Sciences in Cold and Arid Regions ›› 2018, Vol. 10 ›› Issue (6): 482-492.doi: 10.3724/SP.J.1226.2018.00482

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Contrasting vegetation changes in dry and humid regions of the Tibetan Plateau over recent decades

RuiQing Li1,2,YanHong Gao1,*(),DeLiang Chen3,4,YongXin Zhang5,SuoSuo Li1   

  1. 1 Key Laboratory of Land-surface Process and Climate Change in Cold and Arid Regions, Northwest Institute of Eco-environment and Resources, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
    2 Inner Mongolia Autonomous Regional Meteorological Observatory, Hohhot, Inner Mongolia 010051, China
    3 Regional Climate Group, Department of Earth Sciences, University of Gothenburg, Gothenburg, Sweden
    4 Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
    5 National Center for Atmospheric Research, Boulder, Colorado, U.S.A.
  • Received:2017-12-25 Accepted:2018-10-08 Online:2018-12-01 Published:2018-12-29
  • Contact: YanHong Gao
  • Supported by:
    We thank the Center for Global Change Data Processing and Analysis of Beijing Normal University for the GLASS LAI dataset. This work is jointly supported by National Natural Science Foundation of China (91537105, 91537211, 41322033), the Opening Research Foundation of Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, and the Chinese Academy of Sciences (LPCC201504). D. Chen is supported by Swedish VR, STINT, BECC and MERGE, as well as SNIC through S-CMIP.


An overall greening over the Tibetan Plateau (TP) in recent decades has been established through analyses of remotely sensed Normalized Difference Vegetation Index (NDVI), though the regional pattern of the changes and associated drivers remain to be explored. This study used a satellite Leaf Area Index (LAI) dataset (the GLASS LAI dataset) and examined vegetation changes in humid and arid regions of the TP during 1982–2012. Based on distributions of the major vegetation types, the TP was divided roughly into a humid southeastern region dominated by meadow and a dry northwestern region covered mainly by steppe. It was found that the dividing line between the two regions corresponded well with the lines of mean annual precipitation of 400 mm and the mean LAI of 0.3. LAI=0.3 was subsequently used as a threshold for investigating vegetation type changes at the interanual and decadal time scales: if LAI increased from less than 0.3 to greater than 0.3 from one time period to the next, it was regarded as a change from steppe to meadow, and vice versa. The analysis shows that changes in vegetation types occurred primarily around the dividing line of the two regions, with clear growth (reduction) of the area covered by meadow (steppe), in consistency with the findings from using another independent satellite product. Surface air temperature and precipitation (diurnal temperature range) appeared to contribute positively (negatively) to this change though climate variables displayed varying correlation with LAI for different time periods and different regions.

Key words: Tibetan Plateau, vegetation change, leaf area index, climate change

Table 1

Percentage of vegetation covers (coincidence rate) as a function of the GS LAI intervals"

LAI Steppe Meadow Forest Shrub-land Crop
0.0–0.1 86.00 11.88 0.09 1.47 0.57
0.1–0.2 71.45 24.96 0.22 2.99 0.39
0.2–0.3 51.62 42.67 0.35 4.77 0.59
0.3–0.4 36.73 50.99 1.26 10.38 0.65
0.4–0.5 24.10 57.77 2.79 14.27 1.08
0.5–0.6 17.79 60.98 3.39 16.38 1.46
0.6–0.7 16.45 62.93 3.72 15.77 1.13
0.7–0.8 10.94 65.32 4.96 18.34 0.44
0.8–0.9 9.13 66.58 5.15 18.52 0.61
0.9–1.0 7.51 64.01 6.92 21.25 0.32
1.0–1.1 5.44 62.51 9.37 22.24 0.44
1.1–1.2 6.31 55.38 11.50 26.38 0.44
1.2–1.3 5.84 47.61 15.75 30.24 0.56
1.3–1.4 4.11 46.12 19.62 28.73 1.42
1.4–1.5 3.29 40.78 19.25 35.80 0.88
1.5–1.6 3.14 41.39 19.30 35.12 1.05
1.6–1.7 2.74 37.61 22.11 36.80 0.74
1.7–1.8 2.44 37.36 22.56 37.14 0.50
1.8–1.9 1.67 40.33 20.42 36.70 0.87
1.9–2.0 1.22 38.32 23.13 36.41 0.92
2.0–2.1 1.90 38.81 27.54 30.79 0.95
2.1–2.2 1.60 38.87 26.31 31.45 1.77
2.2–2.3 1.39 38.56 28.94 29.64 1.47
2.3–2.4 1.06 46.26 26.47 25.42 0.79
2.4–2.5 1.16 35.65 36.08 25.63 1.48
2.5–2.6 2.27 23.82 47.16 24.96 1.78
2.6–2.7 0.25 10.43 60.05 27.23 2.04
2.7–2.8 0.00 7.69 62.39 27.07 2.85
2.8–2.9 0.00 5.90 60.87 30.43 2.80
2.9–3.0 0.00 3.90 58.16 34.75 3.19
3.0–7.0 0.00 1.86 79.45 11.79 6.89

