Technical Report Series on Global Modeling and Data Assimilation. Volume 12; Comparison of Satellite Global Rainfall Algorithms

NASA Technical Reports Server (NTRS)

Suarez, Max J. (Editor); Chang, Alfred T. C.; Chiu, Long S.

1997-01-01

Seventeen months of rainfall data (August 1987-December 1988) from nine satellite rainfall algorithms (Adler, Chang, Kummerow, Prabhakara, Huffman, Spencer, Susskind, and Wu) were analyzed to examine the uncertainty of satellite-derived rainfall estimates. The variability among algorithms, measured as the standard deviation computed from the ensemble of algorithms, shows regions of high algorithm variability tend to coincide with regions of high rain rates. Histograms of pattern correlation (PC) between algorithms suggest a bimodal distribution, with separation at a PC-value of about 0.85. Applying this threshold as a criteria for similarity, our analyses show that algorithms using the same sensor or satellite input tend to be similar, suggesting the dominance of sampling errors in these satellite estimates.

Influence of high resolution rainfall data on the hydrological response of urban flat catchments

NASA Astrophysics Data System (ADS)

Cristiano, Elena; ten Veldhuis, Marie-claire; van de Giesen, Nick

2016-04-01

In the last decades, cities have become more and more urbanized and population density in urban areas is increased. At the same time, due to the climate changes, rainfall events present higher intensity and shorter duration than in the past. The increase of imperviousness degree, due to urbanization, combined with short and intense rainfall events, determinates a fast hydrological response of the urban catchment and in some cases it can lead to flooding. Urban runoff processes are sensitive to rainfall spatial and temporal variability and, for this reason, high resolution rainfall data are required as input for the hydrological model. A better knowledge of the hydrological response of system can help to prevent damages caused by flooding. This study aims to evaluate the sensitivity of urban hydrological response to spatial and temporal rainfall variability in urban areas, focusing especially on understanding the hydrological behaviour in lowland areas. In flat systems, during intense rainfall events, the flow in the sewer network can be pressurized and it can change direction, depending on the setting of pumping stations and CSOs (combined sewer overflow). In many cases these systems are also looped and it means that the water can follow different paths, depending on the pipe filling process. For these reasons, hydrological response of flat and looped catchments is particularly complex and it can be difficult characterize and predict it. A new dual polarimetric X-band weather radar, able to measure rainfall with temporal resolution of 1 min and spatial resolution of 100mX100m, was recently installed in the city of Rotterdam (NL). With this instrument, high resolution rainfall data were measured and used, in this work, as input for the hydrodynamic model. High detailed, semi-distributed hydrodynamic models of some districts of Rotterdam were used to investigate the hydrological response of flat catchments to high resolution rainfall data. In particular, the hydrological response of some subcatchments of the district of Kralingen was studied. Rainfall data were combined with level and discharge measurements at the pumping station that connects the sewer system with the waste water treatment plane. Using this data it was possible to study the water balance and to have a better idea of the amount of water that leave the system during a specific rainfall events. Results show that the hydrological response of flat and looped catchments is sensitive to spatial and temporal rainfall variability and it can be strongly influenced by rainfall event characteristics, such as intensity, velocity and intermittency of the storm.

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.

Identification of MJO Signal on Various Elevation Station Rainfall in Southern Papua, Indonesia

NASA Astrophysics Data System (ADS)

Sakya, A. E.; Permana, D.; Makmur, E. E. S.; Handayani, A. S.; Hanggoro, W.; Setyadi, G.

2016-12-01

The Madden-Julian Oscillation (MJO) is the dominant mode of intraseasonal variability in tropical rainfall on the large scale, but its signal is often obscured in individual station data, where effects are most directly felt at the local level. The characteristic of the MJO during its propagation through the Maritime Continent has always been a challenge to comprehend despite decades of research attempts in that region. Unique topography over the Maritime Continent is believed to act as one of the vanguard of precipitation triggered by the MJO. Such condition leads to a maximize amplitude of the diurnal cycle of precipitation over land on phase 2 and 5, even before the arrival of the MJO. Papua in Indonesia is one of the wettest regions on Earth and is at the heart of the MJO envelope. Aiming to investigate the effect of topography and coastline distance on MJO in southern Papua, 14 years of rainfall data from 12 stations in PTFI AWS network at various elevations (9 meters to 4400 meters above sea level) have been utilized. The results show a strong MJO modulation in rainfall variability with variance of 30 - 100 days in the region. These results suggest a strong impact of MJO on rainfall at various elevations in southern Papua which confirm the previous studies. The peak rainfall rates were observed at phase 3 at lower elevation and coastline stations and phase 4 at middle and high elevation stations. The study also investigated the relationship between MJO phases and diurnal precipitation cycle at all stations. At low elevation and coastline stations, diurnal rainfall variation is more variable with high rainfall observed at afternoon to midnight and after midnight. This is due to the local effect of land-sea breeze system. While in middle and high elevation stations, rainfall peak was observed at afternoon to midnight. The results show the impact of MJO in diurnal rainfall variation at all stations.

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.

Rainfall pattern variability as climate change impact in The Wallacea Region

NASA Astrophysics Data System (ADS)

Pujiastuti, I.; Nurjani, E.

2018-04-01

The objective of the study is to observe the characteristic variability of rainfall pattern in the city located in every rainfall type, local (Kendari), monsoon (Manado), and equatorial (Palu). The result will be compared to determine which has the most significantly precipitation changing due to climate change impact. Rainfall variability in Indonesia illustrates precipitation variation thus the important variability is the variability of monthly rainfall. Monthly precipitation data for the period of 1961-2010 are collected from Indonesian Agency for Meteorological, Climatological, and Geophysical Agency. This data is calculated with the normal test statistical method to analyze rainfall variability. The result showed the pattern of trend and variability of rainfall in every city with the own characteristic which determines the rainfall type. Moreover, there is comparison of rainfall pattern changing between every rainfall type. This information is useful for climate change mitigation and adaptation strategies especially in water resource management form precipitation as well as the occurrence of meteorological disasters.

Models are likely to underestimate increase in heavy rainfall in regions with high rainfall intensity

NASA Astrophysics Data System (ADS)

Borodina, Aleksandra; Fischer, Erich M.; Knutti, Reto

2017-04-01

Model projections of heavy rainfall are uncertain. On timescales of few decades, internal variability plays an important role and therefore poses a challenge to detect robust model responses. We show that spatial aggregation across regions with intense heavy rainfall events, - defined as grid cells with high annual precipitation maxima (Rx1day), - allows to reduce the role of internal variability and thus to detect a robust signal during the historical period. This enables us to evaluate models with observational datasets and to constrain long-term projections of the intensification of heavy rainfall, i.e., to recalibrate full model ensemble consistent with observations resulting in narrower range of projections. In the regions of intense heavy rainfall, we found two present-day metrics that are related to a model's projection. The first metric is the observed relationship between the area-weighted mean of the annual precipitation maxima (Rx1day) and the global land temperatures. The second is the fraction of land exhibiting statistically significant relationships between local annual precipitation maxima (Rx1day) and global land temperatures over the historical period. The models that simulate high values in both metrics are those that are in better agreement with observations and show strong future intensification of heavy rainfall. This implies that changes in heavy rainfall are likely to be more intense than anticipated from the multi-model mean.

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.

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.

Adaptability and performance of short-season maize hybrids in the southern high plains

USDA-ARS?s Scientific Manuscript database

Drought incidences change with year and location, and are prevalent in the Southern High Plains where annual rainfall is low and highly variable and most maize and other crops are irrigated. The low rainfall and groundwater overuse are leading to shortages of water for crop irrigation in this regio...

Heavy rainfall in Mediterranean cyclones. Part I: contribution of deep convection and warm conveyor belt

NASA Astrophysics Data System (ADS)

Flaounas, Emmanouil; Kotroni, Vassiliki; Lagouvardos, Konstantinos; Gray, Suzanne L.; Rysman, Jean-François; Claud, Chantal

2018-04-01

In this study, we provide an insight to the role of deep convection (DC) and the warm conveyor belt (WCB) as leading processes to Mediterranean cyclones' heavy rainfall. To this end, we use reanalysis data, lighting and satellite observations to quantify the relative contribution of DC and the WCB to cyclone rainfall, as well as to analyse the spatial and temporal variability of these processes with respect to the cyclone centre and life cycle. Results for the period 2005-2015 show that the relationship between cyclone rainfall and intensity has high variability and demonstrate that even intense cyclones may produce low rainfall amounts. However, when considering rainfall averages for cyclone intensity bins, a linear relationship was found. We focus on the 500 most intense tracked cyclones (responsible for about 40-50% of the total 11-year Mediterranean rainfall) and distinguish between the ones producing high and low rainfall amounts. DC and the WCB are found to be the main cause of rainfall for the former (producing up to 70% of cyclone rainfall), while, for the latter, DC and the WCB play a secondary role (producing up to 50% of rainfall). Further analysis showed that rainfall due to DC tends to occur close to the cyclones' centre and to their eastern sides, while the WCBs tend to produce rainfall towards the northeast. In fact, about 30% of rainfall produced by DC overlaps with rainfall produced by WCBs but this represents only about 8% of rainfall produced by WCBs. This suggests that a considerable percentage of DC is associated with embedded convection in WCBs. Finally, DC was found to be able to produce higher rain rates than WCBs, exceeding 50 mm in 3-h accumulated rainfall compared to a maximum of the order of 40 mm for WCBs. Our results demonstrate in a climatological framework the relationship between cyclone intensity and processes that lead to heavy rainfall, one of the most prominent environmental risks in the Mediterranean. Therefore, we set perspectives for a deeper analysis of the favourable atmospheric conditions that yield high impact weather.

Impacts of climate variability and extreme events on soil hydrological processes

NASA Astrophysics Data System (ADS)

Ramos, M. C.; Mulligan, M.

2003-04-01

The Mediterranean climate (dry subhumid), characterised by a high variability, produces in many situations an insufficient water supply to support stable agriculture. Not only is there insufficient rainfall, but its occurrence is also highly variable between years, during the year, and spatially, during a single rainfall event. One of the main climatic characteristics affecting the vulnerability of the Mediterranean region is the high intensity rainfalls which fall after a very dry summer and the high degree of climatic fluctuation in the short and long term, especially in rainfall quantity. In addition, the rainwater penetration and storage of water in the soil are conditioned by the soil characteristics, in some cases modified by changes in land use and with new management practices. The aim of this study was to evaluate the impact of this high variability, from year to year and through the year, on soil hydrological processes, in fields resulted of the mechanisation works in vineyards in a Mediterranean environment. The PATTERNlight model, a simplified two-dimensional version of the hydrological and growth PATTERN model (Mulligan, 1996) is used here to simulate the water balance for three situations: normal, wet and dry years. Ssignificant differences in soil moisture and recharge were observed under vine culture from year to year, giving rise very often, to critical situations for the development of the crops. The distribution of the rainfall through the year together with the intensity of the recorded rainfalls is much very significant for soil hydrology than the total annual rainfall. Very low soil moisture conditions are raised when spring rainfall is scarce, which contribute to exhaustion of profile soil water over the summer, especially if the antecedent soil moisture is low. This low soil moisture has a significant effect on the development of the vine crop. The simulations of leaf and root biomass carried out with the PATTERNLIGHT model indicate the differences in the development of the leaf biomass between wet and dry conditions, especially with dry springs. Wet conditions favour the development of root and leaf biomass in a significant way. Mulligan, M., 1996. Modelling the hydrology of vegetation competition in a degrade semiarid environment. PhD Theses. Department of Geography, King's College London, University of London.

The impact of inter-annual rainfall variability on food production in the Ganges basin

NASA Astrophysics Data System (ADS)

Siderius, Christian; Biemans, Hester; van Walsum, Paul; hellegers, Petra; van Ierland, Ekko; Kabat, Pavel

2014-05-01

Rainfall variability is expected to increase in the coming decades as the world warms. Especially in regions already water stressed, a higher rainfall variability will jeopardize food security. Recently, the impact of inter-annual rainfall variability has received increasing attention in regional to global analysis on water availability and food security. But the description of the dynamics behind it is still incomplete in most models. Contemporary land surface and hydrological models used for such analyses describe variability in production primarily as a function of yield, a process driven by biophysical parameters, thereby neglecting yearly variations in cropped area, a process driven largely by management decisions. Agricultural statistics for northern India show that the latter process could explain up to 40% of the observed inter-annual variation in food production in various states. We added a simple dynamic land use decision module to a land surface model (LPJmL) and analyzed to what extent this improved the estimation of variability in food production. Using this improved modelling framework we then assessed if and at which scale rainfall variability affects meeting the food self-sufficiency threshold. Early results for the Ganges Basin indicate that, while on basin level variability in crop production is still relatively low, several districts and states are highly affected (RSTD > 50%). Such insight can contribute to better recommendations on the most effective measures, at the most appropriate scale, to buffer variability in food production.

The significance of spatial variability of rainfall on streamflow: A synthetic analysis at the Upper Lee catchment, UK

NASA Astrophysics Data System (ADS)

Pechlivanidis, Ilias; McIntyre, Neil; Wheater, Howard

2017-04-01

Rainfall, one of the main inputs in hydrological modeling, is a highly heterogeneous process over a wide range of scales in space, and hence the ignorance of the spatial rainfall information could affect the simulated streamflow. Calibration of hydrological model parameters is rarely a straightforward task due to parameter equifinality and parameters' 'nature' to compensate for other uncertainties, i.e. structural and forcing input. In here, we analyse the significance of spatial variability of rainfall on streamflow as a function of catchment scale and type, and antecedent conditions using the continuous time, semi-distributed PDM hydrological model at the Upper Lee catchment, UK. The impact of catchment scale and type is assessed using 11 nested catchments ranging in scale from 25 to 1040 km2, and further assessed by artificially changing the catchment characteristics and translating these to model parameters with uncertainty using model regionalisation. Synthetic rainfall events are introduced to directly relate the change in simulated streamflow to the spatial variability of rainfall. Overall, we conclude that the antecedent catchment wetness and catchment type play an important role in controlling the significance of the spatial distribution of rainfall on streamflow. Results show a relationship between hydrograph characteristics (streamflow peak and volume) and the degree of spatial variability of rainfall for the impermeable catchments under dry antecedent conditions, although this decreases at larger scales; however this sensitivity is significantly undermined under wet antecedent conditions. Although there is indication that the impact of spatial rainfall on streamflow varies as a function of catchment scale, the variability of antecedent conditions between the synthetic catchments seems to mask this significance. Finally, hydrograph responses to different spatial patterns in rainfall depend on assumptions used for model parameter estimation and also the spatial variation in parameters indicating the need of an uncertainty framework in such investigation.

Partitioning the impacts of spatial and climatological rainfall variability in urban drainage modeling

NASA Astrophysics Data System (ADS)

Peleg, Nadav; Blumensaat, Frank; Molnar, Peter; Fatichi, Simone; Burlando, Paolo

2017-03-01

The performance of urban drainage systems is typically examined using hydrological and hydrodynamic models where rainfall input is uniformly distributed, i.e., derived from a single or very few rain gauges. When models are fed with a single uniformly distributed rainfall realization, the response of the urban drainage system to the rainfall variability remains unexplored. The goal of this study was to understand how climate variability and spatial rainfall variability, jointly or individually considered, affect the response of a calibrated hydrodynamic urban drainage model. A stochastic spatially distributed rainfall generator (STREAP - Space-Time Realizations of Areal Precipitation) was used to simulate many realizations of rainfall for a 30-year period, accounting for both climate variability and spatial rainfall variability. The generated rainfall ensemble was used as input into a calibrated hydrodynamic model (EPA SWMM - the US EPA's Storm Water Management Model) to simulate surface runoff and channel flow in a small urban catchment in the city of Lucerne, Switzerland. The variability of peak flows in response to rainfall of different return periods was evaluated at three different locations in the urban drainage network and partitioned among its sources. The main contribution to the total flow variability was found to originate from the natural climate variability (on average over 74 %). In addition, the relative contribution of the spatial rainfall variability to the total flow variability was found to increase with longer return periods. This suggests that while the use of spatially distributed rainfall data can supply valuable information for sewer network design (typically based on rainfall with return periods from 5 to 15 years), there is a more pronounced relevance when conducting flood risk assessments for larger return periods. The results show the importance of using multiple distributed rainfall realizations in urban hydrology studies to capture the total flow variability in the response of the urban drainage systems to heavy rainfall events.

Reconstruction of rainfall in Zafra (southwest Spain) from 1750 to 1840 from documentary sources

NASA Astrophysics Data System (ADS)

Fernández-Fernández, M. I.; Gallego, M. C.; Domínguez-Castro, F.; Vaquero, J. M.; Moreno González, J. M.; Castillo Durán, J.

2011-11-01

This work presents the first high-resolution reconstruction of rainfall in southwestern Spain during the period 1750-1840. The weather descriptions used are weekly reports describing the most relevant events that occurred in the Duchy of Feria. An index was defined to characterise the weekly rainfall. Monthly indices were obtained by summing the corresponding weekly indices, obtaining cumulative monthly rainfall indices. The reconstruction method consisted of establishing a linear correlation between the monthly rainfall index and monthly instrumental data (1960-1990). The correlation coefficients were greater than 0.80 for all months. The rainfall reconstruction showed major variability similar to natural variability. The reconstructed rainfall series in Zafra was compared with the rainfall series of Cadiz, Gibraltar and Lisbon for the period 1750-1840, with all four series found to have a similar pattern. The influence of the North Atlantic Oscillation (NAO) on the winter rainfall reconstruction was found to behave similarly to that of modern times. Other studies described are of the SLP values over the entire North Atlantic in the months with extreme values of rainfall, and unusual meteorological events (hail, frost, storms and snowfall) in the reports of the Duchy of Feria.

High temporal resolution of extreme rainfall rate variability and the acoustic classification of rainfall

NASA Astrophysics Data System (ADS)

Nystuen, Jeffrey A.; Amitai, Eyal

2003-04-01

The underwater sound generated by raindrop splashes on a water surface is loud and unique allowing detection, classification and quantification of rainfall. One of the advantages of the acoustic measurement is that the listening area, an effective catchment area, is proportional to the depth of the hydrophone and can be orders of magnitude greater than other in situ rain gauges. This feature allows high temporal resolution of the rainfall measurement. A series of rain events with extremely high rainfall rates, over 100 mm/hr, is examined acoustically. Rapid onset and cessation of rainfall intensity are detected within the convective cells of these storms with maximum 5-s resolution values exceeding 1000 mm/hr. The probability distribution functions (pdf) for rainfall rate occurrence and water volume using the longer temporal resolutions typical of other instruments do not include these extreme values. The variance of sound intensity within different acoustic frequency bands can be used as an aid to classify rainfall type. Objective acoustic classification algorithms are proposed. Within each rainfall classification the relationship between sound intensity and rainfall rate is nearly linear. The reflectivity factor, Z, also has a linear relationship with rainfall rate, R, for each rainfall classification.

Variability in rainfall over tropical Australia during summer and relationships with the Bilybara High

NASA Astrophysics Data System (ADS)

Reason, C. J. C.

2018-04-01

Variability in summer rainfall over tropical Australia, defined here as that part of the continent north of 25° S, and its linkages with regional circulation are examined. In particular, relationships with the mid-level anticyclone (termed the Bilybara High) that exists over the northwestern Australia/Timor Sea region between August and April are considered. This High forms to the southwest of the upper-level anticyclone via a balance between the upper-level divergence over the region of tropical precipitation maximum and planetary vorticity advection and moves south and strengthens during the spring and summer. It is shown that variations in the strength and position of the Bilybara High are related to anomalies in precipitation and temperature over large parts of tropical Australia as well as some areas in the south and southeast of the landmass. Some of the interannual variations in the High are related to ENSO, but there are also a number of neutral years with large anomalies in the High and hence in rainfall. On decadal time scales, a strong relationship exists between the leading mode of tropical Australian rainfall and the Bilybara High. On both interannual and decadal scales, the relationships between the High and the regional rainfall involve changes in the monsoonal northwesterlies blowing towards northern Australia, and further south, in the easterly trade winds over the region.

Monsoon rainfall over India in June and link with northwest tropical pacific - June ISMR and link with northwest tropical pacific

NASA Astrophysics Data System (ADS)

Surendran, Sajani; Gadgil, Sulochana; Rajendran, Kavirajan; Varghese, Stella Jes; Kitoh, Akio

2018-03-01

Recent years have witnessed large interannual variation of all-India rainfall (AIR) in June, with intermittent large deficits and excesses. Variability of June AIR is found to have the strongest link with variation of rainfall over northwest tropical Pacific (NWTP), with AIR deficit (excess) associated with enhancement (suppression) of NWTP rainfall. This association is investigated using high-resolution Meteorological Research Institute model which shows high skill in simulating important features of Asian summer monsoon, its variability and the inverse relationship between NWTP rainfall and AIR. Analysis of the variation of NWTP rainfall shows that it is associated with a change in the latitudinal position of subtropical westerly jet over the region stretching from West of Tibetan Plateau (WTP) to NWTP and the phase of Rossby wave steered in it with centres over NWTP and WTP. In years with large rainfall excess/deficit, the strong link between AIR and NWTP rainfall exists through differences in Rossby wave phase steered in the jet. The positive phase of the WTP-NWTP pattern, with troughs over WTP and west of NWTP, tends to be associated with increased rainfall over NWTP and decreased AIR. This scenario is reversed in the opposite phase. Thus, the teleconnection between NWTP rainfall and AIR is a manifestation of the difference in the phase of Rossby wave between excess and deficit years, with centres over WTP and NWTP. This brings out the importance of prediction of phase of Rossby waves over WTP and NWTP in advance, for prediction of June rainfall over India.

Spatial variability of extreme rainfall at radar subpixel scale

NASA Astrophysics Data System (ADS)

Peleg, Nadav; Marra, Francesco; Fatichi, Simone; Paschalis, Athanasios; Molnar, Peter; Burlando, Paolo

2018-01-01

Extreme rainfall is quantified in engineering practice using Intensity-Duration-Frequency curves (IDF) that are traditionally derived from rain-gauges and more recently also from remote sensing instruments, such as weather radars. These instruments measure rainfall at different spatial scales: rain-gauge samples rainfall at the point scale while weather radar averages precipitation on a relatively large area, generally around 1 km2. As such, a radar derived IDF curve is representative of the mean areal rainfall over a given radar pixel and neglects the within-pixel rainfall variability. In this study, we quantify subpixel variability of extreme rainfall by using a novel space-time rainfall generator (STREAP model) that downscales in space the rainfall within a given radar pixel. The study was conducted using a unique radar data record (23 years) and a very dense rain-gauge network in the Eastern Mediterranean area (northern Israel). Radar-IDF curves, together with an ensemble of point-based IDF curves representing the radar subpixel extreme rainfall variability, were developed fitting Generalized Extreme Value (GEV) distributions to annual rainfall maxima. It was found that the mean areal extreme rainfall derived from the radar underestimate most of the extreme values computed for point locations within the radar pixel (on average, ∼70%). The subpixel variability of rainfall extreme was found to increase with longer return periods and shorter durations (e.g. from a maximum variability of 10% for a return period of 2 years and a duration of 4 h to 30% for 50 years return period and 20 min duration). For the longer return periods, a considerable enhancement of extreme rainfall variability was found when stochastic (natural) climate variability was taken into account. Bounding the range of the subpixel extreme rainfall derived from radar-IDF can be of major importance for different applications that require very local estimates of rainfall extremes.

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.

Applications of high resolution rainfall radar data to quantify water temperature dynamics in urban catchments

NASA Astrophysics Data System (ADS)

Croghan, Danny; Van Loon, Anne; Bradley, Chris; Sadler, Jon; Hannnah, David

2017-04-01

Studies relating rainfall events to river water quality are frequently hindered by the lack of high resolution rainfall data. Local studies are particularly vulnerable due to the spatial variability of precipitation, whilst studies in urban environments require precipitation data at high spatial and temporal resolutions. The use of point-source data makes identifying causal effects of storms on water quality problematic and can lead to erroneous interpretations. High spatial and temporal resolution rainfall radar data offers great potential to address these issues. Here we use rainfall radar data with a 1km spatial resolution and 5 minute temporal resolution sourced from the UK Met Office Nimrod system to study the effects of storm events on water temperature (WTemp) in Birmingham, UK. 28 WTemp loggers were placed over 3 catchments on a rural-urban land use gradient to identify trends in WTemp during extreme events within urban environments. Using GIS, the catchment associated with each logger was estimated, and 5 min. rainfall totals and intensities were produced for each sub-catchment. Comparisons of rainfall radar data to meteorological stations in the same grid cell revealed the high accuracy of rainfall radar data in our catchments (<5% difference for studied months). The rainfall radar data revealed substantial differences in rainfall quantity between the three adjacent catchments. The most urban catchment generally received more rainfall, with this effect greatest in the highest intensity storms, suggesting the possibility of urban heat island effects on precipitation dynamics within the catchment. Rainfall radar data provided more accurate sub-catchment rainfall totals allowing better modelled estimates of storm flow, whilst spatial fluctuations in both discharge and WTemp can be simply related to precipitation intensity. Storm flow inputs for each sub-catchment were estimated and linked to changes in WTemp. WTemp showed substantial fluctuations (>1 °C) over short durations (<30 minutes) during storm events in urbanised sub-catchments, however WTemp recovery times were more prolonged. Use of the rainfall radar data allowed increased accuracy in estimates of storm flow timings and rainfall quantities at each sub-catchment, from which the impact of storm flow on WTemp could be quantified. We are currently using the radar data to derive thresholds for rainfall amount and intensity at which these storm deviations occur for each logger, from which the relative effects of land use and other catchment characteristics in each sub-catchment can be assessed. Our use of the rainfall radar data calls into question the validity of using station based data for small scale studies, particularly in urban areas, with high variation apparent in rainfall intensity both spatially and temporally. Variation was particularly high within the heavily urbanised catchment. For water quality studies, high resolution rainfall radar can be implemented to increase the reliability of interpretations of the response of water quality variables to storm water inputs in urban catchments.

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.

High-resolution stochastic generation of extreme rainfall intensity for urban drainage modelling applications

NASA Astrophysics Data System (ADS)

Peleg, Nadav; Blumensaat, Frank; Molnar, Peter; Fatichi, Simone; Burlando, Paolo

2016-04-01

Urban drainage response is highly dependent on the spatial and temporal structure of rainfall. Therefore, measuring and simulating rainfall at a high spatial and temporal resolution is a fundamental step to fully assess urban drainage system reliability and related uncertainties. This is even more relevant when considering extreme rainfall events. However, the current space-time rainfall models have limitations in capturing extreme rainfall intensity statistics for short durations. Here, we use the STREAP (Space-Time Realizations of Areal Precipitation) model, which is a novel stochastic rainfall generator for simulating high-resolution rainfall fields that preserve the spatio-temporal structure of rainfall and its statistical characteristics. The model enables a generation of rain fields at 102 m and minute scales in a fast and computer-efficient way matching the requirements for hydrological analysis of urban drainage systems. The STREAP model was applied successfully in the past to generate high-resolution extreme rainfall intensities over a small domain. A sub-catchment in the city of Luzern (Switzerland) was chosen as a case study to: (i) evaluate the ability of STREAP to disaggregate extreme rainfall intensities for urban drainage applications; (ii) assessing the role of stochastic climate variability of rainfall in flow response and (iii) evaluate the degree of non-linearity between extreme rainfall intensity and system response (i.e. flow) for a small urban catchment. The channel flow at the catchment outlet is simulated by means of a calibrated hydrodynamic sewer model.

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.

High Resolution Flash Flood Forecasting Using a Wireless Sensor Network in the Dallas-Fort Worth Metroplex

NASA Astrophysics Data System (ADS)

Bartos, M. D.; Kerkez, B.; Noh, S.; Seo, D. J.

2017-12-01

In this study, we develop and evaluate a high resolution urban flash flood monitoring system using a wireless sensor network (WSN), a real-time rainfall-runoff model, and spatially-explicit radar rainfall predictions. Flooding is the leading cause of natural disaster fatalities in the US, with flash flooding in particular responsible for a majority of flooding deaths. While many riverine flood models have been operationalized into early warning systems, there is currently no model that is capable of reliably predicting flash floods in urban areas. Urban flash floods are particularly difficult to model due to a lack of rainfall and runoff data at appropriate scales. To address this problem, we develop a wide-area flood-monitoring wireless sensor network for the Dallas-Fort Worth metroplex, and use this network to characterize rainfall-runoff response over multiple heterogeneous catchments. First, we deploy a network of 22 wireless sensor nodes to collect real-time stream stage measurements over catchments ranging from 2-80 km2 in size. Next, we characterize the rainfall-runoff response of each catchment by combining stream stage data with gage and radar-based precipitation measurements. Finally, we demonstrate the potential for real-time flash flood prediction by joining the derived rainfall-runoff models with real-time radar rainfall predictions. We find that runoff response is highly heterogeneous among catchments, with large variabilities in runoff response detected even among nearby gages. However, when spatially-explicit rainfall fields are included, spatial variability in runoff response is largely captured. This result highlights the importance of increased spatial coverage for flash flood prediction.

Adapting to climate variability and change: experiences from cereal-based farming in the central rift and Kobo Valleys, Ethiopia.

PubMed

Kassie, Belay Tseganeh; Hengsdijk, Huib; Rötter, Reimund; Kahiluoto, Helena; Asseng, Senthold; Van Ittersum, Martin

2013-11-01

Small-holder farmers in Ethiopia are facing several climate related hazards, in particular highly variable rainfall with severe droughts which can have devastating effects on their livelihoods. Projected changes in climate are expected to aggravate the existing challenges. This study examines farmer perceptions on current climate variability and long-term changes, current adaptive strategies, and potential barriers for successful further adaptation in two case study regions-the Central Rift Valley (CRV) and Kobo Valley. The study was based on a household questionnaire, interviews with key stakeholders, and focus group discussions. The result revealed that about 99 % of the respondents at the CRV and 96 % at the Kobo Valley perceived an increase in temperature and 94 % at CRV and 91 % at the Kobo Valley perceived a decrease in rainfall over the last 20-30 years. Inter-annual and intraseasonal rainfall variability also has increased according to the farmers. The observed climate data (1977-2009) also showed an increasing trend in temperature and high inter-annual and intra-seasonal rainfall variability. In contrast to farmers' perceptions of a decrease in rainfall totals, observed rainfall data showed no statistically significant decline. The interaction among various bio-physical and socio-economic factors, changes in rainfall intensity and reduced water available to crops due to increased hot spells, may have influenced the perception of farmers with respect to rainfall trends. In recent decades, farmers in both the CRV and Kobo have changed farming practices to adapt to perceived climate change and variability, for example, through crop and variety choice, adjustment of cropping calendar, and in situ moisture conservation. These relatively low-cost changes in farm practices were within the limited adaptation capacity of farmers, which may be insufficient to deal with the impacts of future climate change. Anticipated climate change is expected to impose new risks outside the range of current experiences. To enable farmers to adapt to these impacts critical technological, institutional, and market-access constraints need to be removed. Inconsistencies between farmers' perceptions and observed climate trends (e.g., decrease in annual rainfall) could lead to sub-optimal or counterproductive adaptations, and therefore must be removed by better communication and capacity building, for example through Climate Field Schools. Enabling strategies, which are among others targeted at agricultural inputs, credit supply, market access, and strengthening of local knowledge and information services need to become integral part of government policies to assist farmers to adapt to the impacts of current and future climate change.

Adapting to Climate Variability and Change: Experiences from Cereal-Based Farming in the Central Rift and Kobo Valleys, Ethiopia

NASA Astrophysics Data System (ADS)

Kassie, Belay Tseganeh; Hengsdijk, Huib; Rötter, Reimund; Kahiluoto, Helena; Asseng, Senthold; Van Ittersum, Martin

2013-11-01

Small-holder farmers in Ethiopia are facing several climate related hazards, in particular highly variable rainfall with severe droughts which can have devastating effects on their livelihoods. Projected changes in climate are expected to aggravate the existing challenges. This study examines farmer perceptions on current climate variability and long-term changes, current adaptive strategies, and potential barriers for successful further adaptation in two case study regions—the Central Rift Valley (CRV) and Kobo Valley. The study was based on a household questionnaire, interviews with key stakeholders, and focus group discussions. The result revealed that about 99 % of the respondents at the CRV and 96 % at the Kobo Valley perceived an increase in temperature and 94 % at CRV and 91 % at the Kobo Valley perceived a decrease in rainfall over the last 20-30 years. Inter-annual and intraseasonal rainfall variability also has increased according to the farmers. The observed climate data (1977-2009) also showed an increasing trend in temperature and high inter-annual and intra-seasonal rainfall variability. In contrast to farmers’ perceptions of a decrease in rainfall totals, observed rainfall data showed no statistically significant decline. The interaction among various bio-physical and socio-economic factors, changes in rainfall intensity and reduced water available to crops due to increased hot spells, may have influenced the perception of farmers with respect to rainfall trends. In recent decades, farmers in both the CRV and Kobo have changed farming practices to adapt to perceived climate change and variability, for example, through crop and variety choice, adjustment of cropping calendar, and in situ moisture conservation. These relatively low-cost changes in farm practices were within the limited adaptation capacity of farmers, which may be insufficient to deal with the impacts of future climate change. Anticipated climate change is expected to impose new risks outside the range of current experiences. To enable farmers to adapt to these impacts critical technological, institutional, and market-access constraints need to be removed. Inconsistencies between farmers’ perceptions and observed climate trends (e.g., decrease in annual rainfall) could lead to sub-optimal or counterproductive adaptations, and therefore must be removed by better communication and capacity building, for example through Climate Field Schools. Enabling strategies, which are among others targeted at agricultural inputs, credit supply, market access, and strengthening of local knowledge and information services need to become integral part of government policies to assist farmers to adapt to the impacts of current and future climate change.

Vertical Variability of Rain Drop Size Distribution from Micro Rain Radar Measurements during IFloodS

NASA Astrophysics Data System (ADS)

Adirosi, Elisa; Tokay, Ali; Roberto, Nicoletta; Gorgucci, Eugenio; Montopoli, Mario; Baldini, Luca

2017-04-01

Ground based weather radars are highly used to generate rainfall products for meteorological and hydrological applications. However, weather radar quantitative rainfall estimation is obtained at a certain altitude that depends mainly on the radar elevation angle and on the distance from the radar. Therefore, depending on the vertical variability of rainfall, a time-height ambiguity between radar measurement and rainfall at the ground can affect the rainfall products. The vertically pointing radars (such as the Micro Rain Radar, MRR) are great tool to investigate the vertical variability of rainfall and its characteristics and ultimately, to fill the gap between the ground level and the first available radar elevation. Furthermore, the knowledge of rain Drop Size Distribution (DSD) variability is linked to the well-known problem of the non-uniform beam filling that is one of the main uncertainties of Global Precipitation Measurement (GPM) mission Dual frequency Precipitation Radar (DPR). During GPM Ground Validation Iowa Flood Studies (IFloodS) field experiment, data collected with 2D video disdrometers (2DVD), Autonomous OTT Parsivel2 Units (APU), and MRR profilers at different sites were available. In three different sites co-located APU, 2DVD and MRR are available and covered by the S-band Dual Polarimetric Doppler radar (NPOL). The first elevation height of the radar beam varies, among the three sites, between 70 m and 1100 m. The IFloodS set-up has been used to compare disdrometers, MRR and NPOL data and to evaluate the uncertainties of those measurements. First, the performance of disdrometers and MRR in determining different rainfall parameters at ground has been evaluated and then the MRR based parameters have been compared with the ones obtained from NPOL data at the lowest elevations. Furthermore, the vertical variability of DSD and integral rainfall parameters within the MRR bins (from ground to 1085 m each 35 m) has been investigated in order to provide some insight on the variability of the rainfall microphysical characteristics within about 1 km above the ground.

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.

Do we really use rainfall observations consistent with reality in hydrological modelling?

NASA Astrophysics Data System (ADS)

Ciampalini, Rossano; Follain, Stéphane; Raclot, Damien; Crabit, Armand; Pastor, Amandine; Moussa, Roger; Le Bissonnais, Yves

2017-04-01

Spatial and temporal patterns in rainfall control how water reaches soil surface and interacts with soil properties (i.e., soil wetting, infiltration, saturation). Once a hydrological event is defined by a rainfall with its spatiotemporal variability and by some environmental parameters such as soil properties (including land use, topographic and anthropic features), the evidence shows that each parameter variation produces different, specific outputs (e.g., runoff, flooding etc.). In this study, we focus on the effect of rainfall patterns because, due to the difficulty to dispose of detailed data, their influence in modelling is frequently underestimated or neglected. A rainfall event affects a catchment non uniformly, it is spatially localized and its pattern moves in space and time. The way and the time how the water reaches the soil and saturates it respect to the geometry of the catchment deeply influences soil saturation, runoff, and then sediment delivery. This research, approaching a hypothetical, simple case, aims to stimulate the debate on the reliability of the rainfall quality used in hydrological / soil erosion modelling. We test on a small catchment of the south of France (Roujan, Languedoc Roussillon) the influence of rainfall variability with the use of a HD hybrid hydrological - soil erosion model, combining a cinematic wave with the St. Venant equation and a simplified "bucket" conceptual model for ground water, able to quantify the effect of different spatiotemporal patterns of a very-high-definition synthetic rainfall. Results indicate that rainfall spatiotemporal patterns are crucial simulating an erosive event: differences between spatially uniform rainfalls, as frequently adopted in simulations, and some hypothetical rainfall patterns here applied, reveal that the outcome of a simulated event can be highly underestimated.

Droughts, rainfall and rural water supply in northern Nigeria

NASA Astrophysics Data System (ADS)

Tarhule, Aondover Augustine

Knowledge concerning various aspects of drought and water scarcity is required to predict, and to articulate strategies to minimize the effects of future events. This thesis investigated different aspects of droughts and rainfall variability at several time scales and described the dynamics of water supply and use in a rural village in northeastern Nigeria. The parallel existence of measured climatic records and information on famine/folklore events is utilized to calibrate the historical information against the measured data. It is shown that famines or historical droughts occurred when the cumulative deficit of rainfall fell below 1.3 times the standard deviation of the long-term mean rainfall. The study demonstrated that famine chronologies are adequate proxy for drought events, providing a means for the reconstruction of the drought/climatic history of the region. Analysis of recent changes in annual rainfall characteristics show that the series of annual rainfall and number of rain days experienced a discontinuity during the 1960's, caused largely by the decrease in the frequency of moderate to high intensity rain events. The periods prior to and after the change point are homogenous and provide an objective basis for the estimation of changes in rainfall characteristics, drought parameters and for demarcating the region into sub-zones. Rainfall variability was unaffected by the abrupt change. Furthermore, the variability is independently distributed and adequately described by the normal distribution. This allows estimates of the probability of various magnitudes or thresholds of variability. The effects of droughts and rainfall variability are most strongly felt in rural areas. Analysis of the patterns of water supply and use in a typical rural village revealed that the hydrologic system is driven by the local rainfall. Perturbations in the rains propagate through the system with short lag time between the various components. Where fadama aquifers occur, they offer a major supplement of water for six to seven months during the dry season. Under traditional systems, the pattern of water withdrawal from the fadama aquifers is designed to accommodate the diverse interests of different groups and to minimize the potential for conflict. The results contribute to our understanding of drought and water scarcity and are useful in various practical applications.

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.

Influence of meteorological variables on rainfall partitioning for deciduous and coniferous tree species in urban area

NASA Astrophysics Data System (ADS)

Zabret, Katarina; Rakovec, Jože; Šraj, Mojca

2018-03-01

Rainfall partitioning is an important part of the ecohydrological cycle, influenced by numerous variables. Rainfall partitioning for pine (Pinus nigra Arnold) and birch (Betula pendula Roth.) trees was measured from January 2014 to June 2017 in an urban area of Ljubljana, Slovenia. 180 events from more than three years of observations were analyzed, focusing on 13 meteorological variables, including the number of raindrops, their diameter, and velocity. Regression tree and boosted regression tree analyses were performed to evaluate the influence of the variables on rainfall interception loss, throughfall, and stemflow in different phenoseasons. The amount of rainfall was recognized as the most influential variable, followed by rainfall intensity and the number of raindrops. Higher rainfall amount, intensity, and the number of drops decreased percentage of rainfall interception loss. Rainfall amount and intensity were the most influential on interception loss by birch and pine trees during the leafed and leafless periods, respectively. Lower wind speed was found to increase throughfall, whereas wind direction had no significant influence. Consideration of drop size spectrum properties proved to be important, since the number of drops, drop diameter, and median volume diameter were often recognized as important influential variables.

Trends and homogeneity of monthly, seasonal, and annual rainfall over arid region of Rajasthan, India

NASA Astrophysics Data System (ADS)

Meena, Hari Mohan; Machiwal, Deepesh; Santra, Priyabrata; Moharana, Pratap Chandra; Singh, D. V.

2018-05-01

Knowledge of rainfall variability is important for regional-scale planning and management of water resources in agriculture. This study explores spatio-temporal variations, trends, and homogeneity in monthly, seasonal, and annual rainfall series of 62 stations located in arid region of Rajasthan, India using 55 year (1957-2011) data. Box-whisker plots indicate presence of outliers and extremes in annual rainfall, which made the distribution of annual rainfall right-skewed. Mean and coefficient of variation (CV) of rainfall reveals a high inter-annual variability (CV > 200%) in the western portion where the mean annual rainfall is very low. A general gradient of the mean monthly, seasonal, and annual rainfall is visible from northwest to southeast direction, which is orthogonal to the gradient of CV. The Sen's innovative trend test is found over-sensitive in evaluating statistical significance of the rainfall trends, while the Mann-Kendall test identifies significantly increasing rainfall trends in June and September. Rainfall in July shows prominently decreasing trends although none of them are found statistically significant. Monsoon and annual rainfall show significantly increasing trends at only four stations. The magnitude of trends indicates that the rainfall is increasing at a mean rate of 1.11, 2.85, and 2.89 mm year-1 in August, monsoon season, and annual series. The rainfall is found homogeneous over most of the area except for few stations situated in the eastern and northwest portions where significantly increasing trends are observed. Findings of this study indicate that there are few increasing trends in rainfall of this Indian arid region.

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.

On the Predictability of Northeast Monsoon Rainfall over South Peninsular India in General Circulation Models

NASA Astrophysics Data System (ADS)

Nair, Archana; Acharya, Nachiketa; Singh, Ankita; Mohanty, U. C.; Panda, T. C.

2013-11-01

In this study the predictability of northeast monsoon (Oct-Nov-Dec) rainfall over peninsular India by eight general circulation model (GCM) outputs was analyzed. These GCM outputs (forecasts for the whole season issued in September) were compared with high-resolution observed gridded rainfall data obtained from the India Meteorological Department for the period 1982-2010. Rainfall, interannual variability (IAV), correlation coefficients, and index of agreement were examined for the outputs of eight GCMs and compared with observation. It was found that the models are able to reproduce rainfall and IAV to different extents. The predictive power of GCMs was also judged by determining the signal-to-noise ratio and the external error variance; it was noted that the predictive power of the models was usually very low. To examine dominant modes of interannual variability, empirical orthogonal function (EOF) analysis was also conducted. EOF analysis of the models revealed they were capable of representing the observed precipitation variability to some extent. The teleconnection between the sea surface temperature (SST) and northeast monsoon rainfall was also investigated and results suggest that during OND the SST over the equatorial Indian Ocean, the Bay of Bengal, the central Pacific Ocean (over Nino3 region), and the north and south Atlantic Ocean enhances northeast monsoon rainfall. This observed phenomenon is only predicted by the CCM3v6 model.

Prediction of early summer rainfall over South China by a physical-empirical model

NASA Astrophysics Data System (ADS)

Yim, So-Young; Wang, Bin; Xing, Wen

2014-10-01

In early summer (May-June, MJ) the strongest rainfall belt of the northern hemisphere occurs over the East Asian (EA) subtropical front. During this period the South China (SC) rainfall reaches its annual peak and represents the maximum rainfall variability over EA. Hence we establish an SC rainfall index, which is the MJ mean precipitation averaged over 72 stations over SC (south of 28°N and east of 110°E) and represents superbly the leading empirical orthogonal function mode of MJ precipitation variability over EA. In order to predict SC rainfall, we established a physical-empirical model. Analysis of 34-year observations (1979-2012) reveals three physically consequential predictors. A plentiful SC rainfall is preceded in the previous winter by (a) a dipole sea surface temperature (SST) tendency in the Indo-Pacific warm pool, (b) a tripolar SST tendency in North Atlantic Ocean, and (c) a warming tendency in northern Asia. These precursors foreshadow enhanced Philippine Sea subtropical High and Okhotsk High in early summer, which are controlling factors for enhanced subtropical frontal rainfall. The physical empirical model built on these predictors achieves a cross-validated forecast correlation skill of 0.75 for 1979-2012. Surprisingly, this skill is substantially higher than four-dynamical models' ensemble prediction for 1979-2010 period (0.15). The results here suggest that the low prediction skill of current dynamical models is largely due to models' deficiency and the dynamical prediction has large room to improve.

Performance of Sorghum Varieties under Variable Rainfall in Central Tanzania

PubMed Central

Tumbo, S. D.; Kihupi, N. I.; Rwehumbiza, Filbert B.

2017-01-01

Rainfall variability has a significant impact on crop production with manifestations in frequent crop failure in semiarid areas. This study used the parameterized APSIM crop model to investigate how rainfall variability may affect yields of improved sorghum varieties based on long-term historical rainfall and projected climate. Analyses of historical rainfall indicate a mix of nonsignificant and significant trends on the onset, cessation, and length of the growing season. The study confirmed that rainfall variability indeed affects yields of improved sorghum varieties. Further analyses of simulated sorghum yields based on seasonal rainfall distribution indicate the concurrence of lower grain yields with the 10-day dry spells during the cropping season. Simulation results for future sorghum response, however, show that impacts of rainfall variability on sorghum will be overridden by temperature increase. We conclude that, in the event where harms imposed by moisture stress in the study area are not abated, even improved sorghum varieties are likely to perform poorly. PMID:28536708

Performance of Sorghum Varieties under Variable Rainfall in Central Tanzania.

PubMed

Msongaleli, Barnabas M; Tumbo, S D; Kihupi, N I; Rwehumbiza, Filbert B

2017-01-01

Rainfall variability has a significant impact on crop production with manifestations in frequent crop failure in semiarid areas. This study used the parameterized APSIM crop model to investigate how rainfall variability may affect yields of improved sorghum varieties based on long-term historical rainfall and projected climate. Analyses of historical rainfall indicate a mix of nonsignificant and significant trends on the onset, cessation, and length of the growing season. The study confirmed that rainfall variability indeed affects yields of improved sorghum varieties. Further analyses of simulated sorghum yields based on seasonal rainfall distribution indicate the concurrence of lower grain yields with the 10-day dry spells during the cropping season. Simulation results for future sorghum response, however, show that impacts of rainfall variability on sorghum will be overridden by temperature increase. We conclude that, in the event where harms imposed by moisture stress in the study area are not abated, even improved sorghum varieties are likely to perform poorly.

Understanding litter decomposition in semiarid ecosystems: linking leaf traits, UV exposure and rainfall variability

PubMed Central

Gaxiola, Aurora; Armesto, Juan J.

2015-01-01

Differences in litter quality, microbial activity or abiotic conditions cannot fully account for the variability in decomposition rates observed in semiarid ecosystems. Here we tested the role of variation in litter quality, water supply, and UV radiation as drivers of litter decomposition in arid lands. And show that carry-over effects of litter photodegradation during dry periods can regulate decomposition during subsequent wet periods. We present data from a two-phase experiment, where we first exposed litter from a drought-deciduous and an evergreen shrub to natural UV levels during five, rainless summer months and, subsequently, in the laboratory, we assessed the carry-over effects of photodegradation on biomass loss under different irrigation treatments representing the observed range of local rainfall variation among years (15–240 mm). Photodegradation of litter in the field produced average carbon losses of 12%, but deciduous Proustia pungens lost >25%, while evergreen Porlieria chilensis less than 5%. Natural exposure to UV significantly reduced carbon-to-nitrogen and lignin:N ratios in Proustia litter but not in Porlieria. During the subsequent wet phase, remaining litter biomass was lower in Proustia than in Porlieria. Indeed UV exposure increased litter decomposition of Proustia under low and medium rainfall treatments, whereas no carry-over effects were detected under high rainfall treatment. Consequently, for deciduous Proustia carry-over effects of UV exposure were negligible under high irrigation. Litter decomposition of the evergreen Porlieria depended solely on levels of rainfall that promote microbial decomposers. Our two-phase experiment revealed that both the carry-over effects of photodegradation and litter quality, modulated by inter-annual variability in rainfall, can explain the marked differences in decomposition rates and the frequent decoupling between rainfall and litter decomposition observed in semiarid ecosystems. PMID:25852705

Understanding litter decomposition in semiarid ecosystems: linking leaf traits, UV exposure and rainfall variability.

PubMed

Gaxiola, Aurora; Armesto, Juan J

2015-01-01

Differences in litter quality, microbial activity or abiotic conditions cannot fully account for the variability in decomposition rates observed in semiarid ecosystems. Here we tested the role of variation in litter quality, water supply, and UV radiation as drivers of litter decomposition in arid lands. And show that carry-over effects of litter photodegradation during dry periods can regulate decomposition during subsequent wet periods. We present data from a two-phase experiment, where we first exposed litter from a drought-deciduous and an evergreen shrub to natural UV levels during five, rainless summer months and, subsequently, in the laboratory, we assessed the carry-over effects of photodegradation on biomass loss under different irrigation treatments representing the observed range of local rainfall variation among years (15-240 mm). Photodegradation of litter in the field produced average carbon losses of 12%, but deciduous Proustia pungens lost >25%, while evergreen Porlieria chilensis less than 5%. Natural exposure to UV significantly reduced carbon-to-nitrogen and lignin:N ratios in Proustia litter but not in Porlieria. During the subsequent wet phase, remaining litter biomass was lower in Proustia than in Porlieria. Indeed UV exposure increased litter decomposition of Proustia under low and medium rainfall treatments, whereas no carry-over effects were detected under high rainfall treatment. Consequently, for deciduous Proustia carry-over effects of UV exposure were negligible under high irrigation. Litter decomposition of the evergreen Porlieria depended solely on levels of rainfall that promote microbial decomposers. Our two-phase experiment revealed that both the carry-over effects of photodegradation and litter quality, modulated by inter-annual variability in rainfall, can explain the marked differences in decomposition rates and the frequent decoupling between rainfall and litter decomposition observed in semiarid ecosystems.

Inter-annual rainfall variability in the eastern Antilles and coupling with the regional and intra-seasonal circulation

NASA Astrophysics Data System (ADS)

Jury, Mark R.

2016-11-01

Climate variability in the eastern Antilles island chain is analyzed via principal component analysis of high-resolution monthly rainfall in the period 1981-2013. The second mode reflecting higher rainfall in July-October season between Martinique and Grenada is the focus of this study. Higher rainfall corresponds with a weakened trade wind and boundary current along the southern edge of the Caribbean. This quells the coastal upwelling off Venezuela and builds the freshwater plume east of Trinidad. There is corresponding upper easterly wind flow that intensifies passing tropical waves. During a storm event over the Antilles on 4-5 October 2010, there was inflow from east of Guyana where low salinity and high sea temperatures enable surplus latent heat fluxes. A N-S convective rain band forms ˜500 km east of the cyclonic vortex. Many features at the weather timescale reflect the seasonal correlation and composite difference maps and El Nino Southern Oscillation (ENSO) modulation of oceanic inter-basin transfers.

Combined effects of constant versus variable intensity simulated rainfall and reduced tillage management on cotton preemergence herbicide runoff.

PubMed

Potter, Thomas L; Truman, Clint C; Strickland, Timothy C; Bosch, David D; Webster, Theodore M; Franklin, Dorcas H; Bednarz, Craig W

2006-01-01

Pesticide runoff research relies heavily on rainfall simulation experiments. Most are conducted at a constant intensity, i.e., at a fixed rainfall rate; however, large differences in natural rainfall intensity is common. To assess implications we quantified runoff of two herbicides, fluometuron and pendimethalin, and applied preemergence after planting cotton on Tifton loamy sand. Rainfall at constant and variable intensity patterns representative of late spring thunderstorms in the Atlantic Coastal Plain region of Georgia (USA) were simulated on 6-m2 plots under strip- (ST) and conventional-tillage (CT) management. The variable pattern produced significantly higher runoff rates of both compounds from CT but not ST plots. However, on an event-basis, runoff totals (% applied) were not significantly different, with one exception: fluometuron runoff from CT plots. There was about 25% more fluometuron runoff with the variable versus the constant intensity pattern (P = 0.10). Study results suggest that conduct of simulations using variable intensity storm patterns may provide more representative rainfall simulation-based estimates of pesticide runoff and that the greatest impacts will be observed with CT. The study also found significantly more fluometuron in runoff from ST than CT plots. Further work is needed to determine whether this behavior may be generalized to other active ingredients with similar properties [low K(oc) (organic carbon partition coefficient) approximately 100 mL g(-1); high water solubility approximately 100 mg L(-1)]. If so, it should be considered when making tillage-specific herbicide recommendations to reduce runoff potential.

Disparity in rainfall trend and patterns among different regions: analysis of 158 years' time series of rainfall dataset across India

NASA Astrophysics Data System (ADS)

Saha, Saurav; Chakraborty, Debasish; Paul, Ranjit Kumar; Samanta, Sandipan; Singh, S. B.

2017-10-01

Rainfall anomaly during crop-growing season can have large impact on the agricultural output of a country, especially like India, where two-thirds of the crop land is rain-fed. In such situation, decreased agricultural production not only challenges food security of the country but directly and immediately hits the livelihood of its farming community. In a vast country like India, rainfall or its anomalies hardly follow a specific pattern, rather it is having high variability in spatial domain. This study focused on the trends of national and regional rainfall anomalies (wetness/dryness) along with their interrelationship using time series data of past 158 years. The significant reducing wetness trend (p < 0.05) over north mountainous India was prominent with an increasing trend over southern peninsular India (p < 0.10). However, long-term annual wetness was increasing over entire peninsular India. The results of change point tests indicate that major abrupt changes occurred between early to mid-twentieth century having regional variations. The regional interrelationship was studied using principal component, hierarchical clustering, and pair-wise difference test, which clearly indicated a significantly different pattern in rainfall anomalies for north east India (p = 0.022), north central India (p = 0.022), and north mountainous India (p = 0.011) from that of the all India. Result of this study affirmed high spatial variability in rainfall anomaly and most importantly established the unalike pattern in trends of regional rainfall vis-à-vis national level, ushering towards paradigm shift in rainfall forecast from country scale to regional scale for pragmatic planning.

Does the Madden-Julian Oscillation influence aerosol variability?

NASA Astrophysics Data System (ADS)

Tian, Baijun; Waliser, Duane E.; Kahn, Ralph A.; Li, Qinbin; Yung, Yuk L.; Tyranowski, Tomasz; Geogdzhayev, Igor V.; Mishchenko, Michael I.; Torres, Omar; Smirnov, Alexander

2008-06-01

We investigate the modulation of aerosols by the Madden-Julian Oscillation (MJO) using multiple, global satellite aerosol products: aerosol index (AI) from the Total Ozone Mapping Spectrometer (TOMS) on Nimbus-7, and aerosol optical thickness (AOT) from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua and the Advanced Very High Resolution Radiometer (AVHRR) on NOAA satellites. A composite MJO analysis indicates that large variations in the TOMS AI and MODIS/AVHRR AOT are found over the equatorial Indian and western Pacific Oceans where MJO convection is active, as well as the tropical Africa and Atlantic Ocean where MJO convection is weak but the background aerosol level is high. A strong inverse linear relationship between the TOMS AI and rainfall anomalies, but a weaker, less coherent positive correlation between the MODIS/AVHRR AOT and rainfall anomalies, were found. The MODIS/AVHRR pattern is consistent with ground-based Aerosol Robotic Network data. These results indicate that the MJO and its associated cloudiness, rainfall, and circulation variability systematically influence the variability in remote sensing aerosol retrieval results. Several physical and retrieval algorithmic factors that may contribute to the observed aerosol-rainfall relationships are discussed. Preliminary analysis indicates that cloud contamination in the aerosol retrievals is likely to be a major contributor to the observed relationships, although we cannot exclude possible contributions from other physical mechanisms. Future research is needed to fully understand these complex aerosol-rainfall relationships.

Indian Summer Monsoon Rainfall: Implications of Contrasting Trends in the Spatial Variability of Means and Extremes

PubMed Central

Ghosh, Subimal; Vittal, H.; Sharma, Tarul; Karmakar, Subhankar; Kasiviswanathan, K. S.; Dhanesh, Y.; Sudheer, K. P.; Gunthe, S. S.

2016-01-01

India’s agricultural output, economy, and societal well-being are strappingly dependent on the stability of summer monsoon rainfall, its variability and extremes. Spatial aggregate of intensity and frequency of extreme rainfall events over Central India are significantly increasing, while at local scale they are spatially non-uniform with increasing spatial variability. The reasons behind such increase in spatial variability of extremes are poorly understood and the trends in mean monsoon rainfall have been greatly overlooked. Here, by using multi-decadal gridded daily rainfall data over entire India, we show that the trend in spatial variability of mean monsoon rainfall is decreasing as exactly opposite to that of extremes. The spatial variability of extremes is attributed to the spatial variability of the convective rainfall component. Contrarily, the decrease in spatial variability of the mean rainfall over India poses a pertinent research question on the applicability of large scale inter-basin water transfer by river inter-linking to address the spatial variability of available water in India. We found a significant decrease in the monsoon rainfall over major water surplus river basins in India. Hydrological simulations using a Variable Infiltration Capacity (VIC) model also revealed that the water yield in surplus river basins is decreasing but it is increasing in deficit basins. These findings contradict the traditional notion of dry areas becoming drier and wet areas becoming wetter in response to climate change in India. This result also calls for a re-evaluation of planning for river inter-linking to supply water from surplus to deficit river basins. PMID:27463092

Indian Summer Monsoon Rainfall: Implications of Contrasting Trends in the Spatial Variability of Means and Extremes.

PubMed

Ghosh, Subimal; Vittal, H; Sharma, Tarul; Karmakar, Subhankar; Kasiviswanathan, K S; Dhanesh, Y; Sudheer, K P; Gunthe, S S

2016-01-01

India's agricultural output, economy, and societal well-being are strappingly dependent on the stability of summer monsoon rainfall, its variability and extremes. Spatial aggregate of intensity and frequency of extreme rainfall events over Central India are significantly increasing, while at local scale they are spatially non-uniform with increasing spatial variability. The reasons behind such increase in spatial variability of extremes are poorly understood and the trends in mean monsoon rainfall have been greatly overlooked. Here, by using multi-decadal gridded daily rainfall data over entire India, we show that the trend in spatial variability of mean monsoon rainfall is decreasing as exactly opposite to that of extremes. The spatial variability of extremes is attributed to the spatial variability of the convective rainfall component. Contrarily, the decrease in spatial variability of the mean rainfall over India poses a pertinent research question on the applicability of large scale inter-basin water transfer by river inter-linking to address the spatial variability of available water in India. We found a significant decrease in the monsoon rainfall over major water surplus river basins in India. Hydrological simulations using a Variable Infiltration Capacity (VIC) model also revealed that the water yield in surplus river basins is decreasing but it is increasing in deficit basins. These findings contradict the traditional notion of dry areas becoming drier and wet areas becoming wetter in response to climate change in India. This result also calls for a re-evaluation of planning for river inter-linking to supply water from surplus to deficit river basins.

Influences of Appalachian orography on heavy rainfall and rainfall variability associated with the passage of hurricane Isabel by ensemble simulations

NASA Astrophysics Data System (ADS)

Oldaker, Guy; Liu, Liping; Lin, Yuh-Lang

2017-12-01

This study focuses on the heavy rainfall event associated with hurricane Isabel's (2003) passage over the Appalachian mountains of the eastern United States. Specifically, an ensemble consisting of two groups of simulations using the Weather Research and Forecasting model (WRF), with and without topography, is performed to investigate the orographic influences on heavy rainfall and rainfall variability. In general, the simulated ensemble mean with full terrain is able to reproduce the key observed 24-h rainfall amount and distribution, while the flat-terrain mean lacks in this respect. In fact, 30-h rainfall amounts are reduced by 75% with the removal of topography. Rainfall variability is also significantly increased with the presence of orography. Further analysis shows that the complex interaction between the hurricane and terrain along with contributions from varied microphysics, cumulus parametrization, and planetary boundary layer schemes have a pronounced effect on rainfall and rainfall variability. This study follows closely with a previous study, but for a different TC case of Isabel (2003). It is an important sensitivity test for a different TC in a very different environment. This study reveals that the rainfall variability behaves similarly, even with different settings of the environment.

Skilful Seasonal Predictions of Summer European Rainfall

NASA Astrophysics Data System (ADS)

Dunstone, Nick; Smith, Doug; Scaife, Adam; Hermanson, Leon; Fereday, David; O'Reilly, Chris; Stirling, Alison; Eade, Rosie; Gordon, Margaret; MacLachlan, Craig; Woollings, Tim; Sheen, Katy; Belcher, Stephen

2018-04-01

Year-to-year variability in Northern European summer rainfall has profound societal and economic impacts; however, current seasonal forecast systems show no significant forecast skill. Here we show that skillful predictions are possible (r 0.5, p < 0.001) using the latest high-resolution Met Office near-term prediction system over 1960-2017. The model predictions capture both low-frequency changes (e.g., wet summers 2007-2012) and some of the large individual events (e.g., dry summer 1976). Skill is linked to predictable North Atlantic sea surface temperature variability changing the supply of water vapor into Northern Europe and so modulating convective rainfall. However, dynamical circulation variability is not well predicted in general—although some interannual skill is found. Due to the weak amplitude of the forced model signal (likely caused by missing or weak model responses), very large ensembles (>80 members) are required for skillful predictions. This work is promising for the development of European summer rainfall climate services.

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.

Evaluate Hydrologic Response on Spatiotemporal Characteristics of Rainfall Using High Resolution Radar Rainfall Data and WRF-Hydro Model

NASA Astrophysics Data System (ADS)

Gao, S.; Fang, N. Z.

2017-12-01

A previously developed Dynamic Moving Storm (DMS) generator is a multivariate rainfall model simulating the complex nature of precipitation field: spatial variability, temporal variability, and storm movement. Previous effort by the authors has investigated the sensitivity of DMS parameters on corresponding hydrologic responses by using synthetic storms. In this study, the DMS generator has been upgraded to generate more realistic precipitation field. The dependence of hydrologic responses on rainfall features was investigated by dissecting the precipitation field into rain cells and modifying their spatio-temporal specification individually. To retrieve DMS parameters from radar rainfall data, rain cell segmentation and tracking algorithms were respectively developed and applied on high resolution radar rainfall data (1) to spatially determine the rain cells within individual radar image and (2) to temporally analyze their dynamic behavior. Statistics of DMS parameters were established by processing a long record of rainfall data (10 years) to keep the modification on real storms within the limit of regional climatology. Empirical distributions of the DMS parameters were calculated to reveal any preferential pattern and seasonality. Subsequently, the WRF-Hydro model forced by the remodeled and modified precipitation was used for hydrologic simulation. The study area was the Upper Trinity River Basin (UTRB) watershed, Texas; and two kinds of high resolution radar data i.e. the Next-Generation Radar (NEXRAD) level III Digital Hybrid Reflectivity (DHR) product and Multi-Radar Multi-Sensor (MRMS) precipitation rate product, were utilized to establish parameter statistics and to recreate/remodel historical events respectively. The results demonstrated that rainfall duration is a significant linkage between DMS parameters and their hydrologic impacts—any combination of spatiotemporal characteristics that keep rain cells longer over the catchment will produce higher peak discharge.

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.

A comparative modeling analysis of multiscale temporal variability of rainfall in Australia

NASA Astrophysics Data System (ADS)

Samuel, Jos M.; Sivapalan, Murugesu

2008-07-01

The effects of long-term natural climate variability and human-induced climate change on rainfall variability have become the focus of much concern and recent research efforts. In this paper, we present the results of a comparative analysis of observed multiscale temporal variability of rainfall in the Perth, Newcastle, and Darwin regions of Australia. This empirical and stochastic modeling analysis explores multiscale rainfall variability, i.e., ranging from short to long term, including within-storm patterns, and intra-annual, interannual, and interdecadal variabilities, using data taken from each of these regions. The analyses investigated how storm durations, interstorm periods, and average storm rainfall intensities differ for different climate states and demonstrated significant differences in this regard between the three selected regions. In Perth, the average storm intensity is stronger during La Niña years than during El Niño years, whereas in Newcastle and Darwin storm duration is longer during La Niña years. Increase of either storm duration or average storm intensity is the cause of higher average annual rainfall during La Niña years as compared to El Niño years. On the other hand, within-storm variability does not differ significantly between different ENSO states in all three locations. In the case of long-term rainfall variability, the statistical analyses indicated that in Newcastle the long-term rainfall pattern reflects the variability of the Interdecadal Pacific Oscillation (IPO) index, whereas in Perth and Darwin the long-term variability exhibits a step change in average annual rainfall (up in Darwin and down in Perth) which occurred around 1970. The step changes in Perth and Darwin and the switch in IPO states in Newcastle manifested differently in the three study regions in terms of changes in the annual number of rainy days or the average daily rainfall intensity or both. On the basis of these empirical data analyses, a stochastic rainfall time series model was developed that incorporates the entire range of multiscale variabilities observed in each region, including within-storm, intra-annual, interannual, and interdecadal variability. Such ability to characterize, model, and synthetically generate realistic time series of rainfall intensities is essential for addressing many hydrological problems, including estimation of flood and drought frequencies, pesticide risk assessment, and landslide frequencies.

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 influence on dengue epidemics in Puerto Rico.

PubMed

Jury, Mark R

2008-10-01

The variability of the insect-borne disease dengue in Puerto Rico was studied in relation to climatic variables in the period 1979-2005. Annual and monthly reported dengue cases were compared with precipitation and temperature data. Results show that the incidence of dengue in Puerto Rico was relatively constant over time despite global warming, possibly due to the offsetting effects of declining rainfall, improving health care and little change in population. Seasonal fluctuations of dengue were driven by rainfall increases from May to November. Year-to-year variability in dengue cases was positively related to temperature, but only weakly associated with local rainfall and an index of El Nino Southern Oscillation (ENSO). Climatic conditions were mapped with respect to dengue cases and patterns in high and low years were compared. During epidemics, a low pressure system east of Florida draws warm humid air over the northwestern Caribbean. Long-term trends in past observed and future projected rainfall and temperatures were studied. Rainfall has declined slowly, but temperatures in the Caribbean are rising with the influence of global warming. Thus, dengue may increase in the future, and it will be necessary to anticipate dengue epidemics using climate forecasts, to reduce adverse health impacts.

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

AgMIP Regional Activities in a Global Framework: The Brazil Experience

NASA Technical Reports Server (NTRS)

Assad, Eduardo D.; Marin, Fabio R.; Valdivia, Roberto O.; Rosenzweig, Cynthia E.

2012-01-01

Climate variability and change are projected to increate the frequency of extreme high-temperature events, floods, and droughts, which can lead to subsequent changes in soil moister in many locations (Alexandrov and Hoogenboom, 2000). In Brazil, observations reveal a tendency for increasing frequency of extreme rainfall events particularly in south Brazil (Alexander et al., 2006; Carvalho et al., 2014; Groissman et al., 2005), as well as projections for increasing extremes in both maximum and minimum temperatures and high spatial variability for rainfall under the IPCC SRES A2 and B2 scenarios (Marengo et al., 2009).

Intra-Seasonal Rainfall Variations and Linkage with Kharif Crop Production: An Attempt to Evaluate Predictability of Sub-Seasonal Rainfall Events

NASA Astrophysics Data System (ADS)

Singh, Ankita; Ghosh, Kripan; Mohanty, U. C.

2018-03-01

The sub-seasonal variation of Indian summer monsoon rainfall highly impacts Kharif crop production in comparison with seasonal total rainfall. The rainfall frequency and intensity corresponding to various rainfall events are found to be highly related to crop production and therefore, the predictability of such events are considered to be diagnosed. Daily rainfall predictions are made available by one of the coupled dynamical model National Centers for Environmental Prediction Climate Forecast System (NCEPCFS). A large error in the simulation of daily rainfall sequence influences to take up a bias correction and for that reason, two approaches are used. The bias-corrected GCM is able to capture the inter-annual variability in rainfall events. Maximum prediction skill of frequency of less rainfall (LR) event is observed during the month of September and a similar result is also noticed for moderate rainfall event with maximum skill over the central parts of the country. On the other hand, the impact of rainfall weekly rainfall intensity is evaluated against the Kharif rice production. It is found that weekly rainfall intensity during July is having a significant impact on Kharif rice production, but the corresponding skill was found very low in GCM. The GCM are able to simulate the less and moderate rainfall frequency with significant skill.

Automated canopy estimator (ACE): Enhancing crop modelling and decision making in agriculture

USDA-ARS?s Scientific Manuscript database

The Caribbean agriculture sector is dominated by small holdings, which are overly reliant on rainfall and highly dependent on manual means of optimization. The sector is therefore very vulnerable to the vagaries of climate variability and change, with rainfall variations being of particular concern...

Could Malaria Control Programmes be Timed to Coincide with Onset of Rainfall?

PubMed

Komen, Kibii

2017-06-01

Malaria cases in South Africa's Northern Province of Limpopo have surpassed known endemic KwaZulu Natal and Mpumalanga Provinces. This paper applies statistical methods: regression analysis and impulse response function to understand the timing of impact and the length that such impacts last. Climate data (rainfall and temperature) are obtained from South African Weather Services (SAWs); global data from the European Centre for Medium-Range Weather Forecasts (ECMWF), while clinical malaria data came from Malaria Control Centre in Tzaneen (Limpopo Province). Data collected span from January 1998 to July 2007. Signs of the coefficients are positive for rainfall and temperature and negative for their exponents. Three out of five independent variables consistently maintain a very high statistical level of significance. The coefficients for climate variables describe an inverted u-shape: parameters for the exponents of rainfall (-0.02, -0.01, -0.02, -0.00) and temperature (-46.61, -47.46, -48.14, -36.04) are both negative. A one standard deviation rise in rainfall (rainfall onset) increases malaria cases, and the effects become sustained for at least 3 months and conclude that onset of rainfall therefore triggers a 'malaria season'. Malaria control programme and early warning system should be intensified in the first 3 months following the onset of rainfall.

Forecasting Andean rainfall and crop yield from the influence of El Nino on Pleiades visibility

PubMed

Orlove; Chiang; Cane

2000-01-06

Farmers in drought-prone regions of Andean South America have historically made observations of changes in the apparent brightness of stars in the Pleiades around the time of the southern winter solstice in order to forecast interannual variations in summer rainfall and in autumn harvests. They moderate the effect of reduced rainfall by adjusting the planting dates of potatoes, their most important crop. Here we use data on cloud cover and water vapour from satellite imagery, agronomic data from the Andean altiplano and an index of El Nino variability to analyse this forecasting method. We find that poor visibility of the Pleiades in June-caused by an increase in subvisual high cirrus clouds-is indicative of an El Nino year, which is usually linked to reduced rainfall during the growing season several months later. Our results suggest that this centuries-old method of seasonal rainfall forecasting may be based on a simple indicator of El Nino variability.

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.

Rainfall spatiotemporal variability relation to wetlands hydroperiods

NASA Astrophysics Data System (ADS)

Serrano-Hidalgo, Carmen; Guardiola-Albert, Carolina; Fernandez-Naranjo, Nuria

2017-04-01

Doñana natural space (Southwestern Spain) is one of the largest protected wetlands in Europe. The wide marshes present in this natural space have such ecological value that this wetland has been declared a Ramsar reserve in 1982. Apart from the extensive marsh, there are also small lagoons and seasonally flooded areas which are likewise essential to maintain a wide variety of valuable habitats. Hydroperiod, the length of time each point remains flooded along an annual cycle, is a critical ecological parameter that shapes aquatic plants and animals distribution and determines available habitat for many of the living organisms in the marshes. Recently, there have been published two different works estimating the hydroperiod of Doñana lagoons with Landsat Time Series images (Cifuentes et al., 2015; Díaz-Delgado et al., 2016). In both works the flooding cycle hydroperiod in Doñana marshes reveals a flooding regime mainly driven by rainfall, evapotranspiration, topography and local hydrological management actions. The correlation found between rainfall and hydroperiod is studied differently in both works. While in one the rainfall is taken from one raingauge (Cifuentes et al., 2015), the one performed by Díaz-Delgado (2016) uses annual rainfall maps interpolated with the inverse of the distance method. The rainfall spatiotemporal variability in this area can be highly significant; however the amount of this importance has not been quantified at the moment. In the present work the geostatistical tool known as spatiotemporal variogram is used to study the rainfall spatiotemporal variability. The spacetime package implemented in R (Pebesma, 2012) facilities its computation from a high rainfall data base of more than 100 raingauges from 1950 to 2016. With the aid of these variograms the rainfall spatiotemporal variability is quantified. The principal aim of the present work is the study of the relation between the rainfall spatiotemporal variability and the hydroperiods of wetlands present in Doñana natural space. Key issues: spatiotemporal variability, geostatistics, hydroperiod, wetlands. References: Cifuentes, V., García, M.A., Checa, M.J. & Escudero, R. (2015). Estimación por teledetección de la superficie de la lámina de agua y los niveles de profundidad de las lagunas en los humedales de la Campiña Andaluza Central incluidos en la demarcación hidrográfica del Guadalquivir. Teledetección: Humedales y Espacios Protegidos. Presented in XVI Congreso de la Asociación Española de Teledetección. pp. 322-325. Sevilla 21-23 octubre 2015. http://ocs.ebd.csic.es/index.php/AET/2015/schedConf/presentations Díaz-Delgado, R., Carro, F., Herruzo, F. Q., Osuna, A., & Baena, M. (2016). Contribución del seguimiento ecológico a largo plazo a la investigación y la gestión en la plataforma LTSER-Doñana. Revista Ecosistemas, 25(1), 9-18. Pebesma, E. (2012). spacetime: Spatio-temporal data in r. Journal of Statistical Software, 51(7), 1-30.

Rainfall variation and child health: effect of rainfall on diarrhea among under 5 children in Rwanda, 2010.

PubMed

Mukabutera, Assumpta; Thomson, Dana; Murray, Megan; Basinga, Paulin; Nyirazinyoye, Laetitia; Atwood, Sidney; Savage, Kevin P; Ngirimana, Aimable; Hedt-Gauthier, Bethany L

2016-08-05

Diarrhea among children under 5 years of age has long been a major public health concern. Previous studies have suggested an association between rainfall and diarrhea. Here, we examined the association between Rwandan rainfall patterns and childhood diarrhea and the impact of household sanitation variables on this relationship. We derived a series of rain-related variables in Rwanda based on daily rainfall measurements and hydrological models built from daily precipitation measurements collected between 2009 and 2011. Using these data and the 2010 Rwanda Demographic and Health Survey database, we measured the association between total monthly rainfall, monthly rainfall intensity, runoff water and anomalous rainfall and the occurrence of diarrhea in children under 5 years of age. Among the 8601 children under 5 years of age included in the survey, 13.2 % reported having diarrhea within the 2 weeks prior to the survey. We found that higher levels of runoff were protective against diarrhea compared to low levels among children who lived in households with unimproved toilet facilities (OR = 0.54, 95 % CI: [0.34, 0.87] for moderate runoff and OR = 0.50, 95 % CI: [0.29, 0.86] for high runoff) but had no impact among children in household with improved toilets. Our finding that children in households with unimproved toilets were less likely to report diarrhea during periods of high runoff highlights the vulnerabilities of those living without adequate sanitation to the negative health impacts of environmental events.

What rainfall events trigger landslides on the West Coast US?

NASA Astrophysics Data System (ADS)

Biasutti, Michela; Seager, Richard; Kirschbaum, Dalia

2016-04-01

A dataset of landslide occurrences compiled by collating google news reports covers 9 full years of data. We show that, while this compilation cannot provide consistent and widespread monitoring everywhere, it is adequate to capture the distribution of events in the major urban areas of the West Coast US and it can be used to provide a quantitative relationship between landslides and rainfall events. The case of the Seattle metropolitan area is presented as an example. The landslide dataset shows a clear seasonality in landslide occurrence, corresponding to the seasonality of rainfall, modified by the accumulation of soil moisture as winter progresses. Interannual variability of landslide occurrences is also linked to interannual variability of monthly rainfall. In most instances, landslides are clustered on consecutive days or at least within the same pentad and correspond to days of large rainfall accumulation at the regional scale. A joint analysis of the landslide data and of the high-resolution PRISM daily rainfall accumulation shows that on days when landslides occurred, the distribution of rainfall was shifted, with rainfall accumulation higher than 10mm/day being more common. Accumulations above 50mm/day much increase the probability of landslides, including the possibility of a major landslide event (one with multiple landslides in a day). The synoptic meteorological conditions associated with these major events show a mid-tropospheric ridge to the south of the target area steering a surface low and bringing enhanced precipitable water towards the Pacific North West. The interaction of the low-level flow with the local orography results in instances of a strong Puget Sound Convergence Zone, with widespread rainfall accumulation above 30mm/day and localized maxima as high as 100mm/day or more.

Nonstationary Intensity-Duration-Frequency Curves for Drainge Infrastructure Coping with Climate Change

NASA Astrophysics Data System (ADS)

Kim, Byung Sik; Jeung, Se Jin; Lee, Dong Seop; Han, Woo Suk

2015-04-01

As the abnormal rainfall condition has been more and more frequently happen and serious by climate change and variabilities, the question whether the design of drainage system could be prepared with abnormal rainfall condition or not has been on the rise. Usually, the drainage system has been designed by rainfall I-D-F (Intensity-Duration-Frequency) curve with assumption that I-D-F curve is stationary. The design approach of the drainage system has limitation not to consider the extreme rainfall condition of which I-D-F curve is non-stationary by climate change and variabilities. Therefore, the assumption that the I-D-F curve is stationary to design drainage system maybe not available in the climate change period, because climate change has changed the characteristics of extremes rainfall event to be non-stationary. In this paper, design rainfall by rainfall duration and non-stationary I-D-F curve are derived by the conditional GEV distribution considering non-stationary of rainfall characteristics. Furthermore, the effect of designed peak flow with increase of rainfall intensity was analyzed by distributed rainfall-runoff model, S-RAT(Spatial Runoff Assessment Tool). Although there are some difference by rainfall duration, the traditional I-D-F curves underestimates the extreme rainfall events for high-frequency rainfall condition. As a result, this paper suggest that traditional I-D-F curves could not be suitable for the design of drainage system under climate change condition. Keywords : Drainage system, Climate Change, non-stationary, I-D-F curves This research was supported by a grant 'Development of multi-function debris flow control technique considering extreme rainfall event' [NEMA-Natural-2014-74] from the Natural Hazard Mitigation Research Group, National Emergency Management Agency of KOREA

Adapting to the unpredictable: reproductive biology of vertebrates in the Australian wet-dry tropics.

