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Updated: Jun 27, 2026
08:20
In Situ Soil Moisture Sensors in Undisturbed Soils
Published on: November 18, 2022
Remotely Sensed Surface Water Storage Shows Distinct Patterns From SWAT-Simulated Data
W Dolan1, M K Vanderhoof1, J R Christensen2
1U.S. Geological Survey, Geosciences and Environmental Change Science Center, Denver Federal Center, Denver, CO, USA.
Water Resources Research
|June 26, 2026
Summary
Hydrologic models often misrepresent surface water storage (SW storage) due to static data. New remote sensing data show significant discrepancies, highlighting the need for improved model calibration for accurate streamflow projections.
Area of Science:
- Hydrology
- Remote Sensing
- Water Resource Management
Background:
- Watershed hydrologic models commonly use static digital elevation model (DEM) data to represent surface water storage (SW storage) in lakes and wetlands.
- Model calibration typically focuses on observed discharge, neglecting the validation of SW storage dynamics using external datasets, especially in large river basins.
Purpose of the Study:
- To develop and assess a novel remote sensing-based (RS) SW storage dataset for verifying Soil and Water Assessment Tool (SWAT) model simulations in the Upper Mississippi River Basin (UMRB).
- To evaluate the adequacy of static DEM-based parameterization and discharge-only calibration for capturing SW storage dynamics.
Main Methods:
- Utilized Sentinel-1 and Sentinel-2 satellite data to create an RS-based SW storage dataset for the UMRB.
- Compared RS-derived SW storage estimates with those simulated by a SWAT model.
- Validated RS SW storage estimates against bathymetric data in selected wetlands.
Main Results:
- SWAT model simulations showed mean SW storage 74% higher than RS estimates, with underestimation in wetland-rich areas and overestimation in agricultural, tile-drained areas.
- Temporal correlations between SWAT and RS SW storage were significant in only 38.8% of subbasins.
- Static DEM parameterization and discharge-only calibration inadequately captured spatial and temporal SW storage dynamics.
Conclusions:
- The developed RS SW storage dataset offers a promising approach for improving the parameterization and calibration of hydrologic models like SWAT.
- Addressing SW storage dynamics is crucial for enhancing the accuracy of simulated streamflow, particularly in regions with extensive wetlands.
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