Table 2

Percentage of areas covered by each vegetation type in GS over ROI1 and ROI2 (Unit: %)"

LAI-VEG Steppe Meadow Forest Shrub-land Crop Sum
ROI1 33.70 10.01 0.08 1.18 0.23 45.21
ROI2 4.72 25.26 10.83 13.26 0.71 54.79
SUM 38.43 35.28 10.91 14.44 0.94 100.00

Figure 1

Spatial distributions of (a) annual mean precipitation (mm) during 1982–2012 and the 400 mm dividing line, (b) major vegetation cover types and the dividing line between steppe- and meadow-dominated regions, (c) GS LAI during 1982–2012 and the LAI=0.3 dividing line, and (d) the dividing lines from (a) (blue), (b) (red) and (c) (green). The vegetation cover types map was released in 2001 as an average since 1950"

Figure 2

Yearly locations of the dividing lines determined by (a) annual precipitation of 400 mm and (b) LAI of 0.3. Time series and linear trends of the areal coverage percentage of the humid region ROI2 in relation to the entire TP as determined by LAI (red lines) and precipitation (Precip, blue lines) are presented in (c). The time period is 1982–2012 and for the growing season. In (a) and (b), the yellow lines represent the earlier years while the green lines represent more recent years. In (c), coefficient of determination R2, statistical significance p-value, regression coefficient and correlation are indicated "

Table 3

Temporal correlation of annual LAI with annual mean surface air temperature (Tmean), precipitation (Precip), and diurnal temperature ranger (DTR) in the three decades (1982–2012), the first two decades (1982–2001) and the last two decades (1993–2012). Correlation coefficients that are statistically significant at the 0.001 level based on the two-tailed t-test are marked with a"

CORR Tmean Precip DTR
1982–2012 0.59a 0.62a ?0.53a
1982–2001 0.43 0.40 ?0.21
1993–2012 0.23 0.65a ?0.69a

Figure 3

Decadal locations of the dividing lines determined by (a) annual precipitation of 400 mm and (b) LAI of 0.3. Decadal means of Tmean, Precip, DTR and LAI over the intersection of ROI1 and ROI2 are presented in (c). The time period in 1982–2012 and the three decades are 1982–1991, 1992–2001, 2002–2012. In (a) and (b), the light gray, the light black, and the dark black lines represent the first, second, and third decade, respectively"

Table 4

Means before and after 1997/1998 (i.e., 1982–1997 and 1998–2012) of GS LAI and three climate variables over the two subregions and the entire TP (Precip: precipitation; Tmean: mean surface air temperature; DTR: diurnal temperature range. Unit for Precip is mm, unit for Tmean and DTR is °C) "

Mean Arid sub-region Humid sub-region Whole TP
before after before after before After
LAI 0.13 0.18 1.38 1.51 0.76 0.85
Precip 31.6 38.1 92.6 94.3 62.1 66.2
Tmean 3.4 4.2 6.3 7.0 4.8 5.6
DTR 13.0 12.4 11.8 11.6 12.4 12.0

Figure 4

Time series of the yearly areas of arid and humid regions during 1982–2012 (a, c) and spatial distributions of the shift between steppe and meadow in the last decade (2003–2012) compared to the first decade (1982–1991) for GLASS LAI (a, b) and GeoLand2 LAI (c, d). Coefficient of determination R2, statistical significance p-value, and regression coefficient for the arid region are indicated in (a) and (c). Green shadings in (b) and (d) denote the locations where steppe was replaced by meadow and red shadings denote the locations where meadow was replaced by steppe "

Table 5

Same as Table 4 except for the linear trends of the variables for 1982–1997 and 1998–2012 (Unit for Precip is mm/10a, unit for Tmean and DTR is °C/10a)"

