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Accurate subsurface data is crucial for continental-scale hydrological modeling. This study recommends a specific dataset configuration for improved water resource and process understanding.

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Area of Science:

  • Earth and Environmental Sciences
  • Hydrology
  • Geoscience

Background:

  • Integrated hydrological models require accurate spatial datasets for physical representation.
  • Subsurface properties significantly influence hydrologic cycle processes and water resource quantification.
  • Existing subsurface datasets present challenges for continental-scale modeling.

Purpose of the Study:

  • To evaluate data-driven approaches for estimating continental-scale hydraulic conductivity and subsurface properties.
  • To assess the impact of different subsurface configurations on integrated hydrological model performance.
  • To identify key factors influencing subsurface data representation in large-scale hydrologic models.

Main Methods:

  • Development and testing of multiple data-driven subsurface configurations.
  • Utilizing integrated hydrological models in two large-scale U.S. watersheds.
  • Comparison of model outputs with observed streamflow and water table depth (WTD).

Main Results:

  • Water table depth (WTD) and surface water partitioning are critical performance indicators.
  • Geology data source, subsurface depth, anisotropy, and vertical flow barriers significantly impact model results.
  • A range of subsurface configurations demonstrated viability for hydrological modeling.

Conclusions:

  • The selection of appropriate subsurface data is paramount for accurate continental-scale hydrological modeling.
  • A recommended "Selected National Configuration" subsurface dataset at 1km resolution is proposed.
  • This dataset can enhance distributed large- and continental-scale hydrologic modeling efforts.