Improving long-term global precipitation dataset using multi-sensor surface soil moisture retrievals and the soil moisture analysis rainfall tool (SMART)

USDA-ARS?s Scientific Manuscript database

Using multiple historical satellite surface soil moisture products, the Kalman Filtering-based Soil Moisture Analysis Rainfall Tool (SMART) is applied to improve the accuracy of a multi-decadal global daily rainfall product that has been bias-corrected to match the monthly totals of available rain g...

Comparison of rainfall products over Africa

NASA Astrophysics Data System (ADS)

Le Coz, Camille; van de Giesen, Nick

2017-04-01

There are many rainfall products available, some of them are global and others have been developed specifically for Africa or another region. They are based on different methods and use different sources of data. All these products have different advantages and limitations and they are suitable for different purposes, like climate or drought monitoring. They can be divided in three categories: the gauge-only products, the satellite-based products and the reanalyses products. These different types of products are compared through literature study in order to find their strengths and weaknesses. The performance of each product varies from region to region, the behaviour of a product can be very different over two separate regions. The gauge-only products are dependent on the gauge coverage which is poor in many regions of Africa. The performance of the satellite-based products are influenced by several factors such as the orography, the rainfall regime and the gauge density (for the ones using them). The reanalyses products are outperformed by the other products but can be improved through downscaling.

Rainfall estimation by inverting SMOS soil moisture estimates: A comparison of different methods over Australia

NASA Astrophysics Data System (ADS)

Brocca, Luca; Pellarin, Thierry; Crow, Wade T.; Ciabatta, Luca; Massari, Christian; Ryu, Dongryeol; Su, Chun-Hsu; Rüdiger, Christoph; Kerr, Yann

2016-10-01

Remote sensing of soil moisture has reached a level of maturity and accuracy for which the retrieved products can be used to improve hydrological and meteorological applications. In this study, the soil moisture product from the Soil Moisture and Ocean Salinity (SMOS) satellite is used for improving satellite rainfall estimates obtained from the Tropical Rainfall Measuring Mission multisatellite precipitation analysis product (TMPA) using three different "bottom up" techniques: SM2RAIN, Soil Moisture Analysis Rainfall Tool, and Antecedent Precipitation Index Modification. The implementation of these techniques aims at improving the well-known "top down" rainfall estimate derived from TMPA products (version 7) available in near real time. Ground observations provided by the Australian Water Availability Project are considered as a separate validation data set. The three algorithms are calibrated against the gauge-corrected TMPA reanalysis product, 3B42, and used for adjusting the TMPA real-time product, 3B42RT, using SMOS soil moisture data. The study area covers the entire Australian continent, and the analysis period ranges from January 2010 to November 2013. Results show that all the SMOS-based rainfall products improve the performance of 3B42RT, even at daily time scale (differently from previous investigations). The major improvements are obtained in terms of estimation of accumulated rainfall with a reduction of the root-mean-square error of more than 25%. Also, in terms of temporal dynamic (correlation) and rainfall detection (categorical scores) the SMOS-based products provide slightly better results with respect to 3B42RT, even though the relative performance between the methods is not always the same. The strengths and weaknesses of each algorithm and the spatial variability of their performances are identified in order to indicate the ways forward for this promising research activity. Results show that the integration of bottom up and top down approaches has the potential to improve the quality of near-real-time rainfall estimates from remote sensing in the near future.

Propagation of Satelite Rainfall Products uncertainties in hydrological applications : Examples in West-Africa in the framework of the Megha-Tropiques Satellite Mission

NASA Astrophysics Data System (ADS)

Casse, C.; Gosset, M.; Peugeot, C.; Boone, A.; Pedinotti, V.

2013-12-01

The use of satellite based rainfall in research or operational Hydrological application is becoming more and more frequent. This is specially true in the Tropics where ground based gauge (or radar) network are generally scarce and often degrading. Member of the GPM constellation, the new French-Indian satellite Mission Megha-Tropiques (MT) dedicated to the water and energy budget in the tropical atmosphere contributes to a better monitoring of rainfall in the inter-tropical zone. As part of this mission, research is developed on the use of MT rainfall products for hydrological research or operational application such as flood monitoring. A key issue for such applications is how to account for rainfall products biases and uncertainties, and how to propagate them in the end user models ? Another important question is how to choose the best space-time resolution for the rainfall forcing, given that both model performances and rain-product uncertainties are resolution dependent. This talk will present on going investigations and perspectives on this subject, with examples from the Megha_tropiques Ground validation sites in West Africa. The CNRM model Surfex-ISBA/TRIP has been set up to simulate the hydrological behavior of the Niger River. This modeling set up is being used to study the predictability of Niger Floods events in the city of Niamey and the performances of satellite rainfall products as forcing for such predictions. One of the interesting feature of the Niger outflow in Niamey is its double peak : a first peak attributed to 'local' rainfall falling in small to medium size basins situated in the region of Niamey, and a second peak linked to the rainfall falling in the upper par of the river, and slowly propagating through the river towards Niamey. The performances of rainfall products are found to differ between the wetter/upper part of the basin, and the local/sahelian areas. Both academic tests with artificially biased or 'perturbed' rainfield and actual rainfall products are carried out. A simple method based on probability matching is applied to correct the RT products from their main biases. Several sensitivity studies have also been carried out in the Oueme Basin in Benin, West Africa, one of the instrumented basin used for MT products direct and hydrological validation. The tests highlight the fact that not only total biases but also the distribution of rain rates are key players for explaining the hydrological model sensitivity. (a) TMPAv7 total rainfall in 2010 in West Africa. Solid gray line delimits Niger river catchment, and dotted lines delimit Niger right bank tributary catchments. (b) Observed and simulated discharge at Niamey station. Preliminary results using the SURFEX hydrological model over Niger catchment and different satellite rainfall products as forcing.

Satellite-based Flood Modeling Using TRMM-based Rainfall Products.

PubMed

Harris, Amanda; Rahman, Sayma; Hossain, Faisal; Yarborough, Lance; Bagtzoglou, Amvrossios C; Easson, Greg

2007-12-20

Increasingly available and a virtually uninterrupted supply of satellite-estimatedrainfall data is gradually becoming a cost-effective source of input for flood predictionunder a variety of circumstances. However, most real-time and quasi-global satelliterainfall products are currently available at spatial scales ranging from 0.25 o to 0.50 o andhence, are considered somewhat coarse for dynamic hydrologic modeling of basin-scaleflood events. This study assesses the question: what are the hydrologic implications ofuncertainty of satellite rainfall data at the coarse scale? We investigated this question onthe 970 km² Upper Cumberland river basin of Kentucky. The satellite rainfall productassessed was NASA's Tropical Rainfall Measuring Mission (TRMM) Multi-satellitePrecipitation Analysis (TMPA) product called 3B41RT that is available in pseudo real timewith a latency of 6-10 hours. We observed that bias adjustment of satellite rainfall data canimprove application in flood prediction to some extent with the trade-off of more falsealarms in peak flow. However, a more rational and regime-based adjustment procedureneeds to be identified before the use of satellite data can be institutionalized among floodmodelers.

From TRMM to GPM: How well can heavy rainfall be detected from space?

NASA Astrophysics Data System (ADS)

Prakash, Satya; Mitra, Ashis K.; Pai, D. S.; AghaKouchak, Amir

2016-02-01

In this study, we investigate the capabilities of the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) and the recently released Integrated Multi-satellitE Retrievals for GPM (IMERG) in detecting and estimating heavy rainfall across India. First, the study analyzes TMPA data products over a 17-year period (1998-2014). While TMPA and reference gauge-based observations show similar mean monthly variations of conditional heavy rainfall events, the multi-satellite product systematically overestimates its inter-annual variations. Categorical as well as volumetric skill scores reveal that TMPA over-detects heavy rainfall events (above 75th percentile of reference data), but it shows reasonable performance in capturing the volume of heavy rain across the country. An initial assessment of the GPM-based multi-satellite IMERG precipitation estimates for the southwest monsoon season shows notable improvements over TMPA in capturing heavy rainfall over India. The recently released IMERG shows promising results to help improve modeling of hydrological extremes (e.g., floods and landslides) using satellite observations.

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

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

Climatological Processing and Product Development for the TRMM Ground Validation Program

NASA Technical Reports Server (NTRS)

Marks, D. A.; Kulie, M. S.; Robinson, M.; Silberstein, D. S.; Wolff, D. B.; Ferrier, B. S.; Amitai, E.; Fisher, B.; Wang, J.; Augustine, D.;

Rainy Day: A Remote Sensing-Driven Extreme Rainfall Simulation Approach for Hazard Assessment

NASA Astrophysics Data System (ADS)

Wright, Daniel; Yatheendradas, Soni; Peters-Lidard, Christa; Kirschbaum, Dalia; Ayalew, Tibebu; Mantilla, Ricardo; Krajewski, Witold

2015-04-01

Progress on the assessment of rainfall-driven hazards such as floods and landslides has been hampered by the challenge of characterizing the frequency, intensity, and structure of extreme rainfall at the watershed or hillslope scale. Conventional approaches rely on simplifying assumptions and are strongly dependent on the location, the availability of long-term rain gage measurements, and the subjectivity of the analyst. Regional and global-scale rainfall remote sensing products provide an alternative, but are limited by relatively short (~15-year) observational records. To overcome this, we have coupled these remote sensing products with a space-time resampling framework known as stochastic storm transposition (SST). SST "lengthens" the rainfall record by resampling from a catalog of observed storms from a user-defined region, effectively recreating the regional extreme rainfall hydroclimate. This coupling has been codified in Rainy Day, a Python-based platform for quickly generating large numbers of probabilistic extreme rainfall "scenarios" at any point on the globe. Rainy Day is readily compatible with any gridded rainfall dataset. The user can optionally incorporate regional rain gage or weather radar measurements for bias correction using the Precipitation Uncertainties for Satellite Hydrology (PUSH) framework. Results from Rainy Day using the CMORPH satellite precipitation product are compared with local observations in two examples. The first example is peak discharge estimation in a medium-sized (~4000 square km) watershed in the central United States performed using CUENCAS, a parsimonious physically-based distributed hydrologic model. The second example is rainfall frequency analysis for Saint Lucia, a small volcanic island in the eastern Caribbean that is prone to landslides and flash floods. The distinct rainfall hydroclimates of the two example sites illustrate the flexibility of the approach and its usefulness for hazard analysis in data-poor regions.

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

NASA Astrophysics Data System (ADS)

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

2014-12-01

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

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

NASA Astrophysics Data System (ADS)

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

2017-09-01

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

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.

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.

TRMM- and GPM-based precipitation analysis and modelling in the Tropical Andes

NASA Astrophysics Data System (ADS)

Manz, Bastian; Buytaert, Wouter; Zulkafli, Zed; Onof, Christian

2016-04-01

Despite wide-spread applications of satellite-based precipitation products (SPPs) throughout the TRMM-era, the scarcity of ground-based in-situ data (high density gauge networks, rainfall radar) in many hydro-meteorologically important regions, such as tropical mountain environments, has limited our ability to evaluate both SPPs and individual satellite-based sensors as well as accurately model or merge rainfall at high spatial resolutions, particularly with respect to extremes. This has restricted both the understanding of sensor behaviour and performance controls in such regions as well as the accuracy of precipitation estimates and respective hydrological applications ranging from water resources management to early warning systems. Here we report on our recent research into precipitation analysis and modelling using various TRMM and GPM products (2A25, 3B42 and IMERG) in the tropical Andes. In an initial study, 78 high-frequency (10-min) recording gauges in Colombia and Ecuador are used to generate a ground-based validation dataset for evaluation of instantaneous TRMM Precipitation Radar (TPR) overpasses from the 2A25 product. Detection ability, precipitation time-series, empirical distributions and statistical moments are evaluated with respect to regional climatological differences, seasonal behaviour, rainfall types and detection thresholds. Results confirmed previous findings from extra-tropical regions of over-estimation of low rainfall intensities and under-estimation of the highest 10% of rainfall intensities by the TPR. However, in spite of evident regionalised performance differences as a function of local climatological regimes, the TPR provides an accurate estimate of climatological annual and seasonal rainfall means. On this basis, high-resolution (5 km) climatological maps are derived for the entire tropical Andes. The second objective of this work is to improve the local precipitation estimation accuracy and representation of spatial patterns of extreme rainfall probabilities over the region. For this purpose, an ensemble of high-resolution rainfall fields is generated by stochastic simulation using space-time averaged, coarse-scale (daily, 0.25°) satellite-based rainfall inputs (TRMM 3B42/ -RT) and the high-resolution climatological information derived from the TPR as spatial disaggregation proxies. For evaluation and merging, gridded ground-based rainfall fields are generated from gauge data using sequential simulation. Satellite and ground-based ensembles are subsequently merged using an inverse error weighting scheme. The model was tested over a case study in the Colombian Andes with optional coarse-scale bias correction prior to disaggregation and merging. The resulting outputs were assessed in the context of Generalized Extreme Value theory and showed improved estimation of extreme rainfall probabilities compared to the original TMPA inputs. Initial findings using GPM-IMERG inputs are also presented.

Rainfall model investigation and scenario analyses of the effect of government reforestation policy on seasonal rainfalls: A case study from Northern Thailand

NASA Astrophysics Data System (ADS)

Duangdai, Eakkapong; Likasiri, Chulin

2017-03-01

In this work, 4 models for predicting rainfall amounts are investigated and compared using Northern Thailand's seasonal rainfall data for 1973-2008. Two models, global temperature, forest area and seasonal rainfall (TFR) and modified TFR based on a system of differential equations, give the relationships between global temperature, Northern Thailand's forest cover and seasonal rainfalls in the region. The other two models studied are time series and Autoregressive Moving Average (ARMA) models. All models are validated using the k-fold cross validation method with the resulting errors being 0.971233, 0.740891, 2.376415 and 2.430891 for time series, ARMA, TFR and modified TFR models, respectively. Under Business as Usual (BaU) scenario, seasonal rainfalls in Northern Thailand are projected through the year 2020 using all 4 models. TFR and modified TFR models are also used to further analyze how global temperature rise and government reforestation policy affect seasonal rainfalls in the region. Rainfall projections obtained via the two models are also compared with those from the International Panel on Climate Change (IPCC) under IS92a scenario. Results obtained through a mathematical model for global temperature, forest area and seasonal rainfall show that the higher the forest cover, the less fluctuation there is between rainy-season and summer rainfalls. Moreover, growth in forest cover also correlates with an increase in summer rainfalls. An investigation into the relationship between main crop productions and rainfalls in dry and rainy seasons indicates that if the rainy-season rainfall is high, that year's main-crop rice production will decrease but the second-crop rice, maize, sugarcane and soybean productions will increase in the following year.

Evaluating the streamflow simulation capability of PERSIANN-CDR daily rainfall products in two river basins on the Tibetan Plateau

DOE PAGES

Liu, Xiaomang; Yang, Tiantian; Hsu, Koulin; ...

2017-01-10

On the Tibetan Plateau, the limited ground-based rainfall information owing to a harsh environment has brought great challenges to hydrological studies. Satellite-based rainfall products, which allow for a better coverage than both radar network and rain gauges on the Tibetan Plateau, can be suitable alternatives for studies on investigating the hydrological processes and climate change. In this study, a newly developed daily satellite-based precipitation product, termed Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks $-$ Climate Data Record (PERSIANN-CDR), is used as input for a hydrologic model to simulate streamflow in the upper Yellow and Yangtze River basinsmore » on the Tibetan Plateau. The results show that the simulated streamflows using PERSIANN-CDR precipitation and the Global Land Data Assimilation System (GLDAS) precipitation are closer to observation than that using limited gauge-based precipitation interpolation in the upper Yangtze River basin. The simulated streamflow using gauge-based precipitation are higher than the streamflow observation during the wet season. In the upper Yellow River basin, gauge-based precipitation, GLDAS precipitation, and PERSIANN-CDR precipitation have similar good performance in simulating streamflow. Finally, the evaluation of streamflow simulation capability in this study partly indicates that the PERSIANN-CDR rainfall product has good potential to be a reliable dataset and an alternative information source of a limited gauge network for conducting long-term hydrological and climate studies on the Tibetan Plateau.« less

Evaluating the streamflow simulation capability of PERSIANN-CDR daily rainfall products in two river basins on the Tibetan Plateau

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

Liu, Xiaomang; Yang, Tiantian; Hsu, Koulin

On the Tibetan Plateau, the limited ground-based rainfall information owing to a harsh environment has brought great challenges to hydrological studies. Satellite-based rainfall products, which allow for a better coverage than both radar network and rain gauges on the Tibetan Plateau, can be suitable alternatives for studies on investigating the hydrological processes and climate change. In this study, a newly developed daily satellite-based precipitation product, termed Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks $-$ Climate Data Record (PERSIANN-CDR), is used as input for a hydrologic model to simulate streamflow in the upper Yellow and Yangtze River basinsmore » on the Tibetan Plateau. The results show that the simulated streamflows using PERSIANN-CDR precipitation and the Global Land Data Assimilation System (GLDAS) precipitation are closer to observation than that using limited gauge-based precipitation interpolation in the upper Yangtze River basin. The simulated streamflow using gauge-based precipitation are higher than the streamflow observation during the wet season. In the upper Yellow River basin, gauge-based precipitation, GLDAS precipitation, and PERSIANN-CDR precipitation have similar good performance in simulating streamflow. Finally, the evaluation of streamflow simulation capability in this study partly indicates that the PERSIANN-CDR rainfall product has good potential to be a reliable dataset and an alternative information source of a limited gauge network for conducting long-term hydrological and climate studies on the Tibetan Plateau.« less

On using TRMM data and rainfall forecasts from meteorological models in data-scarce transboundary catchments - an example of Bangladesh

NASA Astrophysics Data System (ADS)

Bhattacharya, Biswa; Tohidul Islam, Md.

2014-05-01

This research focuses on the flood risk of the Haor region in the north-eastern part of Bangladesh. The prediction of the hydrological variables at different spatial and temporal scales in the Haor region is dependent on the influence of several upstream rivers in the Meghalaya catchment in India. Limitation in hydro-meteorological data collection and data sharing issues between the two countries dominate the feasibility of hydrological studies, particularly for near-realtime predictions. One of the possible solutions seems to be in making use of the variety of satellite based and meteorological model products for rainfall. The abundance of a variety of rainfall products provides a good basis of hydrological modelling of a part of the Ganges and Brahmaputra basin. In this research the TRMM data and rainfall forecasts from ECMWF have been compared with the scarce rain gauge data from the upstream Meghalaya catchment. Subsequently, the TRMM data and rainfall forecasts from ECMWF have been used as the meteorological input to a rainfall-runoff model of the Meghalaya catchment. The rainfall-runoff model of Meghalaya has been developed using the DEM data from SRTM. The generated runoff at the outlet of Meghalaya has been used as the upstream boundary condition in the existing rainfall-runoff model of the Haor region. The simulation results have been compared with the existing results based on simulations without any information of the rainfall-runoff in the upstream Meghalaya catchment. The comparison showed that the forecasting lead time has been substantially increased. As per the existing results the forecasting lead time at a number of locations in the catchment was about 6 to 8 hours. With the new results the forecasting lead time has gone up, with different levels of accuracy, to about 24 hours. This additional lead time will be highly beneficial in managing flood risk of the Haor region of Bangladesh. The research shows that satellite based rainfall products and rainfall forecasts from meteorological models can be very useful in flood risk management, particularly for data scarce regions and/or transboundary regions with data sharing issues. Keywords: flood risk management, TRMM, ECMWF, flood forecasting, Haor, Bangladesh. Abbreviations: TRMM: Tropical Rainfall Measuring Mission ECMWF: European Centre for Medium-Range Weather Forecasts DEM: Digital Elevation Model SRTM: Shuttle Radar Topography Mission

Rainfall: State of the Science

NASA Astrophysics Data System (ADS)

Testik, Firat Y.; Gebremichael, Mekonnen

Rainfall: State of the Science offers the most up-to-date knowledge on the fundamental and practical aspects of rainfall. Each chapter, self-contained and written by prominent scientists in their respective fields, provides three forms of information: fundamental principles, detailed overview of current knowledge and description of existing methods, and emerging techniques and future research directions. The book discusses • Rainfall microphysics: raindrop morphodynamics, interactions, size distribution, and evolution • Rainfall measurement and estimation: ground-based direct measurement (disdrometer and rain gauge), weather radar rainfall estimation, polarimetric radar rainfall estimation, and satellite rainfall estimation • Statistical analyses: intensity-duration-frequency curves, frequency analysis of extreme events, spatial analyses, simulation and disaggregation, ensemble approach for radar rainfall uncertainty, and uncertainty analysis of satellite rainfall products The book is tailored to be an indispensable reference for researchers, practitioners, and graduate students who study any aspect of rainfall or utilize rainfall information in various science and engineering disciplines.

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.

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.

Climate change induced rainfall patterns affect wheat productivity and agroecosystem functioning dependent on soil types

NASA Astrophysics Data System (ADS)

Tabi Tataw, James; Baier, Fabian; Krottenthaler, Florian; Pachler, Bernadette; Schwaiger, Elisabeth; Whylidal, Stefan; Formayer, Herbert; Hösch, Johannes; Baumgarten, Andreas; Zaller, Johann G.

2014-05-01

Wheat is a crop of global importance supplying more than half of the world's population with carbohydrates. We examined, whether climate change induced rainfall patterns towards less frequent but heavier events alter wheat agroecosystem productivity and functioning under three different soil types. Therefore, in a full-factorial experiment Triticum aestivum L. was cultivated in 3 m2 lysimeter plots containing the soil types sandy calcaric phaeozem, gleyic phaeozem or calcic chernozem. Prognosticated rainfall patterns based on regionalised climate change model calculations were compared with current long-term rainfall patterns; each treatment combination was replicated three times. Future rainfall patterns significantly reduced wheat growth and yield, reduced the leaf area index, accelerated crop development, reduced arbuscular mycorrhizal fungi colonisation of roots, increased weed density and the stable carbon isotope signature (δ13C) of both old and young wheat leaves. Different soil types affected wheat growth and yield, ecosystem root production as well as weed abundance and biomass. The interaction between climate and soil type was significant only for the harvest index. Our results suggest that even slight changes in rainfall patterns can significantly affect the functioning of wheat agroecosystems. These rainfall effects seemed to be little influenced by soil types suggesting more general impacts of climate change across different soil types. Wheat production under future conditions will likely become more challenging as further concurrent climate change factors become prevalent.

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.

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.

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

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

Comparison of TRMM 2A25 Products Version 6 and Version 7 with NOAA/NSSL Ground Radar-Based National Mosaic QPE

NASA Technical Reports Server (NTRS)

Kirstetter, Pierre-Emmanuel; Hong, Y.; Gourley, J. J.; Schwaller, M.; Petersen, W; Zhang, J.

2012-01-01

Characterization of the error associated to satellite rainfall estimates is a necessary component of deterministic and probabilistic frameworks involving spaceborne passive and active microwave measurements for applications ranging from water budget studies to forecasting natural hazards related to extreme rainfall events. We focus here on the error structure of Tropical Rainfall Measurement Mission (TRMM) Precipitation Radar (PR) quantitative precipitation estimation (QPE) at ground. The problem was addressed in a previous paper by comparison of 2A25 version 6 (V6) product with reference values derived from NOAA/NSSL's ground radar-based National Mosaic and QPE system (NMQ/Q2). The primary contribution of this study is to compare the new 2A25 version 7 (V7) products that were recently released as a replacement of V6. This new version is considered superior over land areas. Several aspects of the two versions are compared and quantified including rainfall rate distributions, systematic biases, and random errors. All analyses indicate V7 is an improvement over V6.

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

Characterizing Congo Basin rainfall and climate using Tropical Rainfall Measuring Mission (TRMM) satellite data and limited rain gauge ground observations

USGS Publications Warehouse

Munzimi, Yolande A.; Hansen, Matthew C.; Adusei, Bernard; Senay, Gabriel B.

2015-01-01

Quantitative understanding of Congo River basin hydrological behavior is poor because of the basin’s limited hydrometeorological observation network. In cases such as the Congo basin where ground data are scarce, satellite-based estimates of rainfall, such as those from the joint NASA/JAXA Tropical Rainfall Measuring Mission (TRMM), can be used to quantify rainfall patterns. This study tests and reports the use of limited rainfall gauge data within the Democratic Republic of Congo (DRC) to recalibrate a TRMM science product (TRMM 3B42, version 6) in characterizing precipitation and climate in the Congo basin. Rainfall estimates from TRMM 3B42, version 6, are compared and adjusted using ground precipitation data from 12 DRC meteorological stations from 1998 to 2007. Adjustment is achieved on a monthly scale by using a regression-tree algorithm. The output is a new, basin-specific estimate of monthly and annual rainfall and climate types across the Congo basin. This new product and the latest version-7 TRMM 3B43 science product are validated by using an independent long-term dataset of historical isohyets. Standard errors of the estimate, root-mean-square errors, and regression coefficients r were slightly and uniformly better with the recalibration from this study when compared with the 3B43 product (mean monthly standard errors of 31 and 40 mm of precipitation and mean r2 of 0.85 and 0.82, respectively), but the 3B43 product was slightly better in terms of bias estimation (1.02 and 1.00). Despite reasonable doubts that have been expressed in studies of other tropical regions, within the Congo basin the TRMM science product (3B43) performed in a manner that is comparable to the performance of the recalibrated product that is described in this study.

Assessment of the GPM and TRMM Precipitation Products Using the Rain Gauge Network over the Tibetan Plateau

NASA Astrophysics Data System (ADS)

Zhang, Sijia; Wang, Donghai; Qin, Zhengkun; Zheng, Yaoyao; Guo, Jianping

2018-04-01

Using high-quality hourly observations from national-level ground-based stations, the satellite-based rainfall products from both the Global Precipitation Measurement (GPM) Integrated MultisatellitE Retrievals for GPM (IMERG) and its predecessor, the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), are statistically evaluated over the Tibetan Plateau (TP), with an emphasis on the diurnal variation. The results indicate that: (1) the half-hourly IMERG rainfall product can explicitly describe the diurnal variation over the TP, but with discrepancies in the timing of the greatest precipitation intensity and an overestimation of the maximum rainfall intensity over the whole TP. In addition, the performance of IMERG on the hourly timescale, in terms of the correlation coefficient and relative bias, is different for regions with sea level height below or above 3500 m; (2) the IMERG products, having higher correlation and lower root-mean-square error, perform better than the TMPA products on the daily and monthly timescales; and (3) the detection ability of IMERG is superior to that of TMPA, as corroborated by a higher Hanssen and Kuipers score, a higher probability of detection, a lower false alarm ratio, and a lower bias. Compared to TMPA, the IMERG products ameliorate the overestimation across the TP. In conclusion, GPM IMERG is superior to TRMM TMPA over the TP on multiple timescales.

Formulation Effects and the Off-target Transport of Pyrethroid Insecticides from Urban Hard Surfaces

PubMed Central

Jorgenson, Brant C.; Young, Thomas M.

2010-01-01

Controlled rainfall experiments utilizing drop forming rainfall simulators were conducted to study various factors contributing to off-target transport of off-the-shelf formulated pyrethroid insecticides from concrete surfaces. Factors evaluated included active ingredient, product formulation, time between application and rainfall (set time), and rainfall intensity. As much as 60% and as little as 0.8% of pyrethroid applied could be recovered in surface runoff depending primarily on product formulation, and to a lesser extent on product set time. Resulting wash-off profiles during one-hour storm simulations could be categorized based on formulation, with formulations utilizing emulsifying surfactants rather than organic solvents resulting in unique wash-off profiles with overall higher wash-off efficiency. These higher wash-off efficiency profiles were qualitatively replicated by applying formulation-free neat pyrethroid in the presence of independently applied linear alkyl benzene sulfonate (LAS) surfactant, suggesting that the surfactant component of some formulated products may be influential in pyrethroid wash-off from urban hard surfaces. PMID:20524665

Impacts of the seasonal distribution of rainfall on vegetation productivity across the Sahel

NASA Astrophysics Data System (ADS)

Zhang, Wenmin; Brandt, Martin; Tong, Xiaoye; Tian, Qingjiu; Fensholt, Rasmus

2018-01-01

Climate change in drylands has caused alterations in the seasonal distribution of rainfall including increased heavy-rainfall events, longer dry spells, and a shifted timing of the wet season. Yet the aboveground net primary productivity (ANPP) in drylands is usually explained by annual-rainfall sums, disregarding the influence of the seasonal distribution of rainfall. This study tested the importance of rainfall metrics in the wet season (onset and cessation of the wet season, number of rainy days, rainfall intensity, number of consecutive dry days, and heavy-rainfall events) for growing season ANPP. We focused on the Sahel and northern Sudanian region (100-800 mm yr-1) and applied daily satellite-based rainfall estimates (CHIRPS v2.0) and growing-season-integrated normalized difference vegetation index (NDVI; MODIS) as a proxy for ANPP over the study period: 2001-2015. Growing season ANPP in the arid zone (100-300 mm yr-1) was found to be rather insensitive to variations in the seasonal-rainfall metrics, whereas vegetation in the semi-arid zone (300-700 mm yr-1) was significantly impacted by most metrics, especially by the number of rainy days and timing (onset and cessation) of the wet season. We analysed critical breakpoints for all metrics to test if vegetation response to changes in a given rainfall metric surpasses a threshold beyond which vegetation functioning is significantly altered. It was shown that growing season ANPP was particularly negatively impacted after > 14 consecutive dry days and that a rainfall intensity of ˜ 13 mm day-1 was detected for optimum growing season ANPP. We conclude that the number of rainy days and the timing of the wet season are seasonal-rainfall metrics that are decisive for favourable vegetation growth in the semi-arid Sahel and need to be considered when modelling primary productivity from rainfall in the drylands of the Sahel and elsewhere.

Operational Processing of Ground Validation Data for the Tropical Rainfall Measuring Mission

NASA Technical Reports Server (NTRS)

Kulie, Mark S.; Robinson, Mike; Marks, David A.; Ferrier, Brad S.; Rosenfeld, Danny; Wolff, David B.

1999-01-01

The Tropical Rainfall Measuring Mission (TRMM) satellite was successfully launched in November 1997. A primary goal of TRMM is to sample tropical rainfall using the first active spaceborne precipitation radar. To validate TRMM satellite observations, a comprehensive Ground Validation (GV) Program has been implemented for this mission. A key component of GV is the analysis and quality control of meteorological ground-based radar data from four primary sites: Melbourne, FL; Houston, TX; Darwin, Australia; and Kwajalein Atoll, RMI. As part of the TRMM GV effort, the Joint Center for Earth Systems Technology (JCET) at the University of Maryland, Baltimore County, has been tasked with developing and implementing an operational system to quality control (QC), archive, and provide data for subsequent rainfall product generation from the four primary GV sites. This paper provides an overview of the JCET operational environment. A description of the QC algorithm and performance, in addition to the data flow procedure between JCET and the TRNM science and Data Information System (TSDIS), are presented. The impact of quality-controlled data on higher level rainfall and reflectivity products will also be addressed, Finally, a brief description of JCET's expanded role into producing reference rainfall products will be discussed.

Evaluation of satellite rainfall estimates for drought and flood monitoring in Mozambique

USGS Publications Warehouse

Tote, Carolien; Patricio, Domingos; Boogaard, Hendrik; van der Wijngaart, Raymond; Tarnavsky, Elena; Funk, Christopher C.

2015-01-01

Satellite derived rainfall products are useful for drought and flood early warning and overcome the problem of sparse, unevenly distributed and erratic rain gauge observations, provided their accuracy is well known. Mozambique is highly vulnerable to extreme weather events such as major droughts and floods and thus, an understanding of the strengths and weaknesses of different rainfall products is valuable. Three dekadal (10-day) gridded satellite rainfall products (TAMSAT African Rainfall Climatology And Time-series (TARCAT) v2.0, Famine Early Warning System NETwork (FEWS NET) Rainfall Estimate (RFE) v2.0, and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS)) are compared to independent gauge data (2001–2012). This is done using pairwise comparison statistics to evaluate the performance in estimating rainfall amounts and categorical statistics to assess rain-detection capabilities. The analysis was performed for different rainfall categories, over the seasonal cycle and for regions dominated by different weather systems. Overall, satellite products overestimate low and underestimate high dekadal rainfall values. The RFE and CHIRPS products perform as good, generally outperforming TARCAT on the majority of statistical measures of skill. TARCAT detects best the relative frequency of rainfall events, while RFE underestimates and CHIRPS overestimates the rainfall events frequency. Differences in products performance disappear with higher rainfall and all products achieve better results during the wet season. During the cyclone season, CHIRPS shows the best results, while RFE outperforms the other products for lower dekadal rainfall. Products blending thermal infrared and passive microwave imagery perform better than infrared only products and particularly when meteorological patterns are more complex, such as over the coastal, central and south regions of Mozambique, where precipitation is influenced by frontal systems.

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

NASA Astrophysics Data System (ADS)

Brauer, Claudia; Overeem, Aart; Uijlenhoet, Remko

2015-04-01

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

Does GPM-based multi-satellite precipitation enhance rainfall estimates over Pakistan and Bolivia arid regions?

NASA Astrophysics Data System (ADS)

Hussain, Y.; Satgé, F.; Bonnet, M. P.; Pillco, R.; Molina, J.; Timouk, F.; Roig, H.; Martinez-Carvajal, H., Sr.; Gulraiz, A.

2016-12-01

Arid regions are sensitive to rainfall variations which are expressed in the form of flooding and droughts. Unfortunately, those regions are poorly monitored and high quality rainfall estimates are still needed. The Global Precipitation Measurement (GPM) mission released two new satellite rainfall products named Integrated Multisatellite Retrievals GPM (IMERG) and Global Satellite Mapping of Precipitation version 6 (GSMaP-v6) bringing the possibility of accurate rainfall monitoring over these countries. This study assessed both products at monthly scale over Pakistan considering dry and wet season over the 4 main climatic zones from 2014 to 2016. With similar climatic conditions, the Altiplano region of Bolivia is considered to quantify the influence of big lakes (Titicaca and Poopó) in rainfall estimates. For comparison, the widely used TRMM-Multisatellite Precipitation Analysis 3B43 (TMPA-3B43) version 7 is also involved in the analysis to observe the potential enhancement in rainfall estimate brought by GPM products. Rainfall estimates derived from 110 rain-gauges are used as reference to compare IMERG, GSMaP-v6 and TMPA-3B43 at the 0.1° and 0.25° spatial resolution. Over both regions, IMERG and GSMaP-v6 capture the spatial pattern of precipitation as well as TMPA-3B43. All products tend to over estimates rainfall over very arid regions. This feature is even more marked during dry season. However, during this season, both reference and estimated rainfall remain very low and do not impact seasonal water budget computation. On a general way, IMERG slightly outperforms TMPA-3B43 and GSMaP-v6 which provides the less accurate rainfall estimate. The TMPA-3B43 rainfall underestimation previously found over Lake Titicaca is still observed in IMERG estimates. However, GSMaP-v6 considerably decreases the underestimation providing the most accurate rainfall estimate over the lake. MOD11C3 Land Surface Temperature (LST) and ASTER Global Emissivity Dataset reveal strong LST and Emissivity anomaly over the lake in comparison with surrounding lands. These anomalies should explain rainfall underestimations tendency over this lake. LST and Emissivity of lake Poopó are closest to surrounding land and the slight observed rainfall overestimation appears to be related to the very arid context of the region.

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

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

An Experimental Global Monitoring System for Rainfall-triggered Landslides using Satellite Remote Sensing Information

NASA Technical Reports Server (NTRS)

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

2006-01-01

Landslides triggered by rainfall can possibly be foreseen in real time by jointly using rainfall intensity-duration thresholds and information related to land surface susceptibility. However, no system exists at either a national or a global scale to monitor or detect rainfall conditions that may trigger landslides due to the lack of extensive ground-based observing network in many parts of the world. Recent advances in satellite remote sensing technology and increasing availability of high-resolution geospatial products around the globe have provided an unprecedented opportunity for such a study. In this paper, a framework for developing an experimental real-time monitoring system to detect rainfall-triggered landslides is proposed by combining two necessary components: surface landslide susceptibility and a real-time space-based rainfall analysis system (http://trmm.gsfc.nasa.aov). First, a global landslide susceptibility map is derived from a combination of semi-static global surface characteristics (digital elevation topography, slope, soil types, soil texture, and land cover classification etc.) using a GIs weighted linear combination approach. Second, an adjusted empirical relationship between rainfall intensity-duration and landslide occurrence is used to assess landslide risks at areas with high susceptibility. A major outcome of this work is the availability of a first-time global assessment of landslide risk, which is only possible because of the utilization of global satellite remote sensing products. This experimental system can be updated continuously due to the availability of new satellite remote sensing products. This proposed system, if pursued through wide interdisciplinary efforts as recommended herein, bears the promise to grow many local landslide hazard analyses into a global decision-making support system for landslide disaster preparedness and risk mitigation activities across the world.

Rainfall Product Evaluation for the TRMM Ground Validation Program

NASA Technical Reports Server (NTRS)

Amitai, E.; Wolff, D. B.; Robinson, M.; Silberstein, D. S.; Marks, D. A.; Kulie, M. S.; Fisher, B.; Einaudi, Franco (Technical Monitor)

2000-01-01

Evaluation of the Tropical Rainfall Measuring Mission (TRMM) satellite observations is conducted through a comprehensive Ground Validation (GV) Program. Standardized instantaneous and monthly rainfall products are routinely generated using quality-controlled ground based radar data from four primary GV sites. As part of the TRMM GV program, effort is being made to evaluate these GV products and to determine the uncertainties of the rainfall estimates. The evaluation effort is based on comparison to rain gauge data. The variance between the gauge measurement and the true averaged rain amount within the radar pixel is a limiting factor in the evaluation process. While monthly estimates are relatively simple to evaluate, the evaluation of the instantaneous products are much more of a challenge. Scattegrams of point comparisons between radar and rain gauges are extremely noisy for several reasons (e.g. sample volume discrepancies, timing and navigation mismatches, variability of Z(sub e)-R relationships), and therefore useless for evaluating the estimates. Several alternative methods, such as the analysis of the distribution of rain volume by rain rate as derived from gauge intensities and from reflectivities above the gauge network will be presented. Alternative procedures to increase the accuracy of the estimates and to reduce their uncertainties also will be discussed.

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.

Use of a large-scale rainfall simulator reveals novel insights into stemflow generation

NASA Astrophysics Data System (ADS)

Levia, D. F., Jr.; Iida, S. I.; Nanko, K.; Sun, X.; Shinohara, Y.; Sakai, N.

2017-12-01

Detailed knowledge of stemflow generation and its effects on both hydrological and biogoechemical cycling is important to achieve a holistic understanding of forest ecosystems. Field studies and a smaller set of experiments performed under laboratory conditions have increased our process-based knowledge of stemflow production. Building upon these earlier works, a large-scale rainfall simulator was employed to deepen our understanding of stemflow generation processes. The use of the large-scale rainfall simulator provides a unique opportunity to examine a range of rainfall intensities under constant conditions that are difficult under natural conditions due to the variable nature of rainfall intensities in the field. Stemflow generation and production was examined for three species- Cryptomeria japonica D. Don (Japanese cedar), Chamaecyparis obtusa (Siebold & Zucc.) Endl. (Japanese cypress), Zelkova serrata Thunb. (Japanese zelkova)- under both leafed and leafless conditions at several different rainfall intensities (15, 20, 30, 40, 50, and 100 mm h-1) using a large-scale rainfall simulator in National Research Institute for Earth Science and Disaster Resilience (Tsukuba, Japan). Stemflow production and rates and funneling ratios were examined in relation to both rainfall intensity and canopy structure. Preliminary results indicate a dynamic and complex response of the funneling ratios of individual trees to different rainfall intensities among the species examined. This is partly the result of different canopy structures, hydrophobicity of vegetative surfaces, and differential wet-up processes across species and rainfall intensities. This presentation delves into these differences and attempts to distill them into generalizable patterns, which can advance our theories of stemflow generation processes and ultimately permit better stewardship of forest resources. ________________ Funding note: This research was supported by JSPS Invitation Fellowship for Research in Japan (Grant Award No.: S16088) and JSPS KAKENHI (Grant Award No.: JP15H05626).

Analyzing remotely sensed datasets for improved characterization of field-scale interventions for food security

NASA Astrophysics Data System (ADS)

Limaye, A. S.; Ellenburg, W. L., II; Coffee, K.; Ashmall, W.; Stanton, K.; Burks, J.; Irwin, D.

2017-12-01

Agriculture interventions such as irrigation, improved fertilization, and advanced cultivars have the potential to increase food security and ensure climate resilience. However, in order broaden the support of activities like these, environmental managers must be able to assess their impact. Often field data are difficult to obtain and decisions are made with limited information. Satellite products can provide relevant information at field and village wide scales that can assist in this process. SERVIR is taking an aim of helping connect the space-based products to help the efficacy of village scale interventions through a couple of web-based tools, called ClimateSERV and AgriSERV. ClimateSERV has been active since 2014, and has increased in the data holdings and access points. Currently, ClimateSERV enables users to create geographic regions of their choosing and to compute key statistics for those regions. Rainfall (GPM IMERG, CHIRPS), vegetation indices (eMODIS Normalized Difference Vegetation Index - NDVI; Evaporative Stress Index), and North American Multi-model Ensemble-based seasonal climate forecasts of rainfall and temperature. ClimateSERV can also query the Google Earth Engine holdings for datasets, currently, ClimateSERV provides access to the daytime MODIS Land Surface Temperature (LST). Our first such derived product is a monthly rainfall analysis feature which combines CHIRPS historic rainfall with seasonal forecast models AgriSERV is a derived web-based tool based on the ClimateSERV data holdings. It is designed to provide easy to interpret analysis, based NDVI and rainfall. This tool allows users to draw two areas of interest, one control with no intervention and another that has experienced intervention. An on-demand comparative analysis is performed and the user is presented with side-by-side charts and summary data that highlight the differences of the two areas in terms of vegetation health, derived growing season lengths and rainfall. The analysis is based on an area-weighted average of the gridded NDVI and rainfall data. The users can download the summary data table as well as the full dataset for the period specified. This presentation is intended to showcase the utility of the intervention programs and to provide an objective rationale for expansion of those intervention programs.

Verification of the skill of numerical weather prediction models in forecasting rainfall from U.S. landfalling tropical cyclones

NASA Astrophysics Data System (ADS)

Luitel, Beda; Villarini, Gabriele; Vecchi, Gabriel A.

2018-01-01

The goal of this study is the evaluation of the skill of five state-of-the-art numerical weather prediction (NWP) systems [European Centre for Medium-Range Weather Forecasts (ECMWF), UK Met Office (UKMO), National Centers for Environmental Prediction (NCEP), China Meteorological Administration (CMA), and Canadian Meteorological Center (CMC)] in forecasting rainfall from North Atlantic tropical cyclones (TCs). Analyses focus on 15 North Atlantic TCs that made landfall along the U.S. coast over the 2007-2012 period. As reference data we use gridded rainfall provided by the Climate Prediction Center (CPC). We consider forecast lead-times up to five days. To benchmark the skill of these models, we consider rainfall estimates from one radar-based (Stage IV) and four satellite-based [Tropical Rainfall Measuring Mission - Multi-satellite Precipitation Analysis (TMPA, both real-time and research version); Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN); the CPC MORPHing Technique (CMORPH)] rainfall products. Daily and storm total rainfall fields from each of these remote sensing products are compared to the reference data to obtain information about the range of errors we can expect from "observational data." The skill of the NWP models is quantified: (1) by visual examination of the distribution of the errors in storm total rainfall for the different lead-times, and numerical examination of the first three moments of the error distribution; (2) relative to climatology at the daily scale. Considering these skill metrics, we conclude that the NWP models can provide skillful forecasts of TC rainfall with lead-times up to 48 h, without a consistently best or worst NWP model.

The assessment of Global Precipitation Measurement estimates over the Indian subcontinent

NASA Astrophysics Data System (ADS)

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

2017-08-01

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

In Situ Verification of the NASA D3R's Hydrometeor Classification and Rainfall Products during the OLYMPEx Field Campaign

NASA Astrophysics Data System (ADS)

Chen, H.; Chandra, C. V.

2017-12-01

As a ground validation (GV) radar for the Global Precipitation Measurement (GPM) satellite mission, the NASA dual-frequency, dual-polarization, Doppler radar (D3R) was deployed just north of Pacific Beach, WA between November 8th, 2015 and January 15th, 2016, as part of the Olympic Mountains Experiment (OLYMPEx). The D3R's observations were coordinated with a diverse array of instruments including the NASA NPOL S-band radar, Autonomous Parsivel Unit (APU) disdrometers, rain gauges, and airborne probe. The Ku- and Ka-band D3R is analogous to the GPM core satellite dual-frequency precipitation radar (DPR), but can provide more detailed insight into the precipitation microphysics through the ground-based dual-frequency dual-polarization observations. Previous studies have revealed that the dual polarization radar can be used to identify different hydrometeor types and their size and shape information. However, most of the previous studies are devoted to S-, C-, and/or X-band frequencies since they are standard operating frequency in many countries. This paper presents a region-based hydrometeor classification methodology applied for the NASA D3R measurements collected during OLYMPEx. This paper also details the differential phase based attenuation correction methodology and rainfall algorithm developed for the D3R. The D3R's hydrometeor classification and rainfall products are evaluated using other remote sensors and in situ measurements. In particular, the derived hydrometeor types are cross compared with collocated S-band products and images collected by the airborne probe. The rainfall performance are assessed using rain gauge and disdrometer observations. Results show that the NASA D3R has great potential for monitoring precipitation microphysics and rainfall estimation, especially light rainfall that is hard to be observed by traditional ground or space based sensors.

Application of satellite products and hydrological modelling for flood early warning

NASA Astrophysics Data System (ADS)

Koriche, Sifan A.; Rientjes, Tom H. M.

2016-06-01

Floods have caused devastating impacts to the environment and society in Awash River Basin, Ethiopia. Since flooding events are frequent, this marks the need to develop tools for flood early warning. In this study, we propose a satellite based flood index to identify the runoff source areas that largely contribute to extreme runoff production and floods in the basin. Satellite based products used for development of the flood index are CMORPH (Climate Prediction Center MORPHing technique: 0.25° by 0.25°, daily) product for calculation of the Standard Precipitation Index (SPI) and a Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) for calculation of the Topographic Wetness Index (TWI). Other satellite products used in this study are for rainfall-runoff modelling to represent rainfall, potential evapotranspiration, vegetation cover and topography. Results of the study show that assessment of spatial and temporal rainfall variability by satellite products may well serve in flood early warning. Preliminary findings on effectiveness of the flood index developed in this study indicate that the index is well suited for flood early warning. The index combines SPI and TWI, and preliminary results illustrate the spatial distribution of likely runoff source areas that cause floods in flood prone areas.

Evaluation of Various Radar Data Quality Control Algorithms Based on Accumulated Radar Rainfall Statistics

NASA Technical Reports Server (NTRS)

Robinson, Michael; Steiner, Matthias; Wolff, David B.; Ferrier, Brad S.; Kessinger, Cathy; Einaudi, Franco (Technical Monitor)

2000-01-01

The primary function of the TRMM Ground Validation (GV) Program is to create GV rainfall products that provide basic validation of satellite-derived precipitation measurements for select primary sites. A fundamental and extremely important step in creating high-quality GV products is radar data quality control. Quality control (QC) processing of TRMM GV radar data is based on some automated procedures, but the current QC algorithm is not fully operational and requires significant human interaction to assure satisfactory results. Moreover, the TRMM GV QC algorithm, even with continuous manual tuning, still can not completely remove all types of spurious echoes. In an attempt to improve the current operational radar data QC procedures of the TRMM GV effort, an intercomparison of several QC algorithms has been conducted. This presentation will demonstrate how various radar data QC algorithms affect accumulated radar rainfall products. In all, six different QC algorithms will be applied to two months of WSR-88D radar data from Melbourne, Florida. Daily, five-day, and monthly accumulated radar rainfall maps will be produced for each quality-controlled data set. The QC algorithms will be evaluated and compared based on their ability to remove spurious echoes without removing significant precipitation. Strengths and weaknesses of each algorithm will be assessed based on, their abilit to mitigate both erroneous additions and reductions in rainfall accumulation from spurious echo contamination and true precipitation removal, respectively. Contamination from individual spurious echo categories will be quantified to further diagnose the abilities of each radar QC algorithm. Finally, a cost-benefit analysis will be conducted to determine if a more automated QC algorithm is a viable alternative to the current, labor-intensive QC algorithm employed by TRMM GV.

High Resolution Monthly Oceanic Rainfall Based on Microwave Brightness Temperature Histograms

NASA Astrophysics Data System (ADS)

Shin, D.; Chiu, L. S.

2005-12-01

A statistical emission-based passive microwave retrieval algorithm has been developed by Wilheit, Chang and Chiu (1991) to estimate space/time oceanic rainfall. The algorithm has been applied to Special Sensor Microwave Imager (SSM/I) data taken on board the Defense Meteorological Satellite Program (DMSP) satellites to provide monthly oceanic rainfall over 2.5ox2.5o and 5ox5o latitude-longitude boxes by the Global Precipitation Climatology Project-Polar Satellite Precipitation Data Center (GPCP-PSPDC, URL: http://gpcp-pspdc.gmu.edu/) as part of NASA's contribution to the GPCP. The algorithm has been modified and applied to the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) data to produce a TRMM Level 3 standard product (3A11) over 5ox5o latitude/longitude boxes. In this study, the algorithm code is modified to retrieve rain rates at 2.5ox2.5o and 1ox1o resolutions for TMI. Two months of TMI data have been tested and the results compared with the monthly mean rain rates derived from TRMM Level 2 TMI rain profile algorithm (2A12) and the original 5ox5o data from 3A11. The rainfall pattern is very similar to the monthly average of 2A12, although the intensity is slightly higher. Details in the rain pattern, such as rain shadow due to island blocking, which were not discernible from the low resolution products, are now easily discernible. The spatial average of the higher resolution rain rates are in general slightly higher than lower resolution rain rates, although a Student-t test shows no significant difference. This high resolution product will be useful for the calibration of IR rain estimates for the production of the GPCP merge rain product.

Acquisition Of Rainfall Dataset And The Application For The Automatic Harvester In The Chesapeake Bay Region

NASA Astrophysics Data System (ADS)

Choi, Y.; Piasecki, M.

2008-12-01

The objective of this study is the preparation and indexing of rainfall data products for ingestion into the Chesapeake Bay Environmental Observatory (CBEO) node of the CUAHSI/WATERs network. Rainfall products (which are obtained and then processed based on the WSR-88D NEXRAD network) are obtained from the NOAA/NWS Advanced Hydrologic Prediction Service that combines the Multi-sensor Precipitation Estimate (MPE) data generated by the Regional River Forecast Centers and Hydro-NEXRAD rainfall data generated as a service by the University of Iowa. The former is collected on 4*4 km grid (HRAP) with a daily average temporal resolution and the latter on a 1minute*1minute degree grid with hourly values. We have generated a cut-out for the Chesapeake Bay Basin that contains about 9,300 nodes (sites) for the MPE data and about 300,000 nodes (sites) for the Hydro-NEXRAD product. Automated harvesting services have been implemented for both data products. The MPE data is harvested from its download site using ArcGIS which in turn is used to extract the data for the Chesapeake Bay watershed before a scripting program is used to scatter the data into the ODM. The Hydro-NEXRAD is downloaded from a web-based system at the University of Iowa which permits downloads for large scale watersheds organized by Hydraulic Unit Codes (HUC). The resulting ASCII is then automatically parsed and the information stored alongside the MPE data. The two data products stored side-by-side then allows a comparison between them addressing the accuracy and agreement between the methods used to arrive at rainfall data as both use the raw reflectivity data from the WSD-88D system.

Soil water sensor-based and evapotranspiration-based irrigation scheduling for soybean production on a Blackland Prairie soil in humid climate

USDA-ARS?s Scientific Manuscript database

In east-central Mississippi, annual rainfall was 1307 mm and reference evapotranspiration (ETo) was 1210 mm for the 120-year period from 1894 to 2014. From May to October, when major crops are typically grown in this area, monthly rainfall ranged from 72 to 118 mm, and monthly ETo from 94 to 146 mm ...

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

NASA Astrophysics Data System (ADS)

Shehu, Bora; Haberlandt, Uwe

2016-04-01

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

Precipitation Estimation from the ARM Distributed Radar Network during the MC3E Campaign

DOE PAGES

Giangrande, Scott E.; Collis, Scott; Theisen, Adam K.; ...

2014-09-12

This study presents radar-based precipitation estimates collected during the two-month DOE ARM - NASA Midlatitude Continental Convective Clouds Experiment (MC3E). Emphasis is on the usefulness of radar observations from the C-band and X-band scanning ARM precipitation radars (CSAPR, XSAPR) for rainfall estimation products to distances within 100 km of the Oklahoma SGP facility. A dense collection of collocated ARM, NASA GPM and nearby surface Oklahoma Mesonet gauge records are consulted to evaluate potential ARM radar-based hourly rainfall products and campaign optimized methods over individual gauge and areal characterizations. Rainfall products are evaluated against the performance of the regional operational NWSmore » NEXRAD S-band radar polarimetric product. Results indicate that the ARM C-band system may achieve similar point and areal-gauge bias and root mean square (rms) error performance to the NEXRAD standard for the variety of MC3E deep convective events sampled when capitalizing on differential phase measurements. The best campaign rainfall performance was achieved when applying radar relations capitalizing on estimates of the specific attenuation from the CSAPR system. The ARM X-band systems only demonstrate solid capabilities as compared to NEXRAD standards for hourly point and areal rainfall accumulations under 10 mm. Here, all methods exhibit a factor of 1.5 to 2.5 reduction in rms errors for areal accumulations over a 15 km2 NASA dense network housing 16 sites having collocated bucket gauges, with the higher error reductions best associated with polarimetric methods.« less

Precipitation Estimation from the ARM Distributed Radar Network during the MC3E Campaign

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

Giangrande, Scott E.; Collis, Scott; Theisen, Adam K.

This study presents radar-based precipitation estimates collected during the two-month DOE ARM - NASA Midlatitude Continental Convective Clouds Experiment (MC3E). Emphasis is on the usefulness of radar observations from the C-band and X-band scanning ARM precipitation radars (CSAPR, XSAPR) for rainfall estimation products to distances within 100 km of the Oklahoma SGP facility. A dense collection of collocated ARM, NASA GPM and nearby surface Oklahoma Mesonet gauge records are consulted to evaluate potential ARM radar-based hourly rainfall products and campaign optimized methods over individual gauge and areal characterizations. Rainfall products are evaluated against the performance of the regional operational NWSmore » NEXRAD S-band radar polarimetric product. Results indicate that the ARM C-band system may achieve similar point and areal-gauge bias and root mean square (rms) error performance to the NEXRAD standard for the variety of MC3E deep convective events sampled when capitalizing on differential phase measurements. The best campaign rainfall performance was achieved when applying radar relations capitalizing on estimates of the specific attenuation from the CSAPR system. The ARM X-band systems only demonstrate solid capabilities as compared to NEXRAD standards for hourly point and areal rainfall accumulations under 10 mm. Here, all methods exhibit a factor of 1.5 to 2.5 reduction in rms errors for areal accumulations over a 15 km2 NASA dense network housing 16 sites having collocated bucket gauges, with the higher error reductions best associated with polarimetric methods.« less

Evaluating the use of different precipitation datasets in simulating a flood event

NASA Astrophysics Data System (ADS)

Akyurek, Z.; Ozkaya, A.

2016-12-01

Floods caused by convective storms in mountainous regions are sensitive to the temporal and spatial variability of rainfall. Space-time estimates of rainfall from weather radar, satellites and numerical weather prediction models can be a remedy to represent pattern of the rainfall with some inaccuracy. However, there is a strong need for evaluation of the performance and limitations of these estimates in hydrology. This study aims to provide a comparison of gauge, radar, satellite (Hydro-Estimator (HE)) and numerical weather prediciton model (Weather Research and Forecasting (WRF)) precipitation datasets during an extreme flood event (22.11.2014) lasting 40 hours in Samsun-Turkey. For this study, hourly rainfall data from 13 ground observation stations were used in the analyses. This event having a peak discharge of 541 m3/sec created flooding at the downstream of Terme Basin. Comparisons were performed in two parts. First the analysis were performed in areal and point based manner. Secondly, a semi-distributed hydrological model was used to assess the accuracy of the rainfall datasets to simulate river flows for the flood event. Kalman Filtering was used in the bias correction of radar rainfall data compared to gauge measurements. Radar, gauge, corrected radar, HE and WRF rainfall data were used as model inputs. Generally, the HE product underestimates the cumulative rainfall amounts in all stations, radar data underestimates the results in cumulative sense but keeps the consistency in the results. On the other hand, almost all stations in WRF mean statistics computations have better results compared to the HE product but worse than the radar dataset. Results in point comparisons indicated that, trend of the rainfall is captured by the radar rainfall estimation well but radar underestimates the maximum values. According to cumulative gauge value, radar underestimated the cumulative rainfall amount by % 32. Contrary to other datasets, the bias of WRF is positive due to the overestimation of rainfall forecasts. It was seen that radar-based flow predictions demonstrated good potential for successful hydrological modeling. Moreover, flow predictions obtained from bias corrected radar rainfall values produced an increase in the peak flows compared to the ones obtained from radar data itself.

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

PubMed Central

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

2014-01-01

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

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

PubMed

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

2014-01-01

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

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.

Internationally coordinated multi-mission planning is now critical to sustain the space-based rainfall observations needed for managing floods globally

NASA Astrophysics Data System (ADS)

Reed, Patrick M.; Chaney, Nathaniel W.; Herman, Jonathan D.; Ferringer, Matthew P.; Wood, Eric F.

2015-02-01

At present 4 of 10 dedicated rainfall observing satellite systems have exceeded their design life, some by more than a decade. Here, we show operational implications for flood management of a ‘collapse’ of space-based rainfall observing infrastructure as well as the high-value opportunities for a globally coordinated portfolio of satellite missions and data services. Results show that the current portfolio of rainfall missions fails to meet operational data needs for flood management, even when assuming a perfectly coordinated data product from all current rainfall-focused missions (i.e., the full portfolio). In the full portfolio, satellite-based rainfall data deficits vary across the globe and may preclude climate adaptation in locations vulnerable to increasing flood risks. Moreover, removing satellites that are currently beyond their design life (i.e., the reduced portfolio) dramatically increases data deficits globally and could cause entire high intensity flood events to be unobserved. Recovery from the reduced portfolio is possible with internationally coordinated replenishment of as few as 2 of the 4 satellite systems beyond their design life, yielding rainfall data coverages that outperform the current full portfolio (i.e., an optimized portfolio of eight satellites can outperform ten satellites). This work demonstrates the potential for internationally coordinated satellite replenishment and data services to substantially enhance the cost-effectiveness, sustainability and operational value of space-based rainfall observations in managing evolving flood risks.

A preliminary assessment of GPM-based multi-satellite precipitation estimates over a monsoon dominated region

NASA Astrophysics Data System (ADS)

Prakash, Satya; Mitra, Ashis K.; AghaKouchak, Amir; Liu, Zhong; Norouzi, Hamidreza; Pai, D. S.

2018-01-01

Following the launch of the Global Precipitation Measurement (GPM) Core Observatory, two advanced high resolution multi-satellite precipitation products namely, Integrated Multi-satellitE Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) version 6 are released. A critical evaluation of these newly released precipitation data sets is very important for both the end users and data developers. This study provides a comprehensive assessment of IMERG research product and GSMaP estimates over India at a daily scale for the southwest monsoon season (June to September 2014). The GPM-based precipitation products are inter-compared with widely used TRMM Multi-satellite Precipitation Analysis (TMPA), and gauge-based observations over India. Results show that the IMERG estimates represent the mean monsoon rainfall and its variability more realistically than the gauge-adjusted TMPA and GSMaP data. However, GSMaP has relatively smaller root-mean-square error than IMERG and TMPA, especially over the low mean rainfall regimes and along the west coast of India. An entropy-based approach is employed to evaluate the distributions of the selected precipitation products. The results indicate that the distribution of precipitation in IMERG and GSMaP has been improved markedly, especially for low precipitation rates. IMERG shows a clear improvement in missed and false precipitation bias over India. However, all the three satellite-based rainfall estimates show exceptionally smaller correlation coefficient, larger RMSE, larger negative total bias and hit bias over the northeast India where precipitation is dominated by orographic effects. Similarly, the three satellite-based estimates show larger false precipitation over the southeast peninsular India which is a rain-shadow region. The categorical verification confirms that these satellite-based rainfall estimates have difficulties in detection of rain over the southeast peninsula and northeast India. These preliminary results need to be confirmed in other monsoon seasons in future studies when the fully GPM-based IMERG retrospectively processed data prior to 2014 are available.

Uncertainty Analysis of Radar and Gauge Rainfall Estimates in the Russian River Basin

NASA Astrophysics Data System (ADS)

Cifelli, R.; Chen, H.; Willie, D.; Reynolds, D.; Campbell, C.; Sukovich, E.

2013-12-01

Radar Quantitative Precipitation Estimation (QPE) has been a very important application of weather radar since it was introduced and made widely available after World War II. Although great progress has been made over the last two decades, it is still a challenging process especially in regions of complex terrain such as the western U.S. It is also extremely difficult to make direct use of radar precipitation data in quantitative hydrologic forecasting models. To improve the understanding of rainfall estimation and distributions in the NOAA Hydrometeorology Testbed in northern California (HMT-West), extensive evaluation of radar and gauge QPE products has been performed using a set of independent rain gauge data. This study focuses on the rainfall evaluation in the Russian River Basin. The statistical properties of the different gridded QPE products will be compared quantitatively. The main emphasis of this study will be on the analysis of uncertainties of the radar and gauge rainfall products that are subject to various sources of error. The spatial variation analysis of the radar estimates is performed by measuring the statistical distribution of the radar base data such as reflectivity and by the comparison with a rain gauge cluster. The application of mean field bias values to the radar rainfall data will also be described. The uncertainty analysis of the gauge rainfall will be focused on the comparison of traditional kriging and conditional bias penalized kriging (Seo 2012) methods. This comparison is performed with the retrospective Multisensor Precipitation Estimator (MPE) system installed at the NOAA Earth System Research Laboratory. The independent gauge set will again be used as the verification tool for the newly generated rainfall products.

Assessment of GPM and SM2RAIN-ASCAT rainfall products over complex terrain in southern Italy

NASA Astrophysics Data System (ADS)

Chiaravalloti, Francesco; Brocca, Luca; Procopio, Antonio; Massari, Christian; Gabriele, Salvatore

2018-07-01

The assessment of precipitation over land is extremely important for a number of scientific purposes related to the mitigation of natural hazards, climate modelling and prediction, famine and disease monitoring, to cite a few. Due to the difficulties and the cost to maintain ground monitoring networks, i.e., raingauges and meteorological radars, remote sensing is receiving more and more attention in the recent decade(s). However, the accuracy of satellite observations of rainfall should be assessed with ground information as it is affected by a number of factors (topography, vegetation density, land-sea interface). Calabria is a peninsular region in southern Italy characterized by complex topography, dense vegetation and a narrow North-South elongated shape, thus being a very challenging place for rainfall retrieval from remote sensing. In this study, we built a high-quality rainfall datasets from raingauges and meteorological radars for testing three remotely sensed rainfall products: 1) the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement product (IMERG), 2) the SM2RASC product obtained from the application of SM2RAIN (Soil Moisture TO RAIN) algorithm to the Advanced SCATterometer (ASCAT) derived satellite soil moisture data, and 3) a product derived from a simple combination of IMERG and SM2RASC. The assessment of the products is carried out at different rainfall time accumulation (e.g., from 0.5 to 24 h) for a 2-year period from 10th March 2015, to 31st December 2016. Results show that IMERG has good performance at time resolutions higher than 6 h. At daily time scale, IMERG and SM2RASC show similar results with median correlations, R, 0.60, and root mean square error, RMSE, 7.6 mm/day (BIAS is -0.85 and +0.51 mm/day, respectively). The combined product outperforms the parent products (median R > 0.70, RMSE<6.5 mm/day, BIAS -0.07 mm/day). Among the different factors affecting products quality, topographic complexity seems to play the more relevant role, particularly for SM2RASC but also for IMERG. Overall, this study shows that the investigated satellite-based products agree reasonably well with observations notwithstanding the challenging features of the region, and the combination of IMERG and SM2RASC provides a way to overcome their limitations and to produce a higher quality satellite rainfall product.

Stochastic generation of hourly rainstorm events in Johor

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

Nojumuddin, Nur Syereena; Yusof, Fadhilah; Yusop, Zulkifli

2015-02-03

Engineers and researchers in water-related studies are often faced with the problem of having insufficient and long rainfall record. Practical and effective methods must be developed to generate unavailable data from limited available data. Therefore, this paper presents a Monte-Carlo based stochastic hourly rainfall generation model to complement the unavailable data. The Monte Carlo simulation used in this study is based on the best fit of storm characteristics. Hence, by using the Maximum Likelihood Estimation (MLE) and Anderson Darling goodness-of-fit test, lognormal appeared to be the best rainfall distribution. Therefore, the Monte Carlo simulation based on lognormal distribution was usedmore » in the study. The proposed model was verified by comparing the statistical moments of rainstorm characteristics from the combination of the observed rainstorm events under 10 years and simulated rainstorm events under 30 years of rainfall records with those under the entire 40 years of observed rainfall data based on the hourly rainfall data at the station J1 in Johor over the period of 1972–2011. The absolute percentage error of the duration-depth, duration-inter-event time and depth-inter-event time will be used as the accuracy test. The results showed the first four product-moments of the observed rainstorm characteristics were close with the simulated rainstorm characteristics. The proposed model can be used as a basis to derive rainfall intensity-duration frequency in Johor.« less

Adjusting Satellite Rainfall Error in Mountainous Areas for Flood Modeling Applications

NASA Astrophysics Data System (ADS)

Zhang, X.; Anagnostou, E. N.; Astitha, M.; Vergara, H. J.; Gourley, J. J.; Hong, Y.

2014-12-01

This study aims to investigate the use of high-resolution Numerical Weather Prediction (NWP) for evaluating biases of satellite rainfall estimates of flood-inducing storms in mountainous areas and associated improvements in flood modeling. Satellite-retrieved precipitation has been considered as a feasible data source for global-scale flood modeling, given that satellite has the spatial coverage advantage over in situ (rain gauges and radar) observations particularly over mountainous areas. However, orographically induced heavy precipitation events tend to be underestimated and spatially smoothed by satellite products, which error propagates non-linearly in flood simulations.We apply a recently developed retrieval error and resolution effect correction method (Zhang et al. 2013*) on the NOAA Climate Prediction Center morphing technique (CMORPH) product based on NWP analysis (or forecasting in the case of real-time satellite products). The NWP rainfall is derived from the Weather Research and Forecasting Model (WRF) set up with high spatial resolution (1-2 km) and explicit treatment of precipitation microphysics.In this study we will show results on NWP-adjusted CMORPH rain rates based on tropical cyclones and a convective precipitation event measured during NASA's IPHEX experiment in the South Appalachian region. We will use hydrologic simulations over different basins in the region to evaluate propagation of bias correction in flood simulations. We show that the adjustment reduced the underestimation of high rain rates thus moderating the strong rainfall magnitude dependence of CMORPH rainfall bias, which results in significant improvement in flood peak simulations. Further study over Blue Nile Basin (western Ethiopia) will be investigated and included in the presentation. *Zhang, X. et al. 2013: Using NWP Simulations in Satellite Rainfall Estimation of Heavy Precipitation Events over Mountainous Areas. J. Hydrometeor, 14, 1844-1858.

Rainfall frequency analysis for ungauged sites using satellite precipitation products

NASA Astrophysics Data System (ADS)

Gado, Tamer A.; Hsu, Kuolin; Sorooshian, Soroosh

2017-11-01

The occurrence of extreme rainfall events and their impacts on hydrologic systems and society are critical considerations in the design and management of a large number of water resources projects. As precipitation records are often limited or unavailable at many sites, it is essential to develop better methods for regional estimation of extreme rainfall at these partially-gauged or ungauged sites. In this study, an innovative method for regional rainfall frequency analysis for ungauged sites is presented. The new method (hereafter, this is called the RRFA-S) is based on corrected annual maximum series obtained from a satellite precipitation product (e.g., PERSIANN-CDR). The probability matching method (PMM) is used here for bias correction to match the CDF of satellite-based precipitation data with the gauged data. The RRFA-S method was assessed through a comparative study with the traditional index flood method using the available annual maximum series of daily rainfall in two different regions in USA (11 sites in Colorado and 18 sites in California). The leave-one-out cross-validation technique was used to represent the ungauged site condition. Results of this numerical application have found that the quantile estimates obtained from the new approach are more accurate and more robust than those given by the traditional index flood method.

Influence of Rainfall Product on Hydrological and Sediment Outputs when Calibrating the STREAP Rainfall Generator for the CAESAR-Lisflood Landscape Evolution Model

NASA Astrophysics Data System (ADS)

Skinner, Christopher; Peleg, Nadav; Quinn, Niall

2017-04-01

The use of Landscape Evolution Models often requires a timeseries of rainfall to drive the model. The spatial and temporal resolution of the driving data has an impact on several model outputs, including the shape of the landscape itself. Attempts to compensate for the spatiotemporal smoothing of local rainfall intensities are insufficient and may exacerbate these issues, meaning that to produce the best results the model needs to be run with data of highest spatial and temporal resolutions available. Some rainfall generators are able to produce timeseries with high spatial and temporal resolution. Observed data is used for the calibration of these generators. However, rainfall observations are highly uncertain and vary between different products (e.g. raingauges, weather radar) which may cascade through the Landscape Evolution Model. Here, we used the STREAP rainfall generator to produce high spatial (1km) and temporal (hourly) resolution ensembles of rainfall for a 50-year period, and used these to drive the CAESAR-Lisflood Landscape Evolution Model for a test catchment. Three different calibrations of STREAP were used against different products: gridded raingauge (TBR), weather radar (NIMROD), and a merged of the two. Analysis of the discharge and sediment yields from the model runs showed that the models run by STREAP calibrated by the different products were statistically significantly different, with the raingauge calibration producing 12.4 % more sediment on average over the 50-year period. The merged product produced results which were between the raingauge and radar products. The results demonstrate the importance of considering the selection of rainfall driving data on Landscape Evolution Modelling. Rainfall products are highly uncertain, different instruments will observe rainfall differently, and these uncertainties are clearly shown to cascade through the calibration of the rainfall generator and the Landscape Evolution Model. Merging raingauge and radar products is a common practise operationally, and by using features of both to calibrate the rainfall generator it is likely a more robust rainfall timeseries is produced.

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

NASA Technical Reports Server (NTRS)

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

2013-01-01

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

Development of microwave rainfall retrieval algorithm for climate applications

NASA Astrophysics Data System (ADS)

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

2014-12-01

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

Regional statistical assessment of WRF-Hydro and IFC Model stream Flow uncertainties over the State of Iowa

NASA Astrophysics Data System (ADS)

ElSaadani, M.; Quintero, F.; Goska, R.; Krajewski, W. F.; Lahmers, T.; Small, S.; Gochis, D. J.

