TL;DR
The results demonstrate that deep learning models, particularly when enriched with spatially interpolated inputs and land cover variability, significantly enhance forecasting accuracy and provide valuable projections for future water availability but also informs sustainable water management strategies in climate-sensitive regions.
Accurate streamflow prediction is critical for effective water resource management, and recent advances in deep learning and multi‐basin data sets have significantly improved model skill. Global deep‐learning models, trained on thousands of catchments have demonstrated strong generalization across diverse climates an...
The findings highlight the value of training strategies that allow models to directly learn bias correction during forecast transitions, emphasize the operational potential of combining sequential processing with near real-time discharge observations and identify physiographic catchment characteristics as key modulator...
Accurate streamflow prediction in snow-dominated regions is challenging due to complex nonlinear interactions and significant temporal lags between snow accumulation and melt. This study evaluates the efficacy of a spatially explicit cascade-forward deep learning framework compared to traditional lumped methods in...
It is shown that a regionally trained probabilistic deep learning model improves operational streamflow forecasts 5 for the Rhine basin and highlights that deep learning can complement established operational systems without replacing them, offering a practical pathway to integrate data-driven methods into operational...
DL‐based downscaling substantially improves the spatial fidelity and usability of climate projections for regional adaptation planning and suggests intensifying regional hydroclimatic contrasts and enhanced spatial polarisation of monsoon rainfall under continued warming.
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