Global rainfall erosivity assessment based on high-temporal resolution rainfall records.

PubMed

Panagos, Panos; Borrelli, Pasquale; Meusburger, Katrin; Yu, Bofu; Klik, Andreas; Jae Lim, Kyoung; Yang, Jae E; Ni, Jinren; Miao, Chiyuan; Chattopadhyay, Nabansu; Sadeghi, Seyed Hamidreza; Hazbavi, Zeinab; Zabihi, Mohsen; Larionov, Gennady A; Krasnov, Sergey F; Gorobets, Andrey V; Levi, Yoav; Erpul, Gunay; Birkel, Christian; Hoyos, Natalia; Naipal, Victoria; Oliveira, Paulo Tarso S; Bonilla, Carlos A; Meddi, Mohamed; Nel, Werner; Al Dashti, Hassan; Boni, Martino; Diodato, Nazzareno; Van Oost, Kristof; Nearing, Mark; Ballabio, Cristiano

2017-06-23

The exposure of the Earth's surface to the energetic input of rainfall is one of the key factors controlling water erosion. While water erosion is identified as the most serious cause of soil degradation globally, global patterns of rainfall erosivity remain poorly quantified and estimates have large uncertainties. This hampers the implementation of effective soil degradation mitigation and restoration strategies. Quantifying rainfall erosivity is challenging as it requires high temporal resolution(<30 min) and high fidelity rainfall recordings. We present the results of an extensive global data collection effort whereby we estimated rainfall erosivity for 3,625 stations covering 63 countries. This first ever Global Rainfall Erosivity Database was used to develop a global erosivity map at 30 arc-seconds(~1 km) based on a Gaussian Process Regression(GPR). Globally, the mean rainfall erosivity was estimated to be 2,190 MJ mm ha -1 h -1 yr -1 , with the highest values in South America and the Caribbean countries, Central east Africa and South east Asia. The lowest values are mainly found in Canada, the Russian Federation, Northern Europe, Northern Africa and the Middle East. The tropical climate zone has the highest mean rainfall erosivity followed by the temperate whereas the lowest mean was estimated in the cold climate zone.

The Role of Rainfall Patterns in Seasonal Malaria Transmission

NASA Astrophysics Data System (ADS)

Bomblies, A.

2010-12-01

Seasonal total precipitation is well known to affect malaria transmission because Anopheles mosquitoes depend on standing water for breeding habitat. However, the within-season temporal pattern of the rainfall influences persistence of standing water and thus rainfall patterns also affect mosquito population dynamics. In this talk, I show that intraseasonal rainfall pattern describes 40% of the variance in simulated mosquito abundance in a Niger Sahel village where malaria is endemic but highly seasonal, demonstrating the necessity for detailed distributed hydrology modeling to explain the variance from this important effect. I apply a field validated, high spatial- and temporal-resolution hydrology model coupled with an entomology model. Using synthetic rainfall time series generated using a stationary first-order Markov Chain model, I hold all variables except hourly rainfall constant, thus isolating the contribution of rainfall pattern to variance in mosquito abundance. I further show the utility of hydrology modeling to assess precipitation effects by analyzing collected water. Time-integrated surface area of pools explains 70% of the variance in mosquito abundance, and time-integrated surface area of pools persisting longer than seven days explains 82% of the variance, showing an improved predictive ability when pool persistence is explicitly modeled at high spatio-temporal resolution. I extend this analysis to investigate the impacts of this effect on malaria vector mosquito populations under climate shift scenarios, holding all climate variables except precipitation constant. In these scenarios, rainfall mean and variance change with climatic change, and the modeling approach evaluates the impact of non-stationarity in rainfall and the associated rainfall patterns on expected mosquito activity.

Flood and Landslide Applications of High Time Resolution Satellite Rain Products

NASA Technical Reports Server (NTRS)

Adler, Robert F.; Hong, Yang; Huffman, George J.

2006-01-01

Experimental, potentially real-time systems to detect floods and landslides related to heavy rain events are described. A key basis for these applications is high time resolution satellite rainfall analyses. Rainfall is the primary cause for devastating floods across the world. However, in many countries, satellite-based precipitation estimation may be the best source of rainfall data due to insufficient ground networks and absence of data sharing along many trans-boundary river basins. Remotely sensed precipitation from the NASA's TRMM Multi-satellite Precipitation Analysis (TMPA) operational system (near real-time precipitation at a spatial-temporal resolution of 3 hours and 0.25deg x 0.25deg) is used to monitor extreme precipitation events. Then these data are ingested into a macro-scale hydrological model which is parameterized using spatially distributed elevation, soil and land cover datasets available globally from satellite remote sensing. Preliminary flood results appear reasonable in terms of location and frequency of events, with implementation on a quasi-global basis underway. With the availability of satellite rainfall analyses at fine time resolution, it has also become possible to assess landslide risk on a near-global basis. Early results show that landslide occurrence is closely associated with the spatial patterns and temporal distribution of TRMM rainfall characteristics. Particularly, the number of landslides triggered by rainfall is related to rainfall climatology, antecedent rainfall accumulation, and intensity-duration of rainstorms. For the purpose of prediction, an empirical TMPA-based rainfall intensity-duration threshold is developed and shown to have skill in determining potential areas of landslides. These experimental findings, in combination with landslide surface susceptibility information based on satellite-based land surface information, form a starting point towards a potential operational landslide monitoring/warning system around the globe.

The Effect of Rainfall Measurement Technique and Its Spatiotemporal Resolution on Discharge Predictions in the Netherlands

NASA Astrophysics Data System (ADS)

Uijlenhoet, R.; Brauer, C.; Overeem, A.; Sassi, M.; Rios Gaona, M. F.

2014-12-01

Several rainfall measurement techniques are available for hydrological applications, each with its own spatial and temporal resolution. We investigated the effect of these spatiotemporal resolutions on discharge simulations in lowland catchments by forcing a novel rainfall-runoff model (WALRUS) with rainfall data from gauges, radars and microwave links. The hydrological model used for this analysis is the recently developed Wageningen Lowland Runoff Simulator (WALRUS). WALRUS is a rainfall-runoff model accounting for hydrological processes relevant to areas with shallow groundwater (e.g. groundwater-surface water feedback). Here, we used WALRUS for case studies in a freely draining lowland catchment and a polder with controlled water levels. We used rain gauge networks with automatic (hourly resolution but low spatial density) and manual gauges (high spatial density but daily resolution). Operational (real-time) and climatological (gauge-adjusted) C-band radar products and country-wide rainfall maps derived from microwave link data from a cellular telecommunication network were also used. Discharges simulated with these different inputs were compared to observations. We also investigated the effect of spatiotemporal resolution with a high-resolution X-band radar data set for catchments with different sizes. Uncertainty in rainfall forcing is a major source of uncertainty in discharge predictions, both with lumped and with distributed models. For lumped rainfall-runoff models, the main source of input uncertainty is associated with the way in which (effective) catchment-average rainfall is estimated. When catchments are divided into sub-catchments, rainfall spatial variability can become more important, especially during convective rainfall events, leading to spatially varying catchment wetness and spatially varying contribution of quick flow routes. Improving rainfall measurements and their spatiotemporal resolution can improve the performance of rainfall-runoff models, indicating their potential for reducing flood damage through real-time control.

Connecting spatial and temporal scales of tropical precipitation in observations and the MetUM-GA6

NASA Astrophysics Data System (ADS)

Martin, Gill M.; Klingaman, Nicholas P.; Moise, Aurel F.

2017-01-01

This study analyses tropical rainfall variability (on a range of temporal and spatial scales) in a set of parallel Met Office Unified Model (MetUM) simulations at a range of horizontal resolutions, which are compared with two satellite-derived rainfall datasets. We focus on the shorter scales, i.e. from the native grid and time step of the model through sub-daily to seasonal, since previous studies have paid relatively little attention to sub-daily rainfall variability and how this feeds through to longer scales. We find that the behaviour of the deep convection parametrization in this model on the native grid and time step is largely independent of the grid-box size and time step length over which it operates. There is also little difference in the rainfall variability on larger/longer spatial/temporal scales. Tropical convection in the model on the native grid/time step is spatially and temporally intermittent, producing very large rainfall amounts interspersed with grid boxes/time steps of little or no rain. In contrast, switching off the deep convection parametrization, albeit at an unrealistic resolution for resolving tropical convection, results in very persistent (for limited periods), but very sporadic, rainfall. In both cases, spatial and temporal averaging smoothes out this intermittency. On the ˜ 100 km scale, for oceanic regions, the spectra of 3-hourly and daily mean rainfall in the configurations with parametrized convection agree fairly well with those from satellite-derived rainfall estimates, while at ˜ 10-day timescales the averages are overestimated, indicating a lack of intra-seasonal variability. Over tropical land the results are more varied, but the model often underestimates the daily mean rainfall (partly as a result of a poor diurnal cycle) but still lacks variability on intra-seasonal timescales. Ultimately, such work will shed light on how uncertainties in modelling small-/short-scale processes relate to uncertainty in climate change projections of rainfall distribution and variability, with a view to reducing such uncertainty through improved modelling of small-/short-scale processes.

Estimation of the fractional coverage of rainfall in climate models

NASA Technical Reports Server (NTRS)

Eltahir, E. A. B.; Bras, R. L.

1993-01-01

The fraction of the grid cell area covered by rainfall, mu, is an essential parameter in descriptions of land surface hydrology in climate models. A simple procedure is presented for estimating this fraction, based on extensive observations of storm areas and rainfall volumes. Storm area and rainfall volume are often linearly related; this relation can be used to compute the storm area from the volume of rainfall simulated by a climate model. A formula is developed for computing mu, which describes the dependence of the fractional coverage of rainfall on the season of the year, the geographical region, rainfall volume, and the spatial and temporal resolution of the model. The new formula is applied in computing mu over the Amazon region. Significant temporal variability in the fractional coverage of rainfall is demonstrated. The implications of this variability for the modeling of land surface hydrology in climate models are discussed.

A two-parameter design storm for Mediterranean convective rainfall

NASA Astrophysics Data System (ADS)

García-Bartual, Rafael; Andrés-Doménech, Ignacio

2017-05-01

The following research explores the feasibility of building effective design storms for extreme hydrological regimes, such as the one which characterizes the rainfall regime of the east and south-east of the Iberian Peninsula, without employing intensity-duration-frequency (IDF) curves as a starting point. Nowadays, after decades of functioning hydrological automatic networks, there is an abundance of high-resolution rainfall data with a reasonable statistic representation, which enable the direct research of temporal patterns and inner structures of rainfall events at a given geographic location, with the aim of establishing a statistical synthesis directly based on those observed patterns. The authors propose a temporal design storm defined in analytical terms, through a two-parameter gamma-type function. The two parameters are directly estimated from 73 independent storms identified from rainfall records of high temporal resolution in Valencia (Spain). All the relevant analytical properties derived from that function are developed in order to use this storm in real applications. In particular, in order to assign a probability to the design storm (return period), an auxiliary variable combining maximum intensity and total cumulated rainfall is introduced. As a result, for a given return period, a set of three storms with different duration, depth and peak intensity are defined. The consistency of the results is verified by means of comparison with the classic method of alternating blocks based on an IDF curve, for the above mentioned study case.

Rainfall Erosivity Database on the European Scale (REDES): A product of a high temporal resolution rainfall data collection in Europe

NASA Astrophysics Data System (ADS)

Panagos, Panos; Ballabio, Cristiano; Borrelli, Pasquale; Meusburger, Katrin; Alewell, Christine

2016-04-01

The erosive force of rainfall is expressed as rainfall erosivity. Rainfall erosivity considers the rainfall amount and intensity, and is most commonly expressed as the R-factor in the (R)USLE model. The R-factor is calculated from a series of single storm events by multiplying the total storm kinetic energy with the measured maximum 30-minutes rainfall intensity. This estimation requests high temporal resolution (e.g. 30 minutes) rainfall data for sufficiently long time periods (i.e. 20 years) which are not readily available at European scale. The European Commission's Joint Research Centre(JRC) in collaboration with national/regional meteorological services and Environmental Institutions made an extensive data collection of high resolution rainfall data in the 28 Member States of the European Union plus Switzerland in order to estimate rainfall erosivity in Europe. This resulted in the Rainfall Erosivity Database on the European Scale (REDES) which included 1,541 rainfall stations in 2014 and has been updated with 134 additional stations in 2015. The interpolation of those point R-factor values with a Gaussian Process Regression (GPR) model has resulted in the first Rainfall Erosivity map of Europe (Science of the Total Environment, 511, 801-815). The intra-annual variability of rainfall erosivity is crucial for modelling soil erosion on a monthly and seasonal basis. The monthly feature of rainfall erosivity has been added in 2015 as an advancement of REDES and the respective mean annual R-factor map. Almost 19,000 monthly R-factor values of REDES contributed to the seasonal and monthly assessments of rainfall erosivity in Europe. According to the first results, more than 50% of the total rainfall erosivity in Europe takes place in the period from June to September. The spatial patterns of rainfall erosivity have significant differences between Northern and Southern Europe as summer is the most erosive period in Central and Northern Europe and autumn in the Mediterranean area. This spatio-temporal analysis of rainfall erosivity at European scale is very important for policy makers and farmers for soil conservation, optimization of agricultural land use and natural hazards prediction. REDES is also used in combination with future rainfall data from WorldClim to run climate change scenarios. The projection of REDES combined with climate change scenarios (HADGEM2, RCP4.5) and using a robust geo-statistical model resulted in a 10-20% increase of the R-factor in Europe till 2050.

Pluviometric characterization of the Coca river basin by using a stochastic rainfall model

NASA Astrophysics Data System (ADS)

González-Zeas, Dunia; Chávez-Jiménez, Adriadna; Coello-Rubio, Xavier; Correa, Ángel; Martínez-Codina, Ángela

2014-05-01

An adequate design of the hydraulic infrastructures, as well as, the prediction and simulation of a river basin require historical records with a greater temporal and spatial resolution. However, the lack of an extensive network of precipitation data, the short time scale data and the incomplete information provided by the available rainfall stations limit the analysis and design of complex hydraulic engineering systems. As a consequence, it is necessary to develop new quantitative tools in order to face this obstacle imposed by ungauged or poorly gauged basins. In this context, the use of a spatial-temporal rainfall model allows to simulate the historical behavior of the precipitation and at the same time, to obtain long-term synthetic series that preserve the extremal behavior. This paper provides a characterization of the precipitation in the Coca river basin located in Ecuador by using RainSim V3, a robust and well tested stochastic rainfall model based on a spatial-temporal Neyman-Scott rectangular pulses process. A preliminary consistency analysis of the historical rainfall data available has been done in order to identify climatic regions with similar precipitation behavior patterns. Mean and maximum yearly and monthly fields of precipitation of high resolution spaced grids have been obtained through the use of interpolation techniques. According to the climatological similarity, long time series of daily temporal resolution of precipitation have been generated in order to evaluate the model skill in capturing the structure of daily observed precipitation. The results show a good performance of the model in reproducing very well the gross statistics, including the extreme values of rainfall at daily scale. The spatial pattern represented by the observed and simulated precipitation fields highlights the existence of two important regions characterized by different pluviometric comportment, with lower precipitation in the upper part of the basin and higher precipitation in the lower part of the basin.

Geo-statistical model of Rainfall erosivity by using high temporal resolution precipitation data in Europe

NASA Astrophysics Data System (ADS)

Panagos, Panos; Ballabio, Cristiano; Borrelli, Pasquale; Meusburger, Katrin; Alewell, Christine

2015-04-01

Rainfall erosivity (R-factor) is among the 6 input factors in estimating soil erosion risk by using the empirical Revised Universal Soil Loss Equation (RUSLE). R-factor is a driving force for soil erosion modelling and potentially can be used in flood risk assessments, landslides susceptibility, post-fire damage assessment, application of agricultural management practices and climate change modelling. The rainfall erosivity is extremely difficult to model at large scale (national, European) due to lack of high temporal resolution precipitation data which cover long-time series. In most cases, R-factor is estimated based on empirical equations which take into account precipitation volume. The Rainfall Erosivity Database on the European Scale (REDES) is the output of an extensive data collection of high resolution precipitation data in the 28 Member States of the European Union plus Switzerland taking place during 2013-2014 in collaboration with national meteorological/environmental services. Due to different temporal resolutions of the data (5, 10, 15, 30, 60 minutes), conversion equations have been applied in order to homogenise the database at 30-minutes interval. The 1,541 stations included in REDES have been interpolated using the Gaussian Process Regression (GPR) model using as covariates the climatic data (monthly precipitation, monthly temperature, wettest/driest month) from WorldClim Database, Digital Elevation Model and latitude/longitude. GPR has been selected among other candidate models (GAM, Regression Kriging) due the best performance both in cross validation (R2=0.63) and in fitting dataset (R2=0.72). The highest uncertainty has been noticed in North-western Scotland, North Sweden and Finland due to limited number of stations in REDES. Also, in highlands such as Alpine arch and Pyrenees the diversity of environmental features forced relatively high uncertainty. The rainfall erosivity map of Europe available at 500m resolution plus the standard error and the erosivity density (Rainfall erosivity per mm of precipitation) are available in the European Soil Data Centre (ESDAC). The highest erosivity has been found in the mediterrean countries (Italy, Western Greece, Spain, Northern Portugal), South Austria, Slovenia, Croatia and Western United Kingdom.

Variability in rainfall at monitoring stations and derivation of a long-term rainfall intensity record in the Grand Canyon Region, Arizona, USA

USGS Publications Warehouse

Caster, Joshua J.; Sankey, Joel B.

2016-04-11

In this study, we examine rainfall datasets of varying temporal length, resolution, and spatial distribution to characterize rainfall depth, intensity, and seasonality for monitoring stations along the Colorado River within Marble and Grand Canyons. We identify maximum separation distances between stations at which rainfall measurements might be most useful for inferring rainfall characteristics at other locations. We demonstrate a method for applying relations between daily rainfall depth and intensity, from short-term high-resolution data to lower-resolution longer-term data, to synthesize a long-term record of daily rainfall intensity from 1950–2012. We consider the implications of our spatio-temporal characterization of rainfall for understanding local landscape change in sedimentary deposits and archaeological sites, and for better characterizing past and present rainfall and its potential role in overland flow erosion within the canyons. We find that rainfall measured at stations within the river corridor is spatially correlated at separation distances of tens of kilometers, and is not correlated at the large elevation differences that separate stations along the Colorado River from stations above the canyon rim. These results provide guidance for reasonable separation distances at which rainfall measurements at stations within the Grand Canyon region might be used to infer rainfall at other nearby locations along the river. Like other rugged landscapes, spatial variability between rainfall measured at monitoring stations appears to be influenced by canyon and rim physiography and elevation, with preliminary results suggesting the highest elevation landform in the region, the Kaibab Plateau, may function as an important orographic influence. Stations at specific locations within the canyons and along the river, such as in southern (lower) Marble Canyon and eastern (upper) Grand Canyon, appear to have strong potential to receive high-intensity rainfall that can generate runoff which may erode alluvium. The characterization of past and present rainfall variability in this study will be useful for future studies that evaluate more spatially continuous datasets in order to better understand the rainfall dynamics within this, and potentially other, deep canyons.

Bias adjustment of infrared-based rainfall estimation using Passive Microwave satellite rainfall data

NASA Astrophysics Data System (ADS)

Karbalaee, Negar; Hsu, Kuolin; Sorooshian, Soroosh; Braithwaite, Dan

2017-04-01

This study explores using Passive Microwave (PMW) rainfall estimation for spatial and temporal adjustment of Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS). The PERSIANN-CCS algorithm collects information from infrared images to estimate rainfall. PERSIANN-CCS is one of the algorithms used in the Integrated Multisatellite Retrievals for GPM (Global Precipitation Mission) estimation for the time period PMW rainfall estimations are limited or not available. Continued improvement of PERSIANN-CCS will support Integrated Multisatellite Retrievals for GPM for current as well as retrospective estimations of global precipitation. This study takes advantage of the high spatial and temporal resolution of GEO-based PERSIANN-CCS estimation and the more effective, but lower sample frequency, PMW estimation. The Probability Matching Method (PMM) was used to adjust the rainfall distribution of GEO-based PERSIANN-CCS toward that of PMW rainfall estimation. The results show that a significant improvement of global PERSIANN-CCS rainfall estimation is obtained.

Critical Phenomena of Rainfall in Ecuador

NASA Astrophysics Data System (ADS)

Serrano, Sh.; Vasquez, N.; Jacome, P.; Basile, L.

2014-02-01

Self-organized criticality (SOC) is characterized by a power law behavior over complex systems like earthquakes and avalanches. We study rainfall using data of one day, 3 hours and 10 min temporal resolution from INAMHI (Instituto Nacional de Meteorologia e Hidrologia) station at Izobamba, DMQ (Metropolitan District of Quito), satellite data over Ecuador from Tropical Rainfall Measure Mission (TRMM,) and REMMAQ (Red Metropolitana de Monitoreo Atmosferico de Quito) meteorological stations over, respectively. Our results show a power law behavior of the number of rain events versus mm of rainfall measured for the high resolution case (10 min), and as the resolution decreases this behavior gets lost. This statistical property is the fingerprint of a self-organized critical process (Peter and Christensen, 2002) and may serve as a benchmark for models of precipitation based in phase transitions between water vapor and precipitation (Peter and Neeling, 2006).

Does the GPM mission resolve the systematic error dependence with climatology and topography - a statistical and hydrologic evaluation over India?

NASA Astrophysics Data System (ADS)

Beria, H.; Nanda, T., Sr.; Bisht, D. S.; Chatterjee, C.

2016-12-01

Increasing hydrologic extremes in a changing climate with lack of quality rainfall forcings have inspired the development of a number of satellite and reanalysis based precipitation products in the past decade. Tropical Rainfall Measuring Mission (TRMM) has emerged as the front runner in this race, providing high quality precipitation forcings in the tropical part of the world. However, TRMM is known to suffer from its poor sensitivity to low rainfall intensities due to limited resolving power of its sensors, and is also not known to accurately resolve topography in its rainfall estimates. The Global Precipitation Mission (GPM), a follow-up mission of TRMM, promises enhanced spatio-temporal resolution along with upgrades in sensors and rainfall estimation techniques. In this study, the rainfall estimates of Integrated Multi-satellitE Retrievals for GPM (IMERG), was compared with those of TRMM for the major basins in India for the year 2014. IMERG depicted higher skill (in terms of correlation) for the majority of basins at all rainfall intensities, with a drastic improvement in low rainfall estimates (smaller biases in 75 out of 86 basins). IMERG was found to improve the topographic resolution, with lower error in high elevation basins. IMERG could better resolve the sharp topographic gradient in the Western Ghat region of India. However, IMERG suffered from poor skill in the semi-arid basins of Rajasthan, at all rainfall intensities. Rainfall-runoff exercise over Mahanadi River basin (a flood prone basin on the Eastern coast of India) using Variable Infiltration Capacity Model (VIC) showed better simulations with TRMM, mainly due to the overestimation of low rainfall events by IMERG. Also, the calibration scheme could be put to fault as the period of availability of IMERG is rather small, and more in-depth hydrologic analysis could only be carried out with sufficiently longer time series. Overall, the fine spatial and temporal resolution along with improved accuracy, promises new horizons in hydrologic forecasting under data scarcity.

A stochastical event-based continuous time step rainfall generator based on Poisson rectangular pulse and microcanonical random cascade models

NASA Astrophysics Data System (ADS)

Pohle, Ina; Niebisch, Michael; Zha, Tingting; Schümberg, Sabine; Müller, Hannes; Maurer, Thomas; Hinz, Christoph

2017-04-01

Rainfall variability within a storm is of major importance for fast hydrological processes, e.g. surface runoff, erosion and solute dissipation from surface soils. To investigate and simulate the impacts of within-storm variabilities on these processes, long time series of rainfall with high resolution are required. Yet, observed precipitation records of hourly or higher resolution are in most cases available only for a small number of stations and only for a few years. To obtain long time series of alternating rainfall events and interstorm periods while conserving the statistics of observed rainfall events, the Poisson model can be used. Multiplicative microcanonical random cascades have been widely applied to disaggregate rainfall time series from coarse to fine temporal resolution. We present a new coupling approach of the Poisson rectangular pulse model and the multiplicative microcanonical random cascade model that preserves the characteristics of rainfall events as well as inter-storm periods. In the first step, a Poisson rectangular pulse model is applied to generate discrete rainfall events (duration and mean intensity) and inter-storm periods (duration). The rainfall events are subsequently disaggregated to high-resolution time series (user-specified, e.g. 10 min resolution) by a multiplicative microcanonical random cascade model. One of the challenges of coupling these models is to parameterize the cascade model for the event durations generated by the Poisson model. In fact, the cascade model is best suited to downscale rainfall data with constant time step such as daily precipitation data. Without starting from a fixed time step duration (e.g. daily), the disaggregation of events requires some modifications of the multiplicative microcanonical random cascade model proposed by Olsson (1998): Firstly, the parameterization of the cascade model for events of different durations requires continuous functions for the probabilities of the multiplicative weights, which we implemented through sigmoid functions. Secondly, the branching of the first and last box is constrained to preserve the rainfall event durations generated by the Poisson rectangular pulse model. The event-based continuous time step rainfall generator has been developed and tested using 10 min and hourly rainfall data of four stations in North-Eastern Germany. The model performs well in comparison to observed rainfall in terms of event durations and mean event intensities as well as wet spell and dry spell durations. It is currently being tested using data from other stations across Germany and in different climate zones. Furthermore, the rainfall event generator is being applied in modelling approaches aimed at understanding the impact of rainfall variability on hydrological processes. Reference Olsson, J.: Evaluation of a scaling cascade model for temporal rainfall disaggregation, Hydrology and Earth System Sciences, 2, 19.30

Gauge-adjusted rainfall estimates from commercial microwave links

NASA Astrophysics Data System (ADS)

Fencl, Martin; Dohnal, Michal; Rieckermann, Jörg; Bareš, Vojtěch

2017-01-01

Increasing urbanization makes it more and more important to have accurate stormwater runoff predictions, especially with potentially severe weather and climatic changes on the horizon. Such stormwater predictions in turn require reliable rainfall information. Especially for urban centres, the problem is that the spatial and temporal resolution of rainfall observations should be substantially higher than commonly provided by weather services with their standard rainfall monitoring networks. Commercial microwave links (CMLs) are non-traditional sensors, which have been proposed about a decade ago as a promising solution. CMLs are line-of-sight radio connections widely used by operators of mobile telecommunication networks. They are typically very dense in urban areas and can provide path-integrated rainfall observations at sub-minute resolution. Unfortunately, quantitative precipitation estimates (QPEs) from CMLs are often highly biased due to several epistemic uncertainties, which significantly limit their usability. In this manuscript we therefore suggest a novel method to reduce this bias by adjusting QPEs to existing rain gauges. The method has been specifically designed to produce reliable results even with comparably distant rain gauges or cumulative observations. This eliminates the need to install reference gauges and makes it possible to work with existing information. First, the method is tested on data from a dedicated experiment, where a CML has been specifically set up for rainfall monitoring experiments, as well as operational CMLs from an existing cellular network. Second, we assess the performance for several experimental layouts of ground truth from rain gauges (RGs) with different spatial and temporal resolutions. The results suggest that CMLs adjusted by RGs with a temporal aggregation of up to 1 h (i) provide precise high-resolution QPEs (relative error < 7 %, Nash-Sutcliffe efficiency coefficient > 0.75) and (ii) that the combination of both sensor types clearly outperforms each individual monitoring system. Unfortunately, adjusting CML observations to RGs with longer aggregation intervals of up to 24 h has drawbacks. Although it substantially reduces bias, it unfavourably smoothes out rainfall peaks of high intensities, which is undesirable for stormwater management. A similar, but less severe, effect occurs due to spatial averaging when CMLs are adjusted to remote RGs. Nevertheless, even here, adjusted CMLs perform better than RGs alone. Furthermore, we provide first evidence that the joint use of multiple CMLs together with RGs also reduces bias in their QPEs. In summary, we believe that our adjustment method has great potential to improve the space-time resolution of current urban rainfall monitoring networks. Nevertheless, future work should aim to better understand the reason for the observed systematic error in QPEs from CMLs.

Seasonal variation and climate change impact in Rainfall Erosivity across Europe

NASA Astrophysics Data System (ADS)

Panagos, Panos; Borrelli, Pasquale; Meusburger, Katrin; Alewell, Christine; Ballabio, Cristiano

2017-04-01

Rainfall erosivity quantifies the climatic effect on water erosion and is of high importance for soil scientists, land use planners, agronomists, hydrologists and environmental scientists in general. The rainfall erosivity combines the influence of rainfall duration, magnitude, frequency and intensity. Rainfall erosivity is calculated from a series of single storm events by multiplying the total storm kinetic energy with the measured maximum 30-minute rainfall intensity. This estimation requests high temporal resolution (e.g. 30 minutes) rainfall data for sufficiently long time periods (i.e. 20 years). The European Commission's Joint Research Centr(JRC) in collaboration with national/regional meteorological services and Environmental Institutions made an extensive data collection of high resolution rainfall data in the 28 Member States of the European Union plus Switzerland to estimate rainfall erosivity in Europe. This resulted in the Rainfall Erosivity Database on the European Scale (REDES) which included 1,675 stations. The interpolation of those point erosivity values with a Gaussian Process Regression (GPR) model has resulted in the first Rainfall Erosivity map of Europe (Science of the Total Environment, 511: 801-815). In 2016, REDES extended with a monthly component, which allowed developing monthly and seasonal erosivity maps and assessing rainfall erosivity both spatially and temporally for European Union and Switzerland. The monthly erosivity maps have been used to develop composite indicators that map both intra-annual variability and concentration of erosive events (Science of the Total Environment, 579: 1298-1315). Consequently, spatio-temporal mapping of rainfall erosivity permits to identify the months and the areas with highest risk of soil loss where conservation measures should be applied in different seasons of the year. Finally, the identification of the most erosive month allows recommending certain agricultural management practices (crop residues, reduced tillage) in regions with high erosivity. Besides soil erosion mapping, the intra-annual analysis of rainfall erosivity is an important step towards flood prevention, hazard mitigation, ecosystem services, land use change and agricultural production. The application of REDES in combination with moderate climate change scenarios scenario (HadGEM RCP 4.5) resulted in predictions of erosivity in 2050. The overall increase of rainfall erosivity in Europe by 18% until 2050 are in line with projected increases of 17% for the U.S.A. The predicted mean rise of erosivity is also expected to increase the threat of soil erosion in Europe. The most noticeable increase of erosivity is projected for North-Central Europe, the English Channel, The Netherlands and Northern France. On the contrary, the Mediterranean basin show mixed trends. The success story with the compilation of REDES and first rainfall erosivity map of Europe was a driver to implement a Global Rainfall Erosivity Database (GloREDa). During the last 3 years, JRC was leading an effort to collect high temporal resolution rainfall data worldwide. In collaboration with 50 scientists worldwide and 100+ Meteorological and environmental Organisations, we have developed a Global Erosivity Database. In this database, we managed to include calculated erosivity values for 3,625 stations covering 63 countries worldwide.

Validation Of TRMM For Hazard Assessment In The Remote Context Of Tropical Africa

NASA Astrophysics Data System (ADS)

Monsieurs, E.; Kirschbaum, D.; Tan, J.; Jacobs, L.; Kervyn, M.; Demoulin, A.; Dewitte, O.

2017-12-01

Accurate rainfall data is fundamental for understanding and mitigating the disastrous effects of many rainfall-triggered hazards, especially when one considers the challenges arising from climate change and rainfall variability. In tropical Africa in particular, the sparse operational rainfall gauging network hampers the ability to understand these hazards. Satellite rainfall estimates (SRE) can therefore be of great value. Yet, rigorous validation is required to identify the uncertainties when using SRE for hazard applications. We evaluated the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42 Research Derived Daily Product from 1998 to 2017, at 0.25° x 0.25° spatial and 24 h temporal resolution. The validation was done over the western branch of the East African Rift, with the perspective of regional landslide hazard assessment in mind. Even though we collected an unprecedented dataset of 47 gauges with a minimum temporal resolution of 24 h, the sparse and heterogeneous temporal coverage in a region with high rainfall variability poses challenges for validation. In addition, the discrepancy between local-scale gauge data and spatially averaged ( 775 km²) TMPA data in the context of local convective storms and orographic rainfall is a crucial source of uncertainty. We adopted a flexible framework for SRE validation that fosters explorative research in a remote context. Results show that TMPA performs reasonably well during the rainy seasons for rainfall intensities <20 mm/day. TMPA systematically underestimates rainfall, but most problematic is the decreasing probability of detection of high intensity rainfalls. We suggest that landslide hazard might be efficiently assessed if we take account of the systematic biases in TMPA data and determine rainfall thresholds modulated by controls on, and uncertainties of, TMPA revealed in this study. Moreover, it is found relevant in mapping regional-scale rainfall-triggered hazards that are in any case poorly covered by the sparse available gauges. We anticipate validation of TMPA's successor (Integrated Multi-satellitE Retrievals for Global Precipitation Measurement; 10 km × 10 km, half-hourly) using the proposed framework, as soon as this product will be available in early 2018 for the 1998-present period.

A new, long-term daily satellite-based rainfall dataset for operational monitoring in Africa

NASA Astrophysics Data System (ADS)

Maidment, Ross I.; Grimes, David; Black, Emily; Tarnavsky, Elena; Young, Matthew; Greatrex, Helen; Allan, Richard P.; Stein, Thorwald; Nkonde, Edson; Senkunda, Samuel; Alcántara, Edgar Misael Uribe

2017-05-01

Rainfall information is essential for many applications in developing countries, and yet, continually updated information at fine temporal and spatial scales is lacking. In Africa, rainfall monitoring is particularly important given the close relationship between climate and livelihoods. To address this information gap, this paper describes two versions (v2.0 and v3.0) of the TAMSAT daily rainfall dataset based on high-resolution thermal-infrared observations, available from 1983 to the present. The datasets are based on the disaggregation of 10-day (v2.0) and 5-day (v3.0) total TAMSAT rainfall estimates to a daily time-step using daily cold cloud duration. This approach provides temporally consistent historic and near-real time daily rainfall information for all of Africa. The estimates have been evaluated using ground-based observations from five countries with contrasting rainfall climates (Mozambique, Niger, Nigeria, Uganda, and Zambia) and compared to other satellite-based rainfall estimates. The results indicate that both versions of the TAMSAT daily estimates reliably detects rainy days, but have less skill in capturing rainfall amount—results that are comparable to the other datasets.

A DETERMINISTIC GEOMETRIC REPRESENTATION OF TEMPORAL RAINFALL: SENSITIVITY ANALYSIS FOR A STORM IN BOSTON. (R824780)

EPA Science Inventory

In an earlier study, Puente and Obregón [Water Resour. Res. 32(1996)2825] reported on the usage of a deterministic fractal–multifractal (FM) methodology to faithfully describe an 8.3 h high-resolution rainfall time series in Boston, gathered every 15 s ...

Models for estimating daily rainfall erosivity in China

NASA Astrophysics Data System (ADS)

Xie, Yun; Yin, Shui-qing; Liu, Bao-yuan; Nearing, Mark A.; Zhao, Ying

2016-04-01

The rainfall erosivity factor (R) represents the multiplication of rainfall energy and maximum 30 min intensity by event (EI30) and year. This rainfall erosivity index is widely used for empirical soil loss prediction. Its calculation, however, requires high temporal resolution rainfall data that are not readily available in many parts of the world. The purpose of this study was to parameterize models suitable for estimating erosivity from daily rainfall data, which are more widely available. One-minute resolution rainfall data recorded in sixteen stations over the eastern water erosion impacted regions of China were analyzed. The R-factor ranged from 781.9 to 8258.5 MJ mm ha-1 h-1 y-1. A total of 5942 erosive events from one-minute resolution rainfall data of ten stations were used to parameterize three models, and 4949 erosive events from the other six stations were used for validation. A threshold of daily rainfall between days classified as erosive and non-erosive was suggested to be 9.7 mm based on these data. Two of the models (I and II) used power law functions that required only daily rainfall totals. Model I used different model coefficients in the cool season (Oct.-Apr.) and warm season (May-Sept.), and Model II was fitted with a sinusoidal curve of seasonal variation. Both Model I and Model II estimated the erosivity index for average annual, yearly, and half-month temporal scales reasonably well, with the symmetric mean absolute percentage error MAPEsym ranging from 10.8% to 32.1%. Model II predicted slightly better than Model I. However, the prediction efficiency for the daily erosivity index was limited, with the symmetric mean absolute percentage error being 68.0% (Model I) and 65.7% (Model II) and Nash-Sutcliffe model efficiency being 0.55 (Model I) and 0.57 (Model II). Model III, which used the combination of daily rainfall amount and daily maximum 60-min rainfall, improved predictions significantly, and produced a Nash-Sutcliffe model efficiency for daily erosivity index prediction of 0.93. Thus daily rainfall data was generally sufficient for estimating annual average, yearly, and half-monthly time scales, while sub-daily data was needed when estimating daily erosivity values.

Validation of the CHIRPS Satellite Rainfall Estimates over Eastern of Africa

NASA Astrophysics Data System (ADS)

Dinku, T.; Funk, C. C.; Tadesse, T.; Ceccato, P.

2017-12-01

Long and temporally consistent rainfall time series are essential in climate analyses and applications. Rainfall data from station observations are inadequate over many parts of the world due to sparse or non-existent observation networks, or limited reporting of gauge observations. As a result, satellite rainfall estimates have been used as an alternative or as a supplement to station observations. However, many satellite-based rainfall products with long time series suffer from coarse spatial and temporal resolutions and inhomogeneities caused by variations in satellite inputs. There are some satellite rainfall products with reasonably consistent time series, but they are often limited to specific geographic areas. The Climate Hazards Group Infrared Precipitation (CHIRP) and CHIRP combined with station observations (CHIRPS) are recently produced satellite-based rainfall products with relatively high spatial and temporal resolutions and quasi-global coverage. In this study, CHIRP and CHIRPS were evaluated over East Africa at daily, dekadal (10-day) and monthly time scales. The evaluation was done by comparing the satellite products with rain gauge data from about 1200 stations. The is unprecedented number of validation stations for this region covering. The results provide a unique region-wide understanding of how satellite products perform over different climatic/geographic (low lands, mountainous regions, and coastal) regions. The CHIRP and CHIRPS products were also compared with two similar satellite rainfall products: the African Rainfall Climatology version 2 (ARC2) and the latest release of the Tropical Applications of Meteorology using Satellite data (TAMSAT). The results show that both CHIRP and CHIRPS products are significantly better than ARC2 with higher skill and low or no bias. These products were also found to be slightly better than the latest version of the TAMSAT product. A comparison was also done between the latest release of the TAMSAT product (TAMSAT3) and the earlier version(TAMSAT2), which has shown that the latest version is a substantial improvement over the previous one, particularly with regards to the bias statistics.

Estimating Vegetation Rainfall Interception Using Remote Sensing Observations at Very High Resolution

NASA Astrophysics Data System (ADS)

Cui, Y.; Zhao, P.; Hong, Y.; Fan, W.; Yan, B.; Xie, H.

2017-12-01

Abstract: As an important compont of evapotranspiration, vegetation rainfall interception is the proportion of gross rainfall that is intercepted, stored and subsequently evaporated from all parts of vegetation during or following rainfall. Accurately quantifying the vegetation rainfall interception at a high resolution is critical for rainfall-runoff modeling and flood forecasting, and is also essential for understanding its further impact on local, regional, and even global water cycle dynamics. In this study, the Remote Sensing-based Gash model (RS-Gash model) is developed based on a modified Gash model for interception loss estimation using remote sensing observations at the regional scale, and has been applied and validated in the upper reach of the Heihe River Basin of China for different types of vegetation. To eliminate the scale error and the effect of mixed pixels, the RS-Gash model is applied at a fine scale of 30 m with the high resolution vegetation area index retrieved by using the unified model of bidirectional reflectance distribution function (BRDF-U) for the vegetation canopy. Field validation shows that the RMSE and R2 of the interception ratio are 3.7% and 0.9, respectively, indicating the model's strong stability and reliability at fine scale. The temporal variation of vegetation rainfall interception loss and its relationship with precipitation are further investigated. In summary, the RS-Gash model has demonstrated its effectiveness and reliability in estimating vegetation rainfall interception. When compared to the coarse resolution results, the application of this model at 30-m fine resolution is necessary to resolve the scaling issues as shown in this study. Keywords: rainfall interception; remote sensing; RS-Gash analytical model; high resolution

Assessment on spatiotemporal relationship between rainfall and cloud top temperature from new generation weather satellite imagery

NASA Astrophysics Data System (ADS)

Wei, Chiang; Yeh, Hui-Chung; Chen, Yen-Chang

2017-04-01

This study addressed the relationship between rainfall and cloud top temperature (CCT) from new generation satellite Himawari-8 imagery at different spatiotemporal scale. This satellite provides higher band, more bits for data format, spatial and temporal resolution compared with previous GMS series. The multi-infrared channels with 10-minute and 1-2 km resolution make it possible for rainfall estimating/forecasting in small/medium watershed. The preliminary result investigated at Chenyulan watershed (443.6 square kilometer) of Central Taiwan in 2016 Typhoon Megi shows the regression coefficient fitted by negative exponential equation of largest rainfall vs. CCT (B8 band) at pixel scale increases as time scales enlarges and reach 0.462 for 120-minute accumulative rainfall; the value (CTT of B15 band) decreases from 0.635 for 10-minute to 0.423 for 120-minute accumulative rainfall at basin-wide scale. More rainfall events for different regime are yet to evaluate to get solid results.

Determining hydroclimatic extreme events over the south-central Andes

NASA Astrophysics Data System (ADS)

RamezaniZiarani, Maryam; Bookhagen, Bodo; Schmidt, Torsten; Wickert, Jens; de la Torre, Alejandro; Volkholz, Jan

2017-04-01

The south-central Andes in NW Argentina are characterized by a strong rainfall asymmetry. In the east-west direction exists one of the steepest rainfall gradients on Earth, resulting from the large topographic differences in this region. In addition, in the north-south direction the rainfall intensity varies as the climatic regime shifts from the tropical central Andes to the subtropical south-central Andes. In this study, we investigate hydroclimatic extreme events over the south-central Andes using ERA-Interim reanalysis data of the ECMWF (European Centre for Medium-Range Weather Forecasts), the high resolution regional climate model (COSMO-CLM) data and TRMM (Tropical Rainfall Measuring Mission) data. We divide the area in three different study regions based on elevation: The high-elevation Altiplano-Puna plateau, an intermediate area characterized by intramontane basins, and the foreland area. We analyze the correlations between climatic variables, such as specific humidity, zonal wind component, meridional wind component and extreme rainfall events in all three domains. The results show that there is a high positive temporal correlation between extreme rainfall events (90th and 99th percentile rainfall) and extreme specific humidity events (90th and 99th percentile specific humidity). In addition, the temporal variations analysis represents a trend of increasing specific humidity with time during time period (1994-2013) over the Altiplano-Puna plateau which is in agreement with rainfall trend. Regarding zonal winds, our results indicate that 99th percentile rainfall events over the Altiplano-Puna plateau coincide temporally with strong easterly winds from intermountain and foreland regions in the east. In addition, the results regarding the meridional wind component represent strong northerly winds in the foreland region coincide temporally with 99th percentile rainfall over the Altiplano-Puna plateau.

A new, long-term daily satellite-based rainfall dataset for operational monitoring in Africa

PubMed Central

Maidment, Ross I.; Grimes, David; Black, Emily; Tarnavsky, Elena; Young, Matthew; Greatrex, Helen; Allan, Richard P.; Stein, Thorwald; Nkonde, Edson; Senkunda, Samuel; Alcántara, Edgar Misael Uribe

2017-01-01

Rainfall information is essential for many applications in developing countries, and yet, continually updated information at fine temporal and spatial scales is lacking. In Africa, rainfall monitoring is particularly important given the close relationship between climate and livelihoods. To address this information gap, this paper describes two versions (v2.0 and v3.0) of the TAMSAT daily rainfall dataset based on high-resolution thermal-infrared observations, available from 1983 to the present. The datasets are based on the disaggregation of 10-day (v2.0) and 5-day (v3.0) total TAMSAT rainfall estimates to a daily time-step using daily cold cloud duration. This approach provides temporally consistent historic and near-real time daily rainfall information for all of Africa. The estimates have been evaluated using ground-based observations from five countries with contrasting rainfall climates (Mozambique, Niger, Nigeria, Uganda, and Zambia) and compared to other satellite-based rainfall estimates. The results indicate that both versions of the TAMSAT daily estimates reliably detects rainy days, but have less skill in capturing rainfall amount—results that are comparable to the other datasets. PMID:28534868

STEP-TRAMM - A modeling interface for simulating localized rainfall induced shallow landslides and debris flow runout pathways

NASA Astrophysics Data System (ADS)

von Ruette, Jonas; Lehmann, Peter; Fan, Linfeng; Bickel, Samuel; Or, Dani

2017-04-01

Landslides and subsequent debris-flows initiated by rainfall represent a ubiquitous natural hazard in steep mountainous regions. We integrated a landslide hydro-mechanical triggering model and associated debris flow runout pathways with a graphical user interface (GUI) to represent these natural hazards in a wide range of catchments over the globe. The STEP-TRAMM GUI provides process-based locations and sizes of landslides patterns using digital elevation models (DEM) from SRTM database (30 m resolution) linked with soil maps from global database SoilGrids (250 m resolution) and satellite based information on rainfall statistics for the selected region. In a preprocessing step STEP-TRAMM models soil depth distribution and complements soil information that jointly capture key hydrological and mechanical properties relevant to local soil failure representation. In the presentation we will discuss feature of this publicly available platform and compare landslide and debris flow patterns for different regions considering representative intense rainfall events. Model outcomes will be compared for different spatial and temporal resolutions to test applicability of web-based information on elevation and rainfall for hazard assessment.

Using Remotely Sensed Information for Near Real-Time Landslide Hazard Assessment

NASA Technical Reports Server (NTRS)

Kirschbaum, Dalia; Adler, Robert; Peters-Lidard, Christa

2013-01-01

The increasing availability of remotely sensed precipitation and surface products provides a unique opportunity to explore how landslide susceptibility and hazard assessment may be approached at larger spatial scales with higher resolution remote sensing products. A prototype global landslide hazard assessment framework has been developed to evaluate how landslide susceptibility and satellite-derived precipitation estimates can be used to identify potential landslide conditions in near-real time. Preliminary analysis of this algorithm suggests that forecasting errors are geographically variable due to the resolution and accuracy of the current susceptibility map and the application of satellite-based rainfall estimates. This research is currently working to improve the algorithm through considering higher spatial and temporal resolution landslide susceptibility information and testing different rainfall triggering thresholds, antecedent rainfall scenarios, and various surface products at regional and global scales.

Comparison of online and offline based merging methods for high resolution rainfall intensities

NASA Astrophysics Data System (ADS)

Shehu, Bora; Haberlandt, Uwe

2016-04-01

Accurate rainfall intensities with high spatial and temporal resolution are crucial for urban flow prediction. Commonly, raw or bias corrected radar fields are used for forecasting, while different merging products are employed for simulation. The merging products are proven to be adequate for rainfall intensities estimation, however their application in forecasting is limited as they are developed for offline mode. This study aims at adapting and refining the offline merging techniques for the online implementation, and at comparing the performance of these methods for high resolution rainfall data. Radar bias correction based on mean fields and quantile mapping are analyzed individually and also are implemented in conditional merging. Special attention is given to the impact of different spatial and temporal filters on the predictive skill of all methods. Raw radar data and kriging interpolation of station data are considered as a reference to check the benefit of the merged products. The methods are applied for several extreme events in the time period 2006-2012 caused by different meteorological conditions, and their performance is evaluated by split sampling. The study area is located within the 112 km radius of Hannover radar in Lower Saxony, Germany and the data set constitutes of 80 recording stations in 5 min time steps. The results of this study reveal how the performance of the methods is affected by the adjustment of radar data, choice of merging method and selected event. Merging techniques can be used to improve the performance of online rainfall estimation, which gives way to the application of merging products in forecasting.

Unbiased estimation of oceanic mean rainfall from satellite borne radiometer measurements

NASA Technical Reports Server (NTRS)

Mittal, M. C.

1981-01-01

The statistical properties of the radar derived rainfall obtained during the GARP Atlantic Tropical Experiment (GATE) are used to derive quantitative estimates of the spatial and temporal sampling errors associated with estimating rainfall from brightness temperature measurements such as would be obtained from a satelliteborne microwave radiometer employing a practical size antenna aperture. A basis for a method of correcting the so called beam filling problem, i.e., for the effect of nonuniformity of rainfall over the radiometer beamwidth is provided. The method presented employs the statistical properties of the observations themselves without need for physical assumptions beyond those associated with the radiative transfer model. The simulation results presented offer a validation of the estimated accuracy that can be achieved and the graphs included permit evaluation of the effect of the antenna resolution on both the temporal and spatial sampling errors.

A parametric approach for simultaneous bias correction and high-resolution downscaling of climate model rainfall

NASA Astrophysics Data System (ADS)

Mamalakis, Antonios; Langousis, Andreas; Deidda, Roberto; Marrocu, Marino

2017-03-01

Distribution mapping has been identified as the most efficient approach to bias-correct climate model rainfall, while reproducing its statistics at spatial and temporal resolutions suitable to run hydrologic models. Yet its implementation based on empirical distributions derived from control samples (referred to as nonparametric distribution mapping) makes the method's performance sensitive to sample length variations, the presence of outliers, the spatial resolution of climate model results, and may lead to biases, especially in extreme rainfall estimation. To address these shortcomings, we propose a methodology for simultaneous bias correction and high-resolution downscaling of climate model rainfall products that uses: (a) a two-component theoretical distribution model (i.e., a generalized Pareto (GP) model for rainfall intensities above a specified threshold u*, and an exponential model for lower rainrates), and (b) proper interpolation of the corresponding distribution parameters on a user-defined high-resolution grid, using kriging for uncertain data. We assess the performance of the suggested parametric approach relative to the nonparametric one, using daily raingauge measurements from a dense network in the island of Sardinia (Italy), and rainfall data from four GCM/RCM model chains of the ENSEMBLES project. The obtained results shed light on the competitive advantages of the parametric approach, which is proved more accurate and considerably less sensitive to the characteristics of the calibration period, independent of the GCM/RCM combination used. This is especially the case for extreme rainfall estimation, where the GP assumption allows for more accurate and robust estimates, also beyond the range of the available data.

A Multiplicative Cascade Model for High-Resolution Space-Time Downscaling of Rainfall

NASA Astrophysics Data System (ADS)

Raut, Bhupendra A.; Seed, Alan W.; Reeder, Michael J.; Jakob, Christian

2018-02-01

Distributions of rainfall with the time and space resolutions of minutes and kilometers, respectively, are often needed to drive the hydrological models used in a range of engineering, environmental, and urban design applications. The work described here is the first step in constructing a model capable of downscaling rainfall to scales of minutes and kilometers from time and space resolutions of several hours and a hundred kilometers. A multiplicative random cascade model known as the Short-Term Ensemble Prediction System is run with parameters from the radar observations at Melbourne (Australia). The orographic effects are added through multiplicative correction factor after the model is run. In the first set of model calculations, 112 significant rain events over Melbourne are simulated 100 times. Because of the stochastic nature of the cascade model, the simulations represent 100 possible realizations of the same rain event. The cascade model produces realistic spatial and temporal patterns of rainfall at 6 min and 1 km resolution (the resolution of the radar data), the statistical properties of which are in close agreement with observation. In the second set of calculations, the cascade model is run continuously for all days from January 2008 to August 2015 and the rainfall accumulations are compared at 12 locations in the greater Melbourne area. The statistical properties of the observations lie with envelope of the 100 ensemble members. The model successfully reproduces the frequency distribution of the 6 min rainfall intensities, storm durations, interarrival times, and autocorrelation function.

Remote rainfall sensing for landslide hazard analysis

USGS Publications Warehouse

Wieczorek, Gerald F.; McWreath, Harry; Davenport, Clay

2001-01-01

Methods of assessing landslide hazards and providing warnings are becoming more advanced as remote sensing of rainfall provides more detailed temporal and spatial data on rainfall distribution. Two recent landslide disasters are examined noting the potential for using remotely sensed rainfall data for landslide hazard analysis. For the June 27, 1995, storm in Madison County, Virginia, USA, National Weather Service WSR-88D Doppler radar provided rainfall estimates based on a relation between cloud reflectivity and moisture content on a 1 sq. km. resolution every 6 minutes. Ground-based measurements of rainfall intensity and precipitation total, in addition to landslide timing and distribution, were compared with the radar-derived rainfall data. For the December 14-16, 1999, storm in Vargas State, Venezuela, infrared sensing from the GOES-8 satellite of cloud top temperatures provided the basis for NOAA/NESDIS rainfall estimates on a 16 sq. km. resolution every 30 minutes. These rainfall estimates were also compared with ground-based measurements of rainfall and landslide distribution. In both examples, the remotely sensed data either overestimated or underestimated ground-based values by up to a factor of 2. The factors that influenced the accuracy of rainfall data include spatial registration and map projection, as well as prevailing wind direction, cloud orientation, and topography.

Evaluating RGB photogrammetry and multi-temporal digital surface models for detecting soil erosion

NASA Astrophysics Data System (ADS)

Anders, Niels; Keesstra, Saskia; Seeger, Manuel

2013-04-01

Photogrammetry is a widely used tool for generating high-resolution digital surface models. Unmanned Aerial Vehicles (UAVs), equipped with a Red Green Blue (RGB) camera, have great potential in quickly acquiring multi-temporal high-resolution orthophotos and surface models. Such datasets would ease the monitoring of geomorphological processes, such as local soil erosion and rill formation after heavy rainfall events. In this study we test a photogrammetric setup to determine data requirements for soil erosion studies with UAVs. We used a rainfall simulator (5 m2) and above a rig with attached a Panasonic GX1 16 megapixel digital camera and 20mm lens. The soil material in the simulator consisted of loamy sand at an angle of 5 degrees. Stereo pair images were taken before and after rainfall simulation with 75-85% overlap. Acquired images were automatically mosaicked to create high-resolution orthorectified images and digital surface models (DSM). We resampled the DSM to different spatial resolutions to analyze the effect of cell size to the accuracy of measured rill depth and soil loss estimations, and determined an optimal cell size (thus flight altitude). Furthermore, the high spatial accuracy of the acquired surface models allows further analysis of rill formation and channel initiation related to e.g. surface roughness. We suggest implementing near-infrared and temperature sensors to combine soil moisture and soil physical properties with surface morphology for future investigations.

Censored rainfall modelling for estimation of fine-scale extremes

NASA Astrophysics Data System (ADS)

Cross, David; Onof, Christian; Winter, Hugo; Bernardara, Pietro

2018-01-01

Reliable estimation of rainfall extremes is essential for drainage system design, flood mitigation, and risk quantification. However, traditional techniques lack physical realism and extrapolation can be highly uncertain. In this study, we improve the physical basis for short-duration extreme rainfall estimation by simulating the heavy portion of the rainfall record mechanistically using the Bartlett-Lewis rectangular pulse (BLRP) model. Mechanistic rainfall models have had a tendency to underestimate rainfall extremes at fine temporal scales. Despite this, the simple process representation of rectangular pulse models is appealing in the context of extreme rainfall estimation because it emulates the known phenomenology of rainfall generation. A censored approach to Bartlett-Lewis model calibration is proposed and performed for single-site rainfall from two gauges in the UK and Germany. Extreme rainfall estimation is performed for each gauge at the 5, 15, and 60 min resolutions, and considerations for censor selection discussed.

Uncertainties in TRMM-Era multisatellite-based tropical rainfall estimates over the Maritime Continent

NASA Astrophysics Data System (ADS)

Rauniyar, S. P.; Protat, A.; Kanamori, H.

2017-05-01

This study investigates the regional and seasonal rainfall rate retrieval uncertainties within nine state-of-the-art satellite-based rainfall products over the Maritime Continent (MC) region. The results show consistently larger differences in mean daily rainfall among products over land, especially over mountains and along coasts, compared to over ocean, by about 20% for low to medium rain rates and 5% for heavy rain rates. However, rainfall differences among the products do not exhibit any seasonal dependency over both surface types (land and ocean) of the MC region. The differences between products largely depends on the rain rate itself, with a factor 2 difference for light rain and 30% for intermediate and high rain rates over ocean. The rain-rate products dominated by microwave measurements showed less spread among themselves over ocean compared to the products dominated by infrared measurements. Conversely, over land, the rain gauge-adjusted post-real-time products dominated by microwave measurements produced the largest spreads, due to the usage of different gauge analyses for the bias corrections. Intercomparisons of rainfall characteristics of these products revealed large discrepancies in detecting the frequency and intensity of rainfall. These satellite products are finally evaluated at subdaily, daily, monthly, intraseasonal, and seasonal temporal scales against high-quality gridded rainfall observations in the Sarawak (Malaysia) region for the 4 year period 2000-2003. No single satellite-based rainfall product clearly outperforms the other products at all temporal scales. General guidelines are provided for selecting a product that could be best suited for a particular application and/or temporal resolution.

Signal to Noise Ratio for Different Gridded Rainfall Products of Indian Monsoon

NASA Astrophysics Data System (ADS)

Nehra, P.; Shastri, H. K.; Ghosh, S.; Mishra, V.; Murtugudde, R. G.

2014-12-01

Gridded rainfall datasets provide useful information of spatial and temporal distribution of precipitation over a region. For India, there are 3 gridded rainfall data products available from India Meteorological Department (IMD), Tropical Rainfall Measurement Mission (TRMM) and Asian Precipitation - Highly Resolved Observational Data Integration towards Evaluation of Water Resources (APHRODITE), these compile precipitation information obtained through satellite based measurement and ground station based data. The gridded rainfall data from IMD is available at spatial resolution of 1°, 0.5° and 0.25° where as TRMM and APHRODITE is available at 0.25°. Here, we employ 7 years (1998-2004) of common time period amongst the 3 data products for the south-west monsoon season, i.e., the months June to September. We examine temporal mean and standard deviation of these 3 products to observe substantial variation amongst them at 1° resolution whereas for 0.25° resolution, all the data types are nearly identical. We determine the Signal to Noise Ratio (SNR) of the 3 products at 1° and 0.25° resolution based on noise separation technique adopting horizontal separation of the power spectrum generated with the Fast Fourier Transformation (FFT). A methodology is developed for threshold based separation of signal and noise from the power spectrum, treating the noise as white. The variance of signal to that of noise is computed to obtain SNR. Determination of SNR for different regions over the country shows the highest SNR with APHRODITE at 0.25° resolution. It is observed that the eastern part of India has the highest SNR in all cases considered whereas the northern and southern most Indian regions have lowest SNR. An incremental linear trend is observed among the SNR values and the spatial variance of corresponding region. Relationship between the computed SNR values and the interpolation method used with the dataset is analyzed. The SNR analysis provides an effective tool to evaluate the gridded precipitation data products. However detailed analysis is needed to determine the processes that lead to these SNR distributions so that the quality of the gridded rainfall data products can be further improved and transferability of the gridding algorithms can be explored to produce a unified high-quality rainfall dataset.

Characterization of intermittency and statistical properties of high-resolution rainfall observations across a topographic transect in Northwest Mexico

NASA Astrophysics Data System (ADS)

Mascaro, G.; Vivoni, E. R.; Gochis, D. J.; Watts, C. J.; Rodriguez, J. C.

2013-12-01

In northwest Mexico, the statistical properties of rainfall at high temporal resolution (up to 1 min) have been poorly characterized, mainly due to a lack of observations. Under a combined effort of US and Mexican institutions initiated during the North American Monsoon-Soil Moisture Experiment in 2004 (NAME-SMEX04), a network of 8 tipping-bucket rain gauges were installed across a topographic transect in the Sierra Los Locos basin of Sonora, Mexico. The transect spans a distance of ~14 km and an elevation difference of 748 m, thus including valley, mid-elevation and ridge sites where rainfall generation mechanisms in the summer and winter seasons are potentially affected by orography. In this study, we used the data collected during the period of 2007-2010 to characterize the rainfall statistical properties in a wide range of time scales (1 min to ~45 days) and analyzed how these properties change as a function of elevation, the gauge separation distance, and the summer and winter seasons. We found that the total summer (winter) rainfall decreases (increases) with elevation, and that rainfall has a clear diurnal cycle in the summertime, with a peak around 9 pm at all gauges. The correlation structure across the transect indicates that: (i) when times series are aggregated at a resolution greater than 3 hours, the correlation distance is greater than the maximum separation distance (~14 km), while it dramatically decreases for lower time resolutions (e.g., it is ~1.5 km when the resolution is 10 min). Consistent with other semiarid regions, spectral and scale invariance analyses show the presence of different scaling regimes, which are associated to single convective events and larger stratiform systems, with different intermittency properties dependent on the rainfall season. Results of this work are useful for the interpretation of storm generation mechanisms and hydrologic response in the region, as well as for the calibration of high-resolution, stochastic rainfall models used in climate, hydrology, and engineering applications.

Rainfall erosivity factor estimation in Republic of Moldova

NASA Astrophysics Data System (ADS)

Castraveš, Tudor; Kuhn, Nikolaus

2017-04-01

Rainfall erosivity represents a measure of the erosive force of rainfall. Typically, it is expressed as variable such as the R factor in the Universal Soil Loss Equation (USLE) (Wischmeier and Smith, 1965, 1978) or its derivates. The rainfall erosivity index for a rainfall event (EI30) is calculated from the total kinetic energy and maximum 30 minutes intensity of individual events. However, these data are often unavailable for wide regions and countries. Usually, there are three issues regarding precipitation data: low temporal resolution, low spatial density and limited access to the data. This is especially true for some of postsoviet countries from Eastern Europe, such as Republic of Moldova, where soil erosion is a real and persistent problem (Summer, 2003) and where soils represents the main natural resource of the country. Consequently, researching and managing soil erosion is particularly important. The purpose of this study is to develop a model based on commonly available rainfall data, such as event, daily or monthly amounts, to calculate rainfall erosivity for the territory of Republic of Moldova. Rainfall data collected during 1994-2015 period at 15 meteorological stations in the Republic of Moldova, with 10 minutes temporal resolution, were used to develop and calibrate a model to generate an erosivity map of Moldova. References 1. Summer, W., (2003). Soil erosion in the Republic of Moldova — the importance of institutional arrangements. Erosion Prediction in Ungauged Basins: Integrating Methods and Techniques (Proceedings of symposium HS01 held during IUGG2003 at Sapporo. July 2003). IAHS Publ. no. 279. 2. Wischmeier, W.H., and Smith, D.D. (1965). Predicting rainfall-erosion losses from cropland east of the Rocky Mountains. Agr. Handbook No. 282, U.S. Dept. Agr., Washington, DC 3. Wischmeier, W.H., and Smith, D.D. (1978). Predicting rainfall erosion losses. Agr. handbook No. 537, U.S. Dept. of Agr., Science and Education Administration.

Rainfall disaggregation for urban hydrology: Effects of spatial consistence

NASA Astrophysics Data System (ADS)

Müller, Hannes; Haberlandt, Uwe

2015-04-01

For urban hydrology rainfall time series with a high temporal resolution are crucial. Observed time series of this kind are very short in most cases, so they cannot be used. On the contrary, time series with lower temporal resolution (daily measurements) exist for much longer periods. The objective is to derive time series with a long duration and a high resolution by disaggregating time series of the non-recording stations with information of time series of the recording stations. The multiplicative random cascade model is a well-known disaggregation model for daily time series. For urban hydrology it is often assumed, that a day consists of only 1280 minutes in total as starting point for the disaggregation process. We introduce a new variant for the cascade model, which is functional without this assumption and also outperforms the existing approach regarding time series characteristics like wet and dry spell duration, average intensity, fraction of dry intervals and extreme value representation. However, in both approaches rainfall time series of different stations are disaggregated without consideration of surrounding stations. This yields in unrealistic spatial patterns of rainfall. We apply a simulated annealing algorithm that has been used successfully for hourly values before. Relative diurnal cycles of the disaggregated time series are resampled to reproduce the spatial dependence of rainfall. To describe spatial dependence we use bivariate characteristics like probability of occurrence, continuity ratio and coefficient of correlation. Investigation area is a sewage system in Northern Germany. We show that the algorithm has the capability to improve spatial dependence. The influence of the chosen disaggregation routine and the spatial dependence on overflow occurrences and volumes of the sewage system will be analyzed.

Multifractal Downscaling of Rainfall Using Normalized Difference Vegetation Index (NDVI) in the Andes Plateau.

PubMed

Duffaut Espinosa, L A; Posadas, A N; Carbajal, M; Quiroz, R

2017-01-01

In this paper, a multifractal downscaling technique is applied to adequately transformed and lag corrected normalized difference vegetation index (NDVI) in order to obtain daily estimates of rainfall in an area of the Peruvian Andean high plateau. This downscaling procedure is temporal in nature since the original NDVI information is provided at an irregular temporal sampling period between 8 and 11 days, and the desired final scale is 1 day. The spatial resolution of approximately 1 km remains the same throughout the downscaling process. The results were validated against on-site measurements of meteorological stations distributed in the area under study.

Multifractal Downscaling of Rainfall Using Normalized Difference Vegetation Index (NDVI) in the Andes Plateau

PubMed Central

Posadas, A. N.; Carbajal, M.; Quiroz, R.

2017-01-01

In this paper, a multifractal downscaling technique is applied to adequately transformed and lag corrected normalized difference vegetation index (NDVI) in order to obtain daily estimates of rainfall in an area of the Peruvian Andean high plateau. This downscaling procedure is temporal in nature since the original NDVI information is provided at an irregular temporal sampling period between 8 and 11 days, and the desired final scale is 1 day. The spatial resolution of approximately 1 km remains the same throughout the downscaling process. The results were validated against on-site measurements of meteorological stations distributed in the area under study. PMID:28125607

Using Low-Cost GNSS Receivers to Investigate the Small-Scale Precipitable Water Vapor Variability in the Atmosphere for Improving High Resolution Rainfall Forecasts

NASA Astrophysics Data System (ADS)

Krietemeyer, Andreas; ten Veldhuis, Marie-claire; van de Giesen, Nick

2017-04-01

Recent research has shown that assimilation of Precipitable Water Vapor (PWV) measurements into numerical weather predictions models improve the quality of rainfall now- and forecasting. Local PWV fluctuations may be related with water vapor increases in the lower troposphere which lead to deep convection. Prior studies show that about 20 minutes before rain occurs, the amount of water vapor in the atmosphere at 1 km height increases. Monitoring the small-scale temporal and spatial variability of PWV is therefore crucial to improve the weather now- and forecasting for convective storms, that are typically critical for urban stormwater systems. One established technique to obtain PWV measurements in the atmosphere is to exploit signal delays from GNSS satellites to dual-frequency receivers on the ground. Existing dual-frequency receiver networks typically have inter-station distances in the order of tens of kilometers, which is not sufficiently dense to capture the small-scale PWV variations. In this study, we will add low-cost, single-frequency GNSS receivers to an existing dual-frequency receiver network to obtain an inter-station distance of about 1 km in the Rotterdam area (Netherlands). The aim is to investigate the spatial variability of PWV in the atmosphere at this scale. We use the surrounding dual-frequency network (distributed over a radius of approximately 25 km) to apply an ionospheric delay model that accounts for the delay in the ionosphere (50-1000 km altitude) that cannot be eliminated by single-frequency receivers. The results are validated by co-aligning a single-frequency receiver to a dual-frequency receiver. In the next steps, we will investigate how the high temporal and increased spatial resolution network can help to improve high-resolution rainfall forecasts. Their supposed improved forecasting results will be evaluated based on high-resolution rainfall estimates from a polarimetric X-band rainfall radar installed in the city of Rotterdam.

Mixed memory, (non) Hurst effect, and maximum entropy of rainfall in the tropical Andes

NASA Astrophysics Data System (ADS)

Poveda, Germán

2011-02-01

Diverse linear and nonlinear statistical parameters of rainfall under aggregation in time and the kind of temporal memory are investigated. Data sets from the Andes of Colombia at different resolutions (15 min and 1-h), and record lengths (21 months and 8-40 years) are used. A mixture of two timescales is found in the autocorrelation and autoinformation functions, with short-term memory holding for time lags less than 15-30 min, and long-term memory onwards. Consistently, rainfall variance exhibits different temporal scaling regimes separated at 15-30 min and 24 h. Tests for the Hurst effect evidence the frailty of the R/ S approach in discerning the kind of memory in high resolution rainfall, whereas rigorous statistical tests for short-memory processes do reject the existence of the Hurst effect. Rainfall information entropy grows as a power law of aggregation time, S( T) ˜ Tβ with < β> = 0.51, up to a timescale, TMaxEnt (70-202 h), at which entropy saturates, with β = 0 onwards. Maximum entropy is reached through a dynamic Generalized Pareto distribution, consistently with the maximum information-entropy principle for heavy-tailed random variables, and with its asymptotically infinitely divisible property. The dynamics towards the limit distribution is quantified. Tsallis q-entropies also exhibit power laws with T, such that Sq( T) ˜ Tβ( q) , with β( q) ⩽ 0 for q ⩽ 0, and β( q) ≃ 0.5 for q ⩾ 1. No clear patterns are found in the geographic distribution within and among the statistical parameters studied, confirming the strong variability of tropical Andean rainfall.

Comparison of different synthetic 5-min rainfall time series regarding their suitability for urban drainage modelling

NASA Astrophysics Data System (ADS)

van der Heijden, Sven; Callau Poduje, Ana; Müller, Hannes; Shehu, Bora; Haberlandt, Uwe; Lorenz, Manuel; Wagner, Sven; Kunstmann, Harald; Müller, Thomas; Mosthaf, Tobias; Bárdossy, András

2015-04-01

For the design and operation of urban drainage systems with numerical simulation models, long, continuous precipitation time series with high temporal resolution are necessary. Suitable observed time series are rare. As a result, intelligent design concepts often use uncertain or unsuitable precipitation data, which renders them uneconomic or unsustainable. An expedient alternative to observed data is the use of long, synthetic rainfall time series as input for the simulation models. Within the project SYNOPSE, several different methods to generate synthetic precipitation data for urban drainage modelling are advanced, tested, and compared. The presented study compares four different approaches of precipitation models regarding their ability to reproduce rainfall and runoff characteristics. These include one parametric stochastic model (alternating renewal approach), one non-parametric stochastic model (resampling approach), one downscaling approach from a regional climate model, and one disaggregation approach based on daily precipitation measurements. All four models produce long precipitation time series with a temporal resolution of five minutes. The synthetic time series are first compared to observed rainfall reference time series. Comparison criteria include event based statistics like mean dry spell and wet spell duration, wet spell amount and intensity, long term means of precipitation sum and number of events, and extreme value distributions for different durations. Then they are compared regarding simulated discharge characteristics using an urban hydrological model on a fictitious sewage network. First results show a principal suitability of all rainfall models but with different strengths and weaknesses regarding the different rainfall and runoff characteristics considered.

Quantifying discharge uncertainty from remotely sensed precipitation data products in Puerto Rico

NASA Astrophysics Data System (ADS)

Weerasinghe, H.; Raoufi, R.; Yoon, Y.; Beighley, E., II; Alshawabkeh, A.

2014-12-01

Preterm birth is a serious health issue in the United States that contributes to over one-third of all infant deaths. Puerto Rico being one of the hot spots, preliminary research found that the high preterm birth rate can be associated with exposure to some contaminants in water used on daily basis. Puerto Rico has more than 200 contaminated sites including 16 active Superfund sites. Risk of exposure to contaminants is aggravated by unlined landfills lying over the karst regions, highly mobile and dynamic nature of the karst aquifers, and direct contact with surface water through sinkholes and springs. Much of the population in the island is getting water from natural springs or artesian wells that are connected with many of these potentially contaminated karst aquifers. Mobility of contaminants through surface water flows and reservoirs are largely known and are highly correlated with the variations in hydrologic events and conditions. In this study, we quantify the spatial and temporal distribution of Puerto Rico's surface water stores and fluxes to better understand potential impacts on the distribution of groundwater contamination. To quantify and characterize Puerto Rico's surface waters, hydrologic modeling, remote sensing and field measurements are combined. Streamflow measurements are available from 27 U.S. Geological Survey (USGS) gauging stations with drainage areas ranging from 2 to 510 km2. Hillslope River Routing (HRR) model is used to simulate hourly streamflow from watersheds larger than 1 km2 that discharge to ocean. HRR model simulates vertical water balance, lateral surface and subsurface runoff and river discharge. The model consists of 4418 sub-catchments with a mean model unit area (i.e., sub-catchment) of 1.8 km2. Using gauged streamflow measurements for validation, we first assess model results for simulated discharge using three precipitation products: TRMM-3B42 (3 hour temporal resolution, 0.25 degree spatial resolution); NWS stage-III radar rainfall (~ 5 min temporal resolution and 4 km spatial resolution); and gauge measurements from 37 rainfall stations for the period 2000-2012. We then explore methods for combining each product to improve overall model performance. Effects of varied spatial and temporal rainfall resolutions on simulated discharge are also investigated.

Hydrometeorological and statistical analyses of heavy rainfall in Midwestern USA

NASA Astrophysics Data System (ADS)

Thorndahl, S.; Smith, J. A.; Krajewski, W. F.

2012-04-01

During the last two decades the mid-western states of the United States of America has been largely afflicted by heavy flood producing rainfall. Several of these storms seem to have similar hydrometeorological properties in terms of pattern, track, evolution, life cycle, clustering, etc. which raise the question if it is possible to derive general characteristics of the space-time structures of these heavy storms. This is important in order to understand hydrometeorological features, e.g. how storms evolve and with what frequency we can expect extreme storms to occur. In the literature, most studies of extreme rainfall are based on point measurements (rain gauges). However, with high resolution and quality radar observation periods exceeding more than two decades, it is possible to do long-term spatio-temporal statistical analyses of extremes. This makes it possible to link return periods to distributed rainfall estimates and to study precipitation structures which cause floods. However, doing these statistical frequency analyses of rainfall based on radar observations introduces some different challenges, converting radar reflectivity observations to "true" rainfall, which are not problematic doing traditional analyses on rain gauge data. It is for example difficult to distinguish reflectivity from high intensity rain from reflectivity from other hydrometeors such as hail, especially using single polarization radars which are used in this study. Furthermore, reflectivity from bright band (melting layer) should be discarded and anomalous propagation should be corrected in order to produce valid statistics of extreme radar rainfall. Other challenges include combining observations from several radars to one mosaic, bias correction against rain gauges, range correction, ZR-relationships, etc. The present study analyzes radar rainfall observations from 1996 to 2011 based the American NEXRAD network of radars over an area covering parts of Iowa, Wisconsin, Illinois, and Lake Michigan. The radar observations are processed using Hydro-NEXRAD algorithms in order to produce rainfall estimates with a spatial resolution of 1 km and a temporal resolution of 15 min. The rainfall estimates are bias-corrected on a daily basis using a network of rain gauges. Besides a thorough evaluation of the different challenges in investigating heavy rain as described above the study includes suggestions for frequency analysis methods as well as studies of hydrometeorological features of single events.

Evaluation and intercomparison of GPM-IMERG and TRMM 3B42 daily precipitation products over Greece

NASA Astrophysics Data System (ADS)

Kazamias, A. P.; Sapountzis, M.; Lagouvardos, K.

2017-09-01

Accurate precipitation data at high temporal and spatial resolutions are needed for numerous applications in hydrology, water resources management and flood risk management. Satellite-based precipitation estimations/products offer a potential alternative source of rainfall data for regions with sparse rain gauge network. The recently launched Global Precipitation Measurement (GPM) mission is the successor of Tropical Rainfall Measuring Mission (TRMM) providing global precipitation estimates at spatial resolution of 0.1 degree x 0.1 degree and half-hourly temporal resolution. This study aims at evaluating the accuracy of the Integrated Multi-satellite Retrievals for GPM (IMERG) near-real-time daily product (GPM-3IMERGDL) against rain gauge observations from a network of stations distributed across Greece for the year 2016. Moreover, the GPM-IMERG product is also compared with its predecessor, the Version-7 near-real-time (3B42RT) daily product of TRMM Multisatellite Precipitation Analysis (TMPA). Several statistical metrics are used to quantitatively evaluate the performance of the satellite-based precipitation estimates against rain gauge observations. In addition, categorical statistical indices are used to assess rain detection capabilities of the two satellite products. The GPM-IMERG daily product shows reasonable agreement (CC=0.60) against rain gauge observations, with the exception of coastal areas in which low correlations are achieved. The GPM-IMERG daily precipitation product tends to overestimate rainfall, especially in complex terrain areas with high annual precipitation. In particular, rainfall estimates in western Greece have a strong positive bias. On the other hand, the TRMM 3B42 product shows low correlation (CC=0.45) against rain gauge observations and slightly underestimates rainfall. This study is a first attempt to evaluate and compare the newly introduced GPM-IMERG and the TRMM 3B42 rainfall products at daily timescale over Greece.

Review of FEWS NET Biophysical Monitoring Requirements

NASA Technical Reports Server (NTRS)

Ross, K. W.; Brown, Molly E.; Verdin, J.; Underwood, L. W.

2009-01-01

The Famine Early Warning System Network (FEWS NET) provides monitoring and early warning support to decision makers responsible for responding to famine and food insecurity. FEWS NET transforms satellite remote sensing data into rainfall and vegetation information that can be used by these decision makers. The National Aeronautics and Space Administration has recently funded activities to enhance remote sensing inputs to FEWS NET. To elicit Earth observation requirements, a professional review questionnaire was disseminated to FEWS NET expert end-users: it focused upon operational requirements to determine additional useful remote sensing data and; subsequently, beneficial FEWS NET biophysical supplementary inputs. The review was completed by over 40 experts from around the world, enabling a robust set of professional perspectives to be gathered and analyzed rapidly. Reviewers were asked to evaluate the relative importance of environmental variables and spatio-temporal requirements for Earth science data products, in particular for rainfall and vegetation products. The results showed that spatio-temporal resolution requirements are complex and need to vary according to place, time, and hazard: that high resolution remote sensing products continue to be in demand, and that rainfall and vegetation products were valued as data that provide actionable food security information.

Rainfall estimates for hydrological models: Comparing rain gauge, radar and microwave link data as input for the Wageningen Lowland Runoff Simulator (WALRUS)

NASA Astrophysics Data System (ADS)

Brauer, Claudia; Overeem, Aart; Uijlenhoet, Remko

2015-04-01

Several rainfall measurement techniques are available for hydrological applications, each with its own spatial and temporal resolution. We investigated the effect of differences in rainfall estimates on discharge simulations in a lowland catchment by forcing a novel rainfall-runoff model (WALRUS) with rainfall data from gauges, radars and microwave links. The hydrological model used for this analysis is the recently developed Wageningen Lowland Runoff Simulator (WALRUS). WALRUS is a rainfall-runoff model accounting for hydrological processes relevant to areas with shallow groundwater (e.g. groundwater-surface water feedback). Here, we used WALRUS for case studies in the Hupsel Brook catchment. We used two automatic rain gauges with hourly resolution, located inside the catchment (the base run) and 30 km northeast. Operational (real-time) and climatological (gauge-adjusted) C-band radar products and country-wide rainfall maps derived from microwave link data from a cellular telecommunication network were also used. Discharges simulated with these different inputs were compared to observations. Traditionally, the precipitation research community places emphasis on quantifying spatial errors and uncertainty, but for hydrological applications, temporal errors and uncertainty should be quantified as well. Its memory makes the hydrologic system sensitive to missed or badly timed rainfall events, but also emphasizes the effect of a bias in rainfall estimates. Systematic underestimation of rainfall by the uncorrected operational radar product leads to very dry model states and an increasing underestimation of discharge. Using the rain gauge 30 km northeast of the catchment yields good results for climatological studies, but not for forecasting individual floods. Simulating discharge using the maps derived from microwave link data and the gauge-adjusted radar product yields good results for both events and climatological studies. This indicates that these products can be used in catchments without gauges in or near the catchment. Uncertainty in rainfall forcing is a major source of uncertainty in discharge predictions, both with lumped and with distributed models. For lumped rainfall-runoff models, the main source of input uncertainty is associated with the way in which (effective) catchment-average rainfall is estimated. Improving rainfall measurements can improve the performance of rainfall-runoff models, indicating their potential for reducing flood damage through real-time control.

Land surface sensitivity of monsoon depressions formed over Bay of Bengal using improved high-resolution land state

NASA Astrophysics Data System (ADS)

Rajesh, P. V.; Pattnaik, S.; Mohanty, U. C.; Rai, D.; Baisya, H.; Pandey, P. C.

2017-12-01

Monsoon depressions (MDs) constitute a large fraction of the total rainfall during the Indian summer monsoon season. In this study, the impact of high-resolution land state is addressed by assessing the evolution of inland moving depressions formed over the Bay of Bengal using a mesoscale modeling system. Improved land state is generated using High Resolution Land Data Assimilation System employing Noah-MP land-surface model. Verification of soil moisture using Soil Moisture and Ocean Salinity (SMOS) and soil temperature using tower observations demonstrate promising results. Incorporating high-resolution land state yielded least root mean squared errors with higher correlation coefficient in the surface and mid tropospheric parameters. Rainfall forecasts reveal that simulations are spatially and quantitatively in accordance with observations and provide better skill scores. The improved land surface characteristics have brought about the realistic evolution of surface, mid-tropospheric parameters, vorticity and moist static energy that facilitates the accurate MDs dynamics in the model. Composite moisture budget analysis reveals that the surface evaporation is negligible compared to moisture flux convergence of water vapor, which supplies moisture into the MDs over land. The temporal relationship between rainfall and moisture convergence show high correlation, suggesting a realistic representation of land state help restructure the moisture inflow into the system through rainfall-moisture convergence feedback.

High-resolution space-time characterization of convective rain cells: implications on spatial aggregation and temporal sampling operated by coarser resolution instruments

NASA Astrophysics Data System (ADS)

Marra, Francesco; Morin, Efrat

2017-04-01

Forecasting the occurrence of flash floods and debris flows is fundamental to save lives and protect infrastructures and properties. These natural hazards are generated by high-intensity convective storms, on space-time scales that cannot be properly monitored by conventional instrumentation. Consequently, a number of early-warning systems are nowadays based on remote sensing precipitation observations, e.g. from weather radars or satellites, that proved effective in a wide range of situations. However, the uncertainty affecting rainfall estimates represents an important issue undermining the operational use of early-warning systems. The uncertainty related to remote sensing estimates results from (a) an instrumental component, intrinsic of the measurement operation, and (b) a discretization component, caused by the discretization of the continuous rainfall process. Improved understanding on these sources of uncertainty will provide crucial information to modelers and decision makers. This study aims at advancing knowledge on the (b) discretization component. To do so, we take advantage of an extremely-high resolution X-Band weather radar (60 m, 1 min) recently installed in the Eastern Mediterranean. The instrument monitors a semiarid to arid transition area also covered by an accurate C-Band weather radar and by a relatively sparse rain gauge network ( 1 gauge/ 450 km2). Radar quantitative precipitation estimation includes corrections reducing the errors due to ground echoes, orographic beam blockage and attenuation of the signal in heavy rain. Intense, convection-rich, flooding events recently occurred in the area serve as study cases. We (i) describe with very high detail the spatiotemporal characteristics of the convective cores, and (ii) quantify the uncertainty due to spatial aggregation (spatial discretization) and temporal sampling (temporal discretization) operated by coarser resolution remote sensing instruments. We show that instantaneous rain intensity decreases very steeply with the distance from the core of convection with intensity observed at 1 km (2 km) being 10-40% (1-20%) of the core value. The use of coarser temporal resolutions leads to gaps in the observed rainfall and even relatively high resolutions (5 min) can be affected by the problem. We conclude providing to the final user indications about the effects of the discretization component of estimation uncertainty and suggesting viable ways to decrease them.

Assessment of a climate model to reproduce rainfall variability and extremes over Southern Africa

NASA Astrophysics Data System (ADS)

Williams, C. J. R.; Kniveton, D. R.; Layberry, R.

2010-01-01

It is increasingly accepted that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. A change in the distribution and magnitude of extreme rainfall events (associated with changing variability), such as droughts or flooding, may have a far greater impact on human and natural systems than a changing mean. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The sub-continent is considered especially vulnerable to and ill-equipped (in terms of adaptation) for extreme events, due to a number of factors including extensive poverty, famine, disease and political instability. Rainfall variability and the identification of rainfall extremes is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. The majority of previous climate model verification studies have compared model output with observational data at monthly timescales. In this research, the assessment of ability of a state of the art climate model to simulate climate at daily timescales is carried out using satellite-derived rainfall data from the Microwave Infrared Rainfall Algorithm (MIRA). This dataset covers the period from 1993 to 2002 and the whole of southern Africa at a spatial resolution of 0.1° longitude/latitude. This paper concentrates primarily on the ability of the model to simulate the spatial and temporal patterns of present-day rainfall variability over southern Africa and is not intended to discuss possible future changes in climate as these have been documented elsewhere. Simulations of current climate from the UK Meteorological Office Hadley Centre's climate model, in both regional and global mode, are firstly compared to the MIRA dataset at daily timescales. Secondly, the ability of the model to reproduce daily rainfall extremes is assessed, again by a comparison with extremes from the MIRA dataset. The results suggest that the model reproduces the number and spatial distribution of rainfall extremes with some accuracy, but that mean rainfall and rainfall variability is under-estimated (over-estimated) over wet (dry) regions of southern Africa.

Use of radar QPE for the derivation of Intensity-Duration-Frequency curves in a range of climatic regimes

NASA Astrophysics Data System (ADS)

Marra, Francesco; Morin, Efrat

2015-12-01

Intensity-Duration-Frequency (IDF) curves are widely used in flood risk management because they provide an easy link between the characteristics of a rainfall event and the probability of its occurrence. Weather radars provide distributed rainfall estimates with high spatial and temporal resolutions and overcome the scarce representativeness of point-based rainfall for regions characterized by large gradients in rainfall climatology. This work explores the use of radar quantitative precipitation estimation (QPE) for the identification of IDF curves over a region with steep climatic transitions (Israel) using a unique radar data record (23 yr) and combined physical and empirical adjustment of the radar data. IDF relationships were derived by fitting a generalized extreme value distribution to the annual maximum series for durations of 20 min, 1 h and 4 h. Arid, semi-arid and Mediterranean climates were explored using 14 study cases. IDF curves derived from the study rain gauges were compared to those derived from radar and from nearby rain gauges characterized by similar climatology, taking into account the uncertainty linked with the fitting technique. Radar annual maxima and IDF curves were generally overestimated but in 70% of the cases (60% for a 100 yr return period), they lay within the rain gauge IDF confidence intervals. Overestimation tended to increase with return period, and this effect was enhanced in arid climates. This was mainly associated with radar estimation uncertainty, even if other effects, such as rain gauge temporal resolution, cannot be neglected. Climatological classification remained meaningful for the analysis of rainfall extremes and radar was able to discern climatology from rainfall frequency analysis.

Distributed modelling of water resources in the Lower Jordan River Basin - from present day variability to suitability for new water sources

NASA Astrophysics Data System (ADS)

Gunkel, Anne; Lange, Jens

2010-05-01

The Middle East is characterized by a high temporal and spatial variability of rainfall. As a result, water resources are not reliable and severe drought events are frequent, worsening the natural water scarcity. Single high magnitude events may dominate the water balance of entire seasons - a fact that is poorly represented in the assessments of available water resources that are normally based on long term averages. Therefore, a distributed hydrological model with a high temporal and spatial resolution is applied to the Lower Jordan River basin (LJRB). The focus is hereby to capture the variability of rainfall and to investigate how this signal is amplified in the hydrological cycle in this arid and semi arid environment. Rainfall variability is addressed through a volume scanning rainfall radar providing precipitation data with a resolution of 5 minutes for entire seasons that serves as input to a conceptual hydrological model. The raw radar data recorded by a C-Band system was pre-corrected by a multiple regression approach prior to regionalization to the LJRB, ground truthing with rainfall station data and conditional merging. Despite certain uncertainties, the data documents the accentuated rainfall variability in the entire LJRB. In order to include the full range of present rainfall variability, one average and two extreme seasons (wet and dry) are studied. Hydrological modelling is undertaken with a new modelling tool created by coupling two hydrological models, TRAIN and ZIN, complementing each other in respect to the addressed processes and water fluxes. The resulting modelling tool enables conceptual modelling of the processes relevant for semi-arid / arid environments with a high temporal and spatial resolution. The model is applied to the large scale LJRB (16,000 km²) in order to simulate all components of the water balance for three rainy seasons representing the present climate variability. Under given conditions of low data availability, the results give a basin wide view on the availability of surface water resources without human intervention with a high resolution in time (5 min) and space (up to 250 x 250 m²). The scarcity of water resources in many areas within the region is illustrated and detailed maps of the water balance components reveal spatial pattern of water availability characterizing the different potentials of regions or sub basins for water management options. Moreover, comparing different climate conditions provides valuable information for water management, including insights into the relation between green and blue water. For instance, runoff generation and percolation react stronger to changes in precipitation than evapotranspiration and the changes in runoff and percolation are considerably higher than the differences in rainfall between the three years. This amplification of rainfall variability by the hydrological cycle is significant for water management. Based on the results for current conditions, the impact of different scenarios and management options is analyzed, e.g. the effect of land use changes or the suitability of different regions for rainwater harvesting, one of the urgently needed new water sources.

Identifying and Analyzing Uncertainty Structures in the TRMM Microwave Imager Precipitation Product over Tropical Ocean Basins

NASA Technical Reports Server (NTRS)

Liu, Jianbo; Kummerow, Christian D.; Elsaesser, Gregory S.

2016-01-01

Despite continuous improvements in microwave sensors and retrieval algorithms, our understanding of precipitation uncertainty is quite limited, due primarily to inconsistent findings in studies that compare satellite estimates to in situ observations over different parts of the world. This study seeks to characterize the temporal and spatial properties of uncertainty in the Tropical Rainfall Measuring Mission Microwave Imager surface rainfall product over tropical ocean basins. Two uncertainty analysis frameworks are introduced to qualitatively evaluate the properties of uncertainty under a hierarchy of spatiotemporal data resolutions. The first framework (i.e. 'climate method') demonstrates that, apart from random errors and regionally dependent biases, a large component of the overall precipitation uncertainty is manifested in cyclical patterns that are closely related to large-scale atmospheric modes of variability. By estimating the magnitudes of major uncertainty sources independently, the climate method is able to explain 45-88% of the monthly uncertainty variability. The percentage is largely resolution dependent (with the lowest percentage explained associated with a 1 deg x 1 deg spatial/1 month temporal resolution, and highest associated with a 3 deg x 3 deg spatial/3 month temporal resolution). The second framework (i.e. 'weather method') explains regional mean precipitation uncertainty as a summation of uncertainties associated with individual precipitation systems. By further assuming that self-similar recurring precipitation systems yield qualitatively comparable precipitation uncertainties, the weather method can consistently resolve about 50 % of the daily uncertainty variability, with only limited dependence on the regions of interest.

A probabilistic approach of the Flash Flood Early Warning System (FF-EWS) in Catalonia based on radar ensemble generation

NASA Astrophysics Data System (ADS)

Velasco, David; Sempere-Torres, Daniel; Corral, Carles; Llort, Xavier; Velasco, Enrique

2010-05-01

Early Warning Systems (EWS) are commonly identified as the most efficient tools in order to improve the preparedness and risk management against heavy rains and Flash Floods (FF) with the objective of reducing economical losses and human casualties. In particular, flash floods affecting torrential Mediterranean catchments are a key element to be incorporated within operational EWSs. The characteristic high spatial and temporal variability of the storms requires high-resolution data and methods to monitor/forecast the evolution of rainfall and its hydrological impact in small and medium torrential basins. A first version of an operational FF-EWS has been implemented in Catalonia (NE Spain) under the name of EHIMI system (Integrated Tool for Hydrometeorological Forecasting) with the support of the Catalan Water Agency (ACA) and the Meteorological Service of Catalonia (SMC). Flash flood warnings are issued based on radar-rainfall estimates. Rainfall estimation is performed on radar observations with high spatial and temporal resolution (1km2 and 10 minutes) in order to adapt the warning scale to the 1-km grid of the EWS. The method is based on comparing observed accumulated rainfall against rainfall thresholds provided by the regional Intensity-Duration-Frequency (IDF) curves. The so-called "aggregated rainfall warning" at every river cell is obtained as the spatially averaged rainfall over its associated upstream draining area. Regarding the time aggregation of rainfall, the critical duration is thought to be an accumulation period similar to the concentration time of each cachtment. The warning is issued once the forecasted rainfall accumulation exceeds the rainfall thresholds mentioned above, which are associated to certain probability of occurrence. Finally, the hazard warning is provided and shown to the decision-maker in terms of exceeded return periods at every river cell covering the whole area of Catalonia. The objective of the present work includes the probabilistic component to the FF-EWS. As a first step, we have incorporated the uncertainty in rainfall estimates and forecasts based on an ensemble of equiprobable rainfall scenarios. The presented study has focused on a number of rainfall events and the performance of the FF-EWS evaluated in terms of its ability to produce probabilistic hazard warnings for decision-making support.

Analysis of the sensitivity to rainfall spatio-temporal variability of an operational urban rainfall-runoff model in a multifractal framework

NASA Astrophysics Data System (ADS)

Gires, A.; Tchiguirinskaia, I.; Schertzer, D. J.; Lovejoy, S.

2011-12-01

In large urban areas, storm water management is a challenge with enlarging impervious areas. Many cities have implemented real time control (RTC) of their urban drainage system to either reduce overflow or limit urban contamination. A basic component of RTC is hydraulic/hydrologic model. In this paper we use the multifractal framework to suggest an innovative way to test the sensitivity of such a model to the spatio-temporal variability of its rainfall input. Indeed the rainfall variability is often neglected in urban context, being considered as a non-relevant issue at the scales involve. Our results show that on the contrary the rainfall variability should be taken into account. Universal multifractals (UM) rely on the concept of multiplicative cascade and are a standard tool to analyze and simulate with a reduced number of parameters geophysical processes that are extremely variable over a wide range of scales. This study is conducted on a 3 400 ha urban area located in Seine-Saint-Denis, in the North of Paris (France). We use the operational semi-distributed model that was calibrated by the local authority (Direction Eau et Assainnissement du 93) that is in charge of urban drainage. The rainfall data comes from the C-Band radar of Trappes operated by Météo-France. The rainfall event of February 9th, 2009 was used. A stochastic ensemble approach was implemented to quantify the uncertainty on discharge associated to the rainfall variability occurring at scales smaller than 1 km x 1 km x 5 min that is usually available with C-band radar networks. An analysis of the quantiles of the simulated peak flow showed that the uncertainty exceeds 20 % for upstream links. To evaluate a potential gain from a direct use of the rainfall data available at the resolution of X-band radar, we performed similar analysis of the rainfall fields of the degraded resolution of 9 km x 9 km x 20 min. The results show a clear decrease in uncertainty when the original resolution of C-band radar data is used. This analysis highlights the interest of implementing X-band radars in urban areas. Indeed such radars provide the rainfall data at a hectometric resolution that would enable a better nowcasting and management of storm water. The multifractal properties of the simulated hydrographs were analysed with the help of simulated rainfall fields of resolution 111 m x 111 m x 1 min, lasting 4 hours, and corresponding to a 5 year return period event. On the whole, the discharge exhibits a good scaling behaviour over the range 4 h - 5 min. Both UM parameters tend to be greater for the discharge than for the rainfall. The notion of maximum probable singularity was used to clarify the consequences on the assessment of extremes. It appears that the urban drainage network basically reproduces the extremes, or only slightly damps them, at least in terms of multifractal statistics. The results were obtained with the financial support from the EU FP7 SMARTesT Project and the Chair "Hydrology for Resilient Cities" (sponsored by Veolia) of Ecole des Ponts ParisTech.

An operational ensemble prediction system for catchment rainfall over eastern Africa spanning multiple temporal and spatial scales

NASA Astrophysics Data System (ADS)

Riddle, E. E.; Hopson, T. M.; Gebremichael, M.; Boehnert, J.; Broman, D.; Sampson, K. M.; Rostkier-Edelstein, D.; Collins, D. C.; Harshadeep, N. R.; Burke, E.; Havens, K.

2017-12-01

While it is not yet certain how precipitation patterns will change over Africa in the future, it is clear that effectively managing the available water resources is going to be crucial in order to mitigate the effects of water shortages and floods that are likely to occur in a changing climate. One component of effective water management is the availability of state-of-the-art and easy to use rainfall forecasts across multiple spatial and temporal scales. We present a web-based system for displaying and disseminating ensemble forecast and observed precipitation data over central and eastern Africa. The system provides multi-model rainfall forecasts integrated to relevant hydrological catchments for timescales ranging from one day to three months. A zoom-in features is available to access high resolution forecasts for small-scale catchments. Time series plots and data downloads with forecasts, recent rainfall observations and climatological data are available by clicking on individual catchments. The forecasts are calibrated using a quantile regression technique and an optimal multi-model forecast is provided at each timescale. The forecast skill at the various spatial and temporal scales will discussed, as will current applications of this tool for managing water resources in Sudan and optimizing hydropower operations in Ethiopia and Tanzania.

A Comprehensive Framework for Use of NEXRAD Data in Hydrometeorology and Hydrology

NASA Astrophysics Data System (ADS)

Krajewski, W. F.; Bradley, A.; Kruger, A.; Lawrence, R. E.; Smith, J. A.; Steiner, M.; Ramamurthy, M. K.; del Greco, S. A.

2004-12-01

The overall objective of this project is to provide the broad science and engineering communities with ready access to the vast archives and real-time information collected by the national network of NEXRAD weather radars. The main focus is on radar-rainfall data for use in hydrology, hydrometeorology, and water resources. Currently, the NEXRAD data, which are archived at NOAA's National Climatic Data Center (NCDC), are converted to operational products and used by forecasters in real time. The scientific use of the full resolution NEXRAD information is presently limited because current methods of accessing this data require considerable expertise in weather radars, data quality control, formatting and handling, and radar-rainfall algorithms. The goal is to provide professionals in the scientific, engineering, education, and public policy sectors with on-demand NEXRAD data and custom products that are at high spatial and temporal resolutions. Furthermore, the data and custom products will be of a quality suitable for scientific discovery in hydrology and hydrometeorology and in data formats that are convenient to a wide spectrum of users. We are developing a framework and a set of tools for access, visualization, management, rainfall estimation algorithms, and scientific analysis of full resolution NEXRAD data. The framework will address the issues of data dissemination, format conversions and compression, management of terabyte-sized datasets, rapid browsing and visualization, metadata selection and calculation, relational and XML databases, integration with geographic information systems, data queries and knowledge mining, and Web Services. The tools will perform instantaneous comprehensive quality control and radar-rainfall estimation using a variety of algorithms. The algorithms that the user can select will range from "quick look" to complex, and computing-intensive and will include operational algorithms used by federal agencies as well as research grade experimental methods. Options available to the user will include user-specified spatial and temporal resolution, ancillary products such as storm advection velocity fields, estimation of uncertainty associated with rainfall maps, and mathematical synthesis of the products. The data and the developed tools will be provided to the community via the services and the infrastructure of Unidata and the NCDC.

Nonlinear responses of southern African rainfall to forcing from Atlantic SST in a high-resolution regional climate model

NASA Astrophysics Data System (ADS)

Williams, C.; Kniveton, D.; Layberry, R.

2009-04-01

It is increasingly accepted that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. A change in the distribution and magnitude of extreme rainfall events (associated with changing variability), such as droughts or flooding, may have a far greater impact on human and natural systems than a changing mean. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The subcontinent is considered especially vulnerable to and ill-equipped (in terms of adaptation) for extreme events, due to a number of factors including extensive poverty, famine, disease and political instability. Rainfall variability is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. In this research, high resolution satellite derived rainfall data from the Microwave Infra-Red Algorithm (MIRA) are used as a basis for undertaking model experiments using a state-of-the-art regional climate model. The MIRA dataset covers the period from 1993-2002 and the whole of southern Africa at a spatial resolution of 0.1 degree longitude/latitude. Once the model's ability to reproduce extremes has been assessed, idealised regions of sea surface temperature (SST) anomalies are used to force the model, with the overall aim of investigating the ways in which SST anomalies influence rainfall extremes over southern Africa. In this paper, results from sensitivity testing of the regional climate model's domain size are briefly presented, before a comparison of simulated daily rainfall from the model with the satellite-derived dataset. Secondly, simulations of current climate and rainfall extremes from the model are compared to the MIRA dataset at daily timescales. Finally, the results from the idealised SST experiments are presented, suggesting highly nonlinear associations between rainfall extremes remote SST anomalies.

The need to consider temporal variability when modelling exchange at the sediment-water interface

USGS Publications Warehouse

Rosenberry, Donald O.

2011-01-01

Most conceptual or numerical models of flows and processes at the sediment-water interface assume steady-state conditions and do not consider temporal variability. The steady-state assumption is required because temporal variability, if quantified at all, is usually determined on a seasonal or inter-annual scale. In order to design models that can incorporate finer-scale temporal resolution we first need to measure variability at a finer scale. Automated seepage meters that can measure flow across the sediment-water interface with temporal resolution of seconds to minutes were used in a variety of settings to characterize seepage response to rainfall, wind, and evapotranspiration. Results indicate that instantaneous seepage fluxes can be much larger than values commonly reported in the literature, although seepage does not always respond to hydrological processes. Additional study is needed to understand the reasons for the wide range and types of responses to these hydrologic and atmospheric events.

Exploring Agro-Climatic Trends in Ethiopia Using CHIRPS

NASA Astrophysics Data System (ADS)

Pedreros, D. H.; Funk, C. C.; Brown, M. E.; Korecha, D.; Seid, Y. M.

2015-12-01

The Famine Early Warning Systems Network (FEWS NET) uses the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) to monitor agricultural food production in different regions of the world. CHIRPS is a 1981-present, 5 day, approximately 5km resolution, rainfall product based on a combination of geostationary satellite observations, a high resolution climatology and in situ station observations. Furthermore, FEWS NET has developed a gridded implementation of the Water Requirement Satisfaction Index (WRSI), a water balance measurement indicator of crop performance. This study takes advantage of the CHIRPS' long term period of record and high spatial and temporal resolution to examine agro-climatic trends in Ethiopia. We use the CHIRPS rainfall dataset to calculate the WRSI for the boreal spring and summer crop seasons, as well as for spring-summer rangelands conditions. We find substantial long term rainfall declines in the spring and summer seasons across southeastern and northeastern Ethiopia. Crop Model results indicate that rainfall declines in the cropped regions have been associated with water deficits during the critical grain filling periods in well populated and/or highly vulnerable parts of eastern Ethiopia. WRSI results in the pastoral areas indicate substantial reductions in rangeland health during the later part of the growing seasons. These health declines correspond to the regions of Somaliland and Afar that have experienced chronic severe food insecurity since 2010. Key words: CHIRPS, satellite estimated rainfall, agricultural production

Comparison of radar and gauge precipitation data in watershed models across varying spatial and temporal scales

EPA Science Inventory

Precipitation is a key control on watershed hydrologic modelling output, with errors in rainfall propagating through subsequent stages of water quantity and quality analysis. Most watershed models incorporate precipitation data from rain gauges; higher-resolution data sources are...

