Comparison of deep learning models and a typical process-based model in glacio-hydrology simulation

Glacier hydrology has profound implications for socio-economic development and nature conservation in arid Central Asia. Process-based hydrological models, which are the traditional tools used to simulate glacier melting, have made considerable contributions to advance our understanding of glacio-hydrology. Simultaneously, deep learning (DL) models have achieved excellent performance in many complex tasks and provide high accuracy. However, it is uncertain whether glacio-hydrological studies can benefit from the application of DL models. In this study, to help us assess water resource change for glacier-influenced regions, we used DL models to simulate glacio-hydrological processes in the Urumqi Glacier No. 1 in northwest China. First, we proposed a newly DL model called Exogenous Regularization Network (ERNet), which focuses on the relationship between exogenous (temperature and precipitation) and endogenous (runoff) variables, balancing the roles of different variables in the simulation process. Second, we compared ERNet with a stacked long short-term memory (LSTM) model and a process-based glacio-hydrology model, FLEX^G. Experiments showed that compared with the other two models, ERNet not only performed well in runoff and peak flow simulations but also displayed superior transferability. Third, given that the DL model is data-driven, we experimentally compared the importance of air temperature and precipitation to glacial runoff processes. The results show that air temperature plays a dominant role in glacier runoff generation. We believe that the proposed model provides a useful predictive tool and that the results shed light on the future implication in cold region hydrology.