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.
The complex ecohydrological processes of rangelands can be studied through the framework of ecological sites (ESs) or hillslope‐scale soil–vegetation complexes. High‐quality hydrologic field investigations are needed to quantitatively link ES characteristics to hydrologic function. Geophysical tools are useful in this context because they provide valuable information about the subsurface at appropriate spatial scales. We conducted 20 field experiments in which we deployed time‐lapse electrical resistivity tomography (ERT), variable intensity rainfall simulation, ground‐penetrating radar (GPR), and seismic refraction, on hillslope plots at five different ESs within the Upper Crow Creek Watershed in south‐east Wyoming. Surface runoff was measured using a precalibrated flume. Infiltration data from the rainfall simulations, coupled with site‐specific resistivity–water content relationships and ERT datasets, were used to spatially and temporally track the progression of the wetting front. First‐order constraints on subsurface structure were made at each ES using the geophysical methods. Sites ranged from infiltrating 100% of applied rainfall to infiltrating less than 60%. Analysis of covariance results indicated significant differences in the rate of wetting front progression, ranging from 0.346 m min−1/2for sites with a subsurface dominated by saprolitic material to 0.156 m min−1/2for sites with a well‐developed soil profile. There was broad agreement in subsurface structure between the geophysical methods with GPR typically providing the most detail. Joint interpretation of the geophysics showed that subsurface features such as soil layer thickness and the location of subsurface obstructions such as granite corestones and material boundaries had a large effect on the rate of infiltration and subsurface flow processes. These features identified through the geophysics varied significantly by ES. By linking surface hydrologic information from the rainfall simulations with subsurface information provided by the geophysics, we can characterize the ES‐specific hydrologic response. Both surface and subsurface flow processes differed among sites and are directly linked to measured characteristics.
more » « less- NSF-PAR ID:
- 10462804
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Hydrological Processes
- Volume:
- 33
- Issue:
- 5
- ISSN:
- 0885-6087
- Page Range / eLocation ID:
- p. 759-774
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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