2015-12-01

This study examines the performance of different Hydrologic models in estimating peak flows over the state of Iowa. In this study I will compare the output of the Iowa Flood Center (IFC) hydrologic model and WRF-Hydro (NFIE configuration) to the observed flows at the USGS stream gauges. During the National Flood Interoperability Experiment I explored the performance of WRF-Hydro over the state of Iowa using different rainfall products and the resulting hydrographs showed a "flashy" behavior of the model output due to lack of calibration and bad initial flows due to short model spin period. I would like to expand this study by including a second well established hydrologic model and include more rain gauge vs. radar rainfall direct comparisons. The IFC model is expected to outperform WRF-Hydro's out of the box results, however, I will test different calibration options for both the Noah-MP land surface model and RAPID, which is the routing component of the NFIE-Hydro configuration, to see if this will improve the model results. This study will explore the statistical structure of model output uncertainties across scales (as a function of drainage areas and/or stream orders). I will also evaluate the performance of different radar-based Quantitative Precipitation Estimation (QPE) products (e.g. Stage IV, MRMS and IFC's NEXRAD based radar rainfall product. Different basins will be evaluated in this study and they will be selected based on size, amount of rainfall received over the basin area and location. Basin location will be an important factor in this study due to our prior knowledge of the performance of different NEXRAD radars that cover the region, this will help observe the effect of rainfall biases on stream flows. Another possible addition to this study is to apply controlled spatial error fields to rainfall inputs and observer the propagation of these errors through the stream network.

Design and development of surface rainfall forecast products on GRAPES_MESO model

NASA Astrophysics Data System (ADS)

Zhili, Liu

2016-04-01

In this paper, we designed and developed the surface rainfall forecast products using medium scale GRAPES_MESO model precipitation forecast products. The horizontal resolution of GRAPES_MESO model is 10km*10km, the number of Grids points is 751*501, vertical levels is 26, the range is 70°E－145.15°E, 15°N－64.35 °N. We divided the basin into 7 major watersheds. Each watersheds was divided into a number of sub regions. There were 95 sub regions in all. Tyson polygon method is adopted in the calculation of surface rainfall. We used 24 hours forecast precipitation data of GRAPES_MESO model to calculate the surface rainfall. According to the site of information and boundary information of the 95 sub regions, the forecast surface rainfall of each sub regions was calculated. We can provide real-time surface rainfall forecast products every day. We used the method of fuzzy evaluation to carry out a preliminary test and verify about the surface rainfall forecast product. Results shows that the fuzzy score of heavy rain, rainstorm and downpour level forecast rainfall were higher, the fuzzy score of light rain level was lower. The forecast effect of heavy rain, rainstorm and downpour level surface rainfall were better. The rate of missing and empty forecast of light rainfall level surface rainfall were higher, so it's fuzzy score were lower.

Rainfall estimation over-land using SMOS soil moisture observations: SM2RAIN, LMAA and SMART algorithms

NASA Astrophysics Data System (ADS)

Massari, Christian; Brocca, Luca; Pellarin, Thierry; Kerr, Yann; Crow, Wade; Cascon, Carlos; Ciabatta, Luca

2016-04-01

Recent advancements in the measurement of precipitation from space have provided estimates at scales that are commensurate with the needs of the hydrological and land-surface model communities. However, as demonstrated in a number of studies (Ebert et al. 2007, Tian et al. 2007, Stampoulis et al. 2012) satellite rainfall estimates are characterized by low accuracy in certain conditions and still suffer from a number of issues (e.g., bias) that may limit their utility in over-land applications (Serrat-Capdevila et al. 2014). In recent years many studies have demonstrated that soil moisture observations from ground and satellite sensors can be used for correcting satellite precipitation estimates (e.g. Crow et al., 2011; Pellarin et al., 2013), or directly estimating rainfall (SM2RAIN, Brocca et al., 2014). In this study, we carried out a detailed scientific analysis in which these three different methods are used for: i) estimating rainfall through satellite soil moisture observations (SM2RAIN, Brocca et al., 2014); ii) correcting rainfall through a Land surface Model Assimilation Algorithm (LMAA) (an improvement of a previous work of Crow et al. 2011 and Pellarin et al. 2013) and through the Soil Moisture Analysis Rainfall Tool (SMART, Crow et al. 2011). The analysis is carried within the ESA project "SMOS plus Rainfall" and involves 9 sites in Europe, Australia, Africa and USA containing high-quality hydrometeorological and soil moisture observations. Satellite soil moisture data from Soil Moisture and Ocean Salinity (SMOS) mission are employed for testing their potential in deriving a cumulated rainfall product at different temporal resolutions. The applicability and accuracy of the three algorithms is investigated also as a function of climatic and soil/land use conditions. A particular attention is paid to assess the expected limitations soil moisture based rainfall estimates such as soil saturation, freezing/snow conditions, SMOS RFI, irrigated areas, contribution of surface runoff and evapotranspiration, vegetation coverage, temporal sampling, and the assimilation/modelling approach. The 9 selected sites gather such potential problems which are shown and discussed at the conference. REFERENCES Ebert, E. E.; Janowiak, J. E.; Kidd, C. Comparison of Near-Real-Time Precipitation Estimates from Satellite Observations and Numerical Models. Bull. Am. Meteorol. Soc. 2007, 88, 47-64. Tian, Y.; Peters-Lidard, C. D.; Choudhury, B. J.; Garcia, M. Multitemporal Analysis of TRMM-Based Satellite Precipitation Products for Land Data Assimilation Applications. J. Hydrometeorol. 2007, 8, 1165-1183. Stampoulis, D.; Anagnostou, E. N. Evaluation of Global Satellite Rainfall Products over Continental Europe. J. Hydrometeorol. 2012, 13, 588-603. Serrat-Capdevila, A.; Valdes, J. B.; Stakhiv, E. Z. Water Management Applications for Satellite Precipitation Products: Synthesis and Recommendations. JAWRA J. Am. Water Resour. Assoc. 2014, 50, 509-525. Crow, W. T.; van den Berg, M. J.; Huffman, G. J.; Pellarin, T. Correcting rainfall using satellite-based surface soil moisture retrievals: The Soil Moisture Analysis Rainfall Tool (SMART). Water Resour. Res. 2011, 47, W08521. Pellarin, T.; Louvet, S.; Gruhier, C.; Quantin, G.; Legout, C. A simple and effective method for correcting soil moisture and precipitation estimates using AMSR-E measurements. Remote Sens. Environ. 2013, 136, 28-36. Brocca, L.; Ciabatta, L.; Massari, C.; Moramarco, T.; Hahn, S.; Hasenauer, S.; Kidd, R.; Dorigo, W.; Wagner, W.; Levizzani, V. Soil as a natural rain gauge: Estimating global rainfall from satellite soil moisture data. J. Geophys. Res. Atmos. 2014, 119, 5128-5141.

Earth observation based assessment of the water production and water consumption of Nile Basin agro-ecosystems

USGS Publications Warehouse

Bastiaanssen, Wim G.M.; Karimi, Poolad; Rebelo, Lisa-Maria; Duan, Zheng; Senay, Gabriel; Muthuwatte, Lal; Smakhtin, Vladimir

2014-01-01

The increasing competition for water resources requires a better understanding of flows, fluxes, stocks, and the services and benefits related to water consumption. This paper explains how public domain Earth Observation data based on Moderate Resolution Imaging Spectroradiometer (MODIS), Second Generation Meteosat (MSG), Tropical Rainfall Measurement Mission (TRMM) and various altimeter measurements can be used to estimate net water production (rainfall (P) > evapotranspiration (ET)) and net water consumption (ET > P) of Nile Basin agro-ecosystems. Rainfall data from TRMM and the Famine Early Warning System Network (FEWS-NET) RainFall Estimates (RFE) products were used in conjunction with actual evapotranspiration from the Operational Simplified Surface Energy Balance (SSEBop) and ETLook models. Water flows laterally between net water production and net water consumption areas as a result of runoff and withdrawals. This lateral flow between the 15 sub-basins of the Nile was estimated, and partitioned into stream flow and non-stream flow using the discharge data. A series of essential water metrics necessary for successful integrated water management are explained and computed. Net water withdrawal estimates (natural and humanly instigated) were assumed to be the difference between net rainfall (Pnet) and actual evapotranspiration (ET) and some first estimates of withdrawals—without flow meters—are provided. Groundwater-dependent ecosystems withdraw large volumes of groundwater, which exceed water withdrawals for the irrigation sector. There is a strong need for the development of more open-access Earth Observation databases, especially for information related to actual ET. The fluxes, flows and storage changes presented form the basis for a global framework to describe monthly and annual water accounts in ungauged river basins.

Comparisons of Rain Estimates from Ground Radar and Satellite Over Mountainous Regions

NASA Technical Reports Server (NTRS)

Lin, Xin; Kidd, Chris; Tao, Jing; Barros, Ana

2016-01-01

A high-resolution rainfall product merging surface radar and an enhanced gauge network is used as a reference to examine two operational surface radar rainfall products over mountain areas. The two operational rainfall products include radar-only and conventional-gauge-corrected radar rainfall products. Statistics of rain occurrence and rain amount including their geographical, seasonal, and diurnal variations are examined using 3-year data. It is found that the three surface radar rainfall products in general agree well with one another over mountainous regions in terms of horizontal mean distributions of rain occurrence and rain amount. Frequency of rain occurrence and fraction of rain amount also indicate similar distribution patterns as a function of rain intensity. The diurnal signals of precipitation over mountain ridges are well captured and joint distributions of coincident raining samples indicate reasonable correlations during both summer and winter. Factors including undetected low-level precipitation, limited availability of gauges for correcting the Z-R relationship over the mountains, and radar beam blocking by mountains are clearly noticed in the two conventional radar rainfall products. Both radar-only and conventional-gauge-corrected radar rainfall products underestimate the rain occurrence and fraction of rain amount at intermediate and heavy rain intensities. Comparison of PR and TMI against a surface radar-only rainfall product indicates that the PR performs equally well with the high-resolution radar-only rainfall product over complex terrains at intermediate and heavy rain intensities during the summer and winter. TMI, on the other hand, requires improvement to retrieve wintertime precipitation over mountain areas.

Increased evapotranspiration demand in a Mediterranean climate might cause a decline in fungal yields under global warming.

PubMed

Ágreda, Teresa; Águeda, Beatriz; Olano, José M; Vicente-Serrano, Sergio M; Fernández-Toirán, Marina

2015-09-01

Wild fungi play a critical role in forest ecosystems, and its recollection is a relevant economic activity. Understanding fungal response to climate is necessary in order to predict future fungal production in Mediterranean forests under climate change scenarios. We used a 15-year data set to model the relationship between climate and epigeous fungal abundance and productivity, for mycorrhizal and saprotrophic guilds in a Mediterranean pine forest. The obtained models were used to predict fungal productivity for the 2021-2080 period by means of regional climate change models. Simple models based on early spring temperature and summer-autumn rainfall could provide accurate estimates for fungal abundance and productivity. Models including rainfall and climatic water balance showed similar results and explanatory power for the analyzed 15-year period. However, their predictions for the 2021-2080 period diverged. Rainfall-based models predicted a maintenance of fungal yield, whereas water balance-based models predicted a steady decrease of fungal productivity under a global warming scenario. Under Mediterranean conditions fungi responded to weather conditions in two distinct periods: early spring and late summer-autumn, suggesting a bimodal pattern of growth. Saprotrophic and mycorrhizal fungi showed differences in the climatic control. Increased atmospheric evaporative demand due to global warming might lead to a drop in fungal yields during the 21st century. © 2015 John Wiley & Sons Ltd.

Flood and Landslide Applications of High Time Resolution Satellite Rain Products

NASA Technical Reports Server (NTRS)

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

2006-01-01

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

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

NASA Astrophysics Data System (ADS)

Veerakachen, Watcharee; Raksapatcharawong, Mongkol

2015-09-01

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

A Web-based Data Intensive Visualization of Real-time River Drainage Network Response to Rainfall

NASA Astrophysics Data System (ADS)

Demir, I.; Krajewski, W. F.

2012-04-01

The Iowa Flood Information System (IFIS) is a web-based platform developed by the Iowa Flood Center (IFC) to provide access to and visualization of flood inundation maps, real-time flood conditions, flood forecasts both short-term and seasonal, and other flood-related data for communities in Iowa. The key element of the system's architecture is the notion of community. Locations of the communities, those near streams and rivers, define basin boundaries. The IFIS streams rainfall data from NEXRAD radar, and provides three interfaces including animation for rainfall intensity, daily rainfall totals and rainfall accumulations for past 14 days for Iowa. A real-time interactive visualization interface is developed using past rainfall intensity data. The interface creates community-based rainfall products on-demand using watershed boundaries of each community as a mask. Each individual rainfall pixel is tracked in the interface along the drainage network, and the ones drains to same pixel location are accumulated. The interface loads recent rainfall data in five minute intervals that are combined with current values. Latest web technologies are utilized for the development of the interface including HTML 5 Canvas, and JavaScript. The performance of the interface is optimized to run smoothly on modern web browsers. The interface controls allow users to change internal parameters of the system, and operation conditions of the animation. The interface will help communities understand the effects of rainfall on water transport in stream and river networks and make better-informed decisions regarding the threat of floods. This presentation provides an overview of a unique visualization interface and discusses future plans for real-time dynamic presentations of streamflow forecasting.

Simulated sensitivity of African terrestrial ecosystem photosynthesis to rainfall frequency, intensity, and rainy season length

NASA Astrophysics Data System (ADS)

Guan, Kaiyu; Good, Stephen P.; Caylor, Kelly K.; Medvigy, David; Pan, Ming; Wood, Eric F.; Sato, Hisashi; Biasutti, Michela; Chen, Min; Ahlström, Anders; Xu, Xiangtao

2018-02-01

There is growing evidence of ongoing changes in the statistics of intra-seasonal rainfall variability over large parts of the world. Changes in annual total rainfall may arise from shifts, either singly or in a combination, of distinctive intra-seasonal characteristics -i.e. rainfall frequency, rainfall intensity, and rainfall seasonality. Understanding how various ecosystems respond to the changes in intra-seasonal rainfall characteristics is critical for predictions of future biome shifts and ecosystem services under climate change, especially for arid and semi-arid ecosystems. Here, we use an advanced dynamic vegetation model (SEIB-DGVM) coupled with a stochastic rainfall/weather simulator to answer the following question: how does the productivity of ecosystems respond to a given percentage change in the total seasonal rainfall that is realized by varying only one of the three rainfall characteristics (rainfall frequency, intensity, and rainy season length)? We conducted ensemble simulations for continental Africa for a realistic range of changes (-20% ~ +20%) in total rainfall amount. We find that the simulated ecosystem productivity (measured by gross primary production, GPP) shows distinctive responses to the intra-seasonal rainfall characteristics. Specifically, increase in rainfall frequency can lead to 28% more GPP increase than the same percentage increase in rainfall intensity; in tropical woodlands, GPP sensitivity to changes in rainy season length is ~4 times larger than to the same percentage changes in rainfall frequency or intensity. In contrast, shifts in the simulated biome distribution are much less sensitive to intra-seasonal rainfall characteristics than they are to total rainfall amount. Our results reveal three major distinctive productivity responses to seasonal rainfall variability—‘chronic water stress’, ‘acute water stress’ and ‘minimum water stress’ - which are respectively associated with three broad spatial patterns of African ecosystem physiognomy, i.e. savannas, woodlands, and tropical forests.

Congo Basin rainfall climatology: can we believe the climate models?

PubMed

Washington, Richard; James, Rachel; Pearce, Helen; Pokam, Wilfried M; Moufouma-Okia, Wilfran

2013-01-01

The Congo Basin is one of three key convective regions on the planet which, during the transition seasons, dominates global tropical rainfall. There is little agreement as to the distribution and quantity of rainfall across the basin with datasets differing by an order of magnitude in some seasons. The location of maximum rainfall is in the far eastern sector of the basin in some datasets but the far western edge of the basin in others during March to May. There is no consistent pattern to this rainfall distribution in satellite or model datasets. Resolving these differences is difficult without ground-based data. Moisture flux nevertheless emerges as a useful variable with which to study these differences. Climate models with weak (strong) or even divergent moisture flux over the basin are dry (wet). The paper suggests an approach, via a targeted field campaign, for generating useful climate information with which to confront rainfall products and climate models.

The Use of Radar-Based Products for Deriving Extreme Rainfall Frequencies Using Regional Frequency Analysis with Application in South Louisiana

NASA Astrophysics Data System (ADS)

Eldardiry, H. A.; Habib, E. H.

2014-12-01

Radar-based technologies have made spatially and temporally distributed quantitative precipitation estimates (QPE) available in an operational environmental compared to the raingauges. The floods identified through flash flood monitoring and prediction systems are subject to at least three sources of uncertainties: (a) those related to rainfall estimation errors, (b) those due to streamflow prediction errors due to model structural issues, and (c) those due to errors in defining a flood event. The current study focuses on the first source of uncertainty and its effect on deriving important climatological characteristics of extreme rainfall statistics. Examples of such characteristics are rainfall amounts with certain Average Recurrence Intervals (ARI) or Annual Exceedance Probability (AEP), which are highly valuable for hydrologic and civil engineering design purposes. Gauge-based precipitation frequencies estimates (PFE) have been maturely developed and widely used over the last several decades. More recently, there has been a growing interest by the research community to explore the use of radar-based rainfall products for developing PFE and understand the associated uncertainties. This study will use radar-based multi-sensor precipitation estimates (MPE) for 11 years to derive PFE's corresponding to various return periods over a spatial domain that covers the state of Louisiana in southern USA. The PFE estimation approach used in this study is based on fitting generalized extreme value distribution to hydrologic extreme rainfall data based on annual maximum series (AMS). Some of the estimation problems that may arise from fitting GEV distributions at each radar pixel is the large variance and seriously biased quantile estimators. Hence, a regional frequency analysis approach (RFA) is applied. The RFA involves the use of data from different pixels surrounding each pixel within a defined homogenous region. In this study, region of influence approach along with the index flood technique are used in the RFA. A bootstrap technique procedure is carried out to account for the uncertainty in the distribution parameters to construct 90% confidence intervals (i.e., 5% and 95% confidence limits) on AMS-based precipitation frequency curves.

ENSO Precipitation Variations as Seen by GPM and TRMM Radar and Passive Microwave Observations

NASA Astrophysics Data System (ADS)

Adler, R. F.; Wang, J. J.

2017-12-01

Tropical precipitation variations related to ENSO are the largest-scale such variations both spatially and in magnitude and are also the main driver of surface temperature-surface rainfall relationships on the inter-annual scale. GPM (and TRMM before it) provide a unique capability to examine these relations with both the passive and active microwave approaches. Documenting the phase and magnitudes of these relationships are important to understand these large-scale processes and to validate climate models. However, as past research by the authors have shown, the results of these relations have been different for passive vs. radar retrievals. In this study we re-examine these relations with the new GPM Version 5 products, focusing on the 2015-2016 El Nino event. The recent El Nino peaked in Dec. 2015 through Feb. 2016 with the usual patterns of precipitation anomalies across the Tropics as evident in both the GPM GMI and the Near Surface (NS) DPR (single frequency) retrievals. Integrating both the rainfall anomalies and the SST anomalies over the entire tropical ocean area (25N-25S) and comparing how they vary as a function of time on a monthly scale during the GPM era (2014-2017), the radar-based results show contrasting results to those from the GMI-based (and GPCP) results. The passive microwave data (GMI and GPCP) indicates a slope of 17%/C for the precipitation variations, while the radar NS indicates about half that ( 8%/C). This NS slope is somewhat less than calculated before with GPM's V4 data, but is larger than obtained with TRMM PR data ( 0%/C) for an earlier period during the TRMM era. Very similar results as to the DPR NS calculations are also obtained for rainfall at 2km and 4km altitude and for the Combined (DPR + GMI) product. However, at 6km altitude, although the reflectivity and rainfall magnitudes are much less than at lower altitudes, the slope of the rainfall/SST relation is 17%/C, the same as calculated with the passive microwave data. The reasons for these differences are explored and lead to conclusions that the radar-based estimates of surface rainfall with GPM have limitations (and are negatively biased) in relatively intense rainfall and this leads to an underestimation of large-scale rainfall under El Nino conditions, where more oceanic rainfall, and more intense rainfall are prevalent.

Cloud-based NEXRAD Data Processing and Analysis for Hydrologic Applications

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

Rain-fed fig yield as affected by rainfall distribution

NASA Astrophysics Data System (ADS)

Bagheri, Ensieh; Sepaskhah, Ali Reza

2014-08-01

Variable annual rainfall and its uneven distribution are the major uncontrolled inputs in rain-fed fig production and possibly the main cause of yield fluctuation in Istahban region of Fars Province, I.R. of Iran. This introduces a considerable risk in rain-fed fig production. The objective of this study was to find relationships between seasonal rainfall distribution and rain-fed fig production in Istahban region to determine the critical rainfall periods for rain-fed fig production and supplementary irrigation water application. Further, economic analysis for rain-fed fig production was considered in this region to control the risk of production. It is concluded that the monthly, seasonal and annual rainfall indices are able to show the effects of rainfall and its distribution on the rain-fed fig yield. Fig yield with frequent occurrence of 80 % is 374 kg ha-1. The internal rates of return for interest rate of 4, 8 and 12 % are 21, 58 and 146 %, respectively, that are economically feasible. It is concluded that the rainfall in spring especially in April and in December has negatively affected fig yield due to its interference with the life cycle of Blastophaga bees for pollination. Further, it is concluded that when the rainfall is limited, supplementary irrigation can be scheduled in March.

Extreme rainfall-induced landslide changes based on landslide susceptibility in China, 1998-2015

NASA Astrophysics Data System (ADS)

Li, Weiyue; Liu, Chun; Hong, Yang

2017-04-01

Nowadays, landslide has been one of the most frequent and seriously widespread natural hazards all over the world. Rainfall, especially heavy rainfall is a trigger to cause the landslide occurrence, by increasing soil pore water pressures. In China, rainfall-induced landslides have risen up over to 90% of the total number. Rainfall events sometimes generate a trend of extremelization named rainfall extremes that induce the slope failure suddenly and severely. This study shows a method to simulate the rainfall-induced landslide spatio-temporal distribution on the basis of the landslide susceptibility index. First, the study on landslide susceptibility in China is introduced. We set the values of the index to the range between 0 and 1. Second, we collected TRMM 3B42 precipitation products spanning the years 1998-2015 and extracted the daily rainfall events greater than 50mm/day as extreme rainfall. Most of the rainfall duration time that may trigger a landslide has resulted between 3 hours and 45 hours. The combination of these two aspects can be exploited to simulate extreme rainfall-induced landslide distribution and illustrate the changes in 17 years. This study shows a useful tool to be part of rainfall-induced landslide simulation methodology for landslide early warning.

Climatological Processing of Radar Data for the TRMM Ground Validation Program

NASA Technical Reports Server (NTRS)

Kulie, Mark; Marks, David; Robinson, Michael; Silberstein, David; Wolff, David; Ferrier, Brad; Amitai, Eyal; Fisher, Brad; Wang, Jian-Xin; Augustine, David;

Improving a spatial rainfall product using multiple-point geostatistical simulations and its effect on a national hydrological model.

NASA Astrophysics Data System (ADS)

Oriani, F.; Stisen, S.

2016-12-01

Rainfall amount is one of the most sensitive inputs to distributed hydrological models. Its spatial representation is of primary importance to correctly study the uncertainty of basin recharge and its propagation to the surface and underground circulation. We consider here the 10-km-grid rainfall product provided by the Danish Meteorological Institute as input to the National Water Resources Model of Denmark. Due to a drastic reduction in the rain gauge network in recent years (from approximately 500 stations in the period 1996-2006, to 250 in the period 2007-2014), the grid rainfall product, based on the interpolation of these data, is much less reliable. Consequently, the related hydrological model shows a significantly lower prediction power. To give a better estimation of spatial rainfall at the grid points far from ground measurements, we use the direct sampling technique (DS) [1], belonging to the family of multiple-point geostatistics. DS, already applied to rainfall and spatial variable estimation [2, 3], simulates a grid value by sampling a training data set where a similar data neighborhood occurs. In this way, complex statistical relations are preserved by generating similar spatial patterns to the ones found in the training data set. Using the reliable grid product from the period 1996-2006 as training data set, we first test the technique by simulating part of this data set, then we apply the technique to the grid product of the period 2007-2014, and subsequently analyzing the uncertainty propagation to the hydrological model. We show that DS can improve the reliability of the rainfall product by generating more realistic rainfall patterns, with a significant repercussion on the hydrological model. The reduction of rain gauge networks is a global phenomenon which has huge implications for hydrological model performance and the uncertainty assessment of water resources. Therefore, the presented methodology can potentially be used in many regions where historical records can act as training data. [1] G.Mariethoz et al. (2010), Water Resour. Res., 10.1029/2008WR007621.[2] F. Oriani et al. (2014), Hydrol. Earth Syst. Sc., 10.5194/hessd-11-3213-2014. [3] G. Mariethoz et al. (2012), Water Resour. Res., 10.1029/2012WR012115.

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.

A multi-source precipitation approach to fill gaps over a radar precipitation field

NASA Astrophysics Data System (ADS)

Tesfagiorgis, K. B.; Mahani, S. E.; Khanbilvardi, R.

2012-12-01

Satellite Precipitation Estimates (SPEs) may be the only available source of information for operational hydrologic and flash flood prediction due to spatial limitations of radar and gauge products. The present work develops an approach to seamlessly blend satellite, radar, climatological and gauge precipitation products to fill gaps over ground-based radar precipitation fields. To mix different precipitation products, the bias of any of the products relative to each other should be removed. For bias correction, the study used an ensemble-based method which aims to estimate spatially varying multiplicative biases in SPEs using a radar rainfall product. Bias factors were calculated for a randomly selected sample of rainy pixels in the study area. Spatial fields of estimated bias were generated taking into account spatial variation and random errors in the sampled values. A weighted Successive Correction Method (SCM) is proposed to make the merging between error corrected satellite and radar rainfall estimates. In addition to SCM, we use a Bayesian spatial method for merging the gap free radar with rain gauges, climatological rainfall sources and SPEs. We demonstrate the method using SPE Hydro-Estimator (HE), radar- based Stage-II, a climatological product PRISM and rain gauge dataset for several rain events from 2006 to 2008 over three different geographical locations of the United States. Results show that: the SCM method in combination with the Bayesian spatial model produced a precipitation product in good agreement with independent measurements. The study implies that using the available radar pixels surrounding the gap area, rain gauge, PRISM and satellite products, a radar like product is achievable over radar gap areas that benefits the scientific community.

RAINLINK: Retrieval algorithm for rainfall monitoring employing microwave links from a cellular communication network

NASA Astrophysics Data System (ADS)

Uijlenhoet, R.; Overeem, A.; Leijnse, H.; Rios Gaona, M. F.

2017-12-01

The basic principle of rainfall estimation using microwave links is as follows. Rainfall attenuates the electromagnetic signals transmitted from one telephone tower to another. By measuring the received power at one end of a microwave link as a function of time, the path-integrated attenuation due to rainfall can be calculated, which can be converted to average rainfall intensities over the length of a link. Microwave links from cellular communication networks have been proposed as a promising new rainfall measurement technique for one decade. They are particularly interesting for those countries where few surface rainfall observations are available. Yet to date no operational (real-time) link-based rainfall products are available. To advance the process towards operational application and upscaling of this technique, there is a need for freely available, user-friendly computer code for microwave link data processing and rainfall mapping. Such software is now available as R package "RAINLINK" on GitHub (https://github.com/overeem11/RAINLINK). It contains a working example to compute link-based 15-min rainfall maps for the entire surface area of The Netherlands for 40 hours from real microwave link data. This is a working example using actual data from an extensive network of commercial microwave links, for the first time, which will allow users to test their own algorithms and compare their results with ours. The package consists of modular functions, which facilitates running only part of the algorithm. The main processings steps are: 1) Preprocessing of link data (initial quality and consistency checks); 2) Wet-dry classification using link data; 3) Reference signal determination; 4) Removal of outliers ; 5) Correction of received signal powers; 6) Computation of mean path-averaged rainfall intensities; 7) Interpolation of rainfall intensities ; 8) Rainfall map visualisation. Some applications of RAINLINK will be shown based on microwave link data from a temperate climate (the Netherlands), and from a subtropical climate (Brazil). We hope that RAINLINK will promote the application of rainfall monitoring using microwave links in poorly gauged regions around the world. We invite researchers to contribute to RAINLINK to make the code more generally applicable to data from different networks and climates.

Evaluation of Bias Correction Method for Satellite-Based Rainfall Data

PubMed Central

Bhatti, Haris Akram; Rientjes, Tom; Haile, Alemseged Tamiru; Habib, Emad; Verhoef, Wouter

2016-01-01

With the advances in remote sensing technology, satellite-based rainfall estimates are gaining attraction in the field of hydrology, particularly in rainfall-runoff modeling. Since estimates are affected by errors correction is required. In this study, we tested the high resolution National Oceanic and Atmospheric Administration’s (NOAA) Climate Prediction Centre (CPC) morphing technique (CMORPH) satellite rainfall product (CMORPH) in the Gilgel Abbey catchment, Ethiopia. CMORPH data at 8 km-30 min resolution is aggregated to daily to match in-situ observations for the period 2003–2010. Study objectives are to assess bias of the satellite estimates, to identify optimum window size for application of bias correction and to test effectiveness of bias correction. Bias correction factors are calculated for moving window (MW) sizes and for sequential windows (SW’s) of 3, 5, 7, 9, …, 31 days with the aim to assess error distribution between the in-situ observations and CMORPH estimates. We tested forward, central and backward window (FW, CW and BW) schemes to assess the effect of time integration on accumulated rainfall. Accuracy of cumulative rainfall depth is assessed by Root Mean Squared Error (RMSE). To systematically correct all CMORPH estimates, station based bias factors are spatially interpolated to yield a bias factor map. Reliability of interpolation is assessed by cross validation. The uncorrected CMORPH rainfall images are multiplied by the interpolated bias map to result in bias corrected CMORPH estimates. Findings are evaluated by RMSE, correlation coefficient (r) and standard deviation (SD). Results showed existence of bias in the CMORPH rainfall. It is found that the 7 days SW approach performs best for bias correction of CMORPH rainfall. The outcome of this study showed the efficiency of our bias correction approach. PMID:27314363

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.

Evaluation of Bias Correction Method for Satellite-Based Rainfall Data.

PubMed

Bhatti, Haris Akram; Rientjes, Tom; Haile, Alemseged Tamiru; Habib, Emad; Verhoef, Wouter

2016-06-15

With the advances in remote sensing technology, satellite-based rainfall estimates are gaining attraction in the field of hydrology, particularly in rainfall-runoff modeling. Since estimates are affected by errors correction is required. In this study, we tested the high resolution National Oceanic and Atmospheric Administration's (NOAA) Climate Prediction Centre (CPC) morphing technique (CMORPH) satellite rainfall product (CMORPH) in the Gilgel Abbey catchment, Ethiopia. CMORPH data at 8 km-30 min resolution is aggregated to daily to match in-situ observations for the period 2003-2010. Study objectives are to assess bias of the satellite estimates, to identify optimum window size for application of bias correction and to test effectiveness of bias correction. Bias correction factors are calculated for moving window (MW) sizes and for sequential windows (SW's) of 3, 5, 7, 9, …, 31 days with the aim to assess error distribution between the in-situ observations and CMORPH estimates. We tested forward, central and backward window (FW, CW and BW) schemes to assess the effect of time integration on accumulated rainfall. Accuracy of cumulative rainfall depth is assessed by Root Mean Squared Error (RMSE). To systematically correct all CMORPH estimates, station based bias factors are spatially interpolated to yield a bias factor map. Reliability of interpolation is assessed by cross validation. The uncorrected CMORPH rainfall images are multiplied by the interpolated bias map to result in bias corrected CMORPH estimates. Findings are evaluated by RMSE, correlation coefficient (r) and standard deviation (SD). Results showed existence of bias in the CMORPH rainfall. It is found that the 7 days SW approach performs best for bias correction of CMORPH rainfall. The outcome of this study showed the efficiency of our bias correction approach.

Quantifying Uncertainty in Instantaneous Orbital Data Products of TRMM over Indian Subcontinent

NASA Astrophysics Data System (ADS)

Jayaluxmi, I.; Nagesh, D.

2013-12-01

In the last 20 years, microwave radiometers have taken satellite images of earth's weather proving to be a valuable tool for quantitative estimation of precipitation from space. However, along with the widespread acceptance of microwave based precipitation products, it has also been recognized that they contain large uncertainties. While most of the uncertainty evaluation studies focus on the accuracy of rainfall accumulated over time (e.g., season/year), evaluation of instantaneous rainfall intensities from satellite orbital data products are relatively rare. These instantaneous products are known to potentially cause large uncertainties during real time flood forecasting studies at the watershed scale. Especially over land regions, where the highly varying land surface emissivity offer a myriad of complications hindering accurate rainfall estimation. The error components of orbital data products also tend to interact nonlinearly with hydrologic modeling uncertainty. Keeping these in mind, the present study fosters the development of uncertainty analysis using instantaneous satellite orbital data products (version 7 of 1B11, 2A25, 2A23) derived from the passive and active sensors onboard Tropical Rainfall Measuring Mission (TRMM) satellite, namely TRMM microwave imager (TMI) and Precipitation Radar (PR). The study utilizes 11 years of orbital data from 2002 to 2012 over the Indian subcontinent and examines the influence of various error sources on the convective and stratiform precipitation types. Analysis conducted over the land regions of India investigates three sources of uncertainty in detail. These include 1) Errors due to improper delineation of rainfall signature within microwave footprint (rain/no rain classification), 2) Uncertainty offered by the transfer function linking rainfall with TMI low frequency channels and 3) Sampling errors owing to the narrow swath and infrequent visits of TRMM sensors. Case study results obtained during the Indian summer monsoon months of June-September are presented using contingency table statistics, performance diagram, scatter plots and probability density functions. Our study demonstrates that theory of copula can be efficiently used to represent the highly non linear dependency structure of rainfall with respect to TMI low frequency channels of 19, 21, 37 GHz. This questions the exclusive usage of high frequency 85 GHz channel for TMI overland rainfall retrieval algorithms. Further, the PR sampling errors revealed using a statistical bootstrap technique was found to incur relative sampling errors <30% (for 2 degree grids) over India whose magnitudes were biased towards stratiform rainfall type and sampling technique employed. These findings clearly document that proper characterization of error structure offered by TMI and PR has wider implications for decision making prior to incorporating the resulting orbital products for basin scale hydrologic modeling.

Asymmetric impact of rainfall on India's food grain production: evidence from quantile autoregressive distributed lag model

NASA Astrophysics Data System (ADS)

Pal, Debdatta; Mitra, Subrata Kumar

2018-01-01

This study used a quantile autoregressive distributed lag (QARDL) model to capture asymmetric impact of rainfall on food production in India. It was found that the coefficient corresponding to the rainfall in the QARDL increased till the 75th quantile and started decreasing thereafter, though it remained in the positive territory. Another interesting finding is that at the 90th quantile and above the coefficients of rainfall though remained positive was not statistically significant and therefore, the benefit of high rainfall on crop production was not conclusive. However, the impact of other determinants, such as fertilizer and pesticide consumption, is quite uniform over the whole range of the distribution of food grain production.

Exploring Agro-Climatic Trends in Ethiopia Using CHIRPS

NASA Astrophysics Data System (ADS)

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

2015-12-01

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

Reducing Production Basis Risk through Rainfall Intensity Frequency (RIF) Indexes: Global Sensitivity Analysis' Implication on Policy Design

NASA Astrophysics Data System (ADS)

Muneepeerakul, Chitsomanus; Huffaker, Ray; Munoz-Carpena, Rafael

2016-04-01

The weather index insurance promises financial resilience to farmers struck by harsh weather conditions with swift compensation at affordable premium thanks to its minimal adverse selection and moral hazard. Despite these advantages, the very nature of indexing causes the presence of "production basis risk" that the selected weather indexes and their thresholds do not correspond to actual damages. To reduce basis risk without additional data collection cost, we propose the use of rain intensity and frequency as indexes as it could offer better protection at the lower premium by avoiding basis risk-strike trade-off inherent in the total rainfall index. We present empirical evidences and modeling results that even under the similar cumulative rainfall and temperature environment, yield can significantly differ especially for drought sensitive crops. We further show that deriving the trigger level and payoff function from regression between historical yield and total rainfall data may pose significant basis risk owing to their non-unique relationship in the insured range of rainfall. Lastly, we discuss the design of index insurance in terms of contract specifications based on the results from global sensitivity analysis.

New and Improved Remotely Sensed Products and Tools for Agricultural Monitoring Applications in Support of Famine Early Warning

NASA Astrophysics Data System (ADS)

Budde, M. E.; Rowland, J.; Senay, G. B.; Funk, C. C.; Pedreros, D.; Husak, G. J.; Bohms, S.

2011-12-01

The high global food prices in 2008 led to the acknowledgement that there is a need to monitor the inter-connectivity of global and regional markets and their potential impacts on food security in many more regions than previously considered. The crisis prompted an expansion of monitoring by the Famine Early Warning Systems Network (FEWS NET) to include additional countries, beyond those where food security has long been of concern. Scientists at the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and the University of California Santa Barbara Climate Hazards Group have provided new and improved data products as well as visualization and analysis tools in support of this increased mandate for remote monitoring. We present a new product for measuring actual evapotranspiration (ETa) based on the implementation of a surface energy balance model and site improvements of two standard FEWS NET monitoring products: normalized difference vegetation index (NDVI) and satellite-based rainfall estimates. USGS FEWS NET has implemented a simplified surface energy balance model to produce operational ETa anomalies for Africa. During the growing season, ETa anomalies express surplus or deficit crop water use which is directly related to crop condition and biomass. The expedited Moderate Resolution Imaging Spectroradiometer (eMODIS) production system provides FEWS NET with a much improved NDVI dataset for crop and rangeland monitoring. eMODIS NDVI provides a reliable data stream with a vastly improved spatial resolution (250-m) and short latency period (less than 12 hours) which allows for better operational vegetation monitoring. FEWS NET uses satellite rainfall estimates as inputs for monitoring agricultural food production. By combining high resolution (0.05 deg) rainfall mean fields with Tropical Rainfall Measuring Mission rainfall estimates and infrared temperature data, we provide pentadal (5-day) rainfall fields suitable for crop monitoring and modeling. We also present two new monitoring tools, the Early Warning eXplorer (EWX) and the Decision Support Interface (DSI). The EWX is a data analysis tool which provides the ability to rapidly visualize multiple remote sensing datasets and compare standardized anomaly maps and time series. The DSI uses remote sensing data in an automated fashion to map areas of drought concern and ranks their severity at both crop zone and administrative levels. New and improved data products and more targeted analysis tools are a necessity as food security monitoring requirements expand and resources become limited.

Effect of rainfall seasonality on carbon storage in tropical dry ecosystems

NASA Astrophysics Data System (ADS)

Rohr, Tyler; Manzoni, Stefano; Feng, Xue; Menezes, Rômulo S. C.; Porporato, Amilcare

2013-07-01

seasonally dry conditions are typical of large areas of the tropics, their biogeochemical responses to seasonal rainfall and soil carbon (C) sequestration potential are not well characterized. Seasonal moisture availability positively affects both productivity and soil respiration, resulting in a delicate balance between C deposition as litterfall and C loss through heterotrophic respiration. To understand how rainfall seasonality (i.e., duration of the wet season and rainfall distribution) affects this balance and to provide estimates of long-term C sequestration, we develop a minimal model linking the seasonal behavior of the ensemble soil moisture, plant productivity, related C inputs through litterfall, and soil C dynamics. A drought-deciduous caatinga ecosystem in northeastern Brazil is used as a case study to parameterize the model. When extended to different patterns of rainfall seasonality, the results indicate that for fixed annual rainfall, both plant productivity and soil C sequestration potential are largely, and nonlinearly, dependent on wet season duration. Moreover, total annual rainfall is a critical driver of this relationship, leading at times to distinct optima in both production and C storage. These theoretical predictions are discussed in the context of parameter uncertainties and possible changes in rainfall regimes in tropical dry ecosystems.

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.

Congo Basin rainfall climatology: can we believe the climate models?

PubMed Central

Washington, Richard; James, Rachel; Pearce, Helen; Pokam, Wilfried M.; Moufouma-Okia, Wilfran

2013-01-01

The Congo Basin is one of three key convective regions on the planet which, during the transition seasons, dominates global tropical rainfall. There is little agreement as to the distribution and quantity of rainfall across the basin with datasets differing by an order of magnitude in some seasons. The location of maximum rainfall is in the far eastern sector of the basin in some datasets but the far western edge of the basin in others during March to May. There is no consistent pattern to this rainfall distribution in satellite or model datasets. Resolving these differences is difficult without ground-based data. Moisture flux nevertheless emerges as a useful variable with which to study these differences. Climate models with weak (strong) or even divergent moisture flux over the basin are dry (wet). The paper suggests an approach, via a targeted field campaign, for generating useful climate information with which to confront rainfall products and climate models. PMID:23878328

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

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

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

NASA Astrophysics Data System (ADS)

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

2017-05-01

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

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

NASA Astrophysics Data System (ADS)

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

2016-04-01

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

Based on the rainfall system platform raindrops research and analysis of pressure loss

NASA Astrophysics Data System (ADS)

Cao, Gang; Sun, Jian

2018-01-01

With the rapid development of China’s military career, land, sea and air force all services and equipment of modern equipment need to be in the rain test, and verify its might suffer during transportation, storage or use a different environment temperature lower water or use underwater, the water is derived from the heavy rain, the wind and rain, sprinkler system, splash water, water wheel, a violent shock waves or use underwater, etcTest the product performance and quality, under the condition of rainfall system platform in the process of development, how to control the raindrops pressure loss becomes the key to whether the system can simulate the real rainfall [1], this paper is according to the rainfall intensity, nozzle flow resistance, meet water flow of rain pressure loss calculation and analysis, and system arrangement of the optimal solution of rainfall is obtained [2].

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.

Early Results from the Global Precipitation Measurement (GPM) Mission in Japan

NASA Astrophysics Data System (ADS)

Kachi, Misako; Kubota, Takuji; Masaki, Takeshi; Kaneko, Yuki; Kanemaru, Kaya; Oki, Riko; Iguchi, Toshio; Nakamura, Kenji; Takayabu, Yukari N.

2015-04-01

The Global Precipitation Measurement (GPM) mission is an international collaboration to achieve highly accurate and highly frequent global precipitation observations. The GPM mission consists of the GPM Core Observatory jointly developed by U.S. and Japan and Constellation Satellites that carry microwave radiometers and provided by the GPM partner agencies. The Dual-frequency Precipitation Radar (DPR) was developed by the Japan Aerospace Exploration Agency (JAXA) and the National Institute of Information and Communications Technology (NICT), and installed on the GPM Core Observatory. The GPM Core Observatory chooses a non-sun-synchronous orbit to carry on diurnal cycle observations of rainfall from the Tropical Rainfall Measuring Mission (TRMM) satellite and was successfully launched at 3:37 a.m. on February 28, 2014 (JST), while the Constellation Satellites, including JAXA's Global Change Observation Mission (GCOM) - Water (GCOM-W1) or "SHIZUKU," are launched by each partner agency sometime around 2014 and contribute to expand observation coverage and increase observation frequency JAXA develops the DPR Level 1 algorithm, and the NASA-JAXA Joint Algorithm Team develops the DPR Level 2 and DPR-GMI combined Level2 algorithms. JAXA also develops the Global Rainfall Map (GPM-GSMaP) algorithm, which is a latest version of the Global Satellite Mapping of Precipitation (GSMaP), as national product to distribute hourly and 0.1-degree horizontal resolution rainfall map. Major improvements in the GPM-GSMaP algorithm is; 1) improvements in microwave imager algorithm based on AMSR2 precipitation standard algorithm, including new land algorithm, new coast detection scheme; 2) Development of orographic rainfall correction method for warm rainfall in coastal area (Taniguchi et al., 2012); 3) Update of database, including rainfall detection over land and land surface emission database; 4) Development of microwave sounder algorithm over land (Kida et al., 2012); and 5) Development of gauge-calibrated GSMaP algorithm (Ushio et al., 2013). In addition to those improvements in the algorithms number of passive microwave imagers and/or sounders used in the GPM-GSMaP was increased compared to the previous version. After the early calibration and validation of the products and evaluation that all products achieved the release criteria, all GPM standard products and the GPM-GSMaP product has been released to the public since September 2014. The GPM products can be downloaded via the internet through the JAXA G-Portal (https://www.gportal.jaxa.jp).

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

NASA Astrophysics Data System (ADS)

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

2015-12-01

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

Rainfall and crop modeling-based water stress assessment for rainfed maize cultivation in peninsular India

NASA Astrophysics Data System (ADS)

Manivasagam, V. S.; Nagarajan, R.

2018-04-01

Water stress due to uneven rainfall distribution causes a significant impact on the agricultural production of monsoon-dependent peninsular India. In the present study, water stress assessment for rainfed maize crop is carried out for kharif (June-October) and rabi (October-February) cropping seasons which coincide with two major Indian monsoons. Rainfall analysis (1976-2010) shows that the kharif season receives sufficient weekly rainfall (28 ± 32 mm) during 26th-39th standard meteorological weeks (SMWs) from southwest monsoon, whereas the rabi season experiences a major portion of its weekly rainfall due to northeast monsoon between the 42nd and 51st SMW (31 ± 42 mm). The later weeks experience minimal rainfall (5.5 ± 15 mm) and thus expose the late sown maize crops to a severe water stress during its maturity stage. Wet and dry spell analyses reveal a substantial increase in the rainfall intensity over the last few decades. However, the distribution of rainfall shows a striking decrease in the number of wet spells, with prolonged dry spells in both seasons. Weekly rainfall classification shows that the flowering and maturity stages of kharif maize (33rd-39th SMWs) can suffer around 30-40% of the total water stress. In the case of rabi maize, the analysis reveals that a shift in the sowing time from the existing 42nd SMW (16-22 October) to the 40th SMW (1-7 October) can avoid terminal water stress. Further, AquaCrop modeling results show that one or two minimal irrigations during the flowering and maturity stages (33rd-39th SMWs) of kharif maize positively avoid the mild water stress exposure. Similarly, rabi maize requires an additional two or three lifesaving irrigations during its flowering and maturity stages (48th-53rd SMWs) to improve productivity. Effective crop planning with appropriate sowing time, short duration crop, and high yielding drought-resistant varieties will allow for better utilization of the monsoon rain, thus reducing water stress with an increase in rainfed maize productivity.

A Prototype Visualization of Real-time River Drainage Network Response to Rainfall

NASA Astrophysics Data System (ADS)

Demir, I.; Krajewski, W. F.

2011-12-01

The Iowa Flood Information System (IFIS) is a web-based platform developed by the Iowa Flood Center (IFC) to provide access to and visualization of flood inundation maps, real-time flood conditions, flood forecasts both short-term and seasonal, and other flood-related data for communities in Iowa. The key element of the system's architecture is the notion of community. Locations of the communities, those near streams and rivers, define basin boundaries. The IFIS streams rainfall data from NEXRAD radar, and provides three interfaces including animation for rainfall intensity, daily rainfall totals and rainfall accumulations for past 14 days for Iowa. A real-time interactive visualization interface is developed using past rainfall intensity data. The interface creates community-based rainfall products on-demand using watershed boundaries of each community as a mask. Each individual rainfall pixel is tracked in the interface along the drainage network, and the ones drains to same pixel location are accumulated. The interface loads recent rainfall data in five minute intervals that are combined with current values. Latest web technologies are utilized for the development of the interface including HTML 5 Canvas, and JavaScript. The performance of the interface is optimized to run smoothly on modern web browsers. The interface controls allow users to change internal parameters of the system, and operation conditions of the animation. The interface will help communities understand the effects of rainfall on water transport in stream and river networks and make better-informed decisions regarding the threat of floods. This presentation provides an overview of a unique visualization interface and discusses future plans for real-time dynamic presentations of streamflow forecasting.

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

NASA Astrophysics Data System (ADS)

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

2011-12-01

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

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.

Analysis of Non-Tropical Cyclone Induced Flood Events over South East Asia: Investigating Flood Frequency and Extremes in the Philippines

NASA Astrophysics Data System (ADS)

Marcella, M. P.; CHEN, C.; Senarath, S. U.

2013-12-01

Much work has been completed in analyzing Southeast Asia's tropical cyclone climatology and the associated flooding throughout the region. Although, an active and strong monsoon season also brings major flooding across the Philippines resulting in the loss of lives and significant economic impacts, only a limited amount of research work has been conducted to investigate the frequency and flood loss estimates of these non-tropical cyclone (TC) storms. In this study, using the TRMM 3-hourly rainfall product, tropical cyclone rainfall is removed to construct a non-TC rainfall climatology across the region. Given this data, stochastically generated rainfall that is both spatially and temporally correlated across the country is created to generate a longer historically-based record of non-TC precipitation. After defining the rainfall criteria that constitutes a flood event based on observed floods and TRMM data, this event definition is applied to the stochastic catalog of rainfall to determine flood events. Subsequently, a thorough analysis of non-TC flood extremes, frequency, and distribution is completed for the country of the Philippines. As a result, the above methodology and datasets provide a unique opportunity to further study flood occurrences and their extremes across most of South East Asia.

Evapotranspiration variability and its association with vegetation dynamics in the Nile Basin, 2002–2011

USGS Publications Warehouse

Alemu, Henok; Senay, Gabriel B.; Kaptue, Armel T.; Kovalskyy, Valeriy

2014-01-01

Evapotranspiration (ET) is a vital component in land-atmosphere interactions. In drylands, over 90% of annual rainfall evaporates. The Nile Basin in Africa is about 42% dryland in a region experiencing rapid population growth and development. The relationship of ET with climate, vegetation and land cover in the basin during 2002–2011 is analyzed using thermal-based Simplified Surface Energy Balance Operational (SSEBop) ET, Normalized Difference Vegetation Index (NDVI)-based MODIS Terrestrial (MOD16) ET, MODIS-derived NDVI as a proxy for vegetation productivity and rainfall from Tropical Rainfall Measuring Mission (TRMM). Interannual variability and trends are analyzed using established statistical methods. Analysis based on thermal-based ET revealed that >50% of the study area exhibited negative ET anomalies for 7 years (2009, driest), while >60% exhibited positive ET anomalies for 3 years (2007, wettest). NDVI-based monthly ET correlated strongly (r > 0.77) with vegetation than thermal-based ET (0.52 < r < 0.73) at p < 0.001. Climate-zone averaged thermal-based ET anomalies positively correlated (r = 0.6, p < 0.05) with rainfall in 4 of the 9 investigated climate zones. Thermal-based and NDVI-based ET estimates revealed minor discrepancies over rainfed croplands (60 mm/yr higher for thermal-based ET), but a significant divergence over wetlands (440 mm/yr higher for thermal-based ET). Only 5% of the study area exhibited statistically significant trends in ET.

An Updated TRMM Composite Climatology of Tropical Rainfall and Its Validation

NASA Technical Reports Server (NTRS)

Wang, Jian-Jian; Adler, Robert F.; Huffman, George; Bolvin, David

2013-01-01

An updated 15-yr Tropical Rainfall Measuring Mission (TRMM) composite climatology (TCC) is presented and evaluated. This climatology is based on a combination of individual rainfall estimates made with data from the primaryTRMMinstruments: theTRMM Microwave Imager (TMI) and the precipitation radar (PR). This combination climatology of passive microwave retrievals, radar-based retrievals, and an algorithm using both instruments simultaneously provides a consensus TRMM-based estimate of mean precipitation. The dispersion of the three estimates, as indicated by the standard deviation sigma among the estimates, is presented as a measure of confidence in the final estimate and as an estimate of the uncertainty thereof. The procedures utilized by the compositing technique, including adjustments and quality-control measures, are described. The results give a mean value of the TCC of 4.3mm day(exp -1) for the deep tropical ocean beltbetween 10 deg N and 10 deg S, with lower values outside that band. In general, the TCC values confirm ocean estimates from the Global Precipitation Climatology Project (GPCP) analysis, which is based on passive microwave results adjusted for sampling by infrared-based estimates. The pattern of uncertainty estimates shown by sigma is seen to be useful to indicate variations in confidence. Examples include differences between the eastern and western portions of the Pacific Ocean and high values in coastal and mountainous areas. Comparison of the TCC values (and the input products) to gauge analyses over land indicates the value of the radar-based estimates (small biases) and the limitations of the passive microwave algorithm (relatively large biases). Comparison with surface gauge information from western Pacific Ocean atolls shows a negative bias (16%) for all the TRMM products, although the representativeness of the atoll gauges of open-ocean rainfall is still in question.

Effects of shifting seasonal rainfall patterns on net primary productivity and carbon storage in tropical seasonally dry ecosystems

NASA Astrophysics Data System (ADS)

Rohr, T.; Manzoni, S.; Feng, X.; Menezes, R.; Porporato, A. M.

2013-12-01

Although seasonally dry ecosystems (SDEs), identified by prolonged drought followed by a short, but intense, rainy season, cover large regions of the tropics, their biogeochemical response to seasonal rainfall and soil carbon (C) sequestration potential are not well characterized. Both productivity and soil respiration are positively affected by seasonal soil moisture availability, creating a delicate balance between C deposition through litterfall and C losses through heterotrophic respiration. As climate change projections for the tropics predict decreased annual rainfall and increased dry season length, it is critical to understand how variations in seasonal rainfall distributions control this balance. To address this question, we develop a minimal model linking the seasonal behavior of the ensemble soil moisture, plant productivity, the related soil C inputs through litterfall, and soil C dynamics. The model is parameterized for a case study from a drought-deciduous caatinga ecosystem in northeastern Brazil. Results indicate that when altering the seasonal rainfall patterns for a fixed annual rainfall, both plant productivity and soil C sequestration potential are largely, and nonlinearly, dependent on wet season duration. Moreover, total annual rainfall plays a dominant role in describing this relationship, leading at times to the emergence of distinct optima in both primary production and C sequestration. Examining these results in the context of climate-driven changes to wet season duration and mean annual precipitation indicate that the initial hydroclimatic regime of a particular ecosystem is an important factor to predict both the magnitude and direction of the effects of shifting seasonal distributions on productivity and C storage. Although highly productive ecosystems will likely experience declining C storage with predicted climate shifts, those currently operating well below peak production can potentially see improved C stocks with the onset of declining rainfall due to reduced soil respiration. a) Annual average net primary productivity and b) the temporally averaged ensemble soil carbon concentration <(C_yr )> are plotted against the length of the wet season T_W, for six annual rainfall rates (m yr-1).

Trends and Bioclimatic Assessment of Extreme Indices: Emerging Insights for Rainfall Derivative Crop Microinsurance in Central-West Nigeria

NASA Astrophysics Data System (ADS)

Awolala, D. O.

2015-12-01

Scientific predictions have forecasted increasing economic losses by which farming households will be forced to consider new adaptation pathways to close the food gap and be income secure. Pro-poor adaptation planning decisions therefore must rely on location-specific details from systematic assessment of extreme climate indices to provide template for most suitable financial adaptation instruments. This paper examined critical loss point to water stress in maize production and risk-averse behaviour to extreme local climate in Central West Nigeria. Trends of extreme indices and bio-climatic assessment based on RClimDex for numerical weather predictions were carried out using a 3-decade time series daily observational climate data of the sub-humid region. The study reveals that the flowering and seed formation stage was identified as the most critical loss point when seed formation is a function of per unit soil water available for uptake. The sub-humid has a bi-modal rainfall pattern but faces longer dry spell with a fast disappearing mild climate measured by budyko evaporation of 80.1%. Radiation index of dryness of 1.394 confirms the region is rapidly becoming drier at an evaporation rate of 949 mm/year and rainfall deficit of 366 mm/year. Net primary production from rainfall is fast declining by 1634 g(DM)/m2/year. These conditions influenced by monthly rainfall uncertainties are associated with losses of standing crops because farmers are uncertain of rainfall probability distribution especially during most important vegetative stage. In a simulated warmer climate, an absolute dryness of months was observed compared with 4 dry months in a normal climate which explains triggers of food deficits and income losses. Positive coefficients of tropical nights (TR20), warm nights (TN90P) and warm days (TX90P), and the negative coefficient of cold days (TX10P) with time are significant at P<0.05. The increasing gradient of warm spell indicator (WSDI), the decreasing gradients of cold nights (TN10P) and cold days (TX10P) are added evidence of aridity arising from increasing rainfall deficits. This paper recommends that the region needs rainfall-based index microinsurance adaptation financial instruments capable of sharing covariate shocks with farmers within an incentive-based risk sharing framework.

Developing New Rainfall Estimates to Identify the Likelihood of Agricultural Drought in Mesoamerica

NASA Astrophysics Data System (ADS)

Pedreros, D. H.; Funk, C. C.; Husak, G. J.; Michaelsen, J.; Peterson, P.; Lasndsfeld, M.; Rowland, J.; Aguilar, L.; Rodriguez, M.

2012-12-01

The population in Central America was estimated at ~40 million people in 2009, with 65% in rural areas directly relying on local agricultural production for subsistence, and additional urban populations relying on regional production. Mapping rainfall patterns and values in Central America is a complex task due to the rough topography and the influence of two oceans on either side of this narrow land mass. Characterization of precipitation amounts both in time and space is of great importance for monitoring agricultural food production for food security analysis. With the goal of developing reliable rainfall fields, the Famine Early warning Systems Network (FEWS NET) has compiled a dense set of historical rainfall stations for Central America through cooperation with meteorological services and global databases. The station database covers the years 1900-present with the highest density between 1970-2011. Interpolating station data by themselves does not provide a reliable result because it ignores topographical influences which dominate the region. To account for this, climatological rainfall fields were used to support the interpolation of the station data using a modified Inverse Distance Weighting process. By blending the station data with the climatological fields, a historical rainfall database was compiled for 1970-2011 at a 5km resolution for every five day interval. This new database opens the door to analysis such as the impact of sea surface temperature on rainfall patterns, changes to the typical dry spell during the rainy season, characterization of drought frequency and rainfall trends, among others. This study uses the historical database to identify the frequency of agricultural drought in the region and explores possible changes in precipitation patterns during the past 40 years. A threshold of 500mm of rainfall during the growing season was used to define agricultural drought for maize. This threshold was selected based on assessments of crop conditions from previous seasons, and was identified as an amount roughly corresponding to significant crop loss for maize, a major crop in most of the region. Results identify areas in central Honduras and Nicaragua as well as the Altiplano region in Guatemala that experienced 15 seasons of agricultural drought for the period May-July during the years 1970-2000. Preliminary results show no clear trend in rainfall, but further investigation is needed to confirm that agricultural drought is not becoming more frequent in this region.

Monitoring Global Food Security with New Remote Sensing Products and Tools

NASA Astrophysics Data System (ADS)

Budde, M. E.; Rowland, J.; Senay, G. B.; Funk, C. C.; Husak, G. J.; Magadzire, T.; Verdin, J. P.

2012-12-01

Global agriculture monitoring is a crucial aspect of monitoring food security in the developing world. The Famine Early Warning Systems Network (FEWS NET) has a long history of using remote sensing and crop modeling to address food security threats in the form of drought, floods, pests, and climate change. In recent years, it has become apparent that FEWS NET requires the ability to apply monitoring and modeling frameworks at a global scale to assess potential impacts of foreign production and markets on food security at regional, national, and local levels. Scientists at the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and the University of California Santa Barbara (UCSB) Climate Hazards Group have provided new and improved data products as well as visualization and analysis tools in support of the increased mandate for remote monitoring. We present our monitoring products for measuring actual evapotranspiration (ETa), normalized difference vegetation index (NDVI) in a near-real-time mode, and satellite-based rainfall estimates and derivatives. USGS FEWS NET has implemented a Simplified Surface Energy Balance (SSEB) model to produce operational ETa anomalies for Africa and Central Asia. During the growing season, ETa anomalies express surplus or deficit crop water use, which is directly related to crop condition and biomass. We present current operational products and provide supporting validation of the SSEB model. The expedited Moderate Resolution Imaging Spectroradiometer (eMODIS) production system provides FEWS NET with an improved NDVI dataset for crop and rangeland monitoring. eMODIS NDVI provides a reliable data stream with a relatively high spatial resolution (250-m) and short latency period (less than 12 hours) which allows for better operational vegetation monitoring. We provide an overview of these data and cite specific applications for crop monitoring. FEWS NET uses satellite rainfall estimates as inputs for monitoring agricultural food production and driving crop water balance models. We present a series of derived rainfall products and provide an update on efforts to improve satellite-based estimates. We also present advancements in monitoring tools, namely, the Early Warning eXplorer (EWX) and interactive rainfall and NDVI time series viewers. The EWX is a data analysis and visualization tool that allows users to rapidly visualize multiple remote sensing datasets and compare standardized anomaly maps and time series. The interactive time series viewers allow users to analyze rainfall and NDVI time series over multiple spatial domains. New and improved data products and more targeted analysis tools are a necessity as food security monitoring requirements expand and resources become limited.

Precipitation Estimation from the ARM Distributed Radar Network During the MC3E Campaign

NASA Astrophysics Data System (ADS)

Theisen, A. K.; Giangrande, S. E.; Collis, S. M.

2012-12-01

The DOE - NASA Midlatitude Continental Convective Cloud Experiment (MC3E) was the first demonstration of the Atmospheric Radiation Measurement (ARM) Climate Research Facility scanning precipitation radar platforms. A goal for the MC3E field campaign over the Southern Great Plains (SGP) facility was to demonstrate the capabilities of ARM polarimetric radar systems for providing unique insights into deep convective storm evolution and microphysics. One practical application of interest for climate studies and the forcing of cloud resolving models is improved Quantitative Precipitation Estimates (QPE) from ARM radar systems positioned at SGP. This study presents the results of ARM radar-based precipitation estimates during the 2-month MC3E campaign. Emphasis is on the usefulness of polarimetric C-band radar observations (CSAPR) for rainfall estimation to distances within 100 km of the Oklahoma SGP facility. Collocated ground disdrometer resources, precipitation profiling radars and nearby surface Oklahoma Mesonet gauge records are consulted to evaluate potential ARM radar-based rainfall products and optimal methods. Rainfall products are also evaluated against the regional NEXRAD-standard observations.

Hydrological simulation of the Brahmaputra basin using global datasets

NASA Astrophysics Data System (ADS)

Bhattacharya, Biswa; Conway, Crystal; Craven, Joanne; Masih, Ilyas; Mazzolini, Maurizio; Shrestha, Shreedeepy; Ugay, Reyne; van Andel, Schalk Jan

2017-04-01

Brahmaputra River flows through China, India and Bangladesh to the Bay of Bengal and is one of the largest rivers of the world with a catchment size of 580K km2. The catchment is largely hilly and/or forested with sparse population and with limited urbanisation and economic activities. The catchment experiences heavy monsoon rainfall leading to very high flood discharges. Large inter-annual variation of discharge leading to flooding, erosion and morphological changes are among the major challenges. The catchment is largely ungauged; moreover, limited availability of hydro-meteorological data limits the possibility of carrying out evidence based research, which could provide trustworthy information for managing and when needed, controlling, the basin processes by the riparian countries for overall basin development. The paper presents initial results of a current research project on Brahmaputra basin. A set of hydrological and hydraulic models (SWAT, HMS, RAS) are developed by employing publicly available datasets of DEM, land use and soil and simulated using satellite based rainfall products, evapotranspiration and temperature estimates. Remotely sensed data are compared with sporadically available ground data. The set of models are able to produce catchment wide hydrological information that potentially can be used in the future in managing the basin's water resources. The model predications should be used with caution due to high level of uncertainty because the semi-calibrated models are developed with uncertain physical representation (e.g. cross-section) and simulated with global meteorological forcing (e.g. TRMM) with limited validation. Major scientific challenges are seen in producing robust information that can be reliably used in managing the basin. The information generated by the models are uncertain and as a result, instead of using them per se, they are used in improving the understanding of the catchment, and by running several scenarios with varying catchment conditions the catchment dynamics is explored. Objectives are set that suit the data availability. For example, patterns (e.g., variation of rainfall in the lower basin) and aggregates/averages (seasonal averages) are preferred over point information. Instead of simulating instantaneous flood propagation flood extent corresponding to a frequency is followed. As satellite rainfall products may be erroneous so a variety of satellite based products are used as ensemble input. Satellite rainfall estimates are corrected for bias and different rainfall products are aggregated in a data fusion framework. Finally, the linkages between catchment erosion, hydrology and morphological changes are investigated and validated with remote sensing imageries. Keywords: Brahmaputra, hydrology, TRMM, data fusion, ungauged basin.

Dutch national rainfallradar project: a unique corporation

NASA Astrophysics Data System (ADS)

Schuurmans, Hanneke; Maarten Verbree, Jan; Leijnse, Hidde; van Heeringen, Klaas-Jan; Uijlenhoet, Remko; Bierkens, Mark; van de Giesen, Nick; Gooijer, Jan; van den Houten, Gert

2013-04-01

Since January 2013 Dutch watermanagers have access to innovative high-quality rainfall data. This product is innovative because of the following reasons. (i) The product is developed in a 'golden triangle' construction - corporation between government, business and research institutes. (ii) Second the rainfall products are developed according to the open-source GPL license. The initiative comes from a group of water boards in the Netherlands that joined their forces to fund the development of a new rainfall product. Not only data from Dutch radar stations (as is currently done by the Dutch meteorological organization KNMI) is used but also data from radars in Germany and Belgium. After a radarcomposite is made, it is adjusted according to data from raingauges (ground truth). This results in 9 different rainfall products that give for each moment the best rainfall data. This data will be used, depending on the end-user for several applications: (i) forecasts: input for flood early warning systems, (ii) water system analysis: hydrological model input, (iii) optimization: real time control and (iv) investigation of incidents: in case of flooding, who's responsible. The latter is mainly insight in the return period of heavy rainfall events. More info (in Dutch): www.nationaleregenradar.nl

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.

Seasonal forecasting of fire over Kalimantan, Indonesia

NASA Astrophysics Data System (ADS)

Spessa, A. C.; Field, R. D.; Pappenberger, F.; Langner, A.; Englhart, S.; Weber, U.; Stockdale, T.; Siegert, F.; Kaiser, J. W.; Moore, J.

2015-03-01

Large-scale fires occur frequently across Indonesia, particularly in the southern region of Kalimantan and eastern Sumatra. They have considerable impacts on carbon emissions, haze production, biodiversity, health, and economic activities. In this study, we demonstrate that severe fire and haze events in Indonesia can generally be predicted months in advance using predictions of seasonal rainfall from the ECMWF System 4 coupled ocean-atmosphere model. Based on analyses of long, up-to-date series observations on burnt area, rainfall, and tree cover, we demonstrate that fire activity is negatively correlated with rainfall and is positively associated with deforestation in Indonesia. There is a contrast between the southern region of Kalimantan (high fire activity, high tree cover loss, and strong non-linear correlation between observed rainfall and fire) and the central region of Kalimantan (low fire activity, low tree cover loss, and weak, non-linear correlation between observed rainfall and fire). The ECMWF seasonal forecast provides skilled forecasts of burnt and fire-affected area with several months lead time explaining at least 70% of the variance between rainfall and burnt and fire-affected area. Results are strongly influenced by El Niño years which show a consistent positive bias. Overall, our findings point to a high potential for using a more physical-based method for predicting fires with several months lead time in the tropics rather than one based on indexes only. We argue that seasonal precipitation forecasts should be central to Indonesia's evolving fire management policy.

Spatial rainfall data in open source environment

NASA Astrophysics Data System (ADS)

Schuurmans, Hanneke; Maarten Verbree, Jan; Leijnse, Hidde; van Heeringen, Klaas-Jan; Uijlenhoet, Remko; Bierkens, Marc; van de Giesen, Nick; Gooijer, Jan; van den Houten, Gert

2013-04-01

Since January 2013 The Netherlands have access to innovative high-quality rainfall data that is used for watermanagers. This product is innovative because of the following reasons. (i) The product is developed in a 'golden triangle' construction - corporation between government, business and research. (ii) Second the rainfall products are developed according to the open-source GPL license. The initiative comes from a group of water boards in the Netherlands that joined their forces to fund the development of a new rainfall product. Not only data from Dutch radar stations (as is currently done by the Dutch meteorological organization KNMI) is used but also data from radars in Germany and Belgium. After a radarcomposite is made, it is adjusted according to data from raingauges (ground truth). This results in 9 different rainfall products that give for each moment the best rainfall data. Specific knowledge is necessary to develop these kind of data. Therefore a pool of experts (KNMI, Deltares and 3 universities) participated in the development. The philosophy of the developers (being corporations) is that products like this should be developed in open source. This way knowledge is shared and the whole community is able to make suggestions for improvement. In our opinion this is the only way to make real progress in product development. Furthermore the financial resources of government organizations are optimized. More info (in Dutch): www.nationaleregenradar.nl

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

NASA Astrophysics Data System (ADS)

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

2014-05-01

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

Toward a Global Map of Raindrop Size Distributions. Part 1; Rain-Type Classification and Its Implications for Validating Global Rainfall Products

NASA Technical Reports Server (NTRS)

L'Ecuyer, Tristan S.; Kummerow, Christian; Berg,Wesley

2004-01-01

Variability in the global distribution of precipitation is recognized as a key element in assessing the impact of climate change for life on earth. The response of precipitation to climate forcings is, however, poorly understood because of discrepancies in the magnitude and sign of climatic trends in satellite-based rainfall estimates. Quantifying and ultimately removing these biases is critical for studying the response of the hydrologic cycle to climate change. In addition, estimates of random errors owing to variability in algorithm assumptions on local spatial and temporal scales are critical for establishing how strongly their products should be weighted in data assimilation or model validation applications and for assigning a level of confidence to climate trends diagnosed from the data. This paper explores the potential for refining assumed drop size distributions (DSDs) in global radar rainfall algorithms by establishing a link between satellite observables and information gleaned from regional validation experiments where polarimetric radar, Doppler radar, and disdrometer measurements can be used to infer raindrop size distributions. By virtue of the limited information available in the satellite retrieval framework, the current method deviates from approaches adopted in the ground-based radar community that attempt to relate microphysical processes and resultant DSDs to local meteorological conditions. Instead, the technique exploits the fact that different microphysical pathways for rainfall production are likely to lead to differences in both the DSD of the resulting raindrops and the three-dimensional structure of associated radar reflectivity profiles. Objective rain-type classification based on the complete three-dimensional structure of observed reflectivity profiles is found to partially mitigate random and systematic errors in DSDs implied by differential reflectivity measurements. In particular, it is shown that vertical and horizontal reflectivity structure obtained from spaceborne radar can be used to reproduce significant differences in Z(sub dr) between the easterly and westerly climate regimes observed in the Tropical Rainfall Measuring Mission Large-scale Biosphere-Atmosphere (TRMM-LBA) field experiment as well as the even larger differences between Amazonian rainfall and that observed in eastern Colorado. As such, the technique offers a potential methodology for placing locally observed DSD information into a global framework.

