Explainable Spatially Distributed Hydrologic Modeling of a Snow
Dominated Mountainous Karst Watershed Using Attention
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.