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Journal of Arid Land
Journal of Arid Land  2025, Vol. 17 Issue (1): 58-73    DOI: 10.1007/s40333-025-0002-1     CSTR: 32276.14.JAL.02500021
Research article     
Spatiotemporal variation and influencing factors of desertification sensitivity on the Qinghai-Xizang Plateau, China
PAN Meihui1,2,*(), CHEN Qing1, LI Chenlu1, LI Na1, GONG Yifu1
1College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China
2Key Laboratory of Resource Environment and Sustainable Development of Oasis, Northwest Normal University, Lanzhou 730070, China
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Abstract  

Due to irrational human activities and extreme climate, the Qinghai-Xizang Plateau, China, faces a serious threat of desertification. Desertification has a detrimental effect on the ecological environment and socioeconomic development. In this study, the desertification sensitivity index (DSI) model was established by integrating the spatial distance model and environmentally sensitive area index evaluation method, and then the model was used to quantitatively analyze the spatial and temporal characteristics of desertification sensitivity of the Qinghai-Xizang Plateau from 1990 to 2020. The results revealed that: (1) a general increasing tendency from southeast to northwest was identified in the spatial distribution of desertification sensitivity. The low-sensitivity areas were mostly concentrated in the Hengduan and Nyaingqêntanglha mountains and surrounding forest and meadow areas. The high-sensitivity areas were located mainly in the Kunlun and Altun mountains and surrounding decertified areas. The center of gravity of all types of desertification-sensitive areas moved to the northwest, and the desertification sensitivity showed a decreasing trend as a whole; (2) the area of highly sensitive desertification areas decreased by 8.37%, with extreme sensitivity being the largest change among the sensitivity types. The desertification sensitivity transfer was characterized by a greater shift to lower sensitivity levels (24.56%) than to higher levels (2.03%), which demonstrated a declining trend; (3) since 1990, the change in desertification sensitivity has been dominated by the stabilizing type I (29.30%), with the area of continuously increasing desertification sensitivity accounting for only 1.10%, indicating that the management of desertification has achieved positive results in recent years; and (4) natural factors have had a more significant impact on desertification sensitivity on the Xizang Plateau, whereas socioeconomic factors affected only localized areas. The main factors influencing desertification sensitivity were vegetation drought tolerance and aridity index. Studying spatiotemporal variations in desertification sensitivity and its influencing factors can provide a scientific foundation for developing strategies to control desertification on the Qinghai-Xizang Plateau. Dividing different desertification-sensitive areas on the basis of these patterns of change can facilitate the formulation of more targeted management and protection measures, contributing to ecological construction and sustainable economic development in the area.

Key words： desertification sensitivity      geodetector      gravity center transfer model      spatiotemporal change      Qinghai- Xizang Plateau     
Received: 14 May 2024      Published: 31 January 2025
Corresponding Authors: *PAN Meihui (E-mail: panmh@nwnu.edu.cn)
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PAN Meihui, CHEN Qing, LI Chenlu, LI Na, GONG Yifu. Spatiotemporal variation and influencing factors of desertification sensitivity on the Qinghai-Xizang Plateau, China. Journal of Arid Land, 2025, 17(1): 58-73.

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http://jal.xjegi.com/10.1007/s40333-025-0002-1     OR     http://jal.xjegi.com/Y2025/V17/I1/58

Background index Formula Index composition Index processing Index description
Vegetation background index (VBI) $\text{VBI=}\sqrt{\underset{i\text{=1}}{\overset{n}{\mathop \sum }}\,{{\text{(}{{V}_{i}}-{{V}_{i\text{-low}}}\text{)}}^{\text{2}}}}$ Vegetation drought tolerance + Vegetation drought tolerance was determined by quantifying the types of vegetation (Li et al., 2018).
NDVI -
Climatic background index (CBI) $\text{CBI=}\sqrt{\underset{i\text{=1}}{\overset{n}{\mathop \sum }}\,{{\text{(}{{C}_{i}}-{{C}_{i\text{-low}}}\text{)}}^{\text{2}}}}$ Annual average ground temperature (°C) + Aridity index=$P\text{/(}{{t}^{\text{0}}}\text{+10}$), where P is the mean annual precipitation (mm), and t0 is the annual mean temperature (°C) (Zhang, 1994).
Annual average wind speed (m/s) +
Aridity index -
Hydrological background index (HBI) $\text{HBI=}\sqrt{\underset{i\text{=1}}{\overset{n}{\mathop \sum }}\,{{\text{(}{{H}_{i}}-{{H}_{i\text{-low}}}\text{)}}^{\text{2}}}}$ Distance to glaciers (km) + The distances to glaciers, rivers, lakes, and reservoirs were calculated using the Euclidean distance tool in ArcGIS software (Wei et al., 2021).
