Vegetation dynamics and its response to climate change in Central Asia
YIN Gang1,2,3, HU Zengyun1, CHEN Xi1*, TIYIP Tashpolat2
1 State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China; 2 College of Resources and Environmental Sciences, Xinjiang University, Urumqi 830046, China; 3 University of Chinese Academy of Sciences, Beijing 100049, China
Vegetation dynamics and its response to climate change in Central Asia
YIN Gang1,2,3, HU Zengyun1, CHEN Xi1*, TIYIP Tashpolat2
1 State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China; 2 College of Resources and Environmental Sciences, Xinjiang University, Urumqi 830046, China; 3 University of Chinese Academy of Sciences, Beijing 100049, China
摘要 The plant ecosystems are particularly sensitive to climate change in arid and semi-arid regions. However, the responses of vegetation dynamics to climate change in Central Asia are still unclear. In this study, we used the normalized difference vegetation index (NDVI) data to analyze the spatial-temporal changes of vegetation and the correlation of vegetation and climatic variables over the period of 1982–2012 in Central Asia by using the empirical orthogonal function and least square methods. The results showed that the annual NDVI in Central Asia experienced a weak increasing trend overall during the study period. Specifically, the annual NDVI showed a significant increasing trend between1982 and 1994, and exhibited a decreasing trend since 1994. The regions where the annual NDVI decreased were mainly distributed in western Central Asia, which may be caused by the decreased precipitation. The NDVI exhibited a larger increasing trend in spring than in the other three seasons. In mountainous areas, the NDVI had a significant increasing trend at the annual and seasonal scales; further, the largest increasing trend of NDVI mainly appeared in the middle mountain belt (1,700–2,650 m asl). The annual NDVI was positively correlated with annual precipitation in Central Asia, and there was a weak negative correlation between annual NDVI and temperature. Moreover, a one-month time lag was found in the response of NDVI to temperature from June to September in Central Asia during 1982–2012.
Abstract: The plant ecosystems are particularly sensitive to climate change in arid and semi-arid regions. However, the responses of vegetation dynamics to climate change in Central Asia are still unclear. In this study, we used the normalized difference vegetation index (NDVI) data to analyze the spatial-temporal changes of vegetation and the correlation of vegetation and climatic variables over the period of 1982–2012 in Central Asia by using the empirical orthogonal function and least square methods. The results showed that the annual NDVI in Central Asia experienced a weak increasing trend overall during the study period. Specifically, the annual NDVI showed a significant increasing trend between1982 and 1994, and exhibited a decreasing trend since 1994. The regions where the annual NDVI decreased were mainly distributed in western Central Asia, which may be caused by the decreased precipitation. The NDVI exhibited a larger increasing trend in spring than in the other three seasons. In mountainous areas, the NDVI had a significant increasing trend at the annual and seasonal scales; further, the largest increasing trend of NDVI mainly appeared in the middle mountain belt (1,700–2,650 m asl). The annual NDVI was positively correlated with annual precipitation in Central Asia, and there was a weak negative correlation between annual NDVI and temperature. Moreover, a one-month time lag was found in the response of NDVI to temperature from June to September in Central Asia during 1982–2012.
The Innovation Research Group Program of Chinese Academy of Sciences and State Administration of Foreign Experts Affairs of China (KZCX2-YW-T09)
The West Light Foundation of Chinese Academy of Sciences (2015-XBQN-B-20)
通讯作者:
CHEN Xi
E-mail: chenxi@ms.xjb.ac.cn
引用本文:
YIN Gang, HU Zengyun, CHEN Xi, TIYIP Tashpolat. Vegetation dynamics and its response to climate change in Central Asia[J]. 干旱区科学, 2016, 8(3): 375-388.
YIN Gang, HU Zengyun, CHEN Xi, TIYIP Tashpolat. Vegetation dynamics and its response to climate change in Central Asia. Journal of Arid Land, 2016, 8(3): 375-388.
