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The demand for accurate satellite rainfall products is increasing particularly in Africa where ground-based data are mostly unavailable, timely inaccessible, and unreliable. In this study, the accuracy of three widely used, near-global, high-resolution satellite rainfall products (CMORPH, TMPA-RT v7, TMPA-RP v7), with a spatial resolution of 0.25° and a temporal resolution of 3 h, is assessed over the Blue Nile River Basin, a basin characterized by complex terrain and tropical monsoon. The assessment is made using relatively dense experimental networks of rain gauges deployed at two, 0.25° × 0.25°, sites that represent contrasting topographic features: lowland plain (mean elevation of 719 m.a.s.l.) and highland mountain (mean elevation of 2268 m.a.s.l.). The investigation period covers the summer seasons of 2012 and 2013. Compared to the highland mountain site, the lowland plain site exhibits marked extremes of rain intensity, higher mean rain intensity when it rains, lower frequency of rain occurrence, and smaller seasonal rainfall accumulation. All the satellite products considered tend to overestimate the mean rainfall rate at the lowland plain site, but underestimate it at the highland mountain site. The satellite products miss more rainfall at the highland mountain site than at the lowland plain site, and underestimate the heavy rain rates at both sites. Both sites have uncertainty (root mean square error) values greater than 100% for 3 h accumulations of

Motivated by the field observations of fall velocity and axis ratio deviations from predicted terminal velocity and equilibrium axis ratio values, the combined effects of raindrop fall velocity and axis ratio deviations on dual-polarization radar rainfall estimations were investigated. A radar rainfall retrieval algorithm [Colorado State University–Hydrometeor Identification Rainfall Optimization (CSU-HIDRO)] served as the test bed. Subsequent investigations determined that the available field measurements, which were very limited in scope, of the fall velocity and axis ratio deviations indicated rain-rate estimation errors of approximately 20%. Based on these findings, a sensitivity study was then performed using uncorrelated fall velocity and axis ratio deviations around the predicted values. Significant rain-rate estimation errors were observed for the realistic combinations of fall velocity and axis ratio deviations. It was shown that the maximum rain-rate estimation error can reach up to approximately 200% for combinations of fall velocity and axis ratio deviations (5000 drop size distribution samples were simulated for each combination) between −10% and +10% of the predicted values for each. The maximum standard deviation of errors was as great as 75% for the same combinations of fall velocity and axis ratio deviations. The authors found that use of dual-polarization radars to accurately estimate rainfall, during natural rain events, also requires a reanalysis of the parameterizations for raindrop fall velocity and axis ratio. These parameterizations should consider both the coupling between these two parameters and factors that may introduce any possible deviations of the predicted values of these parameters.

High-resolution satellite precipitation estimates, such as the Climate Prediction Center morphing technique (CMORPH), provide alternative sources of precipitation data for hydrological applications, especially in regions where adequate ground-based instruments are unavailable. These estimates are, however, subject to large errors, especially at times of heavy precipitation. This paper presents a method to distributionally convert a set of CMORPH estimates into ground-based Next Generation Weather Radar (NEXRAD) estimates. As our concern lies with floods and extreme precipitation events, a peaks-over-threshold extreme value approach is adopted that fits a generalized Pareto distribution to the large precipitation estimates. A quantile matching transformation is then used to convert CMORPH values into NEXRAD values. The methods are applied in the analysis of 6 yr of precipitation observations from 625 pixels centered around eastern Oklahoma.

An integrated assessment of crop-energy-water (CEW) nexus is critical to understand the tradeoffs and synergies for better management of sustainable agricultural systems. In this study, we evaluate the historic evolution of CEW interactions in the Central Valley, California, a critical agricultural region that produces approximately 50% of US fruits, nuts and vegetables. Specifically, we consider three nexus elements, including water use for irrigation (blue water), energy use for groundwater pumping, and crop yield (for all crops aggregated, almond and cotton). To quantify the interactions between CEW elements, we estimate the water use for cropping (water footprint) and energy use for cropping (energy footprint). We conduct the analyses for four historical periods, i.e., 2007&ndash, 2009 (Drought 1), 2010&ndash, 2011 (Post-drought 1), 2012&ndash, 2015 (Drought 2) and 2016&ndash, 2018 (Post-drought 2). We find that the southern regions (San Joaquin and Tulare) are susceptible to greater stress on energy and water, especially during droughts. The groundwater footprint (GWF) has been continuously increasing due to greater crop water use and a shift from row crops to profitable water-intensive tree crops. The GWF in Tulare during Drought 2 was around 60% higher than Drought 1, where the GWF in Tulare was almost twice that of Sacramento. The energy and water uses for almond production have increased during the recent periods, whereas their uses have mostly decreased for cotton. On average, energy and water footprints under almond crop scenario are around 3&ndash, 3.5 times as much as the footprints under all crops scenario.

A simple remote sensing evapotranspiration (ET) model (Sim-ReSET) has been proposed but only tested using field measurements at a site with a semi-arid climate. Its performance for mapping ET using only satellite data remained unknown. In this study, the Sim-ReSET model was further evaluated for ET estimation driven by only MODIS data products. The estimated ET rates were compared with ground-based observational data from a variety of ecosystems and climates across China. The results show that MODIS-based ET estimates are consistent with both the ET measurements from eddy covariance flux towers and those from the Penman-Monteith method combined with micrometeorological data. Evaporation fraction (EF) is indicative of land surface moisture. The derivative EF maps demonstrate that the proposed ET data set obtained from the Sim-ReSET model and MODIS data is capable of capturing the spatio-temporal pattern of land surface moisture for different land covers with different climates. Editor Z.W. Kundzew...

article i nfo In this work we demonstrate the utility of satellite remote sensing for river discharge nowcasting and forecasting for two major rivers, the Ganges and Brahmaputra, in southern Asia. Passive microwave sensing of the river and floodplain at more than twenty locations upstream of Hardinge Bridge (Ganges) and Bahadurabad (Brahmaputra) gauging stations are used to: 1) examine the capability of remotely sensed flow information to track the down- stream propagation of river flow waves and 2) evaluate their use in producing river flow nowcasts, and forecasts at 1-15 days lead time. The pattern of correlation between upstream satellite data and in situ observations of downstream discharge is used to estimate wave propagation time. This pattern of correlation is combined with a cross-validation method to select the satellite sites that produce the most accurate river discharge estimates in a lagged regression model. The results show that the well-correlated satellite-derived flow (SDF) signals were able to detect the propagation of a river flow wave along both river channels. The daily river discharge (contem- poraneous) nowcast produced from the upstream SDFs could be used to provide missing data estimates given its Nash-Sutcliffe coefficient of 0.8 for both rivers; and forecasts have considerably better skill than autoregressive moving-average (ARMA) model beyond 3-day lead time for Brahmaputra. Due to the expected better accuracy of the SDF for detecting large flows, the forecast error is found to be lower for high flows compared to low flows. Overall, we conclude that satellite-based flow estimates are a useful source of dynamical surface water information in data-scarce regions and that they could be used for model calibration and data assimilation purposes in