Distance to rivers (km) +
Distance to lakes and reservoirs (km) +
Soil background index (SBI) $\text{SBI=}\sqrt{\underset{i\text{=1}}{\overset{n}{\mathop \sum }}\,{{\text{(}{{S}_{i}}-{{S}_{i}}_{\text{-low}}\text{)}}^{\text{2}}}}$ Soil organic matter content (%) - Soil organic matter content=soil organic carbon/0.58 (Wei et al., 2021).
Soil erosion intensity +
Soil sand content (%) +
Soil depth (cm) +
Topographic background index (TBI) $\text{TBI=}\sqrt{\underset{i\text{=1}}{\overset{n}{\mathop \sum }}\,{{\text{(}{{T}_{i}}-{{T}_{i\text{-low}}}\text{)}}^{\text{2}}}}$ Slope (°) - Aspect is assigned values as follows: flat=1; west, northwest, and north=2; northeast and east=3; southeast, south, and southwest=4 (Xu et al., 2019).
Altitude (m) -
Aspect +
Table 1 Construction methods for various indices
Fig. 1 Spatial distributions of the five background indices on the Qinghai-Xizang Plateau in 2020. (a), TBI (topographic background index); (b), SBI, (soil background index); (c), VBI (vegetation background index); (d), CBI (climatic background index); (e), HBI (hydrological background index). The abbreviations are the same in the following figures.
Fig. 2 Average values of the five background indices from 1990 to 2020. High values of the background index indicate poor quality.
Fig. 3 Spatial distribution of desertification sensitivity index (DSI) on the Qinghai-Xizang Plateau from 1990 to 2020. (a), 1990; (b), 2000; (c), 2010; (d), 2020.
Fig. 4 Gravity center distribution of desertification sensitivity on the Qinghai-Xizang Plateau from 1990 to 2020. (a), overall distribution of desertification sensitivity; (b), extreme sensitivity; (c), severe sensitivity; (d), moderate sensitivity; (e), mild sensitivity; (f), nonsensitivity. The map is provided by the National Tibetan Plateau Data Center (http://data.tpdc.ac.cn/) and the boundary of the map is not modified.
Desertification sensitivity type Area proportion (%) Dynamic degree (%)
1990 2000 2010 2020 1990-2020
Nonsensitivity 8.74 19.66 16.55 10.88 0.66
Mild sensitivity 22.91 29.88 30.42 28.24 0.63
Moderate sensitivity 30.35 25.50 27.36 31.26 0.10
Severe sensitivity 24.04 16.30 17.05 20.29 -0.62
Extreme sensitivity 13.95 8.67 8.62 9.33 -1.65
Table 2 Area proportion and dynamic degree of desertification sensitivity on the Qinghai-Xizang Plateau from 1990 to 2020
Desertification sensitivity type Nonsensitivity Mild sensitivity Moderate sensitivity Severe sensitivity Extreme sensitivity
(%)
Nonsensitivity 92.10 7.89 0.00 0.00 0.00
Mild sensitivity 12.28 84.18 3.54 0.00 0.00
Moderate sensitivity 0.05 27.11 71.32 1.51 0.00
Severe sensitivity 0.00 0.12 36.56 63.01 0.31
Extreme sensitivity 0.00 0.00 0.03 33.56 66.41
Table 3 Transfer probability matrix of desertification sensitivity types on the Qinghai-Xizang Plateau from 1990 to 2020
Change type Meaning
Stabilizing type I Sensitivity was perennially non-sensitive, mildly sensitive, and moderately sensitive
Decreasing type Desertification sensitivity continued to decrease
Fluctuating type Sensitivity fluctuated
Increasing type Desertification sensitivity continued to increase
Stabilizing type II Sensitivity was perennially severe and extreme
Table 4 Classification of change types in desertification sensitivity
Fig. 5 Spatial distribution of change type in desertification sensitivity on the Qinghai-Xizang Plateau
Driving factor 1990 2000 2010 2020
VBI 0.4680 0.5025 0.5204 0.5036
NDVI 0.1923 0.2497 0.1945 0.1864
VDT 0.4672 0.5025 0.5193 0.5036
SBI 0.1325 0.1760 0.1760 0.1588
SOM 0.0887 0.0993 0.0962 0.0946
SE 0.0900 0.1073 0.1096 0.1034
SS 0.1282 0.1596 0.1593 0.1519
SD 0.0602 0.0710 0.0962 0.0726
TBI 0.1022 0.1033 0.1073 0.1163
AL 0.0736 0.0883 0.0822 0.0735
SL 0.1089 0.0984 0.1087 0.1083
AS 0.0411 0.0452 0.0464 0.0543
CBI 0.4704 0.4271 0.3843 0.2599
AI 0.4733 0.4755 0.4430 0.3436
WS 0.0719 0.0832 0.0900 0.0663
GT 0.0648 0.0817 0.0616 0.0676
HBI 0.1361 0.1644 0.1620 0.1633
DS 0.0097 0.0175 0.0199 0.0257
DR 0.1752 0.1714 0.1787 0.1664
DL 0.0202 0.0171 0.0137 0.0340
GDP - 0.0002 0.0000 0.0002
POP - 0.0005 0.0000 0.0005
Table 5 q-values of interaction detector
Fig. 6 Results of the interaction detector on the Qinghai-Xizang Plateau from 1990 to 2020. (a), 1990; (b), 2000; (c), 2010; (d), 2020. * is two-factor enhanced and the rest are nonlinear enhanced.
