Spatiotemporal patterns of drought-driven mechanism transition in the mu us Sandy land: A multi-scale observational perspective based on vegetation dynamics

This study investigates a potential transition in vegetation drought-driving mechanisms within arid and semi-arid regions—from traditional water-supply dominance toward growing atmospheric-demand dominance. Focusing on China's Mu Us Sandy Land (2000−2022), we characterise this transition's spatiotemporal dynamics using multi-source remote sensing data integrated with Theil-Sen trend analysis, Mann-Kendall change detection, moving window regression, and spatial autocorrelation. The key findings are as follows: (1) Significant vegetation greening occurred (NDVI increased at 95.7% of sites), concurrently with stable water supply (SPI) and intensifying atmospheric aridity. This ‘greening-while-drying’ pattern challenges traditional water-supply-centric models. (2) Moving window regression identified a critical transition circa 2012, during which the dominance of the evaporative demand drought index (EDDI) strengthened markedly. By 2016, a new regional steady-state had emerged, signifying a gradual transition from “water supply limitation” to “water demand limitation”. (3) Spatial differentiation was pronounced: atmospheric demand-driven zones (26.4%) 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. (4) Land use and topography jointly moderated this pattern. Forests exhibited the highest sensitivity to atmospheric drought (55.6% atmosphere-driven), in contrast to the more resilient grasslands. Low-elevation areas proved more vulnerable to atmospheric drought stress. The absence of “Coordinated Improvement” and “Water-Limited Degradation” types provides supporting evidence for the mechanism transition, corroborating that historical vegetation degradation is linked to atmospheric drought rather than precipitation decline. These findings necessitate a strategic reorientation in arid-land management: moving from pursuing vegetation coverage alone toward building climate-resilient ecosystems adapted to the “demand-dominant” paradigm. • Reveals a “greening while drying” paradox, challenging traditional hydrological understanding in drylands. • Identifies a shift from water supply-limited to atmospheric demand-dominated drought driving in 2012. • Elucidates spatial heterogeneity jointly regulated by land use and topographic factors. • Demonstrates that future degradation risk stems mainly from enhanced atmospheric drought.

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Publication Details

Journal
Ecological Indicators
Published
2026-04-01
DOI
https://doi.org/10.1016/j.ecolind.2026.114832
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
0.00

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article

Spatiotemporal patterns of drought-driven mechanism transition in the mu us Sandy land: A multi-scale observational perspective based on vegetation dynamics

Xiaoze Jin, Zhaoquan He, Yingying Xing, Xiukang Wang et al.
Ecological Indicators
Remote Sensing in Agriculture
article

Spatiotemporal patterns of drought-driven mechanism transition in the mu us Sandy land: A multi-scale observational perspective based on vegetation dynamics

Xiaoze Jin, Zhaoquan He, Yingying Xing, Xiukang Wang, Xue Shang, Wenbo Chen
article en

Abstract

This study investigates a potential transition in vegetation drought-driving mechanisms within arid and semi-arid regions—from traditional water-supply dominance toward growing atmospheric-demand dominance. Focusing on China's Mu Us Sandy Land (2000−2022), we characterise this transition's spatiotemporal dynamics using multi-source remote sensing data integrated with Theil-Sen trend analysis, Mann-Kendall change detection, moving window regression, and spatial autocorrelation. The key findings are as follows: (1) Significant vegetation greening occurred (NDVI increased at 95.7% of sites), concurrently with stable water supply (SPI) and intensifying atmospheric aridity. This ‘greening-while-drying’ pattern challenges traditional water-supply-centric models. (2) Moving window regression identified a critical transition circa 2012, during which the dominance of the evaporative demand drought index (EDDI) strengthened markedly. By 2016, a new regional steady-state had emerged, signifying a gradual transition from “water supply limitation” to “water demand limitation”. (3) Spatial differentiation was pronounced: atmospheric demand-driven zones (26.4%) 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. (4) Land use and topography jointly moderated this pattern. Forests exhibited the highest sensitivity to atmospheric drought (55.6% atmosphere-driven), in contrast to the more resilient grasslands. Low-elevation areas proved more vulnerable to atmospheric drought stress. The absence of “Coordinated Improvement” and “Water-Limited Degradation” types provides supporting evidence for the mechanism transition, corroborating that historical vegetation degradation is linked to atmospheric drought rather than precipitation decline. These findings necessitate a strategic reorientation in arid-land management: moving from pursuing vegetation coverage alone toward building climate-resilient ecosystems adapted to the “demand-dominant” paradigm. • Reveals a “greening while drying” paradox, challenging traditional hydrological understanding in drylands. • Identifies a shift from water supply-limited to atmospheric demand-dominated drought driving in 2012. • Elucidates spatial heterogeneity jointly regulated by land use and topographic factors. • Demonstrates that future degradation risk stems mainly from enhanced atmospheric drought.

Ecological IndicatorsVol. 185
Chinese Academy of Sciences (CN), Yan'an University (CN), Northwest Institute of Eco-Environment and Resources (CN), East China University of Technology (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 8%
Remote Sensing in Agriculture
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