Comparing Approaches to Deal With Non-Gaussianity of Rainfall Data in Kriging-Based Radar-Gauge Rainfall Merging

NASA Astrophysics Data System (ADS)

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

2017-11-01

Merging radar and rain gauge rainfall data is a technique used to improve the quality of spatial rainfall estimates and in particular the use of Kriging with External Drift (KED) is a very effective radar-rain gauge rainfall merging technique. However, kriging interpolations assume Gaussianity of the process. Rainfall has a strongly skewed, positive, probability distribution, characterized by a discontinuity due to intermittency. In KED rainfall residuals are used, implicitly calculated as the difference between rain gauge data and a linear function of the radar estimates. Rainfall residuals are non-Gaussian as well. The aim of this work is to evaluate the impact of applying KED to non-Gaussian rainfall residuals, and to assess the best techniques to improve Gaussianity. We compare Box-Cox transformations with λ parameters equal to 0.5, 0.25, and 0.1, Box-Cox with time-variant optimization of λ, normal score transformation, and a singularity analysis technique. The results suggest that Box-Cox with λ = 0.1 and the singularity analysis is not suitable for KED. Normal score transformation and Box-Cox with optimized λ, or λ = 0.25 produce satisfactory results in terms of Gaussianity of the residuals, probability distribution of the merged rainfall products, and rainfall estimate quality, when validated through cross-validation. However, it is observed that Box-Cox transformations are strongly dependent on the temporal and spatial variability of rainfall and on the units used for the rainfall intensity. Overall, applying transformations results in a quantitative improvement of the rainfall estimates only if the correct transformations for the specific data set are used.

The Eastern Pacific ITCZ during the Boreal Spring

NASA Technical Reports Server (NTRS)

Gu, Guojun; Adler, Robert F.; Sobel, Adam H.

2004-01-01

The 6-year (1998-2003) rainfall products from the Tropical Rainfall Measuring Mission (TRMM) are used to quantify the Intertropical Convergence Zone (ITCZ) in the eastern Pacific (defined by longitudinal averages over 90 degrees W-130 degrees W) during boreal spring (March-April). The double ITCZ phenomenon, represented by the occurrence of two maxima with respect to latitude in monthly mean rainfall, is observed in most but not all of the years studied. The relative spatial locations of maxima in sea surface temperature (SST), rainfall, and surface pressure are examined. Interannual and weekly variability are characterized in SST, rainfall, surface convergence, total column water vapor, and cloud water. There appears to be a competition for rainfall between the two hemispheres during this season. When one of the two rainfall maxima is particularly strong, the other tends to be weak, with the total rainfall integrated over the two varying less than does the difference between the rainfall integrated over each separately. There is some evidence for a similar competition between the SST maxima in the two hemispheres, but this is more ambiguous, and there is evidence that some variations in the relative strengths of the two rainfall maxima may be independent of SST. Using a 25-year (1979-2003) monthly rainfall dataset from the Global Precipitation Climatology Project (GPCP), four distinct ITCZ types during March-April are defined, based on the relative strengths of rainfall peaks north and south of, and right over the equator. Composite meridional profiles and spatial distributions of rainfall and SST are documented for each type. Consistent with previous studies, an equatorial cold tongue is essential to the existence of the double ITCZs. However, too strong a cold tongue may dampen either the southern or northern rainfall maximum, depending on the magnitude of SST north of the equator.

Evaluating satellite-derived long-term historical precipitation datasets for drought monitoring in Chile

NASA Astrophysics Data System (ADS)

Zambrano, Francisco; Wardlow, Brian; Tadesse, Tsegaye

2016-10-01

Precipitation is a key parameter for the study of climate change and variability and the detection and monitoring of natural disaster such as drought. Precipitation datasets that accurately capture the amount and spatial variability of rainfall is critical for drought monitoring and a wide range of other climate applications. This is challenging in many parts of the world, which often have a limited number of weather stations and/or historical data records. Satellite-derived precipitation products offer a viable alternative with several remotely sensed precipitation datasets now available with long historical data records (+30 years), which include the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) datasets. This study presents a comparative analysis of three historical satellite-based precipitation datasets that include Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B43 version 7 (1998-2015), PERSIANN-CDR (1983-2015) and CHIRPS 2.0 (1981-2015) over Chile to assess their performance across the country and evaluate their applicability for agricultural drought evaluation when used in the calculation of commonly used drought indicator as the Standardized Precipitation Index (SPI). In this analysis, 278 weather stations of in-situ rainfall measurements across Chile were initially compared to the satellite-based precipitation estimates. The study area (Chile) was divided into five latitudinal zones: North, North-Central, Central, South-Central and South to determine if there were a regional difference among these satellite-based estimates. Nine statistics were used to evaluate the performance of satellite products to estimate the amount and spatial distribution of historical rainfall across Chile. Hierarchical cluster analysis, k-means and singular value decomposition were used to analyze these datasets to better understand their similarities and differences in characterizing rainfall patterns across Chile. Monthly analysis showed that all satellite products highly overestimated precipitation in the arid North zone. However, there were no major difference between all three products from North to South-Central zones. Though, in the South zone, PERSIANN-CDR shows the lowest fit with high underestimation, further CHIRPS 2.0 and TMPA 3B43 v7 had better agreement with in-situ measurements. The accuracy of satellite products were highly dependent on the amount of monthly rainfall with the best results found during winter seasons and in zones (Central to South) with higher amounts of precipitation. PERSIANN-CDR and CHIRPS 2.0 were used to derive SPI at time-scale of 1, 3 and 6 months, both satellite products presented similar results when it was compared in-situ against satellite SPI's. Because of its higher spatial resolution that allows better characterizing of spatial variation in precipitation pattern, the CHIRPS 2.0 was used to mapping the SPI-3 over Chile. The results of this study show that in order to use the CHIRPS 2.0 and PERSIANN-CDR data sets in Chile to monitor spatial patterns in the rainfall and drought intensity conditions, these products should be calibrated to adjust for the overestimation/underestimation of precipitation geographically specially in the North zone and seasonally during the summer and spring months in the other zones.

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.

Seasonality on the rainfall partitioning of a fast-growing tree plantation under Mediterranean conditions

NASA Astrophysics Data System (ADS)

molina, antonio; llorens, pilar; biel, carme

2014-05-01

Studies on rainfall interception in fast-growing tree plantations are less numerous than those in natural forests. Trees in these plantations are regularly distributed, and the canopy cover is clumped but changes quickly, resulting on high variability in the volume and composition of water that reach the soil. In addition, irrigation supply is normally required in semiarid areas to get optimal wood production; consequently, knowing rainfall interception and its yearly evolution is crucial to manage the irrigation scheme properly. This work studies the rainfall partitioning seasonality in a cherry tree (Prunus avium) plantation orientated to timber production under Mediterranean conditions. The monitoring design started on March 2012 and consists of a set of 58 throughfall tipping buckets randomly distributed (based on a 1x1 m2 grid) in a plot of 128 m2 with 8 trees. Stemflow is measured in all the trees with 2 tipping buckets and 6 accumulative collectors. Canopy cover is regularly measured throughout the study period, in leaf and leafless periods, by mean of sky-orientated photographs taken 50 cm above the center of each tipping bucket. Others tree biometrics are also measured such as diameter and leaf area index. Meteorological conditions are measured at 2 m above the forest cover. This work presents the first analyses describing the rainfall partitioning and its dependency on canopy cover, distance to tree and meteorological conditions. The modified Gash' model for rainfall interception in dispersed vegetation is also preliminary evaluated.

Monitoring Rainfall by Combining Ground-based Observed Precipitation and PERSIANN Satellite Product (Case Study Area: Lake Urmia Basin)

NASA Astrophysics Data System (ADS)

Abrishamchi, A.; Mirshahi, A.

2015-12-01

The global coverage, quick access, and appropriate spatial-temporal resolution of satellite precipitation data renders the data appropriate for hydrologic studies, especially in regions with no sufficient rain-gauge network. On the other hand, satellite precipitation products may have major errors. The present study aims at reduction of estimation error of the PERSIANN satellite precipitation product. Bayesian logic employed to develop a statistical relationship between historical ground-based and satellite precipitation data. This relationship can then be used to reduce satellite precipitation product error in near real time, when there is no ground-based precipitation observation. The method was evaluated in the Lake Urmia basin with a monthly time scale; November to May of 2000- 2008 for the purpose of model development and two years of 2009 and 2010 for the validation of the established relationships. Moreover, Kriging interpolation method was employed to estimate the average rainfall in the basin. Furthermore, to downscale the satellite precipitation product from 0.25o to 0.05o, data-location downscaling algorithm was used. In 76 percent of months, the final product, compared with the satellite precipitation, had less error during the validation period. Additionally, its performance was marginally better than adjusted PERSIANN product.

Comparison of TOPMODEL streamflow simulations using NEXRAD-based and measured rainfall data, McTier Creek watershed, South Carolina

USGS Publications Warehouse

Feaster, Toby D.; Westcott, Nancy E.; Hudson, Robert J.M.; Conrads, Paul; Bradley, Paul M.

2012-01-01

Rainfall is an important forcing function in most watershed models. As part of a previous investigation to assess interactions among hydrologic, geochemical, and ecological processes that affect fish-tissue mercury concentrations in the Edisto River Basin, the topography-based hydrological model (TOPMODEL) was applied in the McTier Creek watershed in Aiken County, South Carolina. Measured rainfall data from six National Weather Service (NWS) Cooperative (COOP) stations surrounding the McTier Creek watershed were used to calibrate the McTier Creek TOPMODEL. Since the 1990s, the next generation weather radar (NEXRAD) has provided rainfall estimates at a finer spatial and temporal resolution than the NWS COOP network. For this investigation, NEXRAD-based rainfall data were generated at the NWS COOP stations and compared with measured rainfall data for the period June 13, 2007, to September 30, 2009. Likewise, these NEXRAD-based rainfall data were used with TOPMODEL to simulate streamflow in the McTier Creek watershed and then compared with the simulations made using measured rainfall data. NEXRAD-based rainfall data for non-zero rainfall days were lower than measured rainfall data at all six NWS COOP locations. The total number of concurrent days for which both measured and NEXRAD-based data were available at the COOP stations ranged from 501 to 833, the number of non-zero days ranged from 139 to 209, and the total difference in rainfall ranged from -1.3 to -21.6 inches. With the calibrated TOPMODEL, simulations using NEXRAD-based rainfall data and those using measured rainfall data produce similar results with respect to matching the timing and shape of the hydrographs. Comparison of the bias, which is the mean of the residuals between observed and simulated streamflow, however, reveals that simulations using NEXRAD-based rainfall tended to underpredict streamflow overall. Given that the total NEXRAD-based rainfall data for the simulation period is lower than the total measured rainfall at the NWS COOP locations, this bias would be expected. Therefore, to better assess the use of NEXRAD-based rainfall estimates as compared to NWS COOP rainfall data on the hydrologic simulations, TOPMODEL was recalibrated and updated simulations were made using the NEXRAD-based rainfall data. Comparisons of observed and simulated streamflow show that the TOPMODEL results using measured rainfall data and NEXRAD-based rainfall are comparable. Nonetheless, TOPMODEL simulations using NEXRAD-based rainfall still tended to underpredict total streamflow volume, although the magnitude of differences were similar to the simulations using measured rainfall. The McTier Creek watershed was subdivided into 12 subwatersheds and NEXRAD-based rainfall data were generated for each subwatershed. Simulations of streamflow were generated for each subwatershed using NEXRAD-based rainfall and compared with subwatershed simulations using measured rainfall data, which unlike the NEXRAD-based rainfall were the same data for all subwatersheds (derived from a weighted average of the six NWS COOP stations surrounding the basin). For the two simulations, subwatershed streamflow were summed and compared to streamflow simulations at two U.S. Geological Survey streamgages. The percentage differences at the gage near Monetta, South Carolina, were the same for simulations using measured rainfall data and NEXRAD-based rainfall. At the gage near New Holland, South Carolina, the percentage differences using the NEXRAD-based rainfall were twice as much as those using the measured rainfall. Single-mass curve comparisons showed an increase in the total volume of rainfall from north to south. Similar comparisons of the measured rainfall at the NWS COOP stations showed similar percentage differences, but the NEXRAD-based rainfall variations occurred over a much smaller distance than the measured rainfall. Nonetheless, it was concluded that in some cases, using NEXRAD-based rainfall data in TOPMODEL streamflow simulations may provide an effective alternative to using measured rainfall data. For this investigation, however, TOPMODEL streamflow simulations using NEXRAD-based rainfall data for both calibration and simulations did not show significant improvements with respect to matching observed streamflow over simulations generated using measured rainfall data.

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.

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

NASA Astrophysics Data System (ADS)

Rahmawati, Novi; Lubczynski, Maciek W.

2017-11-01

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

Effect of Erosion on Productivity in Subtropical Red Soil Hilly Region: A Multi-Scale Spatio-Temporal Study by Simulated Rainfall

PubMed Central

Li, Zhongwu; Huang, Jinquan; Zeng, Guangming; Nie, Xiaodong; Ma, Wenming; Yu, Wei; Guo, Wang; Zhang, Jiachao

2013-01-01

The effects of water erosion (including long-term historical erosion and single erosion event) on soil properties and productivity in different farming systems were investigated. A typical sloping cropland with homogeneous soil properties was designed in 2009 and then protected from other external disturbances except natural water erosion. In 2012, this cropland was divided in three equally sized blocks. Three treatments were performed on these blocks with different simulated rainfall intensities and farming methods: (1) high rainfall intensity (1.5 - 1.7 mm min−1), no-tillage operation; (2) low rainfall intensity (0.5 - 0.7 mm min−1), no-tillage operation; and (3) low rainfall intensity, tillage operation. All of the blocks were divided in five equally sized subplots along the slope to characterize the three-year effects of historical erosion quantitatively. Redundancy analysis showed that the effects of long-term historical erosion significantly caused most of the variations in soil productivity in no-tillage and low rainfall erosion intensity systems. The intensities of the simulated rainfall did not exhibit significant effects on soil productivity in no-tillage systems. By contrast, different farming operations induced a statistical difference in soil productivity at the same single erosion intensity. Soil organic carbon (SOC) was the major limiting variable that influenced soil productivity. Most explanations of long-term historical erosion for the variation in soil productivity arose from its sharing with SOC. SOC, total nitrogen, and total phosphorus were found as the regressors of soil productivity because of tillage operation. In general, this study provided strong evidence that single erosion event could also impose significant constraints on soil productivity by integrating with tillage operation, although single erosion is not the dominant effect relative to the long-term historical erosion. Our study demonstrated that an effective management of organic carbon pool should be the preferred option to maintain soil productivity in subtropical red soil hilly region. PMID:24147090

Constraining relationships between rainfall and landsliding with satellite derived rainfall measurements and landslide inventories.

NASA Astrophysics Data System (ADS)

Marc, Odin; Malet, Jean-Philippe; Stumpf, Andre; Gosset, Marielle

2017-04-01

In mountainous and hilly regions, landslides are an important source of damage and fatalities. Landsliding correlates with extreme rainfall events and may increase with climate change. Still, how precipitation drives landsliding at regional scales is poorly understood quantitatively in part because constraining simultaneously landsliding and rainfall across large areas is challenging. By combining optical images acquired from satellite observation platforms and rainfall measurements from satellite constellations we are building a database of landslide events caused by with single storm events. We present results from storm-induced landslides from Brazil, Taiwan, Micronesia, Central America, Europe and the USA. We present scaling laws between rainfall metrics derived by satellites (total rainfall, mean intensity, antecedent rainfall, ...) and statistical descriptors of landslide events (total area and volume, size distribution, mean runout, ...). Total rainfall seems to be the most important parameter driving non-linearly the increase in total landslide number, and area and volume. The maximum size of bedrock landslides correlates with the total number of landslides, and thus with total rainfall, within the limits of available topographic relief. In contrast, the power-law scaling exponent of the size distribution, controlling the relative abundance of small and large landslides, appears rather independent of the rainfall metrics (intensity, duration and total rainfall). These scaling laws seem to explain both the intra-storm pattern of landsliding, at the scale of satellite rainfall measurements ( 25kmx25km), and the different impacts observed for various storms. Where possible, we evaluate the limits of standard rainfall products (TRMM, GPM, GSMaP) by comparing them to in-situ data. Then we discuss how slope distribution and other geomorphic factors (lithology, soil presence,...) modulate these scaling laws. Such scaling laws at the basin scale and based only on a-priori information (topography, lithology, …) and rainfall metrics available from meteorological forecast may allow to better anticipate and mitigates landsliding associated with extreme rainfall events.

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

NASA Astrophysics Data System (ADS)

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

2015-04-01

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

Validation of Satellite-based Rainfall Estimates for Severe Storms (Hurricanes & Tornados)

NASA Astrophysics Data System (ADS)

Nourozi, N.; Mahani, S.; Khanbilvardi, R.

2005-12-01

Severe storms such as hurricanes and tornadoes cause devastating damages, almost every year, over a large section of the United States. More accurate forecasting intensity and track of a heavy storm can help to reduce if not to prevent its damages to lives, infrastructure, and economy. Estimating accurate high resolution quantitative precipitation (QPE) from a hurricane, required to improve the forecasting and warning capabilities, is still a challenging problem because of physical characteristics of the hurricane even when it is still over the ocean. Satellite imagery seems to be a valuable source of information for estimating and forecasting heavy precipitation and also flash floods, particularly for over the oceans where the traditional ground-based gauge and radar sources cannot provide any information. To improve the capability of a rainfall retrieval algorithm for estimating QPE of severe storms, its product is evaluated in this study. High (hourly 4km x 4km) resolutions satellite infrared-based rainfall products, from the NESDIS Hydro-Estimator (HE) and also PERSIANN (Precipitation Estimation from Remotely Sensed Information using an Artificial Neural Networks) algorithms, have been tested against NEXRAD stage-IV and rain gauge observations in this project. Three strong hurricanes: Charley (category 4), Jeanne (category 3), and Ivan (category 3) that caused devastating damages over Florida in the summer 2004, have been considered to be investigated. Preliminary results demonstrate that HE tends to underestimate rain rates when NEXRAD shows heavy storm (rain rates greater than 25 mm/hr) and to overestimate when NEXRAD gives low rainfall amounts, but PERSIANN tends to underestimate rain rates, in general.

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.

Use of a geomorphological transfer function to model design floods in small hillside catchments in semiarid Tunisia

NASA Astrophysics Data System (ADS)

Nasri, S.; Cudennec, C.; Albergel, J.; Berndtsson, R.

2004-02-01

In the beginning of the 1990s, the Tunisian Ministry of Agriculture launched an ambitious program for constructing small hillside reservoirs in the northern and central region of the country. At present, more than 720 reservoirs have been created. They consist of small compacted earth dams supplied with a horizontal overflow weir. Due to lack of hydrological data and the area's extreme floods, however, it is very difficult to design the overflow weirs. Also, catchments are very sensitive to erosion and the reservoirs are rapidly silted up. Consequently, prediction of flood volumes for important rainfall events becomes crucial. Few hydrological observations, however, exist for the catchment areas. For this purpose a geomorphological model methodology is presented to predict shape and volume of hydrographs for important floods. This model is built around a production function that defines the net storm rainfall (portion of rainfall during a storm which reaches a stream channel as direct runoff) from the total rainfall (observed rainfall in the catchment) and a transfer function based on the most complete possible definition of the surface drainage system. Observed rainfall during 5-min time steps was used in the model. The model runoff generation is based on surface drainage characteristics which can be easily extracted from maps. The model was applied to two representative experimental catchments in central Tunisia. The conceptual rainfall-runoff model based on surface topography and drainage network was seen to reproduce observed runoff satisfactory. The calibrated model was used to estimate runoff from 5, 10, 20, and 50 year rainfall return periods regarding runoff volume, maximum runoff, as well as the general shape of the runoff hydrograph. Practical conclusions to design hill reservoirs and to extrapolate results using this model methodology for ungauged small catchments in semiarid Tunisia are made.

Precipitation forecasts for rainfall runoff predictions. A case study in poorly gauged Ribb and Gumara catchments, upper Blue Nile, Ethiopia

NASA Astrophysics Data System (ADS)

Seyoum, Mesgana; van Andel, Schalk Jan; Xuan, Yunqing; Amare, Kibreab

Flow forecasting in poorly gauged, flood-prone Ribb and Gumara sub-catchments of the Blue Nile was studied with the aim of testing the performance of Quantitative Precipitation Forecasts (QPFs). Four types of QPFs namely MM5 forecasts with a spatial resolution of 2 km; the Maximum, Mean and Minimum members (MaxEPS, MeanEPS and MinEPS where EPS stands for Ensemble Prediction System) of the fixed, low resolution (2.5 by 2.5 degrees) National Oceanic and Atmospheric Administration Global Forecast System (NOAA GFS) ensemble forecasts were used. Both the MM5 and the EPS were not calibrated (bias correction, downscaling (for EPS), etc.). In addition, zero forecasts assuming no rainfall in the coming days, and monthly average forecasts assuming average monthly rainfall in the coming days, were used. These rainfall forecasts were then used to drive the Hydrologic Engineering Center’s-Hydrologic Modeling System, HEC-HMS, hydrologic model for flow predictions. The results show that flow predictions using MaxEPS and MM5 precipitation forecasts over-predicted the peak flow for most of the seven events analyzed, whereas under-predicted peak flow was found using zero- and monthly average rainfall. The comparison of observed and predicted flow hydrographs shows that MM5, MaxEPS and MeanEPS precipitation forecasts were able to capture the rainfall signal that caused peak flows. Flow predictions based on MaxEPS and MeanEPS gave results that were quantitatively close to the observed flow for most events, whereas flow predictions based on MM5 resulted in large overestimations for some events. In follow-up research for this particular case study, calibration of the MM5 model will be performed. The overall analysis shows that freely available atmospheric forecasting products can provide additional information on upcoming rainfall and peak flow events in areas where only base-line forecasts such as no-rainfall or climatology are available.

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.

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

NASA Astrophysics Data System (ADS)

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

2013-10-01

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

Evaluating satellite-derived long-term historical precipitation datasets for drought monitoring in Chile

NASA Astrophysics Data System (ADS)

Zambrano, Francisco; Wardlow, Brian; Tadesse, Tsegaye; Lillo-Saavedra, Mario; Lagos, Octavio

2017-04-01

Precipitation is a key parameter for the study of climate change and variability and the detection and monitoring of natural disaster such as drought. Precipitation datasets that accurately capture the amount and spatial variability of rainfall is critical for drought monitoring and a wide range of other climate applications. This is challenging in many parts of the world, which often have a limited number of weather stations and/or historical data records. Satellite-derived precipitation products offer a viable alternative with several remotely sensed precipitation datasets now available with long historical data records (+30years), which include the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) datasets. This study presents a comparative analysis of three historical satellite-based precipitation datasets that include Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B43 version 7 (1998-2015), PERSIANN-CDR (1983-2015) and CHIRPS 2.0 (1981-2015) over Chile to assess their performance across the country and for the case of the two long-term products the applicability for agricultural drought were evaluated when used in the calculation of commonly used drought indicator as the Standardized Precipitation Index (SPI). In this analysis, 278 weather stations of in situ rainfall measurements across Chile were initially compared to the satellite data. The study area (Chile) was divided into five latitudinal zones: North, North-Central, Central, South-Central and South to determine if there were a regional difference among these satellite products, and nine statistics were used to evaluate their performance to estimate the amount and spatial distribution of historical rainfall across Chile. Hierarchical cluster analysis, k-means and singular value decomposition were used to analyze these datasets to better understand their similarities and differences in characterizing rainfall patterns across Chile. Monthly analysis showed that all satellite products highly overestimated rainfall in the arid North zone. However, there were no major difference between all three products from North to South-Central zones. Though, in the South zone, PERSIANN-CDR shows the lowest fit with high underestimation, while CHIRPS 2.0 and TMPA 3B43 v7 had better agreement with in situ measurements. The accuracy of satellite products were highly dependent on the amount of monthly rainfall with the best results found during winter seasons and in zones (Central to South) with higher amounts of precipitation. PERSIANN-CDR and CHIRPS 2.0 were used to derive SPI at time-scale of 1, 3 and 6 months, both satellite products presented similar results when it was compared in situ against satellite SPI's. Because of its higher spatial resolution that allows better characterizing of spatial variation in precipitation pattern, the CHIRPS 2.0 was used to mapping the SPI-3 over Chile. The results of this study show that in order to use the CHIRPS 2.0 and PERSIANN-CDR datasets in Chile to monitor spatial patterns in the rainfall and drought intensity conditions, these products should be calibrated to adjust for the overestimation/underestimation of rainfall geographically specially in the North zone and seasonally during the summer and spring months in the other zones.

Improved spatial mapping of rainfall events with spaceborne SAR imagery

NASA Technical Reports Server (NTRS)

Ulaby, F. T.; Brisco, B.; Dobson, C.

1983-01-01

The Seasat satellite acquired the first spaceborne synthetic-aperture radar (SAR) images of the earth's surface, in 1978, at a frequency of 1.275 GHz (L-band) in a like-polarization mode at incidence angles of 23 + or - 3 deg. Although this may not be the optimum system configuration for radar remote sensing of soil moisture, interpretation of two Seasat images of Iowa demonstrates the sensitivity of microwave backscatter to soil moisture content. In both scenes, increased image brightness, which represents more radar backscatter, can be related to previous rainfall activity in the two areas. Comparison of these images with ground-based rainfall observations illustrates the increased spatial coverage of the rainfall event that can be obtained from the satellite SAR data. These data can then be color-enhanced by a digital computer to produce aesthetically pleasing output products for the user community.

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.

Rainfall Intensity and Frequency Explain Production Basis Risk in Cumulative Rain Index Insurance

NASA Astrophysics Data System (ADS)

Muneepeerakul, Chitsomanus P.; Muneepeerakul, Rachata; Huffaker, Ray G.

2017-12-01

With minimal moral hazard and adverse selection, weather index insurance promises financial resilience to farmers struck by harsh weather conditions through swift compensation at affordable premium. Despite these advantages, the very nature of indexing gives rise to production basis risk as the selected weather indexes do not sufficiently correspond to actual damages. To address this problem, we develop a stochastic yield model, built upon a stochastic soil moisture model driven by marked Poisson rainfall. Our analysis shows that even under similar temperature and rainfall amount yields can differ significantly; this was empirically supported by a 2-year field experiment in which rain-fed maize was grown under very similar total rainfall. Here, the year with more intense, less-frequent rainfall produces a better yield—a rare counter evidence to most climate change projections. Through a stochastic yield model, we demonstrate the crucial roles of rainfall intensity and frequency in determining the yield. Importantly, the model allows us to compute rainfall pattern-related basis risk inherent in cumulative rain index insurance. The model results and a case study herein clearly show that total rainfall is a poor indicator of yield, imposing unnecessary production basis risk on farmers and false-positive payouts on insurers. Incorporating rainfall intensity and frequency in the design of rain index insurance can offer farmers better protection, while maintaining the attractive features of the weather index insurance and thus fulfilling its promise of financial resilience.

Applying Customized Climate Advisory Information to Translate Extreme Rainfall Events into Farming Options in the Sudan-Sahel of West Africa

NASA Astrophysics Data System (ADS)

Salack, S.; Worou, N. O.; Sanfo, S.; Nikiema, M. P.; Boubacar, I.; Paturel, J. E.; Tondoh, E. J.

2017-12-01

In West Africa, the risk of food insecurity linked to the low productivity of small holder farming increases as a result of rainfall extremes. In its recent evolution, the rainy season in the Sudan-Sahel zone presents mixed patterns of extreme climatic events. In addition to intense rain events, the distribution of events is associated with pockets of intra-seasonal long dry spells. The negative consequences of these mixed patterns are obvious on the farm: soil water logging, erosion of arable land, dwartness and dessication of crops, and loss in production. The capacity of local farming communities to respond accordingly to rainfall extreme events is often constrained by lack of access to climate information and advisory on smart crop management practices that can help translate extreme rainfall events into farming options. The objective of this work is to expose the framework and the pre-liminary results of a scheme that customizes climate-advisory information package delivery to subsistence farmers in Bakel (Senegal), Ouahigouya & Dano (Burkina Faso) and Bolgatanga (Ghana) for sustainable family agriculture. The package is based on the provision of timely climate information (48-hours, dekadal & seasonal) embedded with smart crop management practices to explore and exploite the potential advantage of intense rainfall and extreme dry spells in millet, maize, sorghum and cowpea farming communities. It is sent via mobile phones and used on selected farms (i.e agro-climatic farm schools) on which some small on-farm infrastructure were built to alleviate negative impacts of weather. Results provide prominent insight on how co-production of weather/climate information, customized access and guidiance on its use can induce fast learning (capacity building of actors), motivation for adaptation, sustainability, potential changes in cropping system, yields and family income in the face of a rainfall extremes at local scales of Sudan-Sahel of West Africa. Keywords: Climate Information, Smart Practices, Farming Options, Agro-Climatic Farm Schools, Sudan-Sahel

Remote Sensing and Capacity Building to Improve Food Security

NASA Astrophysics Data System (ADS)

Husak, G. J.; Funk, C. C.; Verdin, J. P.; Rowland, J.; Budde, M. E.

2012-12-01

The Famine Early Warning Systems Network (FEWS NET) is a U.S. Agency for International Development (USAID) supported project designed to monitor and anticipate food insecurity in the developing world, primarily Africa, Central America, the Caribbean and Central Asia. This is done through a network of partners involving U.S. government agencies, universities, country representatives, and partner institutions. This presentation will focus on the remotely sensed data used in FEWS NET activities and capacity building efforts designed to expand and enhance the use of FEWS NET tools and techniques. Remotely sensed data are of particular value in the developing world, where ground data networks and data reporting are limited. FEWS NET uses satellite based rainfall and vegetation greenness measures to monitor and assess food production conditions. Satellite rainfall estimates also drive crop models which are used in determining yield potential. Recent FEWS NET products also include estimates of actual evapotranspiration. Efforts are currently underway to assimilate these products into a single tool which would indicate areas experiencing abnormal conditions with implications for food production. FEWS NET is also involved in a number of capacity building activities. Two primary examples are the development of software and training of institutional partners in basic GIS and remote sensing. Software designed to incorporate rainfall station data with existing satellite-derived rainfall estimates gives users the ability to enhance satellite rainfall estimates or long-term means, resulting in gridded fields of rainfall that better reflect ground conditions. Further, this software includes a crop water balance model driven by the improved rainfall estimates. Finally, crop parameters, such as the planting date or length of growing period, can be adjusted by users to tailor the crop model to actual conditions. Training workshops in the use of this software, as well as basic GIS and remote sensing tools, are routinely conducted by FEWS NET representatives at host country meteorological and agricultural services. These institutions are then able to produce information that can more accurately inform food security decision making. Informed decision making reduces the risk associated with a given hazard. In the case of FEWS NET, this involves identification of shocks to food availability, allowing for the pre-positioning of aid to be available when a hazard strikes. Developing tools to incorporate better information in food production estimates and working closely with local staff trained in state-of-the-practice techniques results in a more informed decision making process, reducing the impacts of food security hazards.

The effects of more extreme rainfall patterns on nitrogen leaching from a field crop system in the upper Midwest, USA

NASA Astrophysics Data System (ADS)

Hess, L.; Hinckley, E. L. S.; Robertson, G. P.; Matson, P. A.

2016-12-01

As global surface temperatures rise, the proportion of total rainfall that falls in heavy storm events is increasing in many areas, in particular the US Midwest, a major agricultural region. These changes in rainfall patterns may have consequences for ecosystem nutrient losses, especially from agricultural ecosystems. We conducted a multi-year rainfall manipulation experiment to examine how more extreme rainfall patterns affect nitrogen (N) leaching from row-crop ecosystems in the upper Midwest, and to what extent tillage may moderate these effects. 5x5m rainout shelters were installed in April 2015 to impose control and extreme rainfall patterns in replicated plots under conventional tillage and no-till management at the Kellogg Biological Station LTER site. Plots exposed to the control rainfall treatment received ambient rainfall, and those exposed to the extreme rainfall treatment received the same total amount of water but applied once every 2 weeks, to simulate larger, less frequent storms. N leaching was calculated as the product of measured soil water N concentrations and modeled soil water drainage at 1.2m depth using HYDRUS-1D. Based on data to date, more N has been leached from both tilled and no-till soils exposed to the extreme rainfall treatment compared to the control rainfall treatment. Results thus far suggest that greater soil water drainage is a primary driver of this increase, and changes in within-system nitrogen cycling - such as net N mineralization and crop N uptake - may also play a role. The experiment is ongoing, and our results so far suggest that intensifying precipitation patterns may exacerbate N leaching from agricultural soils, with potentially negative consequences for receiving ground- and surface waters, as well as for farmers.

Dynamic Rainfall Patterns and the Simulation of Changing Scenarios: A behavioral watershed response

NASA Astrophysics Data System (ADS)

Chu, M.; Guzman, J.; Steiner, J. L.; Hou, C.; Moriasi, D.

2015-12-01

Rainfall is one of the fundamental drivers that control hydrologic responses including runoff production and transport phenomena that consequently drive changes in aquatic ecosystems. Quantifying the hydrologic responses to changing scenarios (e.g., climate, land use, and management) using environmental models requires a realistic representation of probable rainfall in its most sensible spatio-temporal dimensions matching that of the phenomenon under investigation. Downscaling projected rainfall from global circulation models (GCMs) is the most common practice in deriving rainfall datasets to be used as main inputs to hydrologic models which in turn are used to assess the impacts of climate changes on ecosystems. Downscaling assumes that local climate is a combination of large-scale climatic/atmospheric conditions and local conditions. However, the representation of the latter is generally beyond the capacity of current GCMs. The main objective of this study was to develop and implement a synthetic rainfall generator to downscale expected rainfall trends to 1 x 1 km rainfall daily patterns that mimic the dynamic propagation of probability distribution functions (pdf) derived from historic rainfall data (rain-gauge or radar estimated). Future projections were determined based on actual and expected changes in the pdf and stochastic processes to account for variability. Watershed responses in terms of streamflow and nutrients loads were evaluated using synthetically generated rainfall patterns and actual data. The framework developed in this study will allow practitioners to generate rainfall datasets that mimic the temporal and spatial patterns exclusive to their study area under full disclosure of the uncertainties involved. This is expected to provide significantly more accurate environmental models than is currently available and would provide practitioners with ways to evaluate the spectrum of systemic responses to changing scenarios.

[Output characteristics of rainfall runoff phosphorus pollution from a typical small watershed in Yimeng mountainous area].

PubMed

Yu, Xing-xiu; Li, Zhen-wei; Liu, Qian-jin; Jing, Guang-hua

2012-08-01

Relationships between phosphorus pollutant concentrations and precipitation-runoff were analyzed by monitoring pollutant losses at outlets of the Menglianggu watershed in 2010. A typical small watershed was selected to examine the runoff and quality parameters such as total phosphorus (TP), particle phosphorus (PP), dissolve phosphorus (DP) and dissolve inorganic phosphorus (DIP) in rainfall-runoff of 10 rainfall events. Precipitation was above 2 mm for all the 10 rainfall events. The results showed that the peak of phosphorus concentrations occurred before the peak of water flows, whereas change processes of the phosphorus fluxes were consistent with that of the water flows and the phosphorus flux also have a strong linear relationship with the water flows. The minimums of the phosphorus concentrations in every 10 natural rainfall events have small differences with each other, but the maximum and EMCs of the phosphorus concentrations have significant differences with each rainfall event. This was mainly influenced by the precipitation, maximum rainfall intensity and mean rainfall intensity (EMCs) and was less influenced by rainfall duration. DP and TP were mainly composed of DIP and PP, respectively. There were no significant correlations between DIP/DP dynamic changes and rainfall characteristics, whereas significant correlations between PP/TP dynamic changes and maximum rainfall intensity were detected. The production of DIP, DP, AND TP were mainly influenced by the direct runoff (DR) and base flow (BF). The EMCs of DIP, DP, TP and the variations of DIP/DP were all found to have significant polynomial relationships with DR/TR., but the dynamic changes of PP/ TP and the EMCS of PP were less influenced by the DR/TR.

Innovative technologies to understand hydrogeomorphic impacts of climate change scenarios on gully development in drylands: case study from Ethiopia

NASA Astrophysics Data System (ADS)

Frankl, Amaury; Stal, Cornelis; Abraha, Amanuel; De Wulf, Alain; Poesen, Jean

2014-05-01

Taking climate change scenarios into account, rainfall patterns are likely to change over the coming decades in eastern Africa. In brief, large parts of eastern Africa are expected to experience a wetting, including seasonality changes. Gullies are threshold phenomena that accomplish most of their geomorphic change during short periods of strong rainfall. Understanding the links between geomorphic change and rainfall characteristics in detail, is thus crucial to ensure the sustainability of future land management. In this study, we present image-based 3D modelling as a low-cost, flexible and rapid method to quantify gully morphology from terrestrial photographs. The methodology was tested on two gully heads in Northern Ethiopia. Ground photographs (n = 88-235) were taken during days with cloud cover. The photographs were processed in PhotoScan software using a semi-automated Structure from Motion-Multi View Stereo (SfM-MVS) workflow. As a result, full 3D models were created, accurate at cm level. These models allow to quantify gully morphology in detail, including information on undercut walls and soil pipe inlets. Such information is crucial for understanding the hydrogeomorphic processes involved. Producing accurate 3D models after each rainfall event, allows to model interrelations between rainfall, land management, runoff and erosion. Expected outcomes are the production of detailed vulnerability maps that allow to design soil and water conservation measures in a cost-effective way. Keywords: 3D model, Ethiopia, Image-based 3D modelling, Gully, PhotoScan, Rainfall.

Convective Cloud and Rainfall Processes Over the Maritime Continent: Simulation and Analysis of the Diurnal Cycle

NASA Astrophysics Data System (ADS)

Gianotti, Rebecca L.

The Maritime Continent experiences strong moist convection, which produces significant rainfall and drives large fluxes of heat and moisture to the upper troposphere. Despite the importance of these processes to global circulations, current predictions of climate change over this region are still highly uncertain, largely due to inadequate representation of the diurnally-varying processes related to convection. In this work, a coupled numerical model of the land-atmosphere system (RegCM3-IBIS) is used to investigate how more physically-realistic representations of these processes can be incorporated into large-scale climate models. In particular, this work improves simulations of convective-radiative feedbacks and the role of cumulus clouds in mediating the diurnal cycle of rainfall. Three key contributions are made to the development of RegCM3-IBIS. Two pieces of work relate directly to the formation and dissipation of convective clouds: a new representation of convective cloud cover, and a new parameterization of convective rainfall production. These formulations only contain parameters that can be directly quantified from observational data, are independent of model user choices such as domain size or resolution, and explicitly account for subgrid variability in cloud water content and nonlinearities in rainfall production. The third key piece of work introduces a new method for representation of cloud formation within the boundary layer. A comprehensive evaluation of the improved model was undertaken using a range of satellite-derived and ground-based datasets, including a new dataset from Singapore's Changi airport that documents diurnal variation of the local boundary layer height. The performance of RegCM3-IBIS with the new formulations is greatly improved across all evaluation metrics, including cloud cover, cloud liquid water, radiative fluxes and rainfall, indicating consistent improvement in physical realism throughout the simulation. This work demonstrates that: (1) moist convection strongly influences the near surface environment by mediating the incoming solar radiation and net radiation at the surface; (2) dissipation of convective cloud via rainfall plays an equally important role in the convectiveradiative feedback as the formation of that cloud; and (3) over parts of the Maritime Continent, rainfall is a product of diurnally-varying convective processes that operate at small spatial scales, on the order of 1 km. (Copies available exclusively from MIT Libraries, libraries.mit.edu/docs - docs@mit.edu)

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

NASA Astrophysics Data System (ADS)

Tang, L.; Hossain, F.

2009-12-01

Understanding the error characteristics of satellite rainfall data at different spatial/temporal scales is critical, especially when the scheduled Global Precipitation Mission (GPM) plans to provide High Resolution Precipitation Products (HRPPs) at global scales. Satellite rainfall data contain errors which need ground validation (GV) data for characterization, while satellite rainfall data will be most useful in the regions that are lacking in GV. Therefore, a critical step is to develop a spatial interpolation scheme for transferring the error characteristics of satellite rainfall data from GV regions to Non-GV regions. As a prelude to GPM, The TRMM Multi-satellite Precipitation Analysis (TMPA) products of 3B41RT and 3B42RT (Huffman et al., 2007) over the US spanning a record of 6 years are used as a representative example of satellite rainfall data. Next Generation Radar (NEXRAD) Stage IV rainfall data are used as the reference for GV data. Initial work by the authors (Tang et al., 2009, GRL) has shown promise in transferring error from GV to Non-GV regions, based on a six-year climatologic average of satellite rainfall data assuming only 50% of GV coverage. However, this transfer of error characteristics needs to be investigated for a range of GV data coverage. In addition, it is also important to investigate if proxy-GV data from an accurate space-borne sensor, such as the TRMM PR (or the GPM DPR), can be leveraged for the transfer of error at sparsely gauged regions. The specific question we ask in this study is, “what is the minimum coverage of GV data required for error transfer scheme to be implemented at acceptable accuracy in hydrological relevant scale?” Three geostatistical interpolation methods are compared: ordinary kriging, indicator kriging and disjunctive kriging. Various error metrics are assessed for transfer such as, Probability of Detection for rain and no rain, False Alarm Ratio, Frequency Bias, Critical Success Index, RMSE etc. Understanding the proper space-time scales at which these metrics can be reasonably transferred is also explored in this study. Keyword: Satellite rainfall, error transfer, spatial interpolation, kriging methods.

Assessing the performance of satellite-based precipitation products over the Mediterranean region

NASA Astrophysics Data System (ADS)

Xaver, Angelika; Dorigo, Wouter; Brocca, Luca; Ciabatta, Luca

2017-04-01

Detailed knowledge about the spatial and temporal patterns and quantities of precipitation is of high importance. This applies especially in the Mediterranean region, where water demand for agricultural, industrial and touristic needs is growing and climate projections foresee a decrease of precipitation amounts and an increase in variability. In this region, ground-based rain gauges are available only limited in number, particularly in northern Africa and the Middle East and lack to capture the high spatio-temporal character of precipitation over large areas. This has motivated the development of a large number of remote sensing products for monitoring rainfall. Satellite-based precipitation products are based on various observation principles and retrieval approaches, i.e. from thermal infra-red and microwaves. Although, many individual validation studies on the performance of these precipitation datasets exist, they mostly examine only one or a few of these rainfall products at the same time and are not targeted at the Mediterranean basin as a whole. Here, we present an extensive comparative study of seven different satellite-based precipitation products, namely CMORPH 30-minutes, CMORPH 3-hourly, GPCP, PERSIANN, SM2Rain CCI, TRMM TMPA 3B42, and TRMM TMPA 3B42RT, focusing on the whole Mediterranean region and on individual Mediterranean catchments. The time frame of investigation is restricted by the common availability of all precipitation products and covers the period 2000-2013. We assess the skill of the satellite products against gridded gauge-based data provided by GPCC and E-OBS. Apart from common characteristics like biases and temporal correlations we evaluate several sophisticated dataset properties that are of particular interest for Mediterranean hydrology, including the capability of the remotely sensed products to capture extreme events and trends. A clear seasonal dependency of the correlation results can be observed for the whole Mediterranean basin as well as for the individual catchments. While high correlation values are achieved for basins north of the Mediterranean Sea, the African Nile catchment is showing the lowest correlation values. When examining the climate indices, e.g. number of (very) heavy precipitation days, the maximum precipitation amount of five consecutive wet days, maximum number of consecutive wet days, it becomes clear that the satellite-based precipitation products are having difficulties in capturing consecutive rainfall events. More promising results are obtained when calculating the total annual amount of precipitation or the number of heavy precipitation days.

Retrieved Latent Heating from TRMM

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo; Smith, Eric A.; Houze Jr, Robert

2008-01-01

The global hydrological cycle is central to the Earth's climate system, with rainfall and the physics of precipitation formation acting as the key links in the cycle. Two-thirds of global rainfall occurs in the tropics with the associated latent heating (LH) accounting for three-fourths of the total heat energy available to the Earth's atmosphere. In addition, fresh water provided by tropical rainfall and its variability exerts a large impact upon the structure and motions of the upper ocean layer. In the last decade, it has been established that standard products of LH from satellite measurements, particularly TRMM measurements, would be a valuable resource for scientific research and applications. Such products would enable new insights and investigations concerning the complexities of convection system life cycles, the diabatic heating controls and feedbacks related to meso-synoptic circulations and their forecasting, the relationship of tropical patterns of LH to the global circulation and climate, and strategies for improving cloud parameterizations in environmental prediction models. The status of retrieved TRMM LH products, TRMM LH inter-comparison and validation project, current TRMM LH applications and critic issues/action items (based on previous five TRMM LH workshops) is presented in this article.

Error threshold inference from Global Precipitation Measurement (GPM) satellite rainfall data and interpolated ground-based rainfall measurements in Metro Manila

NASA Astrophysics Data System (ADS)

Ampil, L. J. Y.; Yao, J. G.; Lagrosas, N.; Lorenzo, G. R. H.; Simpas, J.

2017-12-01

The Global Precipitation Measurement (GPM) mission is a group of satellites that provides global observations of precipitation. Satellite-based observations act as an alternative if ground-based measurements are inadequate or unavailable. Data provided by satellites however must be validated for this data to be reliable and used effectively. In this study, the Integrated Multisatellite Retrievals for GPM (IMERG) Final Run v3 half-hourly product is validated by comparing against interpolated ground measurements derived from sixteen ground stations in Metro Manila. The area considered in this study is the region 14.4° - 14.8° latitude and 120.9° - 121.2° longitude, subdivided into twelve 0.1° x 0.1° grid squares. Satellite data from June 1 - August 31, 2014 with the data aggregated to 1-day temporal resolution are used in this study. The satellite data is directly compared to measurements from individual ground stations to determine the effect of the interpolation by contrast against the comparison of satellite data and interpolated measurements. The comparisons are calculated by taking a fractional root-mean-square error (F-RMSE) between two datasets. The results show that interpolation improves errors compared to using raw station data except during days with very small amounts of rainfall. F-RMSE reaches extreme values of up to 654 without a rainfall threshold. A rainfall threshold is inferred to remove extreme error values and make the distribution of F-RMSE more consistent. Results show that the rainfall threshold varies slightly per month. The threshold for June is inferred to be 0.5 mm, reducing the maximum F-RMSE to 9.78, while the threshold for July and August is inferred to be 0.1 mm, reducing the maximum F-RMSE to 4.8 and 10.7, respectively. The maximum F-RMSE is reduced further as the threshold is increased. Maximum F-RMSE is reduced to 3.06 when a rainfall threshold of 10 mm is applied over the entire duration of JJA. These results indicate that IMERG performs well for moderate to high intensity rainfall and that the interpolation remains effective only when rainfall exceeds a certain threshold value. Over Metro Manila, an F-RMSE threshold of 0.5 mm indicated better correspondence between ground measured and satellite measured rainfall.

Rainfall estimation from soil moisture data: crash test for SM2RAIN algorithm

NASA Astrophysics Data System (ADS)

Brocca, Luca; Albergel, Clement; Massari, Christian; Ciabatta, Luca; Moramarco, Tommaso; de Rosnay, Patricia

2015-04-01

Soil moisture governs the partitioning of mass and energy fluxes between the land surface and the atmosphere and, hence, it represents a key variable for many applications in hydrology and earth science. In recent years, it was demonstrated that soil moisture observations from ground and satellite sensors contain important information useful for improving rainfall estimation. Indeed, soil moisture data have been used for correcting rainfall estimates from state-of-the-art satellite sensors (e.g. Crow et al., 2011), and also for improving flood prediction through a dual data assimilation approach (e.g. Massari et al., 2014; Chen et al., 2014). Brocca et al. (2013; 2014) developed a simple algorithm, called SM2RAIN, which allows estimating rainfall directly from soil moisture data. SM2RAIN has been applied successfully to in situ and satellite observations. Specifically, by using three satellite soil moisture products from ASCAT (Advanced SCATterometer), AMSR-E (Advanced Microwave Scanning Radiometer for Earth Observation) and SMOS (Soil Moisture and Ocean Salinity); it was found that the SM2RAIN-derived rainfall products are as accurate as state-of-the-art products, e.g., the real-time version of the TRMM (Tropical Rainfall Measuring Mission) product. Notwithstanding these promising results, a detailed study investigating the physical basis of the SM2RAIN algorithm, its range of applicability and its limitations on a global scale has still to be carried out. In this study, we carried out a crash test for SM2RAIN algorithm on a global scale by performing a synthetic experiment. Specifically, modelled soil moisture data are obtained from HTESSEL model (Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land) forced by ERA-Interim near-surface meteorology. Afterwards, the modelled soil moisture data are used as input into SM2RAIN algorithm for testing weather or not the resulting rainfall estimates are able to reproduce ERA-Interim rainfall data. Correlation, root mean square differences and categorical scores were used to evaluate the goodness of the results. This analysis wants to draw global picture of the performance of SM2RAIN algorithm in absence of errors in soil moisture and rainfall data. First preliminary results over Europe have shown that SM2RAIN performs particularly well over southern Europe (e.g., Spain, Italy and Greece) while its performances diminish by moving towards Northern latitudes (Scandinavia) and over Alps. The results on a global scale will be shown and discussed at the conference session. REFERENCES Brocca, L., Melone, F., Moramarco, T., Wagner, W. (2013). A new method for rainfall estimation through soil moisture observations. Geophysical Research Letters, 40(5), 853-858. Brocca, L., Ciabatta, L., Massari, C., Moramarco, T., Hahn, S., Hasenauer, S., Kidd, R., Dorigo, W., Wagner, W., Levizzani, V. (2014). Soil as a natural rain gauge: estimating global rainfall from satellite soil moisture data. Journal of Geophysical Research, 119(9), 5128-5141. Chen F, Crow WT, Ryu D. (2014) Dual forcing and state correction via soil moisture assimilation for improved rainfall-runoff modeling. J Hydrometeor, 15, 1832-1848. Crow, W.T., van den Berg, M.J., Huffman, G.J., Pellarin, T. (2011). Correcting rainfall using satellite-based surface soil moisture retrievals: the soil moisture analysis rainfall tool (SMART). Water Resour Res, 47, W08521. Dee, D. P.,et al. (2011). The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. Roy. Meteorol. Soc., 137, 553-597 Massari, C., Brocca, L., Moramarco, T., Tramblay, Y., Didon Lescot, J.-F. (2014). Potential of soil moisture observations in flood modelling: estimating initial conditions and correcting rainfall. Advances in Water Resources, 74, 44-53.

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.

The Status of the Tropical Rainfall Measuring Mission (TRMM) after 2 Years in Orbit

NASA Technical Reports Server (NTRS)

Kummerow, C.; Simpson, J.; Thiele, O.; Barnes, W.; Chang, A. T. C.; Stocker, E.; Adler, R. F.; Hou, A.; Kakar, R.; Wentz, F.

1999-01-01

The Tropical Rainfall Measuring Mission (TRMM) satellite was launched on November 27, 1997, and data from all the instruments first became available approximately 30 days after launch. Since then, much progress has been made in the calibration of the sensors, the improvement of the rainfall algorithms, in related modeling applications and in new datasets tailored specifically for these applications. This paper reports the latest results regarding the calibration of the TRMM Microwave Imager, (TMI), Precipitation Radar (PR) and Visible and Infrared Sensor (VIRS). For the TMI, a new product is in place that corrects for a still unknown source of radiation leaking in to the TMI receiver. The PR calibration has been adjusted upward slightly (by 0.6 dBZ) to better match ground reference targets, while the VIRS calibration remains largely unchanged. In addition to the instrument calibration, great strides have been made with the rainfall algorithms as well, with the new rainfall products agreeing with each other to within less than 20% over monthly zonally averaged statistics. The TRMM Science Data and Information System (TSDIS) has responded equally well by making a number of new products, including real-time and fine resolution gridded rainfall fields available to the modeling community. The TRMM Ground Validation (GV) program is also responding with improved radar calibration techniques and rainfall algorithms to provide more accurate GV products which will be further enhanced with the new multiparameter 10 cm radar being developed for TRMM validation and precipitation studies. Progress in these various areas has, in turn, led to exciting new developments in the modeling area where Data Assimilation, and Weather Forecast models are showing dramatic improvements after the assimilation of observed rainfall fields.

Utilization of Precipitation and Moisture Products Derived from Satellites to Support NOAA Operational Precipitation Forecasts

NASA Astrophysics Data System (ADS)

Ferraro, R.; Zhao, L.; Kuligowski, R. J.; Kusselson, S.; Ma, L.; Kidder, S. Q.; Forsythe, J. M.; Jones, A. S.; Ebert, E. E.; Valenti, E.

2012-12-01

NOAA/NESDIS operates a constellation of polar and geostationary orbiting satellites to support weather forecasts and to monitor the climate. Additionally, NOAA utilizes satellite assets from other U.S. agencies like NASA and the Department of Defense, as well as those from other nations with similar weather and climate responsibilities (i.e., EUMETSAT and JMA). Over the past two decades, through joint efforts between U.S. and international government researchers, academic partners, and private sector corporations, a series of "value added" products have been developed to better serve the needs of weather forecasters and to exploit the full potential of precipitation and moisture products generated from these satellites. In this presentation, we will focus on two of these products - Ensemble Tropical Rainfall Potential (eTRaP) and Blended Total Precipitable Water (bTPW) - and provide examples on how they contribute to hydrometeorological forecasts. In terms of passive microwave satellite products, TPW perhaps is most widely used to support real-time forecasting applications, as it accurately depicts tropospheric water vapor and its movement. In particular, it has proven to be extremely useful in determining the location, timing, and duration of "atmospheric rivers" which contribute to and sustain flooding events. A multi-sensor approach has been developed and implemented at NESDIS in which passive microwave estimates from multiple satellites and sensors are merged to create a seamless, bTPW product that is more efficient for forecasters to use. Additionally, this product is being enhanced for utilization for television weather forecasters. Examples will be shown to illustrate the roll of atmospheric rivers and contribution to flooding events, and how the bTPW product was used to improve the forecast of these events. Heavy rains associated with land falling tropical cyclones (TC) frequently trigger floods that cause millions of dollars of damage and tremendous loss of lives. To provide observations-based forecast guidance for TC heavy rain, the Tropical Rainfall Potential (TRaP), an extrapolation forecast generated by accumulating rainfall estimates from satellites with microwave sensors as the storm is translated along the forecast track, was originally developed to predict the maximum rainfall at landfall, as well as the spatial pattern of precipitation. More recently, an enhancement has been made to combine the TRaP forecasts from multiple sensors and various start times into an ensemble (eTRaP). The ensemble approach provides not only more accurate quantitative precipitation forecasts, including more skillful maximum rainfall amount and location, it also produces probabilistic forecasts of rainfall exceeding various thresholds that decision makers can use to make critical risk assessments. Examples of the utilization and performance of eTRaP will be given in the presentation.

DRI-Grass: A New Experimental Platform for Addressing Grassland Ecosystem Responses to Future Precipitation Scenarios in South-East Australia

PubMed Central

Power, Sally A.; Barnett, Kirk L.; Ochoa-Hueso, Raul; Facey, Sarah L.; Gibson-Forty, Eleanor V. J.; Hartley, Susan E.; Nielsen, Uffe N.; Tissue, David T.; Johnson, Scott N.

2016-01-01

Climate models predict shifts in the amount, frequency and seasonality of rainfall. Given close links between grassland productivity and rainfall, such changes are likely to have profound effects on the functioning of grassland ecosystems and modify species interactions. Here, we introduce a unique, new experimental platform – DRI-Grass (Drought and Root Herbivore Interactions in a Grassland) – that exposes a south-eastern Australian grassland to five rainfall regimes [Ambient (AMB), increased amount (IA, +50%), reduced amount (RA, -50%), reduced frequency (RF, single rainfall event every 21 days, with total amount unchanged) and summer drought (SD, 12–14 weeks without water, December–March)], and contrasting levels of root herbivory. Incorporation of a belowground herbivore (root-feeding scarabs) addition treatment allows novel investigation of ecological responses to the twin stresses of altered rainfall and root herbivory. We quantified effects of permanently installed rain shelters on microclimate by comparison with outside plots, identifying small shelter effects on air temperature (-0.19°C day, +0.26°C night), soil water content (SWC; -8%) and photosynthetically active radiation (PAR; -16%). Shelters were associated with modest increases in net primary productivity (NPP), particularly during the cool season. Rainfall treatments generated substantial differences in SWC, with the exception of IA; the latter is likely due to a combination of higher transpiration rates associated with greater plant biomass in IA and the low water-holding capacity of the well-drained, sandy soil. Growing season NPP was strongly reduced by SD, but did not respond to the other rainfall treatments. Addition of root herbivores did not affect plant biomass and there were no interactions between herbivory and rainfall treatments in the 1st year of study. Root herbivory did, however, induce foliar silicon-based defenses in Cynodon dactylon and Eragrostis curvula. Rapid recovery of NPP following resumption of watering in SD plots indicates high functional resilience at the site, and may reflect adaptation of the vegetation to historically high variability in rainfall, both within- and between years. DRI-Grass provides a unique platform for understanding how ecological interactions will be affected by changing rainfall regimes and, specifically, how belowground herbivory modifies grassland resistance and resilience to climate extremes. PMID:27703458

Assessment of satellite-based precipitation estimates over Paraguay

NASA Astrophysics Data System (ADS)

Oreggioni Weiberlen, Fiorella; Báez Benítez, Julián

2018-04-01

Satellite-based precipitation estimates represent a potential alternative source of input data in a plethora of meteorological and hydrological applications, especially in regions characterized by a low density of rain gauge stations. Paraguay provides a good example of a case where the use of satellite-based precipitation could be advantageous. This study aims to evaluate the version 7 of the Tropical Rainfall Measurement Mission Multi-Satellite Precipitation Analysis (TMPA V7; 3B42 V7) and the version 1.0 of the purely satellite-based product of the Climate Prediction Center Morphing Technique (CMORPH RAW) through their comparison with daily in situ precipitation measurements from 1998 to 2012 over Paraguay. The statistical assessment is conducted with several commonly used indexes. Specifically, to evaluate the accuracy of daily precipitation amounts, mean error (ME), root mean square error (RMSE), BIAS, and coefficient of determination (R 2) are used, and to analyze the capability to correctly detect different precipitation intensities, false alarm ratio (FAR), frequency bias index (FBI), and probability of detection (POD) are applied to various rainfall rates (0, 0.1, 0.5, 1, 2, 5, 10, 20, 40, 60, and 80 mm/day). Results indicate that TMPA V7 has a better performance than CMORPH RAW over Paraguay. TMPA V7 has higher accuracy in the estimation of daily rainfall volumes and greater precision in the detection of wet days (> 0 mm/day). However, both satellite products show a lower ability to appropriately detect high intensity precipitation events.

Fine-tuning satellite-based rainfall estimates

NASA Astrophysics Data System (ADS)

Harsa, Hastuadi; Buono, Agus; Hidayat, Rahmat; Achyar, Jaumil; Noviati, Sri; Kurniawan, Roni; Praja, Alfan S.

2018-05-01

Rainfall datasets are available from various sources, including satellite estimates and ground observation. The locations of ground observation scatter sparsely. Therefore, the use of satellite estimates is advantageous, because satellite estimates can provide data on places where the ground observations do not present. However, in general, the satellite estimates data contain bias, since they are product of algorithms that transform the sensors response into rainfall values. Another cause may come from the number of ground observations used by the algorithms as the reference in determining the rainfall values. This paper describe the application of bias correction method to modify the satellite-based dataset by adding a number of ground observation locations that have not been used before by the algorithm. The bias correction was performed by utilizing Quantile Mapping procedure between ground observation data and satellite estimates data. Since Quantile Mapping required mean and standard deviation of both the reference and the being-corrected data, thus the Inverse Distance Weighting scheme was applied beforehand to the mean and standard deviation of the observation data in order to provide a spatial composition of them, which were originally scattered. Therefore, it was possible to provide a reference data point at the same location with that of the satellite estimates. The results show that the new dataset have statistically better representation of the rainfall values recorded by the ground observation than the previous dataset.

The development of a sub-daily gridded rainfall product to improve hydrological predictions in Great Britain

NASA Astrophysics Data System (ADS)

Quinn, Niall; Freer, Jim; Coxon, Gemma; O'Loughlin, Fiachra; Woods, Ross; Liguori, Sara

2015-04-01

In Great Britain and many other regions of the world, flooding resulting from short duration, high intensity rainfall events can lead to significant economic losses and fatalities. At present, such extreme events are often poorly evaluated using hydrological models due, in part, to their rarity and relatively short duration and a lack of appropriate data. Such storm characteristics are not well represented by daily rainfall records currently available using volumetric gauges and/or derived gridded products. This research aims to address this important data gap by developing a sub-daily gridded precipitation product for Great Britain. Our focus is to better understand these storm events and some of the challenges and uncertainties in quantifying such data across catchment scales. Our goal is to both improve such rainfall characterisation and derive an input to drive hydrological model simulations. Our methodology involves the collation, error checking, and spatial interpolation of approximately 2000 rain gauges located across Great Britain, provided by the Scottish Environment Protection Agency (SEPA) and the Environment Agency (EA). Error checking was conducted over the entirety of the TBR data available, utilising a two stage approach. First, rain gauge data at each site were examined independently, with data exceeding reasonable thresholds marked as suspect. Second, potentially erroneous data were marked using a neighbourhood analysis approach whereby measurements at a given gauge were deemed suspect if they did not fall within defined bounds of measurements at neighbouring gauges. A total of eight error checks were conducted. To provide the user with the greatest flexibility possible, the error markers associated with each check have been recorded at every site. This approach aims to enable the user to choose which checks they deem most suitable for a particular application. The quality assured TBR dataset was then spatially interpolated to produce a national scale gridded rainfall product. Finally, radar rainfall data provided by the UK Met Office was assimilated, where available, to provide an optimal hourly estimate of rainfall, given the error variance associated with both datasets. This research introduces a sub-daily rainfall product that will be of particular value to hydrological modellers requiring rainfall inputs at higher temporal resolutions than those currently available nationally. Further research will aim to quantify the uncertainties in the rainfall product in order to improve our ability to diagnose and identify structural errors in hydrological modelling of extreme events. Here we present our initial findings.

Inter-Comparison of CHARM Data and WSR-88D Storm Integrated Rainfall

NASA Technical Reports Server (NTRS)

Jedlovec, Gary J.; Meyer, Paul J.; Guillory, Anthony R.; Stellman, Keith; Limaye, Ashutosh; Arnold, James E. (Technical Monitor)

2002-01-01

A localized precipitation network has been established over a 4000 sq km region of northern Alabama in support of local weather and climate research at the Global Hydrology and Climate Center (GHCC) in Huntsville. This Cooperative Huntsville-Area Rainfall Measurement (CHARM) network is comprised of over 80 volunteers who manually take daily rainfall measurements from 85 sites. The network also incorporates 20 automated gauges that report data at 1-5 minute intervals on a 24 h a day basis. The average spacing of the gauges in the network is about 6 kin, however coverage in some regions benefit from gauges every 1-2 km. The 24 h rainfall totals from the CHARM network have been used to validate Stage III rainfall estimates of daily and storm totals derived from the WSR-88D radars that cover northern Alabama. The Stage III rainfall product is produced by the Lower Mississippi River Forecast Center (LMRFC) in support of their daily forecast operations. The intercomparisons between the local rain gauge and the radar estimates have been useful to understand the accuracy and utility of the Stage III data. Recently, the Stage III and CHARM rainfall measurements have been combined to produce an hourly rainfall dataset at each CHARM observation site. The procedure matches each CHARM site with a time sequence of Stage III radar estimates of precipitation. Hourly stage III rainfall estimates were used to partition the rain gauge values to the time interval over which they occurred. The new hourly rain gauge dataset is validated at selected points where 1-5 minute rainfall measurements have been made. This procedure greatly enhances the utility of the CHARM data for local weather and hydrologic modeling studies. The conference paper will present highlights of the Stage III intercomparison and some examples of the combined radar / rain gauge product demonstrating its accuracy and utility in deriving an hourly rainfall product from the 24 h CHARM totals.

Evaluation of GPM IMERG Early, Late, and Final rainfall estimates using WegenerNet gauge data in southeastern Austria

NASA Astrophysics Data System (ADS)

O, Sungmin; Foelsche, Ulrich; Kirchengast, Gottfried; Fuchsberger, Juergen; Tan, Jackson; Petersen, Walter A.

2017-12-01

The Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG) products provide quasi-global (60° N-60° S) precipitation estimates, beginning March 2014, from the combined use of passive microwave (PMW) and infrared (IR) satellites comprising the GPM constellation. The IMERG products are available in the form of near-real-time data, i.e., IMERG Early and Late, and in the form of post-real-time research data, i.e., IMERG Final, after monthly rain gauge analysis is received and taken into account. In this study, IMERG version 3 Early, Late, and Final (IMERG-E,IMERG-L, and IMERG-F) half-hourly rainfall estimates are compared with gauge-based gridded rainfall data from the WegenerNet Feldbach region (WEGN) high-density climate station network in southeastern Austria. The comparison is conducted over two IMERG 0.1° × 0.1° grid cells, entirely covered by 40 and 39 WEGN stations each, using data from the extended summer season (April-October) for the first two years of the GPM mission. The entire data are divided into two rainfall intensity ranges (low and high) and two seasons (warm and hot), and we evaluate the performance of IMERG, using both statistical and graphical methods. Results show that IMERG-F rainfall estimates are in the best overall agreement with the WEGN data, followed by IMERG-L and IMERG-E estimates, particularly for the hot season. We also illustrate, through rainfall event cases, how insufficient PMW sources and errors in motion vectors can lead to wide discrepancies in the IMERG estimates. Finally, by applying the method of Villarini and Krajewski (2007), we find that IMERG-F half-hourly rainfall estimates can be regarded as a 25 min gauge accumulation, with an offset of +40 min relative to its nominal time.

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.

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

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

Hydrological impacts of climate change on the Tejo and Guadiana Rivers

NASA Astrophysics Data System (ADS)

Kilsby, C. G.; Tellier, S. S.; Fowler, H. J.; Howels, T. R.

2007-05-01

A distributed daily rainfall runoff model is applied to the Tejo and Guadiana river basins in Spain and Portugal to simulate the effects of climate change on runoff production, river flows and water resource availability with results aggregated to the monthly level. The model is calibrated, validated and then used for a series of climate change impact assessments for the period 2070 2100. Future scenarios are derived from the HadRM3H regional climate model (RCM) using two techniques: firstly a bias-corrected RCM output, with monthly mean correction factors calculated from observed rainfall records; and, secondly, a circulation-pattern-based stochastic rainfall model. Major reductions in rainfall and streamflow are projected throughout the year; these results differ from those for previous studies where winter increases are projected. Despite uncertainties in the representation of heavily managed river systems, the projected impacts are serious and pose major threats to the maintenance of bipartite water treaties between Spain and Portugal and the supply of water to urban and rural regions of Portugal.

A simplified diagnostic model of orographic rainfall for enhancing satellite-based rainfall estimates in data-poor regions

USGS Publications Warehouse

Funk, Christopher C.; Michaelsen, Joel C.

2004-01-01

An extension of Sinclair's diagnostic model of orographic precipitation (“VDEL”) is developed for use in data-poor regions to enhance rainfall estimates. This extension (VDELB) combines a 2D linearized internal gravity wave calculation with the dot product of the terrain gradient and surface wind to approximate terrain-induced vertical velocity profiles. Slope, wind speed, and stability determine the velocity profile, with either sinusoidal or vertically decaying (evanescent) solutions possible. These velocity profiles replace the parameterized functions in the original VDEL, creating VDELB, a diagnostic accounting for buoyancy effects. A further extension (VDELB*) uses an on/off constraint derived from reanalysis precipitation fields. A validation study over 365 days in the Pacific Northwest suggests that VDELB* can best capture seasonal and geographic variations. A new statistical data-fusion technique is presented and is used to combine VDELB*, reanalysis, and satellite rainfall estimates in southern Africa. The technique, matched filter regression (MFR), sets the variance of the predictors equal to their squared correlation with observed gauge data and predicts rainfall based on the first principal component of the combined data. In the test presented here, mean absolute errors from the MFR technique were 35% lower than the satellite estimates alone. VDELB assumes a linear solution to the wave equations and a Boussinesq atmosphere, and it may give unrealistic responses under extreme conditions. Nonetheless, the results presented here suggest that diagnostic models, driven by reanalysis data, can be used to improve satellite rainfall estimates in data-sparse regions.

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.

A new physically-based model considered antecedent rainfall for shallow landslide

NASA Astrophysics Data System (ADS)

Luo, Yu; He, Siming

2017-04-01

Rainfall is the most significant factor to cause landslide especially shallow landslide. In previous studies, rainfall intensity and duration are take part in the physically based model to determining the occurrence of the rainfall-induced landslides, but seldom considered the antecedent rainfall. In this study, antecedent rainfall is took into account to derive a new physically based model for shallow landslides prone area predicting at the basin scale. Based on the Rosso's equation of seepage flow considering the antecedent rainfall to construct the hillslope hydrology model. And then, the infinite slope stability theory is using to construct the slope stability model. At last, the model is apply in the Baisha river basin of Chengdu, Sichuan, China, and the results are compared with the one's from unconsidered antecedent rainfall. The results show that the model is simple, but has the capability of consider antecedent rainfall in the triggering mechanism of shallow landslide. Meanwhile, antecedent rainfall can make an obvious effect on shallow landslides, so in shallow landslide hazard assessment, the influence of the antecedent rainfall can't be ignored.

Merging Satellite Precipitation Products for Improved Streamflow Simulations

NASA Astrophysics Data System (ADS)

Maggioni, V.; Massari, C.; Barbetta, S.; Camici, S.; Brocca, L.

2017-12-01

Accurate quantitative precipitation estimation is of great importance for water resources management, agricultural planning and forecasting and monitoring of natural hazards such as flash floods and landslides. In situ observations are limited around the Earth, especially in remote areas (e.g., complex terrain, dense vegetation), but currently available satellite precipitation products are able to provide global precipitation estimates with an accuracy that depends upon many factors (e.g., type of storms, temporal sampling, season, etc.). The recent SM2RAIN approach proposes to estimate rainfall by using satellite soil moisture observations. As opposed to traditional satellite precipitation methods, which sense cloud properties to retrieve instantaneous estimates, this new bottom-up approach makes use of two consecutive soil moisture measurements for obtaining an estimate of the fallen precipitation within the interval between two satellite overpasses. As a result, the nature of the measurement is different and complementary to the one of classical precipitation products and could provide a different valid perspective to substitute or improve current rainfall estimates. Therefore, we propose to merge SM2RAIN and the widely used TMPA 3B42RT product across Italy for a 6-year period (2010-2015) at daily/0.25deg temporal/spatial scale. Two conceptually different merging techniques are compared to each other and evaluated in terms of different statistical metrics, including hit bias, threat score, false alarm rates, and missed rainfall volumes. The first is based on the maximization of the temporal correlation with a reference dataset, while the second is based on a Bayesian approach, which provides a probabilistic satellite precipitation estimate derived from the joint probability distribution of observations and satellite estimates. The merged precipitation products show a better performance with respect to the parental satellite-based products in terms of categorical statistics, as well as bias reduction and correlation coefficient, with the Bayesian approach being superior to other methods. A study case in the Tiber river basin is also presented to discuss the performance of forcing a hydrological model with the merged satellite precipitation product to simulate streamflow time series.

