Assessing a Model-of-Models Approach for Global Flood Forecasting and Alerting

Abstract

Flooding is a frequent extreme weather event that causes significant financial and societal losses. According to the International Disaster Database (EM-DAT), during January through July 2023, 87 flooding events caused about 2000 deaths and ${\$}$13 billions in damages globally. Among the impacted, low- and medium-income countries with resource scarcity tend to experience high mortality, displacement of people, unmitigated damages, and long-term recovery. Currently, several hydrologic models and Earth observation (EO) datasets are used to forecast flood severity and impacts. However, not all of these models are globally operational or publicly available. The variability in outputs in terms of accuracy, scale, and content also limits their usage for emergency response activities. The Model of Models (MoM), an ensemble approach, integrates hydrologic models and EO datasets 1) to forecast flood risk (probability of occurrence) globally every 24 h at a subwatershed level and 2) to disseminate alert messages and potential impact information to at-risk communities using the Pacific Disaster Center's DisasterAWARE platform. MoM is operational and designed to assist countries with flood risk management and mitigation by providing early warning and situational awareness information. An accuracy assessment of MoM from user-perspective across nine different flood types revealed that 1) the model reliably generated early warning for 100% of the flooded subwatersheds in seven events, and 2) during 2022 flooding, 61% and 89% of the flooded subwatersheds that were identified to be in Watch and Warning categories in Pakistan and Chad, respectively, were detected to be flooding by the Copernicus Global Flood Monitoring system.

Keywords

• Early warning
• Earth observation (EO) data
• global flood forecasting
• hydrologic models
• Model of Models (MoM)