PubMed

Shine, Richard; Brown, Gregory P

2008-01-27

In the wet-dry tropics of northern Australia, temperatures are high and stable year-round but monsoonal rainfall is highly seasonal and variable both annually and spatially. Many features of reproduction in vertebrates of this region may be adaptations to dealing with this unpredictable variation in precipitation, notably by (i) using direct proximate (rainfall-affected) cues to synchronize the timing and extent of breeding with rainfall events, (ii) placing the eggs or offspring in conditions where they will be buffered from rainfall extremes, and (iii) evolving developmental plasticity, such that the timing and trajectory of embryonic differentiation flexibly respond to local conditions. For example, organisms as diverse as snakes (Liasis fuscus, Acrochordus arafurae), crocodiles (Crocodylus porosus), birds (Anseranas semipalmata) and wallabies (Macropus agilis) show extreme annual variation in reproductive rates, linked to stochastic variation in wet season rainfall. The seasonal timing of initiation and cessation of breeding in snakes (Tropidonophis mairii) and rats (Rattus colletti) also varies among years, depending upon precipitation. An alternative adaptive route is to buffer the effects of rainfall variability on offspring by parental care (including viviparity) or by judicious selection of nest sites in oviparous taxa without parental care. A third type of adaptive response involves flexible embryonic responses (including embryonic diapause, facultative hatching and temperature-dependent sex determination) to incubation conditions, as seen in squamates, crocodilians and turtles. Such flexibility fine-tunes developmental rates and trajectories to conditions--especially, rainfall patterns--that are not predictable at the time of oviposition.

Spatiotemporal variability of rainfall extremes in monsoonal climates - examples from the South American Monsoon and the Indian Monsoon Systems (Invited)

NASA Astrophysics Data System (ADS)

Bookhagen, B.; Boers, N.; Marwan, N.; Malik, N.; Kurths, J.

2013-12-01

Monsoonal rainfall is the crucial component for more than half of the world's population. Runoff associated with monsoon systems provide water resources for agriculture, hydropower, drinking-water generation, recreation, and social well-being and are thus a fundamental part of human society. However, monsoon systems are highly stochastic and show large variability on various timescales. Here, we use various rainfall datasets to characterize spatiotemporal rainfall patterns using traditional as well as new approaches emphasizing nonlinear spatial correlations from a complex networks perspective. Our analyses focus on the South American (SAMS) and Indian (ISM) Monsoon Systems on the basis of Tropical Rainfall Measurement Mission (TRMM) using precipitation radar and passive-microwave products with horizontal spatial resolutions of ~5x5 km^2 (products 2A25, 2B31) and 25x25 km^2 (3B42) and interpolated rainfall-gauge data for the ISM (APHRODITE, 25x25 km^2). The eastern slopes of the Andes of South America and the southern front of the Himalaya are characterized by significant orographic barriers that intersect with the moisture-bearing, monsoonal wind systems. We demonstrate that topography exerts a first-order control on peak rainfall amounts on annual timescales in both mountain belts. Flooding in the downstream regions is dominantly caused by heavy rainfall storms that propagate deep into the mountain range and reach regions that are arid and without vegetation cover promoting rapid runoff. These storms exert a significantly different spatial distribution than average-rainfall conditions and assessing their recurrence intervals and prediction is key in understanding flooding for these regions. An analysis of extreme-value distributions of our high-spatial resolution data reveal that semi-arid areas are characterized by low-frequency/high-magnitude events (i.e., are characterized by a ';heavy tail' distribution), whereas regions with high mean annual rainfall have a less skewed distribution. In a second step, an analysis of the spatial characteristics of extreme rainfall synchronicity by means of complex networks reveals patterns of the propagation of extreme rainfall events. These patterns differ substantially from those obtained from the mean annual rainfall distribution. In addition, we have developed a scheme to predict rainfall extreme events in the eastern Central Andes based on event synchronization and spatial patterns of complex networks. The presented methods and result will allow to critically evaluate data and models in space and time.

Throughfall and its spatial variability beneath xerophytic shrub canopies within water-limited arid desert ecosystems

NASA Astrophysics Data System (ADS)

Zhang, Ya-feng; Wang, Xin-ping; Hu, Rui; Pan, Yan-xia

2016-08-01

Throughfall is known to be a critical component of the hydrological and biogeochemical cycles of forested ecosystems with inherently temporal and spatial variability. Yet little is understood concerning the throughfall variability of shrubs and the associated controlling factors in arid desert ecosystems. Here we systematically investigated the variability of throughfall of two morphological distinct xerophytic shrubs (Caragana korshinskii and Artemisia ordosica) within a re-vegetated arid desert ecosystem, and evaluated the effects of shrub structure and rainfall characteristics on throughfall based on heavily gauged throughfall measurements at the event scale. We found that morphological differences were not sufficient to generate significant difference (P < 0.05) in throughfall between two studied shrub species under the same rainfall and meteorological conditions in our study area, with a throughfall percentage of 69.7% for C. korshinskii and 64.3% for A. ordosica. We also observed a highly variable patchy pattern of throughfall beneath individual shrub canopies, but the spatial patterns appeared to be stable among rainfall events based on time stability analysis. Throughfall linearly increased with the increasing distance from the shrub base for both shrubs, and radial direction beneath shrub canopies had a pronounced impact on throughfall. Throughfall variability, expressed as the coefficient of variation (CV) of throughfall, tended to decline with the increase in rainfall amount, intensity and duration, and stabilized passing a certain threshold. Our findings highlight the great variability of throughfall beneath the canopies of xerophytic shrubs and the time stability of throughfall pattern among rainfall events. The spatially heterogeneous and temporally stable throughfall is expected to generate a dynamic patchy distribution of soil moisture beneath shrub canopies within arid desert ecosystems.

Robust signals of future projections of Indian summer monsoon rainfall by IPCC AR5 climate models: Role of seasonal cycle and interannual variability

NASA Astrophysics Data System (ADS)

Jayasankar, C. B.; Surendran, Sajani; Rajendran, Kavirajan

2015-05-01

Coupled Model Intercomparison Project phase 5 (Fifth Assessment Report of Intergovernmental Panel on Climate Change) coupled global climate model Representative Concentration Pathway 8.5 simulations are analyzed to derive robust signals of projected changes in Indian summer monsoon rainfall (ISMR) and its variability. Models project clear future temperature increase but diverse changes in ISMR with substantial intermodel spread. Objective measures of interannual variability (IAV) yields nearly equal chance for future increase or decrease. This leads to discrepancy in quantifying changes in ISMR and variability. However, based primarily on the physical association between mean changes in ISMR and its IAV, and objective methods such as k-means clustering with Dunn's validity index, mean seasonal cycle, and reliability ensemble averaging, projections fall into distinct groups. Physically consistent groups of models with the highest reliability project future reduction in the frequency of light rainfall but increase in high to extreme rainfall and thereby future increase in ISMR by 0.74 ± 0.36 mm d-1, along with increased future IAV. These robust estimates of future changes are important for useful impact assessments.

The Southern Oscillation and Prediction of `Der' Season Rainfall in Somalia.

NASA Astrophysics Data System (ADS)

Hutchinson, P.

1992-05-01

Somalia survives in semiarid to arid conditions, with annual rainfall totals rarely exceeding 700 mm, which are divided between two seasons. Many areas are arid, with negligible precipitation. Seasonal totals are highly variable. Thus, any seasonal rainfall forecast would be of significant importance to both the agricultural and animal husbandry communities. An investigation was carried out to determine whether there is a relationship between the Southern Oscillation and seasonal rainfall. No relationship exists between the Southern Oscillation and rainfall during the midyear `Gu' season, but it is shown that the year-end `Der' season precipitation is attected by the Southern Oscillation in southern and central areas of Somalia. Three techniques were used: correlation, regression, and simple contingency tables. Correlations between the SOI (Southern Oscillation index) and seasonal rainfall vary from zero up to about 0.8, with higher correlations in the south, both for individual stations and for area-averaged rainfall. Regression provides some predictive capacity, but the `explanation' of the variation in rainfall is not particularly high. The contingency tables revealed that there were very few occasions of both high SOI and high seasonal rainfall, although there was a wide scatter of seasonal rainfall associated with a low SOI.It is concluded that the SOI would be useful for planners, governments, and agencies as one tool in food/famine early warning but that the relationships are not strong enough for the average farmer to place much reliance on forecasts produced solely using the SOI.

Role of Oceanic and Terrestrial Atmospheric Moisture Sources in Intraseasonal Variability of Indian Summer Monsoon Rainfall.

PubMed

Pathak, Amey; Ghosh, Subimal; Kumar, Praveen; Murtugudde, Raghu

2017-10-06

Summer Monsoon Rainfall over the Indian subcontinent displays a prominent variability at intraseasonal timescales with 10-60 day periods of high and low rainfall, known as active and break periods, respectively. Here, we study moisture transport from the oceanic and terrestrial sources to the Indian landmass at intraseasonal timescales using a dynamic recycling model, based on a Lagrangian trajectory approach applied to the ECMWF-ERA-interim reanalysis data. Intraseasonal variation of monsoon rainfall is associated with both a north-south pattern from the Indian landmass to the Indian Ocean and an east-west pattern from the Core Monsoon Zone (CMZ) to eastern India. We find that the oceanic sources of moisture, namely western and central Indian Oceans (WIO and CIO) contribute to the former, while the major terrestrial source, Ganga basin (GB) contributes to the latter. The formation of the monsoon trough over Indo-Gangetic plain during the active periods results in a high moisture transport from the Bay of Bengal and GB into the CMZ in addition to the existing southwesterly jet from WIO and CIO. Our results indicate the need for the correct representation of both oceanic and terrestrial sources of moisture in models for simulating the intraseasonal variability of the monsoon.

Organization of vertical shear of wind and daily variability of monsoon rainfall

NASA Astrophysics Data System (ADS)

Gouda, K. C.; Goswami, P.

2016-10-01

Very little is known about the mechanisms that govern the day to day variability of the Indian summer monsoon (ISM) rainfall; in the current dominant view, the daily rainfall is essentially a result of chaotic dynamics. Most studies in the past have thus considered monsoon in terms of its seasonal (June-September) or monthly rainfall. We show here that the daily rainfall in June is associated with vertical shear of horizontal winds at specific scales. While vertical shear had been used in the past to investigate interannual variability of seasonal rainfall, rarely any effort has been made to examine daily rainfall. Our work shows that, at least during June, the daily rainfall variability of ISM rainfall is associated with a large scale dynamical coherence in the sense that the vertical shear averaged over large spatial extents are significantly correlated with area-averaged daily rainfall. An important finding from our work is the existence of a clearly delineated monsoon shear domain (MSD) with strong coherence between area-averaged shear and area-averaged daily rainfall in June; this association of daily rainfall is not significant with shear over only MSD. Another important feature is that the association between daily rainfall and vertical shear is present only during the month of June. Thus while ISM (June-September) is a single seasonal system, it is important to consider the dynamics and variation of June independently of the seasonal ISM rainfall. The association between large-scale organization of circulation and daily rainfall is suggested as a basis for attempting prediction of daily rainfall by ensuring accurate simulation of wind shear.

Assessing operative natural and anthropogenic forcing factors from long-term climate time series of Uttarakhand (India) in the backdrop of recurring extreme rainfall events over northwest Himalaya

NASA Astrophysics Data System (ADS)

Agnihotri, Rajesh; Dimri, A. P.; Joshi, H. M.; Verma, N. K.; Sharma, C.; Singh, J.; Sundriyal, Y. P.

2017-05-01

The entire Indo-Himalayan region from northwest (Kashmir) to northeast (Assam) is facing prevalence of floods and landslides in recent years causing massive loss of property, human and animal lives, infrastructure, and eventually threatening tourist activities substantially. Extremely intense rainfall event of 2013 C.E. (between 15 and 17 June) kicked off mammoth flash floods in the Kedarnath area of Uttarakhand state, resulting in huge socioeconomic losses to the state and country. Uttarakhand is an important hilly region attracting thousands of tourists every year owing to numerous shrines and forested mountainous tourist spots. Though recent studies indicate a plausible weakening of Indian summer monsoon rainfall overall, recurrent anomalous high rainfall events over northwest Himalaya (e.g. -2010, 2013, and 2016) point out the need for a thorough reassessment of long-term time series data of regional rainfall and ambient temperatures in order to trace signatures of a shifting pattern in regional meteorology, if any. Accordingly, here we investigate 100-year-long monthly rainfall and air temperature time series data for a selected grid (28.5°N, 31.25°N; 78.75°E, 81.25°E) covering most parts of Uttarakhand state. We also examined temporal variance in interrelationships among regional meteorological data (temperature and precipitation) and key global climate variability indices using advance statistical methods. Major findings are (i) significant increase in pre-monsoon air temperature over Uttarakhand after 1997, (ii) increasing upward trend in June-July rainfall and its relationship with regional May temperatures (iii) monsoonal rainfall (June, July, August, and September; JJAS) showing covariance with interannual variability in Eurasian snow cover (ESC) extent during the month of March, and (iv) enhancing tendency of anomalous high rainfall events during negative phases of Arctic Oscillation. Obtained results indicate that under warming scenario, JJ rainfall (over AS) may further increase with occasional extreme rainfall spells when AO index (March) is negative.

Patterns and perceptions of climate change in a biodiversity conservation hotspot.

PubMed

Hartter, Joel; Stampone, Mary D; Ryan, Sadie J; Kirner, Karen; Chapman, Colin A; Goldman, Abraham

2012-01-01

Quantifying local people's perceptions to climate change, and their assessments of which changes matter, is fundamental to addressing the dual challenge of land conservation and poverty alleviation in densely populated tropical regions To develop appropriate policies and responses, it will be important not only to anticipate the nature of expected changes, but also how they are perceived, interpreted and adapted to by local residents. The Albertine Rift region in East Africa is one of the world's most threatened biodiversity hotspots due to dense smallholder agriculture, high levels of land and resource pressures, and habitat loss and conversion. Results of three separate household surveys conducted in the vicinity of Kibale National Park during the late 2000s indicate that farmers are concerned with variable precipitation. Many survey respondents reported that conditions are drier and rainfall timing is becoming less predictable. Analysis of daily rainfall data for the climate normal period 1981 to 2010 indicates that total rainfall both within and across seasons has not changed significantly, although the timing and transitions of seasons has been highly variable. Results of rainfall data analysis also indicate significant changes in the intra-seasonal rainfall distribution, including longer dry periods within rainy seasons, which may contribute to the perceived decrease in rainfall and can compromise food security. Our results highlight the need for fine-scale climate information to assist agro-ecological communities in developing effective adaptive management.

Patterns and Perceptions of Climate Change in a Biodiversity Conservation Hotspot

PubMed Central

Hartter, Joel; Stampone, Mary D.; Ryan, Sadie J.; Kirner, Karen; Chapman, Colin A.; Goldman, Abraham

2012-01-01

Quantifying local people's perceptions to climate change, and their assessments of which changes matter, is fundamental to addressing the dual challenge of land conservation and poverty alleviation in densely populated tropical regions To develop appropriate policies and responses, it will be important not only to anticipate the nature of expected changes, but also how they are perceived, interpreted and adapted to by local residents. The Albertine Rift region in East Africa is one of the world's most threatened biodiversity hotspots due to dense smallholder agriculture, high levels of land and resource pressures, and habitat loss and conversion. Results of three separate household surveys conducted in the vicinity of Kibale National Park during the late 2000s indicate that farmers are concerned with variable precipitation. Many survey respondents reported that conditions are drier and rainfall timing is becoming less predictable. Analysis of daily rainfall data for the climate normal period 1981 to 2010 indicates that total rainfall both within and across seasons has not changed significantly, although the timing and transitions of seasons has been highly variable. Results of rainfall data analysis also indicate significant changes in the intra-seasonal rainfall distribution, including longer dry periods within rainy seasons, which may contribute to the perceived decrease in rainfall and can compromise food security. Our results highlight the need for fine-scale climate information to assist agro-ecological communities in developing effective adaptive management. PMID:22384244

Rainfall Climatology over Asir Region, Saudi Arabia

NASA Astrophysics Data System (ADS)

Sharif, H.; Furl, C.; Al-Zahrani, M.

2012-04-01

Arid and semi-arid lands occupy about one-third of the land surface of the earth and support about one-fifth of the world population. The Asir area in Saudi Arabia is an example of these areas faced with the problem of maintaining sustainable water resources. This problem is exacerbated by the high levels of population growth, land use changes, increasing water demand, and climate variability. In this study, the characteristics of decade-scale variations in precipitation are examined in more detail for Asir region. The spatio-temporal distributions of rainfall over the region are analyzed. The objectives are to identify the sensitivity, magnitude, and range of changes in annual and seasonal evapotranspiration resulting from observed decade-scale precipitation variations. An additional objective is to characterize orographic controls on the space-time variability of rainfall. The rainfall data is obtained from more than 30 rain gauges spread over the region.

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.

Mechanisms for Diurnal Variability of Global Tropical Rainfall Observed from TRMM

NASA Technical Reports Server (NTRS)

Yang, Song; Smith, Eric A.

2004-01-01

The behavior and various controls of diurnal variability in tropical-subtropical rainfall are investigated using Tropical Rainfall Measuring Mission (TRMM) precipitation measurements retrieved from: (1) TRMM Microwave Imager (TMI), (2) Precipitation Radar (PR), and (3) TMI/PR Combined, standard level 2 algorithms for the 1998 annual cycle. Results show that the diurnal variability characteristics of precipitation are consistent for all three algorithms, providing assurance that TRMM retrievals are providing consistent estimates of rainfall variability. As anticipated, most ocean areas exhibit more rainfall at night, while over most land areas rainfall peaks during daytime ,however, various important exceptions are found. The dominant feature of the oceanic diurnal cycle is a rainfall maximum in late-evening/early-morning (LE-EM) hours, while over land the dominant maximum occurs in the mid- to late-afternoon (MLA). In conjunction with these maxima are pronounced seasonal variations of the diurnal amplitudes. Amplitude analysis shows that the diurnal pattern and its seasonal evolution are closely related to the rainfall accumulation pattern and its seasonal evolution. In addition, the horizontal distribution of diurnal variability indicates that for oceanic rainfall there is a secondary MLA maximum, co-existing with the LE-EM maximum, at latitudes dominated by large scale convergence and deep convection. Analogously, there is a preponderance for an LE-EM maximum over land, co-existing with the stronger MLA maximum, although it is not evident that this secondary continental feature is closely associated with the large scale circulation. The ocean results clearly indicate that rainfall diurnal variability associated with large scale convection is an integral part of the atmospheric general circulation.

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.

Biotic context and soil properties modulate native plant responses to enhanced rainfall.

PubMed

Eskelinen, Anu; Harrison, Susan

2015-11-01

The environmental and biotic context within which plants grow have a great potential to modify responses to climatic changes, yet few studies have addressed both the direct effects of climate and the modulating roles played by variation in the biotic (e.g. competitors) and abiotic (e.g. soils) environment. In a grassland with highly heterogeneous soils and community composition, small seedlings of two native plants, Lasthenia californica and Calycadenia pauciflora, were transplanted into factorially watered and fertilized plots. Measurements were made to test how the effect of climatic variability (mimicked by the watering treatment) on the survival, growth and seed production of these species was modulated by above-ground competition and by edaphic variables. Increased competition outweighed the direct positive impacts of enhanced rainfall on most fitness measures for both species, resulting in no net effect of enhanced rainfall. Both species benefitted from enhanced rainfall when the absence of competitors was accompanied by high soil water retention capacity. Fertilization did not amplify the watering effects; rather, plants benefitted from enhanced rainfall or competitor removal only in ambient nutrient conditions with high soil water retention capacity. The findings show that the direct effects of climatic variability on plant fitness may be reversed or neutralized by competition and, in addition, may be strongly modulated by soil variation. Specifically, coarse soil texture was identified as a factor that may limit plant responsiveness to altered water availability. These results highlight the importance of considering the abiotic as well as biotic context when making future climate change forecasts. © The Author 2015. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Effects of ambient air temperature, humidity and rainfall on annual survival of adult little penguins Eudyptula minor in southeastern Australia

NASA Astrophysics Data System (ADS)

Ganendran, L. B.; Sidhu, L. A.; Catchpole, E. A.; Chambers, L. E.; Dann, P.

2016-08-01

Seabirds are subject to the influences of local climate variables during periods of land-based activities such as breeding and, for some species, moult; particularly if they undergo a catastrophic moult (complete simultaneous moult) as do penguins. We investigated potential relationships between adult penguin survival and land-based climate variables (ambient air temperature, humidity and rainfall) using 46 years of mark-recapture data of little penguins Eudyptula minor gathered at a breeding colony on Phillip Island in southeastern Australia. Our results showed that adult penguin survival had a stronger association with land-based climate variables during the moult period, when birds were unable to go to sea for up to 3 weeks, than during the breeding period, when birds could sacrifice breeding success in favour of survival. Annual adult survival probability was positively associated with humidity during moult and negatively associated with rainfall during moult. Prolonged heat during breeding and moult had a negative association with annual adult survival. Local climate projections suggest increasing days of high temperatures, fewer days of rainfall which will result in more droughts (and by implication, lower humidity) and more extreme rainfall events. All of these predicted climate changes are expected to have a negative impact on adult penguin survival.

Effects of ambient air temperature, humidity and rainfall on annual survival of adult little penguins Eudyptula minor in southeastern Australia.

PubMed

Ganendran, L B; Sidhu, L A; Catchpole, E A; Chambers, L E; Dann, P

2016-08-01

Seabirds are subject to the influences of local climate variables during periods of land-based activities such as breeding and, for some species, moult; particularly if they undergo a catastrophic moult (complete simultaneous moult) as do penguins. We investigated potential relationships between adult penguin survival and land-based climate variables (ambient air temperature, humidity and rainfall) using 46 years of mark-recapture data of little penguins Eudyptula minor gathered at a breeding colony on Phillip Island in southeastern Australia. Our results showed that adult penguin survival had a stronger association with land-based climate variables during the moult period, when birds were unable to go to sea for up to 3 weeks, than during the breeding period, when birds could sacrifice breeding success in favour of survival. Annual adult survival probability was positively associated with humidity during moult and negatively associated with rainfall during moult. Prolonged heat during breeding and moult had a negative association with annual adult survival. Local climate projections suggest increasing days of high temperatures, fewer days of rainfall which will result in more droughts (and by implication, lower humidity) and more extreme rainfall events. All of these predicted climate changes are expected to have a negative impact on adult penguin survival.

Climate change impacts on rainfall and temperature in sugarcane growing Upper Gangetic Plains of India

NASA Astrophysics Data System (ADS)

Verma, Ram Ratan; Srivastava, Tapendra Kumar; Singh, Pushpa

2018-01-01

Assessment of variability in climate extremes is crucial for managing their aftermath on crops. Sugarcane (Saccharum officinarum L.), a major C4 crop, dominates the Upper Gangetic Plain (UGP) in India and is vulnerable to both direct and indirect effects of changes in temperature and rainfall. The present study was taken up to assess the weekly, monthly, seasonal, and annual trends of rainfall and temperature variability during the period 1956-2015 (60 years) for envisaging the probabilities of different levels of rainfall suitable for sugarcane in UGP in the present climate scenario. The analysis revealed that 87% of total annual rainfall was received during southwest monsoon months (June-September) while post-monsoon (October to February) and pre-monsoon months (March-May) accounted for only 9.4 and 3.6%, respectively. There was a decline in both monthly and annual normal rainfall during the period 1986-2015 as compared to 1956-1985, and an annual rainfall deficiency of 205.3 mm was recorded. Maximum monthly normal rainfall deficiencies of 52.8, 84.2, and 54.0 mm were recorded during the months of July, August, and September, respectively, while a minimum rainfall deficiency of 2.2 mm was observed in November. There was a decline by 196.3 mm in seasonal normal rainfall during June-September (kharif). The initial probability of a week going dry was higher (> 70%) from the 1st to the 25th week; however, standard meteorological weeks (SMW) 26 to 37 had more than 50% probability of going wet. The normal annual maximum temperature (Tmax) decreased by 0.4 °C while normal annual minimum temperatures (Tmin) increased by 0.21 °C. Analysis showed that there was an increase in frequency of drought from 1986 onwards in the zone and a monsoon rainfall deficit by about 21.25% during June-September which coincided with tillering and grand growth stage of sugarcane. The imposed drought during the growth and elongation phase is emerging as a major constraint in realizing high cane productivity in the zone. Strategies for mitigating the negative impacts of rainfall and temperature variability on sugarcane productivity through improvement in existing adaptation strategies are proposed.

Effects of variable regolith depth, hydraulic properties, and rainfall on debris-flow initiation during the September 2013 northern Colorado Front Range rainstorm

NASA Astrophysics Data System (ADS)

Baum, R. L.; Coe, J. A.; Kean, J. W.; Jones, E. S.; Godt, J.

2015-12-01

Heavy rainfall during 9 - 13 September 2013 induced about 1100 debris flows in the foothills and mountains of the northern Colorado Front Range. Weathered bedrock was partially exposed in the basal surfaces of many of the shallow source areas at depths ranging from 0.2 to 5 m. Typical values of saturated hydraulic conductivity of soils and regolith units mapped in the source areas range from about 10-4 - 10-6 m/s, with a median value of 2.8 x 10-5 m/s based on number of source areas in each map unit. Rainfall intensities varied spatially and temporally, from 0 to 2.5 x 10-5 m/s (90 mm/hour), with two periods of relatively heavy rainfall on September 12 - 13. The distribution of debris flows appears to correlate with total storm rainfall, and reported times of greatest landslide activity coincide with times of heaviest rainfall. Process-based models of rainfall infiltration and slope stability (TRIGRS) representing the observed ranges of regolith depth, hydraulic conductivity, and rainfall intensity, provide additional insights about the timing and distribution of debris flows from this storm. For example, small debris flows from shallower source areas (<2 m) occurred late on September 11 and in the early morning of September 12, whereas large debris flows from deeper (3 - 5 m) source areas in the western part of the affected area occurred late on September 12. Timing of these flows can be understood in terms of the time required for pore pressure rise depending on regolith depth and rainfall intensity. The variable hydraulic properties combined with variable regolith depth and slope angles account for much of the observed range in timing in areas of similar rainfall intensity and duration. Modeling indicates that the greatest and most rapid pore pressure rise likely occurred in areas of highest rainfall intensity and amount. This is consistent with the largest numbers of debris flows occurring on steep canyon walls in areas of high total storm rainfall.

Combined effect of MJO, ENSO and IOD on the intraseasonal variability of northeast monsoon rainfall over south peninsular India

NASA Astrophysics Data System (ADS)

Sreekala, P. P.; Rao, S. Vijaya Bhaskara; Rajeevan, K.; Arunachalam, M. S.

2018-02-01

The present study has examined the combined effect of MJO, ENSO and IOD on the intraseasonal and interannual variability of northeast monsoon rainfall over south peninsular India. The study has revealed that the intraseasonal variation of daily rainfall over south peninsular India during NEM season is associated with various phases of eastward propagating MJO life cycle. Positive rainfall anomaly over south peninsular India and surrounding Indian Ocean (IO) is observed during the strong MJO phases 2, 3 and 4; and negative rainfall anomaly during the strong MJO phases 5,6,7,8 and 1. Above normal (below normal) convection over south peninsular India and suppressed convection over east Indian and West Pacific Ocean, high pressure (low pressure) anomaly over West Pacific Ocean, Positive (negative) SST anomalies over equatorial East and Central Pacific Ocean and easterly wind anomaly (westerly anomaly) over equatorial Indian Ocean are the observed features during the first three MJO (5, 6, 7) phases and all these features are observed in the excess (drought) NEMR composite. This suggests that a similar mode of physical mechanism is responsible for the intraseasonal and interannual variability of northeast monsoon rainfall. The number of days during the first three phases (last four phases) of MJO, where the enhanced convection and positive rainfall anomaly is over Indian Ocean (East Indian ocean and West Pacific Ocean), is more (less) during El Nino and IOD years and less during La Nina and NIOD years and vice versa. The observed excess (deficit) rainfall anomaly over west IO and south peninsular India and deficit (excess) rainfall anomaly over east IO including Bay of Bengal and West Pacific Ocean suggest that the more (less) number of first three phases during El Nino and IOD (La Nina and Negative IOD) is due to the interaction between eastward moving MJO and strong easterlies over equatorial IO present during El Nino and IOD years. This interaction would inhibit the development of long duration MJO and would result in short duration high frequency MJO type which confined over Indian Ocean and south peninsular India and hence make all the El Nino and IOD years to be excess rainfall years for NEM season.

Impact of La Niña and La Niña Modoki on Indonesia rainfall variability

NASA Astrophysics Data System (ADS)

Hidayat, R.; Juniarti, MD; Ma’rufah, U.

2018-05-01

La Niña events are indicated by cooling SST in central and eastern equatorial Pacific. While La Niña Modoki occurrences are indicated by cooling SST in central Pacific and warming SST in western and eastern equatorial Pacific. These two events are influencing rainfall variability in several regions including Indonesia. The objective of this study is to analyse the impact of La Niña and La Niña Modoki on Indonesian rainfall variability. We found the Nino 3.4 index is highly correlated (r = -0.95) with Indonesian rainfall. Positive rainfall anomalies up to 200 mm/month occurred mostly in Indonesian region during La Niña events, but in DJF several areas of Sumatera, Kalimantan and eastern Indonesia tend to have negative rainfall. During La Niña Modoki events, positive rainfall anomaly (up to 50 mm/month) occurred in Sumatera Island, Kalimantan, Java and eastern Indonesia in DJF and up to 175 mm/month occurred only in Java Island in MAM season. La Niña events have strong cooling SST in central and eastern equatorial Pacific (-1.5°C) in DJF. While La Niña Modoki events warming SST occurred in western and eastern equatorial Pacific (0.75°C) and cooling SST in central Pacific (- 0.75°C) in DJF and MAM. Walker circulation in La Niña Modoki events (on DJF and MAM) showed strong convergence in eastern Pacific, and weak convergence in western Pacific (Indonesia).

Understanding extreme rainfall events in Australia through historical data

NASA Astrophysics Data System (ADS)

Ashcroft, Linden; Karoly, David John

2016-04-01

Historical climate data recovery is still an emerging field in the Australian region. The majority of Australia's instrumental climate analyses begin in 1900 for rainfall and 1910 for temperature, particularly those focussed on extreme event analysis. This data sparsity for the past in turn limits our understanding of long-term climate variability, constraining efforts to predict the impact of future climate change. To address this need for improved historical data in Australia, a new network of recovered climate observations has recently been developed, centred on the highly populated southeastern Australian region (Ashcroft et al., 2014a, 2014b). The dataset includes observations from more than 39 published and unpublished sources and extends from British settlement in 1788 to the formation of the Australian Bureau of Meteorology in 1908. Many of these historical sources provide daily temperature and rainfall information, providing an opportunity to improve understanding of the multidecadal variability of Australia's extreme events. In this study we combine the historical data for three major Australian cities - Melbourne, Sydney and Adelaide - with modern observations to examine extreme rainfall variability over the past 174 years (1839-2013). We first explore two case studies, combining instrumental and documentary evidence to support the occurrence of severe storms in Sydney in 1841 and 1844. These events appear to be at least as extreme as Sydney's modern 24-hour rainfall record. Next we use a suite of rainfall indices to assess the long-term variability of rainfall in southeastern Australia. In particular, we focus on the stationarity of the teleconnection between the El Niño-Southern Oscillation (ENSO) phenomenon and extreme rainfall events. Using ENSO reconstructions derived from both palaeoclimatic and documentary sources, we determine the historical relationship between extreme rainfall in southeastern Australia and ENSO, and examine whether or not this relationship has remained stable since the early to mid-19th century. Ashcroft, L., Gergis, J., Karoly, D.J., 2014a. A historical climate dataset for southeastern Australia, 1788-1859. Geosci. Data J. 1, 158-178. doi:10.1002/gdj3.19 Ashcroft, L., Karoly, D.J., Gergis, J., 2014b. Southeastern Australian climate variability 1860-2009: A multivariate analysis. Int. J. Climatol. 34, 1928-1944. doi:10.1002/joc.3812

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.

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.

Impacts of rainfall variability and expected rainfall changes on cost-effective adaptation of water systems to climate change.

PubMed

van der Pol, T D; van Ierland, E C; Gabbert, S; Weikard, H-P; Hendrix, E M T

2015-05-01

Stormwater drainage and other water systems are vulnerable to changes in rainfall and runoff and need to be adapted to climate change. This paper studies impacts of rainfall variability and changing return periods of rainfall extremes on cost-effective adaptation of water systems to climate change given a predefined system performance target, for example a flood risk standard. Rainfall variability causes system performance estimates to be volatile. These estimates may be used to recurrently evaluate system performance. This paper presents a model for this setting, and develops a solution method to identify cost-effective investments in stormwater drainage adaptations. Runoff and water levels are simulated with rainfall from stationary rainfall distributions, and time series of annual rainfall maxima are simulated for a climate scenario. Cost-effective investment strategies are determined by dynamic programming. The method is applied to study the choice of volume for a storage basin in a Dutch polder. We find that 'white noise', i.e. trend-free variability of rainfall, might cause earlier re-investment than expected under projected changes in rainfall. The risk of early re-investment may be reduced by increasing initial investment. This can be cost-effective if the investment involves fixed costs. Increasing initial investments, therefore, not only increases water system robustness to structural changes in rainfall, but could also offer insurance against additional costs that would occur if system performance is underestimated and re-investment becomes inevitable. Copyright © 2015 Elsevier Ltd. All rights reserved.

Application of satellite precipitation data to analyse and model arbovirus activity in the tropics

PubMed Central

2011-01-01

Background Murray Valley encephalitis virus (MVEV) is a mosquito-borne Flavivirus (Flaviviridae: Flavivirus) which is closely related to Japanese encephalitis virus, West Nile virus and St. Louis encephalitis virus. MVEV is enzootic in northern Australia and Papua New Guinea and epizootic in other parts of Australia. Activity of MVEV in Western Australia (WA) is monitored by detection of seroconversions in flocks of sentinel chickens at selected sample sites throughout WA. Rainfall is a major environmental factor influencing MVEV activity. Utilising data on rainfall and seroconversions, statistical relationships between MVEV occurrence and rainfall can be determined. These relationships can be used to predict MVEV activity which, in turn, provides the general public with important information about disease transmission risk. Since ground measurements of rainfall are sparse and irregularly distributed, especially in north WA where rainfall is spatially and temporally highly variable, alternative data sources such as remote sensing (RS) data represent an attractive alternative to ground measurements. However, a number of competing alternatives are available and careful evaluation is essential to determine the most appropriate product for a given problem. Results The Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42 product was chosen from a range of RS rainfall products to develop rainfall-based predictor variables and build logistic regression models for the prediction of MVEV activity in the Kimberley and Pilbara regions of WA. Two models employing monthly time-lagged rainfall variables showed the strongest discriminatory ability of 0.74 and 0.80 as measured by the Receiver Operating Characteristics area under the curve (ROC AUC). Conclusions TMPA data provide a state-of-the-art data source for the development of rainfall-based predictive models for Flavivirus activity in tropical WA. Compared to ground measurements these data have the advantage of being collected spatially regularly, irrespective of remoteness. We found that increases in monthly rainfall and monthly number of days above average rainfall increased the risk of MVEV activity in the Pilbara at a time-lag of two months. Increases in monthly rainfall and monthly number of days above average rainfall increased the risk of MVEV activity in the Kimberley at a lag of three months. PMID:21255449

Rainfall prediction with backpropagation method

NASA Astrophysics Data System (ADS)

Wahyuni, E. G.; Fauzan, L. M. F.; Abriyani, F.; Muchlis, N. F.; Ulfa, M.

2018-03-01

Rainfall is an important factor in many fields, such as aviation and agriculture. Although it has been assisted by technology but the accuracy can not reach 100% and there is still the possibility of error. Though current rainfall prediction information is needed in various fields, such as agriculture and aviation fields. In the field of agriculture, to obtain abundant and quality yields, farmers are very dependent on weather conditions, especially rainfall. Rainfall is one of the factors that affect the safety of aircraft. To overcome the problems above, then it’s required a system that can accurately predict rainfall. In predicting rainfall, artificial neural network modeling is applied in this research. The method used in modeling this artificial neural network is backpropagation method. Backpropagation methods can result in better performance in repetitive exercises. This means that the weight of the ANN interconnection can approach the weight it should be. Another advantage of this method is the ability in the learning process adaptively and multilayer owned on this method there is a process of weight changes so as to minimize error (fault tolerance). Therefore, this method can guarantee good system resilience and consistently work well. The network is designed using 4 input variables, namely air temperature, air humidity, wind speed, and sunshine duration and 3 output variables ie low rainfall, medium rainfall, and high rainfall. Based on the research that has been done, the network can be used properly, as evidenced by the results of the prediction of the system precipitation is the same as the results of manual calculations.

A further assessment of vegetation feedback on decadal Sahel rainfall variability

NASA Astrophysics Data System (ADS)

Kucharski, Fred; Zeng, Ning; Kalnay, Eugenia

2013-03-01

The effect of vegetation feedback on decadal-scale Sahel rainfall variability is analyzed using an ensemble of climate model simulations in which the atmospheric general circulation model ICTPAGCM ("SPEEDY") is coupled to the dynamic vegetation model VEGAS to represent feedbacks from surface albedo change and evapotranspiration, forced externally by observed sea surface temperature (SST) changes. In the control experiment, where the full vegetation feedback is included, the ensemble is consistent with the observed decadal rainfall variability, with a forced component 60 % of the observed variability. In a sensitivity experiment where climatological vegetation cover and albedo are prescribed from the control experiment, the ensemble of simulations is not consistent with the observations because of strongly reduced amplitude of decadal rainfall variability, and the forced component drops to 35 % of the observed variability. The decadal rainfall variability is driven by SST forcing, but significantly enhanced by land-surface feedbacks. Both, local evaporation and moisture flux convergence changes are important for the total rainfall response. Also the internal decadal variability across the ensemble members (not SST-forced) is much stronger in the control experiment compared with the one where vegetation cover and albedo are prescribed. It is further shown that this positive vegetation feedback is physically related to the albedo feedback, supporting the Charney hypothesis.

Atmospheric Teleconnection and Climate Variability: Affecting Rice Productivity of Bihar, India

NASA Astrophysics Data System (ADS)

Saini, A.

2017-12-01

Climate variability brought various negative results to the environment around us and area under rice crop in Bihar has also faced a lot of negative impacts due to variability in temperature and rainfall. Location of Bihar in Northern Plain of India automatically makes it prime location for agriculture and therefore variability in climatic variables brings highly sensitive results to the agricultural production (especially rice). In this study, rainfall and temperature variables are taken into consideration to investigate the impact on rice cultivated area. Change in climate variable with the passage of time is prevailing since the start of geological time scale, how the variability in climate variables has affected the major crops. Climate index of Pacific Ocean and Indian Ocean influences the seasonal weather in Bihar and therefore role of ENSO and IOD is an interesting point of inquiry. Does there exists direct relation between climate variability and area under agricultural crops? How many important variables directly signals towards the change in area under agriculture production? These entire questions are answered with respect to change in area under rice cultivation of Bihar State of India. Temperature, rainfall and ENSO are a good indicator with respect to rice cultivation in Indian subcontinent. Impact on the area under rice has been signaled through ONI, Niño3 and DMI. Increasing range of temperature in the rice productivity declining years is observed since 1990.

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.

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.

Spatial structure of monthly rainfall measurements average over 25 years and trends of the hourly variability of a current rainy day in Rwanda.

NASA Astrophysics Data System (ADS)

Nduwayezu, Emmanuel; Kanevski, Mikhail; Jaboyedoff, Michel

2013-04-01

Climate plays a vital role in a wide range of socio-economic activities of most nations particularly of developing countries. Climate (rainfall) plays a central role in agriculture which is the main stay of the Rwandan economy and community livelihood and activities. The majority of the Rwandan population (81,1% in 2010) relies on rain fed agriculture for their livelihoods, and the impacts of variability in climate patterns are already being felt. Climate-related events like heavy rainfall or too little rainfall are becoming more frequent and are impacting on human wellbeing.The torrential rainfall that occurs every year in Rwanda could disturb the circulation for many days, damages houses, infrastructures and causes heavy economic losses and deaths. Four rainfall seasons have been identified, corresponding to the four thermal Earth ones in the south hemisphere: the normal season (summer), the rainy season (autumn), the dry season (winter) and the normo-rainy season (spring). Globally, the spatial rainfall decreasing from West to East, especially in October (spring) and February (summer) suggests an «Atlantic monsoon influence» while the homogeneous spatial rainfall distribution suggests an «Inter-tropical front» mechanism. What is the hourly variability in this mountainous area? Is there any correlation with the identified zones of the monthly average series (from 1965 to 1990 established by the Rwandan meteorological services)? Where could we have hazards with several consecutive rainy days (using forecasted datas from the Norwegian Meteorological Institute)? Spatio-temporal analysis allows for identifying and explaining large-scale anomalies which are useful for understanding hydrological characteristics and subsequently predicting these hydrological events. The objective of our current research (Rainfall variability) is to proceed to an evaluation of the potential rainfall risk by applying advanced geospatial modelling tools in Rwanda: geostatistical predictions and simulations, machine learning algorithm (different types of neural networks) and GIS. Hybrid models - mixing geostatistics and machine learning, will be applied to study spatial non-stationarity of rainfall fields. The research will include rainfalls variability mapping and probabilistic analyses of extreme events. Key words: rainfall variability, Rwanda, extreme event, model, mapping, geostatistics.

Forecasting Global Point Rainfall using ECMWF's Ensemble Forecasting System

NASA Astrophysics Data System (ADS)

Pillosu, Fatima; Hewson, Timothy; Zsoter, Ervin; Baugh, Calum

2017-04-01

ECMWF (the European Centre for Medium range Weather Forecasts), in collaboration with the EFAS (European Flood Awareness System) and GLOFAS (GLObal Flood Awareness System) teams, has developed a new operational system that post-processes grid box rainfall forecasts from its ensemble forecasting system to provide global probabilistic point-rainfall predictions. The project attains a higher forecasting skill by applying an understanding of how different rainfall generation mechanisms lead to different degrees of sub-grid variability in rainfall totals. In turn this approach facilitates identification of cases in which very localized extreme totals are much more likely. This approach aims also to improve the rainfall input required in different hydro-meteorological applications. Flash flood forecasting, in particular in urban areas, is a good example. In flash flood scenarios precipitation is typically characterised by high spatial variability and response times are short. In this case, to move beyond radar based now casting, the classical approach has been to use very high resolution hydro-meteorological models. Of course these models are valuable but they can represent only very limited areas, may not be spatially accurate and may give reasonable results only for limited lead times. On the other hand, our method aims to use a very cost-effective approach to downscale global rainfall forecasts to a point scale. It needs only rainfall totals from standard global reporting stations and forecasts over a relatively short period to train it, and it can give good results even up to day 5. For these reasons we believe that this approach better satisfies user needs around the world. This presentation aims to describe two phases of the project: The first phase, already completed, is the implementation of this new system to provide 6 and 12 hourly point-rainfall accumulation probabilities. To do this we use a limited number of physically relevant global model parameters (i.e. convective precipitation ratio, speed of steering winds, CAPE - Convective Available Potential Energy - and solar radiation), alongside the rainfall forecasts themselves, to define the "weather types" that in turn define the expected sub-grid variability. The calibration and computational strategy intrinsic to the system will be illustrated. The quality of the global point rainfall forecasts is also illustrated by analysing recent case studies in which extreme totals and a greatly elevated flash flood risk could be foreseen some days in advance but especially by a longer-term verification that arises out of retrospective global point rainfall forecasting for 2016. The second phase, currently in development, is focussing on the relationships with other relevant geographical aspects, for instance, orography and coastlines. Preliminary results will be presented. These are promising but need further study to fully understand their impact on the spatial distribution of point rainfall totals.

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.

Assessing future changes in the occurrence of rainfall-induced landslides at a regional scale.

PubMed

Gariano, S L; Rianna, G; Petrucci, O; Guzzetti, F

2017-10-15

According to the fifth report of the Intergovernmental Panel on Climate Change, an increase in the frequency and the intensity of extreme rainfall is expected in the Mediterranean area. Among different impacts, this increase might result in a variation in the frequency and the spatial distribution of rainfall-induced landslides, and in an increase in the size of the population exposed to landslide risk. We propose a method for the regional-scale evaluation of future variations in the occurrence of rainfall-induced landslides, in response to changes in rainfall regimes. We exploit information on the occurrence of 603 rainfall-induced landslides in Calabria, southern Italy, in the period 1981-2010, and daily rainfall data recorded in the same period in the region. Furthermore, we use high-resolution climate projections based on RCP4.5 and RCP8.5 scenarios. In particular, we consider the mean variations between a 30-year future period (2036-2065) and the reference period 1981-2010 in three variables assumed as proxy for landslide activity: annual rainfall, seasonal cumulated rainfall, and annual maxima of daily rainfall. Based on reliable correlations between landslide occurrence and weather variables estimated in the reference period, we assess future variations in rainfall-induced landslide occurrence for all the municipalities of Calabria. A +45.7% and +21.2% average regional variation in rainfall-induced landslide occurrence is expected in the region for the period 2036-2065, under the RCP4.5 and RCP8.5 scenario, respectively. We also investigate the future variations in the impact of rainfall-induced landslides on the population of Calabria. We find a +80.2% and +54.5% increase in the impact on the population for the period 2036-2065, under the RCP4.5 and RCP8.5 scenario, respectively. The proposed method is quantitative and reproducible, thus it can be applied in similar regions, where adequate landslide and rainfall information is available. Copyright © 2017 Elsevier B.V. All rights reserved.

Modelling rainfall amounts using mixed-gamma model for Kuantan district

NASA Astrophysics Data System (ADS)

Zakaria, Roslinazairimah; Moslim, Nor Hafizah

2017-05-01

An efficient design of flood mitigation and construction of crop growth models depend upon good understanding of the rainfall process and characteristics. Gamma distribution is usually used to model nonzero rainfall amounts. In this study, the mixed-gamma model is applied to accommodate both zero and nonzero rainfall amounts. The mixed-gamma model presented is for the independent case. The formulae of mean and variance are derived for the sum of two and three independent mixed-gamma variables, respectively. Firstly, the gamma distribution is used to model the nonzero rainfall amounts and the parameters of the distribution (shape and scale) are estimated using the maximum likelihood estimation method. Then, the mixed-gamma model is defined for both zero and nonzero rainfall amounts simultaneously. The formulae of mean and variance for the sum of two and three independent mixed-gamma variables derived are tested using the monthly rainfall amounts from rainfall stations within Kuantan district in Pahang Malaysia. Based on the Kolmogorov-Smirnov goodness of fit test, the results demonstrate that the descriptive statistics of the observed sum of rainfall amounts is not significantly different at 5% significance level from the generated sum of independent mixed-gamma variables. The methodology and formulae demonstrated can be applied to find the sum of more than three independent mixed-gamma variables.

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, runoff and sediment transport in a Mediterranean mountainous catchment.

PubMed

Tuset, J; Vericat, D; Batalla, R J

2016-01-01

The relation between rainfall, runoff, erosion and sediment transport is highly variable in Mediterranean catchments. Their relation can be modified by land use changes and climate oscillations that, ultimately, will control water and sediment yields. This paper analyses rainfall, runoff and sediment transport relations in a meso-scale Mediterranean mountain catchment, the Ribera Salada (NE Iberian Peninsula). A total of 73 floods recorded between November 2005 and November 2008 at the Inglabaga Sediment Transport Station (114.5 km(2)) have been analysed. Suspended sediment transport and flow discharge were measured continuously. Rainfall data was obtained by means of direct rain gauges and daily rainfall reconstructions from radar information. Results indicate that the annual sediment yield (2.3 t km(-1) y(-1) on average) and the flood-based runoff coefficients (4.1% on average) are low. The Ribera Salada presents a low geomorphological and hydrological activity compared with other Mediterranean mountain catchments. Pearson correlations between rainfall, runoff and sediment transport variables were obtained. The hydrological response of the catchment is controlled by the base flows. The magnitude of suspended sediment concentrations is largely correlated with flood magnitude, while sediment load is correlated with the amount of direct runoff. Multivariate analysis shows that total suspended load can be predicted by integrating rainfall and runoff variables. The total direct runoff is the variable with more weight in the equation. Finally, three main hydro-sedimentary phases within the hydrological year are defined in this catchment: (a) Winter, where the catchment produces only water and very little sediment; (b) Spring, where the majority of water and sediment is produced; and (c) Summer-Autumn, when little runoff is produced but significant amount of sediments is exported out of the catchment. Results show as land use and climate change may have an important role in modifying the cycles of water and sediment yields in Mediterranean mountain catchments. Copyright © 2015 Elsevier B.V. All rights reserved.

Effects of rainfall spatial variability and intermittency on shallow landslide triggering patterns at a catchment scale

NASA Astrophysics Data System (ADS)

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

2014-10-01

The occurrence of shallow landslides is often associated with intense and prolonged rainfall events, where infiltrating water reduces soil strength and may lead to abrupt mass release. Despite general understanding of the role of rainfall water in slope stability, the prediction of rainfall-induced landslides remains a challenge due to natural heterogeneity that affect hydrologic loading patterns and the largely unobservable internal progressive failures. An often overlooked and potentially important factor is the role of rainfall variability in space and time on landslide triggering that is often obscured by coarse information (e.g., hourly radar data at spatial resolution of a few kilometers). To quantify potential effects of rainfall variability on failure dynamics, spatial patterns, landslide numbers and volumes, we employed a physically based "Catchment-scale Hydromechanical Landslide Triggering" (CHLT) model for a study area where a summer storm in 2002 triggered 51 shallow landslides. In numerical experiments based on the CHLT model, we applied the measured rainfall amount of 53 mm in different artificial spatiotemporal rainfall patterns, resulting in between 30 and 100 landslides and total released soil volumes between 3000 and 60,000 m3 for the various scenarios. Results indicate that low intensity rainfall below soil's infiltration capacity resulted in the largest mechanical perturbation. This study illustrates how small-scale rainfall variability that is often overlooked by present operational rainfall data may play a key role in shaping landslide patterns.

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.

Comparative assessment of lowland and highland Smallholder farmers' vulnerability to climate variability in Ethiopia

NASA Astrophysics Data System (ADS)

Ayal, D. Y., Sr.; Abshare, M. W. M.; Desta, S. D.; Filho, W. L.

2015-12-01

Desalegn Yayeh Ayal P.O.BOX 150129 Addis Ababa University Ethiopia Mobil +251910824784 Abstract Smallholder farmers' near term scenario (2010-2039) vulnerability nature and magnitude was examined using twenty-two exposure, sensitivity and adaptive capacity vulnerability indicators. Assessment of smallholder farmers' vulnerability to climate variability revealed the importance of comprehending exposure, sensitivity and adaptive capacity induces. Due to differences in level of change in rainfall, temperature, drought frequency, their environmental interaction and variations on adaptive capacity the nature and magnitude of smallholder farmers vulnerability to physical, biological and epidemiological challenges of crop and livestock production varied within and across agro-ecologies. Highlanders' sensitive relates with high population density, erosion and crop disease and pest damage occurrence. Whereas lowlanders will be more sensitive to high crop disease and pest damage, provenance of livestock disease, absence of alternative water sources, less diversified agricultural practices. However, with little variations in the magnitude and nature of vulnerability, both highlanders and lowlanders are victims of climate variability and change. Given the ever increasing population, temperature and unpredictable nature of rainfall variability, the study concluded that future adaptation strategies should capitalize on preparing smallholder farmers for both extremes- excess rainfall and flooding on the one hand and severe drought on the other.

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.

Ecosystem and Community Responses to Rainfall Manipulations in Shrublands Depends on Dominant Vegetation Cover

NASA Astrophysics Data System (ADS)

Esch, E. H.; Lipson, D.; Kim, J. B.; Cleland, E. E.

2014-12-01

Southern California is predicted to face decreasing precipitation with increased interannual variability in the coming century. Native shrublands in this area are increasingly invaded by exotic annual grasses, though invasion dynamics can vary by rainfall scenario, with wet years generally associated with high invasion pressure. Interplay between rainfall and invasion scenarios can influence carbon stocks and community composition. Here we asked how invasion alters ecosystem and community responses in drought versus high rainfall scenarios, as quantified by community identity, biomass production, and the normalized difference vegetation index (NDVI). To do this, we performed a rainfall manipulation experiment with paired plots dominated either by native shrubs or exotic herbaceous species, subjected to treatments of 50%, 100%, or 150% of ambient rainfall. The study site was located in a coastal sage scrub ecosystem, with patches dominated by native shrubs and exotic grasses located in San Diego County, USA. During two growing seasons, we found that native, herbaceous biomass production was significantly affected by rainfall treatment (p<0.05 for both years), though was not affected by dominant community composition. Photosynthetic biomass production of shrub species also varied by treatment (p=0.035). Exotic biomass production showed a significant interaction between dominant community composition and rainfall treatment, and both individual effects (p<0.001 for all). NDVI showed similar results, but also indicated the importance of rainfall timing on overall biomass production between years. Community composition data showed certain species, of both native and exotic identities, segregating by treatment. These results indicate that exotic species are more sensitive to rainfall, and that increased rainfall may promote greater carbon storage in annual dominated communities when compared to shrub dominated communities in high rainfall years, but with drought, this trend is reversed.

Instrumental record of debris flow initiation during natural rainfall: Implications for modeling slope stability

Treesearch

David R. Montgomery; Kevin M. Schmidt; William E. Dietrich; Jim McKean

2009-01-01

The middle of a hillslope hollow in the Oregon Coast Range failed and mobilized as a debris flow during heavy rainfall in November 1996. Automated pressure transducers recorded high spatial variability of pore water pressure within the area that mobilized as a debris flow, which initiated where local upward flow from bedrock developed into overlying colluvium....

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.

Rainfall variability and drought characteristics in two agro-climatic zones: An assessment of climate change challenges in Africa.

PubMed

Ayanlade, Ayansina; Radeny, Maren; Morton, John F; Muchaba, Tabitha

2018-07-15

This paper examines drought characteristics as an evidence of climate change in two agro-climatic zones of Nigeria and farmers' climate change perceptions of impacts and adaptation strategies. The results show high spatial and temporal rainfall variability for the stations. Consequently, there are several anomalies in rainfall in recent years but much more in the locations around the Guinea savanna. The inter-station and seasonality statistics reveal less variable and wetter early growing seasons and late growing seasons in the Rainforest zone, and more variable and drier growing seasons in other stations. The probability (p) of dry spells exceeding 3, 5 and 10 consecutive days is very high with 0.62≤p≥0.8 in all the stations, though, the p-values for 10day spells drop below 0.6 in Ibadan and Osogbo. The results further show that rainfall is much more reliable from the month of May until July with the coefficient of variance for rainy days <0.30, but less reliable in the months of March, August and October (CV-RD>0.30), though CV-RD appears higher in the month of August for all the stations. It is apparent that farmers' perceptions of drought fundamentally mirror climatic patterns from historical weather data. The study concludes that the adaptation facilities and equipment, hybrids of crops and animals are to be provided to farmers, at a subsidized price by the government, for them to cope with the current condition of climate change. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Post-processing of global model output to forecast point rainfall

NASA Astrophysics Data System (ADS)

Hewson, Tim; Pillosu, Fatima

2016-04-01

ECMWF (the European Centre for Medium range Weather Forecasts) has recently embarked upon a new project to post-process gridbox rainfall forecasts from its ensemble prediction system, to provide probabilistic forecasts of point rainfall. The new post-processing strategy relies on understanding how different rainfall generation mechanisms lead to different degrees of sub-grid variability in rainfall totals. We use a number of simple global model parameters, such as the convective rainfall fraction, to anticipate the sub-grid variability, and then post-process each ensemble forecast into a pdf (probability density function) for a point-rainfall total. The final forecast will comprise the sum of the different pdfs from all ensemble members. The post-processing is essentially a re-calibration exercise, which needs only rainfall totals from standard global reporting stations (and forecasts) to train it. High density observations are not needed. This presentation will describe results from the initial 'proof of concept' study, which has been remarkably successful. Reference will also be made to other useful outcomes of the work, such as gaining insights into systematic model biases in different synoptic settings. The special case of orographic rainfall will also be discussed. Work ongoing this year will also be described. This involves further investigations of which model parameters can provide predictive skill, and will then move on to development of an operational system for predicting point rainfall across the globe. The main practical benefit of this system will be a greatly improved capacity to predict extreme point rainfall, and thereby provide early warnings, for the whole world, of flash flood potential for lead times that extend beyond day 5. This will be incorporated into the suite of products output by GLOFAS (the GLObal Flood Awareness System) which is hosted at ECMWF. As such this work offers a very cost-effective approach to satisfying user needs right around the world. This field has hitherto relied on using very expensive high-resolution ensembles; by their very nature these can only run over small regions, and only for lead times up to about 2 days.

Intrinsic Coupled Ocean-Atmosphere Modes of the Asian Summer Monsoon: A Re-assessment of Monsoon-ENSO Relationships

NASA Technical Reports Server (NTRS)

Lau, K.-M.; Wu, H. T.

2000-01-01

Using global rainfall and sea surface temperature (SST) data for the past two decades (1979-1998), we have investigated the intrinsic modes of Asian summer monsoon (ASM) and ENSO co-variability. Three recurring ASM rainfall-SST coupled modes were identified. The first is a basin scale mode that features SST and rainfall variability over the entire tropics (including the ASM region), identifiable with those occurring during El Nino or La Nina. This mode is further characterized by a pronounced biennial variation in ASM rainfall and SST associated with fluctuations of the anomalous Walker circulation that occur during El Nino/La Nina transitions. The second mode comprises mixed regional and basin-scale rainfall and SST signals, with pronounced intraseasonal and interannual variabilities. This mode features a SST pattern associated with a developing La Nina, with a pronounced low level anticyclone in the subtropics of the western Pacific off the coast of East Asia. The third mode depicts an east-west rainfall and SST dipole across the southern equatorial Indian Ocean, most likely stemming from coupled ocean-atmosphere processes within the ASM region. This mode also possesses a decadal time scale and a linear trend, which are not associated with El Nino/La Nina variability. Possible causes of year-to-year rainfall variability over the ASM and sub-regions have been evaluated from a reconstruction of the observed rainfall from singular eigenvectors of the coupled modes. It is found that while basin-scale SST can account for portions of ASM rainfall variability during ENSO events (up to 60% in 1998), regional processes can accounts up to 20-25% of the rainfall variability in typical non-ENSO years. Stronger monsoon-ENSO relationship tends to occur in the boreal summer immediately preceding a pronounced La Nina, i.e., 1998, 1988 and 1983. Based on these results, we discuss the possible impacts of the ASM on ENSO variability via the west Pacific anticyclone and articulate a hypothesis that anomalous wind forcings derived from the anticyclone may be instrumental in inducing a strong biennial modulation to natural ENSO cycles.

Coherent response of the Indo-African boreal summer monsoon to Pacific SST captured in Ethiopian rain δ18O

NASA Astrophysics Data System (ADS)

Madhavan, M.; Palliyil, L. R.; Ramesh, R.

2017-12-01

Pacific Sea Surface Temperature (SST) plays an important role in the inter-annual to inter-decadal variability of boreal monsoons. We identified a common mode of inter annual variability in the Indian and African boreal summer monsoon (June to September) rainfalls, which is linked to Pacific SSTs, using Empirical Orthogonal Function (EOF) analysis. Temporal coefficients (Principle component: PC1) of the leading mode of variability (EOF-1) is well correlated with the Indian summer monsoon rainfall and Sahel rainfall. About forty year long monthly observations of δ18O (and δD) at Addis Ababa, Ethiopia show a strong association with PC1 (r=0.69 for δ18O and r=0.75 for δD). Analysis of SST, sea level pressure and lower tropospheric winds suggest that 18O depletion in Ethiopian rainfall (and wet phases of PC1) is associated with cooler eastern tropical Pacific and warmer western Pacific and strengthening of Pacific subtropical high in both the hemispheres. Associated changes in the trade winds cause enhanced westerly moisture transport into the Indian subcontinent and northern Africa and cause enhanced rainfall. The intrusion of Atlantic westerly component of moisture transport at Addis Ababa during wet phases of PC1 is clearly recorded in δ18O of rain. We also observe the same common mode of variability (EOF1) of Indo-African boreal summer monsoon rain on decadal time scales. A 100 year long δ18O record of actively growing speleothem from the Mechara cave, Ethiopia, matches very well with the PC1 on the decadal time scale. This highlights the potential of speleothem δ18O and leaf wax δD from Ethiopia to investigate the natural variability and teleconnections of Indo-African boreal monsoon.

Classification and Space-Time Analysis of Precipitation Events in Manizales, Caldas, Colombia.

NASA Astrophysics Data System (ADS)

Suarez Hincapie, J. N.; Vélez, J.; Romo Melo, L.; Chang, P.

2015-12-01

Manizales is a mid-mountain Andean city located near the Nevado del Ruiz volcano in west-central Colombia, this location exposes it to earthquakes, floods, landslides and volcanic eruptions. It is located in the intertropical convergence zone (ITCZ) and presents a climate with a bimodal rainfall regime (Cortés, 2010). Its mean annual rainfall is 2000 mm, one may observe precipitation 70% of the days over a year. This rain which favors the formation of large masses of clouds and the presence of macroclimatic phenomenon as "El Niño South Oscillation", has historically caused great impacts in the region (Vélez et al, 2012). For example the geographical location coupled with rain events results in a high risk of landslides in the city. Manizales has a hydrometeorological network of 40 stations that measure and transmit data of up to eight climate variables. Some of these stations keep 10 years of historical data. However, until now this information has not been used for space-time classification of precipitation events, nor has the meteorological variables that influence them been thoroughly researched. The purpose of this study was to classify historical events of rain in an urban area of Manizales and investigate patterns of atmospheric behavior that influence or trigger such events. Classification of events was performed by calculating the "n" index of the heavy rainfall, describing the behavior of precipitation as a function of time throughout the event (Monjo, 2009). The analysis of meteorological variables was performed using statistical quantification over variable time periods before each event. The proposed classification allowed for an analysis of the evolution of rainfall events. Specially, it helped to look for the influence of different meteorological variables triggering rainfall events in hazardous areas as the city of Manizales.

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.

The evaluation of rainfall influence on combined sewer overflows characteristics: the Berlin case study.

PubMed

Sandoval, S; Torres, A; Pawlowsky-Reusing, E; Riechel, M; Caradot, N

2013-01-01

The present study aims to explore the relationship between rainfall variables and water quality/quantity characteristics of combined sewer overflows (CSOs), by the use of multivariate statistical methods and online measurements at a principal CSO outlet in Berlin (Germany). Canonical correlation results showed that the maximum and average rainfall intensities are the most influential variables to describe CSO water quantity and pollutant loads whereas the duration of the rainfall event and the rain depth seem to be the most influential variables to describe CSO pollutant concentrations. The analysis of partial least squares (PLS) regression models confirms the findings of the canonical correlation and highlights three main influences of rainfall on CSO characteristics: (i) CSO water quantity characteristics are mainly influenced by the maximal rainfall intensities, (ii) CSO pollutant concentrations were found to be mostly associated with duration of the rainfall and (iii) pollutant loads seemed to be principally influenced by dry weather duration before the rainfall event. The prediction quality of PLS models is rather low (R² < 0.6) but results can be useful to explore qualitatively the influence of rainfall on CSO characteristics.

Tree-Ring Reconstruction of Wet Season Rainfall Totals in the Amazon

NASA Astrophysics Data System (ADS)

Stahle, D. W.; Lopez, L.; Granato-Souza, D.; Barbosa, A. C. M. C.; Torbenson, M.; Villalba, R.; Pereira, G. D. A.; Feng, S.; Schongart, J.; Cook, E. R.

2017-12-01

The Amazon Basin is a globally important center of deep atmospheric convection, energy balance, and biodiversity, but only a handful of weather stations in this vast Basin have recorded rainfall measurements for at least 50 years. The available rainfall and river level observations suggest that the hydrologic cycle in the Amazon may have become amplified in the last 40-years, with more extreme rainfall and streamflow seasonality, deeper droughts, and more severe flooding. These changes in the largest hydrological system on earth may be early evidence of the expected consequences of anthropogenic climate change and deforestation in the coming century. Placing these observed and simulated changes in the context of natural climate variability during the late Holocene is a significant challenge for high-resolution paleoclimatology. We have developed exactly dated and well-replicated annual tree-ring chronologies from two native Amazonian tree species (Cedrela sp and Centrolobium microchaete). These moisture sensitive chronologies have been used to compute two reconstructions of wet season rainfall totals, one in the southern Amazon based on Centrolobium and another in the eastern equatorial Amazon using Cedrela. Both reconstructions are over 200-years long and extend the available instrumental observations in each region by over 150-years. These reconstructions are well correlated with the same regional and large-scale climate dynamics that govern the inter-annual variability of the instrumental wet season rainfall totals. Increased multi-decadal variability is reconstructed after 1950 with the Centrolobium chronologies in the southern Amazon. The Cedrela reconstruction from the eastern Amazon exhibits changes in the spatial pattern of correlation with regional rainfall stations and the large-scale sea surface temperature field after 1990 that may be consistent with recent changes in the mean position of the Inter-Tropical Convergence Zone in March over the western Atlantic and South American sector.

Interannual Rainfall Variability in North-East Brazil: Observation and Model Simulation

NASA Astrophysics Data System (ADS)

Harzallah, A.; Rocha de Aragão, J. O.; Sadourny, R.

1996-08-01

The relationship between interannual variability of rainfall in north-east Brazil and tropical sea-surface temperature is studied using observations and model simulations. The simulated precipitation is the average of seven independent realizations performed using the Laboratoire de Météorologie Dynamique atmospheric general model forced by the 1970-1988 observed sea-surface temperature. The model reproduces very well the rainfall anomalies (correlation of 091 between observed and modelled anomalies). The study confirms that precipitation in north-east Brazil is highly correlated to the sea-surface temperature in the tropical Atlantic and Pacific oceans. Using the singular value decomposition method, we find that Nordeste rainfall is modulated by two independent oscillations, both governed by the Atlantic dipole, but one involving only the Pacific, the other one having a period of about 10 years. Correlations between precipitation in north-east Brazil during February-May and the sea-surface temperature 6 months earlier indicate that both modes are essential to estimate the quality of the rainy season.

Rainfall Variability, Adaptation through Irrigation, and Sustainable Management of Water Resources in India

NASA Astrophysics Data System (ADS)

Fishman, R.

2013-12-01

Most studies of the impact of climate change on agriculture account for shifts in temperature and total seasonal (or monthly) precipitation. However, climate change is also projected to increase intra-seasonal precipitation variability in many parts of the world. To provide first estimates of the potential impact, I paired daily rainfall and rice yield data during the period 1970-2004, from across India, where about a fifth of the world's rice is produced, and yields have always been highly dependent on the erratic monsoon rainfall. Multivariate regression models revealed that the number of rainless days during the wet season has a statistically robust negative impact on rice yields that exceeds that of total seasonal rainfall. Moreover, a simulation of climate change impacts found that the negative impact of the projected increase in the number of rainless days will trump the positive impact of the projected increase in total precipitation, and reverse the net precipitation effect on rice production from positive (+3%) to negative (-10%). The results also indicate that higher irrigation coverage is correlated with reduced sensitivity to rainfall variability, suggesting the expansion of irrigation can effectively adapt agriculture to these climate change impacts. However, taking into account limitations on water resource availability in India, I calculate that under current irrigation practices, sustainable use of water can mitigate less than a tenth of the impact.

Changes in Intense Precipitation Events in West Africa and the central U.S. under Global Warming

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

Cook, Kerry H.; Vizy, Edward

The purpose of the proposed project is to improve our understanding of the physical processes and large-scale connectivity of changes in intense precipitation events (high rainfall rates) under global warming in West Africa and the central U.S., including relationships with low-frequency modes of variability. This is in response to the requested subject area #2 “simulation of climate extremes under a changing climate … to better quantify the frequency, duration, and intensity of extreme events under climate change and elucidate the role of low frequency climate variability in modulating extremes.” We will use a regional climate model and emphasize an understandingmore » of the physical processes that lead to an intensification of rainfall. The project objectives are as follows: 1. Understand the processes responsible for simulated changes in warm-season rainfall intensity and frequency over West Africa and the Central U.S. associated with greenhouse gas-induced global warming 2. Understand the relationship between changes in warm-season rainfall intensity and frequency, which generally occur on regional space scales, and the larger-scale global warming signal by considering modifications of low-frequency modes of variability. 3. Relate changes simulated on regional space scales to global-scale theories of how and why atmospheric moisture levels and rainfall should change as climate warms.« less

About climate variabilitiy leading the hydric condition of the soil in the rainfed region of Argentina

NASA Astrophysics Data System (ADS)

Pántano, V. C.; Penalba, O. C.

2013-05-01

Extreme events of temperature and rainfall have a socio-economic impact in the rainfed agriculture production region in Argentina. The magnitude of the impact can be analyzed through the water balance which integrates the characteristics of the soil and climate conditions. Changes observed in climate variables during the last decades affected the components of the water balance. As a result, a displacement of the agriculture border towards the west was produced, improving the agricultural production of the region. The objective of this work is to analyze how the variability of rainfall and temperature leads the hydric condition of the soil, with special focus on extreme events. The hydric conditions of the soil (HC= Excess- Deficit) were estimated from the monthly water balance (Thornthwaite and Mather method, 1957), using monthly potential evapotranspiration (PET) and monthly accumulated rainfall (R) for 33 stations (period 1970-2006). Information of temperature and rainfall was provided by National Weather Service and the effective capacity of soil water was considered from Forte Lay and Spescha (2001). An agricultural extreme condition occurs when soil moisture and rainfall are inadequate or excessive for the development of the crops. In this study, we define an extreme event when the variable is less (greater) than its 20% and 10% (80% and 90%) percentile. In order to evaluate how sensitive is the HC to water and heat stress in the region, different conditional probabilities were evaluated. There is a weaker response of HC to extreme low PET while extreme low R leads high values of HC. However, this behavior is not always observed, especially in the western region where extreme high and low PET show a stronger influence over the HC. Finally, to analyze the temporal variability of extreme PET and R, leading hydric condition of the soil, the number of stations presenting extreme conditions was computed for each month. As an example, interesting results were observed for April. During this month, the water recharge of the soil is crucial to let the winter crops manage with the scarce rainfalls occurring in the following months. In 1970, 1974, 1977, 1978 and 1997 more than 50% of the stations were under extreme high PET; while 1970, 1974, 1978 and 1988 presented more than 40% under extreme low R. Thus, the 70s was the more threatened decade of the period. Since the 80s (except for 1997), extreme dry events due to one variable or the other are mostly presented separately, over smaller areas. The response of the spatial distribution of HC is stronger when both variables present extreme conditions. In particular, during 1997 the region presents extreme low values of HC as a consequence of extreme low R and high PET. Communities dependent on agriculture are highly sensitive to climate variability and its extremes. In the studied region, it was shown that scarce water and heat stress contribute to the resulting hydric condition, producing strong impact over different productive activities. Extreme temperature seems to have a stronger influence over extreme unfavorable hydric conditions.

The magnitude of variability produced by methods used to estimate annual stormwater contaminant loads for highly urbanised catchments.

PubMed

Beck, H J; Birch, G F

2013-06-01

Stormwater contaminant loading estimates using event mean concentration (EMC), rainfall/runoff relationship calculations and computer modelling (Model of Urban Stormwater Infrastructure Conceptualisation--MUSIC) demonstrated high variability in common methods of water quality assessment. Predictions of metal, nutrient and total suspended solid loadings for three highly urbanised catchments in Sydney estuary, Australia, varied greatly within and amongst methods tested. EMC and rainfall/runoff relationship calculations produced similar estimates (within 1 SD) in a statistically significant number of trials; however, considerable variability within estimates (∼50 and ∼25 % relative standard deviation, respectively) questions the reliability of these methods. Likewise, upper and lower default inputs in a commonly used loading model (MUSIC) produced an extensive range of loading estimates (3.8-8.3 times above and 2.6-4.1 times below typical default inputs, respectively). Default and calibrated MUSIC simulations produced loading estimates that agreed with EMC and rainfall/runoff calculations in some trials (4-10 from 18); however, they were not frequent enough to statistically infer that these methods produced the same results. Great variance within and amongst mean annual loads estimated by common methods of water quality assessment has important ramifications for water quality managers requiring accurate estimates of the quantities and nature of contaminants requiring treatment.

Increased Spatial Variability and Intensification of Extreme Monsoon Rainfall due to Urbanization.

PubMed

Paul, Supantha; Ghosh, Subimal; Mathew, Micky; Devanand, Anjana; Karmakar, Subhankar; Niyogi, Dev

2018-03-02

While satellite data provides a strong robust signature of urban feedback on extreme precipitation; urbanization signal is often not so prominent with station level data. To investigate this, we select the case study of Mumbai, India and perform a high resolution (1 km) numerical study with Weather Research and Forecasting (WRF) model for eight extreme rainfall days during 2014-2015. The WRF model is coupled with two different urban schemes, the Single Layer Urban Canopy Model (WRF-SUCM), Multi-Layer Urban Canopy Model (WRF-MUCM). The differences between the WRF-MUCM and WRF-SUCM indicate the importance of the structure and characteristics of urban canopy on modifications in precipitation. The WRF-MUCM simulations resemble the observed distributed rainfall. WRF-MUCM also produces intensified rainfall as compared to the WRF-SUCM and WRF-NoUCM (without UCM). The intensification in rainfall is however prominent at few pockets of urban regions, that is seen in increased spatial variability. We find that the correlation of precipitation across stations within the city falls below statistical significance at a distance greater than 10 km. Urban signature on extreme precipitation will be reflected on station rainfall only when the stations are located inside the urban pockets having intensified precipitation, which needs to be considered in future analysis.

Interpretation of the impact of different managements and the rainfall variability on the soil erosion in a Mediterranean olive orchard microcatchment

NASA Astrophysics Data System (ADS)

Taguas, E. V.; Burguet, M.; Pérez, R.; Ayuso, J. L.; Gómez, J. A.

2012-04-01

The microcatchment is a spatial scale which allows to evaluate and to quantify the erosive processes under conditions close to those perceived by farmers. In this work, soil erosion and runoff over six hydrological years (2005 and 2011) were monitored in an olive orchard microcatchment of 6.4 ha, where different management types were applied. The aim was to evaluate the impact of the management and the rainfall regime variability. Non-tillage was applied during the years 2005-2007, tillage operations were carried in April in the period 2007-2010 while in the year 2010-2011, the tillage was applied in January and mulches (olives leaves and branches) were established for reducing the soil losses, mainly generated from rills. At the annual scale, the variation ranges of the cumulative rainfall depth and of the erosivity were between 600 and 1000 mm and between 600 and 1500 MJ mm ha-1 h-1, respectively. Although there are some gaps in the data series, the annual runoff coefficients calculated were smaller than 5% and the total sediment load range was between less than 1 t ha-1 year-1and more than 20 t ha-1 year-1. During these years olive yield also showed a high degree of variability, between 5000 kg ha-1 year-1and 10000 kg ha-1 year-1, typical of the alternate bearing of this crop, without correlation with annual rainfall. The annual rainfall depth explained significantly the sediment load and the runoff in spite of the different managements applied. At the event scale, rainfall depth was correlated with runoff, however, sediment load was very sensible to management. The high variability of the hydrological regime (inter and intra-annual) and the importance of the precedent hydrological years determine complex interpretations of the impact of the management on the soil losses and the olive yield by the farmers, so the continuity of the data analysis is essential for supporting the suitable taking decisions about the overall farm management.

SCS-CN parameter determination using rainfall-runoff data in heterogeneous watersheds. The two-CN system approach

NASA Astrophysics Data System (ADS)

Soulis, K. X.; Valiantzas, J. D.

2011-10-01

The Soil Conservation Service Curve Number (SCS-CN) approach is widely used as a simple method for predicting direct runoff volume for a given rainfall event. The CN values can be estimated by being selected from tables. However, it is more accurate to estimate the CN value from measured rainfall-runoff data (assumed available) in a watershed. Previous researchers indicated that the CN values calculated from measured rainfall-runoff data vary systematically with the rainfall depth. They suggested the determination of a single asymptotic CN value observed for very high rainfall depths to characterize the watersheds' runoff response. In this paper, the novel hypothesis that the observed correlation between the calculated CN value and the rainfall depth in a watershed reflects the effect of the inevitable presence of soil-cover complex spatial variability along watersheds is being tested. Based on this hypothesis, the simplified concept of a two-CN heterogeneous system is introduced to model the observed CN-rainfall variation by reducing the CN spatial variability into two classes. The behavior of the CN-rainfall function produced by the proposed two-CN system concept is approached theoretically, it is analyzed systematically, and it is found to be similar to the variation observed in natural watersheds. Synthetic data tests, natural watersheds examples, and detailed study of two natural experimental watersheds with known spatial heterogeneity characteristics were used to evaluate the method. The results indicate that the determination of CN values from rainfall runoff data using the proposed two-CN system approach provides reasonable accuracy and it over performs the previous original method based on the determination of a single asymptotic CN value. Although the suggested method increases the number of unknown parameters to three (instead of one), a clear physical reasoning for them is presented.

Seasonal precipitation forecasting for the Melbourne region using a Self-Organizing Maps approach

NASA Astrophysics Data System (ADS)

Pidoto, Ross; Wallner, Markus; Haberlandt, Uwe

2017-04-01

The Melbourne region experiences highly variable inter-annual rainfall. For close to a decade during the 2000s, below average rainfall seriously affected the environment, water supplies and agriculture. A seasonal rainfall forecasting model for the Melbourne region based on the novel approach of a Self-Organizing Map has been developed and tested for its prediction performance. Predictor variables at varying lead times were first assessed for inclusion within the model by calculating their importance via Random Forests. Predictor variables tested include the climate indices SOI, DMI and N3.4, in addition to gridded global sea surface temperature data. Five forecasting models were developed: an annual model and four seasonal models, each individually optimized for performance through Pearson's correlation r and the Nash-Sutcliffe Efficiency. The annual model showed a prediction performance of r = 0.54 and NSE = 0.14. The best seasonal model was for spring, with r = 0.61 and NSE = 0.31. Autumn was the worst performing seasonal model. The sea surface temperature data contributed fewer predictor variables compared to climate indices. Most predictor variables were supplied at a minimum lead, however some predictors were found at lead times of up to a year.

Flexible Strategies for Coping with Rainfall Variability: Seasonal Adjustments in Cropped Area in the Ganges Basin

PubMed Central

Siderius, Christian; Biemans, Hester; van Walsum, Paul E. V.; van Ierland, Ekko C.; Kabat, Pavel; Hellegers, Petra J. G. J.