Comprehensive overview of FPL field testing conducted in the tropics (1945-2005)

Treesearch

Grant T. Kirker; Stan L. Lebow; Mark E. Mankowski

2016-01-01

Tropical exposure often represents a more severe environment for treated wood and wood based products. Accelerated tropical decay rates are typically attributed to higher mean rainfall and temperatures. The Forest Products Laboratory (FPL) in Madison, WI has been conducting tropical field tests in a variety of locations since the early 1940’s. This paper summarizes FPL...

Projected Changes in Seasonal Mean Temperature and Rainfall (2011-2040) in Cagayan Valley, Philippines

NASA Astrophysics Data System (ADS)

Basconcillo, J. Q.; Lucero, A. J. R.; Solis, A. S.; Kanamaru, H.; Sandoval, R. S.; Bautista, E. U.

2014-12-01

Among Filipinos, a meal is most often considered incomplete without rice. There is a high regard for rice in the entire archipelago that in 2012, the country's rice production was accounted to more than 18 million tons with an equivalent harvested area of 4.7 million hectares. This means that from the 5.4 million hectares of arable land in the Philippines, 11 percent are found and being utilized for rice production in Cagayan Valley (CV). In the same year, more than 13 percent of the country's total annual rice production was produced in CV. Rice production also provides employment to 844,000 persons (out of 1.4 million persons) which suggest that occupation and livelihood in Cagayan Valley are strongly anchored in rice production. These figures outline the imaginable vulnerability of rice production in CV amidst varying issues such as land conversion, urbanization, increase in population, retention of farming households, and climate change. While all these issues are of equal importance, this paper is directed towards the understanding the projected changes in seasonal rainfall and mean temperature (2011-2040). It is envisioned by this study that a successful climate change adaptation starts with the provision of climate projections hence this paper's objective to investigate on the changes in climate patterns and extreme events. Projected changes are zonally limited to the Provinces of Cagayan, Isabela, Nueva Vizcaya, and Quirino based on the statistical downscaling of three global climate models (BCM2, CNCM3, and MPEH5) and two emission scenarios (A1B and A2). With the idea that rainfall and temperature varies with topography, the AURELHY technique was utilized in interpolating climate projections. Results obtained from the statistical downscaling showed that there will be significant climate changes from 2011-2040 in terms of rainfall and mean temperature. There are also indications of increasing frequency of extreme 24-hour rainfall and number of dry days (especially in Tuguegarao City). This study was forged in a partnership of PAGASA and FAO AMICAF. Further efforts to improve climate change adaptations in CV are directed towards provision of climate projections as input to crop and water resources modeling, market modeling, hunger and poverty reduction, and policy formulation.

Improving a spatial rainfall product using a data-mining approach and its effect on the hydrological response of a meso-scale catchment.

NASA Astrophysics Data System (ADS)

Oriani, F.; Stisen, S.; Demirel, C.

2017-12-01

The spatial representation of rainfall is of primary importance to correctly study the uncertainty of basin recharge and its propagation to the surface and underground circulation. We consider here the daily grid rainfall product provided by the Danish Meteorological Institute as input to the National Water Resources Model of Denmark. Due to a drastic reduction in the rain gauge network (from approximately 500 stations in the period 1996-2006, to 250 in the period 2007-2014), the grid rainfall product, based on the interpolation of these data, is much less reliable. The research is focused on the Skjern catchment (1,050 km2 western Jutland), where we can dispose of the complete rain-gauge database from the Danish Hydrological Observatory and compute the distributed hydrological response at the 1-km scale.To give a better estimation of the gridded rainfall input, we start from ground measurements by simulating the missing data with a stochastic data-mining approach, then we compute again the grid interpolation. To maximize the predictive power of the technique, combinations of station time-series that are the most informative to each other are selected on the basis of their correlation and available historical data. Then, the missing data inside these time-series are simulated together using the direct sampling technique (DS) [1, 2]. DS simulates a datum by sampling the historical record of the same stations where a similar data pattern occurs, preserving their complex statistical relation. The simulated data are reinjected in the whole dataset and used as well as conditioning data to progressively fill up the gaps in other stations.The results show that the proposed methodology, tested on the period 1995-2012, can increase the realism of the grid rainfall product by regenerating the missing ground measurements. The hydrological response is analyzed considering the observations at 5 hydrological stations. The presented methodology can be used in many regions to regenerate the missing data using the information contained in the historical record and propagate the uncertainty of the prediction to the hydrological response. [1] G.Mariethoz et al. (2010), Water Resour. Res., 10.1029/2008WR007621.[2] F. Oriani et al. (2014), Hydrol. Earth Syst. Sc., 10.5194/hessd-11-3213-2014.

Surrogate modeling of joint flood risk across coastal watersheds

NASA Astrophysics Data System (ADS)

Bass, Benjamin; Bedient, Philip

2018-03-01

This study discusses the development and performance of a rapid prediction system capable of representing the joint rainfall-runoff and storm surge flood response of tropical cyclones (TCs) for probabilistic risk analysis. Due to the computational demand required for accurately representing storm surge with the high-fidelity ADvanced CIRCulation (ADCIRC) hydrodynamic model and its coupling with additional numerical models to represent rainfall-runoff, a surrogate or statistical model was trained to represent the relationship between hurricane wind- and pressure-field characteristics and their peak joint flood response typically determined from physics based numerical models. This builds upon past studies that have only evaluated surrogate models for predicting peak surge, and provides the first system capable of probabilistically representing joint flood levels from TCs. The utility of this joint flood prediction system is then demonstrated by improving upon probabilistic TC flood risk products, which currently account for storm surge but do not take into account TC associated rainfall-runoff. Results demonstrate the source apportionment of rainfall-runoff versus storm surge and highlight that slight increases in flood risk levels may occur due to the interaction between rainfall-runoff and storm surge as compared to the Federal Emergency Management Association's (FEMAs) current practices.

An assessment of the feasibility of the use of satellite-only rainfall estimates for the hydrological monitoring in central Italy

NASA Astrophysics Data System (ADS)

Campo, Lorenzo; Caparrini, Francesca

2013-04-01

The need for accurate distributed hydrological modelling has constantly increased in last years for several purposes: agricultural applications, water resources management, hydrological balance at watershed scale, floods forecast. The main input for the hydrological numerical models is rainfall data that present, at the same time, a large availability of measures (in gauged regions, with respect to other micro-meteorological variables) and the most complex spatial patterns. While also in presence of densely gauged watersheds the spatial interpolation of the rainfall is a non-trivial problem, due to the spatial intermittence of the variable (especially at finer temporal scales), ungauged regions need an alternative source of rainfall data in order to perform the hydrological modelling. Such source can be constituted by the satellite-estimated rainfall fields, with reference to both geostationary and polar-orbit platforms. In this work the rainfall product obtained by the Aqua-AIRS sensor were used in order to assess the feasibility of the use of satellite-based rainfall as input for distributed hydrological modelling. The MOBIDIC (MOdello di BIlancio Distribuito e Continuo) model, developed at the Department of civil and Environmental Engineering of the University of Florence and operationally used by Tuscany Region and Umbria Region for flood prediction and management, was used for the experiments. In particular three experiments were carried on: a) hydrological simulation with the use of rain-gauges data, b) simulation with the use of satellite-only rainfall estimates, c) simulation with the combined use of the two sources of data in order to obtain an optimal estimate of the actual rainfall fields. The domain of the study was the central Italy. Several critical events occurred in the area were analyzed. A discussion of the results is provided.

Robust effects of cloud superparameterization on simulated daily rainfall intensity statistics across multiple versions of the Community Earth System Model

DOE PAGES

Kooperman, Gabriel J.; Pritchard, Michael S.; Burt, Melissa A.; ...

2016-02-01

This study evaluates several important statistics of daily rainfall based on frequency and amount distributions as simulated by a global climate model whose precipitation does not depend on convective parameterization—Super-Parameterized Community Atmosphere Model (SPCAM). Three superparameterized and conventional versions of CAM, coupled within the Community Earth System Model (CESM1 and CCSM4), are compared against two modern rainfall products (GPCP 1DD and TRMM 3B42) to discriminate robust effects of superparameterization that emerge across multiple versions. The geographic pattern of annual-mean rainfall is mostly insensitive to superparameterization, with only slight improvements in the double-ITCZ bias. However, unfolding intensity distributions reveal several improvementsmore » in the character of rainfall simulated by SPCAM. The rainfall rate that delivers the most accumulated rain (i.e., amount mode) is systematically too weak in all versions of CAM relative to TRMM 3B42 and does not improve with horizontal resolution. It is improved by superparameterization though, with higher modes in regions of tropical wave, Madden-Julian Oscillation, and monsoon activity. Superparameterization produces better representations of extreme rates compared to TRMM 3B42, without sensitivity to horizontal resolution seen in CAM. SPCAM produces more dry days over land and fewer over the ocean. Updates to CAM’s low cloud parameterizations have narrowed the frequency peak of light rain, converging toward SPCAM. Poleward of 50°, where more rainfall is produced by resolved-scale processes in CAM, few differences discriminate the rainfall properties of the two models. Lastly, these results are discussed in light of their implication for future rainfall changes in response to climate forcing.« less

TRMM Applications for Rainfall-Induced Landslide Early Warning

NASA Astrophysics Data System (ADS)

Dok, A.; Fukuoka, H.; Hong, Y.

2012-04-01

Early warning system (EWS) is the most effective method in saving lives and reducing property damages resulted from the catastrophic landslides if properly implemented in populated areas of landslide-prone nations. For predicting the occurrence of landslides, it requires examination of empirical relationship between rainfall characteristics and past landslide occurrence. In developed countries like Japan and the US, precipitation is monitored by rain radars and ground-based rain gauge matrix. However, in developing regions like Southeast Asian countries, very limited number of rain gauges is available, and there is no implemented methodology for issuing effective warming of landslides yet. Correspondingly, satellite precipitation monitoring could be therefore a possible and promising solution for launching landslide quasi-real-time early warning system in those countries. It is due to the fact that TMPA (TRMM Multi-satellite Precipitation Analysis) can provides a globally calibration-based sequential scheme for combining precipitation estimates from multiple satellites, and gauge analyses where feasible, at fine scales (3-hourly with 0.25°x0.25° spatial resolution). It is available both after and in quasi-real time, calibrated by TRMM Combined Instrument and TRMM Microwave Imager precipitation product. However, validation of ground based rain gauge and TRMM satellite data in the vulnerable regions is still not yet operative. Snake-line/Critical-line and Soil Water Index (SWI) are used for issuing warning of landslide occurrence in Japan; whereas, Caine criterion is preferable in Europe and western nations. Herewith, it presents rainfall behavior which took place in Beichuan city (located on the 2008 Chinese Wenchuan earthquake fault), Hofu and Shobara cities in Japan where localized heavy rainfall attacked in 2009 and 2010, respectively, from TRMM 3B42RT correlated with ground based rain gauge data. The 1-day rainfall intensity and 15-day cumulative rainfall (snake line) were independently plotted to investigate the impact of short-term rainfall intensity and accumulated effective rainfall volume respectively for obtaining some probabilistic threshold. Japanese SWI was also tested to distribute threshold regarding to highly nonlinear rainfall patterns in predicting the landslide occurrence through the plot of total water of 3 serial tank models and daily precipitation. As a result, the snake line plots using TMPA work well for landslide warning in the selected cities; while SWI plots shows unusual peak value on the day of the debris flow occurrence. Graph of daily precipitation vs SWI implies possible zone of critical line, and second peak appearance 1 day before, indicating possibility of early warning.

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.

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

NASA Astrophysics Data System (ADS)

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

2017-11-01

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

Improving Radar Quantitative Precipitation Estimation over Complex Terrain in the San Francisco Bay Area

NASA Astrophysics Data System (ADS)

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

2017-12-01

A recent study by the State of California's Department of Water Resources has emphasized that the San Francisco Bay Area is at risk of catastrophic flooding. Therefore, accurate quantitative precipitation estimation (QPE) and forecast (QPF) are critical for protecting life and property in this region. Compared to rain gauge and meteorological satellite, ground based radar has shown great advantages for high-resolution precipitation observations in both space and time domain. In addition, the polarization diversity shows great potential to characterize precipitation microphysics through identification of different hydrometeor types and their size and shape information. Currently, all the radars comprising the U.S. National Weather Service (NWS) Weather Surveillance Radar-1988 Doppler (WSR-88D) network are operating in dual-polarization mode. Enhancement of QPE is one of the main considerations of the dual-polarization upgrade. The San Francisco Bay Area is covered by two S-band WSR-88D radars, namely, KMUX and KDAX. However, in complex terrain like the Bay Area, it is still challenging to obtain an optimal rainfall algorithm for a given set of dual-polarization measurements. In addition, the accuracy of rain rate estimates is contingent on additional factors such as bright band contamination, vertical profile of reflectivity (VPR) correction, and partial beam blockages. This presentation aims to improve radar QPE for the Bay area using advanced dual-polarization rainfall methodologies. The benefit brought by the dual-polarization upgrade of operational radar network is assessed. In addition, a pilot study of gap fill X-band radar performance is conducted in support of regional QPE system development. This paper also presents a detailed comparison between the dual-polarization radar-derived rainfall products with various operational products including the NSSL's Multi-Radar/Multi-Sensor (MRMS) system. Quantitative evaluation of various rainfall products is achieved using rainfall measurements from a validation gauge network, which shows that new dual-polarization methods can produce better QPE, and the X-band radar has excellent potential to augment WSR-88D for rainfall monitoring in this region.

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.

Plant and arthropod community sensitivity to rainfall manipulation but not nitrogen enrichment in a successional grassland ecosystem.

PubMed

Lee, Mark A; Manning, Pete; Walker, Catherine S; Power, Sally A

2014-12-01

Grasslands provide many ecosystem services including carbon storage, biodiversity preservation and livestock forage production. These ecosystem services will change in the future in response to multiple global environmental changes, including climate change and increased nitrogen inputs. We conducted an experimental study over 3 years in a mesotrophic grassland ecosystem in southern England. We aimed to expose plots to rainfall manipulation that simulated IPCC 4th Assessment projections for 2100 (+15% winter rainfall and -30% summer rainfall) or ambient climate, achieving +15% winter rainfall and -39% summer rainfall in rainfall-manipulated plots. Nitrogen (40 kg ha(-1) year(-1)) was also added to half of the experimental plots in factorial combination. Plant species composition and above ground biomass were not affected by rainfall in the first 2 years and the plant community did not respond to nitrogen enrichment throughout the experiment. In the third year, above-ground plant biomass declined in rainfall-manipulated plots, driven by a decline in the abundances of grass species characteristic of moist soils. Declining plant biomass was also associated with changes to arthropod communities, with lower abundances of plant-feeding Auchenorrhyncha and carnivorous Araneae indicating multi-trophic responses to rainfall manipulation. Plant and arthropod community composition and plant biomass responses to rainfall manipulation were not modified by nitrogen enrichment, which was not expected, but may have resulted from prior nitrogen saturation and/or phosphorus limitation. Overall, our study demonstrates that climate change may in future influence plant productivity and induce multi-trophic responses in grasslands.

A First Approach to Global Runoff Simulation using Satellite Rainfall Estimation

NASA Technical Reports Server (NTRS)

Hong, Yang; Adler, Robert F.; Hossain, Faisal; Curtis, Scott; Huffman, George J.

2007-01-01

Many hydrological models have been introduced in the hydrological literature to predict runoff but few of these have become common planning or decision-making tools, either because the data requirements are substantial or because the modeling processes are too complicated for operational application. On the other hand, progress in regional or global rainfall-runoff simulation has been constrained by the difficulty of measuring spatiotemporal variability of the primary causative factor, i.e. rainfall fluxes, continuously over space and time. Building on progress in remote sensing technology, researchers have improved the accuracy, coverage, and resolution of rainfall estimates by combining imagery from infrared, passive microwave, and space-borne radar sensors. Motivated by the recent increasing availability of global remote sensing data for estimating precipitation and describing land surface characteristics, this note reports a ballpark assessment of quasi-global runoff computed by incorporating satellite rainfall data and other remote sensing products in a relatively simple rainfall-runoff simulation approach: the Natural Resources Conservation Service (NRCS) runoff Curve Number (CN) method. Using an Antecedent Precipitation Index (API) as a proxy of antecedent moisture conditions, this note estimates time-varying NRCS-CN values determined by the 5-day normalized API. Driven by multi-year (1998-2006) Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis, quasi-global runoff was retrospectively simulated with the NRCS-CN method and compared to Global Runoff Data Centre data at global and catchment scales. Results demonstrated the potential for using this simple method when diagnosing runoff values from satellite rainfall for the globe and for medium to large river basins. This work was done with the simple NRCS-CN method as a first-cut approach to understanding the challenges that lie ahead in advancing the satellite-based inference of global runoff. We expect that the successes and limitations revealed in this study will lay the basis for applying more advanced methods to capture the dynamic variability of the global hydrologic process for global runoff monltongin real time. The essential ingredient in this work is the use of global satellite-based rainfall estimation.

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

NASA Astrophysics Data System (ADS)

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

2017-05-01

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

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.

Merging bottom-up and top-down precipitation products using a stochastic error model

NASA Astrophysics Data System (ADS)

Maggioni, Viviana; Massari, Christian; Brocca, Luca; Ciabatta, Luca

2017-04-01

Accurate quantitative precipitation estimation is of great importance for water resources management, agricultural planning, and forecasting and monitoring of natural hazards such as flash floods and landslides. In situ observations are limited around the Earth, especially in remote areas (e.g., complex terrain, dense vegetation), but currently available satellite precipitation products are able to provide global precipitation estimates with an accuracy that depends upon many factors (e.g., type of storms, temporal sampling, season etc…). Recently, Brocca et al. (2014) have proposed an alternative approach (i.e., SM2RAIN) that allows to estimate rainfall from space by using satellite soil moisture observations. In contrast with classical satellite precipitation products which sense the cloud properties to retrieve the instantaneous precipitation, this new bottom-up approach makes use of two consecutive soil moisture measurements for obtaining an estimate of the fallen precipitation within the interval between two satellite passes. As a result, the nature of the measurement is different and complementary to the one of classical precipitation products and could provide a different valid perspective to improve current satellite rainfall estimates via appropriate integration between the products (i.e., SM2RAIN plus a classical satellite rainfall product). However, whether SM2RAIN is able or not to improve the performance of any state-of-the-art satellite rainfall product is much dependent upon an adequate quantification and characterization of the relative errors of the products. In this study, the stochastic rainfall error model SREM2D (Hossain et al. 2006) is used for characterizing the retrieval error of both SM2RAIN and a state-of-the-art satellite precipitation product (i.e., 3B42RT). The error characterization serves for an optimal integration between SM2RAIN and 3B42RT for enhancing the capability of the resulting integrated product (i.e. SM2RAIN+3B42RT) in operational hydrology. The study, conducted in Italy for a 5-yr period (2010-2014) using a dense network of raingauges (about 3000) as a benchmark, demonstrates that the integration is able to enhance the correlation and the root mean squared error of SM2RAIN+3B42RT with respect to the parent products. This suggests a potential benefit of merging SM2RAIN derived rainfall with state-of-the-art satellite precipitation estimates for creating a product characterized by higher accuracy and better performance when used in the contest of operational hydrology. REFERENCES 1. Brocca, L.; Ciabatta, L.; Massari, C.; Moramarco, T.; Hahn, S.; Hasenauer, S.; Kidd, R.; Dorigo, W.; Wagner, W.; Levizzani, V. Soil as a natural rain gauge: Estimating global rainfall from satellite soil moisture data. J. Geophys. Res. Atmos. 2014, 119, 5128-5141. 2. Hossain, F.; Anagnostou, E. N. A two-dimensional satellite rainfall error model. IEEE Trans. Geosci. Remote Sens. 2006, 44, 1511-1522.

Satellite-based high-resolution mapping of rainfall over southern Africa

NASA Astrophysics Data System (ADS)

Meyer, Hanna; Drönner, Johannes; Nauss, Thomas

2017-06-01

A spatially explicit mapping of rainfall is necessary for southern Africa for eco-climatological studies or nowcasting but accurate estimates are still a challenging task. This study presents a method to estimate hourly rainfall based on data from the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI). Rainfall measurements from about 350 weather stations from 2010-2014 served as ground truth for calibration and validation. SEVIRI and weather station data were used to train neural networks that allowed the estimation of rainfall area and rainfall quantities over all times of the day. The results revealed that 60 % of recorded rainfall events were correctly classified by the model (probability of detection, POD). However, the false alarm ratio (FAR) was high (0.80), leading to a Heidke skill score (HSS) of 0.18. Estimated hourly rainfall quantities were estimated with an average hourly correlation of ρ = 0. 33 and a root mean square error (RMSE) of 0.72. The correlation increased with temporal aggregation to 0.52 (daily), 0.67 (weekly) and 0.71 (monthly). The main weakness was the overestimation of rainfall events. The model results were compared to the Integrated Multi-satellitE Retrievals for GPM (IMERG) of the Global Precipitation Measurement (GPM) mission. Despite being a comparably simple approach, the presented MSG-based rainfall retrieval outperformed GPM IMERG in terms of rainfall area detection: GPM IMERG had a considerably lower POD. The HSS was not significantly different compared to the MSG-based retrieval due to a lower FAR of GPM IMERG. There were no further significant differences between the MSG-based retrieval and GPM IMERG in terms of correlation with the observed rainfall quantities. The MSG-based retrieval, however, provides rainfall in a higher spatial resolution. Though estimating rainfall from satellite data remains challenging, especially at high temporal resolutions, this study showed promising results towards improved spatio-temporal estimates of rainfall over southern Africa.

Inter-Seasonal and Annual Co-Variation of Smallholder Production Portfolios, Volumes and Incomes with Rainfall and Flood Levels in the Amazon Estuary: Implications for Building Livelihood Resilience to Increasing Variability of Hydro-Climatic Regimes

NASA Astrophysics Data System (ADS)

Vogt, N. D.; Fernandes, K.; Pinedo-Vasquez, M.; Brondizio, E. S.; Almeida, O.; Rivero, S.; Rabelo, F. R.; Dou, Y.; Deadman, P.

2014-12-01

In this paper we investigate inter-seasonal and annual co-variations of rainfall and flood levels with Caboclo production portfolios, and proportions of it they sell and consume, in the Amazon Estuary from August 2012 to August 2014. Caboclos of the estuary maintain a diverse and flexible land-use portfolio, with a shift in dominant use from agriculture to agroforestry and forestry since WWII (Vogt et al., 2014). The current landscape is configured for acai, shrimp and fish production. In the last decade the frequency of wet seasons with anomalous flood levels and duration has increased primarily from changes in rainfall and discharge from upstream basins. Local rainfall, though with less influence on extreme estuarine flood levels, is reported to be more sporadic and intense in wet season and variable in both wet and dry seasons, for yet unknown reasons. The current production portfolio and its flexibility are felt to build resilience to these increases in hydro-climatic variability and extreme events. What is less understood, for time and costliness of daily measures at household levels, is how variations in flood and rainfall levels affect shifts in the current production portfolio of estuarine Caboclos, and the proportions of it they sell and consume. This is needed to identify what local hydro-climatic thresholds are extreme for current livelihoods, that is, that most adversely affect food security and income levels. It is also needed identify the large-scale forcings driving those extreme conditions to build forecasts for when they will occur. Here we present results of production, rainfall and flood data collected daily in households from both the North and South Channel of the Amazon estuary over last two years to identify how they co-vary, and robustness of current production portfolio under different hydro-climatic conditions.

Assessment of Satellite Precipitation Products in the Philippine Archipelago

NASA Astrophysics Data System (ADS)

Ramos, M. D.; Tendencia, E.; Espana, K.; Sabido, J.; Bagtasa, G.

2016-06-01

Precipitation is the most important weather parameter in the Philippines. Made up of more than 7100 islands, the Philippine archipelago is an agricultural country that depends on rain-fed crops. Located in the western rim of the North West Pacific Ocean, this tropical island country is very vulnerable to tropical cyclones that lead to severe flooding events. Recently, satellite-based precipitation estimates have improved significantly and can serve as alternatives to ground-based observations. These data can be used to fill data gaps not only for climatic studies, but can also be utilized for disaster risk reduction and management activities. This study characterized the statistical errors of daily precipitation from four satellite-based rainfall products from (1) the Tropical Rainfall Measuring Mission (TRMM), (2) the CPC Morphing technique (CMORPH) of NOAA and (3) the Global Satellite Mapping of Precipitation (GSMAP) and (4) Precipitation Estimation from Remotely Sensed information using Artificial Neural Networks (PERSIANN). Precipitation data were compared to 52 synoptic weather stations located all over the Philippines. Results show GSMAP to have over all lower bias and CMORPH with lowest Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). In addition, a dichotomous rainfall test reveals GSMAP and CMORPH have low Proportion Correct (PC) for convective and stratiform rainclouds, respectively. TRMM consistently showed high PC for almost all raincloud types. Moreover, all four satellite precipitation showed high Correct Negatives (CN) values for the north-western part of the country during the North-East monsoon and spring monsoonal transition periods.

Attributing Asymmetric Productivity Responses to Internal Ecosystem Dynamics and External Drivers Using Probabilistic Models

NASA Astrophysics Data System (ADS)

Parolari, A.; Goulden, M.

2017-12-01

A major challenge to interpreting asymmetric changes in ecosystem productivity is the attribution of these changes to external climate forcing or to internal ecophysiological processes that respond to these drivers (e.g., photosynthesis response to drying soil). For example, positive asymmetry in productivity can result from either positive skewness in the distribution of annual rainfall amount or from negative curvature in the productivity response to annual rainfall. To analyze the relative influences of climate and ecosystem dynamics on both positive and negative asymmetry in multi-year ANPP experiments, we use a multi-scale coupled ecosystem water-carbon model to interpret field experimental results that span gradients of rainfall skewness and ANPP response curvature. The model integrates rainfall variability, soil moisture dynamics, and net carbon assimilation from the daily to inter-annual scales. From the underlying physical basis of the model, we compute the joint probability distribution of the minimum and maximum ANPP for an annual ANPP experiment of N years. The distribution is used to estimate the likelihood that either positive or negative asymmetry will be observed in an experiment, given the annual rainfall distribution and the ANPP response curve. We estimate the total asymmetry as the mode of this joint distribution and the relative contribution attributable to rainfall skewness as the mode for a linear ANPP response curve. Applied to data from several long-term ANPP experiments, we find that there is a wide range of observed ANPP asymmetry (positive and negative) and a spectrum of contributions from internal and external factors. We identify the soil water holding capacity relative to the mean rain event depth as a critical ecosystem characteristic that controls the non-linearity of the ANPP response and positive curvature at high rainfall. Further, the seasonal distribution of rainfall is shown to control the presence or absence of negative curvature at low rainfall. Therefore, a combination of rooting depth, soil texture, and climate seasonality contribute to ANPP response curvature and its contribution to overall observed asymmetry.

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

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

Radar-based Quantitative Precipitation Forecasting using Spatial-scale Decomposition Method for Urban Flood Management

NASA Astrophysics Data System (ADS)

Yoon, S.; Lee, B.; Nakakita, E.; Lee, G.

2016-12-01

Recent climate changes and abnormal weather phenomena have resulted in increased occurrences of localized torrential rainfall. Urban areas in Korea have suffered from localized heavy rainfall, including the notable Seoul flood disaster in 2010 and 2011. The urban hydrological environment has changed in relation to precipitation, such as reduced concentration time, a decreased storage rate, and increased peak discharge. These changes have altered and accelerated the severity of damage to urban areas. In order to prevent such urban flash flood damages, we have to secure the lead time for evacuation through the improvement of radar-based quantitative precipitation forecasting (QPF). The purpose of this research is to improve the QPF products using spatial-scale decomposition method for considering the life time of storm and to assess the accuracy between traditional QPF method and proposed method in terms of urban flood management. The layout of this research is as below. First, this research applies the image filtering to separate the spatial-scale of rainfall field. Second, the separated small and large-scale rainfall fields are extrapolated by each different forecasting method. Third, forecasted rainfall fields are combined at each lead time. Finally, results of this method are evaluated and compared with the results of uniform advection model for urban flood modeling. It is expected that urban flood information using improved QPF will help to reduce casualties and property damage caused by urban flooding through this research.

Performance of bias corrected MPEG rainfall estimate for rainfall-runoff simulation in the upper Blue Nile Basin, Ethiopia

NASA Astrophysics Data System (ADS)

Worqlul, Abeyou W.; Ayana, Essayas K.; Maathuis, Ben H. P.; MacAlister, Charlotte; Philpot, William D.; Osorio Leyton, Javier M.; Steenhuis, Tammo S.

2018-01-01

In many developing countries and remote areas of important ecosystems, good quality precipitation data are neither available nor readily accessible. Satellite observations and processing algorithms are being extensively used to produce satellite rainfall products (SREs). Nevertheless, these products are prone to systematic errors and need extensive validation before to be usable for streamflow simulations. In this study, we investigated and corrected the bias of Multi-Sensor Precipitation Estimate-Geostationary (MPEG) data. The corrected MPEG dataset was used as input to a semi-distributed hydrological model Hydrologiska Byråns Vattenbalansavdelning (HBV) for simulation of discharge of the Gilgel Abay and Gumara watersheds in the Upper Blue Nile basin, Ethiopia. The result indicated that the MPEG satellite rainfall captured 81% and 78% of the gauged rainfall variability with a consistent bias of underestimating the gauged rainfall by 60%. A linear bias correction applied significantly reduced the bias while maintaining the coefficient of correlation. The simulated flow using bias corrected MPEG SRE resulted in a simulated flow comparable to the gauge rainfall for both watersheds. The study indicated the potential of MPEG SRE in water budget studies after applying a linear bias correction.

Breeding decisions and output are correlated with both temperature and rainfall in an arid-region passerine, the sociable weaver

PubMed Central

Paquet, Matthieu; Spottiswoode, Claire N.; Covas, Rita

2017-01-01

Animal reproductive cycles are commonly triggered by environmental cues of favourable breeding conditions. In arid environments, rainfall may be the most conspicuous cue, but the effects on reproduction of the high inter- and intra-annual variation in temperature remain poorly understood, despite being relevant to the current context of global warming. Here, we conducted a multiyear examination of the relationships between a suite of measures of temperature and rainfall, and the onset and length of the breeding season, the probability of breeding and reproductive output in an arid-region passerine, the sociable weaver (Philetairus socius). As expected, reproductive output increased with rainfall, yet specific relationships were conditional on the timing of rainfall: clutch production was correlated with rainfall throughout the season, whereas fledgling production was correlated with early summer rainfall. Moreover, we reveal novel correlations between aspects of breeding and temperature, indicative of earlier laying dates after warmer springs, and longer breeding seasons during cooler summers. These results have implications for understanding population trends under current climate change scenarios and call for more studies on the role of temperature in reproduction beyond those conducted on temperate-region species. PMID:28989782

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.

Meteorological satellite product support and research for project GALE

NASA Technical Reports Server (NTRS)

Velden, Christopher S.; Smith, William L.; Achtor, Thomas H.; Menzel, W. Paul

1988-01-01

This participation in the Genesis of Atlantic Lows Experiment (GALE) focused on three main areas: (1) real-time support of the field phase, centered on a McIDAS workstation; (2) satellite data collection, archive, product generation, and dissemination; and (3) research into satellite rainfall estimation and data assimilation. Accomplishments include production of a videotape of animated GOES satellite imagery, production of an atlas of GOES satellite imagery, production of a set of 12-hour interval analyses; research into 4-D data assimilation, and production of a set of satellite-estimated rainfall maps.

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

NASA Astrophysics Data System (ADS)

Sehad, Mounir; Lazri, Mourad; Ameur, Soltane

2017-03-01

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

The Evolution of Remotely Sensed Precipitation Products for Hydrological Applications with a Focus on the Tropical Rainfall Measurement Mission (TRMM)

NASA Astrophysics Data System (ADS)

Tobin, K. J.; Bennett, M.

2012-12-01

This study examines the evolution of how remotely sensed precipitation products have impacted hydrologic modeling from six basins across the continental United States. Precipitation products include both ground-based (Multisensor Precipitation Estimator - MPE) and space-based products. Two space-based products are from the Tropical Rainfall Measurement Mission (TRMM) and include the real-time TRMM Multi-Satellite Precipitation Analysis (TMPA-RT) and TRMM 3B42 Research product. Precipitation products are compared between early (2004-2008) and late (2008-2010) periods. Additionally, version 6 and the new version 7 of these TRMM products are examined. Watersheds examined were moderately large (1000 to 1,000 square kilometers) and included the San Pedro (Arizona), Cimarron (Oklahoma); Alapaha (Georgia), mid-Nueces (Texas), San Casimiro (Texas), and the mid-Rio Grande basins, which is a bi-national basin that spans the Texas-Mexico border. Precipitation products are used to drive streamflow simulations using the Soil Water Assessment Tool (SWAT). The main results of this study concludes that MPE is a mature remote sensing product that generally supports superior hydrologic simulations based on standard performance metrics such as mass balance error, Nash-Sutcliffe efficiency coefficient, and coefficient of persistence. TRMM products support acceptable simulations and have improved in performance between early and late periods for TMPA-RT (both versions) and version 6 of TRMM 3B42 Research in five out of the six basins examined. This improvement is related to modification of TRMM in January 2009 with the addition of more satellite data and a climatologic bias correction, which greatly improves the real-time TMPA-RT product. Conversely, version 7 of the TRMM 3B42 Research has a positive bias compared to version 6, which is translated into poorer hydrological simulations of streamflow. Future research is urgently needed to determine if the issues observed in this study are indicative of a broader problem associated with the most recent version of TRMM.

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.

Remote rainfall sensing for landslide hazard analysis

USGS Publications Warehouse

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

2001-01-01

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

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

PubMed Central

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

2017-01-01

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

Observed Oceanic and Terrestrial Drivers of North African Climate

NASA Astrophysics Data System (ADS)

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

2015-12-01

Hydrologic variability can pose a serious threat to the poverty-stricken regions of North Africa. Yet, the current understanding of oceanic versus terrestrial drivers of North African droughts/pluvials is largely model-based, with vast disagreement among models. In order to identify the observed drivers of North African climate and develop a benchmark for model evaluations, the multivariate Generalized Equilibrium Feedback Assessment (GEFA) is applied to observations, remotely sensed data, and reanalysis products. The identified primary oceanic drivers of North African rainfall variability are the Atlantic, tropical Indian, and tropical Pacific Oceans and Mediterranean Sea. During the summer monsoon, positive tropical eastern Atlantic sea-surface temperature (SST) anomalies are associated with a southward shift of the Inter-Tropical Convergence Zone, enhanced ocean evaporation, and greater precipitable water across coastal West Africa, leading to increased West African monsoon (WAM) rainfall and decreased Sahel rainfall. During the short rains, positive SST anomalies in the western tropical Indian Ocean and negative anomalies in the eastern tropical Indian Ocean support greater easterly oceanic flow, evaporation over the western ocean, and moisture advection to East Africa, thereby enhancing rainfall. The sign, magnitude, and timing of observed vegetation forcing on rainfall vary across North Africa. The positive feedback of leaf area index (LAI) on rainfall is greatest during DJF for the Horn of Africa, while it peaks in autumn and is weakest during the summer monsoon for the Sahel. Across the WAM region, a positive LAI anomaly supports an earlier monsoon onset, increased rainfall during the pre-monsoon, and decreased rainfall during the wet season. Through unique mechanisms, positive LAI anomalies favor enhanced transpiration, precipitable water, and rainfall across the Sahel and Horn of Africa, and increased roughness, ascent, and rainfall across the WAM region. The current study represents the first attempt to separate the observed roles of oceanic and vegetation feedbacks across North Africa, and provides observational benchmark for model evaluation.

Pattern Analysis of El Nino and La Nina Phenomenon Based on Sea Surface Temperature (SST) and Rainfall Intensity using Oceanic Nino Index (ONI) in West Java Area

NASA Astrophysics Data System (ADS)

Prasetyo, Yudo; Nabilah, Farras

2017-12-01

Climate change occurs in 1998-2016 brings significant alteration in the earth surface. It is affects an extremely anomaly temperature such as El Nino and La Nina or mostly known as ENSO (El Nino Southern Oscillation). West Java is one of the regions in Indonesia that encounters the impact of this phenomenon. Climate change due to ENSO also affects food production and other commodities. In this research, processing data method is conducted using programming language to process SST data and rainfall data from 1998 to 2016. The data are sea surface temperature from NOAA satellite, SST Reynolds (Sea Surface Temperature) and daily rainfall temperature from TRMM satellite. Data examination is done using analysis of rainfall spatial pattern and sea surface temperature (SST) where is affected by El Nino and La Nina phenomenon. This research results distribution map of SST and rainfall for each season to find out the impacts of El Nino and La Nina around West Java. El Nino and La Nina in Java Sea are occurring every August to February. During El Nino, sea surface temperature is between 27°C - 28°C with average temperature on 27.71°C. Rainfall intensity is 1.0 mm/day - 2.0 mm/day and the average are 1.63 mm/day. During La Nina, sea surface temperature is between 29°C - 30°C with average temperature on 29.06°C. Rainfall intensity is 9.0 mm/day - 10 mm/day, and the average is 9.74 mm/day. The correlation between rainfall and SST is 0,413 which is expresses a fairly strong correlation between parameters. The conclusion is, during La Nina SST and rainfall increase. While during El Nino SST and rainfall decrease. Hopefully this research could be a guideline to plan disaster mitigation in West Java region that is related extreme climate change.

Intercomparison of Global Precipitation Products: The Third Precipitation Intercomparison Project (PIP-3)

NASA Technical Reports Server (NTRS)

Adler, Robert F.; Kidd, Christopher; Petty, Grant; Morrissey, Mark; Goodman, H. Michael; Einaudi, Franco (Technical Monitor)

2000-01-01

A set of global, monthly rainfall products has been intercompared to understand the quality and utility of the estimates. The products include 25 observational (satellite-based), four model and two climatological products. The results of the intercomparison indicate a very large range (factor of two or three) of values when all products are considered. The range of values is reduced considerably when the set of observational products is limited to those considered quasi-standard. The model products do significantly poorer in the tropics, but are competitive with satellite-based fields in mid-latitudes over land. Over ocean, products are compared to frequency of precipitation from ship observations. The evaluation of the observational products point to merged data products (including rain gauge information) as providing the overall best results.

Rainfall extremes from TRMM data and the Metastatistical Extreme Value Distribution

NASA Astrophysics Data System (ADS)

Zorzetto, Enrico; Marani, Marco

2017-04-01

A reliable quantification of the probability of weather extremes occurrence is essential for designing resilient water infrastructures and hazard mitigation measures. However, it is increasingly clear that the presence of inter-annual climatic fluctuations determines a substantial long-term variability in the frequency of occurrence of extreme events. This circumstance questions the foundation of the traditional extreme value theory, hinged on stationary Poisson processes or on asymptotic assumptions to derive the Generalized Extreme Value (GEV) distribution. We illustrate here, with application to daily rainfall, a new approach to extreme value analysis, the Metastatistical Extreme Value Distribution (MEVD). The MEVD relaxes the above assumptions and is based on the whole distribution of daily rainfall events, thus allowing optimal use of all available observations. Using a global dataset of rain gauge observations, we show that the MEVD significantly outperforms the Generalized Extreme Value distribution, particularly for long average recurrence intervals and when small samples are available. The latter property suggests MEVD to be particularly suited for applications to satellite rainfall estimates, which only cover two decades, thus making extreme value estimation extremely challenging. Here we apply MEVD to the TRMM TMPA 3B42 product, an 18-year dataset of remotely-sensed daily rainfall providing a quasi-global coverage. Our analyses yield a global scale mapping of daily rainfall extremes and of their distributional tail properties, bridging the existing large gaps in ground-based networks. Finally, we illustrate how our global-scale analysis can provide insight into how properties of local rainfall regimes affect tail estimation uncertainty when using the GEV or MEVD approach. We find a dependence of the estimation uncertainty, for both the GEV- and MEV-based approaches, on the average annual number and on the inter-annual variability of rainy days. In particular, estimation uncertainty decreases 1) as the mean annual number of wet days increases, and 2) as the variability in the number of rainy days, expressed by its coefficient of variation, decreases. We tentatively explain this behavior in terms of the assumptions underlying the two approaches.

Estimating root-zone soil moisture in the West Africa Sahel using remotely sensed rainfall and vegetation

NASA Astrophysics Data System (ADS)

McNally, Amy L.

Agricultural drought is characterized by shortages in precipitation, large differences between actual and potential evapotranspiration, and soil water deficits that impact crop growth and pasture productivity. Rainfall and other agrometeorological gauge networks in Sub-Saharan Africa are inadequate for drought early warning systems and hence, satellite-based estimates of rainfall and vegetation greenness provide the main sources of information. While a number of studies have described the empirical relationship between rainfall and vegetation greenness, these studies lack a process based approach that includes soil moisture storage. In Chapters I and II, I modeled soil moisture using satellite rainfall inputs and developed a new method for estimating soil moisture with NDVI calibrated to in situ and microwave soil moisture observations. By transforming both NDVI and rainfall into estimates of soil moisture I was able to easily compare these two datasets in a physically meaningful way. In Chapter II, I also show how the new NDVI derived soil moisture can be assimilated into a water balance model that calculates an index of crop water stress. Compared to the analogous rainfall derived estimates of soil moisture and crop stress the NDVI derived estimates were better correlated with millet yields. In Chapter III, I developed a metric for defining growing season drought events that negatively impact millet yields. This metric is based on the data and models used in the Chapters I and II. I then use this metric to evaluate the ability of a sophisticated land surface model to detect drought events. The analysis showed that this particular land surface model's soil moisture estimates do have the potential to benefit the food security and drought early warning communities. With a focus on soil moisture, this dissertation introduced new methods that utilized a variety of data and models for agricultural drought monitoring applications. These new methods facilitate a more quantitative, transparent `convergence of evidence' approach to identifying agricultural drought events that lead to food insecurity. Ideally, these new methods will contribute to better famine early warning and the timely delivery of food aid to reduce the human suffering caused by drought.

Developing an operational rangeland water requirement satisfaction index

USGS Publications Warehouse

Senay, Gabriel B.; Verdin, James P.; Rowland, James

2011-01-01

Developing an operational water requirement satisfaction index (WRSI) for rangeland monitoring is an important goal of the famine early warning systems network. An operational WRSI has been developed for crop monitoring, but until recently a comparable WRSI for rangeland was not successful because of the extremely poor performance of the index when based on published crop coefficients (K c) for rangelands. To improve the rangeland WRSI, we developed a simple calibration technique that adjusts the K c values for rangeland monitoring using long-term rainfall distribution and reference evapotranspiration data. The premise for adjusting the K c values is based on the assumption that a viable rangeland should exhibit above-average WRSI (values >80%) during a normal year. The normal year was represented by a median dekadal rainfall distribution (satellite rainfall estimate from 1996 to 2006). Similarly, a long-term average for potential evapotranspiration was used as input to the famine early warning systems network WRSI model in combination with soil-water-holding capacity data. A dekadal rangeland WRSI has been operational for east and west Africa since 2005. User feedback has been encouraging, especially with regard to the end-of-season WRSI anomaly products that compare the index's performance to ‘normal’ years. Currently, rangeland WRSI products are generated on a dekadal basis and posted for free distribution on the US Geological Survey early warning website at http://earlywarning.usgs.gov/adds/

Agricultural Early Warning Informing Humanitarian Response in East Africa for 2012

NASA Astrophysics Data System (ADS)

Husak, G. J.; Funk, C. C.

2012-12-01

Long rains during the March-April-May (MAM) 2011 growing season were a failure for much of the Greater Horn of Africa. These conditions resulted in severe food shortages, with the Famine Early Warning Systems Network (FEWS NET) estimating that 12.4 million people were in need of food assistance in Kenya, Somalia, Ethiopia and Djibouti. Heading into the 2012 season, La Niña conditions, an exceptionally strong western-to-central Pacific sea surface temperature (SST) gradient, and warm SSTs in the eastern Indian Ocean foretold further dryness, compounding the difficulties faced by the already vulnerable populations of this region. In an effort to assess the potential for greater food insecurity in the region, FEWS NET scientists attempted to quantify the likelihood of a dry event. This work used satellite rainfall estimates with a 13-year rainfall history. Weights were assigned to previous years based on the similarity of existing SST conditions to those of previous years in the rainfall record. Scenarios were created by randomly combining dekadal rainfall from the historical record, in accordance with the weights. This bootstrapping resulted in a suite of simulations which could be used to identify the likelihood of specific rainfall outcomes. Areal averages of each simulation were used in the analysis. Analysis of the Global Precipitation Climatology Centre (GPCC) rainfall record, a gridded rainfall product based on available station data, showed that the mean rainfall value for the time period of the satellite data for this region was only about 80% of the 30-year mean. The bootstrapped scenarios were corrected for this bias during the period of the satellite record. Results were expressed as percent of average rather than in absolute rainfall amounts, to account for biases in the satellite products as well as variability in spatial amounts. The results showed that during a normal year the interquartile range is typically 80-120% of normal. However, using the weighted scenarios based on February SSTs, the interquartile range shifted to 75-105% of normal. As the season progressed, March turned out to be exceptionally dry, with a lack of onset of rains for much of the region. This delayed start to the season allowed for the combination of satellite estimates for the start of March to be combined with scenarios to look ahead to end-of-season values. By the end of March, combining estimated 2012 rains with the scenarios built before the season resulted in the interquartile range for expected outcomes dropping to 60-85% of normal. This information was relayed to FEWS NET food security analysts and used in a special report, highlighting the potential for crisis in the region. In April, this forecasting effort, combined with FEWS NET's extensive monitoring activities, helped motivate allocation of an additional $50M in food aid from the U.S. government. This presentation examines the climate conditions associated with MAM drought in the eastern sector of the Greater Horn, reviews the techniques behind the 2012 forecasts, and analyzes the actual outcome for the region. Methods for improving the work to more accurately reflect the variability and future directions and applications will be discussed.

Environmental correlates of breeding in the Crested Caracara (Caracara cheriway)

USGS Publications Warehouse

Morrison, J.L.; Pias, Kyle E.; Cohen, J.B.; Catlin, D.H.

2009-01-01

We evaluated the influence of weather on reproduction of the Crested Caracara (Caracara cheriway) in an agricultural landscape in south-central Florida. We used a mixed logistic-regression modeling approach within an information-theoretic framework to examine the influence of total rainfall, rainfall frequency, and temperature on the number of breeding pairs, timing of breeding, nest success, and productivity of Crested Caracaras during 1994–2000. The best models indicated an influence of rainfall frequency and laying period on reproduction. More individuals nested and more pairs nested earlier during years with more frequent rainfall in late summer and early fall. Pairs that nested later in each breeding season had smaller clutches, lower nest success and productivity, and higher probability of nest failure. More frequent rainfall during early spring months that are usually characterized by water deficit (March–May), more frequent rainfall during the fall drawdown period (September–November), and a shorter winter dry period showed some association with higher probability of brood reduction and lower nest success. The proportion of nests that failed was higher in “wet” years, when total rainfall during the breeding season (September–April) was >10% above the 20-year average. Rainfall may influence reproduction in Crested Caracaras indirectly through food resources. As total rainfall increased during February–April, when most pairs are feeding nestlings or dependent fledglings, the proportion of drawdown-dependent species (those that become available as rainfall decreases and wetlands become isolated and shallow) in the diet of Crested Caracaras declined, which may indicate reduced availability of foraging habitat for this primarily terrestrial raptor.

Systematic Anomalies in Rainfall Intensity Estimates Over the Continental U.S.

NASA Technical Reports Server (NTRS)

Amitai, Eyal; Petersen, Walter Arthur; Llort, Xavier; Vasiloff, Steve

2010-01-01

Rainfall intensities during extreme events over the continental U.S. are compared for several advanced radar products. These products include: 1) TRMM spaceborne radar (PR) near surface estimates; 2) NOAA Next-Generation Quantitative Precipitation Estimation (QPE) very high-resolution (1 km) radar-only national mosaics (Q2); 3) very high-resolution instantaneous gauge adjusted radar national mosaics, which we have developed by applying gauge correction on the Q2 instantaneous radar-only products; and 4) several independent C-band dual-polarimetric radar-estimated rainfall samples collected with the ARMOR radar in northern Alabama. Though accumulated rainfall amounts are often similar, we find the satellite and the ground radar rain rate pdfs to be quite different. PR pdfs are shifted towards lower rain rates, implying a much larger stratiform/convective rain ratio than do ground radar products. The shift becomes more evident during strong continental convective storms and much less during tropical storms. Resolving the continental/maritime regime behavior and other large discrepancies between the products presents an important challenge. A challenge to improve our understanding of the source of the discrepancies, to determine the uncertainties of the estimates, and to improve remote-sensing estimates of precipitation in general.

Estimating methane emissions from landfills based on rainfall, ambient temperature, and waste composition: The CLEEN model.

PubMed

Karanjekar, Richa V; Bhatt, Arpita; Altouqui, Said; Jangikhatoonabad, Neda; Durai, Vennila; Sattler, Melanie L; Hossain, M D Sahadat; Chen, Victoria

2015-12-01

Accurately estimating landfill methane emissions is important for quantifying a landfill's greenhouse gas emissions and power generation potential. Current models, including LandGEM and IPCC, often greatly simplify treatment of factors like rainfall and ambient temperature, which can substantially impact gas production. The newly developed Capturing Landfill Emissions for Energy Needs (CLEEN) model aims to improve landfill methane generation estimates, but still require inputs that are fairly easy to obtain: waste composition, annual rainfall, and ambient temperature. To develop the model, methane generation was measured from 27 laboratory scale landfill reactors, with varying waste compositions (ranging from 0% to 100%); average rainfall rates of 2, 6, and 12 mm/day; and temperatures of 20, 30, and 37°C, according to a statistical experimental design. Refuse components considered were the major biodegradable wastes, food, paper, yard/wood, and textile, as well as inert inorganic waste. Based on the data collected, a multiple linear regression equation (R(2)=0.75) was developed to predict first-order methane generation rate constant values k as functions of waste composition, annual rainfall, and temperature. Because, laboratory methane generation rates exceed field rates, a second scale-up regression equation for k was developed using actual gas-recovery data from 11 landfills in high-income countries with conventional operation. The Capturing Landfill Emissions for Energy Needs (CLEEN) model was developed by incorporating both regression equations into the first-order decay based model for estimating methane generation rates from landfills. CLEEN model values were compared to actual field data from 6 US landfills, and to estimates from LandGEM and IPCC. For 4 of the 6 cases, CLEEN model estimates were the closest to actual. Copyright © 2015 Elsevier Ltd. All rights reserved.

Validation of the TRMM Multi Satellite Rainfall Product 3B42 and estimation of scavenging coefficients for (131)I and (137)Cs using TRMM 3B42 rainfall data.

PubMed

Shrivastava, R; Dash, S K; Hegde, M N; Pradeepkumar, K S; Sharma, D N

2014-12-01

The TRMM rainfall product 3B42 is compared with rain gauge observations for Kaiga, India on monthly and seasonal time scales. This comparison is carried out for the years 2004-2007 spanning four monsoon seasons. A good correlation is obtained between the two data sets however; magnitude wise, the cumulative precipitation of the satellite product on monthly and seasonal time scales is deficient by almost 33-40% as compared to the rain gauge data. The satellite product is also compared with APHRODITE's Monsoon Asia data set on the same time scales. This comparison indicates a much better agreement since both these data sets represent an average precipitation over the same area. The scavenging coefficients for (131)I and (137)Cs are estimated using TRMM 3B42, rain gauge and APHRODITE data. The values obtained using TRMM 3B42 rainfall data compare very well with those obtained using rain gauge and APHRODITE data. Copyright © 2014 Elsevier Ltd. All rights reserved.

Mammals of Australia's Tropical Savannas: A Conceptual Model of Assemblage Structure and Regulatory Factors in the Kimberley Region

PubMed Central

Radford, Ian J.; Dickman, Christopher R.; Start, Antony N.; Palmer, Carol; Carnes, Karin; Everitt, Corrin; Fairman, Richard; Graham, Gordon; Partridge, Thalie; Thomson, Allan

2014-01-01

We construct a state-and-transition model for mammals in tropical savannas in northern Australia to synthesize ecological knowledge and understand mammalian declines. We aimed to validate the existence of alternative mammal assemblage states similar to those in arid Australian grasslands, and to speculate on transition triggers. Based on the arid grassland model, we hypothesized that assemblages are partitioned across rainfall gradients and between substrates. We also predicted that assemblages typical of arid regions in boom periods would be prevalent in savannas with higher and more regular rainfall. Data from eight mammal surveys from the Kimberley region, Western Australia (1994 to 2011) were collated. Survey sites were partitioned across rainfall zones and habitats. Data allowed us to identify three assemblage states: State 0:- low numbers of mammals, State II:- dominated by omnivorous rodents and State III:- dominated by rodents and larger marsupials. Unlike arid grasslands, assemblage dominance by insectivorous dasyurids (State I) did not occur in savannas. Mammal assemblages were partitioned across rainfall zones and between substrates as predicted, but—unlike arid regions—were not related strongly to yearly rainfall. Mammal assemblage composition showed high regional stability, probably related to high annual rainfall and predictable wet season resource pulses. As a consequence, we speculate that perpetually booming assemblages in savannas allow top-down control of the ecosystem, with suppression of introduced cats by the dingo, the region's top predator. Under conditions of low or erratic productivity, imposed increasingly by intense fire regimes and introduced herbivore grazing, dingoes may not limit impacts of cats on native mammals. These interacting factors may explain contemporary declines of savanna mammals as well as historical declines in arid Australia. The cat-ecosystem productivity hypothesis raised here differs from the already-articulated cat-habitat structure hypothesis for mammal declines, and we suggest approaches for explicit testing of transition triggers for competing hypotheses. PMID:24670997

Mammals of Australia's tropical savannas: a conceptual model of assemblage structure and regulatory factors in the Kimberley region.

PubMed

Radford, Ian J; Dickman, Christopher R; Start, Antony N; Palmer, Carol; Carnes, Karin; Everitt, Corrin; Fairman, Richard; Graham, Gordon; Partridge, Thalie; Thomson, Allan

2014-01-01

We construct a state-and-transition model for mammals in tropical savannas in northern Australia to synthesize ecological knowledge and understand mammalian declines. We aimed to validate the existence of alternative mammal assemblage states similar to those in arid Australian grasslands, and to speculate on transition triggers. Based on the arid grassland model, we hypothesized that assemblages are partitioned across rainfall gradients and between substrates. We also predicted that assemblages typical of arid regions in boom periods would be prevalent in savannas with higher and more regular rainfall. Data from eight mammal surveys from the Kimberley region, Western Australia (1994 to 2011) were collated. Survey sites were partitioned across rainfall zones and habitats. Data allowed us to identify three assemblage states: State 0:--low numbers of mammals, State II:--dominated by omnivorous rodents and State III:--dominated by rodents and larger marsupials. Unlike arid grasslands, assemblage dominance by insectivorous dasyurids (State I) did not occur in savannas. Mammal assemblages were partitioned across rainfall zones and between substrates as predicted, but-unlike arid regions-were not related strongly to yearly rainfall. Mammal assemblage composition showed high regional stability, probably related to high annual rainfall and predictable wet season resource pulses. As a consequence, we speculate that perpetually booming assemblages in savannas allow top-down control of the ecosystem, with suppression of introduced cats by the dingo, the region's top predator. Under conditions of low or erratic productivity, imposed increasingly by intense fire regimes and introduced herbivore grazing, dingoes may not limit impacts of cats on native mammals. These interacting factors may explain contemporary declines of savanna mammals as well as historical declines in arid Australia. The cat-ecosystem productivity hypothesis raised here differs from the already-articulated cat-habitat structure hypothesis for mammal declines, and we suggest approaches for explicit testing of transition triggers for competing hypotheses.

Assessment of satellite rainfall products over the Andean plateau

NASA Astrophysics Data System (ADS)

Satgé, Frédéric; Bonnet, Marie-Paule; Gosset, Marielle; Molina, Jorge; Hernan Yuque Lima, Wilson; Pillco Zolá, Ramiro; Timouk, Franck; Garnier, Jérémie

2016-01-01

Nine satellite rainfall estimations (SREs) were evaluated for the first time over the South American Andean plateau watershed by comparison with rain gauge data acquired between 2005 and 2007. The comparisons were carried out at the annual, monthly and daily time steps. All SREs reproduce the salient pattern of the annual rain field, with a marked north-south gradient and a lighter east-west gradient. However, the intensity of the gradient differs among SREs: it is well marked in the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis 3B42 (TMPA-3B42), Precipitation Estimation from remotely Sensed Information using Artificial Neural Networks (PERSIANN) and Global Satellite Mapping of Precipitation (GSMaP) products, and it is smoothed out in the Climate prediction center MORPHing (CMORPH) products. Another interesting difference among products is the contrast in rainfall amounts between the water surfaces (Lake Titicaca) and the surrounding land. Some products (TMPA-3B42, PERSIANN and GSMaP) show a contradictory rainfall deficit over Lake Titicaca, which may be due to the emissivity contrast between the lake and the surrounding lands and warm rain cloud processes. An analysis differentiating coastal Lake Titicaca from inland pixels confirmed this trend. The raw or Real Time (RT) products have strong biases over the study region. These biases are strongly positive for PERSIANN (above 90%), moderately positive for TMPA-3B42 (28%), strongly negative for CMORPH (- 42%) and moderately negative for GSMaP (- 18%). The biases are associated with a deformation of the rain rate frequency distribution: GSMaP underestimates the proportion of rainfall events for all rain rates; CMORPH overestimates the proportion of rain rates below 2 mm day- 1; and the other products tend to overestimate the proportion of moderate to high rain rates. These biases are greatly reduced by the gauge adjustment in the TMPA-3B42, PERSIANN and CMORPH products, whereas a negative bias becomes positive for GSMaP. TMPA-3B42 Adjusted (Adj) version 7 demonstrates the best overall agreement with gauges in terms of correlation, rain rate distribution and bias. However, PERSIANN-Adj's bias in the southern part of the domain is very low.

Climate seasonality limits leaf carbon assimilation and wood productivity in tropical forests

DOE PAGES

Wagner, Fabien H.; Hérault, Bruno; Bonal, Damien; ...

2016-04-28

Here, the seasonal climate drivers of the carbon cycle in tropical forests remain poorly known, although these forests account for more carbon assimilation and storage than any other terrestrial ecosystem. Based on a unique combination of seasonal pan-tropical data sets from 89 experimental sites (68 include aboveground wood productivity measurements and 35 litter productivity measurements), their associated canopy photosynthetic capacity (enhanced vegetation index, EVI) and climate, we ask how carbon assimilation and aboveground allocation are related to climate seasonality in tropical forests and how they interact in the seasonal carbon cycle. We found that canopy photosynthetic capacity seasonality responds positivelymore » to precipitation when rainfall is < 2000 mm yr -1 (water-limited forests) and to radiation otherwise (light-limited forests). On the other hand, independent of climate limitations, wood productivity and litterfall are driven by seasonal variation in precipitation and evapotranspiration, respectively. Consequently, light-limited forests present an asynchronism between canopy photosynthetic capacity and wood productivity. First-order control by precipitation likely indicates a decrease in tropical forest productivity in a drier climate in water-limited forest, and in current light-limited forest with future rainfall < 2000 mm yr -1.« less

Climate seasonality limits leaf carbon assimilation and wood productivity in tropical forests

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

Wagner, Fabien H.; Hérault, Bruno; Bonal, Damien

Here, the seasonal climate drivers of the carbon cycle in tropical forests remain poorly known, although these forests account for more carbon assimilation and storage than any other terrestrial ecosystem. Based on a unique combination of seasonal pan-tropical data sets from 89 experimental sites (68 include aboveground wood productivity measurements and 35 litter productivity measurements), their associated canopy photosynthetic capacity (enhanced vegetation index, EVI) and climate, we ask how carbon assimilation and aboveground allocation are related to climate seasonality in tropical forests and how they interact in the seasonal carbon cycle. We found that canopy photosynthetic capacity seasonality responds positivelymore » to precipitation when rainfall is < 2000 mm yr -1 (water-limited forests) and to radiation otherwise (light-limited forests). On the other hand, independent of climate limitations, wood productivity and litterfall are driven by seasonal variation in precipitation and evapotranspiration, respectively. Consequently, light-limited forests present an asynchronism between canopy photosynthetic capacity and wood productivity. First-order control by precipitation likely indicates a decrease in tropical forest productivity in a drier climate in water-limited forest, and in current light-limited forest with future rainfall < 2000 mm yr -1.« less

Use of Satellite Remote Sensing of Cloud and Rainfall for Selected Operational Applications in the Fields of Applied Hydrology and Food Production.

NASA Astrophysics Data System (ADS)

Power, Clare

Available from UMI in association with The British Library. The material presented in this thesis takes the form of a series of discrete, but inter-related projects on subjects related to the use of satellite remote sensing techniques for selected applications in the fields of cloud, rainfall, vegetation and food production monitoring and assessment. Detailed literature reviews have been carried out on remote sensing techniques in these fields, in particular, for rainfall monitoring and the development of systems for food crop prediction from various rainfall, vegetation and crop monitoring algorithms. The second part of the thesis is devoted to a series of practical projects using five different and contrasting satellite rainfall monitoring techniques using visible and/or infrared imagery, three applied over the Sultanate of Oman and two over West Africa. The case studies applied over the Sultanate of Oman show a range of techniques from manual nephanalyses of Potential Rain Clouds and the derivation of a 20 year record of Tropical Cyclone tracks over the Arabian Sea, to the manual Bristol rainfall monitoring technique and its human-machine interactive successor BIAS, which are applicable to the analysis of short term extreme rainfall events. The remaining two techniques were developed simultaneously over West Africa. The first, namely, PERMIT (the Polar-orbiter Effective Rainfall Monitoring Technique), was developed by the Author, and the second, ADMIT (Agricultural Drought Monitoring Integrated Technique), by a colleague, Giles D'Souza. The development, testing on data from July and August 1985 and July 1986, and subsequent modification of the PERMIT technique is described. The 1986 Case Study results have been compared with the ADMIT results from the same data set, as part of a project funded by FAO to compare the performance of four Meteosat rainfall monitoring techniques (Snijders 1988). PERMIT was designed to be an economic, (in terms of satellite data and computer processing needs), automatic rainfall estimation technique suitable for use in environments where computer facilities are limited. Finally the PERMIT rainfall products have been compared with contemporaneous NOAA AVHRR Normalised Vegetation Index monthly composites. The relationships observed between these two satellite-derived products may contribute to the future development of a simple, low cost crop prediction scheme for developing countries. The main conclusion drawn from this research is that there is an urgent need for simple but effective rainfall and vegetation monitoring systems such as PERMIT, to be implemented operationally on low cost portable microcomputer systems which are readily installed in Developing Countries, where effective monitoring of such environmental elements can provide early warnings and reduce the impacts of drought inflicted famine disasters.

Latent Heating from TRMM Satellite Measurements

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo; Smith, E. A.; Adler, R.; Haddad, Z.; Hou, A.; Iguchi, T.; Kakar, R.; Krishnamurti, T.; Kummerow, C.; Lang, S.

2004-01-01

Rainfall production is the fundamental variable within the Earth's hydrological cycle because it is both the principal forcing term in surface water budgets and its energetics corollary, latent heating, is the principal source of atmospheric diabatic heating. Latent heat release itself is a consequence of phase changes between the vapor, liquid, and frozen states of water. The properties of the vertical distribution of latent heat release modulate large-scale meridional and zonal circulations within the tropics - as well as modifying the energetic efficiencies of midlatitude weather systems. This paper focuses on the retrieval of latent heat release from satellite measurements generated by the Tropical Rainfall Measuring Mission (TRMM) satellite observatory, which was launched in November 1997 as a joint American-Japanese space endeavor. Since then, TRMM measurements have been providing an accurate four-dimensional account of rainfall over the global tropics and sub-tropics, information which can be used to estimate the space-time structure of latent heating across the Earth's low latitudes. The paper examines how the observed TRMM distribution of rainfall has advanced an understanding of the global water and energy cycle and its consequent relationship to the atmospheric general circulation and climate via latent heat release. A set of algorithm methodologies that are being used to estimate latent heating based on rain rate retrievals from the TRMM observations are described. The characteristics of these algorithms and the latent heating products that can be generated from them are also described, along with validation analyses of the heating products themselves. Finally, the investigation provides an overview of how TRMM-derived latent heating information is currently being used in conjunction with global weather and climate models, concluding with remarks intended to stimulate further research on latent heating retrieval from satellites.

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.

Modern proposal of methodology for retrieval of characteristic synthetic rainfall hyetographs

NASA Astrophysics Data System (ADS)

Licznar, Paweł; Burszta-Adamiak, Ewa; Łomotowski, Janusz; Stańczyk, Justyna

2017-11-01

Modern engineering workshop of designing and modelling complex drainage systems is based on hydrodynamic modelling and has a probabilistic character. Its practical application requires a change regarding rainfall models accepted at the input. Previously used artificial rainfall models of simplified form, e.g. block precipitation or Euler's type II model rainfall are no longer sufficient. It is noticeable that urgent clarification is needed as regards the methodology of standardized rainfall hyetographs that would take into consideration the specifics of local storm rainfall temporal dynamics. The aim of the paper is to present a proposal for innovative methodology for determining standardized rainfall hyetographs, based on statistical processing of the collection of actual local precipitation characteristics. Proposed methodology is based on the classification of standardized rainfall hyetographs with the use of cluster analysis. Its application is presented on the example of selected rain gauges localized in Poland. Synthetic rainfall hyetographs achieved as a final result may be used for hydrodynamic modelling of sewerage systems, including probabilistic detection of necessary capacity of retention reservoirs.

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.

Regional maximum rainfall analysis using L-moments at the Titicaca Lake drainage, Peru

NASA Astrophysics Data System (ADS)

Fernández-Palomino, Carlos Antonio; Lavado-Casimiro, Waldo Sven

2017-08-01

The present study investigates the application of the index flood L-moments-based regional frequency analysis procedure (RFA-LM) to the annual maximum 24-h rainfall (AM) of 33 rainfall gauge stations (RGs) to estimate rainfall quantiles at the Titicaca Lake drainage (TL). The study region was chosen because it is characterised by common floods that affect agricultural production and infrastructure. First, detailed quality analyses and verification of the RFA-LM assumptions were conducted. For this purpose, different tests for outlier verification, homogeneity, stationarity, and serial independence were employed. Then, the application of RFA-LM procedure allowed us to consider the TL as a single, hydrologically homogeneous region, in terms of its maximum rainfall frequency. That is, this region can be modelled by a generalised normal (GNO) distribution, chosen according to the Z test for goodness-of-fit, L-moments (LM) ratio diagram, and an additional evaluation of the precision of the regional growth curve. Due to the low density of RG in the TL, it was important to produce maps of the AM design quantiles estimated using RFA-LM. Therefore, the ordinary Kriging interpolation (OK) technique was used. These maps will be a useful tool for determining the different AM quantiles at any point of interest for hydrologists in the region.

Modelling rainfall erosion resulting from climate change

NASA Astrophysics Data System (ADS)

Kinnell, Peter

2016-04-01

It is well known that soil erosion leads to agricultural productivity decline and contributes to water quality decline. The current widely used models for determining soil erosion for management purposes in agriculture focus on long term (~20 years) average annual soil loss and are not well suited to determining variations that occur over short timespans and as a result of climate change. Soil loss resulting from rainfall erosion is directly dependent on the product of runoff and sediment concentration both of which are likely to be influenced by climate change. This presentation demonstrates the capacity of models like the USLE, USLE-M and WEPP to predict variations in runoff and erosion associated with rainfall events eroding bare fallow plots in the USA with a view to modelling rainfall erosion in areas subject to climate change.

Flood and landslide warning based on rainfall thresholds and soil moisture indexes: the HEWS (Hydrohazards Early Warning System) for Sicily

NASA Astrophysics Data System (ADS)

Brigandì, Giuseppina; Tito Aronica, Giuseppe; Bonaccorso, Brunella; Gueli, Roberto; Basile, Giuseppe

2017-09-01

The main focus of the paper is to present a flood and landslide early warning system, named HEWS (Hydrohazards Early Warning System), specifically developed for the Civil Protection Department of Sicily, based on the combined use of rainfall thresholds, soil moisture modelling and quantitative precipitation forecast (QPF). The warning system is referred to 9 different Alert Zones in which Sicily has been divided into and based on a threshold system of three different increasing critical levels: ordinary, moderate and high. In this system, for early flood warning, a Soil Moisture Accounting (SMA) model provides daily soil moisture conditions, which allow to select a specific set of three rainfall thresholds, one for each critical level considered, to be used for issue the alert bulletin. Wetness indexes, representative of the soil moisture conditions of a catchment, are calculated using a simple, spatially-lumped rainfall-streamflow model, based on the SCS-CN method, and on the unit hydrograph approach, that require daily observed and/or predicted rainfall, and temperature data as input. For the calibration of this model daily continuous time series of rainfall, streamflow and air temperature data are used. An event based lumped rainfall-runoff model has been, instead, used for the derivation of the rainfall thresholds for each catchment in Sicily characterised by an area larger than 50 km2. In particular, a Kinematic Instantaneous Unit Hydrograph based lumped rainfall-runoff model with the SCS-CN routine for net rainfall was developed for this purpose. For rainfall-induced shallow landslide warning, empirical rainfall thresholds provided by Gariano et al. (2015) have been included in the system. They were derived on an empirical basis starting from a catalogue of 265 shallow landslides in Sicily in the period 2002-2012. Finally, Delft-FEWS operational forecasting platform has been applied to link input data, SMA model and rainfall threshold models to produce warning on a daily basis for the entire region.

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

NASA Astrophysics Data System (ADS)

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

2017-03-01

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

Wavelet Scale Analysis of Mesoscale Convective Systems for Detecting Deep Convection From Infrared Imagery

NASA Astrophysics Data System (ADS)

Klein, Cornelia; Belušić, Danijel; Taylor, Christopher M.

2018-03-01

Mesoscale convective systems (MCSs) are frequently associated with rainfall extremes and are expected to further intensify under global warming. However, despite the significant impact of such extreme events, the dominant processes favoring their occurrence are still under debate. Meteosat geostationary satellites provide unique long-term subhourly records of cloud top temperatures, allowing to track changes in MCS structures that could be linked to rainfall intensification. Focusing on West Africa, we show that Meteosat cloud top temperatures are a useful proxy for rainfall intensities, as derived from snapshots from the Tropical Rainfall Measuring Mission 2A25 product: MCSs larger than 15,000 km2 at a temperature threshold of -40°C are found to produce 91% of all extreme rainfall occurrences in the study region, with 80% of the storms producing extreme rain when their minimum temperature drops below -80°C. Furthermore, we present a new method based on 2-D continuous wavelet transform to explore the relationship between cloud top temperature and rainfall intensity for subcloud features at different length scales. The method shows great potential for separating convective and stratiform cloud parts when combining information on temperature and scale, improving the common approach of using a temperature threshold only. We find that below -80°C, every fifth pixel is associated with deep convection. This frequency is doubled when looking at subcloud features smaller than 35 km. Scale analysis of subcloud features can thus help to better exploit cloud top temperature data sets, which provide much more spatiotemporal detail of MCS characteristics than available rainfall data sets alone.