Anyamba A, Tucker C J, Mahoney R. 2002. From El Niño to La Niña: Vegetation response patterns over East and southern Africa during the 1997–2000 period. Journal of Climate, 15(12): 3096–3103. Barichivich J, Briffa K R, Myneni R B, et al. 2013. Large-scale variations in the vegetation growing season and annual cycle of atmospheric CO2 at high northern latitudes from 1950 to 2011. Global Change Biology, 19(10): 3167–3183. Beck P S A, Goetz S J. 2011. Satellite observations of high northern latitude vegetation productivity changes between 1982 and 2008: Ecological variability and regional differences. Environmental Research Letters, 6(4): 045501, doi: 10.1088/1748-9326/6/4/045501. Cai D L, Fraedrich K, Sielmann F, et al. 2014. Climate and vegetation: An ERA-interim and GIMMS NDVI analysis. Journal of Climate, 27(13): 5111–5118. Camberlin P, Martiny N, Philippon N, et al. 2007. Determinants of the interannual relationships between remote sensed photosynthetic activity and rainfall in tropical Africa. Remote Sensing of Environment, 106(2): 199–216. Chen X, Xia J, Qian J, et al. 2003. Study on distributed hydrological model in Sangong river basin. Arid Land Geography, 26(4): 305–308. (in Chinese)Cheng W M, Wang N, Zhao S M, et al. 2016. Growth of the Sayram Lake and retreat of its water-supplying glaciers in the Tianshan Mountains from 1972 to 2011. Journal of Arid Land, 8(1): 13–22. Dong J R, Kaufmann R K, Myneni R B, et al. 2003. Remote sensing estimates of boreal and temperate forest woody biomass: carbon pools, sources, and sinks. Remote Sensing of Environment, 84(3): 393–410. Eastman J R, Sangermano F, Machado E A, et al. 2013. Global trends in seasonality of normalized difference vegetation index (NDVI), 1982–2011. Remote Sensing, 5(10): 4799–4818. Goetz S J, Prince S D. 1999. Modelling terrestrial carbon exchange and storage: Evidence and implications of functional convergence in light-use efficiency. Advances in Ecological Research, 28: 57–92. Goetz S J, Bunn A G, Fiske G J, et al. 2005. Satellite-observed photosynthetic trends across boreal North America associated with climate and fire disturbance. Proceeding of the National Academy of Sciences of the United States of America, 102(38): 13521–13525. Guay K C, Beck P S A, Berner L T, et al. 2014. Vegetation productivity patterns at high northern latitudes: a multi-sensor satellite data assessment. Global Change Biology, 20(10): 3147–3158. Hu R J. 2004. Physical Geography of the Tianshan Mountains in China. Beijing: China Environmental Science Press, 264–273. (in Chinese)Hu Z Y, Zhang C. 2014. Evaluation of reanalysis, spatially-interpolated and remote-sensing derived precipitation datasets over Central Asia. Geophysical Research Abstracts, 16, EGU2014-358.Hu Z Y, Zhang C, Hu Q, et al. 2014. Temperature changes in Central Asia from 1979 to 2011 based on multiple datasets. Journal of Climate, 27(3): 1143–1167. Hu Z Y, Li Q X, Chen X, et al. 2015. Climate changes in temperature and precipitation extremes in an alpine grassland of Central Asia. Theoretical and Applied Climatology, doi: 10.1007/s00704-015-1568-x. Ichii K, Kawabata A, Yamaguchi Y. 2002. Global correlation analysis for NDVI and climatic variables and NDVI trends: 1982–1990. International Journal of Remote Sensing, 23(18): 3873–3878. IPCC. 2012. Managing the risks of extreme events and disasters to advance climate change adaptation. In: Field C B, Barros V, Stocker T F, et al. Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change. Cambridge, NY, USA: Cambridge University Press. Koutsoyiannis D. 2003. Climate change, the Hurst phenomenon, and hydrological statistics. Hydrological Sciences Journal, 48(1): 3–24. Koutsoyiannis D, Montanari A. 2007. Statistical analysis of hydroclimatic time series: Uncertainty and insights. Water Resources Research, 43(5): W05429, doi: 10.1029/2006WR005592. Li C F, Zhang C, Luo G P, Chen X. 2013. Modeling the carbon dynamics of the dryland ecosystems in Xinjiang, China from 1981 to 2007—The spatiotemporal patterns and climate controls. Ecological Modelling. 267:148–157. Li C F, Zhang C, Luo G P, et al. 2015. Carbon stock and its responses to climate change in Central Asia. Global Change Biology, 21(5): 1951–1967. Li Q H, Chen Y N, Shen Y J, et al. 2011. Spatial and temporal trends of climate change in Xinjiang, China. Journal of Geographical Sciences, 21(6): 1007–1018. Lioubimtseva E, Cole R. 2006. Uncertainties of climate change in arid environments of Central Asia. Review in Fisheries Science, 14(1–2): 29–49. Lorenz E N. 1956. Empirical orthogonal functions and statistical weather prediction. In: Scientific Report No. 1 of Statistical Forecast Project. Department of Meteorology, MIT. Mann M E. 2011. On long range dependence in global surface temperature series. Climatic Change, 107(3–4): 267–276. Mao D H, Wang