Blending high-resolution satellite rainfall estimates over urban catchment using Bayesian Model Averaging approach

Wegayehu Asfaw*, Tom Rientjes, Alemseged Tamiru Haile

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
62 Downloads (Pure)


Study region
Akaki is a headwater catchment of Awash River Basin that hosts the capital city of Ethiopia, Addis Ababa. The area encompasses several agglomerated towns, water supply, and hydropower reservoirs and is characterized by a chain of mountains and floodplains. Due to basin rainfall, and the expansion of urbanized areas, the catchment is frequently affected by flooding.

Study focus
This study evaluates dynamic Bayesian Model Averaging (BMA) approach to improve rainfall estimation over the catchment by blending four high-resolution satellite rainfall estimate (SRE) products. Using daily data (2003–2019) observed at thirteen stations as a reference, seven statistical metrics served to assess the point and spatial scale accuracy of the rainfall estimates.

New hydrological insights
Main findings from this study are: (i) the blended product outperformed the individual SRE products by notably improving correlation with in-situ observed rainfall, and reducing the error of the estimated rainfall, (ii) the blended and individual SRE products performed better in the highlands than the lowlands of the catchment, and (iii) the amount of daily rainfall during the main-rainy season was mostly overestimated by the individual SRE products but was fairly estimated by the blended product. This study showed the nonexistence of surpassing individual SRE products and emphasized the blending of several products for gaining optimal results from each product.
Original languageEnglish
Article number101287
JournalJournal of Hydrology: Regional Studies
Early online date5 Dec 2022
Publication statusPublished - 1 Feb 2023


  • UT-Gold-D


Dive into the research topics of 'Blending high-resolution satellite rainfall estimates over urban catchment using Bayesian Model Averaging approach'. Together they form a unique fingerprint.

Cite this