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Watershed Planning within a Quantitative Scenario Analysis Framework
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Continental Scale Hydrostratigraphy: Basin-Scale Testing of Alternative Data-Driven Approaches
Danielle Tijerina-Kreuzer1, Jackson S Swilley1,2, Hoang V Tran3
1Integrated GroundWater Modeling Center, Princeton University, Princeton, NJ, USA.
Ground Water
|September 28, 2023
Summary
Accurate subsurface data is crucial for continental-scale hydrological modeling. This study recommends a specific dataset configuration for improved water resource and process understanding.
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.
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