Summary In this paper, a two-level self-organizing map (SOM) clustering technique was used to identify spatially homogeneous clusters of precipitation satellite data, and to choose the most operative and effective data for a feed-forward neural network (FFNN) to model rainfall–runoff process on a daily and multi-step ahead time scale. The wavelet transform (WT) was also used to extract dynamic and multi-scale features of the non-stationary runoff time series and to remove noise. The performance of the coupled SOM–FFNN model was compared to the newly proposed combined SOM–WT–FFNN model. The performance of these two models was also compared to that of a conventional forecasting method, namely the auto regressive integrated moving average with exogenous input (ARIMAX) model. Daily precipitation data from two satellites and four rain gauges, as well as runoff values recorded from January 2003 to December 2007 in the Gilgel Abay watershed in Ethiopia were used to calibrate and validate the models. Runoff predictions via all of the above methods were investigated for both single-step-ahead and multi-step-ahead lead times. The results indicated that the use of spatial and temporal pre-processed data in the FFNN model led to a promising improvement in its performance for rainfall–runoff forecasting. In the validation phase of single and multi-step-ahead forecasting, it was determined that the SOM–WT–FFNN models provide more accurate forecasts than the SOM–FFNN models (the determination coefficients for validation of the SOM–FFNN and SOM–WT–FFNN models were 0.80 and 0.93, respectively). The proposed FFNN model coupled with the SOM clustering method decreased the dimensionality of the input variables and consequently the complexity of the FFNN models. On the other hand, the application of the wavelet transform to the runoff data increased the performance of the FFNN rainfall–runoff models in predicting runoff peak values by removing noise and revealing the dominant periods.

Remote sensing datasets are increasingly being used to provide spatially explicit large scale evapotranspiration (ET) estimates. The focus of this study was to estimate and thematically map pixel-by-pixel basis, and compare the actual evapotranspiration (ETa) of the Wonji Shoa Sugarcane Estate using Surface Energy Balance Algorithm for Land (SEBAL), Simplified Surface Energy Balance (SSEB) and Operational Simplified Surface Energy Balance (SSEBop) algorithms on Landsat7 ETM+ images acquired on four days in 2002. The algorithms were based on image processing which uses spatially distributed spectral satellite data and ground meteorological data to derive the surface energy balance components. The results obtained revealed that the ranges of the daily ETa estimated on January 25, February 26, September 06 and October 08, 2002 using SEBAL were 0.0–6.85, 0.0–9.36, 0.0–3.61, 0.0–6.83 mm/day; using SSEB 0.0–6.78, 0.0–7.81, 0.0–3.65, 0.0–6.46 mm/day, and SSEBop were 0.05–8.25, 0.0–8.82, 0.2–4.0, 0.0–7.40 mm/day, respectively. The Root Mean Square Error (RMSE) values between SSEB and SEBAL, SSEBop and SEBAL, and SSEB and SSEBop were 0.548, 0.548, and 0.99 for January 25, 2002; 0.739, 0.753, and 0.994 for February 26, 2002;0.847, 0.846, and 0.999 for September 06, 2002; 0.573, 0.573, and 1.00 for October 08, 2002, respectively. The standard deviation of ETa over the sugarcane estate showed high spatio-temporal variability perhaps due to soil moisture variability and surface cover. The three algorithm results showed that well watered sugarcane fields in the mid-season growing stage of the crop and water storage areas had higher ETa values compared with the other dry agricultural fields confirming that they consumptively use more water. Generally during the dry season ETa is limited to water surplus areas only and in wet season, ETa was high throughout the entire sugarcane estate. The evaporation fraction (ETrF) results also followed the same pattern as the daily ETa over the sugarcane estate. The total crop and irrigation water requirement and effective rainfall estimated using the Cropwat model were 2468.8, 2061.6 and 423.8 mm/yr for January 2001 planted and 2281.9, 1851.0 and 437.8 mm/yr for March 2001 planted sugarcanes, respectively. The mean annual ETa estimated for the whole estate were 107 Mm3, 140 Mm3, and 178 Mm3 using SEBAL, SSEB, and SSEBop, respectively. Even though the algorithms should be validated through field observation, they have potential to be used for effective estimation of ET in the sugarcane estate.

Most existing remote sensing-based evapotranspiration (ET) algorithms rely exclusively on polar-orbiting satellites with thermal infrared sensors, and therefore the resulting ET values represent only "instantaneous or snapshot" values. However, daily ET is more meaningful and useful in applications. In this study, daily ET estimates are obtained by combining data from the MODIS sensor aboard the polar-orbiting Terra satellite and the SEVIRI sensor aboard the geostationary-orbiting MSG satellite. The procedure consists of estimating the instantaneous evaporative fraction (EF) based on the MODIS/Terra land data products, and estimating the daily net radiation and daily available energy based on the 30-min SEVIRI/MSG data products. Assuming constant EF during the daytime, daily ET is estimated as the product of the SEVIRI/MSG-based daily available energy and MODIS/Terra-based instantaneous EF. The daily ET estimates are evaluated against flux tower measurements at four validation sites in Africa. Results indicate that the synergistic use of SEVIRI/MSG and MODIS/Terra has the potential to provide reliable estimates of daily ET during wet periods when daily ET exceeds 1. mm/day. The satellite-based daily ET estimates however tend to underestimate ET by 13% to 35%. The daily ET estimation algorithm can further be improved by incorporating a temporal data-filling interpolation technique to estimate the unavailable net radiation information during cloudy sky conditions, and by improving the accuracy of the instantaneous EF. The assumption of constant evaporative fraction during the daytime is reasonable, and does not result in substantial errors in the daily ET estimates.

Net radiation is a key component of the energy balance, whose estimation accuracy has an impact on energy flux estimates from satellite data. In typical remote sensing evapotranspiration (ET) algorithms, the outgoing shortwave and longwave components of net radiation are obtained from remote sensing data, while the incoming shortwave (RS ↓) and longwave (RL ↓) components are typically estimated from weather data using empirical equations. This study evaluates the accuracy of empirical equations commonly used in remote sensing ET algorithms for estimating RS ↓ and RL ↓ radiation. Evaluation is carried out through comparison of estimates and observations at five sites that represent different climatic regions from humid to arid. Results reveal (1) both RS ↓ and RL ↓ estimates from all evaluated equations well correlate with observations (R2 ≥ 0.92), (2) RS ↓ estimating equations tend to overestimate, especially at higher values, (3) RL ↓ estimating equations tend to give more biased values in arid and semi-arid regions, (4) a model that parameterizes the diffuse component of radiation using two clearness indices and a simple model that assumes a linear increase of atmospheric transmissivity with elevation give better RS ↓ estimates, and (5) mean relative absolute errors in the net radiation (Rn) estimates caused by the use of RS ↓ and RL ↓ estimating equations varies from 10% to 22%. This study suggests that Rn estimates using recommended incoming radiation estimating equations could improve ET estimates. © 2013 by the authors.

