Potential impacts of climate change on vegetation dynamics and ecosystem function in a mountain watershed on the Qinghai-Tibet Plateau

  • Decheng Zhou
  • Lu HaoEmail author
  • John B. Kim
  • Peilong Liu
  • Cen Pan
  • Yongqiang Liu
  • Ge Sun


The Qinghai-Tibet Plateau constitutes unique mountain ecosystems that can be used for early detection of the impacts of climate change on ecosystem functions. We use the MAPSS-CENTURY 2 (MC2), a dynamic global vegetation model, to examine the potential responses of terrestrial ecosystems to climate change in the past (1961–2010) and future (2011–2080) under one medium-low warming scenario (RCP4.5) at a 1-km spatial resolution in the Upper Heihe River Basin (UHRB), northwestern China. Results showed that 21.4% of the watershed area has experienced changes in potential natural vegetation types in the past and that 42.6% of the land would undergo changes by the 2070s, characterized by a sharp increase in alpine tundra at the cost of cold barren land. Net primary productivity (NPP) and heterotrophic respiration (RH) have increased sharply since the mid-1980s and are projected to remain at reduced rates in the future. Overall, UHRB switched from carbon neutral to a carbon sink in 1961–2010, and the sink strength is projected to decline after 2040. Additionally, future climate change is projected to drive a decrease in water yield due to a slight decrease in precipitation and an increase in evapotranspiration (ET). Furthermore, we find large spatial variations in simulated ecosystem dynamics, including an upward trend of NPP, RH, and ET in the alpine zone, but a downward trend in the mid-elevation forest zone. These results underscore the complexity of potential impacts of climate change on mountain watersheds that represent the headwaters of inland river systems in an arid environment.



We thank the Cold and Arid Regions Science Data Center for sharing the climate and vegetation data. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modeling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Fig. 1 of this paper) for producing and making available their model output.

Funding information

This research was funded by the Natural Science Foundation of China (Grant No. 91425301, 41571026, and 41601196) and the Qinglan Project of Jiangsu Province of China. Partial support was provided by the Southern Research Station of the US Department of Agriculture Forest Service.

Supplementary material

10584_2019_2524_MOESM1_ESM.docx (623 kb)
ESM 1 (DOCX 623 kb)


