Evaluation of daily based satellite rainfall estimates for flood monitoring in Gumera Watershed, Amhara region, Ethiopia
In developing countries, including Ethiopia, ground-based rain gauge stations are limited and unevenly distributed. This makes it difficult to find accurate spatial data on the amount of rainfall. However, since the 1980s, several satellites have been used as alternative rainfall data sources. The d...
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Taylor & Francis Group
2025-12-01
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| Series: | Cogent Social Sciences |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/23311886.2024.2433690 |
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| author | Gebrie Tsegaye Mersha Asnake Mekuriaw |
| author_facet | Gebrie Tsegaye Mersha Asnake Mekuriaw |
| author_sort | Gebrie Tsegaye Mersha |
| collection | DOAJ |
| description | In developing countries, including Ethiopia, ground-based rain gauge stations are limited and unevenly distributed. This makes it difficult to find accurate spatial data on the amount of rainfall. However, since the 1980s, several satellites have been used as alternative rainfall data sources. The data could be used for different hydrological analyses including flood monitoring. This study aims to evaluate the performances of daily-based satellite rainfall estimates for flood monitoring in the Gumera Watershed of Amhara region, Ethiopia. Three daily satellite rainfall estimates (Tropical Application of Meteorology using Satellite and ground-based observations (TA MSAT -V3.1), Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS-V2) and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks Climate Data Record (PERSIANN-CDR)) are evaluated against independent gauge data (2004–2019). Observed rainfall, estimated rainfall, and flood events were the data used for the study. Continuous and categorical statistical indices were used for data analysis. The result shows that all three satellite datasets underestimate the highest amount of observed rainfall and overestimate the lowest amount of daily rainfall conditions. Relatively, CHIRPS-V2 had the best skill in estimating the highest daily rainfall observed in the study area. Future research shall be focused on the flood prediction performance of satellite-derived rainfall data. |
| format | Article |
| id | doaj-art-1218944a64f1423f82ec7551b1ac16a6 |
| institution | Kabale University |
| issn | 2331-1886 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Social Sciences |
| spelling | doaj-art-1218944a64f1423f82ec7551b1ac16a62025-01-06T11:10:32ZengTaylor & Francis GroupCogent Social Sciences2331-18862025-12-0111110.1080/23311886.2024.2433690Evaluation of daily based satellite rainfall estimates for flood monitoring in Gumera Watershed, Amhara region, EthiopiaGebrie Tsegaye Mersha0Asnake Mekuriaw1Kotebe University of Education, Addis Ababa, EthiopiaDepartment of Geography and Environmental Studies, Addis Ababa University, Addis Ababa, EthiopiaIn developing countries, including Ethiopia, ground-based rain gauge stations are limited and unevenly distributed. This makes it difficult to find accurate spatial data on the amount of rainfall. However, since the 1980s, several satellites have been used as alternative rainfall data sources. The data could be used for different hydrological analyses including flood monitoring. This study aims to evaluate the performances of daily-based satellite rainfall estimates for flood monitoring in the Gumera Watershed of Amhara region, Ethiopia. Three daily satellite rainfall estimates (Tropical Application of Meteorology using Satellite and ground-based observations (TA MSAT -V3.1), Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS-V2) and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks Climate Data Record (PERSIANN-CDR)) are evaluated against independent gauge data (2004–2019). Observed rainfall, estimated rainfall, and flood events were the data used for the study. Continuous and categorical statistical indices were used for data analysis. The result shows that all three satellite datasets underestimate the highest amount of observed rainfall and overestimate the lowest amount of daily rainfall conditions. Relatively, CHIRPS-V2 had the best skill in estimating the highest daily rainfall observed in the study area. Future research shall be focused on the flood prediction performance of satellite-derived rainfall data.https://www.tandfonline.com/doi/10.1080/23311886.2024.2433690Remote sensingflood monitoringsatelliteperformance evaluationHazards & DisastersGIS, Remote Sensing & Cartography |
| spellingShingle | Gebrie Tsegaye Mersha Asnake Mekuriaw Evaluation of daily based satellite rainfall estimates for flood monitoring in Gumera Watershed, Amhara region, Ethiopia Cogent Social Sciences Remote sensing flood monitoring satellite performance evaluation Hazards & Disasters GIS, Remote Sensing & Cartography |
| title | Evaluation of daily based satellite rainfall estimates for flood monitoring in Gumera Watershed, Amhara region, Ethiopia |
| title_full | Evaluation of daily based satellite rainfall estimates for flood monitoring in Gumera Watershed, Amhara region, Ethiopia |
| title_fullStr | Evaluation of daily based satellite rainfall estimates for flood monitoring in Gumera Watershed, Amhara region, Ethiopia |
| title_full_unstemmed | Evaluation of daily based satellite rainfall estimates for flood monitoring in Gumera Watershed, Amhara region, Ethiopia |
| title_short | Evaluation of daily based satellite rainfall estimates for flood monitoring in Gumera Watershed, Amhara region, Ethiopia |
| title_sort | evaluation of daily based satellite rainfall estimates for flood monitoring in gumera watershed amhara region ethiopia |
| topic | Remote sensing flood monitoring satellite performance evaluation Hazards & Disasters GIS, Remote Sensing & Cartography |
| url | https://www.tandfonline.com/doi/10.1080/23311886.2024.2433690 |
| work_keys_str_mv | AT gebrietsegayemersha evaluationofdailybasedsatelliterainfallestimatesforfloodmonitoringingumerawatershedamhararegionethiopia AT asnakemekuriaw evaluationofdailybasedsatelliterainfallestimatesforfloodmonitoringingumerawatershedamhararegionethiopia |