Z M, Han J X, et al. 2012. Spatio-temporal pattern of net primary productivity and its driven factors in Northeast China in 1982–2010. Scientia Geographica Sinica, 32(9): 1106–1111. (in Chinese)Martiny N, Richard Y, Camberlin P. 2005. Interannual persistence effects in vegetation dynamics of semi-arid Africa. Geophysical Research Letters, 32(24), doi: 10.1029/2005GL024634. Myneni R B, Keeling C D, Tucker C J, et al. 1997. Increased plant growth in the northern high latitudes from 1981 to 1991. Nature, 386(6626): 698–702. North G R, Bell T L, Cahalan R F. 1982. Sampling errors in the estimation of empirical orthogonal functions. Monthly Weather Review, 110(7): 699–706. Peng S S, Piao S L, Ciais P, et al. 2013. Asymmetric effects of daytime and night-time warming on Northern Hemisphere vegetation. Nature, 501(7465): 88–92. Piao S L, Fang J Y, Zhou L M, et al. 2003. Interannual variations of monthly and seasonal normalized difference vegetation index (NDVI) in China from 1982 to 1999. Journal of Geophysical Research-Atmospheres, 108(D14), 4401, doi: 10.1029/2002JD002848. Piao S L, Mohammat A, Fang J Y, et al. 2006. NDVI-based increase in growth of temperate grasslands and its responses to climate changes in China. Global Environmental Change, 16(4): 340–348. Potter C S, Brooks V. 1998. Global analysis of empirical relations between annual climate and seasonality of NDVI. International Journal of Remote Sensing, 19(15): 2921–2948. Propastin P A, Kappas M W, Herrmann S M, et al. 2012. Modified light use efficiency model for assessment of carbon sequestration in grasslands of Kazakhstan: combining ground biomass data and remote-sensing. International Journal of Remote Sensing, 33(5): 1465–1487. Racheva-Iotova B, Samorodnitsky G. 2003. Long range dependence in heavy tailed stochastic processes. In: Rachev S T. Handbook of Heavy Tailed Distributions in Finance. Amsterdam: Elsevier, 641–662. Ran M, Zhang C J, Feng Z D. 2015. Climatic and hydrological variations during the past 8000 years in northern Xinjiang of China and the associated mechanisms. Quaternary International, 358: 21–34. Rienecker M M, Suarez M J, Gelaro R, et al. 2011. MERRA: NASA’s modern-era retrospective analysis for research and applications. Journal of Climate, 24(14): 3624–3648. Samorodnitsky G. 2007. Long range dependence. Foundations and Trends® in Stochastic Systems, 1(3): 163–257. Schiemann R, Lüthi D, Vidale P L, et al. 2008. The precipitation climate of Central Asia–intercomparison of observational and numerical data sources in a remote semiarid region. International Journal of Climatology, 28(13): 295–314. Shen M G, Tang Y H, Chen J, et al. 2011. Influences of temperature and precipitation before the growing season on spring phenology in grasslands of the central and eastern Qinghai-Tibetan Plateau. Agricultural and Forest Meteorology, 151(12): 1711–1722. Suo Y X, Wang Z X, Liu C, et al. 2009. Relationship between NDVI and precipitation and temperature in Middle Asia during 1982–2002. Resources Science, 31(8): 1422–1429. (in Chinese)Tucker C J, Townshend J R G, Goff T E. 1985. African land-cover classification using satellite data. Science, 227(4685): 369–375.United Nations Statistics Division. 2013. Composition of macro geographical (continental) regions, geographical sub-regions, and selected economic and other groupings. United Nations Statistics Division, United States of America. [2011-10-23]. http://unstats.un.org/unsd/methods/m49/m49regin.htm#asia.Wang J, Rich P M, Price K P. 2003. Temporal responses of NDVI to precipitation and temperature in the central Great Plains, USA. International Journal of Remote Sensing, 24(11): 2345–2364. Wu D H, Zhao X, Liang S L, et al. 2015. Time-lag effects of global vegetation responses to climate change. Global Change Biology, 21(9): 3520–3531. Yang W, Yang L, Merchant J W. 1997. An assessment of AVHRR/NDVI-ecoclimatological relations in Nebraska, U.S.A. International Journal of Remote Sensing, 18(10): 2161–2180. Yuan X L, Li L H, Chen X, et al. 2015. Effects of precipitation intensity and temperature on NDVI-based grass change over northern China during the Period from 1982 to 2011. Remote Sensing, 7(8): 10164–10183. Zhang C, Li C F, Chen X, et al. 2013. A spatial-explicit dynamic vegetation model that couples carbon, water, and nitrogen processes for arid and semi-arid ecosystems. Journal of Arid Land, 5(1): 102–117.Zhou L M, Tucker C J, Kaufmann R K, et al. 2001. Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999. Journal of Geophysical Research-Atmospheres, 106(D17): 20069–20083. Zhou W, Gang C C, Chen Y Z et al. 2014. Grassland coverage inter-annual variation and its coupling relation with hydrothermal factors in China during 1982–2010. Journal of Geographical Sciences, 24(4): 593–611.
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