2016-01-01

One of the main manifestations of climate change will be increased rainfall variability. How to deal with this in agriculture will be a major societal challenge. In this paper we explore flexibility in land use, through deliberate seasonal adjustments in cropped area, as a specific strategy for coping with rainfall variability. Such adjustments are not incorporated in hydro-meteorological crop models commonly used for food security analyses. Our paper contributes to the literature by making a comprehensive model assessment of inter-annual variability in crop production, including both variations in crop yield and cropped area. The Ganges basin is used as a case study. First, we assessed the contribution of cropped area variability to overall variability in rice and wheat production by applying hierarchical partitioning on time-series of agricultural statistics. We then introduced cropped area as an endogenous decision variable in a hydro-economic optimization model (WaterWise), coupled to a hydrology-vegetation model (LPJmL), and analyzed to what extent its performance in the estimation of inter-annual variability in crop production improved. From the statistics, we found that in the period 1999–2009 seasonal adjustment in cropped area can explain almost 50% of variability in wheat production and 40% of variability in rice production in the Indian part of the Ganges basin. Our improved model was well capable of mimicking existing variability at different spatial aggregation levels, especially for wheat. The value of flexibility, i.e. the foregone costs of choosing not to crop in years when water is scarce, was quantified at 4% of gross margin of wheat in the Indian part of the Ganges basin and as high as 34% of gross margin of wheat in the drought-prone state of Rajasthan. We argue that flexibility in land use is an important coping strategy to rainfall variability in water stressed regions. PMID:26934389

Comparison of Two Stochastic Daily Rainfall Models and their Ability to Preserve Multi-year Rainfall Variability

NASA Astrophysics Data System (ADS)

Kamal Chowdhury, AFM; Lockart, Natalie; Willgoose, Garry; Kuczera, George; Kiem, Anthony; Parana Manage, Nadeeka

2016-04-01

Stochastic simulation of rainfall is often required in the simulation of streamflow and reservoir levels for water security assessment. As reservoir water levels generally vary on monthly to multi-year timescales, it is important that these rainfall series accurately simulate the multi-year variability. However, the underestimation of multi-year variability is a well-known issue in daily rainfall simulation. Focusing on this issue, we developed a hierarchical Markov Chain (MC) model in a traditional two-part MC-Gamma Distribution modelling structure, but with a new parameterization technique. We used two parameters of first-order MC process (transition probabilities of wet-to-wet and dry-to-dry days) to simulate the wet and dry days, and two parameters of Gamma distribution (mean and standard deviation of wet day rainfall) to simulate wet day rainfall depths. We found that use of deterministic Gamma parameter values results in underestimation of multi-year variability of rainfall depths. Therefore, we calculated the Gamma parameters for each month of each year from the observed data. Then, for each month, we fitted a multi-variate normal distribution to the calculated Gamma parameter values. In the model, we stochastically sampled these two Gamma parameters from the multi-variate normal distribution for each month of each year and used them to generate rainfall depth in wet days using the Gamma distribution. In another study, Mehrotra and Sharma (2007) proposed a semi-parametric Markov model. They also used a first-order MC process for rainfall occurrence simulation. But, the MC parameters were modified by using an additional factor to incorporate the multi-year variability. Generally, the additional factor is analytically derived from the rainfall over a pre-specified past periods (e.g. last 30, 180, or 360 days). They used a non-parametric kernel density process to simulate the wet day rainfall depths. In this study, we have compared the performance of our hierarchical MC model with the semi-parametric model in preserving rainfall variability in daily, monthly, and multi-year scales. To calibrate the parameters of both models and assess their ability to preserve observed statistics, we have used ground based data from 15 raingauge stations around Australia, which consist a wide range of climate zones including coastal, monsoonal, and arid climate characteristics. In preliminary results, both models show comparative performances in preserving the multi-year variability of rainfall depth and occurrence. However, the semi-parametric model shows a tendency of overestimating the mean rainfall depth, while our model shows a tendency of overestimating the number of wet days. We will discuss further the relative merits of the both models for hydrology simulation in the presentation.

Vegetation response to rainfall seasonality and interannual variability in tropical dry forests

NASA Astrophysics Data System (ADS)

Feng, X.; Silva Souza, R. M.; Souza, E.; Antonino, A.; Montenegro, S.; Porporato, A. M.

2015-12-01

We analyzed the response of tropical dry forests to seasonal and interannual rainfall variability, focusing on the caatinga biome in semi-arid in Northeast Brazil. We selected four sites across a gradient of rainfall amount and seasonality and analyzed daily rainfall and biweekly Normalized Difference Vegetation Index (NDVI) in the period 2000-2014. The seasonal and interannual rainfall statistics were characterized using recently developed metrics describing duration, location, and intensity of wet season and compared them with those of NDVI time series and modelled soil moisture. A model of NDVI was also developed and forced by different rainfall scenarios (combination amount of rainfall and duration of wet season). The results show that the caatinga tends to have a more stable response characterized by longer and less variable growing seasons (of duration 3.1±0.1 months) compared to the rainfall wet seasons (2.0±0.5 months). Even for more extreme rainfall conditions, the ecosystem shows very little sensitivity to duration of wet season in relation to the amount of rainfall, however the duration of wet season is most evident for wetter sites. This ability of the ecosystem in buffering the interannual variability of rainfall is corroborated by the stability of the centroid location of the growing season compared to the wet season for all sites. The maximal biomass production was observed at intermediate levels of seasonality, suggesting a possible interesting trade-off in the effects of intensity (i.e., amount) and duration of the wet season on vegetation growth.

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.

Relationships between atmospheric circulation indices and rainfall in Northern Algeria and comparison of observed and RCM-generated rainfall

NASA Astrophysics Data System (ADS)

Taibi, S.; Meddi, M.; Mahé, G.; Assani, A.

2017-01-01

This work aims, as a first step, to analyze rainfall variability in Northern Algeria, in particular extreme events, during the period from 1940 to 2010. Analysis of annual rainfall shows that stations in the northwest record a significant decrease in rainfall since the 1970s. Frequencies of rainy days for each percentile (5th, 10th, 25th, 50th, 75th, 90th, 95th, and 99th) and each rainfall interval class (1-5, 5-10, 10-20, 20-50, and ≥50 mm) do not show a significant change in the evolution of daily rainfall. The Tenes station is the only one to show a significant decrease in the frequency of rainy days up to the 75th percentile and for the 10-20-mm interval class. There is no significant change in the temporal evolution of extreme events in the 90th, 95th, and 99th percentiles. The relationships between rainfall variability and general atmospheric circulation indices for interannual and extreme event variability are moderately influenced by the El Niño-Southern Oscillation and Mediterranean Oscillation. Significant correlations are observed between the Southern Oscillation Index and annual rainfall in the northwestern part of the study area, which is likely linked with the decrease in rainfall in this region. Seasonal rainfall in Northern Algeria is affected by the Mediterranean Oscillation and North Atlantic Oscillation in the west. The ENSEMBLES regional climate models (RCMs) are assessed using the bias method to test their ability to reproduce rainfall variability at different time scales. The Centre National de Recherches Météorologiques (CNRM), Czech Hydrometeorological Institute (CHMI), Eidgenössische Technische Hochschule Zürich (ETHZ), and Forschungszentrum Geesthacht (GKSS) models yield the least biased results.

Malaria control under unstable dynamics: reactive vs. climate-based strategies.

PubMed

Baeza, Andres; Bouma, Menno J; Dhiman, Ramesh; Pascual, Mercedes

2014-01-01

In areas of the world where malaria prevails under unstable conditions, attacking the adult vector population through insecticide-based Indoor Residual Spraying (IRS) is the most common method for controlling epidemics. Defined in policy guidance, the use of Annual Parasitic Incidence (API) is an important tool for assessing the effectiveness of control and for planning new interventions. To investigate the consequences that a policy based on API in previous seasons might have on the population dynamics of the disease and on control itself in regions of low and seasonal transmission, we formulate a mathematical malaria model that couples epidemiologic and vector dynamics with IRS intervention. This model is parameterized for a low transmission and semi-arid region in northwest India, where epidemics are driven by high rainfall variability. We show that this type of feedback mechanism in control strategies can generate transient cycles in malaria even in the absence of environmental variability, and that this tendency to cycle can in turn limit the effectiveness of control in the presence of such variability. Specifically, for realistic rainfall conditions and over a range of control intensities, the effectiveness of such 'reactive' intervention is compared to that of an alternative strategy based on rainfall and therefore vector variability. Results show that the efficacy of intervention is strongly influenced by rainfall variability and the type of policy implemented. In particular, under an API 'reactive' policy, high vector populations can coincide more frequently with low control coverage, and in so doing generate large unexpected epidemics and decrease the likelihood of elimination. These results highlight the importance of incorporating information on climate variability, rather than previous incidence, in planning IRS interventions in regions of unstable malaria. These findings are discussed in the more general context of elimination and other low transmission regions such as highlands. Copyright © 2013. Published by Elsevier B.V.

A sensitivity study of the coupled simulation of the Northeast Brazil rainfall variability

NASA Astrophysics Data System (ADS)

Misra, Vasubandhu

2007-06-01

Two long-term coupled ocean-land-atmosphere simulations with slightly different parameterization of the diagnostic shallow inversion clouds in the atmospheric general circulation model (AGCM) of the Center for Ocean-Land-Atmosphere Studies (COLA) coupled climate model are compared for their annual cycle and interannual variability of the northeast Brazil (NEB) rainfall variability. It is seen that the solar insolation affected by the changes to the shallow inversion clouds results in large scale changes to the gradients of the SST and the surface pressure. The latter in turn modulates the surface convergence and the associated Atlantic ITCZ precipitation and the NEB annual rainfall variability. In contrast, the differences in the NEB interannual rainfall variability between the two coupled simulations is attributed to their different remote ENSO forcing.

Seasonal Evolution and Variability Associated with the West African Monsoon System

NASA Technical Reports Server (NTRS)

Gu, Guojun; Adler, Robert F.

2003-01-01

In this study, we investigate the seasonal variations in surface rainfall and associated large-scale processes in the tropical eastern Atlantic and West African region. The 5-yr (1998-2002) high-quality TRMM rainfall, sea surface temperature (SST), water vapor and cloud liquid water observations are applied along with the NCEP/NCAR reanalysis wind components and a 3-yr (2000-2002) Quickscat satellite-observed surface wind product. Major mean rainfall over West Africa tends to be concentrated in two regions and is observed in two different seasons, manifesting an abrupt shift of the mean rainfall zone during June-July. (i) Near the Gulf of Guinea (about 5 degN), intense convection and rainfall are seen during April-June and roughly follow the seasonality of SST in the tropical eastern Atlantic. (ii) Along the latitudes of about 10 deg. N over the interior West African continent, a second intense rain belt begins to develop from July and remains there during the later summer season. This belt co-exists with a northwardmoved African Easterly Jet (AEJ) and its accompanying horizonal and vertical shear zones, the appearance and intensification of an upper tropospheric Tropical Easterly Jet (TEJ), and a strong low-level westerly flow. Westward-propagating wave signals [ i e . , African easterly waves (AEWs)] dominate the synoptic-scale variability during July-September, in contrast to the evident eastward-propagating wave signals during May- June. The abrupt shift of mean rainfall zone thus turns out to be a combination of two different physical processes: (i) Evident seasonal cycles in the tropical eastern Atlantic ocean which modulate convection and rainfall in the Gulf of Guinea by means of SST thermal forcing and SST-related meridional gradient; (ii) The interaction among the AEJ, TEJ, low-level westerly flow, moist convection and AEWs during July-September which modulates rainfall variability in the interior West Africa, primarily within the ITCZ rain band. Evident seasonality in synoptic-scale wave signals is shown to be a good evidence for this seasonal evolution.

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...

Rainfall control of debris-flow triggering in the Réal Torrent, Southern French Prealps

NASA Astrophysics Data System (ADS)

Bel, Coraline; Liébault, Frédéric; Navratil, Oldrich; Eckert, Nicolas; Bellot, Hervé; Fontaine, Firmin; Laigle, Dominique

2017-08-01

This paper investigates the occurrence of debris flow due to rainfall forcing in the Réal Torrent, a very active debris flow-prone catchment in the Southern French Prealps. The study is supported by a 4-year record of flow responses and rainfall events, from three high-frequency monitoring stations equipped with geophones, flow stage sensors, digital cameras, and rain gauges measuring rainfall at 5-min intervals. The classic method of rainfall intensity-duration (ID) threshold was used, and a specific emphasis was placed on the objective identification of rainfall events, as well as on the discrimination of flow responses observed above the ID threshold. The results show that parameters used to identify rainfall events significantly affect the ID threshold and are likely to explain part of the threshold variability reported in the literature. This is especially the case regarding the minimum duration of rain interruption (MDRI) between two distinct rainfall events. In the Réal Torrent, a 3-h MDRI appears to be representative of the local rainfall regime. A systematic increase in the ID threshold with drainage area was also observed from the comparison of the three stations, as well as from the compilation of data from experimental debris-flow catchments. A logistic regression used to separate flow responses above the ID threshold, revealed that the best predictors are the 5-min maximum rainfall intensity, the 48-h antecedent rainfall, the rainfall amount and the number of days elapsed since the end of winter (used as a proxy of sediment supply). This emphasizes the critical role played by short intense rainfall sequences that are only detectable using high time-resolution rainfall records. It also highlights the significant influence of antecedent conditions and the seasonal fluctuations of sediment supply.

Variability of rainfall over small areas

NASA Technical Reports Server (NTRS)

Runnels, R. C.

1983-01-01

A preliminary investigation was made to determine estimates of the number of raingauges needed in order to measure the variability of rainfall in time and space over small areas (approximately 40 sq miles). The literature on rainfall variability was examined and the types of empirical relationships used to relate rainfall variations to meteorological and catchment-area characteristics were considered. Relations between the coefficient of variation and areal-mean rainfall and area have been used by several investigators. These parameters seemed reasonable ones to use in any future study of rainfall variations. From a knowledge of an appropriate coefficient of variation (determined by the above-mentioned relations) the number rain gauges needed for the precise determination of areal-mean rainfall may be calculated by statistical estimation theory. The number gauges needed to measure the coefficient of variation over a 40 sq miles area, with varying degrees of error, was found to range from 264 (10% error, mean precipitation = 0.1 in) to about 2 (100% error, mean precipitation = 0.1 in).

Effect of spatial variability of storm on the optimal placement of best management practices (BMPs).

PubMed

Chang, C L; Chiueh, P T; Lo, S L

2007-12-01

It is significant to design best management practices (BMPs) and determine the proper BMPs placement for the purpose that can not only satisfy the water quantity and water quality standard, but also lower the total cost of BMPs. The spatial rainfall variability can have much effect on its relative runoff and non-point source pollution (NPSP). Meantime, the optimal design and placement of BMPs would be different as well. The objective of this study was to discuss the relationship between the spatial variability of rainfall and the optimal BMPs placements. Three synthetic rainfall storms with varied spatial distributions, including uniform rainfall, downstream rainfall and upstream rainfall, were designed. WinVAST model was applied to predict runoff and NPSP. Additionally, detention pond and swale were selected for being structural BMPs. Scatter search was applied to find the optimal BMPs placement. The results show that mostly the total cost of BMPs is higher in downstream rainfall than in upstream rainfall or uniform rainfall. Moreover, the cost of detention pond is much higher than swale. Thus, even though detention pond has larger efficiency for lowering peak flow and pollutant exports, it is not always the determined set in each subbasin.

Climate Change Impact on Rainfall: How will Threaten Wheat Yield?

NASA Astrophysics Data System (ADS)

Tafoughalti, K.; El Faleh, E. M.; Moujahid, Y.; Ouargaga, F.

2018-05-01

Climate change has a significant impact on the environmental condition of the agricultural region. Meknes has an agrarian economy and wheat production is of paramount importance. As most arable area are under rainfed system, Meknes is one of the sensitive regions to rainfall variability and consequently to climate change. Therefore, the use of changes in rainfall is vital for detecting the influence of climate system on agricultural productivity. This article identifies rainfall temporal variability and its impact on wheat yields. We used monthly rainfall records for three decades and wheat yields records of fifteen years. Rainfall variability is assessed utilizing the precipitation concentration index and the variation coefficient. The association between wheat yields and cumulative rainfall amounts of different scales was calculated based on a regression model. The analysis shown moderate seasonal and irregular annual rainfall distribution. Yields fluctuated from 210 to 4500 Kg/ha with 52% of coefficient of variation. The correlation results shows that wheat yields are strongly correlated with rainfall of the period January to March. This investigation concluded that climate change is altering wheat yield and it is crucial to adept the necessary adaptation to challenge the risk.

Optimal traits of plant hydraulic capacitance as an adaptation to hydroclimatic variability

NASA Astrophysics Data System (ADS)

Hartzell, S. R.; Bartlett, M. S., Jr.; Porporato, A. M.

2016-12-01

Hydraulic capacitance allows plants to uptake and store water when it is abundant. This stored water is utilized during periods of water stress, decreasing tissue damage and increasing carbon assimilation. By providing a more consistent and readily accessible water supply, it buffers water stress variability across daily and seasonal timescales. The rate of plant water storage and withdrawal varies widely between plant species and is principally governed by several plant hydraulic parameters, principally the hydraulic capacitance, the total water storage capacity, and the conductance between xylem and water storage tissue. The timescale of the plant response to changes in environmental conditions may be related to the timescale of relevant environmental variability. For example, the Baobab tree (Adansonia), which grows in an environment with very strong seasonal rainfall variability, has a relatively long timescale of hydraulic response, while an evergreen tree such as Pinus taeda, which mainly contends with daily and inter-rainfall moisture variability, has a much shorter timescale of hydraulic response. Here a model of hydraulic capacitance is coupled to a resistance model of soil-plant-atmosphere continuum. We force this model with stochastic rainfall and examine plant responses to moisture variability at various timescales. Optimal plant hydraulic properties are examined as a function of mean soil moisture (daily variability), mean period between rainfall events (inter-rainfall variability), and seasonal rainfall variability, and the relative importance of each type of variability in shaping plant water use strategies is assessed. Results are compared to typical hydraulic parameters of plants growing under specific environmental conditions. Values of hydraulic traits which optimize carbon assimilation and water use efficiency are found; these values are dependent on mean environmental conditions as well as the timescale of environmental variability.

Reconstruction and analysis of spring rainfall over the southeastern US for the past 1000 years

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

Stahle, D.W.; Cleaveland, M.K.

1992-12-01

Tree-ring chronologies can provide surprisingly accurate estimates of the natural variability of important climate parameters such as precipitation and temperature during the centuries prior to the industrial Revolution. Bald cypress tree-ring chronologies have been used to reconstruct spring rainfall for the past 1000 years in North Carolina, South Carolina, and Georgia. These rainfall reconstructions explain from 54% to 68% of the spring rainfall variance in each state, and are well verified against independent rainfall measurements. In fact, these tree-ring data explain only 6% to 13% less statewide rainfall variance than is explained by the same number of instrumental raingage records.more » The reconstructions indicate that the spring rainfall extremes and decade-long regimes witnessed during the past century of instrumental observation have been a prominent feature of southeastern United States climate over the past millennium. These spring rainfall regimes are linked in part to anomalies in the seasonal expansion and migration of the subtropical anticyclone over the North Atlantic. The western sector of the Bermuda high often ridges strongly westward into the southeastern United States during dry springs, but during wet springs it is usually located east of its mean position and well offshore. Similar anomalies in the western sector of the Bermuda high occurred during multidecadal regimes of spring rainfall over the Southeast. During the relatively dry springs from 1901 to 1939, the high often ridged into the Southeast, but the western periphery of the high was more frequently located offshore during the relatively wet period from 1940 to 1980. Spring and summer rainfall extremes and decade-long regimes over the Southeast are frequently out of phase, and the tendency for wet (dry) springs to be followed by dry (wet) summers also appears to reflect anomalies in the zonal position of the Bermuda high during spring and summer.« less

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.

Challenges in predicting and simulating summer rainfall in the eastern China

NASA Astrophysics Data System (ADS)

Liang, Ping; Hu, Zeng-Zhen; Liu, Yunyun; Yuan, Xing; Li, Xiaofan; Jiang, Xingwen

2018-05-01

To demonstrate the challenge of summer rainfall prediction and simulation in the eastern China, in this work, we examine the skill of the state-of-the-art climate models, evaluate the impact of sea surface temperature (SST) on forecast skill and estimate the predictability by using perfect model approach. The challenge is further demonstrated by assessing the ability of various reanalyses in capturing the observed summer rainfall variability in the eastern China and by examining the biases in reanalyses and in a climate model. Summer rainfall forecasts (hindcasts) initiated in May from eight seasonal forecast systems have low forecast skill with linear correlation of - 0.3 to 0.5 with observations. The low forecast skill is consistent with the low perfect model score ( 0.1-0.3) of atmospheric model forced by observed SST, due to the fact that external forcing (SST) may play a secondary role in the summer rainfall variation in the eastern China. This is a common feature for the climate variation over the middle and high latitude lands, where the internal dynamical processes dominate the rainfall variation in the eastern China and lead to low predictability, and external forcing (such as SST) plays a secondary role and is associated with predictable fraction. Even the reanalysis rainfall has some remarkable disagreements with the observation. Statistically, more than 20% of the observed variance is not captured by the mean of six reanalyses. Among the reanalyses, JRA55 stands out as the most reliable one. In addition, the reanalyses and climate model have pronounced biases in simulating the mean rainfall. These defaults mean an additional challenge in predicting the summer rainfall variability in the eastern China that has low predictability in nature.

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.

Sensitivity of Catchment Transit Times to Rainfall Variability Under Present and Future Climates

NASA Astrophysics Data System (ADS)

Wilusz, Daniel C.; Harman, Ciaran J.; Ball, William P.

2017-12-01

Hydrologists have a relatively good understanding of how rainfall variability shapes the catchment hydrograph, a reflection of the celerity of hydraulic head propagation. Much less is known about the influence of rainfall variability on catchment transit times, a reflection of water velocities that control solute transport. This work uses catchment-scale lumped parameter models to decompose the relationship between rainfall variability and an important metric of transit times, the time-varying fraction of young water (<90 days old) in streams (FYW). A coupled rainfall-runoff model and rank StorAge Selection (rSAS) transit time model were calibrated to extensive hydrometric and environmental tracer data from neighboring headwater catchments in Plynlimon, Wales from 1999 to 2008. At both sites, the mean annual FYW increased more than 13 percentage points from the driest to the wettest year. Yearly mean rainfall explained most between-year variation, but certain signatures of rainfall pattern were also associated with higher FYW including: more clustered storms, more negatively skewed storms, and higher covariance between daily rainfall and discharge. We show that these signatures are symptomatic of an "inverse storage effect" that may be common among watersheds. Looking to the future, changes in rainfall due to projected climate change caused an up to 19 percentage point increase in simulated mean winter FYW and similarly large decreases in the mean summer FYW. Thus, climate change could seasonally alter the ages of water in streams at these sites, with concomitant impacts on water quality.

Variability of rainfall over Lake Kariba catchment area in the Zambezi river basin, Zimbabwe

NASA Astrophysics Data System (ADS)

Muchuru, Shepherd; Botai, Joel O.; Botai, Christina M.; Landman, Willem A.; Adeola, Abiodun M.

2016-04-01

In this study, average monthly and annual rainfall totals recorded for the period 1970 to 2010 from a network of 13 stations across the Lake Kariba catchment area of the Zambezi river basin were analyzed in order to characterize the spatial-temporal variability of rainfall across the catchment area. In the analysis, the data were subjected to intervention and homogeneity analysis using the Cumulative Summation (CUSUM) technique and step change analysis using rank-sum test. Furthermore, rainfall variability was characterized by trend analysis using the non-parametric Mann-Kendall statistic. Additionally, the rainfall series were decomposed and the spectral characteristics derived using Cross Wavelet Transform (CWT) and Wavelet Coherence (WC) analysis. The advantage of using the wavelet-based parameters is that they vary in time and can therefore be used to quantitatively detect time-scale-dependent correlations and phase shifts between rainfall time series at various localized time-frequency scales. The annual and seasonal rainfall series were homogeneous and demonstrated no apparent significant shifts. According to the inhomogeneity classification, the rainfall series recorded across the Lake Kariba catchment area belonged to category A (useful) and B (doubtful), i.e., there were zero to one and two absolute tests rejecting the null hypothesis (at 5 % significance level), respectively. Lastly, the long-term variability of the rainfall series across the Lake Kariba catchment area exhibited non-significant positive and negative trends with coherent oscillatory modes that are constantly locked in phase in the Morlet wavelet space.

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.

SUB-PIXEL RAINFALL VARIABILITY AND THE IMPLICATIONS FOR UNCERTAINTIES IN RADAR RAINFALL ESTIMATES

EPA Science Inventory

Radar estimates of rainfall are subject to significant measurement uncertainty. Typically, uncertainties are measured by the discrepancies between real rainfall estimates based on radar reflectivity and point rainfall records of rain gauges. This study investigates how the disc...

Temporal and spatial changes of rainfall and streamflow in the Upper Tekezē-Atbara river basin, Ethiopia

NASA Astrophysics Data System (ADS)

Gebremicael, Tesfay G.; Mohamed, Yasir A.; Zaag, Pieter v.; Hagos, Eyasu Y.

2017-04-01

The Upper Tekezē-Atbara river sub-basin, part of the Nile Basin, is characterized by high temporal and spatial variability of rainfall and streamflow. In spite of its importance for sustainable water use and food security, the changing patterns of streamflow and its association with climate change is not well understood. This study aims to improve the understanding of the linkages between rainfall and streamflow trends and identify possible drivers of streamflow variabilities in the basin. Trend analyses and change-point detections of rainfall and streamflow were analysed using Mann-Kendall and Pettitt tests, respectively, using data records for 21 rainfall and 9 streamflow stations. The nature of changes and linkages between rainfall and streamflow were carefully examined for monthly, seasonal and annual flows, as well as indicators of hydrologic alteration (IHA). The trend and change-point analyses found that 19 of the tested 21 rainfall stations did not show statistically significant changes. In contrast, trend analyses on the streamflow showed both significant increasing and decreasing patterns. A decreasing trend in the dry season (October to February), short season (March to May), main rainy season (June to September) and annual totals is dominant in six out of the nine stations. Only one out of nine gauging stations experienced significant increasing flow in the dry and short rainy seasons, attributed to the construction of Tekezē hydropower dam upstream this station in 2009. Overall, streamflow trends and change-point timings were found to be inconsistent among the stations. Changes in streamflow without significant change in rainfall suggests factors other than rainfall drive the change. Most likely the observed changes in streamflow regimes could be due to changes in catchment characteristics of the basin. Further studies are needed to verify and quantify the hydrological changes shown in statistical tests by identifying the physical mechanisms behind those changes. The findings from this study are useful as a prerequisite for studying the effects of catchment management dynamics on the hydrological variabilities in the basin.

Precipitation Changes Throughout the South Pacific Convergence Zone During the Last 2000 Years

NASA Astrophysics Data System (ADS)

Maloney, A. E.; Nelson, D. B.; Sachs, J. P.

2016-12-01

The South Pacific Convergence Zone (SPCZ) is the southern hemisphere's most prominent precipitation feature extending 3000km southeastwards from Papua New Guinea to French Polynesia. Seasonal and interannual variability in SPCZ rainfall is well characterized by satellite data, however an understanding of this feature prior to the instrumental record is lacking. Rainfall in the western tropical Pacific is difficult to reconstruct due to a dearth of archives that are both high-resolution and continuous. Here we present molecular fossil hydroclimate reconstructions from the last 2000 years. The hydrogen isotopic composition of the algal lipid biomarker dinosterol was measured in 10 freshwater lake sediment cores from 7 lakes on 4 islands in Vanuatu, the Solomon Islands, and Wallis and Futuna. Coretop δ2Hdinosterol values were well correlated with satellite-derived rainfall rates, providing a transfer function for deriving precipitation rates from sedimentary δ2Hdinosterol values. The Vanuatu and Wallis records indicate that the south-western portion of the SPCZ was driest during the transition from the Medieval Warm Period (MWP) to the Little Ice Age (LIA) (1200-1400 CE) with rainfall rates as low as 2mm/day compare to more typical values of 4mm/day. Conversely, the central SPCZ (Solomon Islands) experienced the driest conditions ( 5mm/day) during the MWP (600-1200 CE) and has maintained high ( 9mm/day) rainfall rates since the LIA. The tropical water cycle influences global climate and these quantitative precipitation records are important for understanding SPCZ natural variability.

Use of a scenario-neutral approach to identify the key hydro-meteorological attributes that impact runoff from a natural catchment

NASA Astrophysics Data System (ADS)

Guo, Danlu; Westra, Seth; Maier, Holger R.

2017-11-01

Scenario-neutral approaches are being used increasingly for assessing the potential impact of climate change on water resource systems, as these approaches allow the performance of these systems to be evaluated independently of climate change projections. However, practical implementations of these approaches are still scarce, with a key limitation being the difficulty of generating a range of plausible future time series of hydro-meteorological data. In this study we apply a recently developed inverse stochastic generation approach to support the scenario-neutral analysis, and thus identify the key hydro-meteorological variables to which the system is most sensitive. The stochastic generator simulates synthetic hydro-meteorological time series that represent plausible future changes in (1) the average, extremes and seasonal patterns of rainfall; and (2) the average values of temperature (Ta), relative humidity (RH) and wind speed (uz) as variables that drive PET. These hydro-meteorological time series are then fed through a conceptual rainfall-runoff model to simulate the potential changes in runoff as a function of changes in the hydro-meteorological variables, and runoff sensitivity is assessed with both correlation and Sobol' sensitivity analyses. The method was applied to a case study catchment in South Australia, and the results showed that the most important hydro-meteorological attributes for runoff were winter rainfall followed by the annual average rainfall, while the PET-related meteorological variables had comparatively little impact. The high importance of winter rainfall can be related to the winter-dominated nature of both the rainfall and runoff regimes in this catchment. The approach illustrated in this study can greatly enhance our understanding of the key hydro-meteorological attributes and processes that are likely to drive catchment runoff under a changing climate, thus enabling the design of tailored climate impact assessments to specific water resource systems.

Scaling Linguistic Characterization of Precipitation Variability

NASA Astrophysics Data System (ADS)

Primo, C.; Gutierrez, J. M.

2003-04-01

Rainfall variability is influenced by changes in the aggregation of daily rainfall. This problem is of great importance for hydrological, agricultural and ecological applications. Rainfall averages, or accumulations, are widely used as standard climatic parameters. However different aggregation schemes may lead to the same average or accumulated values. In this paper we present a fractal method to characterize different aggregation schemes. The method provides scaling exponents characterizing weekly or monthly rainfall patterns for a given station. To this aim, we establish an analogy with linguistic analysis, considering precipitation as a discrete variable (e.g., rain, no rain). Each weekly, or monthly, symbolic precipitation sequence of observed precipitation is then considered as a "word" (in this case, a binary word) which defines a specific weekly rainfall pattern. Thus, each site defines a "language" characterized by the words observed in that site during a period representative of the climatology. Then, the more variable the observed weekly precipitation sequences, the more complex the obtained language. To characterize these languages, we first applied the Zipf's method obtaining scaling histograms of rank ordered frequencies. However, to obtain significant exponents, the scaling must be maintained some orders of magnitude, requiring long sequences of daily precipitation which are not available at particular stations. Thus this analysis is not suitable for applications involving particular stations (such as regionalization). Then, we introduce an alternative fractal method applicable to data from local stations. The so-called Chaos-Game method uses Iterated Function Systems (IFS) for graphically representing rainfall languages, in a way that complex languages define complex graphical patterns. The box-counting dimension and the entropy of the resulting patterns are used as linguistic parameters to quantitatively characterize the complexity of the patterns. We illustrate the high climatological discrimination power of the linguistic parameters in the Iberian peninsula, when compared with other standard techniques (such as seasonal mean accumulated precipitation). As an example, standard and linguistic parameters are used as inputs for a clustering regionalization method, comparing the resulting clusters.

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.

Coupling of Indian and East Asian Monsoon Precipitation in July-August

NASA Astrophysics Data System (ADS)

Day, J. A.; Fung, I. Y.; Risi, C. M.

2014-12-01

Recent work suggests that summer rainfall in the Indian and East Asian monsoons results from different mechanisms. The onset of intense convection in India is mediated by Hadley Cell transitions, whereas frontal rainfall in China (most notably during Meiyu season in June) arises from forced meridional convergence and zonal heat transport in the wake of the Tibetan Plateau. However, the leading mode of July-August interannual rainfall variability for All-Asia (defined as the region within 68E-140E and 5N-45N) demonstrates a statistically significant coupling between monthly anomalies in India and China. In particular, positive anomalies along the Himalayan Foothills are associated with positive anomalies along the Yangtze River, and also with negative anomalies over central India and northern and southern China. The entire pattern reverses in dry years over the Himalayan Foothills. This coupling is not significantly correlated with ENSO, the leading mode of global interannual variability. We propose that a channel of moisture transport links the Bay of Bengal to the Yangtze River valley across the high terrain of the Yunnan Plateau, on the southeast edge of the Tibetan Plateau. This channel only activates in July, when the maximum of moist static energy (MSE) shifts to land, and weakens in September with the cooling of Bay of Bengal SST. Our mechanism is substantiated by analysis of output from the LMDZ5 model, which includes a high-resolution nested grid nudged to reanalysis, improving the simulation of the Indian Monsoon and performance near high topography. Potential changes in moisture transport across the Yunnan Plateau under 21st century warming conditions may lead to modified interannual variability of Asian rainfall.

Exploring the Role of Soil Moisture Conditions for Rainfall Triggered Landslides on Catchment Scale: the case of the Ialomita Sub Carpathians, Romania

NASA Astrophysics Data System (ADS)

Chitu, Zenaida; Bogaard, Thom; Adler, Mary-Jeanne; Steele-Dunne, Susan; Hrachowitz, Markus; Busuioc, Aristita; Sandric, Ionut; Istrate, Alexandru

2014-05-01

Like in many parts of the world, landslides represent in Romania recurrent phenomena that produce numerous damages to the infrastructure every few years. The high frequency of landslide events over the world has resulted to the development of many early warning systems that are based on the definition of rainfall thresholds triggering landslides. In Romania in particular, recent studies exploring the temporal occurrence of landslides have revealed that rainfall represents the most important triggering factor for landslides. The presence of low permeability soils and gentle slope degrees in the Ialomita Subcarpathians of Romania makes that cumulated precipitation over variable time interval and the hydraulic response of the soil plays a key role in landslides triggering. In order to identify the slope responses to rainfall events in this particular area we investigate the variability of soil moisture and its relationship to landslide events in three Subcarpathians catchments (Cricovul Dulce, Bizididel and Vulcana) by combining in situ measurements, satellite-based radiometry and hydrological modelling. For the current study, hourly soil moisture measurements from six soil moisture monitoring stations that are fitted with volumetric soil moisture sensors, temperature soil sensors and rain gauges sensors are used. Pedotransfer functions will be applied in order to infer hydraulic soil properties from soil texture sampled from 50 soil profiles. The information about spatial and temporal variability of soil moisture content will be completed with the Level 2 soil moisture products from the Soil Moisture and Ocean Salinity (SMOS) mission. A time series analysis of soil moisture is planned to be integrated to landslide and rainfall time series in order to determine a preliminary rainfall threshold triggering landslides in Ialomita Subcarpathians.

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.

GPM Precipitation Estimates over the Walnut Gulch Experimental Watershed/LTAR site in Southeastern Arizona

NASA Astrophysics Data System (ADS)

Goodrich, D. C.; Tan, J.; Petersen, W. A.; Unkrich, C. C.; Demaria, E. M.; Hazenberg, P.; Lakshmi, V.

2017-12-01

Precipitation profiles from the GPM Core Observatory Dual-frequency Precipitation Radar (DPR) form part of the a priori database used in GPM Goddard Profiling (GPROF) algorithm passive microwave radiometer retrievals of rainfall. The GPROF retrievals are in turn used as high quality precipitation estimates in gridded products such as IMERG. Due to the variability in and high surface emissivity of land surfaces, GPROF performs precipitation retrievals as a function of surface classes. As such, different surface types may possess different error characteristics, especially over arid regions where high quality ground measurements are often lacking. Importantly, the emissive properties of land also result in GPROF rainfall estimates being driven primarily by the higher frequency radiometer channels (e.g., > 89 GHz) where precipitation signals are most sensitive to coupling between the ice-phase and rainfall production. In this study, we evaluate the rainfall estimates from the Ku channel of the DPR as well as GPROF estimates from various passive microwave sensors. Our evaluation is conducted at the level of individual satellite pixels (5 to 15 km in diameter), against a dense network of weighing rain gauges (90 in 150 km2) in the USDA-ARS Walnut Gulch Experimental Watershed and Long-Term Agroecosystem Research (LTAR) site in southeastern Arizona. The multiple gauges in each satellite pixel and precise accumulation about the overpass time allow a spatially and temporally representative comparison between the satellite estimates and ground reference. Over Walnut Gulch, both the Ku and GPROF estimates are challenged to delineate between rain and no-rain. Probabilities of detection are relatively high, but false alarm ratios are also high. The rain intensities possess a negative bias across nearly all sensors. It is likely that storm types, arid conditions and the highly variable precipitation regime present a challenge to both rainfall retrieval algorithms. An array of ground-based sensors is being deployed during the 2017 monsoon season to better understand possible reasons for this discrepancy.

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.

Rainfall and streamflow from small tree-covered and fern-covered and burned watersheds in Hawaii

Treesearch

H. W. Anderson; P. D. Duffy; Teruo Yamamoto

1966-01-01

Streamflow from two 30-acre watersheds near Honolulu was studied by using principal components regression analysis. Models using data on monthly, storm, and peak discharges were tested against several variables expressing amount and intensity of rainfall, and against variables expressing antecedent rainfall. Explained variation ranged from 78 to 94 percent. The...

The Influence of ENSO to the Rainfall Variability in North Sumatra Province

NASA Astrophysics Data System (ADS)

Irwandi, H.; Pusparini, N.; Ariantono, J. Y.; Kurniawan, R.; Tari, C. A.; Sudrajat, A.

2018-04-01

The El Niño Southern Oscillation (ENSO) is a global phenomenon that affects the variability of rainfall in North Sumatra. The influence of ENSO will be different for each region. This review will analyse the influence of ENSO activity on seasonal and annual rainfall variability. In this research, North Sumatra Province will be divided into 4 (four) regions based on topographical conditions, such as: East Coast (EC), East Slope (ES), Mountains (MT), and West Coast (WC). The method used was statistical and descriptive analysis. Data used in this research were rainfall data from 15 stations / climate observation posts which spread in North Sumatera region and also anomaly data of Nino 3.4 region from period 1981-2016. The results showed that the active El Niño had an effect on the decreasing the rainfall during the period of DJF, JJA and SON in East Coast, East Slope, and Mountains with the decreasing of average percentage of annual rainfall up to 7%. On the contrary, the active La Nina had an effect on the addition of rainfall during the period DJF and JJA in the East Coast and Mountains with the increasing of average percentage of annual rainfall up to 6%.

Quantification of Holocene Asian monsoon rainfall from spatially separated cave records

NASA Astrophysics Data System (ADS)

Hu, Chaoyong; Henderson, Gideon M.; Huang, Junhua; Xie, Shucheng; Sun, Ying; Johnson, Kathleen R.

2008-02-01

A reconstruction of Holocene rainfall is presented for southwest China — an area prone to drought and flooding due to variability in the East Asian monsoon. The reconstruction is derived by comparing a new high-resolution stalagmite δ18O record with an existing record from the same moisture transport pathway. The new record is from Heshang Cave (30°27'N, 110°25'E; 294 m) and shows no sign of kinetic or evaporative effects so can be reliably interpreted as a record of local rainfall composition and temperature. Heshang lies 600 km downwind from Dongge Cave which has a published high-resolution δ18O record (Wang, Y.J., Cheng, H., Edwards, R.L., He, Y.Q., Kong, X.G., An, Z.S., Wu, J.Y., Kelly, M.J., Dykoski, C.A., Li, X.D., 2005. The Holocene Asian monsoon: links to solar changes and North Atlantic climate. Science 308, 854-857). By differencing co-eval δ18O values for the two caves, secondary controls on δ18O (e.g. moisture source, moisture transport, non-local rainfall, temperature) are circumvented and the resulting Δ δ18O signal is controlled directly by the amount of rain falling between the two sites. This is confirmed by comparison with rainfall data from the instrumental record, which also allows a calibration of the Δ δ18O proxy. The calibrated Δ δ18O record provides a quantitative history of rainfall in southwest China which demonstrates that rainfall was 8% higher than today during the Holocene climatic optimum (≈ 6 ka), but only 3% higher during the early Holocene. Significant multi-centennial variability also occurred, with notable dry periods at 8.2 ka, 4.8-4.1 ka, 3.7-3.1 ka, 1.4-1.0 ka and during the Little Ice Age. This Holocene rainfall record provides a good target with which to test climate models. The approach used here, of combining stalagmite records from more than one location, will also allow quantification of rainfall patterns for past times in other regions.

Determining the influence of rainfall patterns and carbendazim on the surface activity of the earthworm Lumbricus terrestris.

PubMed

Ellis, Sian R; Hodson, Mark E; Wege, Phil

2010-08-01

Carbendazim is highly toxic to earthworms and is used as a standard control substance when running field-based trials of pesticides, but results using carbendazim are highly variable. In the present study, impacts of timing of rainfall events following carbendazim application on earthworms were investigated. Lumbricus terrestris were maintained in soil columns to which carbendazim and then deionized water (a rainfall substitute) were applied. Carbendazim was applied at 4 kg/ha, the rate recommended in pesticide field trials. Three rainfall regimes were investigated: initial and delayed heavy rainfall 24 h and 6 d after carbendazim application, and frequent rainfall every 48 h. Earthworm mortality and movement of carbendazim through the soil was assessed 14 d after carbendazim application. No detectable movement of carbendazim occurred through the soil in any of the treatments or controls. Mortality in the initial heavy and frequent rainfall was significantly higher (approximately 55%) than in the delayed rainfall treatment (approximately 25%). This was due to reduced bioavailability of carbendazim in the latter treatment due to a prolonged period of sorption of carbendazim to soil particles before rainfall events. The impact of carbendazim application on earthworm surface activity was assessed using video cameras. Carbendazim applications significantly reduced surface activity due to avoidance behavior of the earthworms. Surface activity reductions were least in the delayed rainfall treatment due to the reduced bioavailability of the carbendazim. The nature of rainfall events' impacts on the response of earthworms to carbendazim applications, and details of rainfall events preceding and following applications during field trials should be made at a higher level of resolution than is currently practiced according to standard International Organization for Standardization protocols. Copyright 2010 SETAC

Rainfall erosivity in the Fukushima Prefecture: implications for radiocesium mobilization and migration

NASA Astrophysics Data System (ADS)

Laceby, J. Patrick; Chartin, Caroline; Degan, Francesca; Onda, Yuichi; Evrard, Olivier; Cerdan, Olivier; Ayrault, Sophie

2015-04-01

The Fukushima Dai-ichi nuclear power plant (FDNPP) accident in March 2011 led to the fallout of predominantly radiocesium (137Cs and 134Cs) on soils of the Fukushima Prefecture. This radiocesium was primarily fixated to fine soil particles. Subsequently, rainfall and snow melt run-off events result in significant quantities of radiocesium being eroded and transported throughout the coastal catchments and ultimately exported to the Pacific Ocean. Erosion models, such as the Universal Soil Loss Equation (USLE), relate rainfall directly to soil erosion in that an increase in rainfall one month will directly result in a proportional increase in sediment generation. Understanding the rainfall regime of the region is therefore fundamental to modelling and predicting long-term radiocesium export. Here, we analyze rainfall data for ~40 stations within a 100 km radius of the FDNPP. First we present general information on the rainfall regime in the region based on monthly and annual rainfall totals. Second we present general information on rainfall erosivity, the R-factor of the USLE equation and its relationship to the general rainfall data. Third we examine rainfall trends over the last 100 years at several of the rainfall stations to understand temporal trends and whether ~20 years of data is sufficient to calculate the R-factor for USLE models. Fourth we present monthly R-factor maps for the Fukushima coastal catchments impacted by the FDNPP accident. The variability of the rainfall in the region, particularly during the typhoon season, is likely resulting in a similar variability in the transfer and migration of radiocesium throughout the coastal catchments of the Fukushima Prefecture. Characterizing the region's rainfall variability is fundamental to modelling sediment and the concomitant radiocesium migration and transfer throughout these catchments and ultimately to the Pacific Ocean.

Secular spring rainfall variability at local scale over Ethiopia: trend and associated dynamics

NASA Astrophysics Data System (ADS)

Tsidu, Gizaw Mengistu

2017-10-01

Spring rainfall secular variability is studied using observations, reanalysis, and model simulations. The joint coherent spatio-temporal secular variability of gridded monthly gauge rainfall over Ethiopia, ERA-Interim atmospheric variables and sea surface temperature (SST) from Hadley Centre Sea Ice and SST (HadISST) data set is extracted using multi-taper method singular value decomposition (MTM-SVD). The contemporaneous associations are further examined using partial Granger causality to determine presence of causal linkage between any of the climate variables. This analysis reveals that only the northwestern Indian Ocean secular SST anomaly has direct causal links with spring rainfall over Ethiopia and mean sea level pressure (MSLP) over Africa inspite of the strong secular covariance of spring rainfall, SST in parts of subtropical Pacific, Atlantic, Indian Ocean and MSLP. High secular rainfall variance and statistically significant linear trend show consistently that there is a massive decline in spring rain over southern Ethiopia. This happened concurrently with significant buildup of MSLP over East Africa, northeastern Africa including parts of the Arabian Peninsula, some parts of central Africa and SST warming over all ocean basins with the exception of the ENSO regions. The east-west pressure gradient in response to the Indian Ocean warming led to secular southeasterly winds over the Arabian Sea, easterly over central Africa and equatorial Atlantic. These flows weakened climatological northeasterly flow over the Arabian Sea and southwesterly flow over equatorial Atlantic and Congo basins which supply moisture into the eastern Africa regions in spring. The secular divergent flow at low level is concurrent with upper level convergence due to the easterly secular anomalous flow. The mechanisms through which the northwestern Indian Ocean secular SST anomaly modulates rainfall are further explored in the context of East Africa using a simplified atmospheric general circulation model (AGCM) coupled to mixed-layer oceanic model. The rainfall anomaly (with respect to control simulation), forced by the northwestern Indian Ocean secular SST anomaly and averaged over the 30-year period, exhibits prevalence of dry conditions over East and equatorial Africa in agreement with observation. The atmospheric response to secular SST warming anomaly led to divergent flow at low levels and subsidence at the upper troposphere over regions north of 5° S on the continent and vice versa over the Indian Ocean. This surface difluence over East Africa, in addition to its role in suppressing convective activity, deprives the region of moisture supply from the Indian Ocean as well as the Atlantic and Congo basins.

Rainfall erosivity and sediment load over the Poyang Lake Basin under variable climate and human activities since the 1960s

NASA Astrophysics Data System (ADS)

Gu, Chaojun; Mu, Xingmin; Gao, Peng; Zhao, Guangju; Sun, Wenyi; Yu, Qiang

2018-03-01

Accelerated soil erosion exerts adverse effects on water and soil resources. Rainfall erosivity reflects soil erosion potential driven by rainfall, which is essential for soil erosive risk assessment. This study investigated the spatiotemporal variation of rainfall erosivity and its impacts on sediment load over the largest freshwater lake basin of China (the Poyang Lake Basin, abbreviate to PYLB). The spatiotemporal variations of rainfall erosivity from 1961 to 2014 based on 57 meteorological stations were detected using the Mann-Kendall test, linear regression, and kriging interpolation method. The sequential t test analysis of regime shift (STARS) was employed to identify the abrupt changes of sediment load, and the modified double mass curve was used to assess the impacts of rainfall erosivity variability on sediment load. It was found that there was significant increase (P < 0.05) in rainfall erosivity in winter due to the significant increase in January over the last 54 years, whereas no trend in year and other seasons. Annual sediment load into the Poyang Lake (PYL) decreased significantly (P < 0.01) between 1961 and 2014, and the change-points were identified in both 1985 and 2003. It was found that take annual rainfall erosivity as the explanatory variables of the double mass curves is more reasonable than annual rainfall and erosive rainfall. The estimation via the modified double mass curve demonstrated that compared with the period before change-point (1961-1984), the changes of rainfall erosivity increased 8.0 and 2.1% of sediment load during 1985-2002 and 2003-2014, respectively. Human activities decreased 50.2 and 69.7% of sediment load during the last two periods, which indicated effects of human activities on sediment load change was much larger than that of rainfall erosivity variability in the PYLB.

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.

High resolution stalagmite climate record from the Yucatán Peninsula spanning the Maya terminal classic period

NASA Astrophysics Data System (ADS)

Medina-Elizalde, Martín; Burns, Stephen J.; Lea, David W.; Asmerom, Yemane; von Gunten, Lucien; Polyak, Victor; Vuille, Mathias; Karmalkar, Ambarish

2010-09-01

The decline of the Classic Maya civilization was complex and geographically variable, and occurred over a ~ 150-year interval, known as the Terminal Classic Period (TCP, C.E. 800-950). Paleoclimate studies based on lake sediments from the Yucatán Peninsula lowlands suggested that drought prevailed during the TCP and was likely an important factor in the disintegration of the Classic Maya civilization. The lacustrine evidence for decades of severe drought in the Yucatán Peninsula, however, does not readily explain the long 150-year socio-political decline of the Classic Maya civilization. Here we present a new, absolute-dated, high-resolution stalagmite δ18O record from the northwest Yucatán Peninsula that provides a much more detailed picture of climate variability during the last 1500 years. Direct calibration between stalagmite δ18O and rainfall amount offers the first quantitative estimation of rainfall variability during the Terminal Classic Period. Our results show that eight severe droughts, lasting from 3 to 18 years, occurred during major depopulation events of Classic Maya city-states. During these droughts, rainfall was reduced by 52% to 36%. The number and short duration of the dry intervals help explain why the TCP collapse of the Mayan civilization occurred over 150 years.

Global Climatic Indices Influence on Rainfall Spatiotemporal Distribution : A Case Study from Morocco

NASA Astrophysics Data System (ADS)

Elkadiri, R.; Zemzami, M.; Phillips, J.

2017-12-01

The climate of Morocco is affected by the Mediterranean Sea, the Atlantic Ocean the Sahara and the Atlas mountains, creating a highly variable spatial and temporal distribution. In this study, we aim to decompose the rainfall in Morocco into global and local signals and understand the contribution of the climatic indices (CIs) on rainfall. These analyses will contribute in understanding the Moroccan climate that is typical of other Mediterranean and North African climatic zones. In addition, it will contribute in a long-term prediction of climate. The constructed database ranges from 1950 to 2013 and consists of monthly data from 147 rainfall stations and 37 CIs data provided mostly by the NOAA Climate Prediction Center. The next general steps were followed: (1) the study area was divided into 9 homogenous climatic regions and weighted precipitation was calculated for each region to reduce the local effects. (2) Each CI was decomposed into nine components of different frequencies (D1 to D9) using wavelet multiresolution analysis. The four lowest frequencies of each CI were selected. (3) Each of the original and resulting signals were shifted from one to six months to account for the effect of the global patterns. The application of steps two and three resulted in the creation of 1225 variables from the original 37 CIs. (4) The final 1225 variables were used to identify links between the global and regional CIs and precipitation in each of the nine homogenous regions using stepwise regression and decision tree. The preliminary analyses and results were focused on the north Atlantic zone and have shown that the North Atlantic Oscillation (PC-based) from NCAR (NAOPC), the Arctic Oscillation (AO), the North Atlantic Oscillation (NAO), the Western Mediterranean Oscillation (WMO) and the Extreme Eastern Tropical Pacific Sea Surface Temperature (NINO12) have the highest correlation with rainfall (33%, 30%, 27%, 21% and -20%, respectively). In addition the 4-months lagged NINO12 and the 6-months lagged NAOPC and WMO have a collective contribution of more than 45% of the rainfall signal. Low frequencies are also represented in the rainfall; especially the 5th and 4th components of the decomposed CIs (48% and 42% of the frequencies, respectively) suggesting their potential contribution in the interannual rainfall variability.

Reconstructing pre-instrumental streamflow in Eastern Australia using a water balance approach

NASA Astrophysics Data System (ADS)

Tozer, C. R.; Kiem, A. S.; Vance, T. R.; Roberts, J. L.; Curran, M. A. J.; Moy, A. D.

2018-03-01

Streamflow reconstructions based on paleoclimate proxies provide much longer records than the short instrumental period records on which water resource management plans are currently based. In Australia there is a lack of in-situ high resolution paleoclimate proxy records, but remote proxies with teleconnections to Australian climate have utility in producing streamflow reconstructions. Here we investigate, via a case study for a catchment in eastern Australia, the novel use of an Antarctic ice-core based rainfall reconstruction within a Budyko-framework to reconstruct ∼1000 years of annual streamflow. The resulting streamflow reconstruction captures interannual to decadal variability in the instrumental streamflow, validating both the use of the ice core rainfall proxy record and the Budyko-framework method. In the preinstrumental era the streamflow reconstruction shows longer wet and dry epochs and periods of streamflow variability that are higher than observed in the instrumental era. Importantly, for both the instrumental record and preinstrumental reconstructions, the wet (dry) epochs in the rainfall record are shorter (longer) in the streamflow record and this non-linearity must be considered when inferring hydroclimatic risk or historical water availability directly from rainfall proxy records alone. These insights provide a better understanding of present infrastructure vulnerability in the context of past climate variability for eastern Australia. The streamflow reconstruction presented here also provides a better understanding of the range of hydroclimatic variability possible, and therefore represents a more realistic baseline on which to quantify the potential impacts of anthropogenic climate change on water security.

The variability of the rainfall rate as a function of area

NASA Astrophysics Data System (ADS)

Jameson, A. R.; Larsen, M. L.

2016-01-01

Distributions of drop sizes can be expressed as DSD = Nt × PSD, where Nt is the total number of drops in a sample and PSD is the frequency distribution of drop diameters (D). Their discovery permitted remote sensing techniques for rainfall estimation using radars and satellites measuring over large domains of several kilometers. Because these techniques depend heavily on higher moments of the PSD, there has been a bias toward attributing the variability of the intrinsic rainfall rates R over areas (σR) to the variability of the PSDs. While this variability does increase up to a point with increasing domain dimension L, the variability of the rainfall rate R also depends upon the variability in the total number of drops Nt. We show that while the importance of PSDs looms large for small domains used in past studies, it is the variability of Nt that dominates the variability of R as L increases to 1 km and beyond. The PSDs contribute to the variability of R through the relative dispersion of χ = D3Vt, where Vt is the terminal fall speed of drops of diameter D. However, the variability of χ is inherently limited because drop sizes and fall speeds are physically limited. In contrast, it is shown that the variance of Nt continuously increases as the domain expands for physical reasons explained below. Over domains larger than around 1 km, it is shown that Nt dominates the variance of the rainfall rate with increasing L regardless of the PSD.

Regional changes in extreme monsoon rainfall deficit and excess in India

NASA Astrophysics Data System (ADS)

Pal, Indrani; Al-Tabbaa, Abir

2010-04-01

With increasing concerns about climate change, the need to understand the nature and variability of monsoon climatic conditions and to evaluate possible future changes becomes increasingly important. This paper deals with the changes in frequency and magnitudes of extreme monsoon rainfall deficiency and excess in India from 1871 to 2005. Five regions across India comprising variable climates were selected for the study. Apart from changes in individual regions, changing tendencies in extreme monsoon rainfall deficit and excess were also determined for the Indian region as a whole. The trends and their significance were assessed using non-parametric Mann-Kendall technique. The results show that intra-region variability for extreme monsoon seasonal precipitation is large and mostly exhibited a negative tendency leading to increasing frequency and magnitude of monsoon rainfall deficit and decreasing frequency and magnitude of monsoon rainfall excess.

Interannual rainfall variability and SOM-based circulation classification

NASA Astrophysics Data System (ADS)

Wolski, Piotr; Jack, Christopher; Tadross, Mark; van Aardenne, Lisa; Lennard, Christopher

2018-01-01

Self-Organizing Maps (SOM) based classifications of synoptic circulation patterns are increasingly being used to interpret large-scale drivers of local climate variability, and as part of statistical downscaling methodologies. These applications rely on a basic premise of synoptic climatology, i.e. that local weather is conditioned by the large-scale circulation. While it is clear that this relationship holds in principle, the implications of its implementation through SOM-based classification, particularly at interannual and longer time scales, are not well recognized. Here we use a SOM to understand the interannual synoptic drivers of climate variability at two locations in the winter and summer rainfall regimes of South Africa. We quantify the portion of variance in seasonal rainfall totals that is explained by year to year differences in the synoptic circulation, as schematized by a SOM. We furthermore test how different spatial domain sizes and synoptic variables affect the ability of the SOM to capture the dominant synoptic drivers of interannual rainfall variability. Additionally, we identify systematic synoptic forcing that is not captured by the SOM classification. The results indicate that the frequency of synoptic states, as schematized by a relatively disaggregated SOM (7 × 9) of prognostic atmospheric variables, including specific humidity, air temperature and geostrophic winds, captures only 20-45% of interannual local rainfall variability, and that the residual variance contains a strong systematic component. Utilising a multivariate linear regression framework demonstrates that this residual variance can largely be explained using synoptic variables over a particular location; even though they are used in the development of the SOM their influence, however, diminishes with the size of the SOM spatial domain. The influence of the SOM domain size, the choice of SOM atmospheric variables and grid-point explanatory variables on the levels of explained variance, is consistent with the general understanding of the dominant processes and atmospheric variables that affect rainfall variability at a particular location.

Animal perception of seasonal thresholds: changes in elephant movement in relation to rainfall patterns.

PubMed

Birkett, Patricia J; Vanak, Abi T; Muggeo, Vito M R; Ferreira, Salamon M; Slotow, Rob

2012-01-01

The identification of temporal thresholds or shifts in animal movement informs ecologists of changes in an animal's behaviour, which contributes to an understanding of species' responses in different environments. In African savannas, rainfall, temperature and primary productivity influence the movements of large herbivores and drive changes at different scales. Here, we developed a novel approach to define seasonal shifts in movement behaviour by examining the movements of a highly mobile herbivore (elephant; Loxodonta africana), in relation to local and regional rainfall patterns. We used speed to determine movement changes of between 8 and 14 GPS-collared elephant cows, grouped into five spatial clusters, in Kruger National Park, South Africa. To detect broad-scale patterns of movement, we ran a three-year daily time-series model for each individual (2007-2009). Piecewise regression models provided the best fit for elephant movement, which exhibited a segmented, waveform pattern over time. Major breakpoints in speed occurred at the end of the dry and wet seasons of each year. During the dry season, female elephant are constrained by limited forage and thus the distances they cover are shorter and less variable. Despite the inter-annual variability of rainfall, speed breakpoints were strongly correlated with both local and regional rainfall breakpoints across all three years. Thus, at a multi-year scale, rainfall patterns significantly affect the movements of elephant. The variability of both speed and rainfall breakpoints across different years highlights the need for an objective definition of seasonal boundaries. By using objective criteria to determine behavioural shifts, we identified a biologically meaningful indicator of major changes in animal behaviour in different years. We recommend the use of such criteria, from an animal's perspective, for delineating seasons or other extrinsic shifts in ecological studies, rather than arbitrarily fixed definitions based on convention or common practice.

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.

Are revised models better models? A skill score assessment of regional interannual variability

NASA Astrophysics Data System (ADS)

Sperber, Kenneth R.; Participating AMIP Modelling Groups

1999-05-01

Various skill scores are used to assess the performance of revised models relative to their original configurations. The interannual variability of all-India, Sahel and Nordeste rainfall and summer monsoon windshear is examined in integrations performed under the experimental design of the Atmospheric Model Intercomparison Project. For the indices considered, the revised models exhibit greater fidelity at simulating the observed interannual variability. Interannual variability of all-India rainfall is better simulated by models that have a more realistic rainfall climatology in the vicinity of India, indicating the beneficial effect of reducing systematic model error.

Are revised models better models? A skill score assessment of regional interannual variability

NASA Astrophysics Data System (ADS)

Participating AMIP Modelling Groups,; Sperber, Kenneth R.

Various skill scores are used to assess the performance of revised models relative to their original configurations. The interannual variability of all-India, Sahel and Nordeste rainfall and summer monsoon windshear is examined in integrations performed under the experimental design of the Atmospheric Model Intercomparison Project. For the indices considered, the revised models exhibit greater fidelity at simulating the observed interannual variability. Interannual variability of all-India rainfall is better simulated by models that have a more realistic rainfall climatology in the vicinity of India, indicating the beneficial effect of reducing systematic model error.

Analysis of spatial autocorrelation patterns of heavy and super-heavy rainfall in Iran

NASA Astrophysics Data System (ADS)

Rousta, Iman; Doostkamian, Mehdi; Haghighi, Esmaeil; Ghafarian Malamiri, Hamid Reza; Yarahmadi, Parvane

2017-09-01

Rainfall is a highly variable climatic element, and rainfall-related changes occur in spatial and temporal dimensions within a regional climate. The purpose of this study is to investigate the spatial autocorrelation changes of Iran's heavy and super-heavy rainfall over the past 40 years. For this purpose, the daily rainfall data of 664 meteorological stations between 1971 and 2011 are used. To analyze the changes in rainfall within a decade, geostatistical techniques like spatial autocorrelation analysis of hot spots, based on the Getis-Ord G i statistic, are employed. Furthermore, programming features in MATLAB, Surfer, and GIS are used. The results indicate that the Caspian coast, the northwest and west of the western foothills of the Zagros Mountains of Iran, the inner regions of Iran, and southern parts of Southeast and Northeast Iran, have the highest likelihood of heavy and super-heavy rainfall. The spatial pattern of heavy rainfall shows that, despite its oscillation in different periods, the maximum positive spatial autocorrelation pattern of heavy rainfall includes areas of the west, northwest and west coast of the Caspian Sea. On the other hand, a negative spatial autocorrelation pattern of heavy rainfall is observed in central Iran and parts of the east, particularly in Zabul. Finally, it is found that patterns of super-heavy rainfall are similar to those of heavy rainfall.

Comparison of four machine learning algorithms for their applicability in satellite-based optical rainfall retrievals

NASA Astrophysics Data System (ADS)

Meyer, Hanna; Kühnlein, Meike; Appelhans, Tim; Nauss, Thomas

2016-03-01

Machine learning (ML) algorithms have successfully been demonstrated to be valuable tools in satellite-based rainfall retrievals which show the practicability of using ML algorithms when faced with high dimensional and complex data. Moreover, recent developments in parallel computing with ML present new possibilities for training and prediction speed and therefore make their usage in real-time systems feasible. This study compares four ML algorithms - random forests (RF), neural networks (NNET), averaged neural networks (AVNNET) and support vector machines (SVM) - for rainfall area detection and rainfall rate assignment using MSG SEVIRI data over Germany. Satellite-based proxies for cloud top height, cloud top temperature, cloud phase and cloud water path serve as predictor variables. The results indicate an overestimation of rainfall area delineation regardless of the ML algorithm (averaged bias = 1.8) but a high probability of detection ranging from 81% (SVM) to 85% (NNET). On a 24-hour basis, the performance of the rainfall rate assignment yielded R2 values between 0.39 (SVM) and 0.44 (AVNNET). Though the differences in the algorithms' performance were rather small, NNET and AVNNET were identified as the most suitable algorithms. On average, they demonstrated the best performance in rainfall area delineation as well as in rainfall rate assignment. NNET's computational speed is an additional advantage in work with large datasets such as in remote sensing based rainfall retrievals. However, since no single algorithm performed considerably better than the others we conclude that further research in providing suitable predictors for rainfall is of greater necessity than an optimization through the choice of the ML algorithm.

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.

Spatial averaging of oceanic rainfall variability using underwater sound: Ionian Sea rainfall experiment 2004.

PubMed

Nystuen, Jeffrey A; Amitai, Eyal; Anagnostou, Emmanuel N; Anagnostou, Marios N

2008-04-01

An experiment to evaluate the inherent spatial averaging of the underwater acoustic signal from rainfall was conducted in the winter of 2004 in the Ionian Sea southwest of Greece. A mooring with four passive aquatic listeners (PALs) at 60, 200, 1000, and 2000 m was deployed at 36.85 degrees N, 21.52 degrees E, 17 km west of a dual-polarization X-band coastal radar at Methoni, Greece. The acoustic signal is classified into wind, rain, shipping, and whale categories. It is similar at all depths and rainfall is detected at all depths. A signal that is consistent with the clicking of deep-diving beaked whales is present 2% of the time, although there was no visual confirmation of whale presence. Co-detection of rainfall with the radar verifies that the acoustic detection of rainfall is excellent. Once detection is made, the correlation between acoustic and radar rainfall rates is high. Spatial averaging of the radar rainfall rates in concentric circles over the mooring verifies the larger inherent spatial averaging of the rainfall signal with recording depth. For the PAL at 2000 m, the maximum correlation was at 3-4 km, suggesting a listening area for the acoustic rainfall measurement of roughly 30-50 km(2).

SCS-CN parameter determination using rainfall-runoff data in heterogeneous watersheds - the two-CN system approach

NASA Astrophysics Data System (ADS)

Soulis, K. X.; Valiantzas, J. D.

2012-03-01

The Soil Conservation Service Curve Number (SCS-CN) approach is widely used as a simple method for predicting direct runoff volume for a given rainfall event. The CN parameter values corresponding to various soil, land cover, and land management conditions can be selected from tables, but it is preferable to estimate the CN value from measured rainfall-runoff data if available. However, previous researchers indicated that the CN values calculated from measured rainfall-runoff data vary systematically with the rainfall depth. Hence, they suggested the determination of a single asymptotic CN value observed for very high rainfall depths to characterize the watersheds' runoff response. In this paper, the hypothesis that the observed correlation between the calculated CN value and the rainfall depth in a watershed reflects the effect of soils and land cover spatial variability on its hydrologic response is being tested. Based on this hypothesis, the simplified concept of a two-CN heterogeneous system is introduced to model the observed CN-rainfall variation by reducing the CN spatial variability into two classes. The behaviour of the CN-rainfall function produced by the simplified two-CN system is approached theoretically, it is analysed systematically, and it is found to be similar to the variation observed in natural watersheds. Synthetic data tests, natural watersheds examples, and detailed study of two natural experimental watersheds with known spatial heterogeneity characteristics were used to evaluate the method. The results indicate that the determination of CN values from rainfall runoff data using the proposed two-CN system approach provides reasonable accuracy and it over performs the previous methods based on the determination of a single asymptotic CN value. Although the suggested method increases the number of unknown parameters to three (instead of one), a clear physical reasoning for them is presented.

ENSO-Based Index Insurance: Approach and Peru Flood Risk Management Application

NASA Astrophysics Data System (ADS)

Khalil, A. F.; Kwon, H.; Lall, U.; Miranda, M. J.; Skees, J. R.

2006-12-01

Index insurance has recently been advocated as a useful risk transfer tool for disaster management situations where rapid fiscal relief is desirable, and where estimating insured losses may be difficult, time consuming, or subject to manipulation and falsification. For climate related hazards, a rainfall or temperature index may be proposed. However, rainfall may be highly spatially variable relative to the gauge network, and in many locations data are inadequate to develop an index due to short time-series and the spatial dispersion of stations. In such cases, it may be helpful to consider a climate proxy index as a regional rainfall index. This is particularly useful if a long record is available for the climate index through an independent source and it is well correlated with the regional rainfall hazard. Here, ENSO related climate indices are explored for use as a proxy to extreme rainfall in one of the departments of Peru -- Piura. The ENSO index insurance product may be purchased by banks or microfinance institutions (MFIs) to aid agricultural damage relief in Peru. Crop losses in the region are highly correlated with floods, but are difficult to assess directly. Beyond agriculture, many other sectors suffer as well. Basic infrastructure is destroyed during the most severe events. This disrupts trade for many micro-enterprises. The reliability and quality of the local rainfall data is variable. Averaging the financial risk across the region is desirable. Some issues with the implementation of the proxy ENSO index are identified and discussed. Specifically, we explore (a) the reliability of the index at different levels of probability of exceedance of maximum seasonal rainfall; (b) the potential for clustering of payoffs; (c) the potential that the index could be predicted with some lead time prior to the flood season; and (d) evidence for climate change or non-stationarity in the flood exceedance probability from the long ENSO record. Finally, prospects for the global application of an ENSO based index insurance product are discussed.

Stress modulation of cellular metabolic sensors: interaction of stress from temperature and rainfall on the intertidal limpet Cellana toreuma.

PubMed

Dong, Yun-Wei; Han, Guo-Dong; Huang, Xiong-Wei

2014-09-01

In the natural environment, organisms are exposed to large variations in physical conditions. Quantifying such physiological responses is, however, often performed in laboratory acclimation studies, in which usually only a single factor is varied. In contrast, field acclimatization may expose organisms to concurrent changes in several environmental variables. The interactions of these factors may have strong effects on organismal function. In particular, rare events that occur stochastically and have relatively short duration may have strong effects. The present experiments studied levels of expression of several genes associated with cellular stress and metabolic regulation in a field population of limpet Cellana toreuma that encountered a wide range of temperatures plus periodic rain events. Physiological responses to these variable conditions were quantified by measuring levels of mRNA of genes encoding heat-shock proteins (Hsps) and metabolic sensors (AMPKs and Sirtuin 1). Our results reveal high ratios of individuals in upregulation group of stress-related gene expression at high temperature and rainy days, indicating the occurrence of stress from both prevailing high summer temperatures and occasional rainfall during periods of emersion. At high temperature, stress due to exposure to rainfall may be more challenging than heat stress alone. The highly variable physiological performances of limpets in their natural habitats indicate the possible differences in capability for physiological regulation among individuals. Our results emphasize the importance of studies of field acclimatization in unravelling the effects of environmental change on organisms, notably in the context of multiple changes in abiotic factors that are accompanying global change. © 2014 John Wiley & Sons Ltd.

Use of dynamical downscaling to improve the simulation of Central U.S. warm season precipitation in CMIP5 models

NASA Astrophysics Data System (ADS)

Harding, Keith J.; Snyder, Peter K.; Liess, Stefan

2013-11-01

supporting exceptionally productive agricultural lands, the Central U.S. is susceptible to severe droughts and floods. Such precipitation extremes are expected to worsen with climate change. However, future projections are highly uncertain as global climate models (GCMs) generally fail to resolve precipitation extremes. In this study, we assess how well models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulate summer means, variability, extremes, and the diurnal cycle of Central U.S. summer rainfall. Output from a subset of historical CMIP5 simulations are used to drive the Weather Research and Forecasting model to determine whether dynamical downscaling improves the representation of Central U.S. rainfall. We investigate which boundary conditions influence dynamically downscaled precipitation estimates and identify GCMs that can reasonably simulate precipitation when downscaled. The CMIP5 models simulate the seasonal mean and variability of summer rainfall reasonably well but fail to resolve extremes, the diurnal cycle, and the dynamic forcing of precipitation. Downscaling to 30 km improves these characteristics of precipitation, with the greatest improvement in the representation of extremes. Additionally, sizeable diurnal cycle improvements occur with higher (10 km) resolution and convective parameterization disabled, as the daily rainfall peak shifts 4 h closer to observations than 30 km resolution simulations. This lends greater confidence that the mechanisms responsible for producing rainfall are better simulated. Because dynamical downscaling can more accurately simulate these aspects of Central U.S. summer rainfall, policymakers can have added confidence in dynamically downscaled rainfall projections, allowing for more targeted adaptation and mitigation.

Atmospheric water budget over the South Asian summer monsoon region

NASA Astrophysics Data System (ADS)

Unnikrishnan, C. K.; Rajeevan, M.

2018-04-01

High resolution hybrid atmospheric water budget over the South Asian monsoon region is examined. The regional characteristics, variability, regional controlling factors and the interrelations of the atmospheric water budget components are investigated. The surface evapotranspiration was created using the High Resolution Land Data Assimilation System (HRLDAS) with the satellite-observed rainfall and vegetation fraction. HRLDAS evapotranspiration shows significant similarity with in situ observations and MODIS satellite-observed evapotranspiration. Result highlights the fundamental importance of evapotranspiration over northwest and southeast India on atmospheric water balance. The investigation shows that the surface net radiation controls the annual evapotranspiration over those regions, where the surface evapotranspiration is lower than 550 mm. The rainfall and evapotranspiration show a linear relation over the low-rainfall regions (<500 mm/year). Similar result is observed in in NASA GLDAS data (1980-2014). The atmospheric water budget shows annual, seasonal, and intra-seasonal variations. Evapotranspiration does not show a high intra-seasonal variability as compared to other water budget components. The coupling among the water budget anomalies is investigated. The results show that regional inter-annual evapotranspiration anomalies are not exactly in phase with rainfall anomalies; it is strongly influenced by the surface conditions and other atmospheric forcing (like surface net radiation). The lead and lag correlation of water budget components show that the water budget anomalies are interrelated in the monsoon season even up to 4 months lead. These results show the important regional interrelation of water budget anomalies on south Asian monsoon.

An interdecadal regime shift in rainfall predictability related to the Ningaloo Nino in the late 1990s

NASA Astrophysics Data System (ADS)

Doi, T.; Behera, S. K.; Yamagata, T.

2016-02-01

The global warming and the Interdecadal Pacific Oscillation (IPO) started influencing the coastal ocean off Western Australia, leading to a dramatic change in the regional climate predictability. The warmer ocean started driving rainfall variability regionally there after the late 1990s. Because of this, rainfall predictability near the coastal region of Western Australia on a seasonal time scale was drastically enhanced in the late 1990s; it is significantly predictable 5 months ahead after the late 1990s. The high prediction skill of the rainfall in recent decades is very encouraging and would help to develop an early warning system of Ningaloo Nino/Nina events to mitigate possible societal as well as agricultural impacts in the granary of Western Australia.

An interdecadal regime shift in rainfall predictability related to the Ningaloo Niño in the late 1990s

NASA Astrophysics Data System (ADS)

Doi, Takeshi; Behera, Swadhin K.; Yamagata, Toshio

2015-02-01

The global warming and the Interdecadal Pacific Oscillation (IPO) started influencing the coastal ocean off Western Australia, leading to a dramatic change in the regional climate predictability. The warmer ocean started driving rainfall variability regionally there after the late 1990s. Because of this, rainfall predictability near the coastal region of Western Australia on a seasonal time scale was drastically enhanced in the late 1990s; it is significantly predictable 5 months ahead after the late 1990s. The high prediction skill of the rainfall in recent decades is very encouraging and would help to develop an early warning system of Ningaloo Niño/Niña events to mitigate possible societal as well as agricultural impacts in the granary of Western Australia.

Evaluation of Stochastic Rainfall Models in Capturing Climate Variability for Future Drought and Flood Risk Assessment

NASA Astrophysics Data System (ADS)

Chowdhury, A. F. M. K.; Lockart, N.; Willgoose, G. R.; Kuczera, G. A.; Kiem, A.; Nadeeka, P. M.

2016-12-01

One of the key objectives of stochastic rainfall modelling is to capture the full variability of climate system for future drought and flood risk assessment. However, it is not clear how well these models can capture the future climate variability when they are calibrated to Global/Regional Climate Model data (GCM/RCM) as these datasets are usually available for very short future period/s (e.g. 20 years). This study has assessed the ability of two stochastic daily rainfall models to capture climate variability by calibrating them to a dynamically downscaled RCM dataset in an east Australian catchment for 1990-2010, 2020-2040, and 2060-2080 epochs. The two stochastic models are: (1) a hierarchical Markov Chain (MC) model, which we developed in a previous study and (2) a semi-parametric MC model developed by Mehrotra and Sharma (2007). Our hierarchical model uses stochastic parameters of MC and Gamma distribution, while the semi-parametric model uses a modified MC process with memory of past periods and kernel density estimation. This study has generated multiple realizations of rainfall series by using parameters of each model calibrated to the RCM dataset for each epoch. The generated rainfall series are used to generate synthetic streamflow by using a SimHyd hydrology model. Assessing the synthetic rainfall and streamflow series, this study has found that both stochastic models can incorporate a range of variability in rainfall as well as streamflow generation for both current and future periods. However, the hierarchical model tends to overestimate the multiyear variability of wet spell lengths (therefore, is less likely to simulate long periods of drought and flood), while the semi-parametric model tends to overestimate the mean annual rainfall depths and streamflow volumes (hence, simulated droughts are likely to be less severe). Sensitivity of these limitations of both stochastic models in terms of future drought and flood risk assessment will be discussed.

Analysis of the influence of rainfall variables on urban effluents concentrations and fluxes in wet weather

NASA Astrophysics Data System (ADS)

Gooré Bi, Eustache; Monette, Frédéric; Gasperi, Johnny

2015-04-01

Urban rainfall runoff has been a topic of increasing importance over the past years, a result of both the increase in impervious land area arising from constant urban growth and the effects of climate change on urban drainage. The main goal of the present study is to assess and analyze the correlations between rainfall variables and common indicators of urban water quality, namely event mean concentrations (EMCs) and event fluxes (EFs), in order to identify and explain the impacts of each of the main rainfall variables on the generation process of urban pollutants during wet periods. To perform this analysis, runoff from eight summer rainfall events that resulted in combined sewer overflow (CSO) was sampled simultaneously from two distinct catchment areas in order to quantify discharges at the respective outfalls. Pearson statistical analysis of total suspended solids (TSS), chemical oxygen demand (COD), carbonaceous biochemical oxygen demand at 5 days (CBOD5), total phosphorus (Ptot) and total kedjal nitrogen (N-TKN) showed significant correlations (ρ = 0.05) between dry antecedent time (DAT) and EMCs on one hand, and between total rainfall (TR) and the volume discharged (VD) during EFs, on the other. These results show that individual rainfall variables strongly affect either EMCs or EFs and are good predictors to consider when selecting variables for statistical modeling of urban runoff quality. The results also show that in a combined sewer network, there is a linear relationship between TSS event fluxes and COD, CBOD5, Ptot, and N-TKN event fluxes; this explains 97% of the variability of these pollutants which adsorb onto TSS during wet weather, which therefore act as tracers. Consequently, the technological solution selected for TSS removal will also lead to a reduction of these pollutants. Given the huge volumes involved, urban runoffs contribute substantially to pollutant levels in receiving water bodies, a situation which, in a climate change context, may get much worse as a result of more frequent, shorter, but more intense rainfall events.

Studying the Diurnal Cycle of Convection Using a TRMM-Calibrated Infrared Rain Algorithm

NASA Technical Reports Server (NTRS)

Negri, Andrew J.

2005-01-01

The development of a satellite infrared (IR) technique for estimating convective and stratiform rainfall and its application in studying the diurnal variability of rainfall on a global scale is presented. The Convective-Stratiform Technique (CST), calibrated by coincident, physically retrieved rain rates from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR), is applied over the global tropics. The technique makes use of the IR data from the TRMM Visible/Infrared Scanner (VIRS) before application to global geosynchronous satellite data. The calibrated CST technique has the advantages of high spatial resolution (4 km), filtering of nonraining cirrus clouds, and the stratification of the rainfall into its convective and stratiform components, the last being important for the calculation of vertical profiles of latent heating. The diurnal cycle of rainfall, as well as the division between convective and Stratiform rainfall will be presented. The technique is validated using available data sets and compared to other global rainfall products such as Global Precipitation Climatology Project (GPCP) IR product, calibrated with TRMM Microwave Imager (TMI) data. Results from five years of PR data will show the global-tropical partitioning of convective and stratiform rainfall.

Climate variables as predictors for seasonal forecast of dengue occurrence in Chennai, Tamil Nadu

NASA Astrophysics Data System (ADS)

Subash Kumar, D. D.; Andimuthu, R.

2013-12-01

Background Dengue is a recently emerging vector borne diseases in Chennai. As per the WHO report in 2011 dengue is one of eight climate sensitive disease of this century. Objective Therefore an attempt has been made to explore the influence of climate parameters on dengue occurrence and use for forecasting. Methodology Time series analysis has been applied to predict the number of dengue cases in Chennai, a metropolitan city which is the capital of Tamil Nadu, India. Cross correlation of the climate variables with dengue cases revealed that the most influential parameters were monthly relative humidity, minimum temperature at 4 months lag and rainfall at one month lag (Table 1). However due to intercorrelation of relative humidity and rainfall was high and therefore for predictive purpose the rainfall at one month lag was used for the model development. Autoregressive Integrated Moving Average (ARIMA) models have been applied to forecast the occurrence of dengue. Results and Discussion The best fit model was ARIMA (1,0,1). It was seen that the monthly minimum temperature at four months lag (β= 3.612, p = 0.02) and rainfall at one month lag (β= 0.032, p = 0.017) were associated with dengue occurrence and they had a very significant effect. Mean Relative Humidity had a directly significant positive correlation at 99% confidence level, but the lagged effect was not prominent. The model predicted dengue cases showed significantly high correlation of 0.814(Figure 1) with the observed cases. The RMSE of the model was 18.564 and MAE was 12.114. The model is limited by the scarcity of the dataset. Inclusion of socioeconomic conditions and population offset are further needed to be incorporated for effective results. Conclusion Thus it could be claimed that the change in climatic parameters is definitely influential in increasing the number of dengue occurrence in Chennai. The climate variables therefore can be used for seasonal forecasting of dengue with rise in minimum temperature and rainfall at a city level. Table 1. Cross correlation of climate variables with dengue cases in Chennai ** p<0.01,*p<0.05

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.