Rainfall variability and extremes over southern Africa: assessment of a climate model to reproduce daily extremes

NASA Astrophysics Data System (ADS)

Williams, C.; Kniveton, D.; Layberry, R.

2009-04-01

It is increasingly accepted that that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. A change in the distribution and magnitude of extreme rainfall events (associated with changing variability), such as droughts or flooding, may have a far greater impact on human and natural systems than a changing mean. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The subcontinent is considered especially vulnerable to and ill-equipped (in terms of adaptation) for extreme events, due to a number of factors including extensive poverty, famine, disease and political instability. Rainfall variability and the identification of rainfall extremes is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. The majority of previous climate model verification studies have compared model output with observational data at monthly timescales. In this research, the assessment of ability of a state of the art climate model to simulate climate at daily timescales is carried out using satellite derived rainfall data from the Microwave Infra-Red Algorithm (MIRA). This dataset covers the period from 1993-2002 and the whole of southern Africa at a spatial resolution of 0.1 degree longitude/latitude. The ability of a climate model to simulate current climate provides some indication of how much confidence can be applied to its future predictions. In this paper, simulations of current climate from the UK Meteorological Office Hadley Centre's climate model, in both regional and global mode, are firstly compared to the MIRA dataset at daily timescales. This concentrates primarily on the ability of the model to simulate the spatial and temporal patterns of rainfall variability over southern Africa. Secondly, the ability of the model to reproduce daily rainfall extremes will be assessed, again by a comparison with extremes from the MIRA dataset.

A spatio-temporal evaluation of the WRF physical parameterisations for numerical rainfall simulation in semi-humid and semi-arid catchments of Northern China

NASA Astrophysics Data System (ADS)

Tian, Jiyang; Liu, Jia; Wang, Jianhua; Li, Chuanzhe; Yu, Fuliang; Chu, Zhigang

2017-07-01

Mesoscale Numerical Weather Prediction systems can provide rainfall products at high resolutions in space and time, playing an increasingly more important role in water management and flood forecasting. The Weather Research and Forecasting (WRF) model is one of the most popular mesoscale systems and has been extensively used in research and practice. However, for hydrologists, an unsolved question must be addressed before each model application in a different target area. That is, how are the most appropriate combinations of physical parameterisations from the vast WRF library selected to provide the best downscaled rainfall? In this study, the WRF model was applied with 12 designed parameterisation schemes with different combinations of physical parameterisations, including microphysics, radiation, planetary boundary layer (PBL), land-surface model (LSM) and cumulus parameterisations. The selected study areas are two semi-humid and semi-arid catchments located in the Daqinghe River basin, Northern China. The performance of WRF with different parameterisation schemes is tested for simulating eight typical 24-h storm events with different evenness in space and time. In addition to the cumulative rainfall amount, the spatial and temporal patterns of the simulated rainfall are evaluated based on a two-dimensional composed verification statistic. Among the 12 parameterisation schemes, Scheme 4 outperforms the other schemes with the best average performance in simulating rainfall totals and temporal patterns; in contrast, Scheme 6 is generally a good choice for simulations of spatial rainfall distributions. Regarding the individual parameterisations, Single-Moment 6 (WSM6), Yonsei University (YSU), Kain-Fritsch (KF) and Grell-Devenyi (GD) are better choices for microphysics, planetary boundary layers (PBL) and cumulus parameterisations, respectively, in the study area. These findings provide helpful information for WRF rainfall downscaling in semi-humid and semi-arid areas. The methodologies to design and test the combination schemes of parameterisations can also be regarded as a reference for generating ensembles in numerical rainfall predictions using the WRF model.

Exploratory analysis of rainfall events in Coimbra, Portugal: variability of raindrop characteristics

NASA Astrophysics Data System (ADS)

Carvalho, S. C. P.; de Lima, M. I. P.; de Lima, J. L. M. P.

2012-04-01

Laser disdrometers can monitor efficiently rainfall characteristics at small temporal scales, providing data on rain intensity, raindrop diameter and fall speed, and raindrop counts over time. This type of data allows for the increased understanding of the rainfall structure at small time scales. Of particular interest for many hydrological applications is the characterization of the properties of extreme events, including the intra-event variability, which are affected by different factors (e.g. geographical location, rainfall generating mechanisms). These properties depend on the microphysical, dynamical and kinetic processes that interact to produce rain. In this study we explore rainfall data obtained during two years with a laser disdrometer installed in the city of Coimbra, in the centre region of mainland Portugal. The equipment was developed by Thies Clima. The data temporal resolution is one-minute. Descriptive statistics of time series of raindrop diameter (D), fall speed, kinetic energy, and rain rate were studied at the event scale; for different variables, the average, maximum, minimum, median, variance, standard deviation, quartile, coefficient of variation, skewness and kurtosis were determined. The empirical raindrop size distribution, N(D), was also calculated. Additionally, the parameterization of rainfall was attempted by investigating the applicability of different theoretical statistical distributions to fit the empirical data (e.g. exponential, gamma and lognormal distributions). As expected, preliminary results show that rainfall properties and structure vary with rainfall type and weather conditions over the year. Although only two years were investigated, already some insight into different rain events' structure was obtained.

The Soil Moisture Dependence of TRMM Microwave Imager Rainfall Estimates

NASA Astrophysics Data System (ADS)

Seyyedi, H.; Anagnostou, E. N.

2011-12-01

This study presents an in-depth analysis of the dependence of overland rainfall estimates from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) on the soil moisture conditions at the land surface. TMI retrievals are verified against rainfall fields derived from a high resolution rain-gauge network (MESONET) covering Oklahoma. Soil moisture (SOM) patterns are extracted based on recorded data from 2000-2007 with 30 minutes temporal resolution. The area is divided into wet and dry regions based on normalized SOM (Nsom) values. Statistical comparison between two groups is conducted based on recorded ground station measurements and the corresponding passive microwave retrievals from TMI overpasses at the respective MESONET station location and time. The zero order error statistics show that the Probability of Detection (POD) for the wet regions (higher Nsom values) is higher than the dry regions. The Falls Alarm Ratio (FAR) and volumetric FAR is lower for the wet regions. The volumetric missed rain for the wet region is lower than dry region. Analysis of the MESONET-to-TMI ratio values shows that TMI tends to overestimate for surface rainfall intensities less than 12 (mm/h), however the magnitude of the overestimation over the wet regions is lower than the dry regions.

Urban rainfall estimation employing commercial microwave links

NASA Astrophysics Data System (ADS)

Overeem, Aart; Leijnse, Hidde; Uijlenhoet, Remko; ten Veldhuis, Marie-claire

2015-04-01

Urban areas often lack rainfall information. To increase the number of rainfall observations in cities, microwave links from operational cellular telecommunication networks may be employed. Although this new potential source of rainfall information has been shown to be promising, its quality needs to be demonstrated more extensively. In the Rain Sense kickstart project of the Amsterdam Institute for Advanced Metropolitan Solutions (AMS), sensors and citizens are preparing Amsterdam for future weather. Part of this project is rainfall estimation using new measurement techniques. Innovative sensing techniques will be utilized such as rainfall estimation from microwave links, umbrellas for weather sensing, low-cost sensors at lamp posts and in drainage pipes for water level observation. These will be combined with information provided by citizens in an active way through smartphone apps and in a passive way through social media posts (Twitter, Flickr etc.). Sensor information will be integrated, visualized and made accessible to citizens to help raise citizen awareness of urban water management challenges and promote resilience by providing information on how citizens can contribute in addressing these. Moreover, citizens and businesses can benefit from reliable weather information in planning their social and commercial activities. In the end city-wide high-resolution rainfall maps will be derived, blending rainfall information from microwave links and weather radars. This information will be used for urban water management. This presentation focuses on rainfall estimation from commercial microwave links. Received signal levels from tens of microwave links within the Amsterdam region (roughly 1 million inhabitants) in the Netherlands are utilized to estimate rainfall with high spatial and temporal resolution. Rainfall maps will be presented and compared to a gauge-adjusted radar rainfall data set. Rainfall time series from gauge(s), radars and links will be compared.

Evaluation of topographical and seasonal feature using GPM IMERG and TRMM 3B42 over Far-East Asia

NASA Astrophysics Data System (ADS)

Kim, Kiyoung; Park, Jongmin; Baik, Jongjin; Choi, Minha

2017-05-01

The acquisition of accurate precipitation data is essential for analyzing various hydrological phenomena and climate change. Recently, the Global Precipitation Measurement (GPM) satellites were launched as a next-generation rainfall mission for observing global precipitation characteristics. The main objective in this study is to assess precipitation products from GPM, especially the Integrated Multi-satellitE Retrievals (GPM-3IMERGHH) and the Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), using gauge-based precipitation data from Far-East Asia during the pre-monsoon and monsoon seasons. Evaluation was performed by focusing on three different factors: geographical aspects, seasonal factors, and spatial distributions. In both mountainous and coastal regions, the GPM-3IMERGHH product showed better performance than the TRMM 3B42 V7, although both rainfall products showed uncertainties caused by orographic convection and the land-ocean classification algorithm. GPM-3IMERGHH performed about 8% better than TRMM 3B42 V7 during the pre-monsoon and monsoon seasons due to the improvement of loaded sensor and reinforcement in capturing convective rainfall, respectively. In depicting the spatial distribution of precipitation, GPM-3IMERGHH was more accurate than TRMM 3B42 V7 because of its enhanced spatial and temporal resolutions of 10 km and 30 min, respectively. Based on these results, GPM-3IMERGHH would be helpful for not only understanding the characteristics of precipitation with high spatial and temporal resolution, but also for estimating near-real-time runoff patterns.

A space-time multifractal analysis on radar rainfall sequences from central Poland

NASA Astrophysics Data System (ADS)

Licznar, Paweł; Deidda, Roberto

2014-05-01

Rainfall downscaling belongs to most important tasks of modern hydrology. Especially from the perspective of urban hydrology there is real need for development of practical tools for possible rainfall scenarios generation. Rainfall scenarios of fine temporal scale reaching single minutes are indispensable as inputs for hydrological models. Assumption of probabilistic philosophy of drainage systems design and functioning leads to widespread application of hydrodynamic models in engineering practice. However models like these covering large areas could not be supplied with only uncorrelated point-rainfall time series. They should be rather supplied with space time rainfall scenarios displaying statistical properties of local natural rainfall fields. Implementation of a Space-Time Rainfall (STRAIN) model for hydrometeorological applications in Polish conditions, such as rainfall downscaling from the large scales of meteorological models to the scale of interest for rainfall-runoff processes is the long-distance aim of our research. As an introduction part of our study we verify the veracity of the following STRAIN model assumptions: rainfall fields are isotropic and statistically homogeneous in space; self-similarity holds (so that, after having rescaled the time by the advection velocity, rainfall is a fully homogeneous and isotropic process in the space-time domain); statistical properties of rainfall are characterized by an "a priori" known multifractal behavior. We conduct a space-time multifractal analysis on radar rainfall sequences selected from the Polish national radar system POLRAD. Radar rainfall sequences covering the area of 256 km x 256 km of original 2 km x 2 km spatial resolution and 15 minutes temporal resolution are used as study material. Attention is mainly focused on most severe summer convective rainfalls. It is shown that space-time rainfall can be considered with a good approximation to be a self-similar multifractal process. Multifractal analysis is carried out assuming Taylor's hypothesis to hold and the advection velocity needed to rescale the time dimension is assumed to be equal about 16 km/h. This assumption is verified by the analysis of autocorrelation functions along the x and y directions of "rainfall cubes" and along the time axis rescaled with assumed advection velocity. In general for analyzed rainfall sequences scaling is observed for spatial scales ranging from 4 to 256 km and for timescales from 15 min to 16 hours. However in most cases scaling break is identified for spatial scales between 4 and 8, corresponding to spatial dimensions of 16 km to 32 km. It is assumed that the scaling break occurrence at these particular scales in central Poland conditions could be at least partly explained by the rainfall mesoscale gap (on the edge of meso-gamma, storm-scale and meso-beta scale).

Toward seamless high-resolution flash flood forecasting over Europe based on radar nowcasting and NWP: An evaluation with case studies

NASA Astrophysics Data System (ADS)

Park, Shinju; Berenguer, Marc; Sempere-Torres, Daniel; Baugh, Calum; Smith, Paul

2017-04-01

Flash floods induced by heavy rain are one of the hazardous natural events that significantly affect human lives. Because flash floods are characterized by their rapid onset, forecasting flash flood to lead an effective response requires accurate rainfall predictions with high spatial and temporal resolution and adequate representation of the hydrologic and hydraulic processes within a catchment that determine rainfall-runoff accumulations. We present extreme flash flood cases which occurred throughout Europe in 2015-2016 that were identified and forecasted by two real-time approaches: 1) the European Rainfall-Induced Hazard Assessment System (ERICHA) and 2) the European Runoff Index based on Climatology (ERIC). ERICHA is based on the nowcasts of accumulated precipitation generated from the pan-European radar composites produced by the EUMETNET project OPERA. It has the advantage of high-resolution precipitation inputs and rapidly updated forecasts (every 15 minutes), but limited forecast lead time (up to 8 hours). ERIC, on the other hand, provides 5-day forecasts based on the COSMO-LEPS NWP simulations updated 2 times a day but is only produced at a 7 km resolution. We compare the products from both systems and focus on showing the advantages, limitations and complementarities of ERICHA and ERIC for seamless high-resolution flash flood forecasting.

Spatio-temporal trends of rainfall across Indian river basins

NASA Astrophysics Data System (ADS)

Bisht, Deepak Singh; Chatterjee, Chandranath; Raghuwanshi, Narendra Singh; Sridhar, Venkataramana

2018-04-01

Daily gridded high-resolution rainfall data of India Meteorological Department at 0.25° spatial resolution (1901-2015) was analyzed to detect the trend in seasonal, annual, and maximum cumulative rainfall for 1, 2, 3, and 5 days. The present study was carried out for 85 river basins of India during 1901-2015 and pre- and post-urbanization era, i.e., 1901-1970 and 1971-2015, respectively. Mann-Kendall ( α = 0.05) and Theil-Sen's tests were employed for detecting the trend and percentage of change over the period of time, respectively. Daily extreme rainfall events, above 95 and 99 percentile threshold, were also analyzed to detect any trend in their magnitude and number of occurrences. The upward trend was found for the majority of the sub-basins for 1-, 2-, 3-, and 5-day maximum cumulative rainfall during the post-urbanization era. The magnitude of extreme threshold events is also found to be increasing in the majority of the river basins during the post-urbanization era. A 30-year moving window analysis further revealed a widespread upward trend in a number of extreme threshold rainfall events possibly due to urbanization and climatic factors. Overall trends studied against intra-basin trend across Ganga basin reveal the mixed pattern of trends due to inherent spatial heterogeneity of rainfall, therefore, highlighting the importance of scale for such studies.

An improved rainfall disaggregation technique for GCMs

NASA Astrophysics Data System (ADS)

Onof, C.; Mackay, N. G.; Oh, L.; Wheater, H. S.

1998-08-01

Meteorological models represent rainfall as a mean value for a grid square so that when the latter is large, a disaggregation scheme is required to represent the spatial variability of rainfall. In general circulation models (GCMs) this is based on an assumption of exponentiality of rainfall intensities and a fixed value of areal rainfall coverage, dependent on rainfall type. This paper examines these two assumptions on the basis of U.K. and U.S. radar data. Firstly, the coverage of an area is strongly dependent on its size, and this dependence exhibits a scaling law over a range of sizes. Secondly, the coverage is, of course, dependent on the resolution at which it is measured, although this dependence is weak at high resolutions. Thirdly, the time series of rainfall coverages has a long-tailed autocorrelation function which is comparable to that of the mean areal rainfalls. It is therefore possible to reproduce much of the temporal dependence of coverages by using a regression of the log of the mean rainfall on the log of the coverage. The exponential assumption is satisfactory in many cases but not able to reproduce some of the long-tailed dependence of some intensity distributions. Gamma and lognormal distributions provide a better fit in these cases, but they have their shortcomings and require a second parameter. An improved disaggregation scheme for GCMs is proposed which incorporates the previous findings to allow the coverage to be obtained for any area and any mean rainfall intensity. The parameters required are given and some of their seasonal behavior is analyzed.

Time distribution of heavy rainfall events in south west of Iran

NASA Astrophysics Data System (ADS)

Ghassabi, Zahra; kamali, G. Ali; Meshkatee, Amir-Hussain; Hajam, Sohrab; Javaheri, Nasrolah

2016-07-01

Accurate knowledge of rainfall time distribution is a fundamental issue in many Meteorological-Hydrological studies such as using the information of the surface runoff in the design of the hydraulic structures, flood control and risk management, and river engineering studies. Since the main largest dams of Iran are in the south-west of the country (i.e. South Zagros), this research investigates the temporal rainfall distribution based on an analytical numerical method to increase the accuracy of hydrological studies in Iran. The United States Soil Conservation Service (SCS) estimated the temporal rainfall distribution in various forms. Hydrology studies usually utilize the same distribution functions in other areas of the world including Iran due to the lack of sufficient observation data. However, we first used Weather Research Forecasting (WRF) model to achieve the simulated rainfall results of the selected storms on south west of Iran in this research. Then, a three-parametric Logistic function was fitted to the rainfall data in order to compute the temporal rainfall distribution. The domain of the WRF model is 30.5N-34N and 47.5E-52.5E with a resolution of 0.08 degree in latitude and longitude. We selected 35 heavy storms based on the observed rainfall data set to simulate with the WRF Model. Storm events were scrutinized independently from each other and the best analytical three-parametric logistic function was fitted for each grid point. The results show that the value of the coefficient a of the logistic function, which indicates rainfall intensity, varies from the minimum of 0.14 to the maximum of 0.7. Furthermore, the values of the coefficient B of the logistic function, which indicates rain delay of grid points from start time of rainfall, vary from 1.6 in south-west and east to more than 8 in north and central parts of the studied area. In addition, values of rainfall intensities are lower in south west of IRAN than those of observed or proposed by the SCS values in the US.

Evaluation of High-Resolution Precipitation Estimates from Satellites during July 2012 Beijing Flood Event Using Dense Rain Gauge Observations

PubMed Central

Chen, Sheng; Liu, Huijuan; You, Yalei; Mullens, Esther; Hu, Junjun; Yuan, Ye; Huang, Mengyu; He, Li; Luo, Yongming; Zeng, Xingji; Tang, Guoqiang; Hong, Yang

2014-01-01

Satellite-based precipitation estimates products, CMORPH and PERSIANN-CCS, were evaluated with a dense rain gauge network over Beijing and adjacent regions for an extremely heavy precipitation event on July 21 2012. CMORPH and PEERSIANN-CSS misplaced the region of greatest rainfall accumulation, and failed to capture the spatial pattern of precipitation, evidenced by a low spatial correlation coefficient (CC). CMORPH overestimated the daily accumulated rainfall by 22.84% while PERSIANN-CCS underestimated by 72.75%. In the rainfall center, both CMORPH and PERSIANN-CCS failed to capture the temporal variation of the rainfall, and underestimated rainfall amounts by 43.43% and 87.26%, respectively. Based on our results, caution should be exercised when using CMORPH and PERSIANN-CCS as input for monitoring and forecasting floods in Beijing urban areas, and the potential for landslides in the mountainous zones west and north of Beijing. PMID:24691358

Evaluation of high-resolution precipitation estimates from satellites during July 2012 Beijing flood event using dense rain gauge observations.

PubMed

Chen, Sheng; Liu, Huijuan; You, Yalei; Mullens, Esther; Hu, Junjun; Yuan, Ye; Huang, Mengyu; He, Li; Luo, Yongming; Zeng, Xingji; Tang, Guoqiang; Hong, Yang

2014-01-01

Satellite-based precipitation estimates products, CMORPH and PERSIANN-CCS, were evaluated with a dense rain gauge network over Beijing and adjacent regions for an extremely heavy precipitation event on July 21 2012. CMORPH and PEERSIANN-CSS misplaced the region of greatest rainfall accumulation, and failed to capture the spatial pattern of precipitation, evidenced by a low spatial correlation coefficient (CC). CMORPH overestimated the daily accumulated rainfall by 22.84% while PERSIANN-CCS underestimated by 72.75%. In the rainfall center, both CMORPH and PERSIANN-CCS failed to capture the temporal variation of the rainfall, and underestimated rainfall amounts by 43.43% and 87.26%, respectively. Based on our results, caution should be exercised when using CMORPH and PERSIANN-CCS as input for monitoring and forecasting floods in Beijing urban areas, and the potential for landslides in the mountainous zones west and north of Beijing.

The development of an hourly gridded rainfall product for hydrological applications in England and Wales

NASA Astrophysics Data System (ADS)

Liguori, Sara; O'Loughlin, Fiachra; Souvignet, Maxime; Coxon, Gemma; Freer, Jim; Woods, Ross

2014-05-01

This research presents a newly developed observed sub-daily gridded precipitation product for England and Wales. Importantly our analysis specifically allows a quantification of rainfall errors from grid to the catchment scale, useful for hydrological model simulation and the evaluation of prediction uncertainties. Our methodology involves the disaggregation of the current one kilometre daily gridded precipitation records available for the United Kingdom[1]. The hourly product is created using information from: 1) 2000 tipping-bucket rain gauges; and 2) the United Kingdom Met-Office weather radar network. These two independent datasets provide rainfall estimates at temporal resolutions much smaller than the current daily gridded rainfall product; thus allowing the disaggregation of the daily rainfall records to an hourly timestep. Our analysis is conducted for the period 2004 to 2008, limited by the current availability of the datasets. We analyse the uncertainty components affecting the accuracy of this product. Specifically we explore how these uncertainties vary spatially, temporally and with climatic regimes. Preliminary results indicate scope for improvement of hydrological model performance by the utilisation of this new hourly gridded rainfall product. Such product will improve our ability to diagnose and identify structural errors in hydrological modelling by including the quantification of input errors. References [1] Keller V, Young AR, Morris D, Davies H (2006) Continuous Estimation of River Flows. Technical Report: Estimation of Precipitation Inputs. in Agency E (ed.). Environmental Agency.

Monthly Rainfall Erosivity Assessment for Switzerland

NASA Astrophysics Data System (ADS)

Schmidt, Simon; Meusburger, Katrin; Alewell, Christine

2016-04-01

Water erosion is crucially controlled by rainfall erosivity, which is quantified out of the kinetic energy of raindrop impact and associated surface runoff. Rainfall erosivity is often expressed as the R-factor in soil erosion risk models like the Universal Soil Loss Equation (USLE) and its revised version (RUSLE). Just like precipitation, the rainfall erosivity of Switzerland has a characteristic seasonal dynamic throughout the year. This inter-annual variability is to be assessed by a monthly and seasonal modelling approach. We used a network of 86 precipitation gauging stations with a 10-minute temporal resolution to calculate long-term average monthly R-factors. Stepwise regression and Monte Carlo Cross Validation (MCCV) was used to select spatial covariates to explain the spatial pattern of R-factor for each month across Switzerland. The regionalized monthly R-factor is mapped by its individual regression equation and the ordinary kriging interpolation of its residuals (Regression-Kriging). As covariates, a variety of precipitation indicator data has been included like snow height, a combination of hourly gauging measurements and radar observations (CombiPrecip), mean monthly alpine precipitation (EURO4M-APGD) and monthly precipitation sums (Rhires). Topographic parameters were also significant explanatory variables for single months. The comparison of all 12 monthly rainfall erosivity maps showed seasonality with highest rainfall erosivity in summer (June, July, and August) and lowest rainfall erosivity in winter months. Besides the inter-annual temporal regime, a seasonal spatial variability was detectable. Spatial maps of monthly rainfall erosivity are presented for the first time for Switzerland. The assessment of the spatial and temporal dynamic behaviour of the R-factor is valuable for the identification of more susceptible seasons and regions as well as for the application of selective erosion control measures. A combination with monthly vegetation cover (C-factor) maps would enable the assessment of seasonal dynamics of erosion processes in Switzerland.

Rift Valley fever in a zone potentially occupied by Aedes vexans in Senegal: dynamics and risk mapping

NASA Astrophysics Data System (ADS)

Tourre, Y. M.; Vignolles, C.; Lacaux, J.-P.; Bigeard, G.; Ndione, J.-A.; Lafaye, M.

2009-09-01

This paper presents an analysis of the interaction between the various variables associated with Rift Valley fever (RVF) such as the mosquito vector, available hosts and rainfall distribution. To that end, the varying zones potentially occupied by mosquitoes (ZPOM), rainfall events and pond dynamics, and the associated exposure of hosts to the RVF virus by Aedes vexans, were analyzed in the Barkedji area of the Ferlo, Senegal, during the 2003 rainy season. Ponds were identified by remote sensing using a high-resolution SPOT-5 satellite image. Additional data on ponds and rainfall events from the Tropical Rainfall Measuring Mission were combined with in-situ entomological and limnimetric measurements, and the localization of vulnerable ruminant hosts (data derived from QuickBird satellite). Since "Ae. vexans productive events” are dependent on the timing of rainfall for their embryogenesis (six days without rain are necessary to trigger hatching), the dynamic spatio-temporal distribution of Ae. vexans density was based on the total rainfall amount and pond dynamics. Detailed ZPOM mapping was obtained on a daily basis and combined with aggressiveness temporal profiles. Risks zones, i.e. zones where hazards and vulnerability are combined, are expressed by the percentages of parks where animals are potentially exposed to mosquito bites. This new approach, simply relying upon rainfall distribution evaluated from space, is meant to contribute to the implementation of a new, operational early warning system for RVF based on environmental risks linked to climatic and environmental conditions.

Rainfall variability and extremes over southern Africa: Assessment of a climate model to reproduce daily extremes

NASA Astrophysics Data System (ADS)

Williams, C. J. R.; Kniveton, D. R.; Layberry, R.

2009-04-01

It is increasingly accepted that that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. A change in the distribution and magnitude of extreme rainfall events (associated with changing variability), such as droughts or flooding, may have a far greater impact on human and natural systems than a changing mean. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The subcontinent is considered especially vulnerable to and ill-equipped (in terms of adaptation) for extreme events, due to a number of factors including extensive poverty, famine, disease and political instability. Rainfall variability and the identification of rainfall extremes is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. The majority of previous climate model verification studies have compared model output with observational data at monthly timescales. In this research, the assessment of ability of a state of the art climate model to simulate climate at daily timescales is carried out using satellite derived rainfall data from the Microwave Infra-Red Algorithm (MIRA). This dataset covers the period from 1993-2002 and the whole of southern Africa at a spatial resolution of 0.1 degree longitude/latitude. The ability of a climate model to simulate current climate provides some indication of how much confidence can be applied to its future predictions. In this paper, simulations of current climate from the UK Meteorological Office Hadley Centre's climate model, in both regional and global mode, are firstly compared to the MIRA dataset at daily timescales. This concentrates primarily on the ability of the model to simulate the spatial and temporal patterns of rainfall variability over southern Africa. Secondly, the ability of the model to reproduce daily rainfall extremes will be assessed, again by a comparison with extremes from the MIRA dataset. The paper will conclude by discussing the user needs of satellite rainfall retrievals from a climate change modelling prospective.

Spatial and temporal features of heavy rainstorm events in Calabria, Southern Italy

NASA Astrophysics Data System (ADS)

Terranova, Oreste Giuseppe; Gariano, Stefano Luigi; Greco, Raffaele

2015-04-01

Heavy rainstorms often induce flash floods, shallow landslides and debris flows, which cause several damage to manmade infrastructures and loss of lives. The analysis of spatial distribution and temporal features of intense rainfall events is a fundamental step for a better understanding of the phenomena and for its possible prediction. The present study is an attempt to improve, from a statistical point of view, the understanding at sub-hourly scale of the temporal and spatial structure of intense rainfall events, by examining those that have hit Calabria (Southern Italy) in the years 1998-2008. More in detail, a considerable amount of series with high temporal detail (5 min) related to 155 sites (one rain gauge per less than 100 sq km), were analysed. First, more than 152 thousands rainfall events, separated by at least 6 hours of dry weather, were recognized. Then, less than a third (45,533) were selected, since denoted as erosive. Finally, several heavy rainstorm events (HREs) were chosen by considering the rainfall events recorded simultaneously at different rain gauges, even non-contiguous, within the region. In particular, this further selection was conducted, based on heuristic threshold values of cumulated rainfall (≥ 100 mm), maximum intensity (≥ 50 mm/h), and kinetic energy (≥ 29 MJ/ha). Therefore, 25 distinct HREs, including all the well-known catastrophic geo-hydrological events, were subjected to thorough investigation. The obtained HREs, automatically classified according to their structure in time, were analysed as regards both spatial and temporal evolution. At this end, the 25 HREs were distinguished as widespread (17) or localized (8), if the affected area is ≥ 500 sq km or < 500 sq km, respectively. In particular, the temporal storm structure was described by means of the standardized rainfall profile (rainfall amount vs. duration, in terms on cumulative percentages). Then, a 4-digit binary shape code was adopted to automatically identify the shape of the profile (Terranova and Iaquinta, 2011; Terranova and Gariano, 2014). HREs have different spatial extents and temporal patterns. A wide spatial extent of the events does not imply damage proportionally high. Generally, a peak at the beginning of the event (thunderstorm-type) characterizes localized events. On the contrary, widespread events present mixed temporal structures with peaks localized in the last half of their duration. The proposed method improves the knowledge regarding the input of rainfall-runoff watershed models. These models can benefit from design storms, based on the synthesis of recorded rainstorms, having a time structure integrated with the results of the spatial analysis. The notable size of the employed sample, including data with a very detailed time resolution that relate to several rain gauges well distributed throughout the region, gives robustness to the obtained results. References O.G. Terranova, and P. Iaquinta.: Temporal properties of rainfall events in Calabria (southern Italy). Nat. Hazards Earth Syst. Sci., 11, 751-757, 2011. O.G. Terranova, and S.L. Gariano.: Rainstorms able to induce flash floods in a Mediterranean-climate region (Calabria, southern Italy). Nat. Hazards Earth Syst. Sci., 14, 2423-2434, 2014.

Merging of rain gauge and radar data for urban hydrological modelling

NASA Astrophysics Data System (ADS)

Berndt, Christian; Haberlandt, Uwe

2015-04-01

Urban hydrological processes are generally characterised by short response times and therefore rainfall data with a high resolution in space and time are required for their modelling. In many smaller towns, no recordings of rainfall data exist within the urban catchment. Precipitation radar helps to provide extensive rainfall data with a temporal resolution of five minutes, but the rainfall amounts can be highly biased and hence the data should not be used directly as a model input. However, scientists proposed several methods for adjusting radar data to station measurements. This work tries to evaluate rainfall inputs for a hydrological model regarding the following two different applications: Dimensioning of urban drainage systems and analysis of single event flow. The input data used for this analysis can be divided into two groups: Methods, which rely on station data only (Nearest Neighbour Interpolation, Ordinary Kriging), and methods, which incorporate station as well as radar information (Conditional Merging, Bias correction of radar data based on quantile mapping with rain gauge recordings). Additionally, rainfall intensities that were directly obtained from radar reflectivities are used. A model of the urban catchment of the city of Brunswick (Lower Saxony, Germany) is utilised for the evaluation. First results show that radar data cannot help with the dimensioning task of sewer systems since rainfall amounts of convective events are often overestimated. Gauges in catchment proximity can provide more reliable rainfall extremes. Whether radar data can be helpful to simulate single event flow depends strongly on the data quality and thus on the selected event. Ordinary Kriging is often not suitable for the interpolation of rainfall data in urban hydrology. This technique induces a strong smoothing of rainfall fields and therefore a severe underestimation of rainfall intensities for convective events.

Rainfall estimation using microwave links. Results from an experimental setup in Luxembourg

NASA Astrophysics Data System (ADS)

Fenicia, Fabrizio; Matgen, Patrick; Pfister, Laurent

2010-05-01

Microwave links represent a valid alternative to traditional rainfall estimation methods. They are commonly used in mobile phone communication, and they constitute built-in widely distributed networks. Due to their ability of providing high temporal and spatial resolution measurements, their use is particularly suitable in urban settings. We here show results from an experimental setup in Luxembourg City, where two dual frequency links have been installed. The links cover a distance of about 4km, and measure power attenuation at 1 min. timestep. The links have been equipped with several recording raingauges, which measure rainfall in real-time communicating through a wireless connection. This set-up has been used to analyze in detail the mapping between attenuation and rainfall intensity, and gain insights into the potential accuracy of these instruments. In addition, we investigated the relation between rainfall and discharge response of the urban area of Luxembourg, which shows the potential utility of high frequency rainfall measurements for urban environments.

Regionalization of monthly rainfall erosivity patternsin Switzerland

NASA Astrophysics Data System (ADS)

Schmidt, Simon; Alewell, Christine; Panagos, Panos; Meusburger, Katrin

2016-10-01

One major controlling factor of water erosion is rainfall erosivity, which is quantified as the product of total storm energy and a maximum 30 min intensity (I30). Rainfall erosivity is often expressed as R-factor in soil erosion risk models like the Universal Soil Loss Equation (USLE) and its revised version (RUSLE). As rainfall erosivity is closely correlated with rainfall amount and intensity, the rainfall erosivity of Switzerland can be expected to have a regional characteristic and seasonal dynamic throughout the year. This intra-annual variability was mapped by a monthly modeling approach to assess simultaneously spatial and monthly patterns of rainfall erosivity. So far only national seasonal means and regional annual means exist for Switzerland. We used a network of 87 precipitation gauging stations with a 10 min temporal resolution to calculate long-term monthly mean R-factors. Stepwise generalized linear regression (GLM) and leave-one-out cross-validation (LOOCV) were used to select spatial covariates which explain the spatial and temporal patterns of the R-factor for each month across Switzerland. The monthly R-factor is mapped by summarizing the predicted R-factor of the regression equation and the corresponding residues of the regression, which are interpolated by ordinary kriging (regression-kriging). As spatial covariates, a variety of precipitation indicator data has been included such as snow depths, a combination product of hourly precipitation measurements and radar observations (CombiPrecip), daily Alpine precipitation (EURO4M-APGD), and monthly precipitation sums (RhiresM). Topographic parameters (elevation, slope) were also significant explanatory variables for single months. The comparison of the 12 monthly rainfall erosivity maps showed a distinct seasonality with the highest rainfall erosivity in summer (June, July, and August) influenced by intense rainfall events. Winter months have the lowest rainfall erosivity. A proportion of 62 % of the total annual rainfall erosivity is identified within four months only (June-September). The highest erosion risk can be expected in July, where not only rainfall erosivity but also erosivity density is high. In addition to the intra-annual temporal regime, a spatial variability of this seasonality was detectable between different regions of Switzerland. The assessment of the dynamic behavior of the R-factor is valuable for the identification of susceptible seasons and regions.

Accounting for Rainfall Spatial Variability in Prediction of Flash Floods

NASA Astrophysics Data System (ADS)

Saharia, M.; Kirstetter, P. E.; Gourley, J. J.; Hong, Y.; Vergara, H. J.

2016-12-01

Flash floods are a particularly damaging natural hazard worldwide in terms of both fatalities and property damage. In the United States, the lack of a comprehensive database that catalogues information related to flash flood timing, location, causative rainfall, and basin geomorphology has hindered broad characterization studies. First a representative and long archive of more than 20,000 flooding events during 2002-2011 is used to analyze the spatial and temporal variability of flash floods. We also derive large number of spatially distributed geomorphological and climatological parameters such as basin area, mean annual precipitation, basin slope etc. to identify static basin characteristics that influence flood response. For the same period, the National Severe Storms Laboratory (NSSL) has produced a decadal archive of Multi-Radar/Multi-Sensor (MRMS) radar-only precipitation rates at 1-km spatial resolution with 5-min temporal resolution. This provides an unprecedented opportunity to analyze the impact of event-level precipitation variability on flooding using a big data approach. To analyze the impact of sub-basin scale rainfall spatial variability on flooding, certain indices such as the first and second scaled moment of rainfall, horizontal gap, vertical gap etc. are computed from the MRMS dataset. Finally, flooding characteristics such as rise time, lag time, and peak discharge are linked to derived geomorphologic, climatologic, and rainfall indices to identify basin characteristics that drive flash floods. Next the model is used to predict flash flooding characteristics all over the continental U.S., specifically over regions poorly covered by hydrological observations. So far studies involving rainfall variability indices have only been performed on a case study basis, and a large scale approach is expected to provide a deeper insight into how sub-basin scale precipitation variability affects flooding. Finally, these findings are validated using the National Weather Service storm reports and a historical flood fatalities database. This analysis framework will serve as a baseline for evaluating distributed hydrologic model simulations such as the Flooded Locations And Simulated Hydrographs Project (FLASH) (http://flash.ou.edu).

Accounting for rainfall spatial variability in the prediction of flash floods

NASA Astrophysics Data System (ADS)

Saharia, Manabendra; Kirstetter, Pierre-Emmanuel; Gourley, Jonathan J.; Hong, Yang; Vergara, Humberto; Flamig, Zachary L.

2017-04-01

Flash floods are a particularly damaging natural hazard worldwide in terms of both fatalities and property damage. In the United States, the lack of a comprehensive database that catalogues information related to flash flood timing, location, causative rainfall, and basin geomorphology has hindered broad characterization studies. First a representative and long archive of more than 15,000 flooding events during 2002-2011 is used to analyze the spatial and temporal variability of flash floods. We also derive large number of spatially distributed geomorphological and climatological parameters such as basin area, mean annual precipitation, basin slope etc. to identify static basin characteristics that influence flood response. For the same period, the National Severe Storms Laboratory (NSSL) has produced a decadal archive of Multi-Radar/Multi-Sensor (MRMS) radar-only precipitation rates at 1-km spatial resolution with 5-min temporal resolution. This provides an unprecedented opportunity to analyze the impact of event-level precipitation variability on flooding using a big data approach. To analyze the impact of sub-basin scale rainfall spatial variability on flooding, certain indices such as the first and second scaled moment of rainfall, horizontal gap, vertical gap etc. are computed from the MRMS dataset. Finally, flooding characteristics such as rise time, lag time, and peak discharge are linked to derived geomorphologic, climatologic, and rainfall indices to identify basin characteristics that drive flash floods. The database has been subjected to rigorous quality control by accounting for radar beam height and percentage snow in basins. So far studies involving rainfall variability indices have only been performed on a case study basis, and a large scale approach is expected to provide a deeper insight into how sub-basin scale precipitation variability affects flooding. Finally, these findings are validated using the National Weather Service storm reports and a historical flood fatalities database. This analysis framework will serve as a baseline for evaluating distributed hydrologic model simulations such as the Flooded Locations And Simulated Hydrographs Project (FLASH) (http://flash.ou.edu).

An Ultra-high Resolution Synthetic Precipitation Data for Ungauged Sites

NASA Astrophysics Data System (ADS)

Kim, Hong-Joong; Choi, Kyung-Min; Oh, Jai-Ho

2018-05-01

Despite the enormous damage caused by record heavy rainfall, the amount of precipitation in areas without observation points cannot be known precisely. One way to overcome these difficulties is to estimate meteorological data at ungauged sites. In this study, we have used observation data over Seoul city to calculate high-resolution (250-meter resolution) synthetic precipitation over a 10-year (2005-2014) period. Furthermore, three cases are analyzed by evaluating the rainfall intensity and performing statistical analysis over the 10-year period. In the case where the typhoon "Meari" passed to the west coast during 28-30 June 2011, the Pearson correlation coefficient was 0.93 for seven validation points, which implies that the temporal correlation between the observed precipitation and synthetic precipitation was very good. It can be confirmed that the time series of observation and synthetic precipitation in the period almost completely matches the observed rainfall. On June 28-29, 2011, the estimation of 10 to 30 mm h-1 of continuous strong precipitation was correct. In addition, it is shown that the synthetic precipitation closely follows the observed precipitation for all three cases. Statistical analysis of 10 years of data reveals a very high correlation coefficient between synthetic precipitation and observed rainfall (0.86). Thus, synthetic precipitation data show good agreement with the observations. Therefore, the 250-m resolution synthetic precipitation amount calculated in this study is useful as basic data in weather applications, such as urban flood detection.

Improving PERSIANN-CCS rain estimation using probabilistic approach and multi-sensors information

NASA Astrophysics Data System (ADS)

Karbalaee, N.; Hsu, K. L.; Sorooshian, S.; Kirstetter, P.; Hong, Y.

2016-12-01

This presentation discusses the recent implemented approaches to improve the rainfall estimation from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Cloud Classification System (PERSIANN-CCS). PERSIANN-CCS is an infrared (IR) based algorithm being integrated in the IMERG (Integrated Multi-Satellite Retrievals for the Global Precipitation Mission GPM) to create a precipitation product in 0.1x0.1degree resolution over the chosen domain 50N to 50S every 30 minutes. Although PERSIANN-CCS has a high spatial and temporal resolution, it overestimates or underestimates due to some limitations.PERSIANN-CCS can estimate rainfall based on the extracted information from IR channels at three different temperature threshold levels (220, 235, and 253k). This algorithm relies only on infrared data to estimate rainfall indirectly from this channel which cause missing the rainfall from warm clouds and false estimation for no precipitating cold clouds. In this research the effectiveness of using other channels of GOES satellites such as visible and water vapors has been investigated. By using multi-sensors the precipitation can be estimated based on the extracted information from multiple channels. Also, instead of using the exponential function for estimating rainfall from cloud top temperature, the probabilistic method has been used. Using probability distributions of precipitation rates instead of deterministic values has improved the rainfall estimation for different type of clouds.

High-resolution Rainfall Mapping in Dallas-Fort Worth (DFW) Urban Network of Radars at Multiple Frequencies

NASA Astrophysics Data System (ADS)

Chandra, Chandrasekar V.; Chen*, Haonan

2015-04-01

Urban flash flood is one of the most commonly encountered hazardous weather phenomena. Unfortunately, the rapid urbanization has made the densely populated areas even more vulnerable to flood risks. Hence, accurate and timely monitoring of rainfall at high spatiotemporal resolution is critical to severe weather warning and civil defense, especially in urban areas. However, it is still challenging to produce high-resolution products based on the large S-band National Weather Service (NWS) Next-Generation Weather Radar (NEXRAD), due to the sampling limitations and Earth curvature effect. Since 2012, the U.S. National Science Foundation Engineering Research Center (NSF-ERC) for Collaborative Adaptive Sensing of the Atmosphere (CASA) has initiated the development of Dallas-Fort Worth (DFW) radar remote sensing network for urban weather hazards mitigation. The DFW urban radar network consists of a combination of high-resolution X-band radars and a standard NWS NEXRAD radar operating at S-band frequency. High-resolution quantitative precipitation estimation (QPE) is one of the major research goals in the deployment of this urban radar network. It has been shown in the literature that the dual-polarization radar techniques can improve the QPE accuracy over traditional single-polarization radars by rendering more measurements to enhance the data quality, providing more information about rain drop size distribution (DSD), and implying more characteristics of different hydrometeor types. This paper will present the real-time dual-polarization CASA DFW QPE system, which is developed via fusion of observations from both the high-resolution X band radar network and the S-band NWS radar. The specific dual-polarization rainfall algorithms at different frequencies (i.e., S- and X-band) will be described in details. In addition, the fusion methodology combining observations at different temporal resolution will be presented. In order to demonstrate the capability of rainfall estimation of the CASA DFW QPE system, rainfall measurements from ground rain gauges will be used for evaluation purposes. This high-resolution QPE system is used for urban flash flood forecasting when coupled with hydrological models.

Analysis of the precipitation and streamflow extremes in Northern Italy using high resolution reanalysis dataset Express-Hydro

NASA Astrophysics Data System (ADS)

Silvestro, Francesco; Parodi, Antonio; Campo, Lorenzo

2017-04-01

The characterization of the hydrometeorological extremes, both in terms of rainfall and streamflow, in a given region plays a key role in the environmental monitoring provided by the flood alert services. In last years meteorological simulations (both near real-time and historical reanalysis) were available at increasing spatial and temporal resolutions, making possible long-period hydrological reanalysis in which the meteo dataset is used as input in distributed hydrological models. In this work, a very high resolution meteorological reanalysis dataset, namely Express-Hydro (CIMA, ISAC-CNR, GAUSS Special Project PR45DE), was employed as input in the hydrological model Continuum in order to produce long time series of streamflows in the Liguria territory, located in the Northern part of Italy. The original dataset covers the whole Europe territory in the 1979-2008 period, at 4 km of spatial resolution and 3 hours of time resolution. Analyses in terms of comparison between the rainfall estimated by the dataset and the observations (available from the local raingauges network) were carried out, and a bias correction was also performed in order to better match the observed climatology. An extreme analysis was eventually carried on the streamflows time series obtained by the simulations, by comparing them with the results of the same hydrological model fed with the observed time series of rainfall. The results of the analysis are shown and discussed.

Precipitation Characteristics in Tropical Africa Using Satellite and In-Situ Observations

NASA Technical Reports Server (NTRS)

Dezfuli, Amin; Ichoku, Charles; Huffman, George; Mohr, Karen

2017-01-01

Tropical Africa receives nearly all its precipitation as a result of convection. The characteristics of rain-producing systems in this region, despite their crucial role in regional and global circulation, have not been well-understood. This is mainly due to the lack of in situ observations. Here, we have used precipitation records from the Trans-African Hydro-Meteorological Observatory (TAHMO) to improve our knowledge about the rainfall systems in the region, and to validate the recently-released IMERG precipitation product. The high temporal resolution of the gauge data has allowed us to identify three classes of rain events based on their duration and intensity. The contribution of each class to the total rainfall and the favorable surface atmospheric conditions for each class have been examined. As IMERG aims to continue the legacy of its predecessor, TMPA, and provide higher resolution data, continent-wide comparisons are made between these two products. IMERG, due to its improved temporal resolution, shows some advantages over TMPA in capturing the diurnal cycle and propagation of the meso-scale convective systems. However, the performance of the two satellite-based products varies by season, region and the evaluation statistics. The results of this study serve as a basis for our ongoing work on the impacts of biomass burning on precipitation processes in Africa.

On the relation between SMMR 37-GHz polarization difference and the rainfall over Africa and Australia

NASA Technical Reports Server (NTRS)

Choudhury, Bhaskar J.; Digirolamo, Nicolo E.

1994-01-01

A major difficulty in interpreting coarse resolution satellite data in terms of land surface characteristics is unavailability of spatially and temporally representative ground observations. Under certain conditions rainfall has been found to provide a proxy measure for surface characteristics, and thus a relation between satellite observations and rainfall might provide an indirect approach for relating satellite data to these characteristics. Relationship between rainfall over Africa and Australia and 7-year average (1979-1985) polarization difference (PD) at 37 GHz from scanning multichannel microwave radiometer (SMMR) on board the Nimbus-7 satellite is studied in this paper. Quantitative methods have been used to screen (accept or reject) PD data considering antenna pattern, geolocation uncertainty, water contamination, surface roughness, and adverse effect of drought on the relation between rainfall and surface characteristics. The rainfall data used in the present analysis are climatologic averages and also 1979-1985 averages, and no screening has been applied to this data. The PD data has been screened considering only the location of rainfall stations, without any regard to rainfall amounts. The present analysis confirms a non-linear relation between rainfall and PD published previously.

Improving Quantitative Precipitation Estimation via Data Fusion of High-Resolution Ground-based Radar Network and CMORPH Satellite-based Product

NASA Astrophysics Data System (ADS)

Cifelli, R.; Chen, H.; Chandrasekar, V.; Xie, P.

2015-12-01

A large number of precipitation products at multi-scales have been developed based upon satellite, radar, and/or rain gauge observations. However, how to produce optimal rainfall estimation for a given region is still challenging due to the spatial and temporal sampling difference of different sensors. In this study, we develop a data fusion mechanism to improve regional quantitative precipitation estimation (QPE) by utilizing satellite-based CMORPH product, ground radar measurements, as well as numerical model simulations. The CMORPH global precipitation product is essentially derived based on retrievals from passive microwave measurements and infrared observations onboard satellites (Joyce et al. 2004). The fine spatial-temporal resolution of 0.05o Lat/Lon and 30-min is appropriate for regional hydrologic and climate studies. However, it is inadequate for localized hydrometeorological applications such as urban flash flood forecasting. Via fusion of the Regional CMORPH product and local precipitation sensors, the high-resolution QPE performance can be improved. The area of interest is the Dallas-Fort Worth (DFW) Metroplex, which is the largest land-locked metropolitan area in the U.S. In addition to an NWS dual-polarization S-band WSR-88DP radar (i.e., KFWS radar), DFW hosts the high-resolution dual-polarization X-band radar network developed by the center for Collaborative Adaptive Sensing of the Atmosphere (CASA). This talk will present a general framework of precipitation data fusion based on satellite and ground observations. The detailed prototype architecture of using regional rainfall instruments to improve regional CMORPH precipitation product via multi-scale fusion techniques will also be discussed. Particularly, the temporal and spatial fusion algorithms developed for the DFW Metroplex will be described, which utilizes CMORPH product, S-band WSR-88DP, and X-band CASA radar measurements. In order to investigate the uncertainties associated with each individual product and demonstrate the precipitation data fusion performance, both individual and fused QPE products are evaluated using rainfall measurements from a disdrometer and gauge network.

A reference data set of hillslope rainfall-runoff response, Panola Mountain Research Watershed, United States

USGS Publications Warehouse

Tromp-van, Meerveld; James, A.L.; McDonnell, Jeffery J.; Peters, N.E.

2008-01-01

Although many hillslope hydrologic investigations have been conducted in different climate, topographic, and geologic settings, subsurface stormflow remains a poorly characterized runoff process. Few, if any, of the existing data sets from these hillslope investigations are available for use by the scientific community for model development and validation or conceptualization of subsurface stormflow. We present a high-resolution spatial and temporal rainfall-runoff data set generated from the Panola Mountain Research Watershed trenched experimental hillslope. The data set includes surface and subsurface (bedrock surface) topographic information and time series of lateral subsurface flow at the trench, rainfall, and subsurface moisture content (distributed soil moisture content and groundwater levels) from January to June 2002. Copyright 2008 by the American Geophysical Union.

The assessment of Global Precipitation Measurement estimates over the Indian subcontinent

NASA Astrophysics Data System (ADS)

Murali Krishna, U. V.; Das, Subrata Kumar; Deshpande, Sachin M.; Doiphode, S. L.; Pandithurai, G.

2017-08-01

Accurate and real-time precipitation estimation is a challenging task for current and future spaceborne measurements, which is essential to understand the global hydrological cycle. Recently, the Global Precipitation Measurement (GPM) satellites were launched as a next-generation rainfall mission for observing the global precipitation characteristics. The purpose of the GPM is to enhance the spatiotemporal resolution of global precipitation. The main objective of the present study is to assess the rainfall products from the GPM, especially the Integrated Multi-satellitE Retrievals for the GPM (IMERG) data by comparing with the ground-based observations. The multitemporal scale evaluations of rainfall involving subdaily, diurnal, monthly, and seasonal scales were performed over the Indian subcontinent. The comparison shows that the IMERG performed better than the Tropical Rainfall Measuring Mission (TRMM)-3B42, although both rainfall products underestimated the observed rainfall compared to the ground-based measurements. The analyses also reveal that the TRMM-3B42 and IMERG data sets are able to represent the large-scale monsoon rainfall spatial features but are having region-specific biases. The IMERG shows significant improvement in low rainfall estimates compared to the TRMM-3B42 for selected regions. In the spatial distribution, the IMERG shows higher rain rates compared to the TRMM-3B42, due to its enhanced spatial and temporal resolutions. Apart from this, the characteristics of raindrop size distribution (DSD) obtained from the GPM mission dual-frequency precipitation radar is assessed over the complex mountain terrain site in the Western Ghats, India, using the DSD measured by a Joss-Waldvogel disdrometer.

Using Four Downscaling Techniques to Characterize Uncertainty in Updating Intensity-Duration-Frequency Curves Under Climate Change

NASA Astrophysics Data System (ADS)

Cook, L. M.; Samaras, C.; McGinnis, S. A.

2017-12-01

Intensity-duration-frequency (IDF) curves are a common input to urban drainage design, and are used to represent extreme rainfall in a region. As rainfall patterns shift into a non-stationary regime as a result of climate change, these curves will need to be updated with future projections of extreme precipitation. Many regions have begun to update these curves to reflect the trends from downscaled climate models; however, few studies have compared the methods for doing so, as well as the uncertainty that results from the selection of the native grid scale and temporal resolution of the climate model. This study examines the variability in updated IDF curves for Pittsburgh using four different methods for adjusting gridded regional climate model (RCM) outputs into station scale precipitation extremes: (1) a simple change factor applied to observed return levels, (2) a naïve adjustment of stationary and non-stationary Generalized Extreme Value (GEV) distribution parameters, (3) a transfer function of the GEV parameters from the annual maximum series, and (4) kernel density distribution mapping bias correction of the RCM time series. Return level estimates (rainfall intensities) and confidence intervals from these methods for the 1-hour to 48-hour duration are tested for sensitivity to the underlying spatial and temporal resolution of the climate ensemble from the NA-CORDEX project, as well as, the future time period for updating. The first goal is to determine if uncertainty is highest for: (i) the downscaling method, (ii) the climate model resolution, (iii) the climate model simulation, (iv) the GEV parameters, or (v) the future time period examined. Initial results of the 6-hour, 10-year return level adjusted with the simple change factor method using four climate model simulations of two different spatial resolutions show that uncertainty is highest in the estimation of the GEV parameters. The second goal is to determine if complex downscaling methods and high-resolution climate models are necessary for updating, or if simpler methods and lower resolution climate models will suffice. The final results can be used to inform the most appropriate method and climate model resolutions to use for updating IDF curves for urban drainage design.

Temporal rainfall estimation using input data reduction and model inversion

NASA Astrophysics Data System (ADS)

Wright, A. J.; Vrugt, J. A.; Walker, J. P.; Pauwels, V. R. N.

2016-12-01

Floods are devastating natural hazards. To provide accurate, precise and timely flood forecasts there is a need to understand the uncertainties associated with temporal rainfall and model parameters. The estimation of temporal rainfall and model parameter distributions from streamflow observations in complex dynamic catchments adds skill to current areal rainfall estimation methods, allows for the uncertainty of rainfall input to be considered when estimating model parameters and provides the ability to estimate rainfall from poorly gauged catchments. Current methods to estimate temporal rainfall distributions from streamflow are unable to adequately explain and invert complex non-linear hydrologic systems. This study uses the Discrete Wavelet Transform (DWT) to reduce rainfall dimensionality for the catchment of Warwick, Queensland, Australia. The reduction of rainfall to DWT coefficients allows the input rainfall time series to be simultaneously estimated along with model parameters. The estimation process is conducted using multi-chain Markov chain Monte Carlo simulation with the DREAMZS algorithm. The use of a likelihood function that considers both rainfall and streamflow error allows for model parameter and temporal rainfall distributions to be estimated. Estimation of the wavelet approximation coefficients of lower order decomposition structures was able to estimate the most realistic temporal rainfall distributions. These rainfall estimates were all able to simulate streamflow that was superior to the results of a traditional calibration approach. It is shown that the choice of wavelet has a considerable impact on the robustness of the inversion. The results demonstrate that streamflow data contains sufficient information to estimate temporal rainfall and model parameter distributions. The extent and variance of rainfall time series that are able to simulate streamflow that is superior to that simulated by a traditional calibration approach is a demonstration of equifinality. The use of a likelihood function that considers both rainfall and streamflow error combined with the use of the DWT as a model data reduction technique allows the joint inference of hydrologic model parameters along with rainfall.

Intensity-Duration-Frequency curves from remote sensing datasets: direct comparison of weather radar and CMORPH over the Eastern Mediterranean

NASA Astrophysics Data System (ADS)

Morin, Efrat; Marra, Francesco; Peleg, Nadav; Mei, Yiwen; Anagnostou, Emmanouil N.

2017-04-01

Rainfall frequency analysis is used to quantify the probability of occurrence of extreme rainfall and is traditionally based on rain gauge records. The limited spatial coverage of rain gauges is insufficient to sample the spatiotemporal variability of extreme rainfall and to provide the areal information required by management and design applications. Conversely, remote sensing instruments, even if quantitative uncertain, offer coverage and spatiotemporal detail that allow overcoming these issues. In recent years, remote sensing datasets began to be used for frequency analyses, taking advantage of increased record lengths and quantitative adjustments of the data. However, the studies so far made use of concepts and techniques developed for rain gauge (i.e. point or multiple-point) data and have been validated by comparison with gauge-derived analyses. These procedures add further sources of uncertainty and prevent from isolating between data and methodological uncertainties and from fully exploiting the available information. In this study, we step out of the gauge-centered concept presenting a direct comparison between at-site Intensity-Duration-Frequency (IDF) curves derived from different remote sensing datasets on corresponding spatial scales, temporal resolutions and records. We analyzed 16 years of homogeneously corrected and gauge-adjusted C-Band weather radar estimates, high-resolution CMORPH and gauge-adjusted high-resolution CMORPH over the Eastern Mediterranean. Results of this study include: (a) good spatial correlation between radar and satellite IDFs ( 0.7 for 2-5 years return period); (b) consistent correlation and dispersion in the raw and gauge adjusted CMORPH; (c) bias is almost uniform with return period for 12-24 h durations; (d) radar identifies thicker tail distributions than CMORPH and the tail of the distributions depends on the spatial and temporal scales. These results demonstrate the potential of remote sensing datasets for rainfall frequency analysis for management (e.g. warning and early-warning systems) and design (e.g. sewer design, large scale drainage planning)

Spatial distribution and temporal trends of rainfall erosivity in mainland China for 1951-2010

Treesearch

Wei Qin; Qiankun Guo; Changqing Zuo; Zhijie Shan; Liang Ma; Ge Sun

2016-01-01

Rainfall erosivity is an important factor for estimating soil erosion rates. Understanding the spatial distributionand temporal trends of rainfall erosivity is especially critical for soil erosion risk assessment and soil conservationplanning in mainland China. However, reports on the spatial distribution and temporal trends of rainfall...

A gridded hourly rainfall dataset for the UK applied to a national physically-based modelling system

NASA Astrophysics Data System (ADS)

Lewis, Elizabeth; Blenkinsop, Stephen; Quinn, Niall; Freer, Jim; Coxon, Gemma; Woods, Ross; Bates, Paul; Fowler, Hayley

2016-04-01

An hourly gridded rainfall product has great potential for use in many hydrological applications that require high temporal resolution meteorological data. One important example of this is flood risk management, with flooding in the UK highly dependent on sub-daily rainfall intensities amongst other factors. Knowledge of sub-daily rainfall intensities is therefore critical to designing hydraulic structures or flood defences to appropriate levels of service. Sub-daily rainfall rates are also essential inputs for flood forecasting, allowing for estimates of peak flows and stage for flood warning and response. In addition, an hourly gridded rainfall dataset has significant potential for practical applications such as better representation of extremes and pluvial flash flooding, validation of high resolution climate models and improving the representation of sub-daily rainfall in weather generators. A new 1km gridded hourly rainfall dataset for the UK has been created by disaggregating the daily Gridded Estimates of Areal Rainfall (CEH-GEAR) dataset using comprehensively quality-controlled hourly rain gauge data from over 1300 observation stations across the country. Quality control measures include identification of frequent tips, daily accumulations and dry spells, comparison of daily totals against the CEH-GEAR daily dataset, and nearest neighbour checks. The quality control procedure was validated against historic extreme rainfall events and the UKCP09 5km daily rainfall dataset. General use of the dataset has been demonstrated by testing the sensitivity of a physically-based hydrological modelling system for Great Britain to the distribution and rates of rainfall and potential evapotranspiration. Of the sensitivity tests undertaken, the largest improvements in model performance were seen when an hourly gridded rainfall dataset was combined with potential evapotranspiration disaggregated to hourly intervals, with 61% of catchments showing an increase in NSE between observed and simulated streamflows as a result of more realistic sub-daily meteorological forcing.

A Bayesian beta distribution model for estimating rainfall IDF curves in a changing climate

NASA Astrophysics Data System (ADS)

Lima, Carlos H. R.; Kwon, Hyun-Han; Kim, Jin-Young

2016-09-01

The estimation of intensity-duration-frequency (IDF) curves for rainfall data comprises a classical task in hydrology studies to support a variety of water resources projects, including urban drainage and the design of flood control structures. In a changing climate, however, traditional approaches based on historical records of rainfall and on the stationary assumption can be inadequate and lead to poor estimates of rainfall intensity quantiles. Climate change scenarios built on General Circulation Models offer a way to access and estimate future changes in spatial and temporal rainfall patterns at the daily scale at the utmost, which is not as fine temporal resolution as required (e.g. hours) to directly estimate IDF curves. In this paper we propose a novel methodology based on a four-parameter beta distribution to estimate IDF curves conditioned on the observed (or simulated) daily rainfall, which becomes the time-varying upper bound of the updated nonstationary beta distribution. The inference is conducted in a Bayesian framework that provides a better way to take into account the uncertainty in the model parameters when building the IDF curves. The proposed model is tested using rainfall data from four stations located in South Korea and projected climate change Representative Concentration Pathways (RCPs) scenarios 6 and 8.5 from the Met Office Hadley Centre HadGEM3-RA model. The results show that the developed model fits the historical data as good as the traditional Generalized Extreme Value (GEV) distribution but is able to produce future IDF curves that significantly differ from the historically based IDF curves. The proposed model predicts for the stations and RCPs scenarios analysed in this work an increase in the intensity of extreme rainfalls of short duration with long return periods.

Spatial and temporal heterogeneity of water soil erosion in a Mediterranean rain-fed crop

NASA Astrophysics Data System (ADS)

López-Vicente, M.; Quijano, L.; Gaspar, L.; Machín, J.; Navas, A.

2012-04-01

Fertile soil loss by raindrop impact and runoff processes in croplands presents significant variations at temporal and spatial scales. The combined use of advanced GIS techniques and detailed databases allows high resolution mapping of runoff and soil erosion processes. In this study the monthly values of soil loss are calculated in a medium size field of rain-fed winter barley and its drainage area located in the Central Spanish Pre-Pyrenees. The field is surrounded by narrow strips of dense Mediterranean vegetation (mainly holm oaks) and grass. Man-made infrastructures (paved trails and drainage ditches) modify the overland flow pathways and the study site appears hydrologically closed in its northern and western boundaries. This area has a continental Mediterranean climate with two humid periods, one in spring and a second in autumn and a dry summer with rainfall events of high intensity from July to October. The average annual rainfall is 495 mm and the average monthly rainfall intensity ranges from 1.1 mm / h in January to 7.4 mm / h in July. The predicted rates were obtained after running the RMMF model (Morgan, 2001) with the enhancements made to this model by Morgan and Duzant (2008) to the topographic module, and by López-Vicente and Navas (2010) to the hydrological module. A total of 613 soil samples were collected and all input and output maps were generated at high spatial resolution (1 x 1 m of cell size) with ArcMapTM 10.0. A map of effective cumulative runoff was calculated for each month of the year with a weighted multiple flow algorithm and four sub-catchments were distinguished within the field. The average soil erosion in the cultivated area is 1.32 Mg / ha yr and the corresponding map shows a high spatial variability (s.d. = 7.52 Mg / ha yr). The highest values of soil erosion appear in those areas where overland flow is concentrated and slope steepness is higher. The unpaved trail present the highest values of soil erosion with an average value of 72.23 Mg / ha yr, whereas the grass and forested areas have annual rates lower than 0.1 Mg / ha yr. The highest values of soil erosion appear in March, April, May, October and November showing a very good correlation with the depth of monthly rainfall (Pearson's r = 0.97) and a good correlation with the number of rainy days per month (Pearson's r = 0.76). However, no correlation was obtained with the values of monthly rainfall intensity. The availability of a detailed database of soil properties, weather values and a high resolution DEM allows mapping and calculating the spatial and temporal variations of the soil erosion processes within the cultivated area and the area surrounding the crop. Thus, the application of soil erosion models at high spatial and temporal resolution improves their predicting capability due to the complexity and large number of relevant interactions between the different sub-factors.

Characterizing spatial and temporal variability in methane gas-flux dynamics of subtropical wetlands in the Big Cypress National Preserve, Florida

NASA Astrophysics Data System (ADS)

Sirianni, M.; Comas, X.; Shoemaker, B.

2017-12-01

Wetland methane emissions are highly variable both in space and time, and are controlled by changes in certain biogeochemical controls (i.e. organic matter availability; redox potential) and/or other environmental factors (i.e. soil temperature; water level). Consequently, hot spots (areas with disproportionally high emissions) may develop where biogeochemical and environmental conditions are especially conducive for enhancing certain microbial processes such as methanogenesis. The Big Cypress National Preserve is a collection of subtropical wetlands in southwestern Florida, including extensive forested (cypress, pine, hardwood) and sawgrass ecosystems that dry and flood annually in response to rainfall. In addition to rainfall, hydroperiod, fire regime, elevation above mean sea level, dominant vegetation type and underlying geological controls contribute to the development and evolution of organic and calcitic soils found throughout the Preserve. Currently, the U.S. Geological Survey employs eddy covariance methods within the Preserve to quantify carbon and methane exchanges over several spatially extensive vegetation communities. While eddy covariance towers are a convenient tool for measuring gas exchanges at the ecosystem scale, their spatially extensive footprint (hundreds of meters) may mask smaller scale spatial variabilities that may be conducive to the development of hot spots. Similarly, temporal resolution (i.e. sampling effort) at scales smaller that the eddy covariance measurement footprint is important since low resolution data may overlook rapid emission events and the temporal variability of discrete hot spots. In this work, we intend to estimate small-scale contributions of organic and calcitic soils to gas exchanges measured by the eddy covariance towers using a unique combination of ground penetrating radar (GPR), capacitance probes, gas traps, and time-lapse photography. By using an array of methods that vary in spatio-temporal resolution, we hope to better understand the uncertainties associated with measuring wetland methane fluxes across different spatial and temporal scales. Our results have implications for characterizing and refining methane flux estimates in subtropical peat soils that could be used for climate models.

Temporal rainfall disaggregation using a multiplicative cascade model for spatial application in urban hydrology

NASA Astrophysics Data System (ADS)

Müller, H.; Haberlandt, U.

2018-01-01

Rainfall time series of high temporal resolution and spatial density are crucial for urban hydrology. The multiplicative random cascade model can be used for temporal rainfall disaggregation of daily data to generate such time series. Here, the uniform splitting approach with a branching number of 3 in the first disaggregation step is applied. To achieve a final resolution of 5 min, subsequent steps after disaggregation are necessary. Three modifications at different disaggregation levels are tested in this investigation (uniform splitting at Δt = 15 min, linear interpolation at Δt = 7.5 min and Δt = 3.75 min). Results are compared both with observations and an often used approach, based on the assumption that a time steps with Δt = 5.625 min, as resulting if a branching number of 2 is applied throughout, can be replaced with Δt = 5 min (called the 1280 min approach). Spatial consistence is implemented in the disaggregated time series using a resampling algorithm. In total, 24 recording stations in Lower Saxony, Northern Germany with a 5 min resolution have been used for the validation of the disaggregation procedure. The urban-hydrological suitability is tested with an artificial combined sewer system of about 170 hectares. The results show that all three variations outperform the 1280 min approach regarding reproduction of wet spell duration, average intensity, fraction of dry intervals and lag-1 autocorrelation. Extreme values with durations of 5 min are also better represented. For durations of 1 h, all approaches show only slight deviations from the observed extremes. The applied resampling algorithm is capable to achieve sufficient spatial consistence. The effects on the urban hydrological simulations are significant. Without spatial consistence, flood volumes of manholes and combined sewer overflow are strongly underestimated. After resampling, results using disaggregated time series as input are in the range of those using observed time series. Best overall performance regarding rainfall statistics are obtained by the method in which the disaggregation process ends at time steps with 7.5 min duration, deriving the 5 min time steps by linear interpolation. With subsequent resampling this method leads to a good representation of manhole flooding and combined sewer overflow volume in terms of hydrological simulations and outperforms the 1280 min approach.

A Satellite Infrared Technique for Diurnal Rainfall Variability Studies

NASA Technical Reports Server (NTRS)

Anagnostou, Emmanouil

1998-01-01

Reliable information on the distribution of precipitation at high temporal resolution (

Rainfall Induced Landslides in Puerto Rico (Invited)

NASA Astrophysics Data System (ADS)

Lepore, C.; Kamal, S.; Arnone, E.; Noto, V.; Shanahan, P.; Bras, R. L.

2009-12-01

Landslides are a major geologic hazard in the United States, typically triggered by rainfall, earthquakes, volcanoes and human activity. Rainfall-induced landslides are the most common type in the island of Puerto Rico, with one or two large events per year. We performed an island-wide determination of static landslide susceptibility and hazard assessment as well as dynamic modeling of rainfall-induced shallow landslides in a particular hydrologic basin. Based on statistical analysis of past landslides, we determined that reliable prediction of the susceptibility to landslides is strongly dependent on the resolution of the digital elevation model (DEM) employed and the reliability of the rainfall data. A distributed hydrology model capable of simulating landslides, tRIBS-VEGGIE, has been implemented for the first time in a humid tropical environment like Puerto Rico. The Mameyes basin, located in the Luquillo Experimental Forest in Puerto Rico, was selected for modeling based on the availability of soil, vegetation, topographical, meteorological and historic landslide data. .Application of the model yields a temporal and spatial distribution of predicted rainfall-induced landslides, which is used to predict the dynamic susceptibility of the basin to landslides.

Development of microwave rainfall retrieval algorithm for climate applications

NASA Astrophysics Data System (ADS)

KIM, J. H.; Shin, D. B.

2014-12-01

With the accumulated satellite datasets for decades, it is possible that satellite-based data could contribute to sustained climate applications. Level-3 products from microwave sensors for climate applications can be obtained from several algorithms. For examples, the Microwave Emission brightness Temperature Histogram (METH) algorithm produces level-3 rainfalls directly, whereas the Goddard profiling (GPROF) algorithm first generates instantaneous rainfalls and then temporal and spatial averaging process leads to level-3 products. The rainfall algorithm developed in this study follows a similar approach to averaging instantaneous rainfalls. However, the algorithm is designed to produce instantaneous rainfalls at an optimal resolution showing reduced non-linearity in brightness temperature (TB)-rain rate(R) relations. It is found that the resolution tends to effectively utilize emission channels whose footprints are relatively larger than those of scattering channels. This algorithm is mainly composed of a-priori databases (DBs) and a Bayesian inversion module. The DB contains massive pairs of simulated microwave TBs and rain rates, obtained by WRF (version 3.4) and RTTOV (version 11.1) simulations. To improve the accuracy and efficiency of retrieval process, data mining technique is additionally considered. The entire DB is classified into eight types based on Köppen climate classification criteria using reanalysis data. Among these sub-DBs, only one sub-DB which presents the most similar physical characteristics is selected by considering the thermodynamics of input data. When the Bayesian inversion is applied to the selected DB, instantaneous rain rate with 6 hours interval is retrieved. The retrieved monthly mean rainfalls are statistically compared with CMAP and GPCP, respectively.

Flood modelling with global precipitation measurement (GPM) satellite rainfall data: a case study of Dehradun, Uttarakhand, India

NASA Astrophysics Data System (ADS)

Sai Krishna, V. V.; Dikshit, Anil Kumar; Pandey, Kamal

2016-05-01

Urban expansion, water bodies and climate change are inextricably linked with each other. The macro and micro level climate changes are leading to extreme precipitation events which have severe consequences on flooding in urban areas. Flood simulations shall be helpful in demarcation of flooded areas and effective flood planning and preparedness. The temporal availability of satellite rainfall data at varying spatial scale of 0.10 to 0.50 is helpful in near real time flood simulations. The present research aims at analysing stream flow and runoff to monitor flood condition using satellite rainfall data in a hydrologic model. The satellite rainfall data used in the research was NASA's Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG), which is available at 30 minutes temporal resolution. Landsat data was used for mapping the water bodies in the study area. Land use land cover (LULC) data was prepared using Landsat 8 data with maximum likelihood technique that was provided as an input to the HEC-HMS hydrological model. The research was applied to one of the urbanized cities of India, viz. Dehradun, which is the capital of Uttarakhand State. The research helped in identifying the flood vulnerability at the basin level on the basis of the runoff and various socio economic parameters using multi criteria analysis.

A Monte-Carlo Bayesian framework for urban rainfall error modelling

NASA Astrophysics Data System (ADS)

Ochoa Rodriguez, Susana; Wang, Li-Pen; Willems, Patrick; Onof, Christian

2016-04-01

Rainfall estimates of the highest possible accuracy and resolution are required for urban hydrological applications, given the small size and fast response which characterise urban catchments. While significant progress has been made in recent years towards meeting rainfall input requirements for urban hydrology -including increasing use of high spatial resolution radar rainfall estimates in combination with point rain gauge records- rainfall estimates will never be perfect and the true rainfall field is, by definition, unknown [1]. Quantifying the residual errors in rainfall estimates is crucial in order to understand their reliability, as well as the impact that their uncertainty may have in subsequent runoff estimates. The quantification of errors in rainfall estimates has been an active topic of research for decades. However, existing rainfall error models have several shortcomings, including the fact that they are limited to describing errors associated to a single data source (i.e. errors associated to rain gauge measurements or radar QPEs alone) and to a single representative error source (e.g. radar-rain gauge differences, spatial temporal resolution). Moreover, rainfall error models have been mostly developed for and tested at large scales. Studies at urban scales are mostly limited to analyses of propagation of errors in rain gauge records-only through urban drainage models and to tests of model sensitivity to uncertainty arising from unmeasured rainfall variability. Only few radar rainfall error models -originally developed for large scales- have been tested at urban scales [2] and have been shown to fail to well capture small-scale storm dynamics, including storm peaks, which are of utmost important for urban runoff simulations. In this work a Monte-Carlo Bayesian framework for rainfall error modelling at urban scales is introduced, which explicitly accounts for relevant errors (arising from insufficient accuracy and/or resolution) in multiple data sources (in this case radar and rain gauge estimates typically available at present), while at the same time enabling dynamic combination of these data sources (thus not only quantifying uncertainty, but also reducing it). This model generates an ensemble of merged rainfall estimates, which can then be used as input to urban drainage models in order to examine how uncertainties in rainfall estimates propagate to urban runoff estimates. The proposed model is tested using as case study a detailed rainfall and flow dataset, and a carefully verified urban drainage model of a small (~9 km2) pilot catchment in North-East London. The model has shown to well characterise residual errors in rainfall data at urban scales (which remain after the merging), leading to improved runoff estimates. In fact, the majority of measured flow peaks are bounded within the uncertainty area produced by the runoff ensembles generated with the ensemble rainfall inputs. REFERENCES: [1] Ciach, G. J. & Krajewski, W. F. (1999). On the estimation of radar rainfall error variance. Advances in Water Resources, 22 (6), 585-595. [2] Rico-Ramirez, M. A., Liguori, S. & Schellart, A. N. A. (2015). Quantifying radar-rainfall uncertainties in urban drainage flow modelling. Journal of Hydrology, 528, 17-28.

Rainfall variability over southern Africa: an overview of current research using satellite and climate model data

NASA Astrophysics Data System (ADS)

Williams, C.; Kniveton, D.; Layberry, R.

2009-04-01

It is increasingly accepted that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. A change in the distribution and magnitude of extreme rainfall events (associated with changing variability), such as droughts or flooding, may have a far greater impact on human and natural systems than a changing mean. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The subcontinent is considered especially vulnerable to and ill-equipped (in terms of adaptation) for extreme events, due to a number of factors including extensive poverty, famine, disease and political instability. Rainfall variability is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. In this research, satellite-derived rainfall data are used as a basis for undertaking model experiments using a state-of-the-art climate model, run at both high and low spatial resolution. Once the model's ability to reproduce extremes has been assessed, idealised regions of sea surface temperature (SST) anomalies are used to force the model, with the overall aim of investigating the ways in which SST anomalies influence rainfall extremes over southern Africa. In this paper, a brief overview is given of the authors' research to date, pertaining to southern African rainfall. This covers (i) a description of present-day rainfall variability over southern Africa; (ii) a comparison of model simulated daily rainfall with the satellite-derived dataset; (iii) results from sensitivity testing of the model's domain size; and (iv) results from the idealised SST experiments.

Coupling Poisson rectangular pulse and multiplicative microcanonical random cascade models to generate sub-daily precipitation timeseries

NASA Astrophysics Data System (ADS)

Pohle, Ina; Niebisch, Michael; Müller, Hannes; Schümberg, Sabine; Zha, Tingting; Maurer, Thomas; Hinz, Christoph

2018-07-01

To simulate the impacts of within-storm rainfall variabilities on fast hydrological processes, long precipitation time series with high temporal resolution are required. Due to limited availability of observed data such time series are typically obtained from stochastic models. However, most existing rainfall models are limited in their ability to conserve rainfall event statistics which are relevant for hydrological processes. Poisson rectangular pulse models are widely applied to generate long time series of alternating precipitation events durations and mean intensities as well as interstorm period durations. Multiplicative microcanonical random cascade (MRC) models are used to disaggregate precipitation time series from coarse to fine temporal resolution. To overcome the inconsistencies between the temporal structure of the Poisson rectangular pulse model and the MRC model, we developed a new coupling approach by introducing two modifications to the MRC model. These modifications comprise (a) a modified cascade model ("constrained cascade") which preserves the event durations generated by the Poisson rectangular model by constraining the first and last interval of a precipitation event to contain precipitation and (b) continuous sigmoid functions of the multiplicative weights to consider the scale-dependency in the disaggregation of precipitation events of different durations. The constrained cascade model was evaluated in its ability to disaggregate observed precipitation events in comparison to existing MRC models. For that, we used a 20-year record of hourly precipitation at six stations across Germany. The constrained cascade model showed a pronounced better agreement with the observed data in terms of both the temporal pattern of the precipitation time series (e.g. the dry and wet spell durations and autocorrelations) and event characteristics (e.g. intra-event intermittency and intensity fluctuation within events). The constrained cascade model also slightly outperformed the other MRC models with respect to the intensity-frequency relationship. To assess the performance of the coupled Poisson rectangular pulse and constrained cascade model, precipitation events were stochastically generated by the Poisson rectangular pulse model and then disaggregated by the constrained cascade model. We found that the coupled model performs satisfactorily in terms of the temporal pattern of the precipitation time series, event characteristics and the intensity-frequency relationship.

A seasonal Bartlett-Lewis Rectangular Pulse model

NASA Astrophysics Data System (ADS)

Ritschel, Christoph; Agbéko Kpogo-Nuwoklo, Komlan; Rust, Henning; Ulbrich, Uwe; Névir, Peter

2016-04-01

Precipitation time series with a high temporal resolution are needed as input for several hydrological applications, e.g. river runoff or sewer system models. As adequate observational data sets are often not available, simulated precipitation series come to use. Poisson-cluster models are commonly applied to generate these series. It has been shown that this class of stochastic precipitation models is able to well reproduce important characteristics of observed rainfall. For the gauge based case study presented here, the Bartlett-Lewis rectangular pulse model (BLRPM) has been chosen. As it has been shown that certain model parameters vary with season in a midlatitude moderate climate due to different rainfall mechanisms dominating in winter and summer, model parameters are typically estimated separately for individual seasons or individual months. Here, we suggest a simultaneous parameter estimation for the whole year under the assumption that seasonal variation of parameters can be described with harmonic functions. We use an observational precipitation series from Berlin with a high temporal resolution to exemplify the approach. We estimate BLRPM parameters with and without this seasonal extention and compare the results in terms of model performance and robustness of the estimation.

Urban pluvial flood prediction: a case study evaluating radar rainfall nowcasts and numerical weather prediction models as model inputs.

PubMed

Thorndahl, Søren; Nielsen, Jesper Ellerbæk; Jensen, David Getreuer

2016-12-01

Flooding produced by high-intensive local rainfall and drainage system capacity exceedance can have severe impacts in cities. In order to prepare cities for these types of flood events - especially in the future climate - it is valuable to be able to simulate these events numerically, both historically and in real-time. There is a rather untested potential in real-time prediction of urban floods. In this paper, radar data observations with different spatial and temporal resolution, radar nowcasts of 0-2 h leadtime, and numerical weather models with leadtimes up to 24 h are used as inputs to an integrated flood and drainage systems model in order to investigate the relative difference between different inputs in predicting future floods. The system is tested on the small town of Lystrup in Denmark, which was flooded in 2012 and 2014. Results show it is possible to generate detailed flood maps in real-time with high resolution radar rainfall data, but rather limited forecast performance in predicting floods with leadtimes more than half an hour.

Assessing the Change in Rainfall Characteristics and Trends for the Southern African ITCZ Region

NASA Astrophysics Data System (ADS)

Baumberg, Verena; Weber, Torsten; Helmschrot, Jörg

2015-04-01

Southern Africa is strongly influenced by the movement and intensity of the Intertropical Convergence Zone (ITCZ) thus determining the climate in this region with distinct seasonal and inter-annual rainfall dynamics. The amount and variability of rainfall affect the various ecosystems by controlling the hydrological system, regulating water availability and determining agricultural practices. Changes in rainfall characteristics potentially caused by climate change are of uppermost relevance for both ecosystem functioning and human well-being in this region and, thus, need to be investigated. To analyse the rainfall variability governed by the ITCZ in southern Africa, observational daily rainfall datasets with a high spatial resolution of 0.25° x 0.25° (about 28 km x 28 km) from satellite-based Tropical Rainfall Measuring Mission (TRMM) and Global Land Data Assimilation System (GLDAS) are used. These datasets extend from 1998 to 2008 and 1948 to 2010, respectively, and allow for the assessment of rainfall characteristics over different spatial and temporal scales. Furthermore, a comparison of TRMM and GLDAS and, where available, with observed data will be made to determine the differences of both datasets. In order to quantify the intra- and inner-annual variability of rainfall, the amount of total rainfall, duration of rainy seasons and number of dry spells along with further indices are calculated from the observational datasets. Over the southern African ITCZ region, the rainfall characteristics change moving from wetter north to the drier south, but also from west to east, i.e. the coast to the interior. To address expected spatial and temporal variabilities, the assessment of changes in the rainfall parameters will be carried out for different transects in zonal and meridional directions over the region affected by the ITCZ. Revealing trends over more than 60 years, the results will help to identify and understand potential impacts of climate change on rainfall characteristics for the southern African ITCZ region. The findings of this study will feed into various ecosystem assessment and biodiversity change studies in Angola and Zambia.

Sensitivity of Rainfall Extremes Under Warming Climate in Urban India

NASA Astrophysics Data System (ADS)

Ali, H.; Mishra, V.

2017-12-01

Extreme rainfall events in urban India halted transportation, damaged infrastructure, and affected human lives. Rainfall extremes are projected to increase under the future climate. We evaluated the relationship (scaling) between rainfall extremes at different temporal resolutions (daily, 3-hourly, and 30 minutes), daily dewpoint temperature (DPT) and daily air temperature at 850 hPa (T850) for 23 urban areas in India. Daily rainfall extremes obtained from Global Surface Summary of Day Data (GSOD) showed positive regression slopes for most of the cities with median of 14%/K for the period of 1979-2013 for DPT and T850, which is higher than Clausius-Clapeyron (C-C) rate ( 7%). Moreover, sub-daily rainfall extremes are more sensitive to both DPT and T850. For instance, 3-hourly rainfall extremes obtained from Tropical Rainfall Measurement Mission (TRMM 3B42 V7) showed regression slopes more than 16%/K aginst DPT and T850 for the period of 1998-2015. Half-hourly rainfall extremes from the Integrated Multi-satellitE Retrievals (IMERGE) of Global precipitation mission (GPM) also showed higher sensitivity against changes in DPT and T850. The super scaling of rainfall extremes against changes in DPT and T850 can be attributed to convective nature of precipitation in India. Our results show that urban India may witness non-stationary rainfall extremes, which, in turn will affect stromwater designs and frequency and magniture of urban flooding.

Precipitation characteristics in tropical Africa using satellite and in situ observations

NASA Astrophysics Data System (ADS)

Dezfuli, A. K.; Ichoku, I.; Huffman, G. J.; Mohr, K. I.

2017-12-01

Tropical Africa receives nearly all its precipitation as a result of convection. The characteristics of rain-producing systems in this region have not been well-understood, despite their crucial role in regional and global circulation. This is mainly due to the lack of in situ observations. Here, we have used precipitation records from the Trans-African Hydro-Meteorological Observatory (TAHMO) ground-based gauge network to improve our knowledge about the rainfall systems in the region, and to validate the recently-released IMERG precipitation product based on satellite observations from the Global Precipitation Measurement (GPM) constellation. The high temporal resolution of the gauge data has allowed us to identify three classes of rain events based on their duration and intensity. The contribution of each class to the total rainfall and the favorable surface atmospheric conditions for each class have been examined. As IMERG aims to continue the legacy of its predecessor, TRMM Multi-Satellite Precipitation Analysis (TMPA), and provide higher resolution data, continent-wide comparisons are made between these two products. Due to its improved temporal resolution, IMERG shows some advantages over TMPA in capturing the diurnal cycle and propagation of the meso-scale convective systems. However, the performance of the two satellite-based products varies by season, region and the evaluation statistics. The results of this study serve as a basis for our ongoing work on the impacts of biomass burning on precipitation processes in Africa.

Systematic recover of long high-resolution rainfall time series recorded by pluviographs during the 20th century.

NASA Astrophysics Data System (ADS)

Delitala, Alessandro M. S.; Deidda, Roberto; Mascaro, Giuseppe; Piga, Enrico; Querzoli, Giorgio

2010-05-01

During most of the 20th century, precipitation has been continuously measured by means of the so-called "pluviographs", i.e. rain gauges including a mechanical apparatus for continuously recording the depth of water from precipitation on specific strip charts, usually on a weekly basis. The signal recorded on such strips was visually examined by trained personnel on a regular basis, in order to extract the daily precipitation totals and the maximum precipitation intensities over short periods (from a few minutes to hours). The rest of the high-resolution information contained in the signal was usually not extracted, except for specific cases. A systematic recovering of the entire information at high temporal resolution contained in these precipitation signals would provide a fundamental database to improve the characterization of historical rainfall climatology during the previous century. The Department of Land Engineering of the University of Cagliari has recently developed and tested an automatic software, based on image analysis techniques, which is able to acquire the scanned images of the pluviograph strip charts, to automatically digitise the signal and to produce a digital database of continuous precipitation records at the highest possible temporal resolution, i.e. 5 to 10 minutes. Along with that, a significant amount of daily precipitation totals from the late 19th and the 20th century, either elaborated from pluviograph strip charts or simply derived from bucket rain gauges, still exists in paper form, but it has never been digitalized. Within a project partly-funded by the Operational Programme of the European Union "Italia-Francia Marittimo", the Regional Environmental Protection Agency of Sardinia and the University of Cagliari will recover both the high-resolution rainfall signals and the older time series of daily totals recorded by a large number of pluviographs belonging to the historical monitoring networks of the island of Sardinia. Such data will then be used to construct the high-resolution climatology of precipitation over Sardinia, both assuming stationary climate and slowly varying climate. Specific attention will be devoted to a set of critical hydrological basins, often affected by intense precipitation and flash floods. All information will then be made available to researchers, regional officers, technicians (e.g. hydraulic engineers) and the greater public interested into such information. The present poster describes the general scope of the E.U. project and the specific activities in the field of climatology of Sardinia rainfall that will be conducted as well as the expected results. A section will be dedicated to show how the pluviograph strips are automatically digitized.

Sensitivity of CONUS Summer Rainfall to the Selection of Cumulus Parameterization Schemes in NU-WRF Seasonal Simulations

NASA Technical Reports Server (NTRS)

Iguchi, Takamichi; Tao, Wei-Kuo; Wu, Di; Peters-Lidard, Christa; Santanello, Joseph A.; Kemp, Eric; Tian, Yudong; Case, Jonathan; Wang, Weile; Ferraro, Robert;

A High Space-Time Resolution Dataset Linking Meteorological Forcing and Hydro-Sedimentary Response in a Mesoscale Mediterranean Catchment (Auzon) of the Ardèche Region, France

NASA Astrophysics Data System (ADS)

Nord, G.; Braud, I.; Boudevillain, B.; Gérard, S.; Molinié, G.; Vandervaere, J. P.; Huza, J.; Le Coz, J.; Dramais, G.; Legout, C.; Berne, A.; Grazioli, J.; Raupach, T.; Van Baelen, J.; Wijbrans, A.; Delrieu, G.; Andrieu, J.; Caliano, M.; Aubert, C.; Teuling, R.; Le Boursicaud, R.; Branger, F.; Vincendon, B.; Horner, I.

2014-12-01

A comprehensive hydrometeorological dataset is presented spanning the period 1 Jan 2011-31 Dec 2014 to improve the understanding and simulation of the hydrological processes leading to flash floods in a mesoscale catchment (Auzon, 116 km2) of the Mediterranean region. The specificity of the dataset is its high space-time resolution, especially concerning rainfall and the hydrological response which is particularly adapted to the highly spatially variable rainfall events that may occur in this region. This type of dataset is rare in scientific literature because of the quantity and type of sensors for meteorology and surface hydrology. Rainfall data include continuous precipitation measured by rain-gages (5 min time step for the research network of 21 rain-gages and 1h time step for the operational network of 9 rain-gages), S-band Doppler dual-polarization radar (1 km2, 5 min resolution), and disdrometers (11 sensors working at 1 min time step). During the special observation period (SOP-1) and enhanced observation period (Sep-Dec 2012, Sep-Dec 2013) of the HyMeX (Hydrological Cycle in the Mediterranean Experiment) project, two X-band radars provided precipitation measurements at very fine spatial and temporal scales (1 ha, 5 min). Meteorological data are taken from the operational surface weather observation stations of Meteo France at the hourly time resolution (6 stations in the region of interest). The monitoring of surface hydrology and suspended sediment is multi-scale and based on nested catchments. Three hydrometric stations measure water discharge and additional physico-chemical variables at a 2-10 min time resolution. Two experimental plots monitor overland flow and erosion at 1 min time resolution on a hillslope with vineyard. A network of 11 gauges continuously measures water level and temperature in headwater subcatchments at a time resolution of 2-5 min. A network of soil moisture sensors enable the continuous measurement of soil volumetric water content at 20 min time resolution at 9 sites. Additionally, opportunistic observations (soil moisture measurements and stream gauging) were performed during floods between 2012 and 2014. The data are appropriate for understanding rainfall variability, improving areal rainfall estimations and progress in distributed hydrological modelling.

A High Space-Time Resolution Dataset Linking Meteorological Forcing and Hydro-Sedimentary Response in a Mesoscale Mediterranean Catchment (Auzon) of the Ardèche Region, France

NASA Astrophysics Data System (ADS)

Nord, G.; Braud, I.; Boudevillain, B.; Gérard, S.; Molinié, G.; Vandervaere, J. P.; Huza, J.; Le Coz, J.; Dramais, G.; Legout, C.; Berne, A.; Grazioli, J.; Raupach, T.; Van Baelen, J.; Wijbrans, A.; Delrieu, G.; Andrieu, J.; Caliano, M.; Aubert, C.; Teuling, R.; Le Boursicaud, R.; Branger, F.; Vincendon, B.; Horner, I.

2015-12-01

A comprehensive hydrometeorological dataset is presented spanning the period 1 Jan 2011-31 Dec 2014 to improve the understanding and simulation of the hydrological processes leading to flash floods in a mesoscale catchment (Auzon, 116 km2) of the Mediterranean region. The specificity of the dataset is its high space-time resolution, especially concerning rainfall and the hydrological response which is particularly adapted to the highly spatially variable rainfall events that may occur in this region. This type of dataset is rare in scientific literature because of the quantity and type of sensors for meteorology and surface hydrology. Rainfall data include continuous precipitation measured by rain-gages (5 min time step for the research network of 21 rain-gages and 1h time step for the operational network of 9 rain-gages), S-band Doppler dual-polarization radar (1 km2, 5 min resolution), and disdrometers (11 sensors working at 1 min time step). During the special observation period (SOP-1) and enhanced observation period (Sep-Dec 2012, Sep-Dec 2013) of the HyMeX (Hydrological Cycle in the Mediterranean Experiment) project, two X-band radars provided precipitation measurements at very fine spatial and temporal scales (1 ha, 5 min). Meteorological data are taken from the operational surface weather observation stations of Meteo France at the hourly time resolution (6 stations in the region of interest). The monitoring of surface hydrology and suspended sediment is multi-scale and based on nested catchments. Three hydrometric stations measure water discharge and additional physico-chemical variables at a 2-10 min time resolution. Two experimental plots monitor overland flow and erosion at 1 min time resolution on a hillslope with vineyard. A network of 11 gauges continuously measures water level and temperature in headwater subcatchments at a time resolution of 2-5 min. A network of soil moisture sensors enable the continuous measurement of soil volumetric water content at 20 min time resolution at 9 sites. Additionally, opportunistic observations (soil moisture measurements and stream gauging) were performed during floods between 2012 and 2014. The data are appropriate for understanding rainfall variability, improving areal rainfall estimations and progress in distributed hydrological modelling.

WegenerNet 1km-scale sub-daily rainfall data and their application: a hydrological modeling study on the sensitivity of small-catchment runoff to spatial rainfall variability

NASA Astrophysics Data System (ADS)

Oh, Sungmin; Hohmann, Clara; Foelsche, Ulrich; Fuchsberger, Jürgen; Rieger, Wolfgang; Kirchengast, Gottfried

2017-04-01

WegenerNet Feldbach region (WEGN), a pioneering experiment for weather and climate observations, has recently completed its first 10-year precipitation measurement cycle. The WEGN has measured precipitation, temperature, humidity, and other parameters since the beginning of 2007, supporting local-level monitoring and modeling studies, over an area of about 20 km x 15 km centered near the City of Feldbach (46.93 ˚ N, 15.90 ˚ E) in the Alpine forelands of southeast Austria. All the 151 stations in the network are now equipped with high-quality Meteoservis sensors as of August 2016, following an equipment with Friedrichs sensors at most stations before, and continue to provide high-resolution (2 km2/5-min) gauge based precipitation measurements for interested users in hydro-meteorological communities. Here we will present overall characteristics of the WEGN, with a focus on sub-daily precipitation measurements, from the data processing (data quality control, gridded data products generation, etc.) to data applications (e.g., ground validation of satellite estimates). The latter includes our recent study on the propagation of uncertainty from rainfall to runoff. The study assesses responses of small-catchment runoff to spatial rainfall variability in the WEGN region over the Raab valley, using a physics-based distributed hydrological model; Water Flow and Balance Simulation Model (WaSiM), developed at ETH Zurich (Schulla, ETH Zurich, 1997). Given that uncertainty due to resolution of rainfall measurements is believed to be a significant source of error in hydrologic modeling especially for convective rainfall that dominates in the region during summer, the high-resolution of WEGN data furnishes a great opportunity to analyze effects of rainfall events on the runoff at different spatial resolutions. Furthermore, the assessment can be conducted not only for the lower Raab catchment (area of about 500 km2) but also for its sub-catchments (areas of about 30-70 km2). Beside the question how many stations are necessary for reliable hydrological modeling, different interpolation methods like Inverse Distance Interpolation, Elevation Dependent Regression, and combinations will be tested. This presentation will show the first results from a scale-depending analysis of spatial and temporal structures of heavy rainfall events and responses of simulated runoff at the event scale in the WEGN region.

The fractal-multifractal method and temporal resolution: Application to precipitation and streamflow

NASA Astrophysics Data System (ADS)

Maskey, M.; Puente, C. E.; Sivakumar, B.

2017-12-01

In the past, we have established that the deterministic fractal-multifractal (FM) method is a promising geometric tool to analyze hydro-climatic variables, such as precipitation, river flow, and temperature. In this study, we address the issue of temporal resolution to advance the suitability and usefulness of the FM approach in hydro-climate. Specifically, we elucidate the evolution of FM geometric parameters as computed at different time scales ranging from a day to a month (30-day) in increments of a day. For this purpose, both rainfall and river discharge records at Sacramento, California gathered over a year are encoded at different time scales. The analysis reveals that: (a) the FM approach yields faithful encodings of both kinds of data sets at the resolutions considered with reasonably small errors; and (b) the "best" FM parameters ultimately converge when the resolution is increased, thus allowing visualizing both hydrologic attributes. By addressing the scalability of the geometric patterns, these results further advance the suitability of the FM approach.

Analyzing and Visualizing Precipitation and Soil Moisture in ArcGIS

NASA Technical Reports Server (NTRS)

Yang, Wenli; Pham, Long; Zhao, Peisheng; Kempler, Steve; Wei, Jennifer

2016-01-01

Precipitation and soil moisture are among the most important parameters in many land GIS (Geographic Information System) research and applications. These data are available globally from NASA GES DISC (Goddard Earth Science Data and Information Services Center) in GIS-ready format at 10-kilometer spatial resolution and 24-hour or less temporal resolutions. In this presentation, well demonstrate how rainfall and soil moisture data are used in ArcGIS to analyze and visualize spatiotemporal patterns of droughts and their impacts on natural vegetation and agriculture in different parts of the world.

On the uncertainties associated with using gridded rainfall data as a proxy for observed

NASA Astrophysics Data System (ADS)

Tozer, C. R.; Kiem, A. S.; Verdon-Kidd, D. C.

2011-09-01

Gridded rainfall datasets are used in many hydrological and climatological studies, in Australia and elsewhere, including for hydroclimatic forecasting, climate attribution studies and climate model performance assessments. The attraction of the spatial coverage provided by gridded data is clear, particularly in Australia where the spatial and temporal resolution of the rainfall gauge network is sparse. However, the question that must be asked is whether it is suitable to use gridded data as a proxy for observed point data, given that gridded data is inherently "smoothed" and may not necessarily capture the temporal and spatial variability of Australian rainfall which leads to hydroclimatic extremes (i.e. droughts, floods)? This study investigates this question through a statistical analysis of three monthly gridded Australian rainfall datasets - the Bureau of Meteorology (BOM) dataset, the Australian Water Availability Project (AWAP) and the SILO dataset. To demonstrate the hydrological implications of using gridded data as a proxy for gauged data, a rainfall-runoff model is applied to one catchment in South Australia (SA) initially using gridded data as the source of rainfall input and then gauged rainfall data. The results indicate a markedly different runoff response associated with each of the different sources of rainfall data. It should be noted that this study does not seek to identify which gridded dataset is the "best" for Australia, as each gridded data source has its pros and cons, as does gauged or point data. Rather the intention is to quantify differences between various gridded data sources and how they compare with gauged data so that these differences can be considered and accounted for in studies that utilise these gridded datasets. Ultimately, if key decisions are going to be based on the outputs of models that use gridded data, an estimate (or at least an understanding) of the uncertainties relating to the assumptions made in the development of gridded data and how that gridded data compares with reality should be made.

Short-term ensemble radar rainfall forecasts for hydrological applications

NASA Astrophysics Data System (ADS)

Codo de Oliveira, M.; Rico-Ramirez, M. A.

2016-12-01

Flooding is a very common natural disaster around the world, putting local population and economy at risk. Forecasting floods several hours ahead and issuing warnings are of main importance to permit proper response in emergency situations. However, it is important to know the uncertainties related to the rainfall forecasting in order to produce more reliable forecasts. Nowcasting models (short-term rainfall forecasts) are able to produce high spatial and temporal resolution predictions that are useful in hydrological applications. Nonetheless, they are subject to uncertainties mainly due to the nowcasting model used, errors in radar rainfall estimation, temporal development of the velocity field and to the fact that precipitation processes such as growth and decay are not taken into account. In this study an ensemble generation scheme using rain gauge data as a reference to estimate radars errors is used to produce forecasts with up to 3h lead-time. The ensembles try to assess in a realistic way the residual uncertainties that remain even after correction algorithms are applied in the radar data. The ensembles produced are compered to a stochastic ensemble generator. Furthermore, the rainfall forecast output was used as an input in a hydrodynamic sewer network model and also in hydrological model for catchments of different sizes in north England. A comparative analysis was carried of how was carried out to assess how the radar uncertainties propagate into these models. The first named author is grateful to CAPES - Ciencia sem Fronteiras for funding this PhD research.

Continuous Sub-daily Rainfall Simulation for Regional Flood Risk Assessment - Modelling of Spatio-temporal Correlation Structure of Extreme Precipitation in the Austrian Alps

NASA Astrophysics Data System (ADS)

Salinas, J. L.; Nester, T.; Komma, J.; Bloeschl, G.

2017-12-01

Generation of realistic synthetic spatial rainfall is of pivotal importance for assessing regional hydroclimatic hazard as the input for long term rainfall-runoff simulations. The correct reproduction of observed rainfall characteristics, such as regional intensity-duration-frequency curves, and spatial and temporal correlations is necessary to adequately model the magnitude and frequency of the flood peaks, by reproducing antecedent soil moisture conditions before extreme rainfall events, and joint probability of flood waves at confluences. In this work, a modification of the model presented by Bardossy and Platte (1992), where precipitation is first modeled on a station basis as a multivariate autoregressive model (mAr) in a Normal space. The spatial and temporal correlation structures are imposed in the Normal space, allowing for a different temporal autocorrelation parameter for each station, and simultaneously ensuring the positive-definiteness of the correlation matrix of the mAr errors. The Normal rainfall is then transformed to a Gamma-distributed space, with parameters varying monthly according to a sinusoidal function, in order to adapt to the observed rainfall seasonality. One of the main differences with the original model is the simulation time-step, reduced from 24h to 6h. Due to a larger availability of daily rainfall data, as opposite to sub-daily (e.g. hourly), the parameters of the Gamma distributions are calibrated to reproduce simultaneously a series of daily rainfall characteristics (mean daily rainfall, standard deviations of daily rainfall, and 24h intensity-duration-frequency [IDF] curves), as well as other aggregated rainfall measures (mean annual rainfall, and monthly rainfall). The calibration of the spatial and temporal correlation parameters is performed in a way that the catchment-averaged IDF curves aggregated at different temporal scales fit the measured ones. The rainfall model is used to generate 10.000 years of synthetic precipitation, fed into a rainfall-runoff model to derive the flood frequency in the Tirolean Alps in Austria. Given the number of generated events, the simulation framework is able to generate a large variety of rainfall patterns, as well as reproduce the variograms of relevant extreme rainfall events in the region of interest.