[1]   Chen F H, Wu S H, Cui P, et al. 2021. Progress and prospects of applied research on physical geography and the living environment in China over the past 70 years (1949-2019). Journal of Geographical Sciences, 31: 3-45.
doi: 10.1007/s11442-021-1831-2
[2]   Chen T, Song Y G, Li Y. 2016. Wind-blown sand activities in the Qaidam Basin during the Last Glacial Maximum and the Holocene Megathermal Period. Arid Zone Research, 33(4): 877-883. (in Chinese)
[3]   Chen W X, Li J F, Zeng J, et al. 2019. Spatial heterogeneity and formation mechanism of eco-environmental effect of land use change in China. Geographical Research, 38(9): 2173-2187. (in Chinese)
[4]   Ding J C, Chen Y Z, Wang X Q, et al. 2020. Land degradation sensitivity assessment and convergence analysis in Korla of Xinjiang, China. Journal of Arid Land, 12(4): 594-608.
doi: 10.1007/s40333-020-0057-y
[5]   Dong Y X. 2001. Study on the control of land desertification and its project construction in Tibet Autonomous Region. Journal of Natural Resources, 16(2): 145-151. (in Chinese)
doi: 10.11849/zrzyxb.2001.02.008
[6]   Fu X, Shen Y, Dong R C, et al. 2016. Analysis of urbanization based on center-of-gravity movement and characteristics in Songhua River basin of China and its southern source sub-basin between 1990 and 2010. Chinese Geographical Science, 26(1): 117-128.
[7]   Ganjurjav H, Gao Q Z, Gornish E S, et al. 2016. Differential response of alpine steppe and alpine meadow to climate warming in the central Qinghai-Tibetan Plateau. Agricultural and Forest Meteorology, 223: 233-240.
[8]   Guo B, Zang W Q, Yang F, et al. 2020. Spatial and temporal change patterns of net primary productivity and its response to climate change in the Qinghai-Tibet Plateau of China from 2000 to 2015. Journal of Arid Land, 12(1): 1-17.
doi: 10.1007/s40333-019-0070-1
[9]   Guo J S, Sang H Y, Zhai L. 2023. Spatiotemporal variations and driving factors of vegetation coverage on the Qinghai-Tibet Plateau. Chinese Journal of Ecology, 42(11): 2665-2674. (in Chinese)
doi: 10.13292/j.1000-4890.202311.020
[10]   Hu G Y, Dong Z B, Lu J F, et al. 2011. Spatial and temporal changes of desertification land and its influence factors in source region of the Yellow River from 1975 to 2005. Journal of Desert Research, 31(5): 1079-1086. (in Chinese)
[11]   Huang K, Zhang Y J, Zhu J T, et al. 2016. The influences of climate change and human activities on vegetation dynamics in the Qinghai-Tibet Plateau. Remote Sensing, 8(10): 876, doi: 10.3390/rs8100876.
[12]   Jana A, Jat M K, Saxena A, et al. 2022. Prediction of land use land cover changes of a river basin using the CA-Markov model. Geocarto International, 37(26): 14127-14147.
[13]   Jiang L, Jiapaer G, Bao A M, et al. 2019. Monitoring the long-term desertification process and assessing the relative roles of its drivers in Central Asia. Ecological Indicators, 104: 195-208.
