Search for: All records

This dataset contains the electrical resistivity and weather data collected from the Lower Deer Point site of the Dry Creek Experimental Watershed for the calendar year 2023. Hourly monitored data is available for the weather conditions. Daily data is available for the calculation of evapotranspiration and subsurface storage. We have ten electrical resistivity surveys, and the related data of each survey include the measured apparent resistivity measurements and inverted resistivity images with RES2DINV software. 

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. 

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 Headwater catchments play a vital role in regional water supply and ecohydrology, and a quantitative understanding of the hydrological partitioning in these catchments is critically needed, particularly under a changing climate. Recent studies have highlighted the importance of subsurface critical zone (CZ) structure in modulating the partitioning of precipitation in mountainous catchments; however, few existing studies have explicitly taken into account the 3D subsurface CZ structure. In this study, we designed realistic synthetic catchment models based on seismic velocity‐estimated 3D subsurface CZ structures. Integrated hydrologic modeling is then used to study the effects of the shape of the weathered bedrock and the associated storage capacity on various hydrologic fluxes and storages in mountainous headwater catchments. Numerical results show that the weathered bedrock affects not only the magnitude but also the peak time of both streamflow and subsurface dynamic storage. 

This dataset contains the codes and data used in the manuscript “Influence of Subsurface Critical Zone Structure on Hydrological Partitioning in Mountainous Headwater Catchments” submitted to Geophysical Research Letters. The software requirement are summarized in requirement.txt; hydrologic modeling input data are in the folder TLnewtest2sfb2; the observation data used in the simulation are indicated as comments in the python scripts. Note that the hydrologic modeling was run in HPC (Linux system) with parallel computing. Below are the abstract of the manuscript: “Headwater catchments play a vital role in regional water supply and ecohydrology, and a quantitative understanding of the hydrological partitioning in these catchments is critically needed, particularly under a changing climate. Recent studies have highlighted the importance of subsurface critical zone (CZ) structure in modulating the partitioning of precipitation in mountainous catchments; however, few existing studies have explicitly taken into account the 3D subsurface CZ structure. In this study, we designed realistic synthetic catchment models based on seismic velocity-estimated 3D subsurface CZ structures. Integrated hydrologic modeling is then used to study the effect of the shape of the weathered bedrock bottom on various hydrologic fluxes and storages in mountainous headwater catchments. Numerical results show that the shape of the weathered bedrock bottom not only affects the magnitude but also the peak time of both streamflow and subsurface dynamic storage.” 

Abstract Hydrologic modeling has been a useful approach for analyzing water partitioning in catchment systems. It will play an essential role in studying the responses of watersheds under projected climate changes. Numerous studies have shown it is critical to include subsurface heterogeneity in the hydrologic modeling to correctly simulate various water fluxes and processes in the hydrologic system. In this study, we test the idea of incorporating geophysics‐obtained subsurface critical zone (CZ) structures in the hydrologic modeling of a mountainous headwater catchment. The CZ structure is extracted from a three‐dimensional seismic velocity model developed from a series of two‐dimensional velocity sections inverted from seismic travel time measurements. Comparing different subsurface models shows that geophysics‐informed hydrologic modeling better fits the field observations, including streamflow discharge and soil moisture measurements. The results also show that this new hydrologic modeling approach could quantify many key hydrologic fluxes in the catchment, including streamflow, deep infiltration, and subsurface water storage. Estimations of these fluxes from numerical simulations generally have low uncertainties and are consistent with estimations from other methods. In particular, it is straightforward to calculate many hydraulic fluxes or states that may not be measured directly in the field or separated from field observations. Examples include quickflow/subsurface lateral flow, soil/rock moisture, and deep infiltration. Thus, this study provides a useful approach for studying the hydraulic fluxes and processes in the deep subsurface (e.g., weathered bedrock), which needs to be better represented in many earth system models. 

This is for the paper Geophysics-informed hydrologic modeling of a mountain headwater catchment for studying hydrological partitioning in the critical zone. Also check the description in https://github.com/geohang/Geophysics_informed_models 

The mechanisms by which truncating mutations in MYBPC3 (encoding cardiac myosin-binding protein C; cMyBPC) or myosin missense mutations cause hypercontractility and poor relaxation in hypertrophic cardiomyopathy (HCM) are incompletely understood. Using genetic and biochemical approaches, we explored how depletion of cMyBPC altered sarcomere function. We demonstrated that stepwise loss of cMyBPC resulted in reciprocal augmentation of myosin contractility. Direct attenuation of myosin function, via a damaging missense variant (F764L) that causes dilated cardiomyopathy (DCM), normalized the increased contractility from cMyBPC depletion. Depletion of cMyBPC also altered dynamic myosin conformations during relaxation, enhancing the myosin state that enables ATP hydrolysis and thin filament interactions while reducing the super relaxed conformation associated with energy conservation. MYK-461, a pharmacologic inhibitor of myosin ATPase, rescued relaxation deficits and restored normal contractility in mouse and human cardiomyocytes with MYBPC3 mutations. These data define dosage-dependent effects of cMyBPC on myosin that occur across the cardiac cycle as the pathophysiologic mechanisms by which MYBPC3 truncations cause HCM. Therapeutic strategies to attenuate cMyBPC activity may rescue depressed cardiac contractility in patients with DCM, whereas inhibiting myosin by MYK-461 should benefit the substantial proportion of patients with HCM with MYBPC3 mutations.