Spatiotemporal dynamics and driving mechanisms of grassland desertification in the Qilian Mountain National Park, northeast of the Tibetan Plateau

Grasslands are important terrestrial ecosystems that have suffered severe desertification. However, the driving factors and mechanisms of grassland desertification remain unclear. Therefore, elucidating the climatic conditions of past grassland desertification, monitoring current desertification dynamics, and identifying key factors are crucial for combating desertification. Here, the probability density distribution of 208 optically stimulated luminescence ages derived from multiple fixed dunes was analyzed to identify the cluster periods of past grassland desertification. We then compared these periods with reconstructed paleoclimatic indicators to infer the climatic conditions associated with these desertification episodes in the Qilian Mountain National Park (QLMNP). Random forest was employed to map spatial patterns and monitor desertification dynamics from 1986 to 2022. Geodetector and residual analysis were utilized to quantify contributions of key factors influencing desertification. Relatively cold and dry climatic conditions enhanced aeolian activity and accelerated the transition of grassland landscape from alpine steppe to desert, triggering grassland desertification in the western QLMNP during 5–5.18 ka. The combination of intensified human activities and a relatively cold–dry climate promoted desertification in the central QLMNP during 1.28–0.74 ka. Current grassland desertification covers ∼15.2% of the QLMNP, with interactions between climatic and anthropogenic factors accounting for over 50% of the desertification. Less precipitation, high temperatures, and increased population density are the primary drivers of current grassland desertification. This study proposes an integrated framework of historical process-modern dynamics-driver attribution to track the transition of grassland landscape and broaden our understanding of the mechanisms governing grassland desertification. • Cold and dry conditions triggered clustered desertification in 5–5.18 ka • Climatic and human impacts caused desertification development after 1.5 ka • Current grassland desertification areas cover ∼15.2% of the QLMNP • Climate factors and human activities contribute 58% to grassland desertification • Global warming has enhanced the fragmentation of desertification patches

Authors

Institutions

Publication Details

Journal
Ecological Indicators
Published
2026-04-22
DOI
https://doi.org/10.1016/j.ecolind.2026.114886
Primary Topic
Geology and Paleoclimatology Research
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Spatiotemporal dynamics and driving mechanisms of grassland desertification in the Qilian Mountain National Park, northeast of the Tibetan Plateau

N Wang, Hang Zhao, Xiaoyang Zhao, Liqiong Bai et al.
Ecological Indicators
Geology and Paleoclimatology Research
article

Spatiotemporal dynamics and driving mechanisms of grassland desertification in the Qilian Mountain National Park, northeast of the Tibetan Plateau

N Wang, Hang Zhao, Xiaoyang Zhao, Liqiong Bai, Nana Zhai, Hongyi Cheng
article en

Abstract

Grasslands are important terrestrial ecosystems that have suffered severe desertification. However, the driving factors and mechanisms of grassland desertification remain unclear. Therefore, elucidating the climatic conditions of past grassland desertification, monitoring current desertification dynamics, and identifying key factors are crucial for combating desertification. Here, the probability density distribution of 208 optically stimulated luminescence ages derived from multiple fixed dunes was analyzed to identify the cluster periods of past grassland desertification. We then compared these periods with reconstructed paleoclimatic indicators to infer the climatic conditions associated with these desertification episodes in the Qilian Mountain National Park (QLMNP). Random forest was employed to map spatial patterns and monitor desertification dynamics from 1986 to 2022. Geodetector and residual analysis were utilized to quantify contributions of key factors influencing desertification. Relatively cold and dry climatic conditions enhanced aeolian activity and accelerated the transition of grassland landscape from alpine steppe to desert, triggering grassland desertification in the western QLMNP during 5–5.18 ka. The combination of intensified human activities and a relatively cold–dry climate promoted desertification in the central QLMNP during 1.28–0.74 ka. Current grassland desertification covers ∼15.2% of the QLMNP, with interactions between climatic and anthropogenic factors accounting for over 50% of the desertification. Less precipitation, high temperatures, and increased population density are the primary drivers of current grassland desertification. This study proposes an integrated framework of historical process-modern dynamics-driver attribution to track the transition of grassland landscape and broaden our understanding of the mechanisms governing grassland desertification. • Cold and dry conditions triggered clustered desertification in 5–5.18 ka • Climatic and human impacts caused desertification development after 1.5 ka • Current grassland desertification areas cover ∼15.2% of the QLMNP • Climate factors and human activities contribute 58% to grassland desertification • Global warming has enhanced the fragmentation of desertification patches

Ecological IndicatorsVol. 186
Lanzhou University (CN)
National Natural Science Foundation of China
Climate action
Openalex Percentile: Top 11%
Geology and Paleoclimatology Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.