The correct forecasting of unrecorded data could be enormously helpful in designing water projects and preventing related damages. The conventional methods available for rainfall estimation usually take a long time to estimate the missing data, and their estimations may have many errors in the long-term simulations. In this study, the capabilities of different Artificial Neural Networks (ANNs) were analyzed in estimating missing data from the Ardabel plain rain gauge stations located in northwestern Iran. Accordingly, six different structures of ANNs were used, and their efficiencies in terms of the mean squared error, training, and validation determination coefficients to select better-estimated missing data were examined. The results revealed that the best model is composed of the feed-forward networks, trained by the Levenberg-Marquardt algorithm and considering only one hidden layer. For each of the stations with a complete data set, an ANN was trained. Data gaps from other stations were obtained by the proposed ANN models. Furthermore, an integrated ANN was developed to investigate the hidden spatial relationships among the rainfall data of the stations as well as temporal auto-correlations. The results indicated the superiority of the proposed integrated model. After the estimation of the rain data gaps, the K-means clustering method was also employed as a data pre-processing method to improve the accuracy of the estimation, and the method led to better results.

This study focuses on evaluating four widely used global high-resolution satellite rainfall products [the Climate Prediction Center’s morphing technique (CMORPH) product, the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) near-real-time product (3B42RT), the TMPA method post-real-time research version product (3B42), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) product] with a spatial resolution of 0.25° and temporal resolution of 3 h through their streamflow simulations in the Soil and Water Assessment Tool (SWAT) hydrologic model of a 299-km2 mountainous watershed in Ethiopia. Results show significant biases in the satellite rainfall estimates. The 3B42RT and CMORPH products perform better than the 3B42 and PERSIANN. The predictive ability of each of the satellite rainfall was examined using a SWAT model calibrated in two different approaches: with rain gauge rainfall as input, and with each of the satellite rainfall products as input. Significant improvements in model streamflow simulations are obtained when the model is calibrated with input-specific rainfall data than with rain gauge data. Calibrating SWAT with satellite rainfall estimates results in curve number values that are by far higher than the standard tabulated values, and therefore caution must be exercised when using standard tabulated parameter values with satellite rainfall inputs. The study also reveals that bias correction of satellite rainfall estimates significantly improves the model simulations. The best-performing model simulations based on satellite rainfall inputs are obtained after bias correction and model recalibration.

Two conditions are required when a remotely sensed vegetation index–land surface temperature VI–Ts diagram is used to estimate the land surface moisture index LSMI and air temperature Ta. First, a suitable sampling window size is required to define an ideal VI–Ts diagram. Second, Ta must be homogeneous across the sampling window. In this study, the Shannon diversity index SDI and the semivariogram method were used to evaluate the VI–Ts diagram for estimating LSMI and Ta from Advanced Spaceborne Thermal Emission Reflection Radiometer ASTER and MODerate-resolution Imaging Spectroradiometer MODIS datasets. The results show that Ta is homogeneous across a sampling window with a width of several tens of kilometres 46.0–83.6 km based on the semivariogram method and spatial autocorrelation analysis of the Ta from 83 meteorological stations in the North China Plain NCP in 2003. When the SDIs of VI and Ts are respectively larger than 77% and 63% of their maximums within predetermined sampling windows, LSMI estimations by ASTER and Ta estimations by ASTER and MODIS are reliable.

Summary Objective To assess the suitability of satellite temperature and precipitation datasets for investigating the dependence of Schistosoma mansoni disease transmission on meteorological conditions in an irrigated agricultural region in Ethiopia. Methods Data used were monthly number of patients infected with S. mansoni and seeking treatment at the local hospital, monthly maximum air temperature from a local weather station, monthly average land surface temperature from MODIS satellite data, monthly total precipitation from a local rain gauge and precipitation estimates from four widely used satellite products, namely, TMPA 3B42RT, TMPA 3B42, CMORPH and PERSIANN. The number of patients was used as proxy for vector abundance. Results Temperature and precipitation play a role in the transmission of S. mansoni disease. There is a weak but significant positive correlation between monthly maximum air temperature derived from a meteorological station (or average land surface temperature derived from MODIS satellite product) and the number of patients in the same month. There is a significant negative correlation between monthly precipitation volume (derived from rain gauge or satellite data) and number of patients at lags of 1 and 2 months. Conclusion Satellite temperature and precipitation products provide useful information to understand and infer the relationship between meteorological conditions and S. mansoni prevalence. Objetivo: Evaluar la idoneidad de los conjuntos de datos satelitales de temperatura y precipitacion para investigar la dependencia de la transmision de la enfermedad por S. mansoni de las condiciones metereologicas en una region agricola e irrigada de Etiopia. Metodos: Los datos utilizados eran el numero mensual de pacientes infectados con S. mansoni que buscaban tratamiento en el hospital local; la temperatura maxima del aire tomada de una estacion metereologica local; la temperatura media mensual de la superficie de datos del sistema satelital MODIS; la precipitacion mensual total de un pluviometro local y la precipitacion estimada a partir de cuatro productos satelitales de amplio uso: TMPA 3B42RT, TMPA 3B42, CMORPH y PERSIANN. El numero de pacientes se utilizo como un proxy de abundancia del vector. Resultados: La temperatura y la precipitacion juegan un papel importante en la transmision de la enfermedad por S. mansoni. Hay una correlacion debil pero significativa entre la temperatura maxima del aire derivada de una estacion metereologica (o la temperatura promedio de la superficie terrestre derivado del producto satelital MODIS) y el numero de pacientes en el mismo mes. Hay una correlacion negativa significativa entre el volumen de precipitacion mensual (derivado del pluviometro o datos satelitales) y el numero de pacientes en intervalos de uno o dos meses. Conclusion: Los productos de temperatura y precipitacion por satelite dan una informacion util para entender e inferir la relacion entre las condiciones metereologicas y la prevalencia de S. mansoni.

The objective is to assess the use of the Climate Prediction Center morphing method (CMORPH) (~0.073° latitude–longitude, 30 min resolution) rainfall product as input to the physics-based fully distributed Gridded Surface–Subsurface Hydrologic Analysis (GSSHA) model for streamflow simulation in the small (21.4 km2) Hortonian watershed of the Goodwin Creek experimental watershed located in northern Mississippi. Calibration is performed in two different ways: using rainfall data from a dense network of 30 gauges as input, and using CMORPH rainfall data as input. The study period covers 4 years, during which there were 24 events, each with peak flow rate higher than 0.5 m3 s−1. Streamflow simulations using CMORPH rainfall are compared against observed streamflows and streamflow simulations using rainfall from a dense rain gauge network. Results show that the CMORPH simulations captured all 24 events. The CMORPH simulations have comparable performance with gauge simulations, which is striking given the significant differences in the spatial scale between the rain gauge network and CMORPH. This study concludes that CMORPH rainfall products have potential value for streamflow simulation in such small watersheds. Overall, the performance of CMORPH-driven simulations increases when the model is calibrated with CMORPH data than when the model is calibrated with rain gauge data.

