Evaluation of satellite precipitation products for water allocation studies in the Sio-Malaba-Malakisi River Basin of East Africa

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: http://doi.org/10.1016/j.ejrh.2021.100983. This is version 1 of this Preprint.


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Paul Omonge, Luke Olang, Mathew Herrnegger, Karsten Schulz 


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 modelling. However, there is need to test their reliabilities across varied hydro-climatic and physiographic conditions to understand their applicability. In this study, we evaluated and compared the Tropical Rainfall Measuring Mission (TRMM-3B42 v7), Climate Hazards Group Infrared Precipitation (CHIRPS v2.0), Multi-Source Weighted-Ensemble Precipitation (MSWEP v2.2), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) and Tropical Applications of Meteorology using Satellite (TAMSAT) against gauge observations, for possible use in water allocation studies. Furthermore, the Continuous Semi-distributed Runoff (COSERO) model was adapted using the SPPs and applied to generate discharges, which were cross-compared with observed discharges. New Hydrological Insights for the region: Our results indicate that the SPPs are able to detect seasonal rainfall patterns throughout the basin. At lower altitudes, the products overestimated rainfall events as indicated by the performance measures. The COSERO results indicate that PERSIANN-CDR and MSWEPv2.2 overcompensated and underestimated discharge throughout the basin. 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.




Physical Sciences and Mathematics


Data-sparse regions, satellite precipitation products, COSERO model, rainfall estimations, Sio-Malaba-Malakisi river basin


Published: 2021-04-15 03:35


CC BY Attribution 4.0 International

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Conflict of interest statement:

Data Availability (Reason not available):
https://pmm.nasa.gov/data-access/downloads/trmm, www.tamsat.org.uk/data/rfe/index.cgi, https://chrsdata.eng.uci.edu/, ftp://chg-ftpout.geog.ucsb.edu/pub/org/chg/products/CHIRPS-2.0/, www.gloh2o.org/

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