[14]   Jiang X H, Han L, Bai Z F, et al. 2023. Analysis of the temporal and spatial evolution pattern and trend of desertification sensitivity in the Inner Mongolia Autonomous Region. Acta Ecologica Sinica, 43(1): 364-378. (in Chinese)
[15]   Karamesouti M, Panagos P, Kosmas C. 2018. Model-based spatio-temporal analysis of land desertification risk in Greece. Catena, 167: 266-275.
[16]   Kosmas C, Kirkby M, Geeson N. 1999. The MEDALUS Project: Mediterranean Desertification and Land Use. Manual on Key Indicators of Desertification Mapping Environmental Sensitive Areas to Desertification. Luxembourg: Office for Official Publications of the European Communities.
[17]   Li D J, Xu D Y, Ding X, et al. 2018. Changes of wind erosion climatic erosivity and vegetation dynamics response in northern China from 1981 to 2010. Research of Soil and Water Conservation, 25(2): 15-20. (in Chinese)
[18]   Li Q, Zhang C L, Shen Y P, et al. 2016. Quantitative assessment of the relative roles of climate change and human activities in desertification processes on the Qinghai-Tibet Plateau based on net primary productivity. Catena, 147: 789-796.
[19]   Li S, Dong G R, Shen J Y, et al. 1999. The formation mechanism and development pattern of aeolian landforms in the Yarlung Zangbo River Valley. Science in China (Series D), 29(1): 88-96. (in Chinese)
[20]   Li Y, Zhou J, Wu X. 2017. Effects of the construction of Qinghai-Tibet railway on the vegetation ecosystem and eco-resilience. Geographical Research, 36(11): 2129-2140.
[21]   Ling Z Y, Jin J H, Wu D, et al. 2019. Aeolian sediments and their paleoenvironmental implication in the Yarlung Zangbo catchment (southern Tibet, China) since MIS3. Acta Geographica Sinica, 74(11): 2385-2400. (in Chinese)
doi: 10.11821/dlxb201911014
[22]   Liu K, Xu W H, Ouyang Z Y, et al. 2002. GIS-based assessment on sensitivity to land desertification in Gansu Province. Bulletin of Soil and Water Conservation, 22(5): 29-31, 35. (in Chinese)
[23]   Liu X, Song L, Jin Y, et al. 2013. History of aeolian deposits in Tibetan Plateau and climate change over Holocene. Journal of Arid Land Resource and Environment, 27: 41-47. (in Chinese)
[24]   Lu H Y, Zhao C F, Mason J, et al. 2010. Holocene climatic changes revealed by aeolian deposits from the Qinghai Lake area (northeastern Qinghai-Tibetan Plateau) and possible forcing mechanisms. The Holocene, 21(2): 297-304.
[25]   Lu R R, Dai E F, Wu C S. 2023. Spatial and temporal evolution characteristics and driving factors of soil conservation services on the Qinghai-Tibet Plateau. Catena, 221: 106766, doi: 10.1016/j.catena.2022.106766.
[26]   Mesquita D P P, Gomes J P P, Junior A H S, et al. 2017. Euclidean distance estimation in incomplete datasets. Neurocomputing, 248: 11-18.
[27]   Prăvălie R, Săvulescu I, Patriche C, et al. 2017. Spatial assessment of land degradation sensitive areas in southwestern Romania using modified MEDALUS method. Catena, 153: 114-130.
[28]   Ren Y, Zhang B, Chen X D, et al. 2024. Analysis of spatial-temporal patterns and driving mechanisms of land desertification in China. Science of the Total Environment, 909: 168429, doi: 10.1016/j.scitotenv.2023.168429.
[29]   Saldarriaga J F, Hua Y. 2019. A gravity model analysis of forced displacement in Colombia. Cities, 95: 102407, doi: 10.1016/j.cities.2019.102407.
[30]   Salvati L, Bajocco S. 2011. Land sensitivity to desertification across Italy: Past, present, and future. Applied Geography, 31(1): 223-231.
[31]   Shao W Y, Wang Q Z, Guan Q Y, et al. 2023. Environmental sensitivity assessment of land desertification in the Hexi Corridor, China. Catena, 220: 106728, doi: 10.1016/j.catena.2022.106728.
[32]   Shen Y L. 2023. Study on influencing factors of spatial distribution of aeolian desertification in Qinghai-Tibetan Plateau. MSc Thesis. Zhejiang: Zhejiang Normal University. (in Chinese)
[33]   Smit M, Malan P, Smit N, et al. 2024. Response of herbaceous vegetation in the southern Kalahari following a prolonged drought. Journal of Arid Environments, 222: 105157, doi: 10.1016/j.jaridenv.2024.105157.
