AN ENSEMBLE APPROACH TO GLOBAL FLOOD SEVERITY FORECASTING AND ALERTING IN NEAR REAL-TIME

Keywords: Flood, risk assessment, remote sensing, ensemble model, near real-time alerts, emergency management
Abstract Type: Paper Abstract

Abstract

Flooding is a frequent disaster that impacts every country worldwide and contributes to significant societal and financial losses. Based on the information provided by the International Disaster Database (EM-DAT), the year 2021 has already seen more than 100 flood events globally of varying intensity, and these events combined have contributed to about $78 billion in damages. While flooding is prevalent, disaster managers still continue to face challenges in undertaking preparedness, response, and recovery efforts as well as in developing mitigation strategies. A major reason for this challenge is the difficulty in accessing information ahead of time and/or in near real-time about flood severity, flood locations, and extent that is crucial for resource planning and management. With advancements in sensor technologies, there is no shortage of Earth observation data to identify flood events and their impact areas. Furthermore, several hydrodynamic and empirical models are available to model and forecast flooding events. In this NASA funded project, we have developed an ensemble approach known as the Model of Models (MoM) that integrates flood outputs derived from hydrologic and hydraulic models and Earth observation data sets (optical imagery) to forecast flood severity (i.e., probability of flood risk) on a daily basis globally at sub-watershed level. The operational model and its outputs are used to disseminate flood alerts to stakeholders globally via the Pacific Disaster Center’s DisasterAWARE® decision support platform. This open access global alerting system also allows decision makers to get access flood impact outputs to support emergency response activities.