Spatial connections in regional climate model rainfall outputs at different temporal scales: Application of network theory

NASA Astrophysics Data System (ADS)

Naufan, Ihsan; Sivakumar, Bellie; Woldemeskel, Fitsum M.; Raghavan, Srivatsan V.; Vu, Minh Tue; Liong, Shie-Yui

2018-01-01

Understanding the spatial and temporal variability of rainfall has always been a great challenge, and the impacts of climate change further complicate this issue. The present study employs the concepts of complex networks to study the spatial connections in rainfall, with emphasis on climate change and rainfall scaling. Rainfall outputs (during 1961-1990) from a regional climate model (i.e. Weather Research and Forecasting (WRF) model that downscaled the European Centre for Medium-range Weather Forecasts, ECMWF ERA-40 reanalyses) over Southeast Asia are studied, and data corresponding to eight different temporal scales (6-hr, 12-hr, daily, 2-day, 4-day, weekly, biweekly, and monthly) are analyzed. Two network-based methods are applied to examine the connections in rainfall: clustering coefficient (a measure of the network's local density) and degree distribution (a measure of the network's spread). The influence of rainfall correlation threshold (T) on spatial connections is also investigated by considering seven different threshold levels (ranging from 0.5 to 0.8). The results indicate that: (1) rainfall networks corresponding to much coarser temporal scales exhibit properties similar to that of small-world networks, regardless of the threshold; (2) rainfall networks corresponding to much finer temporal scales may be classified as either small-world networks or scale-free networks, depending upon the threshold; and (3) rainfall spatial connections exhibit a transition phase at intermediate temporal scales, especially at high thresholds. These results suggest that the most appropriate model for studying spatial connections may often be different at different temporal scales, and that a combination of small-world and scale-free network models might be more appropriate for rainfall upscaling/downscaling across all scales, in the strict sense of scale-invariance. The results also suggest that spatial connections in the studied rainfall networks in Southeast Asia are weak, especially when more stringent conditions are imposed (i.e. when T is very high), except at the monthly scale.

Rainfall estimation for real time flood monitoring using geostationary meteorological satellite data

NASA Astrophysics Data System (ADS)

Veerakachen, Watcharee; Raksapatcharawong, Mongkol

2015-09-01

Rainfall estimation by geostationary meteorological satellite data provides good spatial and temporal resolutions. This is advantageous for real time flood monitoring and warning systems. However, a rainfall estimation algorithm developed in one region needs to be adjusted for another climatic region. This work proposes computationally-efficient rainfall estimation algorithms based on an Infrared Threshold Rainfall (ITR) method calibrated with regional ground truth. Hourly rain gauge data collected from 70 stations around the Chao-Phraya river basin were used for calibration and validation of the algorithms. The algorithm inputs were derived from FY-2E satellite observations consisting of infrared and water vapor imagery. The results were compared with the Global Satellite Mapping of Precipitation (GSMaP) near real time product (GSMaP_NRT) using the probability of detection (POD), root mean square error (RMSE) and linear correlation coefficient (CC) as performance indices. Comparison with the GSMaP_NRT product for real time monitoring purpose shows that hourly rain estimates from the proposed algorithm with the error adjustment technique (ITR_EA) offers higher POD and approximately the same RMSE and CC with less data latency.

Deciphering the expression of climate change within the Lower Colorado River basin by stochastic simulation of convective rainfall

NASA Astrophysics Data System (ADS)

Bliss Singer, Michael; Michaelides, Katerina

2017-10-01

In drylands, convective rainstorms typically control runoff, streamflow, water supply and flood risk to human populations, and ecological water availability at multiple spatial scales. Since drainage basin water balance is sensitive to climate, it is important to improve characterization of convective rainstorms in a manner that enables statistical assessment of rainfall at high spatial and temporal resolution, and the prediction of plausible manifestations of climate change. Here we present a simple rainstorm generator, STORM, for convective storm simulation. It was created using data from a rain gauge network in one dryland drainage basin, but is applicable anywhere. We employ STORM to assess watershed rainfall under climate change simulations that reflect differences in wetness/storminess, and thus provide insight into observed or projected regional hydrologic trends. Our analysis documents historical, regional climate change manifesting as a multidecadal decline in rainfall intensity, which we suggest has negatively impacted ephemeral runoff in the Lower Colorado River basin, but has not contributed substantially to regional negative streamflow trends.

Impact of Urbanization on Spatial Variability of Rainfall-A case study of Mumbai city with WRF Model

NASA Astrophysics Data System (ADS)

Mathew, M.; Paul, S.; Devanand, A.; Ghosh, S.

2015-12-01

Urban precipitation enhancement has been identified over many cities in India by previous studies conducted. Anthropogenic effects such as change in land cover from hilly forest areas to flat topography with solid concrete infrastructures has certain effect on the local weather, the same way the greenhouse gas has on climate change. Urbanization could alter the large scale forcings to such an extent that it may bring about temporal and spatial changes in the urban weather. The present study investigate the physical processes involved in urban forcings, such as the effect of sudden increase in wind velocity travelling through the channel space in between the dense array of buildings, which give rise to turbulence and air mass instability in urban boundary layer and in return alters the rainfall distribution as well as rainfall initiation. A numerical model study is conducted over Mumbai metropolitan city which lies on the west coast of India, to assess the effect of urban morphology on the increase in number of extreme rainfall events in specific locations. An attempt has been made to simulate twenty extreme rainfall events that occurred over the summer monsoon period of the year 2014 using high resolution WRF-ARW (Weather Research and Forecasting-Advanced Research WRF) model to assess the urban land cover mechanisms that influences precipitation variability over this spatially varying urbanized region. The result is tested against simulations with altered land use. The correlation of precipitation with spatial variability of land use is found using a detailed urban land use classification. The initial and boundary conditions for running the model were obtained from the global model ECMWF(European Centre for Medium Range Weather Forecast) reanalysis data having a horizontal resolution of 0.75 °x 0.75°. The high resolution simulations show significant spatial variability in the accumulated rainfall, within a few kilometers itself. Understanding the spatial variability of precipitation will help in the planning and management of the built environment more efficiently.

Spatio-Temporal Variability of Summer Precipitation in Mexico under the Influence of the MJO, with Emphasis on the Bimodal Pattern

NASA Astrophysics Data System (ADS)

Perdigón, J.; Romero-Centeno, R.; Barrett, B.; Ordoñez-Perez, P.

2017-12-01

In many regions of Mexico, precipitation occurs in a very well defined annual cycle with peaks in May-June and September-October and a relative minimum in the middle of the rainy season known as the midsummer drought (MSD). The MJO is the most important mode of intraseasonal variability in the tropics, and, although some studies have shown its evident influence on summer precipitation in Mexico, its role in modulating the bimodal pattern of the summer precipitation cycle is still an open question. The spatio-temporal variability of summer precipitation in Mexico is analyzed through composite analysis according to the phases of the MJO, using the very high resolution CHIRPS precipitation data base and gridded data from the CFSR reanalysis to analyzing the MJO influence on the atmospheric circulation over Mexico and its adjacent basins. In general, during MJO phases 8-2 (4-6) rainfall is above-normal (below-normal), although, in some cases, the summer rainfall patterns during the same phase present considerable differences. The atmospheric circulation shows low (high) troposphere southwesterly (northeasterly) wind anomalies in southern Mexico under wetter conditions compared with climatological patterns, while the inverse pattern is observed under drier conditions. Composite anomalies of several variables also agreed well with those rainfall anomalies. Finally, a MJO complete cycle that reinforces (weakens) the bimodal pattern of summer rainfall in Mexico was found.

Climate impacts on environmental risks evaluated from space: a conceptual approach to the case of Rift Valley Fever in Senegal.

PubMed

Tourre, Yves M; Lacaux, Jean-Pierre; Vignolles, Cécile; Lafaye, Murielle

2009-11-11

Climate and environment vary across many spatio-temporal scales, including the concept of climate change, which impact on ecosystems, vector-borne diseases and public health worldwide. To develop a conceptual approach by mapping climatic and environmental conditions from space and studying their linkages with Rift Valley Fever (RVF) epidemics in Senegal. Ponds in which mosquitoes could thrive were identified from remote sensing using high-resolution SPOT-5 satellite images. Additional data on pond dynamics and rainfall events (obtained from the Tropical Rainfall Measuring Mission) were combined with hydrological in-situ data. Localisation of vulnerable hosts such as penned cattle (from QuickBird satellite) were also used. Dynamic spatio-temporal distribution of Aedes vexans density (one of the main RVF vectors) is based on the total rainfall amount and ponds' dynamics. While Zones Potentially Occupied by Mosquitoes are mapped, detailed risk areas, i.e. zones where hazards and vulnerability occur, are expressed in percentages of areas where cattle are potentially exposed to mosquitoes' bites. This new conceptual approach, using precise remote-sensing techniques, simply relies upon rainfall distribution also evaluated from space. It is meant to contribute to the implementation of operational early warning systems for RVF based on both natural and anthropogenic climatic and environmental changes. In a climate change context, this approach could also be applied to other vector-borne diseases and places worldwide.

Probabilistic forecasts based on radar rainfall uncertainty

NASA Astrophysics Data System (ADS)

Liguori, S.; Rico-Ramirez, M. A.

2012-04-01

The potential advantages resulting from integrating weather radar rainfall estimates in hydro-meteorological forecasting systems is limited by the inherent uncertainty affecting radar rainfall measurements, which is due to various sources of error [1-3]. The improvement of quality control and correction techniques is recognized to play a role for the future improvement of radar-based flow predictions. However, the knowledge of the uncertainty affecting radar rainfall data can also be effectively used to build a hydro-meteorological forecasting system in a probabilistic framework. This work discusses the results of the implementation of a novel probabilistic forecasting system developed to improve ensemble predictions over a small urban area located in the North of England. An ensemble of radar rainfall fields can be determined as the sum of a deterministic component and a perturbation field, the latter being informed by the knowledge of the spatial-temporal characteristics of the radar error assessed with reference to rain-gauges measurements. This approach is similar to the REAL system [4] developed for use in the Southern-Alps. The radar uncertainty estimate can then be propagated with a nowcasting model, used to extrapolate an ensemble of radar rainfall forecasts, which can ultimately drive hydrological ensemble predictions. A radar ensemble generator has been calibrated using radar rainfall data made available from the UK Met Office after applying post-processing and corrections algorithms [5-6]. One hour rainfall accumulations from 235 rain gauges recorded for the year 2007 have provided the reference to determine the radar error. Statistics describing the spatial characteristics of the error (i.e. mean and covariance) have been computed off-line at gauges location, along with the parameters describing the error temporal correlation. A system has then been set up to impose the space-time error properties to stochastic perturbations, generated in real-time at gauges location, and then interpolated back onto the radar domain, in order to obtain probabilistic radar rainfall fields in real time. The deterministic nowcasting model integrated in the STEPS system [7-8] has been used for the purpose of propagating the uncertainty and assessing the benefit of implementing the radar ensemble generator for probabilistic rainfall forecasts and ultimately sewer flow predictions. For this purpose, events representative of different types of precipitation (i.e. stratiform/convective) and significant at the urban catchment scale (i.e. in terms of sewer overflow within the urban drainage system) have been selected. As high spatial/temporal resolution is required to the forecasts for their use in urban areas [9-11], the probabilistic nowcasts have been set up to be produced at 1 km resolution and 5 min intervals. The forecasting chain is completed by a hydrodynamic model of the urban drainage network. The aim of this work is to discuss the implementation of this probabilistic system, which takes into account the radar error to characterize the forecast uncertainty, with consequent potential benefits in the management of urban systems. It will also allow a comparison with previous findings related to the analysis of different approaches to uncertainty estimation and quantification in terms of rainfall [12] and flows at the urban scale [13]. Acknowledgements The authors would like to acknowledge the BADC, the UK Met Office and Dr. Alan Seed from the Australian Bureau of Meteorology for providing the radar data and the nowcasting model. The authors acknowledge the support from the Engineering and Physical Sciences Research Council (EPSRC) via grant EP/I012222/1.

How spatial and temporal rainfall variability affect runoff across basin scales: insights from field observations in the (semi-)urbanised Charlotte watershed

NASA Astrophysics Data System (ADS)

Ten Veldhuis, M. C.; Smith, J. A.; Zhou, Z.

2017-12-01

Impacts of rainfall variability on runoff response are highly scale-dependent. Sensitivity analyses based on hydrological model simulations have shown that impacts are likely to depend on combinations of storm type, basin versus storm scale, temporal versus spatial rainfall variability. So far, few of these conclusions have been confirmed on observational grounds, since high quality datasets of spatially variable rainfall and runoff over prolonged periods are rare. Here we investigate relationships between rainfall variability and runoff response based on 30 years of radar-rainfall datasets and flow measurements for 16 hydrological basins ranging from 7 to 111 km2. Basins vary not only in scale, but also in their degree of urbanisation. We investigated temporal and spatial variability characteristics of rainfall fields across a range of spatial and temporal scales to identify main drivers for variability in runoff response. We identified 3 ranges of basin size with different temporal versus spatial rainfall variability characteristics. Total rainfall volume proved to be the dominant agent determining runoff response at all basin scales, independent of their degree of urbanisation. Peak rainfall intensity and storm core volume are of secondary importance. This applies to all runoff parameters, including runoff volume, runoff peak, volume-to-peak and lag time. Position and movement of the storm with respect to the basin have a negligible influence on runoff response, with the exception of lag times in some of the larger basins. This highlights the importance of accuracy in rainfall estimation: getting the position right but the volume wrong will inevitably lead to large errors in runoff prediction. Our study helps to identify conditions where rainfall variability matters for correct estimation of the rainfall volume as well as the associated runoff response.

Precipitation climatology over India: validation with observations and reanalysis datasets and spatial trends

NASA Astrophysics Data System (ADS)

Kishore, P.; Jyothi, S.; Basha, Ghouse; Rao, S. V. B.; Rajeevan, M.; Velicogna, Isabella; Sutterley, Tyler C.

2016-01-01

Changing rainfall patterns have significant effect on water resources, agriculture output in many countries, especially the country like India where the economy depends on rain-fed agriculture. Rainfall over India has large spatial as well as temporal variability. To understand the variability in rainfall, spatial-temporal analyses of rainfall have been studied by using 107 (1901-2007) years of daily gridded India Meteorological Department (IMD) rainfall datasets. Further, the validation of IMD precipitation data is carried out with different observational and different reanalysis datasets during the period from 1989 to 2007. The Global Precipitation Climatology Project data shows similar features as that of IMD with high degree of comparison, whereas Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation data show similar features but with large differences, especially over northwest, west coast and western Himalayas. Spatially, large deviation is observed in the interior peninsula during the monsoon season with National Aeronautics Space Administration-Modern Era Retrospective-analysis for Research and Applications (NASA-MERRA), pre-monsoon with Japanese 25 years Re Analysis (JRA-25), and post-monsoon with climate forecast system reanalysis (CFSR) reanalysis datasets. Among the reanalysis datasets, European Centre for Medium-Range Weather Forecasts Interim Re-Analysis (ERA-Interim) shows good comparison followed by CFSR, NASA-MERRA, and JRA-25. Further, for the first time, with high resolution and long-term IMD data, the spatial distribution of trends is estimated using robust regression analysis technique on the annual and seasonal rainfall data with respect to different regions of India. Significant positive and negative trends are noticed in the whole time series of data during the monsoon season. The northeast and west coast of the Indian region shows significant positive trends and negative trends over western Himalayas and north central Indian region.

Use of soil moisture dynamics and patterns at different spatio-temporal scales for the investigation of subsurface flow processes

NASA Astrophysics Data System (ADS)

Blume, T.; Zehe, E.; Bronstert, A.

2009-07-01

Spatial patterns as well as temporal dynamics of soil moisture have a major influence on runoff generation. The investigation of these dynamics and patterns can thus yield valuable information on hydrological processes, especially in data scarce or previously ungauged catchments. The combination of spatially scarce but temporally high resolution soil moisture profiles with episodic and thus temporally scarce moisture profiles at additional locations provides information on spatial as well as temporal patterns of soil moisture at the hillslope transect scale. This approach is better suited to difficult terrain (dense forest, steep slopes) than geophysical techniques and at the same time less cost-intensive than a high resolution grid of continuously measuring sensors. Rainfall simulation experiments with dye tracers while continuously monitoring soil moisture response allows for visualization of flow processes in the unsaturated zone at these locations. Data was analyzed at different spacio-temporal scales using various graphical methods, such as space-time colour maps (for the event and plot scale) and binary indicator maps (for the long-term and hillslope scale). Annual dynamics of soil moisture and decimeter-scale variability were also investigated. The proposed approach proved to be successful in the investigation of flow processes in the unsaturated zone and showed the importance of preferential flow in the Malalcahuello Catchment, a data-scarce catchment in the Andes of Southern Chile. Fast response times of stream flow indicate that preferential flow observed at the plot scale might also be of importance at the hillslope or catchment scale. Flow patterns were highly variable in space but persistent in time. The most likely explanation for preferential flow in this catchment is a combination of hydrophobicity, small scale heterogeneity in rainfall due to redistribution in the canopy and strong gradients in unsaturated conductivities leading to self-reinforcing flow paths.

Temporal and spatial variations of rainfall erosivity in Southern Taiwan

NASA Astrophysics Data System (ADS)

Lee, Ming-Hsi; Lin, Huan-Hsuan; Chu, Chun-Kuang

2014-05-01

Soil erosion models are essential in developing effective soil and water resource conservation strategies. Soil erosion is generally evaluated using the Universal Soil Loss Equation (USLE) with an appropriate regional scale description. Among factors in the USLE model, the rainfall erosivity index (R) provides one of the clearest indications of the effects of climate change. Accurate estimation of rainfall erosivity requires continuous rainfall data; however, such data rarely demonstrate good spatial and temporal coverage. The data set consisted of 9240 storm events for the period 1993 to 2011, monitored by 27 rainfall stations of the Central Weather Bureau (CWB) in southern Taiwan, was used to analyze the temporal-spatial variations of rainfall erosivity. The spatial distribution map was plotted based on rainfall erosivity by the Kriging interpolation method. Results indicated that rainfall erosivity is mainly concentrated in rainy season from June to November typically contributed 90% of the yearly R factor. The temporal variations of monthly rainfall erosivity during June to November and annual rainfall erosivity have increasing trend from 1993 to 2011. There is an increasing trend from southwest to northeast in spatial distribution of rainfall erosivity in southern Taiwan. The results further indicated that there is a higher relationship between elevation and rainfall erosivity. The method developed in this study may also be useful for sediment disasters on Climate Change.

Real-time adjusting of rainfall estimates from commercial microwave links

NASA Astrophysics Data System (ADS)

Fencl, Martin; Dohnal, Michal; Bareš, Vojtěch

2017-04-01

Urban stormwater predictions require reliable rainfall information with space-time resolution higher than commonly provided by standard rainfall monitoring networks of national weather services. Rainfall data from commercial microwave links (CMLs) could fill this gap. CMLs are line-of-sight radio connections widely used by cellular operators which operate at millimeter bands, where radio waves are attenuated by raindrops. Attenuation data of each single CML in the cellular network can be remotely accessed in (near) real-time with virtually arbitrary sampling frequency and convert to rainfall intensity. Unfortunately, rainfall estimates from CMLs can be substantially biased. Fencl et al., (2017), therefore, proposed adjusting method which enables to correct for this bias. They used rain gauge (RG) data from existing rainfall monitoring networks, which would have otherwise insufficient spatial and temporal resolution for urban rainfall monitoring when used alone without CMLs. In this investigation, we further develop the method to improve its performance in a real-time setting. First, a shortcoming of the original algorithm which delivers unreliable results at the beginning of a rainfall event is overcome by introducing model parameter prior distributions estimated from previous parameter realizations. Second, weights reflecting variance between RGs are introduced into cost function, which is minimized when optimizing model parameters. Finally, RG data used for adjusting are preprocessed by moving average filter. The performance of improved adjusting method is evaluated on four short CMLs (path length < 2 km) located in the small urban catchment (2.3 km2) in Prague-Letnany (CZ). The adjusted CMLs are compared to reference rainfall calculated from six RGs in the catchment. The suggested improvements of the method lead on average to 10% higher Nash-Sutcliffe efficiency coefficient (median value 0.85) for CML adjustment to hourly RG data. Reliability of CML rainfall estimates is especially improved at the beginning of rainfall events and during strong convective rainfalls, whereas performance during longer frontal rainfalls is almost unchanged. Our results clearly demonstrate that adjusting of CMLs to existing RGs represents a viable approach with great potential for real-time applications in stormwater management. This work was supported by the project of Czech Science Foundation (GACR) No.17-16389S. References: Fencl, M., Dohnal, M., Rieckermann, J. and Bareš, V.: Gauge-Adjusted Rainfall Estimates from Commercial Microwave Links, Hydrol Earth Syst. Sci., 2017 (accepted).

Short-term expansion of glacial lakes in the Himalayas

NASA Astrophysics Data System (ADS)

Nagai, H.; Tadono, T.

2017-12-01

A glacial lake outburst flood (GLOF) is a serious mountainous hazard that is related to glacial shrinkage. Despite technical developments in satellite-based lake expansion monitoring, small glacial lakes were collapsed in Bhutan in June 2015 and in Nepal in May 2017. Relatively heavy rainfall was reported downstream just before the floods. Does a large amount of short-term precipitation have a possibility of triggering a GLOF? To answer this question, the temporal change in the glacial lake area is assessed by means of satellite-based synthetic aperture radar, coupled with satellite-derived spatial and temporal distribution of precipitation to evaluate the contribution of rainfall in glacial lake expansion. The Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) observed the Mande Chu river basin in central Bhutan on Aug 11, 2016. Glacial lakes were manually delineated from the orthorectified backscatter amplitude image. They were compared with those delineated from the old satellite images of ALOS ( 2011), PALSAR-2 (2014-2016), and Landsat-8 (2016). The temporal and spatial distributions of precipitation (2010-2016) are obtained from the Global Satellite Mapping of Precipitation (GSMaP) data (10-km spatial / 60-min. temporal resolutions), calibrated by in situ rain gauges (GSMap_RNL/MVL). The outlines of 11 glacial lakes in the study site were successfully traced from 2011 to 2016; rapid expansion was recorded especially in the period between March and July 2016. In this period, exceeding 500 mm of the total amount of precipitation is recorded by GSMaP, whereas the mean precipitation amount is 300-400 mm in the previous years. This implies that relatively larger precipitation occurred in 2016, which is related to the short-term expansion of the glacial lakes. The rapid expansion of smaller lakes can be explained by their relatively shallow depths, which is sensitive to the increase in inflow water volume. This study highlights the importance of high-resolution, frequent observation of small glacial lakes that can expand, possibly corresponding to heavy rainfall, whereas most previous studies focused on large glacial lakes expanding at annual scales. Extreme precipitation should be considered as one of the factors responsible for glacial lake expansion as well as glacier melt.

Using Conditional Analysis to Investigate Spatial and Temporal patterns in Upland Rainfall

NASA Astrophysics Data System (ADS)

Sakamoto Ferranti, Emma Jayne; Whyatt, James Duncan; Timmis, Roger James

2010-05-01

The seasonality and characteristics of rainfall in the UK are altering under a changing climate. Summer rainfall is generally decreasing whereas winter rainfall is increasing, particularly in northern and western areas (Maraun et al., 2008) and recent research suggests these rainfall increases are amplified in upland areas (Burt and Ferranti, 2010). Conditional analysis has been used to investigate these rainfall patterns in Cumbria, an upland area in northwest England. Cumbria was selected as an example of a topographically diverse mid-latitude region that has a predominately maritime and westerly-defined climate. Moreover it has a dense network of more than 400 rain gauges that have operated for periods between 1900 and present day. Cumbria has experienced unprecedented flooding in the past decade and understanding the spatial and temporal changes in this and other upland regions is important for water resource and ecosystem management. The conditional analysis method examines the spatial and temporal variations in rainfall under different synoptic conditions and in different geographic sub-regions (Ferranti et al., 2009). A daily synoptic typing scheme, the Lamb Weather Catalogue, was applied to classify rainfall into different weather types, for example: south-westerly, westerly, easterly or cyclonic. Topographic descriptors developed using GIS were used to classify rain gauges into 6 directionally-dependant geographic sub-regions: coastal, windward-lowland, windward-upland, leeward-upland, leeward-lowland, secondary upland. Combining these classification methods enabled seasonal rainfall climatologies to be produced for specific weather types and sub-regions. Winter rainfall climatologies were constructed for all 6 sub-regions for 3 weather types - south-westerly (SW), westerly (W), and cyclonic (C); these weather types contribute more than 50% of total winter rainfall. The frequency of wet-days (>0.3mm), the total winter rainfall and the average wet day rainfall amount were analysed for each rainfall sub-region and weather type from 1961-2007 (Ferranti et al., 2010). The conditional analysis showed total rainfall under SW and W weather types to be increasing, with the greatest increases observed in the upland sub-regions. The increase in total SW rainfall is driven by a greater occurrence of SW rain days, and there has been little change to the average wet-day rainfall amount. The increase in total W rainfall is driven in part by an increase in the frequency of wet-days, but more significantly by an increase in the average wet-day rainfall amount. In contrast, total rainfall under C weather types has decreased. Further analysis will investigate how spring, summer and autumn rainfall climatologies have changed for the different weather types and sub-regions. Conditional analysis that combines GIS and synoptic climatology provides greater insights into the processes underlying readily available meteorological data. Dissecting Cumbrian rainfall data under different synoptic and geographic conditions showed the observed changes in winter rainfall are not uniform for the different weather types, nor for the different geographic sub-regions. These intricate details are often lost during coarser resolution analysis, and conditional analysis will provide a detailed synopsis of Cumbrian rainfall processes against which Regional Climate Model (RCM) performance can be tested. Conventionally RCMs try to simulate composite rainfall over many different weather types and sub-regions and by undertaking conditional validation the model performance for individual processes can be tested. This will help to target improvements in model performance, and ultimately lead to better simulation of rainfall in areas of complex topography. BURT, T. P. & FERRANTI, E. J. S. (2010) Changing patterns of heavy rainfall in upland areas: a case study from northern England. Atmospheric Environment, [in review]. FERRANTI, E. J. S., WHYATT, J. D. & TIMMIS, R. J. (2009) Development and application of topographic descriptors for conditional analysis of rainfall. Atmospheric Science Letters, 10, 177-184. FERRANTI, E. J. S., WHYATT, J. D., TIMMIS, R. J. & DAVIES, G. (2010) Using GIS to investigate spatial and temporal variations in upland rainfall. Transactions in GIS, [in press]. MARAUN, D., OSBORN, T. J. & GILLETT, N. P. (2008) United Kingdom daily precipitation intensity: improved early data, error estimates and an update from 2000 to 2006. International Journal of Climatology, 28, 833-842.

High resolution modeling in urban hydrology: comparison between two modeling approaches and their sensitivity to high rainfall variability

NASA Astrophysics Data System (ADS)

Ichiba, Abdellah; Gires, Auguste; Tchiguirinskaia, Ioulia; Bompard, Philippe; Schertzer, Daniel

2015-04-01

Urban water management is becoming increasingly complex, due to the rapid increase of impervious areas, and the potential effects of climate change. The large amount of water generated in a very short period of time and the limited capacity of sewer systems increase the vulnerability of urban environments to flooding risk and make it necessary to implement specific devices in order to handle the volume of water generated. This complex situation in urban environments makes the use of hydrological models as well as the implementation of more accurate and reliable tools for flow and rainfall measurements essential for a good pluvial network management, the use of decision support tools such as real-time radar forecasting system, the developpement of general public communication and warning systems, and the implementation of management strategy participate on limiting the flood damages. The very high spatial variability characteristic of urban environments makes it necessary to integrate the variability of physical properties and precipitation at fine scales in modeling processes, suggesting a high resolution modeling approach. In this paper we suggest a comparison between two modeling approaches and their sensitivity to small-scale rainfall variability on a 2.15 km2 urban area located in the County of Val-de-Marne (South-East of Paris, France). The first model used in this study is CANOE, which is a semi-distributed model widely used in France by practitioners for urban hydrology and urban water management. Two configurations of this model are be used in this study, the first one integrate 9 sub-catchments with sizes range from (1ha to 76ha), in the second configuration, the spatial resolution of this model has been improved with 45 sub-catchments with sizes range from (1ha to 14ha), the aim is to see how the semi-distributed model resolution affects it sensitivity to rainfall variability. The second model is Multi-Hydro fully distributed model developed at the Ecole des Ponts ParisTech. It is an interacting core between open source software packages, each of them representing a portion of the water cycle in urban environment. Multi-Hydro has been set up at two resolutions, 10m and 5m. The validation of these two models is performed using 5 rainfall events that occurred between 2010 and 2013. Radar data comes from the Météo-France radar mosaic and the resolution is 1 km in space and 5 min in time. Raingauge and flow measurements data comes from the General Council of Val-de-Marne County. In this validation part, the hydrological responses given by two models and the different configurations are compared to flow measurements. It appears that CANOE gives better results than Multi-Hydro model, especially when using raingauge data. For some events, we noticed that model responses given when using raingauge and radar data are different, suggesting a sign of sensitivity to the spatial variability of rainfall. 10 high-resolution rainfall events are used in the second part to study the sensitivity of each modeling approach to high rainfall variability. Radar data was available at four spatial resolutions (100, 200, 500 and 1000m) and two temporal resolutions (1min and 5min), for each event, two rainfall directions (parallel and perpendicular) are used, meaning that 16 hydrological responses are simulated for each event and the variability within it analyzed. First results suggest that the fully distributed model is more sensitive to high rainfall variability than the semi-distributed one, the increase of both hydrological model spatial resolution improves their sensitivity to rainfall variability. This study highlights some technical challenges facing the high-resolution modeling, especially the difficulty to obtain reliable input data at an acceptable resolution and also the high computation time noticed particularly for the semi-distributed model making it difficult to use it in real time. The authors greatly acknowledge partial financial support from the project RainGain (http://www.raingain.eu) of the EU Interreg program.

Evaluation of high resolution global satellite precipitation products using daily raingauge data over the Upper Blue Nile Basin

NASA Astrophysics Data System (ADS)

Sahlu, Dejene; Moges, Semu; Anagnostou, Emmanouil; Nikolopoulos, Efthymios; Hailu, Dereje; Mei, Yiwen

2017-04-01

Water resources assessment, planning and management in Africa is often constrained by the lack of reliable spatio-temporal rainfall data. Satellite products are steadily growing and offering useful alternative datasets of rainfall globally. The aim of this paper is to examine the error characteristics of the main available global satellite precipitation products with the view of improving the reliability of wet season (June to September) and small rainy season rainfall datasets over the Upper Blue Nile Basin. The study utilized six satellite derived precipitation datasets at 0.25-deg spatial grid size and daily temporal resolution:1) the near real-time (3B42_RT) and gauge adjusted (3B42_V7) products of Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), 2) gauge adjusted and unadjusted Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products and 3) the gauge adjusted and un-adjusted product of the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center Morphing technique (CMORPH) over the period of 2000 to 2013.The error analysis utilized statistical techniques using bias ratio (Bias), correlation coefficient (CC) and root-mean-square-error (RMSE). Mean relative error (MRE), CC and RMSE metrics are further examined for six categories of 10th, 25th, 50th, 75th, 90thand 95th percentile rainfall thresholds. The skill of the satellite estimates is evaluated using categorical error metrics of missed rainfall volume fraction (MRV), falsely detected rainfall volume fraction (FRV), probability of detection (POD) and False Alarm Ratio (FAR). Results showed that six satellite based rainfall products underestimated wet season (June to September) gauge precipitation, with the exception of non-adjusted PERSIANN that overestimated the initial part of the rainy season (March to May). During the wet season, adjusted CMORPH has relatively better bias ratio (89 %) followed by 3B42_V7 (88%), adjusted-PERSIANN (81%), and non-adjusted products have relatively lower bias ratio. The results from CC statistic range from 0.34 to 0.43 for the wet season with adjusted products having slightly higher values. The initial rainy season has relatively higher CC than the wet season. Results from the categorical error metrics showed that CMORPH products have higher POD (91%), which are better in avoiding detecting false rainfall events in the wet season. For the initial rainy season PERSIANN (<50%), TMPA and CMORPH products are nearly equivalent (63-67%). On the other hand, FAR is below 0.1% for all products while in the wet season is higher (10-25%). In terms of rainfall volume of missed and false detected rainfall, CMORPH exhibited lower MRV ( 4.5%) than the TMPA and PERSIANN products (11-19%.) in the wet season. MRV for the initial rainy season was 20% for TMPA and CMORPH products and above 30% for PERSIANN products. All products are nearly equivalent in the wet season in terms of FRV (< 0.2%). The magnitude of MRE increases with gauge rainfall threshold categories with 3B42-V7 and adjusted CMORPH having lower magnitude, showing that underestimation of rainfall increases with increasing rainfall magnitude. CC also decreases with gauge rainfall threshold categories with CMORPH products having slightly higher values. Overall, all satellite products underestimated (overestimated) lower (higher) quantiles quantiles. We have observed that among the six satellite rainfall products the adjusted CMORPH has relatively better potential to improve wet season rainfall estimate and 3B42-V7 that initial rainy season in the Upper Blue Nile Basin.

Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Cloud Classification System

NASA Astrophysics Data System (ADS)

Hong, Yang

Precipitation estimation from satellite information (VISIBLE , IR, or microwave) is becoming increasingly imperative because of its high spatial/temporal resolution and board coverage unparalleled by ground-based data. After decades' efforts of rainfall estimation using IR imagery as basis, it has been explored and concluded that the limitations/uncertainty of the existing techniques are: (1) pixel-based local-scale feature extraction; (2) IR temperature threshold to define rain/no-rain clouds; (3) indirect relationship between rain rate and cloud-top temperature; (4) lumped techniques to model high variability of cloud-precipitation processes; (5) coarse scales of rainfall products. As continuing studies, a new version of Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network (PERSIANN), called Cloud Classification System (CCS), has been developed to cope with these limitations in this dissertation. CCS includes three consecutive components: (1) a hybrid segmentation algorithm, namely Hierarchically Topographical Thresholding and Stepwise Seeded Region Growing (HTH-SSRG), to segment satellite IR images into separated cloud patches; (2) a 3D feature extraction procedure to retrieve both pixel-based local-scale and patch-based large-scale features of cloud patch at various heights; (3) an ANN model, Self-Organizing Nonlinear Output (SONO) network, to classify cloud patches into similarity-based clusters, using Self-Organizing Feature Map (SOFM), and then calibrate hundreds of multi-parameter nonlinear functions to identify the relationship between every cloud types and their underneath precipitation characteristics using Probability Matching Method and Multi-Start Downhill Simplex optimization techniques. The model was calibrated over the Southwest of United States (100°--130°W and 25°--45°N) first and then adaptively adjusted to the study region of North America Monsoon Experiment (65°--135°W and 10°--50°N) using observations from Geostationary Operational Environmental Satellite (GOES) IR imagery, Next Generation Radar (NEXRAD) rainfall network, and Tropical Rainfall Measurement Mission (TRMM) microwave rain rate estimates. CCS functions as a distributed model that first identifies cloud patches and then dispatches different but the best matching cloud-precipitation function for each cloud patch to estimate instantaneous rain rate at high spatial resolution (4km) and full temporal resolution of GOES IR images (every 30-minute). Evaluated over a range of spatial and temporal scales, the performance of CCS compared favorably with GOES Precipitation Index (GPI), Universal Adjusted GPI (UAGPI), PERSIANN, and Auto-Estimator (AE) algorithms, consistently. Particularly, the large number of nonlinear functions and optimum IR-rain rate thresholds of CCS model are highly variable, reflecting the complexity of dominant cloud-precipitation processes from cloud patch to cloud patch over various regions. As a result, CCS can more successfully capture variability in rain rate at small scales than existing algorithms and potentially provides rainfall product from GOES IR-NEXARD-TRMM TMI (SSM/I) at 0.12° x 0.12° and 3-hour resolution with relative low standard error (˜=3.0mm/hr) and high correlation coefficient (˜=0.65).

Ensemble flood simulation for a small dam catchment in Japan using 10 and 2 km resolution nonhydrostatic model rainfalls

NASA Astrophysics Data System (ADS)

Kobayashi, Kenichiro; Otsuka, Shigenori; Apip; Saito, Kazuo

2016-08-01

This paper presents a study on short-term ensemble flood forecasting specifically for small dam catchments in Japan. Numerical ensemble simulations of rainfall from the Japan Meteorological Agency nonhydrostatic model (JMA-NHM) are used as the input data to a rainfall-runoff model for predicting river discharge into a dam. The ensemble weather simulations use a conventional 10 km and a high-resolution 2 km spatial resolutions. A distributed rainfall-runoff model is constructed for the Kasahori dam catchment (approx. 70 km2) and applied with the ensemble rainfalls. The results show that the hourly maximum and cumulative catchment-average rainfalls of the 2 km resolution JMA-NHM ensemble simulation are more appropriate than the 10 km resolution rainfalls. All the simulated inflows based on the 2 and 10 km rainfalls become larger than the flood discharge of 140 m3 s-1, a threshold value for flood control. The inflows with the 10 km resolution ensemble rainfall are all considerably smaller than the observations, while at least one simulated discharge out of 11 ensemble members with the 2 km resolution rainfalls reproduces the first peak of the inflow at the Kasahori dam with similar amplitude to observations, although there are spatiotemporal lags between simulation and observation. To take positional lags into account of the ensemble discharge simulation, the rainfall distribution in each ensemble member is shifted so that the catchment-averaged cumulative rainfall of the Kasahori dam maximizes. The runoff simulation with the position-shifted rainfalls shows much better results than the original ensemble discharge simulations.

Towards a physically-based multi-scale ecohydrological simulator for semi-arid regions

NASA Astrophysics Data System (ADS)

Caviedes-Voullième, Daniel; Josefik, Zoltan; Hinz, Christoph

2017-04-01

The use of numerical models as tools for describing and understanding complex ecohydrological systems has enabled to test hypothesis and propose fundamental, process-based explanations of the system system behaviour as a whole as well as its internal dynamics. Reaction-diffusion equations have been used to describe and generate organized pattern such as bands, spots, and labyrinths using simple feedback mechanisms and boundary conditions. Alternatively, pattern-matching cellular automaton models have been used to generate vegetation self-organization in arid and semi-arid regions also using simple description of surface hydrological processes. A key question is: How much physical realism is needed in order to adequately capture the pattern formation processes in semi-arid regions while reliably representing the water balance dynamics at the relevant time scales? In fact, redistribution of water by surface runoff at the hillslope scale occurs at temporal resolution of minutes while the vegetation development requires much lower temporal resolution and longer times spans. This generates a fundamental spatio-temporal multi-scale problem to be solved, for which high resolution rainfall and surface topography are required. Accordingly, the objective of this contribution is to provide proof-of-concept that governing processes can be described numerically at those multiple scales. The requirements for a simulating ecohydrological processes and pattern formation with increased physical realism are, amongst others: i. high resolution rainfall that adequately captures the triggers of growth as vegetation dynamics of arid regions respond as pulsed systems. ii. complex, natural topography in order to accurately model drainage patterns, as surface water redistribution is highly sensitive to topographic features. iii. microtopography and hydraulic roughness, as small scale variations do impact on large scale hillslope behaviour iv. moisture dependent infiltration as temporal dynamics of infiltration affects water storage under vegetation and in bare soil Despite the volume of research in this field, fundamental limitations still exist in the models regarding the aforementioned issues. Topography and hydrodynamics have been strongly simplified. Infiltration has been modelled as dependent on depth but independent of soil moisture. Temporal rainfall variability has only been addressed for seasonal rain. Spatial heterogenity of the topography as well as roughness and infiltration properties, has not been fully and explicitly represented. We hypothesize that physical processes must be robustly modelled and the drivers of complexity must be present with as much resolution as possible in order to provide the necessary realism to improve transient simulations, perhaps leading the way to virtual laboratories and, arguably, predictive tools. This work provides a first approach into a model with explicit hydrological processes represented by physically-based hydrodynamic models, coupled with well-accepted vegetation models. The model aims to enable new possibilities relating to spatiotemporal variability, arbitrary topography and representation of spatial heterogeneity, including sub-daily (in fact, arbitrary) temporal variability of rain as the main forcing of the model, explicit representation of infiltration processes, and various feedback mechanisms between the hydrodynamics and the vegetation. Preliminary testing strongly suggests that the model is viable, has the potential of producing new information of internal dynamics of the system, and allows to successfully aggregate many of the sources of complexity. Initial benchmarking of the model also reveals strengths to be exploited, thus providing an interesting research outlook, as well as weaknesses to be addressed in the immediate future.

Using a landslide inventory from online news to evaluate the performance of warning models for rainfall-induced landslides in Italy

NASA Astrophysics Data System (ADS)

Pecoraro, Gaetano; Calvello, Michele

2017-04-01

In Italy rainfall-induced landslides pose a significant and widespread hazard, resulting in a large number of casualties and enormous economic damages. Mitigation of such a diffuse risk cannot be attained with structural measures only. With respect to the risk to life, early warning systems represent a viable and useful tool for landslide risk mitigation over wide areas. Inventories of rainfall-induced landslides are critical to support investigations of where and when landslides have happened and may occur in the future, i.e. to establish reliable correlations between rainfall characteristics and landslide occurrences. In this work a parametric study has been conducted to evaluate the performance of correlation models between rainfall and landslides over the Italian territory using the "FraneItalia" database, an inventory of landslides retrieved from online Italian journalistic news. The information reported for each record of this database always include: the site of occurrence of the landslides, the date of occurrence, the source of the news. Multiple landslides occurring in the same date, within the same province or region, are inventoried together in one single record of the database, in this case also reporting the number of landslides of the event. Each record the database may also include, if the related information is available: hour of occurrence; typology, volume and material of the landslide; activity phase; effects on people, structures, infrastructures, cars or other elements. The database currently contains six complete years of data (2010-2015), including more than 4000 landslide reports, most of them triggered by rainfall. For the aim of this study, different rainfall-landslides correlation models have been tested by analysing the reported landslides, within all the 144 zones identified by the national civil protection for weather-related warnings in Italy, in relation to satellite-based precipitations estimates from the Global Precipitation Measurement (GPM) NASA mission. This remote sensing database contains gridded precipitation and precipitation-error estimates, with a half-hour temporal resolution and a 0.10-degree spatial resolution, covering most of the earth starting from 2014. It is well known that satellite estimates of rainfall have some limitations in resolving specific rainfall features (e.g., shallow orographic events and short-duration, high-intensity events), yet the temporal and spatial accuracy of the GPM data may be considered adequate in relation to the scale of the analysis and the size of the warning zones used for this study. The results of the parametric analysis conducted herein, although providing some indications on the most relevant rainfall conditions leading to widespread landsliding over a warning zone, must be considered preliminary as they show a very heterogeneous behaviour of the employed rainfall-based warning models over the Italian territory. Nevertheless, they clearly show the strong potential of the continuous multi-year landslide records available from the "FraneItalia" database as an important source of information to evaluate the performance of warning models at regional scale throughout Italy.

Changes to Sub-daily Rainfall Patterns in a Future Climate

NASA Astrophysics Data System (ADS)

Westra, S.; Evans, J. P.; Mehrotra, R.; Sharma, A.

2012-12-01

An algorithm is developed for disaggregating daily rainfall into sub-daily rainfall 'fragments' (continuous high temporal-resolution rainfall sequences whose total depth sums to the daily rainfall amount) under a future, warmer climate. The basis of the algorithm is to re-sample sub-daily fragments from the historical record conditional on the total daily rainfall amount and a range of temperature-based atmospheric predictors. The logic is that as the atmosphere warms, future rainfall patterns will be more reflective of historical rainfall patterns which occurred on warmer days at the same location, or at locations which have an atmospheric temperature profile more representative of expected future atmospheric conditions. It was found that the daily to sub-daily scaling relationship varied significantly by season and by location, with rainfall patterns on warmer seasons or at warmer locations typically exhibiting higher rainfall intensity occurring over shorter periods within a day, compared with cooler seasons and locations. Importantly, by regressing against temperature-based atmospheric covariates, this effect was substantially reduced, suggesting that the approach also may be valid when extrapolating to a future climate. An adjusted method of fragments algorithm was then applied to nine stations around Australia, with the results showing that when holding total daily rainfall constant, the maximum intensity of short duration rainfall increased by a median of about 5% per degree for the maximum 6 minute burst, and 3.5% for the maximum one hour burst, whereas the fraction of the day with no rainfall increased by a median of 1.5%. This highlights that a large proportion of the change to the distribution of rainfall is likely to occur at sub-daily timescales, with significant implications for many hydrological systems.

Quantitative precipitation estimation for an X-band weather radar network

NASA Astrophysics Data System (ADS)

Chen, Haonan

Currently, the Next Generation (NEXRAD) radar network, a joint effort of the U.S. Department of Commerce (DOC), Defense (DOD), and Transportation (DOT), provides radar data with updates every five-six minutes across the United States. This network consists of about 160 S-band (2.7 to 3.0 GHz) radar sites. At the maximum NEXRAD range of 230 km, the 0.5 degree radar beam is about 5.4 km above ground level (AGL) because of the effect of earth curvature. Consequently, much of the lower atmosphere (1-3 km AGL) cannot be observed by the NEXRAD. To overcome the fundamental coverage limitations of today's weather surveillance radars, and improve the spatial and temporal resolution issues, the National Science Foundation Engineering Center (NSF-ERC) for Collaborative Adaptive Sensing of the Atmosphere (CASA) was founded to revolutionize weather sensing in the lower atmosphere by deploying a dense network of shorter-range, low-power X-band dual-polarization radars. The distributed CASA radars are operating collaboratively to adapt the changing atmospheric conditions. Accomplishments and breakthroughs after five years operation have demonstrated the success of CASA program. Accurate radar quantitative precipitation estimation (QPE) has been pursued since the beginning of weather radar. For certain disaster prevention applications such as flash flood and landslide forecasting, the rain rate must however be measured at a high spatial and temporal resolution. To this end, high-resolution radar QPE is one of the major research activities conducted by the CASA community. A radar specific differential propagation phase (Kdp)-based QPE methodology has been developed in CASA. Unlike the rainfall estimation based on the power terms such as radar reflectivity (Z) and differential reflectivity (Zdr), Kdp-based QPE is less sensitive to the path attenuation, drop size distribution (DSD), and radar calibration errors. The CASA Kdp-based QPE system is also immune to the partial beam blockage and hail contamination. The performance of the CASA QPE system is validated and evaluated by using rain gauges. In CASA's Integrated Project 1 (IP1) test bed in Southwestern Oklahoma, a network of 20 rainfall gauges is used for cross-comparison. 40 rainfall cases, including severe, multicellular thunderstorms, squall lines and widespread stratiform rain, that happened during years 2007 - 2011, are used for validation and evaluation purpose. The performance scores illustrate that the CASA QPE system is a great improvement compared to the current state-of-the-art. In addition, the high-resolution CASA QPE products such as instantaneous rainfall rate map and hourly rainfall amount measurements can serve as a reliable input for various distributed hydrological models. The CASA QPE system can save lived and properties from hazardous flash floods by incorporating hydraulic and hydrologic models for flood monitoring and warning.

Geo-spatial analysis of the temporal trends of kharif crop phenology metrics over India and its relationships with rainfall parameters.

PubMed

Chakraborty, Abhishek; Seshasai, M V R; Dadhwal, V K

2014-07-01

The Global Inventory Modeling and Mapping Studies bimonthly Normalized Difference Vegetation Index (NDVI) data of 8 × 8 km spatial resolution for the period of 1982-2006 were analyzed to detect the trends of crop phenology metrics (start of the growing season (SGS), seasonal NDVI amplitude (AMP), seasonally integrated NDVI (SiNDVI)) during kharif season (June to October) and their relationships with the amount of rainfall and the number of rainy days over Indian subcontinent. Direction and magnitude of trends were analyzed at pixel level using the Mann-Kendall test and further assessed at meteorological subdivision level using field significance test (α = 0.1). Significant pre-occurrence of the SGS was observed over northern (Punjab, Haryana) and central (Marathwada, Vidarbha and Madhya Maharashtra) parts, whereas delay was found over southern (Rayalaseema, Coastal Andhra Pradesh) and eastern (Bihar, Gangetic West Bengal and Sub-Himalayan West Bengal) parts of India. North, west, and central India showed significant increasing trends of SiNDVI, corroborating the kharif food grain production performance during the time frame. Significant temporal correlation (α = 0.1) between the rainfall/number of rainy days and crop phenology metrics was observed over the rainfed region of India. About 35-40 % of the study area showed significant correlation between the SGS and the rainfall/number of rainy days during June to August. June month rainfall/number of rainy days was found to be the most sensitive to the SGS. The amount of rainfall and the number of rainy days during monsoon were found to have significant influence over the SiNDVI in 24-30 % of the study area. The crop phenology metrics had significant correlation with the number of rainy days over the larger areas than that of the rainfall amount.

The potential of urban rainfall monitoring with crowdsourced automatic weather stations in Amsterdam

NASA Astrophysics Data System (ADS)

de Vos, Lotte; Leijnse, Hidde; Overeem, Aart; Uijlenhoet, Remko

2017-02-01

The high density of built-up areas and resulting imperviousness of the land surface makes urban areas vulnerable to extreme rainfall, which can lead to considerable damage. In order to design and manage cities to be able to deal with the growing number of extreme rainfall events, rainfall data are required at higher temporal and spatial resolutions than those needed for rural catchments. However, the density of operational rainfall monitoring networks managed by local or national authorities is typically low in urban areas. A growing number of automatic personal weather stations (PWSs) link rainfall measurements to online platforms. Here, we examine the potential of such crowdsourced datasets for obtaining the desired resolution and quality of rainfall measurements for the capital of the Netherlands. Data from 63 stations in Amsterdam (˜ 575 km2) that measure rainfall over at least 4 months in a 17-month period are evaluated. In addition, a detailed assessment is made of three Netatmo stations, the largest contributor to this dataset, in an experimental setup. The sensor performance in the experimental setup and the density of the PWS network are promising. However, features in the online platforms, like rounding and thresholds, cause changes from the original time series, resulting in considerable errors in the datasets obtained. These errors are especially large during low-intensity rainfall, although they can be reduced by accumulating rainfall over longer intervals. Accumulation improves the correlation coefficient with gauge-adjusted radar data from 0.48 at 5 min intervals to 0.60 at hourly intervals. Spatial rainfall correlation functions derived from PWS data show much more small-scale variability than those based on gauge-adjusted radar data and those found in similar research using dedicated rain gauge networks. This can largely be attributed to the noise in the PWS data resulting from both the measurement setup and the processes occurring in the data transfer to the online PWS platform. A double mass comparison with gauge-adjusted radar data shows that the median of the stations resembles the rainfall reference better than the real-time (unadjusted) radar product. Averaging nearby raw PWS measurements further improves the match with gauge-adjusted radar data in that area. These results confirm that the growing number of internet-connected PWSs could successfully be used for urban rainfall monitoring.

Urban rainfall monitoring with crowdsourced automatic weather stations in Amsterdam

NASA Astrophysics Data System (ADS)

de Vos, Lotte; Leijnse, Hidde; Overeem, Aart; Uijlenhoet, Remko

2017-04-01

The high density of built-up areas and resulting imperviousness of the land surface makes urban areas vulnerable to extreme rainfall, which can lead to considerable damage. In order to design and manage cities to be able to deal with the growing number of extreme rainfall events, rainfall data is required at higher temporal and spatial resolutions than those needed for rural catchments. However, the density of operational rainfall monitoring networks managed by local or national authorities is typically low in urban areas. A growing number of automatic personal weather stations (PWSs) link rainfall measurements to online platforms. Here, we examine the potential of such crowdsourced datasets for obtaining the desired resolution and quality of rainfall measurements for the capital of the Netherlands. Data from 63 stations in Amsterdam (˜575 km2}) that measure rainfall over at least 4 months in a 17-month period are evaluated. In addition, a detailed assessment is made of three Netatmo stations, the largest contributor to this dataset, in an experimental set-up. The sensor performance in the experimental set-up and the density of the PWS-network are promising. However, features in the online platforms, like rounding and thresholds, cause changes from the original time series, resulting in considerable errors in the datasets obtained. These errors are especially large during low intensity rainfall, although they can be reduced by accumulating rainfall over longer intervals. Accumulation improves the correlation coefficient with gauge-adjusted radar data from 0.48 at 5 min intervals to 0.60 at hourly intervals. Spatial rainfall correlation functions derived from PWS data show much more small-scale variability than those based on gauge-adjusted radar data and those found in similar research using dedicated rain gauge networks. This can largely be attributed to the noise in the PWS data resulting from both the measurement setup and the processes occurring in the data transfer to the online PWS-platform. A double mass comparison with gauge-adjusted radar data shows that the median of the stations resembles the rainfall reference better than the real-time (unadjusted) radar product. Averaging nearby raw PWS measurements further improves the match with gauge-adjusted radar data in that area. These results confirm that the growing number of internet-connected PWSs could successfully be used for urban rainfall monitoring.

Spectral analysis of temporal non-stationary rainfall-runoff processes

NASA Astrophysics Data System (ADS)

Chang, Ching-Min; Yeh, Hund-Der

2018-04-01

This study treats the catchment as a block box system with considering the rainfall input and runoff output being a stochastic process. The temporal rainfall-runoff relationship at the catchment scale is described by a convolution integral on a continuous time scale. Using the Fourier-Stieltjes representation approach, a frequency domain solution to the convolution integral is developed to the spectral analysis of runoff processes generated by temporal non-stationary rainfall events. It is shown that the characteristic time scale of rainfall process increases the runoff discharge variability, while the catchment mean travel time constant plays the role in reducing the variability of runoff discharge. Similar to the behavior of groundwater aquifers, catchments act as a low-pass filter in the frequency domain for the rainfall input signal.

First results from comparison of rainfall estimations by GPM IMERG with rainfall measurements from the WegenerNet high density network

NASA Astrophysics Data System (ADS)

Oo, Sungmin; Foelsche, Ulrich; Kirchengast, Gottfried; Fuchsberger, Jürgen

2016-04-01

The research level products of the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG "Final" run datasets) were compared with rainfall measurements from the WegenerNet high density network as part of ground validation (GV) projects of GPM missions. The WegenerNet network comprises 151 ground level weather stations in an area of 15 km × 20 km in south-eastern Austria (Feldbach region, ˜46.93° N, ˜15.90° E) designed to serve as a long-term monitoring and validation facility for weather and climate research and applications. While the IMERG provides rainfall estimations every half hour at 0.1° resolution, the WegenerNet network measures rainfall every 5 minutes at around 2 km2 resolution and produces 200 m × 200 m gridded datasets. The study was conducted on the domain of the WegenerNet network; eight IMERG grids are overlapped with the network, two of which are entirely covered by the WegenerNet (40 and 39 stations in each grid). We investigated data from April to September of the years 2014 to 2015; the date of first two years after the launch of the GPM Core Observatory. Since the network has a flexibility to work with various spatial and temporal scales, the comparison could be conducted on average-points to pixel basis at both sub-daily and daily timescales. This presentation will summarize the first results of the comparison and future plans to explore the characteristics of errors in the IMERG datasets.

Comparison of different synthetic 5-min rainfall time series on the results of rainfall runoff simulations in urban drainage modelling

NASA Astrophysics Data System (ADS)

Krämer, Stefan; Rohde, Sophia; Schröder, Kai; Belli, Aslan; Maßmann, Stefanie; Schönfeld, Martin; Henkel, Erik; Fuchs, Lothar

2015-04-01

The design of urban drainage systems with numerical simulation models requires long, continuous rainfall time series with high temporal resolution. However, suitable observed time series are rare. As a result, usual design concepts often use uncertain or unsuitable rainfall data, which renders them uneconomic or unsustainable. An expedient alternative to observed data is the use of long, synthetic rainfall time series as input for the simulation models. Within the project SYNOPSE, several different methods to generate synthetic rainfall data as input for urban drainage modelling are advanced, tested, and compared. Synthetic rainfall time series of three different precipitation model approaches, - one parametric stochastic model (alternating renewal approach), one non-parametric stochastic model (resampling approach), one downscaling approach from a regional climate model-, are provided for three catchments with different sewer system characteristics in different climate regions in Germany: - Hamburg (northern Germany): maritime climate, mean annual rainfall: 770 mm; combined sewer system length: 1.729 km (City center of Hamburg), storm water sewer system length (Hamburg Harburg): 168 km - Brunswick (Lower Saxony, northern Germany): transitional climate from maritime to continental, mean annual rainfall: 618 mm; sewer system length: 278 km, connected impervious area: 379 ha, height difference: 27 m - Friburg in Brisgau (southern Germany): Central European transitional climate, mean annual rainfall: 908 mm; sewer system length: 794 km, connected impervious area: 1 546 ha, height difference 284 m Hydrodynamic models are set up for each catchment to simulate rainfall runoff processes in the sewer systems. Long term event time series are extracted from the - three different synthetic rainfall time series (comprising up to 600 years continuous rainfall) provided for each catchment and - observed gauge rainfall (reference rainfall) according national hydraulic design standards. The synthetic and reference long term event time series are used as rainfall input for the hydrodynamic sewer models. For comparison of the synthetic rainfall time series against the reference rainfall and against each other the number of - surcharged manholes, - surcharges per manhole, - and the average surcharge volume per manhole are applied as hydraulic performance criteria. The results are discussed and assessed to answer the following questions: - Are the synthetic rainfall approaches suitable to generate high resolution rainfall series and do they produce, - in combination with numerical rainfall runoff models - valid results for design of urban drainage systems? - What are the bounds of uncertainty in the runoff results depending on the synthetic rainfall model and on the climate region? The work is carried out within the SYNOPSE project, funded by the German Federal Ministry of Education and Research (BMBF).

Application of satellite estimates of rainfall distribution to simulate the potential for malaria transmission in Africa

NASA Astrophysics Data System (ADS)

Yamana, T. K.; Eltahir, E. A.

2009-12-01

The Hydrology, Entomology and Malaria Transmission Simulator (HYDREMATS) is a mechanistic model developed to assess malaria risk in areas where the disease is water-limited. This model relies on precipitation inputs as its primary forcing. Until now, applications of the model have used ground-based precipitation observations. However, rain gauge networks in the areas most affected by malaria are often sparse. The increasing availability of satellite based rainfall estimates could greatly extend the range of the model. The minimum temporal resolution of precipitation data needed was determined to be one hour. The CPC Morphing technique (CMORPH ) distributed by NOAA fits this criteria, as it provides 30-minute estimates at 8km resolution. CMORPH data were compared to ground observations in four West African villages, and calibrated to reduce overestimation and false alarm biases. The calibrated CMORPH data were used to force HYDREMATS, resulting in outputs for mosquito populations, vectorial capacity and malaria transmission.

Analysing the Advantages of High Temporal Resolution Geostationary MSG SEVIRI Data Compared to Polar Operational Environmental Satellite Data for Land Surface Monitoring in Africa

NASA Technical Reports Server (NTRS)

Fensholt, R.; Anyamba, A.; Huber, S.; Proud, S. R.; Tucker, C. J.; Small, J.; Pak, E.; Rasmussen, M. O.; Sandholt, I.; Shisanya, C.

2011-01-01

Since 1972, satellite remote sensing of the environment has been dominated by polar-orbiting sensors providing useful data for monitoring the earth s natural resources. However their observation and monitoring capacity are inhibited by daily to monthly looks for any given ground surface which often is obscured by frequent and persistent cloud cover creating large gaps in time series measurements. The launch of the Meteosat Second Generation (MSG) satellite into geostationary orbit has opened new opportunities for land surface monitoring. The Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument on-board MSG with an imaging capability every 15 minutes which is substantially greater than any temporal resolution that can be obtained from existing polar operational environmental satellites (POES) systems currently in use for environmental monitoring. Different areas of the African continent were affected by droughts and floods in 2008 caused by periods of abnormally low and high rainfall, respectively. Based on the effectiveness of monitoring these events from Earth Observation (EO) data the current analyses show that the new generation of geostationary remote sensing data can provide higher temporal resolution cloud-free (less than 5 days) measurements of the environment as compared to existing POES systems. SEVIRI MSG 5-day continental scale composites will enable rapid assessment of environmental conditions and improved early warning of disasters for the African continent such as flooding or droughts. The high temporal resolution geostationary data will complement existing higher spatial resolution polar-orbiting satellite data for various dynamic environmental and natural resource applications of terrestrial ecosystems.

Hydrological Applications of a High-Resolution Radar Precipitation Data Base for Sweden

NASA Astrophysics Data System (ADS)

Olsson, Jonas; Berg, Peter; Norin, Lars; Simonsson, Lennart

2017-04-01

There is an increasing need for high-resolution observations of precipitation on local, regional, national and even continental level. Urbanization and other environmental changes often make societies more vulnerable to intense short-duration rainfalls (cloudbursts) and their consequences in terms of e.g. flooding and landslides. Impact and forecasting models of these hazards put very high demands on the rainfall input in terms of both resolution and accuracy. Weather radar systems obviously have a great potential in this context, but also limitations with respect to e.g. conversion algorithms and various error sources that may have a significant impact on the subsequent hydrological modelling. In Sweden, the national weather radar network has been in operation for nearly three decades, but until recently the hydrological applications have been very limited. This is mainly because of difficulties in managing the different errors and biases in the radar precipitation product, which made it hard to demonstrate any distinct added value as compared with gauge-based precipitation products. In the last years, however, in light of distinct progress in developing error correction procedures, substantial efforts have been made to develop a national gauge-adjusted radar precipitation product - HIPRAD (High-Resolution Precipitation from Gauge-Adjusted Weather Radar). In HIPRAD, the original radar precipitation data are scaled to match the monthly accumulations in a national grid (termed PTHBV) created by optimal interpolation of corrected daily gauge observations, with the intention to attain both a high spatio-temporal resolution and accurate long-term accumulations. At present, HIPRAD covers the period 2000-present with resolutions 15 min and 2×2 km2. A key motivation behind the development of HIPRAD is the intention to increase the temporal resolution in the national flood forecasting system from 1 day to 1 hour. Whereas a daily time step is sufficient to describe the rainfall-runoff process in large, slow river basins, which traditionally has been the main focus in the national forecasting, an hourly time step (or preferably even shorter) is required to simulate the flow in fast-responding basins. At the daily scale, the PTHBV product is used for model initialization prior to the forecasts but with its daily resolution it is not applicable at the hourly scale. For this purpose, a real-time version of HIPRAD has been developed which is currently running operationally. HIPRAD is also being used for historical simulations with an hourly time step, which is important for e.g. water quality assessment. Finally, we will use HIPRAD to gain an improved knowledge of the short-duration precipitation climate in Sweden. Currently there are many open issues with respect to e.g. geographical differences, spatial correlations and areal extremes. Here we will show and discuss selected results from the ongoing development and validation of HIPRAD as well as its various applications for hydrological forecasting and risk assessment. Further, web resources containing radar-based observation and forecasting for hydrological applications will be demonstrated. Finally, some future research directions will be outlined. Fast responding hydrological catchments require fine spatial and temporal resolution of the precipitation input data to provide realistic results.

Climate impacts on environmental risks evaluated from space: a conceptual approach to the case of Rift Valley Fever in Senegal

PubMed Central

Tourre, Yves M.; Lacaux, Jean-Pierre; Vignolles, Cécile; Lafaye, Murielle

2009-01-01

Background Climate and environment vary across many spatio-temporal scales, including the concept of climate change, which impact on ecosystems, vector-borne diseases and public health worldwide. Objectives To develop a conceptual approach by mapping climatic and environmental conditions from space and studying their linkages with Rift Valley Fever (RVF) epidemics in Senegal. Design Ponds in which mosquitoes could thrive were identified from remote sensing using high-resolution SPOT-5 satellite images. Additional data on pond dynamics and rainfall events (obtained from the Tropical Rainfall Measuring Mission) were combined with hydrological in-situ data. Localisation of vulnerable hosts such as penned cattle (from QuickBird satellite) were also used. Results Dynamic spatio-temporal distribution of Aedes vexans density (one of the main RVF vectors) is based on the total rainfall amount and ponds’ dynamics. While Zones Potentially Occupied by Mosquitoes are mapped, detailed risk areas, i.e. zones where hazards and vulnerability occur, are expressed in percentages of areas where cattle are potentially exposed to mosquitoes’ bites. Conclusions This new conceptual approach, using precise remote-sensing techniques, simply relies upon rainfall distribution also evaluated from space. It is meant to contribute to the implementation of operational early warning systems for RVF based on both natural and anthropogenic climatic and environmental changes. In a climate change context, this approach could also be applied to other vector-borne diseases and places worldwide. PMID:20052381

Climate impacts on environmental risks evaluated from space: a contribution to social benefits within the GEOSS Health Area: The case of Rift Valley Fever in Senegal

NASA Astrophysics Data System (ADS)

Tourre, Y. M.

2009-12-01

Climate and environment vary on many spatio-temporal scales, including climate change, with impacts on ecosystems, vector-borne diseases and public health worldwide. This study is to enable societal benefits from a conceptual approach by mapping climatic and environmental conditions from space and understanding the mechanisms within the Health Social Benefit GEOSS area. The case study is for Rift Valley Fever (RVF) epidemics in Senegal is presented. Ponds contributing to mosquitoes’ thriving, were identified from remote sensing using high-resolution SPOT-5 satellite images. Additional data on ponds’ dynamics and rainfall events (obtained from the Tropical Rainfall Measuring Mission) were combined with hydrological in-situ data. Localization of vulnerable hosts such as parked cattle (from QuickBird satellite) are also used. Dynamic spatio-temporal distribution of Aedes vexans density (one of the main RVF vectors) is based on the total rainfall amount and ponds’ dynamics. While Zones Potentially Occupied by Mosquitoes (ZPOM) are mapped, detailed risks areas, i.e. zones where hazards and vulnerability occur, are expressed in percentages of parks where cattle is potentially exposed to mosquitoes’ bites. This new conceptual approach, using remote-sensing techniques belonging to GEOSS, simply relies upon rainfall distribution also evaluated from space. It is meant to contribute to the implementation of integrated operational early warning system within the health application communities since climatic and environmental conditions (both natural and anthropogenic) are changing rapidly.

Establishing a method of short-term rainfall forecasting based on GNSS-derived PWV and its application.

PubMed

Yao, Yibin; Shan, Lulu; Zhao, Qingzhi

2017-09-29

Global Navigation Satellite System (GNSS) can effectively retrieve precipitable water vapor (PWV) with high precision and high-temporal resolution. GNSS-derived PWV can be used to reflect water vapor variation in the process of strong convection weather. By studying the relationship between time-varying PWV and rainfall, it can be found that PWV contents increase sharply before raining. Therefore, a short-term rainfall forecasting method is proposed based on GNSS-derived PWV. Then the method is validated using hourly GNSS-PWV data from Zhejiang Continuously Operating Reference Station (CORS) network of the period 1 September 2014 to 31 August 2015 and its corresponding hourly rainfall information. The results show that the forecasted correct rate can reach about 80%, while the false alarm rate is about 66%. Compared with results of the previous studies, the correct rate is improved by about 7%, and the false alarm rate is comparable. The method is also applied to other three actual rainfall events of different regions, different durations, and different types. The results show that the method has good applicability and high accuracy, which can be used for rainfall forecasting, and in the future study, it can be assimilated with traditional weather forecasting techniques to improve the forecasted accuracy.

Projected rainfall and temperature changes over Malaysia at the end of the 21st century based on PRECIS modelling system

NASA Astrophysics Data System (ADS)

Loh, Jui Le; Tangang, Fredolin; Juneng, Liew; Hein, David; Lee, Dong-In

2016-05-01

This study investigates projected changes in rainfall and temperature over Malaysia by the end of the 21st century based on the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios (SRES) A2, A1B and B2 emission scenarios using the Providing Regional Climates for Impacts Studies (PRECIS). The PRECIS regional climate model (HadRM3P) is configured in 0.22° × 0.22° horizontal grid resolution and is forced at the lateral boundaries by the UKMO-HadAM3P and UKMOHadCM3Q0 global models. The model performance in simulating the present-day climate was assessed by comparing the modelsimulated results to the Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE) dataset. Generally, the HadAM3P/PRECIS and HadCM3Q0/PRECIS simulated the spatio-temporal variability structure of both temperature and rainfall reasonably well, albeit with the presence of cold biases. The cold biases appear to be associated with the systematic error in the HadRM3P. The future projection of temperature indicates widespread warming over the entire country by the end of the 21st century. The projected temperature increment ranges from 2.5 to 3.9°C, 2.7 to 4.2°C and 1.7 to 3.1°C for A2, A1B and B2 scenarios, respectively. However, the projection of rainfall at the end of the 21st century indicates substantial spatio-temporal variation with a tendency for drier condition in boreal winter and spring seasons while wetter condition in summer and fall seasons. During the months of December to May, ~20-40% decrease of rainfall is projected over Peninsular Malaysia and Borneo, particularly for the A2 and B2 emission scenarios. During the summer months, rainfall is projected to increase by ~20-40% across most regions in Malaysia, especially for A2 and A1B scenarios. The spatio-temporal variations in the projected rainfall can be related to the changes in the weakening monsoon circulations, which in turn alter the patterns of regional moisture convergences in the region.

Use of soil moisture dynamics and patterns for the investigation of runoff generation processes with emphasis on preferential flow

NASA Astrophysics Data System (ADS)

Blume, T.; Zehe, E.; Bronstert, A.

2007-08-01

Spatial patterns as well as temporal dynamics of soil moisture have a major influence on runoff generation. The investigation of these dynamics and patterns can thus yield valuable information on hydrological processes, especially in data scarce or previously ungauged catchments. The combination of spatially scarce but temporally high resolution soil moisture profiles with episodic and thus temporally scarce moisture profiles at additional locations provides information on spatial as well as temporal patterns of soil moisture at the hillslope transect scale. This approach is better suited to difficult terrain (dense forest, steep slopes) than geophysical techniques and at the same time less cost-intensive than a high resolution grid of continuously measuring sensors. Rainfall simulation experiments with dye tracers while continuously monitoring soil moisture response allows for visualization of flow processes in the unsaturated zone at these locations. Data was analyzed at different spacio-temporal scales using various graphical methods, such as space-time colour maps (for the event and plot scale) and indicator maps (for the long-term and hillslope scale). Annual dynamics of soil moisture and decimeter-scale variability were also investigated. The proposed approach proved to be successful in the investigation of flow processes in the unsaturated zone and showed the importance of preferential flow in the Malalcahuello Catchment, a data-scarce catchment in the Andes of Southern Chile. Fast response times of stream flow indicate that preferential flow observed at the plot scale might also be of importance at the hillslope or catchment scale. Flow patterns were highly variable in space but persistent in time. The most likely explanation for preferential flow in this catchment is a combination of hydrophobicity, small scale heterogeneity in rainfall due to redistribution in the canopy and strong gradients in unsaturated conductivities leading to self-reinforcing flow paths.

Signature of present and projected climate change at an urban scale: The case of Addis Ababa

NASA Astrophysics Data System (ADS)

Arsiso, Bisrat Kifle; Mengistu Tsidu, Gizaw; Stoffberg, Gerrit Hendrik

2018-06-01

Understanding climate change and variability at an urban scale is essential for water resource management, land use planning, development of adaption plans, mitigation of air and water pollution. However, there are serious challenges to meet these goals due to unavailability of observed and/or simulated high resolution spatial and temporal climate data. The statistical downscaling of general circulation climate model, for instance, is usually driven by sparse observational data hindering the use of downscaled data to investigate urban scale climate variability and change in the past. Recently, these challenges are partly resolved by concerted international effort to produce global and high spatial resolution climate data. In this study, the 1 km2 high resolution NIMR-HadGEM2-AO simulations for future projections under Representative Concentration Pathways (RCP4.5 and RCP8.5) scenarios and gridded observations provided by Worldclim data center are used to assess changes in rainfall, minimum and maximum temperature expected under the two scenarios over Addis Ababa city. The gridded 1 km2 observational data set for the base period (1950-2000) is compared to observation from a meteorological station in the city in order to assess its quality for use as a reference (baseline) data. The comparison revealed that the data set has a very good quality. The rainfall anomalies under RCPs scenarios are wet in the 2030s (2020-2039), 2050s (2040-2069) and 2080s (2070-2099). Both minimum and maximum temperature anomalies under RCPs are successively getting warmer during these periods. Thus, the projected changes under RCPs scenarios show a general increase in rainfall and temperatures with strong variabilities in rainfall during rainy season implying level of difficulty in water resource use and management as well as land use planning and management.

On the uncertainties associated with using gridded rainfall data as a proxy for observed

NASA Astrophysics Data System (ADS)

Tozer, C. R.; Kiem, A. S.; Verdon-Kidd, D. C.

2012-05-01

Gridded rainfall datasets are used in many hydrological and climatological studies, in Australia and elsewhere, including for hydroclimatic forecasting, climate attribution studies and climate model performance assessments. The attraction of the spatial coverage provided by gridded data is clear, particularly in Australia where the spatial and temporal resolution of the rainfall gauge network is sparse. However, the question that must be asked is whether it is suitable to use gridded data as a proxy for observed point data, given that gridded data is inherently "smoothed" and may not necessarily capture the temporal and spatial variability of Australian rainfall which leads to hydroclimatic extremes (i.e. droughts, floods). This study investigates this question through a statistical analysis of three monthly gridded Australian rainfall datasets - the Bureau of Meteorology (BOM) dataset, the Australian Water Availability Project (AWAP) and the SILO dataset. The results of the monthly, seasonal and annual comparisons show that not only are the three gridded datasets different relative to each other, there are also marked differences between the gridded rainfall data and the rainfall observed at gauges within the corresponding grids - particularly for extremely wet or extremely dry conditions. Also important is that the differences observed appear to be non-systematic. To demonstrate the hydrological implications of using gridded data as a proxy for gauged data, a rainfall-runoff model is applied to one catchment in South Australia initially using gauged data as the source of rainfall input and then gridded rainfall data. The results indicate a markedly different runoff response associated with each of the different sources of rainfall data. It should be noted that this study does not seek to identify which gridded dataset is the "best" for Australia, as each gridded data source has its pros and cons, as does gauged data. Rather, the intention is to quantify differences between various gridded data sources and how they compare with gauged data so that these differences can be considered and accounted for in studies that utilise these gridded datasets. Ultimately, if key decisions are going to be based on the outputs of models that use gridded data, an estimate (or at least an understanding) of the uncertainties relating to the assumptions made in the development of gridded data and how that gridded data compares with reality should be made.

Gauging Through the Crowd: A Crowd-Sourcing Approach to Urban Rainfall Measurement and Storm Water Modeling Implications

NASA Astrophysics Data System (ADS)

Yang, Pan; Ng, Tze Ling

2017-11-01

Accurate rainfall measurement at high spatial and temporal resolutions is critical for the modeling and management of urban storm water. In this study, we conduct computer simulation experiments to test the potential of a crowd-sourcing approach, where smartphones, surveillance cameras, and other devices act as precipitation sensors, as an alternative to the traditional approach of using rain gauges to monitor urban rainfall. The crowd-sourcing approach is promising as it has the potential to provide high-density measurements, albeit with relatively large individual errors. We explore the potential of this approach for urban rainfall monitoring and the subsequent implications for storm water modeling through a series of simulation experiments involving synthetically generated crowd-sourced rainfall data and a storm water model. The results show that even under conservative assumptions, crowd-sourced rainfall data lead to more accurate modeling of storm water flows as compared to rain gauge data. We observe the relative superiority of the crowd-sourcing approach to vary depending on crowd participation rate, measurement accuracy, drainage area, choice of performance statistic, and crowd-sourced observation type. A possible reason for our findings is the differences between the error structures of crowd-sourced and rain gauge rainfall fields resulting from the differences between the errors and densities of the raw measurement data underlying the two field types.

Online Visualization and Analysis of Global Half-Hourly Infrared Satellite Data

NASA Technical Reports Server (NTRS)

Liu, Zhong; Ostrenga, Dana; Leptoukh, Gregory

2011-01-01

nfrared (IR) images (approximately 11-micron channel) recorded by satellite sensors have been widely used in weather forecasting, research, and classroom education since the Nimbus program. Unlike visible images, IR imagery can reveal cloud features without sunlight illumination; therefore, they can be used to monitor weather phenomena day and night. With geostationary satellites deployed around the globe, it is possible to monitor weather events 24/7 at a temporal resolution that polar-orbiting satellites cannot achieve at the present time. When IR data from multiple geostationary satellites are merged to form a single product--also known as a merged product--it allows for observing weather on a global scale. Its high temporal resolution (e.g., every half hour) also makes it an ideal ancillary dataset for supporting other satellite missions, such as the Tropical Rainfall Measuring Mission (TRMM), etc., by providing additional background information about weather system evolution.

Changes in intensity of precipitation extremes in Romania on very hight temporal scale and implications on the validity of the Clausius-Clapeyron relation

NASA Astrophysics Data System (ADS)

Busuioc, Aristita; Baciu, Madalina; Breza, Traian; Dumitrescu, Alexandru; Stoica, Cerasela; Baghina, Nina

2016-04-01

Many observational, theoretical and based on climate model simulation studies suggested that warmer climates lead to more intense precipitation events, even when the total annual precipitation is slightly reduced. In this way, it was suggested that extreme precipitation events may increase at Clausius-Clapeyron (CC) rate under global warming and constraint of constant relative humidity. However, recent studies show that the relationship between extreme rainfall intensity and atmospheric temperature is much more complex than would be suggested by the CC relationship and is mainly dependent on precipitation temporal resolution, region, storm type and whether the analysis is conducted on storm events rather than fixed data. The present study presents the dependence between the very hight temporal scale extreme rainfall intensity and daily temperatures, with respect to the verification of the CC relation. To solve this objective, the analysis is conducted on rainfall event rather than fixed interval using the rainfall data based on graphic records including intensities (mm/min.) calculated over each interval with permanent intensity per minute. The annual interval with available a such data (April to October) is considered at 5 stations over the interval 1950-2007. For Bucuresti-Filaret station the analysis is extended over the longer interval (1898-2007). For each rainfall event, the maximum intensity (mm/min.) is retained and these time series are considered for the further analysis (abbreviated in the following as IMAX). The IMAX data were divided based on the daily mean temperature into bins 2oC - wide. The bins with less than 100 values were excluded. The 90th, 99th and 99.9th percentiles were computed from the binned data using the empirical distribution and their variability has been compared to the CC scaling (e.g. exponential relation given by a 7% increase per temperature degree rise). The results show a dependence close to double the CC relation for temperatures less than ~ 220C and negative scaling rates for higher temperatures. This behaviour is similar for all the 5 analysed stations over the common interval 1950-2007. This scaling is more exactly for the 90th percentile, while for the higher percentiles the rainfall intensity in response to warming exceeds sometimes the CC rate. For Bucuresti-Filaret station, the results are similar over a longer interval (1898-2007) showing that these findings are robust. Similar techniques has been previously applied to the hourly rainfall intensities recorded at 9 stations (including the 5 ones) and the results are slightly different: the 90th percentile shows dependence close to the CC relation for all temperatures; the 99th and 99.9th percentiles exhibit rates close to double the CC rate for temperatures between ~ 100C and ~ 220C and negative scaling rates for higher temperatures. In conclusion, these results show that the dependence between the extreme precipitation intensity and atmospheric temperature in Romania is mainly dependent on the temporal precipitation resolution and the degree of the extreme precipitation event (moderate or stronger); these findings are mainly in agreenment with the conclusions presented by previous international studies (mentioned above), with some regional specific features, showing the importance of the regional studies. The results presented is this study were funded by the Executive Agency for Higher Education, Research, Development and Innovation Funding (UEFISCDI) through the research project CLIMHYDEX, "Changes in climate extremes and associated impact in hydrological events in Romania", code PNII-ID-2011-2-0073 (http://climhydex.meteoromania.ro).

Assessment of Developing Intensity Duration Frequency Curves using Satellite Observations (Case Study)

NASA Astrophysics Data System (ADS)

Ombadi, Mohammed; Nguyen, Phu; Sorooshian, Soroosh

2017-12-01

Intensity Duration Frequency (IDF) curves are essential for the resilient design of infrastructures. Since their earlier development, IDF relationships have been derived using precipitation records from rainfall gauge stations. However, with the recent advancement in satellite observation of precipitation which provides near global coverage and high spatiotemporal resolution, it is worthy of attention to investigate the validity of utilizing the relatively short record length of satellite rainfall to generate robust IDF relationships. These satellite-based IDF can address the paucity of such information in the developing countries. Few studies have used satellite precipitation data in IDF development but mainly focused on merging satellite and gauge precipitation. In this study, however, IDF have been derived solely from satellite observations using PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record). The unique PERSIANN-CDR attributes of high spatial resolution (0.25°×0.25°), daily temporal resolution and a record dating back to 1983 allow for the investigation at fine resolution. The results are compared over most of the contiguous United States against NOAA Atlas 14. The impact of using different methods of sampling, distribution estimators and regionalization in the resulting relationships is investigated. Main challenges to estimate robust and accurate IDF from satellite observations are also highlighted.

Extreme Rainfall Events Over Southern Africa: Assessment of a Climate Model to Reproduce Daily Extremes

NASA Astrophysics Data System (ADS)

Williams, C.; Kniveton, D.; Layberry, R.

2007-12-01

It is increasingly accepted that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The subcontinent is considered especially vulnerable extreme events, due to a number of factors including extensive poverty, disease and political instability. Rainfall variability and the identification of rainfall extremes is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. The majority of previous climate model verification studies have compared model output with observational data at monthly timescales. In this research, the assessment of a state-of-the-art climate model to simulate climate at daily timescales is carried out using satellite derived rainfall data from the Microwave Infra-Red Algorithm (MIRA). This dataset covers the period from 1993-2002 and the whole of southern Africa at a spatial resolution of 0.1 degree longitude/latitude. Once the model's ability to reproduce extremes has been assessed, idealised regions of SST anomalies are used to force the model, with the overall aim of investigating the ways in which SST anomalies influence rainfall extremes over southern Africa. In this paper, results from sensitivity testing of the UK Meteorological Office Hadley Centre's climate model's domain size are firstly presented. Then simulations of current climate from the model, operating in both regional and global mode, are compared to the MIRA dataset at daily timescales. Thirdly, the ability of the model to reproduce daily rainfall extremes will be assessed, again by a comparison with extremes from the MIRA dataset. Finally, the results from the idealised SST experiments are briefly presented, suggesting associations between rainfall extremes and both local and remote SST anomalies.

Capabilities of stochastic rainfall models as data providers for urban hydrology

NASA Astrophysics Data System (ADS)

Haberlandt, Uwe

2017-04-01

For planning of urban drainage systems using hydrological models, long, continuous precipitation series with high temporal resolution are needed. Since observed time series are often too short or not available everywhere, the use of synthetic precipitation is a common alternative. This contribution compares three precipitation models regarding their suitability to provide 5 minute continuous rainfall time series for a) sizing of drainage networks for urban flood protection and b) dimensioning of combined sewage systems for pollution reduction. The rainfall models are a parametric stochastic model (Haberlandt et al., 2008), a non-parametric probabilistic approach (Bárdossy, 1998) and a stochastic downscaling of dynamically simulated rainfall (Berg et al., 2013); all models are operated both as single site and multi-site generators. The models are applied with regionalised parameters assuming that there is no station at the target location. Rainfall and discharge characteristics are utilised for evaluation of the model performance. The simulation results are compared against results obtained from reference rainfall stations not used for parameter estimation. The rainfall simulations are carried out for the federal states of Baden-Württemberg and Lower Saxony in Germany and the discharge simulations for the drainage networks of the cities of Hamburg, Brunswick and Freiburg. Altogether, the results show comparable simulation performance for the three models, good capabilities for single site simulations but low skills for multi-site simulations. Remarkably, there is no significant difference in simulation performance comparing the tasks flood protection with pollution reduction, so the models are finally able to simulate both the extremes and the long term characteristics of rainfall equally well. Bárdossy, A., 1998. Generating precipitation time series using simulated annealing. Wat. Resour. Res., 34(7): 1737-1744. Berg, P., Wagner, S., Kunstmann, H., Schädler, G., 2013. High resolution regional climate model simulations for Germany: part I — validation. Climate Dynamics, 40(1): 401-414. Haberlandt, U., Ebner von Eschenbach, A.-D., Buchwald, I., 2008. A space-time hybrid hourly rainfall model for derived flood frequency analysis. Hydrol. Earth Syst. Sci., 12: 1353-1367.

Using Empirical Orthogonal Teleconnections to Analyze Interannual Precipitation Variability in China

NASA Astrophysics Data System (ADS)

Stephan, C.; Klingaman, N. P.; Vidale, P. L.; Turner, A. G.; Demory, M. E.; Guo, L.

2017-12-01

Interannual rainfall variability in China affects agriculture, infrastructure and water resource management. A consistent and objective method, Empirical Orthogonal Teleconnection (EOT) analysis, is applied to precipitation observations over China in all seasons. Instead of maximizing the explained space-time variance, the method identifies regions in China that best explain the temporal variability in domain-averaged rainfall. It produces known teleconnections, that include high positive correlations with ENSO in eastern China in winter, along the Yangtze River in summer, and in southeast China during spring. New findings include that variability along the southeast coast in winter, in the Yangtze valley in spring, and in eastern China in autumn, are associated with extratropical Rossby wave trains. The same analysis is applied to six climate simulations of the Met Office Unified Model with and without air-sea coupling and at various horizontal resolutions of 40, 90 and 200 km. All simulations reproduce the observed patterns of interannual rainfall variability in winter, spring and autumn; the leading pattern in summer is present in all but one simulation. However, only in two simulations are all patterns associated with the observed physical mechanism. Coupled simulations capture more observed patterns of variability and associate more of them with the correct physical mechanism, compared to atmosphere-only simulations at the same resolution. Finer resolution does not improve the fidelity of these patterns or their associated mechanisms. Evaluating climate models by only geographical distribution of mean precipitation and its interannual variance is insufficient; attention must be paid to associated mechanisms.

Regionalizing nonparametric models of precipitation amounts on different temporal scales

NASA Astrophysics Data System (ADS)

Mosthaf, Tobias; Bárdossy, András

2017-05-01

Parametric distribution functions are commonly used to model precipitation amounts corresponding to different durations. The precipitation amounts themselves are crucial for stochastic rainfall generators and weather generators. Nonparametric kernel density estimates (KDEs) offer a more flexible way to model precipitation amounts. As already stated in their name, these models do not exhibit parameters that can be easily regionalized to run rainfall generators at ungauged locations as well as at gauged locations. To overcome this deficiency, we present a new interpolation scheme for nonparametric models and evaluate it for different temporal resolutions ranging from hourly to monthly. During the evaluation, the nonparametric methods are compared to commonly used parametric models like the two-parameter gamma and the mixed-exponential distribution. As water volume is considered to be an essential parameter for applications like flood modeling, a Lorenz-curve-based criterion is also introduced. To add value to the estimation of data at sub-daily resolutions, we incorporated the plentiful daily measurements in the interpolation scheme, and this idea was evaluated. The study region is the federal state of Baden-Württemberg in the southwest of Germany with more than 500 rain gauges. The validation results show that the newly proposed nonparametric interpolation scheme provides reasonable results and that the incorporation of daily values in the regionalization of sub-daily models is very beneficial.

Evaluation of rainfall retrievals from SEVIRI reflectances over West Africa using TRMM-PR and CMORPH

NASA Astrophysics Data System (ADS)

Wolters, E. L. A.; van den Hurk, B. J. J. M.; Roebeling, R. A.

2011-02-01

This paper describes the evaluation of the KNMI Cloud Physical Properties - Precipitation Properties (CPP-PP) algorithm over West Africa. The algorithm combines condensed water path (CWP), cloud phase (CPH), cloud particle effective radius (re), and cloud-top temperature (CTT) retrievals from visible, near-infrared and thermal infrared observations of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) satellites to estimate rain occurrence frequency and rain rate. For the 2005 and 2006 monsoon seasons, it is investigated whether the CPP-PP algorithm is capable of retrieving rain occurrence frequency and rain rate over West Africa with sufficient accuracy, using Tropical Monsoon Measurement Mission Precipitation Radar (TRMM-PR) as reference. As a second goal, it is assessed whether SEVIRI is capable of monitoring the seasonal and daytime evolution of rainfall during the West African monsoon (WAM), using Climate Prediction Center Morphing Technique (CMORPH) rainfall observations. The SEVIRI-detected rainfall area agrees well with TRMM-PR, with the areal extent of rainfall by SEVIRI being ~10% larger than from TRMM-PR. The mean retrieved rain rate from CPP-PP is about 8% higher than from TRMM-PR. Examination of the TRMM-PR and CPP-PP cumulative frequency distributions revealed that differences between CPP-PP and TRMM-PR are generally within +/-10%. Relative to the AMMA rain gauge observations, CPP-PP shows very good agreement up to 5 mm h-1. However, at higher rain rates (5-16 mm h-1) CPP-PP overestimates compared to the rain gauges. With respect to the second goal of this paper, it was shown that both the accumulated precipitation and the seasonal progression of rainfall throughout the WAM is in good agreement with CMORPH, although CPP-PP retrieves higher amounts in the coastal region of West Africa. Using latitudinal Hovmüller diagrams, a fair correspondence between CPP-PP and CMORPH was found, which is reflected by high correlation coefficients (~0.7) for both rain rate and rain occurrence frequency. The daytime cycle of rainfall from CPP-PP shows distinctly different patterns for three different regions in West Africa throughout the WAM, with a decrease in dynamical range of rainfall near the Inter Tropical Convergence Zone (ITCZ). The dynamical range as retrieved from CPP-PP is larger than that from CMORPH. It is suggested that this results from both the better spatio-temporal resolution of SEVIRI, as well as from thermal infrared radiances being partly used by CMORPH, which likely smoothes the daytime precipitation signal, especially in case of cold anvils from convective systems. The promising results show that the CPP-PP algorithm, taking advantage of the high spatio-temporal resolution of SEVIRI, is of added value for monitoring daytime precipitation patterns in tropical areas.

Remote Sensing in a Changing Climate and Environment: the Rift Valley Fever Case

NASA Astrophysics Data System (ADS)

Tourre, Y. M.; Lacaux, J.-P.; Vignolles, C.; Lafaye, M.

2012-07-01

Climate and environment are changing rapidly whilst global population already reached 7 billions people. New public health challenges are posed by new and re-emerging diseases. Innovation is a must i.e., 1) using high resolution remote sensing, 2) re-invent health politics and trans-disciplinary management. The above are part of the 'TransCube Approach' i.e., Transition, Translation, and Transformation. The new concept of Tele-epidemiology includes such approach. A conceptual approach (CA) associated with Rift Valley Fever (RVF) epidemics in Senegal is presented. Ponds are detected using high-resolution SPOT-5 satellite images and radar data from space. Data on rainfall events obtained from the Tropical Rainfall Measuring Mission (NASA/JAXA) are combined with in-situ data. Localization of vulnerable and parked hosts (obtained from QuickBird satellite) is also used. The dynamic spatio-temporal distribution and aggressiveness of RVF mosquitoes, are based on total rainfall amounts, ponds' dynamics and entomological observations. Detailed risks maps (hazards + vulnerability) in real-time are expressed in percentages of parks where animals are potentially at risks. This CA which simply relies upon rainfall distribution from space, is meant to contribute to the implementation of the RVF early warning system (RVFews). It is meant to be applied to other diseases and elsewhere. This is particularly true in new places where new vectors have been rapidly adapting (such as Aedes albopictus) whilst viruses (such as West Nile and Chikungunya,) circulate from constantly moving reservoirs and increasing population.

Analysis of extreme rain and flood events using a regional hydrologically enhanced hydrometeorological system

NASA Astrophysics Data System (ADS)

Yucel, Ismail; Onen, Alper

2013-04-01

Evidence is showing that global warming or climate change has a direct influence on changes in precipitation and the hydrological cycle. Extreme weather events such as heavy rainfall and flooding are projected to become much more frequent as climate warms. Regional hydrometeorological system model which couples the atmosphere with physical and gridded based surface hydrology provide efficient predictions for extreme hydrological events. This modeling system can be used for flood forecasting and warning issues as they provide continuous monitoring of precipitation over large areas at high spatial resolution. This study examines the performance of the Weather Research and Forecasting (WRF-Hydro) model that performs the terrain, sub-terrain, and channel routing in producing streamflow from WRF-derived forcing of extreme precipitation events. The capability of the system with different options such as data assimilation is tested for number of flood events observed in basins of western Black Sea Region in Turkey. Rainfall event structures and associated flood responses are evaluated with gauge and satellite-derived precipitation and measured streamflow values. The modeling system shows skills in capturing the spatial and temporal structure of extreme rainfall events and resulted flood hydrographs. High-resolution routing modules activated in the model enhance the simulated discharges.

Multi-century cool- and warm-season rainfall reconstructions for Australia's major climatic regions

NASA Astrophysics Data System (ADS)

Freund, Mandy; Henley, Benjamin J.; Karoly, David J.; Allen, Kathryn J.; Baker, Patrick J.

2017-11-01

Australian seasonal rainfall is strongly affected by large-scale ocean-atmosphere climate influences. In this study, we exploit the links between these precipitation influences, regional rainfall variations, and palaeoclimate proxies in the region to reconstruct Australian regional rainfall between four and eight centuries into the past. We use an extensive network of palaeoclimate records from the Southern Hemisphere to reconstruct cool (April-September) and warm (October-March) season rainfall in eight natural resource management (NRM) regions spanning the Australian continent. Our bi-seasonal rainfall reconstruction aligns well with independent early documentary sources and existing reconstructions. Critically, this reconstruction allows us, for the first time, to place recent observations at a bi-seasonal temporal resolution into a pre-instrumental context, across the entire continent of Australia. We find that recent 30- and 50-year trends towards wetter conditions in tropical northern Australia are highly unusual in the multi-century context of our reconstruction. Recent cool-season drying trends in parts of southern Australia are very unusual, although not unprecedented, across the multi-century context. We also use our reconstruction to investigate the spatial and temporal extent of historical drought events. Our reconstruction reveals that the spatial extent and duration of the Millennium Drought (1997-2009) appears either very much below average or unprecedented in southern Australia over at least the last 400 years. Our reconstruction identifies a number of severe droughts over the past several centuries that vary widely in their spatial footprint, highlighting the high degree of diversity in historical droughts across the Australian continent. We document distinct characteristics of major droughts in terms of their spatial extent, duration, intensity, and seasonality. Compared to the three largest droughts in the instrumental period (Federation Drought, 1895-1903; World War II Drought, 1939-1945; and the Millennium Drought, 1997-2005), we find that the historically documented Settlement Drought (1790-1793), Sturt's Drought (1809-1830) and the Goyder Line Drought (1861-1866) actually had more regionalised patterns and reduced spatial extents. This seasonal rainfall reconstruction provides a new opportunity to understand Australian rainfall variability by contextualising severe droughts and recent trends in Australia.

Climatology and Interannual Variability of Quasi-Global Intense Precipitation Using Satellite Observations

NASA Technical Reports Server (NTRS)

Ricko, Martina; Adler, Robert F.; Huffman, George J.

2016-01-01

Climatology and variations of recent mean and intense precipitation over a near-global (50 deg. S 50 deg. N) domain on a monthly and annual time scale are analyzed. Data used to derive daily precipitation to examine the effects of spatial and temporal coverage of intense precipitation are from the current Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42 version 7 precipitation product, with high spatial and temporal resolution during 1998 - 2013. Intense precipitation is defined by several different parameters, such as a 95th percentile threshold of daily precipitation, a mean precipitation that exceeds that percentile, or a fixed threshold of daily precipitation value [e.g., 25 and 50 mm day(exp -1)]. All parameters are used to identify the main characteristics of spatial and temporal variation of intense precipitation. High correlations between examined parameters are observed, especially between climatological monthly mean precipitation and intense precipitation, over both tropical land and ocean. Among the various parameters examined, the one best characterizing intense rainfall is a fraction of daily precipitation Great than or equal to 25 mm day(exp. -1), defined as a ratio between the intense precipitation above the used threshold and mean precipitation. Regions that experience an increase in mean precipitation likely experience a similar increase in intense precipitation, especially during the El Nino Southern Oscillation (ENSO) events. Improved knowledge of this intense precipitation regime and its strong connection to mean precipitation given by the fraction parameter can be used for monitoring of intense rainfall and its intensity on a global to regional scale.

Spatio-temporal analysis of annual rainfall in Crete, Greece

NASA Astrophysics Data System (ADS)

Varouchakis, Emmanouil A.; Corzo, Gerald A.; Karatzas, George P.; Kotsopoulou, Anastasia

2018-03-01

Analysis of rainfall data from the island of Crete, Greece was performed to identify key hydrological years and return periods as well as to analyze the inter-annual behavior of the rainfall variability during the period 1981-2014. The rainfall spatial distribution was also examined in detail to identify vulnerable areas of the island. Data analysis using statistical tools and spectral analysis were applied to investigate and interpret the temporal course of the available rainfall data set. In addition, spatial analysis techniques were applied and compared to determine the rainfall spatial distribution on the island of Crete. The analysis presented that in contrast to Regional Climate Model estimations, rainfall rates have not decreased, while return periods vary depending on seasonality and geographic location. A small but statistical significant increasing trend was detected in the inter-annual rainfall variations as well as a significant rainfall cycle almost every 8 years. In addition, statistically significant correlation of the island's rainfall variability with the North Atlantic Oscillation is identified for the examined period. On the other hand, regression kriging method combining surface elevation as secondary information improved the estimation of the annual rainfall spatial variability on the island of Crete by 70% compared to ordinary kriging. The rainfall spatial and temporal trends on the island of Crete have variable characteristics that depend on the geographical area and on the hydrological period.

Precipitation Characteristics in West and East Africa from Satellite and in Situ Observations

NASA Technical Reports Server (NTRS)

Dezfuli, Amin K.; Ichoku, Charles M.; Mohr, Karen I.; Huffman, George J.

2017-01-01

Using in situ data, three precipitation classes are identified for rainy seasons of West and East Africa: weak convective rainfall (WCR), strong convective rainfall (SCR), and mesoscale convective systems (MCSs).Nearly 75% of the total seasonal precipitation is produced by the SCR and MCSs, even though they represent only 8% of the rain events. Rain events in East Africa tend to have a longer duration and lower intensity than in West Africa, reflecting different characteristics of the SCR and MCS events in these two regions. Surface heating seems to be the primary convection trigger for the SCR, particularly in East Africa, whereas the WCR requires a dynamical trigger such as low-level convergence. The data are used to evaluate the performance of the recently launched Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG)project. The IMERG-based precipitation shows significant improvement over its predecessor, the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), particularly in capturing the MCSs, due to its improved temporal resolution.

Trading Space for Time in Design Storm Estimation Using Radar Data

NASA Astrophysics Data System (ADS)

Haberlandt, U.; Berndt, C.

2017-12-01

Intensity-duration-frequency (IDF) curves are frequently used for the derivation of design storms. These curves are usually estimated from rain gauges and are valid for extreme rainfall at local observed points. Two common problems are involved. Regionalization of rainfall statistics for unobserved locations and the use of areal reduction factors (ARF) for the adjustment to larger catchments are required. Weather radar data are available with large spatial coverage and high resolution in space and could be used for a direct derivation of areal design storms for any location and catchment size. However, one problem with radar data is the relatively short observation period for the estimation of extreme events. This study deals with the estimation of area-intensity-duration-frequency (AIDF) curves and areal-reduction-factors (ARF) directly from weather radar data. The main objective is to answer the question if it is possible to trade space for time in the estimation of both characteristics to compensate for the short radar observation periods. In addition, a stratification of the temporal sample according to annual temperature indices is tried to distinguish "colder" and "warmer" climate years. This might eventually show a way for predicting future changes in AIDF curves and ARFs. First, radar data are adjusted with rainfall observations from the daily station network. Thereafter, AIDF curves and ARFs are calculated for different spatial and temporal sample sizes. The AIDF and ARFs are compared regarding their temporal and spatial variability considering also the temperature conditions. In order to reduce spatial variability a grouping of locations according to their climatological and physiographical characteristics is carried out. The data used for this study cover about 20 years of observations from the radar device located near Hanover in Northern Germany and 500 non-recording rain gauges as well as a set of 8 recording rain gauges for validation. AIDF curves and ARFS are analyzed for rainfall durations from 5 minutes to 24 hours and return periods from 1 year to 30 years. It is hypothesized, that the spatial variability of AIDF and ARF characteristics decreases with increasing sample size, grouping and normalization and is finally comparable to temporal variability.

Diurnal, Seasonal, and Interannual Variations of Cloud Properties Derived for CERES From Imager Data

NASA Technical Reports Server (NTRS)

Minnis, Patrick; Young, David F.; Sun-Mack, Sunny; Trepte, Qing Z.; Chen, Yan; Brown, Richard R.; Gibson, Sharon; Heck, Patrick W.

2004-01-01

Simultaneous measurement of the radiation and cloud fields on a global basis is a key component in the effort to understand and model the interaction between clouds and radiation at the top of the atmosphere, at the surface, and within the atmosphere. The NASA Clouds and Earth s Radiant Energy System (CERES) Project, begun in 1998, is meeting this need. Broadband shortwave (SW) and longwave radiance measurements taken by the CERES scanners at resolutions between 10 and 20 km on the Tropical Rainfall Measuring Mission (TRMM), Terra, and Aqua satellites are matched to simultaneous retrievals of cloud height, phase, particle size, water path, and optical depth OD from the TRMM Visible Infrared Scanner (VIRS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua. Besides aiding the interpretation of the broadband radiances, the CERES cloud properties are valuable for understanding cloud variations at a variety of scales. In this paper, the resulting CERES cloud data taken to date are averaged at several temporal scales to examine the temporal and spatial variability of the cloud properties on a global scale at a 1 resolution.

How runoff begins (and ends): characterizing hydrologic response at the catchment scale

USGS Publications Warehouse

Mirus, Benjamin B.; Loague, Keith

2013-01-01

Improved understanding of the complex dynamics associated with spatially and temporally variable runoff response is needed to better understand the hydrology component of interdisciplinary problems. The objective of this study was to quantitatively characterize the environmental controls on runoff generation for the range of different streamflow-generation mechanisms illustrated in the classic Dunne diagram. The comprehensive physics-based model of coupled surface-subsurface flow, InHM, is employed in a heuristic mode. InHM has been employed previously to successfully simulate the observed hydrologic response at four diverse, well-characterized catchments, which provides the foundation for this study. The C3 and CB catchments are located within steep, forested terrain; the TW and R5 catchments are located in gently sloping rangeland. The InHM boundary-value problems for these four catchments provide the corner-stones for alternative simulation scenarios designed to address the question of how runoff begins (and ends). Simulated rainfall-runoff events are used to systematically explore the impact of soil-hydraulic properties and rainfall characteristics. This approach facilitates quantitative analysis of both integrated and distributed hydrologic responses at high-spatial and temporal resolution over the wide range of environmental conditions represented by the four catchments. The results from 140 unique simulation scenarios illustrate how rainfall intensity/depth, subsurface permeability contrasts, characteristic curve shapes, and topography provide important controls on the hydrologic-response dynamics. The processes by which runoff begins (and ends) are shown, in large part, to be defined by the relative rates of rainfall, infiltration, lateral flow convergence, and storage dynamics within the variably saturated soil layers.

Temporal distribution of rainfall in Virginia : final report.