High resolution satellite-based rainfall estimates (SREs) have enormous potential for use in hydrological applications, particularly in the developing world as an alternative to conventional rain gauges which are typically sparse. In this study, three SREs have been evaluated against collocated rain gauge measurements in Ethiopia across six river basins that represent different rainfall regimes and topography. The comparison is made using five-year (2003–2007) averages, and results are stratified by river basin, elevation and season. The SREs considered are: the Climate Prediction Center morphing method (CMORPH), Precipitation Estimation from Remotely Sensed Information Using Neural Networks (PERSIANN) and the real-time version of the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42RT. Overall, the microwave-based products TMPA 3B42RT and CMORPH outperform the infrared-based product PERSIANN: PERSIANN tends to underestimate rainfall by 43 %, while CMORPH tends to underestimate by 11 % and TMPA 3B42RT tends to overestimate by 5 %. The bias in the satellite rainfall estimates depends on the rainfall regime, and, in some regimes, the elevation. In the northwest region, which is characterized mainly by highland topography, a humid climate and a strong Intertropical Convergence Zone (ITCZ) effect, elevation has a strong influence on the accuracy of the SREs: TMPA 3B42RT and CMORPH tend to overestimate at low elevations but give reasonably accurate results at high elevations, whereas PERSIANN gives reasonably accurate values at low elevations but underestimates at high elevations. In the southeast region, which is characterized mainly by lowland topography, a semi-arid climate and southerly winds, elevation does not have a significant influence on the accuracy of the SREs, and all the SREs underestimate rainfall across almost all elevations.

The objective is to assess the suitability of commonly used high-resolution satellite rainfall products (CMORPH, TMPA 3B42RT, TMPA 3B42 and PERSIANN) as input to the semi-distributed hydrological model SWAT for daily streamflow simulation in two watersheds (Koga at 299 km2 and Gilgel Abay at 1656 km2) of the Ethiopian highlands. First, the model is calibrated for each watershed with respect to each rainfall product input for the period 2003–2004. Then daily streamflow simulations for the validation period 2006–2007 are made from SWAT using rainfall input from each source and corresponding model parameters; comparison of the simulations to the observed streamflow at the outlet of each watershed forms the basis for the conclusions of this study. Results reveal that the utility of satellite rainfall products as input to SWAT for daily streamflow simulation strongly depends on the product type. The 3B42RT and CMORPH simulations show consistent and modest skills in their simulations but underestimate the large flood peaks, while the 3B42 and PERSIANN simulations have inconsistent performance with poor or no skills. Not only are the microwave-based algorithms (3B42RT, CMORPH) better than the infrared-based algorithm (PERSIANN), but the infrared-based algorithm PERSIANN also has poor or no skills for streamflow simulations. The satellite-only product (3B42RT) performs much better than the satellite-gauge product (3B42), indicating that the algorithm used to incorporate rain gauge information with the goal of improving the accuracy of the satellite rainfall products is actually making the products worse, pointing to problems in the algorithm. The effect of watershed area on the suitability of satellite rainfall products for streamflow simulation also depends on the rainfall product. Increasing the watershed area from 299 km2 to 1656 km2 improves the simulations obtained from the 3B42RT and CMORPH (i.e. products that are more reliable and consistent) rainfall inputs while it deteriorates the simulations obtained from the 3B42 and PERSIANN (i.e. products that are unstable and inconsistent) rainfall inputs.

In this study, the rainfall-runoff response of the Ammameh watershed located in Tehran, Iran, was studied using TOPMODEL which is a semi-distributed and geomorphologic model that simulates runoff at the watershed's outlet on the basis of saturation excess runoff and subsurface flow concepts. Topographic index as an indicator of the spatial distribution of excess runoff generation in the catchment was calculated using the flow direction, output from two different methods, i.e. Dinf and D8. The analysis was done at three time scales: event, daily, and monthly. The modeling performance of TOPMODEL in simulating runoff process for each of three types of time series was analyzed and compared visually and statistically. Also, the effects of D8 and Dinf methods on rainfall-runoff modeling were compared and it was realized that modeling result of Dinf algorithm, especially in event-based rainfall- runoff modeling was more accurate than the D8 algorithm; whereas, the difference between the results were not notable in the daily modeling. Although the obtained results demonstrate the capability of the TOPMODEL in both event-based and daily simulations, the model could lead to the more reliable results in daily modeling because of considering the watershed soil moisture conditions.

Motivated by the need for rainfall prediction models in data scarce areas, we adapted a simple storage based cloud model to use routinely available thermal infrared (TIR) data. The data is obtained from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) of the Meteosat Second Generation (MSG-2) satellite. Model inputs are TIR cloud top temperatures at 15-min intervals and observations of pressure, temperature, and dew point temperatures from ground-based stations at 30-min intervals. The sensitivity of the parsimonious cloud model to its parameters is evaluated by a regional sensitivity analysis (RSA) which suggests that model performance is sensitive to few parameters. The model was calibrated and tested for four convective events that were observed during the wet season in the source basin of the Upper Blue Nile River. The difference between the simulated and the observed depth of the selected rain events varies between 0.2 and 1.8 mm with a root mean square error of smaller than 0.5 mm for each event. It is shown that the updraft velocity characteristic can provide relevant information for rainfall forecasting. The simulation results suggest the effectiveness of the model approach as evaluated by selected performance measures. The various characteristics of the rainfall events as simulated generally match to observed counter parts when ground-based and remote sensing observations are combined.

The authors study the spatial variability of daily rainfall in a mountainous terrain and tropical humid region within a grid of 6 km × 6 km using daily rainfall data from 22 non-recording rain gauges over a period of 39 days. Results indicate that (1) the daily coefficient of variation varied from 15% to 53%, and (2) among all, five of the rain gauge locations consistently gave estimates of the spatial daily mean rainfall within root mean square error of 20%, ten locations gave estimates with root mean square errors ranging from 20% to 30%, and the remaining seven locations gave estimates with root mean square error ranging from 30% to 41%. These levels of errors must be acknowledged when using single rain gauges to estimate local-scale spatial means at the daily scale. This also indicates that there are temporally stable rain gauge locations that give consistently lower errors in spatial mean estimates. Such short-term field campaigns can be used to identify time stable rain gauge locations for rain gauge network design.

The aircraft-based ESTAR soil moisture fields from the Southern Great Plains 1997 (SGP97) Hydrology Experiment are compared to the simulated ones obtained by Bertoldi et al. [1] with the GEOtop model [2], with a particular focus on their capability in capturing the critical point behaviour in their space-time dynamics (see [3]). The critical point behaviour should denote the transition of soil moisture spatial patterns from an unorganized to organized appearance, as conditions become wetter. The study region is the Little Washita watershed, located in the southwest Oklahoma, in the Southern Great Plains region of the USA. The case study takes place from June 27 to July 16 and encompasses wetting and drying cycles allowing for exploring the behaviour under transient conditions. Results show that the critical probability value is 0.85 for GEOtop, and 0.80 for ESTAR. The GEOtop patterns appear more fragmented, being more reluctant to organization, as confirmed by the higher value of critical probability. Such behaviour is probably inherited by the model’s parameterization: land use and soil classes impose additional spatial structures to those related to the meteorological forcings and the hillslope morphology, driving to higher degrees of heterogeneity.

