Wang et al. (2025) Spatiotemporal dynamics of soil moisture in a watershed in the Loess Plateau during vegetation restoration

Identification

• Journal: Environmental Monitoring and Assessment
• Year: 2025
• Date: 2025-10-29
• Authors: Zihan Wang, Yang Yu, Hongsheng Zhu, Tao Ma, Jiongchang Zhao, Daoming Ma, Liping Wang, Marco Cavalli
• DOI: 10.1007/s10661-025-14728-6

Research Groups

• School of Soil and Water Conservation, Beijing Forestry University, Beijing, China
• Jixian National Forest Ecosystem Observation and Research Station, CNERN, Beijing Forestry University, Beijing, China
• Gansu Institute of Soil and Water Conservation, Lanzhou, China
• National Research Council of Italy, Research Institute for Geo-Hydrological Protection, Padua, Italy

Short Summary

This study analyzed the spatiotemporal dynamics and temporal stability of soil moisture in a watershed on the Loess Plateau during vegetation restoration through two years of field monitoring. It revealed distinct seasonal and spatial patterns influenced by rainfall and vegetation, identified strong spatial autocorrelation, and pinpointed a specific location (S25) as the most temporally stable representative point for the entire watershed.

Objective

• To analyze the relationship between soil moisture mean values, the coefficient of variation (CV) of soil moisture, and rainfall.
• To assess the spatial distribution characteristics of soil moisture at the watershed scale using spatial interpolation and spatial autocorrelation analysis.
• To calculate the temporal stability of soil moisture at the watershed scale and identify an observation point that can represent the soil moisture dynamics of the entire watershed.

Study Configuration

• Spatial Scale: Caijiachuan Watershed, Ji County, Shanxi Province, China (40 km² total area, plantation forested sub-watershed of 1.52 km²). Monitoring points at 34 locations across 10 soil depth intervals from 0 to 180 cm.
• Temporal Scale: Two consecutive growing seasons (May to October) from 2023 to 2024, with biweekly field measurements.

Methodology and Data

• Models used:
  • Inverse Distance Weighting (IDW) for spatial interpolation.
  • Pearson correlation coefficient for correlation analysis.
  • Cross-Correlation Function (CCF) for time-lag characteristics.
  • Global and Local Moran's I index for spatial autocorrelation analysis.
  • Mean Relative Difference (MRD), Standard Deviation of Relative Difference (SDRD), Temporal Stability Index (ITS), and Mean Absolute Bias Error (MABE) for temporal stability analysis.
• Data sources:
  • Field in situ monitoring of volumetric soil water content using Time-Domain Reflectometry (TDR) probes.
  • Rainfall data collected using rain gauges.
  • Global Positioning System (GPS) for monitoring point coordinates and topographic data.

Main Results

• The total cumulative rainfall during the growing seasons of 2023 and 2024 was 644.5 mm.
• Soil moisture exhibited clear seasonal patterns, with the highest mean value of 19.29% in May (0–120 cm layer) and the lowest of 13.00% in September (0–120 cm layer).
• The coefficient of variation (CV) for soil moisture ranged from 0.12 to 0.27, with surface soil (0–40 cm) showing greater spatial heterogeneity (mean CV of 0.20) than deeper layers (mean CV of 0.17).
• Spatially, soil moisture consistently decreased from the southeastern to the northwestern regions of the watershed across all months.
• A strong positive spatial autocorrelation was observed in soil moisture distribution (Moran's I = 0.99, z = 122.96), indicating clustering of high and low moisture areas.
• Soil moisture distribution varied by vegetation type: forest land had high shallow soil moisture (13.5–16.4% in 0–20 cm), grassland showed higher moisture below 20 cm than forest, and farmland had consistently lower moisture at all depths.
• Observation point S25 was identified as the most temporally stable representative point for the entire watershed, with a mean relative difference of 0.002 and a temporal stability index of 0.990, meeting the ITS < 0.1 condition in 6 out of 10 soil layers.

Contributions

• Advances the understanding of vegetation restoration's role in soil moisture stability and dynamics at the watershed scale.
• Provides empirical evidence to support ecological restoration initiatives and sustainable development on the Loess Plateau.
• Offers a new perspective for analyzing the temporal stability of soil moisture at the catchment scale.
• Facilitates the optimization of strategies for irrigation management, vegetation restoration, and water resource protection by identifying representative soil moisture monitoring points.

Funding

• National Key Research and Development Program of China (No. 2023YFF1305101)
• National Natural Science Foundation of China (42177310 and 42377331)

Citation

@article{Wang2025Spatiotemporal,
  author = {Wang, Zihan and Yu, Yang and Zhu, Hongsheng and Ma, Tao and Zhao, Jiongchang and Ma, Daoming and Wang, Liping and Cavalli, Marco},
  title = {Spatiotemporal dynamics of soil moisture in a watershed in the Loess Plateau during vegetation restoration},
  journal = {Environmental Monitoring and Assessment},
  year = {2025},
  doi = {10.1007/s10661-025-14728-6},
  url = {https://doi.org/10.1007/s10661-025-14728-6}
}