Improving AI-based discharge forecasting through hydrograph decomposition and data assimilation

Effective discharge forecasting is critical in operational hydrology. This study explores novel methods to improve forecast accuracy by combining data assimilation techniques and hydrograph decomposition. Traditional rainfall-runoff modeling, including AI-based approaches, typically simulates the entire discharge signal using a single model. However, runoff is generated by multiple processes with contrasting kinetics, which a single-model approach may fail to capture adequately. This study proposes using hydrograph decomposition to separate baseflow and quickflow components, training specific forecasting models for each component individually, and then merging their outputs to reconstruct the total discharge signal. This approach is expected to enhance forecast accuracy for both floods and droughts, identifying long-term dependencies governing baseflow to improve seasonal low-flow forecasts. Experiments will be conducted using a subset of the CAMELS dataset.