scholarly journals Investigating the Applicability of Error Correction Ensembles of Satellite Rainfall Products in River Flow Simulations

2013 ◽  
Vol 14 (4) ◽  
pp. 1194-1211 ◽  
Author(s):  
Viviana Maggioni ◽  
Humberto J. Vergara ◽  
Emmanouil N. Anagnostou ◽  
Jonathan J. Gourley ◽  
Yang Hong ◽  
...  
Keyword(s):  
Error Propagation ◽  
River Flow ◽  
Tropical Rainfall Measuring Mission ◽  
Global Scale ◽  
Hydrologic Model ◽  
Runoff Simulation ◽  
Stochastic Error ◽  
Precipitation Estimation ◽  
Satellite Rainfall ◽  
The Impact

Abstract This study uses a stochastic ensemble-based representation of satellite rainfall error to predict the propagation in flood simulation of three quasi-global-scale satellite rainfall products across a range of basin scales. The study is conducted on the Tar-Pamlico River basin in the southeastern United States based on 2 years of data (2004 and 2006). The NWS Multisensor Precipitation Estimator (MPE) dataset is used as the reference for evaluating three satellite rainfall products: the Tropical Rainfall Measuring Mission (TRMM) real-time 3B42 product (3B42RT), the Climate Prediction Center morphing technique (CMORPH), and the Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS). Both ground-measured runoff and streamflow simulations, derived from the NWS Research Distributed Hydrologic Model forced with the MPE dataset, are used as benchmarks to evaluate ensemble streamflow simulations obtained by forcing the model with satellite rainfall corrected using stochastic error simulations from a two-dimensional satellite rainfall error model (SREM2D). The ability of the SREM2D ensemble error corrections to improve satellite rainfall-driven runoff simulations and to characterize the error variability of those simulations is evaluated. It is shown that by applying the SREM2D error ensemble to satellite rainfall, the simulated runoff ensemble is able to envelope both the reference runoff simulation and observed streamflow. The best (uncorrected) product is 3B42RT, but after applying SREM2D, CMORPH becomes the most accurate of the three products in the prediction of runoff variability. The impact of spatial resolution on the rainfall-to-runoff error propagation is also evaluated for a cascade of basin scales (500–5000 km2). Results show a doubling in the bias from rainfall to runoff at all basin scales. Significant dependency to catchment area is exhibited for the random error propagation component.

Satellite soil moisture improves rainfall just where needed

2020 ◽  
Author(s):  
Luca Brocca ◽  
Stefania Camici ◽  
Christian Massari ◽  
Luca Ciabatta ◽  
Paolo Filippucci ◽  
...  
Keyword(s):  
Soil Moisture ◽  
River Discharge ◽  
Large Scale ◽  
River Flow ◽  
Global Scale ◽  
Rain Gauge ◽  
Rainfall Runoff ◽  
Global Precipitation Climatology Centre ◽  
Satellite Rainfall ◽  
Global Precipitation

<p>Soil moisture is a fundamental variable in the water and energy cycle and its knowledge in many applications is crucial. In the last decade, some authors have proposed the use of satellite soil moisture for estimating and improving rainfall, doing hydrology backward. From this research idea, several studies have been published and currently preoperational satellite rainfall products exploiting satellite soil moisture products have been made available.</p><p>The assessment of such products on a global scale has revealed an important result, i.e., the soil moisture based products perform better than state of the art products exactly over regions in which the data are needed: Africa and South America. However, over these areas the assessment against rain gauge observations is problematic and independent approaches are needed to assess the quality of such products and their potential benefit in hydrological applications. On this basis, the use of the satellite rainfall products as input into rainfall-runoff models, and their indirect assessment through river discharge observations is an alternative and valuable approach for evaluating their quality.</p><p>For this study, a newly developed large scale dataset of river discharge observations over 500+ basins throughout Africa has been exploited. Based on such unique dataset, a large scale assessment of multiple near real time satellite rainfall products has been performed: (1) the Early Run version of the Integrated Multi-Satellite Retrievals for GPM (Global Precipitation Measurement), IMERG Early Run, (2) SM2RAIN-ASCAT (https://doi.org/10.5281/zenodo.3405563), and (3) GPM+SM2RAIN (http://doi.org/10.5281/zenodo.3345323). Additionally, gauge-based and reanalysis rainfall products have been considered, i.e., (4) the Global Precipitation Climatology Centre (GPCC), and (5) the latest European Centre for Medium-Range Weather Forecasts reanalysis, ERA5. As rainfall-runoff model, the semi-distributed MISDc (Modello Idrologico Semi-Distribuito in continuo) model has been employed in the period 2007-2018 at daily temporal scale.</p><p>First results over a part of the dataset reveal the great value of satellite soil moisture products in improving satellite rainfall estimates for river flow prediction in Africa. Such results highlight the need to exploit such products for operational systems in Africa addressed to the mitigation of the flood risk and water resources management.</p>

scholarly journals Application of Remotely Sensed Rainfall Data in Rainfall-Runoff Modelling. A Case of Pangani River Basin, Tanzania

