Liu et al. (2026) Revisiting and Improving Soil Surface Resistance Schemes for Land Surface Modeling

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

Identification

• Journal: Journal of Geophysical Research Atmospheres
• Year: 2026
• Date: 2026-10-10
• Authors: Zhuo Liu, Hua Yuan, Wenzong Dong, Zhongwang Wei, Shupeng Zhang, Jiayi Xiang, Nan Wei, Qingliang Li, Jiahao Shi, Cheng Zhang, Han Zhang, Wanyi Lin, Xinran Yu, Fei Chen, Yongjiu Dai
• DOI: 10.1029/2025jd046279

Research Groups

Not explicitly mentioned in the abstract.

Short Summary

This study systematically evaluates existing soil evaporation schemes, identifies sand volume fraction as a key driver for evaporation suppression, and develops an improved scheme (S92_sand) that significantly enhances the simulation of soil evaporation and wetness in land surface models.

Objective

• To compare and evaluate typical soil surface resistance schemes for soil evaporation.
• To identify key drivers influencing soil evaporation suppression (β factor).
• To develop an improved soil surface resistance scheme (S92_sand) by incorporating the identified key driver.
• To assess the impact and performance of the new scheme in a land surface model (CoLM) at site and regional scales.

Study Configuration

• Spatial Scale: Site-level (FLUXNET sites) and regional (across China).
• Temporal Scale: Summer (for CoLM impact assessment), and general long-term (implied by FLUXNET data and regional assessments).

Methodology and Data

• Models used: Common Land Model (CoLM), XGBoost (machine learning), GLEAM v4.2a (Global Land Evaporation Amsterdam Model).
• Data sources: FLUXNET data, observation-derived β, GLEAM v4.2a (for regional assessment of soil evaporation).

Main Results

• Existing soil surface resistance schemes showed substantial discrepancies, with over tenfold differences in values and varying sensitivity parameters under intermediate soil wetness.
• Incorporating these schemes into CoLM altered mean soil wetness by approximately 5%–35% and evaporation by approximately 9%–73% in summer.
• Sand volume fraction was identified as a key driver of β (evaporation suppression factor) using FLUXNET data and XGBoost.
• The newly developed S92_sand scheme, incorporating sand volume fraction, better reproduced observation-derived β compared to typical schemes.
• When implemented in CoLM, S92_sand reduced the mean absolute error and root mean square error (RMSE) of soil wetness by approximately 10% at evaporation-dominant sites compared to the original S92 scheme.
• Regional assessments across China using GLEAM v4.2a demonstrated S92_sand's superiority over S92, mitigating systematic overestimation of soil evaporation and reducing RMSE across 90% of the region, with improvements up to 1 mm day⁻¹ over the Qinghai-Tibet Plateau and the North China Plain.
• Simulations of soil wetness and evapotranspiration also showed noticeable regional improvements.

Contributions

• Provides a systematic evaluation of multiple soil surface resistance schemes, highlighting their significant discrepancies and impacts on land surface model simulations.
• Identifies sand volume fraction as a crucial, previously under-emphasized, driver of soil evaporation suppression (β factor).
• Develops an improved soil surface resistance scheme (S92_sand) by integrating sand volume fraction into a widely adopted framework (Sellers et al., 1992).
• Demonstrates the superior performance of S92_sand in reducing errors in soil wetness and evaporation simulations at both site and regional scales, advancing land surface modeling of soil evaporation.

Funding

Not explicitly mentioned in the abstract.

Citation

@article{Liu2026Revisiting,
  author = {Liu, Zhuo and Yuan, Hua and Dong, Wenzong and Wei, Zhongwang and Zhang, Shupeng and Xiang, Jiayi and Wei, Nan and Li, Qingliang and Shi, Jiahao and Zhang, Cheng and Zhang, Han and Lin, Wanyi and Yu, Xinran and Chen, Fei and Dai, Yongjiu},
  title = {Revisiting and Improving Soil Surface Resistance Schemes for Land Surface Modeling},
  journal = {Journal of Geophysical Research Atmospheres},
  year = {2026},
  doi = {10.1029/2025jd046279},
  url = {https://doi.org/10.1029/2025jd046279}
}