Evaluation of The Performance of High-Resolution Satellite Rainfall Products for Stream Flow Simulation (Case Study: Genale Dawa River basin)
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Date
2020-07
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Addis Ababa University
Abstract
Precipitation data is the most important input parameter to simulate rainfall-runoff processes,
since it's strongly hooked into the accuracy of the spatial and temporal representation of the
precipitation. In regions where rainfall stations are scarce, additional data sources could also
be needed. Satellite platforms has provided a satisfactory alternative because of its global
coverage. Although a good range of satellite-based estimations of precipitation is out there, not
all the satellite products are suitable for all regions. Moreover, in data-scarce regions in which
interpolation schemes are applied, it becomes difficult to have an accurate performance
assessment; another comparison tool is required as rainfall-runoff models. Remotely-sensed
estimates need to generate realistic and reliable data to be used in water resource assessments.
Therefore there is a need to evaluate the accuracy of remote sensing techniques. This study
investigated the reliability of the following satellite-derived rainfall estimates; Tropical
Applications of Meteorology using SATellite (TAMSAT) and Climate Hazards Group InfraRed
Precipitation with Stations (CHIRPS) in Genale Dawa river basin, Ethiopia where climate data
scarcity problem extremely high. Besides, the study evaluated the performance of satellite
precipitation estimates with ground observations from the most representative rain gauge for
nine stations at daily, monthly and yearly timescale. Intercomparison between Satellite rainfall
product and observed data were done using point to grid method selecting nine representative
metrological stations namely, Bore, Robe, Delomena, Ginir, Moyale, Finchawa, KibreMengist,
Negele, and Filtu. TAMSAT shows unacceptable linear correlation coefficient with rain gauges
while CHRIPS shows a good linear correlation coefficient with rain gauges. Therefore bias
correction was done for TAMSAT. The average correlation R is 0.45 and the average NS is
0.028 for Raw TAMSAT. After bias correction, this value was improved to the average value of
R=0.87 and NS =0.764. Considering four Categorical index POD, FAR, FB and HSS, the
average value were (0.49, 0.4, 0.84 and 0.41) respectively before Bias correction and improved
to (0.71, 0.22, 0.92 and 0.66) respectively after bias correction. For CHRIPS average R and NS
are 0.88 and 0.755 respectively and categorical index POD, FAR, FB and HSS were (0.8, 0.05,
0.85 and 0.81) respectively The study model streamflow using both CHRIPS and TAMSAT
rainfall products by using the SWAT model from 1983-2017). The model is calibrated from 1998
to 2003 and validated from 2004 to 2007 using SUFI-2 algorithm embodied in the SWAT-CUP.
Comparisons of the simulations to the observed streamflow for the four discharge gauging
stations namely Dawa at Melka Guba, Welme at Melka Amana, Dimtu Nr. Bore and Genale Nr.
Halwen. The Nash-Sutcliffe Efficiency (NSE), linear correlation coefficient (R) and BIAS indices
were used to benchmark the model performance and shows very good result (having R
and
NS=0.71-0.95 during calibration and 0.72-0.97 during validation).
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Keywords
CHRIPS, Model, TAMSAT, Satellite rainfall products, SUFI-2, SWAT, SWAT-CUP