2014 ◽  
Vol 35 (1) ◽  
pp. 1-14
Author(s):  
Joel Nobert ◽  
Patric Kibasa
Keyword(s):  
River Basin ◽  
Average Method ◽  
River Flow ◽  
Tropical Rainfall Measuring Mission ◽  
Rainfall Data ◽  
Coefficient Of Determination ◽  
Rainfall Runoff ◽  
Data Set ◽  
Satellite Rainfall ◽  
Areal Rainfall

Rainfall runoff modelling in a river basin is vital for number of hydrologic applicationincluding water resources assessment. However, rainfall data from sparse gauging stationsare usually inadequate for modelling which is a major concern in Tanzania. This studypresents the results of comparison of Tropical Rainfall Measuring Mission (TRMM)satellite rainfall products at daily and monthly time-steps with ground stations rainfalldata; and explores the possibility of using satellite rainfall data for rainfall runoffmodelling in Pangani River Basin, Tanzania. Statistical analysis was carried out to find thecorrelation between the ground stations data and TRMM estimates. It was found thatTRMM estimates at monthly scale compare reasonably well with ground stations data.Time series comparison was also done at daily and annual time scales. Monthly and annualtime series compared well with coefficient of determination of 0.68 and 0.70, respectively.It was also found that areal rainfall comparison in the northern parts of the study area hadpoor results compared to the rest of areas. On the other hand, rainfall runoff modellingwith ground stations data alone and TRMM data set alone was carried out using five Real-Time River Flow Forecasting System models and then outputs combined by Models OutputsCombination Techniques. The results showed that ground stations data performed betterduring calibration period with coefficient of efficiency of 76.7%, 81.7% and 89.1% forSimple Average Method, Weight Average Method and Neural Network Method respectively.Simulation results using TRMM data were 59.8%, 73.5% and 76.8%. It can therefore beconcluded that TRMM data are adequate and promising in hydrological modelling.

scholarly journals Evaluation of Global Satellite Rainfall Products over Continental Europe

2012 ◽  
Vol 13 (2) ◽  
pp. 588-603 ◽  
Author(s):  
Dimitrios Stampoulis ◽  
Emmanouil N. Anagnostou
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Error Variance ◽  
Temporal Variations ◽  
Cold Season ◽  
Global Scale ◽  
Error Statistics ◽  
Extensive Evaluation ◽  
Significant Difference ◽  
Satellite Rainfall ◽  
Trmm 3B42

Abstract An extensive evaluation of two global-scale high-resolution satellite rainfall products is performed using 8 yr (2003–10) of reference rainfall data derived from a network of rain gauges over Europe. The comparisons are performed at a daily temporal scale and 0.25° spatial grid resolution. The satellite rainfall techniques investigated in this study are the Tropical Rainfall Measuring Mission (TRMM) 3B42 V6 (gauge-calibrated version) and the Climate Prediction Center morphing technique (CMORPH). The intercomparison and validation of these satellite products is performed both qualitatively and quantitatively. In the qualitative part of the analysis, error maps of various validation statistics are shown, whereas the quantitative analysis provides information about the performance of the satellite products relative to the rainfall magnitude or ground elevation. Moreover, a time series analysis of certain error statistics is used to depict the temporal variations of the accuracy of the two satellite techniques. The topographical and seasonal influences on the performance of the two satellite products over the European domain are also investigated. The error statistics presented herein indicate that both orography and seasonal variability affect the efficiency of the satellite rainfall retrieval techniques. Specifically, both satellite techniques underestimate rainfall over higher elevations, especially during the cold season, and their performance is subject to seasonal changes. A significant difference between the two satellite products is that TRMM 3B42 V6 generally overestimates rainfall, while CMORPH underestimates it. CMORPH’s mean error is shown to be of higher magnitude than that of 3B42 V6, while in terms of random error variance, CMORPH exhibits lower (higher) values than those of 3B42 V6 in the winter (summer) months.

