Evaluation and Comparison of Satellite and GCM Rainfall Estimates for the Mara River Basin, Kenya/Tanzania

2013 ◽  
pp. 29-45 ◽  
Author(s):  
Shimelis Behailu Dessu ◽  
Assefa M. Melesse
Keyword(s):  
River Basin ◽  
Rainfall Estimates

scholarly journals Nine-Year Systematic Evaluation of the GPM and TRMM Precipitation Products in the Shuaishui River Basin in East-Central China

2020 ◽  
Vol 12 (6) ◽  
pp. 1042 ◽  
Author(s):  
Xiaoying Yang ◽  
Yang Lu ◽  
Mou Leong Tan ◽  
Xiaogang Li ◽  
Guoqing Wang ◽  
...  
Keyword(s):  
River Basin ◽  
Time Scales ◽  
Daily Rainfall ◽  
Central China ◽  
Spatiotemporal Resolution ◽  
Study Region ◽  
East Central ◽  
Hourly Rainfall ◽  
Rainfall Estimates ◽  
Different Time Scales

Owing to their advantages of wide coverage and high spatiotemporal resolution, satellite precipitation products (SPPs) have been increasingly used as surrogates for traditional ground observations. In this study, we have evaluated the accuracy of the latest five GPM IMERG V6 and TRMM 3B42 V7 precipitation products across the monthly, daily, and hourly scale in the hilly Shuaishui River Basin in East-Central China. For evaluation, a total of four continuous and three categorical metrics have been calculated based on SPP estimates and historical rainfall records at 13 stations over a period of 9 years from 2009 to 2017. One-way analysis of variance (ANOVA) and multiple posterior comparison tests are used to assess the significance of the difference in SPP rainfall estimates. Our evaluation results have revealed a wide-ranging performance among the SPPs in estimating rainfall at different time scales. Firstly, two post-time SPPs (IMERG_F and 3B42) perform considerably better in estimating monthly rainfall. Secondly, with IMERG_F performing the best, the GPM products generally produce better daily rainfall estimates than the TRMM products. Thirdly, with their correlation coefficients all falling below 0.6, neither GPM nor TRMM products could estimate hourly rainfall satisfactorily. In addition, topography tends to impose similar impact on the performance of SPPs across different time scales, with more estimation deviations at high altitude. In general, the post-time IMERG_F product may be considered as a reliable data source of monthly or daily rainfall in the study region. Effective bias-correction algorithms incorporating ground rainfall observations, however, are needed to further improve the hourly rainfall estimates of the SPPs to ensure the validity of their usage in real-world applications.

scholarly journals A bootstrap method to estimate the influence of rainfall spatial uncertainty in hydrological simulations

2017 ◽  
Author(s):  
Ang Zhang ◽  
Haiyun Shi ◽  
Tiejian Li ◽  
Xudong Fu
Keyword(s):  
River Basin ◽  
Yellow River ◽  
Bootstrap Method ◽  
Spatial Uncertainty ◽  
Hydrological Simulation ◽  
Rainfall Events ◽  
Simulation Performance ◽  
Rainfall Station ◽  
Areal Rainfall ◽  
Rainfall Estimates

Abstract. Rainfall stations with a certain number and spatial distribution supply sampling records of rainfall processes in a river basin. Uncertainty may be introduced when the station records are spatially interpolated for the purpose of hydrological simulations. This study adopts a bootstrap method to quantitatively estimate the uncertainty of areal rainfall estimates and its effects on hydrological simulations. The observed rainfall records are first analysed using clustering and correlation methods, and possible average basin rainfall amounts are calculated with a bootstrap method using various combinations of rainfall station subsets. Then, the uncertainty of simulated runoff, which is propagated through a hydrological model from the spatial uncertainty of rainfall estimates, is analysed with the bootstrapped rainfall inputs. By comparing the uncertainties of rainfall and runoff, the responses of the hydrological simulation to the spatial uncertainty of rainfall are discussed. Analyses are performed for three rainfall events in the upstream of the Qingjian River basin, a sub-basin of the Yellow River. Using the Digital Yellow River Integrated Model, the results show that the uncertainty of rainfall estimates derived from rainfall station network has a direct influence on simulated runoff processes. This quantified relationship between rainfall input and simulation performance can provide useful information on managing rainfall station density in river basins. The proposed method could be a guide to quantify an approximate range of simulated error caused by the spatial uncertainty of rainfall input.

Hydrological appraisal of rainfall estimates from radar, satellite, raingauge and satellite–gauge combination on the Qinhuai River Basin, China

2019 ◽  
Vol 64 (16) ◽  
pp. 1957-1971 ◽  
Author(s):  
Muhammad Ilyas Abro ◽  
Dehua Zhu ◽  
Ming Wei ◽  
Asghar Ali Majidano ◽  
Murad Ali Khaskheli ◽  
...  
Keyword(s):  
River Basin ◽  
Radar Satellite ◽  
Rainfall Estimates ◽  
Qinhuai River

Generation of Stochastic Rainfall Estimates for the Mekong River Basin

2015 ◽  
Author(s):  
Dumindu L. Jayasekera ◽  
Chia-Jeng Chen ◽  
Sharika U. S. Senarath ◽  
Marc P. Marcella
Keyword(s):  
River Basin ◽  
Mekong River ◽  
Mekong River Basin ◽  
Rainfall Estimates

scholarly journals Bias correction schemes for CMORPH satellite rainfall estimates in the Zambezi River Basin

2016 ◽  
Author(s):  
W. Gumindoga ◽  
T. H. M. Rientjes ◽  
A. T. Haile ◽  
H. Makurira ◽  
P. Reggiani
Keyword(s):  
River Basin ◽  
Bias Correction ◽  
Large Scale ◽  
Statistical Error ◽  
Correction Method ◽  
Correction Scheme ◽  
Satellite Rainfall ◽  
Zambezi River Basin ◽  
Rainfall Estimates ◽  
Zambezi River

Abstract. Obtaining reliable records of rainfall from satellite rainfall estimates (SREs) is a challenge as SREs are an indirect rainfall estimate from visible, infrared (IR), and/or microwave (MW) based information of cloud properties. SREs also contain inherent biases which exaggerate or underestimate actual rainfall values hence the need to apply bias correction methods to improve accuracies. We evaluate the performance of five bias correction schemes for CMORPH satellite-based rainfall estimates. We use 54 raingauge stations in the Zambezi Basin for the period 1998–2013 for comparison and correction. Analysis shows that SREs better match to gauged estimates in the Upper Zambezi Basin than the Lower and Middle Zambezi basins but performance is not clearly related to elevation. Findings indicate that rainfall in the Upper Zambezi Basin is best estimated by an additive bias correction scheme (Distribution transformation). The linear based (Spatio-temporal) bias correction scheme successfully corrected the daily mean of CMORPH estimates for 70 % of the stations and also was most effective in reducing the rainfall bias. The nonlinear bias correction schemes (Power transform and the Quantile based empirical-statistical error correction method) proved most effective in reproducing the rainfall totals. Analyses through bias correction indicate that bias of CMORPH estimates has elevation and seasonality tendencies across the Zambezi river basin area of large scale.

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