Trend Arid sub-region Humid sub-region Whole TP
before after before after before after
LAI 0.01 0.01 0.06 ?0.02 0.04 ?0.01
Precip 0.9 7.4 ?2.6 ?0.8 ?0.9 3.3
Tmean 0.5 0.1 0.2 0.5 0.3 0.3
DTR 0.02 ?0.50 ?0.13 0.05 ?0.06 ?0.23
1 Bao Y, Gao YH, Lü SH, et al. Evaluation of CMIP5 earth system models in reproducing leaf area index and vegetation cover over the Tibetan Plateau. Journal of Meteorological Research 2014; 28: 6 1041- 1060.
doi: 10.1007/s13351-014-4023-5
2 Baret F, Weiss M, Lacaze R, et al. GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part1: Principles of development and production. Remote Sensing of Environment 2013; 137: 299- 309.
doi: 10.1016/j.rse.2012.12.027
3 Brovkin V Climate-vegetation interaction. Journal De Physique IV 2002; 12: 10 57- 72.
doi: 10.1051/jp4:20020452
4 Che ML, Chen BZ, Innes JL, et al. Spatial and temporal variations in the end date of the vegetation growing season throughout the Qinghai-Tibetan Plateau from 1982 to 2011. Agricultural and Forest Meteorology 2014; 189–190: 81- 90.
doi: 10.1016/j.agrformet.2014.01.004
5 Cui QH, Jiang ZG, Liu JK, et al. A review of the cause of rangeland degradation on Qinghai-Tibet Plateau. Pratacultural Science 2007; 24: 5 20- 26.
doi: 10.3969/j.issn.1001-0629.2007.05.004
6 Dong ZB, Hu GY, Yan CZ, et al., 2012. Aeolian Desertification in the Source Regions of Yangtze River and Yellow River. Beijing: Scientific Press, pp. 343.
7 Editorial Board of Vegetation Map of China, Chinese Academy of Sciences. 2001. Vegetation Atlas of China (1:1,000,000). Beijing: Science Press.
8 Gao YH, Cuo L, Zhang YX Changes in moisture flux over the Tibetan Plateau during 1979–2011 and possible mechanisms. Journal of Climate 2014; 27: 5 1876- 1893.
doi: 10.1175/jcli-d-13-00321.1
9 Gao YH, Li K, Chen F, et al. Assessing and improving Noah-MP land model simulations for the central Tibetan Plateau. Journal of Geophysical Research: Atmospheres 2015a; 120: 18 9258- 9278.
doi: 10.1002/2015jd023404
10 Gao YH, Li X, Leung LR, et al. Aridity changes in the Tibetan Plateau in a warming climate. Environmental Research Letters 2015b; 10: 3 034013.
doi: 10.1088/1748-9326/10/3/034013
11 Goetz SJ, Bunn AG, Fiske GJ, et al. Satellite-observed photosynthetic trends across boreal North America associated with climate and fire disturbance. Proceedings of the National Academy of Sciences of the United States of America 2005; 102: 38 13521- 13525.
doi: 10.1073/pnas.0506179102
12 Jeong JH, Kug JS, Kim BM, et al. Greening in the circumpolar high-latitude may amplify warming in the growing season. Climate Dynamics 2012; 38: 7–8 1421- 1431.
doi: 10.1007/s00382-011-1142-x
13 Jeong JH, Kug JS, Linderholm HW, et al. Intensified Arctic warming under greenhouse warming by vegetation-atmosphere-sea ice interaction. Environmental Research Letters 2014; 9: 9 094007.
doi: 10.1088/1748-9326/9/9/094007
14 Kang HS, Xue YK, Collatz GJ Impact assessment of satellite-derived leaf area index datasets using a general circulation model. Journal of Climate 2007; 20: 6 993- 1015.
doi: 10.1175/jcli4054.1
15 Li SS, Lü SH, Gao YH, et al. The change of climate and terrestrial carbon cycle over Tibetan Plateau in CMIP5 models. International Journal of Climatology 2015; 35: 14 4359- 4369.
doi: 10.1002/joc.4293
16 Liang SL, Zhao X, Liu SH, et al. A long-term Global Land Surface Satellite (GLASS) data-set for environmental studies. International Journal of Digital Earth 2013; 6: S1 5- 33.
doi: 10.1080/17538947.2013.805262
17 Lin HL, Wang ZQ, Shang ZH Features on fractal dimension of barren patch and mouse hole among different degenerated succession stages on alpine meadow in the source region of the Yangtze and Yellow River, Qinghai-Tibetan Plateau, China. Acta Agrestia Sinica 2010; 18: 4 477- 484.
doi: 10.11733/j.issn.1007-0435.2010.04.001
18 Liu XD, Chen BD Climatic warming in the Tibetan Plateau during recent decades. International Journal of Climatology 2000; 20: 14 1729- 1742.
doi: 10.1002/1097-0088(20001130)20:14<1729::aid-joc556>;2-y
19 Muschitiello F, Zhang Q, Sundqvist HS, et al. Arctic climate response to the termination of the African Humid Period. Quaternary Science Reviews 2015; 125: 91- 97.
doi: 10.1016/j.quascirev.2015.08.012
