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Abstract 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.
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This content will become publicly available on February 26, 2026
AutoCalNet - Continous Real-Time Calibration of Networked Mobile Volumetric Cameras
AutoCalNet enables continuous real-time calibration of mobile 3D cameras by decoupling calibration from content streaming. It leverages a scalable device-edge-cloud network to minimize bandwidth, manage latency, and maintain high precision in calibration data, prioritizing trackable regions and feature points that will facilitate spatiotemporal tracking. This approach provides a flexible, efficient solution for networked camera systems without being constrained by content-specific requirements.
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- Award ID(s):
- 1942844
- PAR ID:
- 10580752
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400714030
- Page Range / eLocation ID:
- 129 to 129
- Format(s):
- Medium: X
- Location:
- La Quinta CA USA
- Sponsoring Org:
- National Science Foundation
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