DOT National Transportation Integrated Search

1984-01-01

The temporal distribution of Virginia rainstorms was examined by statistically analyzing approximately 1,400 storm events recorded throughout the state. Rainfall time distribution curves were constructed and were compared with several nationally reco...

Simulation of localized heavy precipitation in South Korea on 20 June 2014: sensitivity test of integration time-step size and an effect of topographic resolution using WRF model

NASA Astrophysics Data System (ADS)

Roh, Joon-Woo; Jee, Joon-Bum; Lim, A.-Young; Choi, Young-Jean

2015-04-01

Korean warm-season rainfall, accounting for about three-fourths of the annual precipitation, is primarily caused by Changma front, which is a kind of the East Asian summer monsoon, and localized heavy rainfall with convective instability. Various physical mechanisms potentially exert influences on heavy precipitation over South Korea. Representatively, the middle latitude and subtropical weather fronts, associated with a quasi-stationary moisture convergence zone among varying air masses, make up one of the main rain-bearing synoptic scale systems. Localized heavy rainfall events in South Korea generally arise from mesoscale convective systems embedded in these synoptic scale disturbances along the Changma front or convective instabilities resulted from unstable air mass including the direct or indirect effect of typhoons. In recent years, torrential rainfalls, which are more than 30mm/hour of precipitation amount, in warm-season has increased threefold in Seoul, which is a metropolitan city in South Korea. In order to investigate multiple potential causes of warm-season localized heavy precipitation in South Korea, a localized heavy precipitation case took place on 20 June 2014 at Seoul. This case was mainly seen to be caused by short-wave trough, which is associated with baroclinic instability in the northwest of Korea, and a thermal low, which has high moist and warm air through analysis. This structure showed convective scale torrential rain was embedded in the dynamic and in the thermodynamic structures. In addition to, a sensitivity of rainfall amount and maximum rainfall location to the integration time-step sizes was investigated in the simulations of a localized heavy precipitation case using Weather Research and Forecasting model. The simulation of time-step sizes of 9-27s corresponding to a horizontal resolution of 4.5km and 1.5km varied slightly difference of the maximum rainfall amount. However, the sensitivity of spatial patterns and temporal variations in rainfall were relatively small for the time-step sizes. The effect of topography was also important in the localized heavy precipitation simulation.

Temporal and spatial characteristics of annual and seasonal rainfall in Malawi

NASA Astrophysics Data System (ADS)

Ngongondo, Cosmo; Xu, Chong-Yu; Gottschalk, Lars; Tallaksen, Lena M.; Alemaw, Berhanu

2010-05-01

An understanding of the temporal and spatial characteristics of rainfall is central to water resources planning and management. However, such information is often limited in many developing countries like Malawi. In an effort to bridge the information gap, this study examined the temporal and spatial charecteristics of rainfall in Malawi. Rainfall readings from 42 stations across Malawi from 1960 to 2006 were analysed at monthly, annual and seasonal scales. The Malawian rainfall season lasts from November to April. The data were firstly subjected to quality checks through the cumulative deviations test and the Standard Normal Homogeinity Test (SNHT). Monthly distribution in a typical year, called heterogeneity, was investigated using the Precipitation Concentration Index (PCI). Further, normalized precipitation anomaly series of annual rainfall series (AR) and the PCI (APCI) were used to test for interannual rainfall variability. Spatial variability was characterised by fitting the Spatial Correlation function (SCF). The nonparametric Mann-Kendall statistic was used to investigate the temporal trends of the various rainfall variables. The results showed that 40 of the stations passed both data quality tests. For the two stations that failed, the data were adjusted using nearby stations. Annual and seasonal rainfall were found to be characterised by high spatial variation. The country mean annual rainfall was 1095 mm with mean interannual variability of 26%. The highland areas to the north and southeast of the country exhibited the highest rainfall and lowest interannual variability. Lowest rainfall coupled with high interannual variability was found in the Lower Shire basin, in the southern part of Malawi. This simillarity is the pattern of annual and seasonal rainfall should be expected because all stations had over 90% of their observed annual rainfall in the six month period between November and April. Monthly rainfall was found to be highly variable both temporally and spatially. None of the stations have stable monthly rainfall regimes (mean PCI of less than 10). Stations with the highest mean rainfall were found to have a lower interannual variability. The rainfall stations showed low spatial correlations for annual, monthly as well as seasonal timescales indicating that the data may not be suitable for spatial interpolation. However, some structure (i.e. lower correlation with distance) could be observed when aggregating the data at 50 mile intervals. The annual and seasonal rainfall series were dominated by negative trends. The spatial distribution of the trends can be described as heterogeneous, although most of the stations in the southern region have negative trends. At the monthly timescale, 37 of the stations show a negative trend with four of the stations, all in the south, showing significant negative trends. On the other hand, only 5 stations show positive trends with only one significant trend in the south. Keywords: Malawi, rainfall trends, spatial variation

Verification of Satellite Rainfall Estimates from the Tropical Rainfall Measuring Mission over Ground Validation Sites

NASA Astrophysics Data System (ADS)

Fisher, B. L.; Wolff, D. B.; Silberstein, D. S.; Marks, D. M.; Pippitt, J. L.

2007-12-01

The Tropical Rainfall Measuring Mission's (TRMM) Ground Validation (GV) Program was originally established with the principal long-term goal of determining the random errors and systematic biases stemming from the application of the TRMM rainfall algorithms. The GV Program has been structured around two validation strategies: 1) determining the quantitative accuracy of the integrated monthly rainfall products at GV regional sites over large areas of about 500 km2 using integrated ground measurements and 2) evaluating the instantaneous satellite and GV rain rate statistics at spatio-temporal scales compatible with the satellite sensor resolution (Simpson et al. 1988, Thiele 1988). The GV Program has continued to evolve since the launch of the TRMM satellite on November 27, 1997. This presentation will discuss current GV methods of validating TRMM operational rain products in conjunction with ongoing research. The challenge facing TRMM GV has been how to best utilize rain information from the GV system to infer the random and systematic error characteristics of the satellite rain estimates. A fundamental problem of validating space-borne rain estimates is that the true mean areal rainfall is an ideal, scale-dependent parameter that cannot be directly measured. Empirical validation uses ground-based rain estimates to determine the error characteristics of the satellite-inferred rain estimates, but ground estimates also incur measurement errors and contribute to the error covariance. Furthermore, sampling errors, associated with the discrete, discontinuous temporal sampling by the rain sensors aboard the TRMM satellite, become statistically entangled in the monthly estimates. Sampling errors complicate the task of linking biases in the rain retrievals to the physics of the satellite algorithms. The TRMM Satellite Validation Office (TSVO) has made key progress towards effective satellite validation. For disentangling the sampling and retrieval errors, TSVO has developed and applied a methodology that statistically separates the two error sources. Using TRMM monthly estimates and high-resolution radar and gauge data, this method has been used to estimate sampling and retrieval error budgets over GV sites. More recently, a multi- year data set of instantaneous rain rates from the TRMM microwave imager (TMI), the precipitation radar (PR), and the combined algorithm was spatio-temporally matched and inter-compared to GV radar rain rates collected during satellite overpasses of select GV sites at the scale of the TMI footprint. The analysis provided a more direct probe of the satellite rain algorithms using ground data as an empirical reference. TSVO has also made significant advances in radar quality control through the development of the Relative Calibration Adjustment (RCA) technique. The RCA is currently being used to provide a long-term record of radar calibration for the radar at Kwajalein, a strategically important GV site in the tropical Pacific. The RCA technique has revealed previously undetected alterations in the radar sensitivity due to engineering changes (e.g., system modifications, antenna offsets, alterations of the receiver, or the data processor), making possible the correction of the radar rainfall measurements and ensuring the integrity of nearly a decade of TRMM GV observations and resources.

Multisite rainfall downscaling and disaggregation in a tropical urban area

NASA Astrophysics Data System (ADS)

Lu, Y.; Qin, X. S.

2014-02-01

A systematic downscaling-disaggregation study was conducted over Singapore Island, with an aim to generate high spatial and temporal resolution rainfall data under future climate-change conditions. The study consisted of two major components. The first part was to perform an inter-comparison of various alternatives of downscaling and disaggregation methods based on observed data. This included (i) single-site generalized linear model (GLM) plus K-nearest neighbor (KNN) (S-G-K) vs. multisite GLM (M-G) for spatial downscaling, (ii) HYETOS vs. KNN for single-site disaggregation, and (iii) KNN vs. MuDRain (Multivariate Rainfall Disaggregation tool) for multisite disaggregation. The results revealed that, for multisite downscaling, M-G performs better than S-G-K in covering the observed data with a lower RMSE value; for single-site disaggregation, KNN could better keep the basic statistics (i.e. standard deviation, lag-1 autocorrelation and probability of wet hour) than HYETOS; for multisite disaggregation, MuDRain outperformed KNN in fitting interstation correlations. In the second part of the study, an integrated downscaling-disaggregation framework based on M-G, KNN, and MuDRain was used to generate hourly rainfall at multiple sites. The results indicated that the downscaled and disaggregated rainfall data based on multiple ensembles from HadCM3 for the period from 1980 to 2010 could well cover the observed mean rainfall amount and extreme data, and also reasonably keep the spatial correlations both at daily and hourly timescales. The framework was also used to project future rainfall conditions under HadCM3 SRES A2 and B2 scenarios. It was indicated that the annual rainfall amount could reduce up to 5% at the end of this century, but the rainfall of wet season and extreme hourly rainfall could notably increase.

Rainfall and Extratropical Transition of Tropical Cyclones: Simulation, Prediction, and Projection

NASA Astrophysics Data System (ADS)

Liu, Maofeng

Rainfall and associated flood hazards are one of the major threats of tropical cyclones (TCs) to coastal and inland regions. The interaction of TCs with extratropical systems can lead to enhanced precipitation over enlarged areas through extratropical transition (ET). To achieve a comprehensive understanding of rainfall and ET associated with TCs, this thesis conducts weather-scale analyses by focusing on individual storms and climate-scale analyses by focusing on seasonal predictability and changing properties of climatology under global warming. The temporal and spatial rainfall evolution of individual storms, including Hurricane Irene (2011), Hurricane Hanna (2008), and Hurricane Sandy (2012), is explored using the Weather Research and Forecast (WRF) model and a variety of hydrometeorological datasets. ET and Orographic mechanism are two key players in the rainfall distribution of Irene over regions experiencing most severe flooding. The change of TC rainfall under global warming is explored with the Forecast-oriented Low Ocean Resolution (FLOR) climate model under representative concentration pathway (RCP) 4.5 scenario. Despite decreased TC frequency, FLOR projects increased landfalling TC rainfall over most regions of eastern United States, highlighting the risk of increased flood hazards. Increased storm rain rate is an important player of increased landfalling TC rainfall. A higher atmospheric resolution version of FLOR (HiFLOR) model projects increased TC rainfall at global scales. The increase of TC intensity and environmental water vapor content scaled by the Clausius-Clapeyron relation are two key factors that explain the projected increase of TC rainfall. Analyses on the simulation, prediction, and projection of the ET activity with FLOR are conducted in the North Atlantic. FLOR model exhibits good skills in simulating many aspects of present-day ET climatology. The 21st-century-projection under RCP4.5 scenario demonstrates the dominant role of ET events on the projected increase of TC frequency in the eastern North Atlantic, highlighting increased exposure of the northeastern United States and Western Europe to storm hazards. Retrospective seasonal forecast experiments demonstrate the skill of HiFLOR in predicting basinwide and regional ET frequency. This skill, however, is not seen in the seasonal prediction of ET rate. More work on the property of signal-to-noise ratio of ET rate is needed.

Missing pieces of the puzzle: understanding decadal variability of Sahel Rainfall

NASA Astrophysics Data System (ADS)

Vellinga, Michael; Roberts, Malcolm; Vidale, Pier-Luigi; Mizielinski, Matthew; Demory, Marie-Estelle; Schiemann, Reinhard; Strachan, Jane; Bain, Caroline

2015-04-01

The instrumental record shows that substantial decadal fluctuations affected Sahel rainfall from the West African monsoon throughout the 20th century. Climate models generally underestimate the magnitude of decadal Sahel rainfall changes compared to observations. This shows that the processes that control low-frequency Sahel rainfall change are misrepresented in most CMIP5-era climate models. Reliable climate information of future low-frequency rainfall changes thus remains elusive. Here we identify key processes that control the magnitude of the decadal rainfall recovery in the Sahel since the mid-1980s. We show its sensitivity to model resolution and physics in a suite of experiments with global HadGEM3 model configurations at resolutions between 130-25 km. The decadal rainfall trend increases with resolution and at 60-25 km falls within the observed range. Higher resolution models have stronger increases of moisture supply and of African Easterly wave activity. Easterly waves control the occurrence of strong organised rainfall events which carry most of the decadal trend. Weak rainfall events occur too frequently at all resolutions and at low resolution contribute substantially to the decadal trend. All of this behaviour is seen across CMIP5, including future scenarios. Additional simulations with a global 12km version of HadGEM3 show that treating convection explicitly dramatically improves the properties of Sahel rainfall systems. We conclude that interaction between convective scale and global scale processes is key to decadal rainfall changes in the Sahel. This work is distributed under the Creative Commons Attribution 3.0 Unported License together with an author copyright. This license does not conflict with the regulations of the Crown Copyright.Crown Copyright

Relations between rainfall–runoff-induced erosion and aeolian deposition at archaeological sites in a semi-arid dam-controlled river corridor

USGS Publications Warehouse

Collins, Brian D.; Bedford, David; Corbett, Skye C.; Fairley, Helen C.; Cronkite-Ratcliff, Collin

2016-01-01

Process dynamics in fluvial-based dryland environments are highly complex with fluvial, aeolian, and alluvial processes all contributing to landscape change. When anthropogenic activities such as dam-building affect fluvial processes, the complexity in local response can be further increased by flood- and sediment-limiting flows. Understanding these complexities is key to predicting landscape behavior in drylands and has important scientific and management implications, including for studies related to paleoclimatology, landscape ecology evolution, and archaeological site context and preservation. Here we use multi-temporal LiDAR surveys, local weather data, and geomorphological observations to identify trends in site change throughout the 446-km-long semi-arid Colorado River corridor in Grand Canyon, Arizona, USA, where archaeological site degradation related to the effects of upstream dam operation is a concern. Using several site case studies, we show the range of landscape responses that might be expected from concomitant occurrence of dam-controlled fluvial sand bar deposition, aeolian sand transport, and rainfall-induced erosion. Empirical rainfall-erosion threshold analyses coupled with a numerical rainfall–runoff–soil erosion model indicate that infiltration-excess overland flow and gullying govern large-scale (centimeter- to decimeter-scale) landscape changes, but that aeolian deposition can in some cases mitigate gully erosion. Whereas threshold analyses identify the normalized rainfall intensity (defined as the ratio of rainfall intensity to hydraulic conductivity) as the primary factor governing hydrologic-driven erosion, assessment of false positives and false negatives in the dataset highlight topographic slope as the next most important parameter governing site response. Analysis of 4+ years of high resolution (four-minute) weather data and 75+ years of low resolution (daily) climate records indicates that dryland erosion is dependent on short-term, storm-driven rainfall intensity rather than cumulative rainfall, and that erosion can occur outside of wet seasons and even wet years. These results can apply to other similar semi-arid landscapes where process complexity may not be fully understood.

Validation of satellite daily rainfall estimates in complex terrain of Bali Island, Indonesia

NASA Astrophysics Data System (ADS)

Rahmawati, Novi; Lubczynski, Maciek W.

2017-11-01

Satellite rainfall products have different performances in different geographic regions under different physical and climatological conditions. In this study, the objective was to select the most reliable and accurate satellite rainfall products for specific, environmental conditions of Bali Island. The performances of four spatio-temporal satellite rainfall products, i.e., CMORPH25, CMORPH8, TRMM, and PERSIANN, were evaluated at the island, zonation (applying elevation and climatology as constraints), and pixel scales, using (i) descriptive statistics and (ii) categorical statistics, including bias decomposition. The results showed that all the satellite products had low accuracy because of spatial scale effect, daily resolution and the island complexity. That accuracy was relatively lower in (i) dry seasons and dry climatic zones than in wet seasons and wet climatic zones; (ii) pixels jointly covered by sea and mountainous land than in pixels covered by land or by sea only; and (iii) topographically diverse than uniform terrains. CMORPH25, CMORPH8, and TRMM underestimated and PERSIANN overestimated rainfall when comparing them to gauged rain. The CMORPH25 had relatively the best performance and the PERSIANN had the worst performance in the Bali Island. The CMORPH25 had the lowest statistical errors, the lowest miss, and the highest hit rainfall events; it also had the lowest miss rainfall bias and was relatively the most accurate in detecting, frequent in Bali, ≤ 20 mm day-1 rain events. Lastly, the CMORPH25 coarse grid better represented rainfall events from coastal to inlands areas than other satellite products, including finer grid CMORPH8.

The Hydrometeorological DREAM: A Daily REcharge Assessment Model, for the Israeli Western Mountain Aquifer

NASA Astrophysics Data System (ADS)

Sheffer, N. A.; Dafny, E.; Gvirtzman, H.; Frumkin, A.; Navon, S.; Morin, E.

2008-05-01

The western part of the Israeli Mountain Aquifer (WMA) supplies 360-400 MCM/y of fresh water to the Israeli water budget, which is approximately 20% of the total consumption. The annually recharge to the WMA is considered to be 25-35% of annual rainfall. The high variability in recharge to the WMA is due to spatial and temporal differences in the rain contributing to the aquifer. Different winters producing the same amount of rain may contribute differently to the aquifer due to the locations of the storms, intensity, duration, dry spells between successive rain events, etc. Moreover, besides the climatic-meteorological factors, the recharge is dependent also on geographical factors, such as lithology, pedology, land-use, slope gradient, slope direction etc. The need for a robust reliable Hydrometeorological Daily basis REcharge Assessment Model (Hydrometeorological DREAM) brought us to develop a model with a relatively high spatial and temporal resolution. The concept is based on a relatively simple water budget that states that rainfall over land is added to the soil, and removed later on by means of evapotranspiration, recharge and runoff. The method in use to date at the Hydrological Service for estimating recharge to the WMA is based on an annual regression curve that can be implemented only after the total annual rainfall is known. The DREAM is a near real time estimator of recharge to the WMA using daily rainfall and pan evaporation data. Comparison of the DREAM results with the annual regression curve show a high agreement on an annual basis. The improvements introduced by the DREAM are: 1) Near real time daily values of infiltration, as opposed to calculated annual values established after the rain season is over. 2) High spatial resolution. The DREAM produces daily recharge values in more than 3000 mesh points throughout the 2200 km2 of recharge area. By linking the DREAM output as input to a hydrogeological model (such as FEFLOW, MODFLOW etc.) a completion of the water cycle can by achieved.

New spatial and temporal indices of Indian summer monsoon rainfall

NASA Astrophysics Data System (ADS)

Dwivedi, Sanjeev; Uma, R.; Lakshmi Kumar, T. V.; Narayanan, M. S.; Pokhrel, Samir; Kripalani, R. H.

2018-02-01

The overall yearly seasonal performance of Indian southwest monsoon rainfall (ISMR) for the whole Indian land mass is presently expressed by the India Meteorological Department (IMD) by a single number, the total quantum of rainfall. Any particular year is declared as excess/deficit or normal monsoon rainfall year on the basis of this single number. It is well known that monsoon rainfall also has high interannual variability in spatial and temporal scales. To account for these aspects in ISMR, we propose two new spatial and temporal indices. These indices have been calculated using the 115 years of IMD daily 0.25° × 0.25° gridded rainfall data. Both indices seem to go in tandem with the in vogue seasonal quantum index. The anomaly analysis indicates that the indices during excess monsoon years behave randomly, while for deficit monsoon years the phase of all the three indices is the same. Evaluation of these indices is also studied with respect to the existing dynamical indices based on large-scale circulation. It is found that the new temporal indices have better link with circulation indices as compared to the new spatial indices. El Nino and Southern Oscillation (ENSO) especially over the equatorial Pacific Ocean still have the largest influence in both the new indices. However, temporal indices have much better remote influence as compared to that of spatial indices. Linkages over the Indian Ocean regions are very different in both the spatial and temporal indices. Continuous wavelet transform (CWT) analysis indicates that the complete spectrum of oscillation of the QI is shared in the lower oscillation band by the spatial index and in the higher oscillation band by the temporal index. These new indices may give some extra dimension to study Indian summer monsoon variability.

Climatology of contribution-weighted tropical rain rates based on TRMM 3B42

NASA Astrophysics Data System (ADS)

Venugopal, V.; Wallace, J. M.

2016-10-01

The climatology of annual mean tropical rain rate is investigated based on merged Tropical Rainfall Measuring Mission (TRMM) 3B42 data. At 0.25° × 0.25° spatial resolution, and 3-hourly temporal resolution, half the rain is concentrated within only ˜1% of the area of the tropics at any given instant. When plotted as a function of logarithm of rain rate, the cumulative contribution of rate-ranked rain occurrences to the annual mean rainfall in each grid box is S shaped and its derivative, the contribution-weighted rain rate spectrum, is Gaussian shaped. The 50% intercept of the cumulative contribution R50 is almost equivalent to the contribution-weighted mean logarithmic rain rate RL¯ based on all significant rain occurrences. The spatial patterns of R50 and RL¯ are similar to those obtained by mapping the fraction of the annual accumulation explained by rain occurrences with rates above various specified thresholds. The geographical distribution of R50 confirms the existence of patterns noted in prior analyses based on TRMM precipitation radar data and reveals several previously unnoticed features.

Double Bright Band Observations with High-Resolution Vertically Pointing Radar, Lidar, and Profiles

NASA Technical Reports Server (NTRS)

Emory, Amber E.; Demoz, Belay; Vermeesch, Kevin; Hicks, Michael

2014-01-01

On 11 May 2010, an elevated temperature inversion associated with an approaching warm front produced two melting layers simultaneously, which resulted in two distinct bright bands as viewed from the ER-2 Doppler radar system, a vertically pointing, coherent X band radar located in Greenbelt, MD. Due to the high temporal resolution of this radar system, an increase in altitude of the melting layer of approximately 1.2 km in the time span of 4 min was captured. The double bright band feature remained evident for approximately 17 min, until the lower atmosphere warmed enough to dissipate the lower melting layer. This case shows the relatively rapid evolution of freezing levels in response to an advancing warm front over a 2 h time period and the descent of an elevated warm air mass with time. Although observations of double bright bands are somewhat rare, the ability to identify this phenomenon is important for rainfall estimation from spaceborne sensors because algorithms employing the restriction of a radar bright band to a constant height, especially when sampling across frontal systems, will limit the ability to accurately estimate rainfall.

Double bright band observations with high-resolution vertically pointing radar, lidar, and profilers

NASA Astrophysics Data System (ADS)

Emory, Amber E.; Demoz, Belay; Vermeesch, Kevin; Hicks, Micheal

2014-07-01

On 11 May 2010, an elevated temperature inversion associated with an approaching warm front produced two melting layers simultaneously, which resulted in two distinct bright bands as viewed from the ER-2 Doppler radar system, a vertically pointing, coherent X band radar located in Greenbelt, MD. Due to the high temporal resolution of this radar system, an increase in altitude of the melting layer of approximately 1.2 km in the time span of 4 min was captured. The double bright band feature remained evident for approximately 17 min, until the lower atmosphere warmed enough to dissipate the lower melting layer. This case shows the relatively rapid evolution of freezing levels in response to an advancing warm front over a 2 h time period and the descent of an elevated warm air mass with time. Although observations of double bright bands are somewhat rare, the ability to identify this phenomenon is important for rainfall estimation from spaceborne sensors because algorithms employing the restriction of a radar bright band to a constant height, especially when sampling across frontal systems, will limit the ability to accurately estimate rainfall.

Modelling of Rainfall Induced Landslides in Puerto Rico

NASA Astrophysics Data System (ADS)

Lepore, C.; Arnone, E.; Sivandran, G.; Noto, L. V.; Bras, R. L.

2010-12-01

We performed an island-wide determination of static landslide susceptibility and hazard assessment as well as dynamic modeling of rainfall-induced shallow landslides in a particular hydrologic basin. Based on statistical analysis of past landslides, we determined that reliable prediction of the susceptibility to landslides is strongly dependent on the resolution of the digital elevation model (DEM) employed and the reliability of the rainfall data. A distributed hydrology model, Triangulated Irregular Network (TIN)-based Real-time Integrated Basin Simulator with VEGetation Generator for Interactive Evolution (tRIBS-VEGGIE), tRIBS-VEGGIE, has been implemented for the first time in a humid tropical environment like Puerto Rico and validated against in-situ measurements. A slope-failure module has been added to tRIBS-VEGGIE’s framework, after analyzing several failure criterions to identify the most suitable for our application; the module is used to predict the location and timing of landsliding events. The Mameyes basin, located in the Luquillo Experimental Forest in Puerto Rico, was selected for modeling based on the availability of soil, vegetation, topographical, meteorological and historic landslide data. Application of the model yields a temporal and spatial distribution of predicted rainfall-induced landslides.

Forecasting of monsoon heavy rains: challenges in NWP

NASA Astrophysics Data System (ADS)

Sharma, Kuldeep; Ashrit, Raghavendra; Iyengar, Gopal; Bhatla, R.; Rajagopal, E. N.

2016-05-01

Last decade has seen a tremendous improvement in the forecasting skill of numerical weather prediction (NWP) models. This is attributed to increased sophistication in NWP models, which resolve complex physical processes, advanced data assimilation, increased grid resolution and satellite observations. However, prediction of heavy rains is still a challenge since the models exhibit large error in amounts as well as spatial and temporal distribution. Two state-of-art NWP models have been investigated over the Indian monsoon region to assess their ability in predicting the heavy rainfall events. The unified model operational at National Center for Medium Range Weather Forecasting (NCUM) and the unified model operational at the Australian Bureau of Meteorology (Australian Community Climate and Earth-System Simulator -- Global (ACCESS-G)) are used in this study. The recent (JJAS 2015) Indian monsoon season witnessed 6 depressions and 2 cyclonic storms which resulted in heavy rains and flooding. The CRA method of verification allows the decomposition of forecast errors in terms of error in the rainfall volume, pattern and location. The case by case study using CRA technique shows that contribution to the rainfall errors come from pattern and displacement is large while contribution due to error in predicted rainfall volume is least.

Novel SVM-based technique to improve rainfall estimation over the Mediterranean region (north of Algeria) using the multispectral MSG SEVIRI imagery

NASA Astrophysics Data System (ADS)

Sehad, Mounir; Lazri, Mourad; Ameur, Soltane

2017-03-01

In this work, a new rainfall estimation technique based on the high spatial and temporal resolution of the Spinning Enhanced Visible and Infra Red Imager (SEVIRI) aboard the Meteosat Second Generation (MSG) is presented. This work proposes efficient scheme rainfall estimation based on two multiclass support vector machine (SVM) algorithms: SVM_D for daytime and SVM_N for night time rainfall estimations. Both SVM models are trained using relevant rainfall parameters based on optical, microphysical and textural cloud proprieties. The cloud parameters are derived from the Spectral channels of the SEVIRI MSG radiometer. The 3-hourly and daily accumulated rainfall are derived from the 15 min-rainfall estimation given by the SVM classifiers for each MSG observation image pixel. The SVMs were trained with ground meteorological radar precipitation scenes recorded from November 2006 to March 2007 over the north of Algeria located in the Mediterranean region. Further, the SVM_D and SVM_N models were used to estimate 3-hourly and daily rainfall using data set gathered from November 2010 to March 2011 over north Algeria. The results were validated against collocated rainfall observed by rain gauge network. Indeed, the statistical scores given by correlation coefficient, bias, root mean square error and mean absolute error, showed good accuracy of rainfall estimates by the present technique. Moreover, rainfall estimates of our technique were compared with two high accuracy rainfall estimates methods based on MSG SEVIRI imagery namely: random forests (RF) based approach and an artificial neural network (ANN) based technique. The findings of the present technique indicate higher correlation coefficient (3-hourly: 0.78; daily: 0.94), and lower mean absolute error and root mean square error values. The results show that the new technique assign 3-hourly and daily rainfall with good and better accuracy than ANN technique and (RF) model.

Regional simulation of Indian summer monsoon intraseasonal oscillations at gray-zone resolution

NASA Astrophysics Data System (ADS)

Chen, Xingchao; Pauluis, Olivier M.; Zhang, Fuqing

2018-01-01

Simulations of the Indian summer monsoon by the cloud-permitting Weather Research and Forecasting (WRF) model at gray-zone resolution are described in this study, with a particular emphasis on the model ability to capture the monsoon intraseasonal oscillations (MISOs). Five boreal summers are simulated from 2007 to 2011 using the ERA-Interim reanalysis as the lateral boundary forcing data. Our experimental setup relies on a horizontal grid spacing of 9 km to explicitly simulate deep convection without the use of cumulus parameterizations. When compared to simulations with coarser grid spacing (27 km) and using a cumulus scheme, the 9 km simulations reduce the biases in mean precipitation and produce more realistic low-frequency variability associated with MISOs. Results show that the model at the 9 km gray-zone resolution captures the salient features of the summer monsoon. The spatial distributions and temporal evolutions of monsoon rainfall in the WRF simulations verify qualitatively well against observations from the Tropical Rainfall Measurement Mission (TRMM), with regional maxima located over Western Ghats, central India, Himalaya foothills, and the west coast of Myanmar. The onset, breaks, and withdrawal of the summer monsoon in each year are also realistically captured by the model. The MISO-phase composites of monsoon rainfall, low-level wind, and precipitable water anomalies in the simulations also agree qualitatively with the observations. Both the simulations and observations show a northeastward propagation of the MISOs, with the intensification and weakening of the Somali Jet over the Arabian Sea during the active and break phases of the Indian summer monsoon.

TerraSAR-X high-resolution radar remote sensing: an operational warning system for Rift Valley fever risk.

PubMed

Vignolles, Cécile; Tourre, Yves M; Mora, Oscar; Imanache, Laurent; Lafaye, Murielle

2010-11-01

In the vicinity of the Barkedji village (in the Ferlo region of Senegal), the abundance and aggressiveness of the vector mosquitoes for Rift Valley fever (RVF) are strongly linked to rainfall events and associated ponds dynamics. Initially, these results were obtained from spectral analysis of high-resolution (~10 m) Spot-5 images, but, as a part of the French AdaptFVR project, identification of the free water dynamics within ponds was made with the new high-resolution (down to 3-meter pixels), Synthetic Aperture Radar satellite (TerraSAR-X) produced by Infoterra GmbH, Friedrichshafen/Potsdam, Germany. During summer 2008, within a 30 x 50 km radar image, it was found that identified free water fell well within the footprints of ponds localized by optical data (i.e. Spot-5 images), which increased the confidence in this new and complementary remote sensing technique. Moreover, by using near real-time rainfall data from the Tropical Rainfall Measuring Mission (TRMM), NASA/JAXA joint mission, the filling-up and flushing-out rates of the ponds can be accurately determined. The latter allows for a precise, spatio-temporal mapping of the zones potentially occupied by mosquitoes capable of revealing the variability of pond surfaces. The risk for RVF infection of gathered bovines and small ruminants (~1 park/km(2)) can thus be assessed. This new operational approach (which is independent of weather conditions) is an important development in the mapping of risk components (i.e. hazards plus vulnerability) related to RVF transmission during the summer monsoon, thus contributing to a RVF early warning system.

Temporal and elevation trends in rainfall erosivity on a 149 km2 watershed in a semi-arid region of the American Southwest

USDA-ARS?s Scientific Manuscript database

Temporal changes in rainfall erosivity can be expected to occur with changing climate; and because rainfall amounts are known to be in part a function of elevation, erosivity can be expected to be influenced by elevation as well. This is particularly true in mountainous regions such as are found ove...

Increase in flood risk resulting from climate change in a developed urban watershed - the role of storm temporal patterns

NASA Astrophysics Data System (ADS)

Hettiarachchi, Suresh; Wasko, Conrad; Sharma, Ashish

2018-03-01

The effects of climate change are causing more frequent extreme rainfall events and an increased risk of flooding in developed areas. Quantifying this increased risk is of critical importance for the protection of life and property as well as for infrastructure planning and design. The updated National Oceanic and Atmospheric Administration (NOAA) Atlas 14 intensity-duration-frequency (IDF) relationships and temporal patterns are widely used in hydrologic and hydraulic modeling for design and planning in the United States. Current literature shows that rising temperatures as a result of climate change will result in an intensification of rainfall. These impacts are not explicitly included in the NOAA temporal patterns, which can have consequences on the design and planning of adaptation and flood mitigation measures. In addition there is a lack of detailed hydraulic modeling when assessing climate change impacts on flooding. The study presented in this paper uses a comprehensive hydrologic and hydraulic model of a fully developed urban/suburban catchment to explore two primary questions related to climate change impacts on flood risk. (1) How do climate change effects on storm temporal patterns and rainfall volumes impact flooding in a developed complex watershed? (2) Is the storm temporal pattern as critical as the total volume of rainfall when evaluating urban flood risk? We use the NOAA Atlas 14 temporal patterns, along with the expected increase in temperature for the RCP8.5 scenario for 2081-2100, to project temporal patterns and rainfall volumes to reflect future climatic change. The model results show that different rainfall patterns cause variability in flood depths during a storm event. The changes in the projected temporal patterns alone increase the risk of flood magnitude up to 35 %, with the cumulative impacts of temperature rise on temporal patterns and the storm volume increasing flood risk from 10 to 170 %. The results also show that regional storage facilities are sensitive to rainfall patterns that are loaded in the latter part of the storm duration, while extremely intense short-duration storms will cause flooding at all locations. This study shows that changes in temporal patterns will have a significant impact on urban/suburban flooding and need to be carefully considered and adjusted to account for climate change when used for the design and planning of future storm water systems.

Dynamics and spatio-temporal variability of environmental factors in Eastern Australia using functional principal component analysis

USGS Publications Warehouse

Szabo, J.K.; Fedriani, E.M.; Segovia-Gonzalez, M. M.; Astheimer, L.B.; Hooper, M.J.

2010-01-01

This paper introduces a new technique in ecology to analyze spatial and temporal variability in environmental variables. By using simple statistics, we explore the relations between abiotic and biotic variables that influence animal distributions. However, spatial and temporal variability in rainfall, a key variable in ecological studies, can cause difficulties to any basic model including time evolution. The study was of a landscape scale (three million square kilometers in eastern Australia), mainly over the period of 19982004. We simultaneously considered qualitative spatial (soil and habitat types) and quantitative temporal (rainfall) variables in a Geographical Information System environment. In addition to some techniques commonly used in ecology, we applied a new method, Functional Principal Component Analysis, which proved to be very suitable for this case, as it explained more than 97% of the total variance of the rainfall data, providing us with substitute variables that are easier to manage and are even able to explain rainfall patterns. The main variable came from a habitat classification that showed strong correlations with rainfall values and soil types. ?? 2010 World Scientific Publishing Company.

A 507-year rainfall and runoff reconstruction for the Monsoonal North West, Australia derived from remote paleoclimate archives

NASA Astrophysics Data System (ADS)

Verdon-Kidd, Danielle C.; Hancock, Gregory R.; Lowry, John B.

2017-11-01

The Monsoonal North West (MNW) region of Australia faces a number of challenges adapting to anthropogenic climate change. These have the potential to impact on a range of industries, including agricultural, pastoral, mining and tourism. However future changes to rainfall regimes remain uncertain due to the inability of Global Climate Models to adequately capture the tropical weather/climate processes that are known to be important for this region. Compounding this is the brevity of the instrumental rainfall record for the MNW, which is unlikely to represent the full range of climatic variability. One avenue for addressing this issue (the focus of this paper) is to identify sources of paleoclimate information that can be used to reconstruct a plausible pre-instrumental rainfall history for the MNW. Adopting this approach we find that, even in the absence of local sources of paleoclimate data at a suitable temporal resolution, remote paleoclimate records can resolve 25% of the annual variability observed in the instrumental rainfall record. Importantly, the 507-year rainfall reconstruction developed using the remote proxies displays longer and more intense wet and dry periods than observed during the most recent 100 years. For example, the maximum number of consecutive years of below (above) average rainfall is 90% (40%) higher in the rainfall reconstruction than during the instrumental period. Further, implications for flood and drought risk are studied via a simple GR1A rainfall runoff model, which again highlights the likelihood of extremes greater than that observed in the limited instrumental record, consistent with previous paleoclimate studies elsewhere in Australia. Importantly, this research can assist in informing climate related risks to infrastructure, agriculture and mining, and the method can readily be applied to other regions in the MNW and beyond.

Reclaimed mineland curve number response to temporal distribution of rainfall

USGS Publications Warehouse

Warner, R.C.; Agouridis, C.T.; Vingralek, P.T.; Fogle, A.W.

2010-01-01

The curve number (CN) method is a common technique to estimate runoff volume, and it is widely used in coal mining operations such as those in the Appalachian region of Kentucky. However, very little CN data are available for watersheds disturbed by surface mining and then reclaimed using traditional techniques. Furthermore, as the CN method does not readily account for variations in infiltration rates due to varying rainfall distributions, the selection of a single CN value to encompass all temporal rainfall distributions could lead engineers to substantially under- or over-size water detention structures used in mining operations or other land uses such as development. Using rainfall and runoff data from a surface coal mine located in the Cumberland Plateau of eastern Kentucky, CNs were computed for conventionally reclaimed lands. The effects of temporal rainfall distributions on CNs was also examined by classifying storms as intense, steady, multi-interval intense, or multi-interval steady. Results indicate that CNs for such reclaimed lands ranged from 62 to 94 with a mean value of 85. Temporal rainfall distributions were also shown to significantly affect CN values with intense storms having significantly higher CNs than multi-interval storms. These results indicate that a period of recovery is present between rainfall bursts of a multi-interval storm that allows depressional storage and infiltration rates to rebound. ?? 2010 American Water Resources Association.

Climate-change driven increase in high intensity rainfall events: Analysis of development in the last decades and towards an extrapolation of future progression

NASA Astrophysics Data System (ADS)

Müller, Eva; Pfister, Angela; Gerd, Büger; Maik, Heistermann; Bronstert, Axel

2015-04-01

Hydrological extreme events can be triggered by rainfall on different spatiotemporal scales: river floods are typically caused by event durations of between hours and days, while urban flash floods as well as soil erosion or contaminant transport rather result from storms events of very short duration (minutes). Still, the analysis of climate change impacts on rainfall-induced extreme events is usually carried out using daily precipitation data at best. Trend analyses of extreme rainfall at sub-daily or even sub-hourly time scales are rare. In this contribution two lines of research are combined: first, we analyse sub-hourly rainfall data for several decades in three European regions.Second, we investigate the scaling behaviour of heavy short-term precipitation with temperature, i.e. the dependence of high intensity rainfall on the atmospheric temperature at that particular time and location. The trend analysis of high-resolution rainfall data shows for the first time that the frequency of short and intensive storm events in the temperate lowland regions in Germany has increased by up to 0.5 events per year over the last decades. I.e. this trend suggests that the occurrence of these types of storms have multiplied over only a few decades. Parallel to the changes in the rainfall regime, increases in the annual and seasonal average temperature and changes in the occurrence of circulation patterns responsible for the generation of high-intensity storms have been found. The analysis of temporally highly resolved rainfall records from three European regions further indicates that extreme precipitation events are more intense with warmer temperatures during the rainfall event. These observations follow partly the Clausius-Clapeyron relation. Based on this relation one may derive a general rule of maximum rainfall intensity associated to the event temperature, roughly following the Clausius-Clapeyron (CC) relation. This rule might be used for scenarios of future maximum rainfall intensities under a warming climate.

The impact of soil moisture extremes and their spatiotemporal variability on Zambian maize yields

NASA Astrophysics Data System (ADS)

Zhao, Y.; Estes, L. D.; Vergopolan, N.

2017-12-01

Food security in sub-Saharan Africa is highly sensitive to climate variability. While it is well understood that extreme heat has substantial negative impacts on crop yield, the impacts of precipitation extremes, particularly over large spatial extents, are harder to quantify. There are three primary reasons for this difficulty, which are (1) lack of high quality, high resolution precipitation data, (2) rainfall data provide incomplete information on plant water availability, the variable that most directly affects crop performance, and (3) the type of rainfall extreme that most affects crop yields varies throughout the crop development stage. With respect to the first reason, the spatial and temporal variation of precipitation is much greater than that of temperature, yet the spatial resolution of rainfall data is typically even coarser than it is for temperature, particularly within Africa. Even if there were high-resolution rainfall data, the amount of water available to crops also depends on other physical factors that affect evapotranspiration, which are strongly influenced by heterogeneity in the land surface related to topography, soil properties, and land cover. In this context, soil moisture provides a better measure of crop water availability than rainfall. Furthermore, soil moisture has significantly different influences on crop yield depending on the crop's growth stage. The goal of this study is to understand how the spatiotemporal scales of soil moisture extremes interact with crops, more specifically, the timing and the spatial scales of extreme events like droughts and flooding. In this study, we simulate daily-1km soil moisture using HydroBlocks - a physically based land surface model - and compare it with precipitation and remote sensing derived maize yields between 2000 and 2016 in Zambia. We use a novel combination of the SCYM (scalable satellite-based yield mapper) method with DSSAT crop model, which is a mechanistic model responsive to water stress. Understanding the relationships between soil moisture spatiotemporal variability and yields can help to improve agricultural drought risk assessment and seasonal crop yield forecasting as well as early season warning of potential famines.

Final Technical Report for "High-resolution temporal variations in groundwater chemistry: Tracing the links between climate, hydrology, and element mobility in the vadose zone"

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jay L. Banner

2002-04-23

In spite of a developing emphasis on geochemical methods in studies of modern hydrologic systems, there have been few attempts to examine temporal fluctuations in groundwater chemistry and element mobility in the near-surface environment. Relatively little is known regarding how groundwaters evolve over 10 to 10,000 year scales, yet this knowledge provides a critical framework for understanding the links between climate and hydrology, the evolution of soils, and element migration in the vadose environment. Recent analytical advances allow U-series measurements to be applied to developing high-resolution chronologies of Pleistocene and Holocene carbonates. The potential of these new tools is examinedmore » through an analysis of two well-defined, active karst systems in (1) Barbados and (2) Texas. (1) The research effort on Barbados has developed methods of estimating recharge and inferring the spatial and seasonal distribution of recharge to the Pleistocene limestone aquifer on Barbados. A new method has been developed to estimate recharge based on oxygen isotope variations in rainwater and groundwater. Inter-annual recharge variations indicate that recharge is dependent on the distribution of rainfall throughout the year rather than total annual rainfall. Consequently, a year when rainfall occurs primarily during the peak wet season months (August through November) may have more recharge than a year when rainfall is more evenly distributed through the year. These results lay important groundwork for analysis of rainfall/recharge variations over different time scales based on isotopic records presently being constructed using Barbados speleothems from the same aquifer. (2) The chronology of speleothems (cave calcite deposits) from three caves across 130 kilometers in central Texas provides a 71,000-year record of temporal changes in hydrology and climate. Fifty-three ages were determined by mass spectrometric 238U - 230Th and 235U - 231Pa analyses. The accuracy of the ages and the closed-system behavior of the speleothems are indicated by inter-laboratory comparisons, concordancy of 230Th and 231Pa ages, and the result that all ages are in correct stratigraphic order. Over the last 71,000 years, the stalagmites have similar growth histories with alternating periods of relatively rapid and slow growth. The growth rates vary over more than two orders-of-magnitude, with three periods of rapid growth from 71-60 ka, 39-33 ka, and 24-12 ka. These growth rate shifts correspond in part with global glacial-interglacial climatic shifts. The potential effects of temporal variations in precession of Earth?s orbit on regional effective moisture may provide a mechanism for increased effective moisture coincident with the observed intervals of increased speleothem growth. The stalagmites all exhibit a large drop in growth rate between 15 and 12 ka, and very slow growth up to the present, consistent with drier climate during the Holocene. These results illustrate that speleothem growth rates can reflect the regional response of a hydrologic system to regional and global climate variability.« less

Development of Radar-Satellite Blended QPF (Quantitative Precipitation Forecast) Technique for heavy rainfall

NASA Astrophysics Data System (ADS)

Jang, Sangmin; Yoon, Sunkwon; Rhee, Jinyoung; Park, Kyungwon

2016-04-01

Due to the recent extreme weather and climate change, a frequency and size of localized heavy rainfall increases and it may bring various hazards including sediment-related disasters, flooding and inundation. To prevent and mitigate damage from such disasters, very short range forecasting and nowcasting of precipitation amounts are very important. Weather radar data very useful in monitoring and forecasting because weather radar has high resolution in spatial and temporal. Generally, extrapolation based on the motion vector is the best method of precipitation forecasting using radar rainfall data for a time frame within a few hours from the present. However, there is a need for improvement due to the radar rainfall being less accurate than rain-gauge on surface. To improve the radar rainfall and to take advantage of the COMS (Communication, Ocean and Meteorological Satellite) data, a technique to blend the different data types for very short range forecasting purposes was developed in the present study. The motion vector of precipitation systems are estimated using 1.5km CAPPI (Constant Altitude Plan Position Indicator) reflectivity by pattern matching method, which indicates the systems' direction and speed of movement and blended radar-COMS rain field is used for initial data. Since the original horizontal resolution of COMS is 4 km while that of radar is about 1 km, spatial downscaling technique is used to downscale the COMS data from 4 to 1 km pixels in order to match with the radar data. The accuracies of rainfall forecasting data were verified utilizing AWS (Automatic Weather System) observed data for an extreme rainfall occurred in the southern part of Korean Peninsula on 25 August 2014. The results of this study will be used as input data for an urban stream real-time flood early warning system and a prediction model of landslide. Acknowledgement This research was supported by a grant (13SCIPS04) from Smart Civil Infrastructure Research Program funded by Ministry of Land, Infrastructure and Transport (MOLIT) of Korea government and Korea Agency for Infrastructure Technology Advancement (KAIA).

A high space-time resolution dataset linking meteorological forcing and hydro-sedimentary response in a mesoscale Mediterranean catchment (Auzon) of the Ardèche region, France

NASA Astrophysics Data System (ADS)

Nord, Guillaume; Boudevillain, Brice; Berne, Alexis; Branger, Flora; Braud, Isabelle; Dramais, Guillaume; Gérard, Simon; Le Coz, Jérôme; Legoût, Cédric; Molinié, Gilles; Van Baelen, Joel; Vandervaere, Jean-Pierre; Andrieu, Julien; Aubert, Coralie; Calianno, Martin; Delrieu, Guy; Grazioli, Jacopo; Hachani, Sahar; Horner, Ivan; Huza, Jessica; Le Boursicaud, Raphaël; Raupach, Timothy H.; Teuling, Adriaan J.; Uber, Magdalena; Vincendon, Béatrice; Wijbrans, Annette

2017-03-01

A comprehensive hydrometeorological dataset is presented spanning the period 1 January 2011-31 December 2014 to improve the understanding of the hydrological processes leading to flash floods and the relation between rainfall, runoff, erosion and sediment transport in a mesoscale catchment (Auzon, 116 km2) of the Mediterranean region. Badlands are present in the Auzon catchment and well connected to high-gradient channels of bedrock rivers which promotes the transfer of suspended solids downstream. The number of observed variables, the various sensors involved (both in situ and remote) and the space-time resolution ( ˜ km2, ˜ min) of this comprehensive dataset make it a unique contribution to research communities focused on hydrometeorology, surface hydrology and erosion. Given that rainfall is highly variable in space and time in this region, the observation system enables assessment of the hydrological response to rainfall fields. Indeed, (i) rainfall data are provided by rain gauges (both a research network of 21 rain gauges with a 5 min time step and an operational network of 10 rain gauges with a 5 min or 1 h time step), S-band Doppler dual-polarization radars (1 km2, 5 min resolution), disdrometers (16 sensors working at 30 s or 1 min time step) and Micro Rain Radars (5 sensors, 100 m height resolution). Additionally, during the special observation period (SOP-1) of the HyMeX (Hydrological Cycle in the Mediterranean Experiment) project, two X-band radars provided precipitation measurements at very fine spatial and temporal scales (1 ha, 5 min). (ii) Other meteorological data are taken from the operational surface weather observation stations of Météo-France (including 2 m air temperature, atmospheric pressure, 2 m relative humidity, 10 m wind speed and direction, global radiation) at the hourly time resolution (six stations in the region of interest). (iii) The monitoring of surface hydrology and suspended sediment is multi-scale and based on nested catchments. Three hydrometric stations estimate water discharge at a 2-10 min time resolution. Two of these stations also measure additional physico-chemical variables (turbidity, temperature, conductivity) and water samples are collected automatically during floods, allowing further geochemical characterization of water and suspended solids. Two experimental plots monitor overland flow and erosion at 1 min time resolution on a hillslope with vineyard. A network of 11 sensors installed in the intermittent hydrographic network continuously measures water level and water temperature in headwater subcatchments (from 0.17 to 116 km2) at a time resolution of 2-5 min. A network of soil moisture sensors enables the continuous measurement of soil volumetric water content at 20 min time resolution at 9 sites. Additionally, concomitant observations (soil moisture measurements and stream gauging) were performed during floods between 2012 and 2014. Finally, this dataset is considered appropriate for understanding the rainfall variability in time and space at fine scales, improving areal rainfall estimations and progressing in distributed hydrological and erosion modelling. DOI of the referenced dataset: doi:10.6096/MISTRALS-HyMeX.1438.

Rainfall Downscaling Conditional on Upper-air Atmospheric Predictors: Improved Assessment of Rainfall Statistics in a Changing Climate

NASA Astrophysics Data System (ADS)

Langousis, Andreas; Mamalakis, Antonis; Deidda, Roberto; Marrocu, Marino

2015-04-01

To improve the level skill of Global Climate Models (GCMs) and Regional Climate Models (RCMs) in reproducing the statistics of rainfall at a basin level and at hydrologically relevant temporal scales (e.g. daily), two types of statistical approaches have been suggested. One is the statistical correction of climate model rainfall outputs using historical series of precipitation. The other is the use of stochastic models of rainfall to conditionally simulate precipitation series, based on large-scale atmospheric predictors produced by climate models (e.g. geopotential height, relative vorticity, divergence, mean sea level pressure). The latter approach, usually referred to as statistical rainfall downscaling, aims at reproducing the statistical character of rainfall, while accounting for the effects of large-scale atmospheric circulation (and, therefore, climate forcing) on rainfall statistics. While promising, statistical rainfall downscaling has not attracted much attention in recent years, since the suggested approaches involved complex (i.e. subjective or computationally intense) identification procedures of the local weather, in addition to demonstrating limited success in reproducing several statistical features of rainfall, such as seasonal variations, the distributions of dry and wet spell lengths, the distribution of the mean rainfall intensity inside wet periods, and the distribution of rainfall extremes. In an effort to remedy those shortcomings, Langousis and Kaleris (2014) developed a statistical framework for simulation of daily rainfall intensities conditional on upper air variables, which accurately reproduces the statistical character of rainfall at multiple time-scales. Here, we study the relative performance of: a) quantile-quantile (Q-Q) correction of climate model rainfall products, and b) the statistical downscaling scheme of Langousis and Kaleris (2014), in reproducing the statistical structure of rainfall, as well as rainfall extremes, at a regional level. This is done for an intermediate-sized catchment in Italy, i.e. the Flumendosa catchment, using climate model rainfall and atmospheric data from the ENSEMBLES project (http://ensembleseu.metoffice.com). In doing so, we split the historical rainfall record of mean areal precipitation (MAP) in 15-year calibration and 45-year validation periods, and compare the historical rainfall statistics to those obtained from: a) Q-Q corrected climate model rainfall products, and b) synthetic rainfall series generated by the suggested downscaling scheme. To our knowledge, this is the first time that climate model rainfall and statistically downscaled precipitation are compared to catchment-averaged MAP at a daily resolution. The obtained results are promising, since the proposed downscaling scheme is more accurate and robust in reproducing a number of historical rainfall statistics, independent of the climate model used and the length of the calibration period. This is particularly the case for the yearly rainfall maxima, where direct statistical correction of climate model rainfall outputs shows increased sensitivity to the length of the calibration period and the climate model used. The robustness of the suggested downscaling scheme in modeling rainfall extremes at a daily resolution, is a notable feature that can effectively be used to assess hydrologic risk at a regional level under changing climatic conditions. Acknowledgments The research project is implemented within the framework of the Action «Supporting Postdoctoral Researchers» of the Operational Program "Education and Lifelong Learning" (Action's Beneficiary: General Secretariat for Research and Technology), and is co-financed by the European Social Fund (ESF) and the Greek State. CRS4 highly acknowledges the contribution of the Sardinian regional authorities.

Prognostic Aspects of Sub-seasonal Rainfall Characteristics using the Outputs of General Circulation Model: An Application of Statistical Downscaling and Temporal Disaggregation

NASA Astrophysics Data System (ADS)

Singh, A.; Mohanty, U. C.; Ghosh, K.

2015-12-01

Most regions of India experience varied rainfall duration during the southwest monsoon, changes in which exhibit major impact not only agriculture, but also other sectors like hydrology, agriculture, food and fodder storage etc. In addition, changes in sub-seasonal rainfall characteristics highly impact the rice production. As part of the endeavor seasonal climate outlook, as well as information for weather within climate may be helpful for advance planning and risk management in agriculture. The General Circulation Model (GCM) provide an alternative to gather information for weather within climate but variability is very low in comparison to observation. On the other hand, the spatial resolution of GCM predicted rainfall is not found at the observed station/grid point. To tackle the problem, initially a statistical downscaling over 19 station of Odisha state is undertaken using the atmospheric parameters predicted by a GCM (NCEP-CFSv2). For the purpose, an extended domain is taken for analyzing the significant zone for the atmospheric parameters like zonal wind at 850hPa, Sea Surface Temperature (SST), geopotential height. A statistical model using the pattern projection method is further developed based on empirical orthogonal function. The downscaled rainfall is found better in association with station observation in comparison to raw GCM prediction in view of deterministic and probabilistic skill measure. Further, the sub-seasonal and seasonal forecast from the GCMs can be used at different time steps for risk management. Therefore, downscaled seasonal/monthly rainfall is further converted to sub-seasonal/daily time scale using a non-homogeneous markov model. The simulated weather sequences are further compared with the observed sequence in view of categorical rainfall events. The outcomes suggest that the rainfall amount are overestimated for excess rainfall and henceforth larger excess rainfall events can be realized. The skill for prediction of rainfall events corresponding to lower thresholds is found higher. A detail discussion regarding skill of spatial downscale rainfall at observed stations and its further representation of sub-seasonal characteristics (spells, less rainfall, heavy rainfall, and moderate rainfall events) of rainfall for disaggregated outputs will be presented.

Ecosystem productivity and water stress in tropical East Africa: A Case Study of the 2010-11 drought

NASA Astrophysics Data System (ADS)

Robinson, E. S.; Yang, X.; Lee, J. E.

2015-12-01

The characterization of changes in ecosystem productivity as a consequence of water stress and changing precipitation regimes is critical in defining the response of tropical ecosystems to water stress and projecting future land cover transitions in the East African tropics. Through the analysis of solar-induced chlorophyll fluorescence (SIF), soil moisture, rainfall and reanalysis data, this paper characterizes the 2010-11 drought in tropical East Africa. We demonstrated that SIF, a proxy of ecosystem productivity, varied with water availability during the 2010-11 drought. A comparison of the 2010-11 drought to previous regional droughts revealed that the consecutive failure of rainy seasons in fall 2010 and spring 2011 yielded a drought that is distinguished not only in intensity, but also in spatial and temporal extent as compared to an average of previous regional droughts: the 2010-11 event extended further east and with greater intensity in the southern hemisphere. Anomalously low SIF values during the 2010-11 drought are strongly correlated with those of soil moisture and precipitation. SIF also demonstrated a stronger temporal sensitivity to accumulated water deficit as compared to the conventional Normalized Difference Vegetation Index (NDVI), which approximates photosynthetic potential (chlorophyll content and leaf mass), from the Moderate Resolution Imaging Spectroradiometer (MODIS). Anomalously high rainfall during the dry seasons preceding failed rainy seasons suggest that the seasonality of East African rainfall may be transitioning from a regime characterized by biannual monsoons to one with increasing convective rainfall. Rising boundary layer height during the dry season further substantiates this conclusion by suggesting a transition towards increased deep convection during the summers. This work demonstrated the unique characteristics of the 2010-11 East African drought, and the ability of SIF to track the levels of water stress during the drought.

Short-term rainfall: its scaling properties over Portugal

NASA Astrophysics Data System (ADS)

de Lima, M. Isabel P.

2010-05-01

The characterization of rainfall at a variety of space- and time-scales demands usually that data from different origins and resolution are explored. Different tools and methodologies can be used for this purpose. In regions where the spatial variation of rain is marked, the study of the scaling structure of rainfall can lead to a better understanding of the type of events affecting that specific area, which is essential for many engineering applications. The relevant factors affecting rain variability, in time and space, can lead to contrasting statistics which should be carefully taken into account in design procedures and decision making processes. One such region is Mainland Portugal; the territory is located in the transitional region between the sub-tropical anticyclone and the subpolar depression zones and is characterized by strong north-south and east-west rainfall gradients. The spatial distribution and seasonal variability of rain are particularly influenced by the characteristics of the global circulation. One specific feature is the Atlantic origin of many synoptic disturbances in the context of the regional geography (e.g. latitude, orography, oceanic and continental influences). Thus, aiming at investigating the statistical signature of rain events of different origins, resulting from the large number of mechanisms and factors affecting the rainfall climate over Portugal, scale-invariant analyses of the temporal structure of rain from several locations in mainland Portugal were conducted. The study used short-term rainfall time series. Relevant scaling ranges were identified and characterized that help clarifying the small-scale behaviour and statistics of this process.

Water balance dynamics in the Nile Basin

USGS Publications Warehouse

Senay, Gabriel B.; Asante, Kwabena; Artan, Guleid A.

2009-01-01

Understanding the temporal and spatial dynamics of key water balance components of the Nile River will provide important information for the management of its water resources. This study used satellite-derived rainfall and other key weather variables derived from the Global Data Assimilation System to estimate and map the distribution of rainfall, actual evapotranspiration (ETa), and runoff. Daily water balance components were modelled in a grid-cell environment at 0·1 degree (∼10 km) spatial resolution for 7 years from 2001 through 2007. Annual maps of the key water balance components and derived variables such as runoff and ETa as a percent of rainfall were produced. Generally, the spatial patterns of rainfall and ETa indicate high values in the upstream watersheds (Uganda, southern Sudan, and southwestern Ethiopia) and low values in the downstream watersheds. However, runoff as a percent of rainfall is much higher in the Ethiopian highlands around the Blue Nile subwatershed. The analysis also showed the possible impact of land degradation in the Ethiopian highlands in reducing ETa magnitudes despite the availability of sufficient rainfall. Although the model estimates require field validation for the different subwatersheds, the runoff volume estimate for the Blue Nile subwatershed is within 7·0% of a figure reported from an earlier study. Further research is required for a thorough validation of the results and their integration with ecohydrologic models for better management of water and land resources in the various Nile Basin ecosystems.

SDCLIREF - A sub-daily gridded reference dataset

NASA Astrophysics Data System (ADS)

Wood, Raul R.; Willkofer, Florian; Schmid, Franz-Josef; Trentini, Fabian; Komischke, Holger; Ludwig, Ralf

2017-04-01

Climate change is expected to impact the intensity and frequency of hydrometeorological extreme events. In order to adequately capture and analyze extreme rainfall events, in particular when assessing flood and flash flood situations, data is required at high spatial and sub-daily resolution which is often not available in sufficient density and over extended time periods. The ClimEx project (Climate Change and Hydrological Extreme Events) addresses the alteration of hydrological extreme events under climate change conditions. In order to differentiate between a clear climate change signal and the limits of natural variability, unique Single-Model Regional Climate Model Ensembles (CRCM5 driven by CanESM2, RCP8.5) were created for a European and North-American domain, each comprising 50 members of 150 years (1951-2100). In combination with the CORDEX-Database, this newly created ClimEx-Ensemble is a one-of-a-kind model dataset to analyze changes of sub-daily extreme events. For the purpose of bias-correcting the regional climate model ensembles as well as for the baseline calibration and validation of hydrological catchment models, a new sub-daily (3h) high-resolution (500m) gridded reference dataset (SDCLIREF) was created for a domain covering the Upper Danube and Main watersheds ( 100.000km2). As the sub-daily observations lack a continuous time series for the reference period 1980-2010, the need for a suitable method to bridge the gap of the discontinuous time series arouse. The Method of Fragments (Sharma and Srikanthan (2006); Westra et al. (2012)) was applied to transform daily observations to sub-daily rainfall events to extend the time series and densify the station network. Prior to applying the Method of Fragments and creating the gridded dataset using rigorous interpolation routines, data collection of observations, operated by several institutions in three countries (Germany, Austria, Switzerland), and the subsequent quality control of the observations was carried out. Among others, the quality control checked for steps, extensive dry seasons, temporal consistency and maximum hourly values. The resulting SDCLIREF dataset provides a robust precipitation reference for hydrometeorological applications in unprecedented high spatio-temporal resolution. References: Sharma, A.; Srikanthan, S. (2006): Continuous Rainfall Simulation: A Nonparametric Alternative. In: 30th Hydrology and Water Resources Symposium 4-7 December 2006, Launceston, Tasmania. Westra, S.; Mehrotra, R.; Sharma, A.; Srikanthan, R. (2012): Continuous rainfall simulation. 1. A regionalized subdaily disaggregation approach. In: Water Resour. Res. 48 (1). DOI: 10.1029/2011WR010489.

Continuous rainfall simulation for regional flood risk assessment - application in the Austrian Alps

NASA Astrophysics Data System (ADS)

Salinas, Jose Luis; Nester, Thomas; Komma, Jürgen; Blöschl, Günter

2017-04-01

Generation of realistic synthetic spatial rainfall is of pivotal importance for assessing regional hydroclimatic hazard as the input for long term rainfall-runoff simulations. The correct reproduction of the observed rainfall characteristics, such as regional intensity-duration-frequency curves, is necessary to adequately model the magnitude and frequency of the flood peaks. Furthermore, the replication of the observed rainfall spatial and temporal correlations allows to model important other hydrological features like antecedent soil moisture conditions before extreme rainfall events. In this work, we present an application in the Tirol region (Austrian alps) of a modification of the model presented by Bardossy and Platte (1992), where precipitation is modeled on a station basis as a mutivariate autoregressive model (mAr) in a Normal space, and then transformed to a Gamma-distributed space. For the sake of simplicity, the parameters of the Gamma distributions are assumed to vary monthly according to a sinusoidal function, and are calibrated trying to simultaneously reproduce i) mean annual rainfall, ii) mean daily rainfall amounts, iii) standard deviations of daily rainfall amounts, and iv) 24-hours intensity duration frequency curve. The calibration of the spatial and temporal correlation parameters is performed in a way that the intensity-duration-frequency curves aggregated at different spatial and temporal scales reproduce the measured ones. Bardossy, A., and E. J. Plate (1992), Space-time model for daily rainfall using atmospheric circulation patterns, Water Resour. Res., 28(5), 1247-1259, doi:10.1029/91WR02589.

Evaluation and correction of uncertainty due to Gaussian approximation in radar - rain gauge merging using kriging with external drift

NASA Astrophysics Data System (ADS)

Cecinati, F.; Wani, O.; Rico-Ramirez, M. A.

2016-12-01

It is widely recognised that merging radar rainfall estimates (RRE) with rain gauge data can improve the RRE and provide areal and temporal coverage that rain gauges cannot offer. Many methods to merge radar and rain gauge data are based on kriging and require an assumption of Gaussianity on the variable of interest. In particular, this work looks at kriging with external drift (KED), because it is an efficient, widely used, and well performing merging method. Rainfall, especially at finer temporal scale, does not have a normal distribution and presents a bi-modal skewed distribution. In some applications a Gaussianity assumption is made, without any correction. In other cases, variables are transformed in order to obtain a distribution closer to Gaussian. This work has two objectives: 1) compare different transformation methods in merging applications; 2) evaluate the uncertainty arising when untransformed rainfall data is used in KED. The comparison of transformation methods is addressed under two points of view. On the one hand, the ability to reproduce the original probability distribution after back-transformation of merged products is evaluated with qq-plots, on the other hand the rainfall estimates are compared with an independent set of rain gauge measurements. The tested methods are 1) no transformation, 2) Box-Cox transformations with parameter equal to λ=0.5 (square root), 3) λ=0.25 (square root - square root), and 4) λ=0.1 (almost logarithmic), 5) normal quantile transformation, and 6) singularity analysis. The uncertainty associated with the use of non-transformed data in KED is evaluated in comparison with the best performing product. The methods are tested on a case study in Northern England, using hourly data from 211 tipping bucket rain gauges from the Environment Agency and radar rainfall data at 1 km/5-min resolutions from the UK Met Office. In addition, 25 independent rain gauges from the UK Met Office were used to assess the merged products.

Transient deterministic shallow landslide modeling: Requirements for susceptibility and hazard assessments in a GIS framework

USGS Publications Warehouse

Godt, J.W.; Baum, R.L.; Savage, W.Z.; Salciarini, D.; Schulz, W.H.; Harp, E.L.

2008-01-01

Application of transient deterministic shallow landslide models over broad regions for hazard and susceptibility assessments requires information on rainfall, topography and the distribution and properties of hillside materials. We survey techniques for generating the spatial and temporal input data for such models and present an example using a transient deterministic model that combines an analytic solution to assess the pore-pressure response to rainfall infiltration with an infinite-slope stability calculation. Pore-pressures and factors of safety are computed on a cell-by-cell basis and can be displayed or manipulated in a grid-based GIS. Input data are high-resolution (1.8??m) topographic information derived from LiDAR data and simple descriptions of initial pore-pressure distribution and boundary conditions for a study area north of Seattle, Washington. Rainfall information is taken from a previously defined empirical rainfall intensity-duration threshold and material strength and hydraulic properties were measured both in the field and laboratory. Results are tested by comparison with a shallow landslide inventory. Comparison of results with those from static infinite-slope stability analyses assuming fixed water-table heights shows that the spatial prediction of shallow landslide susceptibility is improved using the transient analyses; moreover, results can be depicted in terms of the rainfall intensity and duration known to trigger shallow landslides in the study area.

HD Hydrological modelling at catchment scale using rainfall radar observations

NASA Astrophysics Data System (ADS)

Ciampalini Rossano. Ciampalini@Gmail. Com), Rossano; Follain, Stéphane; Raclot, Damien; Crabit, Armand; Pastor, Amandine; Augas, Julien; Moussa, Roger; Colin, François; Le Bissonnais, Yves

2017-04-01

Hydrological simulations at catchment scale repose on the quality and data availability both for soil and rainfall data. Soil data are quite easy to be collected, although their quality depends on the resources devoted to this task, rainfall data observations, instead, need further effort because of their spatiotemporal variability. Rainfalls are normally recorded with rain gauges located in the catchment, they can provide detailed temporal data, but, the representativeness is limited to the point where the data are collected. Combining different gauges in space can provide a better representation of the rainfall event but the spatialization is often the main obstacle to obtain data close to the reality. Since several years, radar observations overcome this gap providing continuous data registration, that, when properly calibrated, can offer an adequate, continuous, cover in space and time for medium-wide catchments. Here, we use radar records for the south of the France on the La Peyne catchment with the protocol there adopted by the national meteo agency, with resolution of 1 km space and 5' time scale observations. We present here the realisation of a model able to perform from rainfall radar observations, continuous hydrological and soil erosion simulations. The model is semi-theoretically based, once it simulates water fluxes (infiltration-excess overland flow, saturation overland flow, infiltration and channel routing) with a cinematic wave using the St. Venant equation on a simplified "bucket" conceptual model for ground water, and, an empirical representation of sediment load as adopted in models such as STREAM-LANDSOIL (Cerdan et al., 2002, Ciampalini et al., 2012). The advantage of this approach is to furnish a dynamic representation - simulation of the rainfall-runoff events more easily than using spatialized rainfalls from meteo stations and to offer a new look on the spatial component of the events.

Rainfall-triggered shallow landslides at catchment scale: Threshold mechanics-based modeling for abruptness and localization

NASA Astrophysics Data System (ADS)

von Ruette, J.; Lehmann, P.; Or, D.

2013-10-01

Rainfall-induced shallow landslides may occur abruptly without distinct precursors and could span a wide range of soil mass released during a triggering event. We present a rainfall-induced landslide-triggering model for steep catchments with surfaces represented as an assembly of hydrologically and mechanically interconnected soil columns. The abruptness of failure was captured by defining local strength thresholds for mechanical bonds linking soil and bedrock and adjacent columns, whereby a failure of a single bond may initiate a chain reaction of subsequent failures, culminating in local mass release (a landslide). The catchment-scale hydromechanical landslide-triggering model (CHLT) was applied to results from two event-based landslide inventories triggered by two rainfall events in 2002 and 2005 in two nearby catchments located in the Prealps in Switzerland. Rainfall radar data, surface elevation and vegetation maps, and a soil production model for soil depth distribution were used for hydromechanical modeling of failure patterns for the two rainfall events at spatial and temporal resolutions of 2.5 m and 0.02 h, respectively. The CHLT model enabled systematic evaluation of the effects of soil type, mechanical reinforcement (soil cohesion and lateral root strength), and initial soil water content on landslide characteristics. We compared various landslide metrics and spatial distribution of simulated landslides in subcatchments with observed inventory data. Model parameters were optimized for the short but intense rainfall event in 2002, and the calibrated model was then applied for the 2005 rainfall, yielding reasonable predictions of landslide events and volumes and statistically reproducing localized landslide patterns similar to inventory data. The model provides a means for identifying local hot spots and offers insights into the dynamics of locally resolved landslide hazards in mountainous regions.

Exploring the Variability of Short-term Precipitation and Hydrological Response of Small Czech Watersheds

NASA Astrophysics Data System (ADS)

Kavka, Petr; Strouhal, Ludek; Weyskrabova, Lenka; Müller, Miloslav; Kozant, Petr

2017-04-01

The short-term rainfall temporal distribution is known to have a significant effect on the small watersheds' hydrological response. In Czech Republic there are limited publicly available data on rainfall patterns of short-term precipitation. On one side there are catalogues of very short-term synthetic rainfalls used in urban drainage planning and on the other side hourly distribution of daily totals of rainfalls with long return period for larger catchments analyses. This contribution introduces the preliminary outcomes of a running three years' project, which should bridge this gap and provide such data and methodology to the community of scientists, state administration as well as design planners. Six generalized 6-hours hyetographs with 1 minute resolution were derived from 10 years of radar and gauging stations data. These hyetographs are accompanied with information concerning the region of occurrence as well as their frequency related to the rainfall amount. In the next step these hyetographs are used in a complex sensitivity analysis focused on a rainfall-runoff response of small watersheds. This analysis takes into account the uncertainty related to type of the hydrological model, watershed characteristics and main model routines parameterization. Five models with different methods and structure are considered and each model is applied on 5 characteristic watersheds selected from a classification of 7700 small Czech watersheds. For each combination of model and watershed 30, rainfall scenarios were simulated and other scenarios will be used to address the parameters uncertainty. In the last step the variability of outputs will be assessed in the context of economic impacts on design of landscape water structures or mitigation measures. The research is supported by the grant QJ1520265 of the Czech Ministry of Agriculture, rainfall data were provided by the Czech Hydrometeorological Institute.

Errors and uncertainties in regional climate simulations of rainfall variability over Tunisia: a multi-model and multi-member approach

NASA Astrophysics Data System (ADS)

Fathalli, Bilel; Pohl, Benjamin; Castel, Thierry; Safi, Mohamed Jomâa

2018-02-01

Temporal and spatial variability of rainfall over Tunisia (at 12 km spatial resolution) is analyzed in a multi-year (1992-2011) ten-member ensemble simulation performed using the WRF model, and a sample of regional climate hindcast simulations from Euro-CORDEX. RCM errors and skills are evaluated against a dense network of local rain gauges. Uncertainties arising, on the one hand, from the different model configurations and, on the other hand, from internal variability are furthermore quantified and ranked at different timescales using simple spread metrics. Overall, the WRF simulation shows good skill for simulating spatial patterns of rainfall amounts over Tunisia, marked by strong altitudinal and latitudinal gradients, as well as the rainfall interannual variability, in spite of systematic errors. Mean rainfall biases are wet in both DJF and JJA seasons for the WRF ensemble, while they are dry in winter and wet in summer for most of the used Euro-CORDEX models. The sign of mean annual rainfall biases over Tunisia can also change from one member of the WRF ensemble to another. Skills in regionalizing precipitation over Tunisia are season dependent, with better correlations and weaker biases in winter. Larger inter-member spreads are observed in summer, likely because of (1) an attenuated large-scale control on Mediterranean and Tunisian climate, and (2) a larger contribution of local convective rainfall to the seasonal amounts. Inter-model uncertainties are globally stronger than those attributed to model's internal variability. However, inter-member spreads can be of the same magnitude in summer, emphasizing the important stochastic nature of the summertime rainfall variability over Tunisia.

An Environmental Data Set for Vector-Borne Disease Modeling and Epidemiology

PubMed Central

Chabot-Couture, Guillaume; Nigmatulina, Karima; Eckhoff, Philip

2014-01-01

Understanding the environmental conditions of disease transmission is important in the study of vector-borne diseases. Low- and middle-income countries bear a significant portion of the disease burden; but data about weather conditions in those countries can be sparse and difficult to reconstruct. Here, we describe methods to assemble high-resolution gridded time series data sets of air temperature, relative humidity, land temperature, and rainfall for such areas; and we test these methods on the island of Madagascar. Air temperature and relative humidity were constructed using statistical interpolation of weather station measurements; the resulting median 95th percentile absolute errors were 2.75°C and 16.6%. Missing pixels from the MODIS11 remote sensing land temperature product were estimated using Fourier decomposition and time-series analysis; thus providing an alternative to the 8-day and 30-day aggregated products. The RFE 2.0 remote sensing rainfall estimator was characterized by comparing it with multiple interpolated rainfall products, and we observed significant differences in temporal and spatial heterogeneity relevant to vector-borne disease modeling. PMID:24755954

Definition of a temporal distribution index for high temporal resolution precipitation data over Peninsular Spain and the Balearic Islands: the fractal dimension; and its synoptic implications

NASA Astrophysics Data System (ADS)

Meseguer-Ruiz, Oliver; Osborn, Timothy J.; Sarricolea, Pablo; Jones, Philip D.; Cantos, Jorge Olcina; Serrano-Notivoli, Roberto; Martin-Vide, Javier

2018-03-01

Precipitation on the Spanish mainland and in the Balearic archipelago exhibits a high degree of spatial and temporal variability, regardless of the temporal resolution of the data considered. The fractal dimension indicates the property of self-similarity, and in the case of this study, wherein it is applied to the temporal behaviour of rainfall at a fine (10-min) resolution from a total of 48 observatories, it provides insights into its more or less convective nature. The methodology of Jenkinson & Collison which automatically classifies synoptic situations at the surface, as well as an adaptation of this methodology at 500 hPa, was applied in order to gain insights into the synoptic implications of extreme values of the fractal dimension. The highest fractal dimension values in the study area were observed in places with precipitation that has a more random behaviour over time with generally high totals. Four different regions in which the atmospheric mechanisms giving rise to precipitation at the surface differ from the corresponding above-ground mechanisms have been identified in the study area based on the fractal dimension. In the north of the Iberian Peninsula, high fractal dimension values are linked to a lower frequency of anticyclonic situations, whereas the opposite occurs in the central region. In the Mediterranean, higher fractal dimension values are associated with a higher frequency of the anticyclonic type and a lower frequency of the advective type from the east. In the south, lower fractal dimension values indicate higher frequency with respect to the anticyclonic type from the east and lower frequency with respect to the cyclonic type.

Intra-storm temporal patterns of rainfall in China using Huff curves

USDA-ARS?s Scientific Manuscript database

The intra-storm temporal distributions of precipitation are important to infiltration, runoff and erosion processes and models. A convenient and established method for characterizing precipitation hyetographs is with the use of Huff curves. In this study, 11,801 erosive rainfall events with one-mi...

Interannual Tropical Rainfall Variability in General Circulation Model Simulations Associated with the Atmospheric Model Intercomparison Project.

NASA Astrophysics Data System (ADS)

Sperber, K. R.; Palmer, T. N.

1996-11-01

The interannual variability of rainfall over the Indian subcontinent, the African Sahel, and the Nordeste region of Brazil have been evaluated in 32 models for the period 1979-88 as part of the Atmospheric Model Intercomparison Project (AMIP). The interannual variations of Nordeste rainfall are the most readily captured, owing to the intimate link with Pacific and Atlantic sea surface temperatures. The precipitation variations over India and the Sahel are less well simulated. Additionally, an Indian monsoon wind shear index was calculated for each model. Evaluation of the interannual variability of a wind shear index over the summer monsoon region indicates that the models exhibit greater fidelity in capturing the large-scale dynamic fluctuations than the regional-scale rainfall variations. A rainfall/SST teleconnection quality control was used to objectively stratify model performance. Skill scores improved for those models that qualitatively simulated the observed rainfall/El Niño- Southern Oscillation SST correlation pattern. This subset of models also had a rainfall climatology that was in better agreement with observations, indicating a link between systematic model error and the ability to simulate interannual variations.A suite of six European Centre for Medium-Range Weather Forecasts (ECMWF) AMIP runs (differing only in their initial conditions) have also been examined. As observed, all-India rainfall was enhanced in 1988 relative to 1987 in each of these realizations. All-India rainfall variability during other years showed little or no predictability, possibly due to internal chaotic dynamics associated with intraseasonal monsoon fluctuations and/or unpredictable land surface process interactions. The interannual variations of Nordeste rainfall were best represented. The State University of New York at Albany/National Center for Atmospheric Research Genesis model was run in five initial condition realizations. In this model, the Nordeste rainfall variability was also best reproduced. However, for all regions the skill was less than that of the ECMWF model.The relationships of the all-India and Sahel rainfall/SST teleconnections with horizontal resolution, convection scheme closure, and numerics have been evaluated. Models with resolution T42 performed more poorly than lower-resolution models. The higher resolution models were predominantly spectral. At low resolution, spectral versus gridpoint numerics performed with nearly equal verisimilitude. At low resolution, moisture convergence closure was slightly more preferable than other convective closure techniques. At high resolution, the models that used moisture convergence closure performed very poorly, suggesting that moisture convergence may be problematic for models with horizontal resolution T42.

Representing radar rainfall uncertainty with ensembles based on a time-variant geostatistical error modelling approach

NASA Astrophysics Data System (ADS)

Cecinati, Francesca; Rico-Ramirez, Miguel Angel; Heuvelink, Gerard B. M.; Han, Dawei

2017-05-01

The application of radar quantitative precipitation estimation (QPE) to hydrology and water quality models can be preferred to interpolated rainfall point measurements because of the wide coverage that radars can provide, together with a good spatio-temporal resolutions. Nonetheless, it is often limited by the proneness of radar QPE to a multitude of errors. Although radar errors have been widely studied and techniques have been developed to correct most of them, residual errors are still intrinsic in radar QPE. An estimation of uncertainty of radar QPE and an assessment of uncertainty propagation in modelling applications is important to quantify the relative importance of the uncertainty associated to radar rainfall input in the overall modelling uncertainty. A suitable tool for this purpose is the generation of radar rainfall ensembles. An ensemble is the representation of the rainfall field and its uncertainty through a collection of possible alternative rainfall fields, produced according to the observed errors, their spatial characteristics, and their probability distribution. The errors are derived from a comparison between radar QPE and ground point measurements. The novelty of the proposed ensemble generator is that it is based on a geostatistical approach that assures a fast and robust generation of synthetic error fields, based on the time-variant characteristics of errors. The method is developed to meet the requirement of operational applications to large datasets. The method is applied to a case study in Northern England, using the UK Met Office NIMROD radar composites at 1 km resolution and at 1 h accumulation on an area of 180 km by 180 km. The errors are estimated using a network of 199 tipping bucket rain gauges from the Environment Agency. 183 of the rain gauges are used for the error modelling, while 16 are kept apart for validation. The validation is done by comparing the radar rainfall ensemble with the values recorded by the validation rain gauges. The validated ensemble is then tested on a hydrological case study, to show the advantage of probabilistic rainfall for uncertainty propagation. The ensemble spread only partially captures the mismatch between the modelled and the observed flow. The residual uncertainty can be attributed to other sources of uncertainty, in particular to model structural uncertainty, parameter identification uncertainty, uncertainty in other inputs, and uncertainty in the observed flow.

Amazon river basin evapotranspiration and its influence on the rainfall in southern Brazil

NASA Astrophysics Data System (ADS)

Folegatti, M. V.; Wolff, W.

2017-12-01

Amazon river basin (ARB) presents a positive water balance, i.e. the precipitation is higher than evapotranspiration. Regarding the regional circulation, ARB evapotranspiration represents an important source of humidity for the South of Brazil. Thus, the aim of this work is to answer the question: how much is the correlation between ARB evapotranspiration and rainfall in South of Brazil? The shapefiles data of ARB and countries/states boundary were obtained through the Oak Ridge National Laboratory (ORNL) and Instituto Brasileiro de Geografia e Estatística (IBGE), respectively. According to rasters data, the precipitation was obtained from study of Numerical Terradynamic Simulation Group (NTSG) for images of Moderate Resolution Imaging Spectroradiometer (MODIS), under code MOD16A2, whereas rasters data for evapotranspiration were obtained from National Aeronautics and Space Administration (NASA) by Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA), under code 3B43_V7. The products MOD16A2 and 3B43_V7 have a respective spatial resolution of 0.5º and 0.25º, and a monthly temporal resolution from January/2000 to December/2014. For ARB and South region of Brazil was calculated the mean evapotranspiration and mean precipitation through the pixels within of the respective polygons. To answer the question of this work was performed the cross-correlation analysis between these time series. We observed the highest value for the lag that corresponds the begin of spring (October), being 0.3 approximately. As a result, the mean precipitation on South region of Brazil during spring and summer was in the order of 15% to 30 %, explained by ARB evapotranspiration. For this reason, the maintenance of ARB is extremely important for water resource grant in South of Brazil.

Spatio-temporal analysis of sub-hourly rainfall over Mumbai, India: Is statistical forecasting futile?

NASA Astrophysics Data System (ADS)

Singh, Jitendra; Sekharan, Sheeba; Karmakar, Subhankar; Ghosh, Subimal; Zope, P. E.; Eldho, T. I.

2017-04-01

Mumbai, the commercial and financial capital of India, experiences incessant annual rain episodes, mainly attributable to erratic rainfall pattern during monsoons and urban heat-island effect due to escalating urbanization, leading to increasing vulnerability to frequent flooding. After the infamous episode of 2005 Mumbai torrential rains when only two rain gauging stations existed, the governing civic body, the Municipal Corporation of Greater Mumbai (MCGM) came forward with an initiative to install 26 automatic weather stations (AWS) in June 2006 (MCGM 2007), which later increased to 60 AWS. A comprehensive statistical analysis to understand the spatio-temporal pattern of rainfall over Mumbai or any other coastal city in India has never been attempted earlier. In the current study, a thorough analysis of available rainfall data for 2006-2014 from these stations was performed; the 2013-2014 sub-hourly data from 26 AWS was found useful for further analyses due to their consistency and continuity. Correlogram cloud indicated no pattern of significant correlation when we considered the closest to the farthest gauging station from the base station; this impression was also supported by the semivariogram plots. Gini index values, a statistical measure of temporal non-uniformity, were found above 0.8 in visible majority showing an increasing trend in most gauging stations; this sufficiently led us to conclude that inconsistency in daily rainfall was gradually increasing with progress in monsoon. Interestingly, night rainfall was lesser compared to daytime rainfall. The pattern-less high spatio-temporal variation observed in Mumbai rainfall data signifies the futility of independently applying advanced statistical techniques, and thus calls for simultaneous inclusion of physics-centred models such as different meso-scale numerical weather prediction systems, particularly the Weather Research and Forecasting (WRF) model.

Toward Characterizing the 4D Structure of Precipitation at the Headwater Catchment Scale in Mountainous Regions

NASA Astrophysics Data System (ADS)

Barros, A. P.; Prat, O. P.; Sun, X.; Shrestha, P.; Miller, D.

2009-04-01

The classic conceptual model of orographic rainfall depicts strong stationary horizontal gradients in rainfall accumulations and landcover contrasts across topographic divides (i.e. the rainshadow) at the broad scale of mountain ranges, or isolated orographic features. Whereas this model is sufficient to fingerprint the land-modulation of precipitation at the macroscale in climate studies, and can be useful to force geological models of land evolution for example, it fails to describe the active 4D space-time gradients that are critical at the fundamental scale of mountain hydrometeorology and hydrology, that is the headwater catchment. That is, the scale at which flash-floods are generated and landslides are triggered. Our work surveying the spatial and temporal habits of clouds and rainfall for some of the world's major mountain ranges from remotely-sensed data shows a close alignment of spatial scaling behavior with landform down to the mountain fold scale, that is the ridge-valley. Likewise, we find that diurnal and seasonal cycles are organized and constrained by topography from the macro- to the meso- to the alpha-scale of individual basins varying with synoptic weather conditions. At the catchment scale, the diurnal cycle exhibits an oscillatory behavior with storm features moving up and down from the ridge crests to the valley floor and back and forth from head to mouth along the valley with strong variations in rainfall intensity and duration. Direct observations to provide quantitative estimates of precipitation at this scale are beyond the capability of satellite-based observations present and anticipated in the next 10-20 years. This limitation can be addressed by assimilating the space-time modes of variability of rainfall into satellite-observations at coarser scale using multiscale blending algorithms. The challenge is to characterize the modes of space-time variability of precipitation in a systematic, and quantitative fashion that can be generalized. It requires understanding the physical controls that govern the diurnal cycle and how these physical controls translate into spatial and temporal variability of dynamics and microphysics of precipitation in headwater catchments, and especially in the context of extreme events for natural hazards assessments. Toward this goal, we have initiated a sequence of number of intense observing period (IOP) campaigns in the Great Smoky Mountains National Park using radiosondes, tethersondes, microrain radars, and a high resolution raingauge network that for the first time monitors rainfall systematically along ridges in the Appalachians. Along with field observations, a high-resolution coupled model has been implemented to diagnose the evolution of the 4D structure of regional circulations and associated precipitation for IOP conditions and for reconstructing historical extremes associated with the interaction of tropical cyclones with the mountains. A synthesis of data analysis and model simulations will be presented.

Variability of East Asian summer monsoon precipitation during the Holocene and possible forcing mechanisms

NASA Astrophysics Data System (ADS)

Lu, Fuzhi; Ma, Chunmei; Zhu, Cheng; Lu, Huayu; Zhang, Xiaojian; Huang, Kangyou; Guo, Tianhong; Li, Kaifeng; Li, Lan; Li, Bing; Zhang, Wenqing

2018-03-01

Projecting how the East Asian summer monsoon (EASM) rainfall will change with global warming is essential for human sustainability. Reconstructing Holocene climate can provide critical insight into its forcing and future variability. However, quantitative reconstructions of Holocene summer precipitation are lacking for tropical and subtropical China, which is the core region of the EASM influence. Here we present high-resolution annual and summer rainfall reconstructions covering the whole Holocene based on the pollen record at Xinjie site from the lower Yangtze region. Summer rainfall was less seasonal and 30% higher than modern values at 10-6 cal kyr BP and gradually declined thereafter, which broadly followed the Northern Hemisphere summer insolation. Over the last two millennia, however, the summer rainfall has deviated from the downward trend of summer insolation. We argue that greenhouse gas forcing might have offset summer insolation forcing and contributed to the late Holocene rainfall anomaly, which is supported by the TraCE-21 ka transient simulation. Besides, tropical sea-surface temperatures could modulate summer rainfall by affecting evaporation of seawater. The rainfall pattern concurs with stalagmite and other proxy records from southern China but differs from mid-Holocene rainfall maximum recorded in arid/semiarid northern China. Summer rainfall in northern China was strongly suppressed by high-northern-latitude ice volume forcing during the early Holocene in spite of high summer insolation. In addition, the El Niño/Southern Oscillation might be responsible for droughts of northern China and floods of southern China during the late Holocene. Furthermore, quantitative rainfall reconstructions indicate that the Paleoclimate Modeling Intercomparison Project (PMIP) simulations underestimate the magnitude of Holocene precipitation changes. Our results highlight the spatial and temporal variability of the Holocene EASM precipitation and potential forcing mechanisms, which are very helpful for calibration of paleoclimate models and prediction of future precipitation changes in East Asia in the scenario of global warming.

Study of the Formation and Evolution of Precipitation Induced Sea Surface Salinity Minima in the Tropical Pacific Using HYCOM

NASA Astrophysics Data System (ADS)

Gallagher, R. L.

2016-02-01

During heavy rain events in the tropics, areas of relatively low salinity water collect on the ocean surface. Rainfall events increase the buoyancy of the ocean surface and impact upper ocean salinity and temperature profiles. This resists downward mixing and as a result can persist (SPURS II planning group, 2012; Oceanography 28(1) 150-159). Salinity at the surface adjusts through advective and diffusive mixing processes (Scott, J. et al, 2013; AGU Fall meeting abstracts). This project investigates the upper ocean salinity response in both advection and diffusion dominated regions. The changes in ocean surface salinity are tracked before, during, and after rainfall events. Data from a standard oceanographic model, HYCOM, are used to identify areas where each surface process is significant. Rainfall events are identified using a TRMM dataset. It provides a tropical rainfall analysis which uses amalgamated satellite data to develop detailed global precipitation grids between 50 o north and south latitude. TRMM is useful due its high temporal and spatial resolutions. The salinity response in HYCOM is tested against simple theoretical advective and diffusive mixing models. The magnitude of sea surface salinity minima, their persistence and the precision by which HYCOM can resolve these phenomena are of interest.

Revisiting the latent heat nudging scheme for the rainfall assimilation of a simulated convective storm

NASA Astrophysics Data System (ADS)

Leuenberger, D.; Rossa, A.

2007-12-01

Next-generation, operational, high-resolution numerical weather prediction models require economical assimilation schemes for radar data. In the present study we evaluate and characterise the latent heat nudging (LHN) rainfall assimilation scheme within a meso-γ scale NWP model in the framework of identical twin simulations of an idealised supercell storm. Consideration is given to the model’s dynamical response to the forcing as well as to the sensitivity of the LHN scheme to uncertainty in the observations and the environment. The results indicate that the LHN scheme is well able to capture the dynamical structure and the right rainfall amount of the storm in a perfect environment. This holds true even in degraded environments but a number of important issues arise. In particular, changes in the low-level humidity field are found to affect mainly the precipitation amplitude during the assimilation with a fast adaptation of the storm to the system dynamics determined by the environment during the free forecast. A constant bias in the environmental wind field, on the other hand, has the potential to render a successful assimilation with the LHN scheme difficult, as the velocity of the forcing is not consistent with the system propagation speed determined by the wind. If the rainfall forcing moves too fast, the system propagation is supported and the assimilated storm and forecasts initialised therefrom develop properly. A too slow forcing, on the other hand, can decelerate the system and eventually disturb the system dynamics by decoupling the low-level moisture inflow from the main updrafts during the assimilation. This distortion is sustained in the free forecast. It has further been found that a sufficient temporal resolution of the rainfall input is crucial for the successful assimilation of a fast moving, coherent convective storm and that the LHN scheme, when applied to a convective storm, appears to necessitate a careful tuning.

WRF model performance under flash-flood associated rainfall

NASA Astrophysics Data System (ADS)

Mejia-Estrada, Iskra; Bates, Paul; Ángel Rico-Ramírez, Miguel

2017-04-01

Understanding the natural processes that precede the occurrence of flash floods is crucial to improve the future flood projections in a changing climate. Using numerical weather prediction tools allows to determine one of the triggering conditions for these particularly dangerous events, difficult to forecast due to their short lead-time. However, simulating the spatial and temporal evolution of the rainfall that leads to a rapid rise in river levels requires determining the best model configuration without compromising the computational efficiency. The current research involves the results of the first part of a cascade modeling approach, where the Weather Research and Forecasting (WRF) model is used to simulate the heavy rainfall in the east of the UK in June 2012 when stationary thunderstorms caused 2-hour accumulated values to match those expected in the whole month of June over the city of Newcastle. The optimum model set-up was obtained after extensive testing regarding physics parameterizations, spin-up times, datasets used as initial conditions and model resolution and nesting, hence determining its sensitivity to reproduce localised events of short duration. The outputs were qualitatively and quantitatively assessed using information from the national weather radar network as well as interpolated rainfall values from gauges, respectively. Statistical and skill score values show that the model is able to produce reliable accumulated precipitation values while explicitly solving the atmospheric equations in high resolution domains as long as several hydrometeors are considered with a spin-up time that allows the model to assimilate the initial conditions without going too far back in time from the event of interest. The results from the WRF model will serve as input to run a semi-distributed hydrological model to determine the rainfall-runoff relationship within an uncertainty assessment framework that will allow evaluating the implications of assumptions at the top of the modeling process in the final outputs of the cascade.

Validation of satellite-based rainfall in Kalahari

NASA Astrophysics Data System (ADS)

Lekula, Moiteela; Lubczynski, Maciek W.; Shemang, Elisha M.; Verhoef, Wouter

2018-06-01

Water resources management in arid and semi-arid areas is hampered by insufficient rainfall data, typically obtained from sparsely distributed rain gauges. Satellite-based rainfall estimates (SREs) are alternative sources of such data in these areas. In this study, daily rainfall estimates from FEWS-RFE∼11 km, TRMM-3B42∼27 km, CMOPRH∼27 km and CMORPH∼8 km were evaluated against nine, daily rain gauge records in Central Kalahari Basin (CKB), over a five-year period, 01/01/2001-31/12/2005. The aims were to evaluate the daily rainfall detection capabilities of the four SRE algorithms, analyze the spatio-temporal variability of rainfall in the CKB and perform bias-correction of the four SREs. Evaluation methods included scatter plot analysis, descriptive statistics, categorical statistics and bias decomposition. The spatio-temporal variability of rainfall, was assessed using the SREs' mean annual rainfall, standard deviation, coefficient of variation and spatial correlation functions. Bias correction of the four SREs was conducted using a Time-Varying Space-Fixed bias-correction scheme. The results underlined the importance of validating daily SREs, as they had different rainfall detection capabilities in the CKB. The FEWS-RFE∼11 km performed best, providing better results of descriptive and categorical statistics than the other three SREs, although bias decomposition showed that all SREs underestimated rainfall. The analysis showed that the most reliable SREs performance analysis indicator were the frequency of "miss" rainfall events and the "miss-bias", as they directly indicated SREs' sensitivity and bias of rainfall detection, respectively. The Time Varying and Space Fixed (TVSF) bias-correction scheme, improved some error measures but resulted in the reduction of the spatial correlation distance, thus increased, already high, spatial rainfall variability of all the four SREs. This study highlighted SREs as valuable source of daily rainfall data providing good spatio-temporal data coverage especially suitable for areas with limited rain gauges, such as the CKB, but also emphasized SREs' drawbacks, creating avenue for follow up research.

Optimization of rainfall networks using information entropy and temporal variability analysis

NASA Astrophysics Data System (ADS)

Wang, Wenqi; Wang, Dong; Singh, Vijay P.; Wang, Yuankun; Wu, Jichun; Wang, Lachun; Zou, Xinqing; Liu, Jiufu; Zou, Ying; He, Ruimin

2018-04-01

Rainfall networks are the most direct sources of precipitation data and their optimization and evaluation are essential and important. Information entropy can not only represent the uncertainty of rainfall distribution but can also reflect the correlation and information transmission between rainfall stations. Using entropy this study performs optimization of rainfall networks that are of similar size located in two big cities in China, Shanghai (in Yangtze River basin) and Xi'an (in Yellow River basin), with respect to temporal variability analysis. Through an easy-to-implement greedy ranking algorithm based on the criterion called, Maximum Information Minimum Redundancy (MIMR), stations of the networks in the two areas (each area is further divided into two subareas) are ranked during sliding inter-annual series and under different meteorological conditions. It is found that observation series with different starting days affect the ranking, alluding to the temporal variability during network evaluation. We propose a dynamic network evaluation framework for considering temporal variability, which ranks stations under different starting days with a fixed time window (1-year, 2-year, and 5-year). Therefore, we can identify rainfall stations which are temporarily of importance or redundancy and provide some useful suggestions for decision makers. The proposed framework can serve as a supplement for the primary MIMR optimization approach. In addition, during different periods (wet season or dry season) the optimal network from MIMR exhibits differences in entropy values and the optimal network from wet season tended to produce higher entropy values. Differences in spatial distribution of the optimal networks suggest that optimizing the rainfall network for changing meteorological conditions may be more recommended.

Critical scales to explain urban hydrological response: an application in Cranbrook, London

NASA Astrophysics Data System (ADS)

Cristiano, Elena; ten Veldhuis, Marie-Claire; Gaitan, Santiago; Ochoa Rodriguez, Susana; van de Giesen, Nick

2018-04-01

Rainfall variability in space and time, in relation to catchment characteristics and model complexity, plays an important role in explaining the sensitivity of hydrological response in urban areas. In this work we present a new approach to classify rainfall variability in space and time and we use this classification to investigate rainfall aggregation effects on urban hydrological response. Nine rainfall events, measured with a dual polarimetric X-Band radar instrument at the CAESAR site (Cabauw Experimental Site for Atmospheric Research, NL), were aggregated in time and space in order to obtain different resolution combinations. The aim of this work was to investigate the influence that rainfall and catchment scales have on hydrological response in urban areas. Three dimensionless scaling factors were introduced to investigate the interactions between rainfall and catchment scale and rainfall input resolution in relation to the performance of the model. Results showed that (1) rainfall classification based on cluster identification well represents the storm core, (2) aggregation effects are stronger for rainfall than flow, (3) model complexity does not have a strong influence compared to catchment and rainfall scales for this case study, and (4) scaling factors allow the adequate rainfall resolution to be selected to obtain a given level of accuracy in the calculation of hydrological response.

An assessment of temporal effect on extreme rainfall estimates

NASA Astrophysics Data System (ADS)

Das, Samiran; Zhu, Dehua; Chi-Han, Cheng

2018-06-01

This study assesses the temporal behaviour in terms of inter-decadal variability of extreme daily rainfall of stated return period relevant for hydrologic risk analysis using a novel regional parametric approach. The assessment is carried out based on annual maximum daily rainfall series of 180 meteorological stations of Yangtze River Basin over a 50-year period (1961-2010). The outcomes of the analysis reveal that while there were effects present indicating higher quantile values when estimated from data of the 1990s, it is found not to be noteworthy to exclude the data of any decade from the extreme rainfall estimation process for hydrologic risk analysis.

Rainfall height stochastic modelling as a support tool for landslides early warning

NASA Astrophysics Data System (ADS)

Capparelli, G.; Giorgio, M.; Greco, R.; Versace, P.

2009-04-01

Occurrence of landslides is uneasy to predict, since it is affected by a number of variables, such as mechanical and hydraulic soil properties, slope morphology, vegetation coverage, rainfall spatial and temporal variability. Although heavy landslides frequently occurred in Campania, southern Italy, during the last decade, no complete data sets are available for natural slopes where landslides occurred. As a consequence, landslide risk assessment procedures and early warning systems in Campania still rely on simple empirical models based on correlation between daily rainfall records and observed landslides, like FLAIR model [Versace et al., 2003]. Effectiveness of such systems could be improved by reliable quantitative rainfall prediction. In mountainous areas, rainfall spatial and temporal variability are very pronounced due to orographic effects, making predictions even more complicated. Existing rain gauge networks are not dense enough to resolve the small scale spatial variability, and the same limitation of spatial resolution affects rainfall height maps provided by radar sensors as well as by meteorological physically based models. Therefore, analysis of on-site recorded rainfall height time series still represents the most effective approach for a reliable prediction of local temporal evolution of rainfall. Hydrological time series analysis is a widely studied field in hydrology, often carried out by means of autoregressive models, such as AR and ARMA [Box and Jenkins, 1976]. Sometimes exogenous information coming from additional series of observations is also taken into account, and the models are called ARX and ARMAX (e.g. Salas [1992]). Such models gave the best results when applied to the analysis of autocorrelated hydrological time series, like river flow or level time series. Conversely, they are not able to model the behaviour of intermittent time series, like point rainfall height series usually are, especially when recorded with short sampling time intervals. More useful for this issue are the so-called DRIP (Disaggregated Rectangular Intensity Pulse) and NSRP (Neymann-Scott Rectangular Pulse) model [Heneker et al., 2001; Cowpertwait et al., 2002], usually adopted to generate synthetic point rainfall series. In this paper, the DRIP model approach is adopted in conjunction with FLAIR model to calculate the probability of flowslides occurrence. The final aim of the study is in fact to provide a useful tool to implement an early warning system for hydrogeological risk management. Model calibration has been carried out with hourly rainfall hieght data provided by the rain gauges of Campania Region civil protection agency meteorological warning network. So far, the model has been applied only to data series recorded at a single rain gauge. Future extension will deal with spatial correlation between time series recorded at different gauges. ACKNOWLEDGEMENTS The research was co-financed by the Italian Ministry of University, by means of the PRIN 2006 PRIN program, within the research project entitled ‘Definition of critical rainfall thresholds for destructive landslides for civil protection purposes'. REFERENCES Box, G.E.P. and Jenkins, G.M., 1976. Time Series Analysis Forecasting and Control, Holden-Day, San Francisco. Cowpertwait, P.S.P., Kilsby, C.G. and O'Connell, P.E., 2002. A space-time Neyman-Scott model of rainfall: Empirical analysis of extremes, Water Resources Research, 38(8):1-14. Salas, J.D., 1992. Analysis and modeling of hydrological time series, in D.R. Maidment, ed., Handbook of Hydrology, McGraw-Hill, New York. Heneker, T.M., Lambert, M.F. and Kuczera G., 2001. A point rainfall model for risk-based design, Journal of Hydrology, 247(1-2):54-71. Versace, P., Sirangelo. B. and Capparelli, G., 2003. Forewarning model of landslides triggered by rainfall. Proc. 3rd International Conference on Debris-Flow Hazards Mitigation: Mechanics, Prediction and Assessment, Davos.

