Flood nowcasting based on deep learning and radar rainfall estimates: A reliable and efficient framework for diverse flood regimes

Abstract. Floods pose a severe risk to lives, infrastructure, and ecosystems, necessitating accurate and timely forecasts to support early warning and emergency response. Flash floods in particular are among the most destructive flood hazards due to their rapid onset, short response times, and thus limited time for warning. However, physics-based or conceptual hydrological models often struggle to deliver reliable short lead-time predictions, particularly in small or fast-responding catchments where complex and rapidly evolving hydrological processes are at play. Additionally, in the case of flashy catchments, there is often insufficient time to run numerical models and issue timely warnings. This study explores the use of encode-decode Long Short-Term Memory (LSTM) networks for short-term flood forecasting using high-frequency radar rainfall and river level data across three locations representing diverse flood regimes in the Hunter Valley in Australia. Validation across these locations demonstrates strong agreement between predicted and observed flood levels, with an average RMSE of 0.09 m and MAE of 0.06 m at a 90-minute lead time, and 0.45 m (RMSE) and 0.24 m (MAE) at a 12-hour lead time. By coupling LSTM-predicted water levels with airborne LiDAR-derived digital elevation models (DEMs), inundation maps were generated to translate point-based flood level predictions into spatially distributed flood extent information. These maps showed high agreement with Sentinel-2 and Sentinel-1derived flood products, achieving over 94 % overall accuracy and up to 84.5 % critical success index at a 12-hour lead time. We also demonstrated the workflow’s operational capability, achieving accurate flood level forecasts supported by radar-based rainfall nowcasts and numerical weather predictions, albeit lower quality longer 12-hour forecasts when input precipitation forecasts have high uncertainty and error. Overall, this study presents a scalable, data-driven approach for real-time flood nowcasting, providing a practical tool to support early warning systems and inform emergency planning in vulnerable regions.