  1. Bachelet D, Lenihan JM, Daly C, Neilson RP, Ojima DS, Parton WJ (2001) MC1: a dynamic vegetation model for estimating the distribution of vegetation and associated carbon, nutrients, and water—technical documentation. Version 1.0. Gen. Tech. Rep. PNW-GTR-508. U.S. Department of Agriculture, Forest Service, Pacific northwest Research Station, Portland, ORGoogle Scholar
  2. Bachelet D et al. (2003) Simulating past and future dynamics of natural ecosystems in the United States. Glob Biogeochem Cycles 17Google Scholar
  3. Bachelet D, Ferschweiler K, Sheehan TJ, Sleeter BM, Zhu Z (2015) Projected carbon stocks in the conterminous USA with land use and variable fire regimes. Glob Chang Biol 21:4548–4560CrossRefGoogle Scholar
  4. Beckage B, Osborne B, Gavin DG, Pucko C, Siccama T, Perkins T (2008) A rapid upward shift of a forest ecotone during 40 years of warming in the Green Mountains of Vermont. Proc Natl Acad Sci 105:4197–4202CrossRefGoogle Scholar
  5. Beniston M (2003) Climatic change in mountain regions: a review of possible impacts. Clim Chang 59:5–31CrossRefGoogle Scholar
  6. Case MJ et al. (2018) Climate change, vegetation, and disturbance in South Central Oregon. In: Halofsky JE, Peterson DL, Ho JJ (eds) Climate change vulnerability and adaptation in South Central Oregon. USDA Forest Service, Pacific Northwest Research Station, Portland, OR. (In Press), General Technical Report PNW-GTR-xxxx,Google Scholar
  7. Cheng G, Li X, Zhao W, Xu Z, Feng Q, Xiao S, Xiao H (2014) Integrated study of the water–ecosystem–economy in the Heihe River Basin. Natl Sci Rev 1:413–428CrossRefGoogle Scholar
  8. Conklin DR, Lenihan JM, Bachelet D, Neilson RP, Kim JB (2016) MCFire model technical description. Gen. Tech. Rep. PNW-GTR-926. U.S. Department of Agriculture, Forest Service, Pacific northwest Research Station, Portland, ORGoogle Scholar
  9. Cramer W et al (2001) Global response of terrestrial ecosystem structure and function to CO2 and climate change: results from six dynamic global vegetation models. Glob Chang Biol 7:357–373CrossRefGoogle Scholar
  10. Dolezal J et al (2016) Vegetation dynamics at the upper elevational limit of vascular plants in Himalaya. Sci Rep 6:24881CrossRefGoogle Scholar
  11. Drapek RJ, Kim JB, Neilson RP (2015) Continent-wide simulations of a dynamic global vegetation model over the United States and Canada under nine AR4 future scenarios. In: Bachelet D, Turner D (eds) Global Vegetation Dynamics. Geophysical Monograph Series. doi:
  12. Gao B, Qin Y, Wang Y, Yang D, Zheng Y (2016) Modeling ecohydrological processes and spatial patterns in the Upper Heihe Basin in China forests 7:10Google Scholar
  13. Gobiet A, Kotlarski S, Beniston M, Heinrich G, Rajczak J, Stoffel M (2014) 21st century climate change in the European Alps—a review. Sci Total Environ 493:1138–1151CrossRefGoogle Scholar
  14. Gonzalez P, Neilson RP, Lenihan JM, Drapek RJ (2010) Global patterns in the vulnerability of ecosystems to vegetation shifts due to climate change. Glob Ecol Biogeogr 19:755–768CrossRefGoogle Scholar
  15. Gottfried M et al (2012) Continent-wide response of mountain vegetation to climate change. Nat Clim Chang 2:111CrossRefGoogle Scholar
  16. Hao L et al (2016) Detection of the coupling between vegetation leaf area and climate in a multifunctional watershed, Northwestern China. Remote Sens 8:1032CrossRefGoogle Scholar
  17. Hickler T et al (2012) Projecting the future distribution of European potential natural vegetation zones with a generalized, tree species-based dynamic vegetation model. Glob Ecol Biogeogr 21:50–63CrossRefGoogle Scholar
  18. Huntzinger D et al. (2018) NACP MsTMIP: global 0.5-degree model outputs in standard format, version 1.0. ORNL Distributed Active Archive CenterGoogle Scholar
  19. IPCC (2013) Intergovernmental Panel on Climate Change. Climate Change 2013: The physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge Univ. Press, Cambridge, UK and New York, N. YGoogle Scholar
  20. Jin H, Li A, Bian J, Nan X, Zhao W, Zhang Z, Yin G (2017) Intercomparison and validation of MODIS and GLASS leaf area index (LAI) products over mountain areas: a case study in southwestern China. Int J Appl Earth Obs Geoinf 55:52–67CrossRefGoogle Scholar
