Performance evaluation of multiple satellite rainfall products for Dhidhessa River Basin (DRB), Ethiopia

Gizachew Kabite Wedajo,Misgana Kebede Muleta,Berhan Gessesse Awoke

doi:10.5194/amt-14-2299-2021

Abstract

Abstract. Precipitation is a crucial driver of hydrological processes. Ironically, a reliable characterization of its spatiotemporal variability is challenging. Ground-based rainfall measurement using rain gauges is more accurate. However, installing a dense gauging network to capture rainfall variability can be impractical. Satellite-based rainfall estimates (SREs) could be good alternatives, especially for data-scarce basins like in Ethiopia. However, SRE rainfall is plagued with uncertainties arising from many sources. The objective of this study was to evaluate the performance of the latest versions of several SRE products (i.e., CHIRPS2, IMERG6, TAMSAT3 and 3B42/3) for the Dhidhessa River Basin (DRB). Both statistical and hydrological modeling approaches were used for the performance evaluation. The Soil and Water Analysis Tool (SWAT) was used for hydrological simulations. The results showed that whereas all four SRE products are promising to estimate and detect rainfall for the DRB, the CHIRPS2 dataset performed the best at annual, seasonal and monthly timescales. The hydrological simulation-based evaluation showed that SWAT's calibration results are sensitive to the rainfall dataset. The hydrological response of the basin is found to be dominated by the subsurface processes, primarily by the groundwater flux. Overall, the study showed that both CHIRPS2 and IMERG6 products could be reliable rainfall data sources for the hydrological analysis of the DRB. Moreover, the climatic season in the DRB influences rainfall and streamflow estimation. Such information is important for rainfall estimation algorithm developers.

Highlights

Precipitation is an important hydrological component (Behrangi et al, 2011; Meng et al, 2014)
Mean annual rainfall for the Dhidhessa River Basin (DRB) is 1650 mm yr−1 based on the rain gauge data, which is within 1.8 % to 3 % of the estimates provided by the products
The results show that the CHIRPS2 performed better for the DRB with relatively higher r and E and lower bias ratio (BIAS), Mean error (ME) and root mean square error (RMSE) for annual and monthly timescales, respectively

Summary

Introduction

Precipitation is an important hydrological component (Behrangi et al, 2011; Meng et al, 2014). Precipitation is one of the most challenging hydrometeorological data to be accurately represented (Yong et al, 2014). Rainfall is measured either using ground-based (i.e., rain gauge and radar) or satellite sensors, in which all measurement methods exhibit limitations (Thiemig et al, 2013). Ground-based rainfall measurements using rain gauges are direct and generally accurate near the sensor location. Rain gauges, for instance, either are of poor density to represent spatial and temporal variability in precipitation or may not even exist in many basins, especially in developing countries (Behrangi et al, 2011).

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Discussion

Conclusion