Summary Copulas have recently emerged as a practical method for multivariate modeling. To date, only limited amount of work has been done to apply copula-based modeling in the context of extreme rainfall analysis, and no work exists on modeling multiple characteristics of rainfall events from data at resolutions finer than hourly. In this study, trivariate copula-based modeling is applied to annual extreme rainfall events constructed from 15-min time series precipitation data at 12 stations within the state of Connecticut. Three characteristics (volume, duration, and peak intensity) are modeled by a multivariate distribution specified by three marginal distributions and a dependence structure via copula. A major issue in this application is that, because the 15-min precipitation data are only available fairly recently, the sample size at most stations is small, ranging from 10 to 33 years. For each station, we estimate the model parameters by maximizing a weighted likelihood, which assigns weight to data at stations nearby, borrowing strengths from them. The weights are assigned by a kernel function whose bandwidth is chosen by cross-validation in terms of predictive loglikelihood. The fitted model and sampling algorithms provide new knowledge on design storms and risk assessment in Connecticut.

Most of the existing satellite-based evapotranspiration algorithms have been developed using fine-resolution Landsat TM and ASTER data. However, these algorithms are often applied to coarse-resolution MODIS data. In this paper, we investigate the feasibility of this approach using data from ASTER overpasses of the Las Cruces region in New Mexico, which consisted of irrigated and bare land surface conditions in a desert climate, and a satellite-based evapotranspiration algorithm which has been developed for the Las Cruces region using fine-resolution ASTER data. The evaluation technique consists of comparing evapotranspiration calculated in two ways: first, calculate at the ASTER resolution and average to the MODIS resolution; and second, calculate directly at the MODIS resolution by aggregating the ASTER data to the MODIS resolution. Results show that applying the satellite-based algorithms, which are developed at ASTER resolution, to MODIS resolution leads to ET estimates that (1) preserve the overall spatial pattern (spatial correlation in excess of 0.90), (2) increase the spatial standard deviation and maximum value, and (3) have modest conditional bias: underestimate low ET rates (

This study focuses on the evaluation of 3-hourly, 0.25° × 0.25°, satellite-based precipitation products: the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42RT, the NOAA/Climate Prediction Center morphing technique (CMORPH), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). CMORPH is primarily microwave based, 3B42RT is primarily microwave based when microwave data are available and infrared based when microwave data are not available, and PERSIANN is primarily infrared based. The results show that 1) 3B42RT and CMORPH give similar rainfall fields (in terms of bias, spatial structure, elevation-dependent trend, and distribution function), which are different from PERSIANN rainfall fields; 2) PERSIANN does not show the elevation-dependent trend observed in rain gauge values, 3B42RT, and CMORPH; and 3) PERSIANN considerably underestimates rainfall in high-elevation areas.

Gebremichael, Mekonnen, Emmanouil N. Anagnostou, and Menberu M. Bitew, 2010. Critical Steps for Continuing Advancement of Satellite Rainfall Applications for Surface Hydrology in the Nile River Basin. Journal of the American Water Resources Association (JAWRA) 46(2):361-366. DOI: 10.1111/j.1752-1688.2010.00428.x. Abstract: Given the increasingly higher resolution and data accessibility, satellite precipitation products could be useful for hydrological application in the Nile River Basin, which is characterized by lack of reasonably dense hydrological in situ sensors and lack of access to the existing dataset. However, in the absence of both extreme caution and research results for the Nile basin, the satellite rainfall (SR) products may not be used, or may even be used erroneously. We identify two steps that are critical to enhance the value of SR products for hydrological applications in the Nile basin. The first step is to establish representative validation sites in the Nile basin. The validation site will help to quantify the errors in the different kinds of SR products, which will be used to select the best products for the Nile basin, include the errors in decision making, and design strategies to minimize the errors. Using rainfall measurements collected from the unprecedented high-density rain gauge network over a small region within the Nile basin, we indicate that SR estimates could be subject to significant errors, and quantification of estimation errors by way of establishing validation sites is critically important in order to use the SR products. The second step is to identify the degree of hydrologic model complexity required to obtain more accurate hydrologic simulation results for the Nile basin when using SR products as input. The level of model complexity may depend on basin size and SR algorithm, and further research is needed to spell out this dependence for the Nile basin.

Most remote sensing based rainfall products have spatial resolutions ≥0.25° and temporal resolutions ≥1 day which are coarser than what is typically needed in hydrology. In this study, satellite data obtained from the precipitation radar (PR) of the Tropical Rainfall Measuring Mission (TRMM) which acquires data at 5 km resolution once or twice a day and from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) of the Meteosat Second Generation (MSG-2) which acquires data at 3 km resolution at 15 min interval. We evaluated three MSG-2 channels for rainfall detection in the Upper Blue Nile area in Ethiopia by the following indices: (1) 10.8 μm brightness temperature, (2) rate of change of the 10.8 μm brightness temperature, (3) space gradient of the 10.8 μm brightness temperature, (4) brightness temperature difference (BTD) at the 10.8 and 6.2 μm and (5) BTD at the 10.8 and 12.0 μm channels. The evaluation was made through categorical statistics that are bias, probability of detection, false alarm ratio and Heidke skill score. In this work also, an exponential model was developed for thermal infrared based rainfall estimation. The model was evaluated using observations from a rain gauge network that we installed at the source of the Blue Nile River in Ethiopia.

Scintillometers are becoming increasingly popular for the validation of satellite remote sensing sensible heat-flux estimates due to the comparable spatial resolutions. However, it is important to gain confidence in the accuracy of the sensible heat-flux measurements obtained by the scintillometer. Large aperture scintillometer (LAS) and eddy-covariance (EC) measurements were collected over a homogeneous, dry and semi-arid region near Las Cruces, New Mexico, USA, where the homogeneity allowed direct comparison of the two instruments despite their differences in footprint sizes. The differences between the sensible heat-flux measured by both LAS and EC systems fall within the differences between two EC systems. We conclude that the large aperture scintillometer is a reliable system for measuring sensible heat flux in a dry semiarid region.

The lack of uncertainty measures in operational satellite rainfall (SR) products leads to a situation where users of the SR products know that there are significant errors in the products, but they have no quantitative information about the distribution of these errors. The authors propose a semiparametric model to characterize the conditional distribution of actual rainfall (AR) given measures from SR products. The model consists of two components: a conditional gamma density given each SR, and a smooth functional relationship between the gamma parameters and SR. The model is developed for monthly rainfall, estimated from a satellite with sampling frequency once a day, averaged over an area of 512 × 512 km 2 in the Mississippi River basin. The conditional distribution results are more informative than deterministic SR products since the whole conditional distribution enables users to take appropriate actions according to their own risk assessments and cost/benefit analyses.

