Developing hybrid distributed models for hydrological simulation and climate change assessment in large alpine basins

Large alpine basins on the Tibetan Plateau (TP) provide abundant water resources crucial for hydropower generation, irrigation, and daily life. In recent decades, the TP has been significantly affected by climate change, making it crucial to understand the runoff response to climate change are essential for water resources management. While limited knowledge of specific alpine hydrological processes has constrained the accuracy of hydrological models and heightened uncertainties in climate change assessments. Recently, hybrid hydrological models have come to the forefront, synergizing the exceptional learning capacity of deep learning with a rigorous adherence to hydrological knowledge of process-based models. These models exhibit considerable promise in achieving precision in hydrological simulations and conducting climate change assessments. However, a notable limitation of existing hybrid models lies in their failure to incorporate spatial information and describe alpine hydrological processes, which restricts their applicability in hydrological modeling and climate change assessment in large alpine basins. To address this issue, we develop a set of hybrid distributed hydrological models by employing a distributed process-based model as the backbone, and utilizing embedded neural networks (ENNs) to parameterize and replace different internal modules. The proposed models are tested on three large alpine basins on the Tibetan Plateau. Results are compared to those obtained from hybrid lumped models, state-of-the-art distributed hydrological model, and DL models. A climate perturbation method is further used to evaluate the alpine basins' runoff response to climate change.Results indicate that proposed hybrid hydrological models can perform well in predicting runoff in large alpine basins. The optimal hybrid model with Nash-Sutcliffe efficiency coefficients (NSEs) higher than 0.87 shows comparable performance to state-of-the-art DL models. The hybrid distributed model also exhibits remarkable capability in simulating hydrological processes at ungauged sites within the basin, markedly surpassing traditional distributed models. Besides, runoff exhibits an amplification effect in response to precipitation changes, with a 10% precipitation change resulting in a 15–20% runoff change in large alpine basins. An increase in temperature enhances evaporation capacity and changes the redistribution of rainfall and snowfall and the timing of snowmelt, leading to a decrease in the total runoff and a reduction in the intra-annual variability of runoff. Overall, this study provides a high-performance tool enriched with explicit hydrological knowledge for hydrological prediction and improves our understanding about runoff’s response to climate change in large alpine basins on the TP.