  21. Kim JB et al (2017) Assessing climate change impacts, benefits of mitigation, and uncertainties on major global forest regions under multiple socioeconomic and emissions scenarios. Environ Res Lett 12:045001CrossRefGoogle Scholar
  22. Kim JB, Kerns BK, Drapek RJ, Pitts GS, Halofsky JE (2018) Simulating vegetation response to climate change in the Blue Mountains with MC2 dynamic global vegetation model. Clim Serv 10:20–32Google Scholar
  23. Lenihan JM, Daly C, Bachelet D, Neilson RP (1998) Simulating broad-scale fire severity in a dynamic global vegetation model. Northwest Sci 72:92–103Google Scholar
  24. Li Z, Li C, Xu Z, Zhou X (2014) Frequency analysis of precipitation extremes in Heihe River basin based on generalized Pareto distribution. Stoch Env Res Risk A 28:1709–1721CrossRefGoogle Scholar
  25. Liu P, Hao L, Pan C, Zhou D, Liu Y, Sun G (2017) Combined effects of climate and land management on watershed vegetation dynamics in an arid environment. Sci Total Environ 589:73–88CrossRefGoogle Scholar
  26. Liu S et al. (2018) The Heihe integrated observatory network: a basin-scale land surface processes observatory in China Vadose Zone J 17Google Scholar
  27. Luo Y, Wan S, Hui D, Wallace LL (2001) Acclimatization of soil respiration to warming in a tall grass prairie. Nature 413:622–625CrossRefGoogle Scholar
  28. Meinshausen M et al (2011) The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Clim Chang 109:213CrossRefGoogle Scholar
  29. Mu C et al (2014) Stable carbon isotopes as indicators for permafrost carbon vulnerability in upper reach of Heihe River basin, northwestern China. Quat Int 321:71–77CrossRefGoogle Scholar
  30. Neilson RP (1995) A model for predicting continental-scale vegetation distribution and water balance. Ecol Appl 5:362–385CrossRefGoogle Scholar
  31. Nemani RR et al (2003) Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 300:1560–1563CrossRefGoogle Scholar
  32. Palomo I (2017) Climate change impacts on ecosystem services in high mountain areas: a literature review. Mt Res Dev 37:179–187CrossRefGoogle Scholar
  33. Parton WJ et al (1993) Observations and modeling of biomass and soil organic matter dynamics for the grassland biome worldwide. Glob Biogeochem Cycles 7:785–809CrossRefGoogle Scholar
  34. Peterman W, Bachelet D, Ferschweiler K, Sheehan T (2014) Soil depth affects simulated carbon and water in the MC2 dynamic global vegetation model. Ecol Model 294:84–93CrossRefGoogle Scholar
  35. Piao S, Fang J, Ciais P, Peylin P, Huang Y, Sitch S, Wang T (2009) The carbon balance of terrestrial ecosystems in China. Nature 458:1009CrossRefGoogle Scholar
  36. Qin D, Ding Y, Mu M (2016a) Climate and environmental change in China: 1951–2012. Springer, Berlin HeidelbergGoogle Scholar
  37. Qin Y, Qi F, Holden NM, Cao J (2016b) Variation in soil organic carbon by slope aspect in the middle of the Qilian Mountains in the upper Heihe River Basin, China. Catena 147:308–314CrossRefGoogle Scholar
  38. Ruan H et al (2017) Runoff simulation by SWAT model using high-resolution gridded precipitation in the Upper Heihe River Basin, Northeastern Tibetan Plateau. Water 9:866CrossRefGoogle Scholar
  39. Running SW, Zhao M (2015) Daily GPP and annual NPP (MOD17A2/A3) products NASA Earth Observing System MODIS land algorithm MOD17 User’s GuideGoogle Scholar
  40. Sheehan T, Bachelet D, Ferschweiler K (2015) Projected major fire and vegetation changes in the Pacific Northwest of the conterminous United States under selected CMIP5 climate futures. Ecol Model 317:16–29CrossRefGoogle Scholar
  41. Shim C et al (2014) Evaluation of MODIS GPP over a complex ecosystem in East Asia: a case study at Gwangneung flux tower in Korea. Adv Space Res 54:2296–2308CrossRefGoogle Scholar
  42. Song X-D, Brus DJ, Liu F, Li D-C, Zhao Y-G, Yang J-L, Zhang G-L (2016) Mapping soil organic carbon content by geographically weighted regression: a case study in the Heihe River basin, China. Geoderma 261:11–22CrossRefGoogle Scholar
