High-resolution operational Flood monitoring in India

Flood is a recurrent natural disaster, causing socio-economic losses and affecting millions of people every year. High-resolution and near-real time monitoring of flood disasters is critical for a densely populated and hydro-climatically diverse country like India. Existing operational frameworks in India largely rely on coarse-resolution hydrological models and hydrodynamic models, biased meteorological forecasts, limited gauge networks, missing observed data for model setup, static land use, and deterministic forecasts, which constrain their ability to capture basin heterogeneity, reservoir regulation, agriculture expansion, urban influences, and short-term extremes. Here, we present a high-resolution, integrated operational flood monitoring framework using hydrological, hydrodynamic, and data-driven models to provide 5-day ahead forecasts of streamflow, water level, and flood inundation at more than 350 stations across India. We first evaluate meteorological forecasts from UKMO, KMA, ECMWF, and GEFS products to quantify their spatio-temporal skill and estimated systematic biases across hydro-climatic regimes. Consequently, we apply a knowledge distillation–based bias correction approach trained on observed rainfall and temperature data from the India Meteorological Department (IMD), enabling the physically consistent correction of meteorological inputs. These corrected forecasts are then integrated with a process-based hydrological model and a sequential long short-term memory network augmented with a multi-headed attention mechanism, which explicitly learns temporal dependencies, upstream connectivity, and the dynamic relevance of predictors. The forecasted streamflow is then fed into the large-scale hydrodynamic model to forecast water level and flood inundation maps. The proposed stochastic framework aims to achieve substantial improvements in short-lead flood prediction skill, enhanced representation of peak flows and water levels, and more realistic flood inundation dynamics compared to existing operational systems. By combining machine learning-based forecast correction, high-resolution modelling, and advanced deep learning, this study provides a scalable pathway for next-generation flood early warning systems in India, offering direct benefits for evacuation and rescue operations, reservoir operation, agriculture management, and disaster risk reduction at both national and sub-basin scales.