Evaluation of WRF model rainfall forecast using citizen science in a data-scarce urban catchment: Addis Ababa, Ethiopia
Study region: The Akaki catchment is found in the Upper Awash River Basin in Ethiopia. Study focus: Understanding the accuracy of rainfall forecasts in the data-scarce urban catchment has a multitude of benefits given the increased urban flood risk caused by climate change and urbanization. In this...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Language: | Inglés |
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Elsevier
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/126410 |
| _version_ | 1855514577254481920 |
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| author | Tedla, H. Z. Taye, E. F. Walker, D. W. Haile, Alemseged Tamiru |
| author_browse | Haile, Alemseged Tamiru Taye, E. F. Tedla, H. Z. Walker, D. W. |
| author_facet | Tedla, H. Z. Taye, E. F. Walker, D. W. Haile, Alemseged Tamiru |
| author_sort | Tedla, H. Z. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Study region: The Akaki catchment is found in the Upper Awash River Basin in Ethiopia. Study focus: Understanding the accuracy of rainfall forecasts in the data-scarce urban catchment has a multitude of benefits given the increased urban flood risk caused by climate change and urbanization. In this study, accuracy of the weather research and forecasting (WRF) model rainfall forecast was evaluated using citizen science data. Categorical and continuous accuracy evaluation metrics were used beside gauge representativeness effect. New hydrological insights for the region: The rainfall forecasts performance accuracy is high for 1–3- days lead-time but deteriorates for 4–5-days lead-time. The WRF model captured the temporal dynamics and the rainfall amount according to the estimated KGE values. The model has relatively higher detection performance for no rain and light rain events (< 6 mm/day), but it has lower performance for moderate and heavy rain events (> 6 mm/day). Use of data from a single rain gauge misrepresents the accuracy level of the rainfall forecast in the study area. The gauge representativeness error contributed a variance of 28.08–83.33 % to the variance of WRF-gauge rainfall difference. Thus, the use of citizen science rainfall monitoring program is an essential alternative source of information where in-situ rainfall monitoring is limited that can be used to understand the “true” accuracy of WRF rainfall forecasts. |
| format | Journal Article |
| id | CGSpace126410 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1264102025-12-08T10:11:39Z Evaluation of WRF model rainfall forecast using citizen science in a data-scarce urban catchment: Addis Ababa, Ethiopia Tedla, H. Z. Taye, E. F. Walker, D. W. Haile, Alemseged Tamiru rain weather forecasting models citizen science urban areas catchment areas weather data monitoring Study region: The Akaki catchment is found in the Upper Awash River Basin in Ethiopia. Study focus: Understanding the accuracy of rainfall forecasts in the data-scarce urban catchment has a multitude of benefits given the increased urban flood risk caused by climate change and urbanization. In this study, accuracy of the weather research and forecasting (WRF) model rainfall forecast was evaluated using citizen science data. Categorical and continuous accuracy evaluation metrics were used beside gauge representativeness effect. New hydrological insights for the region: The rainfall forecasts performance accuracy is high for 1–3- days lead-time but deteriorates for 4–5-days lead-time. The WRF model captured the temporal dynamics and the rainfall amount according to the estimated KGE values. The model has relatively higher detection performance for no rain and light rain events (< 6 mm/day), but it has lower performance for moderate and heavy rain events (> 6 mm/day). Use of data from a single rain gauge misrepresents the accuracy level of the rainfall forecast in the study area. The gauge representativeness error contributed a variance of 28.08–83.33 % to the variance of WRF-gauge rainfall difference. Thus, the use of citizen science rainfall monitoring program is an essential alternative source of information where in-situ rainfall monitoring is limited that can be used to understand the “true” accuracy of WRF rainfall forecasts. 2022-12 2022-12-31T23:48:54Z 2022-12-31T23:48:54Z Journal Article https://hdl.handle.net/10568/126410 en Open Access Elsevier Tedla, H. Z.; Taye, E. F.; Walker, D. W.; Haile, Alemseged Tamiru. 2022. Evaluation of WRF model rainfall forecast using citizen science in a data-scarce urban catchment: Addis Ababa, Ethiopia. Journal of Hydrology: Regional Studies, 44:101273. [doi: https://doi.org/10.1016/j.ejrh.2022.101273] |
| spellingShingle | rain weather forecasting models citizen science urban areas catchment areas weather data monitoring Tedla, H. Z. Taye, E. F. Walker, D. W. Haile, Alemseged Tamiru Evaluation of WRF model rainfall forecast using citizen science in a data-scarce urban catchment: Addis Ababa, Ethiopia |
| title | Evaluation of WRF model rainfall forecast using citizen science in a data-scarce urban catchment: Addis Ababa, Ethiopia |
| title_full | Evaluation of WRF model rainfall forecast using citizen science in a data-scarce urban catchment: Addis Ababa, Ethiopia |
| title_fullStr | Evaluation of WRF model rainfall forecast using citizen science in a data-scarce urban catchment: Addis Ababa, Ethiopia |
| title_full_unstemmed | Evaluation of WRF model rainfall forecast using citizen science in a data-scarce urban catchment: Addis Ababa, Ethiopia |
| title_short | Evaluation of WRF model rainfall forecast using citizen science in a data-scarce urban catchment: Addis Ababa, Ethiopia |
| title_sort | evaluation of wrf model rainfall forecast using citizen science in a data scarce urban catchment addis ababa ethiopia |
| topic | rain weather forecasting models citizen science urban areas catchment areas weather data monitoring |
| url | https://hdl.handle.net/10568/126410 |
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