You et al. (2025) Sensitivity of Soil Moisture Simulations to Noah-MP Parameterization Schemes in a Semi-Arid Inland River Basin, China

Identification

• Journal: Agriculture
• Year: 2025
• Date: 2025-11-03
• Authors: Yuanhong You, Yanyu Lu, Yu Wang, Houfu Zhou, Y. X. Hao, Weijing Chen, Zuo Wang
• DOI: 10.3390/agriculture15212286

Research Groups

• Anhui Province Key Laboratory of Atmospheric Science and Satellite Remote Sensing, Anhui Institute of Meteorological Sciences, Hefei, China
• School of Geography and Tourism, Anhui Normal University, Wuhu, China
• School of Earth and Space Sciences, University of Science and Technology of China, Hefei, China
• Huaihe River Basin Meteorological Center, Anhui Meteorological Bureau, Hefei, China
• School of Aeronautic Engineering, Changsha University of Science and Technology, Changsha, China

Short Summary

This study evaluated the Noah-Multiparameterization Land Surface Model (Noah-MP)'s ability to simulate soil moisture in a semi-arid inland river basin, identifying key sensitive physical processes and quantifying their contributions to simulation uncertainty. The findings provide guidance for improving parameterization schemes to reduce model uncertainty in such regions.

Objective

• To evaluate the performance of the Noah-MP model in simulating soil moisture across different layers and climate types in the Heihe River Basin (HRB), China.
• To investigate the sensitivity of soil moisture simulations to various parameterization schemes within the Noah-MP model under different underlying surface conditions.
• To quantify the contribution of specific physical processes to the overall uncertainty in ensemble soil moisture simulations.

Study Configuration

• Spatial Scale: Heihe River Basin (HRB), China, focusing on three representative meteorological sites: Arou (upper reaches, subalpine mountain meadow, dark cold calcareous soil, elevation 3032.8 m), Heihe (middle reaches, cropland, sandy loam, elevation 1560 m), and Sidaoqiao (lower reaches, sparse vegetation, saline-alkali soil, elevation 873 m). Soil column divided into four layers with thicknesses of 0.1 m, 0.3 m, 0.6 m, and 1.0 m.
• Temporal Scale: Soil moisture simulations and observations for the period from January to December 2016 for Arou and Sidaoqiao sites, and extended into 2017 for the Heihe site. Meteorological driving data were at a 1-hour time scale. A spin-up simulation was performed using data from the year prior to the study period to achieve soil-state equilibrium.

Methodology and Data

• Models used: Noah-Multiparameterization Land Surface Model (Noah-MP) version 5.0.
• Data sources:
  • High-precision meteorological and soil moisture observation data from the Arou, Heihe, and Sidaoqiao sites, downloaded from the National Tibetan Plateau Data Center (https://data.tpdc.ac.cn).
  • Model initialization data included soil type, land cover type, and elevation.
  • A large physics-ensemble experiment was conducted, comprising 17,280 simulations per site, by combining different parameterization schemes for 10 main physical processes.
  • Sensitivity analysis methods: Natural Selection and Tukey’s Test.
  • Uncertainty quantification method: Uncertainty Contribution Analysis.

Main Results

• The Noah-MP model captured soil moisture variability in the first and second soil layers across the three sites but exhibited notable biases (underestimation at Arou and Sidaoqiao, overestimation at Heihe). The model largely failed to simulate soil moisture variations in the third and fourth layers.
• Sensitive Physical Processes:
  • Frozen soil permeability (INF), supercooled liquid water in frozen soil (FRZ), and ground resistance to sublimation (SRE) were sensitive at all three sites.
  • Bare soil evaporation (BTR) and surface exchange coefficients (SFC) showed sensitivity at the midstream (Heihe) and downstream (Sidaoqiao) sites.
  • Bottom temperature (TBOT) and canopy air temperature (TEMP) processes were sensitive at the upstream (Arou) and midstream (Heihe) sites.
  • Precipitation (PCP) sensitivity was observed only at the upstream (Arou) alpine region.
• Insensitive Physical Processes: Radiation transfer (RAD) and surface albedo (ALB) processes were consistently insensitive to soil moisture simulations across all sites.
• Uncertainty Contribution:
  • At the upstream Arou site, the FRZ process contributed nearly 50% (48.70%) of the ensemble uncertainty, followed by TEMP (17.86%) and INF (14.03%).
  • At the midstream Heihe site, FRZ was the dominant contributor (52.96%), with INF contributing 30.86%.
  • At the downstream Sidaoqiao site, the SRE process accounted for 50.92% of the uncertainty, and INF contributed 31.07%.
  • Contributions from other physical processes were generally below 1% at these sites.

Contributions

• Provided a comprehensive evaluation of Noah-MP's soil moisture simulation performance in a semi-arid inland river basin with diverse climate and land cover types.
• Identified specific Noah-MP parameterization schemes that significantly influence soil moisture simulation accuracy across different regions of the Heihe River Basin.
• Quantified the contribution of individual physical processes to the overall uncertainty in soil moisture ensemble simulations, highlighting the primary drivers of model uncertainty in this context.
• Offered a scientific basis for selecting optimal parameterization schemes for soil moisture simulation in semi-arid regions and for developing more accurate land surface process parameterization schemes.

Funding

• National Natural Science Foundation of China (U2342206, 42201425, 42101361)
• Jianghuai Meteorological Joint Project of Anhui Natural Science Foundation (2408055UQ006)
• Scientific Research Project of Higher Education Institutions in Anhui province (2023AH050143)
• China Postdoctoral Science Foundation (2024M753092)

Citation

@article{You2025Sensitivity,
  author = {You, Yuanhong and Lu, Yanyu and Wang, Yu and Zhou, Houfu and Hao, Y. X. and Chen, Weijing and Wang, Zuo},
  title = {Sensitivity of Soil Moisture Simulations to Noah-MP Parameterization Schemes in a Semi-Arid Inland River Basin, China},
  journal = {Agriculture},
  year = {2025},
  doi = {10.3390/agriculture15212286},
  url = {https://doi.org/10.3390/agriculture15212286}
}