Systematic errors in the simulation of the Asian summer monsoon: the role of rainfall variability on a range of time and space scales

NASA Astrophysics Data System (ADS)

Martin, Gill; Levine, Richard; Klingaman, Nicholas; Bush, Stephanie; Turner, Andrew; Woolnough, Steven

2015-04-01

Despite considerable efforts worldwide to improve model simulations of the Asian summer monsoon, significant biases still remain in climatological seasonal mean rainfall distribution, timing of the onset, and northward and eastward extent of the monsoon domain (Sperber et al., 2013). Many modelling studies have shown sensitivity to convection and boundary layer parameterization, cloud microphysics and land surface properties, as well as model resolution. Here we examine the problems in representing short-timescale rainfall variability (related to convection parameterization), problems in representing synoptic-scale systems such as monsoon depressions (related to model resolution), and the relationship of each of these with longer-term systematic biases. Analysis of the spatial distribution of rainfall intensity on a range of timescales ranging from ~30 minutes to daily, in the MetUM and in observations (where available), highlights how rainfall biases in the South Asian monsoon region on different timescales in different regions can be achieved in models through a combination of the incorrect frequency and/or intensity of rainfall. Over the Indian land area, the typical dry bias is related to sub-daily rainfall events being too infrequent, despite being too intense when they occur. In contrast, the wet bias regions over the equatorial Indian Ocean are mainly related to too frequent occurrence of lower-than-observed 3-hourly rainfall accumulations which result in too frequent occurrence of higher-than-observed daily rainfall accumulations. This analysis sheds light on the model deficiencies behind the climatological seasonal mean rainfall biases that many models exhibit in this region. Changing physical parameterizations alters this behaviour, with associated adjustments in the climatological rainfall distribution, although the latter is not always improved (Bush et al., 2014). This suggests a more complex interaction between the diabatic heating and the large-scale circulation than is indicated by the intensity and frequency of rainfall alone. Monsoon depressions and low pressure systems are important contributors to monsoon rainfall over central and northern India, areas where MetUM climate simulations typically show deficient monsoon rainfall. Analysis of MetUM climate simulations at resolutions ranging from N96 (~135km) to N512 (~25km) suggests that at lower resolution the numbers and intensities of monsoon depressions and low pressure systems and their associated rainfall are very low compared with re-analyses/observations. We show that there are substantial increases with horizontal resolution, but resolution is not the only factor. Idealised simulations, either using nudged atmospheric winds or initialised coupled hindcasts, which improve (strengthen) the mean state monsoon and cyclonic circulation over the Indian peninsula, also result in a substantial increase in monsoon depressions and associated rainfall. This suggests that a more realistic representation of monsoon depressions is possible even at lower resolution if the larger-scale systematic error pattern in the monsoon is improved.

Spatio-temporal modelling of rainfall in the Murray-Darling Basin

NASA Astrophysics Data System (ADS)

Nowak, Gen; Welsh, A. H.; O'Neill, T. J.; Feng, Lingbing

2018-02-01

The Murray-Darling Basin (MDB) is a large geographical region in southeastern Australia that contains many rivers and creeks, including Australia's three longest rivers, the Murray, the Murrumbidgee and the Darling. Understanding rainfall patterns in the MDB is very important due to the significant impact major events such as droughts and floods have on agricultural and resource productivity. We propose a model for modelling a set of monthly rainfall data obtained from stations in the MDB and for producing predictions in both the spatial and temporal dimensions. The model is a hierarchical spatio-temporal model fitted to geographical data that utilises both deterministic and data-derived components. Specifically, rainfall data at a given location are modelled as a linear combination of these deterministic and data-derived components. A key advantage of the model is that it is fitted in a step-by-step fashion, enabling appropriate empirical choices to be made at each step.

Cluster analysis applied to the spatial and temporal variability of monthly rainfall in Mato Grosso do Sul State, Brazil

NASA Astrophysics Data System (ADS)

Teodoro, Paulo Eduardo; de Oliveira-Júnior, José Francisco; da Cunha, Elias Rodrigues; Correa, Caio Cezar Guedes; Torres, Francisco Eduardo; Bacani, Vitor Matheus; Gois, Givanildo; Ribeiro, Larissa Pereira

2016-04-01

The State of Mato Grosso do Sul (MS) located in Brazil Midwest is devoid of climatological studies, mainly in the characterization of rainfall regime and producers' meteorological systems and rain inhibitors. This state has different soil and climatic characteristics distributed among three biomes: Cerrado, Atlantic Forest and Pantanal. This study aimed to apply the cluster analysis using Ward's algorithm and identify those meteorological systems that affect the rainfall regime in the biomes. The rainfall data of 32 stations (sites) of the MS State were obtained from the Agência Nacional de Águas (ANA) database, collected from 1954 to 2013. In each of the 384 monthly rainfall temporal series was calculated the average and applied the Ward's algorithm to identify spatial and temporal variability of rainfall. Bartlett's test revealed only in January homogeneous variance at all sites. Run test showed that there was no increase or decrease in trend of monthly rainfall. Cluster analysis identified five rainfall homogeneous regions in the MS State, followed by three seasons (rainy, transitional and dry). The rainy season occurs during the months of November, December, January, February and March. The transitional season ranges between the months of April and May, September and October. The dry season occurs in June, July and August. The groups G1, G4 and G5 are influenced by South Atlantic Subtropical Anticyclone (SASA), Chaco's Low (CL), Bolivia's High (BH), Low Levels Jet (LLJ) and South Atlantic Convergence Zone (SACZ) and Maden-Julian Oscillation (MJO). Group G2 is influenced by Upper Tropospheric Cyclonic Vortex (UTCV) and Front Systems (FS). The group G3 is affected by UTCV, FS and SACZ. The meteorological systems' interaction that operates in each biome and the altitude causes the rainfall spatial and temporal diversity in MS State.

Detecting Climate Variability in Tropical Rainfall

NASA Astrophysics Data System (ADS)

Berg, W.

2004-05-01

A number of satellite and merged satellite/in-situ rainfall products have been developed extending as far back as 1979. While the availability of global rainfall data covering over two decades and encompassing two major El Niño events is a valuable resource for a variety of climate studies, significant differences exist between many of these products. Unfortunately, issues such as availability often determine the use of a product for a given application instead of an understanding of the strengths and weaknesses of the various products. Significant efforts have been made to address the impact of sparse sampling by satellite sensors of variable rainfall processes by merging various satellite and in-situ rainfall products. These combine high spatial and temporal frequency satellite infrared data with higher quality passive microwave observations and rain gauge observations. Combining such an approach with spatial and temporal averaging of the data can reduce the large random errors inherent in satellite rainfall estimates to very small levels. Unfortunately, systematic biases can and do result in artificial climate signals due to the underconstrained nature of the rainfall retrieval problem. Because all satellite retrieval algorithms make assumptions regarding the cloud structure and microphysical properties, systematic changes in these assumed parameters between regions and/or times results in regional and/or temporal biases in the rainfall estimates. These biases tend to be relatively small compared to random errors in the retrieval, however, when random errors are reduced through spatial and temporal averaging for climate applications, they become the dominant source of error. Whether or not such biases impact the results for climate studies is very much dependent on the application. For example, all of the existing satellite rainfall products capture the increased rainfall in the east Pacific associated with El Niño, however, the resulting tropical response to El Niño is substantially smaller due to decreased rainfall in the west Pacific partially canceling increases in the central and east Pacific. These differences are not limited to the long-term merged rainfall products using infrared data, but are also exist in state-of-the-art rainfall retrievals from the active and passive microwave sensors on board the Tropical Rainfall Measuring Mission (TRMM). For example, large differences exist in the response of tropical mean rainfall retrieved from the TRMM microwave imager (TMI) 2A12 algorithm and the precipitation radar (PR) 2A25 algorithm to the 1997/98 El Niño. To assist scientists attempting to wade through the vast array of climate rainfall products currently available, and to help them determine whether systematic biases in these rainfall products impact the conclusions of a given study, we have developed a Climate Rainfall Data Center (CRDC). The CRDC web site (rain.atmos.colostate.edu/CRDC) provides climate researchers information on the various rainfall datasets available as well as access to experts in the field of satellite rainfall retrievals to assist them in the appropriate selection and use of climate rainfall products.

Evaluation of LIS-based Soil Moisture and Evapotranspiration in the Korean Peninsula

NASA Astrophysics Data System (ADS)

Jung, H. C.; Kang, D. H.; Kim, E. J.; Yoon, Y.; Kumar, S.; Peters-Lidard, C. D.; Baeck, S. H.; Hwang, E.; Chae, H.

2017-12-01

K-water is the South Korean national water agency. It is the government-funded private agency for water resource development that provides both civil and industrial water in S. Korea. K-water is interested in exploring how earth remote sensing and modeling can help their tasks. In this context, the NASA Land Information System (LIS) is implemented to simulate land surface processes in the Korean Peninsula. The Noah land surface model with Multi-Parameterization, version 3.6 (Noah-MP) is used to reproduce the water budget variables on a 1 km spatial resolution grid with a daily temporal resolution. The Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) datasets is used to force the system. The rainfall data are spatially downscaled from high resolution WorldClim precipitation climatology. The other meteorological inputs (i.e. air temperature, humidity, pressure, winds, radiation) are also downscaled by statistical methods (i.e. lapse-rate, slope-aspect). Additional model experiments are conducted with local rainfall datasets and soil maps to replace the downscaled MERRA-2 precipitation field and the hybrid STATSGO/FAO soil texture, respectively. For the evaluation of model performance, daily soil moisture and evapotranspiration measurements at several stations are compared to the LIS-based outputs. This study demonstrates that application of NASA's LIS can enhance drought and flood prediction capabilities in South Asia and Korea.

Exploring the relationship between malaria, rainfall intermittency, and spatial variation in rainfall seasonality

NASA Astrophysics Data System (ADS)

Merkord, C. L.; Wimberly, M. C.; Henebry, G. M.; Senay, G. B.

2014-12-01

Malaria is a major public health problem throughout tropical regions of the world. Successful prevention and treatment of malaria requires an understanding of the environmental factors that affect the life cycle of both the malaria pathogens, protozoan parasites, and its vectors, anopheline mosquitos. Because the egg, larval, and pupal stages of mosquito development occur in aquatic habitats, information about the spatial and temporal distribution of rainfall is critical for modeling malaria risk. Potential sources of hydrological data include satellite-derived rainfall estimates (TRMM and GPM), evapotranspiration derived from a simplified surface energy balance, and estimates of soil moisture and fractional water cover from passive microwave imagery. Previous studies have found links between malaria cases and total monthly or weekly rainfall in areas where both are highly seasonal. However it is far from clear that monthly or weekly summaries are the best metrics to use to explain malaria outbreaks. It is possible that particular temporal or spatial patterns of rainfall result in better mosquito habitat and thus higher malaria risk. We used malaria case data from the Amhara region of Ethiopia and satellite-derived rainfall estimates to explore the relationship between malaria outbreaks and rainfall with the goal of identifying the most useful rainfall metrics for modeling malaria occurrence. First, we explored spatial variation in the seasonal patterns of both rainfall and malaria cases in Amhara. Second, we assessed the relative importance of different metrics of rainfall intermittency, including alternation of wet and dry spells, the strength of intensity fluctuations, and spatial variability in these measures, in determining the length and severity of malaria outbreaks. We also explored the sensitivity of our results to the choice of method for describing rainfall intermittency and the spatial and temporal scale at which metrics were calculated. Results demonstrate that information about the seasonality and intermittency of rainfall has the potential to improve our understanding of malaria epidemiology and improve our ability to forecast malaria outbreaks.

Moving towards a new paradigm for global flood risk estimation

NASA Astrophysics Data System (ADS)

Troy, Tara J.; Devineni, Naresh; Lima, Carlos; Lall, Upmanu

2013-04-01

Traditional approaches to flood risk assessment are typically indexed to an instantaneous peak flow event at a specific recording gage on a river, and then extrapolated through hydraulic modeling of that peak flow to the potential area that is likely to be inundated. Recent research shows that property losses tend to be determined as much by the duration of flooding as by the depth and velocity of inundation. The existing notion of a flood return period based on just the instantaneous peak flow rate at a stream gauge consequently needs to be revisited, especially for floods due to persistent rainfall as seen recently in Thailand, Pakistan, the Ohio and the Mississippi Rivers, France, and Germany. Depending on the flood event type considered, different rainfall inducing mechanisms (tropical storm, local convection, frontal system, recurrent tropical waves) may be involved. Each of these will have a characteristic spatial scale, expression and orientation and temporal characteristics. We develop stochastic models that can reproduce these attributes with appropriate intensity-duration-frequency and spatial expression, and hence provide a basis for conditioning basin hydrologic attributes for flood risk assessment. Past work on Non-homogeneous Hidden Markov Models (NHMM) is used as a basis to develop this capability at regional scales. In addition, a dynamic hierarchical Bayesian network model that is continuous and not based on discretization to states is tested and compared against NHMM. The exogenous variables in these models comes from the analysis of key synoptic circulation patterns which will be used as predictors for the regional spatio-temporal models. The stochastic simulations of rainfall are then used as input to a flood modeling system, which consists of a series of physically based models. Rainfall-runoff generation is produced by the Variable Infiltration Capacity (VIC) model. When the modeled streamflow crosses a threshold, a full kinematic wave routing model is implemented at a finer resolution (<=1km) in order to more accurately model streamflow under flood conditions and estimate inundation. This approach allows for efficient computational simulation of the hydrology when not under potential for flooding with high-resolution flood wave modeling when there is flooding potential. We demonstrate the results of this flood risk estimation system for the Ohio River basin in the United States, a large river basin that is historically prone to flooding, with the intention of using it to do global flood risk assessment.

Urban RoGeR: Merging process-based high-resolution flash flood model for urban areas with long-term water balance predictions

NASA Astrophysics Data System (ADS)

Weiler, M.

2016-12-01

Heavy rain induced flash floods are still a serious hazard and generate high damages in urban areas. In particular in the spatially complex urban areas, the temporal and spatial pattern of runoff generation processes at a wide spatial range during extreme rainfall events need to be predicted including the specific effects of green infrastructure and urban forests. In addition, the initial conditions (soil moisture pattern, water storage of green infrastructure) and the effect of lateral redistribution of water (run-on effects and re-infiltration) have to be included in order realistically predict flash flood generation. We further developed the distributed, process-based model RoGeR (Runoff Generation Research) to include the relevant features and processes in urban areas in order to test the effects of different settings, initial conditions and the lateral redistribution of water on the predicted flood response. The uncalibrated model RoGeR runs at a spatial resolution of 1*1m² (LiDAR, degree of sealing, landuse), soil properties and geology (1:50.000). In addition, different green infrastructures are included into the model as well as the effect of trees on interception and transpiration. A hydraulic model was included into RoGeR to predict surface runoff, water redistribution, and re-infiltration. During rainfall events, RoGeR predicts at 5 min temporal resolution, but the model also simulates evapotranspiration and groundwater recharge during rain-free periods at a longer time step. The model framework was applied to several case studies in Germany where intense rainfall events produced flash floods causing high damage in urban areas and to a long-term research catchment in an urban setting (Vauban, Freiburg), where a variety of green infrastructures dominates the hydrology. Urban-RoGeR allowed us to study the effects of different green infrastructures on reducing the flood peak, but also its effect on the water balance (evapotranspiration and groundwater recharge). We could also show that infiltration of surface runoff from areas with a low infiltration (lateral redistribution) reduce the flood peaks by over 90% in certain areas and situations. Finally, we also evaluated the model to long-term runoff observations (surface runoff, ET, roof runoff) and to flood marks in the selected case studies.

Development of Spatiotemporal Bias-Correction Techniques for Downscaling GCM Predictions

NASA Astrophysics Data System (ADS)

Hwang, S.; Graham, W. D.; Geurink, J.; Adams, A.; Martinez, C. J.

2010-12-01

Accurately representing the spatial variability of precipitation is an important factor for predicting watershed response to climatic forcing, particularly in small, low-relief watersheds affected by convective storm systems. Although Global Circulation Models (GCMs) generally preserve spatial relationships between large-scale and local-scale mean precipitation trends, most GCM downscaling techniques focus on preserving only observed temporal variability on point by point basis, not spatial patterns of events. Downscaled GCM results (e.g., CMIP3 ensembles) have been widely used to predict hydrologic implications of climate variability and climate change in large snow-dominated river basins in the western United States (Diffenbaugh et al., 2008; Adam et al., 2009). However fewer applications to smaller rain-driven river basins in the southeastern US (where preserving spatial variability of rainfall patterns may be more important) have been reported. In this study a new method was developed to bias-correct GCMs to preserve both the long term temporal mean and variance of the precipitation data, and the spatial structure of daily precipitation fields. Forty-year retrospective simulations (1960-1999) from 16 GCMs were collected (IPCC, 2007; WCRP CMIP3 multi-model database: https://esg.llnl.gov:8443/), and the daily precipitation data at coarse resolution (i.e., 280km) were interpolated to 12km spatial resolution and bias corrected using gridded observations over the state of Florida (Maurer et al., 2002; Wood et al, 2002; Wood et al, 2004). In this method spatial random fields which preserved the observed spatial correlation structure of the historic gridded observations and the spatial mean corresponding to the coarse scale GCM daily rainfall were generated. The spatiotemporal variability of the spatio-temporally bias-corrected GCMs were evaluated against gridded observations, and compared to the original temporally bias-corrected and downscaled CMIP3 data for the central Florida. The hydrologic response of two southwest Florida watersheds to the gridded observation data, the original bias corrected CMIP3 data, and the new spatiotemporally corrected CMIP3 predictions was compared using an integrated surface-subsurface hydrologic model developed by Tampa Bay Water.

Effect of Spatio-Temporal Variability of Rainfall on Stream flow Prediction of Birr Watershed

NASA Astrophysics Data System (ADS)

Demisse, N. S.; Bitew, M. M.; Gebremichael, M.

2012-12-01

The effect of rainfall variability on our ability to forecast flooding events was poorly studied in complex terrain region of Ethiopia. In order to establish relation between rainfall variability and stream flow, we deployed 24 rain gauges across Birr watershed. Birr watershed is a medium size mountainous watershed with an area of 3000 km2 and elevation ranging between 1435 m.a.s.l and 3400 m.a.s.l in the central Ethiopia highlands. One summer monsoon rainfall of 2012 recorded at high temporal scale of 15 minutes interval and stream flow recorded at an hourly interval in three sub-watershed locations representing different scales were used in this study. Based on the data obtained from the rain gauges and stream flow observations, we quantify extent of temporal and spatial variability of rainfall across the watershed using standard statistical measures including mean, standard deviation and coefficient of variation. We also establish rainfall-runoff modeling system using a physically distributed hydrological model: the Soil and Water Assessment Tool (SWAT) and examine the effect of rainfall variability on stream flow prediction. The accuracy of predicted stream flow is measured through direct comparison with observed flooding events. The results demonstrate the significance of relation between stream flow prediction and rainfall variability in the understanding of runoff generation mechanisms at watershed scale, determination of dominant water balance components, and effect of variability on accuracy of flood forecasting activities.

Soil erosion predictions from a landscape evolution model - An assessment of a post-mining landform using spatial climate change analogues.

PubMed

Hancock, G R; Verdon-Kidd, D; Lowry, J B C

2017-12-01

Landscape Evolution Modelling (LEM) technologies provide a means by which it is possible to simulate the long-term geomorphic stability of a conceptual rehabilitated landform. However, simulations rarely consider the potential effects of anthropogenic climate change and consequently risk not accounting for the range of rainfall variability that might be expected in both the near and far future. One issue is that high resolution (both spatial and temporal) rainfall projections incorporating the potential effects of greenhouse forcing are required as input. However, projections of rainfall change are still highly uncertain for many regions, particularly at sub annual/seasonal scales. This is the case for northern Australia, where a decrease or an increase in rainfall post 2030 is considered equally likely based on climate model simulations. The aim of this study is therefore to investigate a spatial analogue approach to develop point scale hourly rainfall scenarios to be used as input to the CAESAR - Lisflood LEM to test the sensitivity of the geomorphic stability of a conceptual rehabilitated landform to potential changes in climate. Importantly, the scenarios incorporate the range of projected potential increase/decrease in rainfall for northern Australia and capture the expected envelope of erosion rates and erosion patterns (i.e. where erosion and deposition occurs) over a 100year modelled period. We show that all rainfall scenarios produce sediment output and gullying greater than that of the surrounding natural system, however a 'wetter' future climate produces the highest output. Importantly, incorporating analogue rainfall scenarios into LEM has the capacity to both improve landform design and enhance the modelling software. Further, the method can be easily transferred to other sites (both nationally and internationally) where rainfall variability is significant and climate change impacts are uncertain. Crown Copyright © 2017. Published by Elsevier B.V. All rights reserved.

Combining InSAR and GPS to Determine Transient Movement and Thickness of a Seasonally Active Low-Gradient Translational Landslide

NASA Astrophysics Data System (ADS)

Hu, Xie; Lu, Zhong; Pierson, Thomas C.; Kramer, Rebecca; George, David L.

2018-02-01

The combined application of continuous Global Positioning System data (high temporal resolution) with spaceborne interferometric synthetic aperture radar data (high spatial resolution) can reveal much more about the complexity of large landslide movement than is possible with geodetic measurements tied to only a few specific measurement sites. This approach is applied to an 4 km2 reactivated translational landslide in the Columbia River Gorge (Washington State), which moves mainly during the winter rainy season. Results reveal the complex three-dimensional shape of the landslide mass, how onset of sliding relates to cumulative rainfall, how surface velocity during sliding varies with location on the topographically complex landslide surface, and how the ground surface subsides slightly in weeks prior to downslope sliding.

Combining InSAR and GPS to determine transient movement and thickness of a seasonally active low-gradient translational landslide

USGS Publications Warehouse

Hu, Xie; Lu, Zhong; Pierson, Thomas C.; Kramer, Rebecca; George, David L.

2018-01-01

The combined application of continuous Global Positioning System data (high temporal resolution) with spaceborne interferometric synthetic aperture radar data (high spatial resolution) can reveal much more about the complexity of large landslide movement than is possible with geodetic measurements tied to only a few specific measurement sites. This approach is applied to an ~4 km2 reactivated translational landslide in the Columbia River Gorge (Washington State), which moves mainly during the winter rainy season. Results reveal the complex three-dimensional shape of the landslide mass, how onset of sliding relates to cumulative rainfall, how surface velocity during sliding varies with location on the topographically complex landslide surface, and how the ground surface subsides slightly in weeks prior to downslope sliding.

Hydrological and hydrochemical impact studies in the urbanised Petrusse river basin (Luxembourg)

NASA Astrophysics Data System (ADS)

Pfister, L.; Iffly, J.; Guignard, C.; Krein, A.; Matgen, P.; Salvia-Castellvi, M.; van den Bos, R.; Tailliez, C.; Barnich, F.; Hofmmann, L.

2009-04-01

On the basis of ancient topographical maps, the growing urbanisation of the Petrusse river basin (42.9 km2) has been documented on 50-year time steps since 1770. While until the 1950's urban areas remained below 10% of total basin area, they are now close to 50%. This rapid change has consisted mainly in a change from cropland into built areas. As a direct consequence of these considerable changes in landuse, the basin presumably has undergone significant modifications of both its hydrological regime and the quality of the flowing surface waters. In the framework of a national monitoring programme, the Petrusse basin has been progressively equipped with 3 recording streamgauges between 1999 and 2003. Several meteorological stations are located in the immediate vicinity of the basin. The hydrological regime revealed by the 15-minute recordings of the streamgauges is very specific to heavily urbanised basins, i.e. characterised by quick reactions to incoming rainfall, as well as very limited contributions from sub-surface and groundwater reservoirs. A conceptual hydrological model has been used to evaluate roughly the impact of the progressive urbanisation of the Petrusse basin since 1770 on the rainfall-runoff relationship. Major changes were found for summer months, with significantly higher peak discharges and increasingly rapid reactions to rainfall events. However, the limitations of the spatial density of rainfall recordings (only 1 rainfall measurement site available between 1854 - 1949) cause severe shortcomings in the accuracy of the incoming rainfall estimations, especially in the case of convective rainfall events. This in turn also considerably reduces the accuracy of the historical rainfall-runoff simulations. Between 2002 and 2004, several monitoring campaigns have been carried out in the Petrusse basin in order to determine the impact of sewer system contributions from the urbanised areas to the water quality within the Petrusse. The investigations have shown a very strong so-called first-flush effect. During dry sequences, numerous deposits on roads and roofs (heavy metals, oils, etc.) accumulate, before being washed away during the first minutes of rainfall events and being ultimately being transported to the Petrusse river via the sewer systems, causing considerable pollution peaks. Current investigations target a reduction of this pollution. The involved volumes of polluted water are of such extent, that they cannot be dealt with by conventional waste water treatment systems. The currently existing rainfall measurement network around the city of Luxembourg has a spatial resolution that is still too low to capture accurately convective rainfall events. A new rainfall measurement approach will soon be tested to estimate spatio-temporal rainfall dynamics with a high resolution above the city of Luxembourg. Based on a combination of conventional raingauges, weather radar and microwave measurements (via cell-phone networks) this approach is supposed to provide data that might ultimately contribute to a real-time management of the first flush pollutions in the Petrusse river basin.

Hydroclimate temporal variability in a coastal Mediterranean watershed: the Tafna basin, North-West Algeria

NASA Astrophysics Data System (ADS)

Boulariah, Ouafik; Longobardi, Antonia; Meddi, Mohamed

2017-04-01

One of the major challenges scientists, practitioners and stakeholders are nowadays involved in, is to provide the worldwide population with reliable water supplies, protecting, at the same time, the freshwater ecosystems quality and quantity. Climate and land use changes undermine the balance between water demand and water availability, causing alteration of rivers flow regime. Knowledge of hydro-climate variables temporal and spatial variability is clearly helpful to plan drought and flood hazard mitigation strategies but also to adapt them to future environmental scenarios. The present study relates to the coastal semi-arid Tafna catchment, located in the North-West of Algeria, within the Mediterranean basin. The aim is the investigation of streamflow and rainfall indices temporal variability in six sub-basins of the large catchment Tafna, attempting to relate streamflow and rainfall changes. Rainfall and streamflow time series have been preliminary tested for data quality and homogeneity, through the coupled application of two-tailed t test, Pettitt test and Cumsum tests (significance level of 0.1, 0.05 and 0.01). Subsequently maximum annual daily rainfall and streamflow and average daily annual rainfall and streamflow time series have been derived and tested for temporal variability, through the application of the Mann Kendall and Sen's test. Overall maximum annual daily streamflow time series exhibit a negative trend which is however significant for only 30% of the station. Maximum annual daily rainfall also e exhibit a negative trend which is intend significant for the 80% of the stations. In the case of average daily annual streamflow and rainfall, the tendency for decrease in time is unclear and, in both cases, appear significant for 60% of stations.

A statistical model of extreme storm rainfall

NASA Astrophysics Data System (ADS)

Smith, James A.; Karr, Alan F.

1990-02-01

A model of storm rainfall is developed for the central Appalachian region of the United States. The model represents the temporal occurrence of major storms and, for a given storm, the spatial distribution of storm rainfall. Spatial inhomogeneities of storm rainfall and temporal inhomogeneities of the storm occurrence process are explicitly represented. The model is used for estimating recurrence intervals of extreme storms. The parameter estimation procedure developed for the model is based on the substitution principle (method of moments) and requires data from a network of rain gages. The model is applied to a 5000 mi2 (12,950 km2) region in the Valley and Ridge Province of Virginia and West Virginia.

Impact of temporal resolution of inputs on hydrological model performance: An analysis based on 2400 flood events

NASA Astrophysics Data System (ADS)

Ficchì, Andrea; Perrin, Charles; Andréassian, Vazken

2016-07-01

Hydro-climatic data at short time steps are considered essential to model the rainfall-runoff relationship, especially for short-duration hydrological events, typically flash floods. Also, using fine time step information may be beneficial when using or analysing model outputs at larger aggregated time scales. However, the actual gain in prediction efficiency using short time-step data is not well understood or quantified. In this paper, we investigate the extent to which the performance of hydrological modelling is improved by short time-step data, using a large set of 240 French catchments, for which 2400 flood events were selected. Six-minute rain gauge data were available and the GR4 rainfall-runoff model was run with precipitation inputs at eight different time steps ranging from 6 min to 1 day. Then model outputs were aggregated at seven different reference time scales ranging from sub-hourly to daily for a comparative evaluation of simulations at different target time steps. Three classes of model performance behaviour were found for the 240 test catchments: (i) significant improvement of performance with shorter time steps; (ii) performance insensitivity to the modelling time step; (iii) performance degradation as the time step becomes shorter. The differences between these groups were analysed based on a number of catchment and event characteristics. A statistical test highlighted the most influential explanatory variables for model performance evolution at different time steps, including flow auto-correlation, flood and storm duration, flood hydrograph peakedness, rainfall-runoff lag time and precipitation temporal variability.

Utilizing Mobile-Phone-Link Data to Improve Rainfall Monitoring over Cyprus

NASA Astrophysics Data System (ADS)

Lange, Manfred; Alpert, Pinhas; David, Noam

2013-04-01

There is a need for spatially denser and temporally more detailed observations of precipitation in most countries, including Cyprus. Traditionally, rainfall is measured with rain gauges that are either read manually once a day or automatically at higher temporal frequencies. However, these are point measurements with poor spatial representativeness due to the small sampling area. In Cyprus rain gauges are typically installed close to populated communities and built-up areas, leaving substantial parts of the country only sparsely covered. On the other hand, wireless communication networks are widely spread across the terrain, at heights of only a few tens of meters off the surface. The microwave links, used in these networks, are deployed by cellular providers for backhaul communication between base stations. Since these links operate at frequencies of tens of GHz, atmospheric conditions and particularly rainfall affect the electromagnetic channel causing attenuations to the microwave signals between the radio masts. Cellular networks infrastructures can therefore be regarded as relatively high-precision atmospheric observation system offering fairly dense spatial coverage and high temporal resolution. The principal feasibility of rainfall delineation by microwave attenuation between commercial radio links has been shown by Messer et al. (2006). In the meantime, the group of P. Alpert and H. Messer at Tel Aviv University, and additional groups around the world (e.g. Leijnse et al 2010; Chwala et al., 2012; Wang et al., 2012 ) have further developed the methodology. The objectives of the current project, which is still being implemented, can be summarized as follows: i. to carry out a feasibility study on the utilization of mobile-phone-link-data for rainfall observations in Cyprus; ii. to evaluate, adapt and implement the analysis methodology/software developed and used at TAU at the Energy, Environment and Water Research Center of The Cyprus Institute; iii. to carry out a pilot study to evaluate the performance of the methodology in an area of good meteorological-observation coverage in Cyprus and based on dedicated field observations. The rich experience and the successful application of the methodology for using mobile-phone-link-data in Israel offers good prospects for a successful implementation of this methodology in Cyprus. This presentation will provide insights into the background and rationale of the project and will discuss its current status and the next steps to be taken. References Chwala, C., Gmeiner, A., Qiu, W., Hipp, S., Nienaber, D., Siart, U., Eibert, T., Pohl, M., Seltmann, J., Fritz, J., and Kunstmann, H., 2012. Precipitation observation using microwave backhaul links in the alpine and pre-alpine region of Southern Germany, Hydrol. Earth Syst. Sci., 16, 2647-2661, doi: 10.5194/hess-16-2647-2012. Leijnse, H., Uijlenhoet, R., and Berne, A., 2010. Errors and uncertainties in microwave link rainfall estimation explored using drop size measurements and high-resolution radar data. J. Hydrometeor., 11, 1330-1344. Messer, H., A. Zinevich, and P. Alpert, 2006: Environmental Monitoring by Wireless Communication Networks. Science, 312, 713 Wang, Z., Schleiss, M., Jaffrain, J., Berne, A., Rieckermann, J., 2012. Using Markov switching models to infer dry and rainy periods from telecommunication microwave link signals, Atmos. Meas. Tech., 5, 1847-1859, doi:10.5194/amt-5-1847-2012.

Synthetic generation of spatially high resolution extreme rainfall in Japan using Monte Carlo simulation with AMeDAS analyzed rainfall data sets

NASA Astrophysics Data System (ADS)

Haruki, W.; Iseri, Y.; Takegawa, S.; Sasaki, O.; Yoshikawa, S.; Kanae, S.

2016-12-01

Natural disasters caused by heavy rainfall occur every year in Japan. Effective countermeasures against such events are important. In 2015, a catastrophic flood occurred in Kinu river basin, which locates in the northern part of Kanto region. The remarkable feature of this flood event was not only in the intensity of rainfall but also in the spatial characteristics of heavy rainfall area. The flood was caused by continuous overlapping of heavy rainfall area over the Kinu river basin, suggesting consideration of spatial extent is quite important to assess impacts of heavy rainfall events. However, the spatial extent of heavy rainfall events cannot be properly measured through rainfall measurement by rain gauges at observation points. On the other hand, rainfall measurements by radar observations provide spatially and temporarily high resolution rainfall data which would be useful to catch the characteristics of heavy rainfall events. For long term effective countermeasure, extreme heavy rainfall scenario considering rainfall area and distribution is required. In this study, a new method for generating extreme heavy rainfall events using Monte Carlo Simulation has been developed in order to produce extreme heavy rainfall scenario. This study used AMeDAS analyzed precipitation data which is high resolution grid precipitation data made by Japan Meteorological Agency. Depth area duration (DAD) analysis has been conducted to extract extreme rainfall events in the past, considering time and spatial scale. In the Monte Carlo Simulation, extreme rainfall event is generated based on events extracted by DAD analysis. Extreme heavy rainfall events are generated in specific region in Japan and the types of generated extreme heavy rainfall events can be changed by varying the parameter. For application of this method, we focused on Kanto region in Japan. As a result, 3000 years rainfall data are generated. 100 -year probable rainfall and return period of flood in Kinu River Basin (2015) are obtained using generated data. We compared 100-year probable rainfall calculated by this method with other traditional method. New developed method enables us to generate extreme rainfall events considering time and spatial scale and produce extreme rainfall scenario.

Reply to the comment on "Rainfall erosivity in Europe" by Auerswald et al.

PubMed

Panagos, Panos; Meusburger, Katrin; Ballabio, Cristiano; Borrelli, Pasquale; Beguería, Santiago; Klik, Andreas; Rymszewicz, Anna; Michaelides, Silas; Olsen, Preben; Tadić, Melita Perčec; Aalto, Juha; Lakatos, Mónika; Dumitrescu, Alexandru; Rousseva, Svetla; Montanarella, Luca; Alewell, Christine

2015-11-01

Recently, in the Auerswald et al. (2015) comment on "Rainfall erosivity in Europe", 5 criticisms were addressed: i) the neglect of seasonal erosion indices, ii) the neglect of published studies and data, iii) the low temporal resolution of the data, especially of the maximum rain intensity, iv) the use of precipitation data instead of rain data and the subsequent deviation of the R-factor in Germany and Austria compared with previous studies, and v) the differences in considered time periods between countries. We reply as follows: (i) An evaluation of the seasonal erosion index at the European scale is, to our knowledge, not achievable at present with the available data but would be a future goal. Synchronous publication of the seasonal erosion index is not mandatory, specifically because seasonal soil loss ratios are not available at this scale to date. We are looking forward to the appropriate study by the authors of the comment, who assert that they have access to the required data. (ii) We discuss and evaluate relevant studies in our original work and in this reply; however, we cannot consider what is not available to the scientific community. (iii) The third point of critique was based on a misunderstanding by Auerswald et al. (2015), as we did indeed calculate the maximum intensity with the highest resolution of data available. (iv) The low R-factor values in Germany and the higher values in Austria compared with previous studies are not due to the involvement of snow but are rather due to a Pan-European interpolation. We argue that an interpolation across the borders of Austria creates a more reliable data set. (v) We agree that the use of a short time series or time series from different periods is generally a problem in all large-scale studies and requires improvement in the future. However, because this affects countries with a rather low variability of the R-factor in our study, we are confident that the overall results of the map are not biased. In conclusion, the Pan-European rainfall data compilation (REDES) was a great success and yielded data from 1541 stations with an average length of 17.1years and a temporal resolution of <60min. However, a Pan-European data collection will never be complete without the help and supply of data from its users. Thus, we invite the authors of the comment to share their data in the open REDES to help build even better rainfall-erosivity maps at regional or European scales. Copyright © 2015 Elsevier B.V. All rights reserved.

Spatial Precipitation Frequency of an Extreme Event: the July 2006 Mesoscale Convective Complexes and Debris Flows in Southeastern Arizona

NASA Astrophysics Data System (ADS)

Griffiths, P. G.; Webb, W. H.; Magirl, C. S.; Pytlak, E.

2008-12-01

An extreme, multi-day rainfall event over southeastern Arizona during 27-31 July 2006 culminated in an historically unprecedented spate of 435 slope failures and associated debris flows in the Santa Catalina Mountains north of Tucson. Previous to this occurrence, only twenty small debris flows had been observed in this region over the past 100 years. Although intense orographic precipitation is routinely delivered by single- cell thunderstorms to the Santa Catalinas during the North American monsoon, in this case repeated nocturnal mesoscale convective systems were induced over southeastern Arizona by an upper-level low- pressure system centered over the Four Corners region for five continuous days, generating five-day rainfall totals up to 360 mm. Calibrating weather radar data with point rainfall data collected at 31 rain gages, mean-area storms totals for the southern Santa Catalina Mountains were calculated for 754 radar grid cells at a resolution of approximately 1 km2 to provide a detailed picture of the spatial and temporal distribution of rainfall during the event. Precipitation intensity for the 31 July storms was typical for monsoonal precipitation in this region, with peak 15-minute rainfall averaging 17 mm/hr for a recurrence interval (RI) < 1 yr. However, RI > 50 yrs for four-day rainfall totals overall, RI > 100 yrs where slope failures occurred, and RI > 1000 yrs for individual grid cells in the heart of the slope failure zone. A comparison of rainfall at locations where debris-flows did and did not occur suggests an intensity (I)-duration (D) threshold for debris flow occurrence for the Santa Catalina Mountains of I = 14.82D-0.39(I in mm/hr). This threshold falls slightly higher than the 1000-year rainfall predicted for this area. The relatively large exponent reflects the high frequency of short-duration, high-intensity rainfall and the relative rarity of the long-duration rainfall that triggered these debris flows. Analysis of the rainfall/runoff ratio in the drainage basin at the heart of the debris flows confirms that sediments were nearly saturated before debris flows were initiated on July 31.

The Amazon forest-rainfall feedback: the roles of transpiration and interception

NASA Astrophysics Data System (ADS)

Dekker, Stefan; Staal, Arie; Tuinenburg, Obbe

2017-04-01

In the Amazon, deep-rooted trees increase local transpiration and high tree cover increase local interception evaporation. These increased local evapotranspiration fluxes to the atmosphere have both positive effects on forests down-wind, as they stimulate rainfall. Although important for the functioning of the Amazon, we have an inadequate assessment on the strength and the timing of these forest-rainfall feedbacks. In this study we (i) estimate local forest transpiration and local interception evaporation, (ii) simulate the trajectories of these moisture flows through the atmosphere and (iii) quantify their contributions to the forest-rainfall feedback for the whole Amazon basin. To determine the atmospheric moisture flows in tropical South America we use a Lagrangian moisture tracking algorithm on 0.25° (c. 25 km) resolution with eight atmospheric layers on a monthly basis for the period 2003-2015. With our approach we account for multiple re-evaporation cycles of this moisture. We also calculate for each month the potential effects of forest loss on evapotranspiration. Combined, these calculations allow us to simulate the effects of land-cover changes on rainfall in downwind areas and estimate the effect on the forest. We found large regional and temporal differences in the importance how forest contribute to rainfall. The transpiration-rainfall feedback is highly important during the dry season. Between September-November, when large parts of the Amazon are at the end of the dry season, more than 50% of the rainfall is caused by the forests upstream. This means that droughts in the Amazon are alleviated by the forest. Furthermore, we found that much moisture cycles several times during its trajectory over the Amazon. After one evapotranspiration-rainfall cycle, more than 40% of the moisture is re-evaporated again. The interception-evaporation feedback is less important during droughts. Finally from our analysis, we show that the forest-rainfall feedback is essential for the resilience of the south-western and northern parts of the Amazon forest. Without the forest-rainfall feedbacks, these forest wouldn't exist.

Mountain Heavy Rainfall Measurement Experiments in a Subtropical Monsoon Environment

NASA Astrophysics Data System (ADS)

Jong-Dao Jou, Ben; Chi-June Jung, Ultimate; Lai, Hsiao-Wei; Feng, Lei

2014-05-01

Quantitative rainfall measurement experiments have been conducted in Taiwan area for the past 5 years (since 2008), especially over the complex terrain region. In this paper, results from these experiments will be analyzed and discussed, especially those associated with heavy rain events in the summer monsoon season. Observations from s-band polarimetric radar (SPOL of NCAR) and also x-band vertically-pointing radar are analyzed to reveal the high resolution temporal and spatial variation of precipitation structure. May and June, the Meiyu season in the area, are months with subtropical frontal rainfall events. Mesoscale convective systems, i.e., pre-frontal squall lines and frontal convective rainbands, are very active and frequently produce heavy rain events over mountain areas. Accurate quantitative precipitation measurements are needed in order to meet the requirement for landslide and flood early warning purpose. Using ground-based disdrometers and vertically-pointing radar, we have been trying to modify the quantitative precipitation estimation in the mountain region by using coastal operational radar. In this paper, the methodology applied will be presented and the potential of its application will be discussed. *corresponding author: Ben Jong-Dao Jou, jouben43@gmail.com

Interannual rainfall variability over China in the MetUM GA6 and GC2 configurations

NASA Astrophysics Data System (ADS)

Stephan, Claudia Christine; Klingaman, Nicholas P.; Vidale, Pier Luigi; Turner, Andrew G.; Demory, Marie-Estelle; Guo, Liang

2018-05-01

Six climate simulations of the Met Office Unified Model Global Atmosphere 6.0 and Global Coupled 2.0 configurations are evaluated against observations and reanalysis data for their ability to simulate the mean state and year-to-year variability of precipitation over China. To analyse the sensitivity to air-sea coupling and horizontal resolution, atmosphere-only and coupled integrations at atmospheric horizontal resolutions of N96, N216 and N512 (corresponding to ˜ 200, 90 and 40 km in the zonal direction at the equator, respectively) are analysed. The mean and interannual variance of seasonal precipitation are too high in all simulations over China but improve with finer resolution and coupling. Empirical orthogonal teleconnection (EOT) analysis is applied to simulated and observed precipitation to identify spatial patterns of temporally coherent interannual variability in seasonal precipitation. To connect these patterns to large-scale atmospheric and coupled air-sea processes, atmospheric and oceanic fields are regressed onto the corresponding seasonal mean time series. All simulations reproduce the observed leading pattern of interannual rainfall variability in winter, spring and autumn; the leading pattern in summer is present in all but one simulation. However, only in two simulations are the four leading patterns associated with the observed physical mechanisms. Coupled simulations capture more observed patterns of variability and associate more of them with the correct physical mechanism, compared to atmosphere-only simulations at the same resolution. However, finer resolution does not improve the fidelity of these patterns or their associated mechanisms. This shows that evaluating climate models by only geographical distribution of mean precipitation and its interannual variance is insufficient. The EOT analysis adds knowledge about coherent variability and associated mechanisms.

Soil Erosion map of Europe based on high resolution input datasets

NASA Astrophysics Data System (ADS)

Panagos, Panos; Borrelli, Pasquale; Meusburger, Katrin; Ballabio, Cristiano; Alewell, Christine

2015-04-01

Modelling soil erosion in European Union is of major importance for agro-environmental policies. Soil erosion estimates are important inputs for the Common Agricultural Policy (CAP) and the implementation of the Soil Thematic Strategy. Using the findings of a recent pan-European data collection through the EIONET network, it was concluded that most Member States are applying the empirical Revised Universal Soil Loss Equation (RUSLE) for the modelling soil erosion at National level. This model was chosen for the pan-European soil erosion risk assessment and it is based on 6 input factors. Compared to past approaches, each of the factors is modelled using the latest pan-European datasets, expertise and data from Member states and high resolution remote sensing data. The soil erodibility (K-factor) is modelled using the recently published LUCAS topsoil database with 20,000 point measurements and incorporating the surface stone cover which can reduce K-factor by 15%. The rainfall erosivity dataset (R-factor) has been implemented using high temporal resolution rainfall data from more than 1,500 precipitation stations well distributed in Europe. The cover-management (C-factor) incorporates crop statistics and management practices such as cover crops, tillage practices and plant residuals. The slope length and steepness (combined LS-factor) is based on the first ever 25m Digital Elevation Model (DEM) of Europe. Finally, the support practices (P-factor) is modelled for first time at this scale taking into account the 270,000 LUCAS earth observations and the Good Agricultural and Environmental Condition (GAEC) that farmers have to follow in Europe. The high resolution input layers produce the final soil erosion risk map at 100m resolution and allow policy makers to run future land use, management and climate change scenarios.

High-Resolution Mesoscale Simulations of the 6-7 May 2000 Missouri Flash Flood: Impact of Model Initialization and Land Surface Treatment

NASA Technical Reports Server (NTRS)

Baker, R. David; Wang, Yansen; Tao, Wei-Kuo; Wetzel, Peter; Belcher, Larry R.

2004-01-01

High-resolution mesoscale model simulations of the 6-7 May 2000 Missouri flash flood event were performed to test the impact of model initialization and land surface treatment on timing, intensity, and location of extreme precipitation. In this flash flood event, a mesoscale convective system (MCS) produced over 340 mm of rain in roughly 9 hours in some locations. Two different types of model initialization were employed: 1) NCEP global reanalysis with 2.5-degree grid spacing and 12-hour temporal resolution, and 2) Eta reanalysis with 40- km grid spacing and $hour temporal resolution. In addition, two different land surface treatments were considered. A simple land scheme. (SLAB) keeps soil moisture fixed at initial values throughout the simulation, while a more sophisticated land model (PLACE) allows for r interactive feedback. Simulations with high-resolution Eta model initialization show considerable improvement in the intensity of precipitation due to the presence in the initialization of a residual mesoscale convective vortex (hlCV) from a previous MCS. Simulations with the PLACE land model show improved location of heavy precipitation. Since soil moisture can vary over time in the PLACE model, surface energy fluxes exhibit strong spatial gradients. These surface energy flux gradients help produce a strong low-level jet (LLJ) in the correct location. The LLJ then interacts with the cold outflow boundary of the MCS to produce new convective cells. The simulation with both high-resolution model initialization and time-varying soil moisture test reproduces the intensity and location of observed rainfall.

Efficient Probabilistic Forecasting for High-Resolution Models through Clustered-State Data Assimilation

NASA Astrophysics Data System (ADS)

Hamidi, A.; Grossberg, M.; Khanbilvardi, R.

2016-12-01

Flood response in an urban area is the product of interactions of spatially and temporally varying rainfall and infrastructures. In urban areas, however, the complex sub-surface networks of tunnels, waste and storm water drainage systems are often inaccessible, pose challenges for modeling and prediction of the drainage infrastructure performance. The increased availability of open data in cities is an emerging information asset for a better understanding of the dynamics of urban water drainage infrastructure. This includes crowd sourced data and community reporting. A well-known source of this type of data is the non-emergency hotline "311" which is available in many US cities, and may contain information pertaining to the performance of physical facilities, condition of the environment, or residents' experience, comfort and well-being. In this study, seven years of New York City 311 (NYC311) call during 2010-2016 is employed, as an alternative approach for identifying the areas of the city most prone to sewer back up flooding. These zones are compared with the hydrologic analysis of runoff flooding zones to provide a predictive model for the City. The proposed methodology is an example of urban system phenomenology using crowd sourced, open data. A novel algorithm for calculating the spatial distribution of flooding complaints across NYC's five boroughs is presented in this study. In this approach, the features that represent reporting bias are separated from those that relate to actual infrastructure system performance. The sewer backup results are assessed with the spatial distribution of runoff in NYC during 2010-2016. With advances in radar technologies, a high spatial-temporal resolution data set for precipitation is available for most of the United States that can be implemented in hydrologic analysis of dense urban environments. High resolution gridded Stage IV radar rainfall data along with the high resolution spatially distributed land cover data are employed to investigate the urban pluvial flooding. The monthly results of excess runoff are compared with the sewer backup in NYC to build a predictive model of flood zones according to the 311 phone calls.

Improving precipitation estimates over the western United States using GOES-R precipitation data

NASA Astrophysics Data System (ADS)

Karbalaee, N.; Kirstetter, P. E.; Gourley, J. J.

2017-12-01

Satellite remote sensing data with fine spatial and temporal resolution are widely used for precipitation estimation for different applications such as hydrological modeling, storm prediction, and flash flood monitoring. The Geostationary Operational Environmental Satellites-R series (GOES-R) is the next generation of environmental satellites that provides hydrologic, atmospheric, and climatic information every 30 seconds over the western hemisphere. The high-resolution and low-latency of GOES-R observations is essential for the monitoring and prediction of floods, specifically in the Western United States where the vantage point of space can complement the degraded weather radar coverage of the NEXRAD network. The GOES-R rainfall rate algorithm will yield deterministic quantitative precipitation estimates (QPE). Accounting for inherent uncertainties will further advance the GOES-R QPEs since with quantifiable error bars, the rainfall estimates can be more readily fused with ground radar products. On the ground, the high-resolution NEXRAD-based precipitation estimation from the Multi-Radar/Multi-Sensor (MRMS) system, which is now operational in the National Weather Service (NWS), is challenged due to a lack of suitable coverage of operational weather radars over complex terrain. Distribution of QPE uncertainties associated with the GOES-R deterministic retrievals are derived and analyzed using MRMS over regions with good radar coverage. They will be merged with MRMS-based probabilistic QPEs developed to advance multisensor QPE integration. This research aims at improving precipitation estimation over the CONUS by combining the observations from GOES-R and MRMS to provide consistent, accurate and fine resolution precipitation rates with uncertainties over the CONUS.

Physically based modeling of rainfall-triggered landslides: a case study in the Luquillo forest, Puerto Rico

NASA Astrophysics Data System (ADS)

Lepore, C.; Arnone, E.; Noto, L. V.; Sivandran, G.; Bras, R. L.

2013-09-01

This paper presents the development of a rainfall-triggered landslide module within an existing physically based spatially distributed ecohydrologic model. The model, tRIBS-VEGGIE (Triangulated Irregular Networks-based Real-time Integrated Basin Simulator and Vegetation Generator for Interactive Evolution), is capable of a sophisticated description of many hydrological processes; in particular, the soil moisture dynamics are resolved at a temporal and spatial resolution required to examine the triggering mechanisms of rainfall-induced landslides. The validity of the tRIBS-VEGGIE model to a tropical environment is shown with an evaluation of its performance against direct observations made within the study area of Luquillo Forest. The newly developed landslide module builds upon the previous version of the tRIBS landslide component. This new module utilizes a numerical solution to the Richards' equation (present in tRIBS-VEGGIE but not in tRIBS), which better represents the time evolution of soil moisture transport through the soil column. Moreover, the new landslide module utilizes an extended formulation of the factor of safety (FS) to correctly quantify the role of matric suction in slope stability and to account for unsaturated conditions in the evaluation of FS. The new modeling framework couples the capabilities of the detailed hydrologic model to describe soil moisture dynamics with the infinite slope model, creating a powerful tool for the assessment of rainfall-triggered landslide risk.

Coupled surface and subsurface flow modeling of natural hillslopes in the Aburrá Valley (Medellín, Colombia)

NASA Astrophysics Data System (ADS)

Blessent, Daniela; Barco, Janet; Temgoua, André Guy Tranquille; Echeverrri-Ramirez, Oscar

2017-03-01

Numerical results are presented of surface-subsurface water modeling of a natural hillslope located in the Aburrá Valley, in the city of Medellín (Antioquia, Colombia). The integrated finite-element hydrogeological simulator HydroGeoSphere is used to conduct transient variably saturated simulations. The objective is to analyze pore-water pressure and saturation variation at shallow depths, as well as volumes of water infiltrated in the porous medium. These aspects are important in the region of study, which is highly affected by soil movements, especially during the high-rain seasons that occur twice a year. The modeling exercise considers rainfall events that occurred between October and December 2014 and a hillslope that is currently monitored because of soil instability problems. Simulation results show that rainfall temporal variability, mesh resolution, coupling length, and the conceptual model chosen to represent the heterogeneous soil, have a noticeable influence on results, particularly for high rainfall intensities. Results also indicate that surface-subsurface coupled modeling is required to avoid unrealistic increase in hydraulic heads when high rainfall intensities cause top-down saturation of soil. This work is a first effort towards fostering hydrogeological modeling expertise that may support the development of monitoring systems and early landslide warning in a country where the rainy season is often the cause of hydrogeological tragedies associated with landslides, mud flow or debris flow.

Cloud-based NEXRAD Data Processing and Analysis for Hydrologic Applications

NASA Astrophysics Data System (ADS)

Seo, B. C.; Demir, I.; Keem, M.; Goska, R.; Weber, J.; Krajewski, W. F.

2016-12-01

The real-time and full historical archive of NEXRAD Level II data, covering the entire United States from 1991 to present, recently became available on Amazon cloud S3. This provides a new opportunity to rebuild the Hydro-NEXRAD software system that enabled users to access vast amounts of NEXRAD radar data in support of a wide range of research. The system processes basic radar data (Level II) and delivers radar-rainfall products based on the user's custom selection of features such as space and time domain, river basin, rainfall product space and time resolution, and rainfall estimation algorithms. The cloud-based new system can eliminate prior challenges faced by Hydro-NEXRAD data acquisition and processing: (1) temporal and spatial limitation arising from the limited data storage; (2) archive (past) data ingestion and format conversion; and (3) separate data processing flow for the past and real-time Level II data. To enhance massive data processing and computational efficiency, the new system is implemented and tested for the Iowa domain. This pilot study begins by ingesting rainfall metadata and implementing Hydro-NEXRAD capabilities on the cloud using the new polarimetric features, as well as the existing algorithm modules and scripts. The authors address the reliability and feasibility of cloud computation and processing, followed by an assessment of response times from an interactive web-based system.

Evaluation of radar rainfall estimates and nowcasts to prevent flash flood in real time by using a road submersion warning tool

NASA Astrophysics Data System (ADS)

Versini, Pierre-Antoine; Sempere-Torres, Daniel

2010-05-01

Important damages occur in small headwater catchments when they are hit by severe storms with complex spatio-temporal structure, sometimes resulting in flash floods. As these catchments are mostly not covered by sensor networks, it is difficult to forecast these floods. This is particularly true for road submersions. These are major concerns for flood event managers. The use of Quantitative Precipitation Estimates and Forecasts (QPE/QPF) especially based on radar measurements could particularly be adequate to evaluate rainfall-induced risks. Although their characteristic time and space scales would make them suitable for flash flood modelling, the impact of their uncertainties remain uncertain and have to be evaluated. The Gard region (France) has been chosen as case study. This area is frequently affected by severe flash floods and different kinds of rainfall observations are available in real time: radar rainfall estimates and nowcasts from METEO FRANCE and the CALAMAR system from SPC (state authority in charge of flood forecasting). An application devoted to the road network, has also been recently developed for this region. It combines distributed hydro-meteorological very short range forecasts and vulnerability analysis to provide warnings of road submersions. The first results demonstrate that it is technically possible to provide distributed short-term forecasts for a large number of sites. The study also demonstrates that a reliable estimation of the spatial distribution of rainfall is essential. For this reason, the road submersion warning system can be used to evaluate the quality of rainfall estimates and nowcasts. The warning system has been tested on the specific storm of the 29-30 September 2007. During this event, more than 300mm dropped on the South part of the Gard and many roads were submerged. Each of the mentioned rainfall datasets (i.e. estimates and nowcasts) was available in real time. They have been used to forecast the exact location of road submersions and the results have been compared to the effective road submersions actually occurred during the event as listed by the emergency services. The results confirm that the road submersion warning system represents a promising tool for anticipating and quantifying the consequences of storm events at ground. It rates the submersion risk with an acceptable level of accuracy and a reasonable false alarm ratio. It demonstrates also the quality of high spatial and temporal resolution radar rainfall data in real time, and the possibility to use them despite their uncertainties. However because of the quality of rainfall nowcasts falls drastically with time, it is not often sufficient to provide valuable information for lead times exceeding one hour.

SM2RAIN-CCI: a new global long-term rainfall data set derived from ESA CCI soil moisture

NASA Astrophysics Data System (ADS)

Ciabatta, Luca; Massari, Christian; Brocca, Luca; Gruber, Alexander; Reimer, Christoph; Hahn, Sebastian; Paulik, Christoph; Dorigo, Wouter; Kidd, Richard; Wagner, Wolfgang

2018-02-01

Accurate and long-term rainfall estimates are the main inputs for several applications, from crop modeling to climate analysis. In this study, we present a new rainfall data set (SM2RAIN-CCI) obtained from the inversion of the satellite soil moisture (SM) observations derived from the ESA Climate Change Initiative (CCI) via SM2RAIN (Brocca et al., 2014). Daily rainfall estimates are generated for an 18-year long period (1998-2015), with a spatial sampling of 0.25° on a global scale, and are based on the integration of the ACTIVE and the PASSIVE ESA CCI SM data sets.The quality of the SM2RAIN-CCI rainfall data set is evaluated by comparing it with two state-of-the-art rainfall satellite products, i.e. the Tropical Measurement Mission Multi-satellite Precipitation Analysis 3B42 real-time product (TMPA 3B42RT) and the Climate Prediction Center Morphing Technique (CMORPH), and one modeled data set (ERA-Interim). A quality check is carried out on a global scale at 1° of spatial sampling and 5 days of temporal sampling by comparing these products with the gauge-based Global Precipitation Climatology Centre Full Data Daily (GPCC-FDD) product. SM2RAIN-CCI shows relatively good results in terms of correlation coefficient (median value > 0.56), root mean square difference (RMSD, median value < 10.34 mm over 5 days) and bias (median value < -14.44 %) during the evaluation period. The validation has been carried out at original resolution (0.25°) over Europe, Australia and five other areas worldwide to test the capabilities of the data set to correctly identify rainfall events under different climate and precipitation regimes.The SM2RAIN-CCI rainfall data set is freely available at https://doi.org/10.5281/zenodo.846259.

Simulating Future Changes in Spatio-temporal Precipitation by Identifying and Characterizing Individual Rainstorm Events

NASA Astrophysics Data System (ADS)

Chang, W.; Stein, M.; Wang, J.; Kotamarthi, V. R.; Moyer, E. J.

2015-12-01

A growing body of literature suggests that human-induced climate change may cause significant changes in precipitation patterns, which could in turn influence future flood levels and frequencies and water supply and management practices. Although climate models produce full three-dimensional simulations of precipitation, analyses of model precipitation have focused either on time-averaged distributions or on individual timeseries with no spatial information. We describe here a new approach based on identifying and characterizing individual rainstorms in either data or model output. Our approach enables us to readily characterize important spatio-temporal aspects of rainstorms including initiation location, intensity (mean and patterns), spatial extent, duration, and trajectory. We apply this technique to high-resolution precipitation over the continental U.S. both from radar-based observations (NCEP Stage IV QPE product, 1-hourly, 4 km spatial resolution) and from model runs with dynamical downscaling (WRF regional climate model, 3-hourly, 12 km spatial resolution). In the model studies we investigate the changes in storm characteristics under a business-as-usual warming scenario to 2100 (RCP 8.5). We find that in these model runs, rainstorm intensity increases as expected with rising temperatures (approximately 7%/K, following increased atmospheric moisture content), while total precipitation increases by a lesser amount (3%/K), consistent with other studies. We identify for the first time the necessary compensating mechanism: in these model runs, individual precipitation events become smaller. Other aspects are approximately unchanged in the warmer climate. Because these spatio-temporal changes in rainfall patterns would impact regional hydrology, it is important that they be accurately incorporated into any impacts assessment. For this purpose we have developed a methodology for producing scenarios of future precipitation that combine observational data and model-projected changes. We statistically describe the future changes in rainstorm characteristics suggested by the WRF model and apply those changes to observational data. The resulting high spatial and temporal resolution scenarios have immediate applications for impacts assessment and adaptation studies.

Ability of WRF to Simulate Rainfall Distribution Over West Africa: Role of Horizontal Resolution and Dynamical Processes

NASA Astrophysics Data System (ADS)

Kouadio, K.; Konare, A.; Bastin, S.; Ajayi, V. O.

2016-12-01

This research work focused on the thorny problem of the representation of rainfall over West Africa and particularly in the Gulf of Guinea and its surroundings by Regional Climate Models (RCMs). The sensitivities of Weather Research and Forecasting (WRF) Model are tested for changes in horizontal resolution (convective permitting versus parameterized) on the replication of West African Climate in year 2014 and also changes in microphysics (MP) and planetary boundary layer (PBL) schemes on June 2014. The sensitivity to horizontal resolution study show that both runs at 24km and 4km (explicit convection) resolution fairly replicate the general distribution of the rainfall over West African region. The analysis also reveals a good replication of the dynamical features of West African monsoon system including Tropical Easterly Jet (TEJ), African Easterly Jet (AEJ), monsoon flow and the West African Heat Low (WAHL). Some differences have been noticed between WRF and ERA-interim outputs irrespective to the spectral nudging used in the experiment which then suggest strong interactions between scales. The link between the seasonal displacement of the WAHL and the spatial distribution of the rainfall and the Sahelian onset is confirmed in this study. The results also show an improvement on the replication of rainfall with the very high resolution run observed at daily scale over the Sahel while a dry bias is observed in WRF simulations of the rainfall over Ivorian Coast and in the Gulf of Guinea. Generally, over the Guinean coast the high resolution run did not provide subsequent improvement on the replication of rainfall. The sensitivity of WRF to MP and PBL on rainfall replication study reveals that the most significant added value over the Guinean coast and surroundings area is provided by the configurations that used the PBL Asymmetric Convective Model V2 (ACM2) suggesting more influence of the PBL compared to MP. The change on microphysics and planetary boundary layer schemes in general, seems to have less effect on the explicit runs into the replication of the rainfall over the Gulf of Guinea and the surroundings seaboard.

Multiscale responses of soil stability and invasive plants to removal of non-native grazers from an arid conservation reserve

USGS Publications Warehouse

Beever, E.A.; Huso, M.; Pyke, D.A.

2006-01-01

Disturbances and ecosystem recovery from disturbance both involve numerous processes that operate on multiple spatial and temporal scales. Few studies have investigated how gradients of disturbance intensity and ecosystem responses are distributed across multiple spatial resolutions and also how this relationship changes through time during recovery. We investigated how cover of non-native species and soil-aggregate stability (a measure of vulnerability to erosion by water) in surface and subsurface soils varied spatially during grazing by burros and cattle and whether patterns in these variables changed after grazer removal from Mojave National Preserve, California, USA. We compared distance from water and number of ungulate defecations - metrics of longer-term and recent grazing intensity, respectively, - as predictors of our response variables. We used information-theoretic analyses to compare hierarchical linear models that accounted for important covariates and allowed for interannual variation in the disturbance-response relationship at local and landscape scales. Soil stability was greater under perennial vegetation than in bare interspaces, and surface soil stability decreased with increasing numbers of ungulate defecations. Stability of surface samples was more affected by time since removal of grazers than was stability of subsurface samples, and subsurface soil stability in bare spaces was not related to grazing intensity, time since removal, or any of our other predictors. In the high rainfall year (2003) after cattle had been removed for 1-2 years, cover of all non-native plants averaged nine times higher than in the low-rainfall year (2002). Given the heterogeneity in distribution of large-herbivore impacts that we observed at several resolutions, hierarchical analyses provided a more complete understanding of the spatial and temporal complexities of disturbance and recovery processes in arid ecosystems. ?? 2006 Blackwell Publishing Ltd.

Multi-scale responses of soil stability and invasive plants to removal of non-native grazers from an arid conservation reserve

USGS Publications Warehouse

Beever, Erik A.; Huso, Manuela M. P.; Pyke, David A.

2006-01-01

Disturbances and ecosystem recovery from disturbance both involve numerous processes that operate on multiple spatial and temporal scales. Few studies have investigated how gradients of disturbance intensity and ecosystem responses are distributed across multiple spatial resolutions and also how this relationship changes through time during recovery. We investigated how cover of non-native species and soil-aggregate stability (a measure of vulnerability to erosion by water) in surface and subsurface soils varied spatially during grazing by burros and cattle and whether patterns in these variables changed after grazer removal from Mojave National Preserve, California, USA. We compared distance from water and number of ungulate defecations — metrics of longer-term and recent grazing intensity, respectively, — as predictors of our response variables. We used information-theoretic analyses to compare hierarchical linear models that accounted for important covariates and allowed for interannual variation in the disturbance–response relationship at local and landscape scales. Soil stability was greater under perennial vegetation than in bare interspaces, and surface soil stability decreased with increasing numbers of ungulate defecations. Stability of surface samples was more affected by time since removal of grazers than was stability of subsurface samples, and subsurface soil stability in bare spaces was not related to grazing intensity, time since removal, or any of our other predictors. In the high rainfall year (2003) after cattle had been removed for 1–2 years, cover of all non-native plants averaged nine times higher than in the low-rainfall year (2002). Given the heterogeneity in distribution of large-herbivore impacts that we observed at several resolutions, hierarchical analyses provided a more complete understanding of the spatial and temporal complexities of disturbance and recovery processes in arid ecosystems.

Water isotope variability across single rainfall events in the tropical Pacific

NASA Astrophysics Data System (ADS)

Cobb, K. M.; Moerman, J. W.; Ellis, S. A.; Bennett, L.; Bosma, C.; Hitt, N. T.

2017-12-01

Water isotopologues provide a powerful diagnostic tool for probing the dynamical processes involved in the initiation and evolution of tropical convective events, yet water isotope observations rarely meet the temporal resolution required to resolve such processes. Here we present timeseries of rainfall oxygen and hydrogen isotopologues across over 30 individual convective events sampled at 1- to 5-minute intervals at both terrestrial (Gunung Mulu National Park, 4N, 115W) and maritime (Kiritimati Island, 2N, 157W) sites located in the equatorial Pacific. The sites are the loci of significant paleoclimate research that employ water isotopologues to reconstruct a variety of climatic parameters of interest over the last century, in the case of coral d18O, to hundreds of thousands of years before present, in the case of stalagmite d18O. As such, there is significant scientific value in refining our understanding of water isotope controls at these particular sites. Our results illustrate large, short-term excursions in water isotope values that far exceed the signals recovered in daily timeseries of rainfall isotopologues from the sites, illustrating the fundamental contribution of mesoscale processes in driving rainfall isotope variability. That said, the cross-event profiles exhibit a broad range of trajectories, even for events collected at the same time of day on adjoining days. Profiles collected at different phases of the 2015-2017 strong El Nino-Southern Oscillation cycle also exhibit appreciable variability. We compare our observations to hypothetical profiles from a 1-dimensional model of each rainfall event, as well as to output from 4-dimensional isotope-equipped, ocean-atmosphere coupled models of rainfall isotope variability in the tropical Pacific. We discuss the implications of our findings for the interpretation of water isotope-based reconstructions of hydroclimate in the tropics.

SMOS+RAINFALL: Evaluating the ability of different methodologies to improve rainfall estimations using soil moisture data from SMOS

NASA Astrophysics Data System (ADS)

Pellarin, Thierry; Brocca, Luca; Crow, Wade; Kerr, Yann; Massari, Christian; Román-Cascón, Carlos; Fernández, Diego

2017-04-01

Recent studies have demonstrated the usefulness of soil moisture retrieved from satellite for improving rainfall estimations of satellite based precipitation products (SBPP). The real-time version of these products are known to be biased from the real precipitation observed at the ground. Therefore, the information contained in soil moisture can be used to correct the inaccuracy and uncertainty of these products, since the value and behavior of this soil variable preserve the information of a rain event even for several days. In this work, we take advantage of the soil moisture data from the Soil Moisture and Ocean Salinity (SMOS) satellite, which provides information with a quite appropriate temporal and spatial resolution for correcting rainfall events. Specifically, we test and compare the ability of three different methodologies for this aim: 1) SM2RAIN, which directly relate changes in soil moisture to rainfall quantities; 2) The LMAA methodology, which is based on the assimilation of soil moisture in two models of different complexity (see EGU2017-5324 in this same session); 3) The SMART method, based on the assimilation of soil moisture in a simple hydrological model with a different assimilation/modelling technique. The results are tested for 6 years over 10 sites around the world with different features (land surface, rainfall climatology, orography complexity, etc.). These preliminary and promising results are shown here for the first time to the scientific community, as also the observed limitations of the different methodologies. Specific remarks on the technical configurations, filtering/smoothing of SMOS soil moisture or re-scaling techniques are also provided from the results of different sensitivity experiments.

Stochastic rainfall synthesis for urban applications using different regionalization methods

NASA Astrophysics Data System (ADS)

Callau Poduje, A. C.; Leimbach, S.; Haberlandt, U.

2017-12-01

The proper design and efficient operation of urban drainage systems require long and continuous rainfall series in a high temporal resolution. Unfortunately, these time series are usually available in a few locations and it is therefore suitable to develop a stochastic precipitation model to generate rainfall in locations without observations. The model presented is based on an alternating renewal process and involves an external and an internal structure. The members of these structures are described by probability distributions which are site specific. Different regionalization methods based on site descriptors are presented which are used for estimating the distributions for locations without observations. Regional frequency analysis, multiple linear regressions and a vine-copula method are applied for this purpose. An area located in the north-west of Germany is used to compare the different methods and involves a total of 81 stations with 5 min rainfall records. The site descriptors include information available for the whole region: position, topography and hydrometeorologic characteristics which are estimated from long term observations. The methods are compared directly by cross validation of different rainfall statistics. Given that the model is stochastic the evaluation is performed based on ensembles of many long synthetic time series which are compared with observed ones. The performance is as well indirectly evaluated by setting up a fictional urban hydrological system to test the capability of the different methods regarding flooding and overflow characteristics. The results show a good representation of the seasonal variability and good performance in reproducing the sample statistics of the rainfall characteristics. The copula based method shows to be the most robust of the three methods. Advantages and disadvantages of the different methods are presented and discussed.

Data-Driven Geospatial Visual Analytics for Real-Time Urban Flooding Decision Support

NASA Astrophysics Data System (ADS)

Liu, Y.; Hill, D.; Rodriguez, A.; Marini, L.; Kooper, R.; Myers, J.; Wu, X.; Minsker, B. S.

2009-12-01

Urban flooding is responsible for the loss of life and property as well as the release of pathogens and other pollutants into the environment. Previous studies have shown that spatial distribution of intense rainfall significantly impacts the triggering and behavior of urban flooding. However, no general purpose tools yet exist for deriving rainfall data and rendering them in real-time at the resolution of hydrologic units used for analyzing urban flooding. This paper presents a new visual analytics system that derives and renders rainfall data from the NEXRAD weather radar system at the sewershed (i.e. urban hydrologic unit) scale in real-time for a Chicago stormwater management project. We introduce a lightweight Web 2.0 approach which takes advantages of scientific workflow management and publishing capabilities developed at NCSA (National Center for Supercomputing Applications), streaming data-aware semantic content management repository, web-based Google Earth/Map and time-aware KML (Keyhole Markup Language). A collection of polygon-based virtual sensors is created from the NEXRAD Level II data using spatial, temporal and thematic transformations at the sewershed level in order to produce persistent virtual rainfall data sources for the animation. Animated color-coded rainfall map in the sewershed can be played in real-time as a movie using time-aware KML inside the web browser-based Google Earth for visually analyzing the spatiotemporal patterns of the rainfall intensity in the sewershed. Such system provides valuable information for situational awareness and improved decision support during extreme storm events in an urban area. Our further work includes incorporating additional data (such as basement flooding events data) or physics-based predictive models that can be used for more integrated data-driven decision support.

Dynamical downscaling with WRF for the Middle-East and North Africa

NASA Astrophysics Data System (ADS)

Dezfuli, A. K.; Zaitchik, B. F.; Badr, H. S.; Bergaoui, K.; Zaaboul, R.; Bhattacharjee, P.

2014-12-01

The Middle-East and North Africa (MENA) experience the highest risk of water stress in the world. This underlines the importance of climate analysis for water resources management and climate change adaptation for this region, particularly in transboundary basins such as the Tigris-Euphrates system. Such analysis, however, is difficult due to a paucity of high quality precipitation data. The network of gauge stations is quite sparse and the data are often available only at monthly time-scale. Satellite-based products, such as the Tropical Rainfall Measuring Mission (TRMM), offer better temporal resolution; however, these data are available only for periods that are short for hydroclimatic analysis, and they often misrepresent precipitation over regions with complex topography or strong convection. To fill this gap, we have implemented the Weather Research and Forecasting (WRF) Model, initialized with the NCEP/NCAR Reanalysis II, to generate high-resolution precipitation estimates for MENA. Several sensitivity analyses have been performed in order to find a set of physics parameters that appropriately captures the annual cycle and year-to-year variability of rainfall over select areas in MENA. The results show that WRF, particularly over highlands, estimates the precipitation more accurately than the satellite products. In addition to these reanalysis-driven simulations, we have performed several simulations using the historical and twenty first century outputs of a select number of GCMs available at the CMIP5 archive. These runs enable us to detect changes in rainfall behavior under different greenhouse gas scenarios and reveal synoptic to mesoscale mechanisms responsible for such changes.

Rainfall Patterns Analysis over Ampangan Muda, Kedah from 2007 - 2016

NASA Astrophysics Data System (ADS)

Chooi Tan, Kok

2018-04-01

The scientific knowledge about climate change and climate variability over Malaysia pertaining to the extreme water-related disaster such as drought and flood. A deficit or increment in precipitation occurred over the past century becomes a useful tool to understand the climate change in Malaysia. The purpose of this work is to examine the rainfall patterns over Ampangan Muda, Kedah. Daily rainfall data is acquired from Malaysian Meteorological Department to analyse the temporal and trends of the monthly and annual rainfall over the study area from 2007 to 2016. The obtained results show that the temporal and patterns of the rainfall over Ampangan Muda, Kedah is largely affected by the regional phenomena such as monsoon, El Niño Southern Oscillation (ENSO), and the Madden-Julian Oscillation. In addition, backward trajectories analysis is also used to identify the patterns for long-range of synoptic circulation over the region.

Evaluation of different rainfall products over India for the summer monsoon

NASA Astrophysics Data System (ADS)

Prakash, Satya; Mitra, Ashis; Turner, Andrew; Collins, Mathew; AchutoRao, Krishna

2015-04-01

Summer rainfall over India forms an integral part of the Asian monsoon, which plays a key role in the global water cycle and climate system through coupled atmospheric and oceanic processes. Accurate prediction of Indian summer monsoon rainfall and its variability at various spatiotemporal scales are crucial for agriculture, water resources and hydroelectric-power sectors. Reliable rainfall observations are very important for verification of numerical model outputs and model development. However, high spatiotemporal variability of rainfall makes it difficult to measure adequately with ground-based instruments over a large region of various surface types from deserts to oceans. A number of multi-satellite rainfall products are available to users at different spatial and temporal scales. Each rainfall product has some advantages as well as limitations, hence it is essential to find a suitable region-specific data set among these rainfall products for a particular user application, such as water resources, agricultural modelling etc. In this study, we examine seasonal-mean and daily rainfall datasets for monsoon model validation. First, six multi-satellite and gauge-only rainfall products were evaluated over India at seasonal scale for 27 (JJAS 1979-2005) summer monsoon seasons against gridded 0.5-degree IMD gauge-based rainfall. Various skill metrics are computed to assess the potential of these data sets in representation of large-scale monsoon rainfall at all-India and sub-regional scales. Among the gauge-only data sets, APHRODITE and GPCC appear to outperform the others whereas GPCP is better than CMAP in the merged multi-satellite category. However, there are significant differences among these data sets indicating uncertainty in the observed rainfall over this region, with important implications for the evaluation of model simulations. At the daily scale, TRMM TMPA-3B42 is one of the best available products and is widely used for various hydro-meteorological applications. The existing version 6 (V6) products of TRMM underwent major changes and version 7 (V7) products were released in late 2012, and we compare these to the IMD daily gridded data over the 1998-2010 period. We show a clear improvement in V7 over V6 in the South Asian monsoon region using various skill metrics. Over typical monsoon rainfall zones, biases are improved by 5-10% in V7 over higher-rainfall regions. These results will help users to select appropriate rainfall product for their application. With the recent launch of the GPM Core Observatory, the release of a more advanced high-resolution multi-satellite rainfall product is expected soon.

Rainfall simulators in hydrological and geomorphological sciences: benefits, applications and future research directions

NASA Astrophysics Data System (ADS)

Iserloh, Thomas; Cerdà, Artemi; Fister, Wolfgang; Seitz, Steffen; Keesstra, Saskia; Green, Daniel; Gabriels, Donald

2017-04-01

Rainfall simulators are used extensively within the hydrological and geomorphological sciences and provide a useful investigative tool to understand many processes, such as: (i) plot-scale runoff, infiltration and erosion; (ii) irrigation and crop management, and; (iii) investigations into flooding within a laboratory setting. Although natural rainfall is desirable as it represents actual conditions in a given geographic location, data acquisition relying on natural rainfall is often hindered by its unpredictable nature. Furthermore, rainfall characteristics such as the intensity, duration, drop size distribution and kinetic energy cannot be spatially or temporally regulated or repeated between experimentation. Rainfall simulators provide a suitable method to overcome the issues associated with depending on potentially erratic and unpredictable natural rainfall as they allow: (i) multiple measurements to be taken quickly without waiting for suitable natural rainfall conditions; (ii) the simulation of spatially and/or temporally controlled rainfall patterns over a given plot area, and; (iii) the creation of a closed environment, allowing simplified measurement of input and output conditions. There is no standardisation of rainfall simulation and as such, rainfall simulators differ in their design, rainfall characteristics and research application. Although this impedes drawing meaningful comparisons between studies, this allows researchers to create a bespoke and tailored rainfall simulator for the specific research application. This paper summarises the rainfall simulators used in European research institutions (Universities of Trier, Valencia, Basel, Tuebingen, Wageningen, Loughborough and Ghent) to investigate a number of hydrological and geomorphological issues and includes details on the design specifications (such as the extent and characteristics of simulated rainfall), as well as a discussion of the purpose and application of the rainfall simulator.

TRMM rainfall estimative coupled with Bell (1969) methodology for extreme rainfall characterization

NASA Astrophysics Data System (ADS)

Schiavo Bernardi, E.; Allasia, D.; Basso, R.; Freitas Ferreira, P.; Tassi, R.

2015-06-01

The lack of rainfall data in Brazil, and, in particular, in Rio Grande do Sul State (RS), hinders the understanding of the spatial and temporal distribution of rainfall, especially in the case of the more complex extreme events. In this context, rainfall's estimation from remote sensors is seen as alternative to the scarcity of rainfall gauges. However, as they are indirect measures, such estimates needs validation. This paper aims to verify the applicability of the Tropical Rainfall Measuring Mission (TRMM) satellite information for extreme rainfall determination in RS. The analysis was accomplished at different temporal scales that ranged from 5 min to daily rainfall while spatial distribution of rainfall was investigated by means of regionalization. An initial test verified TRMM rainfall estimative against measured rainfall at gauges for 1998-2013 period considering different durations and return periods (RP). Results indicated that, for the RP of 2, 5, 10 and 15 years, TRMM overestimated on average 24.7% daily rainfall. As TRMM minimum time-steps is 3 h, in order to verify shorter duration rainfall, the TRMM data were adapted to fit Bell's (1969) generalized IDF formula (based on the existence of similarity between the mechanisms of extreme rainfall events as they are associated to convective cells). Bell`s equation error against measured precipitation was around 5-10%, which varied based on location, RP and duration while the coupled BELL+TRMM error was around 10-35%. However, errors were regionally distributed, allowing a correction to be implemented that reduced by half these values. These findings in turn permitted the use of TRMM+Bell estimates to improve the understanding of spatiotemporal distribution of extreme hydrological rainfall events.

Rainfall From Resolved Rather Than Parameterized Processes Better Represents the Present-Day and Climate Change Response of Moderate Rates in the Community Atmosphere Model

DOE PAGES

Kooperman, Gabriel J.; Pritchard, Michael S.; O'Brien, Travis A.; ...

2018-04-01

Deficiencies in the parameterizations of convection used in global climate models often lead to a distorted representation of the simulated rainfall intensity distribution (i.e., too much rainfall from weak rain rates). While encouraging improvements in high percentile rainfall intensity have been found as the horizontal resolution of the Community Atmosphere Model is increased to ~25 km, we demonstrate no corresponding improvement in the moderate rain rates that generate the majority of accumulated rainfall. Using a statistical framework designed to emphasize links between precipitation intensity and accumulated rainfall beyond just the frequency distribution, we show that CAM cannot realistically simulate moderatemore » rain rates, and cannot capture their intensification with climate change, even as resolution is increased. However, by separating the parameterized convective and large-scale resolved contributions to total rainfall, we find that the intensity, geographic pattern, and climate change response of CAM's large-scale rain rates are more consistent with observations (TRMM 3B42), superparameterization, and theoretical expectations, despite issues with parameterized convection. Increasing CAM's horizontal resolution does improve the representation of total rainfall intensity, but not due to changes in the intensity of large-scale rain rates, which are surprisingly insensitive to horizontal resolution. Rather, improvements occur through an increase in the relative contribution of the large-scale component to the total amount of accumulated rainfall. Analysis of sensitivities to convective timescale and entrainment rate confirm the importance of these parameters in the possible development of scale-aware parameterizations, but also reveal unrecognized trade-offs from the entanglement of precipitation frequency and total amount.« less

Rainfall From Resolved Rather Than Parameterized Processes Better Represents the Present-Day and Climate Change Response of Moderate Rates in the Community Atmosphere Model

NASA Astrophysics Data System (ADS)

Kooperman, Gabriel J.; Pritchard, Michael S.; O'Brien, Travis A.; Timmermans, Ben W.

2018-04-01

Deficiencies in the parameterizations of convection used in global climate models often lead to a distorted representation of the simulated rainfall intensity distribution (i.e., too much rainfall from weak rain rates). While encouraging improvements in high percentile rainfall intensity have been found as the horizontal resolution of the Community Atmosphere Model is increased to ˜25 km, we demonstrate no corresponding improvement in the moderate rain rates that generate the majority of accumulated rainfall. Using a statistical framework designed to emphasize links between precipitation intensity and accumulated rainfall beyond just the frequency distribution, we show that CAM cannot realistically simulate moderate rain rates, and cannot capture their intensification with climate change, even as resolution is increased. However, by separating the parameterized convective and large-scale resolved contributions to total rainfall, we find that the intensity, geographic pattern, and climate change response of CAM's large-scale rain rates are more consistent with observations (TRMM 3B42), superparameterization, and theoretical expectations, despite issues with parameterized convection. Increasing CAM's horizontal resolution does improve the representation of total rainfall intensity, but not due to changes in the intensity of large-scale rain rates, which are surprisingly insensitive to horizontal resolution. Rather, improvements occur through an increase in the relative contribution of the large-scale component to the total amount of accumulated rainfall. Analysis of sensitivities to convective timescale and entrainment rate confirm the importance of these parameters in the possible development of scale-aware parameterizations, but also reveal unrecognized trade-offs from the entanglement of precipitation frequency and total amount.

Rainfall From Resolved Rather Than Parameterized Processes Better Represents the Present‐Day and Climate Change Response of Moderate Rates in the Community Atmosphere Model

PubMed Central

Pritchard, Michael S.; O'Brien, Travis A.; Timmermans, Ben W.

2018-01-01

Abstract Deficiencies in the parameterizations of convection used in global climate models often lead to a distorted representation of the simulated rainfall intensity distribution (i.e., too much rainfall from weak rain rates). While encouraging improvements in high percentile rainfall intensity have been found as the horizontal resolution of the Community Atmosphere Model is increased to ∼25 km, we demonstrate no corresponding improvement in the moderate rain rates that generate the majority of accumulated rainfall. Using a statistical framework designed to emphasize links between precipitation intensity and accumulated rainfall beyond just the frequency distribution, we show that CAM cannot realistically simulate moderate rain rates, and cannot capture their intensification with climate change, even as resolution is increased. However, by separating the parameterized convective and large‐scale resolved contributions to total rainfall, we find that the intensity, geographic pattern, and climate change response of CAM's large‐scale rain rates are more consistent with observations (TRMM 3B42), superparameterization, and theoretical expectations, despite issues with parameterized convection. Increasing CAM's horizontal resolution does improve the representation of total rainfall intensity, but not due to changes in the intensity of large‐scale rain rates, which are surprisingly insensitive to horizontal resolution. Rather, improvements occur through an increase in the relative contribution of the large‐scale component to the total amount of accumulated rainfall. Analysis of sensitivities to convective timescale and entrainment rate confirm the importance of these parameters in the possible development of scale‐aware parameterizations, but also reveal unrecognized trade‐offs from the entanglement of precipitation frequency and total amount. PMID:29861837

Rainfall From Resolved Rather Than Parameterized Processes Better Represents the Present-Day and Climate Change Response of Moderate Rates in the Community Atmosphere Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kooperman, Gabriel J.; Pritchard, Michael S.; O'Brien, Travis A.

Deficiencies in the parameterizations of convection used in global climate models often lead to a distorted representation of the simulated rainfall intensity distribution (i.e., too much rainfall from weak rain rates). While encouraging improvements in high percentile rainfall intensity have been found as the horizontal resolution of the Community Atmosphere Model is increased to ~25 km, we demonstrate no corresponding improvement in the moderate rain rates that generate the majority of accumulated rainfall. Using a statistical framework designed to emphasize links between precipitation intensity and accumulated rainfall beyond just the frequency distribution, we show that CAM cannot realistically simulate moderatemore » rain rates, and cannot capture their intensification with climate change, even as resolution is increased. However, by separating the parameterized convective and large-scale resolved contributions to total rainfall, we find that the intensity, geographic pattern, and climate change response of CAM's large-scale rain rates are more consistent with observations (TRMM 3B42), superparameterization, and theoretical expectations, despite issues with parameterized convection. Increasing CAM's horizontal resolution does improve the representation of total rainfall intensity, but not due to changes in the intensity of large-scale rain rates, which are surprisingly insensitive to horizontal resolution. Rather, improvements occur through an increase in the relative contribution of the large-scale component to the total amount of accumulated rainfall. Analysis of sensitivities to convective timescale and entrainment rate confirm the importance of these parameters in the possible development of scale-aware parameterizations, but also reveal unrecognized trade-offs from the entanglement of precipitation frequency and total amount.« less

Synchrony, compensatory dynamics, and the functional trait basis of phenological diversity in a tropical dry forest tree community: effects of rainfall seasonality

NASA Astrophysics Data System (ADS)

Lasky, Jesse R.; Uriarte, María; Muscarella, Robert

2016-11-01

Interspecific variation in phenology is a key axis of functional diversity, potentially mediating how communities respond to climate change. The diverse drivers of phenology act across multiple temporal scales. For example, abiotic constraints favor synchronous reproduction (positive covariance among species), while biotic interactions can favor synchrony or compensatory dynamics (negative covariance). We used wavelet analyses to examine phenology of community flower and seed production for 45 tree species across multiple temporal scales in a tropical dry forest in Puerto Rico with marked rainfall seasonality. We asked three questions: (1) do species exhibit synchronous or compensatory temporal dynamics in reproduction, (2) do interspecific differences in phenology reflect variable responses to rainfall, and (3) is interspecific variation in phenology and response to a major drought associated with functional traits that mediate responses to moisture? Community-level flowering was synchronized at seasonal scales (˜5-6 mo) and at short scales (˜1 mo, following rainfall). However, seed rain exhibited significant compensatory dynamics at intraseasonal scales (˜3 mo), suggesting interspecific variation in temporal niches. Species with large leaves (associated with sensitivity to water deficit) peaked in reproduction synchronously with the peak of seasonal rainfall (˜5 mo scale). By contrast, species with high wood specific gravity (associated with drought resistance) tended to flower in drier periods. Flowering of tall species and those with large leaves was most tightly linked to intraseasonal (˜2 mo scale) rainfall fluctuations. Although the 2015 drought dramatically reduced community-wide reproduction, functional traits were not associated with the magnitude of species-specific declines. Our results suggest opposing drivers of synchronous versus compensatory dynamics at different temporal scales. Phenology associations with functional traits indicated that distinct strategies for coping with seasonality underlie phenological diversity. Observed drought responses highlight the importance of non-linear community responses to climate. Community phenology exhibits scale-specific patterns highlighting the need for multi-scale approaches to community dynamics.

South American Monsoon precipitation trends from 1948-2006

NASA Astrophysics Data System (ADS)

Araujo, L. R.; De Mattos, J. Z.; Goncalves, L.

2013-05-01

In South America the monsoon system affects the Amazon region extending to the center of the South American continent to the northeast and southeast coastal strip. The characteristic South America Monsoon System (SAMS) is not classical, as in East Asia and India. The SAMS do not show a typical seasonal reversal in wind circulation regime, however indicates aspect of monsoon climate, as well as the seasonal cycle of rainfall over much of the continent, this is, a period of intense rain in summer and winter extremely dry. Despite the precipitation rate in the region of the SAMS being lower than other monsoon areas of the globe, it has a great influency in the major human and economical activities in that continent what motivetis the goal of this work which is to study the trend of rainfall over South America during the monsoon season in South America. For this study Climate Research Unit (CRU) precipitation data was used for the period between 1948 and 2006 during the months from November to March. The spatial resolution of the data is 1 degree and 3 hours temporal resolution. Preliminary results show that there was a pattern of positive trend in precipitation for the months of January, February, March, months in the seasonal cycle of precipitation SAMS.

Analysis of convection-permitting simulations for capturing heavy rainfall events over Myanmar Region

NASA Astrophysics Data System (ADS)

Acierto, R. A. E.; Kawasaki, A.

2017-12-01

Perennial flooding due to heavy rainfall events causes strong impacts on the society and economy. With increasing pressures of rapid development and potential for climate change impacts, Myanmar experiences a rapid increase in disaster risk. Heavy rainfall hazard assessment is key on quantifying such disaster risk in both current and future conditions. Downscaling using Regional Climate Models (RCM) such as Weather Research and Forecast model have been used extensively for assessing such heavy rainfall events. However, usage of convective parameterizations can introduce large errors in simulating rainfall. Convective-permitting simulations have been used to deal with this problem by increasing the resolution of RCMs to 4km. This study focuses on the heavy rainfall events during the six-year (2010-2015) wet period season from May to September in Myanmar. The investigation primarily utilizes rain gauge observation for comparing downscaled heavy rainfall events in 4km resolution using ERA-Interim as boundary conditions using 12km-4km one-way nesting method. The study aims to provide basis for production of high-resolution climate projections over Myanmar in order to contribute for flood hazard and risk assessment.

Machine learning methods for the classification of extreme rainfall and hail events

NASA Astrophysics Data System (ADS)

Teschl, Reinhard; Süsser-Rechberger, Barbara; Paulitsch, Helmut

2015-04-01

In this study, an analysis of a meteorological data set with machine learning tools is presented. The aim was to identify characteristic patterns in different sources of remote sensing data that are associated with hazards like extreme rainfall and hail. The data set originates from a project that was started in 2007 with the goal to document and mitigate hail events in the province of Styria, Austria. It consists of three dimensional weather radar data from a C-band Doppler radar, cloud top temperature information from infrared channels of a weather satellite, as well as the height of the 0° C isotherm from the forecast of the national weather service. The 3D radar dataset has a spatial resolution of 1 km x 1 km x 1 km, up to a height of 16 km above mean sea level, and a temporal resolution of 5 minutes. The infrared satellite image resolution is about 3 km x 3 km, the images are updated every 30 minutes. The study area has approx. 16,000 square kilometers. So far, different criteria for the occurrence of hail (and its discrimination from heavy rain) have been found and are documented in the literature. When applying these criteria to our data and contrasting them with damage reports from an insurance company, a need for adaption was identified. Here we are using supervised learning paradigms to find tailored relationships for the study area, validated by a sub-dataset that was not involved in the training process.

Spatio-temporal variability and trends of precipitation and extreme rainfall events in Ethiopia in 1980-2010

NASA Astrophysics Data System (ADS)

Gummadi, Sridhar; Rao, K. P. C.; Seid, Jemal; Legesse, Gizachew; Kadiyala, M. D. M.; Takele, Robel; Amede, Tilahun; Whitbread, Anthony

2017-12-01

This article summarizes the results from an analysis conducted to investigate the spatio-temporal variability and trends in the rainfall over Ethiopia over a period of 31 years from 1980 to 2010. The data is mostly observed station data supplemented by bias-corrected AgMERRA climate data. Changes in annual and Belg (March-May) and Kiremt (June to September) season rainfalls and rainy days have been analysed over the entire Ethiopia. Rainfall is characterized by high temporal variability with coefficient of variation (CV, %) varying from 9 to 30% in the annual, 9 to 69% during the Kiremt season and 15-55% during the Belg season rainfall amounts. Rainfall variability increased disproportionately as the amount of rainfall declined from 700 to 100 mm or less. No significant trend was observed in the annual rainfall amounts over the country, but increasing and decreasing trends were observed in the seasonal rainfall amounts in some areas. A declining trend is also observed in the number of rainy days especially in Oromia, Benishangul-Gumuz and Gambella regions. Trends in seasonal rainfall indicated a general decline in the Belg season and an increase in the Kiremt season rainfall amounts. The increase in rainfall during the main Kiremt season along with the decrease in the number of rainy days leads to an increase in extreme rainfall events over Ethiopia. The trends in the 95th-percentile rainfall events illustrate that the annual extreme rainfall events are increasing over the eastern and south-western parts of Ethiopia covering Oromia and Benishangul-Gumuz regions. During the Belg season, extreme rainfall events are mostly observed over central Ethiopia extending towards the southern part of the country while during the Kiremt season, they are observed over parts of Oromia, (covering Borena, Guji, Bali, west Harerge and east Harerge), Somali, Gambella, southern Tigray and Afar regions. Changes in the intensity of extreme rainfall events are mostly observed over south-eastern parts of Ethiopia extending to the south-west covering Somali and Oromia regions. Similar trends are also observed in the greatest 3-, 5- and 10-day rainfall amounts. Changes in the consecutive dry and wet days showed that consecutive wet days during Belg and Kiremt seasons decreased significantly in many areas in Ethiopia while consecutive dry days increased. The consistency in the trends over large spatial areas confirms the robustness of the trends and serves as a basis for understanding the projected changes in the climate. These results were discussed in relation to their significance to agriculture.

Daily variability of rainfall and emergency department visits of acute gastrointestinal illness in North Carolina, 2006-2008

EPA Science Inventory

Background & Aims: Projections based on climate models suggest that the frequency of extreme rainfall events will continue to rise over the next several decades. We aim to investigate the temporal relationship between daily variability of rainfall and acute gastrointestinal illne...

A comprehensive analysis of coherent rainfall patterns in China and potential drivers. Part I: Interannual variability

NASA Astrophysics Data System (ADS)

Stephan, Claudia Christine; Klingaman, Nicholas Pappas; Vidale, Pier Luigi; Turner, Andrew George; Demory, Marie-Estelle; Guo, Liang

2018-06-01

Interannual rainfall variability in China affects agriculture, infrastructure and water resource management. To improve its understanding and prediction, many studies have associated precipitation variability with particular causes for specific seasons and regions. Here, a consistent and objective method, Empirical Orthogonal Teleconnection (EOT) analysis, is applied to 1951-2007 high-resolution precipitation observations over China in all seasons. Instead of maximizing the explained space-time variance, the method identifies regions in China that best explain the temporal variability in domain-averaged rainfall. The EOT method is validated by the reproduction of known relationships to the El Niño Southern Oscillation (ENSO): high positive correlations with ENSO are found in eastern China in winter, along the Yangtze River in summer, and in southeast China during spring. New findings include that wintertime rainfall variability along the southeast coast is associated with anomalous convection over the tropical eastern Atlantic and communicated to China through a zonal wavenumber-three Rossby wave. Furthermore, spring rainfall variability in the Yangtze valley is related to upper-tropospheric midlatitude perturbations that are part of a Rossby wave pattern with its origin in the North Atlantic. A circumglobal wave pattern in the northern hemisphere is also associated with autumn precipitation variability in eastern areas. The analysis is objective, comprehensive, and produces timeseries that are tied to specific locations in China. This facilitates the interpretation of associated dynamical processes, is useful for understanding the regional hydrological cycle, and allows the results to serve as a benchmark for assessing general circulation models.

Three millennia of heavy rainfalls in Western Mediterranean: frequency, seasonality and atmospheric drivers

PubMed Central

Corella, J. P.; Valero-Garcés, B. L.; Vicente- Serrano, S. M.; Brauer, A.; Benito, G.

2016-01-01

Documenting subdecadal-scale heavy rainfall (HR) variability over several millennia can rarely be accomplished due to the paucity of high resolution, homogeneous and continuous proxy records. Here, using a unique, seasonally resolved lake record from southern Europe, we quantify temporal changes in extreme HR events for the last 2,800 years in this region and their correlation with negative phases of the Mediterranean Oscillation (MO). Notably, scarce HR dominated by a persistent positive MO mode characterizes the so-called Migration period (CE 370–670). Large hydroclimatic variability, particularly between CE 1012 and 1164, singles out the Medieval Climatic Anomaly, whereas more stationary HR conditions occurred between CE 1537 and 1805 coinciding with the Little Ice Age. This exceptional paleohydrological record highlights that the present-day trend towards strengthened hydrological deficit and less HR in the western Mediterranean is neither acute nor unusual in the context of Late Holocene hydrometeorological variability at centennial to decadal time scales. PMID:27910953

Three millennia of heavy rainfalls in Western Mediterranean: frequency, seasonality and atmospheric drivers

NASA Astrophysics Data System (ADS)

Corella, J. P.; Valero-Garcés, B. L.; Vicente-Serrano, S. M.; Brauer, A.; Benito, G.

2016-12-01

Documenting subdecadal-scale heavy rainfall (HR) variability over several millennia can rarely be accomplished due to the paucity of high resolution, homogeneous and continuous proxy records. Here, using a unique, seasonally resolved lake record from southern Europe, we quantify temporal changes in extreme HR events for the last 2,800 years in this region and their correlation with negative phases of the Mediterranean Oscillation (MO). Notably, scarce HR dominated by a persistent positive MO mode characterizes the so-called Migration period (CE 370-670). Large hydroclimatic variability, particularly between CE 1012 and 1164, singles out the Medieval Climatic Anomaly, whereas more stationary HR conditions occurred between CE 1537 and 1805 coinciding with the Little Ice Age. This exceptional paleohydrological record highlights that the present-day trend towards strengthened hydrological deficit and less HR in the western Mediterranean is neither acute nor unusual in the context of Late Holocene hydrometeorological variability at centennial to decadal time scales.

Assessing the importance of rainfall uncertainty on hydrological models with different spatial and temporal scale

NASA Astrophysics Data System (ADS)

Nossent, Jiri; Pereira, Fernando; Bauwens, Willy

2015-04-01

Precipitation is one of the key inputs for hydrological models. As long as the values of the hydrological model parameters are fixed, a variation of the rainfall input is expected to induce a change in the model output. Given the increased awareness of uncertainty on rainfall records, it becomes more important to understand the impact of this input - output dynamic. Yet, modellers often still have the intention to mimic the observed flow, whatever the deviation of the employed records from the actual rainfall might be, by recklessly adapting the model parameter values. But is it actually possible to vary the model parameter values in such a way that a certain (observed) model output can be generated based on inaccurate rainfall inputs? Thus, how important is the rainfall uncertainty for the model output with respect to the model parameter importance? To address this question, we apply the Sobol' sensitivity analysis method to assess and compare the importance of the rainfall uncertainty and the model parameters on the output of the hydrological model. In order to be able to treat the regular model parameters and input uncertainty in the same way, and to allow a comparison of their influence, a possible approach is to represent the rainfall uncertainty by a parameter. To tackle the latter issue, we apply so called rainfall multipliers on hydrological independent storm events, as a probabilistic parameter representation of the possible rainfall variation. As available rainfall records are very often point measurements at a discrete time step (hourly, daily, monthly,…), they contain uncertainty due to a latent lack of spatial and temporal variability. The influence of the latter variability can also be different for hydrological models with different spatial and temporal scale. Therefore, we perform the sensitivity analyses on a semi-distributed model (SWAT) and a lumped model (NAM). The assessment and comparison of the importance of the rainfall uncertainty and the model parameters is achieved by considering different scenarios for the included parameters and the state of the models.

High-Resolution Rainfall From Radar Reflectivity and Terrestrial Rain Gages for use in Estimating Debris-Flow Susceptibility in the Day Fire, California

NASA Astrophysics Data System (ADS)

Hanshaw, M. N.; Schmidt, K. M.; Jorgensen, D. P.; Stock, J. D.

2007-12-01

Constraining the distribution of rainfall is essential to evaluating the post-fire mass-wasting response of steep soil-mantled landscapes. As part of a pilot early-warning project for flash floods and debris flows, NOAA deployed a portable truck-mounted Shared Mobile Atmospheric Research and Teaching Radar (SMART-R) to the 2006 Day fire in the Transverse Ranges of Southern California. In conjunction with a dense array of ground- based instruments, including 8 tipping-bucket rain gages located within an area of 170 km2, this C-band mobile Doppler radar provided 200-m grid cell estimates of precipitation data at fine temporal and spatial scales in burned steeplands at risk from hazardous flash floods and debris flows. To assess the utility of using this data in process models for flood and debris flow initiation, we converted grids of radar reflectivity to hourly time-steps of precipitation using an empirical relationship for convective storms, sampling the radar data at the locations of each rain gage as determined by GPS. The SMART-R was located 14 km from the farthest rain gage, but <10 km away from our intensive research area, where 5 gages are located within <1-2 km of each other. Analyses of the nine storms imaged by radar throughout the 2006/2007 winter produced similar cumulative rainfall totals between the gages and their SMART-R grid location over the entire season which correlate well on the high side, with gages recording the most precipitation agreeing to within 11% of the SMART-R. In contrast, on the low rainfall side, totals between the two recording systems are more variable, with a 62% variance between the minimums. In addition, at the scale of individual storms, a correlation between ground-based rainfall measurements and radar-based rainfall estimates is less evident, with storm totals between the gages and the SMART-R varying between 7 and 88%, a possible result of these being relatively small, fast-moving storms in an unusually dry winter. The SMART-R also recorded higher seasonal cumulative rainfall than the terrestrial gages, perhaps indicating that not all precipitation reached the ground. For one storm in particular, time-lapse photographs of the ground document snow. This could explain, in part, the discrepancy between storm-specific totals when the rain gages recorded significantly lower totals than the SMART-R. For example, during the storm where snow was observed, the SMART-R recorded a maximum of 66% higher rainfall than the maximum recorded by the gages. Unexpectedly, the highest elevation gage, located in a pre-fire coniferous vegetation community, consistently recorded the lowest precipitation, whereas gages in the lower elevation pre- fire chaparral community recorded the highest totals. The spatial locations of the maximum rainfall inferred by the SMART-R and the terrestrial gages are also offset by 1.6 km, with terrestrial values shifted easterly. The observation that the SMART-R images high rainfall intensities recorded by rain gages suggests that this technology has the ability to quantitatively estimate the spatial distribution over larger areas at a high resolution. Discrepancies on the storm scale, however, need to be investigated further, but we are optimistic that such high resolution data from the SMART-R and the terrestrial gages may lead to the effective application of a prototype debris-flow warning system where such processes put lives at risk.

