The combined impact of data assimilation and machine learning post-processing in improving flood forecasts

Accurate streamflow forecasts are essential for flood risk management and impact mitigation. In recent years, the coupling of hydrological models with machine learning techniques has gained increasing attention to improve forecast skill, with post-processing emerging among the various existing approaches to correct systematic model errors. However, the interaction between machine learning post-processing, data assimilation and calibration strategies remains insufficiently explored. In this study, we assess the contribution of machine learning-based post-processing to hourly streamflow forecasts across 687 catchments in metropolitan France, covering a wide range of hydroclimatic conditions. Streamflow forecasts are generated using the GR5H-RI hydrological model under three forecasting approaches that differ in calibration strategy and use of data assimilation. Two machine learning models, Random Forest and Multilayer Perceptron, are applied to post-process raw forecasts at lead times of 3, 6, 12 and 24 hours. Forecast performance is evaluated using both continuous skill metrics relative to persistence and threshold-based metrics for flood event detection. Results show that post-processing consistently improves forecast skill at short lead times, especially for catchments with slower hydrological responses. The largest relative gains are observed for open-loop forecasts (i.e., without data assimilation), indicating that post-processing can mitigate the absence of state updating, although it does not fully replace it. Neural network-based post-processing slightly outperforms tree-based models in continuous metrics, while differences are more limited for event detection. Overall, results highlight the complementary roles of data assimilation and machine learning post-processing and demonstrate the potential of such frameworks for operational flood forecasting.