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Mid- to high-latitude Asia (MHA) is one of the regions with the strongest warming trend and it is also a region where ecosystems are most sensitive to climate variability. However, how the vegetation in the region will change in the future remains uncertain. Using observation-based Leaf Area Index (LAI) and meteorological data and the multiple regression method, this study analyzes the response of vegetation in the MHA to climate elements during 1982–2020. Then, machine learning prediction models based on the Random Forest (RF) and Extreme Random Tree (ERT) algorithms are built and validated. Based on the calibrated meteorological fields from 17 Coupled Model Intercomparison Project Phase 6 (CMIP6) models under intermediate (SSP2-4.5) and high (SSP5-8.5) emission scenarios and the machine learning models, the LAI over the MHA in 2021–2100 is projected. The historical long-term increasing trends of LAI in the MHA since 1982 are found to be mainly caused by the increasing near-surface air temperature, while the interannual variations of LAI are also greatly affected by precipitation and surface downward solar radiation, especially in summer. The LAI over most of the MHA shows a significant increasing trend in the future, except over some dry areas, and the increasing trends are stronger under the SSP5-8.5 scenario than under the SSP2-4.5 scenario. [ABSTRACT FROM AUTHOR]

• A framework developed for investigating cumulative impact of damming on water. • Tibetan damming having serious downstream impact but limited to local scale. • Runoff increase under warmer climate most likely offsetting damming impact. • This framework can be applicable for various anthropogenic impact analysis. Damming is one of the major methods that human being secure reliable freshwater. It can change flow magnitude and regime along a river, particularly for Transboundary Rivers shared by multiple jurisdictions. This study develops a coupled framework to assess both the current and future cumulative impacts of damming on downstream water availability for data-sparse regions like the Tibetan Plateau. The framework is applied to the Brahmaputra River Basin (BRB), where dam developments on the Tibetan Plateau are highly concerned. The results show that, despite being part of the 'Asian water tower', the Yarlung Tsangpo River (YTR) contributes just 15–16% to Brahmaputra streamflow, for 48% of its contributing area. We found that the maximum impact due to five recently constructed dams in YTR suggests about 8–11% reduction of mean annual streamflow at outlet of Brahmaputra River. The impacts of damming would be largely offset by runoff increases under the climate scenarios projected by 28 CMIP5 model outputs from RCP4.5 median-level emission scenarios and 29 CMIP5 model outputs from RCP8.5 high-level emission scenarios for the period of 2049 to 2075, compared to the historical period of 1979 to 2005. Our results suggest that it is necessary to use our framework or a similar one to comprehensively evaluate the maximum possible damming impact across the Transboundary Rivers. [ABSTRACT FROM AUTHOR]