Assessment of ground-based monitoring techniques applied to landslide investigations

NASA Astrophysics Data System (ADS)

Uhlemann, S.; Smith, A.; Chambers, J.; Dixon, N.; Dijkstra, T.; Haslam, E.; Meldrum, P.; Merritt, A.; Gunn, D.; Mackay, J.

2016-01-01

A landslide complex in the Whitby Mudstone Formation at Hollin Hill, North Yorkshire, UK is periodically re-activated in response to rainfall-induced pore-water pressure fluctuations. This paper compares long-term measurements (i.e., 2009-2014) obtained from a combination of monitoring techniques that have been employed together for the first time on an active landslide. The results highlight the relative performance of the different techniques, and can provide guidance for researchers and practitioners for selecting and installing appropriate monitoring techniques to assess unstable slopes. Particular attention is given to the spatial and temporal resolutions offered by the different approaches that include: Real Time Kinematic-GPS (RTK-GPS) monitoring of a ground surface marker array, conventional inclinometers, Shape Acceleration Arrays (SAA), tilt meters, active waveguides with Acoustic Emission (AE) monitoring, and piezometers. High spatial resolution information has allowed locating areas of stability and instability across a large slope. This has enabled identification of areas where further monitoring efforts should be focused. High temporal resolution information allowed the capture of 'S'-shaped slope displacement-time behaviour (i.e. phases of slope acceleration, deceleration and stability) in response to elevations in pore-water pressures. This study shows that a well-balanced suite of monitoring techniques that provides high temporal and spatial resolutions on both measurement and slope scale is necessary to fully understand failure and movement mechanisms of slopes. In the case of the Hollin Hill landslide it enabled detailed interpretation of the geomorphological processes governing landslide activity. It highlights the benefit of regularly surveying a network of GPS markers to determine areas for installation of movement monitoring techniques that offer higher resolution both temporally and spatially. The small sensitivity of tilt meter measurements to translational movements limited the ability to record characteristic 'S'-shaped landslide movements at Hollin Hill, which were identified using SAA and AE measurements. This high sensitivity to landslide movements indicates the applicability of SAA and AE monitoring to be used in early warning systems, through detecting and quantifying accelerations of slope movement.

The influence of natural factors on the spatio-temporal distribution of Oncomelania hupensis.

PubMed

Cheng, Gong; Li, Dan; Zhuang, Dafang; Wang, Yong

2016-12-01

We analyzed the influence of natural factors, such as temperature, rainfall, vegetation and hydrology, on the spatio-temporal distribution of Oncomelania hupensis and explored the leading factors influencing these parameters. The results will provide reference methods and theoretical a basis for the schistosomiasis control. GIS (Geographic Information System) spatial display and analysis were used to describe the spatio-temporal distribution of Oncomelania hupensis in the study area (Dongting Lake in Hunan Province) from 2004 to 2011. Correlation analysis was used to detect the natural factors associated with the spatio-temporal distribution of O. hupensis. Spatial regression analysis was used to quantitatively analyze the effects of related natural factors on the spatio-temporal distribution of snails and explore the dominant factors influencing this parameter. (1) Overall, the spatio-temporal distribution of O. hupensis was governed by the comprehensive effects of natural factors. In the study area, the average density of living snails showed a downward trend, with the exception of a slight rebound in 2009. The density of living snails showed significant spatial clustering, and the degree of aggregation was initially weak but enhanced later. Regions with high snail density and towns with an HH distribution pattern were mostly distributed in the plain areas in the northwestern and inlet and outlet of the lake. (2) There were space-time differences in the influence of natural factors on the spatio-temporal distribution of O. hupensis. Temporally, the comprehensive influence of natural factors on snail distribution increased first and then decreased. Natural factors played an important role in snail distribution in 2005, 2006, 2010 and 2011. Spatially, it decreased from the northeast to the southwest. Snail distributions in more than 20 towns located along the Yuanshui River and on the west side of the Lishui River were less affected by natural factors, whereas relatively larger in areas around the outlet of the lake (Chenglingji) were more affected. (3) The effects of natural factors on the spatio-temporal distribution of O. hupensis were spatio-temporally heterogeneous. Rainfall, land surface temperature, NDVI, and distance from water sources all played an important role in the spatio-temporal distribution of O. hupensis. In addition, due to the effects of the local geographical environment, the direction of the influences the average annual rainfall, land surface temperature, and NDVI had on the spatio-temporal distribution of O. hupensis were all spatio-temporally heterogeneous, and both the distance from water sources and the history of snail distribution always had positive effects on the distribution O. hupensis, but the direction of the influence was spatio-temporally heterogeneous. (4) Of all the natural factors, the leading factors influencing the spatio-temporal distribution of O. hupensis were rainfall and vegetation (NDVI), and the primary factor alternated between these two. The leading role of rainfall decreased year by year, while that of vegetation (NDVI) increased from 2004 to 2011. The spatio-temporal distribution of O. hupensis was significantly influenced by natural factors, and the influences were heterogeneous across space and time. Additionally, the variation in the spatial-temporal distribution of O. hupensis was mainly affected by rainfall and vegetation. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Sensitivity of the weather research and forecasting model to parameterization schemes for regional climate of Nile River Basin

NASA Astrophysics Data System (ADS)

Tariku, Tebikachew Betru; Gan, Thian Yew

2018-06-01

Regional climate models (RCMs) have been used to simulate rainfall at relatively high spatial and temporal resolutions useful for sustainable water resources planning, design and management. In this study, the sensitivity of the RCM, weather research and forecasting (WRF), in modeling the regional climate of the Nile River Basin (NRB) was investigated using 31 combinations of different physical parameterization schemes which include cumulus (Cu), microphysics (MP), planetary boundary layer (PBL), land-surface model (LSM) and radiation (Ra) schemes. Using the European Centre for Medium-Range Weather Forecast (ECMWF) ERA-Interim reanalysis data as initial and lateral boundary conditions, WRF was configured to model the climate of NRB at a resolution of 36 km with 30 vertical levels. The 1999-2001 simulations using WRF were compared with satellite data combined with ground observation and the NCEP reanalysis data for 2 m surface air temperature (T2), rainfall, short- and longwave downward radiation at the surface (SWRAD, LWRAD). Overall, WRF simulated more accurate T2 and LWRAD (with correlation coefficients >0.8 and low root-mean-square error) than SWRAD and rainfall for the NRB. Further, the simulation of rainfall is more sensitive to PBL, Cu and MP schemes than other schemes of WRF. For example, WRF simulated less biased rainfall with Kain-Fritsch combined with MYJ than with YSU as the PBL scheme. The simulation of T2 is more sensitive to LSM and Ra than to Cu, PBL and MP schemes selected, SWRAD is more sensitive to MP and Ra than to Cu, LSM and PBL schemes, and LWRAD is more sensitive to LSM, Ra and PBL than Cu, and MP schemes. In summary, the following combination of schemes simulated the most representative regional climate of NRB: WSM3 microphysics, KF cumulus, MYJ PBL, RRTM longwave radiation and Dudhia shortwave radiation schemes, and Noah LSM. The above configuration of WRF coupled to the Noah LSM has also been shown to simulate representative regional climate of NRB over 1980-2001 which include a combination of wet and dry years of the NRB.

Characterizing Satellite Rainfall Errors based on Land Use and Land Cover and Tracing Error Source in Hydrologic Model Simulation

NASA Astrophysics Data System (ADS)

Gebregiorgis, A. S.; Peters-Lidard, C. D.; Tian, Y.; Hossain, F.

2011-12-01

Hydrologic modeling has benefited from operational production of high resolution satellite rainfall products. The global coverage, near-real time availability, spatial and temporal sampling resolutions have advanced the application of physically based semi-distributed and distributed hydrologic models for wide range of environmental decision making processes. Despite these successes, the existence of uncertainties due to indirect way of satellite rainfall estimates and hydrologic models themselves remain a challenge in making meaningful and more evocative predictions. This study comprises breaking down of total satellite rainfall error into three independent components (hit bias, missed precipitation and false alarm), characterizing them as function of land use and land cover (LULC), and tracing back the source of simulated soil moisture and runoff error in physically based distributed hydrologic model. Here, we asked "on what way the three independent total bias components, hit bias, missed, and false precipitation, affect the estimation of soil moisture and runoff in physically based hydrologic models?" To understand the clear picture of the outlined question above, we implemented a systematic approach by characterizing and decomposing the total satellite rainfall error as a function of land use and land cover in Mississippi basin. This will help us to understand the major source of soil moisture and runoff errors in hydrologic model simulation and trace back the information to algorithm development and sensor type which ultimately helps to improve algorithms better and will improve application and data assimilation in future for GPM. For forest and woodland and human land use system, the soil moisture was mainly dictated by the total bias for 3B42-RT, CMORPH, and PERSIANN products. On the other side, runoff error was largely dominated by hit bias than the total bias. This difference occurred due to the presence of missed precipitation which is a major contributor to the total bias both during the summer and winter seasons. Missed precipitation, most likely light rain and rain over snow cover, has significant effect on soil moisture and are less capable of producing runoff that results runoff dependency on the hit bias only.

Sensitivity of the weather research and forecasting model to parameterization schemes for regional climate of Nile River Basin

NASA Astrophysics Data System (ADS)

Tariku, Tebikachew Betru; Gan, Thian Yew

2017-08-01

Regional climate models (RCMs) have been used to simulate rainfall at relatively high spatial and temporal resolutions useful for sustainable water resources planning, design and management. In this study, the sensitivity of the RCM, weather research and forecasting (WRF), in modeling the regional climate of the Nile River Basin (NRB) was investigated using 31 combinations of different physical parameterization schemes which include cumulus (Cu), microphysics (MP), planetary boundary layer (PBL), land-surface model (LSM) and radiation (Ra) schemes. Using the European Centre for Medium-Range Weather Forecast (ECMWF) ERA-Interim reanalysis data as initial and lateral boundary conditions, WRF was configured to model the climate of NRB at a resolution of 36 km with 30 vertical levels. The 1999-2001 simulations using WRF were compared with satellite data combined with ground observation and the NCEP reanalysis data for 2 m surface air temperature (T2), rainfall, short- and longwave downward radiation at the surface (SWRAD, LWRAD). Overall, WRF simulated more accurate T2 and LWRAD (with correlation coefficients >0.8 and low root-mean-square error) than SWRAD and rainfall for the NRB. Further, the simulation of rainfall is more sensitive to PBL, Cu and MP schemes than other schemes of WRF. For example, WRF simulated less biased rainfall with Kain-Fritsch combined with MYJ than with YSU as the PBL scheme. The simulation of T2 is more sensitive to LSM and Ra than to Cu, PBL and MP schemes selected, SWRAD is more sensitive to MP and Ra than to Cu, LSM and PBL schemes, and LWRAD is more sensitive to LSM, Ra and PBL than Cu, and MP schemes. In summary, the following combination of schemes simulated the most representative regional climate of NRB: WSM3 microphysics, KF cumulus, MYJ PBL, RRTM longwave radiation and Dudhia shortwave radiation schemes, and Noah LSM. The above configuration of WRF coupled to the Noah LSM has also been shown to simulate representative regional climate of NRB over 1980-2001 which include a combination of wet and dry years of the NRB.

Precipitation in a boiling soup: is microphysics driving the statistical properties of intense turbulent convection?

NASA Astrophysics Data System (ADS)

Parodi, A.; von Hardenberg, J.; Provenzale, A.

2012-04-01

Intense precipitation events are often associated with strong convective phenomena in the atmosphere. A deeper understanding of how microphysics affects the spatial and temporal variability of convective processes is relevant for many hydro-meteorological applications, such as the estimation of rainfall using remote sensing techniques and the ability to predict severe precipitation processes. In this paper, high-resolution simulations (0.1-1 km) of an atmosphere in radiative-convective equilibrium are performed using the Weather Research and Forecasting (WRF) model by prescribing different microphysical parameterizations. The dependence of fine-scale spatio-temporal properties of convective structures on microphysical details are investigated and the simulation results are compared with the known properties of radar maps of precipitation fields. We analyze and discuss similarities and differences and, based also on previous results on the dependence of precipitation statistics on the raindrop terminal velocity, try to draw some general inferences.

A novel method for tracing the movement of multiple individual soil particles under rainfall conditions using florescent videography.

NASA Astrophysics Data System (ADS)

Hardy, Robert; Pates, Jackie; Quinton, John

2016-04-01

The importance of developing new techniques to study soil movement cannot be underestimated especially those that integrate new technology. Currently there are limited empirical data available about the movement of individual soil particles, particularly high quality time-resolved data. Here we present a new technique which allows multiple individual soil particles to be traced in real time under simulated rainfall conditions. The technique utilises fluorescent videography in combination with a fluorescent soil tracer, which is based on natural particles. The system has been successfully used on particles greater than ~130 micrometres diameter. The technique uses HD video shot at 50 frames per second, providing extremely high temporal (0.02 s) and spatial resolution (sub-millimetre) of a particle's location without the need to perturb the system. Once the tracer has been filmed then the images are processed and analysed using a particle analysis and visualisation toolkit written in python. The toolkit enables the creation of 2 and 3-D time-resolved graphs showing the location of 1 or more particles. Quantitative numerical analysis of a pathway (or collection of pathways) is also possible, allowing parameters such as particle speed and displacement to be assessed. Filming the particles removes the need to destructively sample material and has many side-benefits, reducing the time, money and effort expended in the collection, transport and laboratory analysis of soils, while delivering data in a digital form which is perfect for modern computer-driven analysis techniques. There are many potential applications for the technique. High resolution empirical data on how soil particles move could be used to create, parameterise and evaluate soil movement models, particularly those that use the movement of individual particles. As data can be collected while rainfall is occurring it may offer the ability to study systems under dynamic conditions(rather than rainfall of a constant intensity), which are more realistic and this was one motivations behind the development of this technique.

Merging fine and coarse resolution remotely sensed data with household-level survey data to evaluate small-scale vulnerability to climate change in West Africa

NASA Astrophysics Data System (ADS)

Grace, K.; Husak, G. J.

2016-12-01

Climate change, in the form of increasingly variable temperatures and rainfall, is anticipated to have potentially dramatic impacts on subsistence agricultural communities throughout the world. Poor people who depend on rainfall to produce food or to produce products to sell to buy food are expected to be particularly vulnerable to the negative impacts associated with climate change. Poor people have extremely limited resources that can be used to cope with weather events and these resources are even more strained when the individuals live in poor countries. While poor and rural producers are most likely to face high levels of vulnerability to food insecurity due to their dependence on rainfall for their agricultural production, annual agricultural censuses are virtually non-existent. Surveying all of the producers in a country each year is extremely costly owing to difficulties in accessing farmers and the costs associated with extensive surveys. The result, however, is very limited information on the spatial and temporal variation in production and the resulting impacts on micro-scale food insecurity and livelihood stability. In this project we use a combination of fine and coarse resolution remotely sensed data ( 1m data, 250m NDVI data and 10km rainfall data, and others) and recently collected survey data from the World Bank to estimate agricultural and land use characteristics at a fine spatial scale in Burkina Faso, Mali and Niger. The analysis will produce estimates of cultivated area that incorporate spatially dynamic climate and vegetation data but that also account for the variation in agricultural practices associated with the different ethnic and religious groups within each country. The survey data will help to calibrate the models and will also serve as a way to validate the statistical models used to estimate on the ground agricultural practices. The models will then be used to evaluate fine-scale agricultural response to climate change in the form of drying and warming.

Geographic patterns of networks derived from extreme precipitation over the Indian subcontinent

NASA Astrophysics Data System (ADS)

Stolbova, Veronika; Bookhagen, Bodo; Marwan, Norbert; Kurths, Juergen

2014-05-01

Complex networks (CN) and event synchronization (ES) methods have been applied to study a number of climate phenomena such as Indian Summer Monsoon (ISM), South-American Monsoon, and African Monsoon. These methods proved to be powerful tools to infer interdependencies in climate dynamics between geographical sites, spatial structures, and key regions of the considered climate phenomenon. Here, we use these methods to study the spatial temporal variability of the extreme rainfall over the Indian subcontinent, in order to filter the data by coarse-graining the network, and to identify geographic patterns that are signature features (spatial signatures) of the ISM. We find four main geographic patterns of networks derived from extreme precipitation over the Indian subcontinent using up-to-date satellite-derived, and high temporal and spatial resolution rain-gauge interpolated daily rainfall datasets. In order to prove that our results are also relevant for other climatic variables like pressure and temperature, we use re-analysis data provided by the National Center for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR). We find that two of the patterns revealed from the CN extreme rainfall analysis coincide with those obtained for the pressure and temperature fields, and all four above mentioned patterns can be explained by topography, winds, and monsoon circulation. CN and ES enable to select the most informative regions for the ISM, providing realistic description of the ISM dynamics with fewer data, and also help to infer geographic pattern that are spatial signatures of the ISM. These patterns deserve a special attention for the meteorologists and can be used as markers of the ISM variability.

The added value of stochastic spatial disaggregation for short-term rainfall forecasts currently available in Canada

NASA Astrophysics Data System (ADS)

Gagnon, Patrick; Rousseau, Alain N.; Charron, Dominique; Fortin, Vincent; Audet, René

2017-11-01

Several businesses and industries rely on rainfall forecasts to support their day-to-day operations. To deal with the uncertainty associated with rainfall forecast, some meteorological organisations have developed products, such as ensemble forecasts. However, due to the intensive computational requirements of ensemble forecasts, the spatial resolution remains coarse. For example, Environment and Climate Change Canada's (ECCC) Global Ensemble Prediction System (GEPS) data is freely available on a 1-degree grid (about 100 km), while those of the so-called High Resolution Deterministic Prediction System (HRDPS) are available on a 2.5-km grid (about 40 times finer). Potential users are then left with the option of using either a high-resolution rainfall forecast without uncertainty estimation and/or an ensemble with a spectrum of plausible rainfall values, but at a coarser spatial scale. The objective of this study was to evaluate the added value of coupling the Gibbs Sampling Disaggregation Model (GSDM) with ECCC products to provide accurate, precise and consistent rainfall estimates at a fine spatial resolution (10-km) within a forecast framework (6-h). For 30, 6-h, rainfall events occurring within a 40,000-km2 area (Québec, Canada), results show that, using 100-km aggregated reference rainfall depths as input, statistics of the rainfall fields generated by GSDM were close to those of the 10-km reference field. However, in forecast mode, GSDM outcomes inherit of the ECCC forecast biases, resulting in a poor performance when GEPS data were used as input, mainly due to the inherent rainfall depth distribution of the latter product. Better performance was achieved when the Regional Deterministic Prediction System (RDPS), available on a 10-km grid and aggregated at 100-km, was used as input to GSDM. Nevertheless, most of the analyzed ensemble forecasts were weakly consistent. Some areas of improvement are identified herein.

How reliable are satellite precipitation estimates for driving hydrological models: a verification study over the Mediterranean area

NASA Astrophysics Data System (ADS)

Camici, Stefania; Ciabatta, Luca; Massari, Christian; Brocca, Luca

2017-04-01

Floods are one of the most common and dangerous natural hazards, causing every year thousands of casualties and damages worldwide. The main tool for assessing flood risk and reducing damages is represented by hydrologic early warning systems that allow to forecast flood events by using real time data obtained through ground monitoring networks (e.g., raingauges and radars). However, the use of such data, mainly rainfall, presents some issues firstly related to the network density and to the limited spatial representativeness of local measurements. A way to overcome these issues may be the use of satellite-based rainfall products (SRPs) that nowadays are available on a global scale at ever increasing spatial/temporal resolution and accuracy. However, despite the large availability and increased accuracy of SRPs (e.g., the Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA); the Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF); and the recent Global Precipitation Measurement (GPM) mission), remotely sensed rainfall data are scarcely used in hydrological modeling and only a small number of studies have been carried out to outline some guidelines for using satellite data as input for hydrological modelling. Reasons may be related to: 1) the large bias characterizing satellite precipitation estimates, which is dependent on rainfall intensity and season, 2) the spatial/temporal resolution, 3) the timeliness, which is often insufficient for operational purposes, and 4) a general (often not justified) skepticism of the hydrological community in the use of satellite products for land applications. The objective of this study is to explore the feasibility of using SRPs in a lumped hydrologic model (MISDc, "Modello Idrologico Semi-Distribuito in continuo", Masseroni et al., 2017) over 10 basins in the Mediterranean area with different sizes and physiographic characteristics. Specifically, TMPA 3B42-RT, CMORPH, PERSIANN and a new soil moisture-derived rainfall datasets obtained through the application of SM2RAIN algorithm (Brocca et al., 2014) to ASCAT (Advanced SCATterometer) soil moisture product are used in the analysis. The performances obtained with SRPs are compared with those obtained by using ground data during the 6-year period from 2010 to 2015. In addition, the performance obtained by an integration of the above mentioned SRPs is also investigated to see whether merged rainfall observations are able to improve flood simulation. Preliminary analysis were also carried out by using the IMERG early run product of GPM mission. The results highlight that SRPs should be used with caution for rainfall-runoff modelling in the Mediterranean region. Bias correction and model recalibration are necessary steps, even though not always sufficient to achieve satisfactory performances. Indeed, some of the products provide unreliable outcomes, mainly in smaller basins (<500 km2) that, however, represent the main target for flood modelling in the Mediterranean area. The better performances are obtained by integrating different SRPs, and particularly by merging TMPA 3B42-RT and SM2RAIN-ASCAT products. The promising results of the integrated product are expected to increase the confidence on the use of SRPs in hydrological modeling, even in challenging areas as the Mediterranean. REFERENCES Brocca, L., Ciabatta, L., Massari, C., Moramarco, T., Hahn, S., Hasenauer, S., Kidd, R., Dorigo, W., Wagner, W., Levizzani, V. (2014). Soil as a natural rain gauge: estimating global rainfall from satellite soil moisture data. Journal of Geophysical Research, 119(9), 5128-5141, doi:10.1002/2014JD021489. Masseroni, D., Cislaghi, A., Camici, S., Massari, C., Brocca, L. (2017). A reliable rainfall-runoff model for flood forecasting: review and application to a semiurbanized watershed at high flood risk in Italy. Hydrology Research, in press, doi:10.2166/nh.2016.037.

Influence of climate variability versus change at multi-decadal time scales on hydrological extremes

NASA Astrophysics Data System (ADS)

Willems, Patrick

2014-05-01

Recent studies have shown that rainfall and hydrological extremes do not randomly occur in time, but are subject to multidecadal oscillations. In addition to these oscillations, there are temporal trends due to climate change. Design statistics, such as intensity-duration-frequency (IDF) for extreme rainfall or flow-duration-frequency (QDF) relationships, are affected by both types of temporal changes (short term and long term). This presentation discusses these changes, how they influence water engineering design and decision making, and how this influence can be assessed and taken into account in practice. The multidecadal oscillations in rainfall and hydrological extremes were studied based on a technique for the identification and analysis of changes in extreme quantiles. The statistical significance of the oscillations was evaluated by means of a non-parametric bootstrapping method. Oscillations in large scale atmospheric circulation were identified as the main drivers for the temporal oscillations in rainfall and hydrological extremes. They also explain why spatial phase shifts (e.g. north-south variations in Europe) exist between the oscillation highs and lows. Next to the multidecadal climate oscillations, several stations show trends during the most recent decades, which may be attributed to climate change as a result of anthropogenic global warming. Such attribution to anthropogenic global warming is, however, uncertain. It can be done based on simulation results with climate models, but it is shown that the climate model results are too uncertain to enable a clear attribution. Water engineering design statistics, such as extreme rainfall IDF or peak or low flow QDF statistics, obviously are influenced by these temporal variations (oscillations, trends). It is shown in the paper, based on the Brussels 10-minutes rainfall data, that rainfall design values may be about 20% biased or different when based on short rainfall series of 10 to 15 years length, and still 8% for series of 25 years lengths. Methods for bias correction are demonstrated. The definition of "bias" depends on a number of factors, which needs further debate in the hydrological and water engineering community. References: Willems P. (2013), 'Multidecadal oscillatory behaviour of rainfall extremes in Europe', Climatic Change, 120(4), 931-944 Willems, P. (2013). 'Adjustment of extreme rainfall statistics accounting for multidecadal climate oscillations', Journal of Hydrology, 490, 126-133 Willems, P., Olsson, J., Arnbjerg-Nielsen, K., Beecham, S., Pathirana, A., Bülow Gregersen, I., Madsen, H., Nguyen, V-T-V. (2012), 'Impacts of climate change on rainfall extremes and urban drainage', IWA Publishing, 252p., Paperback Print ISBN 9781780401256; Ebook ISBN 9781780401263

Scaling of hydrologic and erosion parameters derived from rainfall simulation

NASA Astrophysics Data System (ADS)

Sheridan, Gary; Lane, Patrick; Noske, Philip; Sherwin, Christopher

2010-05-01

Rainfall simulation experiments conducted at the temporal scale of minutes and the spatial scale of meters are often used to derive parameters for erosion and water quality models that operate at much larger temporal and spatial scales. While such parameterization is convenient, there has been little effort to validate this approach via nested experiments across these scales. In this paper we first review the literature relevant to some of these long acknowledged issues. We then present rainfall simulation and erosion plot data from a range of sources, including mining, roading, and forestry, to explore the issues associated with the scaling of parameters such as infiltration properties and erodibility coefficients.

Spatial and temporal variability in the R-5 infiltration data set: Déjà vu and rainfall-runoff simulations

NASA Astrophysics Data System (ADS)

Loague, Keith; Kyriakidis, Phaedon C.

1997-12-01

This paper is a continuation of the event-based rainfall-runoff model evaluation study reported by Loague and Freeze [1985[. Here we reevaluate the performance of a quasi-physically based rainfall-runoff model for three large events from the well-known R-5 catchment. Five different statistical criteria are used to quantitatively judge model performance. Temporal variability in the large R-5 infiltration data set [Loague and Gander, 1990] is filtered by working in terms of permeability. The transformed data set is reanalyzed via geostatistical methods to model the spatial distribution of permeability across the R-5 catchment. We present new estimates of the spatial distribution of infiltration that are in turn used in our rainfall-runoff simulations with the Horton rainfall-runoff model. The new rainfall-runoff simulations, complicated by reinfiltration impacts at the smaller scales of characterization, indicate that the near-surface hydrologic response of the R-5 catchment is most probably dominated by a combination of the Horton and Dunne overland flow mechanisms.

Coupling of equatorial Atlantic surface stratification to glacial shifts in the tropical rainbelt.

PubMed

Portilho-Ramos, R C; Chiessi, C M; Zhang, Y; Mulitza, S; Kucera, M; Siccha, M; Prange, M; Paul, A

2017-05-08

The modern state of the Atlantic meridional overturning circulation promotes a northerly maximum of tropical rainfall associated with the Intertropical Convergence Zone (ITCZ). For continental regions, abrupt millennial-scale meridional shifts of this rainbelt are well documented, but the behavior of its oceanic counterpart is unclear due the lack of a robust proxy and high temporal resolution records. Here we show that the Atlantic ITCZ leaves a distinct signature in planktonic foraminifera assemblages. We applied this proxy to investigate the history of the Atlantic ITCZ for the last 30,000 years based on two high temporal resolution records from the western Atlantic Ocean. Our reconstruction indicates that the shallowest mixed layer associated with the Atlantic ITCZ unambiguously shifted meridionally in response to changes in the strength of the Atlantic meridional overturning with a southward displacement during Heinrich Stadials 2-1 and the Younger Dryas. We conclude that the Atlantic ITCZ was located at ca. 1°S (ca. 5° to the south of its modern annual mean position) during Heinrich Stadial 1. This supports a previous hypothesis, which postulates a southern hemisphere position of the oceanic ITCZ during climatic states with substantially reduced or absent cross-equatorial oceanic meridional heat transport.

The outlook for precipitation measurements from space

NASA Technical Reports Server (NTRS)

Atlas, D.; Eckerman, J.; Meneghini, R.; Moore, R. K.

1981-01-01

To provide useful precipitation measurements from space, two requirements must be met: adequate spatial and temporal sampling of the storm and sufficient accuracy in the estimate of precipitation intensity. Although presently no single instrument or method completely satisfies both requirements, the visible/IR, microwave radiometer and radar methods can be used in a complementary manner. Visible/IR instruments provide good temporal sampling and rain area depiction, but recourse must be made to microwave measurements for quantitative rainfall estimates. The inadequacy of microwave radiometer measurements over land suggests, in turn, the use of radar. Several recently developed attenuating-wavelength radar methods are discussed in terms of their accuracy, dynamic range and system implementation. Traditionally, the requirements of high resolution and adequate dynamic range led to fairly costly and complex radar systems. Some simplications and cost reduction can be made; however, by using K-band wavelengths which have the advantages of greater sensitivity at the low rain rates and higher resolution capabilities. Several recently proposed methods of this kind are reviewed in terms of accuracy and system implementation. Finally, an adaptive-pointing multi-sensor instrument is described that would exploit certain advantages of the IR, radiometric and radar methods.

Transferring Error Characteristics of Satellite Rainfall Data from Ground Validation (gauged) into Non-ground Validation (ungauged)

NASA Astrophysics Data System (ADS)

Tang, L.; Hossain, F.

2009-12-01

Understanding the error characteristics of satellite rainfall data at different spatial/temporal scales is critical, especially when the scheduled Global Precipitation Mission (GPM) plans to provide High Resolution Precipitation Products (HRPPs) at global scales. Satellite rainfall data contain errors which need ground validation (GV) data for characterization, while satellite rainfall data will be most useful in the regions that are lacking in GV. Therefore, a critical step is to develop a spatial interpolation scheme for transferring the error characteristics of satellite rainfall data from GV regions to Non-GV regions. As a prelude to GPM, The TRMM Multi-satellite Precipitation Analysis (TMPA) products of 3B41RT and 3B42RT (Huffman et al., 2007) over the US spanning a record of 6 years are used as a representative example of satellite rainfall data. Next Generation Radar (NEXRAD) Stage IV rainfall data are used as the reference for GV data. Initial work by the authors (Tang et al., 2009, GRL) has shown promise in transferring error from GV to Non-GV regions, based on a six-year climatologic average of satellite rainfall data assuming only 50% of GV coverage. However, this transfer of error characteristics needs to be investigated for a range of GV data coverage. In addition, it is also important to investigate if proxy-GV data from an accurate space-borne sensor, such as the TRMM PR (or the GPM DPR), can be leveraged for the transfer of error at sparsely gauged regions. The specific question we ask in this study is, “what is the minimum coverage of GV data required for error transfer scheme to be implemented at acceptable accuracy in hydrological relevant scale?” Three geostatistical interpolation methods are compared: ordinary kriging, indicator kriging and disjunctive kriging. Various error metrics are assessed for transfer such as, Probability of Detection for rain and no rain, False Alarm Ratio, Frequency Bias, Critical Success Index, RMSE etc. Understanding the proper space-time scales at which these metrics can be reasonably transferred is also explored in this study. Keyword: Satellite rainfall, error transfer, spatial interpolation, kriging methods.

Precipitation isotopes link regional climate patterns to water supply in a tropical mountain forest, eastern Puerto Rico

USGS Publications Warehouse

Scholl, Martha A.; Murphy, Sheila F.

2014-01-01

Like many mountainous areas in the tropics, watersheds in the Luquillo Mountains of eastern Puerto Rico have abundant rainfall and stream discharge and provide much of the water supply for the densely populated metropolitan areas nearby. Projected changes in regional temperature and atmospheric dynamics as a result of global warming suggest that water availability will be affected by changes in rainfall patterns. It is essential to understand the relative importance of different weather systems to water supply to determine how changes in rainfall patterns, interacting with geology and vegetation, will affect the water balance. To help determine the links between climate and water availability, stable isotope signatures of precipitation from different weather systems were established to identify those that are most important in maintaining streamflow and groundwater recharge. Precipitation stable isotope values in the Luquillo Mountains had a large range, from fog/cloud water with δ2H, δ18O values as high as +12 ‰, −0.73 ‰ to tropical storm rain with values as low as −127 ‰, −16.8 ‰. Temporal isotope values exhibit a reverse seasonality from those observed in higher latitude continental watersheds, with higher isotopic values in the winter and lower values in the summer. Despite the higher volume of convective and low-pressure system rainfall, stable isotope analyses indicated that under the current rainfall regime, frequent trade -wind orographic showers contribute much of the groundwater recharge and stream base flow. Analysis of rain events using 20 years of 15 -minute resolution data at a mountain station (643 m) showed an increasing trend in rainfall amount, in agreement with increased precipitable water in the atmosphere, but differing from climate model projections of drying in the region. The mean intensity of rain events also showed an increasing trend. The determination of recharge sources from stable isotope tracers indicates that water supply will be affected if regional atmospheric dynamics change trade- wind orographic rainfall patterns in the Caribbean.

The capacity of radar, crowdsourced personal weather stations and commercial microwave links to monitor small scale urban rainfall

NASA Astrophysics Data System (ADS)

Uijlenhoet, R.; de Vos, L. W.; Leijnse, H.; Overeem, A.; Raupach, T. H.; Berne, A.

2017-12-01

For the purpose of urban rainfall monitoring high resolution rainfall measurements are desirable. Typically C-band radar can provide rainfall intensities at km grid cells every 5 minutes. Opportunistic sensing with commercial microwave links yields rainfall intensities over link paths within cities. Additionally, recent developments have made it possible to obtain large amounts of urban in situ measurements from weather amateurs in near real-time. With a known high resolution simulated rainfall event the accuracy of these three techniques is evaluated, taking into account their respective existing layouts and sampling methods. Under ideal measurement conditions, the weather station networks proves to be most promising. For accurate estimation with radar, an appropriate choice for Z-R relationship is vital. Though both the microwave links and the weather station networks are quite dense, both techniques will underestimate rainfall if not at least one link path / station captures the high intensity rainfall peak. The accuracy of each technique improves when considering rainfall at larger scales, especially by increasing time intervals, with the steepest improvements found in microwave links.

The extent of wind-induced undercatch in the UK winter storms of 2015

NASA Astrophysics Data System (ADS)

Pollock, Michael; Colli, Matteo; Stagnaro, Mattia; Quinn, Paul; Dutton, Mark; O'Donnell, Greg; Wilkinson, Mark; Black, Andrew; O'Connell, Enda; Lanza, Luca

2016-04-01

The most widely used device for measuring rainfall is the rain gauge, of which the tipping bucket (TBR) is the most prevalent type. Rain gauges are considered by many to be the most accurate method currently available. The data they produce are used in flood-forecasting and flood risk management, water resource management, hydrological modelling and evaluating impacts on climate change; to name but a few. Rain gauges may provide the most accurate measurement of rainfall at a point in space and time, but they are subject to errors - and some gauges are more prone than others. The most significant error is the 'wind-induced undercatch'. This is caused by the gauge itself contributing to an acceleration of the wind speed near the orifice, which disturbs and distorts the airflow. The trajectories of precipitation particles are affected, resulting in an undercatch. Results from Computational Fluid Dynamics (CFD) simulations, presented herein, describe in detail the physical processes contributing to this. High resolution field measurements of rainfall and wind are collected at four field research stations in the UK. Each site is equipped with juxtaposed rain gauges with different funnel profiles, in addition to a WMO reference pit rain gauge measurement. These data describe the rainfall measurement uncertainty. The sites were selected to represent the prevalent rainfall regimes observed in the UK. Two research stations are on the west coast; which is prone to frontal weather systems and storms swept in from the Atlantic, often enhanced by orography. Two are located in the east. Rural lowland and upland areas are represented, both in the west and the east. Urban sites will also have significant undercatch problems but are outside the scope of this study. Data from the four research stations are analysed for the 2015 winter storms which caused devastating flooding in the west of the UK, particularly Cumbria and the Scottish Borders, where two of the sites are located. An assessment of the effect of wind on the rainfall catch during these large storm events is presented for each research station. Based on a reference pit rain gauge, the undercatch for these events is calculated. The difference in rainfall catch between several types of rain gauge mounted at variable heights is also investigated. This work aims to demonstrate the importance of improving the accuracy of rainfall measurements, and to emphasise the need to provide an assessment of the measurement uncertainty. A knowledge gap exists in the understanding of precisely how physical phenomena are contributing to wind-induced undercatch. For instance, a priori, the effect of the wind on the rainfall catch will change depending upon the dimensions of the rain droplets. Rainfall 'type' and rainfall intensity may be able to inform corrections, but rigorous multi-variate statistical analysis of high resolution measurements will be key to the success of these procedures. As the spatio-temporal distribution of rainfall can be highly variable, and each measurement location is different; it is a challenging undertaking to understand and pin down the fundamental processes responsible for the wind-induced undercatch.

Rainfall estimation over-land using SMOS soil moisture observations: SM2RAIN, LMAA and SMART algorithms

NASA Astrophysics Data System (ADS)

Massari, Christian; Brocca, Luca; Pellarin, Thierry; Kerr, Yann; Crow, Wade; Cascon, Carlos; Ciabatta, Luca

2016-04-01

Recent advancements in the measurement of precipitation from space have provided estimates at scales that are commensurate with the needs of the hydrological and land-surface model communities. However, as demonstrated in a number of studies (Ebert et al. 2007, Tian et al. 2007, Stampoulis et al. 2012) satellite rainfall estimates are characterized by low accuracy in certain conditions and still suffer from a number of issues (e.g., bias) that may limit their utility in over-land applications (Serrat-Capdevila et al. 2014). In recent years many studies have demonstrated that soil moisture observations from ground and satellite sensors can be used for correcting satellite precipitation estimates (e.g. Crow et al., 2011; Pellarin et al., 2013), or directly estimating rainfall (SM2RAIN, Brocca et al., 2014). In this study, we carried out a detailed scientific analysis in which these three different methods are used for: i) estimating rainfall through satellite soil moisture observations (SM2RAIN, Brocca et al., 2014); ii) correcting rainfall through a Land surface Model Assimilation Algorithm (LMAA) (an improvement of a previous work of Crow et al. 2011 and Pellarin et al. 2013) and through the Soil Moisture Analysis Rainfall Tool (SMART, Crow et al. 2011). The analysis is carried within the ESA project "SMOS plus Rainfall" and involves 9 sites in Europe, Australia, Africa and USA containing high-quality hydrometeorological and soil moisture observations. Satellite soil moisture data from Soil Moisture and Ocean Salinity (SMOS) mission are employed for testing their potential in deriving a cumulated rainfall product at different temporal resolutions. The applicability and accuracy of the three algorithms is investigated also as a function of climatic and soil/land use conditions. A particular attention is paid to assess the expected limitations soil moisture based rainfall estimates such as soil saturation, freezing/snow conditions, SMOS RFI, irrigated areas, contribution of surface runoff and evapotranspiration, vegetation coverage, temporal sampling, and the assimilation/modelling approach. The 9 selected sites gather such potential problems which are shown and discussed at the conference. REFERENCES Ebert, E. E.; Janowiak, J. E.; Kidd, C. Comparison of Near-Real-Time Precipitation Estimates from Satellite Observations and Numerical Models. Bull. Am. Meteorol. Soc. 2007, 88, 47-64. Tian, Y.; Peters-Lidard, C. D.; Choudhury, B. J.; Garcia, M. Multitemporal Analysis of TRMM-Based Satellite Precipitation Products for Land Data Assimilation Applications. J. Hydrometeorol. 2007, 8, 1165-1183. Stampoulis, D.; Anagnostou, E. N. Evaluation of Global Satellite Rainfall Products over Continental Europe. J. Hydrometeorol. 2012, 13, 588-603. Serrat-Capdevila, A.; Valdes, J. B.; Stakhiv, E. Z. Water Management Applications for Satellite Precipitation Products: Synthesis and Recommendations. JAWRA J. Am. Water Resour. Assoc. 2014, 50, 509-525. Crow, W. T.; van den Berg, M. J.; Huffman, G. J.; Pellarin, T. Correcting rainfall using satellite-based surface soil moisture retrievals: The Soil Moisture Analysis Rainfall Tool (SMART). Water Resour. Res. 2011, 47, W08521. Pellarin, T.; Louvet, S.; Gruhier, C.; Quantin, G.; Legout, C. A simple and effective method for correcting soil moisture and precipitation estimates using AMSR-E measurements. Remote Sens. Environ. 2013, 136, 28-36. Brocca, L.; Ciabatta, L.; Massari, C.; Moramarco, T.; Hahn, S.; Hasenauer, S.; Kidd, R.; Dorigo, W.; Wagner, W.; Levizzani, V. Soil as a natural rain gauge: Estimating global rainfall from satellite soil moisture data. J. Geophys. Res. Atmos. 2014, 119, 5128-5141.

Occurrence analysis of daily rainfalls by using non-homogeneous Poissonian processes

NASA Astrophysics Data System (ADS)

Sirangelo, B.; Ferrari, E.; de Luca, D. L.

2009-09-01

In recent years several temporally homogeneous stochastic models have been applied to describe the rainfall process. In particular stochastic analysis of daily rainfall time series may contribute to explain the statistic features of the temporal variability related to the phenomenon. Due to the evident periodicity of the physical process, these models have to be used only to short temporal intervals in which occurrences and intensities of rainfalls can be considered reliably homogeneous. To this aim, occurrences of daily rainfalls can be considered as a stationary stochastic process in monthly periods. In this context point process models are widely used for at-site analysis of daily rainfall occurrence; they are continuous time series models, and are able to explain intermittent feature of rainfalls and simulate interstorm periods. With a different approach, periodic features of daily rainfalls can be interpreted by using a temporally non-homogeneous stochastic model characterized by parameters expressed as continuous functions in the time. In this case, great attention has to be paid to the parsimony of the models, as regards the number of parameters and the bias introduced into the generation of synthetic series, and to the influence of threshold values in extracting peak storm database from recorded daily rainfall heights. In this work, a stochastic model based on a non-homogeneous Poisson process, characterized by a time-dependent intensity of rainfall occurrence, is employed to explain seasonal effects of daily rainfalls exceeding prefixed threshold values. In particular, variation of rainfall occurrence intensity ? (t) is modelled by using Fourier series analysis, in which the non-homogeneous process is transformed into a homogeneous and unit one through a proper transformation of time domain, and the choice of the minimum number of harmonics is evaluated applying available statistical tests. The procedure is applied to a dataset of rain gauges located in different geographical zones of Mediterranean area. Time series have been selected on the basis of the availability of at least 50 years in the time period 1921-1985, chosen as calibration period, and of all the years of observation in the subsequent validation period 1986-2005, whose daily rainfall occurrence process variability is under hypothesis. Firstly, for each time series and for each fixed threshold value, parameters estimation of the non-homogeneous Poisson model is carried out, referred to calibration period. As second step, in order to test the hypothesis that daily rainfall occurrence process preserves the same behaviour in more recent time periods, the intensity distribution evaluated for calibration period is also adopted for the validation period. Starting from this and using a Monte Carlo approach, 1000 synthetic generations of daily rainfall occurrences, of length equal to validation period, have been carried out, and for each simulation sample ?(t) has been evaluated. This procedure is adopted because of the complexity of determining analytical statistical confidence limits referred to the sample intensity ?(t). Finally, sample intensity, theoretical function of the calibration period and 95% statistical band, evaluated by Monte Carlo approach, are matching, together with considering, for each threshold value, the mean square error (MSE) between the theoretical ?(t) and the sample one of recorded data, and his correspondent 95% one tail statistical band, estimated from the MSE values between the sample ?(t) of each synthetic series and the theoretical one. The results obtained may be very useful in the context of the identification and calibration of stochastic rainfall models based on historical precipitation data. Further applications of the non-homogeneous Poisson model will concern the joint analyses of the storm occurrence process with the rainfall height marks, interpreted by using a temporally homogeneous model in proper sub-year intervals.

Estimation of typhoon rainfall in GaoPing River: A Multivariate Maximum Entropy Method

NASA Astrophysics Data System (ADS)

Pei-Jui, Wu; Hwa-Lung, Yu

2016-04-01

The heavy rainfall from typhoons is the main factor of the natural disaster in Taiwan, which causes the significant loss of human lives and properties. Statistically average 3.5 typhoons invade Taiwan every year, and the serious typhoon, Morakot in 2009, impacted Taiwan in recorded history. Because the duration, path and intensity of typhoon, also affect the temporal and spatial rainfall type in specific region , finding the characteristics of the typhoon rainfall type is advantageous when we try to estimate the quantity of rainfall. This study developed a rainfall prediction model and can be divided three parts. First, using the EEOF(extended empirical orthogonal function) to classify the typhoon events, and decompose the standard rainfall type of all stations of each typhoon event into the EOF and PC(principal component). So we can classify the typhoon events which vary similarly in temporally and spatially as the similar typhoon types. Next, according to the classification above, we construct the PDF(probability density function) in different space and time by means of using the multivariate maximum entropy from the first to forth moment statistically. Therefore, we can get the probability of each stations of each time. Final we use the BME(Bayesian Maximum Entropy method) to construct the typhoon rainfall prediction model , and to estimate the rainfall for the case of GaoPing river which located in south of Taiwan.This study could be useful for typhoon rainfall predictions in future and suitable to government for the typhoon disaster prevention .

The role of stochastic storms on hillslope runoff generation and connectivity in a dryland basin

NASA Astrophysics Data System (ADS)

Michaelides, K.; Singer, M. B.; Mudd, S. M.

2016-12-01

Despite low annual rainfall, dryland basins can generate significant surface runoff during certain rainstorms, which can cause flash flooding and high rates of erosion. However, it remains challenging to anticipate the nature and frequency of runoff generation in hydrological systems which are driven by spatially and temporally stochastic rainstorms. In particular, the stochasticity of rainfall presents challenges to simulating the hydrological response of dryland basins and understanding flow connectivity from hillslopes to the channel. Here we simulate hillslope runoff generation using rainfall characteristics produced by a simple stochastic rainfall generator, which is based on a rich rainfall dataset from the Walnut Gulch Experimental Watershed (WGEW) in Arizona, USA. We assess hillslope runoff generation using the hydrological model, COUP2D, driven by a subset of characteristic output from multiple ensembles of decadal monsoonal rainfall from the stochastic rainfall generator. The rainfall generator operates across WGEW by simulating storms with areas smaller than the basin and enables explicit characterization of rainfall characteristics at any location. We combine the characteristics of rainfall intensity and duration with data on rainstorm area and location to model the surface runoff properties (depth, velocity, duration, distance downslope) on a range of hillslopes within the basin derived from LiDAR analysis. We also analyze connectivity of flow from hillslopes to the channel for various combinations of hillslopes and storms. This approach provides a framework for understanding spatial and temporal dynamics of runoff generation and connectivity that is faithful to the hydrological characteristics of dryland environments.

Palaeoclimate Records in Dryland Dunes: Progress and Remaining Challenges Utilizing the Unsaturated Zone for Palaeomoisture Reconstruction.

NASA Astrophysics Data System (ADS)

Stone, A.

2016-12-01

Reconstructions of past rainfall in dryland regions underpin our understanding the links between climatic forcing and palaeohydrological response. However, there are only few proxies in drylands that record palaeorainfall, or palaeomoisture, in a straightforward manner. The unsaturated zone (USZ) has very significant potential as a novel dryland palaeomoisture archive. The approach is simple, based on variations in the concentration of pore-moisture tracers with depth, representing a hydrostratigraphical record through time. The tracer input is meteoric, with the concentration of this tracer established in the near-surface zone as a function of the level of evapotranspiration before that pore-moisture is transmitted vertically down to the water table. This presentation will highlight key regions where hydrostratigraphies have been successfully applied in drylands. It will also set out challenges regarding the assumptions of the approach, with the intention to stimulate discussion regarding the future development of the unsaturated zone as a palaeoclimate archive over a range of timescales and resolutions. Depending on the rate of moisture flux and the depth of the unsaturated zone, dryland hydrostratigraphies may record (i) broad climatic shifts since the last interglacial at low temporal resolution or multi-millennial length palaeomoisture records with a decadal temporal resolution. USZ hydrostratigraphies may also contain a record of changes in the amount of infiltration (and groundwater recharge) caused by changes to land-use.

Assessment of summer rainfall forecast skill in the Intra-Americas in GFDL high and low-resolution models

NASA Astrophysics Data System (ADS)

Krishnamurthy, Lakshmi; Muñoz, Ángel G.; Vecchi, Gabriel A.; Msadek, Rym; Wittenberg, Andrew T.; Stern, Bill; Gudgel, Rich; Zeng, Fanrong

2018-05-01

The Caribbean low-level jet (CLLJ) is an important component of the atmospheric circulation over the Intra-Americas Sea (IAS) which impacts the weather and climate both locally and remotely. It influences the rainfall variability in the Caribbean, Central America, northern South America, the tropical Pacific and the continental Unites States through the transport of moisture. We make use of high-resolution coupled and uncoupled models from the Geophysical Fluid Dynamics Laboratory (GFDL) to investigate the simulation of the CLLJ and its teleconnections and further compare with low-resolution models. The high-resolution coupled model FLOR shows improvements in the simulation of the CLLJ and its teleconnections with rainfall and SST over the IAS compared to the low-resolution coupled model CM2.1. The CLLJ is better represented in uncoupled models (AM2.1 and AM2.5) forced with observed sea-surface temperatures (SSTs), emphasizing the role of SSTs in the simulation of the CLLJ. Further, we determine the forecast skill for observed rainfall using both high- and low-resolution predictions of rainfall and SSTs for the July-August-September season. We determine the role of statistical correction of model biases, coupling and horizontal resolution on the forecast skill. Statistical correction dramatically improves area-averaged forecast skill. But the analysis of spatial distribution in skill indicates that the improvement in skill after statistical correction is region dependent. Forecast skill is sensitive to coupling in parts of the Caribbean, Central and northern South America, and it is mostly insensitive over North America. Comparison of forecast skill between high and low-resolution coupled models does not show any dramatic difference. However, uncoupled models show improvement in the area-averaged skill in the high-resolution atmospheric model compared to lower resolution model. Understanding and improving the forecast skill over the IAS has important implications for highly vulnerable nations in the region.

Physically based modeling of rainfall-triggered landslides: a case study in the Luquillo Forest, Puerto Rico

NASA Astrophysics Data System (ADS)

Lepore, C.; Arnone, E.; Noto, L. V.; Sivandran, G.; Bras, R. L.

2013-01-01

This paper presents the development of a rainfall-triggered landslide module within a physically based spatially distributed ecohydrologic model. The model, Triangulated Irregular Networks Real-time Integrated Basin Simulator and VEGetation Generator for Interactive Evolution (tRIBS-VEGGIE), is capable of a sophisticated description of many hydrological processes; in particular, the soil moisture dynamics is resolved at a temporal and spatial resolution required to examine the triggering mechanisms of rainfall-induced landslides. The validity of the tRIBS-VEGGIE model to a tropical environment is shown with an evaluation of its performance against direct observations made within the Luquillo Forest (the study area). The newly developed landslide module builds upon the previous version of the tRIBS landslide component. This new module utilizes a numerical solution to the Richards equation to better represent the time evolution of soil moisture transport through the soil column. Moreover, the new landslide module utilizes an extended formulation of the Factor of Safety (FS) to correctly quantify the role of matric suction in slope stability and to account for unsaturated conditions in the evaluation of FS. The new modeling framework couples the capabilities of the detailed hydrologic model to describe soil moisture dynamics with the Infinite Slope model creating a powerful tool for the assessment of landslide risk.

Can we improve streamflow simulation by using higher resolution rainfall information?

NASA Astrophysics Data System (ADS)

Lobligeois, Florent; Andréassian, Vazken; Perrin, Charles

2013-04-01

The catchment response to rainfall is the interplay between space-time variability of precipitation, catchment characteristics and antecedent hydrological conditions. Precipitation dominates the high frequency hydrological response, and its simulation is thus dependent on the way rainfall is represented. One of the characteristics which distinguishes distributed from lumped models is their ability to represent explicitly the spatial variability of precipitation and catchment characteristics. The sensitivity of runoff hydrographs to the spatial variability of forcing data has been a major concern of researchers over the last three decades. However, although the literature on the relationship between spatial rainfall and runoff response is abundant, results are contrasted and sometimes contradictory. Several studies concluded that including information on rainfall spatial distribution improves discharge simulation (e.g. Ajami et al., 2004, among others) whereas other studies showed the lack of significant improvement in simulations with better information on rainfall spatial pattern (e.g. Andréassian et al., 2004, among others). The difficulties to reach a clear consensus is mainly due to the fact that each modeling study is implemented only on a few catchments whereas the impact of the spatial distribution of rainfall on runoff is known to be catchment and event characteristics-dependent. Many studies are virtual experiments and only compare flow simulations, which makes it difficult to reach conclusions transposable to real-life case studies. Moreover, the hydrological rainfall-runoff models differ between the studies and the parameterization strategies sometimes tend to advantage the distributed approach (or the lumped one). Recently, Météo-France developed a rainfall reanalysis over the whole French territory at the 1-kilometer resolution and the hourly time step over a 10-year period combining radar data and raingauge measurements: weather radar data were corrected and adjusted with both hourly and daily raingauge data. Based on this new high resolution product, we propose a framework to evaluate the improvements in streamflow simulation by using higher resolution rainfall information. Semi-distributed modelling is performed for different spatial resolution of precipitation forcing: from lumped to semi-distributed simulations. Here we do not work on synthetic (simulated) streamflow, but with actual measurements, on a large set of 181 French catchments representing a variety of size and climate. The rainfall-runoff model is re-calibrated for each resolution of rainfall spatial distribution over a 5-year sub-period and evaluated on the complementary sub-period in validation mode. The results are analysed by catchment classes based on catchment area and for various types of rainfall events based on the spatial variability of precipitation. References Ajami, N. K., Gupta, H. V, Wagener, T. & Sorooshian, S. (2004) Calibration of a semi-distributed hydrologic model for streamflow estimation along a river system. Journal of Hydrology 298(1-4), 112-135. Andréassian, V., Oddos, A., Michel, C., Anctil, F., Perrin, C. & Loumagne, C. (2004) Impact of spatial aggregation of inputs and parameters on the efficiency of rainfall-runoff models: A theoretical study using chimera watersheds. Water Resources Research 40(5), 1-9.

NOAA AVHRR and its uses for rainfall and evapotranspiration monitoring

NASA Technical Reports Server (NTRS)

Kerr, Yann H.; Imbernon, J.; Dedieu, G.; Hautecoeur, O.; Lagouarde, J. P.

1989-01-01

NOAA-7 Advanced Very High Resolution Radiometer (AVHRR) Global Vegetation Indices (GVI) were used during the 1986 rainy season (June-September) over Senegal to monitor rainfall. The satellite data were used in conjunction with ground-based measurements so as to derive empirical relationships between rainfall and GVI. The regression obtained was then used to map the total rainfall corresponding to the growing season, yielding good results. Normalized Difference Vegetation Indices (NDVI) derived from High Resolution Picture Transmission (HRPT) data were also compared with actual evapotranspiration (ET) data and proved to be closely correlated with it with a time lapse of 20 days.

Propagation of radar rainfall uncertainty in urban flood simulations

NASA Astrophysics Data System (ADS)

Liguori, Sara; Rico-Ramirez, Miguel

2013-04-01

This work discusses the results of the implementation of a novel probabilistic system designed to improve ensemble sewer flow predictions for the drainage network of a small urban area in the North of England. The probabilistic system has been developed to model the uncertainty associated to radar rainfall estimates and propagate it through radar-based ensemble sewer flow predictions. The assessment of this system aims at outlining the benefits of addressing the uncertainty associated to radar rainfall estimates in a probabilistic framework, to be potentially implemented in the real-time management of the sewer network in the study area. Radar rainfall estimates are affected by uncertainty due to various factors [1-3] and quality control and correction techniques have been developed in order to improve their accuracy. However, the hydrological use of radar rainfall estimates and forecasts remains challenging. A significant effort has been devoted by the international research community to the assessment of the uncertainty propagation through probabilistic hydro-meteorological forecast systems [4-5], and various approaches have been implemented for the purpose of characterizing the uncertainty in radar rainfall estimates and forecasts [6-11]. A radar-based ensemble stochastic approach, similar to the one implemented for use in the Southern-Alps by the REAL system [6], has been developed for the purpose of this work. An ensemble generator has been calibrated on the basis of the spatial-temporal characteristics of the residual error in radar estimates assessed with reference to rainfall records from around 200 rain gauges available for the year 2007, previously post-processed and corrected by the UK Met Office [12-13]. Each ensemble member is determined by summing a perturbation field to the unperturbed radar rainfall field. The perturbations are generated by imposing the radar error spatial and temporal correlation structure to purely stochastic fields. A hydrodynamic sewer network model implemented in the Infoworks software was used to model the rainfall-runoff process in the urban area. The software calculates the flow through the sewer conduits of the urban model using rainfall as the primary input. The sewer network is covered by 25 radar pixels with a spatial resolution of 1 km2. The majority of the sewer system is combined, carrying both urban rainfall runoff as well as domestic and trade waste water [11]. The urban model was configured to receive the probabilistic radar rainfall fields. The results showed that the radar rainfall ensembles provide additional information about the uncertainty in the radar rainfall measurements that can be propagated in urban flood modelling. The peaks of the measured flow hydrographs are often bounded within the uncertainty area produced by using the radar rainfall ensembles. This is in fact one of the benefits of using radar rainfall ensembles in urban flood modelling. More work needs to be done in improving the urban models, but this is out of the scope of this research. The rainfall uncertainty cannot explain the whole uncertainty shown in the flow simulations, and additional sources of uncertainty will come from the structure of the urban models as well as the large number of parameters required by these models. Acknowledgements The authors would like to acknowledge the BADC, the UK Met Office and the UK Environment Agency for providing the various data sets. We also thank Yorkshire Water Services Ltd for providing the urban model. The authors acknowledge the support from the Engineering and Physical Sciences Research Council (EPSRC) via grant EP/I012222/1. References [1] Browning KA, 1978. Meteorological applications of radar. Reports on Progress in Physics 41 761 Doi: 10.1088/0034-4885/41/5/003 [2] Rico-Ramirez MA, Cluckie ID, Shepherd G, Pallot A, 2007. A high-resolution radar experiment on the island of Jersey. Meteorological Applications 14: 117-129. [3] Villarini G, Krajewski WF, 2010. Review of the different sources of uncertainty in single polarization radar-based estimates of rainfall. Surveys in Geophysics 31: 107-129. [4] Rossa A, Liechti K, Zappa M, Bruen M, Germann U, Haase G, Keil C, Krahe P, 2011. The COST 731 Action: A review on uncertainty propagation in advanced hydrometeorological forecast systems. Atmospheric Research 100, 150-167. [5] Rossa A, Bruen M, Germann U, Haase G, Keil C, Krahe P, Zappa M, 2010. Overview and Main Results on the interdisciplinary effort in flood forecasting COST 731-Propagation of Uncertainty in Advanced Meteo-Hydrological Forecast Systems. Proceedings of Sixth European Conference on Radar in Meteorology and Hydrology ERAD 2010. [6] Germann U, Berenguer M, Sempere-Torres D, Zappa M, 2009. REAL - ensemble radar precipitation estimation for hydrology in a mountainous region. Quarterly Journal of the Royal Meteorological Society 135: 445-456. [8] Bowler NEH, Pierce CE, Seed AW, 2006. STEPS: a probabilistic precipitation forecasting scheme which merges and extrapolation nowcast with downscaled NWP. Quarterly Journal of the Royal Meteorological Society 132: 2127-2155. [9] Zappa M, Rotach MW, Arpagaus M, Dorninger M, Hegg C, Montani A, Ranzi R, Ament F, Germann U, Grossi G et al., 2008. MAP D-PHASE: real-time demonstration of hydrological ensemble prediction systems. Atmospheric Science Letters 9, 80-87. [10] Liguori S, Rico-Ramirez MA. Quantitative assessment of short-term rainfall forecasts from radar nowcasts and MM5 forecasts. Hydrological Processes, accepted article. DOI: 10.1002/hyp.8415 [11] Liguori S, Rico-Ramirez MA, Schellart ANA, Saul AJ, 2012. Using probabilistic radar rainfall nowcasts and NWP forecasts for flow prediction in urban catchments. Atmospheric Research 103: 80-95. [12] Harrison DL, Driscoll SJ, Kitchen M, 2000. Improving precipitation estimates from weather radar using quality control and correction techniques. Meteorological Applications 7: 135-144. [13] Harrison DL, Scovell RW, Kitchen M, 2009. High-resolution precipitation estimates for hydrological uses. Proceedings of the Institution of Civil Engineers - Water Management 162: 125-135.

Multifractal analysis of different hydrological products of X-band radar

NASA Astrophysics Data System (ADS)

Skouri-Plakali, Ilektra; Da Silva Rocha Paz, Igor; Ichiba, Abdellah; Gires, Auguste; Tchiguirinskaia, Ioulia; Schertzer, Daniel

2017-04-01

Rainfall is widely considered as the hydrological process that triggers all the others. Its accurate measurements are crucial especially when they are used afterwards for the hydrological modeling of urban and peri-urban catchments for decision-making. Rainfall is a complex process and is scale dependent in space and time. Hence a high spatial and temporal resolution of the data is more appropriate for urban modeling. Therefore, a great interest of high-resolution measurements of precipitation in space and time is manifested. Radar technologies have not stopped evolving since their first appearance about the mid-twentieth. Indeed, the turning point work by Marshall-Palmer (1948) has established the Z - R power-law relation that has been widely used, with major scientific efforts being devoted to find "the best choice" of the two associated parameters. Nowadays X-band radars, being provided with dual-polarization and Doppler means, offer more accurate data of higher resolution. The fact that drops are oblate induces a differential phase shift between the two polarizations. The quantity most commonly used for the rainfall rate computation is actually the specific differential phase shift, which is the gradient of the differential phase shift along the radial beam direction. It is even stronger correlated to the rain rate R than reflectivity Z. Hence the rain rate can be computed with a different power-law relation, which again depends on only two parameters. Furthermore, an attenuation correction is needed to adjust the loss of radar energy due to the absorption and scattering as it passes through the atmosphere. Due to natural variations of reflectivity with altitude, vertical profile of reflectivity should be corrected as well. There are some other typical radar data filtering procedures, all resulting in various hydrological products. In this work, we use the Universal Multifractal framework to analyze and to inter-compare different products of X-band radar operated by Ecole des Ponts ParisTech. Several rainfall events selected during the recent period (2015 - 2016) were studied over two different embedded grids (64kmx64km and 32kmx32km, with a resolution of 250 m) covering the test site, using a variety of hydrological products. Obtained results demonstrate that some of these products are much more compatible with the scaling ideas. Indeed, the choice of data filters and/or data conversion procedures with the associated parameters does affect the scaling behavior. In turn, the scaling principals help to revisit and furthermore to optimize the radar technologies, including the choice of the associated parameters.

Exploring public databases to characterize urban flood risks in Amsterdam

NASA Astrophysics Data System (ADS)

Gaitan, Santiago; ten Veldhuis, Marie-claire; van de Giesen, Nick

2015-04-01

Cities worldwide are challenged by increasing urban flood risks. Precise and realistic measures are required to decide upon investment to reduce their impacts. Obvious flooding factors affecting flood risk include sewer systems performance and urban topography. However, currently implemented sewer and topographic models do not provide realistic predictions of local flooding occurrence during heavy rain events. Assessing other factors such as spatially distributed rainfall and socioeconomic characteristics may help to explain probability and impacts of urban flooding. Several public databases were analyzed: complaints about flooding made by citizens, rainfall depths (15 min and 100 Ha spatio-temporal resolution), grids describing number of inhabitants, income, and housing price (1Ha and 25Ha resolution); and buildings age. Data analysis was done using Python and GIS programming, and included spatial indexing of data, cluster analysis, and multivariate regression on the complaints. Complaints were used as a proxy to characterize flooding impacts. The cluster analysis, run for all the variables except the complaints, grouped part of the grid-cells of central Amsterdam into a highly differentiated group, covering 10% of the analyzed area, and accounting for 25% of registered complaints. The configuration of the analyzed variables in central Amsterdam coincides with a high complaint count. Remaining complaints were evenly dispersed along other groups. An adjusted R2 of 0.38 in the multivariate regression suggests that explaining power can improve if additional variables are considered. While rainfall intensity explained 4% of the incidence of complaints, population density and building age significantly explained around 20% each. Data mining of public databases proved to be a valuable tool to identify factors explaining variability in occurrence of urban pluvial flooding, though additional variables must be considered to fully explain flood risk variability.

The creation of future daily gridded datasets of precipitation and temperature with a spatial weather generator, Cyprus 2020-2050

NASA Astrophysics Data System (ADS)

Camera, Corrado; Bruggeman, Adriana; Hadjinicolaou, Panos; Pashiardis, Stelios; Lange, Manfred

2014-05-01

High-resolution gridded daily datasets are essential for natural resource management and the analysis of climate changes and their effects. This study aimed to create gridded datasets of daily precipitation and daily minimum and maximum temperature, for the future (2020-2050). The horizontal resolution of the developed datasets is 1 x 1 km2, covering the area under control of the Republic of Cyprus (5.760 km2). The study is divided into two parts. The first consists of the evaluation of the performance of different interpolation techniques for daily rainfall and temperature data (1980-2010) for the creation of the gridded datasets. Rainfall data recorded at 145 stations and temperature data from 34 stations were used. For precipitation, inverse distance weighting (IDW) performs best for local events, while a combination of step-wise geographically weighted regression and IDW proves to be the best method for large scale events. For minimum and maximum temperature, a combination of step-wise linear multiple regression and thin plate splines is recognized as the best method. Six Regional Climate Models (RCMs) for the A1B SRES emission scenario from the EU ENSEMBLE project database were selected as sources for future climate projections. The RCMs were evaluated for their capacity to simulate Cyprus climatology for the period 1980-2010. Data for the period 2020-2050 from the three best performing RCMs were downscaled, using the change factors approach, at the location of observational stations. Daily time series were created with a stochastic rainfall and temperature generator. The RainSim V3 software (Burton et al., 2008) was used to generate spatial-temporal coherent rainfall fields. The temperature generator was developed in R and modeled temperature as a weakly stationary process with the daily mean and standard deviation conditioned on the wet and dry state of the day (Richardson, 1981). Finally gridded datasets depicting projected future climate conditions were created with the identified best interpolation methods. The difference between the input and simulated mean daily rainfall, averaged over all the stations, was 0.03 mm (2.2%), while the error related to the number of dry days was 2 (0.6%). For mean daily minimum temperature the error was 0.005 ºC (0.04%), while for maximum temperature it was 0.01 ºC (0.04%). Overall, the weather generators were found to be reliable instruments for the downscaling of precipitation and temperature. The resulting datasets indicate a decrease of the mean annual rainfall over the study area between 5 and 70 mm (1-15%) for 2020-2050, relative to 1980-2010. Average annual minimum and maximum temperature over the Republic of Cyprus are projected to increase between 1.2 and 1.5 ºC. The dataset is currently used to compute agricultural production and water use indicators, as part of the AGWATER project (AEIFORIA/GEORGO/0311(BIE)/06), co-financed by the European Regional Development Fund and the Republic of Cyprus through the Research Promotion Foundation. Burton, A., Kilsby, C.G., Fowler, H.J., Cowpertwait, P.S.P., and O'Connell, P.E.: RainSim: A spatial-temporal stochastic rainfall modelling system. Environ. Model. Software 23, 1356-1369, 2008 Richardson, C.W.: Stochastic simulation of daily precipitation, temperature, and solar radiation. Water Resour. Res. 17, 182-190, 1981.

Evaluating the impact of lower resolutions of digital elevation model on rainfall-runoff modeling for ungauged catchments.

PubMed

Ghumman, Abul Razzaq; Al-Salamah, Ibrahim Saleh; AlSaleem, Saleem Saleh; Haider, Husnain

2017-02-01

Geomorphological instantaneous unit hydrograph (GIUH) usually uses geomorphologic parameters of catchment estimated from digital elevation model (DEM) for rainfall-runoff modeling of ungauged watersheds with limited data. Higher resolutions (e.g., 5 or 10 m) of DEM play an important role in the accuracy of rainfall-runoff models; however, such resolutions are expansive to obtain and require much greater efforts and time for preparation of inputs. In this research, a modeling framework is developed to evaluate the impact of lower resolutions (i.e., 30 and 90 m) of DEM on the accuracy of Clark GIUH model. Observed rainfall-runoff data of a 202-km 2 catchment in a semiarid region was used to develop direct runoff hydrographs for nine rainfall events. Geographical information system was used to process both the DEMs. Model accuracy and errors were estimated by comparing the model results with the observed data. The study found (i) high model efficiencies greater than 90% for both the resolutions, and (ii) that the efficiency of Clark GIUH model does not significantly increase by enhancing the resolution of the DEM from 90 to 30 m. Thus, it is feasible to use lower resolutions (i.e., 90 m) of DEM in the estimation of peak runoff in ungauged catchments with relatively less efforts. Through sensitivity analysis (Monte Carlo simulations), the kinematic wave parameter and stream length ratio are found to be the most significant parameters in velocity and peak flow estimations, respectively; thus, they need to be carefully estimated for calculation of direct runoff in ungauged watersheds using Clark GIUH model.

Acquisition Of Rainfall Dataset And The Application For The Automatic Harvester In The Chesapeake Bay Region

NASA Astrophysics Data System (ADS)

Choi, Y.; Piasecki, M.

2008-12-01

The objective of this study is the preparation and indexing of rainfall data products for ingestion into the Chesapeake Bay Environmental Observatory (CBEO) node of the CUAHSI/WATERs network. Rainfall products (which are obtained and then processed based on the WSR-88D NEXRAD network) are obtained from the NOAA/NWS Advanced Hydrologic Prediction Service that combines the Multi-sensor Precipitation Estimate (MPE) data generated by the Regional River Forecast Centers and Hydro-NEXRAD rainfall data generated as a service by the University of Iowa. The former is collected on 4*4 km grid (HRAP) with a daily average temporal resolution and the latter on a 1minute*1minute degree grid with hourly values. We have generated a cut-out for the Chesapeake Bay Basin that contains about 9,300 nodes (sites) for the MPE data and about 300,000 nodes (sites) for the Hydro-NEXRAD product. Automated harvesting services have been implemented for both data products. The MPE data is harvested from its download site using ArcGIS which in turn is used to extract the data for the Chesapeake Bay watershed before a scripting program is used to scatter the data into the ODM. The Hydro-NEXRAD is downloaded from a web-based system at the University of Iowa which permits downloads for large scale watersheds organized by Hydraulic Unit Codes (HUC). The resulting ASCII is then automatically parsed and the information stored alongside the MPE data. The two data products stored side-by-side then allows a comparison between them addressing the accuracy and agreement between the methods used to arrive at rainfall data as both use the raw reflectivity data from the WSD-88D system.

Intra-seasonal rainfall variability during the maize growing season in the northern lowlands of Lesotho

NASA Astrophysics Data System (ADS)

Tongwane, Mphethe Isaac; Moeletsi, Mokhele Edmond

2015-05-01

Intra-seasonal rainfall distribution was identified as a priority gap that needs to be addressed for southern Africa to cope with agro-meteorological risks. The region in the northwest of Lesotho is appropriate for crop cultivation due to its relatively favourable climatic conditions and soils. High rainfall variability is often blamed for poor agricultural production in this region. This study aims to determine the onset of rains, cessation of rains and rainy season duration using historical climate data. Temporal variability of these rainy season characteristics was also investigated. The earliest and latest onset dates of the rainy season are during the last week of October at Butha-Buthe and the third week of November at Mapoteng, respectively. Cessation of the season is predominantly in the first week of April making the season approximately 137-163 days long depending on the location. Average seasonal rainfall ranged from 474 mm at Mapoteng to 668 mm at Butha-Buthe. Onset and cessation of the rainfall season vary by 4-7 weeks and 1 week, respectively. Mean coefficient of variation of seasonal rainfall is 39 %, but monthly variations are higher. These variations make annual crop management and planning difficult each year. Trends show a decrease in the rainfall amounts but improvements in both the temporal distribution of annual rainfall, onset and cessation dates.

Stochastic extreme downscaling model for an assessment of changes in rainfall intensity-duration-frequency curves over South Korea using multiple regional climate models

NASA Astrophysics Data System (ADS)

So, Byung-Jin; Kim, Jin-Young; Kwon, Hyun-Han; Lima, Carlos H. R.

2017-10-01

A conditional copula function based downscaling model in a fully Bayesian framework is developed in this study to evaluate future changes in intensity-duration frequency (IDF) curves in South Korea. The model incorporates a quantile mapping approach for bias correction while integrated Bayesian inference allows accounting for parameter uncertainties. The proposed approach is used to temporally downscale expected changes in daily rainfall, inferred from multiple CORDEX-RCMs based on Representative Concentration Pathways (RCPs) 4.5 and 8.5 scenarios, into sub-daily temporal scales. Among the CORDEX-RCMs, a noticeable increase in rainfall intensity is observed in the HadGem3-RA (9%), RegCM (28%), and SNU_WRF (13%) on average, whereas no noticeable changes are observed in the GRIMs (-2%) for the period 2020-2050. More specifically, a 5-30% increase in rainfall intensity is expected in all of the CORDEX-RCMs for 50-year return values under the RCP 8.5 scenario. Uncertainty in simulated rainfall intensity gradually decreases toward the longer durations, which is largely associated with the enhanced strength of the relationship with the 24-h annual maximum rainfalls (AMRs). A primary advantage of the proposed model is that projected changes in future rainfall intensities are well preserved.

Potential Applications of Remote Sensing Precipitation Data on Urban Stormwater Modeling

NASA Astrophysics Data System (ADS)

Maggioni, V.; Tarantola, R.; Ferreira, C.

2014-12-01

Although stormwater modeling is widely used to plan, manage and operate stormwater systems in the urban environment, accuracy in model development and calibration is still problematic. Precipitation is the major forcing of stormwater modeling and one of the most important variables for accurate representation of the water cycle in urban areas. However, rainfall data availability in both temporal and spatial adequate scales is scarce. Here we investigate the potential to apply satellite precipitation products to small-scale urban watersheds with a focus on real-time data for operational use and historical data for model calibration and planning. We present a study case in Northern Virginia, part of the Washington, D.C. metropolitan region. We compare several rainfall datasets from satellites, radar and rain gauges during 2002-2008, using two multi-satellite precipitation products. The first one is the NASA TRMM TMPA at daily/0.25° time/space resolution, which is available in two forms: 3B42-Real Time and 3B42-Version 7, where the latter is a post-processed product, corrected with ground-based observations. The second one is the NOAA CMORPH at 3hrs/0.25° time/space resolution. The NOAA Climate Prediction Center (CPC) data and NCEP Stage IV radar-based product are used as reference datasets for TMPA and CMORPH, respectively. Statistical analyses are conducted to compare these datasets: correlation coefficient, RMSE, bias, probability of correct no-rain detection and of false alarm were computed with a focus on Fairfax, VA county. Preliminary results show that the TMPA products outperform CMORPH, when compared to rain gauges and radar data over the county. Moreover, no appreciable difference is detected between TMPA-V7 and TMPA-RT, which demonstrates that real-time data could be used over the urban watershed with results that are comparable to the adjusted product. Analyses are undergoing to investigate higher temporal resolution and to include a comparison with the Fairfax county rain gages data. Future work will also evaluate the impacts of different precipitation datasets on stormwater runoff for Fairfax county, using the EPA-SWMM5 storm water model.

Evaluation of Urban Drainage Infrastructure: New York City Case Study

NASA Astrophysics Data System (ADS)

Hamidi, A.; Grossberg, M.; Khanbilvardi, R.

2017-12-01

Flood response in an urban area is the product of interactions of spatially and temporally varying rainfall and infrastructures. In urban areas, however, the complex sub-surface networks of tunnels, waste and storm water drainage systems are often inaccessible, pose challenges for modeling and prediction of the drainage infrastructure performance. The increased availability of open data in cities is an emerging information asset for a better understanding of the dynamics of urban water drainage infrastructure. This includes crowd sourced data and community reporting. A well-known source of this type of data is the non-emergency hotline "311" which is available in many US cities, and may contain information pertaining to the performance of physical facilities, condition of the environment, or residents' experience, comfort and well-being. In this study, seven years of New York City 311 (NYC311) call during 2010-2016 is employed, as an alternative approach for identifying the areas of the city most prone to sewer back up flooding. These zones are compared with the hydrologic analysis of runoff flooding zones to provide a predictive model for the City. The proposed methodology is an example of urban system phenomenology using crowd sourced, open data. A novel algorithm for calculating the spatial distribution of flooding complaints across NYC's five boroughs is presented in this study. In this approach, the features that represent reporting bias are separated from those that relate to actual infrastructure system performance. The sewer backup results are assessed with the spatial distribution of runoff in NYC during 2010-2016. With advances in radar technologies, a high spatial-temporal resolution data set for precipitation is available for most of the United States that can be implemented in hydrologic analysis of dense urban environments. High resolution gridded Stage IV radar rainfall data along with the high resolution spatially distributed land cover data are employed to investigate the urban pluvial flooding. The monthly results of excess runoff are compared with the sewer backup in NYC to build a predictive model of flood zones according to the 311 phone calls.

High resolution weather data for urban hydrological modelling and impact assessment, ICT requirements and future challenges

NASA Astrophysics Data System (ADS)

ten Veldhuis, Marie-claire; van Riemsdijk, Birna

2013-04-01

Hydrological analysis of urban catchments requires high resolution rainfall and catchment information because of the small size of these catchments, high spatial variability of the urban fabric, fast runoff processes and related short response times. Rainfall information available from traditional radar and rain gauge networks does no not meet the relevant scales of urban hydrology. A new type of weather radars, based on X-band frequency and equipped with Doppler and dual polarimetry capabilities, promises to provide more accurate rainfall estimates at the spatial and temporal scales that are required for urban hydrological analysis. Recently, the RAINGAIN project was started to analyse the applicability of this new type of radars in the context of urban hydrological modelling. In this project, meteorologists and hydrologists work closely together in several stages of urban hydrological analysis: from the acquisition procedure of novel and high-end radar products to data acquisition and processing, rainfall data retrieval, hydrological event analysis and forecasting. The project comprises of four pilot locations with various characteristics of weather radar equipment, ground stations, urban hydrological systems, modelling approaches and requirements. Access to data processing and modelling software is handled in different ways in the pilots, depending on ownership and user context. Sharing of data and software among pilots and with the outside world is an ongoing topic of discussion. The availability of high resolution weather data augments requirements with respect to the resolution of hydrological models and input data. This has led to the development of fully distributed hydrological models, the implementation of which remains limited by the unavailability of hydrological input data. On the other hand, if models are to be used in flood forecasting, hydrological models need to be computationally efficient to enable fast responses to extreme event conditions. This presentation will highlight ICT-related requirements and limitations in high resolution urban hydrological modelling and analysis. Further ICT challenges arise in provision of high resolution radar data for diverging information needs as well as in combination with other data sources in the urban environment. Different types of information are required for such diverse activities as operational flood protection, traffic management, large event organisation, business planning in shopping districts and restaurants, timing of family activities. These different information needs may require different configurations and data processing for radars and other data sources. An ICT challenge is to develop techniques for deciding how to automatically respond to these diverging information needs (e.g., through (semi-)automated negotiation). Diverse activities also provide a wide variety of information resources that can supplement traditional networks of weather sensors, such as rain sensors on cars and social media. Another ICT challenge is how to combine data from these different sources for answering a particular information need. Examples will be presented of solutions are currently being explored.

Error threshold inference from Global Precipitation Measurement (GPM) satellite rainfall data and interpolated ground-based rainfall measurements in Metro Manila

NASA Astrophysics Data System (ADS)

Ampil, L. J. Y.; Yao, J. G.; Lagrosas, N.; Lorenzo, G. R. H.; Simpas, J.

2017-12-01

The Global Precipitation Measurement (GPM) mission is a group of satellites that provides global observations of precipitation. Satellite-based observations act as an alternative if ground-based measurements are inadequate or unavailable. Data provided by satellites however must be validated for this data to be reliable and used effectively. In this study, the Integrated Multisatellite Retrievals for GPM (IMERG) Final Run v3 half-hourly product is validated by comparing against interpolated ground measurements derived from sixteen ground stations in Metro Manila. The area considered in this study is the region 14.4° - 14.8° latitude and 120.9° - 121.2° longitude, subdivided into twelve 0.1° x 0.1° grid squares. Satellite data from June 1 - August 31, 2014 with the data aggregated to 1-day temporal resolution are used in this study. The satellite data is directly compared to measurements from individual ground stations to determine the effect of the interpolation by contrast against the comparison of satellite data and interpolated measurements. The comparisons are calculated by taking a fractional root-mean-square error (F-RMSE) between two datasets. The results show that interpolation improves errors compared to using raw station data except during days with very small amounts of rainfall. F-RMSE reaches extreme values of up to 654 without a rainfall threshold. A rainfall threshold is inferred to remove extreme error values and make the distribution of F-RMSE more consistent. Results show that the rainfall threshold varies slightly per month. The threshold for June is inferred to be 0.5 mm, reducing the maximum F-RMSE to 9.78, while the threshold for July and August is inferred to be 0.1 mm, reducing the maximum F-RMSE to 4.8 and 10.7, respectively. The maximum F-RMSE is reduced further as the threshold is increased. Maximum F-RMSE is reduced to 3.06 when a rainfall threshold of 10 mm is applied over the entire duration of JJA. These results indicate that IMERG performs well for moderate to high intensity rainfall and that the interpolation remains effective only when rainfall exceeds a certain threshold value. Over Metro Manila, an F-RMSE threshold of 0.5 mm indicated better correspondence between ground measured and satellite measured rainfall.

Soil organic carbon - a large scale paired catchment assessment

NASA Astrophysics Data System (ADS)

Kunkel, V.; Hancock, G. R.; Wells, T.

2016-12-01

Soil organic carbon (SOC) concentration can vary both spatially and temporally driven by differences in soil properties, topography and climate. However most studies have focused on point scale data sets with a paucity of studies examining larger scale catchments. Here we examine the spatial and temporal distribution of SOC for two large catchments. The Krui (575 km2) and Merriwa River (675km2) catchments (New South Wales, Australia). Both have similar shape, soils, topography and orientation. We show that SOC distribution is very similar for both catchments and that elevation (and associated increase in soil moisture) is a major influence on SOC. We also show that there is little change in SOC from the initial assessment in 2006 to 2015 despite a major drought from 2003 to 2010 and extreme rainfall events in 2007 and 2010 -therefore SOC concentration appears robust. However, we found significant relationships between erosion and deposition patterns (as quantified using 137Cs) and SOC for both catchments again demonstrating a strong geomorphic relationship. Vegetation across the catchments was assessed using remote sensing (Landsat and MODIS). Vegetation patterns were temporally consistent with above ground biomass increasing with elevation. SOC could be predicted using both these low and high resolution remote sensing platforms. Results indicate that, although moderate resolution (250 m) allows for reasonable prediction of the spatial distribution of SOC, the higher resolution (30 m) improved the strength of the SOC-NDVI relationship. The relationship between SOC and 137Cs, as a surrogate for the erosion and deposition of SOC, suggested that sediment transport and deposition influences the distribution of SOC within the catchment. The findings demonstrate that over the large catchment scale and at the decadal time scale that SOC is relatively constant and can largely be predicted by topography.

Landslides, floods and sinkholes in a karst environment: the 1-6 September 2014 Gargano event, southern Italy

NASA Astrophysics Data System (ADS)

Martinotti, Maria Elena; Pisano, Luca; Marchesini, Ivan; Rossi, Mauro; Peruccacci, Silvia; Brunetti, Maria Teresa; Melillo, Massimo; Amoruso, Giuseppe; Loiacono, Pierluigi; Vennari, Carmela; Vessia, Giovanna; Trabace, Maria; Parise, Mario; Guzzetti, Fausto

2017-03-01

In karst environments, heavy rainfall is known to cause multiple geohydrological hazards, including inundations, flash floods, landslides and sinkholes. We studied a period of intense rainfall from 1 to 6 September 2014 in the Gargano Promontory, a karst area in Puglia, southern Italy. In the period, a sequence of torrential rainfall events caused severe damage and claimed two fatalities. The amount and accuracy of the geographical and temporal information varied for the different hazards. The temporal information was most accurate for the inundation caused by a major river, less accurate for flash floods caused by minor torrents and even less accurate for landslides. For sinkholes, only generic information on the period of occurrence of the failures was available. Our analysis revealed that in the promontory, rainfall-driven hazards occurred in response to extreme meteorological conditions and that the karst landscape responded to the torrential rainfall with a threshold behaviour. We exploited the rainfall and the landslide information to design the new ensemble-non-exceedance probability (E-NEP) algorithm for the quantitative evaluation of the possible occurrence of rainfall-induced landslides and of related geohydrological hazards. The ensemble of the metrics produced by the E-NEP algorithm provided better diagnostics than the single metrics often used for landslide forecasting, including rainfall duration, cumulated rainfall and rainfall intensity. We expect that the E-NEP algorithm will be useful for landslide early warning in karst areas and in other similar environments. We acknowledge that further tests are needed to evaluate the algorithm in different meteorological, geological and physiographical settings.

Difference infiltrometer: a method to measure temporally variable infiltration rates during rainstorms

USGS Publications Warehouse

Moody, John A.; Ebel, Brian A.

2012-01-01

We developed a difference infiltrometer to measure time series of non-steady infiltration rates during rainstorms at the point scale. The infiltrometer uses two, tipping bucket rain gages. One gage measures rainfall onto, and the other measures runoff from, a small circular plot about 0.5-m in diameter. The small size allows the infiltration rate to be computed as the difference of the cumulative rainfall and cumulative runoff without having to route water through a large plot. Difference infiltrometers were deployed in an area burned by the 2010 Fourmile Canyon Fire near Boulder, Colorado, USA, and data were collected during the summer of 2011. The difference infiltrometer demonstrated the capability to capture different magnitudes of infiltration rates and temporal variability associated with convective (high intensity, short duration) and cyclonic (low intensity, long duration) rainstorms. Data from the difference infiltrometer were used to estimate saturated hydraulic conductivity of soil affected by the heat from a wildfire. The difference infiltrometer is portable and can be deployed in rugged, steep terrain and does not require the transport of water, as many rainfall simulators require, because it uses natural rainfall. It can be used to assess infiltration models, determine runoff coefficients, identify rainfall depth or rainfall intensity thresholds to initiate runoff, estimate parameters for infiltration models, and compare remediation treatments on disturbed landscapes. The difference infiltrometer can be linked with other types of soil monitoring equipment in long-term studies for detecting temporal and spatial variability at multiple time scales and in nested designs where it can be linked to hillslope and basin-scale runoff responses.

Assessing the quality of rainfall data when aiming to achieve flood resilience

NASA Astrophysics Data System (ADS)

Hoang, C. T.; Tchiguirinskaia, I.; Schertzer, D.; Lovejoy, S.

2012-04-01

A new EU Floods Directive entered into force five years ago. This Directive requires Member States to coordinate adequate measures to reduce flood risk. European flood management systems require reliable rainfall statistics, e.g. the Intensity-duration-Frequency curves for shorter and shorter durations and for a larger and larger range of return periods. Preliminary studies showed that the number of floods was lower when using low time resolution data of high intensity rainfall events, compared to estimates obtained with the help of higher time resolution data. These facts suggest that a particular attention should be paid to the rainfall data quality in order to adequately investigate flood risk aiming to achieve flood resilience. The potential consequences of changes in measuring and recording techniques have been somewhat discussed in the literature with respect to a possible introduction of artificial inhomogeneities in time series. In this paper, we discuss how to detect another artificiality: most of the rainfall time series have a lower recording frequency than that is assumed, furthermore the effective high-frequency limit often depends on the recording year due to algorithm changes. This question is particularly important for operational hydrology, because an error on the effective recording high frequency introduces biases in the corresponding statistics. In this direction, we developed a first version of a SERQUAL procedure to automatically detect the effective time resolution of highly mixed data. Being applied to the 166 rainfall time series in France, the SERQUAL procedure has detected that most of them have an effective hourly resolution, rather than a 5 minutes resolution. Furthermore, series having an overall 5 minute resolution do not have it for all years. These results raise serious concerns on how to benchmark stochastic rainfall models at a sub-hourly resolution, which are particularly desirable for operational hydrology. Therefore, database quality must be checked before use. Due to the fact that the multiple scales and possible scaling behaviour of hydrological data are particularly important for many applications, including flood resilience research, this paper first investigates the sensitivity of the scaling estimates and methods to the deficit of short duration rainfall data, and consequently propose a few simple criteria for a reliable evaluation of the data quality. Then we showed that our procedure SERQUAL enable us to extract high quality sub-series from longer time series that will be much more reliable to calibrate and/or validate short duration quantiles and hydrological models.

Learning to love the rain in Bergen (Norway) and other lessons from a Climate Services neophyte

NASA Astrophysics Data System (ADS)

Sobolowski, Stefan; Wakker, Joyce

2014-05-01

A question that is often asked of regional climate modelers generally, and Climate Service providers specifically, is: "What is the added-value of regional climate simulations and how can I use this information?" The answer is, unsurprisingly, not straightforward and depends greatly on what one needs to know. In particular it is important for scientist to communicate directly with the users of this information to determine what kind of information is important for them to do their jobs. This study is part of the ECLISE project (Enabling Climate Information Services for Europe) and involves a user at the municipality of Bergen's (Norway) water and drainage administration and a provider from Uni Research and the Bjerknes Center for Climate Research. The water and drain administration is responsible for communicating potential future changes in extreme precipitation, particularly short-term high-intensity rainfall, which is common in Bergen and making recommendations to the engineering department for changes in design criteria. Thus, information that enables better decision-making is crucial. This study then actually has two relevant components for climate services: 1) is a scientific exercise to evaluate the performance of high resolution regional climate simulations and their ability to reproduce high intensity short duration precipitation and 2) an exercise in communication between a provider community and user community with different concerns, mandates, methodological approaches and even vocabularies. A set of Weather Research and Forecasting (WRF) simulations was run at high resolution (8km) over a large domain covering much of Scandinavia and Northern Europe. One simulation was driven by so-called "perfect" boundary conditions taken from reanalysis data (ERA-interim, 1989-2010) the second and third simulations used Norway's global climate model as boundary forcing (NorESM) and were run for a historical period (1950-2005) and a 30yr. end of the century time slice under the rcp4.5 "middle of the road" emissions scenario (2071-2100). A unique feature of the WRF modeling system is the ability to write data for selected locations at every time step, thus creating time series of very high temporal resolution which can be compared to observations. This high temporal resolution also allowed us to directly calculate intensity-duration-frequency (IDF) curves for intense precipitation of short to long duration (5 minutes - 1 day) for a number of return periods (2-100 years) with out resorting to factors to calculate rainfall intensities at higher temporal resolutions, as is commonly done. We investigated the IDF curves using a number of parametric and non-parametric approaches. Given the relatively short time periods of the modeled data the standard Gumble approach is presented here. This is also done to maintain consistency with previous calculations by the water and drain administration. Curves were also generated from observed time series at two locations in Bergen. Both the historical, GCM-driven simulation and the ERA-interim driven simulation closely match the observed IDF curves for all return periods up to durations of about 10 minutes where WRF then fails to reproduce the very short, very high intensity events. IDF curves under future conditions were also generated and the changes were compared with the current standard approach of applying climate change-factors to observed extreme precipitation in order to account for structural errors in global and regional climate models. Our investigation suggests that high-resolution regional simulations can capture many of the topographic features and dynamical processes necessary to accurately model extreme rainfall, even in at highly local scales and over complex terrain such as Bergen, Norway. The exercise also produced many lessons for climate service providers and users alike.

Realism of Indian Summer Monsoon Simulation in a Quarter Degree Global Climate Model

NASA Astrophysics Data System (ADS)

Salunke, P.; Mishra, S. K.; Sahany, S.; Gupta, K.

2017-12-01

This study assesses the fidelity of Indian Summer Monsoon (ISM) simulations using a global model at an ultra-high horizontal resolution (UHR) of 0.25°. The model used was the atmospheric component of the Community Earth System Model version 1.2.0 (CESM 1.2.0) developed at the National Center for Atmospheric Research (NCAR). Precipitation and temperature over the Indian region were analyzed for a wide range of space and time scales to evaluate the fidelity of the model under UHR, with special emphasis on the ISM simulations during the period of June-through-September (JJAS). Comparing the UHR simulations with observed data from the India Meteorological Department (IMD) over the Indian land, it was found that 0.25° resolution significantly improved spatial rainfall patterns over many regions, including the Western Ghats and the South-Eastern peninsula as compared to the standard model resolution. Convective and large-scale rainfall components were analyzed using the European Centre for Medium Range Weather Forecast (ECMWF) Re-Analysis (ERA)-Interim (ERA-I) data and it was found that at 0.25° resolution, there was an overall increase in the large-scale component and an associated decrease in the convective component of rainfall as compared to the standard model resolution. Analysis of the diurnal cycle of rainfall suggests a significant improvement in the phase characteristics simulated by the UHR model as compared to the standard model resolution. Analysis of the annual cycle of rainfall, however, failed to show any significant improvement in the UHR model as compared to the standard version. Surface temperature analysis showed small improvements in the UHR model simulations as compared to the standard version. Thus, one may conclude that there are some significant improvements in the ISM simulations using a 0.25° global model, although there is still plenty of scope for further improvement in certain aspects of the annual cycle of rainfall.

Dynamic Rainfall Patterns and the Simulation of Changing Scenarios: A behavioral watershed response

NASA Astrophysics Data System (ADS)

Chu, M.; Guzman, J.; Steiner, J. L.; Hou, C.; Moriasi, D.

2015-12-01

Rainfall is one of the fundamental drivers that control hydrologic responses including runoff production and transport phenomena that consequently drive changes in aquatic ecosystems. Quantifying the hydrologic responses to changing scenarios (e.g., climate, land use, and management) using environmental models requires a realistic representation of probable rainfall in its most sensible spatio-temporal dimensions matching that of the phenomenon under investigation. Downscaling projected rainfall from global circulation models (GCMs) is the most common practice in deriving rainfall datasets to be used as main inputs to hydrologic models which in turn are used to assess the impacts of climate changes on ecosystems. Downscaling assumes that local climate is a combination of large-scale climatic/atmospheric conditions and local conditions. However, the representation of the latter is generally beyond the capacity of current GCMs. The main objective of this study was to develop and implement a synthetic rainfall generator to downscale expected rainfall trends to 1 x 1 km rainfall daily patterns that mimic the dynamic propagation of probability distribution functions (pdf) derived from historic rainfall data (rain-gauge or radar estimated). Future projections were determined based on actual and expected changes in the pdf and stochastic processes to account for variability. Watershed responses in terms of streamflow and nutrients loads were evaluated using synthetically generated rainfall patterns and actual data. The framework developed in this study will allow practitioners to generate rainfall datasets that mimic the temporal and spatial patterns exclusive to their study area under full disclosure of the uncertainties involved. This is expected to provide significantly more accurate environmental models than is currently available and would provide practitioners with ways to evaluate the spectrum of systemic responses to changing scenarios.

RainyDay: An Online, Open-Source Tool for Physically-based Rainfall and Flood Frequency Analysis

NASA Astrophysics Data System (ADS)

Wright, D.; Yu, G.; Holman, K. D.

2017-12-01

Flood frequency analysis in ungaged or changing watersheds typically requires rainfall intensity-duration-frequency (IDF) curves combined with hydrologic models. IDF curves only depict point-scale rainfall depth, while true rainstorms exhibit complex spatial and temporal structures. Floods result from these rainfall structures interacting with watershed features such as land cover, soils, and variable antecedent conditions as well as river channel processes. Thus, IDF curves are traditionally combined with a variety of "design storm" assumptions such as area reduction factors and idealized rainfall space-time distributions to translate rainfall depths into inputs that are suitable for flood hydrologic modeling. The impacts of such assumptions are relatively poorly understood. Meanwhile, modern precipitation estimates from gridded weather radar, grid-interpolated rain gages, satellites, and numerical weather models provide more realistic depictions of rainfall space-time structure. Usage of such datasets for rainfall and flood frequency analysis, however, are hindered by relatively short record lengths. We present RainyDay, an open-source stochastic storm transposition (SST) framework for generating large numbers of realistic rainfall "scenarios." SST "lengthens" the rainfall record by temporal resampling and geospatial transposition of observed storms to extract space-time information from regional gridded rainfall data. Relatively short (10-15 year) records of bias-corrected radar rainfall data are sufficient to estimate rainfall and flood events with much longer recurrence intervals including 100-year and 500-year events. We describe the SST methodology as implemented in RainyDay and compare rainfall IDF results from RainyDay to conventional estimates from NOAA Atlas 14. Then, we demonstrate some of the flood frequency analysis properties that are possible when RainyDay is integrated with a distributed hydrologic model, including robust estimation of flood hazards in a changing watershed. The U.S. Bureau of Reclamation is supporting the development of a web-based variant of RainyDay, a "beta" version of which is available at http://her.cee.wisc.edu/projects/rainyday/.

Towards Near Real-time Convective Rainfall Observations over Kenya

NASA Astrophysics Data System (ADS)

Hoedjes, Joost; Said, Mohammed; Becht, Robert; Kifugo, Shem; Kooiman, André; Limo, Agnes; Maathuis, Ben; Moore, Ian; Mumo, Mark; Nduhiu Mathenge, Joseph; Su, Bob; Wright, Iain

2013-04-01

The existing meteorological infrastructure in Kenya is poorly suited for the countrywide real-time monitoring of precipitation. Rainfall radar is not available, and the existing network of rain gauges is sparse and challenging to maintain. This severely restricts Kenya's capacity to warn for, and respond to, weather related emergencies. Furthermore, the lack of accurate rainfall observations severely limits Kenya's climate change adaptation capabilities. Over the past decade, the mobile telephone network in Kenya has expanded rapidly. This network makes extensive use of terrestrial microwave (MW) links, received signal level (RSL) data from which can be used for the calculation of rainfall intensities. We present a novel method for the near-real time observation of convective rainfall over Kenya, based on the combined use of MW RSL data and Meteosat Second Generation (MSG) satellite data. In this study, the variable density rainfall information derived from several MW links is scaled up using MSG data to provide full rainfall information coverage for the region surrounding the links. Combining MSG data and MW link derived rainfall data for several adjacent MW links makes it possible to make the distinction between wet and dry pixels. This allows the disaggregation of the MW link derived rainfall intensities. With the distinction between wet and dry pixels made, and the MW derived rainfall intensities disaggregated, these data can then be used to develop instantaneous empirical relationships linking rainfall intensities to cloud physical properties. These relationships are then used to calculate rainfall intensities for the MSG scene. Since both the MSG and the MW data are available at the same temporal resolution, unique empirical coefficients can be determined for each interval. This approach ensures that changes in convective conditions from one interval to the next are taken into account. Initial results from a pilot study, which took place from November 2012 until January 2013, are presented. The work has been carried out in close cooperation with mobile telephone operator Safaricom, using RSL data from 15 microwave links in rain prone areas in Western Kenya (out of a total of 3000 MW links operated by Safaricom in Kenya). The data supplied by Safaricom consist of the mean, minimum and maximum RSL for each MW link over a 15 minute interval. For this pilot study, use has been made of the MSG Cloud Top Temperature data product from the Royal Dutch Meteorological Institute's MSG Cloud Physical Properties database (http://msgcpp.knmi.nl/).

Evaluation of sub daily satellite rainfall estimates through flash flood modelling in the Lower Middle Zambezi Basin

NASA Astrophysics Data System (ADS)

Matingo, Thomas; Gumindoga, Webster; Makurira, Hodson

2018-05-01

Flash floods are experienced almost annually in the ungauged Mbire District of the Middle Zambezi Basin. Studies related to hydrological modelling (rainfall-runoff) and flood forecasting require major inputs such as precipitation which, due to shortage of observed data, are increasingly using indirect methods for estimating precipitation. This study therefore evaluated performance of CMORPH and TRMM satellite rainfall estimates (SREs) for 30 min, 1 h, 3 h and daily intensities through hydrologic and flash flood modelling in the Lower Middle Zambezi Basin for the period 2013-2016. On a daily timestep, uncorrected CMORPH and TRMM show Probability of Detection (POD) of 61 and 59 %, respectively, when compared to rain gauge observations. The best performance using Correlation Coefficient (CC) was 70 and 60 % on daily timesteps for CMORPH and TRMM, respectively. The best RMSE for CMORPH was 0.81 % for 30 min timestep and for TRMM was 2, 11 % on 3 h timestep. For the year 2014 to 2015, the HEC-HMS (Hydrological Engineering Centre-Hydrological Modelling System) daily model calibration Nash Sutcliffe efficiency (NSE) for Musengezi sub catchment was 59 % whilst for Angwa it was 55 %. Angwa sub-catchment daily NSE results for the period 2015-2016 was 61 %. HEC-RAS flash flood modeling at 100, 50 and 25 year return periods for Angwa sub catchment, inundated 811 and 867 ha for TRMM rainfall simulated discharge at 3 h and daily timesteps, respectively. For CMORPH generated rainfall, the inundation was 818, 876, 890 and 891 ha at daily, 3 h, 1 h and 30 min timesteps. The 30 min time step for CMORPH effectively captures flash floods with the measure of agreement between simulated flood extent and ground control points of 69 %. For TRMM, the 3 h timestep effectively captures flash floods with coefficient of 67 %. The study therefore concludes that satellite products are most effective in capturing localized hydrological processes such as flash floods for sub-daily rainfall, because of improved spatial and temporal resolution.

Use of radar rainfall estimates and forecasts to prevent flash flood in real time by using a road inundation warning system

NASA Astrophysics Data System (ADS)

Versini, Pierre-Antoine

2012-01-01

SummaryImportant damages occur in small headwater catchments when they are hit by severe storms with complex spatio-temporal structure, sometimes resulting in flash floods. As these catchments are mostly not covered by sensor networks, it is difficult to forecast these floods. This is particularly true for road submersions, representing major concerns for flood event managers. The use of Quantitative Precipitation Estimates and Forecasts (QPE/QPF) especially based on radar measurements could particularly be adequate to evaluate rainfall-induced risks. Although their characteristic time and space scales would make them suitable for flash flood modelling, the impact of their uncertainties remain uncertain and have to be evaluated. The Gard region (France) has been chosen as case study. This area is frequently affected by severe flash floods, and an application devoted to the road network has also been recently developed for the North part of this region. This warning system combines distributed hydro-meteorological modelling and susceptibility analysis to provide warnings of road inundations. The warning system has been tested on the specific storm of the 29-30 September 2007. During this event, around 200 mm dropped on the South part of the Gard and many roads were submerged. Radar-based QPE and QPF have been used to forecast the exact location of road submersions and the results have been compared to the effective road submersions actually occurred during the event as listed by the emergency services. Used on an area it has not been calibrated, the results confirm that the road submersion warning system represents a promising tool for anticipating and quantifying the consequences of storm events at ground. It rates the submersion risk with an acceptable level of accuracy and demonstrates also the quality of high spatial and temporal resolution radar rainfall data in real time, and the possibility to use them despite their uncertainties. However because of the quality of rainfall forecasts falls drastically with time, it is not often sufficient to provide valuable information for lead times exceeding 1 h.