Evaluating Satellite-based Rainfall Estimates for Basin-scale Hydrologic Modeling

NASA Astrophysics Data System (ADS)

Yilmaz, K. K.; Hogue, T. S.; Hsu, K.; Gupta, H. V.; Mahani, S. E.; Sorooshian, S.

2003-12-01

The reliability of any hydrologic simulation and basin outflow prediction effort depends primarily on the rainfall estimates. The problem of estimating rainfall becomes more obvious in basins with scarce or no rain gauges. We present an evaluation of satellite-based rainfall estimates for basin-scale hydrologic modeling with particular interest in ungauged basins. The initial phase of this study focuses on comparison of mean areal rainfall estimates from ground-based rain gauge network, NEXRAD radar Stage-III, and satellite-based PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) and their influence on hydrologic model simulations over several basins in the U.S. Six-hourly accumulations of the above competing mean areal rainfall estimates are used as input to the Sacramento Soil Moisture Accounting Model. Preliminary experiments for the Leaf River Basin in Mississippi, for the period of March 2000 - June 2002, reveals that seasonality plays an important role in the comparison. There is an overestimation during the summer and underestimation during the winter in satellite-based rainfall with respect to the competing rainfall estimates. The consequence of this result on the hydrologic model is that simulated discharge underestimates the major observed peak discharges during early spring for the basin under study. Future research will entail developing correction procedures, which depend on different factors such as seasonality, geographic location and basin size, for satellite-based rainfall estimates over basins with dense rain gauge network and/or radar coverage. Extension of these correction procedures to satellite-based rainfall estimates over ungauged basins with similar characteristics has the potential for reducing the input uncertainty in ungauged basin modeling efforts.

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.

Climate Change and Food Security: The View from Space

NASA Technical Reports Server (NTRS)

Brown, Molly E.

2012-01-01

Global satellite data provides information on land use, rainfall, soil moisture, vegetation vigor and crop yields. Process-based products allows transformation of these data into information that can be used to assess impact of weather on commodity prices and local economic health. Trends and impact of climate change

Precipitation characteristics in tropical Africa using satellite and in situ observations

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Analysis of Rainfall Infiltration Law in Unsaturated Soil Slope

PubMed Central

Zhang, Gui-rong; Qian, Ya-jun; Wang, Zhang-chun; Zhao, Bo

2014-01-01

In the study of unsaturated soil slope stability under rainfall infiltration, it is worth continuing to explore how much rainfall infiltrates into the slope in a rain process, and the amount of rainfall infiltrating into slope is the important factor influencing the stability. Therefore, rainfall infiltration capacity is an important issue of unsaturated seepage analysis for slope. On the basis of previous studies, rainfall infiltration law of unsaturated soil slope is analyzed. Considering the characteristics of slope and rainfall, the key factors affecting rainfall infiltration of slope, including hydraulic properties, water storage capacity (θ s - θ r), soil types, rainfall intensities, and antecedent and subsequent infiltration rates on unsaturated soil slope, are discussed by using theory analysis and numerical simulation technology. Based on critical factors changing, this paper presents three calculation models of rainfall infiltrability for unsaturated slope, including (1) infiltration model considering rainfall intensity; (2) effective rainfall model considering antecedent rainfall; (3) infiltration model considering comprehensive factors. Based on the technology of system response, the relationship of rainfall and infiltration is described, and the prototype of regression model of rainfall infiltration is given, in order to determine the amount of rain penetration during a rain process. PMID:24672332

Analysis of rainfall infiltration law in unsaturated soil slope.

PubMed

Zhang, Gui-rong; Qian, Ya-jun; Wang, Zhang-chun; Zhao, Bo

2014-01-01

In the study of unsaturated soil slope stability under rainfall infiltration, it is worth continuing to explore how much rainfall infiltrates into the slope in a rain process, and the amount of rainfall infiltrating into slope is the important factor influencing the stability. Therefore, rainfall infiltration capacity is an important issue of unsaturated seepage analysis for slope. On the basis of previous studies, rainfall infiltration law of unsaturated soil slope is analyzed. Considering the characteristics of slope and rainfall, the key factors affecting rainfall infiltration of slope, including hydraulic properties, water storage capacity (θs - θr), soil types, rainfall intensities, and antecedent and subsequent infiltration rates on unsaturated soil slope, are discussed by using theory analysis and numerical simulation technology. Based on critical factors changing, this paper presents three calculation models of rainfall infiltrability for unsaturated slope, including (1) infiltration model considering rainfall intensity; (2) effective rainfall model considering antecedent rainfall; (3) infiltration model considering comprehensive factors. Based on the technology of system response, the relationship of rainfall and infiltration is described, and the prototype of regression model of rainfall infiltration is given, in order to determine the amount of rain penetration during a rain process.

MARG - A Low Cost Solid State Microwave Areal Precipitation Measurement System

NASA Astrophysics Data System (ADS)

Paulitsch, Helmut; Dombai, Ferenc; Cremonini, Roberto; Bechini, Renzo

2014-05-01

Water is an essential resource for us so the measurements of its movement throughout the whole cycle is very important. The rainfall is discontinuous in space and in time having large natural variability unlike many other meteorological parameters. The widely used method for getting relatively accurate precipitation data over land is the combination of radar rainfall estimations and rain gauge data. The typically used radar data is coming from long-range weather radars operating in C or S band, or from mini radars operating in X band which is attenuating heavily in strong precipitation. Using such radar data we are facing several constraints: operating costs and limitations of long range radars, X band radars can be blocked totally in heavy thunderstorms even in short range, dual polarization solutions are expensive, etc. Recognizing that an important gap exists in instrumental precipitation measurements over land a consortium has been organized and a project has been established to develop a new measurement device, the so called Microwave Areal Rain Gauge (MARG). MARG is based on FMCW radar principle using solid state transmitter and digital signal processing and operating in C band. The MARG project aims to provide an innovative, real-time, low-cost, user friendly and accurate sensor technology to monitor and to measure continuously the rainfall intensity distribution over an area around some thousand square km. The MARG project proposal has been granted by the EU in FP7-SME-2012 funding scheme. The developed instrument is able to monitor in real-time intensity and spatial distribution of rainfall in rural and urban environments and can be operated by commercial weather data and value-added forecast product suppliers. To achieve sufficient isolation between the transmitter and receiver modules, and to avoid using complex and expensive microwave components, two parabolic antennae are used to transmit and receive the FMCW signal. The radar frontend operates in the C-band at 5.6 GHz with a maximal output power of 20 W continuous and a rainfall detection range of up to 30 km. Doppler processing is included in the signal processing for the purpose of clutter elimination. The reflectivity - rainfall conversion is performed with adjustable parameters as a function of rainfall type derived from morphological parameters of reflectivity fields and disdrometer measurements. Several algorithms, including mean bias correction, range correction and kriging interpolation with existing rain gauge networks to calibrate radar rainfall estimations are also foreseen. The MARG sensor will provide reflectivity, Doppler and precipitation data, but all measurements are organized and stored on the user centre's web server. The database contains precipitation data, measurement identification, and all available auxiliary meteorological data (e.g. temperature and air pressure). Precipitation data are further processed and combined with geographic background information through a GIS system. Finally the processed products, e.g. rainfall accumulation maps, are provided to the users by the GIS-based web service in the MARG user-centre module.

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

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

Global change impacts on wheat production along an environmental gradient in south Australia.

PubMed

Reyenga, P J; Howden, S M; Meinke, H; Hall, W B

2001-09-01

Crop production is likely to change in the future as a result of global changes in CO2 levels in the atmosphere and climate. APSIM, a cropping system model, was used to investigate the potential impact of these changes on the distribution of cropping along an environmental transect in south Australia. The effects of several global change scenarios were studied, including: (1) historical climate and CO2 levels, (2) historic climate with elevated CO2 (700 ppm), (3) warmer climate (+2.4 degrees C) +700 ppm CO2, (4) drier climate (-15% summer, -20% winter rainfall) +2.4 degrees C +700 ppm CO2, (5) wetter climate (+10% summer rainfall) +2.4 degrees C +700 ppm CO2 and (6) most likely climate changes (+1.8 degrees C, -8% annual rainfall) +700 ppm CO2. Based on an analysis of the current cropping boundary, a criterion of 1 t/ha was used to assess potential changes in the boundary under global change. Under most scenarios, the cropping boundary moved northwards with a further 240,000 ha potentially being available for cropping. The exception was the reduced rainfall scenario (4), which resulted in a small retreat of cropping from its current extent. However, the impact of this scenario may only be small (in the order of 10,000-20,000 ha reduction in cropping area). Increases in CO2 levels over the current climate record have resulted in small but significant increases in simulated yields. Model limitations are discussed.

Diagnosis of vegetation recovery within herbaceous sub-systems in the West African Sahel Region

NASA Astrophysics Data System (ADS)

Anchang, J.; Hanan, N. P.; Prihodko, L.; Sathyachandran, S. K.; Ji, W.; Ross, C. W.

2017-12-01

The West African Sahel (WAS) region is an extensive water limited environment that features a delicate balance of herbaceous and woody vegetation sub systems. These play an important role in the cycling of carbon while also supporting the dominant agro-pastoral human activities in the region. Quantifying the temporal trends in vegetation with regard to these two systems is therefore very important in assessing resource sustainability and food security. In water limited areas, rainfall is a primary driver of vegetation productivity and past watershed scale studies in the WAS region have shown that increase in the slope of the productivity-to-rainfall relationship is indicative of increasing cover and density of herbaceous plants. Given the importance of grazing resources to the region, we perform a wall-to-wall pixel based analysis of changing short-term vegetation sensitivity to changing annual rainfall (hereafter referred to as dS) to examine temporal trends in herbaceous vegetation health. Results indicate that 43% of the Sahelian region has experienced changes (P < 0.05) in herbaceous vegetation (dS). Areas with significant increases in dS are well distributed across the region, but with major concentrations in North-Central Senegal, South Western and Central Mali and South Western Niger. Positive dS is indicative of herbaceous vegetation recovery, in response to changing management and rainfall conditions that promote long-term herbaceous community recovery following degradation during the 1970-1980s droughts.

Meteorology Assessment of Historic Rainfall for Los Alamos During September 2013

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

Bruggeman, David Alan; Dewart, Jean Marie

2016-02-12

DOE Order 420.1, Facility Safety, requires that site natural phenomena hazards be evaluated every 10 years to support the design of nuclear facilities. The evaluation requires calculating return period rainfall to determine roof loading requirements and flooding potential based on our on-site rainfall measurements. The return period rainfall calculations are done based on statistical techniques and not site-specific meteorology. This and future studies analyze the meteorological factors that produce the significant rainfall events. These studies provide the meteorology context of the return period rainfall events.

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

NASA Astrophysics Data System (ADS)

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

2017-02-01

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

Urban rainfall monitoring with crowdsourced automatic weather stations in Amsterdam

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

TRMM Fire Algorithm, Product and Applications

NASA Technical Reports Server (NTRS)

Ji, Yi-Min; Stocker, Erich

2003-01-01

Land fires are frequent menaces to human lives and property. They also change the state of the vegetation and contribute to the climate forcing by releasing large amount of aerosols and greenhouse gases into the atmosphere. This paper summarizes methodologies of detecting global land fires from the Tropical Rainfall Measuring Mission (TRMM) Visible Infrared Scanner FIRS) measurements. The TRMM Science Data and Information System (TSDIS) fire products include global images of daily hot spots and monthly fire counts at 0.5 deg. x 0.5 deg. resolution, as well as text fiies that details necessary information of all fire pixels. The information includes date, orbit number, pixel number, local time, solar zenith angle, latitude, longitude, reflectance of visible/near infrared channels, brightness temperatures of infrared channels, as well as background brightness temperatures of infrared channels. These products have been archived since January 1998. The TSDIS fire products are compared with the coincidental European Commission (EC) Joint Research Center (JRC) 1 km AVHRR fire products. Analyses of the TSDIS monthly fire products during the period from 1998 to 2003 manifested seasonal cycles of biomass fires over Southeast Asia, Africa, North America and South America. The data also showed interannual variations associated with the 98/99 ENS0 cycle in Central America and the Indonesian region. In order to understand the variability of global land fires and their effects on the distribution of atmospheric aerosols, statistical methods were applied to the TSDIS fire products as well as to the Total Ozone Mapping Spectrometer (TOMS) aerosol index products for a period of five years from January 1998 to December 2002. The variability of global atmospheric aerosol is consistent with the fire variations over these regions during this period. The correlation between fire count and TOMS aerosol index is about 0.55 for fire pixels in Southeast Asia, Indonesia, and Africa. Parallel statistical analyses such as Empirical Orthogonal Function (EOF) analysis and Singular Spectrum Analysis (SSA) methods were applied to pentad TRMM fire data and TOMS aerosol data. The EOF analyses showed contrast between North and South hemispheres and also inter- continental transitions in Africa and America. EOF and SSA analyses also identified 25-60 day intra-seasonal oscillations that were superimposed on the annual cycles of both fire and aerosol data. The intra-seasonal variability of fires showed similarity of tropical rainfall oscillation modes. The TRMM fire products were also compared to the coincident TRMh4 rainfall and other rainfall products to investigate the interaction between rainfall and fire. The results indicate that the annual, interannual and intraseasonal variability of fire are dominated by global rainfall variations. However, the feedback of fire to the rainfall occurrence at regional scale for certain regions is also evident.

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

NASA Astrophysics Data System (ADS)

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

2017-07-01

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

Validation and correction of rainfall data from the WegenerNet high density network in southeast Austria

NASA Astrophysics Data System (ADS)

O, Sungmin; Foelsche, U.; Kirchengast, G.; Fuchsberger, J.

2018-01-01

Eight years of daily rainfall data from WegenerNet were analyzed by comparison with data from Austrian national weather stations. WegenerNet includes 153 ground level weather stations in an area of about 15 km × 20 km in the Feldbach region in southeast Austria. Rainfall has been measured by tipping bucket gauges at 150 stations of the network since the beginning of 2007. Since rain gauge measurements are considered close to true rainfall, there are increasing needs for WegenerNet data for the validation of rainfall data products such as remote sensing based estimates or model outputs. Serving these needs, this paper aims at providing a clearer interpretation on WegenerNet rainfall data for users in hydro-meteorological communities. Five clusters - a cluster consists of one national weather station and its four closest WegenerNet stations - allowed us close comparison of datasets between the stations. Linear regression analysis and error estimation with statistical indices were conducted to quantitatively evaluate the WegenerNet daily rainfall data. It was found that rainfall data between the stations show good linear relationships with an average correlation coefficient (r) of 0.97 , while WegenerNet sensors tend to underestimate rainfall according to the regression slope (0.87). For the five clusters investigated, the bias and relative bias were - 0.97 mm d-1 and - 11.5 % on average (except data from new sensors). The average of bias and relative bias, however, could be reduced by about 80 % through a simple linear regression-slope correction, with the assumption that the underestimation in WegenerNet data was caused by systematic errors. The results from the study have been employed to improve WegenerNet data for user applications so that a new version of the data (v5) is now available at the WegenerNet data portal (www.wegenernet.org).

Statistical approaches for the definition of landslide rainfall thresholds and their uncertainty using rain gauge and satellite data

NASA Astrophysics Data System (ADS)

Rossi, M.; Luciani, S.; Valigi, D.; Kirschbaum, D.; Brunetti, M. T.; Peruccacci, S.; Guzzetti, F.

2017-05-01

Models for forecasting rainfall-induced landslides are mostly based on the identification of empirical rainfall thresholds obtained exploiting rain gauge data. Despite their increased availability, satellite rainfall estimates are scarcely used for this purpose. Satellite data should be useful in ungauged and remote areas, or should provide a significant spatial and temporal reference in gauged areas. In this paper, the analysis of the reliability of rainfall thresholds based on rainfall remote sensed and rain gauge data for the prediction of landslide occurrence is carried out. To date, the estimation of the uncertainty associated with the empirical rainfall thresholds is mostly based on a bootstrap resampling of the rainfall duration and the cumulated event rainfall pairs (D,E) characterizing rainfall events responsible for past failures. This estimation does not consider the measurement uncertainty associated with D and E. In the paper, we propose (i) a new automated procedure to reconstruct ED conditions responsible for the landslide triggering and their uncertainties, and (ii) three new methods to identify rainfall threshold for the possible landslide occurrence, exploiting rain gauge and satellite data. In particular, the proposed methods are based on Least Square (LS), Quantile Regression (QR) and Nonlinear Least Square (NLS) statistical approaches. We applied the new procedure and methods to define empirical rainfall thresholds and their associated uncertainties in the Umbria region (central Italy) using both rain-gauge measurements and satellite estimates. We finally validated the thresholds and tested the effectiveness of the different threshold definition methods with independent landslide information. The NLS method among the others performed better in calculating thresholds in the full range of rainfall durations. We found that the thresholds obtained from satellite data are lower than those obtained from rain gauge measurements. This is in agreement with the literature, where satellite rainfall data underestimate the "ground" rainfall registered by rain gauges.

Statistical Approaches for the Definition of Landslide Rainfall Thresholds and their Uncertainty Using Rain Gauge and Satellite Data

NASA Technical Reports Server (NTRS)

Rossi, M.; Luciani, S.; Valigi, D.; Kirschbaum, D.; Brunetti, M. T.; Peruccacci, S.; Guzzetti, F.

2017-01-01

Models for forecasting rainfall-induced landslides are mostly based on the identification of empirical rainfall thresholds obtained exploiting rain gauge data. Despite their increased availability, satellite rainfall estimates are scarcely used for this purpose. Satellite data should be useful in ungauged and remote areas, or should provide a significant spatial and temporal reference in gauged areas. In this paper, the analysis of the reliability of rainfall thresholds based on rainfall remote sensed and rain gauge data for the prediction of landslide occurrence is carried out. To date, the estimation of the uncertainty associated with the empirical rainfall thresholds is mostly based on a bootstrap resampling of the rainfall duration and the cumulated event rainfall pairs (D,E) characterizing rainfall events responsible for past failures. This estimation does not consider the measurement uncertainty associated with D and E. In the paper, we propose (i) a new automated procedure to reconstruct ED conditions responsible for the landslide triggering and their uncertainties, and (ii) three new methods to identify rainfall threshold for the possible landslide occurrence, exploiting rain gauge and satellite data. In particular, the proposed methods are based on Least Square (LS), Quantile Regression (QR) and Nonlinear Least Square (NLS) statistical approaches. We applied the new procedure and methods to define empirical rainfall thresholds and their associated uncertainties in the Umbria region (central Italy) using both rain-gauge measurements and satellite estimates. We finally validated the thresholds and tested the effectiveness of the different threshold definition methods with independent landslide information. The NLS method among the others performed better in calculating thresholds in the full range of rainfall durations. We found that the thresholds obtained from satellite data are lower than those obtained from rain gauge measurements. This is in agreement with the literature, where satellite rainfall data underestimate the 'ground' rainfall registered by rain gauges.

Assessment of TRMM Products and Their Influence on Hydrologic Models within the Middle East and North Africa (MENA) Region Using the Soil and Water Assessment Tool (SWAT)

NASA Astrophysics Data System (ADS)

Milewski, A.; El Kadiri, R.; Durham, M. C.

2013-12-01

Satellite remote sensing datasets have been increasingly employed as an ancillary source of essential hydrologic measurements used for the modeling of hydrologic fluxes. Precipitation is one of the most important meteorological forcing parameter in hydrological investigations and land surface modeling, yet it is largely unknown or misused in water budgets and hydrologic models. The Tropical Rainfall Measurement Mission (TRMM) satellite products are widely being used by the scientific community due to the general spatial and temporal paucity of precipitation data in many parts of world and particularly in the Middle East and North Africa (MENA) region. This research utilized a two-fold approach towards understanding the accuracy of satellite-based rainfall and its application in hydrologic models First, we evaluated the uncertainty, accuracy, and precision of various rainfall satellite products (i.e. TRMM 3B42 V6, TRMM 3B42 V7, TRMM 3B42 V7a and TRMM 3B42 RT) in comparison to in situ gauge data from more than 150 rain gauges in Morocco and across the MENA region. Our analyses extend over many parts of the MENA region in order to assess the effect that different climatic regimes and topographic characteristics have on each TRMM product. Secondly, we analyzed and compared the hydrologic fluxes produced from different modeling inputs for several watersheds within the MENA region. SWAT (Soil and Water Assessment Tool) hydrologic models have been developed for the Oum Er Rbia (Morocco), Asyuti (Egypt), and the Sakarya (Turkey) watersheds. SWAT models produced for each watershed include, one model for each of the four satellite TRMM product (STBM-V6, STBM-V7, STBM-V7a, and STBM-RT) and one model for rain gauge based model (RGBM). Findings indicate the best correlation between field-based and satellite-based rainfall measurements is the TRMM V7a (Pearson coefficient: 0.875) product, followed by TRMM V7 (Pearson coefficient: 0.84), then TRMM V6 (Pearson coefficient: 0.805), and finally TRMM RT (Pearson coefficient: 0.715). However, analyses demonstrate that V7a still has an overestimation bias in arid environments (trend line slope: 1.133), and an underestimation bias in both semi-arid environments (trend line slope: 0.5982) and sub humid environments (trend line slope: 0.6800). Results suggest that all versions are consistently better correlated with field gauges in the sub humid environments (V6 Pc: 0.755, V7 Pc: 0.790, V7a Pc: 0.816 and RT Pc: 0728) than the semi-arid environments (V6 Pc: 0.494, V7 Pc: 0.549, V7a Pc: 0.548 and RT Pc: 0.305) and the arid environments (V6 Pc: 0.546, V7 Pc: 0.681, V7a Pc: 0.697 and RT Pc: 0.562). Initial model values for the Oum Er Rbia watershed (area: 48,000 km2, annual precipitation 550 mm/yr.) indicate the satellite TRMM-based models (STBM) underestimated hydrologic variables (precipitation: 19%; runoff: 25%; and recharge 35%) compared to the rain gauge-based model (RGBM). This study demonstrates the accuracy of TRMM precipitation products and shows the opportunities and challenges of their use in data scarce regions of the world.

Herbicides and herbicide degradation products in upper midwest agricultural streams during august base-flow conditions

USGS Publications Warehouse

Kalkhoff, S.J.; Lee, K.E.; Porter, S.D.; Terrio, P.J.; Thurman, E.M.

2003-01-01

Herbicide concentrations in streams of the U.S. Midwest have been shown to decrease through the growing season due to a variety of chemical and physical factors. The occurrence of herbicide degradation products at the end of the growing season is not well known. This study was conducted to document the occurrence of commonly used herbicides and their degradation products in Illinois, Iowa, and Minnesota streams during base-flow conditions in August 1997. Atrazine, the most frequently detected herbicide (94%), was present at relatively low concentrations (median 0.17 μg L−1). Metolachlor was detected in 59% and cyanazine in 37% of the samples. Seven of nine compounds detected in more than 50% of the samples were degradation products. The total concentration of the degradation products (median of 4.4 μg L−1) was significantly greater than the total concentration of parent compounds (median of 0.26 μg L−1). Atrazine compounds were present less frequently and in significantly smaller concentrations in streams draining watersheds with soils developed on less permeable tills than in watersheds with soils developed on more permeable loess. The detection and concentration of triazine compounds was negatively correlated with antecedent rainfall (April–July). In contrast, acetanalide compounds were positively correlated with antecedant rainfall in late spring and early summer that may transport the acetanalide degradates into ground water and subsequently into nearby streams. The distribution of atrazine degradation products suggests regional differences in atrazine degradation processes.

Performance of CMORPH, TMPA, and PERSIANN rainfall datasets over plain, mountainous, and glacial regions of Pakistan

NASA Astrophysics Data System (ADS)

Hussain, Yawar; Satgé, Frédéric; Hussain, Muhammad Babar; Martinez-Carvajal, Hernan; Bonnet, Marie-Paule; Cárdenas-Soto, Martin; Roig, Henrique Llacer; Akhter, Gulraiz

2018-02-01

The present study aims at the assessment of six satellite rainfall estimates (SREs) in Pakistan. For each assessed products, both real-time (RT) and post adjusted (Adj) versions are considered to highlight their potential benefits in the rainfall estimation at annual, monthly, and daily temporal scales. Three geomorphological climatic zones, i.e., plain, mountainous, and glacial are taken under considerations for the determination of relative potentials of these SREs over Pakistan at global and regional scales. All SREs, in general, have well captured the annual north-south rainfall decreasing patterns and rainfall amounts over the typical arid regions of the country. Regarding the zonal approach, the performance of all SREs has remained good over mountainous region comparative to arid regions. This poor performance in accurate rainfall estimation of all the six SREs over arid regions has made their use questionable in these regions. Over glacier region, all SREs have highly overestimated the rainfall. One possible cause of this overestimation may be due to the low surface temperature and radiation absorption over snow and ice cover, resulting in their misidentification with rainy clouds as daily false alarm ratio has increased from mountainous to glacial regions. Among RT products, CMORPH-RT is the most biased product. The Bias was almost removed on CMORPH-Adj thanks to the gauge adjustment. On a general way, all Adj versions outperformed their respective RT versions at all considered temporal scales and have confirmed the positive effects of gauge adjustment. CMORPH-Adj and TMPA-Adj have shown the best agreement with in situ data in terms of Bias, RMSE, and CC over the entire study area.

Bias-adjusted satellite-based rainfall estimates for predicting floods: Narayani Basin

USGS Publications Warehouse

Shrestha, M.S.; Artan, G.A.; Bajracharya, S.R.; Gautam, D.K.; Tokar, S.A.

2011-01-01

In Nepal, as the spatial distribution of rain gauges is not sufficient to provide detailed perspective on the highly varied spatial nature of rainfall, satellite-based rainfall estimates provides the opportunity for timely estimation. This paper presents the flood prediction of Narayani Basin at the Devghat hydrometric station (32000km2) using bias-adjusted satellite rainfall estimates and the Geospatial Stream Flow Model (GeoSFM), a spatially distributed, physically based hydrologic model. The GeoSFM with gridded gauge observed rainfall inputs using kriging interpolation from 2003 was used for calibration and 2004 for validation to simulate stream flow with both having a Nash Sutcliff Efficiency of above 0.7. With the National Oceanic and Atmospheric Administration Climate Prediction Centre's rainfall estimates (CPC-RFE2.0), using the same calibrated parameters, for 2003 the model performance deteriorated but improved after recalibration with CPC-RFE2.0 indicating the need to recalibrate the model with satellite-based rainfall estimates. Adjusting the CPC-RFE2.0 by a seasonal, monthly and 7-day moving average ratio, improvement in model performance was achieved. Furthermore, a new gauge-satellite merged rainfall estimates obtained from ingestion of local rain gauge data resulted in significant improvement in flood predictability. The results indicate the applicability of satellite-based rainfall estimates in flood prediction with appropriate bias correction. ?? 2011 The Authors. Journal of Flood Risk Management ?? 2011 The Chartered Institution of Water and Environmental Management.

Bias-adjusted satellite-based rainfall estimates for predicting floods: Narayani Basin

USGS Publications Warehouse

Artan, Guleid A.; Tokar, S.A.; Gautam, D.K.; Bajracharya, S.R.; Shrestha, M.S.

2011-01-01

In Nepal, as the spatial distribution of rain gauges is not sufficient to provide detailed perspective on the highly varied spatial nature of rainfall, satellite-based rainfall estimates provides the opportunity for timely estimation. This paper presents the flood prediction of Narayani Basin at the Devghat hydrometric station (32 000 km2) using bias-adjusted satellite rainfall estimates and the Geospatial Stream Flow Model (GeoSFM), a spatially distributed, physically based hydrologic model. The GeoSFM with gridded gauge observed rainfall inputs using kriging interpolation from 2003 was used for calibration and 2004 for validation to simulate stream flow with both having a Nash Sutcliff Efficiency of above 0.7. With the National Oceanic and Atmospheric Administration Climate Prediction Centre's rainfall estimates (CPC_RFE2.0), using the same calibrated parameters, for 2003 the model performance deteriorated but improved after recalibration with CPC_RFE2.0 indicating the need to recalibrate the model with satellite-based rainfall estimates. Adjusting the CPC_RFE2.0 by a seasonal, monthly and 7-day moving average ratio, improvement in model performance was achieved. Furthermore, a new gauge-satellite merged rainfall estimates obtained from ingestion of local rain gauge data resulted in significant improvement in flood predictability. The results indicate the applicability of satellite-based rainfall estimates in flood prediction with appropriate bias correction.

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

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

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.

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...

Development of a gridded meteorological dataset over Java island, Indonesia 1985-2014.

PubMed

Yanto; Livneh, Ben; Rajagopalan, Balaji

2017-05-23

We describe a gridded daily meteorology dataset consisting of precipitation, minimum and maximum temperature over Java Island, Indonesia at 0.125°×0.125° (~14 km) resolution spanning 30 years from 1985-2014. Importantly, this data set represents a marked improvement from existing gridded data sets over Java with higher spatial resolution, derived exclusively from ground-based observations unlike existing satellite or reanalysis-based products. Gap-infilling and gridding were performed via the Inverse Distance Weighting (IDW) interpolation method (radius, r, of 25 km and power of influence, α, of 3 as optimal parameters) restricted to only those stations including at least 3,650 days (~10 years) of valid data. We employed MSWEP and CHIRPS rainfall products in the cross-validation. It shows that the gridded rainfall presented here produces the most reasonable performance. Visual inspection reveals an increasing performance of gridded precipitation from grid, watershed to island scale. The data set, stored in a network common data form (NetCDF), is intended to support watershed-scale and island-scale studies of short-term and long-term climate, hydrology and ecology.

Mapping drought risk in Indonesia related to El-Niño hazard

NASA Astrophysics Data System (ADS)

Supari, Muharsyah, R.; Sopaheluwakan, A.

2016-05-01

This work is aimed to identify areas in the country that are at high propensity to the impact of global climate phenomenon i.e. El-Nino. An affected area is recognized when rainfall decreases up to below normal condition which frequently leads drought event. For this purpose, two packages of gridded rainfall data at monthly basis with 0.5 spatial resolutions for 1950 2010 period were used, e.g. GPCC Full Data Reanalysis V.6 (product of Global Precipitation Climatology Centre) and CRU TS3.22 (product of Climatic Research Unit). El-Nino years were labelled based on Oceanic Nino Index, ONI. We applied frequency analysis to quantify the chance of El-Nino impact. GPCC data was found more accurate in representing rainfall observation than CRU data based on correlation test against station data. The results indicate the strong spatial and temporal dependencies of El-Nino impact. During peak of rainy and first transitional season (DJF and MAM), the probability to be affected by El-Nino is mostly less than 20% over whole country In contrast, July-October are months where areas with high and very high risk were observed over many regions such as Southern part of Sumatera, Java, Kalimantan, Sulawesi, Maluku and Papua. Further investigation at province level found that the timing of El-Nino impact starts in June. These results are potential to improve national capacity in risk management related to weather-climate hazards.

Rainfall erosivity estimation based on rainfall data collected over a range of temporal resolutions

USDA-ARS?s Scientific Manuscript database

Rainfall erosivity is the power of rainfall to cause soil erosion by water. The rainfall erosivity index for a rainfall event, EI30, is calculated from the total kinetic energy and maximum 30 minute intensity of individual events. However, these data are often unavailable in many areas of the worl...

Extreme flood event analysis in Indonesia based on rainfall intensity and recharge capacity

NASA Astrophysics Data System (ADS)

Narulita, Ida; Ningrum, Widya

2018-02-01

Indonesia is very vulnerable to flood disaster because it has high rainfall events throughout the year. Flood is categorized as the most important hazard disaster because it is causing social, economic and human losses. The purpose of this study is to analyze extreme flood event based on satellite rainfall dataset to understand the rainfall characteristic (rainfall intensity, rainfall pattern, etc.) that happened before flood disaster in the area for monsoonal, equatorial and local rainfall types. Recharge capacity will be analyzed using land cover and soil distribution. The data used in this study are CHIRPS rainfall satellite data on 0.05 ° spatial resolution and daily temporal resolution, and GSMap satellite rainfall dataset operated by JAXA on 1-hour temporal resolution and 0.1 ° spatial resolution, land use and soil distribution map for recharge capacity analysis. The rainfall characteristic before flooding, and recharge capacity analysis are expected to become the important information for flood mitigation in Indonesia.

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.

Precipitation Regime Shift Enhanced the Rain Pulse Effect on Soil Respiration in a Semi-Arid Steppe

PubMed Central

Yan, Liming; Chen, Shiping; Xia, Jianyang; Luo, Yiqi

2014-01-01

The effect of resource pulses, such as rainfall events, on soil respiration plays an important role in controlling grassland carbon balance, but how shifts in long-term precipitation regime regulate rain pulse effect on soil respiration is still unclear. We first quantified the influence of rainfall event on soil respiration based on a two-year (2006 and 2009) continuously measured soil respiration data set in a temperate steppe in northern China. In 2006 and 2009, soil carbon release induced by rainfall events contributed about 44.5% (83.3 g C m−2) and 39.6% (61.7 g C m−2) to the growing-season total soil respiration, respectively. The pulse effect of rainfall event on soil respiration can be accurately predicted by a water status index (WSI), which is the product of rainfall event size and the ratio between antecedent soil temperature to moisture at the depth of 10 cm (r 2 = 0.92, P<0.001) through the growing season. It indicates the pulse effect can be enhanced by not only larger individual rainfall event, but also higher soil temperature/moisture ratio which is usually associated with longer dry spells. We then analyzed a long-term (1953–2009) precipitation record in the experimental area. We found both the extreme heavy rainfall events (>40 mm per event) and the long dry-spells (>5 days) during the growing seasons increased from 1953–2009. It suggests the shift in precipitation regime has increased the contribution of rain pulse effect to growing-season total soil respiration in this region. These findings highlight the importance of incorporating precipitation regime shift and its impacts on the rain pulse effect into the future predictions of grassland carbon cycle under climate change. PMID:25093573

Precipitation regime shift enhanced the rain pulse effect on soil respiration in a semi-arid steppe.

PubMed

Yan, Liming; Chen, Shiping; Xia, Jianyang; Luo, Yiqi

2014-01-01

The effect of resource pulses, such as rainfall events, on soil respiration plays an important role in controlling grassland carbon balance, but how shifts in long-term precipitation regime regulate rain pulse effect on soil respiration is still unclear. We first quantified the influence of rainfall event on soil respiration based on a two-year (2006 and 2009) continuously measured soil respiration data set in a temperate steppe in northern China. In 2006 and 2009, soil carbon release induced by rainfall events contributed about 44.5% (83.3 g C m(-2)) and 39.6% (61.7 g C m(-2)) to the growing-season total soil respiration, respectively. The pulse effect of rainfall event on soil respiration can be accurately predicted by a water status index (WSI), which is the product of rainfall event size and the ratio between antecedent soil temperature to moisture at the depth of 10 cm (r2 = 0.92, P<0.001) through the growing season. It indicates the pulse effect can be enhanced by not only larger individual rainfall event, but also higher soil temperature/moisture ratio which is usually associated with longer dry spells. We then analyzed a long-term (1953-2009) precipitation record in the experimental area. We found both the extreme heavy rainfall events (>40 mm per event) and the long dry-spells (>5 days) during the growing seasons increased from 1953-2009. It suggests the shift in precipitation regime has increased the contribution of rain pulse effect to growing-season total soil respiration in this region. These findings highlight the importance of incorporating precipitation regime shift and its impacts on the rain pulse effect into the future predictions of grassland carbon cycle under climate change.

A comparison of methods for determining soil water availability in two sites in Panama with similar rainfall but distinct tree communities

Treesearch

Thomas A. Kursar; Bettina M. J. Engelbrecht; Melvin T. Tyree

2005-01-01

Plant productivity, distribution and diversity in tropical rain forests correlate with water availability. Water availability is determined by rainfall and also by the available water capacity of the soil. However, while rainfall is recognized as important, linkages between plant distribution and differences among soils in available water capacity have not been...

Assimilation of passive and active CCI soil moisture products into hydrological modelling: an intercomparison study in Europe

NASA Astrophysics Data System (ADS)

Maggioni, V.; Massari, C.; Camici, S.; Brocca, L.; Marchesini, I.

2017-12-01

Soil moisture (SM) is a key variable in rainfall-runoff partitioning since it acts on the main hydrological processes taking part within a catchment. Modeling SM is often a difficult task due to its large variability at different temporal and spatial scales. Ground soil moisture measurements are a valuable tool for improving runoff prediction but are often limited and suffer from spatial representativeness issues. Remotely sensed observations offer a new source of data able to cope the latter issues thus opening new possibilities for improving flood simulations worldwide. Today, several different SM products are available at increased accuracy with respect to the past. Some interesting products are those derived from the Climate Change Initiative (CCI) which offer the most complete and most consistent global SM data record based on active and passive microwave sensors.Thanks to the combination of multiple sensors within an active, a passive and an active+passive products, the CCI SM is expected to provide a significant benefit for the improvement of rainfall-runoff simulations through data assimilation. However, previous studies have shown that the success of the assimilation is not only related to the accuracy of the observations but also to the specific climate and the catchment physical and hydrological characteristics as well as to many necessary choices related to the assimilation technique. These choices along with the type of SM observations (i.e. passive or active) might play an important role for the success or the failure of the assimilation exercise which is not still clear. In this study, based on a large dataset of catchments covering large part of the Europe, we assimilated satellite SM observations from the passive and the active CCI SM products into Modello Idrologico Semiditribuito in Continuo (MISDc, Brocca et al. 2011). Rainfall and temperature data were collected from the European Climate Assessment & Dataset (E-OBS) while discharge data were obtained from the Global Runoff Data Centre (GRDC). Preliminary results show a general improvement of the hydrological simulations for catchments located in the Mediterranean areas specifically for the active product while lower performance is obtained at northern latitudes due to the presence of snow and ice.

Quantifying Airborne Allergen Levels Before and After Rain Events Using TRMM/GPM and Ground-Sampled Data

NASA Technical Reports Server (NTRS)

Stewart, Randy M.

2006-01-01

Allergies affect millions of Americans, increasing health risks and also increasing absenteeism and reducing productivity in the workplace. Outdoor allergens, such as airborne pollens and mold spores, commonly trigger respiratory distress symptoms, but rainfall reduces the quantity of allergens in the air (EPA, 2003). The current NASA Tropical Rainfall Measuring Mission provides accurate information related to rain events. These capabilities will be further enhanced with the future Global Precipitation Measurement mission. This report examines the effectiveness of combining these NASA resources with established ground-based allergen/spore sampling systems to better understand the benefits that rain provides in removing allergens and spores from the air.

Rainfall erosivity in Central Chile

NASA Astrophysics Data System (ADS)

Bonilla, Carlos A.; Vidal, Karim L.

2011-11-01

SummaryOne of the most widely used indicators of potential water erosion risk is the rainfall-runoff erosivity factor ( R) of the Revised Universal Soil Loss Equation (RUSLE). R is traditionally determined by calculating a long-term average of the annual sum of the product of a storm's kinetic energy ( E) and its maximum 30-min intensity ( I30), known as the EI30. The original method used to calculate EI30 requires pluviograph records for at most 30-min time intervals. Such high resolution data is difficult to obtain in many parts of the world, and processing it is laborious and time-consuming. In Chile, even though there is a well-distributed rain gauge network, there is no systematic characterization of the territory in terms of rainfall erosivity. This study presents a rainfall erosivity map for most of the cultivated land in the country. R values were calculated by the prescribed method for 16 stations with continuous graphical record rain gauges in Central Chile. The stations were distributed along 800 km (north-south), and spanned a precipitation gradient of 140-2200 mm yr -1. More than 270 years of data were used, and 5400 storms were analyzed. Additionally, 241 spatially distributed R values were generated by using an empirical procedure based on annual rainfall. Point estimates generated by both methods were interpolated by using kriging to create a map of rainfall erosivity for Central Chile. The results show that the empirical procedure used in this study predicted the annual rainfall erosivity well (model efficiency = 0.88). Also, an increment in the rainfall erosivities was found as a result of the rainfall depths, a regional feature determined by elevation and increasing with latitude from north to south. R values in the study area range from 90 MJ mm ha -1 h -1 yr -1 in the north up to 7375 MJ mm ha -1 h -1 yr -1 in the southern area, at the foothills of the Andes Mountains. Although the map and the estimates could be improved in the future by generating additional data points, the erosivity map should prove to be a good tool for land-use planners in Chile and other areas with similar rainfall characteristics.

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.

On the use of satellite-based estimates of rainfall temporal distribution to simulate the potential for malaria transmission in rural Africa

NASA Astrophysics Data System (ADS)

Yamana, Teresa K.; Eltahir, Elfatih A. B.

2011-02-01

This paper describes the use of satellite-based estimates of rainfall to force the Hydrology, Entomology and Malaria Transmission Simulator (HYDREMATS), a hydrology-based mechanistic model of malaria transmission. We first examined the temporal resolution of rainfall input required by HYDREMATS. Simulations conducted over Banizoumbou village in Niger showed that for reasonably accurate simulation of mosquito populations, the model requires rainfall data with at least 1 h resolution. We then investigated whether HYDREMATS could be effectively forced by satellite-based estimates of rainfall instead of ground-based observations. The Climate Prediction Center morphing technique (CMORPH) precipitation estimates distributed by the National Oceanic and Atmospheric Administration are available at a 30 min temporal resolution and 8 km spatial resolution. We compared mosquito populations simulated by HYDREMATS when the model is forced by adjusted CMORPH estimates and by ground observations. The results demonstrate that adjusted rainfall estimates from satellites can be used with a mechanistic model to accurately simulate the dynamics of mosquito populations.

Heating Structures Derived from Satellite

NASA Technical Reports Server (NTRS)

Tao, W.-K.; Adler, R.; Haddad, Z.; Hou, A.; Kakar, R.; Krishnamurti, T. N.; Kummerow, C.; Lang, S.; Meneghini, R.; Olson, W.

2004-01-01

Rainfall is a key link in the hydrologic cycle and is a primary heat source for the atmosphere. The vertical distribution of latent-heat release, which is accompanied by rainfall, modulates the large-scale circulations of the tropics and in turn can impact midlatitude weather. This latent heat release is a consequence of phase changes between vapor, liquid, and solid water. The Tropical Rainfall Measuring Mission (TRMM), a joint U.S./Japan space project, was launched in November 1997. It provides an accurate measurement of rainfall over the global tropics which can be used to estimate the four-dimensional structure of latent heating over the global tropics. The distributions of rainfall and inferred heating can be used to advance our understanding of the global energy and water cycle. This paper describes several different algorithms for estimating latent heating using TRMM observations. The strengths and weaknesses of each algorithm as well as the heating products are also discussed. The validation of heating products will be exhibited. Finally, the application of this heating information to global circulation and climate models is presented.

Use of eddy-covariance methods to "calibrate" simple estimators of evapotranspiration

USGS Publications Warehouse

Sumner, David M.; Geurink, Jeffrey S.; Swancar, Amy

2017-01-01

Direct measurement of actual evapotranspiration (ET) provides quantification of this large component of the hydrologic budget, but typically requires long periods of record and large instrumentation and labor costs. Simple surrogate methods of estimating ET, if “calibrated” to direct measurements of ET, provide a reliable means to quantify ET. Eddy-covariance measurements of ET were made for 12 years (2004-2015) at an unimproved bahiagrass (Paspalum notatum) pasture in Florida. These measurements were compared to annual rainfall derived from rain gage data and monthly potential ET (PET) obtained from a long-term (since 1995) U.S. Geological Survey (USGS) statewide, 2-kilometer, daily PET product. The annual proportion of ET to rainfall indicates a strong correlation (r2=0.86) to annual rainfall; the ratio increases linearly with decreasing rainfall. Monthly ET rates correlated closely (r2=0.84) to the USGS PET product. The results indicate that simple surrogate methods of estimating actual ET show positive potential in the humid Florida climate given the ready availability of historical rainfall and PET.

Estimation of debris flow critical rainfall thresholds by a physically-based model

NASA Astrophysics Data System (ADS)

Papa, M. N.; Medina, V.; Ciervo, F.; Bateman, A.

2012-11-01

Real time assessment of debris flow hazard is fundamental for setting up warning systems that can mitigate its risk. A convenient method to assess the possible occurrence of a debris flow is the comparison of measured and forecasted rainfall with rainfall threshold curves (RTC). Empirical derivation of the RTC from the analysis of rainfall characteristics of past events is not possible when the database of observed debris flows is poor or when the environment changes with time. For landslides triggered debris flows, the above limitations may be overcome through the methodology here presented, based on the derivation of RTC from a physically based model. The critical RTC are derived from mathematical and numerical simulations based on the infinite-slope stability model in which land instability is governed by the increase in groundwater pressure due to rainfall. The effect of rainfall infiltration on landside occurrence is modelled trough a reduced form of the Richards equation. The simulations are performed in a virtual basin, representative of the studied basin, taking into account the uncertainties linked with the definition of the characteristics of the soil. A large number of calculations are performed combining different values of the rainfall characteristics (intensity and duration of event rainfall and intensity of antecedent rainfall). For each combination of rainfall characteristics, the percentage of the basin that is unstable is computed. The obtained database is opportunely elaborated to derive RTC curves. The methodology is implemented and tested on a small basin of the Amalfi Coast (South Italy).

Drought in West Africa: How CHIRPS and Reference Evapotranspiration can be used for Index Insurance in a Non-Stationary Setting

NASA Astrophysics Data System (ADS)

Blakeley, S. L.; Husak, G. J.; Harrison, L.; Funk, C. C.; Osgood, D. E.; Peterson, P.

2017-12-01

Index insurance is increasingly used as a safety net and productivity tool in order to improve the resilience of small-holder farmers in developing countries. In West Africa, there are already index insurance projects in many countries, and various non-governmental organizations are eager to expand implementation of this risk management tool. Often, index insurance payouts rely on rainfall to determine drought years, but designation of years based on precipitation variations is particularly complex in places like West Africa where precipitation is subject to much natural variability across timescales [Giannini 2003, among others]. Furthermore, farmers must also rely on other weather factors for good crop yields, such as the availability of moisture for their plants to absorb and maximum daily temperatures staying within an acceptable range for the crops. In this presentation, the payouts of an index based on rainfall (as measured by the Climate Hazards Group Infrared Precipitation with Stations {CHIRPS} dataset) is compared to the payouts of an index using reference evapotranspiration data (using the ASCE's Penmen-Monteith formula and MERRA-2 drivers). The West African rainfall index exhibits a fair amount of long-term variability, reflective of the Atlantic Multidecadal Oscillation, but the reference evapotranspiration index shows different variability, through changes in radiative forcing and temperatures. Therefore, the use of rainfall for an index is appropriate for capturing rainfall deficits, but reference evapotranspiration may also be an appropriate addition to an index or as a stand-alone index for capturing crop stress. In summary, the results point to farmer input as an invaluable source of knowledge in determining the most appropriate dataset as an index for crop insurance. Alessandra Giannini, R Saravanan, and P Chang. Oceanic forcing of Sahel rainfall on interannual to interdecadal time scales. Science, 302(5647):1027-1030, 2003.

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.

Climatic controls on the global distribution, abundance, and species richness of mangrove forests

USGS Publications Warehouse

Osland, Michael J.; Feher, Laura C.; Griffith, Kereen; Cavanaugh, Kyle C.; Enwright, Nicholas M.; Day, Richard H.; Stagg, Camille L.; Krauss, Ken W.; Howard, Rebecca J.; Grace, James B.; Rogers, Kerrylee

2017-01-01

Mangrove forests are highly productive tidal saline wetland ecosystems found along sheltered tropical and subtropical coasts. Ecologists have long assumed that climatic drivers (i.e., temperature and rainfall regimes) govern the global distribution, structure, and function of mangrove forests. However, data constraints have hindered the quantification of direct climate-mangrove linkages in many parts of the world. Recently, the quality and availability of global-scale climate and mangrove data have been improving. Here, we used these data to better understand the influence of air temperature and rainfall regimes upon the distribution, abundance, and species richness of mangrove forests. Although our analyses identify global-scale relationships and thresholds, we show that the influence of climatic drivers is best characterized via regional range limit-specific analyses. We quantified climatic controls across targeted gradients in temperature and/or rainfall within 14 mangrove distributional range limits. Climatic thresholds for mangrove presence, abundance, and species richness differed among the 14 studied range limits. We identified minimum temperature-based thresholds for range limits in eastern North America, eastern Australia, New Zealand, eastern Asia, eastern South America, and southeast Africa. We identified rainfall-based thresholds for range limits in western North America, western Gulf of Mexico, western South America, western Australia, Middle East, northwest Africa, east central Africa, and west central Africa. Our results show that in certain range limits (e.g., eastern North America, western Gulf of Mexico, eastern Asia), winter air temperature extremes play an especially important role. We conclude that rainfall and temperature regimes are both important in western North America, western Gulf of Mexico, and western Australia. With climate change, alterations in temperature and rainfall regimes will affect the global distribution, abundance, and diversity of mangrove forests. In general, warmer winter temperatures are expected to allow mangroves to expand poleward at the expense of salt marshes. However, dispersal and habitat availability constraints may hinder expansion near certain range limits. Along arid and semi-arid coasts, decreases or increases in rainfall are expected to lead to mangrove contraction or expansion, respectively. Collectively, our analyses quantify climate-mangrove linkages and improve our understanding of the expected global- and regional-scale effects of climate change upon mangrove forests.

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.

Simulating Streamflow Using Bias-corrected Multiple Satellite Rainfall Products in the Tekeze Basin, Ethiopia

NASA Astrophysics Data System (ADS)

Abitew, T. A.; Roy, T.; Serrat-Capdevila, A.; van Griensven, A.; Bauwens, W.; Valdes, J. B.

2016-12-01

The Tekeze Basin supports one of Africans largest Arch Dam located in northern Ethiopian has vital role in hydropower generation. However, little has been done on the hydrology of the basin due to limited in situ hydroclimatological data. Therefore, the main objective of this research is to simulate streamflow upstream of the Tekeze Dam using Soil and Water Assessment Tool (SWAT) forced by bias-corrected multiple satellite rainfall products (CMORPH, TMPA and PERSIANN-CCS). This talk will present the potential as well as skills of bias-corrected satellite rainfall products for streamflow prediction in in Tropical Africa. Additionally, the SWAT model results will also be compared with previous conceptual Hydrological models (HyMOD and HBV) from SERVIR Streamflow forecasting in African Basin project (http://www.swaat.arizona.edu/index.html).

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

NASA Technical Reports Server (NTRS)

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

2017-01-01

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

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

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.

How is overland flow produced under intermittent rain? An analysis using plot-scale rainfall simulation on dryland soils

NASA Astrophysics Data System (ADS)

Dunkerley, David

2018-01-01

The characteristic intermittency of rainfall includes temporary cessations (hiatuses), as well as periods of very low intensity within more intense events. To understand how these characteristics of rainfall affect overland flow production, rainfall simulations involving repeated cycles of on-off intermittency were carried out on dryland soils in arid western New South Wales, Australia. Periods of rain (10 mm/h) and no-rain were applied in alternation with cycle times from 3 min to 25 min, in experiments lasting 1-1.5 h. Results showed that intermittency could delay the onset of runoff by more than 30 min, reduce the runoff ratio, reduce the peak runoff rate, and reduce the apparent event infiltration rate by 30-45%. When hiatuses in rainfall were longer than 15-20 min, runoff that had resulted from prior rain ceased completely before the recommencement of rain. Results demonstrate that if rainfall intermittency is not accounted for, estimates of infiltrability based on runoff plot data can be systematically in error. Despite the use of intermittent rain, the episodic occurrence of runoff could be predicted successfully by fitting multiple affine Horton infiltration equations, whose changing f0 and Kf coefficients, but uniform values of fc, reflected the redistribution of soil moisture and the change in the infiltrability f during hiatuses in rainfall. The value of fc varied little among the fitted equations, so constituting an affine set of relationships. This new approach provides an alternative to the use of steady-state methods that are common in rainfall simulation experiments and which typically yield only an estimate of fc. The new field results confirm that intermittency affects infiltration and runoff depths and timing at plot scale and on intra-event timescales. Additional work on other soil types, and at other spatial and temporal scales, is needed to test the generality of these findings.

Effects of Hydrological Parameters on Palm Oil Fresh Fruit Bunch Yield)

NASA Astrophysics Data System (ADS)

Nda, M.; Adnan, M. S.; Suhadak, M. A.; Zakaria, M. S.; Lopa, R. T.

2018-04-01

Climate change effects and variability have been studied by many researchers in diverse geophysical fields. Malaysia produces large volume of palm oil, the effects of climate change on hydrological parameters (rainfall and precipitation) could have adverse effects on palm oil fresh fruit bunch (FFB) production with implications at both local and international market. It is important to understand the effects of climate change on crop yield to adopt new cultivation techniques and guaranteeing food security globally. Based on this background, the paper’s objective is to investigate the effects of rainfall and temperature pattern on crop yield (FFB) within five years period (2013 - 2017) at Batu Pahat District. The Man - Kendall rank technique (trend test) and statistical analyses (correlation and regression) were applied to the dataset used for the study. The results reveal that there are variabilities in rainfall and temperature from one month to the other and the statistical analysis reveals that the hydrological parameters have an insignificant effect on crop yield.

The effects of rainfall and vegetation on litterfall production in the semiarid region of northeastern Brazil.

PubMed

Lopes, M C A; Araújo, V F P; Vasconcellos, A

2015-08-01

Litterfall has a strong influence on biodiversity and on the chemical and physical characteristics of the soil. Its production can be quite variable over time and space, and can be influenced by both natural and anthropogenic factors. We evaluated litterfall production and its relationship with rainfall, species richness, and the densities of the arboreal vegetation. Thirty litter traps were constructed with 1.0 m2 nylon mesh (1.0 mm) and randomly installed within a 2000 m × 500 m area of arboreal/shrub Caatinga (dryland) vegetation. Litter samples were collected monthly from November/2010 to June/2012, and the collected material was classified, dried, and weighted. Species richness and tree densities were determined by conducting phytosociological surveys in 20 m × 20 m plots surrounding each of the litter traps. The litterfall accumulation rate was 3.673 Mgha-1yr-1, similar to values from other seasonally dry tropical forests. Litterfall production was continuous, and principally accompanied the rainfall rate, but with a time interval of 2 to 3 months, with the greatest accumulation at the beginning of the dry season and the least during the rainy season. The different fractions of materials demonstrated distinct accumulation rates, with leaves being the principal category. Litterfall production was found to be related to tree density, but no link was found to species richness. The observed temporal heterogeneity of litterfall production demonstrated a strong link between rainfall and the dynamics of nutrient cycling in the semiarid region of Brazil.

An analysis of the breakdown of paper products (toilet paper, tissues and tampons) in natural environments, Tasmania, Australia.

PubMed

Bridle, Kerry L; Kirkpatrick, J B

2005-01-01

An examination of the relative breakdown rates of unused toilet paper, facial tissues and tampons was undertaken in nine different environments typical of Tasmanian natural areas. Bags of the paper products (toilet paper, facial tissues, tampons) were buried for periods of 6, 12 and 24 months at depths of 5 and 15 cm. A nutrient solution simulating human body wastes was added to half of the samples, to test the hypothesis that the addition of nutrients would enhance the breakdown of paper products buried in the soil. Mean annual rainfall was the most important measured variable determining mean breakdown in the nutrient addition treatment between sites, with high rainfall sites (mean annual rainfall of greater than 650 mm) recording less decayed products than the drier sites (mean annual rainfall of 500-650 mm). Temperature and soil organic content were important influences on the breakdown of the unfertilised products. Toilet paper and tissues decayed more readily than tampons. Nutrient addition enhanced decay for all products across all sites. Depth of burial was not important in determining the degree to which products decayed. In alpine environments, burial under rocks at the surface did not increase the speed of decay of any product. The Western Alpine site, typical of alpine sites in the Tasmanian Wilderness World Heritage Area, showed very little decay over the two-year period, even for nutrient enhanced products. Management prescriptions should be amended to dissuade people from depositing human toilet waste in the extreme (montane to alpine) environments in western Tasmania. Tampons should continue to be carried out as currently prescribed.

Gridded Hourly Text Products: A TRMM Data Reduction Approach

NASA Technical Reports Server (NTRS)

Stocker, Erich; Kwiatkowski, John; Kelley, Owen; Wharton, Stephen W. (Technical Monitor)

2001-01-01

The quantity of precipitation data from satellite-based observations is a blessing and a curse. The sheer volume of the data makes it difficult for many researchers to use in targeted applications. This volume increases further as algorithm improvements lead to the reprocessing of mission data. In addition to the overall volume of data, the size and format complexity of orbital granules contribute to the difficulty in using all the available data. Finally, the number of different instruments available to measure rainfall and related parameters further contributes to the volume concerns. In summary, we have an embarrassment of riches. The science team of the Tropical Rainfall Measuring Mission (TRMM) recognized this dilemma and has developed a strategy to address it. The TRMM Science Data and Information System (TSDIS) produces, at the direction of the Joint TRMM Science Team, a number of instantaneous rainfall products. The TRMM Microwave Imager (TMI), the Precipitation Radar and a Combined TMI/PR are the key "instruments" used in this production. Each of these products contains an entire orbit of data. The algorithm code computes not just rain rates but a large number of other physical parameters as well as information needed for monitoring algorithm performance. That makes these products very large. For example, a single orbit of TMI rain rate product is 99 MB, a single orbit of the combined product yields a granule that is 158 MB, while the 80 vertical levels of rain information from the PR yields an orbital product of 253 MB. These are large products that are often difficult for science users to electronically transfer to their sites especially if they want a large period of time. Level 3 gridded products are much smaller, but their 5 or 30 day temporal resolution is insufficient for many researchers. In addition, TRMM standard products are produced in the HDF format. While a large number of user-friendly tools are available to hide the details of the format (including a toolkit developed at TSDIS for the TRMM science team), many potential users shy away

Habitat Hydrology and Geomorphology Control the Distribution of Malaria Vector Larvae in Rural Africa

PubMed Central

Hardy, Andrew J.; Gamarra, Javier G. P.; Cross, Dónall E.; Macklin, Mark G.; Smith, Mark W.; Kihonda, Japhet; Killeen, Gerry F.; Ling’ala, George N.; Thomas, Chris J.

2013-01-01

Background Larval source management is a promising component of integrated malaria control and elimination. This requires development of a framework to target productive locations through process-based understanding of habitat hydrology and geomorphology. Methods We conducted the first catchment scale study of fine resolution spatial and temporal variation in Anopheles habitat and productivity in relation to rainfall, hydrology and geomorphology for a high malaria transmission area of Tanzania. Results Monthly aggregates of rainfall, river stage and water table were not significantly related to the abundance of vector larvae. However, these metrics showed strong explanatory power to predict mosquito larval abundances after stratification by water body type, with a clear seasonal trend for each, defined on the basis of its geomorphological setting and origin. Conclusion Hydrological and geomorphological processes governing the availability and productivity of Anopheles breeding habitat need to be understood at the local scale for which larval source management is implemented in order to effectively target larval source interventions. Mapping and monitoring these processes is a well-established practice providing a tractable way forward for developing important malaria management tools. PMID:24312606

Habitat hydrology and geomorphology control the distribution of malaria vector larvae in rural Africa.

PubMed

Hardy, Andrew J; Gamarra, Javier G P; Cross, Dónall E; Macklin, Mark G; Smith, Mark W; Kihonda, Japhet; Killeen, Gerry F; Ling'ala, George N; Thomas, Chris J

2013-01-01

Larval source management is a promising component of integrated malaria control and elimination. This requires development of a framework to target productive locations through process-based understanding of habitat hydrology and geomorphology. We conducted the first catchment scale study of fine resolution spatial and temporal variation in Anopheles habitat and productivity in relation to rainfall, hydrology and geomorphology for a high malaria transmission area of Tanzania. Monthly aggregates of rainfall, river stage and water table were not significantly related to the abundance of vector larvae. However, these metrics showed strong explanatory power to predict mosquito larval abundances after stratification by water body type, with a clear seasonal trend for each, defined on the basis of its geomorphological setting and origin. Hydrological and geomorphological processes governing the availability and productivity of Anopheles breeding habitat need to be understood at the local scale for which larval source management is implemented in order to effectively target larval source interventions. Mapping and monitoring these processes is a well-established practice providing a tractable way forward for developing important malaria management tools.

Transfer of uncertainty of space-borne high resolution rainfall products at ungauged regions

NASA Astrophysics Data System (ADS)

Tang, Ling

Hydrologically relevant characteristics of high resolution (˜ 0.25 degree, 3 hourly) satellite rainfall uncertainty were derived as a function of season and location using a six year (2002-2007) archive of National Aeronautics and Space Administration (NASA)'s Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) precipitation data. The Next Generation Radar (NEXRAD) Stage IV rainfall data over the continental United States was used as ground validation (GV) data. A geostatistical mapping scheme was developed and tested for transfer (i.e., spatial interpolation) of uncertainty information from GV regions to the vast non-GV regions by leveraging the error characterization work carried out in the earlier step. The open question explored here was, "If 'error' is defined on the basis of independent ground validation (GV) data, how are error metrics estimated for a satellite rainfall data product without the need for much extensive GV data?" After a quantitative analysis of the spatial and temporal structure of the satellite rainfall uncertainty, a proof-of-concept geostatistical mapping scheme (based on the kriging method) was evaluated. The idea was to understand how realistic the idea of 'transfer' is for the GPM era. It was found that it was indeed technically possible to transfer error metrics from a gauged to an ungauged location for certain error metrics and that a regionalized error metric scheme for GPM may be possible. The uncertainty transfer scheme based on a commonly used kriging method (ordinary kriging) was then assessed further at various timescales (climatologic, seasonal, monthly and weekly), and as a function of the density of GV coverage. The results indicated that if a transfer scheme for estimating uncertainty metrics was finer than seasonal scale (ranging from 3-6 hourly to weekly-monthly), the effectiveness for uncertainty transfer worsened significantly. Next, a comprehensive assessment of different kriging methods for spatial transfer (interpolation) of error metrics was performed. Three kriging methods for spatial interpolation are compared, which are: ordinary kriging (OK), indicator kriging (IK) and disjunctive kriging (DK). Additional comparison with the simple inverse distance weighting (IDW) method was also performed to quantify the added benefit (if any) of using geostatistical methods. The overall performance ranking of the kriging methods was found to be as follows: OK=DK > IDW > IK. Lastly, various metrics of satellite rainfall uncertainty were identified for two large continental landmasses that share many similar Koppen climate zones, United States and Australia. The dependence of uncertainty as a function of gauge density was then investigated. The investigation revealed that only the first and second ordered moments of error are most amenable to a Koppen-type climate type classification in different continental landmasses.

The PRESSCA operational early warning system for landslide forecasting: the 11-12 November 2013 rainfall event in Central Italy.

NASA Astrophysics Data System (ADS)

Ciabatta, Luca; Brocca, Luca; Ponziani, Francesco; Berni, Nicola; Stelluti, Marco; Moramarco, Tommaso

2014-05-01

The Umbria Region, located in Central Italy, is one of the most landslide risk prone area in Italy, almost yearly affected by landslides events at different spatial scales. For early warning procedures aimed at the assessment of the hydrogeological risk, the rainfall thresholds represent the main tool for the Italian Civil Protection System. As shown in previous studies, soil moisture plays a key-role in landslides triggering. In fact, acting on the pore water pressure, soil moisture influences the rainfall amount needed for activating a landslide. In this work, an operational physically-based early warning system, named PRESSCA, that takes into account soil moisture for the definition of rainfall thresholds is presented. Specifically, the soil moisture conditions are evaluated in PRESSCA by using a distributed soil water balance model that is recently coupled with near real-time satellite soil moisture product obtained from ASCAT (Advanced SCATterometer) and from in-situ monitoring data. The integration of three different sources of soil moisture information allows to estimate the most accurate possible soil moisture condition. Then, both observed and forecasted rainfall data are compared with the soil moisture-based thresholds in order to obtain risk indicators over a grid of ~ 5 km. These indicators are then used for the daily hydrogeological risk evaluation and management by the Civil Protection regional service, through the sharing/delivering of near real-time landslide risk scenarios (also through an open source web platform: www.cfumbria.it). On the 11th-12th November, 2013, Umbria Region was hit by an exceptional rainfall event with up to 430mm/72hours that resulted in significant economic damages, but fortunately no casualties among the population. In this study, the results during the rainfall event of PRESSCA system are described, by underlining the model capability to reproduce, two days in advance, landslide risk scenarios in good spatial and temporal agreement with the occurred actual conditions. High-resolution risk scenarios (100mx100m), obtained by coupling PRESSCA forecasts with susceptibility and vulnerability layers, are also produced. The results show good relationship between the PRESSCA forecast and the reported landslides to the Civil Protection Service during the rainfall event, confirming the system robustness. The good forecasts of PRESSCA system have surely contributed to start well in advance the Civil Protection operations (alerting local authorities and population).

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.

Suitability assessment and mapping of Oyo State, Nigeria, for rice cultivation using GIS

NASA Astrophysics Data System (ADS)

Ayoade, Modupe Alake

2017-08-01

Rice is one of the most preferred food crops in Nigeria. However, local rice production has declined with the oil boom of the 1970s causing demand to outstrip supply. Rice production can be increased through the integration of Geographic Information Systems (GIS) and crop-land suitability analysis and mapping. Based on the key predictor variables that determine rice yield mentioned in relevant literature, data on rainfall, temperature, relative humidity, slope, and soil of Oyo state were obtained. To develop rice suitability maps for the state, two MCE-GIS techniques, namely the Overlay approach and weighted linear combination (WLC), using fuzzy AHP were used and compared. A Boolean land use map derived from a landsat imagery was used in masking out areas currently unavailable for rice production. Both suitability maps were classified into four categories of very suitable, suitable, moderate, and fairly moderate. Although the maps differ slightly, the overlay and WLC (AHP) approach found most parts of Oyo state (51.79 and 82.9 % respectively) to be moderately suitable for rice production. However, in areas like Eruwa, Oyo, and Shaki, rainfall amount received needs to be supplemented by irrigation for increased rice yield.

TRMM Latent Heating Retrieval and Comparisons with Field Campaigns and Large-Scale Analyses

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo; Takayabu, Yukuri; Lang, S.; Shige, S.; Olson, W.; Hou, A.; Jiang, X.; Zhang, C.; Lau, W.; Krishnamurti, T.;

Tropical Rainfall Measuring Mission (TRMM). Phase B: Data capture facility definition study

NASA Technical Reports Server (NTRS)

1990-01-01

The National Aeronautics and Aerospace Administration (NASA) and the National Space Development Agency of Japan (NASDA) initiated the Tropical Rainfall Measuring Mission (TRMM) to obtain more accurate measurements of tropical rainfall then ever before. The measurements are to improve scientific understanding and knowledge of the mechanisms effecting the intra-annual and interannual variability of the Earth's climate. The TRMM is largely dependent upon the handling and processing of the data by the TRMM Ground System supporting the mission. The objective of the TRMM is to obtain three years of climatological determinations of rainfall in the tropics, culminating in data sets of 30-day average rainfall over 5-degree square areas, and associated estimates of vertical distribution of latent heat release. The scope of this study is limited to the functions performed by TRMM Data Capture Facility (TDCF). These functions include capturing the TRMM spacecraft return link data stream; processing the data in the real-time, quick-look, and routine production modes, as appropriate; and distributing real time, quick-look, and production data products to users. The following topics are addressed: (1) TRMM end-to-end system description; (2) TRMM mission operations concept; (3) baseline requirements; (4) assumptions related to mission requirements; (5) external interface; (6) TDCF architecture and design options; (7) critical issues and tradeoffs; and (8) recommendation for the final TDCF selection process.

The Goal Structure of a Socratic Tutor. Technical Report No. 3.

ERIC Educational Resources Information Center

Stevens, Albert L.; Collins, Allan

This report describes the current version of the Why System, a script-based Socratic tutor which uses tutoring strategies formulated as production rules. The current system is capable of carrying on a dialogue about the factors influencing rainfall by presenting different cases to the student, asking for predictions, probing for relevant factors,…

Long-term Increases in Flower Production by Growth Forms in Response to Anthropogenic Change in a Tropical Forest

NASA Astrophysics Data System (ADS)

Pau, S.; Wright, S. J.

2016-12-01

There is mounting evidence that anthropogenic global change is altering the ecology of tropical forests. A limited number of studies have focused on long-term trends in tropical reproductive activity, yet differences in reproductive activity should have consequences for demography and ultimately forest carbon, water, and energy balance. Here we analyze a 28-year record of tropical flower production in response to anthropogenic climate change. We show that a multi-decadal increase in flower production is most strongly driven by rising atmospheric CO2, which had approximately 8x the effect of the Multivariate ENSO Index and approximately 13x the effect of rainfall or solar radiation. Interannual peaks in flower production were associated with greater solar radiation and low rainfall during El Niño years. Observed changes in solar radiation explained flower production better than rainfall (models including solar radiation accounted for 94% of cumulative AICc weight compared to 87% for rainfall). All growth forms (lianas, canopy trees, midstory trees, and shrubs) produced more flowers with increasing CO2 except for understory treelets. The increase in flower production was matched by a lengthening of flowering duration for canopy trees and midstory trees; duration was also longer for understory treelets. Given that anthropogenic CO2 emissions will continue to climb over the next century, the long-term increase in flower production may persist unless offset by increasing cloudiness in the tropics, or until rising CO2 and/or warming temperatures associated with the greenhouse effect pass critical thresholds for plant reproduction.

Vertical Profiles of Latent Heat Release over the Global Tropics using TRMM Rainfall Products from December 1997 to November 2002

NASA Technical Reports Server (NTRS)

Tao, W.-K.; Lang, S.; Simpson, J.; Meneghini, R.; Halverson, J.; Johnson, R.; Adler, R.

2003-01-01

NASA Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) derived rainfall information will be used to estimate the four-dimensional structure of global monthly latent heating and rainfall profiles over the global tropics from December 1997 to November 2000. Rainfall, latent heating and radar reflectivity structures between El Nino (DJF 1997-98) and La Nina (DJF 1998-99) will be examined and compared. The seasonal variation of heating over various geographic locations (i.e., oceanic vs continental, Indian ocean vs west Pacific, Africa vs. S. America ) will also be analyzed. In addition, the relationship between rainfall, latent heating (maximum heating level), radar reflectivity and SST is examined and will be presented in the meeting. The impact of random error and bias in stratiform percentage estimates from PR on latent heating profiles is studied and will also be presented in the meeting. The Goddard Cumulus Ensemble Model is being used to simulate various mesoscale convective systems that developed in different geographic locations. Specifically, the model estimated rainfall, radar reflectivity and latent heating profiles will be compared to observational data collected from TRMM field campaigns over the South China Sea in 1998 (SCSMEX), Brazil in 1999 (TRMM-LBA), and the central Pacific in 1999 (KWAJEX). Sounding diagnosed heating budgets and radar reflectivity from these experiments can provide the means to validate (heating product) as well as improve the GCE model. Review of other latent heating algorithms will be discussed in the workshop.

Vertical Profiles of Latent Heat Release over the Global Tropics using TRMM rainfall products from December 1997 to November 2001

NASA Technical Reports Server (NTRS)

Tao, W.-K.; Lang, S.; Simpson, J.; Meneghini, R.; Halverson, J.; Johnson, R.; Adler, R.

