Precipitation Drives the NDVI Distribution on the Tibetan Plateau While High Warming Rates May Intensify Its Ecological Droughts

Climate change has significantly affected the ecosystem of the Tibetan Plateau. There, temperature rises and altered precipitation patterns have led to notable changes in its vegetation growth processes and vegetation cover features. Yet current research still pays relatively little attention to the regional climatic determinants and response patterns of such vegetation dynamics. In this study, spatial patterns in the response of the normalized difference vegetation index (NDVI) to climate change and its dynamic characteristics during the growing season were examined for the Tibetan Plateau, by using a pixel-scale-based geographically weighted regression (GWR) based on the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI data, as well as data for temperature and moisture indices collected at meteorological stations, for the period 1982–2015. The results show the following. Spatial nonstationary relationships, primarily positive, were found between the NDVI and climatic factors in the Tibetan Plateau. However, warming adversely affected vegetation growth and cover in some arid and semiarid regions of the northeast and west Tibetan Plateau. Additionally, precipitation played a dominant role in the NDVI of the Tibetan Plateau in the largest area (accounting for 39.7% of total area). This suggests that increased moisture conditions considerably facilitated vegetation growth and cover in these regions during the study period. Temperature mainly played a dominant role in the NDVI in some parts of the plateau sub-cold zone and some southeastern regions of the Tibetan Plateau. In particular, the minimum temperature was the dominant driver of NDVI over a larger area than any of the other temperature indices. Furthermore, spatial regressions between NDVI dynamics and climatic variability revealed that a faster warming rate in the arid and semiarid regions impeded vegetation growth through mechanisms such as drought intensification. Moisture variability was found to act as a key factor regulating the extent of vegetation cover on the south Tibetan Plateau.

[1]  B. Dong,et al.  Dominant role of greenhouse-gas forcing in the recovery of Sahel rainfall , 2015 .

[2]  Shaohong Wu,et al.  Past and future effects of climate change on spatially heterogeneous vegetation activity in China , 2017 .

[3]  C. Franzke.  Warming trends: Nonlinear climate change , 2014 .

[4]  Binghao Jia,et al.  Contributions of climate change, land use change and CO2 to changes in the gross primary productivity of the Tibetan Plateau , 2019, Atmospheric and Oceanic Science Letters.

[5]  Mariusz Szymanowski,et al.  Local regression models for spatial interpolation of urban heat island—an example from Wrocław, SW Poland , 2012, Theoretical and Applied Climatology.

[6]  Jiquan Chen,et al.  Coupled dynamics of socioeconomic and environmental systems in Tibet , 2018 .

[7]  A. Kerkhoff,et al.  Convergence of terrestrial plant production across global climate gradients , 2014, Nature.

[8]  P. Ciais,et al.  Asymmetric effects of daytime and night-time warming on Northern Hemisphere vegetation , 2013, Nature.

[9]  Jason D. Fridley,et al.  Longer growing seasons shift grassland vegetation towards more-productive species , 2016 .

[10]  Mingyuan Du,et al.  Nonlinear responses of temperature sensitivities of community phenophases to warming and cooling events are mirroring plant functional diversity , 2018 .

[11]  Tai-bao Yang,et al.  Impacts of climate warming on vegetation in Qaidam Area from 1990 to 2003 , 2008, Environmental monitoring and assessment.

[12]  Du Zheng,et al.  Spatial and temporal variability in the net primary production of alpine grassland on the Tibetan Plateau since 1982 , 2014, Journal of Geographical Sciences.

[13]  Yaochen Qin,et al.  Estimating Relations of Vegetation, Climate Change, and Human Activity: A Case Study in the 400 mm Annual Precipitation Fluctuation Zone, China , 2019, Remote. Sens..

[14]  Wei Sun,et al.  Relationship between the Growing Season Maximum Enhanced Vegetation Index and Climatic Factors on the Tibetan Plateau , 2014, Remote. Sens..

[15]  R. Myneni,et al.  Satellite-indicated long-term vegetation changes and their drivers on the Mongolian Plateau , 2014, Landscape Ecology.

[16]  Ling Luo,et al.  Integrating AVHRR and MODIS data to monitor NDVI changes and their relationships with climatic parameters in Northeast China , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[17]  C. Field,et al.  The velocity of climate change , 2009, Nature.

[18]  P. Jones,et al.  Uncertainty estimates in regional and global observed temperature changes: A new data set from 1850 , 2006 .

