Search for: All records

Both hydrological and geophysical data can be used to calibrate hillslope hydrologic models. However, these data often reflect hydrological dynamics occurring at disparate spatial scales. Their use as sole objectives in model calibrations may thus result in different optimum hydraulic parameters and hydrologic model behavior. This is especially true for mountain hillslopes where the subsurface is often heterogeneous and the representative elementary volume can be on the scale of several m³. This study explores differences in hydraulic parameters and hillslope‐scale storage and flux dynamics of models calibrated with different hydrological and geophysical data. Soil water content, groundwater level, and two time‐lapse electrical resistivity tomography (ERT) data sets (transfer resistance and inverted resistivity) from two mountain hillslopes in Wyoming, USA, are used to calibrate physics‐based surface–subsurface hydrologic models of the hillslopes. Calibrations are performed using each data set independently and all data together resulting in five calibrated parameter sets at each site. Model predicted hillslope runoff and internal hydrological dynamics vary significantly depending on the calibration data set. Results indicate that water content calibration data yield models that overestimate near‐surface water storage in mountain hillslopes. Groundwater level calibration data yield models that more reasonably represent hillslope‐scale storage and flux dynamics. Additionally, ERT calibration data yield models with reasonable hillslope runoff predictions but relatively poor predictions of internal hillslope dynamics. These observations highlight the importance of carefully selecting data for hydrologic model calibration in mountain environments. Poor selection of calibration data may yield models with limited predictive capability depending on modeling goals and model complexity.

 

Time‐lapse electrical resistivity tomography (ERT) data are increasingly used to inform the hydrologic dynamics of mountainous environments at the hillslope scale. Despite their popularity and recent advancements in hydrogeophysical inversion methods, few studies have shown how time‐lapse ERT data can be used to determine hydraulic parameters of subsurface water flow models. This study uses synthetic and field‐collected, hillslope‐scale, time‐lapse ERT data to determine subsurface hydraulic properties of a two‐layer, physics‐based, 2‐D vertical flow model with predefined layer and boundary locations. Uncoupled and coupled hydrogeophysical inversion methods are combined with a fine‐earth fraction optimization scheme to reduce the number of parameters needing calibration and interpret the influence of the hydraulic parameters on the hydrologic model predictions. Inversions of synthetic ERT data recover the prescribed fine‐earth fraction bulk density to within 0.1 g cm⁻³. Field‐collected ERT data from a mountain hillslope result in hydrologic model dynamics that are consistent with previous studies and measured water content data but struggle to capture measured groundwater levels. The uncoupled hydrogeophysical inversion method is more sensitive to changes in hydraulic parameter values of the lower hydrologic model layer than the coupled hydrogeophysical inversion method. Time series of minimum objective function value simulations indicate that periodically collected ERT data may recover hydraulic parameters to a similar level of uncertainty as daily ERT data. Using simple hydrologic model domains within hydrogeophysical inversions shows promise for providing reasonable hydrologic predictions while maintaining relatively simple calibration schemes and should be explored further in future studies.

 

The belowground architecture of the critical zone (CZ) consists of soil and rock in various stages of weathering and wetness that acts as a medium for biological growth, mediates chemical reactions, and controls partitioning of hydrologic fluxes. Hydrogeophysical imaging provides unique insights into the geometries and properties of earth materials that are present in the CZ and beyond the reach of direct observation beside sparse wellbores. An improved understanding of CZ architecture can be achieved by leveraging the geophysical measurements of the subsurface. Creating categorical models of the CZ is valuable for driving hydrologic models and comparing belowground architectures between different sites to interpret weathering processes. The CZ architecture is revealed through a novel comparison of hillslopes by applying facies classification in the elastic-electric domain driven by surface-based hydrogeophysical measurements. Three pairs of hillslopes grouped according to common geologic substrates — granite, volcanic extrusive, and glacially altered — are classified by five different hydrofacies classes to reveal the relative wetness and weathering states. The hydrofacies classifications are robust to the choice of initial mean values used in the classification and noncontemporaneous timing of geophysical data acquisition. These results will lead to improved interdisciplinary models of CZ processes at various scales and to an increased ability to predict the hydrologic timing and partitioning. Beyond the hillslope scale, this enhanced capability to compare CZ architecture can also be exploited at the catchment scale with implications for improved understanding of the link between rock weathering, hydrochemical fluxes, and landscape morphology.