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...
The intensification of hydroclimatic extremes under global warming poses serious risks to water security, agriculture and disaster resilience in monsoon‐dependent regions. However, reliable regional‐scale rainfall projections remain challenging due to scale mismatches between global climate models and localised hyd...
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