Bouchard et al. (2026) Enhancing runoff-infiltration partitioning in the SVS land surface model improves streamflow simulations under frozen soil conditions

Identification

• Journal: Hydrology and earth system sciences
• Year: 2026
• Date: 2026-07-30
• Authors: Benjamin Bouchard, Vincent Vionnet, Étienne Gaborit, Vincent Fortin
• DOI: 10.5194/hess-30-4823-2026

Research Groups

• Numerical Modelling and Prediction Research Division, Environment and Climate Change Canada, Dorval, H9P 1J3, Canada

Short Summary

This study proposes a new configuration of the Soil, Vegetation, and Snow (SVS) model that enhances frozen soil infiltration by reducing both surface runoff and sub-surface lateral flow. The enhanced configuration significantly improves streamflow simulations under frozen soil conditions.

Objective

• To improve streamflow prediction under frozen soil conditions by adjusting the existing soil infiltration configuration of the SVS land surface model.

Study Configuration

• Spatial Scale: Regional scale, covering the Great-Lakes and Saint-Lawrence (GLSL) domain in eastern North America.
• Temporal Scale: 5-year period from 2016 to 2021.

Methodology and Data

• Models used: SVS land surface model with an enhanced frozen soil infiltration configuration (Fr-Inf).
• Data sources: Canadian Surface Reanalysis (CaSRv3.1) for atmospheric forcing, and observed streamflow data from USGS and HYDAT databases.

Main Results

• The Fr-Inf configuration significantly improves streamflow simulations under frozen soil conditions, with a Kling-Gupta Efficiency (KGE) of 0.28 compared to the default soil freezing configuration (Fr).
• The enhanced configuration outperforms the no-freezing configuration (noFr) at most stations, indicating its robustness.

Contributions

• This study provides an improved understanding of the impact of frozen soil infiltration on streamflow simulations.
• The proposed Fr-Inf configuration can be used to enhance operational numerical weather prediction and hydrologic forecasting systems.

Funding

• This research was funded by Environment and Climate Change Canada (ECCC) through the National Surface and River Prediction System (NSRPS).

Citation

@article{Bouchard2026Enhancing,
  author = {Bouchard, Benjamin and Vionnet, Vincent and Gaborit, Étienne and Fortin, Vincent},
  title = {Enhancing runoff-infiltration partitioning in the SVS land surface model improves streamflow simulations under frozen soil conditions},
  journal = {Hydrology and earth system sciences},
  year = {2026},
  doi = {10.5194/hess-30-4823-2026},
  url = {https://doi.org/10.5194/hess-30-4823-2026}
}