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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. 

Abstract Streamflow generation in mountain watersheds is strongly influenced by snow accumulation and melt as well as groundwater connectivity. In mountainous regions with limestone and dolomite geology, bedrock formations can host karst aquifers, which play a significant role in snowmelt–discharge dynamics. However, mapping complex karst features and the resulting surface‐groundwater exchanges at large scales remains infeasible. In this study, timeseries analysis of continuous discharge and specific conductance measurements were combined with gridded snowmelt predictions to characterize seasonal streamflow response and evaluate dominant watershed controls across 12 monitoring sites in a karstified 554 km²watershed in northern Utah, USA. Immense surface water hydrologic variability across subcatchments, years and seasons was linked to geologic controls on groundwater dynamics. Unlike many mountain watersheds, the variability between subcatchments could not be well described by typical watershed properties, including elevation or surficial geology. To fill this gap, a conceptual framework was proposed to characterize subsurface controls on snowmelt–discharge dynamics in karst mountain watersheds in terms of conduit flow direction, aquifer storage capacity and connectivity. This framework requires only readily measured surface water and climatic data from nested monitoring sites and was applied to the study watershed to demonstrate its applicability for evaluating dominant controls and climate sensitivity. 

Abstract Snow dominated mountainous karst watersheds are the primary source of water supply in many areas in the western U.S. and worldwide. These watersheds are typically characterized by complex terrain, spatiotemporally varying snow accumulation and melt processes, and duality of flow and storage dynamics because of the juxtaposition of matrix (micropores and small fissures) and karst conduits. As a result, predicting streamflow from meteorological inputs has been challenging due to the inability of physically based or conceptual hydrologic models to represent these unique characteristics. We present a hybrid modeling approach that integrates a physically based, spatially distributed, snow model with a deep learning karst model. More specifically, the high‐resolution snow model captures spatiotemporal variability in snowmelt, and the deep learning model simulates the corresponding response of streamflow as influenced by complex surface and subsurface properties. The deep learning model is based on the Convolutional Long Short‐Term Memory (ConvLSTM) architecture capable of handling spatiotemporal recharge patterns and watershed storage dynamics. The hybrid modeling approach is tested on a watershed in northern Utah with seasonal snow cover and variably karstified carbonate bedrock. The hybrid models were able to simulate streamflow at the watershed outlet with high accuracy. The spatial and temporal recharge and discharge patterns learned by the ConvLSTM model were then examined and compared with known hydrogeologic information. Results suggest that ConvLSTM simulates streamflow with higher accuracy than reference models for the study area and provides insight into spatially influenced hydrologic responses that are unavailable within lumped modeling approaches.