A role of high impact weather events in waterborne disease outbreaks in Canada, 1975 - 2001.

PubMed

Thomas, Kate M; Charron, Dominique F; Waltner-Toews, David; Schuster, Corinne; Maarouf, Abdel R; Holt, John D

2006-06-01

Recent outbreaks of Escherichia coli O157:H7, Campylobacter, and Cryptosporidium have heightened awareness of risks associated with contaminated water supply. The objectives of this research were to describe the incidence and distribution of waterborne disease outbreaks in Canada in relation to preceding weather conditions and to test the association between high impact weather events and waterborne disease outbreaks. We examined extreme rainfall and spring snowmelt in association with 92 Canadian waterborne disease outbreaks between 1975 and 2001, using case-crossover methodology. Explanatory variables including accumulated rainfall, air temperature, and peak stream flow were used to determine the relationship between high impact weather events and the occurrence of waterborne disease outbreaks. Total maximum degree-days above 0 degrees C and accumulated rainfall percentile were associated with outbreak risk. For each degree-day above 0 degrees C the relative odds of an outbreak increased by a factor of 1.007 (95% confidence interval [CI] = 1.002 - 1.012). Accumulated rainfall percentile was dichotomized at the 93rd percentile. For rainfall events greater than the 93rd percentile the relative odds of an outbreak increased by a factor of 2.283 (95% [CI] = 1.216 - 4.285). These results suggest that warmer temperatures and extreme rainfall are contributing factors to waterborne disease outbreaks in Canada. This could have implications for water management and public health initiatives.

Southern Hemisphere rainfall variability over the past 200 years

NASA Astrophysics Data System (ADS)

Gergis, Joëlle; Henley, Benjamin J.

2017-04-01

This study presents an analysis of three palaeoclimate rainfall reconstructions from the Southern Hemisphere regions of south-eastern Australia (SEA), southern South Africa (SAF) and southern South America (SSA). We provide a first comparison of rainfall variations in these three regions over the past two centuries, with a focus on identifying synchronous wet and dry periods. Despite the uncertainties associated with the spatial and temporal limitations of the rainfall reconstructions, we find evidence of dynamically-forced climate influences. An investigation of the twentieth century relationship between regional rainfall and the large-scale climate circulation features of the Pacific, Indian and Southern Ocean regions revealed that Indo-Pacific variations of the El Niño-Southern Oscillation (ENSO) and the Indian Ocean dipole dominate rainfall variability in SEA and SAF, while the higher latitude Southern Annular Mode (SAM) exerts a greater influence in SSA. An assessment of the stability of the regional rainfall-climate circulation modes over the past two centuries revealed a number of non-stationarities, the most notable of which occurs during the early nineteenth century around 1820. This corresponds to a time when the influence of ENSO on SEA, SAF and SSA rainfall weakens and there is a strengthening of the influence of SAM. We conclude by advocating the use of long-term palaeoclimate data to estimate decadal rainfall variability for future water resource management.

Interannual and Decadal Variability of Summer Rainfall over South America

NASA Technical Reports Server (NTRS)

Zhou, Jiayu; Lau, K.-M.

1999-01-01

Using the CPC (Climate Prediction Center) Merged Analysis of Precipitation product along with the Goddard Earth Observing System reanalysis and the Climate Analysis Center sea surface temperature (SST) data, we conduct a diagnostic study of the interannual and decadal scale variability of summer rainfall over South America. Results show three leading modes of rainfall variation identified with interannual, decadal, and long-term trend variability. Together, these modes explain more than half the total variance. The first mode is highly correlated with El Nino/southern oscillation (ENSO), showing severe drought over Northeast Brazil and copious rainfall over the Ecuador coast and the area of Uruguay-Southern Brazil in El Nino years. This pattern is attributed to the large scale zonal shift of the Walker circulation and local Hadley cell anomaly induced by positive (negative) SST anomaly over the eastern (western) equatorial Pacific. In El Nino years, two convective belts indicated by upper tropospheric velocity potential trough and mid-tropospheric rising motion, which are somewhat symmetric about the equator, extend toward the northeast and the southeast into the tropical North and South Atlantic respectively. Sandwiched between the ascent is a region of descending motion over Northeast Brazil. The southern branch of the anomalous Hadley cell is dynamically linked to the increase of rainfall over Uruguay-Southern Brazil. The regional response of anomalous circulation shows a stronger South American summer monsoon and an enhanced (weakened) subtropical high over the South Atlantic (South Pacific) Ocean. The decadal variation displays a meridional shift of the Intertropical Convergence Zone (ITCZ), which is tie to the anomalous cross-equatorial SST gradient over the Atlantic and the eastern Pacific. In conjunction with this mode is a large scale mass swing between the polar regions and midlatitudes in both hemispheres. Over the South Atlantic and the South Pacific, the changes of the strength of the subtropical high and the associated surface wind are dynamically consistent with the distribution of local SST anomalies, suggesting the importance of the atmospheric forcing in the decadal time scale. The decadal mode also presents a weak summer monsoon in its positive phase, which reduces the moisture supply from the equatorial Atlantic and the Amazon Basin and results in negative rainfall anomalies over the central Andes and Gran Chaco. The long-term trend shows decrease of rainfall from the northwest coast to the southeast subtropical region and a southward shift of Atlantic ITCZ that leads to increased rainfall over northern and eastern Brazil. Our result shows a close link of this mode to the observed SST warming trend over the subtropical South Atlantic and a remote connection to the interdecadal SST variation over the extratropical North Atlantic found in previous studies.

Introducing hydrological information in rainfall intensity-duration thresholds

NASA Astrophysics Data System (ADS)

Greco, Roberto; Bogaard, Thom

2016-04-01

Regional landslide hazard assessment is mainly based on empirically derived precipitation-intensity-duration (PID) thresholds. Generally, two features of rainfall events are plotted to discriminate between observed occurrence and absence of occurrence of mass movements. Hereafter, a separation line is drawn in logarithmic space. Although successfully applied in many case studies, such PID thresholds suffer from many false positives as well as limited physical process insight. One of the main limitations is indeed that they do not include any information about the hydrological processes occurring along the slopes, so that the triggering is only related to rainfall characteristics. In order to introduce such an hydrological information in the definition of rainfall thresholds for shallow landslide triggering assessment, in this study the introduction of non-dimensional rainfall characteristics is proposed. In particular, rain storm depth, intensity and duration are divided by a characteristic infiltration depth, a characteristic infiltration rate and a characteristic duration, respectively. These latter variables depend on the hydraulic properties and on the moisture state of the soil cover at the beginning of the precipitation. The proposed variables are applied to the case of a slope covered with shallow pyroclastic deposits in Cervinara (southern Italy), for which experimental data of hourly rainfall and soil suction were available. Rainfall thresholds defined with the proposed non-dimensional variables perform significantly better than those defined with dimensional variables, either in the intensity-duration plane or in the depth-duration plane.

Daily rainfall statistics of TRMM and CMORPH: A case for trans-boundary Gandak River basin

NASA Astrophysics Data System (ADS)

Kumar, Brijesh; Patra, Kanhu Charan; Lakshmi, Venkat

2016-07-01

Satellite precipitation products offer an opportunity to evaluate extreme events (flood and drought) for areas where rainfall data are not available or rain gauge stations are sparse. In this study, daily precipitation amount and frequency of TRMM 3B42V.7 and CMORPH products have been validated against daily rain gauge precipitation for the monsoon months (June-September or JJAS) from 2005-2010 in the trans-boundary Gandak River basin. The analysis shows that the both TRMM and CMORPH can detect rain and no-rain events, but they fail to capture the intensity of rainfall. The detection of precipitation amount is strongly dependent on the topography. In the plains areas, TRMM product is capable of capturing high-intensity rain events but in the hilly regions, it underestimates the amount of high-intensity rain events. On the other hand, CMORPH entirely fails to capture the high-intensity rain events but does well with low-intensity rain events in both hilly regions as well as the plain region. The continuous variable verification method shows better agreement of TRMM rainfall products with rain gauge data. TRMM fares better in the prediction of probability of occurrence of high-intensity rainfall events, but it underestimates intensity at high altitudes. This implies that TRMM precipitation estimates can be used for flood-related studies only after bias adjustment for the topography.

The local and global climate forcings induced inhomogeneity of Indian rainfall.

PubMed

Nair, P J; Chakraborty, A; Varikoden, H; Francis, P A; Kuttippurath, J

2018-04-16

India is home for more than a billion people and its economy is largely based on agrarian society. Therefore, rainfall received not only decides its livelihood, but also influences its water security and economy. This situation warrants continuous surveillance and analysis of Indian rainfall. These kinds of studies would also help forecasters to better tune their models for accurate weather prediction. Here, we introduce a new method for estimating variability and trends in rainfall over different climate regions of India. The method based on multiple linear regression helps to assess contributions of different remote and local climate forcings to seasonal and regional inhomogeneity in rainfall. We show that the Indian Summer Monsoon Rainfall (ISMR) variability is governed by Eastern and Central Pacific El Niño Southern Oscillation, equatorial zonal winds, Atlantic zonal mode and surface temperatures of the Arabian Sea and Bay of Bengal, and the North East Monsoon Rainfall variability is controlled by the sea surface temperature of the North Atlantic and extratropial oceans. Also, our analyses reveal significant positive trends (0.43 mm/day/dec) in the North West for ISMR in the 1979-2017 period. This study cautions against the significant changes in Indian rainfall in a perspective of global climate change.

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.

Simulation of climatology and Interannual Variability of Spring Persistent Rains by Meteorological Research Institute Model: Impacts of different horizontal resolutions

NASA Astrophysics Data System (ADS)

Li, Puxi; Zhou, Tianjun; Zou, Liwei

2016-04-01

The authors evaluated the performance of Meteorological Research Institute (MRI) AGCM3.2 models in the simulations of climatology and interannual variability of the Spring Persistent Rains (SPR) over southeastern China. The possible impacts of different horizontal resolutions were also investigated based on the experiments with three different horizontal resolutions (i.e., 120, 60, and 20km). The model could reasonably reproduce the main rainfall center over southeastern China in boreal spring under the three different resolutions. In comparison with 120 simulation, it revealed that 60km and 20km simulations show the superiority in simulating rainfall centers anchored by the Nanling-Wuyi Mountains, but overestimate rainfall intensity. Water vapor budget diagnosis showed that, the 60km and 20km simulations tended to overestimate the water vapor convergence over southeastern China, which leads to wet biases. In the aspect of interannual variability of SPR, the model could reasonably reproduce the anomalous lower-tropospheric anticyclone in the western North Pacific (WNPAC) and positive precipitation anomalies over southeastern China in El Niño decaying spring. Compared with the 120km resolution, the large positive biases are substantially reduced in the mid and high resolution models which evidently improve the simulation of horizontal moisture advection in El Niño decaying spring. We highlight the importance of developing high resolution climate model as it could potentially improve the climatology and interannual variability of SPR.

A hydro-mechanical framework for early warning of rainfall-induced landslides (Invited)

NASA Astrophysics Data System (ADS)

Godt, J.; Lu, N.; Baum, R. L.

2013-12-01

Landslide early warning requires an estimate of the location, timing, and magnitude of initial movement, and the change in volume and momentum of material as it travels down a slope or channel. In many locations advance assessment of landslide location, volume, and momentum is possible, but prediction of landslide timing entails understanding the evolution of rainfall and soil-water conditions, and consequent effects on slope stability in real time. Existing schemes for landslide prediction generally rely on empirical relations between landslide occurrence and rainfall amount and duration, however, these relations do not account for temporally variable rainfall nor the variably saturated processes that control the hydro-mechanical response of hillside materials to rainfall. Although limited by the resolution and accuracy of rainfall forecasts and now-casts in complex terrain and by the inherent difficulty in adequately characterizing subsurface materials, physics-based models provide a general means to quantitatively link rainfall and landslide occurrence. To obtain quantitative estimates of landslide potential from physics-based models using observed or forecasted rainfall requires explicit consideration of the changes in effective stress that result from changes in soil moisture and pore-water pressures. The physics that control soil-water conditions are transient, nonlinear, hysteretic, and dependent on material composition and history. In order to examine the physical processes that control infiltration and effective stress in variably saturated materials, we present field and laboratory results describing intrinsic relations among soil water and mechanical properties of hillside materials. At the REV (representative elementary volume) scale, the interaction between pore fluids and solid grains can be effectively described by the relation between soil suction, soil water content, hydraulic conductivity, and suction stress. We show that these relations can be obtained independently from outflow, shear strength, and deformation tests for a wide range of earth materials. We then compare laboratory results with measurements of pore pressure and moisture content from landslide-prone settings and demonstrate that laboratory results obtained for hillside materials are representative of field conditions. These fundamental relations provide a basis to combine observed or forecasted rainfall with in-situ measurements of soil water conditions using hydro-mechanical models that simulate transient variably saturated flow and slope stability. We conclude that early warning using an approach in which in-situ observations are used to establish initial conditions for hydro-mechanical models is feasible in areas of high landslide risk where laboratory characterization of materials is practical and accurate rainfall information can be obtained. Analogous to weather and climate forecasting, such models could then be applied in an ensemble fashion to obtain quantitative estimates of landslide probability and error. Application to broader regions likely awaits breakthroughs in the development of remotely sensed proxies of soil properties and subsurface moisture conditions.

Complexity confers stability: Climate variability, vegetation response and sand transport on longitudinal sand dunes in Australia's deserts

NASA Astrophysics Data System (ADS)

Hesse, Paul P.; Telfer, Matt W.; Farebrother, Will

2017-04-01

The relationship between antecedent precipitation, vegetation cover and sand movement on sand dunes in the Simpson and Strzelecki Deserts was investigated by repeated (up to four) surveys of dune crest plots (≈25 × 25 m) over a drought cycle (2002-2012) in both winter (low wind) and spring (high wind). Vegetation varied dramatically between surveys on vegetated and active dune crests. Indices of sand movement had significant correlations with vegetation cover: the depth of loose sand has a strong inverse relationship with crust (cyanobacterial and/or physical) while the area covered by ripples has a strong inverse relationship with the areal cover of vascular plants. However, the relationship between antecedent rainfall and vegetation cover was found to be complex. We tentatively identify two thresholds; (1) >10 mm of rainfall in the preceding 90 days leads to rapid and near total cover of crust and/or small plants <50 cm tall, and (2) >400 mm of rainfall in the preceding three years leads to higher cover of persistent and longer-lived plants >50 cm tall. These thresholds were used to predict days of low vegetation cover on dune crests. The combination of seasonality of predicted bare-crest days, potential sand drift and resultant sand drift direction explains observed patterns of sand drift on these dunes. The complex vegetation and highly variable rainfall regime confer meta-stability on the dunes through the range of responses to different intervals of antecedent rainfall and non-linear growth responses. This suggests that the geomorphic response of dunes to climate variation is complex and non-linear.

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.

A paleoclimate rainfall reconstruction in the Murray-Darling Basin (MDB), Australia: 1. Evaluation of different paleoclimate archives, rainfall networks, and reconstruction techniques

NASA Astrophysics Data System (ADS)

Ho, Michelle; Kiem, Anthony S.; Verdon-Kidd, Danielle C.

2015-10-01

From ˜1997 to 2009 the Murray-Darling Basin (MDB), Australia's largest water catchment and reputed "food bowl," experienced a severe drought termed the "Millennium Drought" or "Big Dry" followed by devastating floods in the austral summers of 2010/2011, 2011/2012, and 2012/2013. The magnitude and severity of these extreme events highlight the limitations associated with assessing hydroclimatic risk based on relatively short instrumental records (˜100 years). An option for extending hydroclimatic records is through the use of paleoclimate records. However, there are few in situ proxies of rainfall or streamflow suitable for assessing hydroclimatic risk in Australia and none are available in the MDB. In this paper, available paleoclimate records are reviewed and those of suitable quality for hydroclimatic risk assessments are used to develop preinstrumental information for the MDB. Three different paleoclimate reconstruction techniques are assessed using two instrumental rainfall networks: (1) corresponding to rainfall at locations where rainfall-sensitive Australian paleoclimate archives currently exist and (2) corresponding to rainfall at locations identified as being optimal for explaining MDB rainfall variability. It is shown that the optimized rainfall network results in a more accurate model of MDB rainfall compared to reconstructions based on rainfall at locations where paleoclimate rainfall proxies currently exist. This highlights the importance of first identifying key locations where existing and as yet unrealized paleoclimate records will be most useful in characterizing variability. These results give crucial insight as to where future investment and research into developing paleoclimate proxies for Australia could be most beneficial, with respect to better understanding instrumental, preinstrumental and potential future variability in the MDB.

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.

Estimating the Risk of Domestic Water Source Contamination following Precipitation Events

PubMed Central

Eisenhauer, Ian F.; Hoover, Christopher M.; Remais, Justin V.; Monaghan, Andrew; Celada, Marco; Carlton, Elizabeth J.

2016-01-01

Climate change is expected to increase precipitation extremes, threatening water quality. In low resource settings, it is unclear which water sources are most vulnerable to contamination following rainfall events. We evaluated the relationship between rainfall and drinking water quality in southwest Guatemala where heavy rainfall is frequent and access to safe water is limited. We surveyed 59 shallow household wells, measured precipitation, and calculated simple hydrological variables. We compared Escherichia coli concentration at wells where recent rainfall had occurred versus had not occurred, and evaluated variability in the association between rainfall and E. coli concentration under different conditions using interaction models. Rainfall in the past 24 hours was associated with greater E. coli concentrations, with the strongest association between rainfall and fecal contamination at wells where pigs were nearby. Because of the small sample size, these findings should be considered preliminary, but provide a model to evaluate vulnerability to climate change. PMID:27114298

Indian Ocean dipole and rainfall drive a Moran effect in East Africa malaria transmission.

PubMed

Chaves, Luis Fernando; Satake, Akiko; Hashizume, Masahiro; Minakawa, Noboru

2012-06-15

Patterns of concerted fluctuation in populations-synchrony-can reveal impacts of climatic variability on disease dynamics. We examined whether malaria transmission has been synchronous in an area with a common rainfall regime and sensitive to the Indian Ocean Dipole (IOD), a global climatic phenomenon affecting weather patterns in East Africa. We studied malaria synchrony in 5 15-year long (1984-1999) monthly time series that encompass an altitudinal gradient, approximately 1000 m to 2000 m, along Lake Victoria basin. We quantified the association patterns between rainfall and malaria time series at different altitudes and across the altitudinal gradient encompassed by the study locations. We found a positive seasonal association of rainfall with malaria, which decreased with altitude. By contrast, IOD and interannual rainfall impacts on interannual disease cycles increased with altitude. Our analysis revealed a nondecaying synchrony of similar magnitude in both malaria and rainfall, as expected under a Moran effect, supporting a role for climatic variability on malaria epidemic frequency, which might reflect rainfall-mediated changes in mosquito abundance. Synchronous malaria epidemics call for the integration of knowledge on the forcing of malaria transmission by environmental variability to develop robust malaria control and elimination programs.

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.

A TRMM-Calibrated Infrared Rainfall Algorithm Applied Over Brazil

NASA Technical Reports Server (NTRS)

Negri, A. J.; Xu, L.; Adler, R. F.; Einaudi, Franco (Technical Monitor)

2000-01-01

The development of a satellite infrared technique for estimating convective and stratiform rainfall and its application in studying the diurnal variability of rainfall in Amazonia are presented. The Convective-Stratiform. Technique, calibrated by coincident, physically retrieved rain rates from the Tropical Rain Measuring Mission (TRMM) Microwave Imager (TMI), is applied during January to April 1999 over northern South America. The diurnal cycle of rainfall, as well as the division between convective and stratiform rainfall is presented. Results compare well (a one-hour lag) with the diurnal cycle derived from Tropical Ocean-Global Atmosphere (TOGA) radar-estimated rainfall in Rondonia. The satellite estimates reveal that the convective rain constitutes, in the mean, 24% of the rain area while accounting for 67% of the rain volume. The effects of geography (rivers, lakes, coasts) and topography on the diurnal cycle of convection are examined. In particular, the Amazon River, downstream of Manaus, is shown to both enhance early morning rainfall and inhibit afternoon convection. Monthly estimates from this technique, dubbed CST/TMI, are verified over a dense rain gage network in the state of Ceara, in northeast Brazil. The CST/TMI showed a high bias equal to +33% of the gage mean, indicating that possibly the TMI estimates alone are also high. The root mean square difference (after removal of the bias) equaled 36.6% of the gage mean. The correlation coefficient was 0.77 based on 72 station-months.

The Roles of Climate Change and El Niño in the Record Low Rainfall in October 2015 in Tasmania, Australia

NASA Astrophysics Data System (ADS)

Karoly, David; Black, Mitchell; Grose, Michael; King, Andrew

2017-04-01

The island state of Tasmania, in southeast Australia, received record low average rainfall of 21 mm in October 2015, 17% of the 1961-90 normal. This had major impacts across the state, affecting agriculture and hydroelectric power generation and preconditioning the landscape for major bushfires the following summer. Rainfall in Tasmania is normally high throughout the year, with variations in Austral spring associated with mean sea level pressure (MSLP) and circulation variations due to El Niño, the Indian Ocean dipole (IOD), and the southern annular mode (SAM). Spring rainfall is declining and projected to decrease further in Tasmania We have investigated the roles of anthropogenic climate change, the 2015/16 El Niño, and internal atmospheric variability on this record low October rainfall using observational data, regional climate simulations driven by specified sea surface temperatures (SSTs) from the weather@home Australia and New Zealand (w@h ANZ) project, and coupled climate model simulations from the Coupled Model Intercomparison Project phase 5. Anthropogenic climate change and the strong El Niño in 2015 very likely increased the chances of breaking the previous record low rainfall in 1965. In terms of contributions to the magnitude of this rainfall deficit, internal atmospheric variability as indicated by the Pacific-South American MSLP pattern was likely the main contributor, with El Niño next and a smaller but significant contribution from anthropogenic climate change. In this case, it was the MSLP and circulation changes associated with anthropogenic climate change in the Southern Hemisphere middle and high latitudes and not the thermodynamic effects of anthropogenic climate change that contributed to this event. Karoly, D. J., M.T. Black, M.R. Grose and A. D. King (2016) The roles of climate change and El Niño in the record low rainfall in October 2015 in Tasmania, Australia [in "Explaining Extremes of 2015 from a Climate Perspective"]. Bull. Am. Met. Soc., 97, S127-S130.

Interannual and intra-annual variability of rainfall in Haiti (1905-2005)

NASA Astrophysics Data System (ADS)

Moron, Vincent; Frelat, Romain; Jean-Jeune, Pierre Karly; Gaucherel, Cédric

2015-08-01

The interannual variability of annual and monthly rainfall in Haiti is examined from a database of 78 rain gauges in 1905-2005. The spatial coherence of annual rainfall is rather low, which is partly due to Haiti's rugged landscape, complex shoreline, and surrounding warm waters (mean sea surface temperatures >27 °C from May to December). The interannual variation of monthly rainfall is mostly shaped by the intensity of the low-level winds across the Caribbean Sea, leading to a drier- (or wetter-) than-average rainy season associated with easterly (or westerly) anomalies, increasing (or decreasing) winds. The varying speed of low-level easterlies across the Caribbean basin may reflect at least four different processes during the year: (1) an anomalous trough/ridge over the western edge of the Azores high from December to February, peaking in January; (2) a zonal pressure gradient between Eastern Pacific and the tropical Northern Atlantic from May/June to September, with a peak in August (i.e. lower-than-average rainfall in Haiti is associated with positive sea level pressure anomalies over the tropical North Atlantic and negative sea level pressure anomalies over the Eastern Pacific); (3) a local ocean-atmosphere coupling between the speed of the Caribbean Low Level Jet and the meridional sea surface temperature (SST) gradient across the Caribbean basin (i.e. colder-than-average SST in the southern Caribbean sea is associated with increased easterlies and below-average rainfall in Haiti). This coupling is triggered when the warmest Caribbean waters move northward toward the Gulf of Mexico; (4) in October/November, a drier- (or wetter-) than-usual rainy season is related to an almost closed anticyclonic (or cyclonic) anomaly located ENE of Haiti on the SW edge of the Azores high. This suggests a main control of the interannual variations of rainfall by intensity, track and/or recurrence of tropical depressions traveling northeast of Haiti. During this period, the teleconnection of Haitian rainfall with synchronous Atlantic and Eastern Pacific SST is at a minimum.

Analysis of global oceanic rainfall from microwave data

NASA Technical Reports Server (NTRS)

Rao, M.

1978-01-01

A Global Rainfall Atlas was prepared from Nimbus 5 ESMR data. The Atlas includes global oceanic rainfall maps based on weekly, monthly and seasonal averages, complete through the end of 1975. Similar maps for 1973 and 1974 were studied. They reveal several previously unknown areas of enhanced rainfall and preliminary data on interannual variability of oceanic rainfall.

Climatic, high tide and vector variables and the transmission of Ross River virus.

PubMed

Tong, S; Hu, W; Nicholls, N; Dale, P; MacKenzie, J S; Patz, J; McMichael, A J

2005-11-01

This report assesses the impact of the variability in environmental and vector factors on the transmission of Ross River virus (RRV) in Brisbane, Australia. Poisson time series regression analyses were conducted using monthly data on the counts of RRV cases, climate variables (Southern Oscillation Index and rainfall), high tides and mosquito density for the period of 1998-2001. The results indicate that increases in the high tide (relative risk (RR): 1.65; 95% confidence interval (CI): 1.20-2.26), rainfall (RR: 1.45; 95% CI: 1.21-1.73), mosquito density (RR: 1.17; 95% CI: 1.09-1.27), the density of Culex annulirostris (RR: 1.25; 95% CI: 1.13-1.37) and the density of Ochlerotatus vigilax (RR: 2.39; 95% CI: 2.30-2.48), each at a lag of 1 month, were statistically significantly associated with the rise of monthly RRV incidence. The results of the present study might facilitate the development of early warning systems for reducing the incidence of this wide-spread disease in Australia and other Pacific island nations.

Indian Monsoon Rainfall Variability During the Common Era: Implications on the Ancient Civilization

NASA Astrophysics Data System (ADS)

Pothuri, D.

2017-12-01

Indian monsoon rainfall variability was reconstructed during last two millennia by using the δ18Ow from a sediment core in the Krishna-Godavari Basin. Higher δ18Ow values during Dark Age Cold Period (DACP) (1550 to 1250 years BP) and Little Ice Age (LIA) (700 to 200 years BP) represent less Indian monsoon rainfall. Whereas during Medieval Warm Period (MWP) (1200 to 800 years BP) and major portion of Roman Warm Period (RWP) 2000 to 1550 years BP) document more rainfall in the Indian subcontinent as evident from lower δ18Ow values. A significant correlation exist between the Bay of Bengal (BoB) sea surface temperature (SST) and Indian monsoon proxy (i.e. δ18Ow), which suggests that; (i) the forcing mechanism of the Indian monsoon rainfall variability during last two millennia was controlled by the thermal contrast between the Indian Ocean and Asian Land Mass, and (ii) the evaporation processes in the BoB and associated SST are strongly coupled with the Indian Monsoon variability over the last two millennia.

Evaluating the variability in surface water reservoir planning characteristics during climate change impacts assessment

NASA Astrophysics Data System (ADS)

Soundharajan, Bankaru-Swamy; Adeloye, Adebayo J.; Remesan, Renji

2016-07-01

This study employed a Monte-Carlo simulation approach to characterise the uncertainties in climate change induced variations in storage requirements and performance (reliability (time- and volume-based), resilience, vulnerability and sustainability) of surface water reservoirs. Using a calibrated rainfall-runoff (R-R) model, the baseline runoff scenario was first simulated. The R-R inputs (rainfall and temperature) were then perturbed using plausible delta-changes to produce simulated climate change runoff scenarios. Stochastic models of the runoff were developed and used to generate ensembles of both the current and climate-change-perturbed future runoff scenarios. The resulting runoff ensembles were used to force simulation models of the behaviour of the reservoir to produce 'populations' of required reservoir storage capacity to meet demands, and the performance. Comparing these parameters between the current and the perturbed provided the population of climate change effects which was then analysed to determine the variability in the impacts. The methodology was applied to the Pong reservoir on the Beas River in northern India. The reservoir serves irrigation and hydropower needs and the hydrology of the catchment is highly influenced by Himalayan seasonal snow and glaciers, and Monsoon rainfall, both of which are predicted to change due to climate change. The results show that required reservoir capacity is highly variable with a coefficient of variation (CV) as high as 0.3 as the future climate becomes drier. Of the performance indices, the vulnerability recorded the highest variability (CV up to 0.5) while the volume-based reliability was the least variable. Such variabilities or uncertainties will, no doubt, complicate the development of climate change adaptation measures; however, knowledge of their sheer magnitudes as obtained in this study will help in the formulation of appropriate policy and technical interventions for sustaining and possibly enhancing water security for irrigation and other uses served by Pong reservoir.

Temperature and rainfall interact to control carbon cycling in tropical forests.

PubMed

Taylor, Philip G; Cleveland, Cory C; Wieder, William R; Sullivan, Benjamin W; Doughty, Christopher E; Dobrowski, Solomon Z; Townsend, Alan R

2017-06-01

Tropical forests dominate global terrestrial carbon (C) exchange, and recent droughts in the Amazon Basin have contributed to short-term declines in terrestrial carbon dioxide uptake and storage. However, the effects of longer-term climate variability on tropical forest carbon dynamics are still not well understood. We synthesised field data from more than 150 tropical forest sites to explore how climate regulates tropical forest aboveground net primary productivity (ANPP) and organic matter decomposition, and combined those data with two existing databases to explore climate - C relationships globally. While previous analyses have focused on the effects of either temperature or rainfall on ANPP, our results highlight the importance of interactions between temperature and rainfall on the C cycle. In cool forests (< 20 °C), high rainfall slowed rates of C cycling, but in warm tropical forests (> 20 °C) it consistently enhanced both ANPP and decomposition. At the global scale, our analysis showed an increase in ANPP with rainfall in relatively warm sites, inconsistent with declines in ANPP with rainfall reported previously. Overall, our results alter our understanding of climate - C cycle relationships, with high precipitation accelerating rates of C exchange with the atmosphere in the most productive biome on earth. © 2017 John Wiley & Sons Ltd/CNRS.

Soil moisture status in a set of rain-fed cereal fields: application of the DR2 model at monthly scale

NASA Astrophysics Data System (ADS)

López-Vicente, M.; Navas, A.

2012-04-01

One important issue in agricultural management and hydrological research is the assessment of water stored during a rainfall event. In this study, the new Distributed Rainfall-Runoff (DR2) model (López-Vicente and Navas, 2012) is used to estimate the volume of actual available water (Waa) and the soil moisture status (SMS) in a set of rain-fed cereal fields (65 ha) located in the Central Spanish Pre-Pyrenees. This model makes the most of GIS techniques (ArcMapTM 10.0) and distinguishes five configurations of the upslope contributing area, infiltration processes and climatic parameters. Results are presented on a monthly basis. The study site has a relatively long history (since the 10th century) of human occupation, agricultural practices and water management. The landscape is representative of the typical former rain-fed Mediterranean agro-ecosystem where small patches of natural and anthropogenic areas are heterogeneously distributed. Climate is continental Mediterranean with a dry summer with rainfall events of high intensity (I30max, higher than 30 mm / h between May and October). Average annual precipitation was 520 mm for the reference period (1961-1990), whereas the average precipitation during the last ten years (2001-2010) was 16% lower (439 mm). Measured antecedent topsoil moisture presented the highest values in autumn (18.3 vol.%) and the lowest in summer (11.2 vol.%). Values of potential overland flow per raster cell (Q0) during maximum rainfall intensity varied notably in terms of time and space. When rainfall intensity is high (May, August, September and October), potential runoff was predicted along the surface of the crops and variability of Q0 was very low, whereas areas with no runoff production appeared when rainfall intensity was low and variability of Q0 values was high. A variance components analysis shows that values of Q0 are mainly explained by variations in the values of saturated hydraulic conductivity (76% of the variability of Q0) and, to a lesser extent, by the values of the antecedent topsoil moisture (23%) and the volumetric content of water of the soil at saturation (1%). Maps of monthly actual available water after maximum rainfall intensity presented a significant spatial variability, though values varied as a function of total rainfall depth and infiltration, and the five different scenarios of cumulative processes considered on the DR2 model. The minimum value of Waa for each month was well correlated with the average values of precipitation (Pearson's r = 0.86), whereas the mean values of Waa showed a close correlation with the values of maximum rainfall intensity (Pearson's r = 0.92). Maps of SMS and their values were reclassified in seven wetness-dryness categories. Predominant wet conditions occurred in May, September, October, November and December, whereas dry conditions appeared in February, March and July. Drying-up conditions were identified in January and June and wetting-up conditions occurred in April and August. The new DR2 model seems to be of interest to monitor humidity variations and trends in time and space in Mediterranean agricultural systems and can provide valuable information for sustainable soil and water resource management in agro-climatic analysis.

Rain rate intensity model for communication link design across the Indian region

NASA Astrophysics Data System (ADS)

Kilaru, Aravind; Kotamraju, Sarat K.; Avlonitis, Nicholas; Sri Kavya, K. Ch.

2016-07-01

A study on rain statistical parameters such as one minute rain intensity, possible number of minute occurrences with respective percentage of time in a year has been evaluated for the purpose of communication link design at Ka, Q, V bands as well as at Free-Space Optical communication links (FSO). To understand possible outage period of a communication links due to rainfall and to investigate rainfall pattern, Automatic Weather Station (AWS) rainfall data is analysed due its ample presence across India. The climates of the examined AWS regions vary from desert to cold climate, heavy rainfall to variable rainfall regions, cyclone effective regions, mountain and coastal regions. In this way a complete and unbiased picture of the rainfall statistics for Indian region is evaluated. The analysed AWS data gives insight into yearly accumulated rainfall, maximum hourly accumulated rainfall, mean hourly accumulated rainfall, number of rainy days and number of rainy hours from 668 AWS locations. Using probability density function the one minute rainfall measurements at KL University is integrated with AWS measurements for estimating number of rain occurrences in terms of one minute rain intensity for annual rainfall accumulated between 100 mm and 5000 mm to give an insight into possible one minute accumulation pattern in an hour for comprehensive analysis of rainfall influence on a communication link for design engineers. So that low availability communications links at higher frequencies can be transformed into a reliable and economically feasible communication links for implementing High Throughput Services (HTS).

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.

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.

Using CHIRPS Rainfall Dataset to detect rainfall trends in West Africa

NASA Astrophysics Data System (ADS)

Blakeley, S. L.; Husak, G. J.

2016-12-01

In West Africa, agriculture is often rain-fed, subjecting agricultural productivity and food availability to climate variability. Agricultural conditions will change as warming temperatures increase evaporative demand, and with a growing population dependent on the food supply, farmers will become more reliant on improved adaptation strategies. Development of such adaptation strategies will need to consider West African rainfall trends to remain relevant in a changing climate. Here, using the CHIRPS rainfall product (provided by the Climate Hazards Group at UC Santa Barbara), I examine trends in West African rainfall variability. My analysis will focus on seasonal rainfall totals, the structure of the rainy season, and the distribution of rainfall. I then use farmer-identified drought years to take an in-depth analysis of intra-seasonal rainfall irregularities. I will also examine other datasets such as potential evapotranspiration (PET) data, other remotely sensed rainfall data, rain gauge data in specific locations, and remotely sensed vegetation data. Farmer bad year data will also be used to isolate "bad" year markers in these additional datasets to provide benchmarks for identification in the future of problematic rainy seasons.

[Ecological suitability regionalization for Gastrodia elata in Zhaotong based on Maxent and ArcGIS].

PubMed

Shi, Zi-Wei; Ma, Cong-Ji; Kang, Chuan-Zhi; Wang, Li; Zhang, Zhi-Hui; Chen, Jun-Fei; Zhang, Xiao-Bo; Liu, Da-Hui

2016-09-01

In this paper, the potential distribution information and ecological suitability regionalization for Gastrodia elata in Zhaotong were studied based on the climate, terrain, soil and vegetation factors analysis by Maxent and ArcGIS. The results showed that the highly potential distribution (suitability index>0.6) mainly located in Zhaotong, Yunnan province（Zhenxiong,Yiliang and Daguan county, with an area of 2 872 km²）, and Bijie, Guizhou province （Hezhang,Bijie,Weining county, 1 251 km²）. The AUC of ROC curve was above 0.99, indicating that the predictive results with the Maxent model were highly precise. The main ecological factors determining the potential distribution were the altitude, average rainfall in November, average rainfall in October, vegetation types, average rainfall in March, average rainfall in April,soil types,isothermal characteristic and average rainfall in June. The environmental variables in the highly potential areas were determined as altitude around 1 450-2 200 m,annual average temperature around 18.0-20.4 ℃,annual average precipitation around 900 mm,yellow soil or yellow brown soil,and acid sandy loam or slightly acidic sandy loam.The results will provide valuable references for plantation regionalization and the siting for imitation wild planting of G. elata in Zhaotong. Copyright© by the Chinese Pharmaceutical Association.

Dendroclimatological spring rainfall reconstruction for an inner Alpine dry valley

NASA Astrophysics Data System (ADS)

Oberhuber, W.; Kofler, W.

Estimates of spring precipitation for the inner Alpine dry valley of the upper Inn (Tyrol, Austria) are made back to A.D. 1724 using a ring width chronology of Scots pine (Pinus sylvestris L.) as predictor. A highly significant agreement in year-to-year ring width changes exists between several chronologies along the dry valley. The dendroclimatic model used for climate reconstruction is a simple linear transfer function that estimates April-June precipitation from current tree-ring width. All verification statistics commonly used in dendroclimatological research are significant ( p<0.01) and indicate that the reconstructed time series provides valuable information on past spring precipitation variability. Reconstructed spring rainfall deficiencies and surpluses >=20% compared to the long-term mean in 1819, 1832, 1834, 1865, 1885, and in 1780, 1782, 1821, 1853, 1910, respectively, are also documented by local historical records. Furthermore, a comparison is made with an independent climate reconstruction based on historical weather indices valid for the northern side of the Swiss Alps. A fairly good agreement is found between both spring rainfall reconstructions at low frequency intervals during 1755-1862 and 1919-1981. This preliminary study shows that tree-rings can be used to reconstruct spring rainfall variability for inner Alpine dry valleys.

Uncertainties on the definition of critical rainfall patterns for debris-flows triggering. Results from the Rebaixader monitoring site (Central Pyrenees)

NASA Astrophysics Data System (ADS)

Hürlimann, Marcel; Abancó, Clàudia; Moya, Jose; Berenguer, Marc

2015-04-01

Empirical rainfall thresholds are a widespread technique in debris-flow hazard assessment and can be established by statistical analysis of historic data. Typically, data from one or several rain gauges located nearby the affected catchment is used to define the triggering conditions. However, this procedure has been demonstrated not to be accurate enough due to the spatial variability of convective rainstorms. In 2009, a monitoring system was installed in the Rebaixader catchment, Central Pyrenees (Spain). Since then, 28 torrential flows (debris flows and debris floods) have occurred and rainfall data of 25 of them are available with a 5-minutes frequency of recording ("event rainfalls"). Other 142 rainfalls that did not trigger events ("no event rainfalls) were also collected and analysed. The goal of this work was threefold: a) characterize rainfall episodes in the Rebaixader catchment and compare rainfall data that triggered torrential events and others that did not; b) define and test Intensity-Duration (ID) thresholds using rainfall data measured inside the catchment; c) estimate the uncertainty derived from the use of rain gauges located outside the catchment based on the spatial correlation depicted by radar rainfall maps. The results of the statistical analysis showed that the parameters that more distinguish between the two populations of rainfalls are the rainfall intensities, the mean rainfall and the total precipitation. On the other side, the storm duration and the antecedent rainfall are not significantly different between "event rainfalls" and "no event rainfalls". Four different ID rainfall thresholds were derived based on the dataset of the first 5 years and tested using the 2014 dataset. The results of the test indicated that the threshold corresponding to the 90% percentile showed the best performance. Weather radar data was used to analyse the spatial variability of the triggering rainfalls. The analysis indicates that rain gauges outside the catchment may be considered useful or not to describe the rainfall depending on the type of rainfall. For widespread rainfalls, further rain gauges can give a reliable measurement, because the spatial correlation decreases slowly with the distance between the rain gauge and the debris-flow initiation area. Contrarily, local storm cells show higher space-time variability and, therefore, representative rainfall measurements are obtained only by the closest rain gauges. In conclusion, the definition of rainfall thresholds is a delicate task. When the rainfall records are coming from gauges that are outside the catchment under consideration, the data should be carefully analysed and crosschecked with radar data (especially for small convective cells).

How effective is the new generation of GPM satellite precipitation in characterizing the rainfall variability over Malaysia?

NASA Astrophysics Data System (ADS)

Mahmud, Mohd Rizaludin; Hashim, Mazlan; Reba, Mohd Nadzri Mohd

2017-08-01

We investigated the potential of the new generation of satellite precipitation product from the Global Precipitation Mission (GPM) to characterize the rainfall in Malaysia. Most satellite precipitation products have limited ability to precisely characterize the high dynamic rainfall variation that occurred at both time and scale in this humid tropical region due to the coarse grid size to meet the physical condition of the smaller land size, sub-continent and islands. Prior to the status quo, an improved satellite precipitation was required to accurately measure the rainfall and its distribution. Subsequently, the newly released of GPM precipitation product at half-hourly and 0.1° resolution served an opportunity to anticipate the aforementioned conflict. Nevertheless, related evidence was not found and therefore, this study made an initiative to fill the gap. A total of 843 rain gauges over east (Borneo) and west Malaysia (Peninsular) were used to evaluate the rainfall the GPM rainfall data. The assessment covered all critical rainy seasons which associated with Asian Monsoon including northeast (Nov. - Feb.), southwest (May - Aug.) and their subsequent inter-monsoon period (Mar. - Apr. & Sep. - Oct.). The ability of GPM to provide quantitative rainfall estimates and qualitative spatial rainfall patterns were analysed. Our results showed that the GPM had good capacity to depict the spatial rainfall patterns in less heterogeneous rainfall patterns (Spearman's correlation, 0.591 to 0.891) compared to the clustered one (r = 0.368 to 0.721). Rainfall intensity and spatial heterogeneity that is largely driven by seasonal monsoon has significant influence on GPM ability to resolve local rainfall patterns. In quantitative rainfall estimation, large errors can be primarily associated with the rainfall intensity increment. 77% of the error variation can be explained through rainfall intensity particularly the high intensity (> 35 mm d-1). A strong relationship between GPM rainfall and error was found from heavy ( 35 mm d-1) to violent rain (160 mm d-1). The output of this study provides reference regarding the performance of GPM data for respective hydrology studies in this region.

Sometimes processes don't matter: the general effect of short term climate variability on erosional systems.

NASA Astrophysics Data System (ADS)

Deal, Eric; Braun, Jean

2017-04-01

Climatic forcing undoubtedly plays an important role in shaping the Earth's surface. However, precisely how climate affects erosion rates, landscape morphology and the sedimentary record is highly debated. Recently there has been a focus on the influence of short-term variability in rainfall and river discharge on the relationship between climate and erosion rates. Here, we present a simple probabilistic argument, backed by modelling, that demonstrates that the way the Earth's surface responds to short-term climatic forcing variability is primarily determined by the existence and magnitude of erosional thresholds. We find that it is the ratio between the threshold magnitude and the mean magnitude of climatic forcing that determines whether variability matters or not and in which way. This is a fundamental result that applies regardless of the nature of the erosional process. This means, for example, that we can understand the role that discharge variability plays in determining fluvial erosion efficiency despite doubts about the processes involved in fluvial erosion. We can use this finding to reproduce the main conclusions of previous studies on the role of discharge variability in determining long-term fluvial erosion efficiency. Many aspects of the landscape known to influence discharge variability are affected by human activity, such as land use and river damming. Another important control on discharge variability, rainfall intensity, is also expected to increase with warmer temperatures. Among many other implications, our findings help provide a general framework to understand and predict the response of the Earth's surface to changes in mean and variability of rainfall and river discharge associated with the anthropogenic activity. In addition, the process independent nature of our findings suggest that previous work on river discharge variability and erosion thresholds can be applied to other erosional systems.

Relative Contributions of Mean-State Shifts and ENSO-Driven Variability to Precipitation Changes in a Warming Climate

NASA Technical Reports Server (NTRS)

Bonfils, Celine J. W.; Santer, Benjamin D.; Phillips, Thomas J.; Marvel, Kate; Leung, L. Ruby; Doutriaux, Charles; Capotondi, Antonietta

2015-01-01

El Niño-Southern Oscillation (ENSO) is an important driver of regional hydroclimate variability through far-reaching teleconnections. This study uses simulations performed with coupled general circulation models (CGCMs) to investigate how regional precipitation in the twenty-first century may be affected by changes in both ENSO-driven precipitation variability and slowly evolving mean rainfall. First, a dominant, time-invariant pattern of canonical ENSO variability (cENSO) is identified in observed SST data. Next, the fidelity with which 33 state-of-the-art CGCMs represent the spatial structure and temporal variability of this pattern (as well as its associated precipitation responses) is evaluated in simulations of twentieth-century climate change. Possible changes in both the temporal variability of this pattern and its associated precipitation teleconnections are investigated in twenty-first-century climate projections. Models with better representation of the observed structure of the cENSO pattern produce winter rainfall teleconnection patterns that are in better accord with twentieth-century observations and more stationary during the twenty-first century. Finally, the model-predicted twenty-first-century rainfall response to cENSO is decomposed into the sum of three terms: 1) the twenty-first-century change in the mean state of precipitation, 2) the historical precipitation response to the cENSO pattern, and 3) a future enhancement in the rainfall response to cENSO, which amplifies rainfall extremes. By examining the three terms jointly, this conceptual framework allows the identification of regions likely to experience future rainfall anomalies that are without precedent in the current climate.

Relative Contributions of Mean-State Shifts and ENSO-Driven Variability to Precipitation Changes in a Warming Climate

NASA Technical Reports Server (NTRS)

Bonfils, Celine J. W.; Santer, Benjamin D.; Phillips, Thomas J.; Marvel, Kate; Leung, L. Ruby; Doutriaux, Charles; Capotondi, Antonietta

2015-01-01

The El Nino-Southern Oscillation (ENSO) is an important driver of regional hydroclimate variability through far-reaching teleconnections. This study uses simulations performed with Coupled General Circulation Models (CGCMs) to investigate how regional precipitation in the 21st century may be affected by changes in both ENSO-driven precipitation variability and slowly-evolving mean rainfall. First, a dominant, time-invariant pattern of canonical ENSO variability (cENSO) is identified in observed SST data. Next, the fidelity with which 33 state-of-the-art CGCMs represent the spatial structure and temporal variability of this pattern (as well as its associated precipitation responses) is evaluated in simulations of 20th century climate change. Possible changes in both the temporal variability of this pattern and its associated precipitation teleconnections are investigated in 21st century climate projections. Models with better representation of the observed structure of the cENSO pattern produce winter rainfall teleconnection patterns that are in better accord with 20th century observations and more stationary during the 21st century. Finally, the model-predicted 21st century rainfall response to cENSO is decomposed into the sum of three terms: 1) the 21st century change in the mean state of precipitation; 2) the historical precipitation response to the cENSO pattern; and 3) a future enhancement in the rainfall response to cENSO, which amplifies rainfall extremes. By examining the three terms jointly, this conceptual framework allows the identification of regions likely to experience future rainfall anomalies that are without precedent in the current climate.

Impact of rainfall spatial variability on Flash Flood Forecasting

NASA Astrophysics Data System (ADS)

Douinot, Audrey; Roux, Hélène; Garambois, Pierre-André; Larnier, Kevin

2014-05-01

According to the United States National Hazard Statistics database, flooding and flash flooding have caused the largest number of deaths of any weather-related phenomenon over the last 30 years (Flash Flood Guidance Improvement Team, 2003). Like the storms that cause them, flash floods are very variable and non-linear phenomena in time and space, with the result that understanding and anticipating flash flood genesis is far from straightforward. In the U.S., the Flash Flood Guidance (FFG) estimates the average number of inches of rainfall for given durations required to produce flash flooding in the indicated county. In Europe, flash flood often occurred on small catchments (approximately 100 km2) and it has been shown that the spatial variability of rainfall has a great impact on the catchment response (Le Lay and Saulnier, 2007). Therefore, in this study, based on the Flash flood Guidance method, rainfall spatial variability information is introduced in the threshold estimation. As for FFG, the threshold is the number of millimeters of rainfall required to produce a discharge higher than the discharge corresponding to the first level (yellow) warning of the French flood warning service (SCHAPI: Service Central d'Hydrométéorologie et d'Appui à la Prévision des Inondations). The indexes δ1 and δ2 of Zoccatelli et al. (2010), based on the spatial moments of catchment rainfall, are used to characterize the rainfall spatial distribution. Rainfall spatial variability impacts on warning threshold and on hydrological processes are then studied. The spatially distributed hydrological model MARINE (Roux et al., 2011), dedicated to flash flood prediction is forced with synthetic rainfall patterns of different spatial distributions. This allows the determination of a warning threshold diagram: knowing the spatial distribution of the rainfall forecast and therefore the 2 indexes δ1 and δ2, the threshold value is read on the diagram. A warning threshold diagram is built for each studied catchment. The proposed methodology is applied on three Mediterranean catchments often submitted to flash floods. The new forecasting method as well as the Flash Flood Guidance method (uniform rainfall threshold) are tested on 25 flash floods events that had occurred on those catchments. Results show a significant impact of rainfall spatial variability. Indeed, it appears that the uniform rainfall threshold (FFG threshold) always overestimates the observed rainfall threshold. The difference between the FFG threshold and the proposed threshold ranges from 8% to 30%. The proposed methodology allows the calculation of a threshold more representative of the observed one. However, results strongly depend on the related event duration and on the catchment properties. For instance, the impact of the rainfall spatial variability seems to be correlated with the catchment size. According to these results, it seems to be interesting to introduce information on the catchment properties in the threshold calculation. Flash Flood Guidance Improvement Team, 2003. River Forecast Center (RFC) Development Management Team. Final Report. Office of Hydrologic Development (OHD), Silver Spring, Mary-land. Le Lay, M. and Saulnier, G.-M., 2007. Exploring the signature of climate and landscape spatial variabilities in flash flood events: Case of the 8-9 September 2002 Cévennes-Vivarais catastrophic event. Geophysical Research Letters, 34(L13401), doi:10.1029/2007GL029746. Roux, H., Labat, D., Garambois, P.-A., Maubourguet, M.-M., Chorda, J. and Dartus, D., 2011. A physically-based parsimonious hydrological model for flash floods in Mediterranean catchments. Nat. Hazards Earth Syst. Sci. J1 - NHESS, 11(9), 2567-2582. Zoccatelli, D., Borga, M., Zanon, F., Antonescu, B. and Stancalie, G., 2010. Which rainfall spatial information for flash flood response modelling? A numerical investigation based on data from the Carpathian range, Romania. Journal of Hydrology, 394(1-2), 148-161.

Sensitivity of Latent Heating Profiles to Environmental Conditions: Implications for TRMM and Climate Research

NASA Technical Reports Server (NTRS)

Shepherd, J. Marshall; Einaudi, Franco (Technical Monitor)

2000-01-01

The Tropical Rainfall Measuring Mission (TRMM) as a part of NASA's Earth System Enterprise is the first mission dedicated to measuring tropical rainfall through microwave and visible sensors, and includes the first spaceborne rain radar. Tropical rainfall comprises two-thirds of global rainfall. It is also the primary distributor of heat through the atmosphere's circulation. It is this circulation that defines Earth's weather and climate. Understanding rainfall and its variability is crucial to understanding and predicting global climate change. Weather and climate models need an accurate assessment of the latent heating released as tropical rainfall occurs. Currently, cloud model-based algorithms are used to derive latent heating based on rainfall structure. Ultimately, these algorithms can be applied to actual data from TRMM. This study investigates key underlying assumptions used in developing the latent heating algorithms. For example, the standard algorithm is highly dependent on a system's rainfall amount and structure. It also depends on an a priori database of model-derived latent heating profiles based on the aforementioned rainfall characteristics. Unanswered questions remain concerning the sensitivity of latent heating profiles to environmental conditions (both thermodynamic and kinematic), regionality, and seasonality. This study investigates and quantifies such sensitivities and seeks to determine the optimal latent heating profile database based on the results. Ultimately, the study seeks to produce an optimized latent heating algorithm based not only on rainfall structure but also hydrometeor profiles.

Modelling Ecuador's rainfall distribution according to geographical characteristics.

NASA Astrophysics Data System (ADS)

Tobar, Vladimiro; Wyseure, Guido

2017-04-01

It is known that rainfall is affected by terrain characteristics and some studies had focussed on its distribution over complex terrain. Ecuador's temporal and spatial rainfall distribution is affected by its location on the ITCZ, the marine currents in the Pacific, the Amazon rainforest, and the Andes mountain range. Although all these factors are important, we think that the latter one may hold a key for modelling spatial and temporal distribution of rainfall. The study considered 30 years of monthly data from 319 rainfall stations having at least 10 years of data available. The relatively low density of stations and their location in accessible sites near to main roads or rivers, leave large and important areas ungauged, making it not appropriate to rely on traditional interpolation techniques to estimate regional rainfall for water balance. The aim of this research was to come up with a useful model for seasonal rainfall distribution in Ecuador based on geographical characteristics to allow its spatial generalization. The target for modelling was the seasonal rainfall, characterized by nine percentiles for each one of the 12 months of the year that results in 108 response variables, later on reduced to four principal components comprising 94% of the total variability. Predictor variables for the model were: geographic coordinates, elevation, main wind effects from the Amazon and Coast, Valley and Hill indexes, and average and maximum elevation above the selected rainfall station to the east and to the west, for each one of 18 directions (50-135°, by 5°) adding up to 79 predictors. A multiple linear regression model by the Elastic-net algorithm with cross-validation was applied for each one of the PC as response to select the most important ones from the 79 predictor variables. The Elastic-net algorithm deals well with collinearity problems, while allowing variable selection in a blended approach between the Ridge and Lasso regression. The model fitting produced explained variances of 59%, 81%, 49% and 17% for PC1, PC2, PC3 and PC4, respectively, backing up the hypothesis of good correlation between geographical characteristics and seasonal rainfall patterns (comprised in the four principal components). With the obtained coefficients from the regression, the 108 rainfall percentiles for each station were back estimated giving very good results when compared with the original ones, with an overall 60% explained variance.

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.

The Impact of Model and Rainfall Forcing Errors on Characterizing Soil Moisture Uncertainty in Land Surface Modeling

NASA Technical Reports Server (NTRS)

Maggioni, V.; Anagnostou, E. N.; Reichle, R. H.

2013-01-01

The contribution of rainfall forcing errors relative to model (structural and parameter) uncertainty in the prediction of soil moisture is investigated by integrating the NASA Catchment Land Surface Model (CLSM), forced with hydro-meteorological data, in the Oklahoma region. Rainfall-forcing uncertainty is introduced using a stochastic error model that generates ensemble rainfall fields from satellite rainfall products. The ensemble satellite rain fields are propagated through CLSM to produce soil moisture ensembles. Errors in CLSM are modeled with two different approaches: either by perturbing model parameters (representing model parameter uncertainty) or by adding randomly generated noise (representing model structure and parameter uncertainty) to the model prognostic variables. Our findings highlight that the method currently used in the NASA GEOS-5 Land Data Assimilation System to perturb CLSM variables poorly describes the uncertainty in the predicted soil moisture, even when combined with rainfall model perturbations. On the other hand, by adding model parameter perturbations to rainfall forcing perturbations, a better characterization of uncertainty in soil moisture simulations is observed. Specifically, an analysis of the rank histograms shows that the most consistent ensemble of soil moisture is obtained by combining rainfall and model parameter perturbations. When rainfall forcing and model prognostic perturbations are added, the rank histogram shows a U-shape at the domain average scale, which corresponds to a lack of variability in the forecast ensemble. The more accurate estimation of the soil moisture prediction uncertainty obtained by combining rainfall and parameter perturbations is encouraging for the application of this approach in ensemble data assimilation systems.

Space based observations: A state of the art solution for spatial monitoring tropical forested watershed productivity at regional scale in developing countries

NASA Astrophysics Data System (ADS)

Mahmud, M. R.

2014-02-01

This paper presents the simplified and operational approach of mapping the water yield in tropical watershed using space-based multi sensor remote sensing data. Two main critical hydrological rainfall variables namely rainfall and evapotranspiration are being estimated by satellite measurement and reinforce the famous Thornthwaite & Mather water balance model. The satellite rainfall and ET estimates were able to represent the actual value on the ground with accuracy under considerable conditions. The satellite derived water yield had good agreement and relation with actual streamflow. A high bias measurement may result due to; i) influence of satellite rainfall estimates during heavy storm, and ii) large uncertainties and standard deviation of MODIS temperature data product. The output of this study managed to improve the regional scale of hydrology assessment in Peninsular Malaysia.

Tree ring reconstructed rainfall over the southern Amazon Basin

NASA Astrophysics Data System (ADS)

Lopez, Lidio; Stahle, David; Villalba, Ricardo; Torbenson, Max; Feng, Song; Cook, Edward

2017-07-01

Moisture sensitive tree ring chronologies of Centrolobium microchaete have been developed from seasonally dry forests in the southern Amazon Basin and used to reconstruct wet season rainfall totals from 1799 to 2012, adding over 150 years of rainfall estimates to the short instrumental record for the region. The reconstruction is correlated with the same atmospheric variables that influence the instrumental measurements of wet season rainfall. Anticyclonic circulation over midlatitude South America promotes equatorward surges of cold and relatively dry extratropical air that converge with warm moist air to form deep convection and heavy rainfall over this sector of the southern Amazon Basin. Interesting droughts and pluvials are reconstructed during the preinstrumental nineteenth and early twentieth centuries, but the tree ring reconstruction suggests that the strong multidecadal variability in instrumental and reconstructed wet season rainfall after 1950 may have been unmatched since 1799.

Characterizing multiscale variability of zero intermittency in spatial rainfall

NASA Technical Reports Server (NTRS)

Kumar, Praveen; Foufoula-Georgiou, Efi

1994-01-01

In this paper the authors study how zero intermittency in spatial rainfall, as described by the fraction of area covered by rainfall, changes with spatial scale of rainfall measurement or representation. A statistical measure of intermittency that describes the size distribution of 'voids' (nonrainy areas imbedded inside rainy areas) as a function of scale is also introduced. Morphological algorithms are proposed for reconstructing rainfall intermittency at fine scales given the intermittency at coarser scales. These algorithms are envisioned to be useful in hydroclimatological studies where the rainfall spatial variability at the subgrid scale needs to be reconstructed from the results of synoptic- or mesoscale meteorological numerical models. The developed methodologies are demsonstrated and tested using data from a severe springtime midlatitude squall line and a mild midlatitude winter storm monitored by a meteorological radar in Norman, Oklahoma.

Assessment of impact of climate change and adaptation strategies on maize production in Uganda

NASA Astrophysics Data System (ADS)

Kikoyo, Duncan A.; Nobert, Joel

2016-06-01

Globally, various climatic studies have estimated a reduction of crop yields due to changes in surface temperature and precipitation especially for the developing countries which is heavily dependent on agriculture and lacks resources to counter the negative effects of climate change. Uganda's economy and the wellbeing of its populace depend on rain-fed agriculture which is susceptible to climate change. This study quantified the impacts of climate change and variability in Uganda and how coping strategies can enhance crop production against climate change and/or variability. The study used statistical methods to establish various climate change and variability indicators across the country, and uses the FAO AquaCrop model to simulate yields under possible future climate scenarios with and without adaptation strategies. Maize, the most widely grown crop was used for the study. Meteorological, soil and crop data were collected for various districts representing the maize growing ecological zones in the country. Based on this study, it was found that temperatures have increased by up to 1 °C across much of Uganda since the 1970s, with rates of warming around 0.3 °C per decade across the country. High altitude, low rainfall regions experience the highest level of warming, with over 0.5 °C/decade recorded in Kasese. Rainfall is variable and does not follow a specific significant increasing or decreasing trend. For both future climate scenarios, Maize yields will reduce in excess of 4.7% for the fast warming-low rainfall climates but increase on average by 3.5% for slow warming-high rainfall regions, by 2050. Improved soil fertility can improve yields by over 50% while mulching and use of surface water management practices improve yields by single digit percentages. The use of fertilizer application needs to go hand in hand with other water management strategies since more yields as a result of the improved soil fertility leads to increased water stress, especially for the dry climates.

The role of storm scale, position and movement in controlling urban flood response

NASA Astrophysics Data System (ADS)

ten Veldhuis, Marie-claire; Zhou, Zhengzheng; Yang, Long; Liu, Shuguang; Smith, James

2018-01-01

The impact of spatial and temporal variability of rainfall on hydrological response remains poorly understood, in particular in urban catchments due to their strong variability in land use, a high degree of imperviousness and the presence of stormwater infrastructure. In this study, we analyze the effect of storm scale, position and movement in relation to basin scale and flow-path network structure on urban hydrological response. A catalog of 279 peak events was extracted from a high-quality observational dataset covering 15 years of flow observations and radar rainfall data for five (semi)urbanized basins ranging from 7.0 to 111.1 km2 in size. Results showed that the largest peak flows in the event catalog were associated with storm core scales exceeding basin scale, for all except the largest basin. Spatial scale of flood-producing storm events in the smaller basins fell into two groups: storms of large spatial scales exceeding basin size or small, concentrated events, with storm core much smaller than basin size. For the majority of events, spatial rainfall variability was strongly smoothed by the flow-path network, increasingly so for larger basin size. Correlation analysis showed that position of the storm in relation to the flow-path network was significantly correlated with peak flow in the smallest and in the two more urbanized basins. Analysis of storm movement relative to the flow-path network showed that direction of storm movement, upstream or downstream relative to the flow-path network, had little influence on hydrological response. Slow-moving storms tend to be associated with higher peak flows and longer lag times. Unexpectedly, position of the storm relative to impervious cover within the basins had little effect on flow peaks. These findings show the importance of observation-based analysis in validating and improving our understanding of interactions between the spatial distribution of rainfall and catchment variability.

SUBPIXEL-SCALE RAINFALL VARIABILITY AND THE EFFECTS ON SEPARATION OF RADAR AND GAUGE RAINFALL ERRORS

EPA Science Inventory

One of the primary sources of the discrepancies between radar-based rainfall estimates and rain gauge measurements is the point-area difference, i.e., the intrinsic difference in the spatial dimensions of the rainfall fields that the respective data sets are meant to represent. ...

The northward shift of Meiyu rain belt and its possible association with rainfall intensity changes and the Pacific-Japan pattern

NASA Astrophysics Data System (ADS)

Gao, Qingjiu; Sun, Yuting; You, Qinglong

2016-12-01

The meridional location change of Meiyu rain belt and its relationship with the rainfall intensity and circulation background changes for the period 1958-2009 are examined using daily rainfall datasets from 756 stations in China, the 6-h ERA-Interim reanalyses, CRU monthly temperature and daily outgoing long-wave radiation (OLR) data from the US National Oceanic and Atmospheric Administration (NOAA). The results indicate that the Meiyu rain belt experienced a northward shift in the late 1990s in response to global warming. Moreover, the intensity of interannual and day-to-day variability of rainfall within Meiyu period has been increasing in the warming climate. The amplification of the variability within Meiyu period over the northern Yangtze-Huai River Valley (YHRV) is much larger than that of the southern YHRV. The large difference in the trends of variance within the Meiyu period between these two regions induces a spatial varying for different rainfall categories in terms of intensity. More significant positive trends in heavy and extreme heavy rainfall occur over northern YHRV compared with southern YHRV, which is a crucial indicator of changes in the rain band, despite the observation of an increase in heavy and very heavy rain events and a decrease in weak events throughout the entire YHRV. A composite of the atmospheric circulation indicates that intense northward horizontal transport and the convergence of water vapor fluxes are the immediate causes of the rain band shift. Besides, through forcing a northward extended convection over the tropics, the Pacific-Japan (P-J) pattern induces a northward expansion of western Pacific Subtropical High, leading to intensified convergence and enhanced rainfall over Northern YHRV.

Enhanced future variability during India's rainy season

NASA Astrophysics Data System (ADS)

Menon, Arathy; Levermann, Anders; Schewe, Jacob

2013-04-01

The Indian summer monsoon shapes the livelihood of a large share of the world's population. About 80% of annual precipitation over India occurs during the monsoon season from June through September. Next to its seasonal mean rainfall the day-to-day variability is crucial for the risk of flooding, national water supply and agricultural productivity. Here we show that the latest ensemble of climate model simulations, prepared for the IPCC's AR-5, consistently projects significant increases in day-to-day rainfall variability under unmitigated climate change. While all models show an increase in day-to-day variability, some models are more realistic in capturing the observed seasonal mean rainfall over India than others. While no model's monsoon rainfall exceeds the observed value by more than two standard deviations, half of the models simulate a significantly weaker monsoon than observed. The relative increase in day-to-day variability by the year 2100 ranges from 15% to 48% under the strongest scenario (RCP-8.5), in the ten models which capture seasonal mean rainfall closest to observations. The variability increase per degree of global warming is independent of the scenario in most models, and is 8% +/- 4% per K on average. This consistent projection across 20 comprehensive climate models provides confidence in the results and suggests the necessity of profound adaptation measures in the case of unmitigated climate change.

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.

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.

Characteristics of aggregation of daily rainfall in a middle-latitudes region during a climate variability in annual rainfall amount

NASA Astrophysics Data System (ADS)

Lucero, Omar A.; Rozas, Daniel

Climate variability in annual rainfall occurs because the aggregation of daily rainfall changes. A topic open to debate is whether that change takes place because rainfall becomes more intense, or because it rains more often, or a combination of both. The answer to this question is of interest for water resources planning, hydrometeorological design, and agricultural management. Change in the number of rainy days can cause major disruptions in hydrological and ecological systems, with important economic and social effects. Furthermore, the characteristics of daily rainfall aggregation in ongoing climate variability provide a reference to evaluate the capability of GCM to simulate changes in the hydrologic cycle. In this research, we analyze changes in the aggregation of daily rainfall producing a climate positive trend in annual rainfall in central Argentina, in the southern middle-latitudes. This state-of-the-art agricultural region has a semiarid climate with dry and wet seasons. Weather effects in the region influence world-market prices of several crops. Results indicate that the strong positive trend in seasonal and annual rainfall amount is produced by an increase in number of rainy days. This increase takes place in the 3-month periods January-March (summer) and April-June (autumn). These are also the 3-month periods showing a positive trend in the mean of annual rainfall. The mean of the distribution of annual number of rainy day (ANRD) increased in 50% in a 36-year span (starting at 44 days/year). No statistically significant indications on time changes in the probability distribution of daily rainfall amount were found. Non-periodic fluctuations in the time series of annual rainfall were analyzed using an integral wavelet transform. Fluctuations with a time scale of about 10 and 20 years construct the trend in annual rainfall amount. These types of non-periodic fluctuations have been observed in other regions of the world. This suggests that results of this research could have further geographical validity.

Receiver Operating Characteristic Curve Analysis of Beach Water Quality Indicator Variables

PubMed Central

Morrison, Ann Michelle; Coughlin, Kelly; Shine, James P.; Coull, Brent A.; Rex, Andrea C.

2003-01-01

Receiver operating characteristic (ROC) curve analysis is a simple and effective means to compare the accuracies of indicator variables of bacterial beach water quality. The indicator variables examined in this study were previous day's Enterococcus density and antecedent rainfall at 24, 48, and 96 h. Daily Enterococcus densities and 15-min rainfall values were collected during a 5-year (1996 to 2000) study of four Boston Harbor beaches. The indicator variables were assessed for their ability to correctly classify water as suitable or unsuitable for swimming at a maximum threshold Enterococcus density of 104 CFU/100 ml. Sensitivity and specificity values were determined for each unique previous day's Enterococcus density and antecedent rainfall volume and used to construct ROC curves. The area under the ROC curve was used to compare the accuracies of the indicator variables. Twenty-four-hour antecedent rainfall classified elevated Enterococcus densities more accurately than previous day's Enterococcus density (P = 0.079). An empirically derived threshold for 48-h antecedent rainfall, corresponding to a sensitivity of 0.75, was determined from the 1996 to 2000 data and evaluated to ascertain if the threshold would produce a 0.75 sensitivity with independent water quality data collected in 2001 from the same beaches. PMID:14602593

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.

Impacts of El Niño Southern Oscillation and Indian Ocean Dipole on dengue incidence in Bangladesh

PubMed Central

Banu, Shahera; Guo, Yuming; Hu, Wenbiao; Dale, Pat; Mackenzie, John S.; Mengersen, Kerrie; Tong, Shilu

2015-01-01

Dengue dynamics are driven by complex interactions between hosts, vectors and viruses that are influenced by environmental and climatic factors. Several studies examined the role of El Niño Southern Oscillation (ENSO) in dengue incidence. However, the role of Indian Ocean Dipole (IOD), a coupled ocean atmosphere phenomenon in the Indian Ocean, which controls the summer monsoon rainfall in the Indian region, remains unexplored. Here, we examined the effects of ENSO and IOD on dengue incidence in Bangladesh. According to the wavelet coherence analysis, there was a very weak association between ENSO, IOD and dengue incidence, but a highly significant coherence between dengue incidence and local climate variables (temperature and rainfall). However, a distributed lag nonlinear model (DLNM) revealed that the association between dengue incidence and ENSO or IOD were comparatively stronger after adjustment for local climate variables, seasonality and trend. The estimated effects were nonlinear for both ENSO and IOD with higher relative risks at higher ENSO and IOD. The weak association between ENSO, IOD and dengue incidence might be driven by the stronger effects of local climate variables such as temperature and rainfall. Further research is required to disentangle these effects. PMID:26537857

Interannual Variability of the Bimodal Distribution of Summertime Rainfall Over Central America and Tropical Storm Activity in the Far-Eastern Pacific

NASA Technical Reports Server (NTRS)

Curtis, Scott; Starr, David OC. (Technical Monitor)

2002-01-01

The summer climate of southern Mexico and Central America is characterized by a mid summer drought (MSD), where rainfall is reduced by 40% in July as compared to June and September. A mid-summer reduction in the climatological number of eastern Pacific tropical cyclones has also been noted. Little is understood about the climatology and interannual variability of these minima. The present study uses a novel approach to quantify the bimodal distribution of summertime rainfall for the globe and finds that this feature of the annual cycle is most extreme over Pan America and adjacent oceans. One dominant interannual signal in this region occurs the summer before a strong winter El Nino/Southern Oscillation ENSO. Before El Nino events the region is dry, the MSD is strong and centered over the ocean, and the mid-summer minimum in tropical cyclone frequency is most pronounced. This is significantly different from Neutral cases (non-El Nino and non-La Nina) when the MSD is weak and positioned over the land bridge. The MSD is highly variable for La Nina years, and there is not an obvious mid-summer minimum in the number of tropical cyclones.

Impacts of El Niño Southern Oscillation and Indian Ocean Dipole on dengue incidence in Bangladesh.

PubMed

Banu, Shahera; Guo, Yuming; Hu, Wenbiao; Dale, Pat; Mackenzie, John S; Mengersen, Kerrie; Tong, Shilu

2015-11-05

Dengue dynamics are driven by complex interactions between hosts, vectors and viruses that are influenced by environmental and climatic factors. Several studies examined the role of El Niño Southern Oscillation (ENSO) in dengue incidence. However, the role of Indian Ocean Dipole (IOD), a coupled ocean atmosphere phenomenon in the Indian Ocean, which controls the summer monsoon rainfall in the Indian region, remains unexplored. Here, we examined the effects of ENSO and IOD on dengue incidence in Bangladesh. According to the wavelet coherence analysis, there was a very weak association between ENSO, IOD and dengue incidence, but a highly significant coherence between dengue incidence and local climate variables (temperature and rainfall). However, a distributed lag nonlinear model (DLNM) revealed that the association between dengue incidence and ENSO or IOD were comparatively stronger after adjustment for local climate variables, seasonality and trend. The estimated effects were nonlinear for both ENSO and IOD with higher relative risks at higher ENSO and IOD. The weak association between ENSO, IOD and dengue incidence might be driven by the stronger effects of local climate variables such as temperature and rainfall. Further research is required to disentangle these effects.

Monsoon variability, crop water requirement, and crop planning for kharif rice in Sagar Island, India.

PubMed

Mandal, S; Choudhury, B U; Satpati, L N

2015-12-01

In the Sagar Island of Bay of Bengal, rainfed lowland rice is the major crop, grown solely depending on erratic distribution of southwest monsoon (SM) rainfall. Lack of information on SM rainfall variability and absence of crop scheduling accordingly results in frequent occurrence of intermittent water stress and occasional crop failure. In the present study, we analyzed long period (1982-2010) SM rainfall behavior (onset, withdrawal, rainfall and wetness indices, dry and wet spells), crop water requirement (CWR, by Food and Agriculture Organization (FAO) 56), and probability of weekly rainfall occurrence (by two-parameter gamma distribution) to assess the variability and impact on water availability, CWR, and rice productivity. Finally, crop planning was suggested to overcome monsoon uncertainties on water availability and rice productivity. Study revealed that the normal onset and withdrawal weeks for SM rainfall were 22nd ± 1 and 43rd ± 2 meteorological weeks (MW), respectively. However, effective monsoon rainfall started at 24th MW (rainfall 92.7 mm, p > 56.7 % for 50 mm rainfall) and was terminated by the end of 40th MW (rainfall 90.7 mm, p < 59.6 % for 50 mm rainfall). During crop growth periods (seed to seed, 21st to 45th MW), the island received an average weekly rainfall of 65.1 ± 25.9 mm, while the corresponding weekly CWR was 47.8 ± 5.4 mm. Despite net water surplus of 353.9 mm during crop growth periods, there was a deficit of 159.5 mm water during MW of 18-23 (seedling raising) and MW of 41-45 (flowering to maturity stages). Water stress was observed in early lag vegetative stage of crop growth (32nd MW). The total dry spell frequency during panicle initiation and heading stage was computed as 40 of which 6 dry spells were >7 days in duration and reflected a significant (p < 0.05) increasing trend (at 0.22 days year(-1)) over the years (1982-2010). The present study highlights the adaptive capacity of crop planning including abiotic stress-tolerant cultivars to monsoon rainfall variability for sustaining rainfed rice production vis-à-vis food and livelihood security in vulnerable islands of coastal ecosystem.

Indian summer monsoon variability forecasts in the North American multimodel ensemble

NASA Astrophysics Data System (ADS)

Singh, Bohar; Cash, Ben; Kinter, James L., III

2018-04-01

The representation of the seasonal mean and interannual variability of the Indian summer monsoon rainfall (ISMR) in nine global ocean-atmosphere coupled models that participated in the North American Multimodal Ensemble (NMME) phase 1 (NMME:1), and in nine global ocean-atmosphere coupled models participating in the NMME phase 2 (NMME:2) from 1982-2009, is evaluated over the Indo-Pacific domain with May initial conditions. The multi-model ensemble (MME) represents the Indian monsoon rainfall with modest skill and systematic biases. There is no significant improvement in the seasonal forecast skill or interannual variability of ISMR in NMME:2 as compared to NMME:1. The NMME skillfully predicts seasonal mean sea surface temperature (SST) and some of the teleconnections with seasonal mean rainfall. However, the SST-rainfall teleconnections are stronger in the NMME than observed. The NMME is not able to capture the extremes of seasonal mean rainfall and the simulated Indian Ocean-monsoon teleconnections are opposite to what are observed.

Throughfall under a teak plantation in Thailand: a multifactorial analysis on the effects of canopy phenology and meteorological conditions

NASA Astrophysics Data System (ADS)

Tanaka, N.; Levia, D. F., Jr.; Igarashi, Y.; Nanko, K.; Yoshifuji, N.; Tanaka, K.; Chatchai, T.; Suzuki, M.; Kumagai, T.

2014-12-01

Teak (Tectona grandis Linn. f.) plantations cover vast areas throughout Southeast Asia and are of great economic importance. This study has sought to increase our understanding of throughfall inputs under teak by analyzing the abiotic and biotic factors governing throughfall amounts and throughfall ratios in relation to three canopy phenophases (leafless, leafing, and leafed). There is no rain during the brief leaf senescence phenophase. Daily data was available for both throughfall volumes and depths as well as leaf area index. Detailed meteorological data were available in situ every ten minutes. Leveraging this high-resolution field data, we employed boosted regression trees (BRT) analysis to identify the primary controls on throughfall amount and ratio during each of the three canopy phenophases. Whereas throughfall amounts were always dominated by the magnitude of rainfall (as expected), throughfall ratios were governed by a suite of predictor variables during each phenophase. The BRT analysis demonstrated that throughfall ratio in the leafless phase was most influenced (in descending order of importance) by air temperature, rainfall amount, maximum wind speed, and rainfall intensity. Throughfall ratio in the leafed phenophase was dominated by rainfall amount which exerted 54.0% of the relative influence. The leafing phenophase was an intermediate case where rainfall amount, air temperature, and vapor pressure deficit were most important. Our results highlight the fact that throughfall ratios are differentially influenced by a suite of meteorological variables during leafless, leafing, and leafed phenophases. Abiotic variables (rainfall amount, air temperature, vapor pressure deficit, and maximum wind speed) trumped leaf area index and stand density in their effect on throughfall ratio. The leafing phenophase, while transitional in nature and short in duration, has a detectable and unique impact on water inputs to teak plantations. Further work is clearly needed to better gauge the importance of the leaf emergence period to the stemflow hydrology and forest biogeochemistry of teak plantations.

A TRMM-Calibrated Infrared Technique for Global Rainfall Estimation

NASA Technical Reports Server (NTRS)

Negri, Andrew J.; Adler, Robert F.

2002-01-01

The development of a satellite infrared (IR) technique for estimating convective and stratiform rainfall and its application in studying the diurnal variability of rainfall on a global scale is presented. The Convective-Stratiform Technique (CST), calibrated by coincident, physically retrieved rain rates from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR), is applied over the global tropics during 2001. The technique is calibrated separately over land and ocean, making ingenious use of the IR data from the TRMM Visible/Infrared Scanner (VIRS) before application to global geosynchronous satellite data. The low sampling rate of TRMM PR imposes limitations on calibrating IR-based techniques; however, our research shows that PR observations can be applied to improve IR-based techniques significantly by selecting adequate calibration areas and calibration length. The diurnal cycle of rainfall, as well as the division between convective and stratiform rainfall will be presented. The technique is validated using available data sets and compared to other global rainfall products such as Global Precipitation Climatology Project (GPCP) IR product, calibrated with TRMM Microwave Imager (TMI) data. The calibrated CST technique has the advantages of high spatial resolution (4 km), filtering of non-raining cirrus clouds, and the stratification of the rainfall into its convective and stratiform components, the latter being important for the calculation of vertical profiles of latent heating.

