Journal
JOURNAL OF HYDROLOGY
Volume 556, Issue -, Pages 1182-1191Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.jhydrol.2017.01.058
Keywords
HBV; Bias; Linear bias; Gilgel Abay; Gumara; Tana
Funding
- Norman E. Borlaug Leadership Enhancement in Agriculture Program - USAID [01625868]
- International Water Management Institute [SAP REF: 45-16214]
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In many developing countries and remote areas of important ecosystems, good quality precipitation data are neither available nor readily accessible. Satellite observations and processing algorithms are being extensively used to produce satellite rainfall products (SREs). Nevertheless, these products are prone to systematic errors and need extensive validation before to be usable for streamflow simulations. In this study, we investigated and corrected the bias of Multi-Sensor Precipitation Estimate-Geostationary (MPEG) data. The corrected MPEG dataset was used as input to a semi-distributed hydrological model Hydrologiska Byrans Vattenbalansavdelning (HBV) for simulation of discharge of the Gilgel Abay and Gumara watersheds in the Upper Blue Nile basin, Ethiopia. The result indicated that the MPEG satellite rainfall captured 81% and 78% of the gauged rainfall variability with a consistent bias of underestimating the gauged rainfall by 60%. A linear bias correction applied significantly reduced the bias while maintaining the coefficient of correlation. The simulated flow using bias corrected MPEG SRE resulted in a simulated flow comparable to the gauge rainfall for both watersheds. The study indicated the potential of MPEG SRE in water budget studies after applying a linear bias correction. (C) 2017 Elsevier B.V. All rights reserved.
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