In many regions globally, snowmelt-recharged mountainous karst aquifers serve as crucial sources for municipal and agricultural water supplies. In these watersheds, complex interplay of meteorological, topographical, and hydrogeological factors leads to intricate recharge-discharge pathways. This study introduces a spatially distributed deep learning precipitation-runoff model that combines Convolutional Long Short-Term Memory (ConvLSTM) with a spatial attention mechanism. The effectiveness of the deep learning model was evaluated using data from the Logan River watershed and subwatersheds, a characteristically karst-dominated hydrological system in northern Utah. Compared to the ConvLSTM baseline, the inclusion of a spatial attention mechanism improved performance for simulating discharge at the watershed outlet. Analysis of attention weights in the trained model unveiled distinct areas contributing the most to discharge under snowmelt and recession conditions. Furthermore, fine-tuning the model at subwatershed scales provided insights into cross-subwatershed subsurface connectivity. These findings align with results obtained from detailed hydrogeochemical tracer studies. Results highlight the potential of the proposed deep learning approach to unravel the complexities of karst aquifer systems, offering valuable insights for water resource management under future climate conditions. Furthermore, results suggest that the proposed explainable, spatially distributed, deep learning approach to hydrologic modeling holds promise for non-karstic watersheds.

中文翻译：

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一种基于注意力的可解释深度学习方法，用于对以雪为主的山地喀斯特流域进行空间分布式水文建模


在全球许多地区，融雪补给的山地喀斯特含水层是市政和农业供水的重要来源。在这些流域中，气象、地形和水文地质因素的复杂相互作用导致了错综复杂的补给-排放路径。本研究引入了一种空间分布的深度学习降水-径流模型，该模型将卷积长短期记忆 （ConvLSTM） 与空间注意力机制相结合。使用来自洛根河流域和子流域的数据评估了深度学习模型的有效性，该流域是犹他州北部一个典型的喀斯特为主的水文系统。与 ConvLSTM 基线相比，包含空间注意机制提高了模拟流域出口流量的性能。对训练模型中注意力权重的分析揭示了在融雪和衰退条件下对排放贡献最大的不同区域。此外，在子流域尺度上微调模型提供了对跨子流域地下连通性的见解。这些发现与详细的水文地球化学示踪剂研究获得的结果一致。结果突出了所提出的深度学习方法在揭示喀斯特含水层系统复杂性方面的潜力，为未来气候条件下的水资源管理提供了有价值的见解。此外，结果表明，所提出的可解释、空间分布、深度学习水文建模方法对非喀斯特流域具有前景。