Deep Learning Framework for High-Resolution Hydrological Forecasting Using Hydrometric Data

Abstract

Multi-site multi-horizon hydrological forecasting is gaining increasing attention in watershed management due to spatially distributed hydrological processes, which cannot be represented using single site models. This study develops a deep learning (DL) framework for forecasting hydrological regime in streams at multiple cross-sections using hydrometric observations. It has been shown that multi-site models with a shared static loss function better represent the sites with regular hydrological regimes and moderate flows, while rapid response sites are not modelled accurately. To address this limitation, an Adaptive Weighted Loss strategy was developed to periodically re-assign weights to site losses depending on the validation performance. The framework was assessed on datasets with different hydrological characteristics. The results highlighted that ALF-based DL framework improved performance at response sites by extending horizon of reliable forecasts. Overall, the study shows that combining watershed-level multi-site forecasting with adaptive weighting provides a scalable and reproducible approach for high-resolution hydrological predictions.