考虑垂直地带性与冰川消融的天山北坡径流模拟研究

潘泰鸿; 李小龙; 何新林; 谷新晨; 杜永军

doi:10.13928/j.cnki.wrahe.2026.01.017

水利水电技术（中英文） ›› 2026, Vol. 57 ›› Issue (1) : 221 -233. DOI: 10.13928/j.cnki.wrahe.2026.01.017

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考虑垂直地带性与冰川消融的天山北坡径流模拟研究

潘泰鸿 ¹^,² ,
李小龙 ¹^,² ,
何新林 ¹^,² ,
谷新晨 ³^,⁴ ,
杜永军 ¹^,²

作者信息 +

Study on runoff simulation on northern slope of Tianshan Mountains considering vertical zonality and glacier ablation

Taihong PAN ¹^,² ,
Xiaolong LI ¹^,² ,
Xinlin HE ¹^,² ,
Xinchen GU ³^,⁴ ,
Yongjun DU ¹^,²

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摘要

【目的】天山北坡寒旱区冰川流域径流受垂直地带性与冰川消融非线性共同影响。而显著的垂直梯度与高冰川径流贡献率,导致该区域存在融雪径流模拟效果不佳、基流物理机制表征不足的突出难题,本文以玛纳斯河流域为研究区,揭示物理机制模型与数据驱动模型在垂直分异下的性能适配规律。【方法】集成CMADS数据集构建SWAT、LSTM及CNN-LSTM并行径流模拟框架,采用纳什效率系数(NSE)、均方根误差(RMSE)和百分比偏差(PBIAS),定量评估1979—2018年径流模拟性能。解析融雪期(4—7月)和枯水期(11月至次年3月)的模型误差成因。【结果】结果表明:CNN-LSTM在整体模拟表现出更优的模拟性能,整体模拟NSE达0.829,较LSTM(0.800)和SWAT(0.811)提升3.6%与2.2%。其中融雪期(4—7月)时CNN-LSTM仍展现最优模拟效果(NSE=0.787),较SWAT(0.737)和LSTM(0.764)提升6.8%与3.0%。而枯水期(11月至次年3月)时CNN-LSTM/LSTM模拟效果相近(NSE=0.859)。考虑枯水期流量基数小,SWAT虽NSE(0.782)低于前两者,但其地下水过程的显式物理参数化,仍赋予结果更高过程稳健性与可解释性,具独特分析价值。【结论】垂直地带性梯度下,模型性能差异由水文过程机制与模型结构优势共同驱动。CNN-LSTM通过数据驱动学习气象要素时空模式,对非线性过程适应性优异。SWAT依托物理机制的显式参数化,仍然具备独特的过程一致性和可解释性。研究揭示寒旱区径流模拟中不同模型的优势边界与适用情境,为物理机制与数据驱动融合的径流模拟模型发展提供关键科学依据与参考。

Abstract

[Objective] Runoff in glacial basins on the cold and arid northern slope of the Tianshan Mountains is jointly influenced by vertical zonality and the nonlinearity of glacier ablation. However, the significant vertical gradient and high glacier runoff contribution rate lead to prominent challenges in this region, namely poor snowmelt runoff simulation and insufficient characterization of the physical mechanism of baseflow. Taking the Manas River Basin as the study area, the performance adaptation patterns of physically-based and data-driven models under vertical differentiation are investigated. [Methods] Integrating the CMADS dataset, a parallel runoff simulation framework combining SWAT, LSTM, and CNN-LSTM was established. The Nash-Sutcliffe Efficiency(NSE), Root Mean Square Error(RMSE), and Percent Bias(PBIAS) were used to quantitatively evaluate the performance of monthly runoff simulation from 1979 to 2018, and the causes of model errors during the snowmelt period(April-July) and low-flow period(November-March) were analyzed. [Results] The result showed that: CNNLSTM exhibited superior overall simulation performance, with an overall NSE of 0. 829—representing a 3. 6% and 2. 2%improvement over LSTM(0. 800) and SWAT(0. 811), respectively. During the snowmelt period(April—July), CNN-LSTM still performed best(NSE= 0. 787), which was 6. 8% and 3. 0% higher than that of SWAT(0. 737) and LSTM(0. 764),respectively. In the low-flow period( November-March), CNN-LSTM and LSTM showed similar performance( both NSE=0. 859). Given the small runoff base in the low-flow period, although SWAT had a lower NSE(0. 782) than the two models, its explicit physical parameterization of groundwater processes still endowed the result with higher process robustness and interpretability, thus possessing unique analytical value. [Conclusion] Under the vertical zonality gradient, differences in model performance are jointly driven by hydrological process mechanisms and model structural advantages. CNN-LSTM learns the spatiotemporal patterns of meteorological factors via a data-driven approach and exhibits excellent adaptability to nonlinear processes. SWAT, with explicit parameterization based on physical mechanisms, exhibits unique process consistency and interpretability. The advantage boundaries and applicable scenarios of different models in runoff simulation in cold and arid regions are identified, providing a key scientific basis for the development of integrated physically-based and data-driven runoff simulation models.