A study on the runoff prediction mechanism of "water-soil-heat" in cold alpine regions with complex spatial...

To address the challenge of runoff prediction in cold alpine regions with complex spatial distributions, this study proposes an integrated "Water-Soil-Hseat" framework for runoff modeling. This framework incorporates key factors such as precipitation, glacier meltwater, soil spatial distribution, and temperature-induced melt processes, providing a more comprehensive understanding of runoff generation mechanisms. Precipitation and glacier meltwater serve as the primary hydrological variables, while soil spatial distribution acts as an impact factor, and temperature-induced melt processes drive the runoff. The model uses these factors to dynamically simulate hydrological behavior under different topographical conditions. By combining a glacier module, a non-glacial area grid runoff generation model (GRGM), and an improved Transformer-based sequence model, the study develops a hybrid runoff prediction model-Glacier-GRGM-Transformer. The model is validated using daily runoff data from the Tailan River Basin from 2001 to 2020. The results show that the dynamic optimization of the Glacier-GRGM runoff generation model achieves a relative error of 6.79 % and a coefficient of determination of 0.97, indicating that the model effectively simulates the runoff generation process of the watershed. The Glacier-GRGM-Transformer runoff prediction model demonstrates strong predictive capability in short-term forecasts, particularly within the first 4 days, with NSE values consistently exceeding 0.7. For forecast periods longer than 2-3 days, the model still provides reasonable forecasts, though with some decrease in accuracy, demonstrating its adaptability over longer timescales. Compared to the SRM and GRU-Transformer models, the Glacier-GRGM-Transformer model outperforms in both prediction accuracy and stability, further validating the adaptability and effectiveness of the "Water-Soil-Heat" framework in complex environments. This study not only effectively captures the spatial heterogeneity of runoff mechanisms but also enhances the model's ability to simulate and predict mixed runoff in complex environments by integrating both glacier and non-glacier hydrological modules. The research provides important scientific insights for hydrological simulation and forecasting in cold alpine regions.