Interannual Rainfall Variability in the Tropical Atlantic Region

NASA Technical Reports Server (NTRS)

Gu, Guojun

2005-01-01

Rainfall variability on seasonal and interannual-to-interdecadal time scales in the tropical Atlantic is quantified using a 25-year (1979-2003) monthly rainfall dataset from the Global Precipitation Climatology Project (GPCP). The ITCZ measured by monthly rainfall between 15-37.5 deg W attains its peak as moving to the northernmost latitude (4-10 deg N) during July-September in which the most total rainfall is observed in the tropical Atlantic basin (17.5 deg S-22.5 deg N, 15 deg-37.5 deg W); the ITCZ becomes weakest during January-February with the least total rainfall as it moves to the south. In contrast, rainfall variability on interannual to interdecadal time scales shows a quite different seasonal preference. The most intense interannual variability occurs during March-May when the ITCZ tends to be near the equator and becomes weaker. Significant, negative correlations between the ITCZ strength and latitude anomalies are observed during boreal spring and early summer. The ITCZ strength and total rainfall amount in the tropical Atlantic basin are significantly modulated by the Pacific El Nino and the Atlantic equatorial mode (or Atlantic Nino) particularly during boreal spring and summer; whereas the impact of the Atlantic interhemispheric mode is considerably weaker. Regarding the anomalous latitudes of the ITCZ, the influence can come from both local, i.e., the Atlantic interhemispheric and equatorial modes, and remote forcings, i. e., El Nino; however, a direct impact of El Nino on the latitudes of the ITCZ can only be found during April-July, not in winter and early spring in which the warmest SST anomalies are usually observed in the equatorial Pacific.

Stemflow in low-density and hedgerow olive orchards in Portugal

NASA Astrophysics Data System (ADS)

Dias, Pedro D.; Valente, Fernanda; Pereira, Fernando L.; Abreu, Francisco G.

2015-04-01

Stemflow (Sf) is responsible for a localized water and solute input to soil around tree's trunks, playing an important eco-hydrological role in forest and agricultural ecosystems. Sf was monitored for seven months in 25 Olea europaea L. trees distributed in three orchards managed in two different ways, traditional low-density and super high density hedgerow. The orchards were located in central Portugal in the regions of Santarém (Várzea and Azóia) and Lisboa (Tapada). Seven olive varieties were analysed: Arbequina, Galega, Picual, Maçanilha, Cordovil, Azeiteira, Negrinha and Blanqueta. Measured Sf ranged from 7.5 to 87.2 mm (relative to crown-projected area), corresponding to 1.2 and 16.7% of gross rainfall (Pg). To understand better the variables that affect Sf and to be able to predict its value, linear regression models were fitted to these data. Whenever possible, the linear models were simplified using the backward stepwise algorithm based on the Akaike information criterion. For each tree, multiple linear regressions were adjusted between Sf and the duration, volume and intensity of rainfall episodes and maximum evaporation rate. In the low-density Várzea grove the more relevant explanatory variables were the three rainfall characteristics. In the super high density Azóia orchard only rainfall volume and intensity were considered relevant. In the low-density Tapada's grove all trees had a different sub-model with Pg being the only common variable. To try to explain differences between trees and to improve the quality of the modeling in each orchard, another set of explanatory variables was added: canopy volume, tree and trunk heights and trunk perimeter at the height of the first branches. The variables present in all sub-models were rainfall volume and intensity and the tree and trunk heights. Canopy volume and rainfall duration were also present in the sub-models of the two low-density groves (Tapada and Várzea). The determination coefficient (R2) of all models ranged from 0.5 to 0.76. The size of leaves was also analysed. Although there were significant differences between varieties and between trees of the same variety, they did not seem to affect the amount of Sf generated. Through analysis of bark storage capacity, it was found that older trees, with rough and thick bark, had higher trunk storage capacity and, therefore, originated less Sf. The results confirm the need for considering the contribution of stemflow when trying to correctly assess interception loss in olive orchards. Although the use of simple and general statistical models may be an attractive option, their precision may be small, making direct measurements or conceptual modelling preferable methods.

Climate variability from the Florida Bay sedimentary record: Possible teleconnections to ENSO, PNA and CNP

USGS Publications Warehouse

Cronin, T. M.; Dwyer, Gary S.; Schwede, S.B.; Vann, C.D.; Dowsett, H.

2002-01-01

We analyzed decadal and interannual climate variability in South Florida since 1880 using geochemical and faunal paleosalinity indicators from isotopically dated sediment cores at Russell Bank in Florida Bay (FB). Using the relative abundance of 2 ostracode species and the Mg/Ca ratios in Loxoconcha matagordensis shells to reconstruct paleosalinity, we found evidence for cyclic oscillations in the salinity of central FB. During this time salinity fluctuated from as low as ~18 parts per thousand (ppt) to as high as ~57 ppt. Time series analyses suggest, in addition to a 5.6 yr Mg/Ca based salinity periodicity, there are 3 other modes of variability in paleosalinity indicators: 6-7, 8-9, and 13-14 yr periods which occur in all paleo-proxies. To search for factors that might cause salinity to vary in FB, we compared the Russell Bank paleosalinity record to South Florida winter rainfall, the Southern Oscillation Index (SOI), winter North Atlantic Oscillation (NAO), and the winter Pacific North American (PNA) index, and a surrogate for the PNA in the winter season, the Central North Pacific (CNP) index. SOI and PNA/CNP appear to be associated with South Florida winter precipitation. Time series analyses of SOI and winter rainfall for the period 1910-1999 suggest ~5, 6-7, 8-9 and 13-14 yr cycles. The 6-7 yr and 13-14 yr cycles correspond to those observed in the faunal and geochemical time series from Russell Bank. The main periods of the CNP index are 5-6 and 13-15 yr, which are similar to those observed in FB paleosalinity. Cross-spectral analyses show that winter rainfall and salinity are coherent at 5.6 yr with a salinity lag of ~1.6 mo. These results suggest that regional rainfall variability influences FB salinity over interannual and decadal timescales and that much of this variability may have its origin in climate variability in the Pacific Ocean/atmosphere system.

Interaction between the effects of evaporation rate and amount of simulated rainfall on development of the free-living stages of Haemonchus contortus.

PubMed

O'Connor, Lauren J; Kahn, Lewis P; Walkden-Brown, Stephen W

2008-08-17

A factorial experiment (3 x 4 x 2 x 3) was conducted in programmable incubators to investigate interaction between the effects of rainfall amount, rainfall distribution and evaporation rate on development of Haemonchus contortus to L3. Sheep faeces containing H. contortus eggs were incubated on sterilised soil under variable temperatures typical of summer in the Northern Tablelands of NSW, Australia. Simulated rainfall was applied in 1 of 3 amounts (12, 24 or 32 mm) and 4 distributions (a single event on the day after deposition, or the same total amount split in 2, 3 or 4 equal events over 2, 3 or 4 days, respectively). Samples were incubated at either a Low or High rate of evaporation (Low: 2.1-3.4 mm/day and High: 3.8-6.1 mm/day), and faeces and soil were destructively sampled at 4, 7 and 14 days post-deposition. Recovery of L3 from the soil (extra-pellet L3) increased over time (up to 0.52% at day 14) and with each increment of rainfall (12 mm: <0.01%; 24 mm: 0.10%; 32 mm: 0.45%) but was reduced under the High evaporation rate (0.01%) compared with the Low evaporation rate (0.31%). All rainfall amounts yielded significantly different recoveries of L3 under Low evaporation rates but there was no difference between the 12 and 24 mm treatments under the High evaporation rate. The distribution of simulated rainfall did not significantly affect recovery of infective larvae. Faecal moisture content was positively associated with L3 recovery, as was the ratio of cumulative precipitation and cumulative evaporation (P/E), particularly when measured in the first 4 days post-deposition. The results show that evaporation rate plays a significant role in regulating the influence of rainfall amount on the success of L3 transmission.

Variable rainfall intensity and tillage effects on runoff, sediment, and carbon losses from a loamy sand under simulated rainfall.

PubMed

Truman, C C; Strickland, T C; Potter, T L; Franklin, D H; Bosch, D D; Bednarz, C W

2007-01-01

The low-carbon, intensively cropped Coastal Plain soils of Georgia are susceptible to runoff, soil loss, and drought. Reduced tillage systems offer the best management tool for sustained row crop production. Understanding runoff, sediment, and chemical losses from conventional and reduced tillage systems is expected to improve if the effect of a variable rainfall intensity storm was quantified. Our objective was to quantify and compare effects of a constant (Ic) intensity pattern and a more realistic, observed, variable (Iv) rainfall intensity pattern on runoff (R), sediment (E), and carbon losses (C) from a Tifton loamy sand cropped to conventional-till (CT) and strip-till (ST) cotton (Gossypium hirsutum L.). Four treatments were evaluated: CT-Ic, CT-Iv, ST-Ic, and ST-Iv, each replicated three times. Field plots (n=12), each 2 by 3 m, were established on each treatment. Each 6-m2 field plot received simulated rainfall at a constant (57 mm h(-1)) or variable rainfall intensity pattern for 70 min (12-run ave.=1402 mL; CV=3%). The Iv pattern represented the most frequent occurring intensity pattern for spring storms in the region. Compared with CT, ST decreased R by 2.5-fold, E by 3.5-fold, and C by 7-fold. Maximum runoff values for Iv events were 1.6-fold higher than those for Ic events and occurred 38 min earlier. Values for Etot and Ctot for Iv events were 19-36% and 1.5-fold higher than corresponding values for Ic events. Values for Emax and Cmax for Iv events were 3-fold and 4-fold higher than corresponding values for Ic events. Carbon enrichment ratios (CER) were or=1.0 for CT plots (except for first 20 min). Maximum CER for CT-Ic, CT-Iv, ST-Ic, and ST-Iv were 2.0, 2.2, 1.0, and 1.2, respectively. Transport of sediment, carbon, and agrichemicals would be better understood if variable rainfall intensity patterns derived from natural rainfall were used in rainfall simulations to evaluate their fate and transport from CT and ST systems.

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.

[Characteristics of rainfall and runoff in urban drainage based on the SWMM model.

PubMed

Xiong, Li Jun; Huang, Fei; Xu, Zu Xin; Li, Huai Zheng; Gong, Ling Ling; Dong, Meng Ke

2016-11-18

The characteristics of 235 rainfall and surface runoff events, from 2009 to 2011 in a typical urban drainage area in Shanghai were analyzed by using SWMM model. The results showed that the rainfall events in the region with high occurrence frequency were characterized by small rainfall amount and low intensity. The most probably occurred rainfall had total amount less than 10 mm, or mean intensity less than 5 mm·h -1 ,or peak intensity less than 10 mm·h -1 , accounting for 66.4%, 88.8% and 79.6% of the total rainfall events, respectively. The study was of great significance to apply low-impact development to reduce runoff and non-point source pollution under condition of less rainfall amount or low mean rainfall intensity in the area. The runoff generally increased with the increase of rainfall. The threshold of regional occurring runoff was controlled by not only rainfall amount, but also mean rainfall intensity and rainfall duration. In general, there was no surface runoff when the rainfall amount was less than 2 mm. When the rainfall amount was between 2 to 4 mm and the mean rainfall intensity was below 1.6 mm·h -1 , the runoff was less than 1 mm. When the rainfall exceeded 4 mm and the mean rainfall intensity was larger than 1.6 mm·h -1 , the runoff would occur generally. Based on the results of the SWMM simulation, three regression equations that were applicable to regional runoff amount and rainfall factors were established. The adjustment R 2 of the three equations were greater than 0.97. This indicated that the equations could reflect well the relationship between runoff and rainfall variables. The results provided the basis of calculations to plan low impact development and better reduce overflow pollution in local drainage area. It also could serve as a useful reference for runoff study in similar drainage areas.

[Spatio-temporal analysis of the biophysical and ecological conditions of Triatoma dimidiata (Hemiptera: Reduviidae: Triatominae) in the northeast region of Colombia].

PubMed

Badel-Mogollón, Jaime; Rodríguez-Figueroa, Laura; Parra-Henao, Gabriel

2017-03-29

Due to the lack of information regarding biophysical and spatio-temporal conditions (hydrometheorologic and vegetal coverage density) in areas with Triatoma dimidiata in the Colombian departments of Santander and Boyacá, there is a need to elucidate the association patterns of these variables to determine the distribution and control of this species. To make a spatio-temporal analysis of biophysical variables related to the distribution of T. dimidiate observed in the northeast region of Colombia. We used the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (IPCC SRES) data bases registering vector presence and hydrometheorologic data. We studied the variables of environmental temperature, relative humidity, rainfall and vegetal coverage density at regional and local levels, and we conducted spatial geostatistic, descriptive statistical and Fourier temporal series analyses. Temperatures two meters above the ground and on covered surface ranged from 14,5°C to 18,8°C in the areas with the higher density of T. dimidiata. The environmental temperature fluctuated between 30 and 32°C. Vegetal coverage density and rainfall showed patterns of annual and biannual peaks. Relative humidity values fluctuated from 66,8 to 85,1%. Surface temperature and soil coverage were the variables that better explained the life cycle of T. dimidiata in the area. High relative humidity promoted the seek of shelters and an increase of the geographic distribution in the annual and biannual peaks of regional rainfall. The ecologic and anthropic conditions suggest that T. dimidiata is a highly resilient species.

Modeling Channel Movement Response to Rainfall Variability and Potential Threats to Post-earthquake Reconstruction

NASA Astrophysics Data System (ADS)

Xie, J.; Wang, M.; Liu, K.

2017-12-01

The 2008 Wenchuan Ms 8.0 earthquake caused overwhelming destruction to vast mountains areas in Sichuan province. Numerous seismic landslides damaged the forest and vegetation cover, and caused substantial loose sediment piling up in the valleys. The movement and fill-up of loose materials led to riverbeds aggradation, thus made the earthquake-struck area more susceptible to flash floods with increasing frequency and intensity of extreme rainfalls. This study investigated the response of sediment and river channel evolution to different rainfall scenarios after the Wenchuan earthquake. The study area was chosen in a catchment affected by the earthquake in Northeast Sichuan province, China. We employed the landscape evolution model CAESAR-lisflood to explore the material migration rules and then assessed the potential effects under two rainfall scenarios. The model parameters were calibrated using the 2013 extreme rainfall event, and the experimental rainfall scenarios were of different intensity and frequency over a 10-year period. The results indicated that CAESAR-lisflood was well adapted to replicate the sediment migration, particularly the fluvial processes after earthquake. With respect to the effects of rainfall intensity, the erosion severity in upstream gullies and the deposition severity in downstream channels, correspondingly increased with the increasing intensity of extreme rainfalls. The modelling results showed that buildings in the catchment suffered from flash floods increased by more than a quarter from the normal to the enhanced rainfall scenarios in ten years, which indicated a potential threat to the exposures nearby the river channel, in the context of climate change. Simulation on landscape change is of great significance, and contributes to early warning of potential geological risks after earthquake. Attention on the high risk area by local government and the public is highly suggested in our study.

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.

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.

Relative contributions of mean-state shifts and ENSO-driven variability to precipitation changes in a warming climate

DOE PAGES

Bonfils, Celine J. W.; Santer, Benjamin D.; Phillips, Thomas J.; ...

2015-12-18

The El Niño–Southern Oscillation (ENSO) is an important driver of regional hydroclimate variability through far-reaching teleconnections. This study uses simulations performed with coupled general circulation models (CGCMs) to investigate how regional precipitation in the twenty-first century may be affected by changes in both ENSO-driven precipitation variability and slowly evolving mean rainfall. First, a dominant, time-invariant pattern of canonical ENSO variability (cENSO) is identified in observed SST data. Next, the fidelity with which 33 state-of-the-art CGCMs represent the spatial structure and temporal variability of this pattern (as well as its associated precipitation responses) is evaluated in simulations of twentieth-century climate change.more » Possible changes in both the temporal variability of this pattern and its associated precipitation teleconnections are investigated in twenty-first-century climate projections. Models with better representation of the observed structure of the cENSO pattern produce winter rainfall teleconnection patterns that are in better accord with twentieth-century observations and more stationary during the twenty-first century. Finally, the model-predicted twenty-first-century rainfall response to cENSO is decomposed into the sum of three terms: 1) the twenty-first-century change in the mean state of precipitation, 2) the historical precipitation response to the cENSO pattern, and 3) a future enhancement in the rainfall response to cENSO, which amplifies rainfall extremes. Lastly, by examining the three terms jointly, this conceptual framework allows the identification of regions likely to experience future rainfall anomalies that are without precedent in the current climate.« less

Relation Between the Rainfall and Soil Moisture During Different Phases of Indian Monsoon

NASA Astrophysics Data System (ADS)

Varikoden, Hamza; Revadekar, J. V.

2018-03-01

Soil moisture is a key parameter in the prediction of southwest monsoon rainfall, hydrological modelling, and many other environmental studies. The studies on relationship between the soil moisture and rainfall in the Indian subcontinent are very limited; hence, the present study focuses the association between rainfall and soil moisture during different monsoon seasons. The soil moisture data used for this study are the ESA (European Space Agency) merged product derived from four passive and two active microwave sensors spanning over the period 1979-2013. The rainfall data used are India Meteorological Department gridded daily data. Both of these data sets are having a spatial resolution of 0.25° latitude-longitude grid. The study revealed that the soil moisture is higher during the southwest monsoon period similar to rainfall and during the pre-monsoon period, the soil moisture is lower. The annual cycle of both the soil moisture and rainfall has the similitude of monomodal variation with a peak during the month of August. The interannual variability of soil moisture and rainfall shows that they are linearly related with each other, even though they are not matched exactly for individual years. The study of extremes also exhibits the surplus amount of soil moisture during wet monsoon years and also the regions of surplus soil moisture are well coherent with the areas of high rainfall.

Technical Note: An operational landslide early warning system at regional scale based on space-time variable rainfall thresholds

NASA Astrophysics Data System (ADS)

Segoni, S.; Battistini, A.; Rossi, G.; Rosi, A.; Lagomarsino, D.; Catani, F.; Moretti, S.; Casagli, N.

2014-10-01

We set up an early warning system for rainfall-induced landslides in Tuscany (23 000 km2). The system is based on a set of state-of-the-art intensity-duration rainfall thresholds (Segoni et al., 2014b), makes use of LAMI rainfall forecasts and real-time rainfall data provided by an automated network of more than 300 rain-gauges. The system was implemented in a WebGIS to ease the operational use in civil protection procedures: it is simple and intuitive to consult and it provides different outputs. Switching among different views, the system is able to focus both on monitoring of real time data and on forecasting at different lead times up to 48 h. Moreover, the system can switch between a very straightforward view where a synoptic scenario of the hazard can be shown all over the region and a more in-depth view were the rainfall path of rain-gauges can be displayed and constantly compared with rainfall thresholds. To better account for the high spatial variability of the physical features, which affects the relationship between rainfall and landslides, the region is subdivided into 25 alert zones, each provided with a specific threshold. The warning system reflects this subdivision: using a network of 332 rain gauges, it allows monitoring each alert zone separately and warnings can be issued independently from an alert zone to another. An important feature of the warning system is the use of thresholds that may vary in time adapting at the conditions of the rainfall path recorded by the rain-gauges. Depending on when the starting time of the rainfall event is set, the comparison with the threshold may produce different outcomes. Therefore, a recursive algorithm was developed to check and compare with the thresholds all possible starting times, highlighting the worst scenario and showing in the WebGIS interface at what time and how much the rainfall path has exceeded or will exceed the most critical threshold. Besides forecasting and monitoring the hazard scenario over the whole region with hazard levels differentiated for 25 distinct alert zones, the system can be used to gather, analyze, visualize, explore, interpret and store rainfall data, thus representing a potential support to both decision makers and scientists.

From GCM grid cell to agricultural plot: scale issues affecting modelling of climate impact

PubMed Central

Baron, Christian; Sultan, Benjamin; Balme, Maud; Sarr, Benoit; Traore, Seydou; Lebel, Thierry; Janicot, Serge; Dingkuhn, Michael

2005-01-01

General circulation models (GCM) are increasingly capable of making relevant predictions of seasonal and long-term climate variability, thus improving prospects of predicting impact on crop yields. This is particularly important for semi-arid West Africa where climate variability and drought threaten food security. Translating GCM outputs into attainable crop yields is difficult because GCM grid boxes are of larger scale than the processes governing yield, involving partitioning of rain among runoff, evaporation, transpiration, drainage and storage at plot scale. This study analyses the bias introduced to crop simulation when climatic data is aggregated spatially or in time, resulting in loss of relevant variation. A detailed case study was conducted using historical weather data for Senegal, applied to the crop model SARRA-H (version for millet). The study was then extended to a 10°N–17° N climatic gradient and a 31 year climate sequence to evaluate yield sensitivity to the variability of solar radiation and rainfall. Finally, a down-scaling model called LGO (Lebel–Guillot–Onibon), generating local rain patterns from grid cell means, was used to restore the variability lost by aggregation. Results indicate that forcing the crop model with spatially aggregated rainfall causes yield overestimations of 10–50% in dry latitudes, but nearly none in humid zones, due to a biased fraction of rainfall available for crop transpiration. Aggregation of solar radiation data caused significant bias in wetter zones where radiation was limiting yield. Where climatic gradients are steep, these two situations can occur within the same GCM grid cell. Disaggregation of grid cell means into a pattern of virtual synoptic stations having high-resolution rainfall distribution removed much of the bias caused by aggregation and gave realistic simulations of yield. It is concluded that coupling of GCM outputs with plot level crop models can cause large systematic errors due to scale incompatibility. These errors can be avoided by transforming GCM outputs, especially rainfall, to simulate the variability found at plot level. PMID:16433096

Demographic patterns of a widespread long-lived tree are associated with rainfall and disturbances along rainfall gradients in SE Australia

PubMed Central

Cohn, Janet S; Lunt, Ian D; Bradstock, Ross A; Hua, Quan; McDonald, Simon

2013-01-01

Predicting species distributions with changing climate has often relied on climatic variables, but increasingly there is recognition that disturbance regimes should also be included in distribution models. We examined how changes in rainfall and disturbances along climatic gradients determined demographic patterns in a widespread and long-lived tree species, Callitris glaucophylla in SE Australia. We examined recruitment since 1950 in relation to annual (200–600 mm) and seasonal (summer, uniform, winter) rainfall gradients, edaphic factors (topography), and disturbance regimes (vertebrate grazing [tenure and species], fire). A switch from recruitment success to failure occurred at 405 mm mean annual rainfall, coincident with a change in grazing regime. Recruitment was lowest on farms with rabbits below 405 mm rainfall (mean = 0–0.89 cohorts) and highest on less-disturbed tenures with no rabbits above 405 mm rainfall (mean = 3.25 cohorts). Moderate levels of recruitment occurred where farms had no rabbits or less disturbed tenures had rabbits above and below 405 mm rainfall (mean = 1.71–1.77 cohorts). These results show that low annual rainfall and high levels of introduced grazing has led to aging, contracting populations, while higher annual rainfall with low levels of grazing has led to younger, expanding populations. This study demonstrates how demographic patterns vary with rainfall and spatial variations in disturbances, which are linked in complex ways to climatic gradients. Predicting changes in tree distribution with climate change requires knowledge of how rainfall and key disturbances (tenure, vertebrate grazing) will shift along climatic gradients. PMID:23919160

Understanding Flood Seasonality and Its Temporal Shifts within the Contiguous United States

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

Ye, Sheng; Li, Hong-Yi; Leung, L. Ruby

2017-07-01

Understanding the causes of flood seasonality is critical for better flood management. This study examines the seasonality of annual maximum floods (AMF) and its changes before and after 1980 at over 250 natural catchments across the contiguous United States. Using circular statistics to define a seasonality index, our analysis focuses on the variability of the flood occurrence date. Generally, catchments with more synchronized seasonal water and energy cycles largely inherit their seasonality of AMF from that of annual maximum rainfall (AMR). In contrast, the seasonality of AMF in catchments with loosely synchronized water and energy cycles are more influenced bymore » high antecedent storage, which is responsible for the amplification of the seasonality of AMF over that of AMR. This understanding then effectively explains a statistically significant shift of flood seasonality detected in some catchments in the recent decades. Catchments where the antecedent soil water storage has increased since 1980 exhibit increasing flood seasonality while catchments that have experienced increases in storm rainfall before the floods have shifted towards floods occurring more variably across the seasons. In the eastern catchments, a concurrent widespread increase in event rainfall magnitude and reduced soil water storage have led to a more variable timing of floods. Our findings of the role of antecedent storage and event rainfall on the flood seasonality provide useful insights for understanding future changes in flood seasonality as climate models projected changes in extreme precipitation and aridity over land.« less

Rainfall Morphology in Semi-Tropical Convergence Zones

NASA Technical Reports Server (NTRS)

Shepherd, J. Marshall; Ferrier, Brad S.; Ray, Peter S.

2000-01-01

Central Florida is the ideal test laboratory for studying convergence zone-induced convection. The region regularly experiences sea breeze fronts and rainfall-induced outflow boundaries. The focus of this study is the common yet poorly-studied convergence zone established by the interaction of the sea breeze front and an outflow boundary. Previous studies have investigated mechanisms primarily affecting storm initiation by such convergence zones. Few have focused on rainfall morphology yet these storms contribute a significant amount precipitation to the annual rainfall budget. Low-level convergence and mid-tropospheric moisture have both been shown to correlate with rainfall amounts in Florida. Using 2D and 3D numerical simulations, the roles of low-level convergence and mid-tropospheric moisture in rainfall evolution are examined. The results indicate that time-averaged, vertical moisture flux (VMF) at the sea breeze front/outflow convergence zone is directly and linearly proportional to initial condensation rates. This proportionality establishes a similar relationship between VMF and initial rainfall. Vertical moisture flux, which encompasses depth and magnitude of convergence, is better correlated to initial rainfall production than surface moisture convergence. This extends early observational studies which linked rainfall in Florida to surface moisture convergence. The amount and distribution of mid-tropospheric moisture determines how rainfall associated with secondary cells develop. Rainfall amount and efficiency varied significantly over an observable range of relative humidities in the 850- 500 mb layer even though rainfall evolution was similar during the initial or "first-cell" period. Rainfall variability was attributed to drier mid-tropospheric environments inhibiting secondary cell development through entrainment effects. Observationally, 850-500 mb moisture structure exhibits wider variability than lower level moisture, which is virtually always present in Florida. A likely consequence of the variability in 850-500 moisture is a stronger statistical correlation to rainfall, which observational studies have noted. The study indicates that vertical moisture flux forcing at convergence zones is critical in determining rainfall in the initial stage of development but plays a decreasing role in rainfall evolution as the system matures. The mid-tropospheric moisture (e.g. environment) plays an increasing role in rainfall evolution as the system matures. This suggests the need to improve measurements of magnitude/depth of convergence and mid-tropospheric moisture distribution. It also highlights the need for better parameterization of entrainment and vertical moisture distribution in larger-scale models.

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.

Internal and International Mobility as Adaptation to Climatic Variability in Contemporary Mexico: Evidence from the Integration of Census and Satellite Data.

PubMed

Leyk, Stefan; Runfola, Dan; Nawrotzki, Raphael J; Hunter, Lori M; Riosmena, Fernando

2017-08-01

Migration provides a strategy for rural Mexican households to cope with, or adapt to, weather events and climatic variability. Yet prior studies on "environmental migration" in this context have not examined the differences between choices of internal (domestic) or international movement. In addition, much of the prior work relied on very coarse spatial scales to operationalize the environmental variables such as rainfall patterns. To overcome these limitations, we use fine-grain rainfall estimates derived from NASA's Tropical Rainfall Measuring Mission (TRMM) satellite. The rainfall estimates are combined with Population and Agricultural Census information to examine associations between environmental changes and municipal rates of internal and international migration 2005-2010. Our findings suggest that municipal-level rainfall deficits relative to historical levels are an important predictor of both international and internal migration, especially in areas dependent on seasonal rainfall for crop productivity. Although our findings do not contradict results of prior studies using coarse spatial resolution, they offer clearer results and a more spatially nuanced examination of migration as related to social and environmental vulnerability and thus higher degrees of confidence.

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 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.

Does internal variability change in response to global warming? A large ensemble modelling study of tropical rainfall

NASA Astrophysics Data System (ADS)

Milinski, S.; Bader, J.; Jungclaus, J. H.; Marotzke, J.

2017-12-01

There is some consensus on mean state changes of rainfall under global warming; changes of the internal variability, on the other hand, are more difficult to analyse and have not been discussed as much despite their importance for understanding changes in extreme events, such as droughts or floodings. We analyse changes in the rainfall variability in the tropical Atlantic region. We use a 100-member ensemble of historical (1850-2005) model simulations with the Max Planck Institute for Meteorology Earth System Model (MPI-ESM1) to identify changes of internal rainfall variability. To investigate the effects of global warming on the internal variability, we employ an additional ensemble of model simulations with stronger external forcing (1% CO2-increase per year, same integration length as the historical simulations) with 68 ensemble members. The focus of our study is on the oceanic Atlantic ITCZ. We find that the internal variability of rainfall over the tropical Atlantic does change due to global warming and that these changes in variability are larger than changes in the mean state in some regions. From splitting the total variance into patterns of variability, we see that the variability on the southern flank of the ITCZ becomes more dominant, i.e. explaining a larger fraction of the total variance in a warmer climate. In agreement with previous studies, we find that changes in the mean state show an increase and narrowing of the ITCZ. The large ensembles allow us to do a statistically robust differentiation between the changes in variability that can be explained by internal variability and those that can be attributed to the external forcing. Furthermore, we argue that internal variability in a transient climate is only well defined in the ensemble domain and not in the temporal domain, which requires the use of a large ensemble.

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.

Accounting for interannual variability: A comparison of options for water resources climate change impact assessments

NASA Astrophysics Data System (ADS)

Johnson, Fiona; Sharma, Ashish

2011-04-01

Empirical scaling approaches for constructing rainfall scenarios from general circulation model (GCM) simulations are commonly used in water resources climate change impact assessments. However, these approaches have a number of limitations, not the least of which is that they cannot account for changes in variability or persistence at annual and longer time scales. Bias correction of GCM rainfall projections offers an attractive alternative to scaling methods as it has similar advantages to scaling in that it is computationally simple, can consider multiple GCM outputs, and can be easily applied to different regions or climatic regimes. In addition, it also allows for interannual variability to evolve according to the GCM simulations, which provides additional scenarios for risk assessments. This paper compares two scaling and four bias correction approaches for estimating changes in future rainfall over Australia and for a case study for water supply from the Warragamba catchment, located near Sydney, Australia. A validation of the various rainfall estimation procedures is conducted on the basis of the latter half of the observational rainfall record. It was found that the method leading to the lowest prediction errors varies depending on the rainfall statistic of interest. The flexibility of bias correction approaches in matching rainfall parameters at different frequencies is demonstrated. The results also indicate that for Australia, the scaling approaches lead to smaller estimates of uncertainty associated with changes to interannual variability for the period 2070-2099 compared to the bias correction approaches. These changes are also highlighted using the case study for the Warragamba Dam catchment.

Distributional changes in rainfall and river flow in Sarawak, Malaysia

NASA Astrophysics Data System (ADS)

Sa'adi, Zulfaqar; Shahid, Shamsuddin; Ismail, Tarmizi; Chung, Eun-Sung; Wang, Xiao-Jun

2017-11-01

Climate change may not change the rainfall mean, but the variability and extremes. Therefore, it is required to explore the possible distributional changes of rainfall characteristics over time. The objective of present study is to assess the distributional changes in annual and northeast monsoon rainfall (November-January) and river flow in Sarawak where small changes in rainfall or river flow variability/distribution may have severe implications on ecology and agriculture. A quantile regression-based approach was used to assess the changes of scale and location of empirical probability density function over the period 1980-2014 at 31 observational stations. The results indicate that diverse variation patterns exist at all stations for annual rainfall but mainly increasing quantile trend at the lowers, and higher quantiles for the month of January and December. The significant increase in annual rainfall is found mostly in the north and central-coastal region and monsoon month rainfalls in the interior and north of Sarawak. Trends in river flow data show that changes in rainfall distribution have affected higher quantiles of river flow in monsoon months at some of the basins and therefore more flooding. The study reveals that quantile trend can provide more information of rainfall change which may be useful for climate change mitigation and adaptation planning.

El Niño-Southern Oscillation-based index insurance for floods: Statistical risk analyses and application to Peru

NASA Astrophysics Data System (ADS)

Khalil, Abedalrazq F.; Kwon, Hyun-Han; Lall, Upmanu; Miranda, Mario J.; Skees, Jerry

2007-10-01

Index insurance has recently been advocated as a useful risk transfer tool for disaster management situations where rapid fiscal relief is desirable and where estimating insured losses may be difficult, time consuming, or subject to manipulation and falsification. For climate-related hazards, a rainfall or temperature index may be proposed. However, rainfall may be highly spatially variable relative to the gauge network, and in many locations, data are inadequate to develop an index because of short time series and the spatial dispersion of stations. In such cases, it may be helpful to consider a climate proxy index as a regional rainfall index. This is particularly useful if a long record is available for the climate index through an independent source and it is well correlated with the regional rainfall hazard. Here El Niño-Southern Oscillation (ENSO) related climate indices are explored for use as a proxy to extreme rainfall in one of the districts of Peru, Piura. The ENSO index insurance product may be purchased by banks or microfinance institutions to aid agricultural damage relief in Peru. Crop losses in the region are highly correlated with floods but are difficult to assess directly. Beyond agriculture, many other sectors suffer as well. Basic infrastructure is destroyed during the most severe events. This disrupts trade for many microenterprises. The reliability and quality of the local rainfall data are variable. Averaging the financial risk across the region is desirable. Some issues with the implementation of the proxy ENSO index are identified and discussed. Specifically, we explore (1) the reliability of the index at different levels of probability of exceedance of maximum seasonal rainfall, (2) the effect of sampling uncertainties and the strength of the proxy's association to local outcome, (3) the potential for clustering of payoffs, (4) the potential that the index could be predicted with some lead time prior to the flood season, and (5) evidence for climate change or nonstationarity in the flood exceedance probability from the long ENSO record.

Linking the North Atlantic Oscillation to Rainfall Over Northern Lake Malawi

NASA Astrophysics Data System (ADS)

Johnson, T. C.; Powers, L. A.; Werne, J. P.; Brown, E. T.; Castaneda, I.; Schouten, S.; Sinninghe-Damste, J.

2005-12-01

Piston and multi-cores recovered from the north basin of Lake Malawi in 1998 by the International Decade for the East African Lakes (IDEAL) have provided a rich history of climate variability spanning the past 25,000 years. As we now begin to analyze the cores recovered by the Malawi Drilling Project in early 2005, we are considering the relationships among sedimentary signals of temperature (TEX86), northerly winds associated with a southward excursion of the Inter-Tropical Convergence Zone (per cent biogenic silica), and rainfall (terrigenous mass accumulation rate) in the well dated 1998 cores. A high-resolution record of the past 800 years suggests that rainfall in this region (10 - 12° S, 30 - 35° E) was relatively low during the Little Ice Age, when northerly winds were more prevalent, attributed to a more southerly position of the ITCZ during austral summers. The TEX86 signal of lake (surface?) temperature ranged mostly between 24 and 26°C during this period, with the coldest temperature of about 22°C around AD1680 and the warmest temperature, exceeding 27°C, in the youngest sediment sample. The cooler water temperatures coincide with periods of highest diatom productivity, consistent with the latter being due to relatively intense upwelling associated with the northerly winds. Our observation of low rainfall during periods of more southerly migration of the ITCZ is consistent with the results of McHugh and Rogers (2001), who linked rainfall in southeastern Africa to the North Atlantic Oscillation (NAO). During years of weak NAO, equatorial westerly transport of Atlantic moisture across Africa during austral summer is relatively intense, causing high rainfall in the East African Rift between the equator and 16° S. Conversely, when the NAO is positive, rainfall is higher south of 15° S than north of this latitude, which is consistent with a southward migration of the ITCZ. McHugh, M. J. and J. C. Rogers (2001). "North Atlantic Oscillation influence on precipitation variability around the southeast African convergence zone." Journal of Climate 14: 3631-3642.

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.

Exogenous factors matter when interpreting the results of an impact evaluation: a case study of rainfall and child health programme intervention in Rwanda.

PubMed

Mukabutera, Assumpta; Thomson, Dana R; Hedt-Gauthier, Bethany L; Atwood, Sidney; Basinga, Paulin; Nyirazinyoye, Laetitia; Savage, Kevin P; Habimana, Marcellin; Murray, Megan

2017-12-01

Public health interventions are often implemented at large scale, and their evaluation seems to be difficult because they are usually multiple and their pathways to effect are complex and subject to modification by contextual factors. We assessed whether controlling for rainfall-related variables altered estimates of the efficacy of a health programme in rural Rwanda and have a quantifiable effect on an intervention evaluation outcomes. We conducted a retrospective quasi-experimental study using previously collected cross-sectional data from the 2005 and 2010 Rwanda Demographic and Health Surveys (DHS), 2010 DHS oversampled data, monthly rainfall data collected from meteorological stations over the same period, and modelled output of long-term rainfall averages, soil moisture, and rain water run-off. Difference-in-difference models were used. Rainfall factors confounded the PIH intervention impact evaluation. When we adjusted our estimates of programme effect by controlling for a variety of rainfall variables, several effectiveness estimates changed by 10% or more. The analyses that did not adjust for rainfall-related variables underestimated the intervention effect on the prevalence of ARI by 14.3%, fever by 52.4% and stunting by 10.2%. Conversely, the unadjusted analysis overestimated the intervention's effect on diarrhoea by 56.5% and wasting by 80%. Rainfall-related patterns have a quantifiable effect on programme evaluation results and highlighted the importance and complexity of controlling for contextual factors in quasi-experimental design evaluations. © 2017 John Wiley & Sons Ltd.

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.

Spatial and Temporal Patterns of Throughfall Amounts and Solutes in a Tropical Montane Forest - Comparisons with Findings From Lowland Rain Forests

NASA Astrophysics Data System (ADS)

Zimmermann, A.

2007-05-01

The diverse tree species composition, irregular shaped tree crowns and a multi-layered forest structure affect the redistribution of rainfall in lower montane rain forests. In addition, abundant epiphyte biomass and associated canopy humus influence spatial patterns of throughfall. The spatial variability of throughfall amounts controls spatial patterns of solute concentrations and deposition. Moreover, the living and dead biomass interacts with the rainwater during the passage through the canopy and creates a chemical variability of its own. Since spatial and temporal patterns are intimately linked, the analysis of temporal solute concentration dynamics is an important step to understand the emerging spatial patterns. I hypothesized that: (1) the spatial variability of volumes and chemical composition of throughfall is particularly high compared with other forests because of the high biodiversity and epiphytism, (2) the temporal stability of the spatial pattern is high because of stable structures in the canopy (e.g. large epiphytes) that show only minor changes during the short term observation period, and (3) the element concentrations decrease with increasing rainfall because of exhausting element pools in the canopy. The study area at 1950 m above sea level is located in the south Ecuadorian Andes far away from anthropogenic emission sources and marine influences. Rain and throughfall were collected from August to October 2005 on an event and within-event basis for five precipitation periods and analyzed for pH, K, Na, Ca, Mg, NH4+, Cl-, NO3-, PO43-, TN, TP and TOC. Throughfall amounts and most of the solutes showed a high spatial variability, thereby the variability of H+, K, Ca, Mg, Cl- and NO3- exceeded those from a Brazilian tropical rain forest. The temporal persistence of the spatial patterns was high for throughfall amounts and varied depending on the solute. Highly persistent time stability patterns were detected for K, Mg and TOC concentrations. Time stability patterns of solute deposition were somewhat weaker than for concentrations for most of the solutes. Epiphytes strongly affected time stability patterns in that collectors situated below thick moss mats or arboreal bromeliads were in large part responsible for the extreme persistence with low throughfall amounts and high ion concentrations (H+ showed low concentrations). Rainfall solute concentrations were low compared with a variety of other tropical lowland and montane forest sites and showed a small temporal variability during the study period for both between and within-event dynamics, respectively. Throughfall solute concentrations were more within the range when compared with other sites and showed highly variable within-event dynamics. For most of the solutes, within-event concentrations did not reach low, constant concentrations in later event stages, rather concentrations fluctuated (e.g. Cl-) or increased (e.g. K and TOC). The within-event throughfall solute concentration dynamics in this lower montane rain forest contrast to recent observations from lowland tropical rain forests in Panama and Brazil. The observed within-event patterns are attributed (1) to the influence of epiphytes and associated canopy humus, and (2) to low rainfall intensities.

Assessing the performance and reliability of PERSIANN-CDR satellite-based rainfall estimates over Spain: case study of rainfall Dry Spell Lengths (DSL)

NASA Astrophysics Data System (ADS)

Garcia Galiano, S. G.; Giraldo Osorio, J. D.; Nguyen, P.; Hsu, K. L.; Braithwaite, D.; Olmos, P.; Sorooshian, S.

2015-12-01

Studying Spain's long-term variability and changing trends in rainfall, due to its unique position in the Mediterranean basin (i.e., the latitudinal gradient from North to South and its orographic variation), can provide a valuable insight into how hydroclimatology of the region has changed. A recently released high resolution satellite-based global daily precipitation climate dataset PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network - Climate Data Record), provided the opportunity to conduct such study. It covers the period 01/01/1983 - to date, at 0.25° resolution. In areas without a dense network of rain-gauges, the PERSIANN-CDR dataset could be useful for identifying the reliability of regional climate models (RCMs), in order to build robust RCMs ensemble for reducing the uncertainties in the climate and hydrological projections. However, before using this data set for RCM evaluation, an assessment of performance of PERSIANN-CDR dataset against in-situ observations is necessary. The high-resolution gridded daily rain-gauge dataset, named Spain02, was employed in this study. The variable Dry Spell Lengths (DSL) considering 1 mm and 10 mm as thresholds of daily rainfall, and the time period 1988-2007 was defined for the study. A procedure for improving the consistency and homogeneity between the two datasets was applied. The assessment is based on distributional similarity and the well-known statistical tests (Smirnov-Kolmogorov of two samples and Chi-Square) are used as fitting criteria. The results demonstrate good fit of PERSIANN-CDR over whole Spain, for threshold 10 mm/day. However, for threshold 1 mm/day PERSIANN-CDR compares well with Spain02 dataset for areas with high values of rainfall (North of Spain); while in semiarid areas (South East of Spain) there is strong overestimation of short DSLs. Overall, PERSIANN-CDR demonstrate its robustness in the simulation of DSLs for the highest thresholds.

Intraseasonal SST-precipitation coupling during the Indian Summer Monsoon, and its modulation by the Indian Ocean Dipole

NASA Astrophysics Data System (ADS)

Jongaramrungruang, S.; Seo, H.; Ummenhofer, C.

2016-02-01

The Indian Summer Monsoon (ISM) plays a crucial role in shaping the large proportion of the total precipitation over the Indian subcontinent each year. The ISM rainfall exhibits a particularly strong intraseasonal variability, that has profound socioeconomic consequences, such as agricultural planning and flood preparation. However, our understanding of the variability on this time scale is still limited due to sparse data availability in the past. In this study, we used a combination of state-of-the-art high-resolution satellite estimate of rainfall, objectively analyzed surface flux, as well as atmospheric reanalysis product to investigate the nature of the ISM intraseasonal rainfall variability and how it varies year to year. The emphasis is placed on the Bay of Bengal (BoB) where the intraseasonal ocean-atmosphere coupling is most prominent. Results show that the maximum warming of SST leads the onset of heavy precipitation event by 3-5 days, and that surface heat flux and surface wind speed are weak prior to the rain but amplifies and peaks after the rain reaches its maximum. Furthermore, the Indian Ocean Dipole (IOD) significantly affects the observed intraseasonal SST-precipitation relationship. The pre-convection SST warming is stronger and more pronounced during the negative phase of the IOD, while the signal is weaker and less organized in the positive phase. This is explained by the column-integrated moisture budget analysis which reveals that, during the ISM heavy rainfall in the BoB, there is more moisture interchange in the form of enhanced vertical advection from the ocean to atmosphere in negative IOD years as compared to positive IOD years. Knowing the distinction of ISM variabilities during opposite phases of the IOD will help contribute to a more reliable prediction of ISM activities.

Climate Variability and Yields of Major Staple Food Crops in Northern Ghana

NASA Astrophysics Data System (ADS)

Amikuzuno, J.

2012-12-01

Climate variability, the short-term fluctuations in average weather conditions, and agriculture affect each other. Climate variability affects the agroecological and growing conditions of crops and livestock, and is recently believed to be the greatest impediment to the realisation of the first Millennium Development Goal of reducing poverty and food insecurity in arid and semi-arid regions of developing countries. Conversely, agriculture is a major contributor to climate variability and change by emitting greenhouse gases and reducing the agroecology's potential for carbon sequestration. What however, is the empirical evidence of this inter-dependence of climate variability and agriculture in Sub-Sahara Africa? In this paper, we provide some insight into the long run relationship between inter-annual variations in temperature and rainfall, and annual yields of the most important staple food crops in Northern Ghana. Applying pooled panel data of rainfall, temperature and yields of the selected crops from 1976 to 2010 to cointegration and Granger causality models, there is cogent evidence of cointegration between seasonal, total rainfall and crop yields; and causality from rainfall to crop yields in the Sudano-Guinea Savannah and Guinea Savannah zones of Northern Ghana. This suggests that inter-annual yields of the crops have been influenced by the total mounts of rainfall in the planting season. Temperature variability over the study period is however stationary, and is suspected to have minimal effect if any on crop yields. Overall, the results confirm the appropriateness of our attempt in modelling long-term relationships between the climate and crop yield variables.

Malaria early warning tool: linking inter-annual climate and malaria variability in northern Guadalcanal, Solomon Islands.

PubMed

Smith, Jason; Tahani, Lloyd; Bobogare, Albino; Bugoro, Hugo; Otto, Francis; Fafale, George; Hiriasa, David; Kazazic, Adna; Beard, Grant; Amjadali, Amanda; Jeanne, Isabelle

2017-11-21

Malaria control remains a significant challenge in the Solomon Islands. Despite progress made by local malaria control agencies over the past decade, case rates remain high in some areas of the country. Studies from around the world have confirmed important links between climate and malaria transmission. This study focuses on understanding the links between malaria and climate in Guadalcanal, Solomon Islands, with a view towards developing a climate-based monitoring and early warning for periods of enhanced malaria transmission. Climate records were sourced from the Solomon Islands meteorological service (SIMS) and historical malaria case records were sourced from the National Vector-Borne Disease Control Programme (NVBDCP). A declining trend in malaria cases over the last decade associated with improved malaria control was adjusted for. A stepwise regression was performed between climate variables and climate-associated malaria transmission (CMT) at different lag intervals to determine where significant relationships existed. The suitability of these results for use in a three-tiered categorical warning system was then assessed using a Mann-Whitney U test. Of the climate variables considered, only rainfall had a consistently significant relationship with malaria in North Guadalcanal. Optimal lag intervals were determined for prediction using R 2 skill scores. A highly significant negative correlation (R = - 0.86, R 2  = 0.74, p < 0.05, n = 14) was found between October and December rainfall at Honiara and CMT in northern Guadalcanal for the subsequent January-June. This indicates that drier October-December periods are followed by higher malaria transmission periods in January-June. Cross-validation emphasized the suitability of this relationship for forecasting purposes [Formula: see text]  as did Mann-Whitney U test results showing that rainfall below or above specific thresholds was significantly associated with above or below normal malaria transmission, respectively. This study demonstrated that rainfall provides the best predictor of malaria transmission in North Guadalcanal. This relationship is thought to be underpinned by the unique hydrological conditions in northern Guadalcanal which allow sandbars to form across the mouths of estuaries which act to develop or increase stagnant brackish marshes in low rainfall periods. These are ideal habitats for the main mosquito vector, Anopheles farauti. High rainfall accumulations result in the flushing of these habitats, reducing their viability. The results of this study are now being used as the basis of a malaria early warning system which has been jointly implemented by the SIMS, NVBDCP and the Australian Bureau of Meteorology.

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.

Calibrating multiple isotopic proxies in a modern aragonite speleothem from northeast India

NASA Astrophysics Data System (ADS)

Ronay, E.; Oster, J. L.; Sharp, W. D.; Marks, N.; Erhardt, A.; Breitenbach, S. F. M.

2017-12-01

Uranium, strontium, and calcium isotope ratios in calcite speleothems are used as proxies for water-soil-rock interactions and prior calcite precipitation, and thus provide information about effective rainfall amount variations, primarily in semi-arid or highly seasonal regions. However, less is known about how these proxies function in humid regions and in aragonite speleothems. In this study, we use meteorological data to calibrate (234U/238U)i and 87Sr/86Sr in a modern aragonite speleothem from northeast India, the rainiest place on Earth, to determine how these proxies reflect effective monsoon rainfall amount. MAW-0201 is an annually laminated aragonite stalagmite that grew from 1960-2013 in Mawmluh Cave, Meghalaya, India. Rainfall here is extremely seasonal due to the Indian Summer Monsoon (ISM), which brings several meters of rain to the region each summer, but with inter-annual variability in total rainfall. The δ18O in Mawmluh dripwater and speleothems reflects moisture source and transport, rather than rainfall amount. Variations in Mg, U, and Ba concentrations in MAW-0201 show seasonal and multi-annual variability. U and Mg are closely correlated, but multi-year periods show significant anti-correlation. The Mg and U distribution coefficients in calcite and aragonite indicate correlated periods are times of prior calcite precipitation (PCP) and anti-correlated periods are times of prior aragonite precipitation (PAP) in the epikarst. We use δ44/40Ca to test this hypothesis, as Ca isotopes fractionate differently during calcite and aragonite precipitation and speleothem δ44/40Ca will record unique PAP and PCP fingerprints. We propose such shifts from PCP to PAP reflect hydrologic variability and/or flow path changes, which provide a useful tool for understanding epikarst hydrology but may also be a complicating factor in speleothem-based paleoclimate interpretations. Preliminary (234U/238U)i (always <1) and 87Sr/86Sr spanning 1991-2009 each show significant variability outside of analytical error. (234U/238U)i displays a decadal trend, gradually increasing until 2000 and decreasing to the end of the record. Several years in the 87Sr/86Sr record have anomalously high values, which may reflect increased sea spray input and provide unique information on the wind component of the ISM.

Critical rainfall conditions for the initiation of torrential flows. Results from the Rebaixader catchment (Central Pyrenees)

NASA Astrophysics Data System (ADS)

Abancó, Clàudia; Hürlimann, Marcel; Moya, José; Berenguer, Marc

2016-10-01

Torrential flows like debris flows or debris floods are fast movements formed by a mix of water and different amounts of unsorted solid material. They generally occur in steep torrents and pose high risk in mountainous areas. Rainfall is their most common triggering factor and the analysis of the critical rainfall conditions is a fundamental research task. Due to their wide use in warning systems, rainfall thresholds for the triggering of torrential flows are an important outcome of such analysis and are empirically derived using data from past events. In 2009, a monitoring system was installed in the Rebaixader catchment, Central Pyrenees (Spain). Since then, rainfall data of 25 torrential flows (;TRIG rainfalls;) were recorded, with a 5-min sampling frequency. Other 142 rainfalls that did not trigger torrential flows (;NonTRIG rainfalls;) were also collected and analyzed. The goal of this work was threefold: (i) characterize rainfall episodes in the Rebaixader catchment and compare rainfall data that triggered torrential flows and others that did not; (ii) define and test Intensity-Duration (ID) thresholds using rainfall data measured inside the catchment by with different techniques; (iii) analyze how the criterion used for defining the rainfall duration and the spatial variability of rainfall influences the value obtained for the thresholds. The statistical analysis of the rainfall characteristics showed that the parameters that discriminate better the TRIG and NonTRIG rainfalls are the rainfall intensities, the mean rainfall and the total rainfall amount. The antecedent rainfall was not significantly different between TRIG and NonTRIG rainfalls, as it can be expected when the source material is very pervious (a sandy glacial soil in the study site). Thresholds were derived from data collected at one rain gauge located inside the catchment. Two different methods were applied to calculate the duration and intensity of rainfall: (i) using total duration, Dtot, and mean intensity, Imean, of the rainfall event, and (ii) using floating durations, D, and intensities, Ifl, based on the maximum values over floating periods of different duration. The resulting thresholds are considerably different (Imean = 6.20 Dtot-0.36 and Ifl_90% = 5.49 D-0.75, respectively) showing a strong dependence on the applied methodology. On the other hand, the definition of the thresholds is affected by several types of uncertainties. Data from both rain gauges and weather radar were used to analyze the uncertainty associated with the spatial variability of the triggering rainfalls. The analysis indicates that the precipitation recorded by the nearby rain gauges can introduce major uncertainties, especially for convective summer storms. Thus, incorporating radar rainfall can significantly improve the accuracy of the measured triggering rainfall. Finally, thresholds were also derived according to three different criteria for the definition of the duration of the triggering rainfall: (i) the duration until the peak intensity, (ii) the duration until the end of the rainfall; and, (iii) the duration until the trigger of the torrential flow. An important contribution of this work is the assessment of the threshold relationships obtained using the third definition of duration. Moreover, important differences are observed in the obtained thresholds, showing that ID relationships are significantly dependent on the applied methodology.

Mapping extreme rainfall in the Northwest Portugal region: statistical analysis and spatial modelling

NASA Astrophysics Data System (ADS)

Santos, Monica; Fragoso, Marcelo

2010-05-01

Extreme precipitation events are one of the causes of natural hazards, such as floods and landslides, making its investigation so important, and this research aims to contribute to the study of the extreme rainfall patterns in a Portuguese mountainous area. The study area is centred on the Arcos de Valdevez county, located in the northwest region of Portugal, the rainiest of the country, with more than 3000 mm of annual rainfall at the Peneda-Gerês mountain system. This work focus on two main subjects related with the precipitation variability on the study area. First, a statistical analysis of several precipitation parameters is carried out, using daily data from 17 rain-gauges with a complete record for the 1960-1995 period. This approach aims to evaluate the main spatial contrasts regarding different aspects of the rainfall regime, described by ten parameters and indices of precipitation extremes (e.g. mean annual precipitation, the annual frequency of precipitation days, wet spells durations, maximum daily precipitation, maximum of precipitation in 30 days, number of days with rainfall exceeding 100 mm and estimated maximum daily rainfall for a return period of 100 years). The results show that the highest precipitation amounts (from annual to daily scales) and the higher frequency of very abundant rainfall events occur in the Serra da Peneda and Gerês mountains, opposing to the valleys of the Lima, Minho and Vez rivers, with lower precipitation amounts and less frequent heavy storms. The second purpose of this work is to find a method of mapping extreme rainfall in this mountainous region, investigating the complex influence of the relief (e.g. elevation, topography) on the precipitation patterns, as well others geographical variables (e.g. distance from coast, latitude), applying tested geo-statistical techniques (Goovaerts, 2000; Diodato, 2005). Models of linear regression were applied to evaluate the influence of different geographical variables (altitude, latitude, distance from sea and distance to the highest orographic barrier) on the rainfall behaviours described by the studied variables. The techniques of spatial interpolation evaluated include univariate and multivariate methods: cokriging, kriging, IDW (inverse distance weighted) and multiple linear regression. Validation procedures were used, assessing the estimated errors in the analysis of descriptive statistics of the models. Multiple linear regression models produced satisfactory results in relation to 70% of the rainfall parameters, suggested by lower average percentage of error. However, the results also demonstrates that there is no an unique and ideal model, depending on the rainfall parameter in consideration. Probably, the unsatisfactory results obtained in relation to some rainfall parameters was motivated by constraints as the spatial complexity of the precipitation patterns, as well as to the deficient spatial coverage of the territory by the rain-gauges network. References Diodato, N. (2005). The influence of topographic co-variables on the spatial variability of precipitation over small regions of complex terrain. Internacional Journal of Climatology, 25(3), 351-363. Goovaerts, P. (2000). Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. Journal of Hydrology, 228, 113 - 129.

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.

Global meteorological influences on the record UK rainfall of winter 2013-14

NASA Astrophysics Data System (ADS)

Knight, Jeff R.; Maidens, Anna; Watson, Peter A. G.; Andrews, Martin; Belcher, Stephen; Brunet, Gilbert; Fereday, David; Folland, Chris K.; Scaife, Adam A.; Slingo, Julia

2017-07-01

The UK experienced record average rainfall in winter 2013-14, leading to widespread and prolonged flooding. The immediate cause of this exceptional rainfall was a very strong and persistent cyclonic atmospheric circulation over the North East Atlantic Ocean. This was related to a very strong North Atlantic jet stream which resulted in numerous damaging wind storms. These exceptional meteorological conditions have led to renewed questions about whether anthropogenic climate change is noticeably influencing extreme weather. The regional weather pattern responsible for the extreme UK winter coincided with highly anomalous conditions across the globe. We assess the contributions from various possible remote forcing regions using sets of ocean-atmosphere model relaxation experiments, where winds and temperatures are constrained to be similar to those observed in winter 2013-14 within specified atmospheric domains. We find that influences from the tropics were likely to have played a significant role in the development of the unusual extra-tropical circulation, including a role for the tropical Atlantic sector. Additionally, a stronger and more stable stratospheric polar vortex, likely associated with a strong westerly phase of the stratospheric Quasi-Biennial Oscillation (QBO), appears to have contributed to the extreme conditions. While intrinsic climatic variability clearly has the largest effect on the generation of extremes, results from an analysis which segregates circulation-related and residual rainfall variability suggest that emerging climate change signals made a secondary contribution to extreme rainfall in winter 2013-14.

Solar Variability Controls on Rainfall in the Last Millennia: Evidence from a Highly Resolved Stalagmite Record from DeSoto Caverns (USA)

NASA Astrophysics Data System (ADS)

Aharon, P.; Lambert, W.; Hellstrom, J.

2009-12-01

Moisture transport from the Gulf of Mexico (GOM) inland has a considerable influence on both regional and continental rainfall patterns. Recent episodes of drought in the Southeastern USA exposed the vulnerability of the regional infrastructure to climate changes and gave rise to inter-state “water wars”. In order to better understand the cause of these periodic droughts and their controlling climate factors we have initiated a study of stalagmites from the DeSoto Caverns (Alabama, USA) that intersect the moisture flow from GOM. Combination of unusually high growth rates (up to 2 mm/decade), prominent dark and light seasonal layers, pristine aragonite mineralogy, precise U/Th dates acquired from mg-size samples and tight sampling (n=195) afforded generation of biannual (δ18O and δ13C of exceptional clarity spanning the last 700 yrs. The stalagmite (DSSG1) top yields isotope values (δ18O=-5.5 per-mill VPDB; δ13C=-10.1 per-mill VPDB) that are in good agreement with the predicted equilibrium isotope values. The oxygen and carbon isotope records exhibit a number of alternating negative and positive phase changes of

Extreme climatic events drive mammal irruptions: regression analysis of 100-year trends in desert rainfall and temperature

PubMed Central

Greenville, Aaron C; Wardle, Glenda M; Dickman, Chris R

2012-01-01

Extreme climatic events, such as flooding rains, extended decadal droughts and heat waves have been identified increasingly as important regulators of natural populations. Climate models predict that global warming will drive changes in rainfall and increase the frequency and severity of extreme events. Consequently, to anticipate how organisms will respond we need to document how changes in extremes of temperature and rainfall compare to trends in the mean values of these variables and over what spatial scales the patterns are consistent. Using the longest historical weather records available for central Australia – 100 years – and quantile regression methods, we investigate if extreme climate events have changed at similar rates to median events, if annual rainfall has increased in variability, and if the frequency of large rainfall events has increased over this period. Specifically, we compared local (individual weather stations) and regional (Simpson Desert) spatial scales, and quantified trends in median (50th quantile) and extreme weather values (5th, 10th, 90th, and 95th quantiles). We found that median and extreme annual minimum and maximum temperatures have increased at both spatial scales over the past century. Rainfall changes have been inconsistent across the Simpson Desert; individual weather stations showed increases in annual rainfall, increased frequency of large rainfall events or more prolonged droughts, depending on the location. In contrast to our prediction, we found no evidence that intra-annual rainfall had become more variable over time. Using long-term live-trapping records (22 years) of desert small mammals as a case study, we demonstrate that irruptive events are driven by extreme rainfalls (>95th quantile) and that increases in the magnitude and frequency of extreme rainfall events are likely to drive changes in the populations of these species through direct and indirect changes in predation pressure and wildfires. PMID:23170202

Dynamical downscaling of historical climate over CORDEX East Asia domain: A comparison of regional ocean-atmosphere coupled model to stand-alone RCM simulations

NASA Astrophysics Data System (ADS)

Zou, Liwei; Zhou, Tianjun; Peng, Dongdong

2016-02-01

The FROALS (flexible regional ocean-atmosphere-land system) model, a regional ocean-atmosphere coupled model, has been applied to the Coordinated Regional Downscaling Experiment (CORDEX) East Asia domain. Driven by historical simulations from a global climate system model, dynamical downscaling for the period from 1980 to 2005 has been conducted at a uniform horizontal resolution of 50 km. The impacts of regional air-sea couplings on the simulations of East Asian summer monsoon rainfall have been investigated, and comparisons have been made to corresponding simulations performed using a stand-alone regional climate model (RCM). The added value of the FROALS model with respect to the driving global climate model was evident in terms of both climatology and the interannual variability of summer rainfall over East China by the contributions of both the high horizontal resolution and the reasonably simulated convergence of the moisture fluxes. Compared with the stand-alone RCM simulations, the spatial pattern of the simulated low-level monsoon flow over East Asia and the western North Pacific was improved in the FROALS model due to its inclusion of regional air-sea coupling. The results indicated that the simulated sea surface temperature (SSTs) resulting from the regional air-sea coupling were lower than those derived directly from the driving global model over the western North Pacific north of 15°N. These colder SSTs had both positive and negative effects. On the one hand, they strengthened the western Pacific subtropical high, which improved the simulation of the summer monsoon circulation over East Asia. On the other hand, the colder SSTs suppressed surface evaporation and favored weaker local interannual variability in the SST, which led to less summer rainfall and weaker interannual rainfall variability over the Korean Peninsula and Japan. Overall, the reference simulation performed using the FROALS model is reasonable in terms of rainfall over the land area of East Asia and will become the basis for the generation of climate change scenarios for the CORDEX East Asia domain that will be described in future reports.

Simulation skill of APCC set of global climate models for Asian summer monsoon rainfall variability

NASA Astrophysics Data System (ADS)

Singh, U. K.; Singh, G. P.; Singh, Vikas

2015-04-01

The performance of 11 Asia-Pacific Economic Cooperation Climate Center (APCC) global climate models (coupled and uncoupled both) in simulating the seasonal summer (June-August) monsoon rainfall variability over Asia (especially over India and East Asia) has been evaluated in detail using hind-cast data (3 months advance) generated from APCC which provides the regional climate information product services based on multi-model ensemble dynamical seasonal prediction systems. The skill of each global climate model over Asia was tested separately in detail for the period of 21 years (1983-2003), and simulated Asian summer monsoon rainfall (ASMR) has been verified using various statistical measures for Indian and East Asian land masses separately. The analysis found a large variation in spatial ASMR simulated with uncoupled model compared to coupled models (like Predictive Ocean Atmosphere Model for Australia, National Centers for Environmental Prediction and Japan Meteorological Agency). The simulated ASMR in coupled model was closer to Climate Prediction Centre Merged Analysis of Precipitation (CMAP) compared to uncoupled models although the amount of ASMR was underestimated in both models. Analysis also found a high spread in simulated ASMR among the ensemble members (suggesting that the model's performance is highly dependent on its initial conditions). The correlation analysis between sea surface temperature (SST) and ASMR shows that that the coupled models are strongly associated with ASMR compared to the uncoupled models (suggesting that air-sea interaction is well cared in coupled models). The analysis of rainfall using various statistical measures suggests that the multi-model ensemble (MME) performed better compared to individual model and also separate study indicate that Indian and East Asian land masses are more useful compared to Asia monsoon rainfall as a whole. The results of various statistical measures like skill of multi-model ensemble, large spread among the ensemble members of individual model, strong teleconnection (correlation analysis) with SST, coefficient of variation, inter-annual variability, analysis of Taylor diagram, etc. suggest that there is a need to improve coupled model instead of uncoupled model for the development of a better dynamical seasonal forecast system.

How important is the spatiotemporal structure of a rainfall field when generating a streamflow hydrograph? An investigation using Reverse Hydrology

NASA Astrophysics Data System (ADS)

Kretzschmar, Ann; Tych, Wlodek; Beven, Keith; Chappell, Nick

2017-04-01

Flooding is the most widely occurring natural disaster affecting thousands of lives and businesses worldwide each year, and the size and frequency of flood-events are predicted to increase with climate change. The main input-variable for models used in flood prediction is rainfall. Estimating the rainfall input is often based on a sparse network of raingauges, which may or may not be representative of the salient rainfall characteristics responsible for generating of storm-hydrographs. A method based on Reverse Hydrology (Kretzschmar et al 2014 Environ Modell Softw) has been developed and is being tested using the intensively-instrumented Brue catchment (Southwest England) to explore the spatiotemporal structure of the rainfall-field (using 23 rain gauges over the 135.2 km2 basin). We compare how well the rainfall measured at individual gauges, or averaged over the basin, represent the rainfall inferred from the streamflow signal. How important is it to get the detail of the spatiotemporal rainfall structure right? Rainfall is transformed by catchment processes as it moves to streams, so exact duplication of the structure may not be necessary. 'True' rainfall estimated using 23 gauges / 135.2 km2 is likely to be a good estimate of the overall-catchment-rainfall, however, the integration process 'smears' the rainfall patterns in time, i.e. reduces the number of and lengthens rain-events as they travel across the catchment. This may have little impact on the simulation of stream-hydrographs when events are extensive across the catchment (e.g., frontal rainfall events) but may be significant for high-intensity, localised convective events. The Reverse Hydrology approach uses the streamflow record to infer a rainfall sequence with a lower time-resolution than the original input time-series. The inferred rainfall series is, however, able simulate streamflow as well as the observed, high resolution rainfall (Kretzschmar et al 2015 Hydrol Res). Most gauged catchments in the UK of a similar size would only have data available for 1 to 3 raingauges. The high density of the Brue raingauge network allows a good estimate of the 'True' catchment rainfall to be made and compared with data from an individual raingauge as if that was the only data available. In addition the rainfall from each raingauge is compared with rainfall inferred from streamflow using data from the selected individual raingauge, and also inferred from the full catchment network. The stochastic structure of the rainfall from all of these datasets is compared using a combination of traditional statistical measures, i.e., the first 4 moments of rainfall totals and its residuals; plus the number, length and distribution of wet and dry periods; rainfall intensity characteristics; and their ability to generate the observed stream hydrograph. Reverse Hydrology, which utilises information present in both the input rainfall and the output hydrograph, has provided a method of investigating the quality of the information each gauge adds to the catchment-average (Kretzschmar et al 2016 Procedia Eng.). Further, it has been used to ascertain how important reproducing the detailed rainfall structure really is, when used for flow prediction.

Ensemble averaging and stacking of ARIMA and GSTAR model for rainfall forecasting

NASA Astrophysics Data System (ADS)

Anggraeni, D.; Kurnia, I. F.; Hadi, A. F.

2018-04-01

Unpredictable rainfall changes can affect human activities, such as in agriculture, aviation, shipping which depend on weather forecasts. Therefore, we need forecasting tools with high accuracy in predicting the rainfall in the future. This research focus on local forcasting of the rainfall at Jember in 2005 until 2016, from 77 rainfall stations. The rainfall here was not only related to the occurrence of the previous of its stations, but also related to others, it’s called the spatial effect. The aim of this research is to apply the GSTAR model, to determine whether there are some correlations of spatial effect between one to another stations. The GSTAR model is an expansion of the space-time model that combines the time-related effects, the locations (stations) in a time series effects, and also the location it self. The GSTAR model will also be compared to the ARIMA model that completely ignores the independent variables. The forcested value of the ARIMA and of the GSTAR models then being combined using the ensemble forecasting technique. The averaging and stacking method of ensemble forecasting method here provide us the best model with higher acuracy model that has the smaller RMSE (Root Mean Square Error) value. Finally, with the best model we can offer a better local rainfall forecasting in Jember for the future.

On interception modelling of a lowland coastal rainforest in northern Queensland, Australia

NASA Astrophysics Data System (ADS)

Wallace, Jim; McJannet, Dave

2006-10-01

SummaryRecent studies of the water balance of tropical rainforests in northern Queensland have revealed that large fractions of rainfall, up to 30%, are intercepted by the canopy and lost as evaporation. These loss rates are much higher than those reported for continental rainforests, for example, in the Amazon basin, where interception is around 9% of rainfall. Higher interception losses have been found in coastal and mountain rainforests and substantial advection of energy during rainfall is proposed to account for these results. This paper uses a process based model of interception to analyse the interception losses at Oliver Creek, a lowland coastal rainforest site in northern Queensland with a mean annual rainfall of 3952 mm. The observed interception loss of 25% of rainfall for the period August 2001 to January 2004 can be reproduced by the model with a suitable choice of the three key controlling variables, the canopy storage capacity, mean rainfall rate and mean wet canopy evaporation rate. Our analyses suggest that the canopy storage capacity of the Oliver Creek rainforest is between 3.0 and 3.5 mm, higher than reported for most other rainforests. Despite the high canopy capacity at our site, the interception losses can only be accounted for with energy advection during rainfall in the range 40-70% of the incident energy.

Assessing the seasonal variability of ephemeral gully erosion using high-frequency monitoring: case study in a fully cultivated catchment (The Pommeroye, Northern France)

NASA Astrophysics Data System (ADS)

Patault, E.; Alary, C.; Franke, C.; Gauthier, A.; Abriak, N. E.

2017-12-01

Gully erosion results in on-site and off-site problems including the loss of cultivated soils, the silting of riverbeds and dams as well as infrastructure and property damage by muddy floods. Regions of intensive agricultural production situated on the European loess belt are particularly affected. Recently a growing interest has focused on ephemeral gullies since there have been recognized as a major contributor to the sediment yield in small agricultural catchment in this area. The aims of this case study are (i) to quantify the sediment yield transported by ephemeral gullies, (ii) to identify parameters that control the function of the hydro-sedimentary response and (iii) to evaluate the influence of seasonal variability on the ephemeral gully erosion. For this study a high-frequency monitoring station was implemented. For each flood event, 8 variables related to hydro-sedimentary and rainfall dynamics are calculated and the relationships between these variables are analyzed using the Pearson correlation matrix and Principal Component Analysis. During the first year of monitoring (03/2016-03/2017), 22 flood events were recorded of which 75% occurred in spring and winter. The specific sediment yield was evaluated to 30 t km-2 yr-1 which is conventional for the study region but the results show a highly variable seasonal distribution; 90% of the sedimentary transfer occurred in winter and autumn. The main reasons were a high cumulative rainfall and a long duration for the events. The maximum suspended sediment concentration at the catchment outlet was observed in spring, likely due to maximum rainfall intensities in that season. Also, a huge variability between the events is observed; e.g. one exceptional rain storm in 11/2016 represents 45% of the total sediment yield of the study period. For the monitored 22 events, 2 different types of hysteresis behavior were observed: (i) clockwise and (ii) complex. In winter, only clockwise hysteresis was observed. These results suggest that other factors have to be considered to better explain the variability of gully erosion, such as the soil surface characteristics (crop cover, crusting stage, roughness). A monitoring of these parameters on experimental plots is in progress.

Discharge variability and bedrock river incision on the Hawaiian island of Kaua'i

NASA Astrophysics Data System (ADS)

Huppert, K.; Deal, E.; Perron, J. T.; Ferrier, K.; Braun, J.

2017-12-01

Bedrock river incision occurs during floods that generate sufficient shear stress to strip riverbeds of sediment cover and erode underlying bedrock. Thresholds for incision can prevent erosion at low flows and slow down erosion at higher flows that do generate excess shear stress. Because discharge distributions typically display power-law tails, with non-negligible frequencies of floods much greater than the mean, models incorporating stochastic discharge and incision thresholds predict that discharge variability can sometimes have greater effects on long-term incision rates than mean discharge. This occurs when the commonly observed inverse scalings between mean discharge and discharge variability are weak or when incision thresholds are high. Because the effects of thresholds and discharge variability have only been documented in a few locations, their influence on long-term river incision rates remains uncertain. The Hawaiian island of Kaua'i provides an ideal natural laboratory to evaluate the effects of discharge variability and thresholds on bedrock river incision because it has one of Earth's steepest spatial gradients in mean annual rainfall and it also experiences dramatic spatial variations in rainfall and discharge variability, spanning a wide range of the conditions reported on Earth. Kaua'i otherwise has minimal variations in lithology, vertical motion, and other factors that can influence erosion. River incision rates averaged over 1.5 - 4.5 Myr timescales can be estimated along the lengths of Kauaian channels from the depths of river canyons and lava flow ages. We characterize rainfall and discharge variability on Kaua'i using records from an extensive network of rain and stream gauges spanning the past century. We use these characterizations to model long-term bedrock river incision along Kauaian channels with a threshold-dependent incision law, modulated by site-specific discharge-channel width scalings. Our comparisons between modeled and observed erosion rates suggest that variations in river incision rates on Kaua'i are dominated by variations in mean rainfall and discharge, rather than by differences in storminess across the island. We explore the implications of this result for the threshold dependence of river incision across Earth's varied climates.

HOW STRONG IS THE RELATIONSHIP BETWEEN RAINFALL VARIABILITY AND CAATINGA PRODUCTIVITY? A CASE STUDY UNDER A CHANGING CLIMATE.

PubMed

Salimon, Cleber; Anderson, Liana

2017-05-22

Despite the knowledge of the influence of rainfall on vegetation dynamics in semiarid tropical Brazil, few studies address and explore quantitatively the various aspects of this relationship. Moreover, Northeast Brazil is expected to have its rainfall reduced by as much as 60% until the end of the 21st Century, under scenario AII of the IPCC Report 2010. We sampled and analyzed satellite-derived monthly rainfall and a vegetation index data for 40 sites with natural vegetation cover in Paraíba State, Brazil from 2001 to 2012. In addition, the anomalies for both variables were calculated. Rainfall variation explained as much as 50% of plant productivity, using the vegetation index as a proxy, and rainfall anomaly explained 80% of the vegetation productivity anomaly. In an extreme dry year (2012), with 65% less rainfall than average for the period 2001-2012, the vegetation index decreased by 25%. If such decrease persists in a long term trend in rainfall reduction, this could lead to a disruption in this ecosystem functioning and the dominant vegetation could become even more xeric or desert-like, bringing serious environmental, social and economical impacts.

Recharge characteristics of an unconfined aquifer from the rainfall-water table relationship

NASA Astrophysics Data System (ADS)

Viswanathan, M. N.

1984-02-01

The determination of recharge levels of unconfined aquifers, recharged entirely by rainfall, is done by developing a model for the aquifer that estimates the water-table levels from the history of rainfall observations and past water-table levels. In the present analysis, the model parameters that influence the recharge were not only assumed to be time dependent but also to have varying dependence rates for various parameters. Such a model is solved by the use of a recursive least-squares method. The variable-rate parameter variation is incorporated using a random walk model. From the field tests conducted at Tomago Sandbeds, Newcastle, Australia, it was observed that the assumption of variable rates of time dependency of recharge parameters produced better estimates of water-table levels compared to that with constant-recharge parameters. It was observed that considerable recharge due to rainfall occurred on the very same day of rainfall. The increase in water-table level was insignificant for subsequent days of rainfall. The level of recharge very much depends upon the intensity and history of rainfall. Isolated rainfalls, even of the order of 25 mm day -1, had no significant effect on the water-table levels.

Seasonal forecasts in the Sahel region: the use of rainfall-based predictive variables

NASA Astrophysics Data System (ADS)

Lodoun, Tiganadaba; Sanon, Moussa; Giannini, Alessandra; Traoré, Pierre Sibiry; Somé, Léopold; Rasolodimby, Jeanne Millogo

2014-08-01

In the Sahel region, seasonal predictions are crucial to alleviate the impacts of climate variability on populations' livelihoods. Agricultural planning (e.g., decisions about sowing date, fertilizer application date, and choice of crop or cultivar) is based on empirical predictive indices whose accuracy to date has not been scientifically proven. This paper attempts to statistically test whether the pattern of rainfall distribution over the May-July period contributes to predicting the real onset date and the nature (wet or dry) of the rainy season, as farmers believe. To that end, we considered historical records of daily rainfall from 51 stations spanning the period 1920-2008 and the different agro-climatic zones in Burkina Faso. We performed (1) principal component analysis to identify climatic zones, based on the patterns of intra-seasonal rainfall, (2) and linear discriminant analysis to find the best rainfall-based variables to distinguish between real and false onset dates of the rainy season, and between wet and dry seasons in each climatic zone. A total of nine climatic zones were identified in each of which, based on rainfall records from May to July, we derived linear discriminant functions to correctly predict the nature of a potential onset date of the rainy season (real or false) and that of the rainy season (dry or wet) in at least three cases out of five. These functions should contribute to alleviating the negative impacts of climate variability in the different climatic zones of Burkina Faso.

Modeling rainfall-runoff relationship using multivariate GARCH model

NASA Astrophysics Data System (ADS)

Modarres, R.; Ouarda, T. B. M. J.

2013-08-01

The traditional hydrologic time series approaches are used for modeling, simulating and forecasting conditional mean of hydrologic variables but neglect their time varying variance or the second order moment. This paper introduces the multivariate Generalized Autoregressive Conditional Heteroscedasticity (MGARCH) modeling approach to show how the variance-covariance relationship between hydrologic variables varies in time. These approaches are also useful to estimate the dynamic conditional correlation between hydrologic variables. To illustrate the novelty and usefulness of MGARCH models in hydrology, two major types of MGARCH models, the bivariate diagonal VECH and constant conditional correlation (CCC) models are applied to show the variance-covariance structure and cdynamic correlation in a rainfall-runoff process. The bivariate diagonal VECH-GARCH(1,1) and CCC-GARCH(1,1) models indicated both short-run and long-run persistency in the conditional variance-covariance matrix of the rainfall-runoff process. The conditional variance of rainfall appears to have a stronger persistency, especially long-run persistency, than the conditional variance of streamflow which shows a short-lived drastic increasing pattern and a stronger short-run persistency. The conditional covariance and conditional correlation coefficients have different features for each bivariate rainfall-runoff process with different degrees of stationarity and dynamic nonlinearity. The spatial and temporal pattern of variance-covariance features may reflect the signature of different physical and hydrological variables such as drainage area, topography, soil moisture and ground water fluctuations on the strength, stationarity and nonlinearity of the conditional variance-covariance for a rainfall-runoff process.

Internally Generated and Externally Forced Multidecadal Oceanic Modes and their Influence on the Summer Rainfall over East Asia

NASA Astrophysics Data System (ADS)

Si, D.; Hu, A.

2017-12-01

The interdecadal oceanic variabilities can be generated from both internal and external processes, and these variabilities can significantly modulate our climate on global and regional scale, such as the warming slowdown in the early 21st century, and the rainfall in East Asia. By analyzing simulations from a unique Community Earth System Model (CESM) Large Ensemble (CESM_LE) project, we show that the Interdecadal Pacific Oscillation (IPO) is primarily an internally generated oceanic variability, while the Atlantic Multidecadal Oscillation (AMO) may be an oceanic variability generated by internal oceanic processes and modulated by external forcings in the 20th century. Although the observed relationship between IPO and the Yangtze-Huaihe River valley (YHRV) summer rainfall in China is well simulated in both the preindustrial control and 20th century ensemble, none of the 20th century ensemble members can reproduce the observed time evolution of both IPO and YHRV due to the unpredictable nature of IPO on multidecade timescale. On the other hand, although CESM_LE cannot reproduce the observed relationship between AMO and Huanghe River valley (HRV) summer rainfall of China in the preindustrial control simulation, this relationship in the 20th century simulations is well reproduced, and the chance to reproduce the observed time evolution of both AMO and HRV rainfall is about 30%, indicating the important role of the interaction between the internal processes and the external forcing to realistically simulate the AMO and HRV rainfall.

Uganda rainfall variability and prediction

NASA Astrophysics Data System (ADS)

Jury, Mark R.