[19]  P. Ciais,et al.  Changes in satellite‐derived vegetation growth trend in temperate and boreal Eurasia from 1982 to 2006 , 2011 .

[20]  Jian Sun,et al.  On the Variation of NDVI with the Principal Climatic Elements in the Tibetan Plateau , 2013, Remote. Sens..

[21]  P. Ciais,et al.  Evaporative cooling over the Tibetan Plateau induced by vegetation growth , 2015, Proceedings of the National Academy of Sciences.

[22]  Cheng Sun,et al.  Significantly lower summer minimum temperature warming trend on the southern Tibetan Plateau than over the Eurasian continent since the Industrial Revolution , 2019, Environmental Research Letters.

[23]  Wenfu Peng,et al.  Quantifying influences of natural factors on vegetation NDVI changes based on geographical detector in Sichuan, western China , 2019, Journal of Cleaner Production.

[24]  Tao Wang,et al.  Responses and feedback of the Tibetan Plateau’s alpine ecosystem to climate change , 2019, Chinese Science Bulletin.

[25]  A. Stewart Fotheringham,et al.  Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity , 2010 .

[26]  Tandong Yao,et al.  Tackling on environmental changes in Tibetan Plateau with focus on water, ecosystem and adaptation. , 2019, Science bulletin.

[27]  Amanda E. Cravens,et al.  Defining Ecological Drought for the Twenty-First Century , 2017 .

[28]  J. Levine.  Ecology: A trail map for trait-based studies , 2015, Nature.

[29]  Xinxing Li,et al.  Modeling soil organic carbon spatial distribution for a complex terrain based on geographically weighted regression in the eastern Qinghai-Tibetan Plateau , 2020 .

[30]  Huabing Huang,et al.  Multisatellite Analyses of Spatiotemporal Variability in Photosynthetic Activity Over the Tibetan Plateau , 2019, Journal of Geophysical Research: Biogeosciences.

[31]  A. Ye,et al.  Spatial and temporal variations in vegetation coverage observed using AVHRR GIMMS and Terra MODIS data in the mainland of China , 2020 .

[32]  Xiaoyang Zhang,et al.  Interannual variations in spring phenology and their response to climate change across the Tibetan Plateau from 1982 to 2013 , 2016, International Journal of Biometeorology.

[33]  Zhanhuan Shang,et al.  The relationship of vegetation and soil differentiation during the formation of black-soil-type degraded meadows in the headwater of the Qinghai-Tibetan Plateau, China , 2013, Environmental Earth Sciences.

[34]  Yuting Zhou,et al.  Experimental warming does not enhance gross primary production and above-ground biomass in the alpine meadow of Tibet , 2013 .

[35]  Jinsheng He,et al.  No upward shift of alpine grassland distribution on the Qinghai-Tibetan Plateau despite rapid climate warming from 2000 to 2014. , 2018, Science of the Total Environment.

[36]  John Tenhunen,et al.  Application of a geographically‐weighted regression analysis to estimate net primary production of Chinese forest ecosystems , 2005 .

[37]  Maosheng Zhao,et al.  Drought-Induced Reduction in Global Terrestrial Net Primary Production from 2000 Through 2009 , 2010, Science.

[38]  Scott Salzman,et al.  Assessment of Spatiotemporal Varying Relationships Between Rainfall, Land Cover and Surface Water Area Using Geographically Weighted Regression , 2012, Environmental Modeling & Assessment.

[39]  Anja Rammig,et al.  A plant's perspective of extremes: terrestrial plant responses to changing climatic variability , 2013, Global change biology.

[40]  J. Canadell,et al.  Greening of the Earth and its drivers , 2016 .

[41]  B. Poulter,et al.  Detection and attribution of vegetation greening trend in China over the last 30 years , 2015, Global change biology.

[42]  M. C. Urban.  Accelerating extinction risk from climate change , 2015, Science.

[43]  Jianping Huang,et al.  Evolution of land surface air temperature trend , 2014 .

[44]  Meng Wang,et al.  Dynamics of vegetation autumn phenology and its response to multiple environmental factors from 1982 to 2012 on Qinghai-Tibetan Plateau in China. , 2018, The Science of the total environment.

[45]  Jiang-bo Gao,et al.  Quantitative assessment of ecosystem vulnerability to climate change: methodology and application in China , 2018, Environmental Research Letters.

[46]  Emilio Chuvieco,et al.  Debating the greening vs. browning of the North American boreal forest: differences between satellite datasets , 2010 .