  43. Tachikawa T, Hato M, Kaku M, Iwasaki A Characteristics of ASTER GDEM version 2. In: 2011 IEEE International Geoscience and Remote Sensing Symposium, 24–29 July 2011 2011. pp 3657–3660. doi:
  44. Tao F, Zhang Z (2010) Dynamic responses of terrestrial ecosystems structure and function to climate change in China. J Geophys Res Biogeosci 115Google Scholar
  45. Theurillat J-P, Guisan A (2001) Potential impact of climate change on vegetation in the European Alps: a review. Clim Chang 50:77–109CrossRefGoogle Scholar
  46. Wang Y, Yang H, Yang D, Qin Y, Gao B, Cong Z (2016) Spatial interpolation of daily precipitation in a high mountainous watershed based on gauge observations and a regional climate model simulation. J Hydrometeorol 18:845–862CrossRefGoogle Scholar
  47. Wei Y et al. (2014) NACP MsTMIP: global and North American driver data for multi-model intercomparison. Data set. Available on-line [] from oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, USA.
  48. Wipf S, Stoeckli V, Bebi P (2009) Winter climate change in alpine tundra: plant responses to changes in snow depth and snowmelt timing. Clim Chang 94:105–121CrossRefGoogle Scholar
  49. Woodward BFI (1987) Climate and plant distribution. Cambridge University PreGoogle Scholar
  50. Wu B, Zhu W, Yan N, Feng X, Xing Q, Zhuang Q (2016) An improved method for deriving daily evapotranspiration estimates from satellite estimates on cloud-free days. IEEE J Selected Topics Appl Earth Obs Remote Sens 9:1323–1330Google Scholar
  51. Xiao Z, Liang S, Wang J, Xiang Y, Zhao X, Song J (2016) Long-time-series global land surface satellite leaf area index product derived From MODIS and AVHRR surface reflectance. IEEE Trans Geosci Remote Sens 54:5301–5318CrossRefGoogle Scholar
  52. Xiong Z, Yan X (2013) Building a high-resolution regional climate model for the Heihe River Basin and simulating precipitation over this region. Chin Sci Bull 58:4670–4678CrossRefGoogle Scholar
  53. Yang DW, Bing G, Yang J, Lei HM, Zhang YL, Yang HB, Cong ZT (2015) A distributed scheme developed for eco-hydrological modeling in the upper Heihe River. Sci China Earth Sci 58:36–45CrossRefGoogle Scholar
  54. Yang R-M et al (2016) Comparison of boosted regression tree and random forest models for mapping topsoil organic carbon concentration in an alpine ecosystem. Ecol Indic 60:870–878CrossRefGoogle Scholar
  55. Yang L, Feng Q, Yin Z, Wen X, Si J, Li C, Deo RC (2017) Identifying separate impacts of climate and land use/cover change on hydrological processes in upper stream of Heihe River, Northwest China. Hydrolog Process 31:1100–1112Google Scholar
  56. You N, Meng J, Zhu L (2018) Sensitivity and resilience of ecosystems to climate variability in the semi-arid to hyper-arid areas of Northern China: a case study in the Heihe River Basin. Ecol Res 33:161–174CrossRefGoogle Scholar
  57. Zhang A, Zheng C, Wang S, Yao Y (2015) Analysis of streamflow variations in the Heihe River Basin, northwest China: trends, abrupt changes, driving factors and ecological influences. J Hydrol Reg Stud 3:106–124Google Scholar
  58. Zhang X, Zhou J, Zheng Y (2016) Vegetation map of the Upper Heihe Basin V2.0 Heihe Plan Science Data Center, Lanzhou, ChinaGoogle Scholar
  59. Zhao Y, Rong Z, Zhang Y, Ye M, Jiang H, Zhao C (2017) Analysis of change in grassland area in the Heihe River basin over the past 30 years and prediction. Acta Pratacul Sin 26:1–15Google Scholar
  60. Zhou D, Zhao S, Liu S, Zhang L (2014) Modeling the effects of the Sloping Land Conversion Program on terrestrial ecosystem carbon dynamics in the Loess Plateau: a case study with Ansai County, Shaanxi province, China. Ecol Model 288:47–54CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Jiangsu Key Laboratory of Agricultural MeteorologyNanjing University of Information Science and TechnologyNanjingChina
  2. 2.Pacific Northwest Research Station, USDA Forest ServiceCorvallisUSA
  3. 3.Centers for Forest Disturbance Science, Southern Research Station, USDA Forest ServiceAthensUSA
  4. 4.Eastern Forest Environmental Threat Assessment Center, Southern Research Station, USDA Forest ServiceResearch Triangle ParkUSA

Personalised recommendations