2018-05-01

This study analyzes large-scale controls on Uganda's rainfall. Unlike past work, here, a May-October season is used because of the year-round nature of agricultural production, vegetation sensitivity to rainfall, and disease transmission. The Uganda rainfall record exhibits steady oscillations of ˜3 and 6 years over 1950-2013. Correlation maps at two-season lead time resolve the subtropical ridge over global oceans as an important feature. Multi-variate environmental predictors include Dec-May south Indian Ocean sea surface temperature, east African upper zonal wind, and South Atlantic wind streamfunction, providing a 33% fit to May-Oct rainfall time series. Composite analysis indicates that cool-phase El Niño Southern Oscillation supports increased May-Oct Uganda rainfall via a zonal overturning lower westerly/upper easterly atmospheric circulation. Sea temperature anomalies are positive in the east Atlantic and negative in the west Indian Ocean in respect of wet seasons. The northern Hadley Cell plays a role in limiting the northward march of the equatorial trough from May to October. An analysis of early season floods found that moist inflow from the west Indian Ocean converges over Uganda, generating diurnal thunderstorm clusters that drift southwestward producing high runoff.

Interannual variability and predictability over the Arabian Penuinsula Winter monsoon region

NASA Astrophysics Data System (ADS)

Adnan Abid, Muhammad; Kucharski, Fred; Almazroui, Mansour; Kang, In-Sik

2016-04-01

Interannual winter rainfall variability and its predictability are analysed over the Arabian Peninsula region by using observed and hindcast datasets from the state-of-the-art European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal prediction System 4 for the period 1981-2010. An Arabian winter monsoon index (AWMI) is defined to highlight the Arabian Peninsula as the most representative region for the Northern Hemispheric winter dominating the summer rainfall. The observations show that the rainfall variability is relatively large over the northeast of the Arabian Peninsula. The correlation coefficient between the Nino3.4 index and rainfall in this region is 0.33, suggesting potentially some modest predictability, and indicating that El Nino increases and La Nina decreases the rainfall. Regression analysis shows that upper-level cyclonic circulation anomalies that are forced by El Nino Southern Oscillation (ENSO) are responsible for the winter rainfall anomalies over the Arabian region. The stronger (weaker) mean transient-eddy activity related to the upper-level trough induced by the warm (cold) sea-surface temperatures during El Nino (La Nina) tends to increase (decrease) the rainfall in the region. The model hindcast dataset reproduces the ENSO-rainfall connection. The seasonal mean predictability of the northeast Arabian rainfall index is 0.35. It is shown that the noise variance is larger than the signal over the Arabian Peninsula region, which tends to limit the prediction skill. The potential predictability is generally increased in ENSO years and is, in particular, larger during La Nina compared to El Nino years in the region. Furthermore, central Pacific ENSO events and ENSO events with weak signals in the Indian Ocean tend to increase predictability over the Arabian region.

A TRMM-Calibrated Infrared Technique for Global Rainfall Estimation

NASA Technical Reports Server (NTRS)

Negri, Andrew J.; Adler, Robert F.; Xu, Li-Ming

2003-01-01

This paper presents the development of a satellite infrared (IR) technique for estimating convective and stratiform rainfall and its application in studying the diurnal variability of rainfall on a global scale. The Convective-Stratiform Technique (CST), calibrated by coincident, physically retrieved rain rates from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR), is applied over the global tropics during summer 2001. The technique is calibrated separately over land and ocean, making ingenious use of the IR data from the TRMM Visible/Infrared Scanner (VIRS) before application to global geosynchronous satellite data. The low sampling rate of TRMM PR imposes limitations on calibrating IR- based techniques; however, our research shows that PR observations can be applied to improve IR-based techniques significantly by selecting adequate calibration areas and calibration length. The diurnal cycle of rainfall, as well as the division between convective and t i f m rainfall will be presented. The technique is validated using available data sets and compared to other global rainfall products such as Global Precipitation Climatology Project (GPCP) IR product, calibrated with TRMM Microwave Imager (TMI) data. The calibrated CST technique has the advantages of high spatial resolution (4 km), filtering of non-raining cirrus clouds, and the stratification of the rainfall into its convective and stratiform components, the latter being important for the calculation of vertical profiles of latent heating.

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

Ghosh, Subimal; Das, Debasish; Kao, Shih-Chieh

Recent studies disagree on how rainfall extremes over India have changed in space and time over the past half century, as well as on whether the changes observed are due to global warming or regional urbanization. Although a uniform and consistent decrease in moderate rainfall has been reported, a lack of agreement about trends in heavy rainfall may be due in part to differences in the characterization and spatial averaging of extremes. Here we use extreme value theory to examine trends in Indian rainfall over the past half century in the context of long-term, low-frequency variability.We show that when generalizedmore » extreme value theory is applied to annual maximum rainfall over India, no statistically significant spatially uniform trends are observed, in agreement with previous studies using different approaches. Furthermore, our space time regression analysis of the return levels points to increasing spatial variability of rainfall extremes over India. Our findings highlight the need for systematic examination of global versus regional drivers of trends in Indian rainfall extremes, and may help to inform flood hazard preparedness and water resource management in the region.« less

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.

Extreme Precipitation and High-Impact Landslides

NASA Technical Reports Server (NTRS)

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

2012-01-01

It is well known that extreme or prolonged rainfall is the dominant trigger of landslides; however, there remain large uncertainties in characterizing the distribution of these hazards and meteorological triggers at the global scale. Researchers have evaluated the spatiotemporal distribution of extreme rainfall and landslides at local and regional scale primarily using in situ data, yet few studies have mapped rainfall-triggered landslide distribution globally due to the dearth of landslide data and consistent precipitation information. This research uses a newly developed Global Landslide Catalog (GLC) and a 13-year satellite-based precipitation record from Tropical Rainfall Measuring Mission (TRMM) data. For the first time, these two unique products provide the foundation to quantitatively evaluate the co-occurence of precipitation and rainfall-triggered landslides globally. The GLC, available from 2007 to the present, contains information on reported rainfall-triggered landslide events around the world using online media reports, disaster databases, etc. When evaluating this database, we observed that 2010 had a large number of high-impact landslide events relative to previous years. This study considers how variations in extreme and prolonged satellite-based rainfall are related to the distribution of landslides over the same time scales for three active landslide areas: Central America, the Himalayan Arc, and central-eastern China. Several test statistics confirm that TRMM rainfall generally scales with the observed increase in landslide reports and fatal events for 2010 and previous years over each region. These findings suggest that the co-occurrence of satellite precipitation and landslide reports may serve as a valuable indicator for characterizing the spatiotemporal distribution of landslide-prone areas in order to establish a global rainfall-triggered landslide climatology. This research also considers the sources for this extreme rainfall, citing teleconnections from ENSO as likely contributors to regional precipitation variability. This work demonstrates the potential for using satellite-based precipitation estimates to identify potentially active landslide areas at the global scale in order to improve landslide cataloging and quantify landslide triggering at daily, monthly and yearly time scales.

Influences of rainfall variables and antecedent discharge on urban effluent concentrations and loads in wet weather.

PubMed

Xu, Zuxin; Xiong, Lijun; Li, Huaizheng; Liao, Zhengliang; Yin, Hailong; Wu, Jun; Xu, Jin; Chen, Hao

2017-04-01

For storm drainages inappropriately connected with sewage, wet weather discharge is a major factor that adversely affects receiving waters. A study of the wet weather influences of rainfall-discharge variables on storm drainages connected with sewage was conducted in the downtown Shanghai area (374 ha). Two indicators, event mean concentration (EMC) and event pollutant load per unit area (EPL), were used to describe the pollution discharge during 20 rain events. The study showed that the total rainfall and discharge volume were important factors that affect the EMCs and EPLs of the chemical oxygen demand, total phosphorus, and especially those of NH 4 + -N. The pollutant concentrations at the beginning of the discharge and the discharge period were also major factors that influence the EMCs of these three pollutants. Regression relationships between the rainfall-discharge variables and discharge volume/ EPLs (R 2 = 0.824-0.981) were stronger than the relationships between the rainfall-discharge variables and EMCs. These regression equations can be considered reliable in the system, with a relative validation error of less than ±10% for the discharge volume, and less than ±20% for the EPLs. The results presented in this paper provide guidance for effectively controlling pollution in similar storm drainages.

Spatial variability of mountain stream dynamics along the Ethiopian Rift Valley escarpment

NASA Astrophysics Data System (ADS)

Asfaha, Tesfaalem-Ghebreyohannes; Frankl, Amaury; Zenebe, Amanuel; Haile, Mitiku; Nyssen, Jan

2014-05-01

Changes in hydrogeomorphic characteristics of mountain streams are generally deemed to be controlled mainly by land use/cover changes and rainfall variability. This study investigates the spatial variability of peak discharge in relation to land cover, rainfall and topographic variables in eleven catchments of the Ethiopian Rift Valley escarpment (average slope gradient = 48% (± 13%). Rapid deforestation of the escarpment in the second half of the 20th century resulted in the occurrence of strong flash floods, transporting large amounts of discharge and sediment to the lower graben bottom. Due to integrated reforestation interventions as of the 1980s, many of these catchments do show improvement in vegetation cover at various degrees. Daily rainfall was measured using seven non-recording rain gauges, while peak stage discharges were measured after floods using crest stage gauges installed at eleven stream reaches. Peak discharges were calculated using the Manning's equation. Daily area-weighted rainfall was computed for each catchment using the Thiessen Polygon method. To estimate the vegetation cover of each catchment, the Normalized Difference Vegetation Index was calculated from Landsat TM imagery (mean = 0.14 ± 0.05). In the rainy season of 2012, there was a positive correlation between daily rainfall and peak discharge in each of the monitored catchments. In a multiple linear regression analysis (R² = 0.83; P<0.01), average daily peak discharge in all rivers was positively related with rainfall depth and catchment size and negatively with vegetation cover (as represented by average NDVI values). Average slope gradient of the catchments and Gravelius's compactness index did not show a statistically significant relation with peak discharge. This study shows that though the average vegetation cover of the catchments is still relatively low, differences in vegetation cover, together with rainfall variability plays a determining role in the amount of peak discharges in flashy mountain streams.

The Microphysical Structure of Extreme Precipitation as Inferred from Ground-Based Raindrop Spectra.

NASA Astrophysics Data System (ADS)

Uijlenhoet, Remko; Smith, James A.; Steiner, Matthias

2003-05-01

The controls on the variability of raindrop size distributions in extreme rainfall and the associated radar reflectivity-rain rate relationships are studied using a scaling-law formalism for the description of raindrop size distributions and their properties. This scaling-law formalism enables a separation of the effects of changes in the scale of the raindrop size distribution from those in its shape. Parameters controlling the scale and shape of the scaled raindrop size distribution may be related to the microphysical processes generating extreme rainfall. A global scaling analysis of raindrop size distributions corresponding to rain rates exceeding 100 mm h1, collected during the 1950s with the Illinois State Water Survey raindrop camera in Miami, Florida, reveals that extreme rain rates tend to be associated with conditions in which the variability of the raindrop size distribution is strongly number controlled (i.e., characteristic drop sizes are roughly constant). This means that changes in properties of raindrop size distributions in extreme rainfall are largely produced by varying raindrop concentrations. As a result, rainfall integral variables (such as radar reflectivity and rain rate) are roughly proportional to each other, which is consistent with the concept of the so-called equilibrium raindrop size distribution and has profound implications for radar measurement of extreme rainfall. A time series analysis for two contrasting extreme rainfall events supports the hypothesis that the variability of raindrop size distributions for extreme rain rates is strongly number controlled. However, this analysis also reveals that the actual shapes of the (measured and scaled) spectra may differ significantly from storm to storm. This implies that the exponents of power-law radar reflectivity-rain rate relationships may be similar, and close to unity, for different extreme rainfall events, but their prefactors may differ substantially. Consequently, there is no unique radar reflectivity-rain rate relationship for extreme rain rates, but the variability is essentially reduced to one free parameter (i.e., the prefactor). It is suggested that this free parameter may be estimated on the basis of differential reflectivity measurements in extreme rainfall.

Rainfall and tillage effects on transport of fecal bacteria and sex hormones 17beta-estradiol and testosterone from broiler litter applications to a Georgia Piedmont Ultisol.

PubMed

Jenkins, Michael B; Truman, Clint C; Siragusa, Gregory; Line, Eric; Bailey, J Stan; Frye, Jonathan; Endale, Dinku M; Franklin, Dorcas H; Schomberg, Harry H; Fisher, Dwight S; Sharpe, Ronald R

2008-09-15

Poultry litter provides nutrients for crop and pasture production; however, it also contains fecal bacteria, sex hormones (17beta-estradiol and testosterone) and antibiotic residues that may contaminate surface waters. Our objective was to quantify transport of fecal bacteria, estradiol, testosterone and antibiotic residues from a Cecil sandy loam managed since 1991 under no-till (NT) and conventional tillage (CT) to which either poultry litter (PL) or conventional fertilizer (CF) was applied based on the nitrogen needs of corn (Zea mays L) in the Southern Piedmont of NE Georgia. Simulated rainfall was applied for 60 min to 2 by 3-m field plots at a constant rate in 2004 and variable rate in 2005. Runoff was continuously measured and subsamples taken for determining flow-weighted concentrations of fecal bacteria, hormones, and antibiotic residues. Neither Salmonella, nor Campylobacter, nor antimicrobial residues were detected in litter, soil, or runoff. Differences in soil concentrations of fecal bacteria before and after rainfall simulations were observed only for Escherichia coli in the constant rainfall intensity experiment. Differences in flow-weighted concentrations were observed only for testosterone in both constant and variable intensity rainfall experiments, and were greatest for treatments that received poultry litter. Total loads of E. coli and fecal enterococci, were largest for both tillage treatments receiving poultry litter for the variable rainfall intensity. Load of testosterone was greatest for no-till plots receiving poultry litter under variable rainfall intensity. Poultry litter application rates commensurate for corn appeared to enhance only soil concentrations of E. coli, and runoff concentrations of testosterone above background levels.

On the distributions of annual and seasonal daily rainfall extremes in central Arizona and their spatial variability

NASA Astrophysics Data System (ADS)

Mascaro, Giuseppe

2018-04-01

This study uses daily rainfall records of a dense network of 240 gauges in central Arizona to gain insights on (i) the variability of the seasonal distributions of rainfall extremes; (ii) how the seasonal distributions affect the shape of the annual distribution; and (iii) the presence of spatial patterns and orographic control for these distributions. For this aim, recent methodological advancements in peak-over-threshold analysis and application of the Generalized Pareto Distribution (GPD) were used to assess the suitability of the GPD hypothesis and improve the estimation of its parameters, while limiting the effect of short sample sizes. The distribution of daily rainfall extremes was found to be heavy-tailed (i.e., GPD shape parameter ξ > 0) during the summer season, dominated by convective monsoonal thunderstorms. The exponential distribution (a special case of GPD with ξ = 0) was instead showed to be appropriate for modeling wintertime daily rainfall extremes, mainly caused by cold fronts transported by westerly flow. The annual distribution exhibited a mixed behavior, with lighter upper tails than those found in summer. A hybrid model mixing the two seasonal distributions was demonstrated capable of reproducing the annual distribution. Organized spatial patterns, mainly controlled by elevation, were observed for the GPD scale parameter, while ξ did not show any clear control of location or orography. The quantiles returned by the GPD were found to be very similar to those provided by the National Oceanic and Atmospheric Administration (NOAA) Atlas 14, which used the Generalized Extreme Value (GEV) distribution. Results of this work are useful to improve statistical modeling of daily rainfall extremes at high spatial resolution and provide diagnostic tools for assessing the ability of climate models to simulate extreme events.

Long-run relative importance of temperature as the main driver to malaria transmission in Limpopo Province, South Africa: a simple econometric approach.

PubMed

Komen, Kibii; Olwoch, Jane; Rautenbach, Hannes; Botai, Joel; Adebayo, Adetunji

2015-03-01

Malaria in Limpopo Province of South Africa is shifting and now observed in originally non-malaria districts, and it is unclear whether climate change drives this shift. This study examines the distribution of malaria at district level in the province, determines direction and strength of the linear relationship and causality between malaria with the meteorological variables (rainfall and temperature) and ascertains their short- and long-run variations. Spatio-temporal method, Correlation analysis and econometric methods are applied. Time series monthly meteorological data (1998-2007) were obtained from South Africa Weather Services, while clinical malaria data came from Malaria Control Centre in Tzaneen (Limpopo Province) and South African Department of Health. We find that malaria changes and pressures vary in different districts with a strong positive correlation between temperature with malaria, r = 0.5212, and a weak positive relationship for rainfall, r = 0.2810. Strong unidirectional causality runs from rainfall and temperature to malaria cases (and not vice versa): F (1, 117) = 3.89, ρ = 0.0232 and F (1, 117) = 20.08, P < 0.001 and between rainfall and temperature, a bi-directional causality exists: F (1, 117) = 19.80; F (1,117) = 17.14, P < 0.001, respectively, meaning that rainfall affects temperature and vice versa. Results show evidence of strong existence of a long-run relationship between climate variables and malaria, with temperature maintaining very high level of significance than rainfall. Temperature, therefore, is more important in influencing malaria transmission in Limpopo Province.

Projecting Changes in S. Florida Rainfall for the 21st century: Scenarios, Downscaling and Analysis

NASA Astrophysics Data System (ADS)

Cioffi, F.; Lall, U.; Monti, A.

2013-12-01

A Non-Homogeneous hidden Markov Models (NHMM) is developed using a 65-years record (1948-2012) of daily rainfall amount at nineteen stations in South Florida and re-analysis atmospheric fields of Temperature (T) at 1000 hPa, Geo Potential Height (GPH) at 1000 hPa, Meridional Winds (MW) and Zonal Winds (ZW) at 850 hPa, and Zonal Winds on the specific latitude of 27N (ZW27N) from 10 to 1000 hPa. The NHMM fitted is then used for predicting future rainfall patterns under global warming scenario (RCP8.5), using predictors from the CMCC-CMS simulations from 1950-2100. The model directly includes a consideration of seasonality through changes in the driving variables thus addressing the question of how future changes in seasonality of precipitation can also be modeled. The results of the simulations obtained by using the downscaling model NHMM, with predictors derived from the simulations of CMCC-CMS CGM, in the worst conditions of global warming as simulated by RCP8.5 scenario, seems to indicate that, as a consequence of increase of CO2 concentration and temperature, South Florida should be subjected to more frequent dry conditions for the most part of the year, due mainly to a reduction of number of wet days and, at the same time, the territory should be also affected by extreme rainfall events that are more intense than the present ones. What appears from results is an increases of rainfall variability. This scenario seems coherent with the trends of rainfall patterns observed in the XX century. An investigation on the causes of such hydrologic changes, and specifically on the role of North Atlantic Subtropical High is pursued.

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.

Indian summer monsoon rainfall: Dancing with the tunes of the sun

NASA Astrophysics Data System (ADS)

Hiremath, K. M.; Manjunath, Hegde; Soon, Willie

2015-02-01

There is strong statistical evidence that solar activity influences the Indian summer monsoon rainfall. To search for a physical link between the two, we consider the coupled cloud hydrodynamic equations, and derive an equation for the rate of precipitation that is similar to the equation of a forced harmonic oscillator, with cloud and rain water mixing ratios as forcing variables. Those internal forcing variables are parameterized in terms of the combined effect of external forcing as measured by sunspot and coronal hole activities with several well known solar periods (9, 13 and 27 days; 1.3, 5, 11 and 22 years). The equation is then numerically solved and the results show that the variability of the simulated rate of precipitation captures very well the actual variability of the Indian monsoon rainfall, yielding vital clues for a physical understanding that has so far eluded analyses based on statistical correlations alone. We also solved the precipitation equation by allowing for the effects of long-term variation of aerosols. We tentatively conclude that the net effects of aerosols variation are small, when compared to the solar factors, in terms of explaining the observed rainfall variability covering the full Indian monsoonal geographical domains.

Rainfall estimation with TFR model using Ensemble Kalman filter

NASA Astrophysics Data System (ADS)

Asyiqotur Rohmah, Nabila; Apriliani, Erna

2018-03-01

Rainfall fluctuation can affect condition of other environment, correlated with economic activity and public health. The increasing of global average temperature is influenced by the increasing of CO2 in the atmosphere, which caused climate change. Meanwhile, the forests as carbon sinks that help keep the carbon cycle and climate change mitigation. Climate change caused by rainfall intensity deviations can affect the economy of a region, and even countries. It encourages research on rainfall associated with an area of forest. In this study, the mathematics model that used is a model which describes the global temperatures, forest cover, and seasonal rainfall called the TFR (temperature, forest cover, and rainfall) model. The model will be discretized first, and then it will be estimated by the method of Ensemble Kalman Filter (EnKF). The result shows that the more ensembles used in estimation, the better the result is. Also, the accurateness of simulation result is influenced by measurement variable. If a variable is measurement data, the result of simulation is better.

Analysis of water-level fluctuations of the US Highway 90 retention pond, Madison, Florida

USGS Publications Warehouse

Bridges, W.C.

1985-01-01

A closed basin stormwater retention pond, located 1 mile west of Madison, Florida, has a maximum storage capacity of 134.1 acre-feet at the overtopping altitude of 100.2 feet. The maximum observed altitude (July 1982 to March 1984) was 99.52 feet (126.7 acre-feet) on March 28, 1984. This report provides a technique for simulating net monthly change-in-altitude in response to rainfall and evaporation. A regression equation was developed which relates net monthly change in altitude (dependent variable) to rainfall and evaporation (independent variables). Rainfall frequency curves were developed using a log-Pearson Type III distribution of the annual, January through April, June through August, and July monthly rainfall totals for the years 1908-72, 1974, 1976-82. The altitude of the retention pond increased almost 7 feet during the 4-month period January through April 1983. The rainfall total was 35.1 inches, and the recurrence interval exceeded the 100-year January-April rainfall. (USGS)

Quantifying Precipitation Variability on Titan Using a GCM and Implications for Observed Geomorphology

NASA Astrophysics Data System (ADS)

Faulk, Sean P.; Mitchell, Jonathan L.; Moon, Seulgi; Lora, Juan Manuel

2016-10-01

Titan's zonal-mean precipitation behavior has been widely investigated using general circulation models (GCMs), but the spatial and temporal variability of rainfall in Titan's active hydrologic cycle is less well understood. We conduct statistical analyses of rainfall, diagnosed from GCM simulations of Titan's atmosphere, to determine storm intensity and frequency. Intense storms of methane have been proposed to be critical for enabling mechanical erosion of Titan's surface, as indicated by observations of dendritic valley networks. Using precipitation outputs from the Titan Atmospheric Model (TAM), a GCM shown to realistically simulate many features of Titan's atmosphere, we quantify the precipitation variability within eight separate latitude bins for a variety of initial surface liquid distributions. We find that while the overall wettest regions are indeed the poles, the most intense rainfall generally occurs in the high mid-latitudes, between 45-67.5 degrees, consistent with recent geomorphological observations of alluvial fans concentrated at those latitudes. We also find that precipitation rates necessary for surface erosion, as estimated by Perron et al. (2006) J. Geophys. Res. 111, E11001, frequently occur at all latitudes, with recurrence intervals of less than one Titan year. Such analysis is crucial towards understanding the complex interaction between Titan's atmosphere and surface and defining the influence of precipitation on observed geomorphology.

Evaluation of atmospheric thermodynamics and dynamics during heavy-rainfall and no-rainfall events in the metropolitan area of Rio de Janeiro, Brazil

NASA Astrophysics Data System (ADS)

da Silva, Fabricio Polifke; Rotunno Filho, Otto Corrêa; Sampaio, Rafael João; Dragaud, Ian Cunha D'amato Viana; de Araújo, Afonso Augusto Magalhães; Justi da Silva, Maria Gertrudes Alvarez; Pires, Gisele Dornelles

2017-12-01

Local prediction of thunderstorms is one of the most challenging tasks in weather forecasting due to their high spatiotemporal variability. An improved understanding of such meteorological phenomena, therefore, requires high-frequency measurements along the vertical profile of the atmosphere of interest. In this context, the evaluation of thermodynamic and dynamic parameters obtained from radiosondes to identify atmospheric conditions favorable to thunderstorm and heavy-rainfall development emerges as a valuable tool for investigations of thunderstorms. In this context, four radiosondes were launched to collect a data set for the area of interest at the sub-daily scale (12 UTC, 16 UTC, 18 UTC, and 00 UTC). The collection period encompassed two dates—November 29 and December 12, 2016—chosen specifically due to the existence of heavy-rainfall warnings in the forecast for the Metropolitan Area of Rio de Janeiro, Brazil ("MARJ") for those days. However, heavy rainfall was registered only for December 12 and not for November 29 (which led us to explore this contrast with the announced rainfall forecasts). Sub-daily radiosonde data showed a clear decrease in atmospheric instability in the early afternoon on November 29. On the other hand, an opposite scenario occurred on December 12, which saw an expressive increase in thermodynamic instability during the day. The meteorological modeling approach used also revealed that the vertical coupling of low-level moisture flux convergence centers and upper-level mass flux divergence centers worked as a dynamic trigger for the thunderstorm and heavy-rainfall developments that took place on December 12, 2016.

Documentary reconstruction of monsoon rainfall variability over western India, 1781-1860

NASA Astrophysics Data System (ADS)

Adamson, George C. D.; Nash, David J.

2014-02-01

Investigations into the climatic forcings that affect the long-term variability of the Indian summer monsoon are constrained by a lack of reliable rainfall data prior to the late nineteenth century. Extensive qualitative and quantitative meteorological information for the pre-instrumental period exists within historical documents, although these materials have been largely unexplored. This paper presents the first reconstruction of monsoon variability using documentary sources, focussing on western India for the period 1781-1860. Three separate reconstructions are generated, for (1) Mumbai, (2) Pune and (3) the area of Gujarat bordering the Gulf of Khambat. A composite chronology is then produced from the three reconstructions, termed the Western India Monsoon Rainfall reconstruction (WIMR). The WIMR exhibits four periods of generally deficient monsoon rainfall (1780-1785, 1799-1806, 1830-1838 and 1845-1857) and three of above-normal rainfall (1788-1794, 1813-1828 and 1839-1844). The WIMR shows good correspondence with a dendroclimatic drought reconstruction for Kerala, although agreement with the western Indian portion of the tree-ring derived Monsoon Asia Drought Atlas is less strong. The reconstruction is used to examine the long-term relationship between the El Nino-Southern Oscillation (ENSO) and monsoon rainfall over western India. This exhibits peaks and troughs in correlation over time, suggesting a regular long-term fluctuation. This may be an internal oscillation in the ENSO-monsoon system or may be related to volcanic aerosol forcings. Further reconstructions of monsoon rainfall are necessary to validate this. The study highlights uncertainties in existing published rainfall records for 1817-1846 for western India.

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.

An Experimental Study of Small-Scale Variability of Raindrop Size Distribution

NASA Technical Reports Server (NTRS)

Tokay, Ali; Bashor, Paul G.

2010-01-01

An experimental study of small-scale variability of raindrop size distributions (DSDs) has been carried out at Wallops Island, Virginia. Three Joss-Waldvogel disdrometers were operated at a distance of 0.65, 1.05, and 1.70 km in a nearly straight line. The main purpose of the study was to examine the variability of DSDs and its integral parameters of liquid water content, rainfall, and reflectivity within a 2-km array: a typical size of Cartesian radar pixel. The composite DSD of rain events showed very good agreement among the disdrometers except where there were noticeable differences in midsize and large drops in a few events. For consideration of partial beam filling where the radar pixel was not completely covered by rain, a single disdrometer reported just over 10% more rainy minutes than the rainy minutes when all three disdrometers reported rainfall. Similarly two out of three disdrometers reported5%more rainy minutes than when all three were reporting rainfall. These percentages were based on a 1-min average, and were less for longer averaging periods. Considering only the minutes when all three disdrometers were reporting rainfall, just over one quarter of the observations showed an increase in the difference in rainfall with distance. This finding was based on a 15-min average and was even less for shorter averaging periods. The probability and cumulative distributions of a gamma-fitted DSD and integral rain parameters between the three disdrometers had a very good agreement and no major variability. This was mainly due to the high percentage of light stratiform rain and to the number of storms that traveled along the track of the disdrometers. At a fixed time step, however, both DSDs and integral rain parameters showed substantial variability. The standard deviation (SD) of rain rate was near 3 mm/h, while the SD of reflectivity exceeded 3 dBZ at the longest separation distance. These standard deviations were at 6-min average and were higher at shorter averaging periods. The correlations decreased with increasing separation distance. For rain rate, the correlations were higher than previous gauge-based studies. This was attributed to the differences in data processing and the difference in rainfall characteristics in different climate regions. It was also considered that the gauge sampling errors could be a factor. In this regard, gauge measurements were simulated employing existing disdrometer dataset. While a difference was noticed in cumulative distribution of rain occurrence between the simulated gauge and disdrometer observations, the correlations in simulated gauge measurements did not differ from the disdrometer measurements.

Impacts of Changing Climate on Agricultural Variability: Implications for Smallholder Farmers in India

NASA Astrophysics Data System (ADS)

Mondal, P.; Jain, M.; DeFries, R. S.; Galford, G. L.; Small, C.

2013-12-01

Agriculture is the largest employment sector in India, where food productivity, and thus food security, is highly dependent on seasonal rainfall and temperature. Projected increase in temperature, along with less frequent but intense rainfall events, will have a negative impact on crop productivity in India in the coming decades. These changes, along with continued ground water depletion, could have serious implications for Indian smallholder farmers, who are among some of the most vulnerable communities to climatic and economic changes. Hence baseline information on agricultural sensitivity to climate variability is important for strategies and policies that promote adaptation to climate variability. This study examines how cropping patterns in different agro-ecological zones in India respond to variations in precipitation and temperature. We specifically examine: a) which climate variables most influence crop cover for monsoon and winter crops? and b) how does the sensitivity of crop cover to climate variability vary in different agro-ecological regions with diverse socio-economic factors? We use remote sensing data (2000-01 - 2012-13) for cropping patterns (developed using MODIS satellite data), climate parameters (derived from MODIS and TRMM satellite data) and agricultural census data. We initially assessed the importance of these climate variables in two agro-ecoregions: a predominantly groundwater irrigated, cash crop region in western India, and a region in central India primarily comprised of rain-fed or surface water irrigated subsistence crops. Seasonal crop cover anomaly varied between -25% and 25% of the 13-year mean in these two regions. Predominantly climate-dependent region in central India showed high anomalies up to 200% of the 13-year crop cover mean, especially during winter season. Winter daytime mean temperature is overwhelmingly the most important climate variable for winter crops irrespective of the varied biophysical and socio-economic conditions across the study regions. Despite access to groundwater irrigation, crop cover in the western Indian study region showed substantial fluctuations during monsoon, probably due to changing planting strategies. This region is less sensitive to precipitation compared to the central Indian study region with predominantly climate-dependent irrigation from surface water. In western Indian study region a greater number of rainy days, increased intensity of rainfall, and cooler daytime and nighttime temperatures lead to increased crop cover during monsoon season, compared to in the central Indian study region where monsoon timing and amount of total rainfall are the most important factors of crop cover. Our findings indicate that different regions respond differently to climate, since socio-economic factors, such as irrigation access, market influences, demography, and policies play critical role in agricultural production. In the wake of projected precipitation and temperature changes, better access to irrigation and heat-tolerant high-yielding crop varieties will be crucial for future food production.

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.

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.

Large-scale assessment of soil erosion in Africa: satellites help to jointly account for dynamic rainfall and vegetation cover

NASA Astrophysics Data System (ADS)

Vrieling, Anton; Hoedjes, Joost C. B.; van der Velde, Marijn

2015-04-01

Efforts to map and monitor soil erosion need to account for the erratic nature of the soil erosion process. Soil erosion by water occurs on sloped terrain when erosive rainfall and consequent surface runoff impact soils that are not well-protected by vegetation or other soil protective measures. Both rainfall erosivity and vegetation cover are highly variable through space and time. Due to data paucity and the relative ease of spatially overlaying geographical data layers into existing models like USLE (Universal Soil Loss Equation), many studies and mapping efforts merely use average annual values for erosivity and vegetation cover as input. We first show that rainfall erosivity can be estimated from satellite precipitation data. We obtained average annual erosivity estimates from 15 yr of 3-hourly TRMM Multi-satellite Precipitation Analysis (TMPA) data (1998-2012) using intensity-erosivity relationships. Our estimates showed a positive correlation (r = 0.84) with long-term annual erosivity values of 37 stations obtained from literature. Using these TMPA erosivity retrievals, we demonstrate the large interannual variability, with maximum annual erosivity often exceeding two to three times the mean value, especially in semi-arid areas. We then calculate erosivity at a 10-daily time-step and combine this with vegetation cover development for selected locations in Africa using NDVI - normalized difference vegetation index - time series from SPOT VEGETATION. Although we do not integrate the data at this point, the joint analysis of both variables stresses the need for joint accounting for erosivity and vegetation cover for large-scale erosion assessment and monitoring.

A predictive model for spotted wilt epidemics in peanut based on local weather conditions and the tomato spotted wilt virus risk index.

PubMed

Olatinwo, R O; Paz, J O; Brown, S L; Kemerait, R C; Culbreath, A K; Beasley, J P; Hoogenboom, G

2008-10-01

Tomato spotted wilt virus (TSWV), a member of the genus Tospovirus (family Bunyaviridae), is an important plant virus that causes severe damage to peanut (Arachis hypogaea) in the southeastern United States. Disease severity has been extremely variable in individual fields in Georgia, due to several factors including variability in weather patterns. A TSWV risk index has been developed by the University of Georgia to aid peanut growers with the assessment and avoidance of high risk situations. This study was conducted to examine the relationship between weather parameters and spotted wilt severity in peanut, and to develop a predictive model that integrates localized weather information into the risk index. On-farm survey data collected during 1999, 2002, 2004, and 2005 growing seasons, and derived weather variables during the same years were analyzed using nonlinear and multiple regression analyses. Meteorological data were obtained from the Georgia Automated Environmental Monitoring Network. The best model explained 61% of the variation in spotted wilt severity (square root transformed) as a function of the interactions between the TSWV risk index, the average daily temperature in April (TavA), the average daily minimum temperature between March and April (TminMA), the accumulated rainfall in March (RainfallM), the accumulated rainfall in April (RainfallA), the number of rain days in April (RainDayA), evapotranspiration in April (EVTA), and the number of days from 1 January to the planting date (JulianDay). Integrating this weather-based model with the TSWV risk index may help peanut growers more effectively manage tomato spotted wilt disease.

Predicting rainfall erosivity by momentum and kinetic energy in Mediterranean environment

NASA Astrophysics Data System (ADS)

Carollo, Francesco G.; Ferro, Vito; Serio, Maria A.

2018-05-01

Rainfall erosivity is an index that describes the power of rainfall to cause soil erosion and it is used around the world for assessing and predicting soil loss on agricultural lands. Erosivity can be represented in terms of both rainfall momentum and kinetic energy, both calculated per unit time and area. Contrasting results on the representativeness of these two variables are available: some authors stated that momentum and kinetic energy are practically interchangeable in soil loss estimation while other found that kinetic energy is the most suitable expression of rainfall erosivity. The direct and continuous measurements of momentum and kinetic energy by a disdrometer allow also to establish a relationship with rainfall intensity at the study site. At first in this paper a comparison between the momentum-rainfall intensity relationships measured at Palermo and El Teularet by an optical disdrometer is presented. For a fixed rainfall intensity the measurements showed that the rainfall momentum values measured at the two experimental sites are not coincident. However both datasets presented a threshold value of rainfall intensity over which the rainfall momentum assumes a quasi-constant value. Then the reliability of a theoretically deduced relationship, linking momentum, rainfall intensity and median volume diameter, is positively verified using measured raindrop size distributions. An analysis to assess which variable, momentum or kinetic energy per unit area and time, is the best predictor of erosivity in Italy and Spain was also carried out. This investigation highlighted that the rainfall kinetic energy per unit area and time can be substituted by rainfall momentum as index for estimating the rainfall erosivity, and this result does not depend on the site where precipitation occurs. Finally, rainfall intensity measurements and soil loss data collected from the bare plots equipped at Sparacia experimental area were used to verify the reliability of some rainfall erosivity indices and their ability to distinguish the type of involved soil erosion processes.

Hydrologic response in karstic-ridge wetlands to rainfall and evapotranspiration, central Florida, 2001-2003

USGS Publications Warehouse

Knowles, Leel; Phelps, G.G.; Kinnaman, Sandra L.; German, Edward R.

2005-01-01

Two internally drained karstic wetlands in central Florida-Boggy Marsh at the Hilochee Wildlife Management Area and a large unnamed wetland at the Lyonia Preserve-were studied during 2001-03 to gain a better understanding of the net-recharge function that these wetlands provide, the significance of exchanges with ground water with regard to wetland water budgets, and the variability in wetland hydrologic response to a range of climate conditions. These natural, relatively remote and unaltered wetlands were selected to provide a baseline of natural wetland hydrologic variability to which anthropogenic influences on wetland hydrology could be compared. Large departures from normal rainfall during the study were fortuitous, and allowed monitoring of hydrologic processes over a wide range of climate conditions. Wetland responses varied greatly as a result of climate conditions that ranged from moderate drought to extremely moist. Anthropogenic activities influenced water levels at both study sites; however, because these activities were brief relative to the duration of the study, sufficient data were collected during unimpacted periods to allow for the following conclusions to be made. Water budgets developed for Boggy Marsh and the Lyonia large wetland showed strong similarity between the flux terms of rainfall, evaporation, net change in storage, and the net ground-water exchange residual. Runoff was assumed to be negligible. Of the total annual flux at Boggy Marsh, rainfall accounted for 45 percent; evaporation accounted for 25 percent; net change in storage accounted for 25 percent; and the net residual accounted for 5 percent. At the Lyonia large wetland, rainfall accounted for 44 percent; evaporation accounted for 29 percent; net change in storage accounted for 21 percent; and the net residual accounted for 6 percent of the total annual flux. Wetland storage and ground-water exchange were important when compared to the total water budget at both wetlands. Even though rainfall was far above average during the study, wetland evaporation volumetrically exceeded rainfall. Ground-water inflow was effective in partially offsetting the negative residual between rainfall and evaporation, thus adding to wetland storage. Ground-water inflow was most common at both wetlands when rainfall continued for days or weeks, or during a week with more than about 2.5 inches of rainfall. Large decreases in wetland storage were associated with large negative fluxes of evaporation and ground-water exchange. The response of wetland water levels to rainfall showed a strong and similar relation at both study sites; however, the greater variability in the relation of wetland water-level change to rainfall at higher rainfall rates indicated that hydrologic processes other than rainfall became more important in the response of the wetland. Changes in wetland water levels seemed to be related more to vertical gradients than to lateral gradients. The largest wetland water-level rises were associated mostly with lower vertical gradients, when vertical head differences were below the 18-month average; however, at the Lyonia large wetland, extremely large lateral gradients toward the wetland during late June 2002 may have contributed to substantial gains in wetland water. During the remainder of the study, wetland water-level rises were associated mostly with decreasing vertical gradients and highly variable lateral gradients. Conversely, wetland water-level decreases were associated mostly with increasing vertical gradients and lateral gradients away from the wetland, particularly during the dry season. The potential for lateral ground-water exchange with the wetlands varied substantially more than that for vertical exchange. Potential for vertical losses of wetland water to ground water was highest during a dry period from December 2001 to June 2002, during the wet season of 2002, and for several months into the following dry season. Lateral he

Effect of monthly areal rainfall uncertainty on streamflow simulation

NASA Astrophysics Data System (ADS)

Ndiritu, J. G.; Mkhize, N.

2017-08-01

Areal rainfall is mostly obtained from point rainfall measurements that are sparsely located and several studies have shown that this results in large areal rainfall uncertainties at the daily time step. However, water resources assessment is often carried out a monthly time step and streamflow simulation is usually an essential component of this assessment. This study set out to quantify monthly areal rainfall uncertainties and assess their effect on streamflow simulation. This was achieved by; i) quantifying areal rainfall uncertainties and using these to generate stochastic monthly areal rainfalls, and ii) finding out how the quality of monthly streamflow simulation and streamflow variability change if stochastic areal rainfalls are used instead of historic areal rainfalls. Tests on monthly rainfall uncertainty were carried out using data from two South African catchments while streamflow simulation was confined to one of them. A non-parametric model that had been applied at a daily time step was used for stochastic areal rainfall generation and the Pitman catchment model calibrated using the SCE-UA optimizer was used for streamflow simulation. 100 randomly-initialised calibration-validation runs using 100 stochastic areal rainfalls were compared with 100 runs obtained using the single historic areal rainfall series. By using 4 rain gauges alternately to obtain areal rainfall, the resulting differences in areal rainfall averaged to 20% of the mean monthly areal rainfall and rainfall uncertainty was therefore highly significant. Pitman model simulations obtained coefficient of efficiencies averaging 0.66 and 0.64 in calibration and validation using historic rainfalls while the respective values using stochastic areal rainfalls were 0.59 and 0.57. Average bias was less than 5% in all cases. The streamflow ranges using historic rainfalls averaged to 29% of the mean naturalised flow in calibration and validation and the respective average ranges using stochastic monthly rainfalls were 86 and 90% of the mean naturalised streamflow. In calibration, 33% of the naturalised flow located within the streamflow ranges with historic rainfall simulations and using stochastic rainfalls increased this to 66%. In validation the respective percentages of naturalised flows located within the simulated streamflow ranges were 32 and 72% respectively. The analysis reveals that monthly areal rainfall uncertainty is significant and incorporating it into streamflow simulation would add validity to the results.

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).

Advancing a Model-Validated Statistical Method for Decomposing the Key Oceanic Drivers of Observed Regional Climate Variability and Evaluating Model Performance: Focus on North African Rainfall in CESM

NASA Astrophysics Data System (ADS)

Wang, F.; Notaro, M.; Yu, Y.; Mao, J.; Shi, X.; Wei, Y.

2016-12-01

North (N.) African rainfall is characterized by dramatic interannual to decadal variability with serious socio-economic ramifications. The Sahel and West African Monsoon (WAM) region experienced a dramatic shift to persistent drought by the late 1960s, while the Horn of Africa (HOA) underwent drying since the 1990s. Large disagreementregarding the dominant oceanic drivers of N. African hydrologic variability exists among modeling studies, leading to notable spread in Sahel summer rainfall projections for this century among Coupled Model Intercomparison Project models. In order to gain a deeper understanding of the oceanic drivers of N. African rainfall and establish a benchmark for model evaluation, a statistical method, the multivariate Generalized Equilibrium Feedback Assessment, is validated and applied to observations and a control run from the Community Earth System Model (CESM). This study represents the first time that the dominant oceanic drivers of N. African rainfall were evaluated and systematically compared between observations and model simulations. CESM and the observations consistently agree that tropical oceanic modes are the dominant controls of N. African rainfall. During the monsoon season, CESM and observations agree that an anomalously warm eastern tropical Pacific shifts the Walker Circulation eastward, with its descending branch supporting Sahel drying. CESM and the observations concur that a warmer tropical eastern Atlantic favors a southward-shifted Intertropical Convergence Zone, which intensifies WAM monsoonal rainfall. An observed reduction in Sahel rainfall accompanies this enhanced WAM rainfall, yet is confined to the Atlantic in CESM. During the short rains, both observations and CESM indicate that a positive phase of tropical Indian Ocean dipole (IOD) mode [anomalously warm (cold) in western (eastern) Indian] enhances HOA rainfall. The observed IOD impacts are limited to the short rains, while the simulated impacts are year-round.

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.

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.

Characterization of the Sahelian-Sudan rainfall based on observations and regional climate models

NASA Astrophysics Data System (ADS)

Salih, Abubakr A. M.; Elagib, Nadir Ahmed; Tjernström, Michael; Zhang, Qiong

2018-04-01

The African Sahel region is known to be highly vulnerable to climate variability and change. We analyze rainfall in the Sahelian Sudan in terms of distribution of rain-days and amounts, and examine whether regional climate models can capture these rainfall features. Three regional models namely, Regional Model (REMO), Rossby Center Atmospheric Model (RCA) and Regional Climate Model (RegCM4), are evaluated against gridded observations (Climate Research Unit, Tropical Rainfall Measuring Mission, and ERA-interim reanalysis) and rain-gauge data from six arid and semi-arid weather stations across Sahelian Sudan over the period 1989 to 2008. Most of the observed rain-days are characterized by weak (0.1-1.0 mm/day) to moderate (> 1.0-10.0 mm/day) rainfall, with average frequencies of 18.5% and 48.0% of the total annual rain-days, respectively. Although very strong rainfall events (> 30.0 mm/day) occur rarely, they account for a large fraction of the total annual rainfall (28-42% across the stations). The performance of the models varies both spatially and temporally. RegCM4 most closely reproduces the observed annual rainfall cycle, especially for the more arid locations, but all of the three models fail to capture the strong rainfall events and hence underestimate its contribution to the total annual number of rain-days and rainfall amount. However, excessive moderate rainfall compensates this underestimation in the models in an annual average sense. The present study uncovers some of the models' limitations in skillfully reproducing the observed climate over dry regions, will aid model users in recognizing the uncertainties in the model output and will help climate and hydrological modeling communities in improving models.

Trends analysis of rainfall and rainfall extremes in Sarawak, Malaysia using modified Mann-Kendall test

NASA Astrophysics Data System (ADS)

Sa'adi, Zulfaqar; Shahid, Shamsuddin; Ismail, Tarmizi; Chung, Eun-Sung; Wang, Xiao-Jun

2017-11-01

This study assesses the spatial pattern of changes in rainfall extremes of Sarawak in recent years (1980-2014). The Mann-Kendall (MK) test along with modified Mann-Kendall (m-MK) test, which can discriminate multi-scale variability of unidirectional trend, was used to analyze the changes at 31 stations. Taking account of the scaling effect through eliminating the effect of autocorrelation, m-MK was employed to discriminate multi-scale variability of the unidirectional trends of the annual rainfall in Sarawak. It can confirm the significance of the MK test. The annual rainfall trend from MK test showed significant changes at 95% confidence level at five stations. The seasonal trends from MK test indicate an increasing rate of rainfall during the Northeast monsoon and a decreasing trend during the Southwest monsoon in some region of Sarawak. However, the m-MK test detected an increasing trend in annual rainfall only at one station and no significant trend in seasonal rainfall at any stations. The significant increasing trends of the 1-h maximum rainfall from the MK test are detected mainly at the stations located in the urban area giving concern to the occurrence of the flash flood. On the other hand, the m-MK test detected no significant trend in 1- and 3-h maximum rainfalls at any location. On the contrary, it detected significant trends in 6- and 72-h maximum rainfalls at a station located in the Lower Rajang basin area which is an extensive low-lying agricultural area and prone to stagnant flood. These results indicate that the trends in rainfall and rainfall extremes reported in Malaysia and surrounding region should be verified with m-MK test as most of the trends may result from scaling effect.

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.

Climate change and the impact of increased rainfall variability on sediment transport and catchment scale water quality

NASA Astrophysics Data System (ADS)

Hancock, G. R.; Willgoose, G. R.; Cohen, S.

2009-12-01

Recently there has been recognition that changing climate will affect rainfall and storm patterns with research directed to examine how the global hydrological cycle will respond to climate change. This study investigates the effect of different rainfall patterns on erosion and resultant water quality for a well studied tropical monsoonal catchment that is undisturbed by Europeans in the Northern Territory, Australia. Water quality has a large affect on a range of aquatic flora and fauna and a significant change in sediment could have impacts on the aquatic ecosystems. There have been several studies of the effect of climate change on rainfall patterns in the study area with projections indicating a significant increase in storm activity. Therefore it is important that the impact of this variability be assessed in terms of catchment hydrology, sediment transport and water quality. Here a numerical model of erosion and hydrology (CAESAR) is used to assess several different rainfall scenarios over a 1000 year modelled period. The results show that that increased rainfall amount and intensity increases sediment transport rates but predicted water quality was variable and non-linear but within the range of measured field data for the catchment and region. Therefore an assessment of sediment transport and water quality is a significant and complex issue that requires further understandings of the role of biophysical feedbacks such as vegetation as well as the role of humans in managing landscapes (i.e. controlled and uncontrolled fire). The study provides a robust methodology for assessing the impact of enhanced climate variability on sediment transport and water quality.

Recent variations in geopotential height associated with West African monsoon variability

NASA Astrophysics Data System (ADS)

Okoro, Ugochukwu K.; Chen, Wen; Nath, Debashis

2018-02-01

In the present study, the atmospheric circulation patterns associated with the seasonal West Africa (WA) monsoon (WAM) rainfall variability has been investigated. The observational rainfall data from the Climatic Research Unit (CRU) and atmospheric fields from the National Center for Environmental Prediction (NCEP) reanalysis 2, from 1979 to 2014, have been used. The rainfall variability extremes, classified as wet or dry years, are the outcomes of simultaneous 6-month SPI at the three rainfall zones, which shows increasing trends [Guinea Coast (GC = 0.012 year-1), Eastern Sudano Sahel (ESS = 0.045 year-1) and Western Sudano Sahel (WSS = 0.056 year-1) from Sen's slope]; however, it is significant only in the Sahel region (α = 0.05 and α = 0.001 at ESS and WSS, respectively, from Mann-Kendall test). The vertical profile of the geopotential height (GpH) during the wet and dry years reveals that the 700 hPa anomalies show remarkable pattern at about 8°N to 13°N. This shows varying correlation with the zonal averaged vertically integrated moisture flux convergence and rainfall anomalies, respectively, as well as the oceanic pulsations indexes [Ocean Nino Index (ONI) and South Atlantic Ocean dipole index (SAODI), significant from t test], identified as precursors to the Sahel and GC rainfall variability respectively. The role of GpH anomalies at 700 hPa has been identified as the facilitator to the West African Westerly Jet's input to the moisture flux transported over the WA. This is a new perspective of the circulation processes associated with WAM and serves as a basis for modeling investigations.

Using Logistic Regression to Predict the Probability of Debris Flows in Areas Burned by Wildfires, Southern California, 2003-2006

USGS Publications Warehouse

Rupert, Michael G.; Cannon, Susan H.; Gartner, Joseph E.; Michael, John A.; Helsel, Dennis R.

2008-01-01

Logistic regression was used to develop statistical models that can be used to predict the probability of debris flows in areas recently burned by wildfires by using data from 14 wildfires that burned in southern California during 2003-2006. Twenty-eight independent variables describing the basin morphology, burn severity, rainfall, and soil properties of 306 drainage basins located within those burned areas were evaluated. The models were developed as follows: (1) Basins that did and did not produce debris flows soon after the 2003 to 2006 fires were delineated from data in the National Elevation Dataset using a geographic information system; (2) Data describing the basin morphology, burn severity, rainfall, and soil properties were compiled for each basin. These data were then input to a statistics software package for analysis using logistic regression; and (3) Relations between the occurrence or absence of debris flows and the basin morphology, burn severity, rainfall, and soil properties were evaluated, and five multivariate logistic regression models were constructed. All possible combinations of independent variables were evaluated to determine which combinations produced the most effective models, and the multivariate models that best predicted the occurrence of debris flows were identified. Percentage of high burn severity and 3-hour peak rainfall intensity were significant variables in all models. Soil organic matter content and soil clay content were significant variables in all models except Model 5. Soil slope was a significant variable in all models except Model 4. The most suitable model can be selected from these five models on the basis of the availability of independent variables in the particular area of interest and field checking of probability maps. The multivariate logistic regression models can be entered into a geographic information system, and maps showing the probability of debris flows can be constructed in recently burned areas of southern California. This study demonstrates that logistic regression is a valuable tool for developing models that predict the probability of debris flows occurring in recently burned landscapes.

Modelling the breeding of Aedes Albopictus species in an urban area in Pulau Pinang using polynomial regression

NASA Astrophysics Data System (ADS)

Salleh, Nur Hanim Mohd; Ali, Zalila; Noor, Norlida Mohd.; Baharum, Adam; Saad, Ahmad Ramli; Sulaiman, Husna Mahirah; Ahmad, Wan Muhamad Amir W.

2014-07-01

Polynomial regression is used to model a curvilinear relationship between a response variable and one or more predictor variables. It is a form of a least squares linear regression model that predicts a single response variable by decomposing the predictor variables into an nth order polynomial. In a curvilinear relationship, each curve has a number of extreme points equal to the highest order term in the polynomial. A quadratic model will have either a single maximum or minimum, whereas a cubic model has both a relative maximum and a minimum. This study used quadratic modeling techniques to analyze the effects of environmental factors: temperature, relative humidity, and rainfall distribution on the breeding of Aedes albopictus, a type of Aedes mosquito. Data were collected at an urban area in south-west Penang from September 2010 until January 2011. The results indicated that the breeding of Aedes albopictus in the urban area is influenced by all three environmental characteristics. The number of mosquito eggs is estimated to reach a maximum value at a medium temperature, a medium relative humidity and a high rainfall distribution.

Effect of initial conditions and of intra-event rainfall intensity variability on shallow landslide triggering return period

NASA Astrophysics Data System (ADS)

Peres, David Johnny; Cancelliere, Antonino

2016-04-01

Assessment of shallow landslide hazard is important for appropriate planning of mitigation measures. Generally, return period of slope instability is assumed as a quantitative metric to map landslide triggering hazard on a catchment. The most commonly applied approach to estimate such return period consists in coupling a physically-based landslide triggering model (hydrological and slope stability) with rainfall intensity-duration-frequency (IDF) curves. Among the drawbacks of such an approach, the following assumptions may be mentioned: (1) prefixed initial conditions, with no regard to their probability of occurrence, and (2) constant intensity-hyetographs. In our work we propose the use of a Monte Carlo simulation approach in order to investigate the effects of the two above mentioned assumptions. The approach is based on coupling a physically based hydrological and slope stability model with a stochastic rainfall time series generator. By this methodology a long series of synthetic rainfall data can be generated and given as input to a landslide triggering physically based model, in order to compute the return period of landslide triggering as the mean inter-arrival time of a factor of safety less than one. In particular, we couple the Neyman-Scott rectangular pulses model for hourly rainfall generation and the TRIGRS v.2 unsaturated model for the computation of transient response to individual rainfall events. Initial conditions are computed by a water table recession model that links initial conditions at a given event to the final response at the preceding event, thus taking into account variable inter-arrival time between storms. One-thousand years of synthetic hourly rainfall are generated to estimate return periods up to 100 years. Applications are first carried out to map landslide triggering hazard in the Loco catchment, located in highly landslide-prone area of the Peloritani Mountains, Sicily, Italy. Then a set of additional simulations are performed in order to compare the results obtained by the traditional IDF-based method with the Monte Carlo ones. Results indicate that both variability of initial conditions and of intra-event rainfall intensity significantly affect return period estimation. In particular, the common assumption of an initial water table depth at the base of the pervious strata may lead in practice to an overestimation of return period up to one order of magnitude, while the assumption of constant-intensity hyetographs may yield an overestimation by a factor of two or three. Hence, it may be concluded that the analysed simplifications involved in the traditional IDF-based approach generally imply a non-conservative assessment of landslide triggering hazard.

How much of the interannual variability of East Asian summer rainfall is forced by SST?

NASA Astrophysics Data System (ADS)

He, Chao; Wu, Bo; Li, Chunhui; Lin, Ailan; Gu, Dejun; Zheng, Bin; Zhou, Tianjun

2016-07-01

It is widely accepted that the interannual variability of East Asian summer rainfall is forced by sea surface temperature (SST), and SST anomalies are widely used as predictors of East Asian summer rainfall. But it is still not very clear what percentage of the interannual rainfall variability is contributed by SST anomalies. In this study, Atmospheric general circulation model simulations forced by observed interannual varying SST are compared with those forced by the fixed annual cycle of SST climatology, and their ratios of interannual variance (IAV) are analyzed. The output of 12 models from the 5th Phase of Coupled Model Intercomparison Project (CMIP5) are adopted, and idealized experiments are done by Community Atmosphere Model version 4 (CAM4). Both the multi-model median of CMIP5 models and CAM4 experiments show that only about 18 % of the IAV of rainfall over East Asian land (EAL) is explained by SST, which is significantly lower than the tropical western Pacific, but comparable to the mid-latitude western Pacific. There is no significant difference between the southern part and the northern part of EAL in the percentages of SST contribution. The remote SST anomalies regulates rainfall over EAL probably by modulating the horizontal water vapor transport rather than the vertical motion, since the horizontal water vapor transport into EAL is strongly modulated by SST but the vertical motion over EAL is not. Previous studies argued about the relative importance of tropical Indian Ocean and tropical Pacific Ocean to East Asian summer rainfall anomalies. Our idealized experiments performed by CAM4 suggest that the contributions from these two ocean basins are comparable to each other, both of which account for approximately 6 % of the total IAV of rainfall over EAL.

Effects of ocean-atmosphere coupling on rainfall over the Indian Ocean and northwestern Pacific Ocean during boreal summer

NASA Astrophysics Data System (ADS)

Zhou, Z. Q.; Xie, S. P.; Zhou, W.

2016-12-01

Atmosphere general circulation model (AGCM), forced with specified SST, has been widely used in climate studies. On one hand, AGCM is much faster to run compared to coupled general circulation model (CGCM). Also, the identical SST forcing allows a clean evaluation of the atmospheric component of CGCM. On the other hand, the coupling between atmosphere and ocean is missed in such atmosphere-only simulations. It is not clear how such simplification could affect the simulate of the atmosphere. In this study, the impact of ocean-atmosphere coupling is studied by comparing a CGCM simulation with an AGCM simulation which is forced with monthly SSTs specified from the CGCM simulation. Particularly, we focus on the climatology and interannual variability of rainfall over the IONWP during boreal summer. The IONWP is a unique region with a strong negative correlation between sea surface temperature (SST) and rainfall during boreal summer on the interannual time scale. The lead/lag correlation analysis suggests a negative feedback of rainfall on SST, which is only reasonably captured by CGCMs. We find that the lack of the negative feedback in AGCM not only enhances the climatology and interannual variability of rainfall but also increases the internal variability of rainfall over the IONWP. A simple mechanism is proposed to explain such enhancement. In addition, AGCM is able to capture the large-scale rainfall pattern over the IONWP during boreal summer, this is because that rainfall here is caused by remote ENSO effect on the interannual time scale. Our results herein suggest that people should be more careful when using an AGCM for climate change studies.

Impact of Climatic Variability on Hydropower Reservoirs in the Paraiba Basin, Southeast of Brazil

NASA Astrophysics Data System (ADS)

Barros, A.; simoes, s

2002-05-01

During 2000/2001, a severe drought greatly reduced the volume of water available to Brazilian hydropower plants and lead to a national water rationing plan. To undestand the potential for climatic change in hydrological regimes and its impact on hydropower we chose the Paraiba Basin located in Southeast Brazil. Three important regional multi-purpose reservoirs are operating in this basin. Moreover, the Paraiba River is of great economic and environmental importance and also constitutes a major corridor connecting the two cities of Sao Paulo and Rio de Janeiro. We analyzed monthly and daily records for rainfall, streamflow and temperature using regression and variance analysis. Rainfall records do not show any significant trend since the 1930s/1940s. By contrast, analysis of seasonal patterns show that in the last twenty years rainfall has increased during autumn and winter (dry season) and decreased during spring and summer (rainy season). Comparison between rainfall and streaflow, from small catchment without man-made influences, shows a more pronounced deficit in streamflow when compared with rainfall. The shifts in seasonal rainfall could indicate a tendency towards a more uniform rainfall pattern and could serve to reduce the streamflow. However, the largest upward trends in temperature were found in the driest months (JJA). The increase in rainfall would not be sufficient to overcome increased of evaporation expect to the same period. Instead, such increase in evaporation could create an over more pronounced streamflow deficit. Climatic variability could be reducing water availability in these reservoirs especially in the driest months. To reduce the uncertainties in hydrological predictions, planners need to incorporate climatic variability, at the catchment scale, in order to accomodate the new conditions resulting from these changes.

Downscaling of Global Climate Change Estimates to Regional Scales: An Application to Iberian Rainfall in Wintertime.

NASA Astrophysics Data System (ADS)

von Storch, Hans; Zorita, Eduardo; Cubasch, Ulrich

1993-06-01

A statistical strategy to deduct regional-scale features from climate general circulation model (GCM) simulations has been designed and tested. The main idea is to interrelate the characteristic patterns of observed simultaneous variations of regional climate parameters and of large-scale atmospheric flow using the canonical correlation technique.The large-scale North Atlantic sea level pressure (SLP) is related to the regional, variable, winter (DJF) mean Iberian Peninsula rainfall. The skill of the resulting statistical model is shown by reproducing, to a good approximation, the winter mean Iberian rainfall from 1900 to present from the observed North Atlantic mean SLP distributions. It is shown that this observed relationship between these two variables is not well reproduced in the output of a general circulation model (GCM).The implications for Iberian rainfall changes as the response to increasing atmospheric greenhouse-gas concentrations simulated by two GCM experiments are examined with the proposed statistical model. In an instantaneous `2 C02' doubling experiment, using the simulated change of the mean North Atlantic SLP field to predict Iberian rainfall yields, there is an insignificant increase of area-averaged rainfall of 1 mm/month, with maximum values of 4 mm/month in the northwest of the peninsula. In contrast, for the four GCM grid points representing the Iberian Peninsula, the change is 10 mm/month, with a minimum of 19 mm/month in the southwest. In the second experiment, with the IPCC scenario A ("business as usual") increase Of C02, the statistical-model results partially differ from the directly simulated rainfall changes: in the experimental range of 100 years, the area-averaged rainfall decreases by 7 mm/month (statistical model), and by 9 mm/month (GCM); at the same time the amplitude of the interdecadal variability is quite different.

Spatial variability of summer Florida precipitation and its impact on microwave radiometer rainfall-measurement systems

NASA Technical Reports Server (NTRS)

Turner, B. J.; Austin, G. L.

1993-01-01

Three-dimensional radar data for three summer Florida storms are used as input to a microwave radiative transfer model. The model simulates microwave brightness observations by a 19-GHz, nadir-pointing, satellite-borne microwave radiometer. The statistical distribution of rainfall rates for the storms studied, and therefore the optimal conversion between microwave brightness temperatures and rainfall rates, was found to be highly sensitive to the spatial resolution at which observations were made. The optimum relation between the two quantities was less sensitive to the details of the vertical profile of precipitation. Rainfall retrievals were made for a range of microwave sensor footprint sizes. From these simulations, spatial sampling-error estimates were made for microwave radiometers over a range of field-of-view sizes. The necessity of matching the spatial resolution of ground truth to radiometer footprint size is emphasized. A strategy for the combined use of raingages, ground-based radar, microwave, and visible-infrared (VIS-IR) satellite sensors is discussed.

Bivariate Rainfall and Runoff Analysis Using Shannon Entropy Theory

NASA Astrophysics Data System (ADS)

Rahimi, A.; Zhang, L.

2012-12-01

Rainfall-Runoff analysis is the key component for many hydrological and hydraulic designs in which the dependence of rainfall and runoff needs to be studied. It is known that the convenient bivariate distribution are often unable to model the rainfall-runoff variables due to that they either have constraints on the range of the dependence or fixed form for the marginal distributions. Thus, this paper presents an approach to derive the entropy-based joint rainfall-runoff distribution using Shannon entropy theory. The distribution derived can model the full range of dependence and allow different specified marginals. The modeling and estimation can be proceeded as: (i) univariate analysis of marginal distributions which includes two steps, (a) using the nonparametric statistics approach to detect modes and underlying probability density, and (b) fitting the appropriate parametric probability density functions; (ii) define the constraints based on the univariate analysis and the dependence structure; (iii) derive and validate the entropy-based joint distribution. As to validate the method, the rainfall-runoff data are collected from the small agricultural experimental watersheds located in semi-arid region near Riesel (Waco), Texas, maintained by the USDA. The results of unviariate analysis show that the rainfall variables follow the gamma distribution, whereas the runoff variables have mixed structure and follow the mixed-gamma distribution. With this information, the entropy-based joint distribution is derived using the first moments, the first moments of logarithm transformed rainfall and runoff, and the covariance between rainfall and runoff. The results of entropy-based joint distribution indicate: (1) the joint distribution derived successfully preserves the dependence between rainfall and runoff, and (2) the K-S goodness of fit statistical tests confirm the marginal distributions re-derived reveal the underlying univariate probability densities which further assure that the entropy-based joint rainfall-runoff distribution are satisfactorily derived. Overall, the study shows the Shannon entropy theory can be satisfactorily applied to model the dependence between rainfall and runoff. The study also shows that the entropy-based joint distribution is an appropriate approach to capture the dependence structure that cannot be captured by the convenient bivariate joint distributions. Joint Rainfall-Runoff Entropy Based PDF, and Corresponding Marginal PDF and Histogram for W12 Watershed The K-S Test Result and RMSE on Univariate Distributions Derived from the Maximum Entropy Based Joint Probability Distribution;

The interaction rainfall vs. weight as determinant of total mercury concentration in fish from a tropical estuary.

PubMed

Barletta, M; Lucena, L R R; Costa, M F; Barbosa-Cintra, S C T; Cysneiros, F J A

2012-08-01

Mercury loads in tropical estuaries are largely controlled by the rainfall regime that may cause biodilution due to increased amounts of organic matter (both live and non-living) in the system. Top predators, as Trichiurus lepturus, reflect the changing mercury bioavailability situations in their muscle tissues. In this work two variables [fish weight (g) and monthly total rainfall (mm)] are presented as being important predictors of total mercury concentration (T-Hg) in fish muscle. These important explanatory variables were identified by a Weibull Regression model, which best fit the dataset. A predictive model using readily available variables as rainfall is important, and can be applied for human and ecological health assessments and decisions. The main contribution will be to further protect vulnerable groups as pregnant women and children. Nature conservation directives could also improve by considering monitoring sample designs that include this hypothesis, helping to establish complete and detailed mercury contamination scenarios. Copyright © 2012 Elsevier Ltd. All rights reserved.

Climate and Leishmaniasis in French Guiana

PubMed Central

Roger, Amaury; Nacher, Mathieu; Hanf, Matthieu; Drogoul, Anne Sophie; Adenis, Antoine; Basurko, Celia; Dufour, Julie; Sainte Marie, Dominique; Blanchet, Denis; Simon, Stephane; Carme, Bernard; Couppié, Pierre

2013-01-01

To study the link between climatic variables and the incidence of leishmaniasis a study was conducted in Cayenne, French Guiana. Patients infected between January 1994 and December 2010. Meteorological data were studied in relation to the incidence of leishmaniasis using an ARIMA model. In the final model, the infections were negatively correlated with rainfall (with a 2-month lag) and with the number of days with rainfall > 50 mm (lags of 4 and 7 months). The variables that were positively correlated were temperature and the Multivariate El Niño Southern Oscillation Index with lags of 8 and 4 months, respectively. Significantly greater correlations were observed in March for rainfall and in November for the Multivariate El Niño/Southern Oscillation Index. Climate thus seems to be a non-negligible explanatory variable for the fluctuations of leishmaniasis. A decrease in rainfall is linked to increased cases 2 months later. This easily perceptible point could lead to an interesting prevention message. PMID:23939706

Suspended sediment flux modeling with artificial neural network: An example of the Longchuanjiang River in the Upper Yangtze Catchment, China

NASA Astrophysics Data System (ADS)

Zhu, Yun-Mei; Lu, X. X.; Zhou, Yue

2007-02-01

Artificial neural network (ANN) was used to model the monthly suspended sediment flux in the Longchuanjiang River, the Upper Yangtze Catchment, China. The suspended sediment flux was related to the average rainfall, temperature, rainfall intensity and water discharge. It is demonstrated that ANN is capable of modeling the monthly suspended sediment flux with fairly good accuracy when proper variables and their lag effect on the suspended sediment flux are used as inputs. Compared with multiple linear regression and power relation models, ANN can generate a better fit under the same data requirement. In addition, ANN can provide more reasonable predictions for extremely high or low values, because of the distributed information processing system and the nonlinear transformation involved. Compared with the ANNs that use the values of the dependent variable at previous time steps as inputs, the ANNs established in this research with only climate variables have an advantage because it can be used to assess hydrological responses to climate change.

Hydropower plans in eastern and southern Africa increase risk of concurrent climate-related electricity supply disruption

NASA Astrophysics Data System (ADS)

Conway, Declan; Dalin, Carole; Landman, Willem A.; Osborn, Timothy J.

2017-12-01

Hydropower comprises a significant and rapidly expanding proportion of electricity production in eastern and southern Africa. In both regions, hydropower is exposed to high levels of climate variability and regional climate linkages are strong, yet an understanding of spatial interdependences is lacking. Here we consider river basin configuration and define regions of coherent rainfall variability using cluster analysis to illustrate exposure to the risk of hydropower supply disruption of current (2015) and planned (2030) hydropower sites. Assuming completion of the dams planned, hydropower will become increasingly concentrated in the Nile (from 62% to 82% of total regional capacity) and Zambezi (from 73% to 85%) basins. By 2030, 70% and 59% of total hydropower capacity will be located in one cluster of rainfall variability in eastern and southern Africa, respectively, increasing the risk of concurrent climate-related electricity supply disruption in each region. Linking of nascent regional electricity sharing mechanisms could mitigate intraregional risk, although these mechanisms face considerable political and infrastructural challenges.

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.

Use of Machine Learning Techniques for Identification of Robust Teleconnections to East African Rainfall Variability

NASA Technical Reports Server (NTRS)

Roberts, J. Brent; Robertson, F. R.; Funk, C.

2014-01-01

Hidden Markov models can be used to investigate structure of subseasonal variability. East African short rain variability has connections to large-scale tropical variability. MJO - Intraseasonal variations connected with appearance of "wet" and "dry" states. ENSO/IOZM SST and circulation anomalies are apparent during years of anomalous residence time in the subseasonal "wet" state. Similar results found in previous studies, but we can interpret this with respect to variations of subseasonal wet and dry modes. Reveal underlying connections between MJO/IOZM/ENSO with respect to East African rainfall.

Review of Malaria Epidemics in Ethiopia using Enhanced Climate Services (ENACTS)

NASA Astrophysics Data System (ADS)

Muhammad, A.

2015-12-01

Malaria is a disease directly linked to temperature and rainfall. In Ethiopia, the influence of climate variables on malaria transmission and the subsequent role of ENSO in the rise of malaria incidence are becoming more recognized. Numerous publications attest to the extreme sensitivity of malaria to climate in Ethiopia. The majority of large-scale epidemics in the past were associated with climatic factors such as temperature and rainfall. However, there is limited information on climate variability and ENSO at the district level to aid in public health decision-making. Since 2008, the National Meteorogy Agency (NMA) and the International Research Institute for Climate and Society (IRI) have been collaborating on improving climate services in Ethiopia. This collaboration spurred the implementation of the Enhancing National Climate Services (ENACTS) initiative and the creation of the IRI Data Library (DL) NMA Ethiopia Maproom. ENACTS provides reliable and readily accessible climate data at high resolutions and the Maproom uses ENACTS to build a collection of maps and other figures that monitor climate and societal conditions at present and in the recent past (1981-2010). A recent analysis exploring the relationship of rainfall and temperature ENACTS products to malaria epidemics in proceeding rainy seasons within 12 woredas found above normal temperature anomalies to be more readily associated with epidemics when compared to above normal rainfall anomalies, regardless of the ENSO phase (Figure 1-2).

Detecting influences on California drought intervals using isotopes in tree-ring cellulose

NASA Astrophysics Data System (ADS)

Kanner, L. C.; Buenning, N. H.; Stott, L. D.; Stahle, D. W.

2012-12-01

Multi-decadal drought events have characterized climate variability in California over the last century. However, the causes of interannual precipitation variability and the origins of multi-decadal drought in California remain unclear. We utilize the oxygen isotopic composition (δ18O) of tree-ring cellulose in combination with previously developed ring-width measurements to trace the delivery of moisture to the region and investigate ocean-atmosphere patterns that might generate prolonged drought. Of the 36 Quercus douglasii (blue oak) sites in the California central valley, we have focused our work at two locations - one north of Los Angeles (34.74°N, 120°W, 1036 masl) and the other east of San Francisco (37.88°N 121.97°W, 182 masl). Using cores from at least five different trees at each location, tree-ring cellulose δ18O was measured for each year of growth from 1954 to 2004. The δ18O values of tree-ring cellulose range from 29‰ to 34‰ (VSMOW) at both sites and exhibit shared interannual variance (r = 0.43, p < 0.01). To trace changes in moisture delivery, we apply a biophysical model of cellulose δ18O and derive a proxy for rainfall δ18O. A reasonable approximation of rainfall δ18O is soil water δ18O, which, based on the biophysical model, can be estimated using cellulose δ18O, relative humidity, and temperature. High-resolution climate data from PRISM are combined with our cellulose measurements to compute soil water δ18O (and thus rainfall δ18O). Calculated rainfall δ18O is well correlated between the two locations (r = 0.55, p < 0.001) and the variance in δ18O at each site is 6‰. In terms of regional climate changes, our rainfall δ18O proxy exhibits a positive correlation with local precipitation amount, inferred from tree-ring width (r = 0.66, p < 0.001). This positive correlation suggests rainfall amount cannot be the main influence on the isotopic composition because changes in δ18O solely due to amount typically occur in the negative direction (the so-called amount effect usually observed in the tropics). Instead, we hypothesize that shifts in the moisture source region are of primary importance because moisture from high latitude sources has a lower isotopic composition compared to subtropical regions. Using NCAR reanalysis data, wind field anomalies suggest that moisture is derived from the north during dry years (low δ18O) and from the subtropics during wet years (high δ18O). Additional processes such as condensation height and post-condensation effects may also be important in controlling isotopic variability.

Effects of rainfall partitioning in the seasonal and spatial variability of soil water content in a Mediterranean downy oak forest

NASA Astrophysics Data System (ADS)

Garcia-Estringana, P.; Latron, J.; Molina, A. J.; Llorens, P.

2012-04-01

Rainfall partitioning fluxes (throughfall and stemflow) have a large degree of temporal and spatial variability and may consequently lead to significant changes in the volume and composition of water that reach the understory and the soil. The objective of this work is to study the effect of rainfall partitioning on the seasonal and spatial variability of the soil water content in a Mediterranean downy oak forest (Quercus pubescens), located in the Vallcebre research catchments (42° 12'N, 1° 49'E). The monitoring design, started on July 2011, consists of a set of 20 automatic rain recorders and 40 automatic soil moisture probes located below the canopy. One hundred 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, in leaf and leafless periods, as well as biometric characteristics of the plot, are also regularly measured. This work presents the first results describing throughfall and soil moisture spatial variability during both the leaf and leafless periods. The main drivers of throughfall variability, as canopy structure and meteorological conditions are also analysed.

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.

Interannual rainfall variability in the Amazon basin and sea-surface temperatures in the equatorial Pacific and the tropical Atlantic Oceans

NASA Astrophysics Data System (ADS)

Ronchail, Josyane; Cochonneau, Gérard; Molinier, Michel; Guyot, Jean-Loup; Chaves, Adriana Goretti De Miranda; Guimarães, Valdemar; de Oliveira, Eurides

2002-11-01

Rainfall variability in the Amazon basin is studied in relation to sea-surface temperatures (SSTs) in the equatorial Pacific and the northern and southern tropical Atlantic during the 1977-99 period, using the HiBAm original rainfall data set and complementary cluster and composite analyses.The northeastern part of the basin, north of 5 °S and east of 60 °W, is significantly related with tropical SSTs: a rainier wet season is observed when the equatorial Pacific and the northern (southern) tropical Atlantic are anomalously cold (warm). A shorter and drier wet season is observed during El Niño events and negative rainfall anomalies are also significantly associated with a warm northern Atlantic in the austral autumn and a cold southern Atlantic in the spring. The northeastern Amazon rainfall anomalies are closely related with El Niño-southern oscillation during the whole year, whereas the relationships with the tropical Atlantic SST anomalies are mainly observed during the autumn. A time-space continuity is observed between El Niño-related rainfall anomalies in the northeastern Amazon, those in the northern Amazon and south-eastern Amazon, and those in northern South America and in the Nordeste of Brazil.A reinforcement of certain rainfall anomalies is observed when specific oceanic events combine. For instance, when El Niño and cold SSTs in the southern Atlantic are associated, very strong negative anomalies are observed in the whole northern Amazon basin. Nonetheless, the comparison of the cluster and the composite analyses results shows that the rainfall anomalies in the northeastern Amazon are not always associated with tropical SST anomalies.In the southern and western Amazon, significant tropical SST-related rainfall anomalies are very few and spatially variable. The precipitation origins differ from those of the northeastern Amazon: land temperature variability, extratropical perturbations and moisture advection are important rainfall factors, as well as SSTs. This could partially explain why: (a) the above-mentioned signals weaken or disappear, with the exception of the relative dryness that is observed at the peak of an El Niño event and during the dry season when northern Atlantic SSTs are warmer than usual; (b) rainfall anomalies tend to resemble those of southeastern South America, noticeably at the beginning and the end of El Niño and La Niña events; (c) some strong excesses of rain are not associated with any SST anomalies and merit further investigation.

Relative roles of aerosols, SST, and snow impurity on snowmelt over the Tibetan Plateau and its their impacts on South Asian summer monsoon

NASA Astrophysics Data System (ADS)

Kim, K. M.; Tsay, S. C.; Lau, W. K. M.; Yasunari, T. J.; Mahanama, S. P. P.; Koster, R. D.; daSilva, A.

2017-12-01

We examine the relative roles of atmospheric aerosol radiative forcing, year-to-year SST (sea surface temperature) variability, and surface radiative forcing by snow impurity on snowmelt over the Tibetan Plateau and their impacts on rainfall and circulation of South Asian summer monsoon. Five-member ensemble experiments are conducted with NASA's GEOS-5 (Goddard Earth Observing System model version 5), equipped with a snow darkening module - GOSWIM (GOddard SnoW Impurity Module), on the Water-Year 2008 (October 2007 to September 2008). Asian summer monsoon in 2008 was near normal in terms of monsoon rainfall over India subcontinent. However, rainfall was excessive in the North while the southern India suffered from the rainfall deficit. The 2008 summer monsoon was accompanied with high loading of aerosols in the Arabian Sea and La Niña condition in the tropical Pacific. To examine the roles high aerosol loading and La Niña condition on the north-south dipole in Indian monsoon rainfall, two sets of experiments, in addition to control runs (CNTRL), are conducted without SST anomalies (CSST) and aerosol radiative feedback (NRF), respectively. Results show that increased aerosol loading in early summer is associated with the increased dust transport during La Niña years. Increased aerosols over the northern India induces EHP-like (elevated heat pump) circulation and increases rainfall over the India subcontinent. Aerosol radiative forcing feedback (CNTRL-NRF) strengthens the EHP-like monsoon circulation even more. Results indicate that anomalous circulation associated with La Niña condition increases aerosol loading by enhancing dust transport as well as by increasing aerosol lifetime. Increased aerosols induces EHP-like feedback processes and increases rainfall over the India subcontinent.

Influences of large-scale convection and moisture source on monthly precipitation isotope ratios observed in Thailand, Southeast Asia

NASA Astrophysics Data System (ADS)

Wei, Zhongwang; Lee, Xuhui; Liu, Zhongfang; Seeboonruang, Uma; Koike, Masahiro; Yoshimura, Kei

2018-04-01

Many paleoclimatic records in Southeast Asia rely on rainfall isotope ratios as proxies for past hydroclimatic variability. However, the physical processes controlling modern rainfall isotopic behaviors in the region is poorly constrained. Here, we combined isotopic measurements at six sites across Thailand with an isotope-incorporated atmospheric circulation model (IsoGSM) and the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to investigate the factors that govern the variability of precipitation isotope ratios in this region. Results show that rainfall isotope ratios are both correlated with local rainfall amount and regional outgoing longwave radiation, suggesting that rainfall isotope ratios in this region are controlled not only by local rain amount (amount effect) but also by large-scale convection. As a transition zone between the Indian monsoon and the western North Pacific monsoon, the spatial difference of observed precipitation isotope among different sites are associated with moisture source. These results highlight the importance of regional processes in determining rainfall isotope ratios in the tropics and provide constraints on the interpretation of paleo-precipitation isotope records in the context of regional climate dynamics.

