Tong, Y., Wang, Y., Li, Z. et al. (7 more authors) (2025) Deciphering spatial pattern and environmental drivers of deep soil moisture security: insights from integrating spatial non-stationarity in large-scale analysis. Catena, 261. 109516. ISSN: 0341-8162
Abstract
Soil moisture (SM) profoundly influences global ecosystem services and climate change, creating the broad needs for SM management. However, existing single-scale studies have limited representativeness and overlook the spatial non-stationarity, while treating regions with diverse SM conditions as a whole may obscure interactions between environment and different SM levels. Therefore, we expanded the study scale both horizontally and vertically and established an “SM security” framework, which classifies SM conditions based on their impacts on evapotranspiration, vegetation growth, and soil quality. SM was measured to 500 cm depth across the Yellow River Basin (YRB) (795,000 km²) and categorized into three security zones including “wet zone” that SM ≥ 80 % * field capacity (FC), “transitional zone” that permanent wilting point (PWP) < SM < 80 % * FC, and “dry zone” that SM ≤ PWP. In the YRB, the transitional zone was predominant (61.58 %), followed by wet (20.24 %) and dry (18.17 %) zones. In relatively stable layers (110–500 cm), the wet zone expanded with depth (17.04 % to 25.86 %) while dry zone contracted (20.76 % to 14.99 %). The mean relative SM (dimensionless) and available SM storage, indicating relative soil saturation and vegetation water availability, were 0.84 and 32.14 cm, respectively. Considering spatial non-stationarity, environmental drivers shaping the SM security pattern were: slope in wet zone, vegetation in dry zone, and clay content, vegetation, and slope in transitional zone, with notable coupling among these factors. These findings help characterizing SM security and developing targeted SM management measures in the YRB and similar regions worldwide.
Metadata
| Item Type: | Article |
|---|---|
| Authors/Creators: |
|
| Copyright, Publisher and Additional Information: | This is an author produced version of an article published in CATENA made available via the University of Leeds Research Outputs Policy under the terms of the Creative Commons Attribution License (CC-BY), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | Deep soil layer; Soil moisture security; Spatial heterogeneity; Hyper-local GWR; Machine learning |
| Dates: |
|
| Institution: | The University of Leeds |
| Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Geography (Leeds) |
| Date Deposited: | 01 Apr 2026 14:20 |
| Last Modified: | 01 Apr 2026 14:22 |
| Published Version: | https://www.sciencedirect.com/science/article/pii/... |
| Status: | Published |
| Publisher: | Elsevier |
| Identification Number: | 10.1016/j.catena.2025.109516 |
| Related URLs: | |
| Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:238668 |
Download
Filename: deciphering_spatial_pattern final submitted draft.pdf
Licence: CC-BY 4.0

CORE (COnnecting REpositories)
CORE (COnnecting REpositories)