Feng et al. (2026) Optimizing Soil Moisture‐Runoff Coupling Strength With Remotely Sensed Soil Moisture for Improved Hydrological Modeling

Identification

• Journal: Water Resources Research
• Year: 2026
• Authors: Huihui Feng, Jianhong Zhou, Zhiyong Wu, Jianzhi Dong, L. Brocca, Long Zhao, Hai He, Hui Fan
• DOI: 10.1029/2024wr039571

Research Groups

• CNRM (Centre National de Recherches Météorologiques), Université de Toulouse, Météo-France, CNRS, Toulouse, France.
• METIS (Milieux Environnementaux, Transferts et Interaction dans les Hydrosystèmes et les Sols), Sorbonne Université, CNRS, EPHE, Paris, France.
• Direction de la Climatologie et des Services Climatiques, Météo-France, Toulouse, France.

Short Summary

The study presents the development and validation of SIM2, a high-resolution hydrometeorological reanalysis for metropolitan France covering the period 1958–2022. By integrating the ISBA-CTRIP model with SAFRAN atmospheric forcing, the system provides a significantly improved estimation of river discharge, snowpack dynamics, and soil moisture compared to its predecessor.

Objective

• To create a consistent, long-term (64-year), high-resolution dataset of the continental water cycle over France.
• To upgrade the national SIM (Safran-Isba-Modcou) system with modernized physics (ISBA-CTRIP) to better support water resource management and climate impact studies.

Study Configuration

• Spatial Scale: Metropolitan France, discretized on an 8 km grid (approximately 550,000 $km^2$).
• Temporal Scale: 1958–2022 (64 years) at an hourly internal time step, with daily and monthly archived outputs.

Methodology and Data

• Models used:
  • SAFRAN: Meteorological reanalysis for atmospheric forcing (precipitation, temperature, etc.).
  • ISBA-CTRIP: Land surface model (within the SURFEX platform) coupled with a river routing model.
  • ISBA-ES: An explicit multi-layer snow scheme (3 layers) for improved mountain hydrology.
• Data sources:
  • Ground-based meteorological observations (for SAFRAN).
  • River discharge data from the French national database (Banque Hydro) for validation (~900 stations).
  • Satellite-derived products for snow cover and soil moisture validation.

Main Results

• Discharge Accuracy: SIM2 shows a marked improvement in river discharge simulation, with median Nash-Sutcliffe Efficiency (NSE) scores increasing significantly across the 891 validated gauging stations compared to SIM1.
• Snow Dynamics: The transition to the multi-layer snow scheme (ISBA-ES) resulted in a more realistic representation of Snow Water Equivalent (SWE) and snowmelt timing in the Alps and Pyrenees.
• Water Balance: The model successfully captures long-term trends, including a notable decrease in soil moisture and an increase in potential evapotranspiration over the 1958–2022 period.
• Spatial Consistency: The 8 km resolution effectively captures the hydrological response of diverse catchments, from Mediterranean ephemeral streams to large oceanic basins like the Seine and Loire.

Contributions

• Modernization of National Tools: Replaces the aging SIM1 system with state-of-the-art land surface physics (diffusive soil water/heat transfer and multi-layer snow).
• Reference Dataset: Provides a new 64-year reference reanalysis for France, essential for the Explore2 project and other climate change adaptation strategies.
• Open Science: The resulting dataset offers a high-resolution benchmark for evaluating drought severity and water availability at the national scale.

Funding

• Météo-France internal funding.
• CNRS (Centre National de la Recherche Scientifique).
• French Ministry of Ecological Transition (supporting the Explore2 and hydrological monitoring programs).

Citation

@article{Feng2026Optimizing,
  author = {Feng, Huihui and Zhou, Jianhong and Wu, Zhiyong and Dong, Jianzhi and Brocca, L. and Zhao, Long and He, Hai and Fan, Hui},
  title = {Optimizing Soil Moisture‐Runoff Coupling Strength With Remotely Sensed Soil Moisture for Improved Hydrological Modeling},
  journal = {Water Resources Research},
  year = {2026},
  doi = {10.1029/2024wr039571},
  url = {https://doi.org/10.1029/2024wr039571}
}

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