Improving short to sub-seasonal streamflow forecast using machine learning

Recent advances in machine learning (ML) for hydrology demonstrate strong potential for improving short to sub-seasonal streamflow forecasting under increasing frequent extreme events. These models leverage large collections of meteorological and hydrological time series dataset that often combined with spatial and network-based information to learn transferable forecasting relationships across diverse hydroclimatic regimes. Here we discuss our recent progress and remaining challenges in developing robust ML-based streamflow forecasting systems that operate beyond traditional short lead times. We developed Future Time Series Transformer (FutureTST), a deep learning architecture designed to explicitly integrate past hydrometeorological conditions with future weather information for streamflow forecast. Unlike conventional autoregressive or process-based approaches, FutureTST independently encodes historical streamflow and meteorological forcings while conditioning forecasts on future atmospheric drivers which helps to capture complex temporal dependencies that govern streamflow at extended lead times. Evaluating across multiple basins, we demonstrate three key advances: (1) Forecast skill improvement across lead times: FutureTST achieves strong performance from short to sub-seasonal period with a mean Nash-Sutcliffe Efficiency (NSE) value of 0.82 at 1-day lead time to 0.67 at 30-day lead time which substantially outperform calibrated process-based hydrological models beyond 4 days, (2) Data filling and network-informed forecasting: Reconstructing lost streamflow information highlights the importance of data filling and spatial connection in a river network for improving forecast for partially gauged or data-sparse basins, and (3) Implications for compound flood prediction: By jointly conditioning on antecedent hydrologic states and meteorological extremes, the ML framework provides an interpretable variable importance for identifying compound flood drivers. Finally, we outline key challenges and future directions from ensemble weather forecasts, uncertainty quantification, and compound event aware training strategies to further improve streamflow forecasting.