An integrated framework for flood risk forecasting utilizing global weather predictions and hydrodynamic Modelling:...

Flood risk forecasting is a critical non-structural component of disaster management and mitigation; however, limited studies in India have addressed it at the river basin scale with an emphasis on response-driven planning. This study proposes a novel and scalable framework for flood risk forecasting that integrates global rainfall forecasts, hydrodynamic modelling, and socioeconomic vulnerability (SEV) assessment to support response prioritization. The methodology is demonstrated over the Krishna River Basin (KRB), a large, geomorphologically diverse, and flood-prone basin spanning four Indian states. Forecasted precipitation from the Global Ensemble Forecast System (GEFS) is used to identify recent extreme rainfall-induced pluvial flood events. The MIKE 21 hydrodynamic model is employed to simulate high-resolution flood inundation maps across multiple lead times. A novel metric, Critical Response Time (CRT), is introduced to quantify the time required for each grid cell to reach a threshold inundation depth, serving as a dynamic spatial indicator of hazard intensity and urgency. SEV is assessed using recent Census data, applying multiple aggregation and ranking techniques to ensure robust, comparative evaluation of vulnerability patterns. Sub-district-level SEV scores are overlaid with CRT-based hazard outputs to produce bivariate choropleth flood risk maps, identifying hazard-driven, vulnerability-driven, and compound high-risk zones. Results indicate that the eastern KRB faces high compound risk, while the western part is predominantly hazard-driven. Validation with recent flood events confirms the framework's capability for anticipatory planning. The proposed approach is generalizable, transparent, and spatially explicit, offering a valuable tool for early warning, response prioritization, and resource allocation to support proactive environmental management in river basins facing hydro-climatic risks.