[34]   Sterk G, Boardman J, Verdoodt A. 2016. Desertification: History, causes and options for its control. Land Degradation & Development, 27(8): 1783-1787.
[35]   Tang Q H, Lan C, Su F G, et al. 2019. Streamflow change on the Qinghai-Tibet Plateau and its impacts. Chinese Science Bulletin, 64(27): 2807-2821. (in Chinese)
[36]   Teng Y M, Zhan J Y, Liu W, et al. 2021. Spatiotemporal dynamics and drivers of wind erosion on the Qinghai-Tibet Plateau, China. Ecological Indicators, 123: 107340, doi: 10.1016/j.ecolind.2021.107340.
[37]   Tian L, Qiu S J, Peng J, et al. 2018. Desertification sensitivity evaluation in Inner Mongolia Autonomous Region based on PSR framework. Progress in Geography, 37(12): 1682-1692. (in Chinese)
doi: 10.18306/dlkxjz.2018.12.010
[38]   Tu Z F, Li M X, Sun T. 2016. The status and trend analysis of desertification and sandification. Forest Resources Management, 45(1): 1-5. (in Chinese)
[39]   UNCCD (United Nations Convention to Combat Desertification). 1994. Intergovernmental Negotiating Committee for a Convention to Combat Desertification, Elaboration of an International Convention to Combat Desertification in Countries Experiencing Serious Drought and/or Desertification, Particularly in Africa. New York: United Nations.
[40]   Wang H B, Ma M G, Geng L Y. 2015. Monitoring the recent trend of aeolian desertification using Landsat TM and Landsat 8 imagery on the north-east Qinghai-Tibet Plateau in the Qinghai Lake basin. Natural Hazards, 79: 1753-1772.
[41]   Wang J F, Li X H, Christakos G, et al. 2010. Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun Region, China. International Journal of Geographical Information Science, 24(1): 107-127.
[42]   Wang J F, Xu C D. 2017. Geodetector: Principle and prospective. Acta Geographica Sinica, 72(1): 116-134. (in Chinese)
doi: 10.11821/dlxb201701010
[43]   Wang T. 2004. Progress in sandy desertification research of China. Journal of Geographical Sciences, 14: 387-400.
[44]   Wang Y H, Zhang L B, Guo Y, et al. 2014. Analysis of spatiotemporal pattern and tendency of land desertification sensitivity in six provinces of China. Research of Soil and Water Conservation, 21(5): 132-137. (in Chinese)
[45]   Wei W, Guo Z C, Xie B B, et al. 2019. Spatiotemporal evolution of environment based on integrated remote sensing indexes in arid inland river basin in Northwest China. Environmental Science and Pollution Research, 26: 13062-13084.
doi: 10.1007/s11356-019-04741-x
[46]   Wei W, Guo Z C, Shi P J, et al. 2021. Spatiotemporal changes of land desertification sensitivity in northwest China from 2000 to 2017. Journal of Geographical Sciences, 31: 46-68.
doi: 10.1007/s11442-021-1832-1
[47]   Wu T H, Ma W S, Wu X D, et al. 2022. Weakening of carbon sink on the Qinghai-Tibet Plateau. Geoderma, 412: 115707, doi: 10.1016/j.geoderma.2022.115707.
[48]   Xu D Y, You X G, Xia C L. 2019. Assessing the spatial-temporal pattern and evolution of areas sensitive to land desertification in North China. Ecological Indicators, 97: 150-158.
[49]   Yang L, Zhao G, Mu X, et al. 2023. Integrated assessments of land degradation on the Qinghai-Tibet plateau. Ecological Indicators, 147: 109945, doi:10.1016/j.ecolind.2023.109945.
[50]   Yao T D, Thompson L G, Mosbrugger V, et al. 2012. Third pole environment (TPE). Environmental Development, 3: 52-64.
[51]   Zhang C L, Li Q, Shen Y P, et al. 2018. Monitoring of aeolian desertification on the Qinghai-Tibet Plateau from the 1970s to 2015 using Landsat images. Science of the Total Environment, 619-620: 1648-1659.
[52]   Zhang X S. 1994. Principles and optimal models for development of Maowusu sandy grassland. Chinese Journal of Plant Ecology, 18(1): 1-16. (in Chinese)
[53]   Zhao Z Y, Zhang Y T, Li T, et al. 2022. Comprehensive evaluation and spatio-temporal variations of ecological sensitivity on the Qinghai-Tibet Plateau based on spatial distance index. Acta Ecologica Sinica, 42(18): 7403-7416. (in Chinese)
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