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Journal of Arid Land
Journal of Arid Land  2025, Vol. 17 Issue (9): 1215-1233    DOI: 10.1007/s40333-025-0087-6     CSTR: 32276.14.JAL.02500876
Research article     
Response of vegetation to climate change along the elevation gradient in High Mountain Asia
HE Bing1,2,3, LI Ying1,2, GAO Fan1,2,*(), XU Hailiang4, WU Bin1,2, YANG Pengnian1,2, BAN Jingya1,2, LIU Zeyi3, LIU Kun1,2, HAN Fanghong1,2, MA Zhenghu1,2, WANG Lu5
1College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, China
2Xinjiang Key Laboratory of Hydraulic Engineering Security and Water Disasters Prevention, Urumqi 830052, China
3Xinjiang Water Conservancy Development Investment (Group) Co., Ltd., Urumqi 830063, China
4Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
5State Key Laboratory of Eco-hydraulics in Northwest Arid Region of China, Xi'an University of Technology, Xi'an 710048, China
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Abstract  

Climate change in High Mountain Asia (HMA) is characterized by elevation dependence, which results in vertical zoning of vegetation distribution. However, few studies have been conducted on the distribution patterns of vegetation, the response of vegetation to climate change, and the key climatic control factors of vegetation along the elevation gradient in this region. In this study, based on the Normalized Difference Vegetation index (NDVI), we investigated the evolution pattern of vegetation in HMA during 2001-2020 using linear trend and Bayesian Estimator of Abrupt change, Seasonality, and Trend (BEAST) methods. Pearson correlation analysis and partial correlation analysis were used to explore the response relationship between vegetation and climatic factors along the elevation gradient. Path analysis was employed to quantitatively reveal the dominant climatic factors affecting vegetation distribution along the elevation gradient. The results showed that NDVI in HMA increased at a rate of 0.011/10a from 2001 to 2020, and the rate of increase abruptly slowed down after 2017. NDVI showed a fluctuating increase at elevation zones 1-2 (<2500 m) and then decreased at elevation zones 3-9 (2500-6000 m) with the increase of elevation. NDVI was most sensitive to precipitation and temperature at a 1-month lag. With the increase of elevation, the positive response relationship of NDVI with precipitation gradually weakened, while that of NDVI with temperature was the opposite. The total effect coefficient of precipitation (0.95) on vegetation was larger than that of temperature (0.87), indicating that precipitation is the dominant control factor affecting vegetation growth. Spacially, vegetation growth is jointly influenced by precipitation and temperature, but the influence of precipitation on vegetation growth is dominant at each elevation zone. The results of this study contribute to understanding how the elevation gradient effect influences the response of vegetation to climate change in alpine ecosystems.

Key words： vegetation growth      climate change      elevation gradient      Normalized Difference Vegetation index (NDVI)      path analysis      High Mountain Asia     
Received: 30 December 2024      Published: 30 September 2025
Corresponding Authors: *GAO Fan (E-mail: gutongfan0202@163.com)
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HE Bing
LI Ying
GAO Fan
XU Hailiang
WU Bin
YANG Pengnian
BAN Jingya
LIU Zeyi
LIU Kun
HAN Fanghong
MA Zhenghu
WANG Lu
Cite this article:

HE Bing, LI Ying, GAO Fan, XU Hailiang, WU Bin, YANG Pengnian, BAN Jingya, LIU Zeyi, LIU Kun, HAN Fanghong, MA Zhenghu, WANG Lu. Response of vegetation to climate change along the elevation gradient in High Mountain Asia. Journal of Arid Land, 2025, 17(9): 1215-1233.

URL:

http://jal.xjegi.com/10.1007/s40333-025-0087-6     OR     http://jal.xjegi.com/Y2025/V17/I9/1215

Fig. 1 Overview of High Mountain Asia (HMA) and spatial distribution of 14 elevation zones
Fig. 2 Variation characteristics of precipitation (a) and temperature (b) along the elevation gradient in HMA from 2001 to 2020
Fig. 3 Spatial distribution of various vegetation types in 2001 (a) and 2020 (b) in HMA
Fig. 4 Flowchart of the research method. MODIS, Moderate Resolution Imaging Spectroradiometer; NDVI, Normalized Difference Vegetation Index; ERA5, the fifth generation of European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalyses of the global climate; ESA, European Space Agency; DEM, digital elevation model; BEAST, Bayesian Estimator of Abrupt Change, Seasonality, and Trend.
Fig. 5 Temporal variations of the monthly NDVI (a) and the average NDVI during the growing season (b), as well as detection of trends and mutation points of the average NDVI during the growing season by the BEAST method (c) from 2001 to 2020. In Figure 5c, trend represents the time series trend; Pr(tcp) represents the change in the probability of a sudden change in the trend; slpSign represents the harmonic order of the trend component; and error represents the residual. The contents related to NDVI in the subsequent figures refer specifically to the NDVI during the growing season.
Fig. 6 Spatial variations of multi-year average NDVI during 2001-2020 (a) and trends in the NDVI during 2001-2017 (b) and 2017-2020 (c)
Fig. 7 Variation characteristics of multi-year average NDVI during 2001-2020 (a), seasonal NDVI from 2001 to 2020 (b), and proportions of NDVI-increasing and NDVI-decreasing pixels before 2017 (c) and after 2017 (d) along the elevation gradient
Fig. 8 Correlation coefficients of NDVI with precipitation (a) and temperature (b) at various time scales along the elevation gradient
Fig. 9 Spatial variations of partial correlation coefficients and area proportion statistics of positive and negative partial correlation coefficients between NDVI and precipitation (a and b) and between NDVI and temperature (c and d) during 2001-2020
Fig. 10 Path analysis results of the influence of precipitation and temperature on NDVI in HMA. (a), path analysis relationship diagram; (b), total effects of precipitation and temperature on NDVI at each elevation zone; (c), spatial distribution of the dominant control factors affecting NDVI; (d), area proportions of dominant control factors affecting NDVI at each elevation zone.
Vegetation type 2001 2020 Area change (km2)
Area (km2) Proportion (%) Area (km2) Proportion (%)
Cropland 419,190.03 8.0 423,664.02 8.1 4473.99
Grassland 2,833,136.82 54.2 2,805,437.25 53.6 -27,699.57
Broad-leaved forest 133,498.53 2.6 137,207.07 2.6 3708.54
Needle-leaf forest 342,392.94 6.5 352,277.28 6.7 9884.34
Shrubland 71,509.23 1.4 72,494.82 1.4 985.59
Lichens and mosses 348.30 0.0 348.30 0.0 0.00
Bare area 1,210,435.92 23.1 1,206,603.45 23.1 -3832.47
Wetland 10,097.46 0.2 10,223.46 0.2 126
Urban area 3398.94 0.1 12,751.65 0.2 9352.71
Water body 65,907.81 1.3 68,908.68 1.3 3000.87
Snow and ice 140,828.94 2.7 140,828.94 2.7 0.00
Table 1 Area changes of vegetation types between 2001 and 2020
Fig. 11 Transformation between vegetation types from 2001 to 2020 (a) and area proportions of vegetation types at each elevation zone in 2001 (b) and 2020 (c)
Fig. 12 Proportions of dominant control factors affecting NDVI for different vegetation types
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