Recent research efforts have been geared towards developing high-resolution rainfall products from satellites for hydrological applications. A necessary step in assessing the potential and utility of these products is to quantify the uncertainty associated with them at validation scales appropriate for hydrological applications. The main objective of this paper is to evaluate the accuracy of the widely-known PERSIANN-CCS high-resolution (hourly, 0.04° × 0.04°) satellite rainfall products against high-quality NEXRAD radar rainfall observations in the Little Washita watershed. Our results reveal that (1) PERSIANN-CCS shows high skills in reproducing the patterns of inter-annual rainfall variability on a monthly basis; (2) both at the hourly and storm scales, the performance statistics of PERSIANN-CCS exhibit large spread, suggesting that the quality of PERSIANN-CCS product is almost unique for each hour and storm; and (3) significant improvement in performance statistics is obtained as PERSIANN-CCS products are averaged to longer sub-daily time scales. The implications of our results are: (1) PERSIANN-CCS could be used with high confidence for inter-annual rainfall variability studies; (2) PERSIANN-CCS products need to be accompanied by corresponding hourly error estimates in order to provide meaningful error estimates for hydrological applications; and (3) research is needed to characterize the tradeoff between the quality of rainfall input and the space-time resolution of hydrological modeling, as a function of watershed size and hydrologic model complexity level.

By providing continuous high-resolution simulations of soil moisture fields, distributed hydrologic models could be powerful tools to advance the scientific community's understanding of the space-time variability and scaling characteristics of soil moisture fields. However, in order to use the soil moisture simulations from hydrologic models with confidence, it is important to understand whether the models are able to represent in a reliable way the processes regulating soil moisture variability. In this study, a comparison of the scaling characteristics of spatial soil moisture fields derived from a set of microwave radiometer observations from the Southern Great Plains 1997 experiment and corresponding simulations using the distributed hydrologic model GEOtop is performed through the use of generalized variograms. Microwave observations and model simulations are in agreement with respect to suggesting the existence of a scale-invariance property in the variograms of spatial soil moisture fields, and indicating that the scaling characteristics vary with changes in the spatial average soil water content. However, observations and simulations give contradictory results regarding the relationship between the scaling parameters (i.e. spatial organization) and average soil water content. The drying process increased the spatial correlation of the microwave observations at both short and long separation distances while increasing the rate of decay of correlation with distance. The effect of drying on the spatial correlation of the model simulations was more complex, depending on the storm and the simulation examined, but for the largest storm in the simulation most similar to the observations, drying increased the long-range correlation but decreased the short-range. This is an indication that model simulations, while reproducing correctly the total streamflow at the outlet of the watershed, may not accurately reproduce the runoff production mechanisms. Consideration of the scaling characteristics of spatial soil moisture fields can therefore serve as a more intensive means for validating distributed hydrologic models, compared to the traditional approach of only comparing the streamflow hydrographs.

In this study, a comparison of the spatial patterns of high-resolution precipitation products obtained from the Climate Prediction Center’s morphing technique (CMORPH), which is a satellite-only product, and gauge-adjusted Next Generation Weather Radar (NEXRAD) rainfall observations is performed using a variety of statistical techniques for the Little Washita watershed region in Oklahoma for a 3-yr period. Results show that 1) the performance statistics of CMORPH show tremendous variability from one hour to the next, suggesting that the performance statistics are dynamic in time, and therefore each satellite rainfall product should be accompanied by an error product to make it more meaningful; 2) CMORPH is positively biased in summer and negatively biased in winter, consistent with the findings of previous studies; 3) CMORPH spatial fields tend to be smoother than NEXRAD output; 4) the errors are temporally correlated, in particular within the range from 1 to 6 accumulation hours, implying that averaging CMORPH products over these time scales does not reduce the errors significantly; and 5) the errors become less correlated in time as the averaging time scale increases to the range from 6 to 24 h.

New versions of evapotranspiration (ET) algorithms based on the Surface Energy Balance Algorithm for Land (SEBAL) are being published, each containing slightly different equations to calculate the energy balance. It is difficult to determine what impact changing one or more of the equations or coefficients in the series of equations of SEBAL has on the final calculation of ET. The objective of this article is to conduct a sensitivity analysis of ET estimates in SEBAL to identify the most sensitive variables and equations. A remote sensing ET model based on SEBAL was programmed and validated against eddy-covariance data. A sensitivity analysis was conducted for three contrasting land surface conditions: full, half, and sparse canopy cover in pecan orchards. Results were most sensitive to the selection (according to temperature) of the dry (~zero ET) reference pixel and to c (the estimated ratio of soil heat flux to net solar radiation). At all the three degrees of canopy cover, estimated ET changed by 40% to 270% (1 to 2 mm d-1) when either variable changed from its baseline value by ±50% of the permissible range. Estimated ET was also sensitive to the selection of the wet (full ET) reference pixel and to dT (aerodynamic difference of air and land temperatures). Changes in ET estimates were 47% to 72% (1.3 to 3.7 mm d-1) at both the full and half canopy areas under changes from baseline values equal to 50% of the permissible range for either variable. In addition, ET was sensitive to the roughness length in areas of half canopy cover (ET changed by 61% [1.5 mm d-1]) and to the value of the normalized difference vegetation index (NDVI) in areas of sparse canopy cover (ET changed by 118% [0.35 mm d-1]). Future research on ET algorithm improvement should focus on the above variables and relative equations. The selection of the wet- and dry-spots should be automated to avoid subjective errors from manual selection.

We used a three-year (1998–2000) dataset of TRMM Precipitation Radar observations to investigate the scaling properties of spatial rainfall fields. This dataset allows consideration of spatial scales ranging from about 4.3 km to 138 km and short temporal scales corresponding to the sensor overpasses. The focus is on the marginal spatial moment scaling, which allows estimation of the scaling parameters from a single scene of data. Here we present a global perspective of the scaling properties of tropical rainfall in terms of its spatial variability, atmospheric forcing, predictability, and applicability. Our results reveal the following: 1) the scaling parameters exhibit strong variability associated with land/ocean contrast and mean precipitation at the synoptic scale; 2) there exists a one-to-one relationship between the scaling parameters and the large-scale spatial average rain rate of a universal functional form; 3) the majority of the scenes are consistent with the hypothesis of scale invariance at the moment orders of 0 and 2; 4) relatively there are more scale-invariant rain scenes over land than over ocean; and 5) for the scenes that are non-scale-invariant, deviation from scale-invariance mainly arises from the increasingly intermittent behavior of rainfall as spatial scale decreases. These results have important implications for the development and calibration of downscaling procedures designed to reproduce rainfall properties at different spatial scales and lead to a better understanding of the nature of tropical rainfall at various spatial resolutions.

