scholarly journals Effect of Bias Correction of Satellite-Rainfall Estimates on Runoff Simulations at the Source of the Upper Blue Nile

2014 ◽  
Vol 6 (7) ◽  
pp. 6688-6708 ◽  
Author(s):  
Emad Habib ◽  
Alemseged Haile ◽  
Nazmus Sazib ◽  
Yu Zhang ◽  
Tom Rientjes
Keyword(s):  
Bias Correction ◽  
Blue Nile ◽  
Satellite Rainfall ◽  
Rainfall Estimates

scholarly journals Evaluation of High-Resolution Satellite Rainfall Products through Streamflow Simulation in a Hydrological Modeling of a Small Mountainous Watershed in Ethiopia

2012 ◽  
Vol 13 (1) ◽  
pp. 338-350 ◽  
Author(s):  
Menberu M. Bitew ◽  
Mekonnen Gebremichael ◽  
Lula T. Ghebremichael ◽  
Yared A. Bayissa
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Bias Correction ◽  
Hydrological Modeling ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Model Simulations ◽  
Mountainous Watershed ◽  
Satellite Rainfall ◽  
Rainfall Estimates

Abstract 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.

scholarly journals Accuracy of satellite rainfall estimates in the Blue Nile Basin: Lowland plain versus highland mountain

2014 ◽  
Vol 50 (11) ◽  
pp. 8775-8790 ◽  
Author(s):  
Mekonnen Gebremichael ◽  
Menberu M. Bitew ◽  
Feyera A. Hirpa ◽  
Gebrehiwot N. Tesfay
Keyword(s):  
Nile Basin ◽  
Blue Nile ◽  
Blue Nile Basin ◽  
Satellite Rainfall ◽  
Rainfall Estimates

scholarly journals Bayesian Bias Correction of Satellite Rainfall Estimates for Climate Studies

2018 ◽  
Vol 10 (7) ◽  
pp. 1074 ◽  
Author(s):  
Margaret Kimani ◽  
Joost Hoedjes ◽  
Zhongbo Su
Keyword(s):  
Bias Correction ◽  
Satellite Rainfall ◽  
Climate Studies ◽  
Rainfall Estimates

scholarly journals Bias correction of satellite rainfall estimation using a radar-gauge product

2010 ◽  
Vol 7 (6) ◽  
pp. 8913-8945 ◽  
Author(s):  
K. Tesfagiorgis ◽  
S. E. Mahani ◽  
R. Khanbilvardi
Keyword(s):  
Bias Correction ◽  
Bias Field ◽  
Systematic Errors ◽  
Daily Basis ◽  
Random Errors ◽  
Field Generation ◽  
Shuffled Complex Evolution ◽  
Hydrologic Prediction ◽  
Satellite Rainfall ◽  
Rainfall Estimates

Abstract. Satellite rainfall estimates can be used in operational hydrologic prediction, but are prone to systematic errors. The goal of this study is to seamlessly blend a radar-gauge product with a corrected satellite product that fills gaps in radar coverage. To blend different rainfall products, they should have similar bias features. The paper presents a pixel by pixel method, which aims to correct biases in hourly satellite rainfall products using a radar-gauge rainfall product. Bias factors are calculated for corresponding rainy pixels, and a desired number of them are randomly selected for the analysis. Bias fields are generated using the selected bias factors. The method takes into account spatial variation and random errors in biases. Bias field parameters were determined on a daily basis using the Shuffled Complex Evolution optimization algorithm. To include more sources of errors, ensembles of bias factors were generated and applied before bias field generation. The procedure of the method was demonstrated using a satellite and a radar-gauge rainfall data for several rainy events in 2006 for the Oklahoma region. The method was compared with bias corrections using interpolation without ensembles, the ratio of mean and maximum ratio. Results show the method outperformed the other techniques such as mean ratio, maximum ratio and bias field generation by interpolation.

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.

Evaluation of satellite rainfall estimates over the Lake Tana basin at the source region of the Blue Nile River

2018 ◽  
Vol 212 ◽  
pp. 43-53 ◽  
Author(s):  
Ayele Almaw Fenta ◽  
Hiroshi Yasuda ◽  
Katsuyuki Shimizu ◽  
Yasuomi Ibaraki ◽  
Nigussie Haregeweyn ◽  
...  
Keyword(s):  
Source Region ◽  
Nile River ◽  
Lake Tana ◽  
Blue Nile ◽  
Satellite Rainfall ◽  
Rainfall Estimates ◽  
Lake Tana Basin

scholarly journals Validation of new satellite rainfall products over the Upper Blue Nile Basin, Ethiopia

2017 ◽  
Author(s):  
Getachew Tesfaye Ayehu ◽  
Tsegaye Tadesse ◽  
Berhan Gessesse ◽  
Tufa Dinku
Keyword(s):  
Validation Study ◽  
Rain Gauge ◽  
Nile Basin ◽  
Spatial And Temporal Patterns ◽  
Blue Nile ◽  
Rainfall Occurrence ◽  
Blue Nile Basin ◽  
Satellite Rainfall ◽  
Rainfall Estimates ◽  
Upper Blue Nile Basin

Abstract. Accurate measurement of rainfall is vital to analyze the spatial and temporal patterns of precipitation at various scales. However, the conventional rain gauge observations in many parts of the world such as Ethiopia are sparse and unevenly distributed. An alternative to traditional rain gauge observations could be satellite-based rainfall estimates. Satellite rainfall estimates could be used as a sole product (e.g. in areas with no (poor) ground observations) or through integrating with rain gauge measurements. In this study, the newly available Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data has been evaluated in comparison to rain gauge data for the period of 2000 to 2015 across the Upper Blue Nile basin in Ethiopia. Besides, the Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT) version 2 and 3 (TAMSAT 2 and TAMSAT 3) and the African Rainfall Climatology (ARC 2) products have been used as a benchmark and compared with CHIRPS. The TAMSAT 2 rainfall estimate was used in this study mainly to assess the improvements made with the recent version of a TAMSAT product (TAMSAT 3). From the overall analysis at dekadal and monthly temporal scale, CHIRPS exhibited higher skills and the best bias value in comparison to ARC 2 but overestimates the frequency of rainfall occurrence particularly during the dry months. On the other hand, TAMSAT 3 has shown very comparable performance with that of CHIRPS product, particularly with regards to bias. The ARC 2 product was found to have the weakest performance underestimating rainfall amounts by about 24 %. The skill of CHIRPS is less affected by variation in elevation in comparison to TAMSAT 3 and ARC 2 products. While ARC 2 was found to be affected by elevation with the average biases of 1.53, 0.86 and 0.77 at lower ( 2000 m a.s.l), respectively. Comparing the overall performance of the three products, CHIRPS exhibited the best performance followed closely by TAMSAT 3. This validation study also shows that the TAMSAT 3 has overcome the main weaknesses of TAMSAT 2, which is underestimation of high rainfall amounts by up to 31 % in this study. Overall, the finding of this validation study shows the potentials of CHIRPS product to be used for various operational applications such as rainfall pattern and variability study in the Upper Blue Nile basin in Ethiopia.

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