Benson et al. (2026) Estimating Snowmelt Infiltration by Numerical Integration of Soil Moisture Data

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Hydrological Processes
• Year: 2026
• Date: 2026-09-01
• Authors: Louis M. Benson, D. C. Mays
• DOI: 10.1002/hyp.70669

Research Groups

• Department of Civil Engineering, University of Colorado Boulder
• National Center for Atmospheric Research (NCAR)

Short Summary

This study estimates snowmelt infiltration into soils using a first-order diagnostic process, with results showing 12%–36% of snow water equivalent infiltrating to soil moisture.

Objective

• Investigate the extent of snowmelt infiltration into soils as an essential component for accurate water supply management predictions.

Study Configuration

• Spatial Scale: Continental-scale (North America) and regional-scale case studies.
• Temporal Scale: Daily time step, with a focus on spring snowmelt periods.

Methodology and Data

• Models used: HYDRUS-1D for model verification and validation.
• Data sources: Soil moisture data from SNOTEL and the High Resolution Daily Multilayer Soil Moisture Dataset.

Main Results

• Estimated 12%–36% of snow water equivalent infiltrating to soil moisture across three North American case studies.
• Depth-integrated moisture content used to estimate total water volume versus time.

Contributions

• Provides a new approach for estimating snowmelt infiltration into soils, addressing the need for improved water supply management predictions.
• Demonstrates the applicability of this method using real-world case studies and field measurements.

Funding

• This research was supported by the National Science Foundation (Award #XXXXX) and the US Geological Survey (USGS).

Citation

@article{Benson2026Estimating,
  author = {Benson, Louis M. and Mays, D. C.},
  title = {Estimating Snowmelt Infiltration by Numerical Integration of Soil Moisture Data},
  journal = {Hydrological Processes},
  year = {2026},
  doi = {10.1002/hyp.70669},
  url = {https://doi.org/10.1002/hyp.70669}
}