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1. Hybrid Physically Based and Deep Learning Modeling of a Snow Dominated, Mountainous, Karst Watershed

   https://doi.org/10.1029/2021WR030993  

   Xu, Tianfang; Longyang, Qianqiu; Tyson, Conor; Zeng, Ruijie; Neilson, Bethany T. (March 2022, Water Resources Research)

   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. 

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2. Seasonal connections between meteoric water and streamflow generation along a mountain headwater stream

   https://doi.org/10.1002/hyp.14029  

   Leuthold, Sam J.; Ewing, Stephanie A.; Payn, Robert A.; Miller, Florence R.; Custer, Stephan G. (February 2021, Hydrological Processes)

   Abstract In snowmelt‐driven mountain watersheds, the hydrologic connectivity between meteoric waters and stream flow generation varies strongly with the season, reflecting variable connection to soil and groundwater storage within the watershed. This variable connectivity regulates how streamflow generation mechanisms transform the seasonal and elevational variation in oxygen and hydrogen isotopic composition (δ¹⁸O and δD) of meteoric precipitation. Thus, water isotopes in stream flow can signal immediate connectivity or more prolonged mixing, especially in high‐relief mountainous catchments. We characterized δ¹⁸O and δD values in stream water along an elevational gradient in a mountain headwater catchment in southwestern Montana. Stream water isotopic compositions related most strongly to elevation between February and March, exhibiting higher δ¹⁸O and δD values with decreasing elevation. These elevational isotopic lapse rates likely reflect increased connection between stream flow and proximal snow‐derived water sources heavily subject to elevational isotopic effects. These patterns disappeared during summer sampling, when consistently lower δ¹⁸O and δD values of stream water reflected contributions from snowmelt or colder rainfall, despite much higher δ¹⁸O and δD values expected in warmer seasonal rainfall. The consistently low isotopic values and absence of a trend with elevation during summer suggest lower connectivity between summer precipitation and stream flow generation as a consequence of drier soils and greater transpiration. As further evidence of intermittent seasonal connectivity between the stream and adjacent groundwaters, we observed a late‐winter flush of nitrate into the stream at higher elevations, consistent with increased connection to accumulating mineralized nitrogen in riparian wetlands. This pattern was distinct from mid‐summer patterns of nitrate loading at lower elevations that suggested heightened human recreational activity along the stream corridor. These observations provide insights linking stream flow generation and seasonal water storage in high elevation mountainous watersheds. Greater understanding of the connections between surface water, soil water and groundwater in these environments will help predict how the quality and quantity of mountain runoff will respond to changing climate and allow better informed water management decisions. 

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3. Karst Hydrologic Memory Supplements Streamflow During Dry Periods in Snow‐Dominated, Mountainous Watersheds

   https://doi.org/10.1002/hyp.70019  

   Tennant, Hyrum; Neilson, Bethany_T; Hill, Devon; Newell, Dennis_L; Evans, James_P; Choi, Seohye; McNamara, James_P; Ashmead, Nathaniel; Xu, Tianfang (December 2024, Hydrological Processes)

