Blending high-resolution satellite rainfall estimates over urban catchment using Bayesian Model Averaging approach
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 th...
| Autores principales: | , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2023
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/126409 |
| _version_ | 1855537237488304128 |
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| author | Asfaw, Wegayehu Rientjes, T. Haile, Alemseged Tamiru |
| author_browse | Asfaw, Wegayehu Haile, Alemseged Tamiru Rientjes, T. |
| author_facet | Asfaw, Wegayehu Rientjes, T. Haile, Alemseged Tamiru |
| author_sort | Asfaw, Wegayehu |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | 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. |
| format | Journal Article |
| id | CGSpace126409 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1264092025-12-08T10:11:39Z Blending high-resolution satellite rainfall estimates over urban catchment using Bayesian Model Averaging approach Asfaw, Wegayehu Rientjes, T. Haile, Alemseged Tamiru rain estimation catchment areas urban areas satellite observation bayesian theory models river basins precipitation 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. 2023-02 2022-12-31T23:45:43Z 2022-12-31T23:45:43Z Journal Article https://hdl.handle.net/10568/126409 en Open Access Elsevier Asfaw, Wegayehu; Rientjes, T.; Haile, Alemseged Tamiru. 2023. Blending high-resolution satellite rainfall estimates over urban catchment using Bayesian Model Averaging approach. Journal of Hydrology: Regional Studies, 45:101287. [doi: https://doi.org/10.1016/j.ejrh.2022.101287] |
| spellingShingle | rain estimation catchment areas urban areas satellite observation bayesian theory models river basins precipitation Asfaw, Wegayehu Rientjes, T. Haile, Alemseged Tamiru Blending high-resolution satellite rainfall estimates over urban catchment using Bayesian Model Averaging approach |
| title | Blending high-resolution satellite rainfall estimates over urban catchment using Bayesian Model Averaging approach |
| title_full | Blending high-resolution satellite rainfall estimates over urban catchment using Bayesian Model Averaging approach |
| title_fullStr | Blending high-resolution satellite rainfall estimates over urban catchment using Bayesian Model Averaging approach |
| title_full_unstemmed | Blending high-resolution satellite rainfall estimates over urban catchment using Bayesian Model Averaging approach |
| title_short | Blending high-resolution satellite rainfall estimates over urban catchment using Bayesian Model Averaging approach |
| title_sort | blending high resolution satellite rainfall estimates over urban catchment using bayesian model averaging approach |
| topic | rain estimation catchment areas urban areas satellite observation bayesian theory models river basins precipitation |
| url | https://hdl.handle.net/10568/126409 |
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