Real-Time Impact-Based Flood Forecasting in the Piracicaba Basin, Brazil

Impact-based flood forecasting remains a major challenge for early warning systems, particularly in regions subject to rapid hydrological transitions and high societal vulnerability. Conventional approaches relying on pre-computed inundation maps and fixed impact thresholds often fail to capture event-specific dynamics, anticipate cascading impacts, and support timely emergency response. This study presents a real-time impact-based forecasting system that integrates physics-enhanced LSTM streamflow prediction, two-dimensional hydrodynamic simulation, and automated GIS-based impact assessment within a unified Python framework.

The workflow begins with a physics-enhanced LSTM model trained to provide short-range streamflow forecasts at key upstream stations. These forecasts drive an automatically executed HEC-RAS 2D model, producing time-evolving floodplain conditions beyond the static assumptions of threshold-based systems. By adopting dynamic simulations rather than pre-calculated inundation products, the system captures spatially and temporally explicit flood characteristics—advancing the representation of timing, extent, and hydraulic intensity during extreme or atypical events.

Hydrodynamic outputs are post-processed through a Python module that derives key impact metrics, including (i) direct economic losses via depth–damage functions, (ii) exposed and affected population, (iii) disruption of transportation links, (iv) impacts on critical facilities (e.g., hospitals, schools, emergency services), and (v) flood arrival times at operationally relevant locations. The arrival-time analysis provides essential lead-time information for emergency mobilisation, substantially enhancing situational awareness.

The system is demonstrated in the 8,850 km² upstream drainage area of the Piracicaba Basin (São Paulo, Brazil), a region characterised by hydrological sensitivity, rapid urbanisation, and recurrent flood emergencies. Results show that integrating machine learning, hydrodynamic modelling, and automated geospatial impact quantification improves the timeliness, accuracy, and operational relevance of flood warnings. The framework advances beyond hazard-centric forecasts by delivering transparent, event-specific impact information essential for effective early action.

All components of the framework rely on free and open-source tools, and all scripts developed in this study are openly available on GitHub to support transparency, reproducibility, and operational scalability.