A Medium-Range Ensemble Flood Forecasting System for India.

We propose a robust framework for flood forecasting that integrates advanced hydrological modeling with high-performance computing to deliver accurate and timely predictions. At its core, the India Land Data Assimilation System (ILDAS) leverages multiple Land Surface Models (LSMs) and routing models to enhance flood prediction capabilities. The framework utilizes the Noah-MP LSM for simulating land surface processes, with runoff routed using mizuRoute, a vector-based hydrodynamic model, to estimate critical flood metrics. Calibration is optimized through an HPC-enabled scheme, ensuring precise model tuning. The system ingests meteorological forecasts from the National Center for Medium-Range Weather Forecasting (NCMRWF), ISRO, and GEFS, offering lead times of 1 to 10 days. Operating at a spatial resolution of 12 meters, it delivers nationwide flood forecasts within 15 minutes, with outputs available at daily and sub-daily temporal resolutions. The flood forecast is improved with an ML based hydrological post-processor to enhance the forecast information. A case study focusing on the flood-prone Brahmaputra basin demonstrates the system's effectiveness in accurately predicting flood events, underscoring its potential as a valuable operational tool for flood preparedness and risk mitigation.