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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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Understanding each other's models: an introduction and a standard representation of 16 global water models to support intercomparison, improvement, and communication
Abstract. Global water models (GWMs) simulate the terrestrial watercycle on the global scale and are used to assess the impacts of climatechange on freshwater systems. GWMs are developed within different modellingframeworks and consider different underlying hydrological processes, leadingto varied model structures. Furthermore, the equations used to describevarious processes take different forms and are generally accessible onlyfrom within the individual model codes. These factors have hindered aholistic and detailed understanding of how different models operate, yetsuch an understanding is crucial for explaining the results of modelevaluation studies, understanding inter-model differences in theirsimulations, and identifying areas for future model development. This studyprovides a comprehensive overview of how 16 state-of-the-art GWMs aredesigned. We analyse water storage compartments, water flows, and humanwater use sectors included in models that provide simulations for theInter-Sectoral Impact Model Intercomparison Project phase 2b (ISIMIP2b). Wedevelop a standard writing style for the model equations to enhance modelintercomparison, improvement, and communication. In this study, WaterGAP2used the highest number of water storage compartments, 11, and CWatM used 10compartments. Six models used six compartments, while four models (DBH,JULES-W1, Mac-PDM.20, and VIC) used the lowest number, three compartments.WaterGAP2 simulates five human water use sectors, while four models (CLM4.5,CLM5.0, LPJmL, and MPI-HM) simulate only water for the irrigation sector. Weconclude that, even though hydrological processes are often based on similarequations for various processes, in the end these equations have beenadjusted or models have used different values for specific parameters orspecific variables. The similarities and differences found among the modelsanalysed in this study are expected to enable us to reduce the uncertaintyin multi-model ensembles, improve existing hydrological processes, andintegrate new processes.
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- Award ID(s):
- 1752729
- PAR ID:
- 10324963
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Date Published:
- Journal Name:
- Geoscientific Model Development
- Volume:
- 14
- Issue:
- 6
- ISSN:
- 1991-9603
- Page Range / eLocation ID:
- 3843 to 3878
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5194/gmd-14-3843-2021