2002-01-01

NASA Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) derived rainfall information will be used to estimate the four-dimensional structure of global monthly latent heating and rainfall profiles over the global tropics from December 1997 to November 2001. Rainfall, latent heating and radar reflectivity structures between El Nino (DE 1997-98) and La Nina (DJF 1998-99) will be examined and compared. The seasonal variation of heating over various geographic locations (i.e., oceanic vs continental, Indian ocean vs. west Pacific, Africa vs. S. America) will also be analyzed. In addition, the relationship between rainfall, latent heating (maximum heating level), radar reflectivity and SST is examined and will be presented in the meeting. The impact of random error and bias in strtaiform percentage estimates from PR on latent heating profiles is studied and will also be presented in the meeting. The Goddard Cumulus Ensemble Model is being used to simulate various mesoscale convective systems that developed in different geographic locations. Specifically, the model estimated rainfall, radar reflectivity and latent heating profiles will be compared to observational data collected from TRMM field campaigns over the South China Sea in 1998 (SCSMEX), Brazil in 1999 (TRMM-LBA), and the central Pacific in 1999 (KWAJEX). Sounding diagnosed heating budgets and radar reflectivity from these experiments can provide the means to validate (heating product) as well as improve the GCE model.

Vertical Profiles of Latent Heat Release Over the Global Tropics using TRMM Rainfall Products from December 1997 to November 2001

NASA Technical Reports Server (NTRS)

Tao, W.-K.; Lang, S.; Simpson, J.; Meneghini, R.; Halverson, J.; Johnson, R.; Adler, R.; Starr, David (Technical Monitor)

2002-01-01

NASA Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) derived rainfall information will be used to estimate the four-dimensional structure of global monthly latent heating and rainfall profiles over the global tropics from December 1997 to November 2000. Rainfall, latent heating and radar reflectivity structures between El Nino (DJF 1997-98) and La Nina (DJF 1998-99) will be examined and compared. The seasonal variation of heating over various geographic locations (i.e., oceanic vs continental, Indian ocean vs west Pacific, Africa vs S. America) will also be analyzed. In addition, the relationship between rainfall, latent heating (maximum heating level), radar reflectivity and SST is examined and will be presented in the meeting. The impact of random error and bias in stratiform percentage estimates from PR on latent heating profiles is studied and will also be presented in the meeting. The Goddard Cumulus Ensemble Model is being used to simulate various mesoscale convective systems that developed in different geographic locations. Specifically, the model estimated rainfall, radar reflectivity and latent heating profiles will be compared to observational data collected from TRMM field campaigns over the South China Sea in 1998 (SCSMEX), Brazil in 1999 (TRMM-LBA), and the central Pacific in 1999 (KWAJEX). Sounding diagnosed heating budgets and radar reflectivity from these experiments can provide the means to validate (heating product) as well as improve the GCE model.

Vertical Profiles of Latent Heat Release over the Global Tropics Using TRMM Rainfall Products from December 1997 to November 2002

NASA Technical Reports Server (NTRS)

Tao, W.-K.

2003-01-01

NASA Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) derived rainfall information will be used to estimate the four-dimensional structure of global monthly latent heating and rainfall profiles over the global tropics from December 1997 to November 2000. Rainfall, latent heating and radar reflectivity structures between El Nino (DJF 1997-98) and La Nina (DJF 1998-99) will be examined and compared. The seasonal variation of heating over various geographic locations (i.e., oceanic vs continental, Indian ocean vs west Pacific, Africa vs S. America) will also be analyzed. In addition, the relationship between rainfall, latent heating (maximum heating level), radar reflectivity and SST is examined and will be presented in the meeting. The impact of random error and bias in straitform percentage estimates from PR on latent heating profiles is studied and will also be presented in the meeting. The Goddard Cumulus Ensemble Model is being used to simulate various mesoscale convective systems that developed in different geographic locations. Specifically, the model estimated rainfall, radar reflectivity and latent heating profiles will be compared to observational data collected from TRMM field campaigns over the South China Sea in 1998 (SCSMXX), Brazil in 1999 (TRMM- LBA), and the central Pacific in 1999 (KWAJEX). Sounding diagnosed heating budgets and radar reflectivity from these experiments can provide the means to validate (heating product) as well as improve the GCE model.

Cluster analysis for characterization of rainfalls and CSO behaviours in an urban drainage area of Tokyo.

PubMed

Yu, Yang; Kojima, Keisuke; An, Kyoungjin; Furumai, Hiroaki

2013-01-01

Combined sewer overflow (CSO) from urban areas is recognized as a major pollutant source to the receiving waters during wet weather. This study attempts to categorize rainfall events and corresponding CSO behaviours to reveal the relationship between rainfall patterns and CSO behaviours in the Shingashi urban drainage areas of Tokyo, Japan where complete service by a combined sewer system (CSS) and CSO often takes place. In addition, outfalls based on their annual overflow behaviours were characterized for effective storm water management. All 117 rainfall events recorded in 2007 were simulated by a distributed model InfoWorks CS to obtain CSO behaviours. The rainfall events were classified based on two sets of parameters of rainfall pattern as well as CSO behaviours. Clustered rainfall and CSO groups were linked by similarity analysis. Results showed that both small and extreme rainfalls had strong correlations with the CSO behaviours, while moderate rainfall had a weak relationship. This indicates that important and negligible rainfalls from the viewpoint of CSO could be identified by rainfall patterns, while influences from the drainage area and network should be taken into account when estimating moderate rainfall-induced CSO. Additionally, outfalls were finally categorized into six groups indicating different levels of impact on the environment.

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.

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.

Quantifying rainfall-derived inflow and infiltration in sanitary sewer systems based on conductivity monitoring

NASA Astrophysics Data System (ADS)

Zhang, Mingkai; Liu, Yanchen; Cheng, Xun; Zhu, David Z.; Shi, Hanchang; Yuan, Zhiguo

2018-03-01

Quantifying rainfall-derived inflow and infiltration (RDII) in a sanitary sewer is difficult when RDII and overflow occur simultaneously. This study proposes a novel conductivity-based method for estimating RDII. The method separately decomposes rainfall-derived inflow (RDI) and rainfall-induced infiltration (RII) on the basis of conductivity data. Fast Fourier transform was adopted to analyze variations in the flow and water quality during dry weather. Nonlinear curve fitting based on the least squares algorithm was used to optimize parameters in the proposed RDII model. The method was successfully applied to real-life case studies, in which inflow and infiltration were successfully estimated for three typical rainfall events with total rainfall volumes of 6.25 mm (light), 28.15 mm (medium), and 178 mm (heavy). Uncertainties of model parameters were estimated using the generalized likelihood uncertainty estimation (GLUE) method and were found to be acceptable. Compared with traditional flow-based methods, the proposed approach exhibits distinct advantages in estimating RDII and overflow, particularly when the two processes happen simultaneously.

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.

Predictability of Seasonal Rainfall over the Greater Horn of Africa

NASA Astrophysics Data System (ADS)

Ngaina, J. N.

2016-12-01

The El Nino-Southern Oscillation (ENSO) is a primary mode of climate variability in the Greater of Africa (GHA). The expected impacts of climate variability and change on water, agriculture, and food resources in GHA underscore the importance of reliable and accurate seasonal climate predictions. The study evaluated different model selection criteria which included the Coefficient of determination (R2), Akaike's Information Criterion (AIC), Bayesian Information Criterion (BIC), and the Fisher information approximation (FIA). A forecast scheme based on the optimal model was developed to predict the October-November-December (OND) and March-April-May (MAM) rainfall. The predictability of GHA rainfall based on ENSO was quantified based on composite analysis, correlations and contingency tables. A test for field-significance considering the properties of finiteness and interdependence of the spatial grid was applied to avoid correlations by chance. The study identified FIA as the optimal model selection criterion. However, complex model selection criteria (FIA followed by BIC) performed better compared to simple approach (R2 and AIC). Notably, operational seasonal rainfall predictions over the GHA makes of simple model selection procedures e.g. R2. Rainfall is modestly predictable based on ENSO during OND and MAM seasons. El Nino typically leads to wetter conditions during OND and drier conditions during MAM. The correlations of ENSO indices with rainfall are statistically significant for OND and MAM seasons. Analysis based on contingency tables shows higher predictability of OND rainfall with the use of ENSO indices derived from the Pacific and Indian Oceans sea surfaces showing significant improvement during OND season. The predictability based on ENSO for OND rainfall is robust on a decadal scale compared to MAM. An ENSO-based scheme based on an optimal model selection criterion can thus provide skillful rainfall predictions over GHA. This study concludes that the negative phase of ENSO (La Niña) leads to dry conditions while the positive phase of ENSO (El Niño) anticipates enhanced wet conditions

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.

Process-based rainfall interception by small trees in Northern China: The effect of rainfall traits and crown structure characteristics

Treesearch

Xiang Li; Qingfu Xiao; Jianzhi Niu; Salli Dymond; Natalie S. van Doorn; Xinxiao Yu; Baoyuan Xie; Xizhi Lv; Kebin Zhang; Jiao Li

2016-01-01

Rainfall interception by a tree's crown is one of the most important hydrological processes in an ecosystem, yet the mechanisms of interception are not well understood. A process-based experiment was conducted under five simulated rainfall intensities (from 10 to 150 mm h−1) to directly quantify tree crown interception and examine the effect...

Aerosols cause intraseasonal short-term suppression of Indian monsoon rainfall.

PubMed

Dave, Prashant; Bhushan, Mani; Venkataraman, Chandra

2017-12-11

Aerosol abundance over South Asia during the summer monsoon season, includes dust and sea-salt, as well as, anthropogenic pollution particles. Using observations during 2000-2009, here we uncover repeated short-term rainfall suppression caused by coincident aerosols, acting through atmospheric stabilization, reduction in convection and increased moisture divergence, leading to the aggravation of monsoon break conditions. In high aerosol-low rainfall regions extending across India, both in deficient and normal monsoon years, enhancements in aerosols levels, estimated as aerosol optical depth and absorbing aerosol index, acted to suppress daily rainfall anomaly, several times in a season, with lags of a few days. A higher frequency of prolonged rainfall breaks, longer than seven days, occurred in these regions. Previous studies point to monsoon rainfall weakening linked to an asymmetric inter-hemispheric energy balance change attributed to aerosols, and short-term rainfall enhancement from radiative effects of aerosols. In contrast, this study uncovers intraseasonal short-term rainfall suppression, from coincident aerosol forcing over the monsoon region, leading to aggravation of monsoon break spells. Prolonged and intense breaks in the monsoon in India are associated with rainfall deficits, which have been linked to reduced food grain production in the latter half of the twentieth century.

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.

Climate variability, rice production and groundwater depletion in India

NASA Astrophysics Data System (ADS)

Bhargava, Alok

2018-03-01

This paper modeled the proximate determinants of rice outputs and groundwater depths in 27 Indian states during 1980-2010. Dynamic random effects models were estimated by maximum likelihood at state and well levels. The main findings from models for rice outputs were that temperatures and rainfall levels were significant predictors, and the relationships were quadratic with respect to rainfall. Moreover, nonlinearities with respect to population changes indicated greater rice production with population increases. Second, groundwater depths were positively associated with temperatures and negatively with rainfall levels and there were nonlinear effects of population changes. Third, dynamic models for in situ groundwater depths in 11 795 wells in mainly unconfined aquifers, accounting for latitudes, longitudes and altitudes, showed steady depletion. Overall, the results indicated that population pressures on food production and environment need to be tackled via long-term healthcare, agricultural, and groundwater recharge policies in India.

Towards estimates of future rainfall erosivity in Europe based on REDES and WorldClim datasets

NASA Astrophysics Data System (ADS)

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

2017-05-01

The policy requests to develop trends in soil erosion changes can be responded developing modelling scenarios of the two most dynamic factors in soil erosion, i.e. rainfall erosivity and land cover change. The recently developed Rainfall Erosivity Database at European Scale (REDES) and a statistical approach used to spatially interpolate rainfall erosivity data have the potential to become useful knowledge to predict future rainfall erosivity based on climate scenarios. The use of a thorough statistical modelling approach (Gaussian Process Regression), with the selection of the most appropriate covariates (monthly precipitation, temperature datasets and bioclimatic layers), allowed to predict the rainfall erosivity based on climate change scenarios. The mean rainfall erosivity for the European Union and Switzerland is projected to be 857 MJ mm ha-1 h-1 yr-1 till 2050 showing a relative increase of 18% compared to baseline data (2010). The changes are heterogeneous in the European continent depending on the future projections of most erosive months (hot period: April-September). The output results report a pan-European projection of future rainfall erosivity taking into account the uncertainties of the climatic models.

Towards estimates of future rainfall erosivity in Europe based on REDES and WorldClim datasets.

PubMed

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

2017-05-01

The policy requests to develop trends in soil erosion changes can be responded developing modelling scenarios of the two most dynamic factors in soil erosion, i.e. rainfall erosivity and land cover change. The recently developed Rainfall Erosivity Database at European Scale (REDES) and a statistical approach used to spatially interpolate rainfall erosivity data have the potential to become useful knowledge to predict future rainfall erosivity based on climate scenarios. The use of a thorough statistical modelling approach (Gaussian Process Regression), with the selection of the most appropriate covariates (monthly precipitation, temperature datasets and bioclimatic layers), allowed to predict the rainfall erosivity based on climate change scenarios. The mean rainfall erosivity for the European Union and Switzerland is projected to be 857 MJ mm ha -1  h -1  yr -1 till 2050 showing a relative increase of 18% compared to baseline data (2010). The changes are heterogeneous in the European continent depending on the future projections of most erosive months (hot period: April-September). The output results report a pan-European projection of future rainfall erosivity taking into account the uncertainties of the climatic models.

L-band Microwave Remote Sensing and Land Data Assimilation Improve the Representation of Prestorm Soil Moisture Conditions for Hydrologic Forecasting

NASA Technical Reports Server (NTRS)

Crow, W. T.; Chen, F.; Reichle, R. H.; Liu, Q.

2017-01-01

Recent advances in remote sensing and land data assimilation purport to improve the quality of antecedent soil moisture information available for operational hydrologic forecasting. We objectively validate this claim by calculating the strength of the relationship between storm-scale runoff ratio (i.e., total stream flow divided by total rainfall accumulation in depth units) and pre-storm surface soil moisture estimates from a range of surface soil moisture data products. Results demonstrate that both satellite-based, L-band microwave radiometry and the application of land data assimilation techniques have significantly improved the utility of surface soil moisture data sets for forecasting stream flow response to future rainfall events.

L-band microwave remote sensing and land data assimilation improve the representation of pre-storm soil moisture conditions for hydrologic forecasting.

PubMed

Crow, W T; Chen, F; Reichle, R H; Liu, Q

2017-06-16

Recent advances in remote sensing and land data assimilation purport to improve the quality of antecedent soil moisture information available for operational hydrologic forecasting. We objectively validate this claim by calculating the strength of the relationship between storm-scale runoff ratio (i.e., total stream flow divided by total rainfall accumulation in depth units) and pre-storm surface soil moisture estimates from a range of surface soil moisture data products. Results demonstrate that both satellite-based, L-band microwave radiometry and the application of land data assimilation techniques have significantly improved the utility of surface soil moisture data sets for forecasting stream flow response to future rainfall events.

L-band microwave remote sensing and land data assimilation improve the representation of pre-storm soil moisture conditions for hydrologic forecasting

PubMed Central

Crow, W.T.; Chen, F.; Reichle, R.H.; Liu, Q.

2018-01-01

Recent advances in remote sensing and land data assimilation purport to improve the quality of antecedent soil moisture information available for operational hydrologic forecasting. We objectively validate this claim by calculating the strength of the relationship between storm-scale runoff ratio (i.e., total stream flow divided by total rainfall accumulation in depth units) and pre-storm surface soil moisture estimates from a range of surface soil moisture data products. Results demonstrate that both satellite-based, L-band microwave radiometry and the application of land data assimilation techniques have significantly improved the utility of surface soil moisture data sets for forecasting stream flow response to future rainfall events. PMID:29657342

Design Considerations and Economics of Water Harvesting System for Crop Production

NASA Astrophysics Data System (ADS)

Pali, A. K.

2016-06-01

By and large, the design of water harvesting pond is generally based on thumb rules and needs to be upgraded on scientific and engineering principles. In this study, the design procedure of on-farm water harvesting pond has been discussed and two farm ponds of circular, rectangular and square shapes were designed for 50, 60, 75 and 80 % probability of occurrence of rainfall and runoff. Though, the circular shape resulted in the least mean water surface area, but due to not being practicable for agricultural operations, it was discarded. The square shaped ponds resulted in giving least water surface areas as 0.761 ha for the micro watershed of 8.19 ha and as 0.246 ha for the micro watershed of 1.7 ha at 80 % probability level of rainfall and runoff at 80 % level of probability. The storage capacity of the first pond was found as 32,314 m3 and it was 12,962 m3 for the second farm pond. The area to be occupied by the two ponds was worked out as about 11 % of the total land area (8.19 ha) of the first micro watershed and about 18-22 % of the area (1.7 ha) of second micro watershed. Results indicated that the designed size of the first farm pond can be acceptable for construction. The economics of farm pond based agricultural production showed that the highest B/C ratio of 2 and 1.9 were possible for the farm pond designed at 80 and 75 % probability of occurrence of rainfall and runoff respectively.

Bias Correction of Satellite Precipitation Products (SPPs) using a User-friendly Tool: A Step in Enhancing Technical Capacity

NASA Astrophysics Data System (ADS)

Rushi, B. R.; Ellenburg, W. L.; Adams, E. C.; Flores, A.; Limaye, A. S.; Valdés-Pineda, R.; Roy, T.; Valdés, J. B.; Mithieu, F.; Omondi, S.

2017-12-01

SERVIR, a joint NASA-USAID initiative, works to build capacity in Earth observation technologies in developing countries for improved environmental decision making in the arena of: weather and climate, water and disasters, food security and land use/land cover. SERVIR partners with leading regional organizations in Eastern and Southern Africa, Hindu Kush-Himalaya, Mekong region, and West Africa to achieve its objectives. SERVIR develops hydrological applications to address specific needs articulated by key stakeholders and daily rainfall estimates are a vital input for these applications. Satellite-derived rainfall is subjected to systemic biases which need to be corrected before it can be used for any hydrologic application such as real-time or seasonal forecasting. SERVIR and the SWAAT team at the University of Arizona, have co-developed an open-source and user friendly tool of rainfall bias correction approaches for SPPs. Bias correction tools were developed based on Linear Scaling and Quantile Mapping techniques. A set of SPPs, such as PERSIANN-CCS, TMPA-RT, and CMORPH, are bias corrected using Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data which incorporates ground based precipitation observations. This bias correction tools also contains a component, which is included to improve monthly mean of CHIRPS using precipitation products of the Global Surface Summary of the Day (GSOD) database developed by the National Climatic Data Center (NCDC). This tool takes input from command-line which makes it user-friendly and applicable in any operating platform without prior programming skills. This presentation will focus on this bias-correction tool for SPPs, including application scenarios.

Landslides triggered by an earthquake and heavy rainfalls at Aso volcano, Japan, detected by UAS and SfM-MVS photogrammetry

NASA Astrophysics Data System (ADS)

Saito, Hitoshi; Uchiyama, Shoichiro; Hayakawa, Yuichi S.; Obanawa, Hiroyuki

2018-12-01

Unmanned aerial systems (UASs) and structure-from-motion multi-view stereo (SfM-MVS) photogrammetry have attracted a tremendous amount of interest for use in the creation of high-definition topographic data for geoscientific studies. By using these techniques, this study examined the topographic characteristics of coseismic landslides triggered by the 2016 Kumamoto earthquake (Mw 7.1) in the Sensuikyo area (1.0 km2) at Aso volcano, Japan. The study area has frequently experienced rainfall-induced landslide events, such as those in 1990, 2001, and 2012. We obtained orthorectified images and digital surface models (DSMs) with a spatial resolution of 0.06 m before and after the 2016 Kumamoto earthquake. By using these high-definition images and DSMs, we detected a total of 54 coseismic landslides with volumes of 9.1-3994.6 m3. These landslides, many of which initiated near topographic ridges, were typically located on upside hillslopes of previous rainfall-induced landslide scars that formed in 2012. This result suggests that the topographic effect on seismic waves, i.e., amplification of ground acceleration, was important for coseismic landslide initiation in the study area. The average depth of the coseismic landslides was 1.5 m, which is deeper than the depth of the rainfall-induced landslides prior to these. The total sediment production of the coseismic landslides reached 2.5 × 104 m3/km2, which is of the same order as the sediment production triggered by the previous single heavy rainfall event. This result indicates that the effects of the 2016 Kumamoto earthquake in terms of sediment production and topographic changes were similar to those of the rainfall-induced landslide event in the study area.

Changes in Water-Food-Energy Nexus in India and its consistency with changes in Monsoon

NASA Astrophysics Data System (ADS)

Barik, B.; Ghosh, S.; Pathak, A.

2017-12-01

Meeting the growing demand for food, water, and energy for a densely populated country like India is a major challenge. Green Revolution helped to maintain the food security, with Government policies such as distribution of electricity at a subsidised rate, resulting in an unregulated withdrawal of groundwater. Thus, the depleting groundwater went unnoticed as the high agricultural productivity overshadowed it. Here we present a comprehensive analysis which assess the present status of the water-food-energy nexus in India. We find that with the growth of population and consequent increase in the food demands, the food production has also increased, and this has been made possible with the intensification of irrigation. However, during the recent decade (after 1996), the increase in food production has not been sufficient to meet its growing demands, precipitating a decline in the per-capita food availability. Also, there has been a decline in the groundwater storage in India during the last decade, as derived from the Gravity Recovery and Climate Experiment (GRACE) data. Regional studies reveal contrasting trends, where North-western India and the middle Ganga basin show a decrease in the groundwater storage as opposed to an increasing storage over western-central India. We also find that, after a drought, the groundwater storage drops but is unable to recover to its original condition even after good monsoon years. The groundwater storage reveals a very strong negative correlation with the electricity consumption for agricultural usage, which may also be considered as a proxy for groundwater pumped for irrigation in a region. The electricity usage for agricultural purposes has an increasing trend and, interestingly, it does not have any correlation with the monsoon rainfall. This reveals an important finding that the irrigation has been intensified irrespective of rainfall. This also resulted in a decreasing correlation between the food production and monsoon rainfall, revealing the increasing dependency of agricultural activities on irrigation. We conclude that irrigation has become essential for agriculture to meet the food demand; hence, it should be judiciously regulated and controlled, based on the water availability from monsoon rainfall.

Towards Near Real-time Convective Rainfall Observations over Kenya

NASA Astrophysics Data System (ADS)

Hoedjes, Joost; Said, Mohammed; Becht, Robert; Kifugo, Shem; Kooiman, André; Limo, Agnes; Maathuis, Ben; Moore, Ian; Mumo, Mark; Nduhiu Mathenge, Joseph; Su, Bob; Wright, Iain

2013-04-01

The existing meteorological infrastructure in Kenya is poorly suited for the countrywide real-time monitoring of precipitation. Rainfall radar is not available, and the existing network of rain gauges is sparse and challenging to maintain. This severely restricts Kenya's capacity to warn for, and respond to, weather related emergencies. Furthermore, the lack of accurate rainfall observations severely limits Kenya's climate change adaptation capabilities. Over the past decade, the mobile telephone network in Kenya has expanded rapidly. This network makes extensive use of terrestrial microwave (MW) links, received signal level (RSL) data from which can be used for the calculation of rainfall intensities. We present a novel method for the near-real time observation of convective rainfall over Kenya, based on the combined use of MW RSL data and Meteosat Second Generation (MSG) satellite data. In this study, the variable density rainfall information derived from several MW links is scaled up using MSG data to provide full rainfall information coverage for the region surrounding the links. Combining MSG data and MW link derived rainfall data for several adjacent MW links makes it possible to make the distinction between wet and dry pixels. This allows the disaggregation of the MW link derived rainfall intensities. With the distinction between wet and dry pixels made, and the MW derived rainfall intensities disaggregated, these data can then be used to develop instantaneous empirical relationships linking rainfall intensities to cloud physical properties. These relationships are then used to calculate rainfall intensities for the MSG scene. Since both the MSG and the MW data are available at the same temporal resolution, unique empirical coefficients can be determined for each interval. This approach ensures that changes in convective conditions from one interval to the next are taken into account. Initial results from a pilot study, which took place from November 2012 until January 2013, are presented. The work has been carried out in close cooperation with mobile telephone operator Safaricom, using RSL data from 15 microwave links in rain prone areas in Western Kenya (out of a total of 3000 MW links operated by Safaricom in Kenya). The data supplied by Safaricom consist of the mean, minimum and maximum RSL for each MW link over a 15 minute interval. For this pilot study, use has been made of the MSG Cloud Top Temperature data product from the Royal Dutch Meteorological Institute's MSG Cloud Physical Properties database (http://msgcpp.knmi.nl/).

On Rainfall Modification by Major Urban Areas. Part 1; Observations from Space-borne Rain Radar Aboard TRMM

NASA Technical Reports Server (NTRS)

Shepherd, J. Marshell; Starr, David OC. (Technical Monitor)

2001-01-01

A novel approach is introduced to correlating urbanization and rainfall modification. This study represents one of the first published attempts (possibly the first) to identify and quantify rainfall modification by urban areas using satellite-based rainfall measurements. Previous investigations successfully used rain gauge networks and around-based radar to investigate this phenomenon but still encountered difficulties due to limited, specialized measurements and separation of topographic and other influences. Three years of mean monthly rainfall rates derived from the first space-based rainfall radar, Tropical Rainfall Measuring Mission's (TRMM) Precipitation Radar, are employed. Analysis of data at half-degree latitude resolution enables identification of rainfall patterns around major metropolitan areas of Atlanta, Montgomery, Nashville, San Antonio, Waco, and Dallas during the warm season. Preliminary results reveal an average increase of 5.6% in monthly rainfall rates (relative to a mean upwind CONTROL area) over the metropolis but an average increase of approx. 28%, in monthly rainfall rates within 30-60 kilometers downwind of the metropolis. Some portions of the downwind area exhibit increases as high as 51%. It was also found that maximum rainfall rates found in the downwind impact area exceeded the mean value in the upwind CONTROL area by 48%-116% and were generally found at an average distance of 39 km from the edge of the urban center or 64 km from the center of the city. These results are quite consistent studies of St. Louis (e.g' METROMEX) and Chicago almost two decades ago and more recent studies in the Atlanta and Mexico City areas.

Heavy rainfall increases nestling mortality of an Arctic top predator: experimental evidence and long-term trend in peregrine falcons.

PubMed

Anctil, Alexandre; Franke, Alastair; Bêty, Joël

2014-03-01

Although animal population dynamics have often been correlated with fluctuations in precipitation, causal relationships have rarely been demonstrated in wild birds. We combined nest observations with a field experiment to investigate the direct effect of rainfall on survival of peregrine falcon (Falco peregrinus) nestlings in the Canadian Arctic. We then used historical data to evaluate if recent changes in the precipitation regime could explain the long-term decline of falcon annual productivity. Rainfall directly caused more than one-third of the recorded nestling mortalities. Juveniles were especially affected by heavy rainstorms (≥8 mm/day). Nestlings sheltered from rainfall by a nest box had significantly higher survival rates. We found that the increase in the frequency of heavy rain over the last three decades is likely an important factor explaining the recent decline in falcon nestling survival rates, and hence the decrease in annual breeding productivity of the population. Our study is among the first experimental demonstrations of the direct link between rainfall and survival in wild birds, and clearly indicates that top arctic predators can be significantly impacted by changes in precipitation regime.

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.

[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.

Study of acid mine drainage management with evaluating climate and rainfall in East Pit 3 West Banko coal mine

NASA Astrophysics Data System (ADS)

Rochyani, Neny

2017-11-01

Acid mine drainage is a major problem for the mining environment. The main factor that formed acid mine drainage is the volume of rainfall. Therefore, it is important to know clearly the main climate pattern of rainfall and season on the management of acid mine drainage. This study focuses on the effects of rainfall on acid mine water management. Based on daily rainfall data, monthly and seasonal patterns by using Gumbel approach is known the amount of rainfall that occurred in East Pit 3 West Banko area. The data also obtained the highest maximum daily rainfall on 165 mm/day and the lowest at 76.4 mm/day, where it is known that the rainfall conditions during the period 2007 - 2016 is from November to April so the use of lime is also slightly, While the low rainfall is from May to October and the use of lime will be more and more. Based on calculation of lime requirement for each return period, it can be seen the total of lime and financial requirement for treatment of each return period.

Sampling errors for satellite-derived tropical rainfall - Monte Carlo study using a space-time stochastic model

NASA Technical Reports Server (NTRS)

Bell, Thomas L.; Abdullah, A.; Martin, Russell L.; North, Gerald R.

1990-01-01

Estimates of monthly average rainfall based on satellite observations from a low earth orbit will differ from the true monthly average because the satellite observes a given area only intermittently. This sampling error inherent in satellite monitoring of rainfall would occur even if the satellite instruments could measure rainfall perfectly. The size of this error is estimated for a satellite system being studied at NASA, the Tropical Rainfall Measuring Mission (TRMM). First, the statistical description of rainfall on scales from 1 to 1000 km is examined in detail, based on rainfall data from the Global Atmospheric Research Project Atlantic Tropical Experiment (GATE). A TRMM-like satellite is flown over a two-dimensional time-evolving simulation of rainfall using a stochastic model with statistics tuned to agree with GATE statistics. The distribution of sampling errors found from many months of simulated observations is found to be nearly normal, even though the distribution of area-averaged rainfall is far from normal. For a range of orbits likely to be employed in TRMM, sampling error is found to be less than 10 percent of the mean for rainfall averaged over a 500 x 500 sq km area.

Rainfall and Sheet Power Equation for Interrill Erosion on Steep Hillslope

NASA Astrophysics Data System (ADS)

Shin, S.; Park, S.; Pierson, F. B.; Al-Hamdan, O. Z.; Williams, C. J.

2012-12-01

Splash and sheet erosion processes dominate on most undisturbed hillslopes of rangeland. Interrill soil erosion should consider the influence of both raindrop and sheet flow to work of soil particles detached by raindrop impact and transported by rainfall-disturbed sheet flow. Interrill erosion equations that combine the influence of both rainfall and runoff have been proposed by several researchers. However most approaches to modeling interrill erosion have been based on statistical relationships given the inherent complexity in derivation of broadly-applicable physically-based erosion parameters. In this study, a rainfall and sheet power equation to evaluate interrill sediment yields (Qs) was derived from the sum of rainfall power and sheet power expressed by rainfall intensity: Qs=a(cosθ/L){α sinθ ∑ I(t)^(11/9)+β tanθ^(1/2) ∑ (1-fr(t))^(5/3) I(t)^(5/3)}^b, where I(t) is rainfall intensity, θ is slope angle, fr(t) is infiltration rate, a, b, α, and β are coefficients, sinθ I(t)^(11/9) is the rainfall power term, and tanθ^(1/2) (1-fr(t))^(5/3) I(t)^(5/3) is the sheet power term. The rainfall power ratio and sheet power ratio decreased and increased with increased rainfall intensity, respectively. The sheet power term depended greatly on infiltration rate controlled by rainfall intensity, vegetation cover, and soil condition. The rainfall and sheet power equation assuming that α and β is 0 was evaluated using field data from plots on steep hillslopes and showed the better correlation with sediment yields than rainfall kinetic energy, runoff discharge, or interrill equations based on rainfall intensity and runoff discharge founded in the literature. This equation successfully explained physical processes for soil erosion that rainfall power is dominant under low rainfall and sheet power is dominant under heavy rainfall. Additional experimental data is needed to assess coefficients of the power equation to determine the relative quantities of rainfall power and sheet power and to evaluate the erosion efficiency of interactions between raindrop impact and sheet flow and soil erodibility. Acknowledgements: This work was supported by a grant (Code#'08 RTIP B-01) from Regional Technology Innovation Program funded by Ministry of Land, Transport and Maritime Affairs of Korean government.;

Regional Assessment of Storm-triggered Shall Landslide Risks using the SLIDE (SLope-Infiltration-Distributed Equilibrium) Model

NASA Astrophysics Data System (ADS)

Hong, Y.; Kirschbaum, D. B.; Fukuoka, H.

2011-12-01

The key to advancing the predictability of rainfall-triggered landslides is to use physically based slope-stability models that simulate the dynamical response of the subsurface moisture to spatiotemporal variability of rainfall in complex terrains. An early warning system applying such physical models has been developed to predict rainfall-induced shallow landslides over Java Island in Indonesia and Honduras. The prototyped early warning system integrates three major components: (1) a susceptibility mapping or hotspot identification component based on a land surface geospatial database (topographical information, maps of soil properties, and local landslide inventory etc.); (2) a satellite-based precipitation monitoring system (http://trmm.gsfc.nasa.gov) and a precipitation forecasting model (i.e. Weather Research Forecast); and (3) a physically-based, rainfall-induced landslide prediction model SLIDE (SLope-Infiltration-Distributed Equilibrium). The system utilizes the modified physical model to calculate a Factor of Safety (FS) that accounts for the contribution of rainfall infiltration and partial saturation to the shear strength of the soil in topographically complex terrains. The system's prediction performance has been evaluated using a local landslide inventory. In Java Island, Indonesia, evaluation of SLIDE modeling results by local news reports shows that the system successfully predicted landslides in correspondence to the time of occurrence of the real landslide events. Further study of SLIDE is implemented in Honduras where Hurricane Mitch triggered widespread landslides in 1998. Results shows within the approximately 1,200 square kilometers study areas, the values of hit rates reached as high as 78% and 75%, while the error indices were 35% and 49%. Despite positive model performance, the SLIDE model is limited in the early warning system by several assumptions including, using general parameter calibration rather than in situ tests and neglecting geologic information. Advantages and limitations of this model will be discussed with respect to future applications of landslide assessment and prediction over large scales. In conclusion, integration of spatially distributed remote sensing precipitation products and in-situ datasets and physical models in this prototype system enable us to further develop a regional early warning tool in the future for forecasting storm-induced landslides.

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

NASA Astrophysics Data System (ADS)

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

2015-06-01

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

Hydro-mechanical mechanism and thresholds of rainfall-induced unsaturated landslides

NASA Astrophysics Data System (ADS)

Yang, Zongji; Lei, Xiaoqin; Huang, Dong; Qiao, Jianping

2017-04-01

The devastating Ms 8 Wenchuan earthquake in 2008 created the greatest number of co-seismic mountain hazards ever recorded in China. However, the dynamics of rainfall induced mass remobilization and transport deposits after giant earthquake are not fully understood. Moreover, rainfall intensity and duration (I-D) methods are the predominant early warning indicators of rainfall-induced landslides in post-earthquake region, which are a convenient and straight-forward way to predict the hazards. However, the rainfall-based criteria and thresholds are generally empirical and based on statistical analysis,consequently, they ignore the failure mechanisms of the landslides. This study examines the mechanism and hydro-mechanical behavior and thresholds of these unsaturated deposits under the influence of rainfall. To accomplish this, in situ experiments were performed in an instrumented landslide deposit, The field experimental tests were conducted on a natural co-seismic fractured slope to 1) simulate rainfall-induced shallow failures in the depression channels of a debris flow catchment in an earthquake-affected region, 2)explore the mechanisms and transient processes associated with hydro-mechanical parameter variations in response to the infiltration of rainfall, and 3) identify the hydrologic parameter thresholds and critical criteria of gravitational erosion in areas prone to mass remobilization as a source of debris flows. These experiments provided instrumental evidence and directly proved that post-earthquake rainfall-induced mass remobilization occurred under unsaturated conditions in response to transient rainfall infiltration, and revealed the presence of transient processes and the dominance of preferential flow paths during rainfall infiltration. A hydro-mechanical method was adopted for the transient hydrologic process modelling and unsaturated slope stability analysis. and the slope failures during the experimental test were reproduced by the model, indicating that the decrease in matrix suction and increase in moisture content in response to rainfall infiltration contributed greatly to post-earthquake shallow mass movement. Thus, a threshold model for the initiation of mass remobilization is proposed based on correlations between slope stability and volumetric water content and matrix suction As a complement to rainfall-based early warning strategies, the water content and suction threshold models based on the water infiltration induced slope failure mechanism. the proposed method are expected to improve the accuracy of prediction and early warnings of post-earthquake mountain hazards

A Remote Sensing-Based Tool for Assessing Rainfall-Driven Hazards

PubMed Central

Wright, Daniel B.; Mantilla, Ricardo; Peters-Lidard, Christa D.

2018-01-01

RainyDay is a Python-based platform that couples rainfall remote sensing data with Stochastic Storm Transposition (SST) for modeling rainfall-driven hazards such as floods and landslides. SST effectively lengthens the extreme rainfall record through temporal resampling and spatial transposition of observed storms from the surrounding region to create many extreme rainfall scenarios. Intensity-Duration-Frequency (IDF) curves are often used for hazard modeling but require long records to describe the distribution of rainfall depth and duration and do not provide information regarding rainfall space-time structure, limiting their usefulness to small scales. In contrast, RainyDay can be used for many hazard applications with 1-2 decades of data, and output rainfall scenarios incorporate detailed space-time structure from remote sensing. Thanks to global satellite coverage, RainyDay can be used in inaccessible areas and developing countries lacking ground measurements, though results are impacted by remote sensing errors. RainyDay can be useful for hazard modeling under nonstationary conditions. PMID:29657544

A Remote Sensing-Based Tool for Assessing Rainfall-Driven Hazards.

PubMed

Wright, Daniel B; Mantilla, Ricardo; Peters-Lidard, Christa D

2017-04-01

RainyDay is a Python-based platform that couples rainfall remote sensing data with Stochastic Storm Transposition (SST) for modeling rainfall-driven hazards such as floods and landslides. SST effectively lengthens the extreme rainfall record through temporal resampling and spatial transposition of observed storms from the surrounding region to create many extreme rainfall scenarios. Intensity-Duration-Frequency (IDF) curves are often used for hazard modeling but require long records to describe the distribution of rainfall depth and duration and do not provide information regarding rainfall space-time structure, limiting their usefulness to small scales. In contrast, RainyDay can be used for many hazard applications with 1-2 decades of data, and output rainfall scenarios incorporate detailed space-time structure from remote sensing. Thanks to global satellite coverage, RainyDay can be used in inaccessible areas and developing countries lacking ground measurements, though results are impacted by remote sensing errors. RainyDay can be useful for hazard modeling under nonstationary conditions.

A Remote Sensing-Based Tool for Assessing Rainfall-Driven Hazards

NASA Technical Reports Server (NTRS)

Wright, Daniel B.; Mantilla, Ricardo; Peters-Lidard, Christa D.

2017-01-01

RainyDay is a Python-based platform that couples rainfall remote sensing data with Stochastic Storm Transposition (SST) for modeling rainfall-driven hazards such as floods and landslides. SST effectively lengthens the extreme rainfall record through temporal resampling and spatial transposition of observed storms from the surrounding region to create many extreme rainfall scenarios. Intensity-Duration-Frequency (IDF) curves are often used for hazard modeling but require long records to describe the distribution of rainfall depth and duration and do not provide information regarding rainfall space-time structure, limiting their usefulness to small scales. In contrast, Rainy Day can be used for many hazard applications with 1-2 decades of data, and output rainfall scenarios incorporate detailed space-time structure from remote sensing. Thanks to global satellite coverage, Rainy Day can be used in inaccessible areas and developing countries lacking ground measurements, though results are impacted by remote sensing errors. Rainy Day can be useful for hazard modeling under nonstationary conditions.

Sensitivity of quantitative groundwater recharge estimates to volumetric and distribution uncertainty in rainfall forcing products

NASA Astrophysics Data System (ADS)

Werner, Micha; Westerhoff, Rogier; Moore, Catherine

2017-04-01

Quantitative estimates of recharge due to precipitation excess are an important input to determining sustainable abstraction of groundwater resources, as well providing one of the boundary conditions required for numerical groundwater modelling. Simple water balance models are widely applied for calculating recharge. In these models, precipitation is partitioned between different processes and stores; including surface runoff and infiltration, storage in the unsaturated zone, evaporation, capillary processes, and recharge to groundwater. Clearly the estimation of recharge amounts will depend on the estimation of precipitation volumes, which may vary, depending on the source of precipitation data used. However, the partitioning between the different processes is in many cases governed by (variable) intensity thresholds. This means that the estimates of recharge will not only be sensitive to input parameters such as soil type, texture, land use, potential evaporation; but mainly to the precipitation volume and intensity distribution. In this paper we explore the sensitivity of recharge estimates due to difference in precipitation volumes and intensity distribution in the rainfall forcing over the Canterbury region in New Zealand. We compare recharge rates and volumes using a simple water balance model that is forced using rainfall and evaporation data from; the NIWA Virtual Climate Station Network (VCSN) data (which is considered as the reference dataset); the ERA-Interim/WATCH dataset at 0.25 degrees and 0.5 degrees resolution; the TRMM-3B42 dataset; the CHIRPS dataset; and the recently releases MSWEP dataset. Recharge rates are calculated at a daily time step over the 14 year period from the 2000 to 2013 for the full Canterbury region, as well as at eight selected points distributed over the region. Lysimeter data with observed estimates of recharge are available at four of these points, as well as recharge estimates from the NGRM model, an independent model constructed using the same base data and forced with the VCSN precipitation dataset. Results of the comparison of the rainfall products show that there are significant differences in precipitation volume between the forcing products; in the order of 20% at most points. Even more significant differences can be seen, however, in the distribution of precipitation. For the VCSN data wet days (defined as >0.1mm precipitation) occur on some 20-30% of days (depending on location). This is reasonably reflected in the TRMM and CHIRPS data, while for the re-analysis based products some 60%to 80% of days are wet, albeit at lower intensities. These differences are amplified in the recharge estimates. At most points, volumetric differences are in the order of 40-60%, though difference may range into several orders of magnitude. The frequency distributions of recharge also differ significantly, with recharge over 0.1 mm occurring on 4-6% of days for the VCNS, CHIRPS, and TRMM datasets, but up to the order of 12% of days for the re-analysis data. Comparison against the lysimeter data show estimates to be reasonable, in particular for the reference datasets. Surprisingly some estimates of the lower resolution re-analysis datasets are reasonable, though this does seem to be due to lower recharge being compensated by recharge occurring more frequently. These results underline the importance of correct representation of rainfall volumes, as well as of distribution, particularly when evaluating possible changes to for example changes in precipitation intensity and volume. This holds for precipitation data derived from satellite based and re-analysis products, but also for interpolated data from gauges, where the distribution of intensities is strongly influenced by the interpolation process.

Compilation of water resources development and hydrologic data of Saipan, Mariana Islands

USGS Publications Warehouse

Van der Brug, Otto

1985-01-01

Saipan is the largest island of the Northern Mariana Islands, a chain of 14 islands north of Guam. Saipan comprises one third of the land area of the islands. No long-term rainfall record is available at any location, but some rainfall records are for periods up to 16 years, some of which began in 1901. Average annual rainfall for the island is 81 inches, with the southern end receiving about 10 inches less annually than the rest of the island. The amount of rainfall which runs off in northeast Saipan ranges from 23 to 64 percent and averages about 40 percent. Runoff on the rest of the island is from springs or occurs only during heavy rainfall. Surface-water development appears impractical. Ground water is the main source of water for the island and production was almost 4 million gallons per day in 1982. However, chloride concentration in ground water exceeds 1,000 milligrams per liter in many locations. The average chloride concentration of the domestic water stays near the maximum permissible level (600 milligrams per liter). This report summarizes the history of the water-resources development and presents all available hydrologic data, including rainfall records since 1901, streamflow records since 1968, and drilling logs, pumping tests, chemical analyses, and production figures from 180 testholes and wells drilled on Saipan. (USGS)

Development of Sub-Daily Intensity Duration Frequency (IDF) Curves for Major Urban Areas in India

NASA Astrophysics Data System (ADS)

Ali, H.; Mishra, V.

2014-12-01

Extreme precipitation events disrupt urban transportation and cause enormous damage to infrastructure. Urban areas are fast responding catchments due to significant impervious surface. Stormwater designs based on daily rainfall data provide inadequate information. We, therefore, develop intensity-duration-frequency curves using sub-daily (1 hour to 12 hour) rainfall data for 57 major urban areas in India. While rain gage stations data from urban areas are most suitable, but stations are unevenly distributed and their data have gaps and inconsistencies. Therefore, we used hourly rainfall data from the Modern Era Retrospective-analysis for Research and Applications (MERRA), which provides a long term data (1979 onwards). Since reanalysis products have uncertainty associated with them we need to enhance their accuracy before their application. We compared daily rain gage station data obtained from Global Surface Summary of Day Data (GSOD) available for 65 stations for the period of 2000-2010 with gridded daily rainfall data provided by Indian Meteorological Department (IMD). 3-hourly data from NOAA/Climate Prediction Center morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) were aggregated to daily for comparison with GSOD station data . TMPA is found to be best correlated with GSOD data. We used TMPA data to correct MERRA's hourly precipitation, which were applied to develop IDF curves. We compared results with IDF curves from empirical methods and found substantial disparities in the existing stormwater designs in India.

Drought stress suppresses phytoalexin production against Fusarium verticilliodes

USDA-ARS?s Scientific Manuscript database

Global climate change involves rising temperatures and potentially decreased rainfall or changes in rainfall patterns, which could dramatically decrease the yield of food crops. Drought alone can impair plant growth and development, but in nature plants are continuously exposed to both abiotic and b...

Statistical bias correction modelling for seasonal rainfall forecast for the case of Bali island

NASA Astrophysics Data System (ADS)

Lealdi, D.; Nurdiati, S.; Sopaheluwakan, A.

2018-04-01

Rainfall is an element of climate which is highly influential to the agricultural sector. Rain pattern and distribution highly determines the sustainability of agricultural activities. Therefore, information on rainfall is very useful for agriculture sector and farmers in anticipating the possibility of extreme events which often cause failures of agricultural production. This research aims to identify the biases from seasonal forecast products from ECMWF (European Centre for Medium-Range Weather Forecasts) rainfall forecast and to build a transfer function in order to correct the distribution biases as a new prediction model using quantile mapping approach. We apply this approach to the case of Bali Island, and as a result, the use of bias correction methods in correcting systematic biases from the model gives better results. The new prediction model obtained with this approach is better than ever. We found generally that during rainy season, the bias correction approach performs better than in dry season.

Predicting water table response to rainfall events, central Florida.

PubMed

van Gaalen, J F; Kruse, S; Lafrenz, W B; Burroughs, S M

2013-01-01

A rise in water table in response to a rainfall event is a complex function of permeability, specific yield, antecedent soil-water conditions, water table level, evapotranspiration, vegetation, lateral groundwater flow, and rainfall volume and intensity. Predictions of water table response, however, commonly assume a linear relationship between response and rainfall based on cumulative analysis of water level and rainfall logs. By identifying individual rainfall events and responses, we examine how the response/rainfall ratio varies as a function of antecedent water table level (stage) and rainfall event size. For wells in wetlands and uplands in central Florida, incorporating stage and event size improves forecasting of water table rise by more than 30%, based on 10 years of data. At the 11 sites studied, the water table is generally least responsive to rainfall at smallest and largest rainfall event sizes and at lower stages. At most sites the minimum amount of rainfall required to induce a rise in water table is fairly uniform when the water table is within 50 to 100 cm of land surface. Below this depth, the minimum typically gradually increases with depth. These observations can be qualitatively explained by unsaturated zone flow processes. Overall, response/rainfall ratios are higher in wetlands and lower in uplands, presumably reflecting lower specific yields and greater lateral influx in wetland sites. Pronounced depth variations in rainfall/response ratios appear to correlate with soil layer boundaries, where corroborating data are available. © 2012, The Author(s). Groundwater © 2012, National Ground Water Association.

Adequacy of satellite derived rainfall data for stream flow modeling

USGS Publications Warehouse

Artan, G.; Gadain, Hussein; Smith, Jodie; Asante, Kwasi; Bandaragoda, C.J.; Verdin, J.P.

2007-01-01

Floods are the most common and widespread climate-related hazard on Earth. Flood forecasting can reduce the death toll associated with floods. Satellites offer effective and economical means for calculating areal rainfall estimates in sparsely gauged regions. However, satellite-based rainfall estimates have had limited use in flood forecasting and hydrologic stream flow modeling because the rainfall estimates were considered to be unreliable. In this study we present the calibration and validation results from a spatially distributed hydrologic model driven by daily satellite-based estimates of rainfall for sub-basins of the Nile and Mekong Rivers. The results demonstrate the usefulness of remotely sensed precipitation data for hydrologic modeling when the hydrologic model is calibrated with such data. However, the remotely sensed rainfall estimates cannot be used confidently with hydrologic models that are calibrated with rain gauge measured rainfall, unless the model is recalibrated. ?? Springer Science+Business Media, Inc. 2007.

Performance of ICTP's RegCM4 in Simulating the Rainfall Characteristics over the CORDEX-SEA Domain

NASA Astrophysics Data System (ADS)

Neng Liew, Ju; Tangang, Fredolin; Tieh Ngai, Sheau; Chung, Jing Xiang; Narisma, Gemma; Cruz, Faye Abigail; Phan Tan, Van; Thanh, Ngo-Duc; Santisirisomboon, Jerasron; Milindalekha, Jaruthat; Singhruck, Patama; Gunawan, Dodo; Satyaningsih, Ratna; Aldrian, Edvin

2015-04-01

The performance of the RegCM4 in simulating rainfall variations over the Southeast Asia regions was examined. Different combinations of six deep convective parameterization schemes, namely i) Grell scheme with Arakawa-Schubert closure assumption, ii) Grell scheme with Fritch-Chappel closure assumption, iii) Emanuel MIT scheme, iv) mixed scheme with Emanuel MIT scheme over the Ocean and the Grell scheme over the land, v) mixed scheme with Grell scheme over the land and Emanuel MIT scheme over the ocean and (vi) Kuo scheme, and three ocean flux treatments were tested. In order to account for uncertainties among the observation products, four different gridded rainfall products were used for comparison. The simulated climate is generally drier over the equatorial regions and slightly wetter over the mainland Indo-China compare to the observation. However, simulation with MIT cumulus scheme used over the land area consistently produces large amplitude of positive rainfall biases, although it simulates more realistic annual rainfall variations. The simulations are found less sensitive to treatment of ocean fluxes. Although the simulations produced the rainfall climatology well, all of them simulated much stronger interannual variability compare to that of the observed. Nevertheless, the time evolution of the inter-annual variations was well reproduced particularly over the eastern part of maritime continent. Over the mainland Southeast Asia (SEA), unrealistic rainfall anomalies processes were simulated. The lacking of summer season air-sea interaction results in strong oceanic forcings over the regions, leading to positive rainfall anomalies during years with warm ocean temperature anomalies. This incurs much stronger atmospheric forcings on the land surface processes compare to that of the observed. A score ranking system was designed to rank the simulations according to their performance in reproducing different aspects of rainfall characteristics. The result suggests that the simulation with Emanuel MIT convective scheme and BATs land surface scheme produces better collective performance compare to the rest of the simulations.

Thermodynamic sensitivities in observed and simulated extreme-rain-producing mesoscale convective systems

NASA Astrophysics Data System (ADS)

Schumacher, R. S.; Peters, J. M.

2015-12-01

Mesoscale convective systems (MCSs) are responsible for a large fraction of warm-season extreme rainfall events over the continental United States, as well as other midlatitude regions globally. The rainfall production in these MCSs is determined by numerous factors, including the large-scale forcing for ascent, the organization of the convection, cloud microphysical processes, and the surrounding thermodynamic and kinematic environment. Furthermore, heavy-rain-producing MCSs are most common at night, which means that well-studied mechanisms for MCS maintenance and organization such as cold pools (gravity currents) are not always at work. In this study, we use numerical model simulations and recent field observations to investigate the sensitivity of low-level MCS structures, and their influences on rainfall, to the details of the thermodynamic environment. In particular, small alterations to the initial conditions in idealized and semi-idealized simulations result in comparatively large precipitation changes, both in terms of the intensity and the spatial distribution. The uncertainties in the thermodynamic enviroments in the model simulations will be compared with high-resolution observations from the Plains Elevated Convection At Night (PECAN) field experiment in 2015. The results have implications for the paradigms of "surface-based" versus "elevated" convection, as well as for the predictability of warm-season convective rainfall.

Development of a gridded meteorological dataset over Java island, Indonesia 1985–2014

PubMed Central

Yanto; Livneh, Ben; Rajagopalan, Balaji

2017-01-01

We describe a gridded daily meteorology dataset consisting of precipitation, minimum and maximum temperature over Java Island, Indonesia at 0.125°×0.125° (~14 km) resolution spanning 30 years from 1985–2014. Importantly, this data set represents a marked improvement from existing gridded data sets over Java with higher spatial resolution, derived exclusively from ground-based observations unlike existing satellite or reanalysis-based products. Gap-infilling and gridding were performed via the Inverse Distance Weighting (IDW) interpolation method (radius, r, of 25 km and power of influence, α, of 3 as optimal parameters) restricted to only those stations including at least 3,650 days (~10 years) of valid data. We employed MSWEP and CHIRPS rainfall products in the cross-validation. It shows that the gridded rainfall presented here produces the most reasonable performance. Visual inspection reveals an increasing performance of gridded precipitation from grid, watershed to island scale. The data set, stored in a network common data form (NetCDF), is intended to support watershed-scale and island-scale studies of short-term and long-term climate, hydrology and ecology. PMID:28534871

Cascade rainfall disaggregation application in U.S. Central Plains

USDA-ARS?s Scientific Manuscript database

Hourly rainfall are increasingly used in complex, process-based simulations of the environment. Long records of daily rainfall are common, but long continuous records of hourly rainfall are rare and must be developed. A Multiplicative Random Cascade (MRC) model is proposed to disaggregate observed d...

A quadratic regression modelling on paddy production in the area of Perlis

NASA Astrophysics Data System (ADS)

Goh, Aizat Hanis Annas; Ali, Zalila; Nor, Norlida Mohd; Baharum, Adam; Ahmad, Wan Muhamad Amir W.

2017-08-01

Polynomial regression models are useful in situations in which the relationship between a response variable and predictor variables is curvilinear. Polynomial regression fits the nonlinear relationship into a least squares linear regression model by decomposing the predictor variables into a kth order polynomial. The polynomial order determines the number of inflexions on the curvilinear fitted line. A second order polynomial forms a quadratic expression (parabolic curve) with either a single maximum or minimum, a third order polynomial forms a cubic expression with both a relative maximum and a minimum. This study used paddy data in the area of Perlis to model paddy production based on paddy cultivation characteristics and environmental characteristics. The results indicated that a quadratic regression model best fits the data and paddy production is affected by urea fertilizer application and the interaction between amount of average rainfall and percentage of area defected by pest and disease. Urea fertilizer application has a quadratic effect in the model which indicated that if the number of days of urea fertilizer application increased, paddy production is expected to decrease until it achieved a minimum value and paddy production is expected to increase at higher number of days of urea application. The decrease in paddy production with an increased in rainfall is greater, the higher the percentage of area defected by pest and disease.

Satellite-Based Assessment of Rainfall-Triggered Landslide Hazard for Situational Awareness

NASA Astrophysics Data System (ADS)

Kirschbaum, Dalia; Stanley, Thomas

2018-03-01

Determining the time, location, and severity of natural disaster impacts is fundamental to formulating mitigation strategies, appropriate and timely responses, and robust recovery plans. A Landslide Hazard Assessment for Situational Awareness (LHASA) model was developed to indicate potential landslide activity in near real-time. LHASA combines satellite-based precipitation estimates with a landslide susceptibility map derived from information on slope, geology, road networks, fault zones, and forest loss. Precipitation data from the Global Precipitation Measurement (GPM) mission are used to identify rainfall conditions from the past 7 days. When rainfall is considered to be extreme and susceptibility values are moderate to very high, a "nowcast" is issued to indicate the times and places where landslides are more probable. When LHASA nowcasts were evaluated with a Global Landslide Catalog, the probability of detection (POD) ranged from 8% to 60%, depending on the evaluation period, precipitation product used, and the size of the spatial and temporal window considered around each landslide point. Applications of the LHASA system are also discussed, including how LHASA is used to estimate long-term trends in potential landslide activity at a nearly global scale and how it can be used as a tool to support disaster risk assessment. LHASA is intended to provide situational awareness of landslide hazards in near real-time, providing a flexible, open-source framework that can be adapted to other spatial and temporal scales based on data availability.

Potential aboveground biomass in drought-prone forest used for rangeland pastoralism.

PubMed

Fensham, R J; Fairfax, R J; Dwyer, J M

2012-04-01

The restoration of cleared dry forest represents an important opportunity to sequester atmospheric carbon. In order to account for this potential, the influences of climate, soils, and disturbance need to be deciphered. A data set spanning a region defined the aboveground biomass of mulga (Acacia aneura) dry forest and was analyzed in relation to climate and soil variables using a Bayesian model averaging procedure. Mean annual rainfall had an overwhelmingly strong positive effect, with mean maximum temperature (negative) and soil depth (positive) also important. The data were collected after a recent drought, and the amount of recent tree mortality was weakly positively related to a measure of three-year rainfall deficit, and maximum temperature (positive), soil depth (negative), and coarse sand (negative). A grazing index represented by the distance of sites to watering points was not incorporated by the models. Stark management contrasts, including grazing exclosures, can represent a substantial part of the variance in the model predicting biomass, but the impact of management was unpredictable and was insignificant in the regional data set. There was no evidence of density-dependent effects on tree mortality. Climate change scenarios represented by the coincidence of historical extreme rainfall deficit with extreme temperature suggest mortality of 30.1% of aboveground biomass, compared to 21.6% after the recent (2003-2007) drought. Projections for recovery of forest using a mapping base of cleared areas revealed that the greatest opportunities for restoration of aboveground biomass are in the higher-rainfall areas, where biomass accumulation will be greatest and droughts are less intense. These areas are probably the most productive for rangeland pastoralism, and the trade-off between pastoral production and carbon sequestration will be determined by market forces and carbon-trading rules.

Design of a reliable and operational landslide early warning system at regional scale

NASA Astrophysics Data System (ADS)

Calvello, Michele; Piciullo, Luca; Gariano, Stefano Luigi; Melillo, Massimo; Brunetti, Maria Teresa; Peruccacci, Silvia; Guzzetti, Fausto

2017-04-01

Landslide early warning systems at regional scale are used to warn authorities, civil protection personnel and the population about the occurrence of rainfall-induced landslides over wide areas, typically through the prediction and measurement of meteorological variables. A warning model for these systems must include a regional correlation law and a decision algorithm. A regional correlation law can be defined as a functional relationship between rainfall and landslides; it is typically based on thresholds of rainfall indicators (e.g., cumulated rainfall, rainfall duration) related to different exceedance probabilities of landslide occurrence. A decision algorithm can be defined as a set of assumptions and procedures linking rainfall thresholds to warning levels. The design and the employment of an operational and reliable early warning system for rainfall-induced landslides at regional scale depend on the identification of a reliable correlation law as well as on the definition of a suitable decision algorithm. Herein, a five-step process chain addressing both issues and based on rainfall thresholds is proposed; the procedure is tested in a landslide-prone area of the Campania region in southern Italy. To this purpose, a database of 96 shallow landslides triggered by rainfall in the period 2003-2010 and rainfall data gathered from 58 rain gauges are used. First, a set of rainfall thresholds are defined applying a frequentist method to reconstructed rainfall conditions triggering landslides in the test area. In the second step, several thresholds at different exceedance probabilities are evaluated, and different percentile combinations are selected for the activation of three warning levels. Subsequently, within steps three and four, the issuing of warning levels is based on the comparison, over time and for each combination, between the measured rainfall and the pre-defined warning level thresholds. Finally, the optimal percentile combination to be employed in the regional early warning system is selected evaluating the model performance in terms of success and error indicators by means of the "event, duration matrix, performance" (EDuMaP) method.

Herbaceous Legume Encroachment Reduces Grass Productivity and Density in Arid Rangelands.

PubMed

Wagner, Thomas C; Hane, Susanne; Joubert, Dave F; Fischer, Christina

2016-01-01

Worldwide savannas and arid grasslands are mainly used for livestock grazing, providing livelihood to over a billion people. While normally dominated by perennial C4 grasses, these rangelands are increasingly affected by the massive spread of native, mainly woody legumes. The consequences are often a repression of grass cover and productivity, leading to a reduced carrying capacity. While such encroachment by woody plants has been extensively researched, studies on similar processes involving herbaceous species are rare. We studied the impact of a sustained and massive spread of the native herbaceous legume Crotalaria podocarpa in Namibia's escarpment region on the locally dominant fodder grasses Stipagrostis ciliata and Stipagrostis uniplumis. We measured tussock densities, biomass production of individual tussocks and tussock dormancy state of Stipagrostis on ten 10 m x 10 m plots affected and ten similarly-sized plots unaffected by C. podocarpa over eight consecutive years and under different seasonal rainfalls and estimated the potential relative productivity of the land. We found the percentage of active Stipagrostis tussocks and the biomass production of individual tussocks to increase asymptotically with higher seasonal rainfall reaching a maximum around 300 mm while the land's relative productivity under average local rainfall conditions reached only 40% of its potential. Crotalaria podocarpa encroachment had no effect on the proportion of productive grass tussocks, but reduced he productivity of individual Stipagrostis tussocks by a third. This effect of C. podocarpa on grass productivity was immediate and direct and was not compensated for by above-average rainfall. Besides this immediate effect, over time, the density of grass tussocks declined by more than 50% in areas encroached by C. podocarpa further and lastingly reducing the lands carrying capacity. The effects of C. podocarpa on grass productivity hereby resemble those of woody encroachers. Therefore, against the background of global change, the spread of herbaceous legumes and the underlying patterns needs to be further investigated to develop adequate counter measures for a sustainable land use.

Toward an Improved Understanding of the Tropical Energy Budget Using TRMM-based Atmospheric Radiative Heating Products

NASA Astrophysics Data System (ADS)

L'Ecuyer, T.; McGarragh, G.; Ellis, T.; Stephens, G.; Olson, W.; Grecu, M.; Shie, C.; Jiang, X.; Waliser, D.; Li, J.; Tian, B.

2008-05-01

It is widely recognized that clouds and precipitation exert a profound influence on the propagation of radiation through the Earth's atmosphere. In fact, feedbacks between clouds, radiation, and precipitation represent one of the most important unresolved factors inhibiting our ability to predict the consequences of global climate change. Since its launch in late 1997, the Tropical Rainfall Measuring Mission (TRMM) has collected more than a decade of rainfall measurements that now form the gold standard of satellite-based precipitation estimates. Although not as widely advertised, the instruments aboard TRMM are also well-suited to the problem of characterizing the distribution of atmospheric heating in the tropics and a series of algorithms have recently been developed for estimating profiles of radiative and latent heating from these measurements. This presentation will describe a new multi-sensor tropical radiative heating product derived primarily from TRMM observations. Extensive evaluation of the products using a combination of ground and satellite-based observations is used to place the dataset in the context of existing techniques for quantifying atmospheric radiative heating. Highlights of several recent applications of the dataset will be presented that illustrate its utility for observation-based analysis of energy and water cycle variability on seasonal to inter-annual timescales and evaluating the representation of these processes in numerical models. Emphasis will be placed on the problem of understanding the impacts of clouds and precipitation on atmospheric heating on large spatial scales, one of the primary benefits of satellite observations like those provided by TRMM.

Monitoring Lake and Reservoir Level: Satellite Observations, Modeling and Prediction

NASA Astrophysics Data System (ADS)

Ricko, M.; Birkett, C. M.; Adler, R. F.; Carton, J.

2013-12-01

Satellite measurements of lake and reservoir water levels complement in situ observations by providing stage information for un-gauged basins and by filling data gaps in gauge records. However, different satellite radar altimeter-derived continental water level products may differ significantly owing to choice of satellites and data processing methods. To explore the impacts of these differences, a direct comparison between three different altimeter-based surface water level estimates (USDA/NASA GRLM, LEGOS and ESA-DMU) will be presented and products validated with lake level gauge time series for lakes and reservoirs of a variety of sizes and conditions. The availability of satellite-based rainfall (i.e., TRMM and GPCP) and satellite-based lake/reservoir levels offers exciting opportunities to estimate and monitor the hydrologic properties of the lake systems. Here, a simple water balance model is utilized to relate net freshwater flux on a catchment basin to lake/reservoir level. Focused on tropical lakes and reservoirs it allows a comparison of the flux to altimetric lake level estimates. The combined use of model, satellite-based rainfall, evaporation information and reanalysis products, can be used to output water-level hindcasts and seasonal future forecasts. Such a tool is fundamental for understanding present-day and future variations in lake/reservoir levels and enabling a better understand of climatic variations on inter-annual to inter-decadal time-scales. New model-derived water level estimates of lakes and reservoirs, on regional to global scales, would assist communities with interests in climate studies focusing on extreme events, such as floods and droughts, and be important for water resources management.

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. ...

Economic Cotton Production over Irrigation Rates in the Southeast United States

USDA-ARS?s Scientific Manuscript database

Regardless of location, water availability affects Cotton (Gossypium hirsutum L.) yield potential and economic stability. Irrigation is used in the Southeast U.S. to supplement rainfall on nearly 50% of cotton acres in Georgia. Rainfall often interferes with the efficiency of irrigation, adding to...

Economic cotton production over irrigation rates in the southeast United States

USDA-ARS?s Scientific Manuscript database

Regardless of location, water availability affects Cotton (Gossypium hirsutum L.) yield potential and economic stability. Irrigation is used in the Southeast U.S. to supplement rainfall on nearly 50% of cotton acres in Georgia. Rainfall often interferes with the efficiency of irrigation, adding to...

Radar-raingauge data combination techniques: a revision and analysis of their suitability for urban hydrology.

PubMed

Wang, Li-Pen; Ochoa-Rodríguez, Susana; Simões, Nuno Eduardo; Onof, Christian; Maksimović, Cedo

2013-01-01

The applicability of the operational radar and raingauge networks for urban hydrology is insufficient. Radar rainfall estimates provide a good description of the spatiotemporal variability of rainfall; however, their accuracy is in general insufficient. It is therefore necessary to adjust radar measurements using raingauge data, which provide accurate point rainfall information. Several gauge-based radar rainfall adjustment techniques have been developed and mainly applied at coarser spatial and temporal scales; however, their suitability for small-scale urban hydrology is seldom explored. In this paper a review of gauge-based adjustment techniques is first provided. After that, two techniques, respectively based upon the ideas of mean bias reduction and error variance minimisation, were selected and tested using as case study an urban catchment (∼8.65 km(2)) in North-East London. The radar rainfall estimates of four historical events (2010-2012) were adjusted using in situ raingauge estimates and the adjusted rainfall fields were applied to the hydraulic model of the study area. The results show that both techniques can effectively reduce mean bias; however, the technique based upon error variance minimisation can in general better reproduce the spatial and temporal variability of rainfall, which proved to have a significant impact on the subsequent hydraulic outputs. This suggests that error variance minimisation based methods may be more appropriate for urban-scale hydrological applications.

Distribution and Prevalence of Parasitic Nematodes of Cowpea (Vigna unguiculata) in Burkina Faso.

PubMed

Sawadogo, A; Thio, B; Kiemde, S; Drabo, I; Dabire, C; Ouedraogo, J; Mullens, T R; Ehlers, J D; Roberts, P A

2009-06-01

A comprehensive survey of the plant parasitic nematodes associated with cowpea (Vigna unguiculata) production fields was carried out in the three primary agro-climatic zones of Burkina Faso in West Africa. Across the three zones, a total of 109 samples were collected from the farms of 32 villages to provide a representative coverage of the cowpea production areas. Samples of rhizosphere soil and samples of roots from actively growing cowpea plants were collected during mid- to late-season. Twelve plant-parasitic nematode genera were identified, of which six appeared to have significant parasitic potential on cowpea based on their frequency and abundance. These included Helicotylenchus, Meloidogyne, Pratylenchus, Scutellonema, Telotylenchus, and Tylenchorhynchus. Criconemella and Rotylenchulus also had significant levels of abundance and frequency, respectively. Of the primary genera, Meloidogyne, Pratylenchus, and Scutellonema contained species which are known or suspected to cause losses of cowpea yield in other parts of the world. According to the prevalence and distribution of these genera in Burkina Faso, their potential for damage to cowpea increased from the dry Sahelian semi-desert zone in the north (annual rainfall < 600 mm/year), through the north-central Soudanian zone (annual rainfall of 600-800 mm/year), to the wet Soudanian zone (annual rainfall ≥ 1000 mm) in the more humid south-western region of the country. This distribution trend was particularly apparent for the endoparasitic nematode Meloidogyne and the migratory endoparasite Pratylenchus.

Latent Heating Structures Derived from TRMM

NASA Technical Reports Server (NTRS)

Tao, W.-K.; Smith, E. A.; Adler, R.; Hou, A.; Kakar, R.; Krishnamurti, T.; Kummerow, C.; Lang, S.; Olson, W.; Satoh, S.

2004-01-01

Rainfall is the fundamental variable within the Earth's hydrological cycle because it is both the main forcing term leading to variations in continental and oceanic surface water budgets. The vertical distribution of latent heat release, which is accompanied with rain, modulates large-scale meridional and zonal circulations within the tropics as well as modifying the energetic efficiency of mid-latitude weather systems. Latent heat release itself is a consequence of phase changes between the vapor, liquid, and frozen states of water.This paper focuses on the retrieval of latent heat release from satellite measurements generated by the Tropical Rainfall Measuring Mission 0. The TRMM observatory, whose development was a joint US-Japan space endeavor, was launched in November 1997. TRMM measurements provide an accurate account of rainfall over the global tropics, information which can be .used to estimate the four-dimensional structure of latent heating across the entire tropical and sub-tropical regions. Various algorithm methodologies for estimating latent heating based on rain rate measurements from TRMM observations are described. The strengths and weaknesses of these algorithms, as well as the latent heating products generated by these algorithms, are also discussed along with validation analyses of the products. The investigation paper provides an overview of how TRMM-derived latent heating information is currently being used in conjunction with global weather and climate models, and concludes with remarks designed to stimulate further research on latent heating retrieval

40 CFR 415.331 - Specialized definitions.

Code of Federal Regulations, 2010 CFR

2010-07-01

... AND STANDARDS INORGANIC CHEMICALS MANUFACTURING POINT SOURCE CATEGORY Carbon Monoxide and By-Product... product, finished product, by-product, or waste product. The term “process wastewater” does not include..., intermediate product, finished product, by-product or waste product by means of (1) rainfall runoff; (2...

Online tools for uncovering data quality issues in satellite-based global precipitation products

NASA Astrophysics Data System (ADS)

Liu, Z.; Heo, G.

2015-12-01

Accurate and timely available global precipitation products are important to many applications such as flood forecasting, hydrological modeling, vector-borne disease research, crop yield estimates, etc. However, data quality issues such as biases and uncertainties are common in satellite-based precipitation products and it is important to understand these issues in applications. In recent years, algorithms using multi-satellites and multi-sensors for satellite-based precipitation estimates have become popular, such as the TRMM (Tropical Rainfall Measuring Mission) Multi-satellite Precipitation Analysis (TMPA) and the latest Integrated Multi-satellitE Retrievals for GPM (IMERG). Studies show that data quality issues for multi-satellite and multi-sensor products can vary with space and time and can be difficult to summarize. Online tools can provide customized results for a given area of interest, allowing customized investigation or comparison on several precipitation products. Because downloading data and software is not required, online tools can facilitate precipitation product evaluation and comparison. In this presentation, we will present online tools to uncover data quality issues in satellite-based global precipitation products. Examples will be presented as well.

