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来源类型Article
规范类型其他
DOI10.1002/joc.3892
Agroclimatic conditions in China under climate change scenarios projected from regional climate models.
Tian Z; Yang X; Sun L; Fischer G; Liang Z; Pan J
发表日期2014
出处International Journal of Climatology 34 (9): 2988-3000
出版年2014
语种英语
摘要The recent warming trends have led to rising concerns on how the changing climate has been altering and will continue to alter agroclimatic conditions in China. In this study, high resolution climate scenarios from regional climate models (RCMs) are used as input to the agro-ecological zones (AEZ) model for China and to compute a comprehensive set of agroclimatic indicators. Uncertainties in the projected impacts of climate change on agroclimatic conditions are also discussed with respect to (1) RCM outputs for different emission scenarios, and (2) differences in projections obtained from two different RCMs under the same emission scenario. The results indicate a significant extension of the crop growing period in tandem with the rising temperatures during the crop growing season, which may lead to increase in multi-cropping opportunities at high latitudes and thus advance the total potential output per unit of cropland. However, the results show that the Southwest China will experience a significant reduction in the value of humidity index, implying severe challenges for future agricultural development in the region. Spatially explicit patterns of changes in future agroclimatic conditions, as revealed in this study, can be beneficial for policy-makers, farming communities and other stakeholders to assess risk factors, design adaptation and mitigation measures, and improve management practices at the local to regional scales.
主题Water (WAT)
关键词Agroclimatic conditions Regional climate model AEZ model Climate change
URLhttp://pure.iiasa.ac.at/id/eprint/10900/
来源智库International Institute for Applied Systems Analysis (Austria)
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资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/129877
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Tian Z,Yang X,Sun L,et al. Agroclimatic conditions in China under climate change scenarios projected from regional climate models.. 2014.
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