A Warning System for Rainfall-Induced Debris Flows: A Integrated Remote Sensing and Data Mining Approach

NASA Astrophysics Data System (ADS)

Elkadiri, R.; Sultan, M.; Nurmemet, I.; Al Harbi, H.; Youssef, A.; Elbayoumi, T.; Zabramwi, Y.; Alzahrani, S.; Bahamil, A.

2014-12-01

We developed methodologies that heavily rely on observations extracted from a wide-range of remote sensing data sets (TRMM, Landsat ETM, ENVISAT, ERS, SPOT, Orbview, GeoEye) to develop a warning system for rainfall-induced debris flows in the Jazan province in the Red Sea Hills. The developed warning system integrates static controlling factors and dynamic triggering factors. The algorithm couples a susceptibility map with a rainfall I-D curve, both are developed using readily available remote sensing datasets. The static susceptibility map was constructed as follows: (1) an inventory was compiled for debris flows identified from high spatial resolution datasets and field verified; (2) 10 topographical and land cover predisposing factors (i.e. slope angle, slope aspect, normalized difference vegetation index, topographical position index, stream power index, flow accumulation, distance to drainage line, soil weathering index, elevation and topographic wetness index) were generated; (3) an artificial neural network model (ANN) was constructed, optimized and validated; (4) a debris-flow susceptibility map was generated using the ANN model and refined (using differential backscatter coefficient radar images). The rainfall threshold curve was derived as follows: (1) a spatial database was generated to host temporal co-registered and radiometrically and atmospherically corrected Landsat images; (2) temporal change detection images were generated for pairs of successively acquired Landsat images and criteria were established to identify "the change" related to debris flows, (3) the duration and intensity of the precipitation event that caused each of the identified debris flow events was assumed to be that of the most intense event within the investigated period; and (4) the I-D curve was extracted using data (intensity and duration of precipitation) for the inventoried events. Our findings include: (1) the spatial controlling factors with the highest predictive power of debris-flow locations are: topographic position index, slope, NDVI and distance to drainage line; (2) the ANN model showed an excellent prediction performance (area under receiver operating characteristic [ROC] curve: 0.961); 3) the preliminary I-D curve is I=39.797×D-0.7355 (I: Intensity and D: duration).

Prediction of heavy rainfall over Chennai Metropolitan City, Tamil Nadu, India: Impact of microphysical parameterization schemes

NASA Astrophysics Data System (ADS)

Singh, K. S.; Bonthu, Subbareddy; Purvaja, R.; Robin, R. S.; Kannan, B. A. M.; Ramesh, R.

2018-04-01

This study attempts to investigate the real-time prediction of a heavy rainfall event over the Chennai Metropolitan City, Tamil Nadu, India that occurred on 01 December 2015 using Advanced Research Weather Research and Forecasting (WRF-ARW) model. The study evaluates the impact of six microphysical (Lin, WSM6, Goddard, Thompson, Morrison and WDM6) parameterization schemes of the model on prediction of heavy rainfall event. In addition, model sensitivity has also been evaluated with six Planetary Boundary Layer (PBL) and two Land Surface Model (LSM) schemes. Model forecast was carried out using nested domain and the impact of model horizontal grid resolutions were assessed at 9 km, 6 km and 3 km. Analysis of the synoptic features using National Center for Environmental Prediction Global Forecast System (NCEP-GFS) analysis data revealed strong upper-level divergence and high moisture content at lower level were favorable for the occurrence of heavy rainfall event over the northeast coast of Tamil Nadu. The study signified that forecasted rainfall was more sensitive to the microphysics and PBL schemes compared to the LSM schemes. The model provided better forecast of the heavy rainfall event using the logical combination of Goddard microphysics, YSU PBL and Noah LSM schemes, and it was mostly attributed to timely initiation and development of the convective system. The forecast with different horizontal resolutions using cumulus parameterization indicated that the rainfall prediction was not well represented at 9 km and 6 km. The forecast with 3 km horizontal resolution provided better prediction in terms of timely initiation and development of the event. The study highlights that forecast of heavy rainfall events using a high-resolution mesoscale model with suitable representations of physical parameterization schemes are useful for disaster management and planning to minimize the potential loss of life and property.

Habitat Hydrology and Geomorphology Control the Distribution of Malaria Vector Larvae in Rural Africa

PubMed Central

Hardy, Andrew J.; Gamarra, Javier G. P.; Cross, Dónall E.; Macklin, Mark G.; Smith, Mark W.; Kihonda, Japhet; Killeen, Gerry F.; Ling’ala, George N.; Thomas, Chris J.

2013-01-01

Background Larval source management is a promising component of integrated malaria control and elimination. This requires development of a framework to target productive locations through process-based understanding of habitat hydrology and geomorphology. Methods We conducted the first catchment scale study of fine resolution spatial and temporal variation in Anopheles habitat and productivity in relation to rainfall, hydrology and geomorphology for a high malaria transmission area of Tanzania. Results Monthly aggregates of rainfall, river stage and water table were not significantly related to the abundance of vector larvae. However, these metrics showed strong explanatory power to predict mosquito larval abundances after stratification by water body type, with a clear seasonal trend for each, defined on the basis of its geomorphological setting and origin. Conclusion Hydrological and geomorphological processes governing the availability and productivity of Anopheles breeding habitat need to be understood at the local scale for which larval source management is implemented in order to effectively target larval source interventions. Mapping and monitoring these processes is a well-established practice providing a tractable way forward for developing important malaria management tools. PMID:24312606

Habitat hydrology and geomorphology control the distribution of malaria vector larvae in rural Africa.

PubMed

Hardy, Andrew J; Gamarra, Javier G P; Cross, Dónall E; Macklin, Mark G; Smith, Mark W; Kihonda, Japhet; Killeen, Gerry F; Ling'ala, George N; Thomas, Chris J

2013-01-01

Larval source management is a promising component of integrated malaria control and elimination. This requires development of a framework to target productive locations through process-based understanding of habitat hydrology and geomorphology. We conducted the first catchment scale study of fine resolution spatial and temporal variation in Anopheles habitat and productivity in relation to rainfall, hydrology and geomorphology for a high malaria transmission area of Tanzania. Monthly aggregates of rainfall, river stage and water table were not significantly related to the abundance of vector larvae. However, these metrics showed strong explanatory power to predict mosquito larval abundances after stratification by water body type, with a clear seasonal trend for each, defined on the basis of its geomorphological setting and origin. Hydrological and geomorphological processes governing the availability and productivity of Anopheles breeding habitat need to be understood at the local scale for which larval source management is implemented in order to effectively target larval source interventions. Mapping and monitoring these processes is a well-established practice providing a tractable way forward for developing important malaria management tools.

High Resolution Monthly Oceanic Rainfall Based on Microwave Brightness Temperature Histograms

NASA Astrophysics Data System (ADS)

Shin, D.; Chiu, L. S.

2005-12-01

A statistical emission-based passive microwave retrieval algorithm has been developed by Wilheit, Chang and Chiu (1991) to estimate space/time oceanic rainfall. The algorithm has been applied to Special Sensor Microwave Imager (SSM/I) data taken on board the Defense Meteorological Satellite Program (DMSP) satellites to provide monthly oceanic rainfall over 2.5ox2.5o and 5ox5o latitude-longitude boxes by the Global Precipitation Climatology Project-Polar Satellite Precipitation Data Center (GPCP-PSPDC, URL: http://gpcp-pspdc.gmu.edu/) as part of NASA's contribution to the GPCP. The algorithm has been modified and applied to the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) data to produce a TRMM Level 3 standard product (3A11) over 5ox5o latitude/longitude boxes. In this study, the algorithm code is modified to retrieve rain rates at 2.5ox2.5o and 1ox1o resolutions for TMI. Two months of TMI data have been tested and the results compared with the monthly mean rain rates derived from TRMM Level 2 TMI rain profile algorithm (2A12) and the original 5ox5o data from 3A11. The rainfall pattern is very similar to the monthly average of 2A12, although the intensity is slightly higher. Details in the rain pattern, such as rain shadow due to island blocking, which were not discernible from the low resolution products, are now easily discernible. The spatial average of the higher resolution rain rates are in general slightly higher than lower resolution rain rates, although a Student-t test shows no significant difference. This high resolution product will be useful for the calibration of IR rain estimates for the production of the GPCP merge rain product.

Estimation of Rainfall Erosivity via 1-Minute to Hourly Rainfall Data from Taipei, Taiwan

NASA Astrophysics Data System (ADS)

Huang, Ting-Yin; Yang, Ssu-Yao; Jan, Chyan-Deng

2017-04-01

Soil erosion is a natural process on hillslopes that threats people's life and properties, having a considerable environmental and economic implications for soil degradation, agricultural activity and water quality. The rainfall erosivity factor (R-factor) in the Universal Soil Loss Equation (USLE), composed of total kinetic energy (E) and the maximum 30-min rainfall intensity (I30), is widely used as an indicator to measure the potential risks of soil loss caused by rainfall at a regional scale. This R factor can represent the detachment and entrainment involved in climate conditions on hillslopes, but lack of 30-min rainfall intensity data usually lead to apply this factor more difficult in many regions. In recent years, fixed-interval, hourly rainfall data is readily available and widely used due to the development of automatic weather stations. Here we assess the estimations of R, E, and I30 based on 1-, 5-, 10-, 15-, 30-, 60-minute rainfall data, and hourly rainfall data obtained from Taipei weather station during 2004 to 2010. Results show that there is a strong correlation among R-factors estimated from different interval rainfall data. Moreover, the shorter time-interval rainfall data (e.g., 1-min) yields larger value of R-factor. The conversion factors of rainfall erosivity (ratio of values estimated from the resolution lower than 30-min rainfall data to those estimated from 60-min and hourly rainfall data, respectively) range from 1.85 to 1.40 (resp. from 1.89 to 1.02) for 60-min (resp. hourly) rainfall data as the time resolution increasing from 30-min to 1-min. This paper provides useful information on estimating R-factor when hourly rainfall data is only available.

Extreme Monsoon Rainfall Signatures Preserved in the Invasive Terrestrial Gastropod Lissachatina fulica

NASA Astrophysics Data System (ADS)

Ghosh, Prosenjit; Rangarajan, Ravi; Thirumalai, Kaustubh; Naggs, Fred

2017-11-01

Indian summer monsoon (ISM) rainfall lasts for a period of 4 months with large variations recorded in terms of rainfall intensity during its period between June and September. Proxy reconstructions of past ISM rainfall variability are required due to the paucity of long instrumental records. However, reconstructing subseasonal rainfall is extremely difficult using conventional hydroclimate proxies due to inadequate sample resolution. Here, we demonstrate the utility of the stable oxygen isotope composition of gastropod shells in reconstructing past rainfall on subseasonal timescales. We present a comparative isotopic study on present day rainwater and stable isotope ratios of precipitate found in the incremental growth bands of giant African land snail Lissachatina fulica (Bowdich) from modern day (2009) and in the historical past (1918). Isotopic signatures present in the growth bands allowed for the identification of ISM rainfall variability in terms of its active and dry spells in the modern as well as past gastropod record. Our results demonstrate the utility of gastropod growth band stable isotope ratios in semiquantitative reconstructions of seasonal rainfall patterns. High resolution climate records extracted from gastropod growth band stable isotopes (museum and archived specimens) can expand the scope for understanding past subseasonal-to-seasonal climate variability.

Rainfall-induced soil aggregate breakdown in field experiments at different rainfall intensities and initial soil moisture conditions

NASA Astrophysics Data System (ADS)

Shi, Pu; Thorlacius, Sigurdur; Keller, Thomas; Keller, Martin; Schulin, Rainer

2017-04-01

Soil aggregate breakdown under rainfall impact is an important process in interrill erosion, but is not represented explicitly in water erosion models. Aggregate breakdown not only reduces infiltration through surface sealing during rainfall, but also determines the size distribution of the disintegrated fragments and thus their availability for size-selective sediment transport and re-deposition. An adequate representation of the temporal evolution of fragment mass size distribution (FSD) during rainfall events and the dependence of this dynamics on factors such as rainfall intensity and soil moisture content may help improve mechanistic erosion models. Yet, little is known about the role of those factors in the dynamics of aggregate breakdown under field conditions. In this study, we conducted a series of artificial rainfall experiments on a field silt loam soil to investigate aggregate breakdown dynamics at different rainfall intensity (RI) and initial soil water content (IWC). We found that the evolution of FSD in the course of a rainfall event followed a consistent two-stage pattern in all treatments. The fragment mean weight diameter (MWD) drastically decreased in an approximately exponential way at the beginning of a rainfall event, followed by a further slow linear decrease in the second stage. We proposed an empirical model that describes this temporal pattern of MWD decrease during a rainfall event and accounts for the effects of RI and IWC on the rate parameters. The model was successfully tested using an independent dataset, showing its potential to be used in erosion models for the prediction of aggregate breakdown. The FSD at the end of the experimental rainfall events differed significantly among treatments, indicating that different aggregate breakdown mechanisms responded differently to the variation in initial soil moisture and rainfall intensity. These results provide evidence that aggregate breakdown dynamics needs to be considered in a case-specific manner in modelling sediment mobilization and transport during water erosion events.

NREPS Applications for Water Supply and Management in California and Tennessee

NASA Technical Reports Server (NTRS)

Gatlin, P.; Scott, M.; Carery, L. D.; Petersen, W. A.

2011-01-01

Management of water resources is a balancing act between temporally and spatially limited sources and competitive needs which can often exceed the supply. In order to manage water resources over a region such as the San Joaquin Valley or the Tennessee River Valley, it is pertinent to know the amount of water that has fallen in the watershed and where the water is going within it. Since rain gauge networks are typically sparsely spaced, it is typical that the majority of rainfall on the region may not be measured. To mitigate this under-sampling of rainfall, weather radar has long been employed to provide areal rainfall estimates. The Next-Generation Weather Radars (NEXRAD) make it possible to estimate rainfall over the majority of the conterminous United States. The NEXRAD Rainfall Estimation Processing System (NREPS) was developed specifically for the purpose of using weather radar to estimate rainfall for water resources management. The NREPS is tailored to meet customer needs on spatial and temporal scales relevant to the hydrologic or land-surface models of the end-user. It utilizes several techniques to mitigate artifacts in the NEXRAD data from contaminating the rainfall field. These techniques include clutter filtering, correction for occultation by topography as well as accounting for the vertical profile of reflectivity. This presentation will focus on improvements made to the NREPS system to map rainfall in the San Joaquin Valley for NASA s Water Supply and Management Project in California, but also ongoing rainfall mapping work in the Tennessee River watershed for the Tennessee Valley Authority and possible future applications in other areas of the continent.

Rainstorms able to induce flash floods in a Mediterranean-climate region (Calabria, southern Italy)

NASA Astrophysics Data System (ADS)

Terranova, O. G.; Gariano, S. L.

2014-03-01

Heavy rainstorms often induce flash flooding, one of the natural disasters most responsible for damage to man-made infrastructure and loss of lives, adversely affecting also the opportunities for socio-economic development of Mediterranean Countries. The frequently dramatic damage of flash floods are often detected with sufficient accuracy by post-event surveys, but rainfall causing them are still only roughly characterized. With the aim of improving the understanding of the temporal structure and spatial distribution of heavy rainstorms in the Mediterranean context, a statistical analysis was carried out in Calabria (southern Italy) concerning rainstorms that mainly induced flash floods, but also shallow landslides and debris-flows. Thus a method is proposed - based on the overcoming of heuristically predetermined threshold values of cumulated rainfall, maximum intensity, and kinetic energy of the rainfall event - to select and characterize the rainstorms able to induce flash floods in the Mediterranean-climate Countries. Therefore the obtained (heavy) rainstorms were automatically classified and studied according to their structure in time, localization and extension. Rainfall-runoff watershed models can consequently benefit from the enhanced identification of design storms, with a realistic time structure integrated with the results of the spatial analysis. A survey of flash flood events recorded in the last decades provides a preliminary validation of the method proposed to identify the heavy rainstorms and synthetically describe their characteristics. The notable size of the employed sample, including data with a very detailed resolution in time, that relate to several rain gauges well-distributed throughout the region, give robustness to the obtained results.

Rainstorms able to induce flash floods in a Mediterranean-climate region (Calabria, southern Italy)

NASA Astrophysics Data System (ADS)

Terranova, O. G.; Gariano, S. L.

2014-09-01

Heavy rainstorms often induce flash flooding, one of the natural disasters most responsible for damage to man-made infrastructures and loss of lives, also adversely affecting the opportunities for socio-economic development of Mediterranean countries. The frequently dramatic damage of flash floods are often detected, with sufficient accuracy, by post-event surveys, but rainfall causing them are still only roughly characterized. With the aim of improving the understanding of the temporal structure and spatial distribution of heavy rainstorms in the Mediterranean context, a statistical analysis was carried out in Calabria (southern Italy) concerning rainstorms that mainly induced flash floods, but also shallow landslides and debris flows. Thus, a method is proposed - based on the overcoming of heuristically predetermined threshold values of cumulated rainfall, maximum intensity, and kinetic energy of the rainfall event - to select and characterize the rainstorms able to induce flash floods in the Mediterranean-climate countries. Therefore, the obtained (heavy) rainstorms were automatically classified and studied according to their structure in time, localization, and extension. Rainfall-runoff watershed models can consequently benefit from the enhanced identification of design storms, with a realistic time structure integrated with the results of the spatial analysis. A survey of flash flood events recorded in the last decades provides a preliminary validation of the method proposed to identify the heavy rainstorms and synthetically describe their characteristics. The notable size of the employed sample, including data with a very detailed resolution in time that relate to several rain gauges well-distributed throughout the region, gives robustness to the obtained results.

Effect of biotic and abiotic factors on inter- and intra-event variability in stemflow rates in oak and pine stands in a Mediterranean mountain area

NASA Astrophysics Data System (ADS)

Cayuela, C.; Llorens, P.; Sánchez-Costa, E.; Levia, D. F.; Latron, J.

2018-05-01

Stemflow, despite being a small proportion of gross rainfall, is an important and understudied flux of water in forested areas. Recent studies have highlighted its complexity and relative importance for understanding soil and groundwater recharge. Stemflow dynamics offer an insight into how rain water is stored and released from the stems of trees to the soil. Past attempts have been made to understand the variability of stemflow under different types of vegetation, but rather few studies have focused on the combined influence of biotic and abiotic factors on inter and intra-storm stemflow variability, and none in Mediterranean climates. This study presents stemflow data collected at high temporal resolution for two species with contrasting canopies and bark characteristics: Quercus pubescens Willd. (downy oak) and Pinus sylvestris L. (Scots pine) in the Vallcebre research catchments (NE of Spain, 42° 12‧N, 1° 49‧E). The main objective was to understand how the interaction of biotic and abiotic factors affected stemflow dynamics. Mean stemflow production was low for both species (∼1% of incident rainfall) and increased with rainfall amount. However, the magnitude of the response depended on the combination of multiple biotic and abiotic factors. Both species produced similar stemflow volumes and the largest differences were found among trees of the same species. The combined analysis of biotic and abiotic factors showed that funneling ratios and stemflow dynamics were highly influenced by the interaction of rainfall intensity and tree size.

Weather model performance on extreme rainfall events simulation's over Western Iberian Peninsula

NASA Astrophysics Data System (ADS)

Pereira, S. C.; Carvalho, A. C.; Ferreira, J.; Nunes, J. P.; Kaiser, J. J.; Rocha, A.

2012-08-01

This study evaluates the performance of the WRF-ARW numerical weather model in simulating the spatial and temporal patterns of an extreme rainfall period over a complex orographic region in north-central Portugal. The analysis was performed for the December month of 2009, during the Portugal Mainland rainy season. The heavy rainfall to extreme heavy rainfall periods were due to several low surface pressure's systems associated with frontal surfaces. The total amount of precipitation for December exceeded, in average, the climatological mean for the 1971-2000 time period in +89 mm, varying from 190 mm (south part of the country) to 1175 mm (north part of the country). Three model runs were conducted to assess possible improvements in model performance: (1) the WRF-ARW is forced with the initial fields from a global domain model (RunRef); (2) data assimilation for a specific location (RunObsN) is included; (3) nudging is used to adjust the analysis field (RunGridN). Model performance was evaluated against an observed hourly precipitation dataset of 15 rainfall stations using several statistical parameters. The WRF-ARW model reproduced well the temporal rainfall patterns but tended to overestimate precipitation amounts. The RunGridN simulation provided the best results but model performance of the other two runs was good too, so that the selected extreme rainfall episode was successfully reproduced.

Radar-raingauge data combination techniques: a revision and analysis of their suitability for urban hydrology.

PubMed

Wang, Li-Pen; Ochoa-Rodríguez, Susana; Simões, Nuno Eduardo; Onof, Christian; Maksimović, Cedo

2013-01-01

The applicability of the operational radar and raingauge networks for urban hydrology is insufficient. Radar rainfall estimates provide a good description of the spatiotemporal variability of rainfall; however, their accuracy is in general insufficient. It is therefore necessary to adjust radar measurements using raingauge data, which provide accurate point rainfall information. Several gauge-based radar rainfall adjustment techniques have been developed and mainly applied at coarser spatial and temporal scales; however, their suitability for small-scale urban hydrology is seldom explored. In this paper a review of gauge-based adjustment techniques is first provided. After that, two techniques, respectively based upon the ideas of mean bias reduction and error variance minimisation, were selected and tested using as case study an urban catchment (∼8.65 km(2)) in North-East London. The radar rainfall estimates of four historical events (2010-2012) were adjusted using in situ raingauge estimates and the adjusted rainfall fields were applied to the hydraulic model of the study area. The results show that both techniques can effectively reduce mean bias; however, the technique based upon error variance minimisation can in general better reproduce the spatial and temporal variability of rainfall, which proved to have a significant impact on the subsequent hydraulic outputs. This suggests that error variance minimisation based methods may be more appropriate for urban-scale hydrological applications.

Prediction of storm transfers and annual loads with data-based mechanistic models using high-frequency data

NASA Astrophysics Data System (ADS)

Ockenden, Mary C.; Tych, Wlodek; Beven, Keith J.; Collins, Adrian L.; Evans, Robert; Falloon, Peter D.; Forber, Kirsty J.; Hiscock, Kevin M.; Hollaway, Michael J.; Kahana, Ron; Macleod, Christopher J. A.; Villamizar, Martha L.; Wearing, Catherine; Withers, Paul J. A.; Zhou, Jian G.; Benskin, Clare McW. H.; Burke, Sean; Cooper, Richard J.; Freer, Jim E.; Haygarth, Philip M.

2017-12-01

Excess nutrients in surface waters, such as phosphorus (P) from agriculture, result in poor water quality, with adverse effects on ecological health and costs for remediation. However, understanding and prediction of P transfers in catchments have been limited by inadequate data and over-parameterised models with high uncertainty. We show that, with high temporal resolution data, we are able to identify simple dynamic models that capture the P load dynamics in three contrasting agricultural catchments in the UK. For a flashy catchment, a linear, second-order (two pathways) model for discharge gave high simulation efficiencies for short-term storm sequences and was useful in highlighting uncertainties in out-of-bank flows. A model with non-linear rainfall input was appropriate for predicting seasonal or annual cumulative P loads where antecedent conditions affected the catchment response. For second-order models, the time constant for the fast pathway varied between 2 and 15 h for all three catchments and for both discharge and P, confirming that high temporal resolution data are necessary to capture the dynamic responses in small catchments (10-50 km2). The models led to a better understanding of the dominant nutrient transfer modes, which will be helpful in determining phosphorus transfers following changes in precipitation patterns in the future.

Spatial and Temporal Variation of Meteorological Drought in the Parambikulam-Aliyar Basin, Tamil Nadu

NASA Astrophysics Data System (ADS)

Manikandan, M.; Tamilmani, D.

2015-09-01

The present study aims to investigate the spatial and temporal variation of meteorological drought in the Parambikulam-Aliyar basin, Tamil Nadu using the Standardized Precipitation Index (SPI) as an indicator of drought severity. The basin was divided into 97 grid-cells of 5 × 5 km with each grid correspondence to approximately 1.03 % of total area. Monthly rainfall data for the period of 40 years (1972-2011) from 28 rain gauge stations in the basin was spatially interpolated and gridded monthly rainfall was created. Regional representative of SPI values calculated from mean areal rainfall were used to analyse the temporal variation of drought at multiple time scales. Spatial variation of drought was analysed based on highest drought severity derived from the monthly gridded SPI values. Frequency analyse was applied to assess the recurrence pattern of drought severity. The temporal analysis of SPI indicated that moderate, severe and extreme droughts are common in the basin and spatial analysis of drought severity identified the areas most frequently affected by drought. The results of this study can be used for developing drought preparedness plan and formulating mitigation strategies for sustainable water resource management within the basin.

Precipitation variability of the Grand Canyon region, 1893 through 2009, and its implications for studying effects of gullying of Holocene terraces and associated archeological sites in Grand Canyon, Arizona

USGS Publications Warehouse

Hereford, Richard; Bennett, Glenn E.; Fairley, Helen C.

2014-01-01

A daily precipitation dataset covering a large part of the American Southwest was compiled for online electronic distribution (http://pubs.usgs.gov/of/2014/1006/). The dataset contains 10.8 million observations spanning January 1893 through January 2009 from 846 weather stations in six states and 13 climate divisions. In addition to processing the data for distribution, water-year totals and other statistical parameters were calculated for each station with more than 2 years of observations. Division-wide total precipitation, expressed as the average deviation from the individual station means of a climate division, shows that the region—including the Grand Canyon, Arizona, area—has been affected by alternating multidecadal episodes of drought and wet conditions. In addition to compiling and analyzing the long-term regional precipitation data, a second dataset consisting of high-temporal-resolution precipitation measurements collected between November 2003 and January 2009 from 10 localities along the Colorado River in Grand Canyon was compiled. An exploratory study of these high-temporal-resolution precipitation measurements suggests that on a daily basis precipitation patterns are generally similar to those at a long-term weather station in the canyon, which in turn resembles the patterns at other long-term stations on the canyon rims; however, precipitation amounts recorded by the individual inner canyon weather stations can vary substantially from station to station. Daily and seasonal rainfall patterns apparent in these data are not random. For example, the inner canyon record, although short and fragmented, reveals three episodes of widespread, heavy precipitation in late summer 2004, early winter 2005, and summer 2007. The 2004 event and several others had sufficient rainfall to initiate potentially pervasive erosion of the late Holocene terraces and related archeological features located along the Colorado River in Grand Canyon.

Soil Infiltration Characteristics in Agroforestry Systems and Their Relationships with the Temporal Distribution of Rainfall on the Loess Plateau in China

PubMed Central

Wang, Lai; Zhong, Chonggao; Gao, Pengxiang; Xi, Weimin; Zhang, Shuoxin

2015-01-01

Many previous studies have shown that land use patterns are the main factors influencing soil infiltration. Thus, increasing soil infiltration and reducing runoff are crucial for soil and water conservation, especially in semi-arid environments. To explore the effects of agroforestry systems on soil infiltration and associated properties in a semi-arid area of the Loess Plateau in China, we compared three plant systems: a walnut (Juglans regia) monoculture system (JRMS), a wheat (Triticum aestivum) monoculture system (TAMS), and a walnut-wheat alley cropping system (JTACS) over a period of 11 years. Our results showed that the JTACS facilitated infiltration, and its infiltration rate temporal distribution showed a stronger relationship coupled with the rainfall temporal distribution compared with the two monoculture systems during the growing season. However, the effect of JTACS on the infiltration capacity was only significant in shallow soil layer, i.e., the 0–40 cm soil depth. Within JTACS, the speed of the wetting front’s downward movement was significantly faster than that in the two monoculture systems when the amount of rainfall and its intensity were higher. The soil infiltration rate was improved, and the two peaks of soil infiltration rate temporal distribution and the rainfall temporal distribution coupled in rainy season in the alley cropping system, which has an important significance in soil and water conservation. The results of this empirical study provide new insights into the sustainability of agroforestry, which may help farmers select rational planting patterns in this region, as well as other regions with similar climatic and environmental characteristics throughout the world. PMID:25893832

Soil Infiltration Characteristics in Agroforestry Systems and Their Relationships with the Temporal Distribution of Rainfall on the Loess Plateau in China.

PubMed

Wang, Lai; Zhong, Chonggao; Gao, Pengxiang; Xi, Weimin; Zhang, Shuoxin

2015-01-01

Many previous studies have shown that land use patterns are the main factors influencing soil infiltration. Thus, increasing soil infiltration and reducing runoff are crucial for soil and water conservation, especially in semi-arid environments. To explore the effects of agroforestry systems on soil infiltration and associated properties in a semi-arid area of the Loess Plateau in China, we compared three plant systems: a walnut (Juglans regia) monoculture system (JRMS), a wheat (Triticum aestivum) monoculture system (TAMS), and a walnut-wheat alley cropping system (JTACS) over a period of 11 years. Our results showed that the JTACS facilitated infiltration, and its infiltration rate temporal distribution showed a stronger relationship coupled with the rainfall temporal distribution compared with the two monoculture systems during the growing season. However, the effect of JTACS on the infiltration capacity was only significant in shallow soil layer, i.e., the 0-40 cm soil depth. Within JTACS, the speed of the wetting front's downward movement was significantly faster than that in the two monoculture systems when the amount of rainfall and its intensity were higher. The soil infiltration rate was improved, and the two peaks of soil infiltration rate temporal distribution and the rainfall temporal distribution coupled in rainy season in the alley cropping system, which has an important significance in soil and water conservation. The results of this empirical study provide new insights into the sustainability of agroforestry, which may help farmers select rational planting patterns in this region, as well as other regions with similar climatic and environmental characteristics throughout the world.

Transfer of uncertainty of space-borne high resolution rainfall products at ungauged regions

NASA Astrophysics Data System (ADS)

Tang, Ling

Hydrologically relevant characteristics of high resolution (˜ 0.25 degree, 3 hourly) satellite rainfall uncertainty were derived as a function of season and location using a six year (2002-2007) archive of National Aeronautics and Space Administration (NASA)'s Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) precipitation data. The Next Generation Radar (NEXRAD) Stage IV rainfall data over the continental United States was used as ground validation (GV) data. A geostatistical mapping scheme was developed and tested for transfer (i.e., spatial interpolation) of uncertainty information from GV regions to the vast non-GV regions by leveraging the error characterization work carried out in the earlier step. The open question explored here was, "If 'error' is defined on the basis of independent ground validation (GV) data, how are error metrics estimated for a satellite rainfall data product without the need for much extensive GV data?" After a quantitative analysis of the spatial and temporal structure of the satellite rainfall uncertainty, a proof-of-concept geostatistical mapping scheme (based on the kriging method) was evaluated. The idea was to understand how realistic the idea of 'transfer' is for the GPM era. It was found that it was indeed technically possible to transfer error metrics from a gauged to an ungauged location for certain error metrics and that a regionalized error metric scheme for GPM may be possible. The uncertainty transfer scheme based on a commonly used kriging method (ordinary kriging) was then assessed further at various timescales (climatologic, seasonal, monthly and weekly), and as a function of the density of GV coverage. The results indicated that if a transfer scheme for estimating uncertainty metrics was finer than seasonal scale (ranging from 3-6 hourly to weekly-monthly), the effectiveness for uncertainty transfer worsened significantly. Next, a comprehensive assessment of different kriging methods for spatial transfer (interpolation) of error metrics was performed. Three kriging methods for spatial interpolation are compared, which are: ordinary kriging (OK), indicator kriging (IK) and disjunctive kriging (DK). Additional comparison with the simple inverse distance weighting (IDW) method was also performed to quantify the added benefit (if any) of using geostatistical methods. The overall performance ranking of the kriging methods was found to be as follows: OK=DK > IDW > IK. Lastly, various metrics of satellite rainfall uncertainty were identified for two large continental landmasses that share many similar Koppen climate zones, United States and Australia. The dependence of uncertainty as a function of gauge density was then investigated. The investigation revealed that only the first and second ordered moments of error are most amenable to a Koppen-type climate type classification in different continental landmasses.

Automated terrestrial laser scanning with near-real-time change detection - monitoring of the Séchilienne landslide

NASA Astrophysics Data System (ADS)

Kromer, Ryan A.; Abellán, Antonio; Hutchinson, D. Jean; Lato, Matt; Chanut, Marie-Aurelie; Dubois, Laurent; Jaboyedoff, Michel

2017-05-01

We present an automated terrestrial laser scanning (ATLS) system with automatic near-real-time change detection processing. The ATLS system was tested on the Séchilienne landslide in France for a 6-week period with data collected at 30 min intervals. The purpose of developing the system was to fill the gap of high-temporal-resolution TLS monitoring studies of earth surface processes and to offer a cost-effective, light, portable alternative to ground-based interferometric synthetic aperture radar (GB-InSAR) deformation monitoring. During the study, we detected the flux of talus, displacement of the landslide and pre-failure deformation of discrete rockfall events. Additionally, we found the ATLS system to be an effective tool in monitoring landslide and rockfall processes despite missing points due to poor atmospheric conditions or rainfall. Furthermore, such a system has the potential to help us better understand a wide variety of slope processes at high levels of temporal detail.

The periodicity of Plasmodium vivax and Plasmodium falciparum in Venezuela.

PubMed

Grillet, María-Eugenia; El Souki, Mayida; Laguna, Francisco; León, José Rafael

2014-01-01

We investigated the periodicity of Plasmodium vivax and P. falciparum incidence in time-series of malaria data (1990-2010) from three endemic regions in Venezuela. In particular, we determined whether disease epidemics were related to local climate variability and regional climate anomalies such as the El Niño Southern Oscillation (ENSO). Malaria periodicity was found to exhibit unique features in each studied region. Significant multi-annual cycles of 2- to about 6-year periods were identified. The inter-annual variability of malaria cases was coherent with that of SSTs (ENSO), mainly at temporal scales within the 3-6 year periods. Additionally, malaria cases were intensified approximately 1 year after an El Niño event, a pattern that highlights the role of climate inter-annual variability in the epidemic patterns. Rainfall mediated the effect of ENSO on malaria locally. Particularly, rains from the last phase of the season had a critical role in the temporal dynamics of Plasmodium. The malaria-climate relationship was complex and transient, varying in strength with the region and species. By identifying temporal cycles of malaria we have made a first step in predicting high-risk years in Venezuela. Our findings emphasize the importance of analyzing high-resolution spatial-temporal data to better understand malaria transmission dynamics. Copyright © 2013 Elsevier B.V. All rights reserved.

Temporal and spatial variability of rainfall over Greece

NASA Astrophysics Data System (ADS)

Markonis, Y.; Batelis, S. C.; Dimakos, Y.; Moschou, E.; Koutsoyiannis, D.

2017-10-01

Recent studies have showed that there is a significant decrease in rainfall over Greece during the last half of the pervious century, following an overall decrease of the precipitation at the eastern Mediterranean. However, during the last decade an increase in rainfall was observed in most regions of the country, contrary to the general circulation climate models forecasts. An updated high-resolution dataset of monthly sums and annual daily maxima records derived from 136 stations during the period 1940-2012 allowed us to present some new evidence for the observed change and its statistical significance. The statistical framework used to determine the significance of the slopes in annual rain was not limited to the time independency assumption (Mann-Kendall test), but we also investigated the effect of short- and long-term persistence through Monte Carlo simulation. Our findings show that (a) change occurs in different scales; most regions show a decline since 1950, an increase since 1980 and remain stable during the last 15 years; (b) the significance of the observed decline is highly dependent to the statistical assumptions used; there are indications that the Mann-Kendall test may be the least suitable method; and (c) change in time is strongly linked with the change in space; for scales below 40 years, relatively close regions may develop even opposite trends, while in larger scales change is more uniform.

Development of a flash flood warning system based on real-time radar data and process-based erosion modelling

NASA Astrophysics Data System (ADS)

Schindewolf, Marcus; Kaiser, Andreas; Buchholtz, Arno; Schmidt, Jürgen

2017-04-01

Extreme rainfall events and resulting flash floods led to massive devastations in Germany during spring 2016. The study presented aims on the development of a early warning system, which allows the simulation and assessment of negative effects on infrastructure by radar-based heavy rainfall predictions, serving as input data for the process-based soil loss and deposition model EROSION 3D. Our approach enables a detailed identification of runoff and sediment fluxes in agricultural used landscapes. In a first step, documented historical events were analyzed concerning the accordance of measured radar rainfall and large scale erosion risk maps. A second step focused on a small scale erosion monitoring via UAV of source areas of heavy flooding events and a model reconstruction of the processes involved. In all examples damages were caused to local infrastructure. Both analyses are promising in order to detect runoff and sediment delivering areas even in a high temporal and spatial resolution. Results prove the important role of late-covering crops such as maize, sugar beet or potatoes in runoff generation. While e.g. winter wheat positively affects extensive runoff generation on undulating landscapes, massive soil loss and thus muddy flows are observed and depicted in model results. Future research aims on large scale model parameterization and application in real time, uncertainty estimation of precipitation forecast and interface developments.

The consecutive dry days to trigger rainfall over West Africa

NASA Astrophysics Data System (ADS)

Lee, J. H.

2018-01-01

In order to resolve contradictions in addressing a soil moisture-precipitation feedback mechanism over West Africa and to clarify the impact of antecedent soil moisture on subsequent rainfall evolution, we first validated various data sets (SMOS satellite soil moisture observations, NOAH land surface model, TRMM rainfall, CMORPH rainfall and HadGEM climate models) with the Analyses Multidisciplinaires de la Mousson Africaine (AMMA) field campaign data. Based on this analysis, it was suggested that biases of data sets might cause contradictions in studying mechanisms. Thus, by taking into account uncertainties in data, it was found that the approach of consecutive dry days (i.e. a relative comparison of time-series) showed consistency across various data sets, while the direct comparison approach for soil moisture state and rainfall did not. Thus, it was discussed that it may be difficult to directly relate rain with soil moisture as the absolute value, however, it may be reasonable to compare a temporal progress of the variables. Based upon the results consistently showing a positive relationship between the consecutive dry days and rainfall, this study supports a negative feedback often neglected by climate model structure. This approach is less sensitive to interpretation errors arising from systematic errors in data sets, as this measures a temporal gradient of soil moisture state.

A Method for Partitioning Surface and Subsurface Flow Using Rainfall Simulaton and Two-Dimensional Surface Electrical Resistivity Imaging

NASA Astrophysics Data System (ADS)

Carey, A. M.; Paige, G. B.; Miller, S. N.; Carr, B. J.; Holbrook, W. S.

2014-12-01

In semi-arid rangeland environments understanding how surface and subsurface flow processes and their interactions are influenced by watershed and rainfall characteristics is critical. However, it is difficult to resolve the temporal variations between mechanisms controlling these processes and challenging to obtain field measurements that document their interactions. Better insight into how these complex systems respond hydrologically is necessary in order to refine hydrologic models and decision support tools. We are conducting field studies integrating high resolution, two-dimensional surface electrical resistivity imaging (ERI) with variable intensity rainfall simulation, to quantify real-time partitioning of rainfall into surface and subsurface response. These studies are being conducted at the hillslope scale on long-term runoff plots on four different ecological sites in the Upper Crow Creek Watershed in southeastern Wyoming. Variable intensity rainfall rates were applied using the Walnut Gulch Rainfall Simulator in which intensities were increased incrementally from 49 to 180 mm hr-1 and steady-state runoff rates for each intensity were measured. Two 13.5 m electrode arrays at 0.5 m spacing were positioned on the surface perpendicular to each plot and potentials were measured at given time intervals prior to, during and following simulations using a dipole-dipole array configuration. The configuration allows for a 2.47 m depth of investigation in which magnitude and direction of subsurface flux can be determined. We used the calculated steady state infiltration rates to quantify the variability in the partial area runoff response on the ecological sites. Coupling this information with time-lapse difference inversions of ERI data, we are able to track areas of increasing and decreasing resistivity in the subsurface related to localized areas of infiltration during and following rainfall events. We anticipate implementing this method across a variety of ecological sites in the Upper Crow Creek in order to characterize the variable hydrologic response of this complex rangeland watershed. This information is being used to refine current physically based hydrologic models and watershed assessment tools.

Required spatial resolution of hydrological models to evaluate urban flood resilience measures

NASA Astrophysics Data System (ADS)

Gires, A.; Giangola-Murzyn, A.; Tchiguirinskaia, I.; Schertzer, D.; Lovejoy, S.

2012-04-01

During a flood in urban area, several non-linear processes (rainfall, surface runoff, sewer flow, and sub-surface flow) interact. Fully distributed hydrological models are a useful tool to better understand these complex interactions between natural processes and man built environment. Developing an efficient model is a first step to improve the understanding of flood resilience in urban area. Given that the previously mentioned underlying physical phenomenon exhibit different relevant scales, determining the required spatial resolution of such model is tricky but necessary issue. For instance such model should be able to properly represent large scale effects of local scale flood resilience measures such as stop logs. The model should also be as simple as possible without being simplistic. In this paper we test two types of model. First we use an operational semi-distributed model over a 3400 ha peri-urban area located in Seine-Saint-Denis (North-East of Paris). In this model, the area is divided into sub-catchments of average size 17 ha that are considered as homogenous, and only the sewer discharge is modelled. The rainfall data, whose resolution is 1 km is space and 5 min in time, comes from the C-band radar of Trappes, located in the West of Paris, and operated by Météo-France. It was shown that the spatial resolution of both the model and the rainfall field did not enable to fully grasp the small scale rainfall variability. To achieve this, first an ensemble of realistic rainfall fields downscaled to a resolution of 100 m is generated with the help of multifractal space-time cascades whose characteristic exponents are estimated on the available radar data. Second the corresponding ensemble of sewer hydrographs is simulated by inputting each rainfall realization to the model. It appears that the probability distribution of the simulated peak flow exhibits a power-law behaviour. This indicates that there is a great uncertainty associated with small scale rainfall. Second we focus on a 50 ha catchment of this area and implement Multi-Hydro, a fully distributed urban hydrological model currently being developed at Ecole des Ponts ParisTech (El Tabach et al., 2009). The version used in this paper consists in an interactive coupling between a 2D model representing infiltration and surface runoff (TREX, Two dimensional Runoff, Erosion and eXport model, Velleux et al., 2011) and a 1D model of sewer networks (SWMM, Storm Water Management Model, Rossman, 2007). Spatial resolution ranging from 2 m to 50 m for land use, topography and rainfall are tested. A special highlight on the impact of small scales rainfall is done. To achieve this the previously mentioned methodology is implemented with rainfall fields downscaled to 10 m in space and 20 s in time. Finally, we will discuss the gains generated by the implementation of the fully distributed model.

Analysis of the daily rainfall events over India using a new long period (1901-2010) high resolution (0.25° × 0.25°) gridded rainfall data set

NASA Astrophysics Data System (ADS)

Pai, D. S.; Sridhar, Latha; Badwaik, M. R.; Rajeevan, M.

2015-08-01

In this study, analysis of the long term climatology, variability and trends in the daily rainfall events of ≥5 mm [or daily rainfall (DR) events] during the southwest monsoon season (June-September) over four regions of India; south central India (SCI), north central India (NCI), northeast India (NEI) and west coast (WC) have been presented. For this purpose, a new high spatial resolution (0.25° × 0.25°) daily gridded rainfall data set covering 110 years (1901-2010) over the Indian main land has been used. The association of monsoon low pressure systems (LPSs) with the DR events of various intensities has also been examined. Major portion of the rainfall over these regions during the season was received in the form of medium rainfall (≥5-100 mm) or moderate rainfall (MR) events. The mean seasonal cycle of the daily frequency of heavy rainfall (HR) (≥100-150 mm) or HR events and very heavy rainfall (VHR) (≥150 mm) or VHR events over each of the four regions showed peak at different parts of the season. The peak in the mean daily HR and VHR events occurred during middle of July to middle of August over SCI, during late part of June to early part of July over NCI, during middle of June to early July over NEI, and during late June to middle July over WC. Significant long term trends in the frequency and intensity of the DR events were observed in all the four geographical regions. Whereas the intensity of the DR events over all the four regions showed significant positive trends during the second half and the total period, the signs and magnitude of the long term trends in the frequency of the various categories of DR events during the total period and its two halves differed from the region to the region. The trend analysis revealed increased disaster potential for instant flooding over SCI and NCI during the recent years due to significant increasing trends in the frequency (areal coverage) and intensity of the HR and VHR events during the recent half of the data period. However, there is increased disaster potential over NEI and WC due to the increasing trends in the intensity of the rainfall events. There is strong association between the LPS days and the DR events in both the spatial and temporal scales. In all the four regions, the contributions to the total MR events by the LPS days were nearly equal. On the other hand, there was relatively large regional difference in the number of combined HR and VHR events associated with LPS days particularly that associated with monsoon depression (LPS stronger than monsoon depression) days. The possible reasons for the same have also been discussed. The increasing trend in the monsoon low (low pressure) days post 1970s is the primary reason for the observed significant increasing trends in the HR and VHR events over SCI and NCI and decreasing trend in HR events over NEI during the recent half (1956-2010). This is in spite of the decreasing trend in the MD days.

High resolution land surface response of inland moving Indian monsoon depressions over Bay of Bengal

NASA Astrophysics Data System (ADS)

Rajesh, P. V.; Pattnaik, S.

2016-05-01

During Indian summer monsoon (ISM) season, nearly about half of the monsoonal rainfall is brought inland by the low pressure systems called as Monsoon Depressions (MDs). These systems bear large amount of rainfall and frequently give copious amount of rainfall over land regions, therefore accurate forecast of these synoptic scale systems at short time scale can help in disaster management, flood relief, food safety. The goal of this study is to investigate, whether an accurate moisture-rainfall feedback from land surface can improve the prediction of inland moving MDs. High Resolution Land Data Assimilation System (HRLDAS) is used to generate improved land state .i.e. soil moisture and soil temperature profiles by means of NOAH-MP land-surface model. Validation of the model simulated basic atmospheric parameters at surface layer and troposphere reveals that the incursion of high resolution land state yields least Root Mean Squared Error (RMSE) with a higher correlation coefficient and facilitates accurate depiction of MDs. Rainfall verification shows that HRLDAS simulations are spatially and quantitatively in more agreement with the observations and the improved surface characteristics could result in the realistic reproduction of the storm spatial structure, movement as well as intensity. These results signify the necessity of investigating more into the land surface-rainfall feedbacks through modifications in moisture flux convergence within the storm.

Challenges with space-time rainfall in urban hydrology highlighted with a semi-distributed model using C-band and X-band radar data

NASA Astrophysics Data System (ADS)

da Silva Rocha Paz, Igor; Ichiba, Abdellah; Skouri-Plakali, Ilektra; Lee, Jisun; Gires, Auguste; Tchiguirinskaia, Ioulia; Schertzer, Daniel

2017-04-01

Climate change and global warming are expected to make precipitation events more frequent, more severe and more local. This may have serious consequences for human health, the environment, cultural heritage, economic activities, utilities and public service providers. Then precipitation risk and water management is a key challenge for densely populated urban areas. Applications derived from high (time and space) resolution observation of precipitations are to make our cities more weather-ready. Finer resolution data available from X-band dual radar measurements enhance engineering tools as used for urban planning policies as well as protection (mitigation/adaptation) strategies to tackle climate-change related weather events. For decades engineering tools have been developed to work conveniently either with very local rain gauge networks, or with mainly C-band weather radars that have gradually been set up for space-time remote sensing of precipitation. Most of the time, the C-band weather radars continue to be calibrated by the existing rain gauge networks. Inhomogeneous distributions of rain gauging networks lead to only a partial information on the rainfall fields. In fact, the statistics of measured rainfall is strongly biased by the fractality of the measuring networks. This fractality needs to be properly taken in to account to retrieve the original properties of the rainfall fields, in spite of the radar data calibration. In this presentation, with the help of multifractal analysis, we first demonstrate that the semi-distributed hydrological models statistically reduce the rainfall fields into rainfall measured by a much scarcer network of virtual rain gauges. For this purpose, we use C-band and X-band radar data. The first has a resolution of 1 km in space and 5 min in time and is in fact a product provided by RHEA SAS after treating the Météo-France C-band radar data. The latter is measured by the radar operated at Ecole des Ponts and has a resolution of 250 m in space and 3.4 min in time. The obtained results suggest that a proper rainfall data re-normalisation is needed either when comparing gauged rainfall with the radar data, or when quantifying the impacts of space-time variability within hydrological modelling. Then, we used the semi-distributed hydrological model InfoWorks CS operated by Veolia over the Bièvre catchment (Paris region) with the same two types of rainfall data as inputs. We simulated six events and analysed the hydrographs resulted from simulations with both data types to show the impacts of initially different resolutions of rainfall fields over the same catchment, especially in respect to the small-scale variability not measured by the C-band radar data. These results encourage us not only to argue the use of higher resolution rainfall data, compare to that has been so claimed in the literature, but also to emphasise the important role of nonlinear geophysics' methods in taking reliable decisions.

Assessing Australian Rainfall Projections in Two Model Resolutions

NASA Astrophysics Data System (ADS)

Taschetto, A.; Haarsma, R. D.; Sen Gupta, A.

2016-02-01

Australian climate is projected to change with increases in greenhouse gases. The IPCC reports an increase in extreme daily rainfall across the country. At the same time, mean rainfall over southeast Australia is projected to reduce during austral winter, but to increase during austral summer, mainly associated with changes in the surrounding oceans. Climate models agree better on the future reduction of average rainfall over the southern regions of Australia compared to the increase in extreme rainfall events. One of the reasons for this disagreement may be related to climate model limitations in simulating the observed mechanisms associated with the mid-latitude weather systems, in particular due to coarse model resolutions. In this study we investigate how changes in sea surface temperature (SST) affect Australian mean and extreme rainfall under global warming, using a suite of numerical experiments at two model resolutions: about 126km (T159) and 25km (T799). The numerical experiments are performed with the earth system model EC-EARTH. Two 6-member ensembles are produced for the present day conditions and a future scenario. The present day ensemble is forced with the observed daily SST from the NOAA National Climatic Data Center from 2002 to 2006. The future scenario simulation is integrated from 2094 to 2098 using the present day SST field added onto the future SST change created from a 17-member ensemble based on the RCP4.5 scenario. Preliminary results show an increase in extreme rainfall events over Tasmania associated with enhanced convection driven by the Tasman Sea warming. We will further discuss how the projected changes in SST will impact the southern mid-latitude weather systems that ultimately affect Australian rainfall.

Data assimilation experiment of precipitable water vapor observed by a hyper-dense GNSS receiver network using a nested NHM-LETKF system

NASA Astrophysics Data System (ADS)

Oigawa, Masanori; Tsuda, Toshitaka; Seko, Hiromu; Shoji, Yoshinori; Realini, Eugenio

2018-05-01

We studied the assimilation of high-resolution precipitable water vapor (PWV) data derived from a hyper-dense global navigation satellite system network around Uji city, Kyoto, Japan, which had a mean inter-station distance of about 1.7 km. We focused on a heavy rainfall event that occurred on August 13-14, 2012, around Uji city. We employed a local ensemble transform Kalman filter as the data assimilation method. The inhomogeneity of the observed PWV increased on a scale of less than 10 km in advance of the actual rainfall detected by the rain gauge. Zenith wet delay data observed by the Uji network showed that the characteristic length scale of water vapor distribution during the rainfall ranged from 1.9 to 3.5 km. It is suggested that the assimilation of PWV data with high horizontal resolution (a few km) improves the forecast accuracy. We conducted the assimilation experiment of high-resolution PWV data, using both small horizontal localization radii and a conventional horizontal localization radius. We repeated the sensitivity experiment, changing the mean horizontal spacing of the PWV data from 1.7 to 8.0 km. When the horizontal spacing of assimilated PWV data was decreased from 8.0 to 3.5 km, the accuracy of the simulated hourly rainfall amount worsened in the experiment that used the conventional localization radius for the assimilation of PWV. In contrast, the accuracy of hourly rainfall amounts improved when we applied small horizontal localization radii. In the experiment that used the small horizontal localization radii, the accuracy of the hourly rainfall amount was most improved when the horizontal resolution of the assimilated PWV data was 3.5 km. The optimum spatial resolution of PWV data was related to the characteristic length scale of water vapor variability.[Figure not available: see fulltext.

Short-Range Prediction of Monsoon Precipitation by NCMRWF Regional Unified Model with Explicit Convection

NASA Astrophysics Data System (ADS)

Mamgain, Ashu; Rajagopal, E. N.; Mitra, A. K.; Webster, S.

2018-03-01

There are increasing efforts towards the prediction of high-impact weather systems and understanding of related dynamical and physical processes. High-resolution numerical model simulations can be used directly to model the impact at fine-scale details. Improvement in forecast accuracy can help in disaster management planning and execution. National Centre for Medium Range Weather Forecasting (NCMRWF) has implemented high-resolution regional unified modeling system with explicit convection embedded within coarser resolution global model with parameterized convection. The models configurations are based on UK Met Office unified seamless modeling system. Recent land use/land cover data (2012-2013) obtained from Indian Space Research Organisation (ISRO) are also used in model simulations. Results based on short-range forecast of both the global and regional models over India for a month indicate that convection-permitting simulations by the high-resolution regional model is able to reduce the dry bias over southern parts of West Coast and monsoon trough zone with more intense rainfall mainly towards northern parts of monsoon trough zone. Regional model with explicit convection has significantly improved the phase of the diurnal cycle of rainfall as compared to the global model. Results from two monsoon depression cases during study period show substantial improvement in details of rainfall pattern. Many categories in rainfall defined for operational forecast purposes by Indian forecasters are also well represented in case of convection-permitting high-resolution simulations. For the statistics of number of days within a range of rain categories between `No-Rain' and `Heavy Rain', the regional model is outperforming the global model in all the ranges. In the very heavy and extremely heavy categories, the regional simulations show overestimation of rainfall days. Global model with parameterized convection have tendency to overestimate the light rainfall days and underestimate the heavy rain days compared to the observation data.

Rainfall estimation from microwave links in São Paulo, Brazil.

NASA Astrophysics Data System (ADS)

Rios Gaona, Manuel Felipe; Overeem, Aart; Leijnse, Hidde; Uijlenhoet, Remko

2017-04-01

Rainfall estimation from microwave link networks has been successfully demonstrated in countries such as the Netherlands, Israel and Germany. The path-averaged rainfall intensity can be computed from the signal attenuation between cell phone towers. Although this technique is still in development, it offers great opportunities to retrieve rainfall rates at high spatiotemporal resolutions very close to the ground surface. High spatiotemporal resolutions and closer-to-ground measurements are highly appreciated, especially in urban catchments where high-impact events such as flash-floods develop in short time scales. We evaluate here this rainfall measurement technique for a tropical climate, something that has hardly been done previously. This is highly relevant since many countries with few surface rainfall observations are located in the tropics. The test-bed is the Brazilian city of São Paulo. The performance of 16 microwave links was evaluated, from a network of 200 links, for the last 3 months of 2014. The open software package RAINLINK was employed to obtain link rainfall estimates. The evaluation was done through a dense automatic gauge network. Results are promising and encouraging, especially for short links for which a high correlation (> 0.9) and a low bias (< 5%) were obtained.

Scale effect challenges in urban hydrology highlighted with a Fully Distributed Model and High-resolution rainfall data

NASA Astrophysics Data System (ADS)

Ichiba, Abdellah; Gires, Auguste; Tchiguirinskaia, Ioulia; Schertzer, Daniel; Bompard, Philippe; Ten Veldhuis, Marie-Claire

2017-04-01

Nowadays, there is a growing interest on small-scale rainfall information, provided by weather radars, to be used in urban water management and decision-making. Therefore, an increasing interest is in parallel devoted to the development of fully distributed and grid-based models following the increase of computation capabilities, the availability of high-resolution GIS information needed for such models implementation. However, the choice of an appropriate implementation scale to integrate the catchment heterogeneity and the whole measured rainfall variability provided by High-resolution radar technologies still issues. This work proposes a two steps investigation of scale effects in urban hydrology and its effects on modeling works. In the first step fractal tools are used to highlight the scale dependency observed within distributed data used to describe the catchment heterogeneity, both the structure of the sewer network and the distribution of impervious areas are analyzed. Then an intensive multi-scale modeling work is carried out to understand scaling effects on hydrological model performance. Investigations were conducted using a fully distributed and physically based model, Multi-Hydro, developed at Ecole des Ponts ParisTech. The model was implemented at 17 spatial resolutions ranging from 100 m to 5 m and modeling investigations were performed using both rain gauge rainfall information as well as high resolution X band radar data in order to assess the sensitivity of the model to small scale rainfall variability. Results coming out from this work demonstrate scale effect challenges in urban hydrology modeling. In fact, fractal concept highlights the scale dependency observed within distributed data used to implement hydrological models. Patterns of geophysical data change when we change the observation pixel size. The multi-scale modeling investigation performed with Multi-Hydro model at 17 spatial resolutions confirms scaling effect on hydrological model performance. Results were analyzed at three ranges of scales identified in the fractal analysis and confirmed in the modeling work. The sensitivity of the model to small-scale rainfall variability was discussed as well.

Linking Vital Rates of Landbirds on a Tropical Island to Rainfall and Vegetation Greenness

PubMed Central

Saracco, James F.; Radley, Paul; Pyle, Peter; Rowan, Erin; Taylor, Ron; Helton, Lauren

2016-01-01

Remote tropical oceanic islands are of high conservation priority, and they are exemplified by range-restricted species with small global populations. Spatial and temporal patterns in rainfall and plant productivity may be important in driving dynamics of these species. Yet, little is known about environmental influences on population dynamics for most islands and species. Here we leveraged avian capture-recapture, rainfall, and remote-sensed habitat data (enhanced vegetation index [EVI]) to assess relationships between rainfall, vegetation greenness, and demographic rates (productivity, adult apparent survival) of three native bird species on Saipan, Northern Mariana Islands: rufous fantail (Rhipidura rufifrons), bridled white-eye (Zosterops conspicillatus), and golden white-eye (Cleptornis marchei). Rainfall was positively related to vegetation greenness at all but the highest rainfall levels. Temporal variation in greenness affected the productivity of each bird species in unique ways. Predicted productivity of rufous fantail was highest when dry and wet season greenness values were high relative to site-specific 5-year seasonal mean values (i.e., relative greenness); while the white-eye species had highest predicted productivity when relative greenness contrasted between wet and dry seasons. Survival of rufous fantail and bridled white eye was positively related to relative dry-season greenness and negatively related to relative wet-season greenness. Bridled white-eye survival also showed evidence of a positive response to overall greenness. Our results highlight the potentially important role of rainfall regimes in affecting population dynamics of species on oceanic tropical islands. Understanding linkages between rainfall, vegetation, and animal population dynamics will be critical for developing effective conservation strategies in this and other regions where the seasonal timing, extent, and variability of rainfall is expected to change in the coming decades. PMID:26863013

Linking Vital Rates of Landbirds on a Tropical Island to Rainfall and Vegetation Greenness.

PubMed

Saracco, James F; Radley, Paul; Pyle, Peter; Rowan, Erin; Taylor, Ron; Helton, Lauren

2016-01-01

Remote tropical oceanic islands are of high conservation priority, and they are exemplified by range-restricted species with small global populations. Spatial and temporal patterns in rainfall and plant productivity may be important in driving dynamics of these species. Yet, little is known about environmental influences on population dynamics for most islands and species. Here we leveraged avian capture-recapture, rainfall, and remote-sensed habitat data (enhanced vegetation index [EVI]) to assess relationships between rainfall, vegetation greenness, and demographic rates (productivity, adult apparent survival) of three native bird species on Saipan, Northern Mariana Islands: rufous fantail (Rhipidura rufifrons), bridled white-eye (Zosterops conspicillatus), and golden white-eye (Cleptornis marchei). Rainfall was positively related to vegetation greenness at all but the highest rainfall levels. Temporal variation in greenness affected the productivity of each bird species in unique ways. Predicted productivity of rufous fantail was highest when dry and wet season greenness values were high relative to site-specific 5-year seasonal mean values (i.e., relative greenness); while the white-eye species had highest predicted productivity when relative greenness contrasted between wet and dry seasons. Survival of rufous fantail and bridled white eye was positively related to relative dry-season greenness and negatively related to relative wet-season greenness. Bridled white-eye survival also showed evidence of a positive response to overall greenness. Our results highlight the potentially important role of rainfall regimes in affecting population dynamics of species on oceanic tropical islands. Understanding linkages between rainfall, vegetation, and animal population dynamics will be critical for developing effective conservation strategies in this and other regions where the seasonal timing, extent, and variability of rainfall is expected to change in the coming decades.

Automatic Extraction of High-Resolution Rainfall Series from Rainfall Strip Charts

NASA Astrophysics Data System (ADS)

Saa-Requejo, Antonio; Valencia, Jose Luis; Garrido, Alberto; Tarquis, Ana M.

2015-04-01

Soil erosion is a complex phenomenon involving the detachment and transport of soil particles, storage and runoff of rainwater, and infiltration. The relative magnitude and importance of these processes depends on a host of factors, including climate, soil, topography, cropping and land management practices among others. Most models for soil erosion or hydrological processes need an accurate storm characterization. However, this data are not always available and in some cases indirect models are generated to fill this gap. In Spain, the rain intensity data known for time periods less than 24 hours back to 1924 and many studies are limited by it. In many cases this data is stored in rainfall strip charts in the meteorological stations but haven't been transfer in a numerical form. To overcome this deficiency in the raw data a process of information extraction from large amounts of rainfall strip charts is implemented by means of computer software. The method has been developed that largely automates the intensive-labour extraction work based on van Piggelen et al. (2011). The method consists of the following five basic steps: 1) scanning the charts to high-resolution digital images, 2) manually and visually registering relevant meta information from charts and pre-processing, 3) applying automatic curve extraction software in a batch process to determine the coordinates of cumulative rainfall lines on the images (main step), 4) post processing the curves that were not correctly determined in step 3, and 5) aggregating the cumulative rainfall in pixel coordinates to the desired time resolution. A colour detection procedure is introduced that automatically separates the background of the charts and rolls from the grid and subsequently the rainfall curve. The rainfall curve is detected by minimization of a cost function. Some utilities have been added to improve the previous work and automates some auxiliary processes: readjust the bands properly, merge bands when those have been scanned in two parts, detect and cut the borders of bands not used (demanded by the software). Also some variations in which colour system is tried basing in HUE or RGB colour have been included. Thanks to apply this digitization rainfall strip charts 209 station-years of three locations in the centre of Spain have been transformed to long-term rainfall time series with 5-min resolution. References van Piggelen, H.E., T. Brandsma, H. Manders, and J. F. Lichtenauer, 2011: Automatic Curve Extraction for Digitizing Rainfall Strip Charts. J. Atmos. Oceanic Technol., 28, 891-906. Acknowledgements Financial support for this research by DURERO Project (Env.C1.3913442) is greatly appreciated.

Assessing Landscape Connectivity and River Water Quality Changes Using an 8-Day, 30-Meter Land Cover Dataset

NASA Astrophysics Data System (ADS)

Kamarinas, I.; Julian, J.; Owsley, B.; de Beurs, K.; Hughes, A.

2014-12-01

Water quality is dictated by interactions among geomorphic processes, vegetation characteristics, weather patterns, and anthropogenic land uses over multiple spatio-temporal scales. In order to understand how changes in climate and land use impact river water quality, a suite of data with high temporal resolution over a long period is needed. Further, all of this data must be analyzed with respect to connectivity to the river, thus requiring high spatial resolution data. Here, we present how changes in climate and land use over the past 25 years have affected water quality in the 268 sq. km Hoteo River catchment in New Zealand. Hydro-climatic data included daily solar radiation, temperature, soil moisture, rainfall, drought indices, and runoff at 5-km resolution. Land cover changes were measured every 8 days at 30-m resolution by fusing Landsat and MODIS satellite imagery. Water quality was assessed using 15-min turbidity (2011-2014) and monthly data for a suite of variables (1990-2014). Watershed connectivity was modeled using a corrected 15-m DEM and a high-resolution drainage network. Our analyses revealed that this catchment experiences cyclical droughts which, when combined with intense land uses such as livestock grazing and plantation forest harvesting, leaves many areas in the catchment disturbed (i.e. exposed soil) that are connected to the river through surface runoff. As a result, flow-normalized turbidity was elevated during droughts and remained relatively low during wet periods. For example, disturbed land area decreased from 9% to 4% over 2009-2013, which was a relatively wet period. During the extreme drought of 2013, disturbed area increased to 6% in less than a year due mainly to slow pasture recovery after heavy stocking rates. The relationships found in this study demonstrate that high spatiotemporal resolution land cover datasets are very important to understanding the interactions between landscape and climate, and how these interactions affect water quality.

Soil conservation service curve number: How to take into account spatial and temporal variability

NASA Astrophysics Data System (ADS)

Rianna, M.; Orlando, D.; Montesarchio, V.; Russo, F.; Napolitano, F.

2012-09-01

The most commonly used method to evaluate rainfall excess, is the Soil Conservation Service (SCS) runoff curve number model. This method is based on the determination of the CN valuethat is linked with a hydrological soil group, cover type, treatment, hydrologic condition and antecedent runoff condition. To calculate the antecedent runoff condition the standard procedure needs to calculate the rainfall over the entire basin during the five days previous to the beginning of the event in order to simulate and then to use that volume of rainfall to calculate the antecedent moisture condition (AMC). This is necessary in order to obtain the correct curve number value. The value of the modified parameter is then kept constant throughout the whole event. The aim of this work is to evaluate the possibility of improving the curve number method. The various assumptions are focused on modifying those related to rainfall and the determination of an AMC condition and their role in the determination of the value of the curve number parameter. In order to consider the spatial variability we assumed that the rainfall which influences the AMC and the CN value does not account for the rainfall over the entire basin, but for the rainfall within a single cell where the basin domain is discretized. Furthermore, in order to consider the temporal variability of rainfall we assumed that the value of the CN of the single cell is not maintained constant during the whole event, but instead varies throughout it according to the time interval used to define the AMC conditions.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yan, Eugene; Pierce, Julia; Mahat, Vinod

This project is a part of the Regional Resiliency Assessment Program, led by the Department of Homeland Security, to address flooding hazards of regional significance for Portland, Maine. The pilot study was performed by Argonne National Laboratory to identify differences in spatial rainfall distributions between the radar-derived and rain-gauge rainfall datasets and to evaluate their impacts on urban flooding. The flooding impact analysis utilized a high-resolution 2-dimensional (2-D) hydrodynamic model (15 ft by 15 ft) incorporating the buildings, streets, stream channels, hydraulic structures, an existing city storm drain system, and assuming a storm surge along the coast coincident with amore » heavy rainfall event. Two historical storm events from April 16, 2007, and September 29, 2015, were selected for evaluation. The radar-derived rainfall data at a 200-m resolution provide spatially-varied rainfall patterns with a wide range of intensities for each event. The resultant maximum flood depth using data from a single rain gauge within the study area could be off (either under- or over-estimated) by more than 10% in the 2007 storm and more than 60% in the 2015 storm compared to the radar-derived rainfall data. The model results also suggest that the inundation area with a flow depth at or greater than 0.5 ft could reach 11% (2007 storm) and 17% (2015 storm) of the total study area, respectively. The lowland areas within the neighborhoods of North Deering, East Deering, East and West Baysides and northeastern Parkside, appear to be more vulnerable to the flood hazard in both storm events. The high-resolution 2-D hydrodynamic model with high-resolution radar-derived rainfall data provides an excellent tool for detailed urban flood analysis and vulnerability assessment. The model developed in this study could be potentially used to evaluate any proposed mitigation measures and optimize their effects in the future for Portland, ME.« less

On the Comparison of the Global Surface Soil Moisture product and Land Surface Modeling

NASA Astrophysics Data System (ADS)

Delorme, B., Jr.; Ottlé, C.; Peylin, P.; Polcher, J.

2016-12-01

Thanks to its large spatio-temporal coverage, the new ESA CCI multi-instruments dataset offers a good opportunity to assess and improve land surface models parametrization. In this study, the ESA CCI surface soil moisture (SSM) combined product (v2.2) has been compared to the simulated top first layers of the ORCHIDEE LSM (the continental part of the IPSL earth system model), in order to evaluate its potential of improvements with data assimilation techniques. The ambition of the work was to develop a comprehensive comparison methodology by analyzing simultaneously the temporal and spatial structures of both datasets. We analyzed the SSM synoptic, seasonal, and inter-annual variations by decomposing the signals into fast and slow components. ORCHIDEE was shown to adequately reproduce the observed SSM dynamics in terms of temporal correlation. However, these correlation scores are supposed to be strongly influenced by SSM seasonal variability and the quality of the model input forcing. Autocorrelation and spectral analyses brought out disagreements in the temporal inertia of the upper soil moisture reservoirs. By linking our results to land cover maps, we found that ORCHIDEE is more dependent on rainfall events compared to the observations in regions with sparse vegetation cover. These diflerences might be due to a wrong partition of rainfall between soil evaporation, transpiration, runofl and drainage in ORCHIDEE. To refine this analysis, a single value decomposition (SVD) of the co-variability between rainfall provided by WFDEI and soil moisture was pursued over Central Europe and South Africa. It showed that spatio-temporal co-varying patterns between ORCHIDEE and rainfall and the ESA-CCI product and rainfall are in relatively good agreement. However, the leading SVD pattern, which exhibits a strong annual cycle and explains the same portion of covariance for both datasets, explains a much larger fraction of variance for ORCHIDEE than for the ESA-CCI product. These results highlight that the role of other surface variables presenting a strong seasonal variability (like vegetation cover, possibly irrigation) is not accounted for similarly in both the model and the product, and that further work is needed to explore these discrepancies.

On storm movement and its applications

NASA Astrophysics Data System (ADS)

Niemczynowicz, Janusz

Rainfall-runoff models applicable for design and analysis of sewage systems in urban areas are further developed in order to represent better different physical processes going on on an urban catchment. However, one important part of the modelling procedure, the generation of the rainfall input is still a weak point. The main problem is lack of adequate rainfall data which represent temporal and spatial variations of the natural rainfall process. Storm movement is a natural phenomenon which influences urban runoff. However, the rainfall movement and its influence on runoff generation process is not represented in presently available urban runoff simulation models. Physical description of the rainfall movement and its parameters is given based on detailed measurements performed on twelve gauges in Lund, Sweden. The paper discusses the significance of the rainfall movement on the runoff generation process and gives suggestions how the rainfall movement parameters may be used in runoff modelling.

The cross wavelet and wavelet coherence analysis of spatio-temporal rainfall-groundwater system in Pingtung plain, Taiwan

NASA Astrophysics Data System (ADS)

Lin, Yuan-Chien; Yu, Hwa-Lung

2013-04-01

The increasing frequency and intensity of extreme rainfall events has been observed recently in Taiwan. Particularly, Typhoon Morakot, Typhoon Fanapi, and Typhoon Megi consecutively brought record-breaking intensity and magnitude of rainfalls to different locations of Taiwan in these two years. However, records show the extreme rainfall events did not elevate the amount of annual rainfall accordingly. Conversely, the increasing frequency of droughts has also been occurring in Taiwan. The challenges have been confronted by governmental agencies and scientific communities to come up with effective adaptation strategies for natural disaster reduction and sustainable environment establishment. Groundwater has long been a reliable water source for a variety of domestic, agricultural, and industrial uses because of its stable quantity and quality. In Taiwan, groundwater accounts for the largest proportion of all water resources for about 40%. This study plans to identify and quantify the nonlinear relationship between precipitation and groundwater recharge, find the non-stationary time-frequency relations between the variations of rainfall and groundwater levels to understand the phase difference of time series. Groundwater level data and over-50-years hourly rainfall records obtained from 20 weather stations in Pingtung Plain, Taiwan has been collected. Extract the space-time pattern by EOF method, which is a decomposition of a signal or data set in terms of orthogonal basis functions determined from the data for both time series and spatial patterns, to identify the important spatial pattern of groundwater recharge and using cross wavelet and wavelet coherence method to identify the relationship between rainfall and groundwater levels. Results show that EOF method can specify the spatial-temporal patterns which represents certain geological characteristics and other mechanisms of groundwater, and the wavelet coherence method can identify general correlation between rainfall and groundwater signal at low frequency and high frequency relationship at some certain extreme rainfall events. Keywords: extreme rainfall, groundwater, EOF, wavelet coherence

Estimation of Rain Intensity Spectra over the Continental US Using Ground Radar-Gauge Measurements

NASA Technical Reports Server (NTRS)

Lin, Xin; Hou, Arthur Y.

2013-01-01

A high-resolution surface rainfall product is used to estimate rain characteristics over the continental US as a function of rain intensity. By defining each data at 4-km horizontal resolutions and 1-h temporal resolutions as an individual precipitating/nonprecipitating sample, statistics of rain occurrence and rain volume including their geographical and seasonal variations are documented. Quantitative estimations are also conducted to evaluate the impact of missing light rain events due to satellite sensors' detection capabilities. It is found that statistics of rain characteristics have large seasonal and geographical variations across the continental US. Although heavy rain events (> 10 mm/hr.) only occupy 2.6% of total rain occurrence, they may contribute to 27% of total rain volume. Light rain events (< 1.0 mm/hr.), occurring much more frequently (65%) than heavy rain events, can also make important contributions (15%) to the total rain volume. For minimum detectable rain rates setting at 0.5 and 0.2 mm/hr which are close to sensitivities of the current and future space-borne precipitation radars, there are about 43% and 11% of total rain occurrence below these thresholds, and they respectively represent 7% and 0.8% of total rain volume. For passive microwave sensors with their rain pixel sizes ranging from 14 to 16 km and the minimum detectable rain rates around 1 mm/hr., the missed light rain events may account for 70% of train occurrence and 16% of rain volume. Statistics of rain characteristics are also examined on domains with different temporal and spatial resolutions. Current issues in estimates of rain characteristics from satellite measurements and model outputs are discussed.

Identifying high production, low production and degraded rangelands in Senegal with normalized difference vegetation index data

USGS Publications Warehouse

Tappan, G. Gray; Wood, Lynette; Moore, Donald G.

1993-01-01

Seasonal herbaceous vegetation production on Senegal's native rangelands exhibits high spatial and temporal variability. This variability can be monitored using normalized difference vegetation index (NDVI) data computed from 1-km resolution Advanced Very High Resolution Radiometer (AVHRR) image data. Although annual fluctuations in rainfall account for some of the variability, numerous long-term production patterns are evident in the AVHRR time-series data. Different n productivity reflect variations in the region's climate, topography, soils, and land use. Areas of overgrazing and intensive cultivation have caused long-term soil and vegetation degradation. Rangelands of high and low productivity, and degraded rangelands were identified using NDVI. Time-series image data from 1987 though 1992 were used to map relative rangeland productivity. The results were compared to detailed resource maps on soils, vegetation and land use. Much of the variation in rangeland productivity correlated well to the known distribution of resources. The study developed an approach that identified a number of areas of degraded soils and low vegetation production.

Very-high-resolution time-lapse photography for plant and ecosystems research.

PubMed

Nichols, Mary H; Steven, Janet C; Sargent, Randy; Dille, Paul; Schapiro, Joshua

2013-09-01

Traditional photography is a compromise between image detail and area covered. We report a new method for creating time-lapse sequences of very-high-resolution photographs to produce zoomable images that facilitate observation across a range of spatial and temporal scales. • A robotic camera mount and software were used to capture images of the growth and movement in Brassica rapa every 15 s in the laboratory. The resultant time-lapse sequence (http://timemachine.gigapan.org/wiki/Plant_Growth) captures growth detail such as circumnutation. A modified, solar-powered system was deployed at a remote field site in southern Arizona. Images were collected every 2 h over a 3-mo period to capture the response of vegetation to monsoon season rainfall (http://timemachine.gigapan.org/wiki/Arizona_Grasslands). • A technique for observing time sequences of both individual plant and ecosystem response at a range of spatial scales is available for use in the laboratory and in the field.

Very-high-resolution time-lapse photography for plant and ecosystems research1

PubMed Central

Nichols, Mary H.; Steven, Janet C.; Sargent, Randy; Dille, Paul; Schapiro, Joshua

2013-01-01

• Premise of the study: Traditional photography is a compromise between image detail and area covered. We report a new method for creating time-lapse sequences of very-high-resolution photographs to produce zoomable images that facilitate observation across a range of spatial and temporal scales. • Methods and Results: A robotic camera mount and software were used to capture images of the growth and movement in Brassica rapa every 15 s in the laboratory. The resultant time-lapse sequence (http://timemachine.gigapan.org/wiki/Plant_Growth) captures growth detail such as circumnutation. A modified, solar-powered system was deployed at a remote field site in southern Arizona. Images were collected every 2 h over a 3-mo period to capture the response of vegetation to monsoon season rainfall (http://timemachine.gigapan.org/wiki/Arizona_Grasslands). • Conclusions: A technique for observing time sequences of both individual plant and ecosystem response at a range of spatial scales is available for use in the laboratory and in the field. PMID:25202588

Analyses of Chinese Hourly Precipitation Using Gauge Observations and Satellite Estimates Products

NASA Astrophysics Data System (ADS)

Pan, Y.; Yu, J.; Shen, Y.

2010-12-01

Highly spatial-temporal and accurate precipitation analyses are essential for monitoring the catastrophic mesoscale weather systems, examining numerical model outputs, and doing dynamic researches on mesoscale meteorology. In recent years, Chinese government has gradually developed a ground-based observational net of 30000 auto-weather-stations (AWS) all over the country, most of which are in the eastern and southern China. The real-time data of gauged rainfall is transported to National Meteorological Information of China (NMIC) every hour, and its quality has been strictly and effectually controlled. Taking advantage of these resources, an hourly Chinese Precipitation Analyses Products (CPAP) with fine resolution is developed. But on the Tibetan Plateau where the AWS is still sparse, the accuracy of precipitation can not satisfy the operational needs yet. Otherwise, CMORPH has a well performance on the space structure of rainfall over China in warm season, but loses on intensity. Thus, we make a merge test analysis at resolution of 0.1 ×0.1 degree , using Optimum Interpolation (OI) to combine hourly CPAP with CMORPH estimates precipitation products. Before OI,the systematic bias in CMORPH have been partly corrected by gauge data through PDF adjustments. The validation of the merge test from June to August 2009 shows that, the combined products can obviously reduce the bias to the gauge analyses CPAP, and also have highly coefficient with it. It is more important that, the combined products provide a reasonable and full-covered precipitation structure over Tibetan Plateau.

Effects of Increased Horizontal Resolution on Simulation of the North American Monsoon in the NCAR CAM3: An Evaluation based on Surface, Satellite, and Reanalysis Data

NASA Astrophysics Data System (ADS)

Collier, J. C.; Zhang, G. J.

2006-05-01

Simulation of the North American monsoon system by the National Center for Atmospheric Research (NCAR) Community Atmosphere Model (CAM3) is evaluated in its sensitivity to increasing horizontal resolution. For two resolutions, T42 and T85, rainfall is compared to TRMM satellite-derived and surface gauge-based rainfall rates over the U.S. and northern Mexico as well as rainfall accumulations in gauges of the North American Monsoon Experiment (NAME) Enhanced Rain Gauge Network (NERN) in the Sierra Madre Occidental mountains. Simulated upper-tropospheric mass and wind fields are compared to those from NCEP-NCAR reanalyses. The comparison presented herein demonstrates that tropospheric motions associated with the North American monsoon system are sensitive to increasing the horizontal resolution of the model. An increase in resolution from T42 to T85 results in changes to a region of large-scale mid-tropospheric descent found north and east of the monsoon anticyclone. Relative to its simulation at T42, this region extends farther south and west at T85. Additionally, at T85, the subsidence is stronger. Consistent with the differences in large-scale descent, the T85 simulation of CAM3 is anomalously dry over Texas and northeastern Mexico during the peak monsoon months. Meanwhile, the geographic distribution of rainfall over the Sierra Madre Occidental region of Mexico is more satisfactorily simulated at T85 than at T42 for July and August. Moisture import into this region is greater at T85 than at T42 during these months. A focused study of the Sierra Madre Occidental region in particular shows that, in the regional average sense, the timing of the peak of the monsoon is relatively insensitive to the horizontal resolution of the model, while a phase bias in the diurnal cycle of monsoon-season precipitation is somewhat reduced in the higher-resolution run. At both resolutions, CAM3 poorly simulates the month-to-month evolution of monsoon rainfall over extreme northwestern Mexico and Arizona, though biases are considerably improved at T85.

Panel regressions to estimate low-flow response to rainfall variability in ungaged basins

USGS Publications Warehouse

Bassiouni, Maoya; Vogel, Richard M.; Archfield, Stacey A.

2016-01-01

Multicollinearity and omitted-variable bias are major limitations to developing multiple linear regression models to estimate streamflow characteristics in ungaged areas and varying rainfall conditions. Panel regression is used to overcome limitations of traditional regression methods, and obtain reliable model coefficients, in particular to understand the elasticity of streamflow to rainfall. Using annual rainfall and selected basin characteristics at 86 gaged streams in the Hawaiian Islands, regional regression models for three stream classes were developed to estimate the annual low-flow duration discharges. Three panel-regression structures (random effects, fixed effects, and pooled) were compared to traditional regression methods, in which space is substituted for time. Results indicated that panel regression generally was able to reproduce the temporal behavior of streamflow and reduce the standard errors of model coefficients compared to traditional regression, even for models in which the unobserved heterogeneity between streams is significant and the variance inflation factor for rainfall is much greater than 10. This is because both spatial and temporal variability were better characterized in panel regression. In a case study, regional rainfall elasticities estimated from panel regressions were applied to ungaged basins on Maui, using available rainfall projections to estimate plausible changes in surface-water availability and usable stream habitat for native species. The presented panel-regression framework is shown to offer benefits over existing traditional hydrologic regression methods for developing robust regional relations to investigate streamflow response in a changing climate.

Panel regressions to estimate low-flow response to rainfall variability in ungaged basins

NASA Astrophysics Data System (ADS)

Bassiouni, Maoya; Vogel, Richard M.; Archfield, Stacey A.

2016-12-01

Multicollinearity and omitted-variable bias are major limitations to developing multiple linear regression models to estimate streamflow characteristics in ungaged areas and varying rainfall conditions. Panel regression is used to overcome limitations of traditional regression methods, and obtain reliable model coefficients, in particular to understand the elasticity of streamflow to rainfall. Using annual rainfall and selected basin characteristics at 86 gaged streams in the Hawaiian Islands, regional regression models for three stream classes were developed to estimate the annual low-flow duration discharges. Three panel-regression structures (random effects, fixed effects, and pooled) were compared to traditional regression methods, in which space is substituted for time. Results indicated that panel regression generally was able to reproduce the temporal behavior of streamflow and reduce the standard errors of model coefficients compared to traditional regression, even for models in which the unobserved heterogeneity between streams is significant and the variance inflation factor for rainfall is much greater than 10. This is because both spatial and temporal variability were better characterized in panel regression. In a case study, regional rainfall elasticities estimated from panel regressions were applied to ungaged basins on Maui, using available rainfall projections to estimate plausible changes in surface-water availability and usable stream habitat for native species. The presented panel-regression framework is shown to offer benefits over existing traditional hydrologic regression methods for developing robust regional relations to investigate streamflow response in a changing climate.

The development of a sub-daily gridded rainfall product to improve hydrological predictions in Great Britain

NASA Astrophysics Data System (ADS)

Quinn, Niall; Freer, Jim; Coxon, Gemma; O'Loughlin, Fiachra; Woods, Ross; Liguori, Sara

2015-04-01

In Great Britain and many other regions of the world, flooding resulting from short duration, high intensity rainfall events can lead to significant economic losses and fatalities. At present, such extreme events are often poorly evaluated using hydrological models due, in part, to their rarity and relatively short duration and a lack of appropriate data. Such storm characteristics are not well represented by daily rainfall records currently available using volumetric gauges and/or derived gridded products. This research aims to address this important data gap by developing a sub-daily gridded precipitation product for Great Britain. Our focus is to better understand these storm events and some of the challenges and uncertainties in quantifying such data across catchment scales. Our goal is to both improve such rainfall characterisation and derive an input to drive hydrological model simulations. Our methodology involves the collation, error checking, and spatial interpolation of approximately 2000 rain gauges located across Great Britain, provided by the Scottish Environment Protection Agency (SEPA) and the Environment Agency (EA). Error checking was conducted over the entirety of the TBR data available, utilising a two stage approach. First, rain gauge data at each site were examined independently, with data exceeding reasonable thresholds marked as suspect. Second, potentially erroneous data were marked using a neighbourhood analysis approach whereby measurements at a given gauge were deemed suspect if they did not fall within defined bounds of measurements at neighbouring gauges. A total of eight error checks were conducted. To provide the user with the greatest flexibility possible, the error markers associated with each check have been recorded at every site. This approach aims to enable the user to choose which checks they deem most suitable for a particular application. The quality assured TBR dataset was then spatially interpolated to produce a national scale gridded rainfall product. Finally, radar rainfall data provided by the UK Met Office was assimilated, where available, to provide an optimal hourly estimate of rainfall, given the error variance associated with both datasets. This research introduces a sub-daily rainfall product that will be of particular value to hydrological modellers requiring rainfall inputs at higher temporal resolutions than those currently available nationally. Further research will aim to quantify the uncertainties in the rainfall product in order to improve our ability to diagnose and identify structural errors in hydrological modelling of extreme events. Here we present our initial findings.

Assimilation of Sentinel-1 estimates of Precipitable Water Vapor (PWV) into a Numerical Weather Model for a more accurate forecast of extreme weather events

NASA Astrophysics Data System (ADS)

Mateus, Pedro; Nico, Giovanni; Catalao, Joao

2017-04-01

In the last two decades, SAR interferometry has been used to obtain maps of Precipitable Water Vapor (PWV).This maps are characterized by their high spatial resolution when compared to the currently available PWV measurements (e.g. GNSS, radiometers or radiosondes). Several previous works have shown that assimilating PWV values, mainly derived from GNSS observations, into Numerical Weather Models (NWMs) can significantly improve rainfall predictions.It is noteworthy that the PWV-derived from GNSS observations have a high temporal resolution but a low spatialone. In addition, there are many regions without any GNSS stations, where temporal and spatial distribution of PWV areonly available through satellite measurements. The first attempt to assimilate InSAR-derived maps of PWV (InSAR-PWV) into a NWM was made by Pichelli et al. [1].They used InSAR-PWV maps obtained from ENVISAT-ASAR images and the mesoscale weather prediction model MM5 over the city of Rome, Italy. The statistical indices show that the InSAR-PWVdata assimilation improves the forecast of weak to moderateprecipitation (< 15 mm/3-h). The second and last attempt, was performed by Mateus et al. [2]. They used the same satellite mission and the Weather Research and Forecast (WRF) model over the city of Lisbon, Portugal, during a light rain event not forecast by the model.Results showed that after data assimilation, there is a bias correction of the PWV field and an improvement in the forecast of the weakto moderate rainfall up to 9 h after the assimilation time. We used, for the first time, the Weather Research and Forecast Data Assimilation (WRFDA) model, at micro-scale resolutions (3 km), over the Iberian Peninsula (focusing on the southern region of Spain) and during a convective cell associated with a local heavy rainfall event, to study the impact of assimilation PWV maps obtained from SAR interferometric phase calculated using images acquired by the Sentinel-1 satellite. It's worth noting that, in this case, the model without assimilation PWV maps fails to reproduce the amount and the region of heavy rainfall. The assimilation of InSAR-PWV maps with high spatial variability by the WRF model, promoted alterations in the buoyancy force over the study area and consequently increased the atmospheric instability, were new convection cells were generated over the correct area. We assessed the results using in-situ meteorological data and with a meteorological radar. With the Sentinel-1 A/B C-band sensors its possible generate maps of PWV over large areas with a length of hundreds of kilometers and a width of about 250 km (country-spanning areas), a spatial resolution of 5×20 m and an absolute revisiting time of 6 days or fewer when combined with other sensors, opening new perspectives to the application of SAR meteorology concept. The availability of interferometric PWV maps on a routine basis can help to capture the high variability of the water vapor distribution at micro-scales. In this study, we show that the knowledge of the PWV with high spatial resolution can change the system thermodynamics to improve the NWP accuracy. Publication supported by the Short-Term Mobility Consiglio Nazionale delle Ricerche programs 2015 and 2016 and the Postdoctoral Grant SFRH/BPD/96069/2013. References: [1] E. Pichelli et al., "InSAR water vapor data assimilation into mesoscale model MM5: Technique and pilot study," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 8, no. 8, pp. 3859-3875, Aug. 2015. [2] P. Mateus, R. Tomé, G. Nico, and J. Catalão, "Three-Dimensional Variational Assimilation of InSAR PWV Using the WRFDA Model," IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 12, pp. 7323-7330, 2016.

Seasonal and spatial variability of rainfall redistribution under Scots pine and Downy oak forests in Mediterranean conditions

NASA Astrophysics Data System (ADS)

Garcia-Estringana, Pablo; Latron, Jérôme; Molina, Antonio J.; Llorens, Pilar

2013-04-01

The large degree of temporal and spatial variability of throughfall input patterns may lead to significant changes in the volume of water that reach the soil in each location, and beyond in the hydrological response of forested hillslopes. To explore the role of vegetation in the temporal and spatial redistribution of rainfall in Mediterranean climatic conditions two contrasted stands were monitored. One is a Downy oak forest (Quercus pubescens) and the other is a Scots pine forest (Pinus sylvestris), both are located in the Vallcebre research catchments (NE Spain, 42° 12'N, 1° 49'E). These plots are representative of Mediterranean mountain areas with spontaneous afforestation by Scots pine as a consequence of the abandonment of agricultural terraces, formerly covered by Downy oaks. The monitoring design of each plot consists of a set of 20 automatic rain recorders and 40 automatic soil moisture probes located below the canopy. 100 hemispheric photographs of the canopy were used to place the instruments at representative locations (in terms of canopy cover) within the plot. Bulk rainfall, stemflow and meteorological conditions above the forest cover are also automatically recorded. Canopy cover as well as biometric characteristics of the plots are also regularly measured. This work presents the first results describing the variability of throughfall beneath each forest stand and compares the persistence of temporal patterns among stands, and for the oaks stand among the leafed and the leafless period. Furthermore, canopy structure, rainfall characteristics and meteorological conditions of rainfall events are evaluated as main drivers of throughfall redistribution.

A Two-year Record of Daily Rainfall Isotopes from Fiji: Implications for Reconstructing Precipitation from Speleothem δ18O

NASA Astrophysics Data System (ADS)

Brett, M.; Mattey, D.; Stephens, M.

2015-12-01

Oxygen isotopes in speleothem provide opportunities to construct precisely dated records of palaeoclimate variability, underpinned by an understanding of both the regional climate and local controls on isotopes in rainfall and groundwater. For tropical islands, a potential means to reconstruct past rainfall variability is to exploit the generally high correlation between rainfall amount and δ18O: the 'amount effect'. The GNIP program provides δ18O data at monthly resolution for several tropical Pacific islands but there are few data for precipitation isotopes at daily resolution, for investigating the amount effect over different timescales in a tropical maritime setting. Timescales are important since meteoric water feeding a speleothem has undergone storage and mixing in the aquifer system and understanding how the isotope amount effect is preserved in aquifer recharge has fundamental implications on the interpretation of speleothem δ18O in terms of palaeo-precipitation. The islands of Fiji host speleothem caves. Seasonal precipitation is related to the movement of the South Pacific Convergence Zone, and interannual variations in rainfall are coupled to ENSO behaviour. Individual rainfall events are stratiform or convective, with proximal moisture sources. We have daily resolution isotope data for rainfall collected at the University of the South Pacific in Suva, covering every rain event in 2012 and 2013. δ18O varies between -18‰ and +3‰ with the annual weighted averages at -7.6‰ and -6.8‰ respectively, while total recorded rainfall amount is similar in both years. We shall present analysis of our data compared with GNIP, meteorological data and back trajectory analyses to demonstrate the nature of the relationship between rainfall amount and isotopic signatures over this short timescale. Comparison with GNIP data for 2012-13 will shed light on the origin of the amount effect at monthly and seasonal timescales in convective, maritime, tropical climates.

The impact of rainfall on the temporal and spatial distribution of taxi passengers

PubMed Central

Zhang, Yong; Gao, Liangpeng; Geng, Nana; Li, Xuefeng

2017-01-01

This paper focuses on the impact of rainfall on the temporal and spatial distribution of taxi passengers. The main objective is to provide guidance for taxi scheduling on rainy days. To this end, we take the occupied and empty states of taxis as units of analysis. By matching a taxi's GPS data to its taximeter data, we can obtain the taxi's operational time and the taxi driver's income from every unit of analysis. The ratio of taxi operation time to taxi drivers' income is used to measure the quality of taxi passengers. The research results show that the spatio-temporal evolution of urban taxi service demand differs based on rainfall conditions and hours of operation. During non-rush hours, taxi demand in peripheral areas is significantly reduced under increasing precipitation conditions, whereas during rush hours, the demand for highly profitable taxi services steadily increases. Thus, as an intelligent response for taxi operations and dispatching, taxi services should guide cruising taxis to high-demand regions to increase their service time and ride opportunities. PMID:28873430

Temporal variation of Mexiconema cichlasomae (Nematoda: Daniconematidae) in the Mayan cichlid fish Cichlasoma urophthalmus and its intermediate host Argulus yucatanus from a tropical coastal lagoon.

PubMed

May-Tec, A L; Pech, D; Aguirre-Macedo, M L; Lewis, J W; Vidal-Martínez, V M

2013-03-01

The aim of the present investigation was to determine whether temporal variation in environmental factors such as rainfall or temperature influence long-term fluctuations in the prevalence and mean abundance of the nematode Mexiconema cichlasomae in the cichlid fish Cichlasoma uropthalmus and its crustacean intermediate host, Argulus yucatanus. The study was undertaken in a tropical coastal lagoon in the Yucatan Peninsula (south-eastern Mexico) over an 8-year period. Variations in temperature, rainfall and monthly infection levels for both hosts were analysed using time series and cross-correlations to detect possible recurrent patterns. Infections of M. cichlasomae in A. yucatanus showed annual peaks, while in C. urophthalmus peaks were bi-annual. The latter appear to be related to the accumulation of several generations of this nematode in C. urophthalmus. Rainfall and temperature appear to be key environmental factors in influencing temporal variation in the infection of M. cichlasomae over periods longer than a year together with the accumulation of larval stages throughout time.

Flood Monitoring using X-band Dual-polarization Radar Network

NASA Astrophysics Data System (ADS)

Chandrasekar, V.; Wang, Y.; Maki, M.; Nakane, K.

2009-09-01

A dense weather radar network is an emerging concept advanced by the Center for Collaborative Adaptive Sensing of the Atmosphere (CASA). Using multiple radars observing over a common will create different data outcomes depending on the characteristics of the radar units employed and the network topology. To define this a general framework is developed to describe the radar network space, and formulations are obtained that can be used for weather radar network characterization. Current weather radar surveillance networks are based upon conventional sensing paradigm of widely-separated, standalone sensing systems using long range radars that operate at wavelengths in 5-10 cm range. Such configuration has limited capability to observe close to the surface of the earth because of the earth's curvature but also has poorer resolution at far ranges. The dense network radar system, observes and measures weather phenomenon such as rainfall and severe weather close to the ground at higher spatial and temporal resolution compared to the current paradigm. In addition the dense network paradigm also is easily adaptable to complex terrain. Flooding is one of the most common natural hazards in the world. Especially, excessive development decreases the response time of urban watersheds and complex terrain to rainfall and increases the chance of localized flooding events over a small spatial domain. Successful monitoring of urban floods requires high spatiotemporal resolution, accurate precipitation estimation because of the rapid flood response as well as the complex hydrologic and hydraulic characteristics in an urban environment. This paper reviews various aspects in radar rainfall mapping in urban coverage using dense X-band dual-polarization radar networks. By reducing the maximum range and operating at X-band, one can ensure good azimuthal resolution with a small-size antenna and keep the radar beam closer to the ground. The networked topology helps to achieve satisfactory sensitivity and fast temporal update across the coverage. Strong clutter is expected from buildings in the neighborhood which act as perfect reflectors. The reduction in radar size enables flexible deployment, such as rooftop installation, with small infrastructure requirement, which is critical in a metropolitan region. Dual-polarization based technologies can be implemented for real-time mitigation of rain attenuations and accurate estimation of rainfall. The NSF Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) is developing the technologies and the systems for network centric weather observation. The Differential propagation phase (Kdp) has higher sensitivity at X-band compared to S and C band. It is attractive to use Kdp to derive Quantitative Precipitation Estimation (QPE) because it is immune to rain attenuation, calibration biases, partial beam blockage, and hail contamination. Despite the advantage of Kdp for radar QPE, the estimation of Kdp itself is a challenge as the range derivative of the differential propagation phase profiles. An adaptive Kdp algorithm was implemented in the CASA IP1 testbed that substantially reduces the fluctuation in light rain and the bias at heavy rain. The Kdp estimation also benefits from the higher resolution in the IP1 radar network. The performance of the IP1 QPE product was evaluated for all major rain events against the USDA Agriculture Research Service's gauge network (MicroNet) in the Little Washita watershed, which comprises 20 weather stations in the center of the test bed. The cross-comparison with gauge measurements shows excellent agreement for the storm events during the Spring Experiments of 2007 and 2008. The hourly rainfall estimates compared to the gauge measurements have a very small bias of few percent and a normalized standard error of 21%. The IP1 testbed was designed with overlapping coverage among its radar nodes. The study area is covered by multiple radars and the aspect of network composition is also evaluated. The independence of Kdp on the radar calibration enables flexibility in combining the collocated Kdp estimates from all the radar nodes. Radar QPE can be improved from the composite Kdp field from the radar with lowest beam height and nearest slant range, or from the radar with the best Kdp estimates. More importantly, the data availability is greatly enhanced by the overlapped topology in cases of heavy rainfall, demonstrating the operational strength of the network centric radar system. The National Research Institute for Earth Science and Disaster Prevention (NIED), Japan, is in the process of establishing an X-band radar network (X-Net) in Metropolitan Tokyo area. Colorado State University and NIED have formed a partnership to initiate a joint program for urban flood monitoring using X-band dual-polarization radar network. This paper will also present some preliminary plans for this program.

Evaluating rainfall kinetic energy - intensity relationships with observed disdrometric data

NASA Astrophysics Data System (ADS)

Angulo-Martinez, Marta; Begueria, Santiago; Latorre, Borja

2016-04-01

Rainfall kinetic energy is required for determining erosivity, the ability of rainfall to detach soil particles and initiate erosion. Its determination relay on the use of disdrometers, i.e. devices capable of measuring the drop size distribution and velocity of falling raindrops. In the absence of such devices, rainfall kinetic energy is usually estimated with empirical expressions relating rainfall energy and intensity. We evaluated the performance of 14 rainfall energy equations in estimating one-minute rainfall energy and event total energy, in comparison with observed data from 821 rainfall episodes (more than 100 thousand one-minute observations) by means of an optical disdrometer. In addition, two sources of bias when using such relationships were evaluated: i) the influence of using theoretical terminal raindrop fall velocities instead of measured values; and ii) the influence of time aggregation (rainfall intensity data every 5-, 10-, 15-, 30-, and 60-minutes). Empirical relationships did a relatively good job when complete events were considered (R2 > 0.82), but offered poorer results for within-event (one-minute resolution) variation. Also, systematic biases where large for many equations. When raindrop size distribution was known, estimating the terminal fall velocities by empirical laws produced good results even at fine time resolution. The influence of time aggregation was very high in the estimated kinetic energy, although linear scaling may allow empirical correction. This results stress the importance of considering all these effects when rainfall energy needs to be estimated from more standard precipitation records. , and recommends the use of disdrometer data to locally determine rainfall kinetic energy.

The PRESSCA operational early warning system for landslide forecasting: the 11-12 November 2013 rainfall event in Central Italy.

NASA Astrophysics Data System (ADS)

Ciabatta, Luca; Brocca, Luca; Ponziani, Francesco; Berni, Nicola; Stelluti, Marco; Moramarco, Tommaso

2014-05-01

The Umbria Region, located in Central Italy, is one of the most landslide risk prone area in Italy, almost yearly affected by landslides events at different spatial scales. For early warning procedures aimed at the assessment of the hydrogeological risk, the rainfall thresholds represent the main tool for the Italian Civil Protection System. As shown in previous studies, soil moisture plays a key-role in landslides triggering. In fact, acting on the pore water pressure, soil moisture influences the rainfall amount needed for activating a landslide. In this work, an operational physically-based early warning system, named PRESSCA, that takes into account soil moisture for the definition of rainfall thresholds is presented. Specifically, the soil moisture conditions are evaluated in PRESSCA by using a distributed soil water balance model that is recently coupled with near real-time satellite soil moisture product obtained from ASCAT (Advanced SCATterometer) and from in-situ monitoring data. The integration of three different sources of soil moisture information allows to estimate the most accurate possible soil moisture condition. Then, both observed and forecasted rainfall data are compared with the soil moisture-based thresholds in order to obtain risk indicators over a grid of ~ 5 km. These indicators are then used for the daily hydrogeological risk evaluation and management by the Civil Protection regional service, through the sharing/delivering of near real-time landslide risk scenarios (also through an open source web platform: www.cfumbria.it). On the 11th-12th November, 2013, Umbria Region was hit by an exceptional rainfall event with up to 430mm/72hours that resulted in significant economic damages, but fortunately no casualties among the population. In this study, the results during the rainfall event of PRESSCA system are described, by underlining the model capability to reproduce, two days in advance, landslide risk scenarios in good spatial and temporal agreement with the occurred actual conditions. High-resolution risk scenarios (100mx100m), obtained by coupling PRESSCA forecasts with susceptibility and vulnerability layers, are also produced. The results show good relationship between the PRESSCA forecast and the reported landslides to the Civil Protection Service during the rainfall event, confirming the system robustness. The good forecasts of PRESSCA system have surely contributed to start well in advance the Civil Protection operations (alerting local authorities and population).

Parameter Estimation for a Model of Space-Time Rainfall

NASA Astrophysics Data System (ADS)

Smith, James A.; Karr, Alan F.

1985-08-01

In this paper, parameter estimation procedures, based on data from a network of rainfall gages, are developed for a class of space-time rainfall models. The models, which are designed to represent the spatial distribution of daily rainfall, have three components, one that governs the temporal occurrence of storms, a second that distributes rain cells spatially for a given storm, and a third that determines the rainfall pattern within a rain cell. Maximum likelihood and method of moments procedures are developed. We illustrate that limitations on model structure are imposed by restricting data sources to rain gage networks. The estimation procedures are applied to a 240-mi2 (621 km2) catchment in the Potomac River basin.