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.

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.

Trends and spatial distribution of annual and seasonal rainfall in Ethiopia

USGS Publications Warehouse

Cheung, W.H.; Senay, G.B.; Singh, A.

2008-01-01

As a country whose economy is heavily dependent on low-productivity rainfed agriculture, rainfall trends are often cited as one of the more important factors in explaining various socio-economic problems such as food insecurity. Therefore, in order to help policymakers and developers make more informed decisions, this study investigated the temporal dynamics of rainfall and its spatial distribution within Ethiopia. Changes in rainfall were examined using data from 134 stations in 13 watersheds between 1960 and 2002. The variability and trends in seasonal and annual rainfall were analysed at the watershed scale with data (1) from all available years, and (2) excluding years that lacked observations from at least 25% of the gauges. Similar analyses were also performed at the gauge, regional, and national levels. By regressing annual watershed rainfall on time, results from the one-sample t-test show no significant changes in rainfall for any of the watersheds examined. However, in our regressions of seasonal rainfall averages against time, we found a significant decline in June to September rainfall (i.e. Kiremt) for the Baro-Akobo, Omo-Ghibe, Rift Valley, and Southern Blue Nile watersheds located in the southwestern and central parts of Ethiopia. While the gauge level analysis showed that certain gauge stations experienced recent changes in rainfall, these trends are not necessarily reflected at the watershed or regional levels.

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

PubMed

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

2017-06-23

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

40 CFR 415.441 - Specialized definitions.

Code of Federal Regulations, 2010 CFR

2010-07-01

... material, intermediate product, finished product, by-product, or waste product. The term “process... any raw material, intermediate product, finished product, by-product or waste product by means of (1) rainfall runoff; (2) accidental spills; (3) accidental leaks caused by the failure of process equipment...

Analysis of extreme rainfall events using attributes control charts in temporal rainfall processes

NASA Astrophysics Data System (ADS)

Villeta, María; Valencia, Jose Luis; Saá-Requejo, Antonio; María Tarquis, Ana

2015-04-01

The impacts of most intense rainfall events on agriculture and insurance industry can be very severe. This research focuses in the analysis of extreme rainfall events throughout the use of attributes control charts, which constitutes a usual tool in Statistical Process Control (SPC) but unusual in climate studios. Here, series of daily precipitations for the years 1931-2009 within a Spanish region are analyzed, based on a new type of attributes control chart that takes into account the autocorrelation between the extreme rainfall events. The aim is to conclude if there exist or not evidence of a change in the extreme rainfall model of the considered series. After adjusting seasonally the precipitation series and considering the data of the first 30 years, a frequency-based criterion allowed fixing specification limits in order to discriminate between extreme observed rainfall days and normal observed rainfall days. The autocorrelation amongst maximum precipitation is taken into account by a New Binomial Markov Extended Process obtained for each rainfall series. These modelling of the extreme rainfall processes provide a way to generate the attributes control charts for the annual fraction of rainfall extreme days. The extreme rainfall processes along the rest of the years under study can then be monitored by such attributes control charts. The results of the application of this methodology show evidence of change in the model of extreme rainfall events in some of the analyzed precipitation series. This suggests that the attributes control charts proposed for the analysis of the most intense precipitation events will be of practical interest to agriculture and insurance sectors in next future.

Rainfall erosivity: An overview of methodologies and applications

USDA-ARS?s Scientific Manuscript database

The rainfall erosivity factor (R-factor) is one of six erosion factors in the Universal Soil Loss Equation (USLE), together which reflect the combined effects that cause soil loss by rill and interrill erosion on hillslopes by precipitation. It is defined as the summation of event EI30 (the product ...

Changes to Sub-daily Rainfall Patterns in a Future Climate

NASA Astrophysics Data System (ADS)

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

2012-12-01

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

Feasibility Study on the Satellite Rainfall Data for Prediction of Sediment- Related Disaster by the Japanese Prediction Methodology

NASA Astrophysics Data System (ADS)

Shimizu, Y.; Ishizuka, T.; Osanai, N.; Okazumi, T.

2014-12-01

In this study, the sediment-related disaster prediction method which based ground gauged rainfall-data, currently practiced in Japan was coupled with satellite rainfall data and applied to domestic large-scale sediment-related disasters. The study confirmed the feasibility of this integrated method. In Asia, large-scale sediment-related disasters which can sweep away an entire settlement occur frequently. Leyte Island suffered from a huge landslide in 2004, and Typhoon Molakot in 2009 caused huge landslides in Taiwan. In the event of these sediment-related disasters, immediate responses by central and local governments are crucial in crisis management. In general, there are not enough rainfall gauge stations in developing countries. Therefore national and local governments have little information to determine the risk level of water induced disasters in their service areas. In the Japanese methodology, a criterion is set by combining two indices: the short-term rainfall index and long-term rainfall index. The short-term rainfall index is defined as the 60-minute total rainfall; the long-term rainfall index as the soil-water index, which is an estimation of the retention status of fallen rainfall in soil. In July 2009, a high-density sediment related disaster, or a debris flow, occurred in Hofu City of Yamaguchi Prefecture, in the western region of Japan. This event was calculated by the Japanese standard methodology, and then analyzed for its feasibility. Hourly satellite based rainfall has underestimates compared with ground based rainfall data. Long-term index correlates with each other. Therefore, this study confirmed that it is possible to deliver information on the risk level of sediment-related disasters such as shallow landslides and debris flows. The prediction method tested in this study is expected to assist for timely emergency responses to rainfall-induced natural disasters in sparsely gauged areas. As the Global Precipitation Measurement (GPM) Plan progresses, spatial resolution, time resolution and accuracy of rainfall data should be further improved and will be more effective in practical use.

Rainfall interception by tree crown and leaf litter: an interactive process

Treesearch

Xiang Li; Qingfu Xiao; Jianzhi Niu; Salli Dymond; E. Gregory McPherson; Natalie van Doorn; Xinxiao Yu; Baoyuan Xie; Kebin Zhang; Jiao Li

2017-01-01

Rainfall interception research in forest ecosystems usually focuses on interception by either tree crown or leaf litter, although the 2 components interact when rainfall occurs. A process-based study was conducted to jointly measure rainfall interception by crown and litter and the interaction between the 2 interception processes for 4 tree species (...

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

USDA-ARS?s Scientific Manuscript database

Rainfall erosivity quantifies the climatic effect on water erosion. In the framework of the Universal Soil Loss Equation, rainfall erosivity, also known as the R-factor, is defined as the mean annual sum of event erosivity values. For a new global soil erosion assessment, also in the broad context...

Some analysis on the diurnal variation of rainfall over the Atlantic Ocean

NASA Technical Reports Server (NTRS)

Gill, T.; Perng, S.; Hughes, A.

1981-01-01

Data collected from the GARP Atlantic Tropical Experiment (GATE) was examined. The data were collected from 10,000 grid points arranged as a 100 x 100 array; each grid covered a 4 square km area. The amount of rainfall was measured every 15 minutes during the experiment periods using c-band radars. Two types of analyses were performed on the data: analysis of diurnal variation was done on each of grid points based on the rainfall averages at noon and at midnight, and time series analysis on selected grid points based on the hourly averages of rainfall. Since there are no known distribution model which best describes the rainfall amount, nonparametric methods were used to examine the diurnal variation. Kolmogorov-Smirnov test was used to test if the rainfalls at noon and at midnight have the same statistical distribution. Wilcoxon signed-rank test was used to test if the noon rainfall is heavier than, equal to, or lighter than the midnight rainfall. These tests were done on each of the 10,000 grid points at which the data are available.

A software-based sensor for combined sewer overflows.

PubMed

Leonhardt, G; Fach, S; Engelhard, C; Kinzel, H; Rauch, W

2012-01-01

A new methodology for online estimation of excess flow from combined sewer overflow (CSO) structures based on simulation models is presented. If sufficient flow and water level data from the sewer system is available, no rainfall data are needed to run the model. An inverse rainfall-runoff model was developed to simulate net rainfall based on flow and water level data. Excess flow at all CSO structures in a catchment can then be simulated with a rainfall-runoff model. The method is applied to a case study and results show that the inverse rainfall-runoff model can be used instead of missing rain gauges. Online operation is ensured by software providing an interface to the SCADA-system of the operator and controlling the model. A water quality model could be included to simulate also pollutant concentrations in the excess flow.

Effect of the spatiotemporal variability of rainfall inputs in water quality integrated catchment modelling for dissolved oxygen concentrations

NASA Astrophysics Data System (ADS)

Moreno Ródenas, Antonio Manuel; Cecinati, Francesca; ten Veldhuis, Marie-Claire; Langeveld, Jeroen; Clemens, Francois

2016-04-01

Maintaining water quality standards in highly urbanised hydrological catchments is a worldwide challenge. Water management authorities struggle to cope with changing climate and an increase in pollution pressures. Water quality modelling has been used as a decision support tool for investment and regulatory developments. This approach led to the development of integrated catchment models (ICM), which account for the link between the urban/rural hydrology and the in-river pollutant dynamics. In the modelled system, rainfall triggers the drainage systems of urban areas scattered along a river. When flow exceeds the sewer infrastructure capacity, untreated wastewater enters the natural system by combined sewer overflows. This results in a degradation of the river water quality, depending on the magnitude of the emission and river conditions. Thus, being capable of representing these dynamics in the modelling process is key for a correct assessment of the water quality. In many urbanised hydrological systems the distances between draining sewer infrastructures go beyond the de-correlation length of rainfall processes, especially, for convective summer storms. Hence, spatial and temporal scales of selected rainfall inputs are expected to affect water quality dynamics. The objective of this work is to evaluate how the use of rainfall data from different sources and with different space-time characteristics affects modelled output concentrations of dissolved oxygen in a simplified ICM. The study area is located at the Dommel, a relatively small and sensitive river flowing through the city of Eindhoven (The Netherlands). This river stretch receives the discharge of the 750,000 p.e. WWTP of Eindhoven and from over 200 combined sewer overflows scattered along its length. A pseudo-distributed water quality model has been developed in WEST (mikedhi.com); this is a lumped-physically based model that accounts for urban drainage processes, WWTP and river dynamics for several pollutant typologies. Different rainfall products are tested: 1) Block kriging of a single reliable rain gauge, 2) Block kriging product from a network of 13 rain gauges and, 3) Universal block kriging with 13 rain gauges and KNMI weather radar estimates as a covariate. Different temporal accumulation levels are compared ranging from 10min to 1h. A geostatistical approach is used to allocate the prediction of the rainfall input in each of the urban hydrological units composing the model. The change in model performance is then assessed by contrasting it with dissolved oxygen monitoring data in a series of events.

Comparison of Imerg Half Hourly Final GPM data with Rain Gauge for Riyadh City, Kingdom of Saudi Arabia

NASA Astrophysics Data System (ADS)

Tekeli, E.; Dönmez, S.

2016-12-01

Being launched in 1997 with the main goal of measuring moderate to heavy rainfall, TRMM enabled invaluable service to remote sensing and hydrology community with data more than 17 years. Based on TRMM experience, GPM was launched in 2014. GPM with increased radar sensitivity and higher spatial resolutions, is expected to enable better light rain and snowfall detection. In here, light rainfall detection capacity of IMERG Half hourly final GPM (IFHH) product is investigated for Riyadh City in Kingdom of Saudi Arabia. A tipping bucket rain gauge located on the roof of King Saud University Civil Engineering Department provided rainfall measurements in 10 minute intervals from 22 November 2014 till 11 Jun 2015. Obtained rain gauge data indicated 72 light rain (rain rate [rr] ≤2.5mm/h) 5 medium rain (2.5mm/hPreliminary results indicate that IFHH overestimate most of the light rain. For the medium and heavy rain rates, IFHH showed under estimations. As one of the major goals of GPM is accurate light rain detection, similar studies should be continued and databases should be formed.

Diurnal variations of summer precipitation over the regions east to Tibetan Plateau

NASA Astrophysics Data System (ADS)

Wu, Yang; Huang, Anning; Huang, Danqing; Chen, Fei; Yang, Ben; Zhou, Yang; Fang, Dexian; Zhang, Lujun; Wen, Lijuan

2017-12-01

Based on the hourly gauge-satellite merged precipitation product with the horizontal resolution of 0.1° latitude/longitude during 2008-2014, diurnal variations of the summer precipitation amount (PA), frequency (PF), and intensity (PI) with different duration time over the regions east to Tibetan Plateau have been systematically revealed in this study. Results indicate that the eight typical precipitation diurnal patterns identified by the cluster analysis display pronounced regional features among the plateaus, basins, plains, hilly and coastal areas. The precipitation diurnal cycles are significantly affected by the sub-grid terrain fluctuations. The PA, PF and PI of the total rainfall show much more pronounced double diurnal peaks with the sub-grid topography standard deviation (SD) decreased. Meanwhile, the diurnal peaks of PA and PF (PI) strengthen (weaken) with the sub-grid topography SD enhanced. Over the elevated mountain ranges, southeastern hilly and coastal regions, the PA and PF diurnal patterns of the total rainfall generally show predominant late-afternoon peaks, which are closely associated with the short-duration (≤slant 3 h) rainfall. Along the Tibetan Plateau to its downstream, the diurnal peaks of PA, PF and PI for the total rainfall all exhibit obvious eastward phase time delay mainly due to the diurnal evolutions of long-duration (> 6 h) rainfall. However, the 4-6 h rainfall leads to the eastward phase time delay of the total rainfall along the Taihang Mountains to its downstream. Further mechanism analysis suggests that the midnight to morning diurnal evolution of the long-duration rainfall is closely associated with the diurnal variations of the upward branches of thermally driven mountain-plain solenoids and the water vapor transport associated with the accelerated nocturnal southwesterly winds. The late-afternoon peak of the short-duration PA over the southeastern hilly and coastal regions is ascribed to the strong local thermal convections due to the solar heating in afternoon, while the early-evening peak of the short-duration PA over the elevated mountain ranges is significantly contributed by the upward warm-moist wind from the surrounding low-lying basins or plains.

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.

Application of Statistical Downscaling Techniques to Predict Rainfall and Its Spatial Analysis Over Subansiri River Basin of Assam, India

NASA Astrophysics Data System (ADS)

Barman, S.; Bhattacharjya, R. K.

2017-12-01

The River Subansiri is the major north bank tributary of river Brahmaputra. It originates from the range of Himalayas beyond the Great Himalayan range at an altitude of approximately 5340m. Subansiri basin extends from tropical to temperate zones and hence exhibits a great diversity in rainfall characteristics. In the Northern and Central Himalayan tracts, precipitation is scarce on account of high altitudes. On the other hand, Southeast part of the Subansiri basin comprising the sub-Himalayan and the plain tract in Arunachal Pradesh and Assam, lies in the tropics. Due to Northeast as well as Southwest monsoon, precipitation occurs in this region in abundant quantities. Particularly, Southwest monsoon causes very heavy precipitation in the entire Subansiri basin during May to October. In this study, the rainfall over Subansiri basin has been studied at 24 different locations by multiple linear and non-linear regression based statistical downscaling techniques and by Artificial Neural Network based model. APHRODITE's gridded rainfall data of 0.25˚ x 0.25˚ resolutions and climatic parameters of HadCM3 GCM of resolution 2.5˚ x 3.75˚ (latitude by longitude) have been used in this study. It has been found that multiple non-linear regression based statistical downscaling technique outperformed the other techniques. Using this method, the future rainfall pattern over the Subansiri basin has been analyzed up to the year 2099 for four different time periods, viz., 2020-39, 2040-59, 2060-79, and 2080-99 at all the 24 locations. On the basis of historical rainfall, the months have been categorized as wet months, months with moderate rainfall and dry months. The spatial changes in rainfall patterns for all these three types of months have also been analyzed over the basin. Potential decrease of rainfall in the wet months and months with moderate rainfall and increase of rainfall in the dry months are observed for the future rainfall pattern of the Subansiri basin.

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.

40 CFR 415.431 - Specialized definitions.

Code of Federal Regulations, 2010 CFR

2010-07-01

..., intermediate product, finished product, by-product, or waste product. The term “process wastewater” does not... material, intermediate product, finished product, by-product or waste product by means of (1) rainfall runoff; (2) accidental spills; (3) accidental leaks caused by the failure of process equipment, which are...

Vertical Profiles of Latent Heat Release Over the Global Tropics using TRMM Rainfall Products from December 1997 to November 2001

NASA Technical Reports Server (NTRS)

Tao, W.-K.; Lang, S.; Simpson, J.; Meneghini, R.; Halverson, J.; Johnson, R.; Adler, R.; Starr, David (Technical Monitor)

2002-01-01

NASA Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) derived rainfall information will be used to estimate the four-dimensional structure of global monthly latent heating and rainfall profiles over the global tropics from December 1997 to November 2000. Rainfall, latent heating and radar reflectivity structures between El Nino (DJF 1997-98) and La Nina (DJF 1998-99) will be examined and compared. The seasonal variation of heating over various geographic locations (i.e., oceanic vs continental, Indian ocean vs west Pacific, Africa vs S. America) will also be analyzed. In addition, the relationship between rainfall, latent heating (maximum heating level), radar reflectivity and SST is examined and will be presented in the meeting. The impact of random error and bias in stratiform percentage estimates from PR on latent heating profiles is studied and will also be presented in the meeting. Additional information is included in the original extended abstract.

Skilful prediction of Sahel summer rainfall on inter-annual and multi-year timescales

PubMed Central

Sheen, K. L.; Smith, D. M.; Dunstone, N. J.; Eade, R.; Rowell, D. P.; Vellinga, M.

2017-01-01

Summer rainfall in the Sahel region of Africa exhibits one of the largest signals of climatic variability and with a population reliant on agricultural productivity, the Sahel is particularly vulnerable to major droughts such as occurred in the 1970s and 1980s. Rainfall levels have subsequently recovered, but future projections remain uncertain. Here we show that Sahel rainfall is skilfully predicted on inter-annual and multi-year (that is, >5 years) timescales and use these predictions to better understand the driving mechanisms. Moisture budget analysis indicates that on multi-year timescales, a warmer north Atlantic and Mediterranean enhance Sahel rainfall through increased meridional convergence of low-level, externally sourced moisture. In contrast, year-to-year rainfall levels are largely determined by the recycling rate of local moisture, regulated by planetary circulation patterns associated with the El Niño-Southern Oscillation. Our findings aid improved understanding and forecasting of Sahel drought, paramount for successful adaptation strategies in a changing climate. PMID:28541288

Linking Science of Flood Forecasts to Humanitarian Actions for Improved Preparedness and Effective Response

NASA Astrophysics Data System (ADS)

Uprety, M.; Dugar, S.; Gautam, D.; Kanel, D.; Kshetri, M.; Kharbuja, R. G.; Acharya, S. H.

2017-12-01

Advances in flood forecasting have provided opportunities for humanitarian responders to employ a range of preparedness activities at different forecast time horizons. Yet, the science of prediction is less understood and realized across the humanitarian landscape, and often preparedness plans are based upon average level of flood risk. Working under the remit of Forecast Based Financing (FbF), we present a pilot from Nepal on how available flood and weather forecast products are informing specific pre-emptive actions in the local preparedness and response plans, thereby supporting government stakeholders and humanitarian agencies to take early actions before an impending flood event. In Nepal, forecasting capabilities are limited but in a state of positive flux. Whilst local flood forecasts based upon rainfall-runoff models are yet to be operationalized, streamflow predictions from Global Flood Awareness System (GLoFAS) can be utilized to plan and implement preparedness activities several days in advance. Likewise, 3-day rainfall forecasts from Nepal Department of Hydrology and Meteorology (DHM) can further inform specific set of early actions for potential flash floods due to heavy precipitation. Existing community based early warning systems in the major river basins of Nepal are utilizing real time monitoring of water levels and rainfall together with localised probabilistic flood forecasts which has increased warning lead time from 2-3 hours to 7-8 hours. Based on these available forecast products, thresholds and trigger levels have been determined for different flood scenarios. Matching these trigger levels and assigning responsibilities to relevant actors for early actions, a set of standard operating procedures (SOPs) are being developed, broadly covering general preparedness activities and science informed anticipatory actions for different forecast lead times followed by the immediate response activities. These SOPs are currently being rolled out and tested by the Ministry of Home Affairs (MoHA) through its district emergency operation centres in West Nepal. Potential scale up and successful implementation of this science based approach would be instrumental to take forward global commitments on disaster risk reduction, climate change adaptation and sustainable goals in Nepal.

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).

Some Precipitation Studies over Andhra Pradesh and the Bay of Bengal using TRMM and SSMI data

NASA Astrophysics Data System (ADS)

Rao, S. Ramalingeswara; Krishna, K. Muni; Kumar, Bhanu

2007-07-01

One of the most difficult issues in modeling the global atmosphere and climate by General Circulation Models is the simulation and initialization of precipitation processes and at the same time rainfall is most important meteorological parameter that effects India's economy. An attempt is made in the present study to evaluate diurnal variation of rain rates over the Bay of Bengal (BoB) for the months June through December during 1999-2002. TMI rainfall product of Wentz and Spencer and SSMI data sets were used in this study. Mean hourly rain rates were calculated over the BoB (10°-15° N and 85°-95°E) and discussed; this study highlights that maximum rain rates are observed in the afternoons during summer monsoon seasons. Secondly mean monthly annual cycle of rainfall is prepared using 3B42RT merged rain product and compared with mean monthly India Meteorological Department (IMD) data for the study period over Andhra Pradesh (A.P). Time series of daily variations of 3B42RT precipitation and observed real time rainfall data over A.P. for the study period is validated and the relationship between them is statistically significant at 1% level. Similarly mean monthly data prepared from the daily analysis and compared with the IMD mean monthly rainfall maps. The comparison suggests that even with only available real time data from 3B42RT and rain gauge, it is possible to construct usable large-scale rainfall maps on regular latitude-longitude grids. This analysis, which uses a high resolution and more local rain gauge data, is able to produce realistic details of Indian summer monsoon rainfall over the study period.

URBAN STORMWATER INVESTIGATIONS BY THE U. S. GEOLOGICAL SURVEY.

USGS Publications Warehouse

Jennings, Marshall E.

1985-01-01

Urban stormwater hydrology studies in the U. S. Geological Survey are currently focused on compilation of national data bases containing flood-peak and short time-interval rainfall, discharge and water-quality information for urban watersheds. Current data bases, updated annually, are nationwide in scope. Supplementing the national data files are published reports of interpretative analyses, a map report and research products including improved instrumentation and deterministic modeling capabilities. New directions of Survey investigations include gaging programs for very small catchments and for stormwater detention facilities.

Characteristics of the water footprint of rice production under different rainfall years in Jilin Province, China.

PubMed

Li, Hongying; Qin, Lijie; He, Hongshi

2018-06-01

Rice is a special crop, and its production differs from that of other crops because it requires a thin layer of water coverage for a long period. The calculation of the water footprint of rice production should differ from that of other crops owing to the rice growing process. This study improved the calculation of blue and grey water footprints of rice production and analyzed the variations in the water footprints for rice production under different rainfall years in Jilin Province. In the drought year, the green water footprint was the lowest and the blue water footprint was the highest among the three years, while in the humid year, the green water footprint was the highest and the blue water footprint was not the lowest. The areas with higher water footprints were found in the east and west regions of Jilin Province, while the areas with lower water footprints were found in the middle east and middle regions of Jilin Province. Blue water was the primary water resource for rice production, although more precipitation provided the highest green water in the humid year; also, the spatial distributions of water footprints were not the same under different rainfall years. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

WPC Excessive Rainfall Forecasts

Science.gov Websites

Summaries Heat Index Tropical Products Daily Weather Map GIS Products Current Watches/ Warnings Satellite and Radar Imagery GOES-East Satellite GOES-West Satellite National Radar Product Archive WPC

A web service and android application for the distribution of rainfall estimates and Earth observation data

NASA Astrophysics Data System (ADS)

Mantas, V. M.; Liu, Z.; Pereira, A. J. S. C.

2015-04-01

The full potential of Satellite Rainfall Estimates (SRE) can only be realized if timely access to the datasets is possible. Existing data distribution web portals are often focused on global products and offer limited customization options, especially for the purpose of routine regional monitoring. Furthermore, most online systems are designed to meet the needs of desktop users, limiting the compatibility with mobile devices. In response to the growing demand for SRE and to address the current limitations of available web portals a project was devised to create a set of freely available applications and services, available at a common portal that can: (1) simplify cross-platform access to Tropical Rainfall Measuring Mission Online Visualization and Analysis System (TOVAS) data (including from Android mobile devices), (2) provide customized and continuous monitoring of SRE in response to user demands and (3) combine data from different online data distribution services, including rainfall estimates, river gauge measurements or imagery from Earth Observation missions at a single portal, known as the Tropical Rainfall Measuring Mission (TRMM) Explorer. The TRMM Explorer project suite includes a Python-based web service and Android applications capable of providing SRE and ancillary data in different intuitive formats with the focus on regional and continuous analysis. The outputs include dynamic plots, tables and data files that can also be used to feed downstream applications and services. A case study in Southern Angola is used to describe the potential of the TRMM Explorer for SRE distribution and analysis in the context of ungauged watersheds. The development of a collection of data distribution instances helped to validate the concept and identify the limitations of the program, in a real context and based on user feedback. The TRMM Explorer can successfully supplement existing web portals distributing SRE and provide a cost-efficient resource to small and medium-sized organizations with specific SRE monitoring needs, namely in developing and transition countries.

Country-wide rainfall maps from cellular communication networks

PubMed Central

Overeem, Aart; Leijnse, Hidde; Uijlenhoet, Remko

2013-01-01

Accurate and timely surface precipitation measurements are crucial for water resources management, agriculture, weather prediction, climate research, as well as ground validation of satellite-based precipitation estimates. However, the majority of the land surface of the earth lacks such data, and in many parts of the world the density of surface precipitation gauging networks is even rapidly declining. This development can potentially be counteracted by using received signal level data from the enormous number of microwave links used worldwide in commercial cellular communication networks. Along such links, radio signals propagate from a transmitting antenna at one base station to a receiving antenna at another base station. Rain-induced attenuation and, subsequently, path-averaged rainfall intensity can be retrieved from the signal’s attenuation between transmitter and receiver. Here, we show how one such a network can be used to retrieve the space–time dynamics of rainfall for an entire country (The Netherlands, ∼35,500 km2), based on an unprecedented number of links (∼2,400) and a rainfall retrieval algorithm that can be applied in real time. This demonstrates the potential of such networks for real-time rainfall monitoring, in particular in those parts of the world where networks of dedicated ground-based rainfall sensors are often virtually absent. PMID:23382210

Automated reconstruction of rainfall events responsible for shallow landslides

NASA Astrophysics Data System (ADS)

Vessia, G.; Parise, M.; Brunetti, M. T.; Peruccacci, S.; Rossi, M.; Vennari, C.; Guzzetti, F.

2014-04-01

Over the last 40 years, many contributions have been devoted to identifying the empirical rainfall thresholds (e.g. intensity vs. duration ID, cumulated rainfall vs. duration ED, cumulated rainfall vs. intensity EI) for the initiation of shallow landslides, based on local as well as worldwide inventories. Although different methods to trace the threshold curves have been proposed and discussed in literature, a systematic study to develop an automated procedure to select the rainfall event responsible for the landslide occurrence has rarely been addressed. Nonetheless, objective criteria for estimating the rainfall responsible for the landslide occurrence (effective rainfall) play a prominent role on the threshold values. In this paper, two criteria for the identification of the effective rainfall events are presented: (1) the first is based on the analysis of the time series of rainfall mean intensity values over one month preceding the landslide occurrence, and (2) the second on the analysis of the trend in the time function of the cumulated mean intensity series calculated from the rainfall records measured through rain gauges. The two criteria have been implemented in an automated procedure written in R language. A sample of 100 shallow landslides collected in Italy by the CNR-IRPI research group from 2002 to 2012 has been used to calibrate the proposed procedure. The cumulated rainfall E and duration D of rainfall events that triggered the documented landslides are calculated through the new procedure and are fitted with power law in the (D,E) diagram. The results are discussed by comparing the (D,E) pairs calculated by the automated procedure and the ones by the expert method.

Estimation of synthetic flood design hydrographs using a distributed rainfall-runoff model coupled with a copula-based single storm rainfall generator

NASA Astrophysics Data System (ADS)

Candela, A.; Brigandì, G.; Aronica, G. T.

2014-07-01

In this paper a procedure to derive synthetic flood design hydrographs (SFDH) using a bivariate representation of rainfall forcing (rainfall duration and intensity) via copulas, which describes and models the correlation between two variables independently of the marginal laws involved, coupled with a distributed rainfall-runoff model, is presented. Rainfall-runoff modelling (R-R modelling) for estimating the hydrological response at the outlet of a catchment was performed by using a conceptual fully distributed procedure based on the Soil Conservation Service - Curve Number method as an excess rainfall model and on a distributed unit hydrograph with climatic dependencies for the flow routing. Travel time computation, based on the distributed unit hydrograph definition, was performed by implementing a procedure based on flow paths, determined from a digital elevation model (DEM) and roughness parameters obtained from distributed geographical information. In order to estimate the primary return period of the SFDH, which provides the probability of occurrence of a hydrograph flood, peaks and flow volumes obtained through R-R modelling were treated statistically using copulas. Finally, the shapes of hydrographs have been generated on the basis of historically significant flood events, via cluster analysis. An application of the procedure described above has been carried out and results presented for the case study of the Imera catchment in Sicily, Italy.

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.

A Metastatistical Approach to Satellite Estimates of Extreme Rainfall Events

NASA Astrophysics Data System (ADS)

Zorzetto, E.; Marani, M.

2017-12-01

The estimation of the average recurrence interval of intense rainfall events is a central issue for both hydrologic modeling and engineering design. These estimates require the inference of the properties of the right tail of the statistical distribution of precipitation, a task often performed using the Generalized Extreme Value (GEV) distribution, estimated either from a samples of annual maxima (AM) or with a peaks over threshold (POT) approach. However, these approaches require long and homogeneous rainfall records, which often are not available, especially in the case of remote-sensed rainfall datasets. We use here, and tailor it to remotely-sensed rainfall estimates, an alternative approach, based on the metastatistical extreme value distribution (MEVD), which produces estimates of rainfall extreme values based on the probability distribution function (pdf) of all measured `ordinary' rainfall event. This methodology also accounts for the interannual variations observed in the pdf of daily rainfall by integrating over the sample space of its random parameters. We illustrate the application of this framework to the TRMM Multi-satellite Precipitation Analysis rainfall dataset, where MEVD optimally exploits the relatively short datasets of satellite-sensed rainfall, while taking full advantage of its high spatial resolution and quasi-global coverage. Accuracy of TRMM precipitation estimates and scale issues are here investigated for a case study located in the Little Washita watershed, Oklahoma, using a dense network of rain gauges for independent ground validation. The methodology contributes to our understanding of the risk of extreme rainfall events, as it allows i) an optimal use of the TRMM datasets in estimating the tail of the probability distribution of daily rainfall, and ii) a global mapping of daily rainfall extremes and distributional tail properties, bridging the existing gaps in rain gauges networks.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Evaluation of the Soil Conservation Service curve number methodology using data from agricultural plots

NASA Astrophysics Data System (ADS)

Lal, Mohan; Mishra, S. K.; Pandey, Ashish; Pandey, R. P.; Meena, P. K.; Chaudhary, Anubhav; Jha, Ranjit Kumar; Shreevastava, Ajit Kumar; Kumar, Yogendra

2017-01-01

The Soil Conservation Service curve number (SCS-CN) method, also known as the Natural Resources Conservation Service curve number (NRCS-CN) method, is popular for computing the volume of direct surface runoff for a given rainfall event. The performance of the SCS-CN method, based on large rainfall (P) and runoff (Q) datasets of United States watersheds, is evaluated using a large dataset of natural storm events from 27 agricultural plots in India. On the whole, the CN estimates from the National Engineering Handbook (chapter 4) tables do not match those derived from the observed P and Q datasets. As a result, the runoff prediction using former CNs was poor for the data of 22 (out of 24) plots. However, the match was little better for higher CN values, consistent with the general notion that the existing SCS-CN method performs better for high rainfall-runoff (high CN) events. Infiltration capacity (fc) was the main explanatory variable for runoff (or CN) production in study plots as it exhibited the expected inverse relationship between CN and fc. The plot-data optimization yielded initial abstraction coefficient (λ) values from 0 to 0.659 for the ordered dataset and 0 to 0.208 for the natural dataset (with 0 as the most frequent value). Mean and median λ values were, respectively, 0.030 and 0 for the natural rainfall-runoff dataset and 0.108 and 0 for the ordered rainfall-runoff dataset. Runoff estimation was very sensitive to λ and it improved consistently as λ changed from 0.2 to 0.03.

40 CFR 415.531 - Specialized definitions.

Code of Federal Regulations, 2010 CFR

2010-07-01

... results from the production or use of any raw material, intermediate product, finished product, by-product... product, by-product or waste product by means of (1) rainfall runoff; (2) accidental spills; (3... shall apply to this subpart. (b) The term product shall mean silver nitrate. (c) The term process...

Multisite rainfall downscaling and disaggregation in a tropical urban area

NASA Astrophysics Data System (ADS)

Lu, Y.; Qin, X. S.

2014-02-01

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

Future climate change enhances rainfall seasonality in a regional model of western Maritime Continent

NASA Astrophysics Data System (ADS)

Kang, Suchul; Im, Eun-Soon; Eltahir, Elfatih A. B.

2018-03-01

In this study, future changes in rainfall due to global climate change are investigated over the western Maritime Continent based on dynamically downscaled climate projections using the MIT Regional Climate Model (MRCM) with 12 km horizontal resolution. A total of nine 30-year regional climate projections driven by multi-GCMs projections (CCSM4, MPI-ESM-MR and ACCESS1.0) under multi-scenarios of greenhouse gases emissions (Historical: 1976-2005, RCP4.5 and RCP8.5: 2071-2100) from phase 5 of the Coupled Model Inter-comparison Project (CMIP5) are analyzed. Focusing on dynamically downscaled rainfall fields, the associated systematic biases originating from GCM and MRCM are removed based on observations using Parametric Quantile Mapping method in order to enhance the reliability of future projections. The MRCM simulations with bias correction capture the spatial patterns of seasonal rainfall as well as the frequency distribution of daily rainfall. Based on projected rainfall changes under both RCP4.5 and RCP8.5 scenarios, the ensemble of MRCM simulations project a significant decrease in rainfall over the western Maritime Continent during the inter-monsoon periods while the change in rainfall is not relevant during wet season. The main mechanism behind the simulated decrease in rainfall is rooted in asymmetries of the projected changes in seasonal dynamics of the meridional circulation along different latitudes. The sinking motion, which is marginally positioned in the reference simulation, is enhanced and expanded under global climate change, particularly in RCP8.5 scenario during boreal fall season. The projected enhancement of rainfall seasonality over the western Maritime Continent suggests increased risk of water stress for natural ecosystems as well as man-made water resources reservoirs.

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.

Rainfall Threshold for Flash Flood Early Warning Based on Rational Equation: A Case Study of Zuojiao Watershed in Yunnan Province

NASA Astrophysics Data System (ADS)

Li, Q.; Wang, Y. L.; Li, H. C.; Zhang, M.; Li, C. Z.; Chen, X.

2017-12-01

Rainfall threshold plays an important role in flash flood warning. A simple and easy method, using Rational Equation to calculate rainfall threshold, was proposed in this study. The critical rainfall equation was deduced from the Rational Equation. On the basis of the Manning equation and the results of Chinese Flash Flood Survey and Evaluation (CFFSE) Project, the critical flow was obtained, and the net rainfall was calculated. Three aspects of the rainfall losses, i.e. depression storage, vegetation interception, and soil infiltration were considered. The critical rainfall was the sum of the net rainfall and the rainfall losses. Rainfall threshold was estimated after considering the watershed soil moisture using the critical rainfall. In order to demonstrate this method, Zuojiao watershed in Yunnan Province was chosen as study area. The results showed the rainfall thresholds calculated by the Rational Equation method were approximated to the rainfall thresholds obtained from CFFSE, and were in accordance with the observed rainfall during flash flood events. Thus the calculated results are reasonable and the method is effective. This study provided a quick and convenient way to calculated rainfall threshold of flash flood warning for the grass root staffs and offered technical support for estimating rainfall threshold.

Rainfall characteristics and thresholds for periglacial debris flows in the Parlung Zangbo Basin, southeast Tibetan Plateau

NASA Astrophysics Data System (ADS)

Deng, Mingfeng; Chen, Ningsheng; Ding, Haitao

2018-02-01

The Parlung Zangbo Basin in the southeastern Tibet Plateau is affected by the summer monsoon from the Indian Ocean, which produces large rainfall gradients in the basin. Rainfall data during 2012-2015 from five new meteorological stations are used to analyse the rainfall characteristics. The daily rainfall, rainfall duration, mean rainfall intensity, and peak rainfall intensity are consistent, but sometimes contrasting. For example, these values decrease with increasing altitude, and the gradient is large downstream and small upstream, respectively. Moreover, the rainfall intensity peaks between 01:00 and 06:00 and increases during the afternoon. Based on the analysis of 14 debris flow cases in the basin, differences in the rainfall threshold differ depending on the location as sediment varieties. The sediment in the middle portions of the basin is wet and well structured; thus, long-duration, high-intensity rainfall is required to generate debris flows. Ravels in the upstream area are arid and not well structured, and short-duration rainfall is required to trigger debris flows. Between the above two locations, either long-duration, low-intensity rainfall or short-duration, high-intensity rainfall could provoke debris flows. Clearly, differences in rainfall characteristics and rainfall thresholds that are associated with the location must be considered in debris flow monitoring and warnings.

Identification of trends in intensity and frequency of extreme rainfall events in part of the Indian Himalaya

NASA Astrophysics Data System (ADS)

Bhardwaj, Alok; Ziegler, Alan D.; Wasson, Robert J.; Chow, Winston; Sharma, Mukat L.

2017-04-01

Extreme monsoon rainfall is the primary reason of floods and other secondary hazards such as landslides in the Indian Himalaya. Understanding the phenomena of extreme monsoon rainfall is therefore required to study the natural hazards. In this work, we study the characteristics of extreme monsoon rainfall including its intensity and frequency in the Garhwal Himalaya in India, with a focus on the Mandakini River Catchment, the site of devastating flood and multiple large landslides in 2013. We have used two long term rainfall gridded data sets: the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) product with daily rainfall data from 1951-2007 and the India Meteorological Department (IMD) product with daily rainfall data from 1901 to 2013. Two methods of Mann Kendall and Sen Slope estimator are used to identify the statistical significance and magnitude of trends in intensity and frequency of extreme monsoon rainfall respectively, at a significance level of 0.05. The autocorrelation in the time series of extreme monsoon rainfall is identified and reduced using the methods of: pre-whitening, trend-free pre-whitening, variance correction, and block bootstrap. We define extreme monsoon rainfall threshold as the 99th percentile of time series of rainfall values and any rainfall depth greater than 99th percentile is considered as extreme in nature. With the IMD data set, significant increasing trend in intensity and frequency of extreme rainfall with slope magnitude of 0.55 and 0.02 respectively was obtained in the north of the Mandakini Catchment as identified by all four methods. Significant increasing trend in intensity with a slope magnitude of 0.3 is found in the middle of the catchment as identified by all methods except block bootstrap. In the south of the catchment, significant increasing trend in intensity with a slope magnitude of 0.86 for pre-whitening method and 0.28 for trend-free pre-whitening and variance correction methods was obtained. Further, increasing trend in frequency with a slope magnitude of 0.01 was identified by three methods except block bootstrap in the south of the catchment. With the APHRODITE data set, we obtained significant increasing trend in intensity with a slope magnitude of 1.27 at the middle of the catchment as identified by all four methods. Collectively, both the datasets show signals of increasing intensity, and IMD shows results for increasing frequency in the Mandakini Catchment. The increasing occurrence of extreme events, as identified here, is becoming more disastrous because of rising human population and infrastructure in the Mandakini Catchment. For example, the 2013 flood due to extreme rainfall was catastrophic in terms of loss of human and animal lives and destruction of the local economy. We believe our results will help understand more about extreme rainfall events in the Mandakini Catchment and in the Indian Himalaya.

Effects of rainfall patterns on toxic cyanobacterial blooms in a changing climate: between simplistic scenarios and complex dynamics.

PubMed

Reichwaldt, Elke S; Ghadouani, Anas

2012-04-01

Toxic cyanobacterial blooms represent a serious hazard to environmental and human health, and the management and restoration of affected waterbodies can be challenging. While cyanobacterial blooms are already a frequent occurrence, in the future their incidence and severity are predicted to increase due to climate change. Climate change is predicted to lead to increased temperature and changes in rainfall patterns, which will both have a significant impact on inland water resources. While many studies indicate that a higher temperature will favour cyanobacterial bloom occurrences, the impact of changed rainfall patterns is widely under-researched and therefore less understood. This review synthesizes the predicted changes in rainfall patterns and their potential impact on inland waterbodies, and identifies mechanisms that influence the occurrence and severity of toxic cyanobacterial blooms. It is predicted that there will be a higher frequency and intensity of rainfall events with longer drought periods in between. Such changes in the rainfall patterns will lead to favourable conditions for cyanobacterial growth due to a greater nutrient input into waterbodies during heavy rainfall events, combined with potentially longer periods of high evaporation and stratification. These conditions are likely to lead to an acceleration of the eutrophication process and prolonged warm periods without mixing of the water column. However, the frequent occurrence of heavy rain events can also lead to a temporary disruption of cyanobacterial blooms due to flushing and de-stratification, and large storm events have been shown to have a long-term negative effect on cyanobacterial blooms. In contrast, a higher number of small rainfall events or wet days can lead to proliferation of cyanobacteria, as they can rapidly use nutrients that are added during rainfall events, especially if stratification remains unchanged. With rainfall patterns changing, cyanobacterial toxin concentration in waterbodies is expected to increase. Firstly, this is due to accelerated eutrophication which supports higher cyanobacterial biomass. Secondly, predicted changes in rainfall patterns produce more favourable growth conditions for cyanobacteria, which is likely to increase the toxin production rate. However, the toxin concentration in inland waterbodies will also depend on the effect of rainfall events on cyanobacterial strain succession, a process that is still little understood. Low light conditions after heavy rainfall events might favour non-toxic strains, whilst inorganic nutrient input might promote the dominance of toxic strains in blooms. This review emphasizes that the impact of changes in rainfall patterns is very complex and will strongly depend on the site-specific dynamics, cyanobacterial species composition and cyanobacterial strain succession. More effort is needed to understand the relationship between rainfall patterns and cyanobacterial bloom dynamics, and in particular toxin production, to be able to assess and mediate the significant threat cyanobacterial blooms pose to our water resources. Copyright © 2011 Elsevier Ltd. All rights reserved.

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.

The contribution of tropical cyclones to rainfall in Mexico

NASA Astrophysics Data System (ADS)

Agustín Breña-Naranjo, J.; Pedrozo-Acuña, Adrián; Pozos-Estrada, Oscar; Jiménez-López, Salma A.; López-López, Marco R.

Investigating the contribution of tropical cyclones to the terrestrial water cycle can help quantify the benefits and hazards caused by the rainfall generated from this type of hydro-meteorological event. Rainfall induced by tropical cyclones can enhance both flood risk and groundwater recharge, and it is therefore important to characterise its minimum, mean and maximum contributions to a region or country's water balance. This work evaluates the rainfall contribution of tropical depressions, storms and hurricanes across Mexico from 1998 to 2013 using the satellite-derived precipitation dataset TMPA 3B42. Additionally, the sensitivity of rainfall to other datasets was assessed: the national rain gauge observation network, real-time satellite rainfall and a merged product that combines rain gauges with non-calibrated space-borne rainfall measurements. The lower Baja California peninsula had the highest contribution from cyclonic rainfall in relative terms (∼40% of its total annual rainfall), whereas the contributions in the rest of the country showed a low-to-medium dependence on tropical cyclones, with mean values ranging from 0% to 20%. In quantitative terms, southern regions of Mexico can receive more than 2400 mm of cyclonic rainfall during years with significant TC activity. Moreover, (a) the number of tropical cyclones impacting Mexico has been significantly increasing since 1998, but cyclonic contributions in relative and quantitative terms have not been increasing, and (b) wind speed and rainfall intensity during cyclones are not highly correlated. Future work should evaluate the impacts of such contributions on surface and groundwater hydrological processes and connect the knowledge gaps between the magnitude of tropical cyclones, flood hazards, and economic losses.

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.

Plant calendar pattern based on rainfall forecast and the probability of its success in Deli Serdang regency of Indonesia

NASA Astrophysics Data System (ADS)

Darnius, O.; Sitorus, S.

2018-03-01

The objective of this study was to determine the pattern of plant calendar of three types of crops; namely, palawija, rice, andbanana, based on rainfall in Deli Serdang Regency. In the first stage, we forecasted rainfall by using time series analysis, and obtained appropriate model of ARIMA (1,0,0) (1,1,1)12. Based on the forecast result, we designed a plant calendar pattern for the three types of plant. Furthermore, the probability of success in the plant types following the plant calendar pattern was calculated by using the Markov process by discretizing the continuous rainfall data into three categories; namely, Below Normal (BN), Normal (N), and Above Normal (AN) to form the probability transition matrix. Finally, the combination of rainfall forecasting models and the Markov process were used to determine the pattern of cropping calendars and the probability of success in the three crops. This research used rainfall data of Deli Serdang Regency taken from the office of BMKG (Meteorologist Climatology and Geophysics Agency), Sampali Medan, Indonesia.

The NASA GPM Iowa Flood Studies Experiment

NASA Astrophysics Data System (ADS)

Petersen, W. A.; Krajewski, W. F.; Peters-Lidard, C. D.; Rutledge, S. A.; Wolff, D. B.

2013-12-01

The overarching objective of NASA Global Precipitation Measurement Mission (GPM) integrated hydrologic ground validation (GV) is to provide a better understanding of the strengths and limitations of the satellite products, in the context of hydrologic applications. Accordingly, the NASA GPM GV program recently completed the first of several hydrology-oriented field efforts: the Iowa Flood Studies (IFloodS) experiment. IFloodS was conducted in central Iowa during the months of April-June, 2013. IFloodS science objectives focused on: a) The collection of reference multi-parameter radar, rain gauge, disdrometer, soil moisture, and hydrologic network measurements to quantify the physical character and space/time variability of rain (e.g., rates, drop size distributions, processes), land surface- state and hydrologic response; b) Application of the ground reference measurements to assessment of satellite-based rainfall estimation uncertainties; c) Propagation of both ground and satellite rainfall estimation uncertainties in coupled hydrologic prediction models to assess impacts on predictive skill; and d) Evaluation of rainfall properties such as rate and accumulation relative to basin hydrologic characteristics in modeled flood genesis. IFloodS observational objectives were achieved via deployments of the NASA NPOL S-band and D3R Ka/Ku-band dual-polarimetric radars (operating in coordinated scanning modes), four University of Iowa X-band dual-polarimetric radars, four Micro Rain Radars, a network of 25 paired rain gauge platforms with attendant soil moisture and temperature probes, a network of six 2D Video and 14 Parsivel disdrometers, and 15 USDA-ARS rain gauge and soil-moisture stations (collaboration with the USDA-ARS and NASA Soil Moisture Active-Passive mission). The aforementioned platforms complemented existing operational WSR-88D S-band polarimetric radar, USGS streamflow, and Iowa Flood Center-affiliated stream monitoring and rainfall measurements. Coincident low-earth orbiter microwave, geostationary infrared, and derived satellite-algorithm rainfall products were also archived during the experiment. Twice daily NASA Unified Weather Research and Forecasting model simulations were conducted to provide weather forecast guidance and a coupled atmospheric/land-surface model simulation benchmark. During the experiment the IFloodS observational domain experienced heavy rainfall (> 250-300 mm) and significant flooding. Deployed observational assets, especially the research radars performed well throughout the experiment, sampling a broad range of precipitation system types including multi-day mixtures of rain and snow, warm-season mesoscale convective systems, and supercell thunderstorms. The variety of regimes and large rain accumulations sampled creates a rich source of data for testing both satellite products and coupled atmospheric, land system, and hydrologic models. In this study we will provide an overview of the IFloodS experiment, datasets, and preliminary observational results.

Rainfall Estimation over the Nile Basin using an Adapted Version of the SCaMPR Algorithm

NASA Astrophysics Data System (ADS)

Habib, E. H.; Kuligowski, R. J.; Elshamy, M. E.; Ali, M. A.; Haile, A.; Amin, D.; Eldin, A.

2011-12-01

Management of Egypt's Aswan High Dam is critical not only for flood control on the Nile but also for ensuring adequate water supplies for most of Egypt since rainfall is scarce over the vast majority of its land area. However, reservoir inflow is driven by rainfall over Sudan, Ethiopia, Uganda, and several other countries from which routine rain gauge data are sparse. Satellite-derived estimates of rainfall offer a much more detailed and timely set of data to form a basis for decisions on the operation of the dam. A single-channel infrared algorithm is currently in operational use at the Egyptian Nile Forecast Center (NFC). This study reports on the adaptation of a multi-spectral, multi-instrument satellite rainfall estimation algorithm (Self-Calibrating Multivariate Precipitation Retrieval, SCaMPR) for operational application over the Nile Basin. The algorithm uses a set of rainfall predictors from multi-spectral Infrared cloud top observations and self-calibrates them to a set of predictands from Microwave (MW) rain rate estimates. For application over the Nile Basin, the SCaMPR algorithm uses multiple satellite IR channels recently available to NFC from the Spinning Enhanced Visible and Infrared Imager (SEVIRI). Microwave rain rates are acquired from multiple sources such as SSM/I, SSMIS, AMSU, AMSR-E, and TMI. The algorithm has two main steps: rain/no-rain separation using discriminant analysis, and rain rate estimation using stepwise linear regression. We test two modes of algorithm calibration: real-time calibration with continuous updates of coefficients with newly coming MW rain rates, and calibration using static coefficients that are derived from IR-MW data from past observations. We also compare the SCaMPR algorithm to other global-scale satellite rainfall algorithms (e.g., 'Tropical Rainfall Measuring Mission (TRMM) and other sources' (TRMM-3B42) product, and the National Oceanographic and Atmospheric Administration Climate Prediction Center (NOAA-CPC) CMORPH product. The algorithm has several potential future applications such as: improving the performance accuracy of hydrologic forecasting models over the Nile Basin, and utilizing the enhanced rainfall datasets and better-calibrated hydrologic models to assess the impacts of climate change on the region's water availability.

Building Climate Service Capacities in Eastern Africa with CHIRP and GeoCLIM

NASA Astrophysics Data System (ADS)

Pedreros, D. H.; Magadzire, T.; Funk, C. C.; Verdin, J. P.; Peterson, P.; Landsfeld, M.; Husak, G. J.

2013-12-01

In developing countries there is a great need for capacity building within national and regional climate agencies to develop and analyze historical and real time gridded rainfall datasets. These datasets are of key importance for monitoring climate and agricultural food production at decadal and seasonal time scales, and for informing local decision makers. The Famine Early Warning Systems Network (FEWS NET), working together with the U.S. Geological Survey (USGS) and the Climate Hazards Group (CHG) of the University of California Santa Barbara, has developed an integrated set of data products and tools to support the development of African climate services. The core data product is the Climate Hazards Group Infrared Precipitation (CHIRP) dataset. The CHIRP is a new rainfall dataset resulting from the blending of satellite estimated precipitation with high resolution precipitation climatology. The CHIRP depicts rainfall on five day totals at 5km spatial resolution from 1981 to present. The CHG is developing and deploying a standalone tool - the GeoCLIM - which will allow national and regional meteorological agencies to blend the CHIRP with station observations, run simple crop water balance models, and conduct climatological, trend, and time series analysis. Blending satellite estimates and gauge data helps overcome limited in situ observing networks. Furthermore, the GeoCLIM combines rainfall, soil, and evapotranspiration data with crop hydrological requirements to calculate agricultural water balance, presented as the Water Requirement Satisfaction Index (WRSI). The WRSI is a measurement of the degree in which a crop's hydrological requirements have been satisfied by rainfall. We present the results of a training session for personnel of the East African Intergovernmental Authority on Development Climate Prediction and Applications Center. The two week training program included the use of the GeoCLIM to improve CHIRP using station data, and to calculate and analyze trends in rainfall, WRSI, and drought frequency in the region.

Contrasting above- and belowground sensitivity of three Great Plains grasslands to altered rainfall regimes.

PubMed

Wilcox, Kevin R; von Fischer, Joseph C; Muscha, Jennifer M; Petersen, Mark K; Knapp, Alan K

2015-01-01

Intensification of the global hydrological cycle with atmospheric warming is expected to increase interannual variation in precipitation amount and the frequency of extreme precipitation events. Although studies in grasslands have shown sensitivity of aboveground net primary productivity (ANPP) to both precipitation amount and event size, we lack equivalent knowledge for responses of belowground net primary productivity (BNPP) and NPP. We conducted a 2-year experiment in three US Great Plains grasslands--the C4-dominated shortgrass prairie (SGP; low ANPP) and tallgrass prairie (TGP; high ANPP), and the C3-dominated northern mixed grass prairie (NMP; intermediate ANPP)--to test three predictions: (i) both ANPP and BNPP responses to increased precipitation amount would vary inversely with mean annual precipitation (MAP) and site productivity; (ii) increased numbers of extreme rainfall events during high-rainfall years would affect high and low MAP sites differently; and (iii) responses belowground would mirror those aboveground. We increased growing season precipitation by as much as 50% by augmenting natural rainfall via (i) many (11-13) small or (ii) fewer (3-5) large watering events, with the latter coinciding with naturally occurring large storms. Both ANPP and BNPP increased with water addition in the two C4 grasslands, with greater ANPP sensitivity in TGP, but greater BNPP and NPP sensitivity in SGP. ANPP and BNPP did not respond to any rainfall manipulations in the C3 -dominated NMP. Consistent with previous studies, fewer larger (extreme) rainfall events increased ANPP relative to many small events in SGP, but event size had no effect in TGP. Neither system responded consistently above- and belowground to event size; consequently, total NPP was insensitive to event size. The diversity of responses observed in these three grassland types underscores the challenge of predicting responses relevant to C cycling to forecast changes in precipitation regimes even within relatively homogeneous biomes such as grasslands. © 2014 John Wiley & Sons Ltd.

WPC Excessive Rainfall and Winter Weather Forecasts

Science.gov Websites

Summaries Heat Index Tropical Products Daily Weather Map GIS Products Current Watches/ Warnings Satellite and Radar Imagery GOES-East Satellite GOES-West Satellite National Radar Product Archive WPC

Hydro-meteorological evaluation of downscaled global ensemble rainfall forecasts

NASA Astrophysics Data System (ADS)

Gaborit, Étienne; Anctil, François; Fortin, Vincent; Pelletier, Geneviève

2013-04-01

Ensemble rainfall forecasts are of high interest for decision making, as they provide an explicit and dynamic assessment of the uncertainty in the forecast (Ruiz et al. 2009). However, for hydrological forecasting, their low resolution currently limits their use to large watersheds (Maraun et al. 2010). In order to bridge this gap, various implementations of the statistic-stochastic multi-fractal downscaling technique presented by Perica and Foufoula-Georgiou (1996) were compared, bringing Environment Canada's global ensemble rainfall forecasts from a 100 by 70-km resolution down to 6 by 4-km, while increasing each pixel's rainfall variance and preserving its original mean. For comparison purposes, simpler methods were also implemented such as the bi-linear interpolation, which disaggregates global forecasts without modifying their variance. The downscaled meteorological products were evaluated using different scores and diagrams, from both a meteorological and a hydrological view points. The meteorological evaluation was conducted comparing the forecasted rainfall depths against nine days of observed values taken from Québec City rain gauge database. These 9 days present strong precipitation events occurring during the summer of 2009. For the hydrologic evaluation, the hydrological models SWMM5 and (a modified version of) GR4J were implemented on a small 6 km2 urban catchment located in the Québec City region. Ensemble hydrologic forecasts with a time step of 3 hours were then performed over a 3-months period of the summer of 2010 using the original and downscaled ensemble rainfall forecasts. The most important conclusions of this work are that the overall quality of the forecasts was preserved during the disaggregation procedure and that the disaggregated products using this variance-enhancing method were of similar quality than bi-linear interpolation products. However, variance and dispersion of the different members were, of course, much improved for the variance-enhanced products, compared to the bi-linear interpolation, which is a decisive advantage. The disaggregation technique of Perica and Foufoula-Georgiou (1996) hence represents an interesting way of bridging the gap between the meteorological models' resolution and the high degree of spatial precision sometimes required by hydrological models in their precipitation representation. References Maraun, D., Wetterhall, F., Ireson, A. M., Chandler, R. E., Kendon, E. J., Widmann, M., Brienen, S., Rust, H. W., Sauter, T., Themeßl, M., Venema, V. K. C., Chun, K. P., Goodess, C. M., Jones, R. G., Onof, C., Vrac, M., and Thiele-Eich, I. 2010. Precipitation downscaling under climate change: recent developments to bridge the gap between dynamical models and the end user. Reviews of Geophysics, 48 (3): RG3003, [np]. Doi: 10.1029/2009RG000314. Perica, S., and Foufoula-Georgiou, E. 1996. Model for multiscale disaggregation of spatial rainfall based on coupling meteorological and scaling descriptions. Journal Of Geophysical Research, 101(D21): 26347-26361. Ruiz, J., Saulo, C. and Kalnay, E. 2009. Comparison of Methods Used to Generate Probabilistic Quantitative Precipitation Forecasts over South America. Weather and forecasting, 24: 319-336. DOI: 10.1175/2008WAF2007098.1 This work is distributed under the Creative Commons Attribution 3.0 Unported License together with an author copyright. This license does not conflict with the regulations of the Crown Copyright.

Rainwater harvesting and management in rainfed agricultural systems in sub-Saharan Africa - A review

NASA Astrophysics Data System (ADS)

Biazin, Birhanu; Sterk, Geert; Temesgen, Melesse; Abdulkedir, Abdu; Stroosnijder, Leo

Agricultural water scarcity in the predominantly rainfed agricultural system of sub-Saharan Africa (SSA) is more related to the variability of rainfall and excessive non-productive losses, than the total annual precipitation in the growing season. Less than 15% of the terrestrial precipitation takes the form of productive ‘green’ transpiration. Hence, rainwater harvesting and management (RWHM) technologies hold a significant potential for improving rainwater-use efficiency and sustaining rainfed agriculture in the region. This paper outlines the various RWHM techniques being practiced in SSA, and reviews recent research results on the performance of selected practices. So far, micro-catchment and in situ rainwater harvesting techniques are more common than rainwater irrigation techniques from macro-catchment systems. Depending on rainfall patterns and local soil characteristics, appropriate application of in situ and micro-catchment techniques could improve the soil water content of the rooting zone by up to 30%. Up to sixfold crop yields have been obtained through combinations of rainwater harvesting and fertiliser use, as compared to traditional practices. Supplemental irrigation of rainfed agriculture through rainwater harvesting not only reduces the risk of total crop failure due to dry spells, but also substantially improves water and crop productivity. Depending on the type of crop and the seasonal rainfall pattern, the application of RWHM techniques makes net profits more possible, compared to the meagre profit or net loss of existing systems. Implementation of rainwater harvesting may allow cereal-based smallholder farmers to shift to diversified crops, hence improving household food security, dietary status, and economic return. The much needed green revolution and adaptations to climate change in SSA should blend rainwater harvesting ideals with agronomic principles. More efforts are needed to improve the indigenous practices, and to disseminate best practices on a wider scale.

A Smallholder Socio-hydrological Modelling Framework

NASA Astrophysics Data System (ADS)

Pande, S.; Savenije, H.; Rathore, P.

2014-12-01

Small holders are farmers who own less than 2 ha of farmland. They often have low productivity and thus remain at subsistence level. A fact that nearly 80% of Indian farmers are smallholders, who merely own a third of total farmlands and belong to the poorest quartile, but produce nearly 40% of countries foodgrains underlines the importance of understanding the socio-hydrology of a small holder. We present a framework to understand the socio-hydrological system dynamics of a small holder. It couples the dynamics of 6 main variables that are most relevant at the scale of a small holder: local storage (soil moisture and other water storage), capital, knowledge, livestock production, soil fertility and grass biomass production. The model incorporates rule-based adaptation mechanisms (for example: adjusting expenditures on food and fertilizers, selling livestocks etc.) of small holders when they face adverse socio-hydrological conditions, such as low annual rainfall, higher intra-annual variability in rainfall or variability in agricultural prices. It allows us to study sustainability of small holder farming systems under various settings. We apply the framework to understand the socio-hydrology of small holders in Aurangabad, Maharashtra, India. This district has witnessed suicides of many sugarcane farmers who could not extricate themselves out of the debt trap. These farmers lack irrigation and are susceptible to fluctuating sugar prices and intra-annual hydroclimatic variability. This presentation discusses two aspects in particular: whether government interventions to absolve the debt of farmers is enough and what is the value of investing in local storages that can buffer intra-annual variability in rainfall and strengthening the safety-nets either by creating opportunities for alternative sources of income or by crop diversification.

An evaluation of soil water outlooks for winter wheat in south-eastern Australia

NASA Astrophysics Data System (ADS)

Western, A. W.; Dassanayake, K. B.; Perera, K. C.; Alves, O.; Young, G.; Argent, R.

2015-12-01

Abstract: Soil moisture is a key limiting resource for rain-fed cropping in Australian broad-acre cropping zones. Seasonal rainfall and temperature outlooks are standard operational services offered by the Australian Bureau of Meteorology and are routinely used to support agricultural decisions. This presentation examines the performance of proposed soil water seasonal outlooks in the context of wheat cropping in south-eastern Australia (autumn planting, late spring harvest). We used weather ensembles simulated by the Predictive Ocean-Atmosphere Model for Australia (POAMA), as input to the Agricultural Production Simulator (APSIM) to construct ensemble soil water "outlooks" at twenty sites. Hindcasts were made over a 33 year period using the 33 POAMA ensemble members. The overall modelling flow involved: 1. Downscaling of the daily weather series (rainfall, minimum and maximum temperature, humidity, radiation) from the ~250km POAMA grid scale to a local weather station using quantile-quantile correction. This was based on a 33 year observation record extracted from the SILO data drill product. 2. Using APSIM to produce soil water ensembles from the downscaled weather ensembles. A warm up period of 5 years of observed weather was followed by a 9 month hindcast period based on each ensemble member. 3. The soil water ensembles were summarized by estimating the proportion of outlook ensembles in each climatological tercile, where the climatology was constructed using APSIM and observed weather from the 33 years of hindcasts at the relevant site. 4. The soil water outlooks were evaluated for different lead times and months using a "truth" run of APSIM based on observed weather. Outlooks generally have useful some forecast skill for lead times of up to two-three months, except late spring; in line with current useful lead times for rainfall outlooks. Better performance was found in summer and autumn when vegetation cover and water use is low.

A flash flood early warning system based on rainfall thresholds and daily soil moisture indexes

NASA Astrophysics Data System (ADS)

Brigandì, Giuseppina; Tito Aronica, Giuseppe

2015-04-01

Main focus of the paper is to present a flash flood early warning system, developed for Civil Protection Agency for the Sicily Region, for alerting extreme hydrometeorological events by using a methodology based on the combined use of rainfall thresholds and soil moisture indexes. As matter of fact, flash flood warning is a key element to improve the Civil Protection achievements to mitigate damages and safeguard the security of people. It is a rather complicated task, particularly in those catchments with flashy response where even brief anticipations are important and welcomed. In this context, some kind of hydrological precursors can be considered to improve the effectiveness of the emergency actions (i.e. early flood warning). Now, it is well known how soil moisture is an important factor in flood formation, because the runoff generation is strongly influenced by the antecedent soil moisture conditions of the catchment. The basic idea of the work here presented is to use soil moisture indexes derived in a continuous form to define a first alert phase in a flash flood forecasting chain and then define a unique rainfall threshold for a given day for the subsequent alarm phases activation, derived as a function of the soil moisture conditions at the beginning of the day. Daily soil moisture indexes, representative of the moisture condition of the catchment, were derived by using a parsimonious and simply to use approach based on the IHACRES model application in a modified form developed by the authors. It is a simple, spatially-lumped rainfall-streamflow model, based on the SCS-CN method and on the unit hydrograph approach that requires only rainfall, streamflow and air temperature data. It consists of two modules. In the first a non linear loss model, based on the SCS-CN method, was used to transform total rainfall into effective rainfall. In the second, a linear convolution of effective rainfall was performed using a total unit hydrograph with a configuration of one parallel channel and reservoir, thereby corresponding to 'quick' and 'slow' components of runoff. In the non linear model a wetness/soil moisture index, varying from 0 to 1, was derived to define daily soil moisture catchment conditions and then conveniently linked to a corresponding CN value to use as input to derive the corresponding rainfall threshold for a given day. Finally, rainfall thresholds for flash flooding were derived using an Instantaneous Unit Hydrograph based lumped rainfall-runoff model with the SCS-CN routine for net rainfall. Application of the proposed methodology was carried out with reference to a river basin in Sicily, Italy.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

[Infiltration characteristics of soil water on loess slope land under intermittent and repetitive rainfall conditions].

PubMed

Li, Yi; Shao, Ming-An

2008-07-01

Based on the experiments of controlled intermittent and repetitive rainfall on slope land, the infiltration and distribution characteristics of soil water on loess slope land were studied. The results showed that under the condition of intermittent rainfall, the cumulative runoff during two rainfall events increased linearly with time, and the wetting front also increased with time. In the interval of the two rainfall events, the wetting front increased slowly, and the infiltration rate was smaller on steeper slope than on flat surface. During the second rainfall event, there was an obvious decreasing trend of infiltration rate with time. The cumulative infiltration on 15 degrees slope land was larger than that of 25 degrees slope land, being 178 mm and 88 mm, respectively. Under the condition of repetitive rainfall, the initial infiltration rate during each rainfall event was relatively large, and during the first rainfall, both the infiltration rate and the cumulative infiltration at various stages were larger than those during the other three rainfall events. However, after the first rainfall, there were no obvious differences in the infiltration rate among the next three rainfall events. The more the rainfall event, the deeper the wetting front advanced.

RainyDay: An Online, Open-Source Tool for Physically-based Rainfall and Flood Frequency Analysis

NASA Astrophysics Data System (ADS)

Wright, D.; Yu, G.; Holman, K. D.

2017-12-01

Flood frequency analysis in ungaged or changing watersheds typically requires rainfall intensity-duration-frequency (IDF) curves combined with hydrologic models. IDF curves only depict point-scale rainfall depth, while true rainstorms exhibit complex spatial and temporal structures. Floods result from these rainfall structures interacting with watershed features such as land cover, soils, and variable antecedent conditions as well as river channel processes. Thus, IDF curves are traditionally combined with a variety of "design storm" assumptions such as area reduction factors and idealized rainfall space-time distributions to translate rainfall depths into inputs that are suitable for flood hydrologic modeling. The impacts of such assumptions are relatively poorly understood. Meanwhile, modern precipitation estimates from gridded weather radar, grid-interpolated rain gages, satellites, and numerical weather models provide more realistic depictions of rainfall space-time structure. Usage of such datasets for rainfall and flood frequency analysis, however, are hindered by relatively short record lengths. We present RainyDay, an open-source stochastic storm transposition (SST) framework for generating large numbers of realistic rainfall "scenarios." SST "lengthens" the rainfall record by temporal resampling and geospatial transposition of observed storms to extract space-time information from regional gridded rainfall data. Relatively short (10-15 year) records of bias-corrected radar rainfall data are sufficient to estimate rainfall and flood events with much longer recurrence intervals including 100-year and 500-year events. We describe the SST methodology as implemented in RainyDay and compare rainfall IDF results from RainyDay to conventional estimates from NOAA Atlas 14. Then, we demonstrate some of the flood frequency analysis properties that are possible when RainyDay is integrated with a distributed hydrologic model, including robust estimation of flood hazards in a changing watershed. The U.S. Bureau of Reclamation is supporting the development of a web-based variant of RainyDay, a "beta" version of which is available at http://her.cee.wisc.edu/projects/rainyday/.

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.

Enhanced Orographic Tropical Rainfall: An Study of the Colombia's rainfall

NASA Astrophysics Data System (ADS)

Peñaranda, V. M.; Hoyos Ortiz, C. D.; Mesa, O. J.

2015-12-01

Convection in tropical regions may be enhanced by orographic barriers. The orographic enhancement is an intensification of rain rates caused by the forced lifting of air over a mountainous structure. Orographic heavy rainfall events, occasionally, comes along by flooding, debris flow and substantial amount of looses, either economics or human lives. Most of the heavy convective rainfall events, occurred in Colombia, have left a lot of victims and material damages by flash flooding. An urgent action is required by either scientific communities or society, helping to find preventive solutions against these kind of events. Various scientific literature reports address the feedback process between the convection and the local orographic structures. The orographic enhancement could arise by several physical mechanism: precipitation transport on leeward side, convection triggered by the forcing of air over topography, the seeder-feeder mechanism, among others. The identification of the physical mechanisms for orographic enhancement of rainfall has not been studied over Colombia. As far as we know, orographic convective tropical rainfall is just the main factor for the altitudinal belt of maximum precipitation, but the lack of detailed hydro-meteorological measurements have precluded a complete understanding of the tropical rainfall in Colombia and its complex terrain. The emergence of the multifractal theory for rainfall has opened a field of research which builds a framework for parsimonious modeling of physical process. Studies about the scaling behavior of orographic rainfall have found some modulating functions between the rainfall intensity probability distribution and the terrain elevation. The overall objective is to advance in the understanding of the orographic influence over the Colombian tropical rainfall based on observations and scaling-analysis techniques. We use rainfall maps, weather radars scans and ground-based rainfall data. The research strategy is the analysis of rainfall fields via first-order statistical properties, scaling functions, structure functions and spectral analysis, taking into account cloud-motion directions over mountainous slopes (windward/leeward side) and timing of the diurnal cycle. The analysis is developed for some Colombia's locations.

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.

