The utility of impact data in flood forecast verification for anticipatory actions: Case studies from Uganda and Kenya
Abstract Skilful flood forecasts have the potential to inform preparedness actions across scales, from smallholder farmers through to humanitarian actors, but require verification first to ensure such early warning information is robust. However, verification efforts in data‐scarce regions are limit...
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| Format: | Article |
| Language: | English |
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Wiley
2025-03-01
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| Series: | Journal of Flood Risk Management |
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| Online Access: | https://doi.org/10.1111/jfr3.12911 |
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| author | Faith Mitheu Elena Tarnavsky Andrea Ficchì Elisabeth Stephens Rosalind Cornforth Celia Petty |
| author_facet | Faith Mitheu Elena Tarnavsky Andrea Ficchì Elisabeth Stephens Rosalind Cornforth Celia Petty |
| author_sort | Faith Mitheu |
| collection | DOAJ |
| description | Abstract Skilful flood forecasts have the potential to inform preparedness actions across scales, from smallholder farmers through to humanitarian actors, but require verification first to ensure such early warning information is robust. However, verification efforts in data‐scarce regions are limited to only a few sparse locations at pre‐existing river gauges. Hence, alternative data sources are urgently needed to enhance flood forecast verification to better guide preparedness actions. In this study, we assess the usefulness of less conventional data such as flood impact data for verifying flood forecasts compared with river‐gauge observations in Uganda and Kenya. The flood impact data contains semi‐quantitative and qualitative information on the location and number of reported flood events derived from five different data repositories (Dartmouth Flood Observatory, DesInventar, Emergency Events Database, GHB, and local) over the 2007–2018 period. In addition, river‐gauge observations from stations located within the affected districts and counties are used as a reference for verification of flood forecasts from the Global Flood Awareness System. Our results reveal both the potential and the challenges of using impact data to improve flood forecast verification in data‐scarce regions. From these, we provide a set of recommendations for using impact data to support anticipatory action planning. |
| format | Article |
| id | doaj-art-df81ec52d9a641b88458700c74e3e1e0 |
| institution | OA Journals |
| issn | 1753-318X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Flood Risk Management |
| spelling | doaj-art-df81ec52d9a641b88458700c74e3e1e02025-08-20T01:49:58ZengWileyJournal of Flood Risk Management1753-318X2025-03-01181n/an/a10.1111/jfr3.12911The utility of impact data in flood forecast verification for anticipatory actions: Case studies from Uganda and KenyaFaith Mitheu0Elena Tarnavsky1Andrea Ficchì2Elisabeth Stephens3Rosalind Cornforth4Celia Petty5Department of Geography and Environmental Science University of Reading Reading UKWalker Institute, University of Reading Reading UKDepartment of Geography and Environmental Science University of Reading Reading UKDepartment of Meteorology University of Reading Reading UKWalker Institute, University of Reading Reading UKWalker Institute, University of Reading Reading UKAbstract Skilful flood forecasts have the potential to inform preparedness actions across scales, from smallholder farmers through to humanitarian actors, but require verification first to ensure such early warning information is robust. However, verification efforts in data‐scarce regions are limited to only a few sparse locations at pre‐existing river gauges. Hence, alternative data sources are urgently needed to enhance flood forecast verification to better guide preparedness actions. In this study, we assess the usefulness of less conventional data such as flood impact data for verifying flood forecasts compared with river‐gauge observations in Uganda and Kenya. The flood impact data contains semi‐quantitative and qualitative information on the location and number of reported flood events derived from five different data repositories (Dartmouth Flood Observatory, DesInventar, Emergency Events Database, GHB, and local) over the 2007–2018 period. In addition, river‐gauge observations from stations located within the affected districts and counties are used as a reference for verification of flood forecasts from the Global Flood Awareness System. Our results reveal both the potential and the challenges of using impact data to improve flood forecast verification in data‐scarce regions. From these, we provide a set of recommendations for using impact data to support anticipatory action planning.https://doi.org/10.1111/jfr3.12911disaster risk reductionfloodsforecast verificationhumanitarian early actionimpactsnon‐traditional verification data |
| spellingShingle | Faith Mitheu Elena Tarnavsky Andrea Ficchì Elisabeth Stephens Rosalind Cornforth Celia Petty The utility of impact data in flood forecast verification for anticipatory actions: Case studies from Uganda and Kenya Journal of Flood Risk Management disaster risk reduction floods forecast verification humanitarian early action impacts non‐traditional verification data |
| title | The utility of impact data in flood forecast verification for anticipatory actions: Case studies from Uganda and Kenya |
| title_full | The utility of impact data in flood forecast verification for anticipatory actions: Case studies from Uganda and Kenya |
| title_fullStr | The utility of impact data in flood forecast verification for anticipatory actions: Case studies from Uganda and Kenya |
| title_full_unstemmed | The utility of impact data in flood forecast verification for anticipatory actions: Case studies from Uganda and Kenya |
| title_short | The utility of impact data in flood forecast verification for anticipatory actions: Case studies from Uganda and Kenya |
| title_sort | utility of impact data in flood forecast verification for anticipatory actions case studies from uganda and kenya |
| topic | disaster risk reduction floods forecast verification humanitarian early action impacts non‐traditional verification data |
| url | https://doi.org/10.1111/jfr3.12911 |
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