Comparisons to ground-based surface soil moisture estimates are necessary to evaluate the capability of remote sensors to determine soil moisture and its spatiotemporal variability. Soil moisture can be especially variable in regions of complex terrain which exhibit large variations in vegetation, soil properties and hydrologic conditions. The objective of this study is to evaluate the spatiotemporal variability of soil moisture in a mountainous basin in northwestern Mexico. Soil moisture estimates from ground sampling over a topographic transect and high resolution retrievals from the Polarimetric Scanning Radiometer are compared during a two week period in August 2004 as part of the Soil Moisture Experiment 2004. Results indicate that the soil moisture estimates exhibit similar variability with mean water content. Statistical analysis, however, reveals clear differences in soil moisture in the basin, in particular for wet periods and high elevations. Despite these differences, the temporal persistence of soil moisture from the estimates agrees well and indicates locations that capture the basin-averaged conditions. Furthermore, the spatiotemporal soil moisture characteristics from the two products are linked to terrain attributes. As a result, a hypsometric technique is shown to improve comparisons between basin-averaged values derived from ground data and remote sensing, as compared to arithmetic averaging. To our knowledge, this study is the first attempt to evaluate PSR/CX retrievals with respect to ground observations over a region of high topographic and vegetation variability using statistical, time-stability and terrain analysis techniques.

A data-based resampling experiment is performed to estimate sampling errors of area-averaged soil moisture estimates due to spatial sampling by ground-based sensors. The data consists of high-resolution soil moisture images derived from the Polarimetric Scanning Radiometer (PSR/CX) sensor flown on an aircraft as part of the summer field experiment (SMEX04 — Soil Moisture Experiment 2004) in the monsoon region of Sonora, Mexico. The sampling characteristics are investigated by accounting for random networks and evenly spaced networks. For random network designs, we develop a simple model that can be used to estimate the sampling uncertainty (expressed as standard deviation of sampling error as a percentage of the areal mean soil moisture) as a function of the number of sensors, mean soil moisture content and averaging area. This model is valid for five or more sensors. The model should prove useful to those wishing to assess the area-averaged performance of a soil moisture network. Furthermore, the method of analysis is applicable to other study regions (Oklahoma, Iowa, Alabama, Georgia, and Arizona) where soil moisture fields have been mapped at high resolution using airborne passive microwave remote sensors.

The dependence between temporal sampling error in satellite-derived rainfall estimates and rainfall rate is of scientific and practical interest. We explore the use of copulas to construct the needed joint distribution between the sampling error and the corresponding rainfall rate. Our approach is to first estimate the marginal distribution functions in a parametric way, and then use these with a number of copula functions in search of the one most appropriate. We use maximum likelihood to estimate the parameters of the copulas. We select the best-fitted parametric copula function as the one that gives the largest likelihood. Our findings have important implications for the interpretation and propagation studies of remote sensing precipitation uncertainties.

The authors analyze information from rain gauges, geostationary infrared satellites, and low earth orbiting radar in order to describe and characterize the submesoscale (

In view of the importance of tropical rainfall and the ubiquitous need for its estimates in climate modeling, the authors assess the ability of the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) to characterize the scaling characteristics of rainfall by comparing the derived results with those obtained from the ground-based radar (GR) data. The analysis is based on 59 months of PR and GR rain rates at three TRMM ground validation (GV) sites: Houston, Texas; Melbourne, Florida; and Kwajalein Atoll, Republic of the Marshall Islands. The authors consider spatial scales ranging from about 4 to 64 km at a fixed temporal scale corresponding to the sensor “instantaneous” snapshots (∼15 min). The focus is on the scaling of the marginal moments, which allows estimation of the scaling parameters from a single scene of data. The standard rainfall products of the PR and the GR are compared in terms of distributions of the scaling parameter estimates, the connection between the scaling parameters and the large-scale spatial average rain rate, and deviations from scale invariance. The five main results are as follows: 1) the PR yields values of the rain intermittence scaling parameter within 20% of the GR estimate; 2) both the PR and GR data show a one-to-one relationship between the intermittence scaling parameter and the large-scale spatial average rain rate that can be fit with the same functional form; 3) the PR underestimates the curvature of the scaling function from 20% to 50%, implying that high rain-rate extremes would be missed in a downscaling procedure; 4) the majority of the scenes (>85%) from both the PR and GR are scale invariant at the moment orders q = 0 and 2; and 5) the scale-invariance property tends to break down more likely over ocean than over land; the rainfall regimes that are not scale invariant are dominated by light storms covering large areas. Our results further show that for a sampling size of one year of data, the TRMM temporal sampling does not significantly affect the derived scaling characteristics. The authors conclude that the TRMM PR has the ability to characterize the basic scaling properties of rainfall, though the resulting parameters are subject to some degree of uncertainty.

Near real-time vegetation indices derived from MODIS (MODerate resolution Imaging Spectroradiometer) observations ( http://modis.gsfc.nasa.gov ) provide a first opportunity to monitor ecohydrological systems globally at a spatial resolution consistent with biophysical processes at the field scale. Here, we present work toward the quantitative estimation of the uncertainty associated with MODIS Gross Primary Productivity (GPP), an end-product that depends on several MODIS derived vegetation indices. GPP products, available at 8-day and 1-km resolutions, were evaluated in two representative tropical ecosystems: a mixed forest site in the humid tropics (the Marsyandi river basin in the Nepalese Himalayas), and an open shrubland site in a semi-arid region (the Sonora river basin in northern Mexico). The MODIS-GPP products were compared against simulations made with a process-based biochemical-hydrology model driven by flux tower meteorological observations. Whereas the temporal march of vegetation indices and GPP products is consistent between the model and the algorithm, our study indicates that that there is a positive bias in the case of the mixed forest biome in the Marsyandi basin, and a negative bias in the case of open shrublands in the Sonora basin. We examined the error contribution from the DAO meteorological data used in the standard MODIS GPP products. The bias between the GPP estimates using DAO and tower meteorology is − 2.77 gC/m 2 /day (i.e., − 77% of the mean of the tower-based GPP) in the Marsyandi, and 0.33 gC/m 2 /day (i.e., 18% of the mean of the tower-based GPP) in Sonora. Analysis of the temporal evolution of the discrepancies between the model and the MODIS algorithm points to the need for examining the light use efficiency parameterization, especially with regard to the representation of nonlinear functional dependencies on vapor pressure deficit (VPD), photosynthetically available radiation (PAR), and seasonal evolution of the productive capacity of vegetation as influenced by water stress.

On the basis of temporally sampled data obtained from satellites, spatial statistics of rainfall can be estimated. In this paper, the authors compare the estimated spatial statistics with their “true” or ensemble values calculated using 5 yr of 15-min radar-based rainfall data at a spatial domain of 512 km × 512 km in the central United States. The authors conducted a Monte Carlo sampling experiment to simulate different sampling scenarios for variable sampling intervals and rainfall averaging periods. The spatial statistics used are the moments of spatial distribution of rainfall, the spatial scaling exponents, and the spatial cross correlations between the sample and ensemble rainfall fields. The results demonstrated that the expected value of the relative error in the mean rain-rate estimate is zero for rainfall averaged over 5 days or longer, better temporal sampling produces average fields that are “less noisy” spatially, an increase in the sampling interval causes the sampled rainfall to be increasingly less correlated with the true rainfall map, and the spatial scaling exponent estimators could give a bias of 40% or less. The results of this study provide a basis for understanding the impact of temporal statistics on inferred spatial statistics.