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.

Statistical and dynamical assessment of land-ocean-atmosphere interactions across North Africa

NASA Astrophysics Data System (ADS)

Yu, Yan

North Africa is highly vulnerable to hydrologic variability and extremes, including impacts of climate change. The current understanding of oceanic versus terrestrial drivers of North African droughts and pluvials is largely model-based, with vast disagreement among models in terms of the simulated oceanic impacts and vegetation feedbacks. Regarding oceanic impacts, the relative importance of the tropical Pacific, tropical Indian, and tropical Atlantic Oceans in regulating the North African rainfall variability, as well as the underlying mechanism, remains debated among different modeling studies. Classic theory of land-atmosphere interactions across the Sahel ecotone, largely based on climate modeling experiments, has promoted positive vegetation-rainfall feedbacks associated with a dominant surface albedo mechanism. However, neither the proposed positive vegetation-rainfall feedback with its underlying albedo mechanism, nor its relative importance compared with oceanic drivers, has been convincingly demonstrated up to now using observational data. Here, the multivariate Generalized Equilibrium Feedback Assessment (GEFA) is applied in order to identify the observed oceanic and terrestrial drivers of North African climate and quantify their impacts. The reliability of the statistical GEFA method is first evaluated against dynamical experiments within the Community Earth System Model (CESM). In order to reduce the sampling error caused by short data records, the traditional GEFA approach is refined through stepwise GEFA, in which unimportant forcings are dropped through stepwise selection. In order to evaluate GEFA's reliability in capturing oceanic impacts, the atmospheric response to a sea-surface temperature (SST) forcing across the tropical Pacific, tropical Indian, and tropical Atlantic Ocean is estimated independently through ensembles of dynamical experiments and compared with GEFA-based assessments. Furthermore, GEFA's performance in capturing terrestrial impacts is evaluated through ensembles of fully coupled CESM dynamical experiments, with modified leaf area index (LAI) and soil moisture across the Sahel or West African Monsoon (WAM) region. The atmospheric responses to oceanic and terrestrial forcings are generally consistent between the dynamical experiments and statistical GEFA, confirming GEFA's capability of isolating the individual impacts of oceanic and terrestrial forcings on North African climate. Furthermore, with the incorporation of stepwise selection, GEFA can now provide reliable estimates of the oceanic and terrestrial impacts on the North African climate with the typical length of observational datasets, thereby enhancing the method's applicability. After the successful validation of GEFA, the key observed oceanic and terrestrial drivers of North African climate are identified through the application of GEFA to gridded observations, remote sensing products, and reanalyses. According to GEFA, oceanic drivers dominate over terrestrial drivers in terms of their observed impacts on North African climate in most seasons. Terrestrial impacts are comparable to, or more important than, oceanic impacts on rainfall during the post-monsoon across the Sahel and WAM region, and after the short rain across the Horn of Africa (HOA). The key ocean basins that regulate North African rainfall are typically located in the tropics. While the observed impacts of SST variability across the tropical Pacific and tropical Atlantic Oceans on the Sahel rainfall are largely consistent with previous model-based findings, minimal impacts from tropical Indian Ocean variability on Sahel rainfall are identified in observations, in contrast to previous modeling studies. The current observational analysis verifies model-hypothesized positive vegetation-rainfall feedback across the Sahel and HOA, which is confined to the post-monsoon and post-short rains season, respectively. However, the observed positive vegetation feedback to rainfall in the semi-arid Sahel and HOA is largely due to moisture recycling, rather than the classic albedo mechanism. Future projections of Sahel rainfall remain highly uncertain in terms of both sign and magnitude within phases three and five of the Coupled Model Intercomparison Project (CMIP3 and CMIP5). The GEFA-based observational analyses will provide a benchmark for evaluating climate models, which will facilitate effective process-based model weighting for more reliable projections of regional climate, as well as model development.

Hydroelectric production from Brazil's São Francisco River could cease due to climate change and inter-annual variability.

PubMed

de Jong, Pieter; Tanajura, Clemente Augusto Souza; Sánchez, Antonio Santos; Dargaville, Roger; Kiperstok, Asher; Torres, Ednildo Andrade

2018-09-01

By the end of this century higher temperatures and significantly reduced rainfall are projected for the Brazilian North and Northeast (NE) regions due to Global Warming. This study examines the impact of these long-term rainfall changes on the Brazilian Northeast's hydroelectric production. Various studies that use different IPCC models are examined in order to determine the average rainfall reduction by the year 2100 in comparison to baseline data from the end of the 20th century. It was found that average annual rainfall in the NE region could decrease by approximately 25-50% depending on the emissions scenario. Analysis of historical rainfall data in the São Francisco basin during the last 57years already shows a decline of more than 25% from the 1961-90 long-term average. Moreover, average annual rainfall in the basin has been below its long-term average every year bar one since 1992. If this declining trend continues, rainfall reduction in the basin could be even more severe than the most pessimistic model projections. That is, the marked drop in average rainfall projected for 2100, based on the IPCC high emissions scenario, could actually eventuate before 2050. Due to the elasticity factor between rainfall and streamflow and because of increased amounts of irrigation in the São Francisco basin, the reduction in the NE's average hydroelectric production in the coming decades could be double the predicted decline in rainfall. Conversely, it is estimated that wind power potential in the Brazilian NE will increase substantially by 2100. Therefore both wind and solar power will need to be significantly exploited in order for the NE region to sustainably replace lost hydroelectric production. Copyright © 2018 Elsevier B.V. All rights reserved.

Small scale rainfall simulators: Challenges for a future use in soil erosion research

NASA Astrophysics Data System (ADS)

Ries, Johannes B.; Iserloh, Thomas; Seeger, Manuel

2013-04-01

Rainfall simulation on micro-plot scale is a method used worldwide to assess the generation of overland flow, soil erosion, infiltration and interrelated processes such as soil sealing, crusting, splash and redistribution of solids and solutes. The produced data are of great significance not only for the analysis of the simulated processes, but also as a source of input-data for soil erosion modelling. The reliability of the data is therefore of paramount importance, and quality management of rainfall simulation procedure a general responsibility of the rainfall simulation community. This was an accepted outcome at the "International Rainfall Simulator Workshop 2011" at Trier University. The challenges of the present and near future use of small scale rainfall simulations concern the comparability of results and scales, the quality of the data for soil erosion modelling, and further technical developments to overcome physical limitations and constraints. Regarding the high number of research questions, different fields of application, and due to the great technical creativity of researchers, a large number of different types of rainfall simulators is available. But each of the devices produces a different rainfall, leading to different kinetic energy values influencing soil surface and erosion processes. Plot sizes are also variable, as well as the experimental simulation procedures. As a consequence, differing runoff and erosion results are produced. The presentation summarises the three important aspects of rainfall simulations, following a processual order: 1. Input-factor "rain" and its calibration 2. Surface-factor "plot" and its documentation 3. Output-factors "runoff" and "sediment concentration" Finally, general considerations about the limitations and challenges for further developments and applications of rainfall simulation data are presented.

The influence of El Niño-Southern Oscillation regimes on eastern African vegetation and its future implications under the RCP8.5 warming scenario

NASA Astrophysics Data System (ADS)

Fer, Istem; Tietjen, Britta; Jeltsch, Florian; Wolff, Christian

2017-09-01

The El Niño-Southern Oscillation (ENSO) is the main driver of the interannual variability in eastern African rainfall, with a significant impact on vegetation and agriculture and dire consequences for food and social security. In this study, we identify and quantify the ENSO contribution to the eastern African rainfall variability to forecast future eastern African vegetation response to rainfall variability related to a predicted intensified ENSO. To differentiate the vegetation variability due to ENSO, we removed the ENSO signal from the climate data using empirical orthogonal teleconnection (EOT) analysis. Then, we simulated the ecosystem carbon and water fluxes under the historical climate without components related to ENSO teleconnections. We found ENSO-driven patterns in vegetation response and confirmed that EOT analysis can successfully produce coupled tropical Pacific sea surface temperature-eastern African rainfall teleconnection from observed datasets. We further simulated eastern African vegetation response under future climate change as it is projected by climate models and under future climate change combined with a predicted increased ENSO intensity. Our EOT analysis highlights that climate simulations are still not good at capturing rainfall variability due to ENSO, and as we show here the future vegetation would be different from what is simulated under these climate model outputs lacking accurate ENSO contribution. We simulated considerable differences in eastern African vegetation growth under the influence of an intensified ENSO regime which will bring further environmental stress to a region with a reduced capacity to adapt effects of global climate change and food security.

Rainfall variability in southern Spain on decadal to centennial time scales

NASA Astrophysics Data System (ADS)

Rodrigo, F. S.; Esteban-Parra, M. J.; Pozo-Vázquez, D.; Castro-Díez, Y.

2000-06-01

In this work a long rainfall series in Andalusia (southern Spain) is analysed. Methods of historical climatology were used to reconstruct a 500-year series from historical sources. Different statistical tools were used to detect and characterize significant changes in this series. Results indicate rainfall fluctuations, without abrupt changes, in the following alternating dry and wet phases: 1501-1589 dry, 1590-1649 wet, 1650-1775 dry, 1776-1937 wet and 1938-1997 dry. Possible causal mechanisms are discussed, emphasizing the important contribution of the North Atlantic Oscillation (NAO) to rainfall variability in the region. Solar activity is discussed in relation to the Maunder Minimum period, and finally the past and present are compared. Results indicate that the magnitude of fluctuations is similar in the past and present.

Quantifying variable rainfall intensity events on runoff and sediment losses

USDA-ARS?s Scientific Manuscript database

Coastal Plain soils in Georgia are susceptible to runoff, sediment, and chemical losses from short duration-high intensity, runoff producing storms at critical times during the growing season. We quantified runoff and sediment losses from a Tifton loamy sand managed under conventional- (CT) and stri...

Rainfall and runoff variability in Ethiopia

NASA Astrophysics Data System (ADS)

Billi, Paolo; Fazzini, Massimiliano; Tadesse Alemu, Yonas; Ciampalini, Rossano

2014-05-01

Rainfall and river flow variability have been deeply investigated and and the impact of climate change on both is rather well known in Europe (EEA, 2012) or in other industrialized countries. Reports of international organizations (IPCC, 2012) and the scientific literature provide results and outlooks that were found contrasting and spatially incoherent (Manton et al., 2001; Peterson et al., 2002; Griffiths et al., 2003; Herath and Ratnayake, 2004) or weakened by limitation of data quality and quantity. According to IPCC (2012), in East Africa precipitation there are contrasting regional and seasonal variations and trends, though Easterling et al. (2000) and Seleshi and Camberlin (2006) report decreasing trends in heavy precipitation over parts of Ethiopia during the period 1965-2002. Literature on the impact of climate change on river flow is scarce in Africa and IPCC Technical Paper VI (IPCC, 2008) concluded that no evidence, based on instrumental records, has been found for a climate-driven globally widespread change in the magnitude/frequency of floods during the last decades (Rosenzweig et al., 2007), though increases in runoff and increased risk of flood events in East Africa are expected. Some papers have faced issues regarding rainfall and river flow variability in Ethiopia (e.g. Seleshi and Demaree, 1995; Osman and Sauerborn, 2002; Seleshi and Zanke, 2004; Meze-Hausken, 2004; Korecha and Barnston, 2006; Cheung et al., 2008) but their investigations are commonly geographically limited or used a small number of rain and flow gauges with the most recent data bound to the beginning of the last decade. In this study an attempt to depict rainfall and river flow variability, considering the longer as possible time series for the largest as possible number of meteo-stations and flow gauge evenly distributed across Ethiopia, is presented. 25 meteo-stations and 21 flow gauges with as much as possible continuous data records were selected. The length of the time series ranges between 35 to 50 and 9 to 49 years for rainfall and river flow, respectively. In order to improve the poor linear correlation model to describe rainfall gradient with altitude a simple topographic parameter is introduced capable to better depict the spatial variability of annual rainfall and its coefficient of variation. The small rains (Belg) were found to be much more unpredictable than the long, monsoon-type rains (Kiremt) and hence much more out of phase with the variation of annual precipitation amount that is significantly influenced by the Kiremt rains. In order to investigate the long term trends, rainfall anomalies were calculated as Z score for annual, Belg and Kiremt precipitation for all the stations and average values are calculated and plotted against time. The three Z trend lines obtained show no marked deviation from the mean as only an almost negligible decreasing trend is observed. Rainfall intensity in 24 hours is analyzed and the trend line of the maximum intensity averaged over the maximum value of each year recorded at each meteo-station is constructed. These data indicate a general decrease in daily rainfall intensity across Ethiopia with clear exceptions in a few selected areas. The same procedure, based on the Z scores, used to analyze rainfall variability is applied also to the river flow data and a similar result is obtained. If compared with rainfall, annual runoff shows a much wider range of variation among the study rivers. This issue is discussed and possible explanations are presented.

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.

Using damage data to estimate the risk from summer convective precipitation extremes

NASA Astrophysics Data System (ADS)

Schroeer, Katharina; Tye, Mari

2017-04-01

This study explores the potential added value from including loss and damage data to understand the risks from high-intensity short-duration convective precipitation events. Projected increases in these events are expected even in regions that are likely to become more arid. Such high intensity precipitation events can trigger hazardous flash floods, debris flows, and landslides that put people and local assets at risk. However, the assessment of local scale precipitation extremes is hampered by its high spatial and temporal variability. In addition to this, not only are extreme events rare, but such small-scale events are likely to be underreported where they do not coincide with the observation network. Reports of private loss and damage on a local administrative unit scale (LAU 2 level) are used to explore the relationship between observed rainfall events and damages reportedly related to hydro-meteorological processes. With 480 Austrian municipalities located within our south-eastern Alpine study region, the damage data are available on a much smaller scale than the available rainfall data. Precipitation is recorded daily at 185 gauges and 52% of these stations additionally deliver sub-hourly rainfall information. To obtain physically plausible information, damage and rainfall data are grouped and analyzed on a catchment scale. The data indicate that rainfall intensities are higher on days that coincide with a damage claim than on days for which no damage was reported. However, approximately one third of the damages related to hydro-meteorological hazards were claimed on days for which no rainfall was recorded at any gauge in the respective catchment. Our goal is to assess whether these events indicate potential extreme events missing in the observations. Damage always is a consequence of an asset being exposed and susceptible to a hazardous process, and naturally, many factors influence whether an extreme rainfall event causes damage. We set up a statistical model to test whether the relationship between extreme rainfall events and damages is robust enough to estimate a potential underrepresentation of high intensity rainfall events in ungauged areas. Risk-relevant factors of socio-economic vulnerability, land cover, streamflow data, and weather type information are included to improve and sharpen the analysis. Within this study, we first aim to identify which rainfall events are most damaging and which factors affect the damages - seen as a proxy for the vulnerability - related to summer convective rainfall extremes in different catchment types. Secondly, we aim to detect potentially unreported damaging rainfall events and estimate the likelihood of such cases. We anticipate this damage perspective on summertime extreme convective precipitation to be beneficial for risk assessment, uncertainty management, and decision making with respect to weather and climate extremes on the regional-to-local level.

Stemflow-induced processes of soil water storage

NASA Astrophysics Data System (ADS)

Germer, Sonja

2013-04-01

Compared to stemflow production studies only few studies deal with the fate of stemflow at the near-stem soil. To investigate stemflow contribution to the root zone soil moisture by young and adult babassu palms (Attalea speciosa Mart.), I studied stemflow generation, subsequent soil water percolation and root distributions. Rainfall, stemflow and perched water tables were monitored on an event basis. Perched water tables were monitored next to adult palms at two depths and three stem distances. Dye tracer experiments monitored stemflow-induced preferential flow paths. Root distributions of fine and coarse roots were related to soil water redistribution. Average rainfall-collecting area per adult palm was 6.4 m², but variability between them was high. Funneling ratios ranged between 16-71 and 4-55 for adult and young palms, respectively. Nonetheless, even very small rainfall events of 1 mm can generate stemflow. On average, 9 liters of adult palm stemflow were intercepted and stemflow tended to decrease for-high intensity rainfall events. Young babassu palms funneled rainfall via their fronds, directly to their subterranean stems. The funneling of rainfall towards adult palm stems, in contrast, led to great stemflow fluxes down to the soil and induced initial horizontal water flows through the soil, leading to perched water tables next to palms, even after small rainfall events. The perched water tables extended, however, only a few decimeters from palm stems. After perched water tables became established, vertical percolation through the soil dominated. To my knowledge, this process has not been described before, and it can be seen as an addition to the two previously described stemflow-induced processes of Horton overland flow and fast, deep percolation along roots. This study has demonstrated that Babassu palms funnel water to their stems and subsequently store it in the soil next to their stems in areas where coarse root length density is very high. This might partly explain the competitive position of babassu palms on pastures or secondary forests.

Passive Microwave Precipitation Retrieval Uncertainty Characterized based on Field Campaign Data over Complex Terrain

NASA Astrophysics Data System (ADS)

Derin, Y.; Anagnostou, E. N.; Anagnostou, M.; Kalogiros, J. A.; Casella, D.; Marra, A. C.; Panegrossi, G.; Sanò, P.

2017-12-01

Difficulties in representation of high rainfall variability over mountainous areas using ground based sensors make satellite remote sensing techniques attractive for hydrologic studies over these regions. Even though satellite-based rainfall measurements are quasi global and available at high spatial resolution, these products have uncertainties that necessitate use of error characterization and correction procedures based upon more accurate in situ rainfall measurements. Such measurements can be obtained from field campaigns facilitated by research quality sensors such as locally deployed weather radar and in situ weather stations. This study uses such high quality and resolution rainfall estimates derived from dual-polarization X-band radar (XPOL) observations from three field experiments in Mid-Atlantic US East Coast (NASA IPHEX experiment), the Olympic Peninsula of Washington State (NASA OLYMPEX experiment), and the Mediterranean to characterize the error characteristics of multiple passive microwave (PMW) sensor retrievals. The study first conducts an independent error analysis of the XPOL radar reference rainfall fields against in situ rain gauges and disdrometer observations available by the field experiments. Then the study evaluates different PMW precipitation products using the XPOL datasets (GR) over the three aforementioned complex terrain study areas. We extracted matchups of PMW/GR rainfall based on a matching methodology that identifies GR volume scans coincident with PMW field-of-view sampling volumes, and scaled GR parameters to the satellite products' nominal spatial resolution. The following PMW precipitation retrieval algorithms are evaluated: the NASA Goddard PROFiling algorithm (GPROF), standard and climatology-based products (V 3, 4 and 5) from four PMW sensors (SSMIS, MHS, GMI, and AMSR2), and the precipitation products based on the algorithms Cloud Dynamics and Radiation Database (CDRD) for SSMIS and Passive microwave Neural network Precipitation Retrieval (PNPR) for AMSU/MHS, developed at ISAC-CNR within the EUMETSAT H-SAF. We will present error analysis results for the different PMW rainfall retrievals and discuss dependences on precipitation type, elevation and precipitation microphysics (derived from XPOL).

Development of a transient, lumped hydrologic model for geomorphologic units in a geomorphology based rainfall-runoff modelling framework

NASA Astrophysics Data System (ADS)

Vannametee, E.; Karssenberg, D.; Hendriks, M. R.; de Jong, S. M.; Bierkens, M. F. P.

2010-05-01

We propose a modelling framework for distributed hydrological modelling of 103-105 km2 catchments by discretizing the catchment in geomorphologic units. Each of these units is modelled using a lumped model representative for the processes in the unit. Here, we focus on the development and parameterization of this lumped model as a component of our framework. The development of the lumped model requires rainfall-runoff data for an extensive set of geomorphological units. Because such large observational data sets do not exist, we create artificial data. With a high-resolution, physically-based, rainfall-runoff model, we create artificial rainfall events and resulting hydrographs for an extensive set of different geomorphological units. This data set is used to identify the lumped model of geomorphologic units. The advantage of this approach is that it results in a lumped model with a physical basis, with representative parameters that can be derived from point-scale measurable physical parameters. The approach starts with the development of the high-resolution rainfall-runoff model that generates an artificial discharge dataset from rainfall inputs as a surrogate of a real-world dataset. The model is run for approximately 105 scenarios that describe different characteristics of rainfall, properties of the geomorphologic units (i.e. slope gradient, unit length and regolith properties), antecedent moisture conditions and flow patterns. For each scenario-run, the results of the high-resolution model (i.e. runoff and state variables) at selected simulation time steps are stored in a database. The second step is to develop the lumped model of a geomorphological unit. This forward model consists of a set of simple equations that calculate Hortonian runoff and state variables of the geomorphologic unit over time. The lumped model contains only three parameters: a ponding factor, a linear reservoir parameter, and a lag time. The model is capable of giving an appropriate representation of the transient rainfall-runoff relations that exist in the artificial data set generated with the high-resolution model. The third step is to find the values of empirical parameters in the lumped forward model using the artificial dataset. For each scenario of the high-resolution model run, a set of lumped model parameters is determined with a fitting method using the corresponding time series of state variables and outputs retrieved from the database. Thus, the parameters in the lumped model can be estimated by using the artificial data set. The fourth step is to develop an approach to assign lumped model parameters based upon the properties of the geomorphological unit. This is done by finding relationships between the measurable physical properties of geomorphologic units (i.e. slope gradient, unit length, and regolith properties) and the lumped forward model parameters using multiple regression techniques. In this way, a set of lumped forward model parameters can be estimated as a function of morphology and physical properties of the geomorphologic units. The lumped forward model can then be applied to different geomorphologic units. Finally, the performance of the lumped forward model is evaluated; the outputs of the lumped forward model are compared with the results of the high-resolution model. Our results show that the lumped forward model gives the best estimates of total discharge volumes and peak discharges when rain intensities are not significantly larger than the infiltration capacities of the units and when the units are small with a flat gradient. Hydrograph shapes are fairly well reproduced for most cases except for flat and elongated units with large runoff volumes. The results of this study provide a first step towards developing low-dimensional models for large ungauged basins.

Hydrological Cycle in the Western Equatorial Warm Pool over the Past 220 k years

NASA Astrophysics Data System (ADS)

Tachikawa, K.; Cartapanis, O.; Vidal, L.; Beaufort, L.; Bard, E.

2008-12-01

The Western Pacific Warm Pool is a major source of heat and moisture to extra-tropical regions, and its condition could have great impact on global climate response to various forcing factors. We reconstructed the rainfall pattern over Papua New Guinea (PNG) for the past 220 kyr using terrigenous elemental contents (Ti, Fe, K and Si) and calcareous productivity (Ca) recorded in a marine sediment core MD05-2920 (2°51.48S, 144°32.04E) from 100 km off the Sepik River mouth in Northern PNG. The core chronostratigraphy is established by 14C dating and benthic foraminiferal oxygen isotopes. The Sepik and Ramu river system forms one of the highest sediment discharge zones in the world because of high rainfall rates, warm and humid climate, steep topography and erodible volcanic rocks in the draining basin. At present, the rainfall over this area is under the influence of both Asia-Australian monsoon and El Niño Southern Oscillation (ENSO). The results obtained by an XRF core scanner indicate that for the whole record major sediment components are of terrigenous river-born nature and biogenic CaCO3. Spectral analysis reveals that dominant peaks for Ti are precession and obliquity periods whereas Ca variability is rather dominated by obliquity. The wet periods appear during maximum local insolation, which is in phase with minimum East Asian summer monsoon strength recorded by Chinese speleothems. Modeled past ENSO activity cannot explain the reconstructed rainfall and productivity patterns. Taken together, the fresh water cycle over New Guinea is better explained by latitudinal shifts of the Intertropical Convergence Zone rather than ENSO-type variability on orbital time scales. The variability of calcareous productivity is likely related to general changes in nutricline depth of the tropical Pacific band.

The Potential Financial Costs of Climate Change on Health of Urban and Rural Citizens: A Case Study of Vibrio cholerae Infections at Bukavu Town, South Kivu Province, Eastern of Democratic Republic of Congo.

PubMed

Munyuli, Mb Théodore; Kavuvu, J-M Mbaka; Mulinganya, Guy; Bwinja, G Mulinganya

2013-01-01

Cholera epidemics have a recorded history in eastern Congo dating to 1971. A study was conducted to find out the linkage between climate variability/change and cholera outbreak and to assess the related economic cost in the management of cholera in Congo. This study integrates historical data (20 years) on temperature and rainfall with the burden of disease from cholera in South-Kivu province, eastern Congo. Analyses of precipitation and temperatures characteristics in South-Kivu provinces showed that cholera epidemics are closely associated with climatic factors variability. Peaks in Cholera new cases were in synchrony with peaks in rainfalls. Cholera infection cases declined significantly (P<0.05) with the rise in the average temperature. The monthly number of new Cholera cases oscillated between 5 and 450. For every rise of the average temperature by 0.35 °C to 0.75 °C degree Celsius, and for every change in the rainfall variability by 10-19%, it is likely cholera infection risks will increase by 17 to 25%. The medical cost of treatment of Cholera case infection was found to be of US$50 to 250 per capita. The total costs of Cholera attributable to climate change were found to fall in the range of 4 to 8% of the per capita in annual income in Bukavu town. It is likely that high rainfall favor multiplication of the bacteria and contamination of water sources by the bacteria (Vibrio cholerae). The consumption of polluted water, promiscuity, population density and lack of hygiene are determinants favoring spread and infection of the bacteria among human beings living in over-crowded environments.

China's water sustainability in the 21st century: a climate-informed water risk assessment covering multi-sector water demands

NASA Astrophysics Data System (ADS)

Chen, X.; Naresh, D.; Upmanu, L.; Hao, Z.; Dong, L.; Ju, Q.; Wang, J.; Wang, S.

2014-05-01

China is facing a water resources crisis with growing concerns as to the reliable supply of water for agricultural, industrial and domestic needs. High inter-annual rainfall variability and increasing consumptive use across the country exacerbates the situation further and is a constraint on future development. For water sustainability, it is necessary to examine the differences in water demand and supply and their spatio-temporal distribution in order to quantify the dimensions of the water risk. Here, a detailed quantitative assessment of water risk as measured by the spatial distribution of cumulated deficits for China is presented. Considering daily precipitation and temperature variability over fifty years and the current water demands, risk measures are developed to inform county level water deficits that account for both within-year and across-year variations in climate. We choose political rather than watershed boundaries since economic activity and water use are organized by county and the political process is best informed through that unit. As expected, the risk measures highlight North China Plain counties as highly water stressed. Regions with high water stress have high inter-annual variability in rainfall and now have depleted groundwater aquifers. The stress components due to agricultural, industrial and domestic water demands are illustrated separately to assess the vulnerability of particular sectors within the country to provide a basis for targeted policy analysis for reducing water stress.

Analysis and predictive models of stormwater runoff volumes, loads, and pollutant concentrations from watersheds in the Twin Cities metropolitan area, Minnesota, USA.

PubMed

Brezonik, Patrick L; Stadelmann, Teresa H

2002-04-01

Urban nonpoint source pollution is a significant contributor to water quality degradation. Watershed planners need to be able to estimate nonpoint source loads to lakes and streams if they are to plan effective management strategies. To meet this need for the twin cities metropolitan area, a large database of urban and suburban runoff data was compiled. Stormwater runoff loads and concentrations of 10 common constituents (six N and P forms, TSS, VSS, COD, Pb) were characterized, and effects of season and land use were analyzed. Relationships between runoff variables and storm and watershed characteristics were examined. The best regression equation to predict runoff volume for rain events was based on rainfall amount, drainage area, and percent impervious area (R2 = 0.78). Median event-mean concentrations (EMCs) tended to be higher in snowmelt runoff than in rainfall runoff, and significant seasonal differences were found in yields (kg/ha) and EMCs for most constituents. Simple correlations between explanatory variables and stormwater loads and EMCs were weak. Rainfall amount and intensity and drainage area were the most important variables in multiple linear regression models to predict event loads, but uncertainty was high in models developed with the pooled data set. The most accurate models for EMCs generally were found when sites were grouped according to common land use and size.

Kinetic Energy of Throughfall in Subtropical Forests of SE China – Effects of Tree Canopy Structure, Functional Traits, and Biodiversity

PubMed Central

Geißler, Christian; Nadrowski, Karin; Kühn, Peter; Baruffol, Martin; Bruelheide, Helge; Schmid, Bernhard; Scholten, Thomas

2013-01-01

Throughfall kinetic energy (TKE) plays an important role in soil erosion in forests. We studied TKE as a function of biodiversity, functional diversity as well as structural stand variables in a secondary subtropical broad-leaved forest in the Gutianshan National Nature Reserve (GNNR) in south-east China, a biodiversity hotspot in the northern hemisphere with more than 250 woody species present. Using a mixed model approach we could identify significant effects of all these variables on TKE: TKE increased with rarefied tree species richness and decreased with increasing proportion of needle-leaved species and increasing leaf area index (LAI). Furthermore, for average rainfall amounts TKE was decreasing with tree canopy height whereas for high rainfall amounts this was not the case. The spatial pattern of throughfall was stable across several rain events. The temporal variation of TKE decreased with rainfall intensity and increased with tree diversity. Our results show that more diverse forest stands over the season have to cope with higher cumulative raindrop energy than less diverse stands. However, the kinetic energy (KE) of one single raindrop is less predictable in diverse stands since the variability in KE is higher. This paper is the first to contribute to the understanding of the ecosystem function of soil erosion prevention in diverse subtropical forests. PMID:23457440

Quantifying Precipitation Variability and Relative Erosion Rates on Titan Using a GCM and Implications for Observed Geomorphology

NASA Astrophysics Data System (ADS)

Faulk, S.; Moon, S.; Mitchell, J.; Lora, J. M.

2016-12-01

Titan's zonal-mean precipitation behavior has been widely investigated using general circulation models (GCMs), but the spatial and temporal variability of rainfall in Titan's active hydrologic cycle is less well understood. We conduct statistical analyses of rainfall, diagnosed from GCM simulations of Titan's atmosphere, to determine storm intensity and frequency. Intense storms of methane have been proposed to be critical for enabling mechanical erosion of Titan's surface, as indicated by extensive observations of dendritic valley networks. Using precipitation outputs from the Titan Atmospheric Model (TAM), a GCM shown to realistically simulate many features of Titan's atmosphere, we quantify the precipitation variability and resulting relative erosion rates within eight separate latitude bins for a variety of initial surface liquid distributions. We find that while the overall wettest regions are indeed the poles, the most intense rainfall generally occurs in the high mid-latitudes, between 45-67.5 degrees, consistent with recent geomorphological observations of alluvial fans concentrated at those latitudes. We also find that precipitation rates necessary for surface erosion, as estimated by Perron et al. (2006) J. Geophys. Res. 111, E11001, frequently occur at all latitudes, with recurrence intervals of less than one Titan year. Such analysis is crucial towards understanding the complex interaction between Titan's atmosphere and surface and defining the influence of precipitation on observed geomorphology.

Contingency in the Direction and Mechanics of Soil Organic Matter Responses to Increased Rainfall

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

Berhe, Asmeret A.; Suttle, K. Blake; Burton, Sarah D.

2012-09-03

Shifts in regional precipitation patterns will be a major component of global climate change. Rainfall will show greater and more variable changes in response to rising earth surface temperatures than most other climatic variables, and will be a major driver of ecosystem change. We studied the consequences of predicted changes in California’s rainy season for storage and stabilization mechanisms of soil organic matter (SOM). In a controlled and replicated experiment, we amended rainfall over large plots of natural grassland in accordance with alternative scenarios of future climate change. Results show that increases in annual rainfall have important consequences for soilmore » C storage, but that the strength and even direction of these effects depend entirely on seasonal timing. Rainfall increases during the winter rainy season led to pronounced C loss from soil while rainfall increases after the typical rainy season increased soil C stocks. Analysis of mineral-OM associations reveals a powerful mechanism underlying this difference: increased winter rainfall vastly diminished the role of Fe and Al oxides in SOM stabilization. Dithionite extractable crystalline Fe oxides explained more than 35 percent of the variability in C storage in ambient control and spring-addition treatments, compared to less than 0.01 percent in the winter-addition treatment. Likewise, poorly crystalline Fe and Al oxides explained more than 25 and 40 percent of the variability in C storage, respectively, in the control and spring-addition treatments compared to less than 5 percent in the -winter-addition treatment. Increases in annual precipitation identical in amount but at three-month offsets produced opposite effects on soil C storage. These results highlight the complexity inherent in biospheric feedbacks to the climate system, and the way that careful experimentation can penetrate that complexity to improve predictions of ecosystem and climatic change.« less

Rainfall thresholds for the activation of shallow landslides in the Italian Alps: the role of environmental conditioning factors

NASA Astrophysics Data System (ADS)

Palladino, M. R.; Viero, A.; Turconi, L.; Brunetti, M. T.; Peruccacci, S.; Melillo, M.; Luino, F.; Deganutti, A. M.; Guzzetti, F.

2018-02-01

The aim of the present work is to investigate the role exerted by selected environmental factors in the activation of rainfall-triggered shallow landslides and to identify site-specific rainfall thresholds. The study concerns the Italian Alps. The region is exposed to widespread slope instability phenomena due to its geological, morphological and climatic features. Furthermore, the high level of anthropization that characterizes wide portions of the territory increases the associated risk. Hence, the analysis of potential predisposing factors influencing landslides triggering is worthwhile to improve the current prediction skills and to enhance the preparedness and the response to these natural hazards. During the last years, the Italian National Research Council's Research Institute for Hydro-geological Protection (CNR-IRPI) has contributed to the analysis of triggering conditions for rainfall-induced landslides in the framework of a national project. The project, funded by the National Department for Civil Protection (DPC), focuses on the identification of the empirical rainfall thresholds for the activation of shallow landslides in Italy. The first outcomes of the project reveal a certain variability of the pluviometric conditions responsible for the mass movements activation, when different environmental settings are compared. This variability is probably related to the action of local environmental factors, such as lithology, climatic regime or soil characteristics. Based on this hypothesis, the present study aims to identify separated domains within the Italian Alps, where different triggering conditions exist and different countermeasures are needed for risk prevention. For this purpose, we collected information concerning 511 landslides activated in the period 2000-2012 and reconstructed 453 rainfall events supposed to be responsible for the activations. Then, we selected a set of thematic maps to represent the hypothesised landslide conditioning factors and to identify the supposed homogeneous domains within the study area. We employed an existing statistical method for the definition of the cumulated event rainfall vs. rainfall duration (ED) thresholds, for both the entire catalogue of rainfall events and for the events falling in the separated domains. The obtained results contribute to a better understanding of the role exerted by geological, pedological and climatic factors in landslides activation and help identifying separated domains where different risk managing strategies should be adopted. The proposed methodology can be a valid support for risk reduction strategies planning at regional scale.

Rainfall variability over the tropical Pacific from July 1987 through December 1991 as inferred via monthly estimates from SSM/I

NASA Technical Reports Server (NTRS)

Berg, Wesley; Avery, Susan K.

1994-01-01

Estimates of monthly rainfall have been computed over the tropical Pacific using passive microwave satellite observations from the Special Sensor Microwave/Imager (SSM/I) for the preiod from July 1987 through December 1991. The monthly estimates were calibrated using measurements from a network of Pacific atoll rain gauges and compared to other satellite-based rainfall estimation techniques. Based on these monthly estimates, an analysis of the variability of large-scale features over intraseasonal to interannual timescales has been performed. While the major precipitation features as well as the seasonal variability distributions show good agreement with expected values, the presence of a moderately intense El Nino during 1986-87 and an intense La Nina during 1988-89 highlights this time period.

Technical Note: An operational landslide early warning system at regional scale based on space-time-variable rainfall thresholds

NASA Astrophysics Data System (ADS)

Segoni, S.; Battistini, A.; Rossi, G.; Rosi, A.; Lagomarsino, D.; Catani, F.; Moretti, S.; Casagli, N.

2015-04-01

We set up an early warning system for rainfall-induced landslides in Tuscany (23 000 km2). The system is based on a set of state-of-the-art intensity-duration rainfall thresholds (Segoni et al., 2014b) and makes use of LAMI (Limited Area Model Italy) rainfall forecasts and real-time rainfall data provided by an automated network of more than 300 rain gauges. The system was implemented in a WebGIS to ease the operational use in civil protection procedures: it is simple and intuitive to consult, and it provides different outputs. When switching among different views, the system is able to focus both on monitoring of real-time data and on forecasting at different lead times up to 48 h. Moreover, the system can switch between a basic data view where a synoptic scenario of the hazard can be shown all over the region and a more in-depth view were the rainfall path of rain gauges can be displayed and constantly compared with rainfall thresholds. To better account for the variability of the geomorphological and meteorological settings encountered in Tuscany, the region is subdivided into 25 alert zones, each provided with a specific threshold. The warning system reflects this subdivision: using a network of more than 300 rain gauges, it allows for the monitoring of each alert zone separately so that warnings can be issued independently. An important feature of the warning system is that the visualization of the thresholds in the WebGIS interface may vary in time depending on when the starting time of the rainfall event is set. The starting time of the rainfall event is considered as a variable by the early warning system: whenever new rainfall data are available, a recursive algorithm identifies the starting time for which the rainfall path is closest to or overcomes the threshold. This is considered the most hazardous condition, and it is displayed by the WebGIS interface. The early warning system is used to forecast and monitor the landslide hazard in the whole region, providing specific alert levels for 25 distinct alert zones. In addition, the system can be used to gather, analyze, display, explore, interpret and store rainfall data, thus representing a potential support to both decision makers and scientists.

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.

Weather models as virtual sensors to data-driven rainfall predictions in urban watersheds

NASA Astrophysics Data System (ADS)

Cozzi, Lorenzo; Galelli, Stefano; Pascal, Samuel Jolivet De Marc; Castelletti, Andrea

2013-04-01

Weather and climate predictions are a key element of urban hydrology where they are used to inform water management and assist in flood warning delivering. Indeed, the modelling of the very fast dynamics of urbanized catchments can be substantially improved by the use of weather/rainfall predictions. For example, in Singapore Marina Reservoir catchment runoff processes have a very short time of concentration (roughly one hour) and observational data are thus nearly useless for runoff predictions and weather prediction are required. Unfortunately, radar nowcasting methods do not allow to carrying out long - term weather predictions, whereas numerical models are limited by their coarse spatial scale. Moreover, numerical models are usually poorly reliable because of the fast motion and limited spatial extension of rainfall events. In this study we investigate the combined use of data-driven modelling techniques and weather variables observed/simulated with a numerical model as a way to improve rainfall prediction accuracy and lead time in the Singapore metropolitan area. To explore the feasibility of the approach, we use a Weather Research and Forecast (WRF) model as a virtual sensor network for the input variables (the states of the WRF model) to a machine learning rainfall prediction model. More precisely, we combine an input variable selection method and a non-parametric tree-based model to characterize the empirical relation between the rainfall measured at the catchment level and all possible weather input variables provided by WRF model. We explore different lead time to evaluate the model reliability for different long - term predictions, as well as different time lags to see how past information could improve results. Results show that the proposed approach allow a significant improvement of the prediction accuracy of the WRF model on the Singapore urban area.

Peak-summer East Asian rainfall predictability and prediction part I: Southeast Asia

NASA Astrophysics Data System (ADS)

Xing, Wen; Wang, Bin; Yim, So-Young

2016-07-01

The interannual variation of East Asia summer monsoon (EASM) rainfall exhibits considerable differences between early summer [May-June (MJ)] and peak summer [July-August (JA)]. The present study focuses on peak summer. During JA, the mean ridge line of the western Pacific subtropical High (WPSH) divides EASM domain into two sub-domains: the tropical EA (5°N-26.5°N) and subtropical-extratropical EA (26.5°N-50°N). Since the major variability patterns in the two sub-domains and their origins are substantially different, the Part I of this study concentrates on the tropical EA or Southeast Asia (SEA). We apply the predictable mode analysis approach to explore the predictability and prediction of the SEA peak summer rainfall. Four principal modes of interannual rainfall variability during 1979-2013 are identified by EOF analysis: (1) the WPSH-dipole sea surface temperature (SST) feedback mode in the Northern Indo-western Pacific warm pool associated with the decay of eastern Pacific El Niño/Southern Oscillation (ENSO), (2) the central Pacific-ENSO mode, (3) the Maritime continent SST-Australian High coupled mode, which is sustained by a positive feedback between anomalous Australian high and sea surface temperature anomalies (SSTA) over Indian Ocean, and (4) the ENSO developing mode. Based on understanding of the sources of the predictability for each mode, a set of physics-based empirical (P-E) models is established for prediction of the first four leading principal components (PCs). All predictors are selected from either persistent atmospheric lower boundary anomalies from March to June or the tendency from spring to early summer. We show that these four modes can be predicted reasonably well by the P-E models, thus they are identified as the predictable modes. Using the predicted PCs and the corresponding observed spatial patterns, we have made a 35-year cross-validated hindcast, setting up a bench mark for dynamic models' predictions. The P-E hindcast prediction skill represented by domain-averaged temporal correlation coefficient is 0.44, which is twice higher than the skill of the current dynamical hindcast, suggesting that the dynamical models have large rooms to improve. The maximum potential attainable prediction skills for the peak summer SEA rainfall is also estimated and discussed by using the PMA. High predictability regions are found over several climatological rainfall centers like Indo-China peninsula, southern coast of China, southeastern SCS, and Philippine Sea.

Linking the Response of Annual Grasslands to Warming and Altered Rainfall Across Scales of Gene Expression, Species, and Ecosystem

NASA Astrophysics Data System (ADS)

Torn, M. S.; Bernard, S. M.; Castanha, C.; Fischer, M. L.; Hopkins, F. M.; Placella, S. A.; St. Clair, S. B.; Salve, R.; Sudderth, E.; Herman, D.; Ackerly, D.; Firestone, M. K.

2007-12-01

Climate change can influence terrestrial ecosystems at multiple biological levels: gene expression, species, and ecosystem. We are studying California grassland mesocosms with seven annual species (five grasses, two forbs) that were started in 2005. In the 2006-2007 growing season, they were exposed to three rainfall treatments (297, 552, and 867 mm y-1) and soil and air temperature (ambient and elevated +4oC) in replicated greenhouses. This presentation will combine plant and ecosystem level results with transcript level analyses associated with key enzymes, such as rubisco and glutamine synthetase (GS). Because rainfall is the dominant climate variable for most processes in this Mediterranean ecosystem, the effect of warming was strongly mediated by rainfall. In fact, we saw significant interactions between temperature and rainfall treatments at all three biological levels. For example, at the ecosystem level, warming led to a decrease in aboveground and total NPP under low rainfall, and an increase under high rainfall. For the dominant species, Avena barbata, warming had no effect under high rainfall, but suppressed Avena NPP in low rainfall. At the same time, warmer, wetter conditions accelerated Avena flowering by almost 15 days. This shift in phenology was presaged by observations at the transcript level. Specifically, in the high temperature, high rainfall treatment, the levels of mRNAs for RbcS and GS2 (encoding the small subunit of rubisco and the chloroplastic isoform of GS, respectively) declined while GS1 (encoding the cytosolic isoform of GS) was upregulated several weeks before heading. The transcript level response (along with soil and plant nitrogen data) indicated the leaf had switched from a carbon and nitrogen sink to a source - consistent with more mature plant function and earlier flowering. Soil CO2 respiration also showed strong rain-by-temperature interactions that were due mainly to changes in root response (respiration and/or exudates) rather than in microbial respiration. Overall, the pervasive rain-by-temperature interactions mean that it may be very difficult to predict the effect of warming alone, without accounting for changes in precipitation (in our Mediterranean system). While predictions of warming of 3-6°C in the next 100 years are fairly certain, changes in precipitation are much more uncertain, with some forecasts drier and others wetter for a given location. We suggest that uncertainty about future precipitation and the interacting influences of temperature and moisture on ecosystems are currently key limitations in predicting ecosystem response to climate change, particularly in Mediterranean ecosystems such as the one studied here.

Rainfall thresholds for possible landslide occurrence in Italy

NASA Astrophysics Data System (ADS)

Peruccacci, Silvia; Brunetti, Maria Teresa; Gariano, Stefano Luigi; Melillo, Massimo; Rossi, Mauro; Guzzetti, Fausto

2017-08-01

The large physiographic variability and the abundance of landslide and rainfall data make Italy an ideal site to investigate variations in the rainfall conditions that can result in rainfall-induced landslides. We used landslide information obtained from multiple sources and rainfall data captured by 2228 rain gauges to build a catalogue of 2309 rainfall events with - mostly shallow - landslides in Italy between January 1996 and February 2014. For each rainfall event with landslides, we reconstructed the rainfall history that presumably caused the slope failure, and we determined the corresponding rainfall duration D (in hours) and cumulated event rainfall E (in mm). Adopting a power law threshold model, we determined cumulated event rainfall-rainfall duration (ED) thresholds, at 5% exceedance probability, and their uncertainty. We defined a new national threshold for Italy, and 26 regional thresholds for environmental subdivisions based on topography, lithology, land-use, land cover, climate, and meteorology, and we used the thresholds to study the variations of the rainfall conditions that can result in landslides in different environments, in Italy. We found that the national and the environmental thresholds cover a small part of the possible DE domain. The finding supports the use of empirical rainfall thresholds for landslide forecasting in Italy, but poses an empirical limitation to the possibility of defining thresholds for small geographical areas. We observed differences between some of the thresholds. With increasing mean annual precipitation (MAP), the thresholds become higher and steeper, indicating that more rainfall is needed to trigger landslides where the MAP is high than where it is low. This suggests that the landscape adjusts to the regional meteorological conditions. We also observed that the thresholds are higher for stronger rocks, and that forested areas require more rainfall than agricultural areas to initiate landslides. Finally, we observed that a 20% exceedance probability national threshold was capable of predicting all the rainfall-induced landslides with casualties between 1996 and 2014, and we suggest that this threshold can be used to forecast fatal rainfall-induced landslides in Italy. We expect the method proposed in this work to define and compare the thresholds to have an impact on the definition of new rainfall thresholds for possible landslide occurrence in Italy, and elsewhere.

Global Distribution of Extreme Precipitation and High-Impact Landslides in 2010 Relative to Previous Years

NASA Technical Reports Server (NTRS)

Kirschbaum, Dalia; Adler, Robert; Adler, David; Peters-Lidard, Christa; Huffman, George

2012-01-01

It is well known that extreme or prolonged rainfall is the dominant trigger of landslides worldwide. While research has evaluated the spatiotemporal distribution of extreme rainfall and landslides at local or regional scales using in situ data, few studies have mapped rainfall-triggered landslide distribution globally due to the dearth of landslide data and consistent precipitation information. This study uses a newly developed Global Landslide Catalog (GLC) and a 13-year satellite-based precipitation record from TRMM data. For the first time, these two unique products provide the foundation to quantitatively evaluate the co-occurrence of precipitation and landslides globally. Evaluation of the GLC indicates that 2010 had a large number of high-impact landslide events relative to previous years. This study considers how variations in extreme and prolonged satellite-based rainfall are related to the distribution of landslides over the same time scales for three active landslide areas: Central America, the Himalayan Arc, and central-eastern China. Several test statistics confirm that TRMM rainfall generally scales with the observed increase in landslide reports and fatal events for 2010 and previous years over each region. These findings suggest that the co-occurrence of satellite precipitation and landslide reports may serve as a valuable indicator for characterizing the spatiotemporal distribution of landslide-prone areas in order to establish a global rainfall-triggered landslide climatology. This study characterizes the variability of satellite precipitation data and reported landslide activity at the globally scale in order to improve landslide cataloging, forecasting and quantify potential triggering sources at daily, monthly and yearly time scales.

The influence of ENSO, PDO and PNA on secular rainfall variations in Hawai`i

NASA Astrophysics Data System (ADS)

Frazier, Abby G.; Elison Timm, Oliver; Giambelluca, Thomas W.; Diaz, Henry F.

2017-11-01

Over the last century, significant declines in rainfall across the state of Hawai`i have been observed, and it is unknown whether these declines are due to natural variations in climate, or manifestations of human-induced climate change. Here, a statistical analysis of the observed rainfall variability was applied as first step towards better understanding causes for these long-term trends. Gridded seasonal rainfall from 1920 to 2012 is used to perform an empirical orthogonal function (EOF) analysis. The leading EOF components are correlated with three indices of natural climate variations (El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and Pacific North American (PNA)), and multiple linear regression (MLR) is used to model the leading components with climate indices. PNA is the dominant mode of wet season (November-April) variability, while ENSO is most significant in the dry season (May-October). To assess whether there is an anthropogenic influence on rainfall, two methods are used: a linear trend term is included in the MLR, and pattern correlation coefficients (PCC) are calculated between recent rainfall trends and future changes in rainfall projected by downscaling methods. PCC results indicate that recent observed rainfall trends in the wet season are positively correlated with future expected changes in rainfall, while dry season PCC results do not show a clear pattern. The MLR results, however, show that the trend term adds significantly to model skill only in the dry season. Overall, MLR and PCC results give weak and inconclusive evidence for detection of anthropogenic signals in the observed rainfall trends.

The use of simulated rainfall to study the discharge process and the influence factors of urban surface runoff pollution loads.

PubMed

Qinqin, Li; Qiao, Chen; Jiancai, Deng; Weiping, Hu

2015-01-01

An understanding of the characteristics of pollutants on impervious surfaces is essential to estimate pollution loads and to design methods to minimize the impacts of pollutants on the environment. In this study, simulated rainfall equipment was constructed to investigate the pollutant discharge process and the influence factors of urban surface runoff (USR). The results indicated that concentrations of total suspended solids (TSS), total nitrogen (TN), total phosphorus (TP) and chemical oxygen demand (COD) appeared to be higher in the early period and then decreased gradually with rainfall duration until finally stabilized. The capacity and particle size of surface dust, rainfall intensity and urban surface slopes affected runoff pollution loads to a variable extent. The loads of TP, TN and COD showed a positive relationship with the surface dust capacity, whereas the maximum TSS load appeared when the surface dust was 0.0317 g·cm⁻². Smaller particle sizes (<0.125 mm) of surface dust generated high TN, TP and COD loads. Increases in rainfall intensity and surface slope enhanced the pollution carrying capacity of runoff, leading to higher pollution loads. Knowledge of the influence factors could assist in the management of USR pollution loads.

Dynamics of changing impacts of tropical Indo-Pacific variability on Indian and Australian rainfall

NASA Astrophysics Data System (ADS)

Li, Ziguang; Cai, Wenju; Lin, Xiaopei

2016-08-01

A positive Indian Ocean Dipole (IOD) and a warm phase of the El Niño-Southern Oscillation (ENSO) reduce rainfall over the Indian subcontinent and southern Australia. However, since the 1980s, El Niño’s influence has been decreasing, accompanied by a strengthening in the IOD’s influence on southern Australia but a reversal in the IOD’s influence on the Indian subcontinent. The dynamics are not fully understood. Here we show that a post-1980 weakening in the ENSO-IOD coherence plays a key role. During the pre-1980 high coherence, ENSO drives both the IOD and regional rainfall, and the IOD’s influence cannot manifest itself. During the post-1980 weak coherence, a positive IOD leads to increased Indian rainfall, offsetting the impact from El Niño. Likewise, the post-1980 weak ENSO-IOD coherence means that El Niño’s pathway for influencing southern Australia cannot fully operate, and as positive IOD becomes more independent and more frequent during this period, its influence on southern Australia rainfall strengthens. There is no evidence to support that greenhouse warming plays a part in these decadal fluctuations.

Dynamics of changing impacts of tropical Indo-Pacific variability on Indian and Australian rainfall.

PubMed

Li, Ziguang; Cai, Wenju; Lin, Xiaopei

2016-08-22

A positive Indian Ocean Dipole (IOD) and a warm phase of the El Niño-Southern Oscillation (ENSO) reduce rainfall over the Indian subcontinent and southern Australia. However, since the 1980s, El Niño's influence has been decreasing, accompanied by a strengthening in the IOD's influence on southern Australia but a reversal in the IOD's influence on the Indian subcontinent. The dynamics are not fully understood. Here we show that a post-1980 weakening in the ENSO-IOD coherence plays a key role. During the pre-1980 high coherence, ENSO drives both the IOD and regional rainfall, and the IOD's influence cannot manifest itself. During the post-1980 weak coherence, a positive IOD leads to increased Indian rainfall, offsetting the impact from El Niño. Likewise, the post-1980 weak ENSO-IOD coherence means that El Niño's pathway for influencing southern Australia cannot fully operate, and as positive IOD becomes more independent and more frequent during this period, its influence on southern Australia rainfall strengthens. There is no evidence to support that greenhouse warming plays a part in these decadal fluctuations.

A vectorial capacity product to monitor changing malaria transmission potential in epidemic regions of Africa

USGS Publications Warehouse

Ceccato, Pietro; Vancutsem, Christelle; Klaver, Robert; Rowland, James; Connor, Stephen J.

2012-01-01

Rainfall and temperature are two of the major factors triggering malaria epidemics in warm semi-arid (desert-fringe) and high altitude (highland-fringe) epidemic risk areas. The ability of the mosquitoes to transmit Plasmodium spp. is dependent upon a series of biological features generally referred to as vectorial capacity. In this study, the vectorial capacity model (VCAP) was expanded to include the influence of rainfall and temperature variables on malaria transmission potential. Data from two remote sensing products were used to monitor rainfall and temperature and were integrated into the VCAP model. The expanded model was tested in Eritrea and Madagascar to check the viability of the approach. The analysis of VCAP in relation to rainfall, temperature and malaria incidence data in these regions shows that the expanded VCAP correctly tracks the risk of malaria both in regions where rainfall is the limiting factor and in regions where temperature is the limiting factor. The VCAP maps are currently offered as an experimental resource for testing within Malaria Early Warning applications in epidemic prone regions of sub-Saharan Africa. User feedback is currently being collected in preparation for further evaluation and refinement of the VCAP model.

Vegetation Interaction Enhances Interdecadal Climate Variability in the Sahel

NASA Technical Reports Server (NTRS)

Zeng, Ning; Neelin, J. David; Lau, William K.-M.

1999-01-01

The role of naturally varying vegetation in influencing the climate variability in the Sahel is explored in a coupled atmosphere-land-vegetation model. The Sahel rainfall variability is influenced by sea surface temperature (SST) variations in the oceans. Land-surface feedback is found to increase this variability both on interannual and interdecadal time scales. Interactive vegetation enhances the interdecadal variation significantly, but can reduce year to year variability due to a phase lag introduced by the relatively slow vegetation adjustment time. Variations in vegetation accompany the changes in rainfall, in particular, the multi-decadal drying trend from the 1950s to the 80s.

Infiltration and Runoff Measurements on Steep Burned Hillslopes Using a Rainfall Simulator with Variable Rain Intensities

USGS Publications Warehouse

Kinner, David A.; Moody, John A.

2008-01-01

Multiple rainfall intensities were used in rainfall-simulation experiments designed to investigate the infiltration and runoff from 1-square-meter plots on burned hillslopes covered by an ash layer of varying thickness. The 1-square-meter plots were on north- and south-facing hillslopes in an area burned by the Overland fire northwest of Boulder near Jamestown on the Front Range of Colorado. A single-nozzle, wide-angle, multi-intensity rain simulator was developed to investigate the infiltration and runoff on steep (30- to 40-percent gradient) burned hillslopes covered with ash. The simulated rainfall was evaluated for spatial variability, drop size, and kinetic energy. Fourteen rainfall simulations, at three intensities (about 20 millimeters per hour [mm/h], 35 mm/h, and 50 mm/h), were conducted on four plots. Measurements during and after the simulations included runoff, rainfall, suspended-sediment concentrations, surface ash layer thickness, soil moisture, soil grain size, soil lost on ignition, and plot topography. Runoff discharge reached a steady state within 7 to 26 minutes. Steady infiltration rates with the 50-mm/h application rainfall intensity approached 20?35 mm/h. If these rates are projected to rainfall application intensities used in many studies of burned area runoff production (about 80 mm/h), the steady discharge rates are on the lower end of measurements from other studies. Experiments using multiple rainfall intensities (three) suggest that runoff begins at rainfall intensities around 20 mm/h at the 1-square-meter scale, an observation consistent with a 10-mm/h rainfall intensity threshold needed for runoff initiation that has been reported in the literature.

Radar-rain-gauge rainfall estimation for hydrological applications in small catchments

NASA Astrophysics Data System (ADS)

Gabriele, Salvatore; Chiaravalloti, Francesco; Procopio, Antonio

2017-07-01

The accurate evaluation of the precipitation's time-spatial structure is a critical step for rainfall-runoff modelling. Particularly for small catchments, the variability of rainfall can lead to mismatched results. Large errors in flow evaluation may occur during convective storms, responsible for most of the flash floods in small catchments in the Mediterranean area. During such events, we may expect large spatial and temporal variability. Therefore, using rain-gauge measurements only can be insufficient in order to adequately depict extreme rainfall events. In this work, a double-level information approach, based on rain gauges and weather radar measurements, is used to improve areal rainfall estimations for hydrological applications. In order to highlight the effect that precipitation fields with different level of spatial details have on hydrological modelling, two kinds of spatial rainfall fields were computed for precipitation data collected during 2015, considering both rain gauges only and their merging with radar information. The differences produced by these two precipitation fields in the computation of the areal mean rainfall accumulation were evaluated considering 999 basins of the region Calabria, southern Italy. Moreover, both of the two precipitation fields were used to carry out rainfall-runoff simulations at catchment scale for main precipitation events that occurred during 2015 and the differences between the scenarios obtained in the two cases were analysed. A representative case study is presented in detail.

Decision tree analysis of factors influencing rainfall-related building damage

NASA Astrophysics Data System (ADS)

Spekkers, M. H.; Kok, M.; Clemens, F. H. L. R.; ten Veldhuis, J. A. E.

2014-04-01

Flood damage prediction models are essential building blocks in flood risk assessments. Little research has been dedicated so far to damage of small-scale urban floods caused by heavy rainfall, while there is a need for reliable damage models for this flood type among insurers and water authorities. The aim of this paper is to investigate a wide range of damage-influencing factors and their relationships with rainfall-related damage, using decision tree analysis. For this, district-aggregated claim data from private property insurance companies in the Netherlands were analysed, for the period of 1998-2011. The databases include claims of water-related damage, for example, damages related to rainwater intrusion through roofs and pluvial flood water entering buildings at ground floor. Response variables being modelled are average claim size and claim frequency, per district per day. The set of predictors include rainfall-related variables derived from weather radar images, topographic variables from a digital terrain model, building-related variables and socioeconomic indicators of households. Analyses were made separately for property and content damage claim data. Results of decision tree analysis show that claim frequency is most strongly associated with maximum hourly rainfall intensity, followed by real estate value, ground floor area, household income, season (property data only), buildings age (property data only), ownership structure (content data only) and fraction of low-rise buildings (content data only). It was not possible to develop statistically acceptable trees for average claim size, which suggest that variability in average claim size is related to explanatory variables that cannot be defined at the district scale. Cross-validation results show that decision trees were able to predict 22-26% of variance in claim frequency, which is considerably better compared to results from global multiple regression models (11-18% of variance explained). Still, a large part of the variance in claim frequency is left unexplained, which is likely to be caused by variations in data at subdistrict scale and missing explanatory variables.

Improve projections of changes in southern African summer rainfall through comprehensive multi-timescale empirical statistical downscaling

NASA Astrophysics Data System (ADS)

Dieppois, B.; Pohl, B.; Eden, J.; Crétat, J.; Rouault, M.; Keenlyside, N.; New, M. G.

2017-12-01

The water management community has hitherto neglected or underestimated many of the uncertainties in climate impact scenarios, in particular, uncertainties associated with decadal climate variability. Uncertainty in the state-of-the-art global climate models (GCMs) is time-scale-dependant, e.g. stronger at decadal than at interannual timescales, in response to the different parameterizations and to internal climate variability. In addition, non-stationarity in statistical downscaling is widely recognized as a key problem, in which time-scale dependency of predictors plays an important role. As with global climate modelling, therefore, the selection of downscaling methods must proceed with caution to avoid unintended consequences of over-correcting the noise in GCMs (e.g. interpreting internal climate variability as a model bias). GCM outputs from the Coupled Model Intercomparison Project 5 (CMIP5) have therefore first been selected based on their ability to reproduce southern African summer rainfall variability and their teleconnections with Pacific sea-surface temperature across the dominant timescales. In observations, southern African summer rainfall has recently been shown to exhibit significant periodicities at the interannual timescale (2-8 years), quasi-decadal (8-13 years) and inter-decadal (15-28 years) timescales, which can be interpret as the signature of ENSO, the IPO, and the PDO over the region. Most of CMIP5 GCMs underestimate southern African summer rainfall variability and their teleconnections with Pacific SSTs at these three timescales. In addition, according to a more in-depth analysis of historical and pi-control runs, this bias is might result from internal climate variability in some of the CMIP5 GCMs, suggesting potential for bias-corrected prediction based empirical statistical downscaling. A multi-timescale regression based downscaling procedure, which determines the predictors across the different timescales, has thus been used to simulate southern African summer rainfall. This multi-timescale procedure shows much better skills in simulating decadal timescales of variability compared to commonly used statistical downscaling approaches.

The role of climate variability in extreme floods in Europe

NASA Astrophysics Data System (ADS)

Guimarães Nobre, Gabriela; Aerts, Jeroen C. J. H.; Jongman, Brenden; Ward, Philip J.

2017-04-01

Between 1980 and 2015, Europe experienced 18% of worldwide weather-related loss events, which accounted for over US500 billion in damage. Consequently, it is urgent to further develop adaptation strategies to mitigate the consequences of weather-related disasters, such as floods. Europe's capability to prepare for such disasters is challenged by a large range of uncertainties and a limited understanding of the driving forces of hydrometeorological hazards. One of the major sources of uncertainty is the relationship between climate variability and weather-related losses. Previous studies show that climate variability drives temporal changes in hydrometereological variables in Europe. However, their influence on flood risk has received little attention. We investigated the influence of the positive and negative phases of El Niño Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), and the Arctic Oscillation (AO), on the seasonal frequency and intensity of extreme rainfall, and anomalies in flood occurrence and damage compared to the neutral phases of the indices of climate variability. Using statistical methods to analyze relationships between the indices of climate variability and four indicators of flooding, we found that positive and negative phases of NAO and AO are associated with more (or less) frequent and intense seasonal extreme rainfall over large areas of Europe. The relationship between ENSO and both the occurrence of extreme rainfall and intensity of extreme rainfall in Europe is much smaller than the relationship with NAO or AO, but still significant in some regions. We observe that flood damage and flood occurrence have strong links with climate variability, especially in southern and eastern Europe. Therefore, when investigating flooding across Europe, all three indices of climate variability should be considered. Seasonal forecasting of flooding could be enhanced by the inclusion of climate variability indicators .

Evaluation of common bean lines for adaptation to high temperatures in Honduras

USDA-ARS?s Scientific Manuscript database

As in other regions worldwide, common bean (Phaseolus vulgaris L.) production in Central America and the Caribbean (CA/C) region is threatened by effects of climate change including increasing temperatures and drought due to variable rainfall patterns. One of the main alternatives for increasing ada...

Extreme rainfall, vulnerability and risk: a continental-scale assessment for South America.

PubMed

Vörösmarty, Charles J; Bravo de Guenni, Lelys; Wollheim, Wilfred M; Pellerin, Brian; Bjerklie, David; Cardoso, Manoel; D'Almeida, Cassiano; Green, Pamela; Colon, Lilybeth

2013-11-13

Extreme weather continues to preoccupy society as a formidable public safety concern bearing huge economic costs. While attention has focused on global climate change and how it could intensify key elements of the water cycle such as precipitation and river discharge, it is the conjunction of geophysical and socioeconomic forces that shapes human sensitivity and risks to weather extremes. We demonstrate here the use of high-resolution geophysical and population datasets together with documentary reports of rainfall-induced damage across South America over a multi-decadal, retrospective time domain (1960-2000). We define and map extreme precipitation hazard, exposure, affectedpopulations, vulnerability and risk, and use these variables to analyse the impact of floods as a water security issue. Geospatial experiments uncover major sources of risk from natural climate variability and population growth, with change in climate extremes bearing a minor role. While rural populations display greatest relative sensitivity to extreme rainfall, urban settings show the highest rates of increasing risk. In the coming decades, rapid urbanization will make South American cities the focal point of future climate threats but also an opportunity for reducing vulnerability, protecting lives and sustaining economic development through both traditional and ecosystem-based disaster risk management systems.

Comparing Satellite Rainfall Estimates with Rain-Gauge Data: Optimal Strategies Suggested by a Spectral Model

NASA Technical Reports Server (NTRS)

Bell, Thomas L.; Kundu, Prasun K.; Lau, William K. M. (Technical Monitor)

2002-01-01

Validation of satellite remote-sensing methods for estimating rainfall against rain-gauge data is attractive because of the direct nature of the rain-gauge measurements. Comparisons of satellite estimates to rain-gauge data are difficult, however, because of the extreme variability of rain and the fact that satellites view large areas over a short time while rain gauges monitor small areas continuously. In this paper, a statistical model of rainfall variability developed for studies of sampling error in averages of satellite data is used to examine the impact of spatial and temporal averaging of satellite and gauge data on intercomparison results. The model parameters were derived from radar observations of rain, but the model appears to capture many of the characteristics of rain-gauge data as well. The model predicts that many months of data from areas containing a few gauges are required to validate satellite estimates over the areas, and that the areas should be of the order of several hundred km in diameter. Over gauge arrays of sufficiently high density, the optimal areas and averaging times are reduced. The possibility of using time-weighted averages of gauge data is explored.

A High-Resolution ENSO-Driven Rainfall Record Derived From an Exceptionally Fast Growing Stalagmite From Niue Island (South Pacific)

NASA Astrophysics Data System (ADS)

Troy, S.; Aharon, P.; Lambert, W. J.

2012-12-01

El Niño-Southern Oscillation's (ENSO) dominant control over the present global climate and its unpredictable response to a global warming makes the study of paleo-ENSO important. So far corals, spanning the Tropical Pacific Ocean, are the most commonly used geological archives of paleo-ENSO. This is because corals typically exhibit high growth rates (>1 cm/yr), and reproduce reliably surface water temperatures at sub-annual resolution. However there are limitations to coral archives because their time span is relatively brief (in the order of centuries), thus far making a long and continuous ENSO record difficult to achieve. On the other hand stalagmites from island settings can offer long and continuous records of ENSO-driven rainfall. Niue Island caves offer an unusual opportunity to investigate ENSO-driven paleo-rainfall because the island is isolated from other large land masses, making it untainted by continental climate artifacts, and its geographical location is within the Tropical Pacific "rain pool" (South Pacific Convergence Zone; SPCZ) that makes the rainfall variability particularly sensitive to the ENSO phase switches. We present here a δ18O and δ13C time series from a stalagmite sampled on Niue Island (19°00' S, 169°50' W) that exhibits exceptionally high growth rates (~1.2 mm/yr) thus affording a resolution comparable to corals but for much longer time spans. A precise chronology, dating back to several millennia, was achieved by U/Th dating of the stalagmite. The stalagmite was sampled using a Computer Automated Mill (CAM) at 300 μm increments in order to receive sub-annual resolution (every 3 months) and calcite powders of 50-100 μg weight were analyzed for δ18O and δ13C using a Continuous Flow Isotope Ratio Mass Spectrometer (CF-IRMS). The isotope time series contains variable shifts at seasonal, inter-annual, and inter-decadal periodicities. The δ13C and δ18O yield ranges of -3.0 to -13.0 (‰ VPDB) and -3.2 to -6.2 (‰ VPDB), respectively. The presentation will describe the factors impacting the seasonal, inter-annual and inter-decadal variability in a highly resolved ENSO record.

Coherent variability between seasonal temperatures and rainfalls in the Iberian Peninsula, 1951-2016

NASA Astrophysics Data System (ADS)

Rodrigo, F. S.

2018-02-01

In this work trends of seasonal mean of daily minimum (TN), maximum (TX), mean (TM) temperatures, daily range of temperature (DTR), and total seasonal rainfall (R) in 35 Iberian stations since mid-twentieth century are studied. The interest is focused on the relationships between temperature variables and rainfall, taking into account the correlation coefficients between R and the temperature variables. The negative link between rainfall and temperatures is detected in the four seasons of the year, except in western stations in winter for TN and TM, and in autumn for TN (for this variable a certain annual cycle is detected, with predominance of positive correlation in winter, negative in spring and summer, and the autumn as transition season). The role of cloud cover is confirmed in those stations with total cloud cover data. Using an average peninsular series, the relationship between nighttime temperature and rainfall related to long wave radiation is confirmed for the four seasons of the year, although in spring and summer has minor importance than in the cold half year. The relationships between R, TN, and TX are in general terms stable after a moving correlation analysis, although the negative correlation between TX and R seems be weakened in spring and autumn and reinforced in summer. The role of convective precipitation in autumn is discussed. The analysis of combined extreme indices in four representative stations shows an increase of warm and dry days, and a decrease of cold and wet days.

Managing the impact of climate change on the hydrology of the Gallocanta Basin, NE-Spain.

PubMed

Kuhn, Nikolaus J; Baumhauer, Roland; Schütt, Brigitta

2011-02-01

The Gallocanta Basin represents an environment highly sensitive to climate change. Over the past 60 years, the Laguna de Gallocanta, an ephemeral lake situated in the closed Gallocanta basin, experienced a sequence of wet and dry phases. The lake and its surrounding wetlands are one of only a few bird sanctuaries left in NE-Spain for grey cranes on their annual migration from Scandinavia to northern Africa. Understanding the impact of climate change on basin hydrology is therefore of utmost importance for the appropriate management of the bird sanctuary. Changes in lake level are only weakly linked to annual rainfall, with reaction times between hours and months after rainfall. Both the total amount of rainfall over the reaction period, as well as individual extreme events, affect lake level. In this study the characteristics and frequencies of daily, event, monthly and bi-monthly rainfall over the past 60 years were analysed. The results revealed a clear link between increased frequencies of high magnitude rainfall and phases of water filling in the Laguna de Gallocanta. In the middle of the 20th century, the absolute amount of rainfall appears to have been more important for lake level, while more recently the frequency of high magnitude rainfall has emerged as the dominant variable. In the Gallocanta Basin, climate change and the distinct and continuing land use change since Spain joined the EU in 1986 have created an environment that is in a more or less constant state of transition. This highlights two challenges faced by hydrologists and climatologists involved in developing water management tools for the Gallocanta Basin in particular, but also other areas with sensitive and rapidly changing environments. Hydrologists have to understand the processes and the spatial and temporal patterns of surface-climate interaction in a watershed to assess the impact of climate change on its hydrology. Climatologists, on the other hand, have to develop climate models which provide the appropriate output data, such as reliable information on rainfall characteristics relevant for environmental management. Copyright © 2009. Published by Elsevier Ltd.

Dealing with deep uncertainties in landslide modelling for disaster risk reduction under climate change

NASA Astrophysics Data System (ADS)

Almeida, Susana; Holcombe, Elizabeth Ann; Pianosi, Francesca; Wagener, Thorsten

2017-02-01

Landslides have large negative economic and societal impacts, including loss of life and damage to infrastructure. Slope stability assessment is a vital tool for landslide risk management, but high levels of uncertainty often challenge its usefulness. Uncertainties are associated with the numerical model used to assess slope stability and its parameters, with the data characterizing the geometric, geotechnic and hydrologic properties of the slope, and with hazard triggers (e.g. rainfall). Uncertainties associated with many of these factors are also likely to be exacerbated further by future climatic and socio-economic changes, such as increased urbanization and resultant land use change. In this study, we illustrate how numerical models can be used to explore the uncertain factors that influence potential future landslide hazard using a bottom-up strategy. Specifically, we link the Combined Hydrology And Stability Model (CHASM) with sensitivity analysis and Classification And Regression Trees (CART) to identify critical thresholds in slope properties and climatic (rainfall) drivers that lead to slope failure. We apply our approach to a slope in the Caribbean, an area that is naturally susceptible to landslides due to a combination of high rainfall rates, steep slopes, and highly weathered residual soils. For this particular slope, we find that uncertainties regarding some slope properties (namely thickness and effective cohesion of topsoil) are as important as the uncertainties related to future rainfall conditions. Furthermore, we show that 89 % of the expected behaviour of the studied slope can be characterized based on only two variables - the ratio of topsoil thickness to cohesion and the ratio of rainfall intensity to duration.

Evaluating spatial and temporal variations of rainfall erosivity, case of Central Rift Valley of Ethiopia

NASA Astrophysics Data System (ADS)

Meshesha, Derege Tsegaye; Tsunekawa, Atsushi; Tsubo, Mitsuru; Haregeweyn, Nigussie; Adgo, Enyew

2015-02-01

Land degradation in many Ethiopian highlands occurs mainly due to high rainfall erosivity and poor soil conservation practices. Rainfall erosivity is an indicator of the precipitation energy and ability to cause soil erosion. In Central Rift Valley (CRV) of Ethiopia, where the climate is characterized as arid and semiarid, rainfall is the main driver of soil erosion that in turn causes a serious expansion in land degradation. In order to evaluate the spatial and temporal variability of rainfall erosivity and its impact on soil erosion, long-term rainfall data (1980-2010) was used, and the monthly Fournier index (FI) and the annual modified Fournier index (MFI) were applied. Student's t test analysis was performed particularly to examine statistical significances of differences in average monthly and annual erosivity values. The result indicated that, in a similar spatial pattern with elevation and rainfall amount, average annual erosivity is also found being higher in western highlands of the valley and gradually decreased towards the east. The long-term average annual erosivity (MFI) showed a general decreasing trend in recent 10 years (2000-2010) as compared to previous 20 years (1980-1999). In most of the stations, average erosivity of main rainy months (May, June, July, and August) showed a decreasing trend, whereby some of them (about 33.3 %) are statically significant at 90 and 95 % confidence intervals but with high variation in spatial pattern of changes. The overall result of the study showed that rainfall aggression (erosivity) in the region has a general decreasing trend in the recent decade as compared to previous decades, especially in the western highlands of the valley. Hence, it implies that anthropogenic factors such as land use change being coupled with topography (steep slope) have largely contributed to increased soil erosion rate in the region.

Year-round breeding equatorial Larks from three climatically-distinct populations do not use rainfall, temperature or invertebrate biomass to time reproduction

PubMed Central

Ndithia, Henry K.; Matson, Kevin D.; Versteegh, Maaike A.; Muchai, Muchane; Tieleman, B. Irene

2017-01-01

Timing of reproduction in birds is important for reproductive success and is known to depend on environmental cues such as day length and food availability. However, in equatorial regions, where day length is nearly constant, other factors such as rainfall and temperature are thought to determine timing of reproduction. Rainfall can vary at small spatial and temporal scales, providing a highly fluctuating and unpredictable environmental cue. In this study we investigated the extent to which spatio-temporal variation in environmental conditions can explain the timing of breeding of Red-capped Lark, Calandrella cinerea, a species that is capable of reproducing during every month of the year in our equatorial east African study locations. For 39 months in three climatically-distinct locations, we monitored nesting activities, sampled ground and flying invertebrates, and quantified rainfall, maximum (Tmax) and minimum (Tmin) temperatures. Among locations we found that lower rainfall and higher temperatures did not coincide with lower invertebrate biomasses and decreased nesting activities, as predicted. Within locations, we found that rainfall, Tmax, and Tmin varied unpredictably among months and years. The only consistent annually recurring observations in all locations were that January and February had low rainfall, high Tmax, and low Tmin. Ground and flying invertebrate biomasses varied unpredictably among months and years, but invertebrates were captured in all months in all locations. Red-capped Larks bred in all calendar months overall but not in every month in every year in every location. Using model selection, we found no clear support for any relationship between the environmental variables and breeding in any of the three locations. Contrary to popular understanding, this study suggests that rainfall and invertebrate biomass as proxy for food do not influence breeding in equatorial Larks. Instead, we propose that factors such as nest predation, female protein reserves, and competition are more important in environments where weather and food meet minimum requirements for breeding during most of the year. PMID:28419105

BN-FLEMOps pluvial - A probabilistic multi-variable loss estimation model for pluvial floods

NASA Astrophysics Data System (ADS)

Roezer, V.; Kreibich, H.; Schroeter, K.; Doss-Gollin, J.; Lall, U.; Merz, B.

2017-12-01

Pluvial flood events, such as in Copenhagen (Denmark) in 2011, Beijing (China) in 2012 or Houston (USA) in 2016, have caused severe losses to urban dwellings in recent years. These floods are caused by storm events with high rainfall rates well above the design levels of urban drainage systems, which lead to inundation of streets and buildings. A projected increase in frequency and intensity of heavy rainfall events in many areas and an ongoing urbanization may increase pluvial flood losses in the future. For an efficient risk assessment and adaptation to pluvial floods, a quantification of the flood risk is needed. Few loss models have been developed particularly for pluvial floods. These models usually use simple waterlevel- or rainfall-loss functions and come with very high uncertainties. To account for these uncertainties and improve the loss estimation, we present a probabilistic multi-variable loss estimation model for pluvial floods based on empirical data. The model was developed in a two-step process using a machine learning approach and a comprehensive database comprising 783 records of direct building and content damage of private households. The data was gathered through surveys after four different pluvial flood events in Germany between 2005 and 2014. In a first step, linear and non-linear machine learning algorithms, such as tree-based and penalized regression models were used to identify the most important loss influencing factors among a set of 55 candidate variables. These variables comprise hydrological and hydraulic aspects, early warning, precaution, building characteristics and the socio-economic status of the household. In a second step, the most important loss influencing variables were used to derive a probabilistic multi-variable pluvial flood loss estimation model based on Bayesian Networks. Two different networks were tested: a score-based network learned from the data and a network based on expert knowledge. Loss predictions are made through Bayesian inference using Markov chain Monte Carlo (MCMC) sampling. With the ability to cope with incomplete information and use expert knowledge, as well as inherently providing quantitative uncertainty information, it is shown that loss models based on BNs are superior to deterministic approaches for pluvial flood risk assessment.

Termites promote resistance of decomposition to spatiotemporal variability in rainfall.

PubMed

Veldhuis, Michiel P; Laso, Francisco J; Olff, Han; Berg, Matty P

2017-02-01

The ecological impact of rapid environmental change will depend on the resistance of key ecosystems processes, which may be promoted by species that exert strong control over local environmental conditions. Recent theoretical work suggests that macrodetritivores increase the resistance of African savanna ecosystems to changing climatic conditions, but experimental evidence is lacking. We examined the effect of large fungus-growing termites and other non-fungus-growing macrodetritivores on decomposition rates empirically with strong spatiotemporal variability in rainfall and temperature. Non-fungus-growing larger macrodetritivores (earthworms, woodlice, millipedes) promoted decomposition rates relative to microbes and small soil fauna (+34%) but both groups reduced their activities with decreasing rainfall. However, fungus-growing termites increased decomposition rates strongest (+123%) under the most water-limited conditions, making overall decomposition rates mostly independent from rainfall. We conclude that fungus-growing termites are of special importance in decoupling decomposition rates from spatiotemporal variability in rainfall due to the buffered environment they create within their extended phenotype (mounds), that allows decomposition to continue when abiotic conditions outside are less favorable. This points at a wider class of possibly important ecological processes, where soil-plant-animal interactions decouple ecosystem processes from large-scale climatic gradients. This may strongly alter predictions from current climate change models. © 2016 by the Ecological Society of America.

Sensitivity of crop cover to climate variability: insights from two Indian agro-ecoregions.

