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 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.

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