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1 Omonge, P.; Feigl, M.; Olang, L.; Schulz, K.; Herrnegger, M. 2022. Evaluation of satellite precipitation products for water allocation studies in the Sio-Malaba-Malakisi River Basin of East Africa. Journal of Hydrology: Regional Studies, 39:100983. [doi: https://doi.org/10.1016/j.ejrh.2021.100983]
Water allocation ; Precipitation ; Satellite observation ; River basins ; Hydrological modelling ; Datasets ; Rainfall patterns ; Estimation ; Runoff ; Water balance ; Rain gauges / East Africa / Kenya / Uganda / Sio-Malaba-Malakisi River Basin / Lake Victoria
(Location: IWMI HQ Call no: e-copy only Record No: H050869)
https://www.sciencedirect.com/science/article/pii/S2214581821002123/pdfft?md5=0175f7656f4a85555f7f4120eaccf665&pid=1-s2.0-S2214581821002123-main.pdf
https://vlibrary.iwmi.org/pdf/H050869.pdf
(12.90 MB) (12.9 MB)
Study region: Sio Malaba Malakisi river basin, East Africa.
Study focus: Poor rain-gauge density is a limitation to comprehensive hydrological studies in Sub-Saharan Africa. Consequently, Satellite precipitation products (SPPs) provide an alternative source of data for possible use in hydrological modeling. However, there is need to test their reliabilities across varied hydro-climatic and physiographic conditions to understand their applicability. Using two approaches, we evaluated the performance of six SPPs against gauge observations for possible water allocation studies in the SMMRB: (i) a point to pixel comparison using different statistical measures; (ii) hydrological evaluation of simulated discharge using the Continuous SEmi-distributed Runoff (COSERO) model approach.
New hydrological insights for the region: Our results indicate that CHIRPSv2 product performed best followed by MSWEPv2.2 as they suitably detected seasonal and annual rainfall amounts throughout the basin. However, at lower altitudes, most of the products overestimated rainfall as indicated by the performance measures. In some parts of the basin, the COSERO output signify an underperformance by PERSIANN-CDR and a good performance by GPM-3IMERG6. This could be attributed to differences in temporal dynamics of the products. In overall, seasonal trends captured by the SPPs can be used to support catchment management efforts in data scarce regions.

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