Shang et al. (2026) Spatiotemporal patterns of drought-driven mechanism transition in the mu us Sandy land: A multi-scale observational perspective based on vegetation dynamics

Identification

• Journal: Ecological Indicators
• Year: 2026
• Date: 2026-04-01
• Authors: Xue Shang, Zhaoquan He, Wenbo Chen, Xiukang Wang, Yingying Xing, Xiaoze Jin
• DOI: 10.1016/j.ecolind.2026.114832

Research Groups

• Office of Information Technology, Yan'an University, Yan'an, China
• School of Life Sciences, Yan'an University, Yan'an, China
• Key Laboratory of Applied Ecology of Universities in Shaanxi Province on the Loess Plateau, Yan'an University, Yan'an, China
• Shaanxi Key Laboratory of Research and Utilization of Resource Plants on the Loess Plateau, College of Life Sciences, Yan'an University, Yan'an, China
• Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, China
• Nanchang Key Laboratory of Landscape Process and Territorial Spatial Ecological Restoration, East China University of Technology, Nanchang, China

Short Summary

This study investigates a potential transition in vegetation drought-driving mechanisms in China's Mu Us Sandy Land from water-supply dominance to atmospheric-demand dominance. It found significant vegetation greening occurred concurrently with stable water supply and intensifying atmospheric aridity, with a critical shift towards atmospheric-demand dominance around 2012.

Objective

• To investigate a potential transition in vegetation drought-driving mechanisms within arid and semi-arid regions, specifically from water-supply dominance toward growing atmospheric-demand dominance.
• To characterize the spatiotemporal dynamics of this transition in China's Mu Us Sandy Land (2000−2022).
• To understand how vegetation greenness and key hydroclimatic variables have evolved spatiotemporally.
• To identify if and when a critical transition occurred in the dominant drought-driving mechanism for vegetation.
• To clarify the spatial differentiation pattern of drought-driving mechanisms and its regulation by land use and topography.

Study Configuration

• Spatial Scale: Mu Us Sandy Land, China (approximately 42,200 square kilometers). Data resampled to 500 meters spatial resolution.
• Temporal Scale: 2000–2022, with a focus on the growing season (May through September). Trend analysis for 2000–2021, moving window regression for 2000–2022.

Methodology and Data

• Models used:
  • Theil-Sen slope estimator
  • Mann-Kendall (M-K) test (for significance and change-point detection)
  • Moving window multiple linear regression (10-year window)
  • Spatial autocorrelation (Global Moran's I, Local Indicators of Spatial Association (LISA) using Anselin Local Moran's I)
  • Decision tree algorithm for coupling type zonation
  • Zonal Statistics
  • One-way analysis of variance (ANOVA) and Tukey's honestly significant difference (HSD) post-hoc test
  • Ordinary kriging for spatial interpolation
• Data sources:
  • Climate Drought Indices: Monthly Standardized Precipitation Index (SPI), Evaporative Demand Drought Index (EDDI), and Vapor Pressure Deficit (VPD) data from Zenodo platform (0.5° resolution).
  • Vegetation Index: MOD13Q1 Normalized Difference Vegetation Index (NDVI) data from NASA's LAADS (250 meters resolution).
  • Land Use Data: Annual 30 meters land cover dataset for China (Wuhan University, 2020 map used).
  • Topographic Data: Digital Elevation Model (DEM) from Geospatial Data Cloud platform (30 meters resolution).
  • Boundary Data: Vector boundary of the Mu Us Sandy Land from the National Ecosystem Science Data Center.
  • Stratified random sampling of 235 points for time series analysis.

Main Results

• The Mu Us Sandy Land exhibited significant vegetation greening from 2000 to 2021, with NDVI increasing at 95.7% of sites, showing a non-linear, stepwise growth pattern.
• This greening occurred concurrently with stable water supply (SPI trend slopes around zero) and intensifying atmospheric aridity (EDDI and VPD trend slopes distinctly positive), indicating a 'greening-while-drying' pattern.
• Moving window regression revealed a critical transition around 2012, where the dominance of atmospheric water demand (EDDI) strengthened, leading to a new regional steady-state by 2016, signifying a shift from "water supply limitation" to "water demand limitation".
• Spatial heterogeneity of drought-driving mechanisms was pronounced: atmospheric demand-driven zones (26.4% of the area) formed patchy clusters in central-eastern and southern low-elevation areas, while water supply-driven zones (11.2%) were dispersed in eastern and southern regions.
• Land use and topography jointly moderated this pattern: forests showed the highest sensitivity to atmospheric drought (55.6% atmosphere-driven), while grasslands were more resilient. Low-elevation areas were more vulnerable to atmospheric drought stress.
• The absence of "Coordinated Improvement" and "Water-Limited Degradation" types supports the mechanism transition, suggesting historical vegetation degradation was linked to atmospheric drought rather than precipitation decline.

Contributions

• Provides a dynamic, multi-indicator framework (integrating SPI, EDDI, VPD with moving-window regression) to capture non-stationary responses of vegetation to drought, which was previously lacking for the Mu Us Sandy Land.
• Systematically elucidates the spatiotemporal dynamics of a drought-driving mechanism transition from water supply to atmospheric demand dominance in an arid/semi-arid region undergoing ecological restoration.
• Quantifies the critical timing of this transition (circa 2012) and the emergence of a new steady-state (by 2016).
• Clarifies the spatial heterogeneity of drought-driving mechanisms and their regulation by land use and topography, offering insights for tailored adaptive management strategies.
• Offers scientific evidence for assessing the long-term sustainability of ecological restoration projects under changing hydroclimatic conditions.

Funding

• National Natural Science Foundation of China (32201902, 52169014)
• The Science Research Launch Project of Ph.D. (205040305)
• The Central Fund for Guiding Local Science and Technology Development (2024ZY-JCYJ-02-04)
• Shaanxi Provincial Department of Education Youth innovation team construction research project (22JP101, 21JP141, 23JP189)

Citation

@article{Shang2026Spatiotemporal,
  author = {Shang, Xue and He, Zhaoquan and Chen, Wenbo and Wang, Xiukang and Xing, Yingying and Jin, Xiaoze},
  title = {Spatiotemporal patterns of drought-driven mechanism transition in the mu us Sandy land: A multi-scale observational perspective based on vegetation dynamics},
  journal = {Ecological Indicators},
  year = {2026},
  doi = {10.1016/j.ecolind.2026.114832},
  url = {https://doi.org/10.1016/j.ecolind.2026.114832}
}