Understanding the Scale Relationships of Uncertainty Propagation of Satellite Rainfall through a Distributed Hydrologic Model

2010 ◽  
Vol 11 (2) ◽  
pp. 520-532 ◽  
Author(s):  
Efthymios I. Nikolopoulos ◽  
Emmanouil N. Anagnostou ◽  
Faisal Hossain ◽  
Mekonnen Gebremichael ◽  
Marco Borga
Keyword(s):  
Error Propagation ◽  
Uncertainty Propagation ◽  
Hydrologic Model ◽  
Rainfall Runoff ◽  
Radar Rainfall ◽  
Flood Simulation ◽  
Small Watersheds ◽  
Actual Error ◽  
High Resolution Radar ◽  
Satellite Rainfall

Abstract The study presents a data-based numerical experiment performed to understand the scale relationships of the error propagation of satellite rainfall for flood evaluation applications in complex terrain basins. A satellite rainfall error model is devised to generate rainfall ensembles based on two satellite products with different retrieval accuracies and space–time resolutions. The generated ensembles are propagated through a distributed physics-based hydrologic model to simulate the rainfall–runoff processes at different basin scales. The resulted hydrographs are compared against the hydrograph obtained by using high-resolution radar rainfall as the “reference” rainfall input. The error propagation of rainfall to stream runoff is evaluated for a number of basin scales ranging between 100 and 1200 km2. The results from this study show that (i) use of satellite rainfall for flood simulation depends strongly on the scale of application (catchment area) and the satellite product resolution, (ii) different satellite products perform differently in terms of hydrologic error propagation, and (iii) the propagation of error depends on the basin size; for example, this study shows that small watersheds (<400 km2) exhibit a higher ability in dampening the error from rainfall to runoff than larger-sized watersheds, although the actual error increases as drainage area decreases.

scholarly journals Future hydrology and hydrological extremes under climate change in Asian river basins

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sangam Shrestha ◽  
Deg-Hyo Bae ◽  
Panha Hok ◽  
Suwas Ghimire ◽  
Yadu Pokhrel
Keyword(s):  
Climate Change ◽  
Southeast Asia ◽  
River Flow ◽  
Climate Change Impacts ◽  
River Basins ◽  
Global Scale ◽  
Climatic Conditions ◽  
Spatiotemporal Variability ◽  
Climatic Extremes ◽  
The Impact

AbstractThe diverse impacts of anthropogenic climate change in the spatiotemporal distribution of global freshwater are generally addressed through global scale studies, which suffer from uncertainties arising from coarse spatial resolution. Multi-catchment, regional studies provide fine-grained details of these impacts but remain less explored. Here, we present a comprehensive analysis of climate change impacts on the hydrology of 19 river basins from different geographical and climatic conditions in South and Southeast Asia. We find that these two regions will get warmer (1.5 to 7.8 °C) and wetter (− 3.4 to 46.2%) with the expected increment in river flow (− 18.5 to 109%) at the end of the twenty-first century under climate change. An increase in seasonal hydro-climatic extremes in South Asia and the rising intensity of hydro-climatic extremes during only one season in Southeast Asia illustrates high spatiotemporal variability in the impact of climate change and augments the importance of similar studies on a larger scale for broader understanding.

scholarly journals Evaluation of satellite rainfall estimates over Ethiopian river basins

2011 ◽  
Vol 15 (5) ◽  
pp. 1505-1514 ◽  
Author(s):  
T. G. Romilly ◽  
M. Gebremichael
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
River Basins ◽  
Rain Gauge ◽  
Rainfall Regime ◽  
Humid Climate ◽  
Precipitation Estimation ◽  
Satellite Rainfall ◽  
Almost All ◽  
High Elevations ◽  
Rainfall Estimates

Abstract. 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.

scholarly journals Adequacy of Near Real-Time Satellite Precipitation Products in Driving Flood Discharge Simulation in the Fuji River Basin, Japan

2021 ◽  
Vol 11 (3) ◽  
pp. 1087
Author(s):  
Li Zhou ◽  
Mohamed Rasmy ◽  
Kuniyoshi Takeuchi ◽  
Toshio Koike ◽  
Hemakanth Selvarajah ◽  
...  
Keyword(s):  
Real Time ◽  
River Basin ◽  
Bias Correction ◽  
Large Scale ◽  
River Flow ◽  
Hydrologic Model ◽  
Satellite Precipitation ◽  
Flood Discharge ◽  
Discharge Simulation ◽  
The Impact

Flood management is an important topic worldwide. Precipitation is the most crucial factor in reducing flood-related risks and damages. However, its adequate quality and sufficient quantity are not met in many parts of the world. Currently, near real-time satellite precipitation products (NRT SPPs) have great potential to supplement the gauge rainfall. However, NRT SPPs have several biases that require corrections before application. As a result, this study investigated two statistical bias correction methods with different parameters for the NRT SPPs and evaluated the adequacy of its application in the Fuji River basin. We employed Global Satellite Mapping of Precipitation (GSMaP)-NRT and Integrated Multi-satellitE Retrievals for GPM (IMERG)-Early for NRT SPPs as well as BTOP model (Block-wise use of the TOPMODEL (Topographic-based hydrologic model)) for flood runoff simulation. The results showed that the corrected SPPs by the 10-day ratio based bias correction method are consistent with the gauge data at the watershed scale. Compared with the original SPPs, the corrected SPPs improved the flood discharge simulation considerably. GSMaP-NRT and IMERG-Early have the potential for hourly river-flow simulation on a basin or large scale after bias correction. These findings can provide references for the applications of NRT SPPs in other basins for flood monitoring and early warning applications. It is necessary to investigate the impact of number of ground observation and their distribution patterns on bias correction and hydrological simulation efficiency, which is the future direction of this study.

