Optimizing forecast-based actions for extreme rainfall events

The last decade has seen a major innovation within disaster risk management through the emergence of standardized forecast-based action and financing protocols. Given sufficient lead time forecast skill, portion relief funds may be shifted from recovery to preparedness, reducing losses in lives property. While short-term early warnings systems are commonplace, forecasts at monthly or seasonal scale relatively underused, despite their potential value. Incorporating both, numerous organizations have developed operational protocols for natural hazards. These plans well-defined forecasts, trigger criteria, identification actions ranging weeks months prior predicted disaster, but many not been explicitly optimized maximize financial utilitarian returns. This study investigates effect different methodologies, performance metrics, levels aversion on optimal decision-making sensitivity analysis an protocol, using case coastal Peru. Results suggest that relative benefit times plays largest role determining decisions, with methodology playing lesser role. optimization framework is designed applicable even absence post-disaster monitoring evaluation, supporting proliferation adaptive more broadly. Forecast-based disasters increasingly common, some adopted identify respond disasters. Because they often new untested, these provide maximum return investment. paper presents way test types decisions including type, willingness take action, ways which calculate benefits. We find preparation—that is, seasons advance—is valuable, value preparation before important than accuracy our risks.