Sediment transport dynamics in steep, tropical volcanic catchments

NASA Astrophysics Data System (ADS)

Birkel, Christian; Solano Rivera, Vanessa; Granados Bolaños, Sebastian; Brenes Cambronero, Liz; Sánchez Murillo, Ricardo; Geris, Josie

2017-04-01

How volcanic landforms in tropical mountainous regions are eroded, and how eroded materials move through these mostly steep landscapes from the headwaters to affect sediment fluxes are critical to water resources management in their downstream rivers. Volcanic landscapes are of particular importance because of the short timescales (< years) over which they transform. Owing to volcanism and seismic activity, landslides and other mass movements frequently occur. These processes are amplified by high intensity precipitation inputs resulting in significant, but natural runoff, erosion and sediment fluxes. Sediment transport is also directly linked to carbon and solute export. However, knowledge on the sediment sources and transport dynamics in the humid tropics remains limited and their fluxes largely unquantified. In order to increase our understanding of the dominant erosion and sediment transport dynamics in humid tropical volcanic landscapes, we conducted an extensive monitoring effort in a pristine and protected (biological reserve Alberto Manuel Brenes, ReBAMB) tropical forest catchment (3.2 km2), located in the Central Volcanic Cordillera of Costa Rica (Figure 1A). Typical for tropical volcanic and montane regions, deeply incised V-form headwaters (Figure 1B) deliver the majority of water (>70%) and sediments to downstream rivers. At the catchment outlet (Figure 1C) of the San Lorencito stream, we established high temporal resolution (5min) water quantity and sediment monitoring (turbidity). We also surveyed the river network on various occasions to characterize fluvial geomorphology including material properties. We could show that the rainfall-runoff-sediment relationships and their characteristic hysteresis patterns are directly linked to variations in the climatic input (storm intensity and duration) and the size, form and mineralogy of the transported material. Such a relationship allowed us to gain the following insights: (i) periodic landslides contribute significant volumes of material (> 100m3 per year) to the stream network, (ii) rainfall events that exceed a threshold of around 30mm/h rain intensity activate superficial flow pathways with associated mobilization of sediments (laminar erosion). However, the erosion processes are spatially very heterogeneous and mostly linked to finer material properties of the soils that mostly developed on more highly weathered bedrock. (iii) extreme events (return period > 50 years) mainly erode the streambed and banks cutting deeper into the bedrock and re-distribute massive amounts of material in the form of removed old alluvial deposits and new deposits created elsewhere, (iv) recovery after such extreme events in the form of fine material transport even during low intensity rainfall towards pre-event rainfall intensity thresholds takes only about two to three months. We conclude that the study catchment geomorphologically represents a low-resistance, but highly resilient catchment that quickly recovers after the impact of extreme rainfall-runoff events. The latter was indicated by a different pre and post-event hysteretic pattern of sediment-runoff dynamics and associated different material properties. The combined use of high-temporal resolution monitoring with spatially distributed surveys provided new insights into the fluvial geomorphology of steep, volcanic headwater catchments with potential to establish more complete sediment budgets and time-scales of land-forming processes of such highly dynamic environments in the humid tropics.

Estimating Vegetation Structure in African Savannas using High Spatial Resolution Imagery

NASA Astrophysics Data System (ADS)

Axelsson, C.; Hanan, N. P.

2016-12-01

High spatial resolution satellite imagery allows for detailed mapping of trees in savanna landscapes, including estimates of woody cover, tree densities, crown sizes, and the spatial pattern of trees. By linking these vegetation parameters to rainfall and soil properties we gain knowledge of how the local environment influences vegetation. A thorough understanding of the underlying ecosystem processes is key to assessing the future productivity and stability of these ecosystems. In this study, we have processed and analyzed hundreds of sites sampled from African savannas across a wide range of rainfall and soil conditions. The vegetation at each site is classified using unsupervised classification with manual assignment into woody, herbaceous and bare cover classes. A crown delineation method further divides the woody areas into individual tree crowns. The results show that rainfall, soil, and topography interactively influence vegetation structure. We see that both total rainfall and rainfall seasonality play important roles and that soil type influences woody cover and the sizes of tree crowns.

Influence of rainfall data scarcity on non-point source pollution prediction: Implications for physically based models

NASA Astrophysics Data System (ADS)

Chen, Lei; Xu, Jiajia; Wang, Guobo; Liu, Hongbin; Zhai, Limei; Li, Shuang; Sun, Cheng; Shen, Zhenyao

2018-07-01

Hydrological and non-point source pollution (H/NPS) predictions in ungagged basins have become the key problem for watershed studies, especially for those large-scale catchments. However, few studies have explored the comprehensive impacts of rainfall data scarcity on H/NPS predictions. This study focused on: 1) the effects of rainfall spatial scarcity (by removing 11%-67% of stations based on their locations) on the H/NPS results; and 2) the impacts of rainfall temporal scarcity (10%-60% data scarcity in time series); and 3) the development of a new evaluation method that incorporates information entropy. A case study was undertaken using the Soil and Water Assessment Tool (SWAT) in a typical watershed in China. The results of this study highlighted the importance of critical-site rainfall stations that often showed greater influences and cross-tributary impacts on the H/NPS simulations. Higher missing rates above a certain threshold as well as missing locations during the wet periods resulted in poorer simulation results. Compared to traditional indicators, information entropy could serve as a good substitute because it reflects the distribution of spatial variability and the development of temporal heterogeneity. This paper reports important implications for the application of Distributed Hydrological Models and Semi-distributed Hydrological Models, as well as for the optimal design of rainfall gauges among large basins.

Reducing bias in rainfall estimates from microwave links by considering variable drop size distribution

NASA Astrophysics Data System (ADS)

Fencl, Martin; Jörg, Rieckermann; Vojtěch, Bareš

2015-04-01

Commercial microwave links (MWL) are point-to-point radio systems which are used in backhaul networks of cellular operators. For several years, they have been suggested as rainfall sensors complementary to rain gauges and weather radars, because, first, they operate at frequencies where rain drops represent significant source of attenuation and, second, cellular networks almost completely cover urban and rural areas. Usually, path-average rain rates along a MWL are retrieved from the rain-induced attenuation of received MWL signals with a simple model based on a power law relationship. The model is often parameterized based on the characteristics of a particular MWL, such as frequency, polarization and the drop size distribution (DSD) along the MWL. As information on the DSD is usually not available in operational conditions, the model parameters are usually considered constant. Unfortunately, this introduces bias into rainfall estimates from MWL. In this investigation, we propose a generic method to eliminate this bias in MWL rainfall estimates. Specifically, we search for attenuation statistics which makes it possible to classify rain events into distinct groups for which same power-law parameters can be used. The theoretical attenuation used in the analysis is calculated from DSD data using T-Matrix method. We test the validity of our approach on observations from a dedicated field experiment in Dübendorf (CH) with a 1.85-km long commercial dual-polarized microwave link transmitting at a frequency of 38 GHz, an autonomous network of 5 optical distrometers and 3 rain gauges distributed along the path of the MWL. The data is recorded at a high temporal resolution of up to 30s. It is further tested on data from an experimental catchment in Prague (CZ), where 14 MWLs, operating at 26, 32 and 38 GHz frequencies, and reference rainfall from three RGs is recorded every minute. Our results suggest that, for our purpose, rain events can be nicely characterized based on only the maximum rain-induced attenuation of an event. Based on our experimental data, optimal results were achieved by classifying the rain events into three distinct groups with different power-law parameters for each group. In general, the classification of rain events based on attenuation data enables to substantially reduce bias in MWL rainfall estimates due to the power-law model. Thus, when using MWLs for rainfall estimation, reference rain events should be first classified and model parameters of a power-law retrieval model should be fitted for each of class separately. However, this at least requires rainfall data in sub-hourly resolution. It seems very promising to further investigate methods to adjust local MWL rainfall estimates to rainfall observations from traditional sensors. Messer, H., Zinevich, A., Alpert, P., 2006: Environmental Monitoring by Wireless Communication Networks. Science 312, 713-713. doi:10.1126/science.1120034 Fencl, M., Rieckermann, J., Sýkora, P., Stránský D. and Bareš V. 2014: Commercial microwave links instead of rain gauges - fiction or reality? Wat. Sci. Tech., in press doi:10.2166/wst.2014.466 Acknowledgements to Czech Science Foundation project No. 14-22978S and Czech Technical University in Prague project No. SGS13/127/OHK1/2T/11.

Modern proposal of methodology for retrieval of characteristic synthetic rainfall hyetographs

NASA Astrophysics Data System (ADS)

Licznar, Paweł; Burszta-Adamiak, Ewa; Łomotowski, Janusz; Stańczyk, Justyna

2017-11-01

Modern engineering workshop of designing and modelling complex drainage systems is based on hydrodynamic modelling and has a probabilistic character. Its practical application requires a change regarding rainfall models accepted at the input. Previously used artificial rainfall models of simplified form, e.g. block precipitation or Euler's type II model rainfall are no longer sufficient. It is noticeable that urgent clarification is needed as regards the methodology of standardized rainfall hyetographs that would take into consideration the specifics of local storm rainfall temporal dynamics. The aim of the paper is to present a proposal for innovative methodology for determining standardized rainfall hyetographs, based on statistical processing of the collection of actual local precipitation characteristics. Proposed methodology is based on the classification of standardized rainfall hyetographs with the use of cluster analysis. Its application is presented on the example of selected rain gauges localized in Poland. Synthetic rainfall hyetographs achieved as a final result may be used for hydrodynamic modelling of sewerage systems, including probabilistic detection of necessary capacity of retention reservoirs.

Effect of DEM resolution on rainfall-triggered landslide modeling within a triangulated network-based model. A case study in the Luquillo Forest, Puerto Rico

NASA Astrophysics Data System (ADS)

Arnone, E.; Dialynas, Y. G.; Noto, L. V.; Bras, R. L.

2013-12-01

Catchment slope distribution is one of the topographic characteristics that significantly control rainfall-triggered landslide modeling, in both direct and indirect ways. Slope directly determines the soil volume associated with instability. Indirectly slope also affects the subsurface lateral redistribution of soil moisture across the basin, which in turn determines the water pore pressure conditions that impact slope stability. In this study, we investigate the influence of DEM resolution on slope stability and the slope stability analysis by using a distributed eco-hydrological and landslide model, the tRIBS-VEGGIE (Triangulated Irregular Network (TIN)-based Real-time Integrated Basin Simulator - VEGetation Generator for Interactive Evolution). The model implements a triangulated irregular network to describe the topography, and it is capable of evaluating vegetation dynamics and predicting shallow landslides triggered by rainfall. The impact of DEM resolution on the landslide prediction was studied using five TINs derived from five grid DEMs at different resolutions, i.e. 10, 20, 30, 50 and 70 m respectively. The analysis was carried out on the Mameyes Basin, located in the Luquillo Experimental Forest in Puerto Rico, where previous landslide analyses have been carried out. Results showed that the use of the irregular mesh reduced the loss of accuracy in the derived slope distribution when coarser resolutions were used. The impact of the different resolutions on soil moisture patterns was important only when the lateral redistribution was considerable, depending on hydrological properties and rainfall forcing. In some cases, the use of different DEM resolutions did not significantly affect tRIBS-VEGGIE landslide output, in terms of landslide locations, and values of slope and soil moisture at failure.

Recent Structural Change in Remote Sensing Data Time Series Linked to Farm Management in Horn of Africa (1999-2009)

NASA Astrophysics Data System (ADS)

Crisci, A.; Vignaroli, P.; Genesio, L.; Grasso, V.; Bacci, M.; Tarchiani, V.; Capecchi, V.

2011-01-01

Food security in East Africa region essentially depends on the stability of rain-fed crops farming, which renders its society vulnerable to climatic fluctuations. These ones in Africa are most widely and directly related to rainfall. In this study, the relation between recent spatial rainfall variability and vegetation dynamics has been investigated for East Africa territories. Satellite raster products SPOT-4 Vegetation 1 km resolution (Saint, 1995) and RFE (rainfall estimates) from Famine Early Warning Systems Network (FEWS NET) are used. The survey is carried out at administrative level scale using 10-day summaries extracted from raster data for each spatial area unit thanks to specific polygonal layers. Time series covers two different periods: 1996-2009 for rainfall estimates and 1999-2009 for NDVI. The first step of the analysis has been to build for each administrative unit a coherent set of data, along the time series, suitable to be processed with state-of-art statistical tools. The analysis is based on the assumption that every structural break in vegetation dynamics could be caused by two alternative/complementary causes, namely: (i) modifications in crop farming systems (adaptation strategy) related to eventual break-shift in rainfall regime and/or (ii) other socio-economic factors. BFAST (Verbesselt et al, 2010) R package are employed to lead a comprehensive breakpoint analysis on 10-day RFE (spatial mean and standard deviation) and 10-day NDVI ones (spatial mean, mode and standard deviation). The cross-viewing of the years where significant breaks have occurred, throughout opportune GIS layering, provides an explorative interpretation of spatial climate/vegetation dynamics in the whole area. Moreover, the spatial and temporal pattern of ecosystem dynamics in response to climatic variability has been investigated using wavelet coherency by SOWAS R package (Maraun, 2007). The wavelet coherency (WCOH) is a normalized time and scale resolved measure for the relationship between two time series (Maraun and Kurths, 2004). This kind of multi-scale temporal investigation provides an explanation of break detected in time series, confirming or not their climatic linkage; results of the analysis are shown. Finally, in order to support the dissemination and sharing of information, interactive vegetation maps have been implemented with Google Earth mash-up. The maturity of Web-based GIS enables the generation of thematic maps dynamically and efficiently, with a thin/thick client or hybrid architectures. This could be a great support for the understanding environmental phenomena.

Upper-soil moisture inter-comparison from SMOS's products and land surface models over the Iberian Peninsula

NASA Astrophysics Data System (ADS)

Polcher, Jan; Barella-Ortiz, Anaïs; Aires, Filipe; Balsamo, Gianpaolo; Gelati, Emiliano; Rodríguez-Fernández, Nemesio

2015-04-01

Soil moisture is a key state variable of the hydrological cycle. It conditions runoff, infiltration and evaporation over continental surfaces, and is key for forecasting droughts and floods. It plays thus an important role in surface-atmosphere interactions. Surface Soil Moisture (SSM) can be measured by in situ measurements, by satellite observations or modelled using land surface models. As a complementary tool, data assimilation can be used to combine both modelling and satellite observations. The work presented here is an inter-comparison of retrieved and modelled SSM data, for the 2010 - 2012 period, over the Iberian Peninsula. The region has been chosen because its vegetation cover is not very dense and includes strong contrasts in the rainfall regimes and thus a diversity of behaviours for SSM. Furthermore this semi-arid region is strongly dependent on a good management of its water resources. Satellite observations correspond to the Soil Moisture and Ocean Salinity (SMOS) retrievals: the L2 product from an optimal interpolation retrieval, and 3 other products using Neural Network retrievals with different input information: SMOS time indexes, purely SMOS data, or addition of the European Advanced Scaterometer (ASCAT) backscattering, and the Moderate-Resolution Imaging Spectrometer (MODIS) surface temperature information. The modelled soil moistures have been taken from the ORCHIDEE (ORganising Carbon and Hydrology In Dynamic EcosystEms) and the HTESSEL (Hydrology-Tiled ECMWF Scheme for Surface Exchanges over Land) land surface models. Both models are forced with the same atmospheric conditions (as part of the Earth2Observe FP7 project) over the period but they represent the surface soil moisture with very different degrees of complexity. ORCHIDEE has 5 levels in the top 5 centimetres of soil while in HTESSEL this variable is part of the top soil moisture level. The two types of SMOS retrievals are compared to the model outputs in their spatial and temporal characteristics. The comparison with the model helps to identify which retrieval configuration is most consistent with our understanding of surface soil moisture in this region. In particular we have determined how each of the soil moisture products is related to the spatio-temporal variations of rainfall. In large parts of the Iberian Peninsula the co-variance of remote sensed SSM and rainfall is consistent with that of the models. But for some regions questions are raised. The variability of SSM observed by SMOS in the North West of the Iberian Peninsula is similar to that of rainfall, at least this relation of SSM and rainfall is closer than suggested by the two models.

Water availability as a driver of spatial and temporal variability in vegetation in the La Mancha plain (Spain): Implications for the land-surface energy, water and carbon budget

NASA Astrophysics Data System (ADS)

Los, Sietse

2017-04-01

Vegetation is water limited in large areas of Spain and therefore a close link exists between vegetation greenness observed from satellite and moisture availability. Here we exploit this link to infer spatial and temporal variability in moisture from MODIS NDVI data and thermal data. Discrepancies in the precipitation - vegetation relationship indicate areas with an alternative supply of water (i.e. not rainfall), this can be natural where moisture is supplied by upwelling groundwater, or can be artificial where crops are irrigated. As a result spatial and temporal variability in vegetation in the La Mancha Plain appears closely linked to topography, geology, rainfall and land use. Crop land shows large variability in year-to-year vegetation greenness; for some areas this variability is linked to variability in rainfall but in other cases this variability is linked to irrigation. The differences in irrigation treatment within one plant functional type, in this case crops, will lead to errors in land surface models when ignored. The magnitude of these effects on the energy, carbon and water balance are assessed at the scale of 250 m to 200 km. Estimating the water balance correctly is of particular important since in some areas in Spain more water is used for irrigation than is supplemented by rainfall.

A Multiseason Comparison of the Forecast Skills among Three Numerical Models over Southcentral United States

NASA Astrophysics Data System (ADS)

Lu, D.; Reddy, S.

2005-05-01

During the summer 2003 and winter 2003-2004, three mesoscale numerical models, the fifth-generation Pennsylvania State University-NCAR Mesoscale Model (MM5), Navy's Coupled Ocean/Atmospheric Mesoscale Prediction System (COAMPS) and the Weather Research and Forecasting model (WRF), were operationally run at a horizontal resolution of 27 km twice daily in Jackson State University (JSU). Three models were run by the initial and lateral boundary conditions from AVN data. The purpose of this paper is to evaluate the performances of three models during these two seasons. It was found that the temporal variation of distribution and strength of mean error (ME) biases at 12, 24 and 36h was rather weak for surface temperature, sea level pressure and surface wind speed. During two seasons, the MM5 underpredicted the seasonal precipitation while the COAMPS and WRF overpredicted. This is consistent with the statistical score analyses of rainfall. The Bias scores revealed that the MM5 yielded an underprediction of precipitation, especially for heavier rainfall events. Due to the under estimate of rainfall areas and strength, the MM5 presented the lower TS, POD and KSS scores at lighter rainfall events compared to the COAMPS and WRF. At moderate to heavier thresholds, three models produced rather low KSS and POD scores that are consistent with the high FAR values. The WRF skills in predicting precipitation heavily depend on the performance of cumulus parameterization scheme. Instead of Kain-Fritsch scheme, using other two schemes, Grell-Devenyi and Bette-Miller-Janjic, in the WRF for warm season 2003 demonstrated that the precipitation overprediction had been efficiently suppressed. Overall, the performances of three models revealed that the best skill is at 12h and the worst at 36h.

Rainwater harvesting possibility under climate change: A basin-scale case study over western province of Saudi Arabia

NASA Astrophysics Data System (ADS)

Almazroui, Mansour; Islam, M. Nazrul; Balkhair, Khaled S.; Şen, Zekâi; Masood, Amjad

2017-06-01

Groundwater reservoirs are important water resources all over the world. Especially, they are of utmost significance for arid and semi-arid regions, and therefore, a sustainable exploitation of these reservoirs needs to be ensured. The natural and most exclusive water supplier to groundwater reservoirs in Saudi Arabia is rainfall, which is characterized by sporadic and random temporal and spatial distributions, particularly under the impacts of climate change; giving rise to uncertainty in groundwater recharge quantification. Although in Saudi Arabia, intense and frequent rainfall events are rare, but they generate significant flash floods with huge amounts of surface water. Under such circumstances, any simple but effective water storage augmentation facility such as rainwater harvesting (RWH) structures gain vital importance for sustainability of water supply and survivals in arid and semi-arid regions. The objective of this study is to explore the possibility of RWH over a basin in the western province of Saudi Arabia called Wadi Al-Lith under climate change. Climatic data is obtained from the IPCC AR5 GCMs, which is further downscaled using a regional climate model RegCM4 for the Arabian Peninsula domain. The RegCM4 is driven to simulate climatic parameters including rainfall at 25 km grid resolution for the present climate (1971-2000), and future climate (2006-2099) with representative concentration pathways, RCP4.5 and RCP8.5. Results indicate that more durable and longer wet durations are expected with increasing surplus rainfall amounts in the far future because of climate change impacts. Consequently, future climate scenarios are expected to enhance floods and flash floods occurrences, which call for progressive measures to harness the RWH opportunity.

High temporal resolution modeling of the impact of rain, tides, and sea level rise on water table flooding in the Arch Creek basin, Miami-Dade County Florida USA.

PubMed

Sukop, Michael C; Rogers, Martina; Guannel, Greg; Infanti, Johnna M; Hagemann, Katherine

2018-03-01

Modeling of groundwater levels in a portion of the low-lying coastal Arch Creek basin in northern Miami-Dade County in Southeast Florida USA, which is subject to repetitive flooding, reveals that rain-induced short-term water table rises can be viewed as a primary driver of flooding events under current conditions. Areas below 0.9m North American Vertical Datum (NAVD) elevation are particularly vulnerable and areas below 1.5m NAVD are vulnerable to exceptionally large rainfall events. Long-term water table rise is evident in the groundwater data, and the rate appears to be consistent with local rates of sea level rise. Linear extrapolation of long-term observed groundwater levels to 2060 suggest roughly a doubling of the number of days when groundwater levels exceed 0.9m NAVD and a threefold increase in the number of days when levels exceed 1.5m NAVD. Projected sea level rise of 0.61m by 2060 together with increased rainfall lead to a model prediction of frequent groundwater-related flooding in areas<0.9m NAVD. However, current simulations do not consider the range of rainfall events that have led to water table elevations>1.5m NAVD and widespread flooding of the area in the past. Tidal fluctuations in the water table are predicted to be more pronounced within 600m of a tidally influenced water control structure that is hydrodynamically connected to Biscayne Bay. The inland influence of tidal fluctuations appears to increase with increased sea level, but the principal driver of high groundwater levels under the 2060 scenario conditions remains groundwater recharge due to rainfall events. Copyright © 2017 Elsevier B.V. All rights reserved.

Dynamics of runoff from high-intensity, short-duration storms.

DOT National Transportation Integrated Search

1985-01-01

The effects of several parameters on the behavior of a runoff hydrograph were analyzed. The temporal distribution of rainfall was simulated using three synthetic storm patterns where the temporal location of the maximum burst was modified; the antece...

Assessment of Land Surface Models in a High-Resolution Atmospheric Model during Indian Summer Monsoon

NASA Astrophysics Data System (ADS)

Attada, Raju; Kumar, Prashant; Dasari, Hari Prasad

2018-04-01

Assessment of the land surface models (LSMs) on monsoon studies over the Indian summer monsoon (ISM) region is essential. In this study, we evaluate the skill of LSMs at 10 km spatial resolution in simulating the 2010 monsoon season. The thermal diffusion scheme (TDS), rapid update cycle (RUC), and Noah and Noah with multi-parameterization (Noah-MP) LSMs are chosen based on nature of complexity, that is, from simple slab model to multi-parameterization options coupled with the Weather Research and Forecasting (WRF) model. Model results are compared with the available in situ observations and reanalysis fields. The sensitivity of monsoon elements, surface characteristics, and vertical structures to different LSMs is discussed. Our results reveal that the monsoon features are reproduced by WRF model with all LSMs, but with some regional discrepancies. The model simulations with selected LSMs are able to reproduce the broad rainfall patterns, orography-induced rainfall over the Himalayan region, and dry zone over the southern tip of India. The unrealistic precipitation pattern over the equatorial western Indian Ocean is simulated by WRF-LSM-based experiments. The spatial and temporal distributions of top 2-m soil characteristics (soil temperature and soil moisture) are well represented in RUC and Noah-MP LSM-based experiments during the ISM. Results show that the WRF simulations with RUC, Noah, and Noah-MP LSM-based experiments significantly improved the skill of 2-m temperature and moisture compared to TDS (chosen as a base) LSM-based experiments. Furthermore, the simulations with Noah, RUC, and Noah-MP LSMs exhibit minimum error in thermodynamics fields. In case of surface wind speed, TDS LSM performed better compared to other LSM experiments. A significant improvement is noticeable in simulating rainfall by WRF model with Noah, RUC, and Noah-MP LSMs over TDS LSM. Thus, this study emphasis the importance of choosing/improving LSMs for simulating the ISM phenomena in a regional model.

Temporal and spatial variations of soil carbon dioxide, methane, and nitrous oxide fluxes in a Southeast Asian tropical rainforest

NASA Astrophysics Data System (ADS)

Itoh, M.; Kosugi, Y.; Takanashi, S.; Hayashi, Y.; Kanemitsu, S.; Osaka, K.; Tani, M.; Nik, A. R.

2010-09-01

To clarify the factors controlling temporal and spatial variations of soil carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) fluxes, we investigated these gas fluxes and environmental factors in a tropical rainforest in Peninsular Malaysia. Temporal variation of CO2 flux in a 2-ha plot was positively related to soil water condition and rainfall history. Spatially, CO2 flux was negatively related to soil water condition. When CO2 flux hotspots were included, no other environmental factors such as soil C or N concentrations showed any significant correlation. Although the larger area sampled in the present study complicates explanations of spatial variation of CO2 flux, our results support a previously reported bipolar relationship between the temporal and spatial patterns of CO2 flux and soil water condition observed at the study site in a smaller study plot. Flux of CH4 was usually negative with little variation, resulting in the soil at our study site functioning as a CH4 sink. Both temporal and spatial variations of CH4 flux were positively related to the soil water condition. Soil N concentration was also related to the spatial distribution of CH4 flux. Some hotspots were observed, probably due to CH4 production by termites, and these hotspots obscured the relationship between both temporal and spatial variations of CH4 flux and environmental factors. Temporal variation of N2O flux and soil N2O concentration was large and significantly related to the soil water condition, or in a strict sense, to rainfall history. Thus, the rainfall pattern controlled wet season N2O production in soil and its soil surface flux. Spatially, large N2O emissions were detected in wet periods at wetter and anaerobic locations, and were thus determined by soil physical properties. Our results showed that, even in Southeast Asian rainforests where distinct dry and wet seasons do not exist, variation in the soil water condition related to rainfall history controlled the temporal variations of soil CO2 flux, CH4 uptake, and N2O emission. The soil water condition associated with soil hydraulic properties was also the important controlling factor of the spatial distributions of these gas fluxes.

Detecting surface runoff location in a small catchment using distributed and simple observation method

NASA Astrophysics Data System (ADS)

Dehotin, Judicaël; Breil, Pascal; Braud, Isabelle; de Lavenne, Alban; Lagouy, Mickaël; Sarrazin, Benoît

2015-06-01

Surface runoff is one of the hydrological processes involved in floods, pollution transfer, soil erosion and mudslide. Many models allow the simulation and the mapping of surface runoff and erosion hazards. Field observations of this hydrological process are not common although they are crucial to evaluate surface runoff models and to investigate or assess different kinds of hazards linked to this process. In this study, a simple field monitoring network is implemented to assess the relevance of a surface runoff susceptibility mapping method. The network is based on spatially distributed observations (nine different locations in the catchment) of soil water content and rainfall events. These data are analyzed to determine if surface runoff occurs. Two surface runoff mechanisms are considered: surface runoff by saturation of the soil surface horizon and surface runoff by infiltration excess (also called hortonian runoff). The monitoring strategy includes continuous records of soil surface water content and rainfall with a 5 min time step. Soil infiltration capacity time series are calculated using field soil water content and in situ measurements of soil hydraulic conductivity. Comparison of soil infiltration capacity and rainfall intensity time series allows detecting the occurrence of surface runoff by infiltration-excess. Comparison of surface soil water content with saturated water content values allows detecting the occurrence of surface runoff by saturation of the soil surface horizon. Automatic records were complemented with direct field observations of surface runoff in the experimental catchment after each significant rainfall event. The presented observation method allows the identification of fast and short-lived surface runoff processes at a small spatial and temporal resolution in natural conditions. The results also highlight the relationship between surface runoff and factors usually integrated in surface runoff mapping such as topography, rainfall parameters, soil or land cover. This study opens interesting prospects for the use of spatially distributed measurement for surface runoff detection, spatially distributed hydrological models implementation and validation at a reasonable cost.

Co-evolution of monsoonal precipitation in East Asia and the tropical Pacific ENSO system since 2.36 Ma: New insights from high-resolution clay mineral records in the West Philippine Sea

NASA Astrophysics Data System (ADS)

Yu, Zhaojie; Wan, Shiming; Colin, Christophe; Yan, Hong; Bonneau, Lucile; Liu, Zhifei; Song, Lina; Sun, Hanjie; Xu, Zhaokai; Jiang, Xuejun; Li, Anchun; Li, Tiegang

2016-07-01

Clay mineralogical analysis and scanning electron microscope (SEM) analysis were performed on deep-sea sediments cored on the Benham Rise (core MD06-3050) in order to reconstruct long-term evolution of East Asian Summer Monsoon (EASM) rainfall in the period since 2.36 Ma. Clay mineralogical variations are due to changes in the ratios of smectite, which derive from weathering of volcanic rocks in Luzon Island during intervals of intensive monsoon rainfall, and illite- and chlorite-rich dusts, which are transported from East Asia by winds associated with the East Asian Winter Monsoon (EAWM). Since Luzon is the main source of smectite to the Benham Rise, long-term consistent variations in the smectite/(illite + chlorite) ratio in core MD06-3050 as well as ODP site 1146 in the Northern South China Sea suggest that minor contributions of eolian dust played a role in the variability of this mineralogical ratio and indicate strengthening EASM precipitation in SE Asia during time intervals from 2360 to 1900 kyr, 1200 to 600 kyr, and after 200 kyr. The EASM rainfall record displays a 30 kyr periodicity suggesting the influence of El Niño-Southern Oscillation (ENSO). These intervals of rainfall intensification on Luzon Island are coeval with a reduction in precipitation over central China and an increase in zonal SST gradient in the equatorial Pacific Ocean, implying a reinforcement of La Niña-like conditions. In contrast, periods of reduced rainfall on Luzon Island are associated with higher precipitation in central China and a weakening zonal SST gradient in the equatorial Pacific Ocean, thereby suggesting the development of dominant El Niño-like conditions. Our study, therefore, highlights for the first time a long-term temporal and spatial co-evolution of monsoonal precipitation in East Asia and of the tropical Pacific ENSO system over the past 2.36 Ma.

The climatic characteristics of extreme precipitations for short-term intervals in the watershed of Lake Maggiore

NASA Astrophysics Data System (ADS)

Saidi, Helmi; Ciampittiello, Marzia; Dresti, Claudia; Ghiglieri, Giorgio

2013-07-01

Alpine and Mediterranean areas are undergoing a profound change in the typology and distribution of rainfall. In particular, there has been an increase in consecutive non-rainy days, and an escalation of extreme rainy events. The climatic characteristic of extreme precipitations over short-term intervals is an object of study in the watershed of Lake Maggiore, the second largest freshwater basin in Italy (located in the north-west of the country) and an important resource for tourism, fishing and commercial flower growing. The historical extreme rainfall series with high-resolution from 5 to 45 min and above: 1, 2, 3, 6, 12 and 24 h collected at different gauges located at representative sites in the watershed of Lake Maggiore, have been computed to perform regional frequency analysis of annual maxima precipitation based on the L-moments approach, and to produce growth curves for different return-period rainfall events. Because of different rainfall-generating mechanisms in the watershed of Lake Maggiore such as elevation, no single parent distribution could be found for the entire study area. This paper concerns an investigation designed to give a first view of the temporal change and evolution of annual maxima precipitation, focusing particularly on both heavy and extreme events recorded at time intervals ranging from few minutes to 24 h and also to create and develop an extreme storm precipitation database, starting from historical sub-daily precipitation series distributed over the territory. There have been two-part changes in extreme rainfall events occurrence in the last 23 years from 1987 to 2009. Little change is observed in 720 min and 24-h precipitations, but the change seen in 5, 10, 15, 20, 30, 45, 60, 120, 180 and 360 min events is significant. In fact, during the 2000s, growth curves have flattened and annual maxima have decreased.

The role of pre-event canopy storage in throughfall and stemflow by using isotopic tracers

Treesearch

S.T. Allen; J.R. Brooks; R.F. Keim; B.J. Bond; J.J. McDonnell

2014-01-01

Stable isotopes can be a valuable tool for tracing the redistribution, storage, and evaporation of water associated with canopy interception of rainfall. Isotopic differences between throughfall and rainfall have been attributed to three mechanisms: evaporative fractionation, isotopic exchange with ambient vapor, and temporal redistribution. We demonstrate the...

Hydrological model parameterization using NDVI values to account for the effects of land-cover change on the rainfall-runoff response

USDA-ARS?s Scientific Manuscript database

Classic rainfall-runoff models usually use historical data to estimate model parameters and mean values of parameters are considered for predictions. However, due to climate changes and human effects, the parameters of model change temporally. To overcome this problem, Normalized Difference Vegetati...

Temporal evolution of the spatial covariability of rainfall in South America

NASA Astrophysics Data System (ADS)

Ciemer, Catrin; Boers, Niklas; Barbosa, Henrique M. J.; Kurths, Jürgen; Rammig, Anja

2017-10-01

The climate of South America exhibits pronounced differences between rainy and dry seasons, associated with specific synoptic features such as the establishment of the South Atlantic convergence zone. Here, we analyze the spatiotemporal correlation structure and in particular teleconnections of daily rainfall associated with these features by means of evolving complex networks. A modification of Pearson's correlation coefficient is introduced to handle the intricate statistical properties of daily rainfall. On this basis, spatial correlation networks are constructed, and new appropriate network measures are introduced in order to analyze the temporal evolution of the networks' characteristics. We particularly focus on the identification of coherent areas of similar rainfall patterns and previously unknown teleconnection structures between remote areas. We show that the monsoon onset is characterized by an abrupt transition from erratic to organized regional connectivity that prevails during the monsoon season, while only the onset times themselves exhibit anomalous large-scale organization of teleconnections. Furthermore, we reveal that the two mega-droughts in the Amazon basin were already announced in the previous year by an anomalous behavior of the connectivity structure.

Evaluation of rainfall structure on hydrograph simulation: Comparison of radar and interpolated methods, a study case in a tropical catchment

NASA Astrophysics Data System (ADS)

Velasquez, N.; Ochoa, A.; Castillo, S.; Hoyos Ortiz, C. D.

2017-12-01

The skill of river discharge simulation using hydrological models strongly depends on the quality and spatio-temporal representativeness of precipitation during storm events. All precipitation measurement strategies have their own strengths and weaknesses that translate into discharge simulation uncertainties. Distributed hydrological models are based on evolving rainfall fields in the same time scale as the hydrological simulation. In general, rainfall measurements from a dense and well maintained rain gauge network provide a very good estimation of the total volume for each rainfall event, however, the spatial structure relies on interpolation strategies introducing considerable uncertainty in the simulation process. On the other hand, rainfall retrievals from radar reflectivity achieve a better spatial structure representation but with higher uncertainty in the surface precipitation intensity and volume depending on the vertical rainfall characteristics and radar scan strategy. To assess the impact of both rainfall measurement methodologies on hydrological simulations, and in particular the effects of the rainfall spatio-temporal variability, a numerical modeling experiment is proposed including the use of a novel QPE (Quantitative Precipitation Estimation) method based on disdrometer data in order to estimate surface rainfall from radar reflectivity. The experiment is based on the simulation of 84 storms, the hydrological simulations are carried out using radar QPE and two different interpolation methods (IDW and TIN), and the assessment of simulated peak flow. Results show significant rainfall differences between radar QPE and the interpolated fields, evidencing a poor representation of storms in the interpolated fields, which tend to miss the precise location of the intense precipitation cores, and to artificially generate rainfall in some areas of the catchment. Regarding streamflow modelling, the potential improvement achieved by using radar QPE depends on the density of the rain gauge network and its distribution relative to the precipitation events. The results for the 84 storms show a better model skill using radar QPE than the interpolated fields. Results using interpolated fields are highly affected by the dominant rainfall type and the basin scale.

Rainfall Estimates from the TMI and the SSM/I

NASA Technical Reports Server (NTRS)

Hong, Ye; Kummerow, Christian D.; Olson, William S.; Viltard, Nicolas

1999-01-01

The Tropical Rainfall Measuring Mission (TRMM), which is a joint Japan-U.S. Earth observing satellite, has been successfully launched from Japan on November 27, 1997. The main purpose of the TRMM is to measure quantitatively rainfall over the tropics for the research of climate and weather. One of three rainfall measuring instruments abroad the TRMM is the high resolution TRMM Microwave Imager (TMI). The TMI instrument is essentially the copy of the SSM/I with a dual-polarized pair of 10.7 GHz channels added to increase the dynamic range of rainfall estimates. In addition, the 21.3 GHz water vapor absorption channel is designed in the TMI as opposed to the 22.235 GHz in the SSM/I to avoid saturation in the tropics. This paper will present instantaneous rain rates estimated from the coincident TMI and SSM/I observations. The algorithm for estimating instantaneous rainfall rates from both sensors is the Goddard Profiling algorithm (Gprof). The Gprof algorithm is a physically based, multichannel rainfall retrieval algorithm, The algorithm is very portable and can be used for various sensors with different channels and resolutions. The comparison of rain rates estimated from TMI and SSM/I on the same rain regions will be performed. The results from the comparison and the insight of tile retrieval algorithm will be given.

Comparison of TOPMODEL streamflow simulations using NEXRAD-based and measured rainfall data, McTier Creek watershed, South Carolina

USGS Publications Warehouse

Feaster, Toby D.; Westcott, Nancy E.; Hudson, Robert J.M.; Conrads, Paul; Bradley, Paul M.

2012-01-01

Rainfall is an important forcing function in most watershed models. As part of a previous investigation to assess interactions among hydrologic, geochemical, and ecological processes that affect fish-tissue mercury concentrations in the Edisto River Basin, the topography-based hydrological model (TOPMODEL) was applied in the McTier Creek watershed in Aiken County, South Carolina. Measured rainfall data from six National Weather Service (NWS) Cooperative (COOP) stations surrounding the McTier Creek watershed were used to calibrate the McTier Creek TOPMODEL. Since the 1990s, the next generation weather radar (NEXRAD) has provided rainfall estimates at a finer spatial and temporal resolution than the NWS COOP network. For this investigation, NEXRAD-based rainfall data were generated at the NWS COOP stations and compared with measured rainfall data for the period June 13, 2007, to September 30, 2009. Likewise, these NEXRAD-based rainfall data were used with TOPMODEL to simulate streamflow in the McTier Creek watershed and then compared with the simulations made using measured rainfall data. NEXRAD-based rainfall data for non-zero rainfall days were lower than measured rainfall data at all six NWS COOP locations. The total number of concurrent days for which both measured and NEXRAD-based data were available at the COOP stations ranged from 501 to 833, the number of non-zero days ranged from 139 to 209, and the total difference in rainfall ranged from -1.3 to -21.6 inches. With the calibrated TOPMODEL, simulations using NEXRAD-based rainfall data and those using measured rainfall data produce similar results with respect to matching the timing and shape of the hydrographs. Comparison of the bias, which is the mean of the residuals between observed and simulated streamflow, however, reveals that simulations using NEXRAD-based rainfall tended to underpredict streamflow overall. Given that the total NEXRAD-based rainfall data for the simulation period is lower than the total measured rainfall at the NWS COOP locations, this bias would be expected. Therefore, to better assess the use of NEXRAD-based rainfall estimates as compared to NWS COOP rainfall data on the hydrologic simulations, TOPMODEL was recalibrated and updated simulations were made using the NEXRAD-based rainfall data. Comparisons of observed and simulated streamflow show that the TOPMODEL results using measured rainfall data and NEXRAD-based rainfall are comparable. Nonetheless, TOPMODEL simulations using NEXRAD-based rainfall still tended to underpredict total streamflow volume, although the magnitude of differences were similar to the simulations using measured rainfall. The McTier Creek watershed was subdivided into 12 subwatersheds and NEXRAD-based rainfall data were generated for each subwatershed. Simulations of streamflow were generated for each subwatershed using NEXRAD-based rainfall and compared with subwatershed simulations using measured rainfall data, which unlike the NEXRAD-based rainfall were the same data for all subwatersheds (derived from a weighted average of the six NWS COOP stations surrounding the basin). For the two simulations, subwatershed streamflow were summed and compared to streamflow simulations at two U.S. Geological Survey streamgages. The percentage differences at the gage near Monetta, South Carolina, were the same for simulations using measured rainfall data and NEXRAD-based rainfall. At the gage near New Holland, South Carolina, the percentage differences using the NEXRAD-based rainfall were twice as much as those using the measured rainfall. Single-mass curve comparisons showed an increase in the total volume of rainfall from north to south. Similar comparisons of the measured rainfall at the NWS COOP stations showed similar percentage differences, but the NEXRAD-based rainfall variations occurred over a much smaller distance than the measured rainfall. Nonetheless, it was concluded that in some cases, using NEXRAD-based rainfall data in TOPMODEL streamflow simulations may provide an effective alternative to using measured rainfall data. For this investigation, however, TOPMODEL streamflow simulations using NEXRAD-based rainfall data for both calibration and simulations did not show significant improvements with respect to matching observed streamflow over simulations generated using measured rainfall data.

Mladotice Lake, Czechia: The unique genesis and evolution of the lake basin

NASA Astrophysics Data System (ADS)

Janský, Bohumír; Šobr, Miroslav; Kliment, Zdeněk; Chalupová, Dagmar

2016-04-01

The Mladotice Lake is a lake of unique genetic type in Czechia. In May 1872 a landslide as a result an extreme rainfall event occurred in western Czechia, blocking the Mladoticky stream valley and creating the Mladotice Lake. The 1952 and 1975 air images document that collective farming had a great impact on the lake basin evolution when balks and field terraces were removed and fields were made much larger. Because of this change in land use we expected higher soil erosion and a related increase in the sedimentation rate. First bathymetric measurements of the newly created lake were carried out in 1972 and were repeated in 1999, in 2003 and in 2014. Our analysis of the sedimentary record aims to identify the sediment stratigraphy, its basic physical and chemical properties, isotope content and thin sections yield a detailed temporal resolution of the sedimentation chronology. In some areas a sediment thickness of 4 m was detected. Hence, the average sedimentation rate is from 2.2 to 2.7 cm per year. KEY WORDS: Mladotice Lake - extreme rainfall event - landslide - land use changes - flood events - bathymetric measurements - sedimentation dynamics - stratigraphy and geochemistry of lake sediments - analyses of isotopes - sedimentation rates.

Influence of topography on debris flow development in Ichino-sawa subwatershed of Ohya-kuzure landslide, Japan

NASA Astrophysics Data System (ADS)

Tsunetaka, H.; Hotta, N.; Imaizumi, F.; Hayakawa, Y. S.

2015-12-01

Large sediment movements, such as deep-seated landslides, produce unstable sediment over the long term. Most of the unstable sediment in a mountain torrent is discharged via the development of debris flows through entrainment. Consequently, after a large sediment movement, debris flows have long-term effects on the watershed regime. However, the development of debris flows in mountain torrents is poorly understood, since the topography is more complicated than downstream. We compared temporal changes in topography to examine how topography affects the development of flows. The study site was the Ichino-sawa subwatershed in the Ohya-kuzure landslide, Japan. Unstable sediment has been produced continuously since the landslide occurred in 1707. Several topographic surveys using a terrestrial laser scanner (TLS) and aerial shoots by an unmanned aerial vehicle (UAV) were performed between November 2011 (TLS) or November 2014 (UAV) and August 2015. High-resolution digital elevation models were created from the TLS and UAV results to detect temporal topographic changes. Debris flow occurrences and rainfall were also monitored using interval cameras and rain gauges. Downstream, the deposit depth decreased after the debris flows. Upstream, more complex changes were detected due to surges in the debris flows, which not only induced entrainment, but were also deposited in the valley floor. Furthermore, sediment was supplied from the stream bank during the debris flows. Consequently, several debris flows of different magnitudes were observed, although the rainfall conditions did not differ significantly. The results imply that the magnitude of the debris flows was affected by successive sediment movement resulting from the changing of the topographic conditions.

Phenologically-tuned MODIS NDVI-based production anomaly estimates for Zimbabwe

USGS Publications Warehouse

Funk, Chris; Budde, Michael E.

2009-01-01

For thirty years, simple crop water balance models have been used by the early warning community to monitor agricultural drought. These models estimate and accumulate actual crop evapotranspiration, evaluating environmental conditions based on crop water requirements. Unlike seasonal rainfall totals, these models take into account the phenology of the crop, emphasizing conditions during the peak grain filling phase of crop growth. In this paper we describe an analogous metric of crop performance based on time series of Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) imagery. A special temporal filter is used to screen for cloud contamination. Regional NDVI time series are then composited for cultivated areas, and adjusted temporally according to the timing of the rainy season. This adjustment standardizes the NDVI response vis-??-vis the expected phenological response of maize. A national time series index is then created by taking the cropped-area weighted average of the regional series. This national time series provides an effective summary of vegetation response in agricultural areas, and allows for the identification of NDVI green-up during grain filling. Onset-adjusted NDVI values following the grain filling period are well correlated with U.S. Department of Agriculture production figures, possess desirable linear characteristics, and perform better than more common indices such as maximum seasonal NDVI or seasonally averaged NDVI. Thus, just as appropriately calibrated crop water balance models can provide more information than seasonal rainfall totals, the appropriate agro-phenological filtering of NDVI can improve the utility and accuracy of space-based agricultural monitoring.

Challenges of flood monitoring in the Senegal river valley using multi-temporal data

NASA Astrophysics Data System (ADS)

Bruckmann, Laurent; Delbart, Nicolas

2017-04-01

In Sub-Saharan Africa, floodplains wetlands play an important role for livelihoods and economy, especially for agriculture and fishing. However, tropical rivers flows are increasingly modified by climate change and dam regulation. In the Senegal river valley, the annual flood, from August to November, is an important water resources creating ecosystems services for people. Senegal river basin face to hydrological changes, due to rainfall diminution during the 1970's and building of large dams during 1980's to secure water resources. Water management and development of irrigation have modified the floodplain functioning. Flood recession agriculture, grazing and fishing are now confronted to a high uncertainty about floods level, duration and extension. Thus, spatiotemporal information of flood extension and duration are important for local communities and stakeholders to ensure food security and ecosystems services. Multi-temporal satellite data demonstrates an important applicability for flood mapping. Aims of this work is to present potentiality of using multi-temporal data from MODIS and new satellite Sentinel-2 for flood monitoring in a Sahelian context. It will also discuss the potential of flood mapping for the analysis of the dynamics of riparian vegetation and flood recession agriculture. This study uses two datasets to explore flood monitoring in Senegal river valley. Firstly, MODIS 8-days data (MOD09A) are first used, because of its temporal resolution of 8 days covering the period from 2000 to 2016. However, MODIS data are limited due to a low spatial resolution, that's why we also use Sentinel-2 data, available since summer 2015. The data were processed by constructing NDWI time-series (NDWI threshold is empirically defined) and extracting NDWI values for each inundated pixel during flood. First results demonstrate that using MODIS on a large scale is enough for analyze interannual variability of the flooded surfaces. We present here maps of flood frequency of the pixels between 2000 and 2016. MODIS spatial resolution is insufficient to analyze the interaction between flood hydrology and vegetation dynamics, whereas flood monitoring by Sentinel data seems to offer better potential. We illustrate our observations through a cartographic example of these interactions at local scale in Senegal river floodplain.

Frequency and distribution of forest, savanna, and crop fires over tropical regions during PEM-Tropics A

NASA Astrophysics Data System (ADS)

Olson, Jennifer R.; Baum, Bryan A.; Cahoon, Donald R.; Crawford, James H.

1999-03-01

Advanced very high resolution radiometer 1.1 km resolution satellite radiance data were used to locate active fires throughout much of the tropical region during NASA's Global Tropospheric Experiment (GTE) Pacific Exploratory Mission-Tropics (PEM-Tropics A) aircraft campaign, held in September and October 1996. The spatial and temporal distributions of the fires in Australia, southern Africa, and South America are presented here. The number of fires over northern Australia, central Africa, and South America appeared to decrease toward the end of the mission period. Fire over eastern Australia was widespread, and temporal patterns showed a somewhat consistent amount of burning with periodic episodes of enhanced fire counts observed. At least one episode of enhanced fire counts corresponded to the passage of a frontal system which brought conditions conducive to fire to the region, with strong westerlies originating over the hot, dry interior continent. Regions that were affected by lower than normal rainfall during the previous wet season (e.g., northern Australia and southwestern Africa) showed relatively few fires during this period. This is consistent with a drought-induced decrease in vegetation and therefore a decreased availability of fuel for burning. Alternatively, a heavier than normal previous wet season along the southeastern coast of South Africa may have contributed to high fuel loading and an associated relatively heavy amount of burning compared to data from previous years.

TRMM- and GPM-based precipitation analysis and modelling in the Tropical Andes

NASA Astrophysics Data System (ADS)

Manz, Bastian; Buytaert, Wouter; Zulkafli, Zed; Onof, Christian

2016-04-01

Despite wide-spread applications of satellite-based precipitation products (SPPs) throughout the TRMM-era, the scarcity of ground-based in-situ data (high density gauge networks, rainfall radar) in many hydro-meteorologically important regions, such as tropical mountain environments, has limited our ability to evaluate both SPPs and individual satellite-based sensors as well as accurately model or merge rainfall at high spatial resolutions, particularly with respect to extremes. This has restricted both the understanding of sensor behaviour and performance controls in such regions as well as the accuracy of precipitation estimates and respective hydrological applications ranging from water resources management to early warning systems. Here we report on our recent research into precipitation analysis and modelling using various TRMM and GPM products (2A25, 3B42 and IMERG) in the tropical Andes. In an initial study, 78 high-frequency (10-min) recording gauges in Colombia and Ecuador are used to generate a ground-based validation dataset for evaluation of instantaneous TRMM Precipitation Radar (TPR) overpasses from the 2A25 product. Detection ability, precipitation time-series, empirical distributions and statistical moments are evaluated with respect to regional climatological differences, seasonal behaviour, rainfall types and detection thresholds. Results confirmed previous findings from extra-tropical regions of over-estimation of low rainfall intensities and under-estimation of the highest 10% of rainfall intensities by the TPR. However, in spite of evident regionalised performance differences as a function of local climatological regimes, the TPR provides an accurate estimate of climatological annual and seasonal rainfall means. On this basis, high-resolution (5 km) climatological maps are derived for the entire tropical Andes. The second objective of this work is to improve the local precipitation estimation accuracy and representation of spatial patterns of extreme rainfall probabilities over the region. For this purpose, an ensemble of high-resolution rainfall fields is generated by stochastic simulation using space-time averaged, coarse-scale (daily, 0.25°) satellite-based rainfall inputs (TRMM 3B42/ -RT) and the high-resolution climatological information derived from the TPR as spatial disaggregation proxies. For evaluation and merging, gridded ground-based rainfall fields are generated from gauge data using sequential simulation. Satellite and ground-based ensembles are subsequently merged using an inverse error weighting scheme. The model was tested over a case study in the Colombian Andes with optional coarse-scale bias correction prior to disaggregation and merging. The resulting outputs were assessed in the context of Generalized Extreme Value theory and showed improved estimation of extreme rainfall probabilities compared to the original TMPA inputs. Initial findings using GPM-IMERG inputs are also presented.

Modeling the temporal variability of zinc concentrations in zinc roof runoff-experimental study and uncertainty analysis.

PubMed

Sage, Jérémie; El Oreibi, Elissar; Saad, Mohamed; Gromaire, Marie-Christine

2016-08-01

This study investigates the temporal variability of zinc concentrations from zinc roof runoff. The influence of rainfall characteristics and dry period duration is evaluated by combining laboratory experiment on small zinc sheets and in situ measurements under real weather conditions from a 1.6-m(2) zinc panel. A reformulation of a commonly used conceptual runoff quality model is introduced and its ability to simulate the evolution of zinc concentrations is evaluated. A systematic and sharp decrease from initially high to relatively low and stable zinc concentrations after 0.5 to 2 mm of rainfall is observed for both experiments, suggesting that highly soluble corrosion products are removed at early stages of runoff. A moderate dependence between antecedent dry period duration and the magnitude of zinc concentrations at the beginning of a rain event is evidenced. Contrariwise, results indicate that concentrations are not significantly influenced by rainfall intensities. Simulated rainfall experiment nonetheless suggests that a slight effect of rainfall intensities may be expected after the initial decrease of concentrations. Finally, this study shows that relatively simple conceptual runoff quality models may be adopted to simulate the variability of zinc concentrations during a rain event and from a rain event to another.

Characterizing land surface phenology and responses to rainfall in the Sahara desert

NASA Astrophysics Data System (ADS)

Yan, Dong; Zhang, Xiaoyang; Yu, Yunyue; Guo, Wei; Hanan, Niall P.

2016-08-01

Land surface phenology (LSP) in the Sahara desert is poorly understood due to the difficulty in detecting subtle variations in vegetation greenness. This study examined the spatial and temporal patterns of LSP and its responses to rainfall seasonality in the Sahara desert. We first generated daily two-band enhanced vegetation index (EVI2) from half-hourly observations acquired by the Spinning Enhanced Visible and Infrared Imager on board the Meteosat Second Generation series of geostationary satellites from 2006 to 2012. The EVI2 time series was used to retrieve LSP based on the Hybrid Piecewise Logistic Model. We further investigated the associations of spatial and temporal patterns in LSP with those in rainfall seasonality derived from the daily rainfall time series of the Tropical Rainfall Measurement Mission. Results show that the spatial shifts in the start of the vegetation growing season generally follow the rainy season onset that is controlled by the summer rainfall regime in the southern Sahara desert. In contrast, the end of the growing season significantly lags the end of the rainy season without any significant dependence. Vegetation growing season can unfold during the dry seasons after onset is triggered during rainy seasons. Vegetation growing season can be as long as 300 days or more in some areas and years. However, the EVI2 amplitude and accumulation across the Sahara region was very low indicating sparse vegetation as expected in desert regions. EVI2 amplitude and accumulated EVI2 strongly depended on rainfall received during the growing season and the preceding dormancy period.

Characteristics Associated with the Madden-Julian Oscillation at Manus Island

DOE Office of Scientific and Technical Information (OSTI.GOV)

Deng, Liping; McFarlane, Sally A.; Flaherty, Julia E.

2013-05-15

Ground-based high temporal and vertical resolution datasets from 2002 to 2008 of observations at the Atmospheric Radiation Measurement (ARM) Tropical Western Pacific (TWP) site on Manus Island, Papua New Guinea are used to examine the evolution of clouds and rainfall associated with the MJO life cycle. A composite MJO event is developed based on the NOAA MJO Index 4 using 13 events. The analysis shows that the cloud evolution during the composited MJO life cycle depicts a two-phase structure consisting of a development phase and a mature phase. During the development phase, congestus is the most important cloud type; duringmore » the mature phase, deep convection is the dominant cloud type. Consistent with this two-phase structure, the heavy rainfall frequency also shows a two-peak structure during the MJO life cycle. Light rainfall does not show a clear relation to the MJO life cycle, but shows variability on shorter time scales. From the development phase to the mature phase, the MJO structure shifts from the Type I to Type II structure, showing a different phase relationship between convection and dynamic fields (or wave motion) in the development and mature phases. During the shift, mid-level clouds play an important role in moving moisture to the mid-troposphere and preparing the atmosphere for the following deep convection. The discharge-recharge theory explains some of observed features of the MJO evolution at the ARM TWP Manus Island site.« less

Enhanced object-based tracking algorithm for convective rain storms and cells

NASA Astrophysics Data System (ADS)

Muñoz, Carlos; Wang, Li-Pen; Willems, Patrick

2018-03-01

This paper proposes a new object-based storm tracking algorithm, based upon TITAN (Thunderstorm Identification, Tracking, Analysis and Nowcasting). TITAN is a widely-used convective storm tracking algorithm but has limitations in handling small-scale yet high-intensity storm entities due to its single-threshold identification approach. It also has difficulties to effectively track fast-moving storms because of the employed matching approach that largely relies on the overlapping areas between successive storm entities. To address these deficiencies, a number of modifications are proposed and tested in this paper. These include a two-stage multi-threshold storm identification, a new formulation for characterizing storm's physical features, and an enhanced matching technique in synergy with an optical-flow storm field tracker, as well as, according to these modifications, a more complex merging and splitting scheme. High-resolution (5-min and 529-m) radar reflectivity data for 18 storm events over Belgium are used to calibrate and evaluate the algorithm. The performance of the proposed algorithm is compared with that of the original TITAN. The results suggest that the proposed algorithm can better isolate and match convective rainfall entities, as well as to provide more reliable and detailed motion estimates. Furthermore, the improvement is found to be more significant for higher rainfall intensities. The new algorithm has the potential to serve as a basis for further applications, such as storm nowcasting and long-term stochastic spatial and temporal rainfall generation.

Continuous country-wide rainfall observation using a large network of commercial microwave links: Challenges, solutions and applications

NASA Astrophysics Data System (ADS)

Chwala, Christian; Boose, Yvonne; Smiatek, Gerhard; Kunstmann, Harald

2017-04-01

Commercial microwave link (CML) networks have proven to be a valuable source for rainfall information over the last years. However, up to now, analysis of CML data was always limited to certain snapshots of data for historic periods due to limited data access. With the real-time availability of CML data in Germany (Chwala et al. 2016) this situation has improved significantly. We are continuously acquiring and processing data from 3000 CMLs in Germany in near real-time with one minute temporal resolution. Currently the data acquisition system is extended to 10000 CMLs so that the whole of Germany is covered and a continuous country-wide rainfall product can be provided. In this contribution we will elaborate on the challenges and solutions regarding data acquisition, data management and robust processing. We will present the details of our data acquisition system that we run operationally at the network of the CML operator Ericsson Germany to solve the problem of limited data availability. Furthermore we will explain the implementation of our data base, its web-frontend for easy data access and present our data processing algorithms. Finally we will showcase an application of our data in hydrological modeling and its potential usage to improve radar QPE. Bibliography: Chwala, C., Keis, F., and Kunstmann, H.: Real-time data acquisition of commercial microwave link networks for hydrometeorological applications, Atmos. Meas. Tech., 9, 991-999, doi:10.5194/amt-9-991-2016, 2016

Mapping drought risk in Indonesia related to El-Niño hazard

NASA Astrophysics Data System (ADS)

Supari, Muharsyah, R.; Sopaheluwakan, A.

2016-05-01

This work is aimed to identify areas in the country that are at high propensity to the impact of global climate phenomenon i.e. El-Nino. An affected area is recognized when rainfall decreases up to below normal condition which frequently leads drought event. For this purpose, two packages of gridded rainfall data at monthly basis with 0.5 spatial resolutions for 1950 2010 period were used, e.g. GPCC Full Data Reanalysis V.6 (product of Global Precipitation Climatology Centre) and CRU TS3.22 (product of Climatic Research Unit). El-Nino years were labelled based on Oceanic Nino Index, ONI. We applied frequency analysis to quantify the chance of El-Nino impact. GPCC data was found more accurate in representing rainfall observation than CRU data based on correlation test against station data. The results indicate the strong spatial and temporal dependencies of El-Nino impact. During peak of rainy and first transitional season (DJF and MAM), the probability to be affected by El-Nino is mostly less than 20% over whole country In contrast, July-October are months where areas with high and very high risk were observed over many regions such as Southern part of Sumatera, Java, Kalimantan, Sulawesi, Maluku and Papua. Further investigation at province level found that the timing of El-Nino impact starts in June. These results are potential to improve national capacity in risk management related to weather-climate hazards.

Diagnosing added value of convection-permitting regional models using precipitation event identification and tracking

NASA Astrophysics Data System (ADS)

Chang, W.; Wang, J.; Marohnic, J.; Kotamarthi, V. R.; Moyer, E. J.

2017-12-01

We use a novel rainstorm identification and tracking algorithm (Chang et al 2016) to evaluate the effects of using resolved convection on improving how faithfully high-resolution regional simulations capture precipitation characteristics. The identification and tracking algorithm allocates all precipitation to individual rainstorms, including low-intensity events with complicated features, and allows us to decompose changes or biases in total mean precipitation into their causes: event size, intensity, number, and duration. It allows lower threshold for tracking so captures nearly all rainfall and improves tracking, so that events that are clearly meteorologically related are tracked across lifespans up to days. We evaluate a series of dynamically downscaled simulations of the summertime United States at 12 and 4 km under different model configurations, and find that resolved convection offers the largest gains in reducing biases in precipitation characteristics, especially in event size. Simulations with parametrized convection produce event sizes 80-220% too large in extent; with resolved convection the bias is reduced to 30%. The identification and tracking algorithm also allows us to demonstrate that the diurnal cycle in rainfall stems not from temporal variation in the production of new events but from diurnal fluctuations in rainfall from existing events. We show further hat model errors in the diurnal cycle biases are best represented as additive offsets that differ by time of day, and again that convection-permitting simulations are most efficient in reducing these additive biases.

In situ adaptive response to climate and habitat quality variation: spatial and temporal variation in European badger (Meles meles) body weight.

PubMed

Byrne, Andrew W; Fogarty, Ursula; O'Keeffe, James; Newman, Chris

2015-09-01

Variation in climatic and habitat conditions can affect populations through a variety of mechanisms, and these relationships can act at different temporal and spatial scales. Using post-mortem badger body weight records from 15 878 individuals captured across the Republic of Ireland (7224 setts across ca. 15 000 km(2) ; 2009-2012), we employed a hierarchical multilevel mixed model to evaluate the effects of climate (rainfall and temperature) and habitat quality (landscape suitability), while controlling for local abundance (unique badgers caught/sett/year). Body weight was affected strongly by temperature across a number of temporal scales (preceding month or season), with badgers being heavier if preceding temperatures (particularly during winter/spring) were warmer than the long-term seasonal mean. There was less support for rainfall across different temporal scales, although badgers did exhibit heavier weights when greater rainfall occurred one or 2 months prior to capture. Badgers were also heavier in areas with higher landscape habitat quality, modulated by the number of individuals captured per sett, consistent with density-dependent effects reducing weights. Overall, the mean badger body weight of culled individuals rose during the study period (2009-2012), more so for males than for females. With predicted increases in temperature, and rainfall, augmented by ongoing agricultural land conversion in this region, we project heavier individual badger body weights in the future. Increased body weight has been associated with higher fecundity, recruitment and survival rates in badgers, due to improved food availability and energetic budgets. We thus predict that climate change could increase the badger population across the Republic of Ireland. Nevertheless, we emphasize that, locally, populations could still be vulnerable to extreme weather variability coupled with detrimental agricultural practice, including population management. © 2015 John Wiley & Sons Ltd.

Trends of rainfall regime in Peninsular Malaysia during northeast and southwest monsoons

NASA Astrophysics Data System (ADS)

Chooi Tan, Kok

2018-04-01

The trends of rainfall regime in Peninsular Malaysia is mainly affected by the seasonal monsoon. The aim of this study is to investigate the impact of northeast and southwest monsoons on the monthly rainfall patterns over Badenoch Estate, Kedah. In addition, the synoptic maps of wind vector also being developed to identify the wind pattern over Peninsular Malaysia from 2007 – 2016. On the other hand, the archived daily rainfall data is acquired from Malaysian Meteorological Department. The temporal and trends of the monthly and annual rainfall over the study area have been analysed from 2007 to 2016. Overall, the average annual precipitation over the study area from 2007 to 2016 recorded by rain gauge is 2562.35 mm per year.

Impacts of rainfall spatial variability on hydrogeological response

NASA Astrophysics Data System (ADS)

Sapriza-Azuri, Gonzalo; Jódar, Jorge; Navarro, Vicente; Slooten, Luit Jan; Carrera, Jesús; Gupta, Hoshin V.

2015-02-01

There is currently no general consensus on how the spatial variability of rainfall impacts and propagates through complex hydrogeological systems. Most studies to date have focused on the effects of rainfall spatial variability (RSV) on river discharge, while paying little attention to other important aspects of system response. Here, we study the impacts of RSV on several responses of a hydrological model of an overexploited system. To this end, we drive a spatially distributed hydrogeological model for the semiarid Upper Guadiana basin in central Spain with stochastic daily rainfall fields defined at three different spatial resolutions (fine → 2.5 km × 2.5 km, medium → 50 km × 50 km, large → lumped). This enables us to investigate how (i) RSV at different spatial resolutions, and (ii) rainfall uncertainty, are propagated through the hydrogeological model of the system. Our results demonstrate that RSV has a significant impact on the modeled response of the system, by specifically affecting groundwater recharge and runoff generation, and thereby propagating through to various other related hydrological responses (river discharge, river-aquifer exchange, groundwater levels). These results call into question the validity of management decisions made using hydrological models calibrated or forced with spatially lumped rainfall.

Modulation of Heavy Rainfall in the Middle East and North Africa by Madden-Julian Oscillation Using High Resolution Atmospheric General Circulation Model

NASA Astrophysics Data System (ADS)

Deng, L.; Stenchikov, G. L.; McCabe, M. F.; Bangalath, H. K.

2014-12-01

Recently, the modulation of subtropical rainfall by the dominant tropical intraseasonal signal of the Madden-Julian Oscillation (MJO), has been explored through the discussion of the MJO-convection-induced Kelvin and Rossby wave related teleconnection patterns. Our study focuses on characterizing the modulation of heavy rainfall in the Middle East and North Africa (MENA) region by the MJO, using the Geophysical Fluid Dynamics Laboratory (GFDL) global High Resolution Atmospheric Model (HIRAM) simulations (25-km; 1979-2012) and a combination of available atmospheric products from satellite, in-situ and reanalysis data. The observed Hadley Centre Global Sea Ice and Sea Surface Temperature (HadISST) and the simulated SST from GFDL's global coupled carbon-climate Earth System Models (ESM2M) are employed in HIRAM to investigate the sensitivity of the simulated heavy rainfall and MJO to SST. The future trend of the extreme rainfalls and their links to the MJO response to climate change are examined using HIRAM simulations of 2012-2050 with the RCP4.5 and RCP 8.5 scenarios to advance the possibility of characterization and forecasting of future extreme rainfall events in the MENA region.

Flood forecasting with DDD-application of a parsimonious hydrological model in operational flood forecasting in Norway

NASA Astrophysics Data System (ADS)

Skaugen, Thomas; Haddeland, Ingjerd

2014-05-01

A new parameter-parsimonious rainfall-runoff model, DDD (Distance Distribution Dynamics) has been run operationally at the Norwegian Flood Forecasting Service for approximately a year. DDD has been calibrated for, altogether, 104 catchments throughout Norway, and provide runoff forecasts 8 days ahead on a daily temporal resolution driven by precipitation and temperature from the meteorological forecast models AROME (48 hrs) and EC (192 hrs). The current version of DDD differs from the standard model used for flood forecasting in Norway, the HBV model, in its description of the subsurface and runoff dynamics. In DDD, the capacity of the subsurface water reservoir M, is the only parameter to be calibrated whereas the runoff dynamics is completely parameterised from observed characteristics derived from GIS and runoff recession analysis. Water is conveyed through the soils to the river network by waves with celerities determined by the level of saturation in the catchment. The distributions of distances between points in the catchment to the nearest river reach and of the river network give, together with the celerities, distributions of travel times, and, consequently unit hydrographs. DDD has 6 parameters less to calibrate in the runoff module than the HBV model. Experiences using DDD show that especially the timing of flood peaks has improved considerably and in a comparison between DDD and HBV, when assessing timeseries of 64 years for 75 catchments, DDD had a higher hit rate and a lower false alarm rate than HBV. For flood peaks higher than the mean annual flood the median hit rate is 0.45 and 0.41 for the DDD and HBV models respectively. Corresponding number for the false alarm rate is 0.62 and 0.75 For floods over the five year return interval, the median hit rate is 0.29 and 0.28 for the DDD and HBV models, respectively with false alarm rates equal to 0.67 and 0.80. During 2014 the Norwegian flood forecasting service will run DDD operationally at a 3h temporal resolution. Running DDD at a 3h resolution will give a better prediction of flood peaks in small catchments, where the averaging over 24 hrs will lead to a underestimation of high events, and we can better describe the progress floods in larger catchments. Also, at a 3h temporal resolution we make better use of the meteorological forecasts that for long have been provided at a very detailed temporal resolution.

Spatio-Temporal Description of the Rainfall for Colombian Andean Mountainous Region for Weather Forecasting Purposes. Case Study: Manizales - Caldas, Colombia

NASA Astrophysics Data System (ADS)

Suarez Hincapie, J. N.

2014-12-01

Manizales is a city located in west-central Colombian Andes in the Caldas province, whose spatial location coincides with one of the most threatened areas of Colombia (landslides, earthquakes, volcanic eruptions, other). As a middle Andean mountainous city and for being located in the area of influence of the ITCZ presents an equatorial mountain climate with a bimodal rainfall regime, and with an average annual rainfall around 2000 mm, it shows very significant rates of precipitation, on average, 70% of the days of the year it is rainy. This situation favors the formation of large masses of clouds and the presence of macroclimatic phenomena such as ENSO, which has historically caused large-scale impacts in both warm and cold phase. Since last decade different entities have implemented a hydro-meteorological network which measures and transmits telemetrically every five minutes hydro-climatic variables. In general, the real-time weather monitoring should be used for a better understanding of our environmental urban environment and to establish indicators of quality of life and welfare for the community. Despite the city has telemetric data on atmospheric and hydrological variables, there is still no tool or a methodology able to generate a spatio-temporal description of these variables. So, the aim of this work is to establish guidelines to sort all this information of atmospheric variables monitored in real time with the help of data mining techniques, machine learning tools to improve the knowledge of atmospheric patterns at Manizales and to serve for territorial planning and decision makers. To reach this purpose the current data warehouse available at the National University of Colombia at Manizales will be used, and it will be fed with observed variables from hydro-meteorological monitoring stations that transmit in real-time. Then, as mentioned this information will make the corresponding processing with data mining techniques to describe the rainfall patterns. All this complemented with the application of statistical techniques for data analysis and exploration. The main contribution of this research is the creation of tools to be used in numerical modeling with forecasting purposes, aiming to improve the resolution given by mesoscale models, which are currently used for weather forecast in Colombia.

Use of High-Resolution Satellite Observations to Evaluate Cloud and Precipitation Statistics from Cloud-Resolving Model Simulations

NASA Astrophysics Data System (ADS)

Zhou, Y.; Tao, W.; Hou, A. Y.; Zeng, X.; Shie, C.

2007-12-01

The cloud and precipitation statistics simulated by 3D Goddard Cumulus Ensemble (GCE) model for different environmental conditions, i.e., the South China Sea Monsoon Experiment (SCSMEX), CRYSTAL-FACE, and KAWJEX are compared with Tropical Rainfall Measuring Mission (TRMM) TMI and PR rainfall measurements and as well as cloud observations from the Earth's Radiant Energy System (CERES) and the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments. It is found that GCE is capable of simulating major convective system development and reproducing total surface rainfall amount as compared with rainfall estimated from the soundings. The model presents large discrepancies in rain spectrum and vertical hydrometer profiles. The discrepancy in the precipitation field is also consistent with the cloud and radiation observations. The study will focus on the effects of large scale forcing and microphysics to the simulated model- observation discrepancies.

A Remote Sensing-Based Tool for Assessing Rainfall-Driven Hazards

PubMed Central

Wright, Daniel B.; Mantilla, Ricardo; Peters-Lidard, Christa D.

2018-01-01

RainyDay is a Python-based platform that couples rainfall remote sensing data with Stochastic Storm Transposition (SST) for modeling rainfall-driven hazards such as floods and landslides. SST effectively lengthens the extreme rainfall record through temporal resampling and spatial transposition of observed storms from the surrounding region to create many extreme rainfall scenarios. Intensity-Duration-Frequency (IDF) curves are often used for hazard modeling but require long records to describe the distribution of rainfall depth and duration and do not provide information regarding rainfall space-time structure, limiting their usefulness to small scales. In contrast, RainyDay can be used for many hazard applications with 1-2 decades of data, and output rainfall scenarios incorporate detailed space-time structure from remote sensing. Thanks to global satellite coverage, RainyDay can be used in inaccessible areas and developing countries lacking ground measurements, though results are impacted by remote sensing errors. RainyDay can be useful for hazard modeling under nonstationary conditions. PMID:29657544

A Remote Sensing-Based Tool for Assessing Rainfall-Driven Hazards.

PubMed

Wright, Daniel B; Mantilla, Ricardo; Peters-Lidard, Christa D

2017-04-01

RainyDay is a Python-based platform that couples rainfall remote sensing data with Stochastic Storm Transposition (SST) for modeling rainfall-driven hazards such as floods and landslides. SST effectively lengthens the extreme rainfall record through temporal resampling and spatial transposition of observed storms from the surrounding region to create many extreme rainfall scenarios. Intensity-Duration-Frequency (IDF) curves are often used for hazard modeling but require long records to describe the distribution of rainfall depth and duration and do not provide information regarding rainfall space-time structure, limiting their usefulness to small scales. In contrast, RainyDay can be used for many hazard applications with 1-2 decades of data, and output rainfall scenarios incorporate detailed space-time structure from remote sensing. Thanks to global satellite coverage, RainyDay can be used in inaccessible areas and developing countries lacking ground measurements, though results are impacted by remote sensing errors. RainyDay can be useful for hazard modeling under nonstationary conditions.

A Remote Sensing-Based Tool for Assessing Rainfall-Driven Hazards

NASA Technical Reports Server (NTRS)

Wright, Daniel B.; Mantilla, Ricardo; Peters-Lidard, Christa D.

2017-01-01

RainyDay is a Python-based platform that couples rainfall remote sensing data with Stochastic Storm Transposition (SST) for modeling rainfall-driven hazards such as floods and landslides. SST effectively lengthens the extreme rainfall record through temporal resampling and spatial transposition of observed storms from the surrounding region to create many extreme rainfall scenarios. Intensity-Duration-Frequency (IDF) curves are often used for hazard modeling but require long records to describe the distribution of rainfall depth and duration and do not provide information regarding rainfall space-time structure, limiting their usefulness to small scales. In contrast, Rainy Day can be used for many hazard applications with 1-2 decades of data, and output rainfall scenarios incorporate detailed space-time structure from remote sensing. Thanks to global satellite coverage, Rainy Day can be used in inaccessible areas and developing countries lacking ground measurements, though results are impacted by remote sensing errors. Rainy Day can be useful for hazard modeling under nonstationary conditions.

STAMMEX high resolution gridded daily precipitation dataset over Germany: a new potential for regional precipitation climate research

NASA Astrophysics Data System (ADS)

Zolina, Olga; Simmer, Clemens; Kapala, Alice; Mächel, Hermann; Gulev, Sergey; Groisman, Pavel

2014-05-01

We present new high resolution precipitation daily grids developed at Meteorological Institute, University of Bonn and German Weather Service (DWD) under the STAMMEX project (Spatial and Temporal Scales and Mechanisms of Extreme Precipitation Events over Central Europe). Daily precipitation grids have been developed from the daily-observing precipitation network of DWD, which runs one of the World's densest rain gauge networks comprising more than 7500 stations. Several quality-controlled daily gridded products with homogenized sampling were developed covering the periods 1931-onwards (with 0.5 degree resolution), 1951-onwards (0.25 degree and 0.5 degree), and 1971-2000 (0.1 degree). Different methods were tested to select the best gridding methodology that minimizes errors of integral grid estimates over hilly terrain. Besides daily precipitation values with uncertainty estimates (which include standard estimates of the kriging uncertainty as well as error estimates derived by a bootstrapping algorithm), the STAMMEX data sets include a variety of statistics that characterize temporal and spatial dynamics of the precipitation distribution (quantiles, extremes, wet/dry spells, etc.). Comparisons with existing continental-scale daily precipitation grids (e.g., CRU, ECA E-OBS, GCOS) which include considerably less observations compared to those used in STAMMEX, demonstrate the added value of high-resolution grids for extreme rainfall analyses. These data exhibit spatial variability pattern and trends in precipitation extremes, which are missed or incorrectly reproduced over Central Europe from coarser resolution grids based on sparser networks. The STAMMEX dataset can be used for high-quality climate diagnostics of precipitation variability, as a reference for reanalyses and remotely-sensed precipitation products (including the upcoming Global Precipitation Mission products), and for input into regional climate and operational weather forecast models. We will present numerous application of the STAMMEX grids spanning from case studies of the major Central European floods to long-term changes in different precipitation statistics, including those accounting for the alternation of dry and wet periods and precipitation intensities associated with prolonged rainy episodes.

Scoping a field experiment: error diagnostics of TRMM precipitation radar estimates in complex terrain as a basis for IPHEx2014

NASA Astrophysics Data System (ADS)

Duan, Y.; Wilson, A. M.; Barros, A. P.

2014-10-01

A diagnostic analysis of the space-time structure of error in Quantitative Precipitation Estimates (QPE) from the Precipitation Radar (PR) on the Tropical Rainfall Measurement Mission (TRMM) satellite is presented here in preparation for the Integrated Precipitation and Hydrology Experiment (IPHEx) in 2014. IPHEx is the first NASA ground-validation field campaign after the launch of the Global Precipitation Measurement (GPM) satellite. In anticipation of GPM, a science-grade high-density raingauge network was deployed at mid to high elevations in the Southern Appalachian Mountains, USA since 2007. This network allows for direct comparison between ground-based measurements from raingauges and satellite-based QPE (specifically, PR 2A25 V7 using 5 years of data 2008-2013). Case studies were conducted to characterize the vertical profiles of reflectivity and rain rate retrievals associated with large discrepancies with respect to ground measurements. The spatial and temporal distribution of detection errors (false alarm, FA, and missed detection, MD) and magnitude errors (underestimation, UND, and overestimation, OVR) for stratiform and convective precipitation are examined in detail toward elucidating the physical basis of retrieval error. The diagnostic error analysis reveals that detection errors are linked to persistent stratiform light rainfall in the Southern Appalachians, which explains the high occurrence of FAs throughout the year, as well as the diurnal MD maximum at midday in the cold season (fall and winter), and especially in the inner region. Although UND dominates the magnitude error budget, underestimation of heavy rainfall conditions accounts for less than 20% of the total consistent with regional hydrometeorology. The 2A25 V7 product underestimates low level orographic enhancement of rainfall associated with fog, cap clouds and cloud to cloud feeder-seeder interactions over ridges, and overestimates light rainfall in the valleys by large amounts, though this behavior is strongly conditioned by the coarse spatial resolution (5 km) of the terrain topography mask used to remove ground clutter effects. Precipitation associated with small-scale systems (< 25 km2) and isolated deep convection tends to be underestimated, which we attribute to non-uniform beam-filling effects due to spatial averaging of reflectivity at the PR resolution. Mixed precipitation events (i.e., cold fronts and snow showers) fall into OVR or FA categories, but these are also the types of events for which observations from standard ground-based raingauge networks are more likely subject to measurement uncertainty, that is raingauge underestimation errors due to under-catch and precipitation phase. Overall, the space-time structure of the errors shows strong links among precipitation, envelope orography, landform (ridge-valley contrasts), and local hydrometeorological regime that is strongly modulated by the diurnal cycle, pointing to three major error causes that are inter-related: (1) representation of concurrent vertically and horizontally varying microphysics; (2) non uniform beam filling (NUBF) effects and ambiguity in the detection of bright band position; and (3) spatial resolution and ground clutter correction.

Scoping a field experiment: error diagnostics of TRMM precipitation radar estimates in complex terrain as a basis for IPHEx2014

NASA Astrophysics Data System (ADS)

Duan, Y.; Wilson, A. M.; Barros, A. P.

2015-03-01

A diagnostic analysis of the space-time structure of error in quantitative precipitation estimates (QPEs) from the precipitation radar (PR) on the Tropical Rainfall Measurement Mission (TRMM) satellite is presented here in preparation for the Integrated Precipitation and Hydrology Experiment (IPHEx) in 2014. IPHEx is the first NASA ground-validation field campaign after the launch of the Global Precipitation Measurement (GPM) satellite. In anticipation of GPM, a science-grade high-density raingauge network was deployed at mid to high elevations in the southern Appalachian Mountains, USA, since 2007. This network allows for direct comparison between ground-based measurements from raingauges and satellite-based QPE (specifically, PR 2A25 Version 7 using 5 years of data 2008-2013). Case studies were conducted to characterize the vertical profiles of reflectivity and rain rate retrievals associated with large discrepancies with respect to ground measurements. The spatial and temporal distribution of detection errors (false alarm, FA; missed detection, MD) and magnitude errors (underestimation, UND; overestimation, OVR) for stratiform and convective precipitation are examined in detail toward elucidating the physical basis of retrieval error. The diagnostic error analysis reveals that detection errors are linked to persistent stratiform light rainfall in the southern Appalachians, which explains the high occurrence of FAs throughout the year, as well as the diurnal MD maximum at midday in the cold season (fall and winter) and especially in the inner region. Although UND dominates the error budget, underestimation of heavy rainfall conditions accounts for less than 20% of the total, consistent with regional hydrometeorology. The 2A25 V7 product underestimates low-level orographic enhancement of rainfall associated with fog, cap clouds and cloud to cloud feeder-seeder interactions over ridges, and overestimates light rainfall in the valleys by large amounts, though this behavior is strongly conditioned by the coarse spatial resolution (5 km) of the topography mask used to remove ground-clutter effects. Precipitation associated with small-scale systems (< 25 km2) and isolated deep convection tends to be underestimated, which we attribute to non-uniform beam-filling effects due to spatial averaging of reflectivity at the PR resolution. Mixed precipitation events (i.e., cold fronts and snow showers) fall into OVR or FA categories, but these are also the types of events for which observations from standard ground-based raingauge networks are more likely subject to measurement uncertainty, that is raingauge underestimation errors due to undercatch and precipitation phase. Overall, the space-time structure of the errors shows strong links among precipitation, envelope orography, landform (ridge-valley contrasts), and a local hydrometeorological regime that is strongly modulated by the diurnal cycle, pointing to three major error causes that are inter-related: (1) representation of concurrent vertically and horizontally varying microphysics; (2) non-uniform beam filling (NUBF) effects and ambiguity in the detection of bright band position; and (3) spatial resolution and ground-clutter correction.

Simulation of the Summer Monsoon Rainfall over East Asia using the NCEP GFS Cumulus Parameterization at Different Horizontal Resolutions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lim, Kyo-Sun; Hong, Song You; Yoon, Jin-Ho

2014-10-01

The most recent version of Simplified Arakawa-Schubert (SAS) cumulus scheme in National Center for Environmental Prediction (NCEP) Global Forecast System (GFS) (GFS SAS) has been implemented into the Weather and Research Forecasting (WRF) model with a modification of triggering condition and convective mass flux to become depending on model’s horizontal grid spacing. East Asian Summer Monsoon of 2006 from June to August is selected to evaluate the performance of the modified GFS SAS scheme. Simulated monsoon rainfall with the modified GFS SAS scheme shows better agreement with observation compared to the original GFS SAS scheme. The original GFS SAS schememore » simulates the similar ratio of subgrid-scale precipitation, which is calculated from a cumulus scheme, against total precipitation regardless of model’s horizontal grid spacing. This is counter-intuitive because the portion of resolved clouds in a grid box should be increased as the model grid spacing decreases. This counter-intuitive behavior of the original GFS SAS scheme is alleviated by the modified GFS SAS scheme. Further, three different cumulus schemes (Grell and Freitas, Kain and Fritsch, and Betts-Miller-Janjic) are chosen to investigate the role of a horizontal resolution on simulated monsoon rainfall. The performance of high-resolution modeling is not always enhanced as the spatial resolution becomes higher. Even though improvement of probability density function of rain rate and long wave fluxes by the higher-resolution simulation is robust regardless of a choice of cumulus parameterization scheme, the overall skill score of surface rainfall is not monotonically increasing with spatial resolution.« less

Rainfall thresholds as a landslide indicator for engineered slopes on the Irish Rail network

NASA Astrophysics Data System (ADS)

Martinović, Karlo; Gavin, Kenneth; Reale, Cormac; Mangan, Cathal

2018-04-01

Rainfall thresholds express the minimum levels of rainfall that need to be reached or exceeded in order for landslides to occur in a particular area. They are a common tool in expressing the temporal portion of landslide hazard analysis. Numerous rainfall thresholds have been developed for different areas worldwide, however none of these are focused on landslides occurring on the engineered slopes on transport infrastructure networks. This paper uses empirical method to develop the rainfall thresholds for landslides on the Irish Rail network earthworks. For comparison, rainfall thresholds are also developed for natural terrain in Ireland. The results show that particular thresholds involving relatively low rainfall intensities are applicable for Ireland, owing to the specific climate. Furthermore, the comparison shows that rainfall thresholds for engineered slopes are lower than those for landslides occurring on the natural terrain. This has severe implications as it indicates that there is a significant risk involved when using generic weather alerts (developed largely for natural terrain) for infrastructure management, and showcases the need for developing railway and road specific rainfall thresholds for landslides.

Improving GEFS Weather Forecasts for Indian Monsoon with Statistical Downscaling

NASA Astrophysics Data System (ADS)

Agrawal, Ankita; Salvi, Kaustubh; Ghosh, Subimal

2014-05-01

Weather forecast has always been a challenging research problem, yet of a paramount importance as it serves the role of 'key input' in formulating modus operandi for immediate future. Short range rainfall forecasts influence a wide range of entities, right from agricultural industry to a common man. Accurate forecasts actually help in minimizing the possible damage by implementing pre-decided plan of action and hence it is necessary to gauge the quality of forecasts which might vary with the complexity of weather state and regional parameters. Indian Summer Monsoon Rainfall (ISMR) is one such perfect arena to check the quality of weather forecast not only because of the level of intricacy in spatial and temporal patterns associated with it, but also the amount of damage it can cause (because of poor forecasts) to the Indian economy by affecting agriculture Industry. The present study is undertaken with the rationales of assessing, the ability of Global Ensemble Forecast System (GEFS) in predicting ISMR over central India and the skill of statistical downscaling technique in adding value to the predictions by taking them closer to evidentiary target dataset. GEFS is a global numerical weather prediction system providing the forecast results of different climate variables at a fine resolution (0.5 degree and 1 degree). GEFS shows good skills in predicting different climatic variables but fails miserably over rainfall predictions for Indian summer monsoon rainfall, which is evident from a very low to negative correlation values between predicted and observed rainfall. Towards the fulfilment of second rationale, the statistical relationship is established between the reasonably well predicted climate variables (GEFS) and observed rainfall. The GEFS predictors are treated with multicollinearity and dimensionality reduction techniques, such as principal component analysis (PCA) and least absolute shrinkage and selection operator (LASSO). Statistical relationship is established between the principal components and observed rainfall over training period and predictions are obtained for testing period. The validations show high improvements in correlation coefficient between observed and predicted data (0.25 to 0.55). The results speak in favour of statistical downscaling methodology which shows the capability to reduce the gap between observed data and predictions. A detailed study is required to be carried out by applying different downscaling techniques to quantify the improvements in predictions.

Stable Isotope Anatomy of Tropical Cyclone Ita, North-Eastern Australia, April 2014

PubMed Central

Munksgaard, Niels C.; Zwart, Costijn; Kurita, Naoyuki; Bass, Adrian; Nott, Jon; Bird, Michael I.

2015-01-01

The isotope signatures registered in speleothems during tropical cyclones (TC) provides information about the frequency and intensity of past TCs but the precise relationship between isotopic composition and the meteorology of TCs remain uncertain. Here we present continuous δ18O and δ2H data in rainfall and water vapour, as well as in discrete rainfall samples, during the passage of TC Ita and relate the evolution in isotopic compositions to local and synoptic scale meteorological observations. High-resolution data revealed a close relationship between isotopic compositions and cyclonic features such as spiral rainbands, periods of stratiform rainfall and the arrival of subtropical and tropical air masses with changing oceanic and continental moisture sources. The isotopic compositions in discrete rainfall samples were remarkably constant along the ~450 km overland path of the cyclone when taking into account the direction and distance to the eye of the cyclone at each sampling time. Near simultaneous variations in δ18O and δ2H values in rainfall and vapour and a near-equilibrium rainfall-vapour isotope fractionation indicates strong isotopic exchange between rainfall and surface inflow of vapour during the approach of the cyclone. In contrast, after the passage of spiral rainbands close to the eye of the cyclone, different moisture sources for rainfall and vapour are reflected in diverging d-excess values. High-resolution isotope studies of modern TCs refine the interpretation of stable isotope signatures found in speleothems and other paleo archives and should aim to further investigate the influence of cyclone intensity and longevity on the isotopic composition of associated rainfall. PMID:25742628

Comparisons of Rain Estimates from Ground Radar and Satellite Over Mountainous Regions

NASA Technical Reports Server (NTRS)

Lin, Xin; Kidd, Chris; Tao, Jing; Barros, Ana

2016-01-01

A high-resolution rainfall product merging surface radar and an enhanced gauge network is used as a reference to examine two operational surface radar rainfall products over mountain areas. The two operational rainfall products include radar-only and conventional-gauge-corrected radar rainfall products. Statistics of rain occurrence and rain amount including their geographical, seasonal, and diurnal variations are examined using 3-year data. It is found that the three surface radar rainfall products in general agree well with one another over mountainous regions in terms of horizontal mean distributions of rain occurrence and rain amount. Frequency of rain occurrence and fraction of rain amount also indicate similar distribution patterns as a function of rain intensity. The diurnal signals of precipitation over mountain ridges are well captured and joint distributions of coincident raining samples indicate reasonable correlations during both summer and winter. Factors including undetected low-level precipitation, limited availability of gauges for correcting the Z-R relationship over the mountains, and radar beam blocking by mountains are clearly noticed in the two conventional radar rainfall products. Both radar-only and conventional-gauge-corrected radar rainfall products underestimate the rain occurrence and fraction of rain amount at intermediate and heavy rain intensities. Comparison of PR and TMI against a surface radar-only rainfall product indicates that the PR performs equally well with the high-resolution radar-only rainfall product over complex terrains at intermediate and heavy rain intensities during the summer and winter. TMI, on the other hand, requires improvement to retrieve wintertime precipitation over mountain areas.

Effects of biotic and abiotic factors on the temporal dynamic of bat-fruit interactions

NASA Astrophysics Data System (ADS)

Laurindo, Rafael de Souza; Gregorin, Renato; Tavares, Davi Castro

2017-08-01

Mutualistic interactions between animals and plants vary over time and space based on the abundance of fruits or animals and seasonality. Little is known about this temporal dynamic and the influence of biotic and abiotic factors on the structure of interaction networks. We evaluated changes in the structure of network interactions between bats and fruits in relation to variations in rainfall. Our results suggest that fruit abundance is the main variable responsible for temporal changes in network attributes, such as network size, connectance, and number of interactions. In the same way, temperature positively affected the abundance of fruits and bats. An increase in temperature and alterations in rainfall patterns, due to human induced climate change, can cause changes in phenological patterns and fruit production, with negative consequences to biodiversity maintenance, ecological interactions, and ecosystem functioning.

Reduced precipitation over large water bodies in the Brazilian Amazon shown from TRMM data

NASA Astrophysics Data System (ADS)

Paiva, Rodrigo Cauduro Dias; Buarque, Diogo Costa; Clarke, Robin T.; Collischonn, Walter; Allasia, Daniel Gustavo

2011-02-01

Tropical Rainfall Measurement Mission (TRMM) data show lower rainfall over large water bodies in the Brazilian Amazon. Mean annual rainfall (P), number of wet days (rainfall > 2 mm) (W) and annual rainfall accumulated over 3-hour time intervals (P3hr) were computed from TRMM 3B42 data for 1998-2009. Reduced rainfall was marked over the Rio Solimões/Amazon, along most Amazon tributaries and over the Balbina reservoir. In a smaller test area, a heuristic argument showed that P and W were reduced by 5% and 6.5% respectively. Allowing for TRMM 3B42 spatial resolution, the reduction may be locally greater. Analyses of diurnal rainfall patterns showed that rainfall is lowest over large rivers during the afternoon, when most rainfall is convective, but at night and early morning the opposite occurs, with increased rainfall over rivers, although this pattern is less marked. Rainfall patterns reported from studies of smaller Amazonian regions therefore exist more widely.

Rainfall continuous time stochastic simulation for a wet climate in the Cantabric Coast

NASA Astrophysics Data System (ADS)

Rebole, Juan P.; Lopez, Jose J.; Garcia-Guzman, Adela

2010-05-01

Rain is the result of a series of complex atmospheric processes which are influenced by numerous factors. This complexity makes its simulation practically unfeasible from a physical basis, advising the use of stochastic diagrams. These diagrams, which are based on observed characteristics (Todorovic and Woolhiser, 1975), allow the introduction of renewal alternating processes, that account for the occurrence of rainfall at different time lapses (Markov chains are a particular case, where lapses can be described by exponential distributions). Thus, a sequential rainfall process can be defined as a temporal series in which rainfall events (periods in which rainfall is recorded) alternate with non rain events (periods in which no rainfall is recorded). The variables of a temporal rain sequence have been characterized (duration of the rainfall event, duration of the non rainfall event, average intensity of the rain in the rain event, and a temporal distribution of the amount of rain in the rain event) in a wet climate such as that of the coastal area of Guipúzcoa. The study has been performed from two series recorded at the meteorological stations of Igueldo-San Sebastián and Fuenterrabia / Airport (data every ten minutes and for its hourly aggregation). As a result of this work, the variables satisfactorily fitted the following distribution functions: the duration of the rain event to a exponential function; the duration of the dry event to a truncated exponential mixed distribution; the average intensity to a Weibull distribution; and the distribution of the rain fallen to the Beta distribution. The characterization was made for an hourly aggregation of the recorded interval of ten minutes. The parameters of the fitting functions were better obtained by means of the maximum likelihood method than the moment method. The parameters obtained from the characterization were used to develop a stochastic rainfall process simulation model by means of a three states Markov chain (Hutchinson, 1990), performed in an hourly basis by García-Guzmán (1993) and Castro et al. (1997, 2005 ). Simulation process results were valid in the hourly case for all the four described variables, with a slightly better response in Fuenterrabia than in Igueldo. In summary, all the variables were better simulated in Fuenterrabia than in Igueldo. Fuenterrabia data series is shorter and with longer sequences without missing data, compared to Igueldo. The latter shows higher number of missing data events, whereas its mean duration is longer in Fuenterrabia.

Temporal Variability in Vertical Groundwater Fluxes and the Effect of Solar Radiation on Streambed Temperatures Based on Vertical High Resolution Distributed Temperature Sensing

NASA Astrophysics Data System (ADS)

Sebok, E.; Karan, S.; Engesgaard, P. K.; Duque, C.

2013-12-01

Due to its large spatial and temporal variability, groundwater discharge to streams is difficult to quantify. Methods using vertical streambed temperature profiles to estimate vertical fluxes are often of coarse vertical spatial resolution and neglect to account for the natural heterogeneity in thermal conductivity of streambed sediments. Here we report on a field investigation in a stream, where air, stream water and streambed sediment temperatures were measured by Distributed Temperature Sensing (DTS) with high spatial resolution to; (i) detect spatial and temporal variability in groundwater discharge based on vertical streambed temperature profiles, (ii) study the thermal regime of streambed sediments exposed to different solar radiation influence, (iii) describe the effect of solar radiation on the measured streambed temperatures. The study was carried out at a field site located along Holtum stream, in Western Denmark. The 3 m wide stream has a sandy streambed with a cobbled armour layer, a mean discharge of 200 l/s and a mean depth of 0.3 m. Streambed temperatures were measured with a high-resolution DTS system (HR-DTS). By helically wrapping the fiber optic cable around two PVC pipes of 0.05 m and 0.075 m outer diameter over 1.5 m length, temperature measurements were recorded with 5.7 mm and 3.8 mm vertical spacing, respectively. The HR-DTS systems were installed 0.7 m deep in the streambed sediments, crossing both the sediment-water and the water-air interface, thus yielding high resolution water and air temperature data as well. One of the HR-DTS systems was installed in the open stream channel with only topographical shading, while the other HR-DTS system was placed 7 m upstream, under the canopy of a tree, thus representing the shaded conditions with reduced influence of solar radiation. Temperature measurements were taken with 30 min intervals between 16 April and 25 June 2013. The thermal conductivity of streambed sediments was calibrated in a 1D flow and heat transport model (HydroGeoSphere). Subsequently, time series of vertical groundwater fluxes were computed based on the high-resolution vertical streambed sediment temperature profiles by coupling the model with PEST. The calculated vertical flux time series show spatial differences in discharge between the two HR-DTS sites. A similar temporal variability in vertical fluxes at the two test sites can also be observed, most likely linked to rainfall-runoff processes. The effect of solar radiation as streambed conduction is visible both at the exposed and shaded test site in form of increased diel temperature oscillations up to 14 cm depth from the streambed surface, with the test site exposed to solar radiation showing larger diel temperature oscillations.

The impact of mesoscale convective systems on global precipitation: A modeling study

NASA Astrophysics Data System (ADS)

Tao, Wei-Kuo

2017-04-01

The importance of precipitating mesoscale convective systems (MCSs) has been quantified from TRMM precipitation radar and microwave imager retrievals. MCSs generate more than 50% of the rainfall in most tropical regions. Typical MCSs have horizontal scales of a few hundred kilometers (km); therefore, a large domain and high resolution are required for realistic simulations of MCSs in cloud-resolving models (CRMs). Almost all traditional global and climate models do not have adequate parameterizations to represent MCSs. Typical multi-scale modeling frameworks (MMFs) with 32 CRM grid points and 4 km grid spacing also might not have sufficient resolution and domain size for realistically simulating MCSs. In this study, the impact of MCSs on precipitation processes is examined by conducting numerical model simulations using the Goddard Cumulus Ensemble model (GCE) and Goddard MMF (GMMF). The results indicate that both models can realistically simulate MCSs with more grid points (i.e., 128 and 256) and higher resolutions (1 or 2 km) compared to those simulations with less grid points (i.e., 32 and 64) and low resolution (4 km). The modeling results also show that the strengths of the Hadley circulations, mean zonal and regional vertical velocities, surface evaporation, and amount of surface rainfall are either weaker or reduced in the GMMF when using more CRM grid points and higher CRM resolution. In addition, the results indicate that large-scale surface evaporation and wind feed back are key processes for determining the surface rainfall amount in the GMMF. A sensitivity test with reduced sea surface temperatures (SSTs) is conducted and results in both reduced surface rainfall and evaporation.

Projections of Future Precipitation Extremes Over Europe: A Multimodel Assessment of Climate Simulations

NASA Astrophysics Data System (ADS)

Rajczak, Jan; Schär, Christoph

2017-10-01

Projections of precipitation and its extremes over the European continent are analyzed in an extensive multimodel ensemble of 12 and 50 km resolution EURO-CORDEX Regional Climate Models (RCMs) forced by RCP2.6, RCP4.5, and RCP8.5 (Representative Concentration Pathway) aerosol and greenhouse gas emission scenarios. A systematic intercomparison with ENSEMBLES RCMs is carried out, such that in total information is provided for an unprecedentedly large data set of 100 RCM simulations. An evaluation finds very reasonable skill for the EURO-CORDEX models in simulating temporal and geographical variations of (mean and heavy) precipitation at both horizontal resolutions. Heavy and extreme precipitation events are projected to intensify across most of Europe throughout the whole year. All considered models agree on a distinct intensification of extremes by often more than +20% in winter and fall and over central and northern Europe. A reduction of rainy days and mean precipitation in summer is simulated by a large majority of models in the Mediterranean area, but intermodel spread between the simulations is large. In central Europe and France during summer, models project decreases in precipitation but more intense heavy and extreme rainfalls. Comparison to previous RCM projections from ENSEMBLES reveals consistency but slight differences in summer, where reductions in southern European precipitation are not as pronounced as previously projected. The projected changes of the European hydrological cycle may have substantial impact on environmental and anthropogenic systems. In particular, the simulations indicate a rising probability of summertime drought in southern Europe and more frequent and intense heavy rainfall across all of Europe.

Tropical Rainfall Measuring Mission (TRMM) Precipitation Data and Services for Research and Applications

NASA Technical Reports Server (NTRS)

Liu, Zhong; Ostrenga, Dana; Teng, William; Kempler, Steven

2012-01-01

Precipitation is a critical component of the Earth's hydrological cycle. Launched on 27 November 1997, TRMM is a joint U.S.-Japan satellite mission to provide the first detailed and comprehensive data set of the four-dimensional distribution of rainfall and latent heating over vastly under-sampled tropical and subtropical oceans and continents (40 S - 40 N). Over the past 14 years, TRMM has been a major data source for meteorological, hydrological and other research and application activities around the world. The purpose of this short article is to inform that the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) provides TRMM archive and near-real-time precipitation data sets and services for research and applications. TRMM data consist of orbital data from TRMM instruments at the sensor s resolution, gridded data at a range of spatial and temporal resolutions, subsets, ground-based instrument data, and ancillary data. Data analysis, display, and delivery are facilitated by the following services: (1) Mirador (data search and access); (2) TOVAS (TRMM Online Visualization and Analysis System); (3) OPeNDAP (Open-source Project for a Network Data Access Protocol); (4) GrADS Data Server (GDS); and (5) Open Geospatial Consortium (OGC) Web Map Service (WMS) for the GIS community. Precipitation data application services are available to support a wide variety of applications around the world. Future plans include enhanced and new services to address data related issues from the user community. Meanwhile, the GES DISC is preparing for the Global Precipitation Measurement (GPM) mission which is scheduled for launch in 2014.

Hillslope-scale experiment demonstrates role of convergence during two-step saturation

USGS Publications Warehouse

Gevaert, A. I.; Teuling, A. J.; Uijlenhoet, R.; DeLong, Stephen B.; Huxman, T. E.; Pangle, L. A.; Breshears, David D.; Chorover, J.; Pelletier, John D.; Saleska, S. R.; Zeng, X.; Troch, Peter A.

2014-01-01

Subsurface flow and storage dynamics at hillslope scale are difficult to ascertain, often in part due to a lack of sufficient high-resolution measurements and an incomplete understanding of boundary conditions, soil properties, and other environmental aspects. A continuous and extreme rainfall experiment on an artificial hillslope at Biosphere 2's Landscape Evolution Observatory (LEO) resulted in saturation excess overland flow and gully erosion in the convergent hillslope area. An array of 496 soil moisture sensors revealed a two-step saturation process. First, the downward movement of the wetting front brought soils to a relatively constant but still unsaturated moisture content. Second, soils were brought to saturated conditions from below in response to rising water tables. Convergent areas responded faster than upslope areas, due to contributions from lateral subsurface flow driven by the topography of the bottom boundary, which is comparable to impermeable bedrock in natural environments. This led to the formation of a groundwater ridge in the convergent area, triggering saturation excess runoff generation. This unique experiment demonstrates, at very high spatial and temporal resolution, the role of convergence on subsurface storage and flow dynamics. The results bring into question the representation of saturation excess overland flow in conceptual rainfall-runoff models and land-surface models, since flow is gravity-driven in many of these models and upper layers cannot become saturated from below. The results also provide a baseline to study the role of the co-evolution of ecological and hydrological processes in determining landscape water dynamics during future experiments in LEO.