scholarly journals Evaluation of Global Flood Detection Using Satellite-Based Rainfall and a Hydrologic Model

2012 ◽  
Vol 13 (4) ◽  
pp. 1268-1284 ◽  
Author(s):  
Huan Wu ◽  
Robert F. Adler ◽  
Yang Hong ◽  
Yudong Tian ◽  
Fritz Policelli
Keyword(s):  
False Alarm ◽  
Event Detection ◽  
Global Scale ◽  
Hydrologic Model ◽  
Flood Frequency ◽  
Probability Of Detection ◽  
Flood Event ◽  
False Alarms ◽  
Pearson Type ◽  
The Impact

Abstract A new version of a real-time global flood monitoring system (GFMS) driven by Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) rainfall has been developed and implemented using a physically based hydrologic model. The purpose of this paper is to evaluate the performance of this new version of the GFMS in terms of flood event detection against flood event archives to establish a baseline of performance and directions for improvement. This new GFMS is quantitatively evaluated in terms of flood event detection during the TRMM era (1998–2010) using a global retrospective simulation (3-hourly and ⅛° spatial resolution) with the TMPA 3B42V6 rainfall. Four methods were explored to define flood thresholds from the model results, including three percentile-based statistical methods and a Log Pearson type-III flood frequency curve method. The evaluation showed the GFMS detection performance improves [increasing probability of detection (POD)] with longer flood durations and larger affected areas. The impact of dams was detected in the validation statistics, with the presence of dams tending to result in more false alarms and greater false-alarm duration. The GFMS validation statistics for flood durations >3 days and for areas without dams vary across the four methods, but center around a POD of ~0.70 and a false-alarm rate (FAR) of ~0.65. The generally positive results indicate the value of this approach for monitoring and researching floods on a global scale, but also indicate limitations and directions for improvement of such approaches. These directions include improving the rainfall estimates, utilizing higher resolution in the runoff-routing model, taking into account the presence of dams, and improving the method for flood identification.

scholarly journals Investigating Spatial Downscaling of Satellite Rainfall Data for Streamflow Simulation in a Medium-Sized Basin

2009 ◽  
Vol 10 (4) ◽  
pp. 1063-1079 ◽  
Author(s):  
Sayma Rahman ◽  
Amvrossios C. Bagtzoglou ◽  
Faisal Hossain ◽  
Ling Tang ◽  
Lance D. Yarbrough ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Rainfall Variability ◽  
Tropical Rainfall Measuring Mission ◽  
Hydrologic Model ◽  
Rainfall Data ◽  
Streamflow Prediction ◽  
Satellite Rainfall ◽  
Spatial Downscaling ◽  
Streamflow Simulation

Abstract The objective of this study was to investigate spatial downscaling of satellite rainfall data for streamflow prediction in a medium-sized (970 km2) river basin prone to flooding. The spatial downscaling scheme used in the study was based on the principle of scale invariance. It reproduced the rainfall variability at finer scales while being conditioned on the large-scale rainfall. Two Tropical Rainfall Measuring Mission (TRMM)-based real-time global satellite rainfall products were analyzed: 1) the infrared (IR)-based 3B41RT product available at 1 hourly and 0.25° scales and 2) the combined passive microwave (PMW) and IR-based 3B42RT product available at 3 hourly and 0.25° scales. The conceptual Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) was used for the simulation of streamflow. It was found that propagation of spatially downscaled satellite rainfall in the hydrologic model increased simulation uncertainty in streamflow as rainfall grid scales became smaller than 0.25°. The streamflow simulation uncertainty for satellite downscaling was found to be very similar to that for ground validation Next Generation Weather Radar (NEXRAD) downscaling at any given scale, indicating that the effectiveness of the spatial downscaling scheme is not influenced by rainfall data type. Closer inspection at the subbasin level revealed that the limitation of the selected spatial downscaling scheme to preserve the mean rainfall intensity for irregularly sized drainage units was responsible for the increase in simulation uncertainty as scales became smaller. Although the findings should not be construed as a generalization for spatial downscaling schemes, there is a need for more rigorous hydrometeorological assessment of downscaled satellite rainfall data prior to institutionalizing its use for real-time streamflow simulation over ungauged basins.

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