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This paper focuses on the use of community-based early warning systems forflood resilience in Nepal. The first part of the work outlines the evolutionand current status of these community-based systems, highlighting the limitedlead times currently available for early warning. The second part of thepaper focuses on the development of a robust operational flood forecastingmethodology for use by the Nepal Department of Hydrology and Meteorology(DHM) to enhance early warning lead times. The methodology uses data-basedphysically interpretable time series models and data assimilation to generateprobabilistic forecasts, which are presented in a simple visual tool. Theapproach is designed to work in situations of limited data availability withan emphasis on sustainability and appropriate technology. The successfulapplication of the forecast methodology to the flood-prone Karnali Riverbasin in western Nepal is outlined, increasing lead times from 2–3 to7–8 h. The challenges faced in communicating probabilistic forecaststo the last mile of the existing community-based early warning systems acrossNepal is discussed. The paper concludes with an assessment of theapplicability of this approach in basins and countries beyond Karnali andNepal and an overview of key lessons learnt from this initiative

Community-based early warning systems for flood risk mitigation in Nepal