20 Parmesan C, Yohe G A globally coherent fingerprint of climate change impacts across natural systems. Nature 2003; 421: 6918 37- 42.
doi: 10.1038/nature01286
21 Piao SL, Fang JY, He JS Variations in vegetation net primary production in the Qinghai-Xizang Plateau, China, from 1982 to 1999. Climatic Change 2006; 74: 1–3 253- 267.
doi: 10.1007/s10584-005-6339-8
22 Piao SL, Cui MD, Chen AP, et al. Altitude and temperature dependence of change in the spring vegetation green-up date from 1982 to 2006 in the Qinghai-Xizang Plateau. Agricultural and Forest Meteorology 2011; 151: 12 1599- 1608.
doi: 10.1016/j.agrformet.2011.06.016
23 Shen MG, Piao SL, Jeong SJ, et al. Evaporative cooling over the Tibetan Plateau induced by vegetation growth. Proceedings of the National Academy of Sciences of the United States of America 2015; 112: 30 9299- 9304.
doi: 10.1073/pnas.1504418112
24 Shen Y, Feng MN, Zhang HZ, et al. Interpolation methods of China daily precipitation data. Journal of Applied Meteorological Science 2010; 21: 3 279- 286.
doi: 10.3969/j.issn.1001-7313.2010.03.003
25 Shen ZX, Fu G, Yu CQ, et al. Relationship between the growing season maximum enhanced vegetation index and climatic factors on the Tibetan Plateau. Remote Sensing 2014; 6: 8 6765- 6789.
doi: 10.3390/rs6086765
26 Song CQ, You SC, Ke LH, et al. Spatio-temporal variation of vegetation phenology in the Northern Tibetan Plateau as detected by MODIS remote sensing. Chinese Journal of Plant Ecology 2011; 35: 8 853- 863.
doi: 10.3724/sp.j.1258.2011.00853
27 National Meteorological Information Center Assessment Report of China's Ground Precipitation 0.5°×0.5° Gridded Dataset (V2.0). Beijing: National Meteorological Information Center 2012.
28 Wang B, Bao Q, Hoskins B, et al. Tibetan plateau warming and precipitation changes in East Asia. Geophysical Research Letters 2008; 35: 14
doi: 10.1029/2008gl034330
29 Wu GX, Duan AM, Zhang XQ, et al. Extreme weather and climate changes and its environmental effects over the Tibetan Plateau. Chinese Journal of Nature 2013; 35: 3 167- 171.
doi: 10.3969/j.issn.0253-9608.2013.03.002
30 Xiao ZQ, Liang SL, Wang JD, et al. Use of general regression neural networks for generating the GLASS leaf area index product from time-series MODIS surface reflectance. IEEE Transactions on Geoscience and Remote Sensing 2014; 52: 1 209- 223.
doi: 10.1109/tgrs.2013.2237780
31 Xu WX, Gu S, Zhao XQ, et al. High positive correlation between soil temperature and NDVI from 1982 to 2006 in alpine meadow of the Three-River Source Region on the Qinghai-Tibetan Plateau. International Journal of Applied Earth Observation and Geoinformation 2011; 13: 4 528- 535.
doi: 10.1016/j.jag.2011.02.001
32 Yang JP, Ding YJ, Chen RS, et al., 2006. Changes of the Ecological System in the Source Regions of the Yangtze and Yellow River Basins. Beijing: Meteorological Press, pp. 181. (in Chinese)
33 Yang K, Ye BS, Zhou DG, et al. Response of hydrological cycle to recent climate changes in the Tibetan Plateau. Climatic Change 2011; 109: 3–4 517- 534.
doi: 10.1007/s10584-011-0099-4
34 Yang MX, Wang SL, Yao TD, et al. Desertification and its relationship with permafrost degradation in Qinghai-Xizang (Tibet) Plateau. Cold Regions Science and Technology 2004; 39: 1 47- 53.
doi: 10.1016/j.coldregions.2004.01.002
35 Zhang L, Guo HD, Ji L, et al. Vegetation greenness trend (2000 to 2009) and the climate controls in the Qinghai-Tibetan Plateau. Journal of Applied Remote Sensing 2013b; 7: 073572.
doi: 10.1117/1.jrs.7.073572
36 Zhang L, Guo HD, Wang CZ, et al. The long-term trends (1982–2006) in vegetation greenness of the alpine ecosystem in the Qinghai-Tibetan Plateau. Environmental Earth Sciences 2014; 72: 6 1827- 1841.
doi: 10.1007/s12665-014-3092-1
37 Zhong L, Ma YM, Salama MS, et al. Assessment of vegetation dynamics and their response to variations in precipitation and temperature in the Tibetan Plateau. Climatic Change 2010; 103: 3–4 519- 535.
doi: 10.1007/s10584-009-9787-8
38 Zhou HJ, Van Rompaey A, Wang JA Detecting the impact of the "Grain for Green" program on the mean annual vegetation cover in the Shaanxi province, China using SPOT-VGT NDVI data. Land Use Policy 2009; 26: 4 954- 960.
doi: 10.1016/j.landusepol.2008.11.006
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