PubMed

Mondal, Pinki; Jain, Meha; DeFries, Ruth S; Galford, Gillian L; Small, Christopher

2015-01-15

Crop productivity in India varies greatly with inter-annual climate variability and is highly dependent on monsoon rainfall and temperature. The sensitivity of yields to future climate variability varies with crop type, access to irrigation and other biophysical and socio-economic factors. To better understand sensitivities to future climate, this study focuses on agro-ecological subregions in Central and Western India that span a range of crops, irrigation, biophysical conditions and socioeconomic characteristics. Climate variability is derived from remotely-sensed data products, Tropical Rainfall Measuring Mission (TRMM - precipitation) and Moderate Resolution Imaging Spectroradiometer (MODIS - temperature). We examined green-leaf phenologies as proxy for crop productivity using the MODIS Enhanced Vegetation Index (EVI) from 2000 to 2012. Using both monsoon and winter growing seasons, we assessed phenological sensitivity to inter-annual variability in precipitation and temperature patterns. Inter-annual EVI phenology anomalies ranged from -25% to 25%, with some highly anomalous values up to 200%. Monsoon crop phenology in the Central India site is highly sensitive to climate, especially the timing of the start and end of the monsoon and intensity of precipitation. In the Western India site, monsoon crop phenology is less sensitive to precipitation variability, yet shows considerable fluctuations in monsoon crop productivity across the years. Temperature is critically important for winter productivity across a range of crop and management types, such that irrigation might not provide a sufficient buffer against projected temperature increases. Better access to weather information and usage of climate-resilient crop types would play pivotal role in maintaining future productivity. Effective strategies to adapt to projected climate changes in the coming decades would also need to be tailored to regional biophysical and socio-economic conditions. Copyright © 2014 Elsevier Ltd. All rights reserved.

Regional landslide hazard assessment in a deep uncertain future

NASA Astrophysics Data System (ADS)

Almeida, Susana; Holcombe, Liz; Pianosi, Francesca; Wagener, Thorsten

2017-04-01

Landslides have many negative economic and societal impacts, including the potential for significant loss of life and damage to infrastructure. These risks are likely to be exacerbated in the future by a combination of climatic and socio-economic factors. Climate change, for example, is expected to increase the occurrence of rainfall-triggered landslides, because a warmer atmosphere tends to produce more high intensity rainfall events. Prediction of future changes in rainfall, however, is subject to high levels of uncertainty, making it challenging for decision-makers to identify the areas and populations that are most vulnerable to landslide hazards. In this study, we demonstrate how a physically-based model - the Combined Hydrology and Stability Model (CHASM) - can be used together with Global Sensitivity Analysis (GSA) to explore the underlying factors controlling the spatial distribution of landslide risks across a regional landscape, while also accounting for deep uncertainty around future rainfall conditions. We demonstrate how GSA can used to analyse CHASM which in turn represents the spatial variability of hillslope characteristics in the study region, while accounting for other uncertainties. Results are presented in the form of landslide hazard maps, utilising high-resolution digital elevation datasets for a case study in St Lucia in the Caribbean. Our findings about spatial landslide hazard drivers have important implications for data collection approaches and for long-term decision-making about land management practices.

Regional Landslide Hazard Assessment Considering Potential Climate Change

NASA Astrophysics Data System (ADS)

Almeida, S.; Holcombe, E.; Pianosi, F.; Wagener, T.

2016-12-01

Landslides have many negative economic and societal impacts, including the potential for significant loss of life and damage to infrastructure. These risks are likely to be exacerbated in the future by a combination of climatic and socio-economic factors. Climate change, for example, is expected to increase the occurrence of rainfall-triggered landslides, because a warmer atmosphere tends to produce more high intensity rainfall events. Prediction of future changes in rainfall, however, is subject to high levels of uncertainty, making it challenging for decision-makers to identify the areas and populations that are most vulnerable to landslide hazards. In this study, we demonstrate how a physically-based model - the Combined Hydrology and Stability Model (CHASM) - can be used together with Global Sensitivity Analysis (GSA) to explore the underlying factors controlling the spatial distribution of landslide risks across a regional landscape, while also accounting for deep uncertainty around potential future rainfall triggers. We demonstrate how GSA can be used to analyse CHASM which in turn represents the spatial variability of hillslope characteristics in the study region, while accounting for other uncertainties. Results are presented in the form of landslide hazard maps, utilising high-resolution digital elevation datasets for a case study in St Lucia in the Caribbean. Our findings about spatial landslide hazard drivers have important implications for data collection approaches and for long-term decision-making about land management practices.

Woody vegetation die off and regeneration in response to rainfall variability in the west African Sahel

USGS Publications Warehouse

Brandt, Martin; Tappan, G. Gray; Aziz Diouf, Abdoul; Beye, Gora; Mbow, Cheikh; Fensholt, Rasmus

2017-01-01

The greening in the Senegalese Sahel has been linked to an increase in net primary productivity, with significant long-term trends being closely related to the woody strata. This study investigates woody plant growth and mortality within greening areas in the pastoral areas of Senegal, and how these dynamics are linked to species diversity, climate, soil and human management. We analyse woody cover dynamics by means of multi-temporal and multi-scale Earth Observation, satellite based rainfall and in situ data sets covering the period 1994 to 2015. We find that favourable conditions (forest reserves, low human population density, sufficient rainfall) led to a rapid growth of Combretaceae and Balanites aegyptiaca between 2000 and 2013 with an average increase of 4% woody cover. However, the increasing dominance and low drought resistance of drought prone species bears the risk of substantial woody cover losses following drought years. This was observed in 2014–2015, with a die off of Guiera senegalensis in most places of the study area. We show that woody cover and woody cover trends are closely related to mean annual rainfall, but no clear relationship with rainfall trends was found over the entire study period. The observed spatial and temporal variation contrasts with the simplified labels of “greening” or “degradation”. While in principal a low woody plant diversity negatively impacts regional resilience, the Sahelian system is showing signs of resilience at decadal time scales through widespread increases in woody cover and high regeneration rates after periodic droughts. We have reaffirmed that the woody cover in Sahel responds to its inherent climatic variability and does not follow a linear trend.

Performance of CMIP3 and CMIP5 GCMs to simulate observed rainfall characteristics over the Western Himalayan region

NASA Astrophysics Data System (ADS)

Meher, J. K.; Das, L.

2017-12-01

The Western Himalayan Region (WHR) was subject to a significant negative trend in the annual and monsoon rainfall during 1902-2005. Annual and seasonal rainfall change over WHR of India was estimated using 22 rain gauge station rainfall data from the India Meteorological Department. The performance of 13 global climate models (GCMs) from the coupled model intercomparison project phase 3 (CMIP3) and 42 GCMs from CMIP5 was evaluated through multiple analysis: the evaluation of the mean annual cycle, annual cycles of interannual variability, spatial patterns, trends and signal-to-noise ratio. In general, CMIP5 GCMs were more skillful in terms of simulating the annual cycle of interannual variability compared to CMIP3 GCMs. The CMIP3 GCMs failed to reproduce the observed trend whereas 50% of the CMIP5 GCMs reproduced the statistical distribution of short-term (30-years) trend-estimates than for the longer term (99-years). GCMs from both CMIP3 and CMIP5 were able to simulate the spatial distribution of observed rainfall in pre-monsoon and winter months. Based on performance, each model of CMIP3 and CMIP5 was given an overall rank, which puts the high resolution version of the MIROC3.2 model (MIROC3.2 hires) and MIROC5 at the top in CMIP3 and CMIP5 respectively. Robustness of the ranking was judged through a sensitivity analysis, which indicated that ranks were independent during the process of adding or removing any individual method. It also revealed that trend analysis was not a robust method of judging performances of the model as compared to other methods.

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.

Decadal features of heavy rainfall events in eastern China

NASA Astrophysics Data System (ADS)

Chen, Huopo; Sun, Jianqi; Fan, Ke

2012-06-01

Based on daily precipitation data, the spatial-temporal features of heavy rainfall events (HREs) during 1960-2009 are investigated. The results indicate that the HREs experienced strong decadal variability in the past 50 years, and the decadal features varied across regions. More HRE days are observed in the 1960s, 1980s, and 1990s over Northeast China (NEC); in the 1960s, 1970s, and 1990s over North China (NC); in the early 1960s, 1980s, and 2000s over the Huaihe River basin (HR); in the 1970s-1990s over the mid-lower reaches of the Yangtze River valley (YR); and in the 1970s and 1990s over South China (SC). These decadal changes of HRE days in eastern China are closely associated with the decadal variations of water content and stratification stability of the local atmosphere. The intensity of HREs in each sub-region is also characterized by strong decadal variability. The HRE intensity and frequency co-vary on the long-term trend, and show consistent variability over NEC, NC, and YR, but inconsistent variability over SC and HR. Further analysis of the relationships between the annual rainfall and HRE frequency as well as intensity indicates that the HRE frequency is the major contributor to the total rainfall variability in eastern China, while the HRE intensity shows only relative weak contribution.

Use of Machine Learning Techniques for Iidentification of Robust Teleconnections to East African Rainfall Variability in Observations and Models

NASA Technical Reports Server (NTRS)

Roberts, J. Brent; Robertson, Franklin R.; Funk, Chris

2014-01-01

Providing advance warning of East African rainfall variations is a particular focus of several groups including those participating in the Famine Early Warming Systems Network. Both seasonal and long-term model projections of climate variability are being used to examine the societal impacts of hydrometeorological variability on seasonal to interannual and longer time scales. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of both seasonal and climate model projections to develop downscaled scenarios for using in impact modeling. The utility of these projections is reliant on the ability of current models to capture the embedded relationships between East African rainfall and evolving forcing within the coupled ocean-atmosphere-land climate system. Previous studies have posited relationships between variations in El Niño, the Walker circulation, Pacific decadal variability (PDV), and anthropogenic forcing. This study applies machine learning methods (e.g. clustering, probabilistic graphical model, nonlinear PCA) to observational datasets in an attempt to expose the importance of local and remote forcing mechanisms of East African rainfall variability. The ability of the NASA Goddard Earth Observing System (GEOS5) coupled model to capture the associated relationships will be evaluated using Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations.

The collaborative historical African rainfall model: description and evaluation

USGS Publications Warehouse

Funk, Christopher C.; Michaelsen, Joel C.; Verdin, James P.; Artan, Guleid A.; Husak, Gregory; Senay, Gabriel B.; Gadain, Hussein; Magadazire, Tamuka

2003-01-01

In Africa the variability of rainfall in space and time is high, and the general availability of historical gauge data is low. This makes many food security and hydrologic preparedness activities difficult. In order to help overcome this limitation, we have created the Collaborative Historical African Rainfall Model (CHARM). CHARM combines three sources of information: climatologically aided interpolated (CAI) rainfall grids (monthly/0.5° ), National Centers for Environmental Prediction reanalysis precipitation fields (daily/1.875° ) and orographic enhancement estimates (daily/0.1° ). The first set of weights scales the daily reanalysis precipitation fields to match the gridded CAI monthly rainfall time series. This produces data with a daily/0.5° resolution. A diagnostic model of orographic precipitation, VDELB—based on the dot-product of the surface wind V and terrain gradient (DEL) and atmospheric buoyancy B—is then used to estimate the precipitation enhancement produced by complex terrain. Although the data are produced on 0.1° grids to facilitate integration with satellite-based rainfall estimates, the ‘true’ resolution of the data will be less than this value, and varies with station density, topography, and precipitation dynamics. The CHARM is best suited, therefore, to applications that integrate rainfall or rainfall-driven model results over large regions. The CHARM time series is compared with three independent datasets: dekadal satellite-based rainfall estimates across the continent, dekadal interpolated gauge data in Mali, and daily interpolated gauge data in western Kenya. These comparisons suggest reasonable accuracies (standard errors of about half a standard deviation) when data are aggregated to regional scales, even at daily time steps. Thus constrained, numerical weather prediction precipitation fields do a reasonable job of representing large-scale diurnal variations.

Identification of tipping elements of the Indian Summer Monsoon using climate network approach

NASA Astrophysics Data System (ADS)

Stolbova, Veronika; Surovyatkina, Elena; Kurths, Jurgen

2015-04-01

Spatial and temporal variability of the rainfall is a vital question for more than one billion of people inhabiting the Indian subcontinent. Indian Summer Monsoon (ISM) rainfall is crucial for India's economy, social welfare, and environment and large efforts are being put into predicting the Indian Summer Monsoon. For predictability of the ISM, it is crucial to identify tipping elements - regions over the Indian subcontinent which play a key role in the spatial organization of the Indian monsoon system. Here, we use climate network approach for identification of such tipping elements of the ISM. First, we build climate networks of the extreme rainfall, surface air temperature and pressure over the Indian subcontinent for pre-monsoon, monsoon and post-monsoon seasons. We construct network of extreme rainfall event using observational satellite data from 1998 to 2012 from the Tropical Rainfall Measuring Mission (TRMM 3B42V7) and reanalysis gridded daily rainfall data for a time period of 57 years (1951-2007) (Asian Precipitation Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources, APHRODITE). For the network of surface air temperature and pressure fields, we use re-analysis data provided by the National Center for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR). Second, we filter out data by coarse-graining the network through network measures, and identify tipping regions of the ISM. Finally, we compare obtained results of the network analysis with surface wind fields and show that occurrence of the tipping elements is mostly caused by monsoonal wind circulation, migration of the Intertropical Convergence Zone (ITCZ) and Westerlies. We conclude that climate network approach enables to select the most informative regions for the ISM, providing realistic description of the ISM dynamics with fewer data, and also help to identify tipping regions of the ISM. Obtained tipping elements deserve a special attention for the meteorologists and can be used as markers of the ISM variability.

Soil erodibility variability in laboratory and field rainfall simulations

NASA Astrophysics Data System (ADS)

Szabó, Boglárka; Szabó, Judit; Jakab, Gergely; Centeri, Csaba; Szalai, Zoltán

2017-04-01

Rainfall simulation experiments are the most common way to observe and to model the soil erosion processes in in situ and ex situ circumstances. During modelling soil erosion, one of the most important factors are the annual soil loss and the soil erodibility which represent the effect of soil properties on soil loss and the soil resistance against water erosion. The amount of runoff and soil loss can differ in case of the same soil type, while it's characteristics determine the soil erodibility factor. This leads to uncertainties regarding soil erodibility. Soil loss and soil erodibility were examined with the investigation of the same soil under laboratory and field conditions with rainfall simulators. The comparative measurement was carried out in a laboratory on 0,5 m2, and in the field (Shower Power-02) on 6 m2 plot size where the applied slope angles were 5% and 12% with 30 and 90 mm/h rainfall intensity. The main idea was to examine and compare the soil erodibility and its variability coming from the same soil, but different rainfall simulator type. The applied model was the USLE, nomograph and other equations which concern single rainfall events. The given results show differences between the field and laboratory experiments and between the different calculations. Concerning for the whole rainfall events runoff and soil loss, were significantly higher at the laboratory experiments, which affected the soil erodibility values too. The given differences can originate from the plot size. The main research questions are that: How should we handle the soil erodibility factors and its significant variability? What is the best solution for soil erodibility determination?

An assessment of the ability of Bartlett-Lewis type of rainfall models to reproduce drought statistics

NASA Astrophysics Data System (ADS)

Pham, M. T.; Vanhaute, W. J.; Vandenberghe, S.; De Baets, B.; Verhoest, N. E. C.

2013-12-01

Of all natural disasters, the economic and environmental consequences of droughts are among the highest because of their longevity and widespread spatial extent. Because of their extreme behaviour, studying droughts generally requires long time series of historical climate data. Rainfall is a very important variable for calculating drought statistics, for quantifying historical droughts or for assessing the impact on other hydrological (e.g. water stage in rivers) or agricultural (e.g. irrigation requirements) variables. Unfortunately, time series of historical observations are often too short for such assessments. To circumvent this, one may rely on the synthetic rainfall time series from stochastic point process rainfall models, such as Bartlett-Lewis models. The present study investigates whether drought statistics are preserved when simulating rainfall with Bartlett-Lewis models. Therefore, a 105 yr 10 min rainfall time series obtained at Uccle, Belgium is used as a test case. First, drought events were identified on the basis of the Effective Drought Index (EDI), and each event was characterized by two variables, i.e. drought duration (D) and drought severity (S). As both parameters are interdependent, a multivariate distribution function, which makes use of a copula, was fitted. Based on the copula, four types of drought return periods are calculated for observed as well as simulated droughts and are used to evaluate the ability of the rainfall models to simulate drought events with the appropriate characteristics. Overall, all Bartlett-Lewis model types studied fail to preserve extreme drought statistics, which is attributed to the model structure and to the model stationarity caused by maintaining the same parameter set during the whole simulation period.

A copula-based assessment of Bartlett-Lewis type of rainfall models for preserving drought statistics

NASA Astrophysics Data System (ADS)

Pham, M. T.; Vanhaute, W. J.; Vandenberghe, S.; De Baets, B.; Verhoest, N. E. C.

2013-06-01

Of all natural disasters, the economic and environmental consequences of droughts are among the highest because of their longevity and widespread spatial extent. Because of their extreme behaviour, studying droughts generally requires long time series of historical climate data. Rainfall is a very important variable for calculating drought statistics, for quantifying historical droughts or for assessing the impact on other hydrological (e.g. water stage in rivers) or agricultural (e.g. irrigation requirements) variables. Unfortunately, time series of historical observations are often too short for such assessments. To circumvent this, one may rely on the synthetic rainfall time series from stochastic point process rainfall models, such as Bartlett-Lewis models. The present study investigates whether drought statistics are preserved when simulating rainfall with Bartlett-Lewis models. Therefore, a 105 yr 10 min rainfall time series obtained at Uccle, Belgium is used as test case. First, drought events were identified on the basis of the Effective Drought Index (EDI), and each event was characterized by two variables, i.e. drought duration (D) and drought severity (S). As both parameters are interdependent, a multivariate distribution function, which makes use of a copula, was fitted. Based on the copula, four types of drought return periods are calculated for observed as well as simulated droughts and are used to evaluate the ability of the rainfall models to simulate drought events with the appropriate characteristics. Overall, all Bartlett-Lewis type of models studied fail in preserving extreme drought statistics, which is attributed to the model structure and to the model stationarity caused by maintaining the same parameter set during the whole simulation period.

Application of Archimedean copulas to the impact assessment of hydro-climatic variables in semi-arid aquifers of western India

NASA Astrophysics Data System (ADS)

Wable, Pawan S.; Jha, Madan K.

2018-02-01

The effects of rainfall and the El Niño Southern Oscillation (ENSO) on groundwater in a semi-arid basin of India were analyzed using Archimedean copulas considering 17 years of data for monsoon rainfall, post-monsoon groundwater level (PMGL) and ENSO Index. The evaluated dependence among these hydro-climatic variables revealed that PMGL-Rainfall and PMGL-ENSO Index pairs have significant dependence. Hence, these pairs were used for modeling dependence by employing four types of Archimedean copulas: Ali-Mikhail-Haq, Clayton, Gumbel-Hougaard, and Frank. For the copula modeling, the results of probability distributions fitting to these hydro-climatic variables indicated that the PMGL and rainfall time series are best represented by Weibull and lognormal distributions, respectively, while the non-parametric kernel-based normal distribution is the most suitable for the ENSO Index. Further, the PMGL-Rainfall pair is best modeled by the Clayton copula, and the PMGL-ENSO Index pair is best modeled by the Frank copula. The Clayton copula-based conditional probability of PMGL being less than or equal to its average value at a given mean rainfall is above 70% for 33% of the study area. In contrast, the spatial variation of the Frank copula-based probability of PMGL being less than or equal to its average value is 35-40% in 23% of the study area during El Niño phase, while it is below 15% in 35% of the area during the La Niña phase. This copula-based methodology can be applied under data-scarce conditions for exploring the impacts of rainfall and ENSO on groundwater at basin scales.

Modelled rainfall skill assessment against a 1000-year time/space isotope dendro-climatology for southern Africa

NASA Astrophysics Data System (ADS)

Woodborne, Stephan; Hall, Grant; Zhang, Qiong

2016-04-01

Palaeoclimate reconstruction using isotopic analysis of tree growth increments has yielded a 1000-year record of rainfall variability in southern Africa. Isotope dendro-climatology reconstructions from baobab trees (Adansonia digitata) provide evidence for rainfall variability from the arid Namib Desert and the Limpopo River Valley. Isotopic analysis of a museum specimen of a yellowwood tree (Podocarps falcatus) yields another record from the southwestern part of the subcontinent. Combined with the limited classic denro-climatologies available in the region these records yield palaeo-rainfall variability in the summer and winter rainfall zones as well as the hyper-arid zone over the last 1000 years. Coherent shifts in all of the records indicate synoptic changes in the westerlies, the inter-tropical convergence zone, and the Congo air boundary. The most substantial rainfall shift takes place at about 1600 CE at the onset of the Little Ice Age. Another distinctive feature of the record is a widespread phenomenon that occurs shortly after 1810 CE that in southern Africa corresponds with a widespread social upheaval known as the Difequane or Mfekane. Large scale forcing of the system includes sea-surface temperatures in the Agulhas Current, the El Nino Southern Oscillation and the Southern Annular Mode. The Little Ice Age and Mfekane climate shifts result from different forcing mechanisms, and the rainfall response in the different regions at these times do not have a fixed phase relationship. This complexity provides a good scenario to test climate models. A first order (wetter versus drier) comparison between each of the tree records and a 1000-year palaeoclimate model simulation for the Little Ice Age and Mfekane transitions demonstrates a generally good correspondence.

Quasi-continuous stochastic simulation framework for flood modelling

NASA Astrophysics Data System (ADS)

Moustakis, Yiannis; Kossieris, Panagiotis; Tsoukalas, Ioannis; Efstratiadis, Andreas

2017-04-01

Typically, flood modelling in the context of everyday engineering practices is addressed through event-based deterministic tools, e.g., the well-known SCS-CN method. A major shortcoming of such approaches is the ignorance of uncertainty, which is associated with the variability of soil moisture conditions and the variability of rainfall during the storm event.In event-based modeling, the sole expression of uncertainty is the return period of the design storm, which is assumed to represent the acceptable risk of all output quantities (flood volume, peak discharge, etc.). On the other hand, the varying antecedent soil moisture conditions across the basin are represented by means of scenarios (e.g., the three AMC types by SCS),while the temporal distribution of rainfall is represented through standard deterministic patterns (e.g., the alternative blocks method). In order to address these major inconsistencies,simultaneously preserving the simplicity and parsimony of the SCS-CN method, we have developed a quasi-continuous stochastic simulation approach, comprising the following steps: (1) generation of synthetic daily rainfall time series; (2) update of potential maximum soil moisture retention, on the basis of accumulated five-day rainfall; (3) estimation of daily runoff through the SCS-CN formula, using as inputs the daily rainfall and the updated value of soil moisture retention;(4) selection of extreme events and application of the standard SCS-CN procedure for each specific event, on the basis of synthetic rainfall.This scheme requires the use of two stochastic modelling components, namely the CastaliaR model, for the generation of synthetic daily data, and the HyetosMinute model, for the disaggregation of daily rainfall to finer temporal scales. Outcomes of this approach are a large number of synthetic flood events, allowing for expressing the design variables in statistical terms and thus properly evaluating the flood risk.

Simulation of the effects of rainfall and groundwater use on historical lake water levels, groundwater levels, and spring flows in central Florida

USGS Publications Warehouse

O'Reilly, Andrew M.; Roehl, Edwin A.; Conrads, Paul; Daamen, Ruby C.; Petkewich, Matthew D.

2014-01-01

The urbanization of central Florida has progressed substantially in recent decades, and the total population in Lake, Orange, Osceola, Polk, and Seminole Counties more than quadrupled from 1960 to 2010. The Floridan aquifer system is the primary source of water for potable, industrial, and agricultural purposes in central Florida. Despite increases in groundwater withdrawals to meet the demand of population growth, recharge derived by infiltration of rainfall in the well-drained karst terrain of central Florida is the largest component of the long-term water balance of the Floridan aquifer system. To complement existing physics-based groundwater flow models, artificial neural networks and other data-mining techniques were used to simulate historical lake water level, groundwater level, and spring flow at sites throughout the area. Historical data were examined using descriptive statistics, cluster analysis, and other exploratory analysis techniques to assess their suitability for more intensive data-mining analysis. Linear trend analyses of meteorological data collected by the National Oceanic and Atmospheric Administration at 21 sites indicate 67 percent of sites exhibited upward trends in air temperature over at least a 45-year period of record, whereas 76 percent exhibited downward trends in rainfall over at least a 95-year period of record. Likewise, linear trend analyses of hydrologic response data, which have varied periods of record ranging in length from 10 to 79 years, indicate that water levels in lakes (307 sites) were about evenly split between upward and downward trends, whereas water levels in 69 percent of wells (out of 455 sites) and flows in 68 percent of springs (out of 19 sites) exhibited downward trends. Total groundwater use in the study area increased from about 250 million gallons per day (Mgal/d) in 1958 to about 590 Mgal/d in 1980 and remained relatively stable from 1981 to 2008, with a minimum of 559 Mgal/d in 1994 and a maximum of 773 Mgal/d in 2000. The change in groundwater-use trend in the early 1980s and the following period of relatively slight trend is attributable to the concomitant effects of increasing public-supply withdrawals and decreasing use of water by the phosphate industry and agriculture. On the basis of available historical data and exploratory analyses, empirical lake water-level, groundwater-level, and spring-flow models were developed for 22 lakes, 23 wells, and 6 springs. Input time series consisting of various frequencies and frequency-band components of daily rainfall (1942 to 2008) and monthly total groundwater use (1957 to 2008) resulted in hybrid signal-decomposition artificial neural network models. The final models explained much of the variability in observed hydrologic data, with 43 of the 51 sites having coefficients of determination exceeding 0.6, and the models matched the magnitude of the observed data reasonably well, such that models for 32 of the 51 sites had root-mean-square errors less than 10 percent of the measured range of the data. The Central Florida Artificial Neural Network Decision Support System was developed to integrate historical databases and the 102 site-specific artificial neural network models, model controls, and model output into a spreadsheet application with a graphical user interface that allows the user to simulate scenarios of interest. Overall, the data-mining analyses indicate that the Floridan aquifer system in central Florida is a highly conductive, dynamic, open system that is strongly influenced by external forcing. The most important external forcing appears to be rainfall, which explains much of the multiyear cyclic variability and long-term downward trends observed in lake water levels, groundwater levels, and spring flows. For most sites, groundwater use explains less of the observed variability in water levels and flows than rainfall. Relative groundwater-use impacts are greater during droughts, however, and long-term trends in water levels and flows were identified that are consistent with historical groundwater-use patterns. The sensitivity of the hydrologic system to rainfall is expected, owing to the well-drained karst terrain and relatively thin confinement of the Floridan aquifer system in much of central Florida. These characteristics facilitate the relatively rapid transmission of infiltrating water from rainfall to the water table and contribute to downward leakage of water to the Floridan aquifer system. The areally distributed nature of rainfall, as opposed to the site-specific nature of groundwater use, and the generally high transmissivity and low storativity properties of the semiconfined Floridan aquifer system contribute to the prevalence of water-level and flow patterns that mimic rainfall patterns. In general, the data-mining analyses demonstrate that the hydrologic system in central Florida is affected by groundwater use differently during wet periods, when little or no system storage is available (high water levels), compared to dry periods, when there is excess system storage (low water levels). Thus, by driving the overall behavior of the system, rainfall indirectly influences the degree to which groundwater use will effect persistent trends in water levels and flows, with groundwater-use impacts more prevalent during periods of low water levels and spring flows caused by low rainfall and less prevalent during periods of high water levels and spring flows caused by high rainfall. Differences in the magnitudes of rainfall and groundwater use during wet and dry periods also are important determinants of hydrologic response. An important implication of the data-mining analyses is that rainfall variability at subannual to multidecadal timescales must be considered in combination with groundwater use to provide robust system-response predictions that enhance sustainable resource management in an open karst aquifer system. The data-driven approach was limited, however, by the confounding effects of correlation between rainfall and groundwater use, the quality and completeness of the historical databases, and the spatial variations in groundwater use. The data-mining analyses indicate that available historical data when used alone do not contain sufficient information to definitively quantify the related individual effects of rainfall and groundwater use on hydrologic response. The knowledge gained from data-driven modeling and the results from physics-based modeling, when compared and used in combination, can yield a more comprehensive assessment and a more robust understanding of the hydrologic system than either of the approaches used separately.

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.

Recent climate variability and its impacts on soybean yields in Southern Brazil

NASA Astrophysics Data System (ADS)

Ferreira, Danielle Barros; Rao, V. Brahmananda

2011-08-01

Recent climate variability in rainfall, temperatures (maximum and minimum), and the diurnal temperature range is studied with emphasis on its influence over soybean yields in southern Brazil, during 1969 to 2002. The results showed that the soybean ( Glycine max L. Merril) yields are more affected by changes in temperature during summer, while changes in rainfall are more important during the beginning of plantation and at its peak of development. Furthermore, soybean yields in Paraná are more sensitive to rainfall variations, while soybean yields in the Rio Grande do Sul are more sensitive to variations in temperature. Effects of interannual climatic variability on soybean yields are evaluated through three agro-meteorological models: additive Stewart, multiplicative Rao, and multiplicative Jensen. The Jensen model is able to reproduce the interannual behavior of soybean yield reasonably well.

Hydrometeorology as an Inversion Problem: Can River Discharge Observations Improve the Atmosphere by Ensemble Data Assimilation?

NASA Astrophysics Data System (ADS)

Sawada, Yohei; Nakaegawa, Tosiyuki; Miyoshi, Takemasa

2018-01-01

We examine the potential of assimilating river discharge observations into the atmosphere by strongly coupled river-atmosphere ensemble data assimilation. The Japan Meteorological Agency's Non-Hydrostatic atmospheric Model (JMA-NHM) is first coupled with a simple rainfall-runoff model. Next, the local ensemble transform Kalman filter is used for this coupled model to assimilate the observations of the rainfall-runoff model variables into the JMA-NHM model variables. This system makes it possible to do hydrometeorology backward, i.e., to inversely estimate atmospheric conditions from the information of river flows or a flood on land surfaces. We perform a proof-of-concept Observing System Simulation Experiment, which reveals that the assimilation of river discharge observations into the atmospheric model variables can improve the skill of the short-term severe rainfall forecast.

The power of remote sensing: Global monitoring of weather, water, and crops with satellites and data integration

USGS Publications Warehouse

Senay, Gabriel

2016-01-01

Imagine a family of six whose livelihood is based on subsistence farming on a small, maybe one hectare, parcel of land somewhere in Africa. The seasonal rainfall varies greatly, from 500 to 800 mm per year, and the land is degraded. Thus, the parcel’s total productivity is not more than 1.5 tonnes in a good year, hardly meeting the family’s food requirements. The lack of surplus grain eliminates the need for grain storage structures, and due to the high rainfall variability, drought hazard is always looming, with an average recurrence interval of five to ten years.

Effect of rain gauge density over the accuracy of rainfall: a case study over Bangalore, India.

PubMed

Mishra, Anoop Kumar

2013-12-01

Rainfall is an extremely variable parameter in both space and time. Rain gauge density is very crucial in order to quantify the rainfall amount over a region. The level of rainfall accuracy is highly dependent on density and distribution of rain gauge stations over a region. Indian Space Research Organisation (ISRO) have installed a number of Automatic Weather Station (AWS) rain gauges over Indian region to study rainfall. In this paper, the effect of rain gauge density over daily accumulated rainfall is analyzed using ISRO AWS gauge observations. A region of 50 km × 50 km box over southern part of Indian region (Bangalore) with good density of rain gauges is identified for this purpose. Rain gauge numbers are varied from 1-8 in 50 km box to study the variation in the daily accumulated rainfall. Rainfall rates from the neighbouring stations are also compared in this study. Change in the rainfall as a function of gauge spacing is studied. Use of gauge calibrated satellite observations to fill the gauge station value is also studied. It is found that correlation coefficients (CC) decrease from 82% to 21% as gauge spacing increases from 5 km to 40 km while root mean square error (RMSE) increases from 8.29 mm to 51.27 mm with increase in gauge spacing from 5 km to 40 km. Considering 8 rain gauges as a standard representative of rainfall over the region, absolute error increases from 15% to 64% as gauge numbers are decreased from 7 to 1. Small errors are reported while considering 4 to 7 rain gauges to represent 50 km area. However, reduction to 3 or less rain gauges resulted in significant error. It is also observed that use of gauge calibrated satellite observations significantly improved the rainfall estimation over the region with very few rain gauge observations.

Variability of CO2 fugacity at the western edge of the tropical Atlantic Ocean from the 8°N to 38°W PIRATA buoy

NASA Astrophysics Data System (ADS)

Bruto, Leonardo; Araujo, Moacyr; Noriega, Carlos; Veleda, Dóris; Lefèvre, Nathalie

2017-06-01

Hourly data of CO2 fugacity (fCO2) at 8°N-38°W were analyzed from 2008 to 2011. Analyses of wind, rainfall, temperature and salinity data from the buoy indicated two distinct seasonal periods. The first period (January to July) had a mean fCO2 of 378.9 μatm (n = 7512). During this period, in which the study area was characterized by small salinity variations, the fCO2 is mainly controlled by sea surface temperature (SST) variations (fCO2 = 24.4*SST-281.1, r2 = 0.8). During the second period (August-December), the mean fCO2 was 421.9 μatm (n = 11571). During these months, the region is subjected to the simultaneous action of (a) rainfall induced by the presence of the Intertropical Convergence Zone (ITCZ); (b) arrival of fresh water from the Amazon River plume that is transported to the east by the North Equatorial Countercurrent (NECC) after the retroflection of the North Brazil Current (NBC); and (c) vertical input of CO2-rich water due to Ekman pumping. The data indicated the existence of high-frequency fCO2 variability (periods less than 24 h). This high variability is related to two different mechanisms. In the first mechanism, fCO2 increases are associated to rapid increases in SST and are attributed to the diurnal cycle of solar radiation. In addition, low wind speed contributes to SST rising by inhibiting vertical mixing. In the second mechanism, fCO2 decreases are associated to SSS decreases caused by heavy rainfall.

A northern Australian coral record of seasonal rainfall and terrestrial runoff (1775-1986)

NASA Astrophysics Data System (ADS)

Patterson, E. W.; Cole, J. E.; Vetter, L.; Lough, J.

2017-12-01

Northern Australia is a climatically dynamic region influenced by both the El Niño-Southern Oscillation (ENSO) and the Australian monsoon. However, this region is largely devoid of long climate records with sub-annual resolution. Understanding long-term climate variations is essential to assess how the storm-prone coasts and rainfall-reliant rangelands of northern Australia have been impacted in the past and may be in the future. In this study, we present a continuous multicentury (1775-1986) coral reconstruction of rainfall and hydroclimate in northern Australia, developed from a Porites spp. coral core collected off the coast of Darwin, Northern Territory, Australia. We combined Ba/Ca measurements with luminescence data as tracers of terrestrial erosion and river discharge respectively. Our results show a strong seasonal cycle in Ba/Ca linked to wet austral summers driven by the Australian monsoon. The Ba/Ca record is corroborated by oxygen isotope data from the same coral and indices of regional river discharge and rainfall. Consistently high levels of Ba measured throughout the record further attest to the importance of river influence on this coral. Our record also shows changes in variability and the baseline level of Ba in coastal waters through time, which may be driven in part by historical land-use change, such as damming or agricultural practices. We will additionally use these records to examine decadal to centennial-scale variability in monsoonal precipitation and regional ENSO signals.

Central American rainfall variations since 100 ka and moisture delivery to Greenland

NASA Astrophysics Data System (ADS)

Lachniet, M. S.; Asmerom, Y.; Johnson, L.; Burns, S.; Polyak, V.; Patterson, W.

2007-12-01

We present a rainfall history for Central America based on oxygen isotope values in Uranium-series dated stalagmites collected from the Pacific Coast of Costa Rica over parts of the Holocene and from 25 to 100 ka. The oxygen isotope values of modern rainfall in our study area within the heart of the Intertropical Convergence Zone (ITCZ) are dominated by the amount effect and moisture source, and we interpret our data as a paleorainfall proxy. Our data show substantial oxygen isotope variability on centennial to multi-millennial time scales. Further, our paleorainfall time series is strongly correlated with the deuterium excess parameter in Greenland Ice, which suggests that the strength of the tropical hydrological cycle has modulated the flow of low-latitude moisture to Greenland on millennial time scales. Our results indicate a strong coupling between tropical and high latitude paleoclimate, that was likely linked via variations in the strength of the Hadley cell and its associated export of atmospheric moisture to the high latitudes. We observe the wettest periods in Central America when the Caribbean was warmer than 26.5 degrees C and the Caribbean to Pacific (cold tongue) SST gradient was largest, suggesting a combined Atlantic and Pacific Ocean control on ITCZ rainfall.

Productivity responses of desert vegetation to precipitation patterns across a rainfall gradient.

PubMed

Li, Fang; Zhao, Wenzhi; Liu, Hu

2015-03-01

The influences of previous-year precipitation and episodic rainfall events on dryland plants and communities are poorly quantified in the temperate desert region of Northwest China. To evaluate the thresholds and lags in the response of aboveground net primary productivity (ANPP) to variability in rainfall pulses and seasonal precipitation along the precipitation-productivity gradient in three desert ecosystems with different precipitation regimes, we collected precipitation data from 2000 to 2012 in Shandan (SD), Linze (LZ) and Jiuquan (JQ) in northwestern China. Further, we extracted the corresponding MODIS Normalized Difference Vegetation Index (NDVI, a proxy for ANPP) datasets at 250 m spatial resolution. We then evaluated different desert ecosystems responses using statistical analysis, and a threshold-delay model (TDM). TDM is an integrative framework for analysis of plant growth, precipitation thresholds, and plant functional type strategies that capture the nonlinear nature of plant responses to rainfall pulses. Our results showed that: (1) the growing season NDVIINT (INT stands for time-integrated) was largely correlated with the warm season (spring/summer) at our mildly-arid desert ecosystem (SD). The arid ecosystem (LZ) exhibited a different response, and the growing season NDVIINT depended highly on the previous year's fall/winter precipitation and ANPP. At the extremely arid site (JQ), the variability of growing season NDVIINT was equally correlated with the cool- and warm-season precipitation; (2) some parameters of threshold-delay differed among the three sites: while the response of NDVI to rainfall pulses began at about 5 mm for all the sites, the maximum thresholds in SD, LZ, and JQ were about 55, 35 and 30 mm respectively, increasing with an increase in mean annual precipitation. By and large, more previous year's fall/winter precipitation, and large rainfall events, significantly enhanced the growth of desert vegetation, and desert ecosystems should be much more adaptive under likely future scenarios of increasing fall/winter precipitation and large rainfall events. These results highlight the inherent complexity in predicting how desert ecosystems will respond to future fluctuations in precipitation.

Indian summer monsoon rainfall variability in response to differences in the decay phase of El Niño

NASA Astrophysics Data System (ADS)

Chowdary, Jasti S.; Harsha, H. S.; Gnanaseelan, C.; Srinivas, G.; Parekh, Anant; Pillai, Prasanth; Naidu, C. V.

2017-04-01

In general the Indian summer monsoon (ISM) rainfall is near normal or excess during the El Niño decay phase. Nevertheless the impact of large variations in decaying El Niño on the ISM rainfall and circulation is not systematically examined. Based on the timing of El Niño decay with respect to boreal summer season, El Niño decay phases are classified into three types in this study using 142 years of sea surface temperature (SST) data, which are as follows: (1) early-decay (ED; decay during spring), (2) mid-summer decay (MD; decay by mid-summer) and (3) no-decay (ND; no decay in summer). It is observed that ISM rainfall is above normal/excess during ED years, normal during MD years and below normal/deficit in ND years, suggesting that the differences in El Niño decay phase display profound impact on the ISM rainfall. Tropical Indian Ocean (TIO) SST warming, induced by El Niño, decays rapidly before the second half of the monsoon season (August and September) in ED years, but persists up to the end of the season in MD years, whereas TIO warming maintained up to winter in ND case. Analysis reveals the existence of strong sub-seasonal ISM rainfall variations in the summer following El Niño years. During ED years, strong negative SST anomalies develop over the equatorial central-eastern Pacific by June and are apparent throughout the summer season accompanied by anomalous moisture divergence and high sea level pressure (SLP). The associated moisture convergence and low SLP over ISM region favour excess rainfall (mainly from July onwards). This circulation and rainfall anomalies are highly influenced by warm TIO SST and Pacific La Niña conditions in ED years. Convergence of southwesterlies from Arabian Sea and northeasterlies from Bay of Bengal leads to positive rainfall over most part of the Indian subcontinent from August onwards in MD years. ND years are characterized by negative rainfall anomaly spatial pattern and weaker circulation over India throughout the summer season, which are mainly due to persisting El Niño related warm SST anomalies over the Pacific. Atmospheric general circulation model simulation supports our hypothesis that El Niño decay variations modulate ISM rainfall and circulation.

Integrated Assessments of the Impact of Climate Change on Agriculture: An Overview of AgMIP Regional Research in South Asia

NASA Technical Reports Server (NTRS)

McDermid, Sonali P.; Dileepkumar, Guntuku; Murthy, K. M. Dakshina; Nedumaran, S.; Singh, Piara; Srinivasa, Chukka; Gangwar, B.; Subash, N.; Ahmad, Ashfaq; Zubair, Lareef;

Statistical Determination of Rainfall-Runoff Erosivity Indices for Single Storms in the Chinese Loess Plateau

PubMed Central

Zheng, Mingguo; Chen, Xiaoan

2015-01-01

Correlation analysis is popular in erosion- or earth-related studies, however, few studies compare correlations on a basis of statistical testing, which should be conducted to determine the statistical significance of the observed sample difference. This study aims to statistically determine the erosivity index of single storms, which requires comparison of a large number of dependent correlations between rainfall-runoff factors and soil loss, in the Chinese Loess Plateau. Data observed at four gauging stations and five runoff experimental plots were presented. Based on the Meng’s tests, which is widely used for comparing correlations between a dependent variable and a set of independent variables, two methods were proposed. The first method removes factors that are poorly correlated with soil loss from consideration in a stepwise way, while the second method performs pairwise comparisons that are adjusted using the Bonferroni correction. Among 12 rainfall factors, I 30 (the maximum 30-minute rainfall intensity) has been suggested for use as the rainfall erosivity index, although I 30 is equally correlated with soil loss as factors of I 20, EI 10 (the product of the rainfall kinetic energy, E, and I 10), EI 20 and EI 30 are. Runoff depth (total runoff volume normalized to drainage area) is more correlated with soil loss than all other examined rainfall-runoff factors, including I 30, peak discharge and many combined factors. Moreover, sediment concentrations of major sediment-producing events are independent of all examined rainfall-runoff factors. As a result, introducing additional factors adds little to the prediction accuracy of the single factor of runoff depth. Hence, runoff depth should be the best erosivity index at scales from plots to watersheds. Our findings can facilitate predictions of soil erosion in the Loess Plateau. Our methods provide a valuable tool while determining the predictor among a number of variables in terms of correlations. PMID:25781173

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.

Downscaling of global climate change estimates to regional scales: An application to Iberian rainfall in wintertime

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

von Storch, H.; Zorita, E.; Cubasch, U.

A statistical strategy to deduct regional-scale features from climate general circulation model (GCM) simulations has been designed and tested. The main idea is to interrelate the characteristic patterns of observed simultaneous variations of regional climate parameters and of large-scale atmospheric flow using the canonical correlation technique. The large-scale North Atlantic sea level pressure (SLP) is related to the regional, variable, winter (DJF) mean Iberian Peninsula rainfall. The skill of the resulting statistical model is shown by reproducing, to a good approximation, the winter mean Iberian rainfall from 1900 to present from the observed North Atlantic mean SLP distributions. It ismore » shown that this observed relationship between these two variables is not well reproduced in the output of a general circulation model (GCM). The implications for Iberian rainfall changes as the response to increasing atmospheric greenhouse-gas concentrations simulated by two GCM experiments are examined with the proposed statistical model. In an instantaneous [open quotes]2 CO[sub 2][close quotes] doubling experiment, using the simulated change of the mean North Atlantic SLP field to predict Iberian rainfall yields, there is an insignificant increase of area-averaged rainfall of I mm/month, with maximum values of 4 mm/month in the northwest of the peninsula. In contrast, for the four GCM grid points representing the lberian Peninsula, the change is - 10 mm/month, with a minimum of - 19 mm/month in the southwest. In the second experiment, with the IPCC scenario A ([open quotes]business as usual[close quotes]) increase of CO[sub 2], the statistical-model results partially differ from the directly simulated rainfall changes: in the experimental range of 100 years, the area-averaged rainfall decreases by 7 mm/month (statistical model), and by 9 mm/month (GCM); at the same time the amplitude of the interdecadal variability is quite different. 17 refs., 10 figs.« less

Statistical determination of rainfall-runoff erosivity indices for single storms in the Chinese Loess Plateau.

PubMed

Zheng, Mingguo; Chen, Xiaoan

2015-01-01

Correlation analysis is popular in erosion- or earth-related studies, however, few studies compare correlations on a basis of statistical testing, which should be conducted to determine the statistical significance of the observed sample difference. This study aims to statistically determine the erosivity index of single storms, which requires comparison of a large number of dependent correlations between rainfall-runoff factors and soil loss, in the Chinese Loess Plateau. Data observed at four gauging stations and five runoff experimental plots were presented. Based on the Meng's tests, which is widely used for comparing correlations between a dependent variable and a set of independent variables, two methods were proposed. The first method removes factors that are poorly correlated with soil loss from consideration in a stepwise way, while the second method performs pairwise comparisons that are adjusted using the Bonferroni correction. Among 12 rainfall factors, I30 (the maximum 30-minute rainfall intensity) has been suggested for use as the rainfall erosivity index, although I30 is equally correlated with soil loss as factors of I20, EI10 (the product of the rainfall kinetic energy, E, and I10), EI20 and EI30 are. Runoff depth (total runoff volume normalized to drainage area) is more correlated with soil loss than all other examined rainfall-runoff factors, including I30, peak discharge and many combined factors. Moreover, sediment concentrations of major sediment-producing events are independent of all examined rainfall-runoff factors. As a result, introducing additional factors adds little to the prediction accuracy of the single factor of runoff depth. Hence, runoff depth should be the best erosivity index at scales from plots to watersheds. Our findings can facilitate predictions of soil erosion in the Loess Plateau. Our methods provide a valuable tool while determining the predictor among a number of variables in terms of correlations.

Tropical warm pool rainfall variability and impact on upper ocean variability throughout the Madden-Julian oscillation

NASA Astrophysics Data System (ADS)

Thompson, Elizabeth J.

Heating and rain freshening often stabilize the upper tropical ocean, bringing the ocean mixed layer depth to the sea surface. Thin mixed layer depths concentrate subsequent fluxes of heat, momentum, and freshwater in a thin layer. Rapid heating and cooling of the tropical sea surface is important for controlling or triggering atmospheric convection. Ocean mixed layer depth and SST variability due to rainfall events have not been as comprehensively explored as the ocean's response to heating or momentum fluxes, but are very important to understand in the tropical warm pool where precipitation exceeds evaporation and many climate phenomena such as ENSO and the MJO (Madden Julian Oscillation) originate. The first part of the dissertation investigates tropical, oceanic convective and stratiform rainfall variability and determines how to most accurately estimate rainfall accumulation with radar from each rain type. The second, main part of the dissertation uses central Indian Ocean salinity and temperature microstructure measurements and surrounding radar-derived rainfall maps throughout two DYNAMO MJO events to determine the impact of precipitating systems on upper-ocean mixed layer depth and resulting SST variability. The ocean mixed layer was as shallow as 0-5 m during 528/1071 observation hours throughout 2 MJOs (54% of the data record). Out of 43 observation days, thirty-eight near-surface mixed layer depth events were attributed to freshwater stabilization, called rain-formed mixed layers (RFLs). Thirty other mixed layer stratification events were classified as diurnal warm layers (DWLs) due to stable temperature stratification by daytime heating. RFLs and DWLs were observed to interact in two ways: 1) RFLs fill preexisting DWLs and add to total near-surface mixed layer stratification, which occurred ten times; 2) RFLs last long enough to heat, creating a new DWL on top of the RFL, which happened nine times. These combination stratification events were responsible for the highest SST warming rates and some of the highest SSTs leading up to the most active precipitation and wind stage of the each MJO. DWLs without RFL interaction helped produce the highest SSTs in suppressed MJO conditions. As storm intensity, frequency, duration, and the ability of storms to maintain stratiform rain areas increased, RFLS became more common in the disturbed and active MJO phases. Along with the barrier layer, DWL and RFL stratification events helped suppress wind-mixing, cooling, and mixed layer deepening throughout the MJO. We hypothesize that both salinity and temperature stratification events, and their interactions, are important for controlling SST variability and therefore MJO initiation in the Indian Ocean. Most RFLs were caused by submesoscale and mesoscale convective systems with stratiform rain components and local rain accumulations above 10 mm but with winds mostly below 8 m s-1. We hypothesize that the stratiform rain components of storms helped stratify the ocean by providing weak but widespread, steady, long-lived freshwater fluxes. Although generally limited to rain rates ≤ 10 mm hr-1, it is demonstrated that stratiform rain can exert a strong buoyancy flux into the ocean, i.e. as high as maximum daytime solar heating. Storm morphology and the preexisting vertical structure of ocean stability were critical in determining ocean mixed layer depth variability in the presence of rain. Therefore, we suggest that high spatial and temporal resolution coupled ocean-atmosphere models that can parameterize or resolve storm morphology as well as ocean mixed layer and barrier layer evolution are needed to reproduce the diurnal and intraseasonal SST variability documented throughout the MJO.

Skill of ENSEMBLES seasonal re-forecasts for malaria prediction in West Africa

NASA Astrophysics Data System (ADS)

Jones, A. E.; Morse, A. P.

2012-12-01

This study examines the performance of malaria-relevant climate variables from the ENSEMBLES seasonal ensemble re-forecasts for sub-Saharan West Africa, using a dynamic malaria model to transform temperature and rainfall forecasts into simulated malaria incidence and verifying these forecasts against simulations obtained by driving the malaria model with General Circulation Model-derived reanalysis. Two subregions of forecast skill are identified: the highlands of Cameroon, where low temperatures limit simulated malaria during the forecast period and interannual variability in simulated malaria is closely linked to variability in temperature, and northern Nigeria/southern Niger, where simulated malaria variability is strongly associated with rainfall variability during the peak rain months.

Vegetation-rainfall feedbacks across the Sahel: a combined observational and modeling study

NASA Astrophysics Data System (ADS)

Yu, Y.; Notaro, M.; Wang, F.; Mao, J.; Shi, X.; Wei, Y.

2016-12-01

The Sahel rainfall is characterized by large interannual variability. Past modeling studies have concluded that the Sahel rainfall variability is primarily driven by oceanic forcings and amplified by land-atmosphere interactions. However, the relative importance of oceanic versus terrestrial drivers has never been assessed from observations. The current understanding of vegetation's impacts on climate, i.e. positive vegetation-rainfall feedback through the albedo, moisture, and momentum mechanisms, comes from untested models. Neither the positive vegetation-rainfall feedback, nor the underlying mechanisms, has been fully resolved in observations. The current study fills the knowledge gap about the observed vegetation-rainfall feedbacks, through the application of the multivariate statistical method Generalized Equilibrium Feedback Assessment (GEFA) to observational data. According to GEFA, the observed oceanic impacts dominate over terrestrial impacts on Sahel rainfall, except in the post-monsoon period. Positive leaf area index (LAI) anomalies favor an extended, wetter monsoon across the Sahel, largely due to moisture recycling. The albedo mechanism is not responsible for this positive vegetation feedback on the seasonal-interannual time scale, which is too short for a grass-desert transition. A low-level stabilization and subsidence is observed in response to increased LAI - potentially responsible for a negative vegetation-rainfall feedback. However, the positive moisture feedback overwhelms the negative momentum feedback, resulting in an observed positive vegetation-rainfall feedback. We further applied GEFA to a fully-coupled Community Earth System Model (CESM) control run, as an example of evaluating climate models against the GEFA-based observational benchmark. In contrast to the observed positive vegetation-rainfall feedbacks, CESM simulates a negative vegetation-rainfall feedback across Sahel, peaking in the pre-monsoon season. The simulated negative feedback is largely due to the low-level stabilization caused by increased LAI. Positive moisture feedback is present in the CESM simulation, but an order weaker than the observed and weaker than the negative momentum feedback, thereby leading to the simulated negative vegetation-rainfall feedbacks.

Climate downscaling over South America for 1971-2000: application in SMAP rainfall-runoff model for Grande River Basin

NASA Astrophysics Data System (ADS)

da Silva, Felipe das Neves Roque; Alves, José Luis Drummond; Cataldi, Marcio

2018-03-01

This paper aims to validate inflow simulations concerning the present-day climate at Água Vermelha Hydroelectric Plant (AVHP—located on the Grande River Basin) based on the Soil Moisture Accounting Procedure (SMAP) hydrological model. In order to provide rainfall data to the SMAP model, the RegCM regional climate model was also used working with boundary conditions from the MIROC model. Initially, present-day climate simulation performed by RegCM model was analyzed. It was found that, in terms of rainfall, the model was able to simulate the main patterns observed over South America. A bias correction technique was also used and it was essential to reduce mistakes related to rainfall simulation. Comparison between rainfall simulations from RegCM and MIROC showed improvements when the dynamical downscaling was performed. Then, SMAP, a rainfall-runoff hydrological model, was used to simulate inflows at Água Vermelha Hydroelectric Plant. After calibration with observed rainfall, SMAP simulations were evaluated in two different periods from the one used in calibration. During calibration, SMAP captures the inflow variability observed at AVHP. During validation periods, the hydrological model obtained better results and statistics with observed rainfall. However, in spite of some discrepancies, the use of simulated rainfall without bias correction captured the interannual flow variability. However, the use of bias removal in the simulated rainfall performed by RegCM brought significant improvements to the simulation of natural inflows performed by SMAP. Not only the curve of simulated inflow became more similar to the observed inflow, but also the statistics improved their values. Improvements were also noticed in the inflow simulation when the rainfall was provided by the regional climate model compared to the global model. In general, results obtained so far prove that there was an added value in rainfall when regional climate model was compared to global climate model and that data from regional models must be bias-corrected so as to improve their results.

Piecewise Disassembly of a Large-Herbivore Community across a Rainfall Gradient: The UHURU Experiment

PubMed Central

Palmer, Todd M.; Charles, Grace K.; Helgen, Kristofer M.; Kinyua, Stephen N.; Maclean, Janet E.; Turner, Benjamin L.; Young, Hillary S.

2013-01-01

Large mammalian herbivores (LMH) strongly influence plant communities, and these effects can propagate indirectly throughout food webs. Most existing large-scale manipulations of LMH presence/absence consist of a single exclusion treatment, and few are replicated across environmental gradients. Thus, important questions remain about the functional roles of different LMH, and how these roles depend on abiotic context. In September 2008, we constructed a series of 1-ha herbivore-exclusion plots across a 20-km rainfall gradient in central Kenya. Dubbed "UHURU" (Ungulate Herbivory Under Rainfall Uncertainty), this experiment aims to illuminate the ecological effects of three size classes of LMH, and how rainfall regimes shape the direction and magnitude of these effects. UHURU consists of four treatments: total-exclusion (all ungulate herbivores), mesoherbivore-exclusion (LMH >120-cm tall), megaherbivore-exclusion (elephants and giraffes), and unfenced open plots. Each treatment is replicated three times at three locations (“sites”) along the rainfall gradient: low (440 mm/year), intermediate (580 mm/year), and high (640 mm/year). There was limited variation across sites in soil attributes and LMH activity levels. Understory-plant cover was greater in plots without mesoherbivores, but did not respond strongly to the exclusion of megaherbivores, or to the additional exclusion of dik-dik and warthog. Eleven of the thirteen understory plant species that responded significantly to exclusion treatment were more common in exclusion plots than open ones. Significant interactions between site and treatment on plant communities, although uncommon, suggested that differences between treatments may be greater at sites with lower rainfall. Browsers reduced densities of several common overstory species, along with growth rates of the three dominant Acacia species. Small-mammal densities were 2–3 times greater in total-exclusion than in open plots at all sites. Although we expect patterns to become clearer with time, results from 2008–2012 show that the effects of excluding successively smaller-bodied subsets of the LMH community are generally non-additive for a given response variable, and inconsistent across response variables, indicating that the different LMH size classes are not functionally redundant. Several response variables showed significant treatment-by-site interactions, suggesting that the nature of plant-herbivore interactions can vary across restricted spatial scales. PMID:23405122

Piecewise disassembly of a large-herbivore community across a rainfall gradient: the UHURU experiment.

PubMed

Goheen, Jacob R; Palmer, Todd M; Charles, Grace K; Helgen, Kristofer M; Kinyua, Stephen N; Maclean, Janet E; Turner, Benjamin L; Young, Hillary S; Pringle, Robert M

2013-01-01

Large mammalian herbivores (LMH) strongly influence plant communities, and these effects can propagate indirectly throughout food webs. Most existing large-scale manipulations of LMH presence/absence consist of a single exclusion treatment, and few are replicated across environmental gradients. Thus, important questions remain about the functional roles of different LMH, and how these roles depend on abiotic context. In September 2008, we constructed a series of 1-ha herbivore-exclusion plots across a 20-km rainfall gradient in central Kenya. Dubbed "UHURU" (Ungulate Herbivory Under Rainfall Uncertainty), this experiment aims to illuminate the ecological effects of three size classes of LMH, and how rainfall regimes shape the direction and magnitude of these effects. UHURU consists of four treatments: total-exclusion (all ungulate herbivores), mesoherbivore-exclusion (LMH >120-cm tall), megaherbivore-exclusion (elephants and giraffes), and unfenced open plots. Each treatment is replicated three times at three locations ("sites") along the rainfall gradient: low (440 mm/year), intermediate (580 mm/year), and high (640 mm/year). There was limited variation across sites in soil attributes and LMH activity levels. Understory-plant cover was greater in plots without mesoherbivores, but did not respond strongly to the exclusion of megaherbivores, or to the additional exclusion of dik-dik and warthog. Eleven of the thirteen understory plant species that responded significantly to exclusion treatment were more common in exclusion plots than open ones. Significant interactions between site and treatment on plant communities, although uncommon, suggested that differences between treatments may be greater at sites with lower rainfall. Browsers reduced densities of several common overstory species, along with growth rates of the three dominant Acacia species. Small-mammal densities were 2-3 times greater in total-exclusion than in open plots at all sites. Although we expect patterns to become clearer with time, results from 2008-2012 show that the effects of excluding successively smaller-bodied subsets of the LMH community are generally non-additive for a given response variable, and inconsistent across response variables, indicating that the different LMH size classes are not functionally redundant. Several response variables showed significant treatment-by-site interactions, suggesting that the nature of plant-herbivore interactions can vary across restricted spatial scales.

Prediction of spatially explicit rainfall intensity-duration thresholds for post-fire debris-flow generation in the western United States

NASA Astrophysics Data System (ADS)

Staley, Dennis; Negri, Jacquelyn; Kean, Jason

2016-04-01

Population expansion into fire-prone steeplands has resulted in an increase in post-fire debris-flow risk in the western United States. Logistic regression methods for determining debris-flow likelihood and the calculation of empirical rainfall intensity-duration thresholds for debris-flow initiation represent two common approaches for characterizing hazard and reducing risk. Logistic regression models are currently being used to rapidly assess debris-flow hazard in response to design storms of known intensities (e.g. a 10-year recurrence interval rainstorm). Empirical rainfall intensity-duration thresholds comprise a major component of the United States Geological Survey (USGS) and the National Weather Service (NWS) debris-flow early warning system at a regional scale in southern California. However, these two modeling approaches remain independent, with each approach having limitations that do not allow for synergistic local-scale (e.g. drainage-basin scale) characterization of debris-flow hazard during intense rainfall. The current logistic regression equations consider rainfall a unique independent variable, which prevents the direct calculation of the relation between rainfall intensity and debris-flow likelihood. Regional (e.g. mountain range or physiographic province scale) rainfall intensity-duration thresholds fail to provide insight into the basin-scale variability of post-fire debris-flow hazard and require an extensive database of historical debris-flow occurrence and rainfall characteristics. Here, we present a new approach that combines traditional logistic regression and intensity-duration threshold methodologies. This method allows for local characterization of both the likelihood that a debris-flow will occur at a given rainfall intensity, the direct calculation of the rainfall rates that will result in a given likelihood, and the ability to calculate spatially explicit rainfall intensity-duration thresholds for debris-flow generation in recently burned areas. Our approach synthesizes the two methods by incorporating measured rainfall intensity into each model variable (based on measures of topographic steepness, burn severity and surface properties) within the logistic regression equation. This approach provides a more realistic representation of the relation between rainfall intensity and debris-flow likelihood, as likelihood values asymptotically approach zero when rainfall intensity approaches 0 mm/h, and increase with more intense rainfall. Model performance was evaluated by comparing predictions to several existing regional thresholds. The model, based upon training data collected in southern California, USA, has proven to accurately predict rainfall intensity-duration thresholds for other areas in the western United States not included in the original training dataset. In addition, the improved logistic regression model shows promise for emergency planning purposes and real-time, site-specific early warning. With further validation, this model may permit the prediction of spatially-explicit intensity-duration thresholds for debris-flow generation in areas where empirically derived regional thresholds do not exist. This improvement would permit the expansion of the early-warning system into other regions susceptible to post-fire debris flow.

A coupled stochastic rainfall-evapotranspiration model for hydrological impact analysis

NASA Astrophysics Data System (ADS)

Pham, Minh Tu; Vernieuwe, Hilde; De Baets, Bernard; Verhoest, Niko E. C.

2018-02-01

A hydrological impact analysis concerns the study of the consequences of certain scenarios on one or more variables or fluxes in the hydrological cycle. In such an exercise, discharge is often considered, as floods originating from extremely high discharges often cause damage. Investigating the impact of extreme discharges generally requires long time series of precipitation and evapotranspiration to be used to force a rainfall-runoff model. However, such kinds of data may not be available and one should resort to stochastically generated time series, even though the impact of using such data on the overall discharge, and especially on the extreme discharge events, is not well studied. In this paper, stochastically generated rainfall and corresponding evapotranspiration time series, generated by means of vine copulas, are used to force a simple conceptual hydrological model. The results obtained are comparable to the modelled discharge using observed forcing data. Yet, uncertainties in the modelled discharge increase with an increasing number of stochastically generated time series used. Notwithstanding this finding, it can be concluded that using a coupled stochastic rainfall-evapotranspiration model has great potential for hydrological impact analysis.

Variability of the recent climate of eastern Africa

NASA Astrophysics Data System (ADS)

Schreck, Carl J., III; Semazzi, Fredrick H. M.

2004-05-01

The primary objective of this study is to investigate the recent variability of the eastern African climate. The region of interest is also known as the Greater Horn of Africa (GHA), and comprises the countries of Burundi, Djibouti, Eritrea, Ethiopia, Kenya, Rwanda, Somalia, Sudan, Uganda, and Tanzania.The analysis was based primarily on the construction of empirical orthogonal functions (EOFs) of gauge rainfall data and on CPC Merged Analysis of Precipitation (CMAP) data, derived from a combination of rain-gauge observations and satellite estimates. The investigation is based on the period 1961-2001 for the short rains season of eastern Africa of October through to December. The EOF analysis was supplemented by projection of National Centers for Environmental Prediction wind data onto the rainfall eigenmodes to understand the rainfall-circulation relationships. Furthermore, correlation and composite analyses have been performed with the Climatic Research Unit globally averaged surface-temperature time series to explore the potential relationship between the climate of eastern Africa and global warming.The most dominant mode of variability (EOF1) based on CMAP data over eastern Africa corresponds to El Niño-southern oscillation (ENSO) climate variability. It is associated with above-normal rainfall amounts during the short rains throughout the entire region, except for Sudan. The corresponding anomalous low-level circulation is dominated by easterly inflow from the Indian Ocean, and to a lesser extent the Congo tropical rain forest, into the positive rainfall anomaly region that extends across most of eastern Africa. The easterly inflow into eastern Africa is part of diffluent outflow from the maritime continent during the warm ENSO events. The second eastern African EOF (trend mode) is associated with decadal variability. In distinct contrast from the ENSO mode pattern, the trend mode is characterized by positive rainfall anomalies over the northern sector of eastern Africa and opposite conditions over the southern sector. This rainfall trend mode eluded detection in previous studies that did not include recent decades of data, because the signal was still relatively weak. The wind projection onto this mode indicates that the primary flow that feeds the positive anomaly region over the northern part of eastern Africa emanates primarily from the rainfall-deficient southern region of eastern Africa and Sudan. Although we do not assign attribution of the trend mode to global warming (in part because of the relatively short period of analysis), the evidence, based on our results and previous studies, strongly suggests a potential connection.

Assessing groundwater recharge in an Andean closed basin using isotopic characterization and a rainfall-runoff model: Salar del Huasco basin, Chile

NASA Astrophysics Data System (ADS)

Uribe, Javier; Muñoz, José F.; Gironás, Jorge; Oyarzún, Ricardo; Aguirre, Evelyn; Aravena, Ramón

2015-11-01

Closed basins are catchments whose drainage networks converge to lakes, salt flats or alluvial plains. Salt flats in the closed basins in arid northern Chile are extremely important ecological niches. The Salar del Huasco, one of these salt flats located in the high plateau (Altiplano), is a Ramsar site located in a national park and is composed of a wetland ecosystem rich in biodiversity. The proper management of the groundwater, which is essential for the wetland function, requires accurate estimates of recharge in the Salar del Huasco basin. This study quantifies the spatio-temporal distribution of the recharge, through combined use of isotopic characterization of the different components of the water cycle and a rainfall-runoff model. The use of both methodologies aids the understanding of hydrological behavior of the basin and enabled estimation of a long-term average recharge of 22 mm/yr (i.e., 15 % of the annual rainfall). Recharge has a high spatial variability, controlled by the geological and hydrometeorological characteristics of the basin, and a high interannual variability, with values ranging from 18 to 26 mm/yr. The isotopic approach allowed not only the definition of the conceptual model used in the hydrological model, but also eliminated the possibility of a hydrogeological connection between the aquifer of the Salar del Huasco basin and the aquifer that feeds the springs of the nearby town of Pica. This potential connection has been an issue of great interest to agriculture and tourism activities in the region.

Interannual variability of Indian monsoon rainfall

NASA Technical Reports Server (NTRS)

Paolino, D. A.; Shukla, J.

1984-01-01

The interannual variability of the Indian summer monsoon and its relationships with other atmospheric fluctuations were studied in hopes of gaining some insight into the predicability of the rainfall. Rainfall data for 31 meteorological subdivisions over India were provided by the India Meteorological Department (IMD). Fifty-three years of seasonal mean anomaly sea-level pressure (SLP) fields were used to determine if any relationships could be detected between fluctuations in Northern Hemisphere surface pressure and Indian monsoon rainfall. Three month running mean sea-level pressure anomalies at Darwin (close to one of the centers of the Southern Oscillation) were compiled for months preceding and following extreme years for rainfall averaged over all of India. Anomalies are small before the monsoon, but are quite large in months following the summer season. However, there is a large decrease in Darwin pressure for months preceding a heavy monsoon, while a deficient monsoon is preceded by a sharp increase in Darwin pressure. If a time series is constructed of the tendency of Darwin SLP between the Northern Hemisphere winter (DJF) and spring (MAM) and a correlation coefficient is computed between it and 81 years of rainfall average over all of India, one gets a C. C. of -.46, which is higher than any other previously computed predictor of the monsoon rainfall. This relationship can also be used to make a qualitative forecast for rainfall over the whole of India by considering the sign of the tendency in extreme monsoon years.

An Investigation of the Hydroclimate Variability of Eastern Africa

NASA Astrophysics Data System (ADS)

Smith, K. A.; Semazzi, F. H. M.

2015-12-01

The flow of the Victoria Nile, and the productivity of the dams along it, is determined by the level of Lake Victoria, which is primarily dictated by the rainfall and temperature variability over the Lake Victoria Basin. Notwithstanding the indisputable decline of water resources over the lake basin during the Long Rains of March - May, there is a strong indication based on IPCC climate projections that this trend, which has persisted for several decades, will reverse in the next few decades. This phenomenon has come to be known as the Eastern-Central African climate change paradox and could have profound implications on sustainable development for the next few decades in Lake Victoria Basin. The purpose of this study is to investigate the climate variability associated with the East African Climate Change Paradox for the recent decades. This research analyzes observations to understand the sources of variability and potential physical mechanisms related to the decline in precipitation over Eastern Africa. We then investigate the hydrological factors involved in the decline of Lake Victoria levels in the context of the decline in rainfall. While East Africa has been experiencing persistent decline of the Long Rains for multiple decades, this same decline is not seen in annual rainfall. The remaining seasons show an increase in rainfall which is compensating for the decline of the Long Rains. It is possible that the Long Rains season is shifting in such a way that the season starts earlier, in February, and ending sooner. The corresponding annual Lake Victoria levels modeled using observed rainfall do not decline in the recent decades, except when the Long Rains seasonal variability is considered without variability from other seasons. This shift could impact hydroelectric power planning on a monthly or seasonal time scale, and could potentially have a large impact on agriculture, since it would shift the growing season in the region.

Distant Influence of Kuroshio Eddies on North Pacific Weather Patterns?

PubMed

Ma, Xiaohui; Chang, Ping; Saravanan, R; Montuoro, Raffaele; Hsieh, Jen-Shan; Wu, Dexing; Lin, Xiaopei; Wu, Lixin; Jing, Zhao

2015-12-04

High-resolution satellite measurements of surface winds and sea-surface temperature (SST) reveal strong coupling between meso-scale ocean eddies and near-surface atmospheric flow over eddy-rich oceanic regions, such as the Kuroshio and Gulf Stream, highlighting the importance of meso-scale oceanic features in forcing the atmospheric planetary boundary layer (PBL). Here, we present high-resolution regional climate modeling results, supported by observational analyses, demonstrating that meso-scale SST variability, largely confined in the Kuroshio-Oyashio confluence region (KOCR), can further exert a significant distant influence on winter rainfall variability along the U.S. Northern Pacific coast. The presence of meso-scale SST anomalies enhances the diabatic conversion of latent heat energy to transient eddy energy, intensifying winter cyclogenesis via moist baroclinic instability, which in turn leads to an equivalent barotropic downstream anticyclone anomaly with reduced rainfall. The finding points to the potential of improving forecasts of extratropical winter cyclones and storm systems and projections of their response to future climate change, which are known to have major social and economic impacts, by improving the representation of ocean eddy-atmosphere interaction in forecast and climate models.

Influence of preonset land atmospheric conditions on the Indian summer monsoon rainfall variability

NASA Astrophysics Data System (ADS)

Rai, Archana; Saha, Subodh K.; Pokhrel, Samir; Sujith, K.; Halder, Subhadeep

2015-05-01

A possible link between preonset land atmospheric conditions and the Indian summer monsoon rainfall (ISMR) is explored. It is shown that, the preonset positive (negative) rainfall anomaly over northwest India, Pakistan, Afghanistan, and Iran is associated with decrease (increase) in ISMR, primarily in the months of June and July, which in turn affects the seasonal mean. ISMR in the months of June and July is also strongly linked with the preonset 2 m air temperature over the same regions. The preonset rainfall/2 m air temperature variability is linked with stationary Rossby wave response, which is clearly evident in the wave activity flux diagnostics. As the predictability of Indian summer monsoon relies mainly on the El Niño-Southern Oscillation (ENSO), the found link may further enhance our ability to predict the monsoon, particularly during a non-ENSO year.

Convective rainfall estimation from digital GOES-1 infrared data

NASA Technical Reports Server (NTRS)

Sickler, G. L.; Thompson, A. H.

1979-01-01

An investigation was conducted to determine the feasibility of developing and objective technique for estimating convective rainfall from digital GOES-1 infrared data. The study area was a 240 km by 240 km box centered on College Station, Texas (Texas A and M University). The Scofield and Oliver (1977) rainfall estimation scheme was adapted and used with the digital geostationary satellite data. The concept of enhancement curves with respect to rainfall approximation is discussed. Raingage rainfall analyses and satellite-derived rainfall estimation analyses were compared. The correlation for the station data pairs (observed versus estimated rainfall amounts) for the convective portion of the storm was 0.92. It was demonstrated that a fairly accurate objective rainfall technique using digital geostationary infrared satellite data is feasible. The rawinsonde and some synoptic data that were used in this investigation came from NASA's Atmospheric Variability Experiment, AVE 7.

Impact of climate, vegetation, soil and crop management variables on multi-year ISBA-A-gs simulations of evapotranspiration over a Mediterranean crop site

NASA Astrophysics Data System (ADS)

Garrigues, S.; Olioso, A.; Carrer, D.; Decharme, B.; Calvet, J.-C.; Martin, E.; Moulin, S.; Marloie, O.

2015-10-01

Generic land surface models are generally driven by large-scale data sets to describe the climate, the soil properties, the vegetation dynamic and the cropland management (irrigation). This paper investigates the uncertainties in these drivers and their impacts on the evapotranspiration (ET) simulated from the Interactions between Soil, Biosphere, and Atmosphere (ISBA-A-gs) land surface model over a 12-year Mediterranean crop succession. We evaluate the forcing data sets used in the standard implementation of ISBA over France where the model is driven by the SAFRAN (Système d'Analyse Fournissant des Renseignements Adaptés à la Nivologie) high spatial resolution atmospheric reanalysis, the leaf area index (LAI) time courses derived from the ECOCLIMAP-II land surface parameter database and the soil texture derived from the French soil database. For climate, we focus on the radiations and rainfall variables and we test additional data sets which include the ERA-Interim (ERA-I) low spatial resolution reanalysis, the Global Precipitation Climatology Centre data set (GPCC) and the MeteoSat Second Generation (MSG) satellite estimate of downwelling shortwave radiations. The evaluation of the drivers indicates very low bias in daily downwelling shortwave radiation for ERA-I (2.5 W m-2) compared to the negative biases found for SAFRAN (-10 W m-2) and the MSG satellite (-12 W m-2). Both SAFRAN and ERA-I underestimate downwelling longwave radiations by -12 and -16 W m-2, respectively. The SAFRAN and ERA-I/GPCC rainfall are slightly biased at daily and longer timescales (1 and 0.5 % of the mean rainfall measurement). The SAFRAN rainfall is more precise than the ERA-I/GPCC estimate which shows larger inter-annual variability in yearly rainfall error (up to 100 mm). The ECOCLIMAP-II LAI climatology does not properly resolve Mediterranean crop phenology and underestimates the bare soil period which leads to an overall overestimation of LAI over the crop succession. The simulation of irrigation by the model provides an accurate irrigation amount over the crop cycle but the timing of irrigation occurrences is frequently unrealistic. Errors in the soil hydrodynamic parameters and the lack of irrigation in the simulation have the largest influence on ET compared to uncertainties in the large-scale climate reanalysis and the LAI climatology. Among climate variables, the errors in yearly ET are mainly related to the errors in yearly rainfall. The underestimation of the available water capacity and the soil hydraulic diffusivity induce a large underestimation of ET over 12 years. The underestimation of radiations by the reanalyses and the absence of irrigation in the simulation lead to the underestimation of ET while the overall overestimation of LAI by the ECOCLIMAP-II climatology induces an overestimation of ET over 12 years. This work shows that the key challenges to monitor the water balance of cropland at regional scale concern the representation of the spatial distribution of the soil hydrodynamic parameters, the variability of the irrigation practices, the seasonal and inter-annual dynamics of vegetation and the spatiotemporal heterogeneity of rainfall.

A physics-based probabilistic forecasting model for rainfall-induced shallow landslides at regional scale

NASA Astrophysics Data System (ADS)

Zhang, Shaojie; Zhao, Luqiang; Delgado-Tellez, Ricardo; Bao, Hongjun

2018-03-01

Conventional outputs of physics-based landslide forecasting models are presented as deterministic warnings by calculating the safety factor (Fs) of potentially dangerous slopes. However, these models are highly dependent on variables such as cohesion force and internal friction angle which are affected by a high degree of uncertainty especially at a regional scale, resulting in unacceptable uncertainties of Fs. Under such circumstances, the outputs of physical models are more suitable if presented in the form of landslide probability values. In order to develop such models, a method to link the uncertainty of soil parameter values with landslide probability is devised. This paper proposes the use of Monte Carlo methods to quantitatively express uncertainty by assigning random values to physical variables inside a defined interval. The inequality Fs < 1 is tested for each pixel in n simulations which are integrated in a unique parameter. This parameter links the landslide probability to the uncertainties of soil mechanical parameters and is used to create a physics-based probabilistic forecasting model for rainfall-induced shallow landslides. The prediction ability of this model was tested in a case study, in which simulated forecasting of landslide disasters associated with heavy rainfalls on 9 July 2013 in the Wenchuan earthquake region of Sichuan province, China, was performed. The proposed model successfully forecasted landslides in 159 of the 176 disaster points registered by the geo-environmental monitoring station of Sichuan province. Such testing results indicate that the new model can be operated in a highly efficient way and show more reliable results, attributable to its high prediction accuracy. Accordingly, the new model can be potentially packaged into a forecasting system for shallow landslides providing technological support for the mitigation of these disasters at regional scale.

Detecting the hydrological impacts of forest cover change in tropical mountain areas: need for detrending time series of rainfall and streamflow data.

NASA Astrophysics Data System (ADS)

Molina, A.; Vanacker, V.; Brisson, E.; Balthazar, V.

2012-04-01

Interactions between human activities and the physical environment have increasingly transformed the hydrological functioning of Andean ecosystems. In these human-modified landscapes, land use/-cover change may have a profound effect on riverine water and sediment fluxes. The hydrological impacts of land use/-cover change are diverse, as changes in vegetation affect the various components of the hydrological cycle including evapotranspiration, infiltration and surface runoff. Quantitative data for tropical mountain regions are scarce, as few long time series on rainfall, water discharge and land use are available. Furthermore, time series of rainfall and streamflow data in tropical mountains are often highly influenced by large inter- and intra-annual variability. In this paper, we analyse the hydrological response to complex forest cover change for a catchment of 280 km2 located in the Ecuadorian Andes. Forest cover change in the Pangor catchment was reconstructed based on airphotos (1963, 1977), LANDSAT TM (1991) and ETM+ data (2001, 2009). From 1963, natural vegetation was converted to agricultural land and pine plantations: forests decreased by a factor 2, and paramo decreased by 20 km2 between 1963 and 2009. For this catchment, there exists an exceptionally long record of rainfall and streamflow data that dates back from the '70s till now, but large variability in hydrometeorological data exists that is partly related to ENSO events. Given the nonstationary and nonlinear character of the ENSO-related changes in rainfall, we used the Hilbert-Huang transformation to detrend the time series of the river flow data from inter- and intra-annual fluctuations in rainfall. After applying adaptive data analysis based on empirical model decomposition techniques, it becomes apparent that the long-term trend in streamflow is different from the long-term trend in rainfall data. While the streamflow data show a long-term decrease in monthly flow, the rainfall data have a trend of increasing and then decreasing precipitation amounts. These results suggest that the land use changes had an important impact on the total water yield of the catchment. Interestingly, the effect of reforestation in the upper part of the catchment with its associated decrease in water yield seems to be dominant over the effect of deforestation in the lower part of the basin.

Vegetation Response to Rainfall and Soil Moisture Variability in Botswana

DTIC Science & Technology

1991-01-01

Effects of Varying Soil Type on the NDVI /Rainfall and NDVI /Soil Moisture...examine the effects of different soil types on the vegetation growth/rainfall relationship. The goals are to determine whether differences in the water-use...34first step" in removing the soil effect (Huete et al., 1985). Indeed, no large-scale soil corrections have been attempted as yet on NDVI data.