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Explainable Spatially Distributed Hydrologic Modeling of a Snow Dominated Mountainous Karst Watershed Using Attention
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  • Qianqiu Longyang,
  • Seohye Choi,
  • Hyrum Tennant,
  • Devon Hill,
  • Nathan Ashmead,
  • Bethany T. Neilson,
  • Dennis L Newell,
  • James P McNamara,
  • Tianfang Xu
Qianqiu Longyang
Arizona State University
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Seohye Choi
Arizona State University
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Hyrum Tennant
Utah State University
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Devon Hill
Utah State University
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Nathan Ashmead
Boise State University
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Bethany T. Neilson
Utah State University
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Dennis L Newell
Utah State University
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James P McNamara
Boise State University
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Tianfang Xu
Arizona State University

Corresponding Author:[email protected]

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Abstract

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.
05 May 2024Submitted to ESS Open Archive
10 May 2024Published in ESS Open Archive