Evaluation and Comparison of Satellite Rainfall Products in the Black Volta Basin
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Evaluation and Comparison of Satellite Rainfall Products in the Black Volta Basin Frederick Yaw Logah 1,2 & Kwaku Amaning Adjei 1 & Emmanuel Obuobie 2 & Charles Gyamfi 1 & Samuel Nii Odai 3,1 Received: 30 April 2020 / Accepted: 11 September 2020/ # Springer Nature Switzerland AG 2020
Abstract
This study evaluated the performance of five satellite rainfall products (CHIRPS, PERSIANN, TRMM, RFE, and ARC) in the Black Volta Basin (BVB) using four performance evaluation methods: pairwise statistics, categorical statistics, rainfall intensity distribution, and extreme rainfall indices. In all, 21 rainfall stations distributed across the BVB with daily data spanning from 1981 to 2010 were used in the study. A high linear relationship was observed between observed and satellite rainfall data at decadal and monthly time scales as compared to weak relationship at the daily and annual time scales. The rainfall amount was least underestimated by CHIRPS at all the time scales. CHIRPS, PERSIANN and RFE performed well with the least deviation (BIAS ≤ 10%) from the observed rainfall amount at all time scales. Considering the high correlation coefficient and good NSE at decadal, monthly, and annual time scales, rainfall in the BVB is best represented by CHIRPS, followed by PERSIANN, RFE, ARC, and TRMM in that order. Though the probability of correctly detecting rainfall events is high (POD = 0.57–0.94), the satellite products were not able to adequately detect rainfall events in the basin at the daily time scale. The TRMM product was better in reproducing a very high rainfall amount (R ≥ 5 mm/day) in the basin as compared to CHIRPS, PERSIANN, RFE, and ARC. Extreme rainfall indices (R20, R99p, CWD and SDII) in the study basin were best represented by CHIRPS. Generally, precipitation in the BVB is best represented by CHIRPS, followed by PERSIANN, TRMM, RFE, and ARC in that order. Keywords Categorical statistics . Extreme rainfall indices . Pairwise statistics . Rainfall intensity distribution . Satellite products
* Frederick Yaw Logah [email protected] Extended author information available on the last page of the article
Logah F.Y. et al.
1 Introduction Information on the availability and pattern of rainfall as well as the impact of climate change on water resources is essential for food security in sub-Saharan Africa. Studies have shown a direct relationship between climate data and river discharge (IPCC 2014; Didoverts et al. 2019), thus indicating that climate data is an important input for any discharge-dependent system including riverine flood forecasting system (Huang et al. 2019; Try et al. 2020). Studies have concluded that hydrological models tend to perform better in catchments with dense networks of rainfall stations (Xu et al. 2013; Emmanuel et al. 2017; Huang et al. 2019). Most of the river basins in the sub-Sahara Africa are not reliable for water resources assessment and hydrological modelling due to the limited network of rainfall stations and discontinuous time series as a result of missing records (Adjei et a
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