Abstract
Hydrologic models play an indispensable role in managing the scarce water resources of a region, and in developing countries, the availability and distribution of data are challenging. This research aimed to integrate and compare the satellite rainfall products, namely, Tropical Rainfall Measuring Mission (TRMM 3B43v7) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), with a GR2M hydrological water balance model over a diversified terrain of the Awash River Basin in Ethiopia. Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), coefficient of determination (R2), and root mean square error (RMSE) and Pearson correlation coefficient (PCC) were used to evaluate the satellite rainfall products and hydrologic model performances of the basin. The satellite rainfall estimations of both products showed a higher PCC (above 0.86) with areal observed rainfall in the Uplands, the Western highlands, and the Lower sub-basins. However, it was weakly associated in the Upper valley and the Eastern catchments of the basin ranging from 0.45 to 0.65. The findings of the assimilated satellite rainfall products with the GR2M model exhibited that 80% of the calibrated and 60% of the validated watersheds in a basin had lower magnitude of PBIAS (<±10), which resulted in better accuracy in flow simulation. The poor performance with higher PBIAS (≥±25) of the GR2M model was observed only in the Melka Kuntire (TRMM 3B43v7 and PERSIANN-CDR), Mojo (PERSIANN-CDR), Metehara (in all rainfall data sets), and Kessem (TRMM 3B43v7) watersheds. Therefore, integrating these satellite rainfall data, particularly in the data-scarce basin, with hydrological data, generally appeared to be useful. However, validation with the ground observed data is required for effective water resources planning and management in a basin. Furthermore, it is recommended to make bias corrections for watersheds with poorlyww performing satellite rainfall products of higher PBIAS before assimilating with the hydrologic model.
Highlights
This study provides insights on the rainfall-runoff modeling using different satellite rainfall (Tropical Rainfall Measurthe rainfall-runoff modeling using different satellite rainfall (Tropical Rainfall Measuring ing Mission (TRMM) 3B43 and Precipitation Estimation from Remotely Sensed InforMission (TRMM) 3B43 and Precipitation Estimation from Remotely Sensed Information mation using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR)) produsing Artificial Neural Networks-Climate Data Record (PERSIANN-CDR)) products with ucts with a GR2M hydrologic water balance model in the water-stressed Awash River Basin (ARB) of Ethiopia
It was PERSIANN-CDR data, but it was completely different in the TRMM 3B43v7 product. This variation might be due to the nature of the product produced, the elevation, and rainfall regime of the basin. Both satellite rainfall estimations showed a higher Pearson correlation coefficient (PCC) with areal observed rainfall in the Uplands, the Western highlands, and the Lower sub-basins
Two satellite rainfall products and the observed rainfall from gauging stations were integrated with the GR2M hydrological water balance model over the complex and diverse terrain of the ARB in Ethiopia
Summary
It is necessary to test whether a specific model fits a particular basin [1,2]. The applicability of these models is mainly constrained by the type and availability of input data in specific basins. The availability and distribution of ground-based rainfall-runoff data in African river basins are sparse [3]. This makes hydrological studies difficult in a basin where gauging stations are poorly distributed, in the river basins of Ethiopia [4,5]
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