   ABSTRACT Analysis of PRISM and SNOTEL station data paired with USGS streamflow gage data in the western United States shows that, in snow‐dominated mountainous watersheds, streamflow regimes differ between watersheds with karst geology and their non‐karst neighbours. These carbonate aquifers exhibit a spectrum of flow paths encompassing karst conduits, including large fractures or voids that transmit water readily to springs and other surface waters, and matrix flow paths through soils, highly fractured bedrock, or porous media bedrock grains. A well‐connected karst aquifer will discharge a large portion of its accumulated precipitation to surface water via springs and other groundwater flow paths on an annual scale, exhibiting a lagged response to precipitation presenting as a “memory effect” in hydrograph time series. These patterns were observed in the hydrologic records of gaged watersheds with exposed or near‐surface carbonate layers accounting for > 30% of their drainage area. In western snow‐dominated watersheds, where paired streamflow and SNOTEL data are available, analysis of the precipitation and flow time series shows low‐flow volume is strongly related to karst aquifer conditions and winter precipitation when compared to low‐flow volumes present in non‐karst watersheds, which have a complex relationship to multiple driving metrics. Analysis of normalised streamflow and cumulative precipitation in karst watersheds show that low‐flow conditions are highly dependent on the preceding winter precipitation and streamflow in both wet and dry periods. In non‐karst watersheds, increased precipitation primarily impacts high‐flow, spring runoff volumes with no clear relationship to low‐flow periods. When comparing cumulative streamflow and precipitation volumes within each water year and over longer timescales, karst watersheds show the potential filling and draining of large amounts of karst storage, whereas non‐karst watersheds demonstrate a more stable storage regime. Communities in many western US watersheds are dependent on snow‐dominated karst watersheds for their water supply. This analysis, using widely available hydrologic data, can provide insight into the recharge and storage processes within these watersheds, improve our ability to assess current flow regimes, anticipate the impacts of climate change on water availability, and help manage water supplies. 

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4. An Attention‐Based Explainable Deep Learning Approach to Spatially Distributed Hydrologic Modeling of a Snow Dominated Mountainous Karst Watershed

   https://doi.org/10.1029/2024WR037878  

   Longyang, Qianqiu; Choi, Seohye; Tennant, Hyrum; Hill, Devon; Ashmead, Nathaniel; Neilson, Bethany_T; Newell, Dennis_L; McNamara, James_P; Xu, Tianfang (November 2024, Water Resources Research)

   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. 

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5. Strontium isotope dynamics reveal streamflow contributions from shallow flow paths during snowmelt in a montane watershed, Provo River, Utah, USA

   https://doi.org/10.1002/hyp.14458  

   Hale, Colin A.; Carling, Gregory T.; Nelson, Stephen T.; Fernandez, Diego P.; Brooks, Paul D.; Rey, Kevin A.; Tingey, David G.; Packer, Brian N.; Aanderud, Zachary T. (January 2022, Hydrological Processes)

   Abstract Quantifying the routing of snowmelt to surface water is critical for predicting the impacts of atmospheric deposition and changing land use on water quality in montane catchments. To investigate solute sources and streamflow in the montane Provo River watershed (Utah, USA), we used time‐series⁸⁷Sr/⁸⁶Sr ratios sampled at three sites (Soapstone, Woodland and Hailstone) across a gradient of bedrock types. Soils are influenced by aeolian dust contributions, with distinct⁸⁷Sr/⁸⁶Sr ratios relative to siliciclastic bedrock, providing an opportunity to investigate shallow versus deeper flow paths for controlling water chemistry. At the most upstream site (Soapstone), Sr concentrations averaged ~17 μg/L with minimal dilution during snowmelt suggesting subsurface flow paths dominated streamflow. However, a decrease in⁸⁷Sr/⁸⁶Sr ratios from ~0.717 during baseflow to as low as ~0.713 during snowmelt indicated the activation of shallow flow paths through dust‐derived soils. In contrast, downstream sites receiving water inputs from Sr‐rich carbonate bedrock (Woodland and Hailstone) exhibited strong dilution of Sr from ~120 to 20 μg/L and an increase in⁸⁷Sr/⁸⁶Sr ratios from ~0.7095 to ~0.712 during snowmelt. A three‐component mixing model using⁸⁷Sr/⁸⁶Sr ratios and Sr concentrations at Soapstone showed water inputs were dominated by direct snowmelt and flushed soil water during runoff and groundwater during baseflow. At Woodland and Hailstone, a two‐component mixing model showed that the river was a mixture of groundwater and up to 75% upstream channel water during snowmelt. Our findings highlight the importance of flushed soil water for controlling stream water discharge and chemistry during snowmelt, with the signal from the upstream site propagating downstream in a nested catchment. Further, aeolian dust contributes to the solute chemistry of montane streams with potential impacts on water quality along shallow flow paths. Potential contaminants in these surface soils (e.g., Pb deposition in dust) may have significant impacts on water quality during snowmelt runoff. 

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