LANZHOU, March 16 (Xinhua) -- Chinese researchers have provided key in-situ evidence that advances understanding of hydrological interactions in marginal permafrost regions, according to the Northwest Institute of Eco-Environment and Resources (NIEER) under the Chinese Academy of Sciences.
The study was conducted by a team from the NIEER in collaboration with researchers from the Nanjing University of Information Science and Technology, Lanzhou University, and other institutions. The findings have been published in Geoderma and the International Journal of Applied Earth Observation and Geoinformation, the NIEER said.
"Given the context of global warming, permafrost degradation is reshaping soil moisture regimes in cold regions. However, the vertical structure and regulatory mechanisms of moisture remain underexplored in marginal permafrost zones," said Hu Guojie, a researcher at NIEER.
As a key variable for permafrost hydrological modeling and vegetation assessment, understanding deep soil moisture has long been constrained by the limited depth of remote sensing and the scarcity of deep-profile field data.
The team conducted field investigations in the Genhe River Basin of the Dahinggan Mountains, a typical marginal permafrost zone. By integrating measurements from 75 shallow profiles and 15 boreholes reaching depths of up to 20 meters across forests, grasslands and wetlands, the researchers examined how vegetation and permafrost jointly regulate volumetric soil water content.
Combining interpretable machine learning with structural equation modeling, the study revealed a distinct vertical stratification of soil moisture and its controls.
Surface factors such as wetland cover and soil organic carbon were found to dominate shallow soil moisture, while bulk density and permafrost extent governed deeper water retention. Climatic influences operated mainly indirectly by altering insulation and freeze-thaw buffering, the study showed.
The findings point to a vertically controlled system shaped by vegetation-permafrost interactions, highlighting the need to incorporate vertical coupling into process-based hydrological models, according to Hu.
The team also developed a mapping algorithm that integrates Earth observation data with interpretable machine learning to produce a soil-moisture stratification dataset for the entire river basin.
"The dataset can accurately depict the moist corridor along river valleys shaped by permafrost and wetlands, providing a valuable mapping scheme and high-precision baseline data for hydrological simulations and ecological management in cold-region basins," Hu said. ■