This study provides an intensive evaluation of the Global Precipitation Climatology Project (GPCP) 1° daily (1DD) rainfall products over the Mississippi River basin, which covers 435 1° latitude × 1° longitude grids for the period of January 1997–December 2000 using radar-based precipitation estimates. The authors’ evaluation criteria include unconditional continuous, conditional (quasi) continuous, and categorical statistics, and their analyses cover annual and seasonal time periods. The authors present spatial maps that reflect the results for the 1° grids and a summary of the results for three selected regions. They also develop a statistical framework that partitions the GPCP–radar difference statistics into GPCP error and radar error statistics. They further partition the GPCP error statistics into sampling error and retrieval error statistics and estimate the sampling error statistics using a data-based resampling experiment. Highlights of the results include the following: 1) the GPCP 1DD product captures the spatial and temporal variability of rainfall to a high degree, with more than 80% of the variance explained, 2) the GPCP 1DD product proficiently detects rainy days at a large range of rainfall thresholds, and 3) in comparison with radar-based estimates the GPCP 1DD product overestimates rainfall.

In this paper, the authors examine models of probability distributions for sampling error in rainfall estimates obtained from discrete satellite sampling in time based on 5 years of 15-min radar rainfall data in the central United States. The sampling errors considered include all combinations of 3, 6, 12, or 24 h sampling of rainfall over 32, 64, 128, 256, or 512 km square domains, and 1, 5, or 30 day rainfall accumulations. Results of this study reveal that the sampling error distribution depends strongly on the rain rate; hence the conditional distribution of sampling error is more informative than its marginal distribution. The distribution of sampling error conditional on rain rate is strongly affected by the sampling interval. At sampling intervals of 3 or 6 h, the logistic distribution appears to fit the conditional sampling error quite well, while the shifted-gamma, shifted-weibull, shifted-lognormal, and normal distributions fit poorly. At sampling intervals of 12 or 24 h, the shifted-gamma, shifted-weibull, or shifted-lognormal distribution fit the conditional sampling error better than the logistics or normal distribution. These results are vital to understanding the accuracy of satellite rainfall products, for performing validation assessment of these products, and for analyzing the effects of rainfall-related errors in hydrological models.

The main objective of this study is to assess the ability of radar-derived rainfall products to characterize the small-scale spatial variability of rainfall. The authors use independent datasets from high-quality dense rain gauge networks employed during the Texas and Florida Underflights (TEFLUN-B) and Tropical Rainfall Measuring Mission component of the Large-Scale Biosphere–Atmosphere (TRMM-LBA) field experiments conducted by NASA in 1998 and 1999. A detailed comparison between gauge- and radar-derived spatial variability estimates is carried out by means of a correlation function, covariance, variogram, scaling characteristics, and variance reduction due to spatial averaging. Emphasis is given to the correlation function because it is involved in most of these statistics. The approach followed in the analysis addresses the problems associated with the traditional estimation methods and the recognized differences in the scales of observation. The performance of the radar-derived correlation fu...

This paper focuses on estimating the error uncertainty of the monthly 2.5° × 2.5° rainfall products of the Global Precipitation Climatology Project (GPCP) using rain gauge observations. Two kinds of GPCP products are evaluated: the satellite-only (MS) product, and the satellite–gauge (SG) merged product. The error variance separation (EVS) method has been proposed previously as a means of estimating the error uncertainty of the GPCP products. In this paper, the accuracy of the EVS results is examined for a variety of gauge densities. Three validation sites—two in North Dakota and one in Thailand—all with a large number of rain gauges, were selected. The very high density of the selected sites justifies the assumption that the errors are negligible if all gauges are used. Monte Carlo simulation studies were performed to evaluate sampling uncertainty for selected rain gauge network densities. Results are presented in terms of EVS error uncertainty normalized by the true error uncertainty. These res...

Nile River basin is a complex hydrological system which consists of distinct hydrological regimes at the different reaches of the river system. The runoff response of the system to the change in future climate is greatly associated to the variation in the behavior of the hydrological regimes of the river system. Developing an appropriate basin, wide adaptation strategies that promote water development in the basin requires an understanding of the sensitivity of the different hydrological regimes of the basin to climate change.

In this study, we integrated satellite-drived precipitation and modeled evapotranspiration data (2000–2012) to describe spatial variability of hydrologic sources and sinks in the Nile Basin. Over 2000–2012 period, 4 out of 11 countries (Ethiopia, Tanzania, Kenya, and Uganda) in the Nile Basin showed a positive water balance while three downstream countries (South Sudan, Sudan, and Egypt) showed a negative balance. Gravity Recovery and Climate Experiment (GRACE) mass deviation in storage data analysis showed that at annual timescales, the Nile Basin storage change is substantial while over longer time periods, it is minimal (

The success of modeling groundwater is strongly influenced by the accuracy of the model parameters that are used to characterize the subsurface system. However, the presence of uncertainty and possibly bias in groundwater model source/sink terms may lead to biased estimates of model parameters and model predictions when the standard regression-based inverse modeling techniques are used. This study first quantifies the levels of bias in groundwater model parameters and predictions due to the presence of errors in irrigation data. Then, a new inverse modeling technique called input uncertainty weighted least-squares (IUWLS) is presented for unbiased estimation of the parameters when pumping and other source/sink data are uncertain. The approach uses the concept of generalized least-squares method with the weight of the objective function depending on the level of pumping uncertainty and iteratively adjusted during the parameter optimization process. We have conducted both analytical and numerical experiments, using irrigation pumping data from the Republican River Basin in Nebraska, to evaluate the performance of ordinary least-squares (OLS) and IUWLS calibration methods under different levels of uncertainty of irrigation data and calibration conditions. The result from the OLS method shows the presence of statistically significant (p

[1] Uncertainty information about river level forecast is as important as the forecast itself for forecast users. This paper presents a flexible, statistical approach that processes deterministic forecasts into probabilistic forecasts. The model is a smoothly changing conditional distribution of river stage given point forecast and other information available, such as lagged river level at the time of forecasting. The parametric distribution is a four-parameter skewt distribution, with each parameter modeled as a smooth function of the point forecast and the 1 day ago observed river level. The model was applied to 9 years of daily 6 h lead forecasts and 24 h lead forecasts in the warm season and their matching observations at the Plymouth station on the Pemigewasset River in New Hampshire. For each point forecast, the conditional distribution and resulting prediction intervals provide uncertainty information that are potentially very important to forecast users and algorithm developers in decision making and improvement of forecast quality.