Climate change indirectly enhances sandstorm prevention services by altering ecosystem patterns on the Qinghai-Tibet Plateau

• 作者: MENG Nan YANG Yan-zheng ZHENG Hua LI Ruo-nan
• 来源期刊: 山地科学学报（英文版）
• 年，卷（期）: 2021,（7）
• 所属期刊栏目: Special Topic on Ecological Assets and Ecosystem Services in the Qinghai-Tibet Plateau
• 页码范围: 1711-1724
• 页数: 14页
• 语种: 英文

摘要

Climate change influences both ecosystems and ecosystem services.The impacts of climate change on ecosystems and ecosystem services have been separately documented.However,it is less well known how ecosystem changes driven by climate change will influence ecosystem services,especially in climate-sensitive regions.Here,we analyzed future climate trends between 2040 and 2100 under four Shared Socioeconomic Pathway(SSP)scenarios(SSP1-2.6,SSP2-4.5,SSP3-7.0,and SSP5-8.5)from the Coupled Model Intercomparison Project 6(CMIP6).We quantified their impacts on ecosystems patterns and on the ecosystem service of sandstorm prevention on the Qinghai-Tibet Plateau(QTP),one of the most climate-sensitive regions in the world,using Random Forest model(RF)and Revised Wind Erosion Equation(RWEQ).Strong warming(0.04℃/yr)and wetting(0.65 mm/yr)trends were projected from 2015 to 2100.Under these trends,there will be increased interspersion in the pattern of grassland and sparse vegetation with meadow and swamp vegetation,although their overall area will remain similar,while the areas of shrub and needle-leaved forest classes will increase and move toward higher altitudes.Driven by the changes in ecosystem patterns caused by climate change indirectly,grassland will play an irreplaceable role in providing sandstorm prevention services,and sandstorm prevention services will increase gradually from 2040 to 2100(1.059-1.070 billion tons)on the QTP.However,some areas show a risk of deterioration in the future and these should be the focus of ecological rehabilitation.Our research helps to understand the cascading relationship among climate change,ecosystem patterns and ecosystem services,which provides important spatio-temporal information for future ecosystem service management.