Encounter risk analysis of rainfall and reference crop evapotranspiration in the irrigation district

NASA Astrophysics Data System (ADS)

Zhang, Jinping; Lin, Xiaomin; Zhao, Yong; Hong, Yang

2017-09-01

Rainfall and reference crop evapotranspiration are random but mutually affected variables in the irrigation district, and their encounter situation can determine water shortage risks under the contexts of natural water supply and demand. However, in reality, the rainfall and reference crop evapotranspiration may have different marginal distributions and their relations are nonlinear. In this study, based on the annual rainfall and reference crop evapotranspiration data series from 1970 to 2013 in the Luhun irrigation district of China, the joint probability distribution of rainfall and reference crop evapotranspiration are developed with the Frank copula function. Using the joint probability distribution, the synchronous-asynchronous encounter risk, conditional joint probability, and conditional return period of different combinations of rainfall and reference crop evapotranspiration are analyzed. The results show that the copula-based joint probability distributions of rainfall and reference crop evapotranspiration are reasonable. The asynchronous encounter probability of rainfall and reference crop evapotranspiration is greater than their synchronous encounter probability, and the water shortage risk associated with meteorological drought (i.e. rainfall variability) is more prone to appear. Compared with other states, there are higher conditional joint probability and lower conditional return period in either low rainfall or high reference crop evapotranspiration. For a specifically high reference crop evapotranspiration with a certain frequency, the encounter risk of low rainfall and high reference crop evapotranspiration is increased with the decrease in frequency. For a specifically low rainfall with a certain frequency, the encounter risk of low rainfall and high reference crop evapotranspiration is decreased with the decrease in frequency. When either the high reference crop evapotranspiration exceeds a certain frequency or low rainfall does not exceed a certain frequency, the higher conditional joint probability and lower conditional return period of various combinations likely cause a water shortage, but the water shortage is not severe.

Toward Continental-scale Rainfall Monitoring Using Commercial Microwave Links From Cellular Communication Networks

NASA Astrophysics Data System (ADS)

Uijlenhoet, R.; Leijnse, H.; Overeem, A.

2017-12-01

Accurate and timely surface precipitation measurements are crucial for water resources management, agriculture, weather prediction, climate research, as well as ground validation of satellite-based precipitation estimates. However, the majority of the land surface of the earth lacks such data, and in many parts of the world the density of surface precipitation gauging networks is even rapidly declining. This development can potentially be counteracted by using received signal level data from the enormous number of microwave links used worldwide in commercial cellular communication networks. Along such links, radio signals propagate from a transmitting antenna at one base station to a receiving antenna at another base station. Rain-induced attenuation and, subsequently, path-averaged rainfall intensity can be retrieved from the signal's attenuation between transmitter and receiver. We have previously shown how one such a network can be used to retrieve the space-time dynamics of rainfall for an entire country (The Netherlands, ˜35,500 km2), based on an unprecedented number of links (˜2,400) and a rainfall retrieval algorithm that can be applied in real time. This demonstrated the potential of such networks for real-time rainfall monitoring, in particular in those parts of the world where networks of dedicated ground-based rainfall sensors are often virtually absent. The presentation will focus on the potential for upscaling this technique to continental-scale rainfall monitoring in Europe. In addition, several examples of recent applications of this technique on other continents (South America, Africa, Asia and Australia) will be given.

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.

Assessing the Relative Performance of Microwave-Based Satellite Rain Rate Retrievals Using TRMM Ground Validation Data

NASA Technical Reports Server (NTRS)

Wolff, David B.; Fisher, Brad L.

2010-01-01

Space-borne microwave sensors provide critical rain information used in several global multi-satellite rain products, which in turn are used for a variety of important studies, including landslide forecasting, flash flood warning, data assimilation, climate studies, and validation of model forecasts of precipitation. This study employs four years (2003-2006) of satellite data to assess the relative performance and skill of SSM/I (F13, F14 and F15), AMSU-B (N15, N16 and N17), AMSR-E (Aqua) and the TRMM Microwave Imager (TMI) in estimating surface rainfall based on direct instantaneous comparisons with ground-based rain estimates from Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) sites at Kwajalein, Republic of the Marshall Islands (KWAJ) and Melbourne, Florida (MELB). The relative performance of each of these satellite estimates is examined via comparisons with space- and time-coincident GV radar-based rain rate estimates. Because underlying surface terrain is known to affect the relative performance of the satellite algorithms, the data for MELB was further stratified into ocean, land and coast categories using a 0.25 terrain mask. Of all the satellite estimates compared in this study, TMI and AMSR-E exhibited considerably higher correlations and skills in estimating/observing surface precipitation. While SSM/I and AMSU-B exhibited lower correlations and skills for each of the different terrain categories, the SSM/I absolute biases trended slightly lower than AMSRE over ocean, where the observations from both emission and scattering channels were used in the retrievals. AMSU-B exhibited the least skill relative to GV in all of the relevant statistical categories, and an anomalous spike was observed in the probability distribution functions near 1.0 mm/hr. This statistical artifact appears to be related to attempts by algorithm developers to include some lighter rain rates, not easily detectable by its scatter-only frequencies. AMSU-B, however, agreed well with GV when the matching data was analyzed on monthly scales. These results signal developers of global rainfall products, such as the TRMM Multi-Satellite Precipitation Analysis (TMPA), and the Climate Data Center s Morphing (CMORPH) technique, that care must be taken when incorporating data from these input satellite estimates in order to provide the highest quality estimates in their products. 3

Assessing the Relative Performance of Microwave-Based Satellite Rain Rate Retrievals Using TRMM Ground Validation Data

NASA Technical Reports Server (NTRS)

Wolff, David B.; Fisher, Brad L.

2011-01-01

Space-borne microwave sensors provide critical rain information used in several global multi-satellite rain products, which in turn are used for a variety of important studies, including landslide forecasting, flash flood warning, data assimilation, climate studies, and validation of model forecasts of precipitation. This study employs four years (2003-2006) of satellite data to assess the relative performance and skill of SSM/I (F13, F14 and F15), AMSU-B (N15, N16 and N17), AMSR-E (Aqua) and the TRMM Microwave Imager (TMI) in estimating surface rainfall based on direct instantaneous comparisons with ground-based rain estimates from Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) sites at Kwajalein, Republic of the Marshall Islands (KWAJ) and Melbourne, Florida (MELB). The relative performance of each of these satellite estimates is examined via comparisons with space- and time-coincident GV radar-based rain rate estimates. Because underlying surface terrain is known to affect the relative performance of the satellite algorithms, the data for MELB was further stratified into ocean, land and coast categories using a 0.25deg terrain mask. Of all the satellite estimates compared in this study, TMI and AMSR-E exhibited considerably higher correlations and skills in estimating/observing surface precipitation. While SSM/I and AMSU-B exhibited lower correlations and skills for each of the different terrain categories, the SSM/I absolute biases trended slightly lower than AMSR-E over ocean, where the observations from both emission and scattering channels were used in the retrievals. AMSU-B exhibited the least skill relative to GV in all of the relevant statistical categories, and an anomalous spike was observed in the probability distribution functions near 1.0 mm/hr. This statistical artifact appears to be related to attempts by algorithm developers to include some lighter rain rates, not easily detectable by its scatter-only frequencies. AMSU-B, however, agreed well with GV when the matching data was analyzed on monthly scales. These results signal developers of global rainfall products, such as the TRMM Multi-Satellite Precipitation Analysis (TMPA), and the Climate Data Center s Morphing (CMORPH) technique, that care must be taken when incorporating data from these input satellite estimates in order to provide the highest quality estimates in their products.

Assessing the Relative Performance of Microwave-based Satellite Rain Rate Retrievals using TRMM Ground Validation Data

NASA Technical Reports Server (NTRS)

Wolff, David B.; Fisher, Brad L.

2008-01-01

Space-borne microwave sensors provide critical rain information used in several global multi-satellite rain products, which in turn are used for a variety of important studies, including landslide forecasting, flash flood warning, data assimilation, climate studies, and validation of model forecast of precipitation. This study employs four years (2003-2006) of satellite data to assess the relative performance and skill of SSM/I (F13, F14 and F15), AMSU-B (N15, N16 and N17), AMSR-E (AQUA) and the TRMM Microwave Imager (TMI) in estimating surface rainfall based on direct instantaneous comparison with ground-based rain estimates from Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) sites at Kwajalein, Republic of the Marshall Islands (KWAJ) and Melbourne, Florida (MELB). The relative performance of each of these satellites is examined via comparisons with GV radar-based rain rate estimates. Because underlying surface terrain is known to affect the relative performance of the satellite algorithms, the data for MELB was further stratified into ocean, land and coast categories using a 0.25 terrain mask. Of all the satellite estimates compared in this study, TMI and AMSR-E exhibited considerably higher correlations and skills in estimating/observing surface precipitation. While SSM/I and AMSU-B exhibited lower correlations and skills for each of the different terrain categories, the SSM/I absolute biases trended slightly lower than AMSRE over ocean, where the observations from both emission and scattering channels were used in the retrievals. AMSU-B exhibited the least skill relative to GV in all of the relevant statistical categories, and an anomalous spike was observed in the probability distribution functions near 1.0 mm hr-1. This statistical artifact appears to be related to attempts by algorithm developers to include some lighter rain rates, not easily detectable by its scatter-only frequencies. AMSU-B, however, agreed well with GV when the matching data was analyzed on monthly scales. These results signal developers of global rainfall products, such as the TRMM Multi-Satellite Precipitation Analysis (TMPA), and the Climate Data Center s Morphing (CMORPH) technique, that care must be taken when incorporating data from these input satellite estimates in order to provide the highest quality estimates in their products.

Uncertainty based modeling of rainfall-runoff: Combined differential evolution adaptive Metropolis (DREAM) and K-means clustering

NASA Astrophysics Data System (ADS)

Zahmatkesh, Zahra; Karamouz, Mohammad; Nazif, Sara

2015-09-01

Simulation of rainfall-runoff process in urban areas is of great importance considering the consequences and damages of extreme runoff events and floods. The first issue in flood hazard analysis is rainfall simulation. Large scale climate signals have been proved to be effective in rainfall simulation and prediction. In this study, an integrated scheme is developed for rainfall-runoff modeling considering different sources of uncertainty. This scheme includes three main steps of rainfall forecasting, rainfall-runoff simulation and future runoff prediction. In the first step, data driven models are developed and used to forecast rainfall using large scale climate signals as rainfall predictors. Due to high effect of different sources of uncertainty on the output of hydrologic models, in the second step uncertainty associated with input data, model parameters and model structure is incorporated in rainfall-runoff modeling and simulation. Three rainfall-runoff simulation models are developed for consideration of model conceptual (structural) uncertainty in real time runoff forecasting. To analyze the uncertainty of the model structure, streamflows generated by alternative rainfall-runoff models are combined, through developing a weighting method based on K-means clustering. Model parameters and input uncertainty are investigated using an adaptive Markov Chain Monte Carlo method. Finally, calibrated rainfall-runoff models are driven using the forecasted rainfall to predict future runoff for the watershed. The proposed scheme is employed in the case study of the Bronx River watershed, New York City. Results of uncertainty analysis of rainfall-runoff modeling reveal that simultaneous estimation of model parameters and input uncertainty significantly changes the probability distribution of the model parameters. It is also observed that by combining the outputs of the hydrological models using the proposed clustering scheme, the accuracy of runoff simulation in the watershed is remarkably improved up to 50% in comparison to the simulations by the individual models. Results indicate that the developed methodology not only provides reliable tools for rainfall and runoff modeling, but also adequate time for incorporating required mitigation measures in dealing with potentially extreme runoff events and flood hazard. Results of this study can be used in identification of the main factors affecting flood hazard analysis.

Effective application of optical sensing technology for sustainable liquid level sensing and rainfall measurement

NASA Astrophysics Data System (ADS)

Afzal, Muhammad Hassan Bin

2015-05-01

Rainfall measurement is performed on regular basis to facilitate effectively the weather stations and local inhabitants. Different types of rain gauges are available with different measuring principle for rainfall measurement. In this research work, a novel optical rain sensor is designed, which precisely calculate the rainfall level according to rainfall intensity. This proposed optical rain sensor model introduced in this paper, which is basically designed for remote sensing of rainfall and it designated as R-ORMS (Remote Optical Rainfall Measurement sensor). This sensor is combination of some improved method of tipping bucket rain gauge and most of the optical hydreon rain sensor's principle. This optical sensor can detect the starting time and ending time of rain, rain intensity and rainfall level. An infrared beam from Light Emitting Diode (LED) through powerful convex lens can accurately determines the diameter of each rain drops by total internal reflection principle. Calculations of these accumulative results determine the rain intensity and rainfall level. Accurate rainfall level is determined by internal optical LED based sensor which is embedded in bucket wall. This internal sensor is also following the total internal reflection (TIR) principle and the Fresnel's law. This is an entirely novel design of optical sensing principle based rain sensor and also suitable for remote sensing rainfall level. The performance of this proposed sensor has been comprehensively compared with other sensors with similar attributes and it showed better and sustainable result. Future related works have been proposed at the end of this paper, to provide improved and enhanced performance of proposed novel rain sensor.

Long-term flow forecasts based on climate and hydrologic modeling: Uruguay River basin

NASA Astrophysics Data System (ADS)

Tucci, Carlos Eduardo Morelli; Clarke, Robin Thomas; Collischonn, Walter; da Silva Dias, Pedro Leite; de Oliveira, Gilvan Sampaio

2003-07-01

This paper describes a procedure for predicting seasonal flow in the Rio Uruguay drainage basin (area 75,000 km2, lying in Brazilian territory), using sequences of future daily rainfall given by the global climate model (GCM) of the Brazilian agency for climate prediction (Centro de Previsão de Tempo e Clima, or CPTEC). Sequences of future daily rainfall given by this model were used as input to a rainfall-runoff model appropriate for large drainage basins. Forecasts of flow in the Rio Uruguay were made for the period 1995-2001 of the full record, which began in 1940. Analysis showed that GCM forecasts underestimated rainfall over almost all the basin, particularly in winter, although interannual variability in regional rainfall was reproduced relatively well. A statistical procedure was used to correct for the underestimation of rainfall. When the corrected rainfall sequences were transformed to flow by the hydrologic model, forecasts of flow in the Rio Uruguay basin were better than forecasts based on historic mean or median flows by 37% for monthly flows and by 54% for 3-monthly flows.

Assessment of the Effects of Various Precipitation Forcings on Flood Forecasting Potential Using WRF-Hydro Modeling

NASA Astrophysics Data System (ADS)

Zhang, J.; Fang, N. Z.

2017-12-01

A potential flood forecast system is under development for the Upper Trinity River Basin (UTRB) in North Central of Texas using the WRF-Hydro model. The Routing Application for the Parallel Computation of Discharge (RAPID) is utilized as channel routing module to simulate streamflow. Model performance analysis was conducted based on three quantitative precipitation estimates (QPE): the North Land Data Assimilation System (NLDAS) rainfall, the Multi-Radar Multi-Sensor (MRMS) QPE and the National Centers for Environmental Prediction (NCEP) quality-controlled stage IV estimates. Prior to hydrologic simulation, QPE performance is assessed on two time scales (daily and hourly) using the Community Collaborative Rain, Hail and Snow Network (CoCoRaHS) and Hydrometeorological Automated Data System (HADS) hourly products. The calibrated WRF-Hydro model was then evaluated by comparing the simulated against the USGS observed using various QPE products. The results imply that the NCEP stage IV estimates have the best accuracy among the three QPEs on both time scales, while the NLDAS rainfall performs poorly because of its coarse spatial resolution. Furthermore, precipitation bias demonstrates pronounced impact on flood forecasting skills, as the root mean squared errors are significantly reduced by replacing NLDAS rainfall with NCEP stage IV estimates. This study also demonstrates that accurate simulated results can be achieved when initial soil moisture values are well understood in the WRF-Hydro model. Future research effort will therefore be invested on incorporating data assimilation with focus on initial states of the soil properties for UTRB.

Vegetation greenness modelling in response to interannual precipitation and temperature changes between 2001 and 2012 in Liao River Basin in Jilin Province, China.

PubMed

Lin, Xiao-Sheng; Tang, Jie; Li, Zhao-Yang; Li, Hai-Yi

2016-01-01

Liao River basin in Jilin Province is the place of origin of the Dongliao River. This study gives a comprehensive analysis of the vegetation coverage in the region and provides a potential theoretical basis for ecological restoration. The seasonal variation of vegetation greenness and dynamics based on the Normalized Difference Vegetation Index (NDVI) in major land cover types in the region was studied. Analyzing the relationship NDVI, temperature and rainfall, we derived a set of predictor variables from 2001 to 2012 using the MODIS Terra level 1 Product (MOD02QKM). The results showed a general increasing trend in NDVI value in the region, while 34.63 % of the region showed degradation. NDVI values begin to rise from April when plants are regreening and they drop in September when temperature are decreasing and the leaves are falling in the study area and temperature was found decreasing during the period of 2001-2012 while rainfall showed an increasing trend. This model could be used to observe the change in vegetation greenness and the dynamic effects of temperature and rainfall. This study provided important data for the environmental protection of the basin area. And we hope to provide scientific analysis for controlling water and soil erosion, maintaining the sustainable productivity of land resources, enhancing the treatment of water pollution and stimulating the virtuous cycle of the ecological system.

On Rainfall Modification by Major Urban Areas. Part 1; Observations from Space-borne Rain Radar on TRMM

NASA Technical Reports Server (NTRS)

Shepherd, J. Marshall; Pierce, Harold; Starr, David OC. (Technical Monitor)

2001-01-01

This study represents one of the first published attempts to identify rainfall modification by urban areas using satellite-based rainfall measurements. Data from the first space-based rain-radar, the Tropical Rainfall Measuring Mission's (TRMM) Precipitation Radar, are employed. Analysis of the data enables identification of rainfall patterns around Atlanta, Montgomery, Nashville, San Antonio, Waco, and Dallas during the warm season. Results reveal an average increase of -28% in monthly rainfall rates within 30-60 kilometers downwind of the metropolis with a modest increase of 5.6% over the metropolis. Portions of the downwind area exhibit increases as high as 51%. The percentage chances are relative to an upwind CONTROL area. It was also found that maximum rainfall rates in the downwind impact area can exceed the mean value in the upwind CONTROL area by 48%-116%. The maximum value was generally found at an average distance of 39 km from the edge of the urban center or 64 km from the center of the city. These results are consistent with METROMEX studies of St. Louis almost two decades ago and more recent studies near Atlanta. Future work will investi(yate hypothesized factors causing rainfall modification by urban areas. Additional work is also needed to provide more robust validation of space-based rain estimates near major urban areas. Such research has implications for urban planning, water resource management, and understanding human impact on the environment.

Analysis of the linkage between rain and flood regime and its application to regional flood frequency estimation

NASA Astrophysics Data System (ADS)

Cunderlik, Juraj M.; Burn, Donald H.

2002-04-01

Improving techniques of flood frequency estimation at ungauged sites is one of the foremost goals of contemporary hydrology. River flood regime is a resultant reflection of a composite catchment hydrologic response to flood producing processes. In this sense the process of identifying homogeneous pooling groups can be plausibly based on catchment similarity in flood regime. Unfortunately the application of any pooling approach that is based on flood regime is restricted to gauged sites. Because flood regime can be markedly determined by rainfall regime, catchment similarity in rainfall regime can be an alternative option for identifying flood frequency pooling groups. An advantage of such a pooling approach is that rainfall data are usually spatially and temporary more abundant than flood data and the approach can also be applied at ungauged sites. Therefore in this study we have quantified the linkage between rainfall and flood regime and explored the appropriateness of substituting rainfall regime for flood regime in regional pooling schemes. Two different approaches to describing rainfall regime similarity using tools of directional statistics have been tested and used for evaluation of the potential of rainfall regime for identification of hydrologically homogeneous pooling groups. The outputs were compared to an existing pooling framework adopted in the Flood Estimation Handbook. The results demonstrate that regional pooling based on rainfall regime information leads to a high number of initially homogeneous groups and seems to be a sound pooling alternative for catchments with a close linkage between rain and flood regimes.

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

Evaluation of rainfall structure on hydrograph simulation: Comparison of radar and interpolated methods, a study case in a tropical catchment

NASA Astrophysics Data System (ADS)

Velasquez, N.; Ochoa, A.; Castillo, S.; Hoyos Ortiz, C. D.

2017-12-01

The skill of river discharge simulation using hydrological models strongly depends on the quality and spatio-temporal representativeness of precipitation during storm events. All precipitation measurement strategies have their own strengths and weaknesses that translate into discharge simulation uncertainties. Distributed hydrological models are based on evolving rainfall fields in the same time scale as the hydrological simulation. In general, rainfall measurements from a dense and well maintained rain gauge network provide a very good estimation of the total volume for each rainfall event, however, the spatial structure relies on interpolation strategies introducing considerable uncertainty in the simulation process. On the other hand, rainfall retrievals from radar reflectivity achieve a better spatial structure representation but with higher uncertainty in the surface precipitation intensity and volume depending on the vertical rainfall characteristics and radar scan strategy. To assess the impact of both rainfall measurement methodologies on hydrological simulations, and in particular the effects of the rainfall spatio-temporal variability, a numerical modeling experiment is proposed including the use of a novel QPE (Quantitative Precipitation Estimation) method based on disdrometer data in order to estimate surface rainfall from radar reflectivity. The experiment is based on the simulation of 84 storms, the hydrological simulations are carried out using radar QPE and two different interpolation methods (IDW and TIN), and the assessment of simulated peak flow. Results show significant rainfall differences between radar QPE and the interpolated fields, evidencing a poor representation of storms in the interpolated fields, which tend to miss the precise location of the intense precipitation cores, and to artificially generate rainfall in some areas of the catchment. Regarding streamflow modelling, the potential improvement achieved by using radar QPE depends on the density of the rain gauge network and its distribution relative to the precipitation events. The results for the 84 storms show a better model skill using radar QPE than the interpolated fields. Results using interpolated fields are highly affected by the dominant rainfall type and the basin scale.

Long-Term Historical Rainfall-Runoff Modeling Using High-Resolution Satellite-based Precipitation Products

NASA Astrophysics Data System (ADS)

Ashouri, H.; Nguyen, P.; Thorstensen, A. R.; Hsu, K. L.; Sorooshian, S.

2014-12-01

This study evaluates the performance of a newly developed long-term high-resolution satellite-based precipitation products, named Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network - Climate Data Record (PERSIANN-CDR), in hydrological modeling. PERSIANN-CDR estimations are biased corrected using GPCP monthly climatology data. PERSIANN-CDR provides daily rainfall estimates at 0.25° x 0.25° grid boxes for 1983-2014 (delayed present). This newly released product makes it feasible to model the streamflow over the past 30 years. Three test basins from the Distributed Hydrologic Model Intercomparison Project - Phase 2 (DMIP 2) are chosen. Comparing with other satellite products, the Version 7 TRMM Multi-satellite Precipitation Analysis (TMPA) product is used. Stage IV radar data is used as a reference data for evaluating the PERSIANN-CDR and TMPA precipitation data. All products are scaled to 0.25° and daily spatiotemporal resolution. The study is performed in two phases. In the first phase, the 2003-2011 period where all the products are available is chosen. Precipitation evaluation results, presented on Taylor Diagrams, show that TMPA and PERSIANN-CDR have close performances. The National Weather Service (NWS) Office of Hydrologic Development (OHD) Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM) is then forced with the PERSIANN-CDR and the TMPA precipitation products, as well as the stage IV radar data. USGS Streamflow observations at the outlet of the basins are used as the reference streamflow data. The results show that in general, in all the three DMIP 2 basins the simulated hydrographs forced with PERSIANN-CDR and TMPA show good agreement, as the statistical measures such as root mean square error, bias, and correlation coefficient are close. In addition, with respect to the streamflow peaks, PERSIANN-CDR shows better performance than Stage IV radar data in capturing the extreme streamflow magnitudes. Based on the results from the first phase of the study and given the fact that PERSIANN-CDR covers the 1983-2014, in the second phase of the study we model the streamflow for the period of 1983-2014. The results will be presented in the meeting.

Simulation of climate characteristics and extremes of the Volta Basin using CCLM and RCA regional climate models

NASA Astrophysics Data System (ADS)

Darko, Deborah; Adjei, Kwaku A.; Appiah-Adjei, Emmanuel K.; Odai, Samuel N.; Obuobie, Emmanuel; Asmah, Ruby

2018-06-01

The extent to which statistical bias-adjusted outputs of two regional climate models alter the projected change signals for the mean (and extreme) rainfall and temperature over the Volta Basin is evaluated. The outputs from two regional climate models in the Coordinated Regional Climate Downscaling Experiment for Africa (CORDEX-Africa) are bias adjusted using the quantile mapping technique. Annual maxima rainfall and temperature with their 10- and 20-year return values for the present (1981-2010) and future (2051-2080) climates are estimated using extreme value analyses. Moderate extremes are evaluated using extreme indices (viz. percentile-based, duration-based, and intensity-based). Bias adjustment of the original (bias-unadjusted) models improves the reproduction of mean rainfall and temperature for the present climate. However, the bias-adjusted models poorly reproduce the 10- and 20-year return values for rainfall and maximum temperature whereas the extreme indices are reproduced satisfactorily for the present climate. Consequently, projected changes in rainfall and temperature extremes were weak. The bias adjustment results in the reduction of the change signals for the mean rainfall while the mean temperature signals are rather magnified. The projected changes for the original mean climate and extremes are not conserved after bias adjustment with the exception of duration-based extreme indices.

Tropical Rainfall Analysis Using TRMM in Combination With Other Satellite Gauge Data: Comparison with Global Precipitation Climatology Project (GPCP) Results

NASA Technical Reports Server (NTRS)

Adler, Robert F.; Huffman, George J.; Bolvin, David; Nelkin, Eric; Curtis, Scott

1999-01-01

This paper describes recent results of using Tropical Rainfall Measuring Mission (TRMM) information as the key calibration tool in a merged analysis on a 1 deg x 1 deg latitude/longitude monthly scale based on multiple satellite sources and raingauge analysis. The procedure used to produce the GPCP data set is a stepwise approach which first combines the satellite low-orbit microwave and geosynchronous IR observations into a "multi-satellite" product and than merges that result with the raingauge analysis. Preliminary results produced with the still-stabilizing TRMM algorithms indicate that TRMM shows tighter spatial gradients in tropical rain maxima with higher peaks in the center of the maxima. The TRMM analyses will be used to evaluate the evolution of the 1998 ENSO variations, again in comparison with the GPCP analyses.

Effects of biotic and abiotic factors on the temporal dynamic of bat-fruit interactions

NASA Astrophysics Data System (ADS)

Laurindo, Rafael de Souza; Gregorin, Renato; Tavares, Davi Castro

2017-08-01

Mutualistic interactions between animals and plants vary over time and space based on the abundance of fruits or animals and seasonality. Little is known about this temporal dynamic and the influence of biotic and abiotic factors on the structure of interaction networks. We evaluated changes in the structure of network interactions between bats and fruits in relation to variations in rainfall. Our results suggest that fruit abundance is the main variable responsible for temporal changes in network attributes, such as network size, connectance, and number of interactions. In the same way, temperature positively affected the abundance of fruits and bats. An increase in temperature and alterations in rainfall patterns, due to human induced climate change, can cause changes in phenological patterns and fruit production, with negative consequences to biodiversity maintenance, ecological interactions, and ecosystem functioning.

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

Singh, Narendra; Solanki, Raman; Ojha, N.

We present the measurements of cloud-base height variations over Aryabhatta Research Institute of Observational Science, Nainital (79.45 degrees E, 29.37 degrees N, 1958 m amsl) obtained from Vaisala Ceilometer, during the nearly year-long Ganges Valley Aerosol Experiment (GVAX). The cloud-base measurements are analysed in conjunction with collocated measurements of rainfall, to study the possible contributions from different cloud types to the observed monsoonal rainfall during June to September 2011. The summer monsoon of 2011 was a normal monsoon year with total accumulated rainfall of 1035.8 mm during June-September with a maximum during July (367.0 mm) and minimum during September (222.3more » mm). The annual mean monsoon rainfall over Nainital is 1440 +/- 430 mm. The total rainfall measured during other months (October 2011-March 2012) was only 9% of that observed during the summer monsoon. The first cloud-base height varied from about 31 m above ground level (AGL) to a maximum of 7.6 km AGL during the summer monsoon period of 2011. It is found that about 70% of the total rain is observed only when the first cloud-base height varies between surface and 2 km AGL, indicating that most of the rainfall at high altitude stations such as Nainital is associated with stratiform low-level clouds. However, about 25% of the total rainfall is being contributed by clouds between 2 and 6 km. The occurrences of high-altitude cumulus clouds are observed to be only 2-4%. This study is an attempt to fill a major gap of measurements over the topographically complex and observationally sparse northern Indian region providing the evaluation data for atmospheric models and therefore, have implications towards the better predictions of monsoon rainfall and the weather components over this region.« less

Vulnerability and Productivity Impacts of Farm-Level Interventions in the Brazilian Sertão

NASA Astrophysics Data System (ADS)

Burney, J. A.

2014-12-01

The Brazilian Sertão exemplifies the complex dynamics between climate, land use, agricultural production, and food security presently playing out across the world's semi-arid tropics. Regional climate change in the past half-century is some of the most dramatic in the world -- +2 degrees average warming in many districts and -300mm rainfall. Crop yields have improved weakly or remained stagnant, in stark contrast with the rest of Brazil. As a result many smallholder farmers have increasingly turned to milk production, but they remain dependent on rainfall for forage growth. During droughts they thus face a choice between overgrazing and letting their cattle die. As a result, deforestation of the native Caatinga biome has been rampant, with estimates of 85% loss. We present the results of controlled tests, conducted with over one hundred farmers, of several on-farm interventions meant to boost on-farm productivity, reduce vulnerability to rainfall shocks, and lessen the incentives for deforestation. These interventions -- water cisterns, smallholder irrigation systems, balancing of animal feed ratios, and cultivation and use of palma forrageira as cattle feed -- are described and presented with results of their impact on productivity and vulnerability/resilience metrics. Estimates of larger-scale social benefits and future land-use change impacts are also discussed.

Review of FEWS NET Biophysical Monitoring Requirements

NASA Technical Reports Server (NTRS)

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

2009-01-01

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

Ecohydrology of managed ecosystems: Linking rainfall unpredictability, agronomic performance, and sustainable water use

NASA Astrophysics Data System (ADS)

Vico, Giulia; Porporato, Amilcare

2014-05-01

The field of ecohydrology, traditionally focusing on natural ecosystems, can offer the necessary quantitative tools to assess and compare the sustainability of agriculture across climates, soil types, crops, and irrigation strategies, including rainfall unpredictability. In particular, irrigation is one of the main strategies to enhance and stabilize agricultural productivity, but represents a cost in terms of often scarce water resources. Here, the sustainability of irrigated and rainfed agriculture is assessed by means of water productivity (defined as the ratio between yield and total supplied water), yields, water requirements, and their variability. These indicators are quantified using a probabilistic description of the soil water balance and crop development. Employing this framework, we interpret changes in water productivity as total water input is altered, in two staple crops (maize and wheat) grown under different soils, climates, and irrigation strategies. Climate change scenarios are explored by using the same approach and altering the rainfall statistics. For a given irrigation strategy, intermediate rainfall inputs leads to the highest variability in yield and irrigation water requirement - it is under these conditions that water management is most problematic. When considering the contrasting needs of limiting water requirements while ensuring adequate yields, micro-irrigation emerges as the most sustainable strategy at the field level, although consideration should be given to its profitability and long-term environmental implications.

Contrasting adaptive strategies to terminal drought-stress gradients in Mediterranean legumes: phenology, productivity, and water relations in wild and domesticated Lupinus luteus L.

PubMed

Berger, J D; Ludwig, C

2014-11-01

Our understanding of within-species annual plant adaptation to rainfall gradients is fragmented. Broad-scale ecological applications of Grime's C-S-R triangle are often superficial, while detailed drought physiology tends to be narrow, focusing on elite cultivars. The former lack the detail to explain how plants respond, while the latter provide little context to investigate trade-offs among traits, to explain where/why these might be adaptive. Ecophysiology, combining the breadth of the former with the detail of the latter, can resolve this disconnect and is applied here to describe adaptive strategies in the Mediterranean legume Lupinus luteus. Wild and domesticated material from low- and high-rainfall environments was evaluated under contrasting terminal drought. These opposing environments have selected for contrasting, integrated, adaptive strategies. Long-season, high-rainfall habitats select for competitive (C) traits: delayed phenology, high above- and below-ground biomass, productivity, and fecundity, leading to high water-use and early stress onset. Terminal drought-prone environments select for the opposite: ruderal (R) traits that facilitate drought escape/avoidance but limit reproductive potential. Surprisingly, high-rainfall ecotypes generate lower critical leaf water potentials under water deficit, maintaining higher relative water content than the latter. Given that L. luteus evolved in sandy, low-water-holding capacity soils, this represents a bet-hedging response to intermittent self-imposed water-deficits associated with a strongly C-selected adaptive strategy that is therefore redundant in R-selected low-rainfall ecotypes. Domesticated L. luteus is even more R-selected, reflecting ongoing selection for early maturity. Introgression of appropriate C-selected adaptive traits from wild germplasm may widen the crop production range. © The Author 2014. Published by Oxford University Press on behalf of the Society for Experimental Biology.

Contrasting adaptive strategies to terminal drought-stress gradients in Mediterranean legumes: phenology, productivity, and water relations in wild and domesticated Lupinus luteus L.

PubMed Central

Berger, J. D.; Ludwig, C.

2014-01-01

Our understanding of within-species annual plant adaptation to rainfall gradients is fragmented. Broad-scale ecological applications of Grime’s C-S-R triangle are often superficial, while detailed drought physiology tends to be narrow, focusing on elite cultivars. The former lack the detail to explain how plants respond, while the latter provide little context to investigate trade-offs among traits, to explain where/why these might be adaptive. Ecophysiology, combining the breadth of the former with the detail of the latter, can resolve this disconnect and is applied here to describe adaptive strategies in the Mediterranean legume Lupinus luteus. Wild and domesticated material from low- and high-rainfall environments was evaluated under contrasting terminal drought. These opposing environments have selected for contrasting, integrated, adaptive strategies. Long-season, high-rainfall habitats select for competitive (C) traits: delayed phenology, high above- and below-ground biomass, productivity, and fecundity, leading to high water-use and early stress onset. Terminal drought-prone environments select for the opposite: ruderal (R) traits that facilitate drought escape/avoidance but limit reproductive potential. Surprisingly, high-rainfall ecotypes generate lower critical leaf water potentials under water deficit, maintaining higher relative water content than the latter. Given that L. luteus evolved in sandy, low-water-holding capacity soils, this represents a bet-hedging response to intermittent self-imposed water-deficits associated with a strongly C-selected adaptive strategy that is therefore redundant in R-selected low-rainfall ecotypes. Domesticated L. luteus is even more R-selected, reflecting ongoing selection for early maturity. Introgression of appropriate C-selected adaptive traits from wild germplasm may widen the crop production range. PMID:24591050

Comparing rainfall patterns between regions in Peninsular Malaysia via a functional data analysis technique

NASA Astrophysics Data System (ADS)

Suhaila, Jamaludin; Jemain, Abdul Aziz; Hamdan, Muhammad Fauzee; Wan Zin, Wan Zawiah

2011-12-01

SummaryNormally, rainfall data is collected on a daily, monthly or annual basis in the form of discrete observations. The aim of this study is to convert these rainfall values into a smooth curve or function which could be used to represent the continuous rainfall process at each region via a technique known as functional data analysis. Since rainfall data shows a periodic pattern in each region, the Fourier basis is introduced to capture these variations. Eleven basis functions with five harmonics are used to describe the unimodal rainfall pattern for stations in the East while five basis functions which represent two harmonics are needed to describe the rainfall pattern in the West. Based on the fitted smooth curve, the wet and dry periods as well as the maximum and minimum rainfall values could be determined. Different rainfall patterns are observed among the studied regions based on the smooth curve. Using the functional analysis of variance, the test results indicated that there exist significant differences in the functional means between each region. The largest differences in the functional means are found between the East and Northwest regions and these differences may probably be due to the effect of topography and, geographical location and are mostly influenced by the monsoons. Therefore, the same inputs or approaches might not be useful in modeling the hydrological process for different regions.

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.

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.

On the Comparison of the Global Surface Soil Moisture product and Land Surface Modeling

NASA Astrophysics Data System (ADS)

Delorme, B., Jr.; Ottlé, C.; Peylin, P.; Polcher, J.

2016-12-01

Thanks to its large spatio-temporal coverage, the new ESA CCI multi-instruments dataset offers a good opportunity to assess and improve land surface models parametrization. In this study, the ESA CCI surface soil moisture (SSM) combined product (v2.2) has been compared to the simulated top first layers of the ORCHIDEE LSM (the continental part of the IPSL earth system model), in order to evaluate its potential of improvements with data assimilation techniques. The ambition of the work was to develop a comprehensive comparison methodology by analyzing simultaneously the temporal and spatial structures of both datasets. We analyzed the SSM synoptic, seasonal, and inter-annual variations by decomposing the signals into fast and slow components. ORCHIDEE was shown to adequately reproduce the observed SSM dynamics in terms of temporal correlation. However, these correlation scores are supposed to be strongly influenced by SSM seasonal variability and the quality of the model input forcing. Autocorrelation and spectral analyses brought out disagreements in the temporal inertia of the upper soil moisture reservoirs. By linking our results to land cover maps, we found that ORCHIDEE is more dependent on rainfall events compared to the observations in regions with sparse vegetation cover. These diflerences might be due to a wrong partition of rainfall between soil evaporation, transpiration, runofl and drainage in ORCHIDEE. To refine this analysis, a single value decomposition (SVD) of the co-variability between rainfall provided by WFDEI and soil moisture was pursued over Central Europe and South Africa. It showed that spatio-temporal co-varying patterns between ORCHIDEE and rainfall and the ESA-CCI product and rainfall are in relatively good agreement. However, the leading SVD pattern, which exhibits a strong annual cycle and explains the same portion of covariance for both datasets, explains a much larger fraction of variance for ORCHIDEE than for the ESA-CCI product. These results highlight that the role of other surface variables presenting a strong seasonal variability (like vegetation cover, possibly irrigation) is not accounted for similarly in both the model and the product, and that further work is needed to explore these discrepancies.

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

NASA Astrophysics Data System (ADS)

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

2004-12-01

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

Merging gauge and satellite rainfall with specification of associated uncertainty across Australia

NASA Astrophysics Data System (ADS)

Woldemeskel, Fitsum M.; Sivakumar, Bellie; Sharma, Ashish

2013-08-01

Accurate estimation of spatial rainfall is crucial for modelling hydrological systems and planning and management of water resources. While spatial rainfall can be estimated either using rain gauge-based measurements or using satellite-based measurements, such estimates are subject to uncertainties due to various sources of errors in either case, including interpolation and retrieval errors. The purpose of the present study is twofold: (1) to investigate the benefit of merging rain gauge measurements and satellite rainfall data for Australian conditions and (2) to produce a database of retrospective rainfall along with a new uncertainty metric for each grid location at any timestep. The analysis involves four steps: First, a comparison of rain gauge measurements and the Tropical Rainfall Measuring Mission (TRMM) 3B42 data at such rain gauge locations is carried out. Second, gridded monthly rain gauge rainfall is determined using thin plate smoothing splines (TPSS) and modified inverse distance weight (MIDW) method. Third, the gridded rain gauge rainfall is merged with the monthly accumulated TRMM 3B42 using a linearised weighting procedure, the weights at each grid being calculated based on the error variances of each dataset. Finally, cross validation (CV) errors at rain gauge locations and standard errors at gridded locations for each timestep are estimated. The CV error statistics indicate that merging of the two datasets improves the estimation of spatial rainfall, and more so where the rain gauge network is sparse. The provision of spatio-temporal standard errors with the retrospective dataset is particularly useful for subsequent modelling applications where input error knowledge can help reduce the uncertainty associated with modelling outcomes.

Extrapolating effects of conservation tillage on yield, soil moisture and dry spell mitigation using simulation modelling

NASA Astrophysics Data System (ADS)

Mkoga, Z. J.; Tumbo, S. D.; Kihupi, N.; Semoka, J.

There is big effort to disseminate conservation tillage practices in Tanzania. Despite wide spread field demonstrations there has been some field experiments meant to assess and verify suitability of the tillage options in local areas. Much of the experiments are short lived and thus long term effects of the tillage options are unknown. Experiments to study long term effects of the tillage options are lacking because they are expensive and cannot be easily managed. Crop simulation models have the ability to use long term weather data and the local soil parameters to assess long term effects of the tillage practices. The Agricultural Production Systems Simulator (APSIM) crop simulation model; was used to simulate long term production series of soil moisture and grain yield based on the soil and weather conditions in Mkoji sub-catchment of the great Ruaha river basin in Tanzania. A 24 year simulated maize yield series based on conventional tillage with ox-plough, without surface crop residues (CT) treatment was compared with similar yield series based on conservation tillage (ox-ripping, with surface crop residues (RR)). Results showed that predicted yield averages were significantly higher in conservation tillage than in conventional tillage ( P < 0.001). Long term analysis, using APSIM simulation model, showed that average soil moisture in the conservation tillage was significantly higher ( P < 0.05) (about 0.29 mm/mm) than in conventional tillage (0.22 mm/mm) treatment during the seasons which received rainfall between 468 and 770 mm. Similarly the conservation tillage treatment recorded significantly higher yields (4.4 t/ha) ( P < 0.01) than the conventional tillage (3.6 t/ha) treatment in the same range of seasonal rainfall. On the other hand there was no significant difference in soil moisture for the seasons which received rainfall above 770 mm. In these seasons grain yield in conservation tillage treatment was significantly lower (3.1 kg/ha) than in the conventional tillage treatment (4.8 kg/ha) ( P < 0.05). Results also indicated a probability of 0.5 of getting higher yield in conservation than in conventional tillage practice. The conservation tillage treatment had the ability to even-out the acute and long intra-seasonal dry spells. For example a 36-days agricultural dry spell which occurred between 85th and 130th day after planting in the 1989/1990 season (in the CT treatment) was mitigated to zero days in the RR treatment by maintaining soil moisture above the critical point. Critical soil moisture for maize was measured at 0.55 of maximum soil moisture that can be depleted crop (0.55 D). It is concluded that conservation tillage practice where ripping and surface crop residues is used is much more effective in mitigating dry spells and increase productivity in a seasonal rainfall range of between 460 and 770 mm. It is recommended that farmers in the area adopt that type of conservation tillage because rainfall was in this range (460-770 mm) in 12 out of the past 24 years, indicating possibility of yield losses once in every 2 years.

An experimental operative system for shallow landslide and flash flood warning based on rainfall thresholds and soil moisture modelling

NASA Astrophysics Data System (ADS)

Brigandı, G.; Aronica, G. T.; Basile, G.; Pasotti, L.; Panebianco, M.

2012-04-01

On November 2011 a thunderstorms became almost exceptional over the North-East part of the Sicily Region (Italy) producing local heavy rainfall, mud-debris flow and flash flooding. The storm was concentrated on the Tyrrhenian sea coast near the city of Barcellona within the Longano catchment. Main focus of the paper is to present an experimental operative system for alerting extreme hydrometeorological events by using a methodology based on the combined use of rainfall thresholds, soil moisture indexes and quantitative precipitation forecasting. As matter of fact, shallow landslide and flash flood warning is a key element to improve the Civil Protection achievements to mitigate damages and safeguard the security of people. It is a rather complicated task, particularly in those catchments with flashy response where even brief anticipations are important and welcomed. It is well known how the triggering of shallow landslides is strongly influenced by the initial soil moisture conditions of catchments. Therefore, the early warning system here applied is based on the combined use of rainfall thresholds, derived both for flash flood and for landslide, and soil moisture conditions; the system is composed of several basic component related to antecedent soil moisture conditions, real-time rainfall monitoring and antecedent rainfall. Soil moisture conditions were estimated using an Antecedent Precipitation Index (API), similar to this widely used for defining soil moisture conditions via Antecedent Moisture conditions index AMC. Rainfall threshold for landslides were derived using historical and statistical analysis. Finally, rainfall thresholds for flash flooding were derived using an Instantaneous Unit Hydrograph based lumped rainfall-runoff model with the SCS-CN routine for net rainfall. After the implementation and calibration of the model, a testing phase was carried out by using real data collected for the November 2001 event in the Longano catchment. Moreover, in order to test the capability of the system to forecast thise event, Quantitative Precipitation Forecasting provided by the SILAM (Sicily Limited Area Model), a meteorological model run by SIAS (Sicilian Agrometeorological Service) with a forecast horizon up to 144 hours, have been used to run the system.

Relationships between Tropical Rainfall Events and Regional Annual Rainfall Anomalies

NASA Astrophysics Data System (ADS)

Painter, C.; Varble, A.; Zipser, E. J.

2016-12-01

Regional annual precipitation anomalies strongly impact the health of regional ecosystems, water resources, agriculture, and the probability of flood and drought conditions. Individual event characteristics, including rain rate, areal coverage, and stratiform fraction are also crucial in considering large-scale impacts on these resources. Therefore, forecasting individual event characteristics is important and could potentially be improved through correlation with longer and better predicted timescale environmental variables such as annual rainfall. This study examines twelve years of retrieved rainfall characteristics from the Tropical Rainfall Measuring Mission (TRMM) satellite at a 5° x 5° resolution between 35°N and 35°S, as a function of annual rainfall anomaly derived from Global Precipitation Climatology Project data. Rainfall event characteristics are derived at a system scale from the University of Utah TRMM Precipitation Features database and at a 5-km pixel scale from TRMM 2A25 products. For each 5° x 5° grid box and year, relationships between these characteristics and annual rainfall anomaly are derived. Additionally, years are separated into wet and dry groups for each grid box and are compared versus one another. Convective and stratiform rain rates, along with system area and volumetric rainfall, generally increase during wetter years, and this increase is most prominent over oceans. This is in agreement with recent studies suggesting that convective systems become larger and rainier when regional annual rainfall increases or when the climate warms. Over some land regions, on the other hand, system rain rate, volumetric rainfall, and area actually decrease as annual rainfall increases. Therefore, land and ocean regions generally exhibit different relationships. In agreement with recent studies of extreme rainfall in a changing climate, the largest and rainiest systems increase in relative size and intensity compared to average systems, and do so as a function of annual rainfall in most tropical regions. However, select land regions such as the Congo fail to follow this tendency. Changes in seasonal and diurnal cycles of PF characteristics as a function of regional annual rainfall anomaly are also analyzed.

Rainfall Controls on Land Surface Phenology over "Never-green" and "Ever-green" Lands in Africa

NASA Astrophysics Data System (ADS)

Yan, D.; Zhang, X.; Yu, Y.; Guo, W.

2015-12-01

The characteristics of land surface phenology (LSP) in the "Never-green" Sahara desert and the "Ever-green" equatorial Congo Basin were rarely discussed due to the extremely low seasonal greenness variations across the Sahara desert and the prolonged cloud cover over the Congo Basin. Based on 30-minute observations acquired by the Spinning Enhanced Visible and Infrared Imager onboard the METEOSAT geostationary satellites, we generated a three-day angularly corrected Two-band Enhanced Vegetation Index (EVI2) time series for each year between 2006 and 2013. We further reconstructed EVI2 temporal trajectories and retrieved LSP transitions using the Hybrid Piecewise Logistic Model. We associated the LSP transitions with the rainy season transitions derived from the Tropical Rainfall Measurement Mission Product 3B42. Results show that LSP within both the Sahara Desert and the Congo Basin was strongly controlled by the rainfall seasonality. Specially, although there is no vegetation growth in most part of the Sahara Desert, recurring LSP was spatially detected in irrigation agriculture and the geomorphological regions of wadis, dayas, chotts/sebkhas and rocky hills. These geomorphological features are able to store moisture in soil to keep plants growing during the long dry seasons after vegetation greenup is triggered by rainfall events. The spatial shift of phenological timing is controlled by the Mediterranean rainfall regime in the north and the rainfalls brought by the Intertropical Convergence Zone (ITCZ) in the south. Across the equatorial Congo Basin, EVI2 time series reveals that canopy greenness cycles (CGC) of the seasonal leaf variation occur in tropical rainforests, which differs from the commonly termed "growing season" with complete leafless canopies. The seasonal EVI2 amplitude is very small and represents the gradual "leaf-exchange" processes. Two annual CGC are found and their spatial shifts closely follow the seasonal migration of ITCZ precipitation.

Estimation and prediction of maximum daily rainfall at Sagar Island using best fit probability models

NASA Astrophysics Data System (ADS)

Mandal, S.; Choudhury, B. U.

2015-07-01

Sagar Island, setting on the continental shelf of Bay of Bengal, is one of the most vulnerable deltas to the occurrence of extreme rainfall-driven climatic hazards. Information on probability of occurrence of maximum daily rainfall will be useful in devising risk management for sustaining rainfed agrarian economy vis-a-vis food and livelihood security. Using six probability distribution models and long-term (1982-2010) daily rainfall data, we studied the probability of occurrence of annual, seasonal and monthly maximum daily rainfall (MDR) in the island. To select the best fit distribution models for annual, seasonal and monthly time series based on maximum rank with minimum value of test statistics, three statistical goodness of fit tests, viz. Kolmogorove-Smirnov test (K-S), Anderson Darling test ( A 2 ) and Chi-Square test ( X 2) were employed. The fourth probability distribution was identified from the highest overall score obtained from the three goodness of fit tests. Results revealed that normal probability distribution was best fitted for annual, post-monsoon and summer seasons MDR, while Lognormal, Weibull and Pearson 5 were best fitted for pre-monsoon, monsoon and winter seasons, respectively. The estimated annual MDR were 50, 69, 86, 106 and 114 mm for return periods of 2, 5, 10, 20 and 25 years, respectively. The probability of getting an annual MDR of >50, >100, >150, >200 and >250 mm were estimated as 99, 85, 40, 12 and 03 % level of exceedance, respectively. The monsoon, summer and winter seasons exhibited comparatively higher probabilities (78 to 85 %) for MDR of >100 mm and moderate probabilities (37 to 46 %) for >150 mm. For different recurrence intervals, the percent probability of MDR varied widely across intra- and inter-annual periods. In the island, rainfall anomaly can pose a climatic threat to the sustainability of agricultural production and thus needs adequate adaptation and mitigation measures.

Use of geostationary meteorological satellite images in convective rain estimation for flash-flood forecasting

NASA Astrophysics Data System (ADS)

Wardah, T.; Abu Bakar, S. H.; Bardossy, A.; Maznorizan, M.

2008-07-01

SummaryFrequent flash-floods causing immense devastation in the Klang River Basin of Malaysia necessitate an improvement in the real-time forecasting systems being used. The use of meteorological satellite images in estimating rainfall has become an attractive option for improving the performance of flood forecasting-and-warning systems. In this study, a rainfall estimation algorithm using the infrared (IR) information from the Geostationary Meteorological Satellite-5 (GMS-5) is developed for potential input in a flood forecasting system. Data from the records of GMS-5 IR images have been retrieved for selected convective cells to be trained with the radar rain rate in a back-propagation neural network. The selected data as inputs to the neural network, are five parameters having a significant correlation with the radar rain rate: namely, the cloud-top brightness-temperature of the pixel of interest, the mean and the standard deviation of the temperatures of the surrounding five by five pixels, the rate of temperature change, and the sobel operator that indicates the temperature gradient. In addition, three numerical weather prediction (NWP) products, namely the precipitable water content, relative humidity, and vertical wind, are also included as inputs. The algorithm is applied for the areal rainfall estimation in the upper Klang River Basin and compared with another technique that uses power-law regression between the cloud-top brightness-temperature and radar rain rate. Results from both techniques are validated against previously recorded Thiessen areal-averaged rainfall values with coefficient correlation values of 0.77 and 0.91 for the power-law regression and the artificial neural network (ANN) technique, respectively. An extra lead time of around 2 h is gained when the satellite-based ANN rainfall estimation is coupled with a rainfall-runoff model to forecast a flash-flood event in the upper Klang River Basin.

Estimating probabilistic rainfall and food security outcomes for eastern and southern Africa

NASA Astrophysics Data System (ADS)

Verdin, J.; Funk, C.; Dettinger, M.; Brown, M.

2009-05-01

Since 1980, the number of undernourished people in eastern and southern Africa has more than doubled. Rural development stalled and rural poverty expanded during the 1990s. Population growth remains high, and declining per-capita agricultural capacity retards development. In September of 2008, Ethiopia, Kenya, Djibouti, and Somalia faced high or extreme conditions of food insecurity caused by repeated droughts and rapid food price inflation. In this talk we present research, performed for the US Agency for International Development on probabilistic projections of rainfall and food security trends for eastern and southern Africa. Analyses of station data and satellite-based estimates of precipitation have identified another problematic trend: main growing- season rainfall has diminished by ~15% in food-insecure countries clustered along the western rim of the Indian Ocean. Occurring during the main growing seasons in poor countries dependent on rain-fed agriculture, these declines constitute a long term danger to subsistence agricultural and pastoral livelihoods. Tracing moisture deficits upstream to an anthropogenically-induced warming Indian Ocean leads us to conclude that further rainfall declines are likely. We present analyses suggesting that warming in the central Indian Ocean disrupts onshore moisture transports, reducing continental rainfall. Thus, late 20th century Indian Ocean warming has probably already produced societally dangerous climate change by creating drought and social disruption in some of the world's most fragile food economies. We quantify the potential impacts of the observed precipitation and agricultural capacity trends by modeling millions of undernourished people as a function of rainfall, population, cultivated area, and seed and fertilizer use. Persistence of current trends may result in a 50% increase in undernourished people. On the other hand, modest increases in per-capita agricultural productivity could more than offset the observed precipitation declines. Increased investment in agricultural development would help mitigate climate change while decreasing rural poverty and vulnerability.

Observed positive vegetation-rainfall feedbacks in the Sahel dominated by a moisture recycling mechanism

DOE PAGES

Yu, Yan; Notaro, Michael; Wang, Fuyao; ...

2017-11-30

Classic, model-based theory of land-atmosphere interactions across the Sahel promote positive vegetation-rainfall feedbacks dominated by surface albedo mechanism. However, neither the proposed positive vegetation-rainfall feedback nor its underlying albedo mechanism has been convincingly demonstrated using observational data. Here, we present observational evidence for the region’s proposed positive vegetation-rainfall feedback on the seasonal to interannual time scale, and find that it is associated with a moisture recycling mechanism, rather than the classic albedo-based mechanism. Positive anomalies of remotely sensed vegetation greenness across the Sahel during the late and post-monsoon periods favor enhanced evapotranspiration, precipitable water, convective activity and rainfall, indicative ofmore » amplified moisture recycling. The identified modest low-level cooling and anomalous atmospheric subsidence in response to positive vegetation greenness anomalies are counter to the responses expected through the classic vegetation-albedo feedback mechanism. The observational analysis further reveals enhanced dust emissions in response to diminished Sahel vegetation growth, potentially contributing to the positive vegetation-rainfall feedback.« less

Observed positive vegetation-rainfall feedbacks in the Sahel dominated by a moisture recycling mechanism

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

Yu, Yan; Notaro, Michael; Wang, Fuyao

Classic, model-based theory of land-atmosphere interactions across the Sahel promote positive vegetation-rainfall feedbacks dominated by surface albedo mechanism. However, neither the proposed positive vegetation-rainfall feedback nor its underlying albedo mechanism has been convincingly demonstrated using observational data. Here, we present observational evidence for the region’s proposed positive vegetation-rainfall feedback on the seasonal to interannual time scale, and find that it is associated with a moisture recycling mechanism, rather than the classic albedo-based mechanism. Positive anomalies of remotely sensed vegetation greenness across the Sahel during the late and post-monsoon periods favor enhanced evapotranspiration, precipitable water, convective activity and rainfall, indicative ofmore » amplified moisture recycling. The identified modest low-level cooling and anomalous atmospheric subsidence in response to positive vegetation greenness anomalies are counter to the responses expected through the classic vegetation-albedo feedback mechanism. The observational analysis further reveals enhanced dust emissions in response to diminished Sahel vegetation growth, potentially contributing to the positive vegetation-rainfall feedback.« less

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.

A Space-Based Perspective of the 2017 Hurricane Season from the Global Precipitation Measurement (GPM) Mission

NASA Astrophysics Data System (ADS)

Skofronick Jackson, G.; Petersen, W. A.; Huffman, G. J.; Kirschbaum, D.; Wolff, D. B.; Tan, J.; Zavodsky, B.

2017-12-01

The Global Precipitation Measurement (GPM) mission collected unique, near real time 3-D satellite-based views of hurricanes in 2017 together with estimated precipitation accumulation using merged satellite data for scientific studies and societal applications. Central to GPM is the NASA-JAXA GPM Core Observatory (CO). The GPM-CO carries an advanced dual-frequency precipitation radar (DPR) and a well-calibrated, multi-frequency passive microwave radiometer that together serve as an on orbit reference for precipitation measurements made by the international GPM satellite constellation. GPM-CO overpasses of major Hurricanes such as Harvey, Irma, Maria, and Ophelia revealed intense convective structures in DPR radar reflectivity together with deep ice-phase microphysics in both the eyewalls and outer rain bands. Of considerable scientific interest, and yet to be determined, will be DPR-diagnosed characteristics of the rain drop size distribution as a function of convective structure, intensity and microphysics. The GPM-CO active/passive suite also provided important decision support information. For example, the National Hurricane Center used GPM-CO observations as a tool to inform track and intensity estimates in their forecast briefings. Near-real-time rainfall accumulation from the Integrated Multi-satellitE Retrievals for GPM (IMERG) was also provided via the NASA SPoRT team to Puerto Rico following Hurricane Maria when ground-based radar systems on the island failed. Comparisons between IMERG, NOAA Multi-Radar Multi-Sensor data, and rain gauge rainfall accumulations near Houston, Texas during Hurricane Harvey revealed spatial biases between ground and IMERG satellite estimates, and a general underestimation of IMERG rain accumulations associated with infrared observations, collectively illustrating the difficulty of measuring rainfall in hurricanes.GPM data continue to advance scientific research on tropical cyclone intensification and structure, and contribute to societal and operational applications for improving storm forecasting. Precipitation accumulations from the multi-satellite product IMERG also contribute to a better understanding of rainfall accumulation, inland flooding, and landslide susceptibility during the passage of these major events.

The effect of differences rainfall data duration and time period in the assessment of rainwater harvesting system performance for domestic water use

NASA Astrophysics Data System (ADS)

Juliana, Imroatul C.; Kusuma, M. Syahril Badri; Cahyono, M.; Martokusumo, Widjaja; Kuntoro, Arno Adi

2017-11-01

One of the attempts to tackle the problem in water resources is to exploit the potential of rainwater volume with rainwater harvesting (RWH) system. A number of rainfall data required for analyzing the RWH system performance. In contrast, the availability of rainfall data is occasionally difficult to obtain. The main objective of this study is to investigate the effect of difference rainfall data duration and time period to assess the RWH system performance. An analysis was conducted on the rainfall data based on rainfall data duration and time period. The analysis was performed considering 15, 5, 3, 2 years, average year, wet year, and dry year for Palembang city in South Sumatera. The RWH system performance is calculated based on the concept of yield before spillage algorithm. A number of scenarios were conducted by varying the tank capacity, roof area, and the rainwater demand. It was observed that the use of data with a smaller duration provides a significant difference, especially for high rainwater demand. In addition, the use of daily rainfall data would describe th e behavior of the system more thoroughly. As for time step, the use of monthly rainfall data is only sufficient for low rainwater demand and bigger tank capacity.

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.

Error and Uncertainty Quantification in Precipitation Retrievals from GPM/DPR Using Ground-based Dual-Polarization Radar Observations

NASA Astrophysics Data System (ADS)

Chandra, Chandrasekar V.; Chen*, Haonan; Petersen, Walter

2017-04-01

The active Dual-frequency Precipitation Radar (DPR) and passive radiometer onboard Global Precipitation Measurement (GPM) mission's Core Observatory extend the observation range attained by Tropical Rainfall Measuring Mission (TRMM) from tropical to most of the globe [1]. Through improved measurements of precipitation, the GPM mission is helping to advance our understanding of Earth's water and energy cycle, as well as climate changes. Ground Validation (GV) is an indispensable part of the GPM satellite mission. In the pre-launch era, several international validation experiments had already generated a substantial dataset that could be used to develop and test the pre-launch GPM algorithms. After launch, more ground validation field campaigns were conducted to further evaluate GPM precipitation data products as well as the sensitivities of retrieval algorithms. Among various validation equipment, ground based dual-polarization radar has shown great advantages to conduct precipitation estimation over a wide area in a relatively short time span. Therefore, radar is always a key component in all the validation field experiments. In addition, the radar polarization diversity has great potential to characterize precipitation microphysics through the identification of raindrop size distribution and different hydrometeor types [2]. Currently, all the radar sites comprising the U.S. National Weather Service (NWS) Weather Surveillance Radar-1988 Doppler (WSR-88DP) network are operating in dual-polarization mode. However, most of the operational radar based precipitation products are produced at coarse resolution typically on 1 km by 1 km spatial grids, focusing on precipitation accumulations at temporal scales of 1-h, 3-h, 6-h, 12-h, and/or 24-h (daily). Their capability for instantaneous GPM product validation is severely limited due to the spatial and temporal mismatching between observations from the ground and space. This paper first presents the rationale and opportunities of using dual-polarization radar in validation of precipitation retrievals from GPM/DPR. A new dual-polarization radar rainfall algorithm is proposed on this ground and implemented for WSR-88DP radar observations, especially when there are GPM satellite overpasses. In addition, an interpolation scheme is developed in order to map the WSR-88DP radar rainfall estimates that are updated every five-six minutes into instantaneous scale ( 1 minute). Detailed comparisons between instantaneous precipitation retrievals from GPM/DPR and WSR-88DP estimates before and after interpolation are investigated from a statistical perspective. [1] Hou, A., R. Kakar, S. Neeck, and Coauthors, 2014: The Global Precipitation Measurement Mission. Bull. Amer. Meteor. Soc., 95, 701-722. [2] Chen, Haonan, V. Chandrasekar, and R. Bechini, 2017: An Improved Dual-Polarization Radar Rainfall Algorithm (DROPS2.0): Application in NASA IFloodS Field Campaign. Journal of Hydrometeorology. doi:10.1175/JHM-D-16-0124.1

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.

A Fresh Start for Flood Estimation in Ungauged UK Catchments

NASA Astrophysics Data System (ADS)

Giani, Giulia; Woods, Ross

2017-04-01

The standard regression-based method for estimating the median annual flood in ungauged UK catchments has a high standard error (95% confidence interval is +/- a factor of 2). This is also the dominant source of uncertainty in statistical estimates of the 100-year flood. Similarly large uncertainties have been reported elsewhere. These large uncertainties make it difficult to do reliable flood design estimates for ungauged catchments. If the uncertainty could be reduced, flood protection schemes could be made significantly more cost-effective. Here we report on attempts to develop a new practical method for flood estimation in ungauged UK catchments, by making more use of knowledge about rainfall-runoff processes. Building on recent research on the seasonality of flooding, we first classify more than 1000 UK catchments into groups according to the seasonality of extreme rainfall and floods, and infer possible causal mechanisms for floods (e.g. Berghuijs et al, Geophysical Research Letters, 2016). For each group we are developing simplified rainfall-runoff-routing relationships (e.g. Viglione et al, Journal of Hydrology, 2010) which can account for spatial and temporal variability in rainfall and flood processes, as well as channel network routing effects. An initial investigation by Viglione et al suggested that the relationship between rainfall amount and flood peak could be summarised through a dimensionless response number that represents the product of the event runoff coefficient and a measure of hydrograph peakedness. Our hypothesis is that this approach is widely applicable, and can be used as the basis for flood estimation. Using subdaily and daily rainfall-runoff data for more than 1000 catchments, we identify a subset of catchments in the west of the UK where floods are generated predominantly in winter through the coincidence of heavy rain and low soil moisture deficits. Floods in these catchments can reliably be simulated with simple rainfall-runoff models, so it is reasonable to expect simple flood estimators. We will report on tests of the several components of the dimensionless response number hypothesis for these catchments.

Rainfall Estimation and Performance Characterization Using an X-band Dual-Polarization Radar in the San Francisco Bay Area

NASA Astrophysics Data System (ADS)

Cifelli, R.; Chen, H.; Chandra, C. V.

2016-12-01

The San Francisco Bay area is home to over 5 million people. In February 2016, the area also hosted the NFL Super bowl, bringing additional people and focusing national attention to the region. Based on the El Nino forecast, public officials expressed concern for heavy rainfall and flooding with the potential for threats to public safety, costly flood damage to infrastructure, negative impacts to water quality (e.g., combined sewer overflows) and major disruptions in transportation. Mitigation of the negative impacts listed above requires accurate precipitation monitoring (quantitative precipitation estimation-QPE) and prediction (including radar nowcasting). The proximity to terrain and maritime conditions as well as the siting of existing NEXRAD radars are all challenges in providing accurate, short-term near surface rainfall estimates in the Bay area urban region. As part of a collaborative effort between the National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory, Colorado State University (CSU), and Santa Clara Valley Water District (SCVWD), an X-band dual-polarization radar was deployed in Santa Clara Valley in February of 2016 to provide support for the National Weather Service during the Super Bowl and NOAA's El Nino Rapid Response field campaign. This high-resolution radar was deployed on the roof of one of the buildings at the Penitencia Water Treatment Plant. The main goal was to provide detailed precipitation information for use in weather forecasting and assists the water district in their ability to predict rainfall and streamflow with real-time rainfall data over Santa Clara County especially during a potentially large El Nino year. The following figure shows the radar's coverage map, as well as sample reflectivity observations on March 06, 2016, at 00:04UTC. This paper presents results from a pilot study from February, 2016 to May, 2016 demonstrating the use of X-band weather radar for quantitative precipitation estimation (QPE) in the Bay Area. The radar rainfall products are evaluated with rain gauge observations collected by SCVWD. The comparison with gages show the excellent performance of X-band radar for rainfall monitoring in the Bay Area.

Status Update on the GPM Ground Validation Iowa Flood Studies (IFloodS) Field Experiment

NASA Astrophysics Data System (ADS)

Petersen, Walt; Krajewski, Witold

2013-04-01

The overarching objective of integrated hydrologic ground validation activities supporting the Global Precipitation Measurement Mission (GPM) is to provide better understanding of the strengths and limitations of the satellite products, in the context of hydrologic applications. To this end, the GPM Ground Validation (GV) program is conducting the first of several hydrology-oriented field efforts: the Iowa Flood Studies (IFloodS) experiment. IFloodS will be conducted in the central to northeastern part of Iowa in Midwestern United States during the months of April-June, 2013. Specific science objectives and related goals for the IFloodS experiment can be summarized as follows: 1. Quantify the physical characteristics and space/time variability of rain (rates, DSD, process/"regime") and map to satellite rainfall retrieval uncertainty. 2. Assess satellite rainfall retrieval uncertainties at instantaneous to daily time scales and evaluate propagation/impact of uncertainty in flood-prediction. 3. Assess hydrologic predictive skill as a function of space/time scales, basin morphology, and land use/cover. 4. Discern the relative roles of rainfall quantities such as rate and accumulation as compared to other factors (e.g. transport of water in the drainage network) in flood genesis. 5. Refine approaches to "integrated hydrologic GV" concept based on IFloodS experiences and apply to future GPM Integrated GV field efforts. These objectives will be achieved via the deployment of the NASA NPOL S-band and D3R Ka/Ku-band dual-polarimetric radars, University of Iowa X-band dual-polarimetric radars, a large network of paired rain gauge platforms with attendant soil moisture and temperature probes, a large network of both 2D Video and Parsivel disdrometers, and USDA-ARS gauge and soil-moisture measurements (in collaboration with the NASA SMAP mission). The aforementioned measurements will be used to complement existing operational WSR-88D S-band polarimetric radar measurements, USGS streamflow, and Iowa Flood Center stream monitoring measurements. Coincident satellite datasets will be archived from current microwave imaging and sounding radiometers flying on NOAA, DMSP, NASA, and EU (METOP) low-earth orbiters, and rapid-scanned IR datasets collected from geostationary (GOES) platforms. Collectively the observational assets will provide a means to create high quality (time and space sampling) ground "reference" rainfall and stream flow datasets. The ground reference radar and rainfall datasets will provide a means to assess uncertainties in both satellite algorithms (physics) and products. Subsequently, the impact of uncertainties in the satellite products can be evaluated in coupled weather, land-surface and distributed hydrologic modeling frameworks as related to flood prediction.

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.

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

PubMed Central

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

2014-01-01

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

Relative importance of impervious area, drainage density, width function, and subsurface storm drainage on flood runoff from an urbanized catchment

NASA Astrophysics Data System (ADS)

Ogden, Fred L.; Raj Pradhan, Nawa; Downer, Charles W.; Zahner, Jon A.

2011-12-01

The literature contains contradictory conclusions regarding the relative effects of urbanization on peak flood flows due to increases in impervious area, drainage density and width function, and the addition of subsurface storm drains. We used data from an urbanized catchment, the 14.3 km2 Dead Run watershed near Baltimore, Maryland, USA, and the physics-based gridded surface/subsurface hydrologic analysis (GSSHA) model to examine the relative effect of each of these factors on flood peaks, runoff volumes, and runoff production efficiencies. GSSHA was used because the model explicitly includes the spatial variability of land-surface and hydrodynamic parameters, including subsurface storm drains. Results indicate that increases in drainage density, particularly increases in density from low values, produce significant increases in the flood peaks. For a fixed land-use and rainfall input, the flood magnitude approaches an upper limit regardless of the increase in the channel drainage density. Changes in imperviousness can have a significant effect on flood peaks for both moderately extreme and extreme storms. For an extreme rainfall event with a recurrence interval in excess of 100 years, imperviousness is relatively unimportant in terms of runoff efficiency and volume, but can affect the peak flow depending on rainfall rate. Changes to the width function affect flood peaks much more than runoff efficiency, primarily in the case of lower density drainage networks with less impermeable area. Storm drains increase flood peaks, but are overwhelmed during extreme rainfall events when they have a negligible effect. Runoff in urbanized watersheds with considerable impervious area shows a marked sensitivity to rainfall rate. This sensitivity explains some of the contradictory findings in the literature.

Physical Validation of TRMM TMI and PR Monthly Rain Products Over Oklahoma

NASA Technical Reports Server (NTRS)

Fisher, Brad L.

2004-01-01

The Tropical Rainfall Measuring Mission (TRMM) provides monthly rainfall estimates using data collected by the TRMM satellite. These estimates cover a substantial fraction of the earth's surface. The physical validation of TRMM estimates involves corroborating the accuracy of spaceborne estimates of areal rainfall by inferring errors and biases from ground-based rain estimates. The TRMM error budget consists of two major sources of error: retrieval and sampling. Sampling errors are intrinsic to the process of estimating monthly rainfall and occur because the satellite extrapolates monthly rainfall from a small subset of measurements collected only during satellite overpasses. Retrieval errors, on the other hand, are related to the process of collecting measurements while the satellite is overhead. One of the big challenges confronting the TRMM validation effort is how to best estimate these two main components of the TRMM error budget, which are not easily decoupled. This four-year study computed bulk sampling and retrieval errors for the TRMM microwave imager (TMI) and the precipitation radar (PR) by applying a technique that sub-samples gauge data at TRMM overpass times. Gridded monthly rain estimates are then computed from the monthly bulk statistics of the collected samples, providing a sensor-dependent gauge rain estimate that is assumed to include a TRMM equivalent sampling error. The sub-sampled gauge rain estimates are then used in conjunction with the monthly satellite and gauge (without sub- sampling) estimates to decouple retrieval and sampling errors. The computed mean sampling errors for the TMI and PR were 5.9% and 7.796, respectively, in good agreement with theoretical predictions. The PR year-to-year retrieval biases exceeded corresponding TMI biases, but it was found that these differences were partially due to negative TMI biases during cold months and positive TMI biases during warm months.