Mei et al. (2025) Soil moisture variability of typical mixed forests in the north-south climatic transitional zone of China

Identification

• Journal: Journal of Environmental Management
• Year: 2025
• Date: 2025-11-20
• Authors: Yu Mei, Qinghe Zhao, Peikun Li, Xiaojian Qin, Boxuan Liu, Zhize Li, Tingting Wang, Shengyan Ding
• DOI: 10.1016/j.jenvman.2025.127953

Research Groups

• College of Geographical Sciences, Faculty of Geographical Science and Engineering, Henan University, Zhengzhou, Henan, China
• Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng, Henan, China
• Henan Dabieshan National Field Observation and Research Station of Forest Ecosystem, Xinyang, Henan, China
• Xiaoqinling Ecological Restoration Field Observation and Research Station of the Yellow River Basin, Henan University, Kaifeng, Henan, China
• Henan Jigongshan National Nature Reserve Affairs Center, Xinyang, Henan, China

Short Summary

This study identified dominant drivers of soil moisture and developed a CEEMDAN-LSTM simulation framework for different mixed forests in China's north-south climatic transitional zone, finding that conifer-broadleaved mixed forests (CBMF) significantly enhance soil moisture and exhibit the highest hydrological conservation capacity under precipitation variation.

Objective

• To identify the dominant drivers of soil moisture and establish a robust simulation framework for different mixed forest types (conifer-broadleaved, deciduous broadleaved-dominated, and conifer-dominated) in the north-south climatic transitional zone of China.

Study Configuration

• Spatial Scale: North-south climatic transitional zone of China, focusing on conifer-broadleaved mixed forests (CBMF), deciduous broadleaved-dominated mixed forests (BDMF), and conifer-dominated mixed forests (CDMF).
• Temporal Scale: Dynamic responses to precipitation variation and climate change scenarios.

Methodology and Data

• Models used: CEEMDAN-LSTM (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise - Long Short-Term Memory) for soil moisture modeling; PCA-VIF (Principal Component Analysis - Variance Inflation Factor) for driver identification.
• Data sources: Field observations and conventional sampling methods.

Main Results

• Conifer-broadleaved mixed forests (CBMF) significantly enhanced soil moisture compared to deciduous broadleaved-dominated mixed forests (BDMF) and conifer-dominated mixed forests (CDMF).
• This enhancement in CBMF was attributed to increases in saturated hydraulic capacity, total porosity, and soil organic carbon (SOC), alongside a reduction in bulk density.
• PCA-VIF analysis identified soil organic carbon (SOC), particle size, and pH as the most significant independent drivers of soil moisture variation.
• A CEEMDAN-LSTM framework was developed for modeling soil moisture at various depths across different mixed forests, demonstrating excellent simulation performance with R² values ranging from 0.80 to 0.92 across all soil layers, and greater accuracy in deeper soil layers (R² > 0.92).
• Simulations revealed that CBMF exhibited the highest soil hydrological conservation capacity under precipitation variation.
• The developed framework effectively simulated the impacts of extreme precipitation on soil moisture under climate change.

Contributions

• Addressed the gap in developing a well-developed framework for modeling soil moisture across different mixed forest types.
• Identified key dominant drivers of soil moisture variation in mixed forests within a climatic transitional zone.
• Established a robust and highly accurate CEEMDAN-LSTM simulation framework for soil moisture at various depths.
• Provided a scientific basis for adaptive management strategies for forest ecosystems and water resources in the face of climate change and extreme precipitation events.
• Highlighted the superior soil hydrological conservation capacity of conifer-broadleaved mixed forests.

Funding

• Not specified in the provided text.

Citation

@article{Mei2025Soil,
  author = {Mei, Yu and Zhao, Qinghe and Li, Peikun and Qin, Xiaojian and Liu, Boxuan and Li, Zhize and Wang, Tingting and Ding, Shengyan},
  title = {Soil moisture variability of typical mixed forests in the north-south climatic transitional zone of China},
  journal = {Journal of Environmental Management},
  year = {2025},
  doi = {10.1016/j.jenvman.2025.127953},
  url = {https://doi.org/10.1016/j.jenvman.2025.127953}
}