Yu Qiying, Bai Yungang, Lu Zhenlin, Liu Chengshuai, Soomro Shan-E-Hyder, Li Wenzhong, Tian Lu, Xu Yingying, Shi Chen, Cao Biao, Hu Caihong
College of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, China; Xinjiang Institute of Water Resources and Hydropower Research, Urumqi 830049, China.
Xinjiang Institute of Water Resources and Hydropower Research, Urumqi 830049, China.
Sci Total Environ. 2025 Jan 1;958:178059. doi: 10.1016/j.scitotenv.2024.178059. Epub 2024 Dec 17.
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.
为应对空间分布复杂的寒冷高山地区径流预测的挑战，本研究提出了一种用于径流建模的综合“水 - 土壤 - 热”框架。该框架纳入了降水、冰川融水、土壤空间分布和温度诱导融解过程等关键因素，能更全面地理解径流产生机制。降水和冰川融水是主要水文变量，土壤空间分布是影响因素，温度诱导融解过程驱动径流。该模型利用这些因素动态模拟不同地形条件下的水文行为。通过结合冰川模块、非冰川区域网格径流生成模型（GRGM）和改进的基于Transformer的序列模型，本研究开发了一种混合径流预测模型——冰川 - GRGM - Transformer。利用2001年至2020年台兰河流域的日径流数据对该模型进行验证。结果表明，冰川 - GRGM径流生成模型的动态优化实现了6.79%的相对误差和0.97的决定系数，表明该模型有效地模拟了流域的径流产生过程。冰川 - GRGM - Transformer径流预测模型在短期预测中表现出强大的预测能力，尤其是在前4天内，NSE值始终超过0.7。对于超过2 - 3天的预测期，该模型仍能提供合理的预测，尽管准确性有所下降，这表明其在较长时间尺度上具有适应性。与SRM和GRU - Transformer模型相比，冰川 - GRGM - Transformer模型在预测准确性和稳定性方面均表现更优，进一步验证了“水 - 土壤 - 热”框架在复杂环境中的适应性和有效性。本研究不仅有效捕捉了径流机制的空间异质性，还通过整合冰川和非冰川水文模块增强了模型在复杂环境中模拟和预测混合径流的能力。该研究为寒冷高山地区的水文模拟和预报提供了重要的科学见解。
