Modeling responses of the meadow steppe dominated by Leymus chinensis to climate change |
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| Authors: | Yuhui Wang Guangsheng Zhou Yonghe Wang |
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| Institution: | (1) Laboratory of Quantitative Vegetation Ecology, Institute of Botany, The Chinese Academy of Sciences, Beijing, 100093, China;(2) Institute of Atmospheric Environment, China Meteorological Administration, Shenyang, 110016, China;(3) Canadian Forest Service, Northern Forestry Centre, 5320-122 St., Edmonton, AB, Canada, T6H3S5 |
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| Abstract: | Grassland is one of the most widespread vegetation types worldwide and plays a significant role in regional climate and global
carbon cycling. Understanding the sensitivity of Chinese grassland ecosystems to climate change and elevated atmospheric CO2 and the effect of these changes on the grassland ecosystems is a key issue in global carbon cycling. China encompasses vast
grassland areas of 354 million ha of 17 major grassland types, according to a national grassland survey. In this study, a
process-based terrestrial model the CENTURY model was used to simulate potential changes in net primary productivity (NPP)
and soil organic carbon (SOC) of the Leymus chinensis meadow steppe (LCMS) under different scenarios of climatic change and elevated atmospheric CO2. The LCMS sensitivities, its potential responses to climate change, and the change in capacity of carbon stock and sequestration
in the future are evaluated. The results showed that the LCMS NPP and SOC are sensitive to climatic change and elevated CO2. In the next 100 years, with doubled CO2 concentration, if temperature increases from 2.7-3.9˚C and precipitation increases by 10% NPP and SOC will increase by 7-21%
and 5-6% respectively. However, if temperature increases by 7.5-7.8˚C and precipitation increases by only 10% NPP and SOC
would decrease by 24% and 8% respectively. Therefore, changes in the NPP and SOC of the meadow steppe are attributed mainly
to the amount of temperature and precipitation change and the atmospheric CO2 concentration in the future. |
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