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中国不同共享社会经济路径-代表性浓度路径情景下基于植物功能类型分类的土地利用变化预测。

Projections of land use changes under the plant functional type classification in different SSP-RCP scenarios in China.

作者信息

Liao Weilin, Liu Xiaoping, Xu Xiyun, Chen Guangzhao, Liang Xun, Zhang Honghui, Li Xia

机构信息

Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China.

Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China.

出版信息

Sci Bull (Beijing). 2020 Nov 30;65(22):1935-1947. doi: 10.1016/j.scib.2020.07.014. Epub 2020 Jul 13.

Abstract

Land use projections are crucial for climate models to forecast the impacts of land use changes on the Earth's system. However, the spatial resolution of existing global land use projections (e.g., 0.25°×0.25° in the Land-Use Harmonization (LUH2) datasets) is still too coarse to drive regional climate models and assess mitigation effectiveness at regional and local scales. To generate a high-resolution land use product with the newest integrated scenarios of the shared socioeconomic pathways and the representative concentration pathways (SSPs-RCPs) for various regional climate studies in China, here we first conduct land use simulations with a newly developed Future Land Uses Simulation (FLUS) model based on the trajectories of land use demands extracted from the LUH2 datasets. On this basis, a new set of land use projections under the plant functional type (PFT) classification, with a temporal resolution of 5 years and a spatial resolution of 5 km, in eight SSP-RCP scenarios from 2015 to 2100 in China is produced. The results show that differences in land use dynamics under different SSP-RCP scenarios are jointly affected by global assumptions and national policies. Furthermore, with improved spatial resolution, the data produced in this study can sufficiently describe the details of land use distribution and better capture the spatial heterogeneity of different land use types at the regional scale. We highlight that these new land use projections at the PFT level have a strong potential for reducing uncertainty in the simulation of regional climate models with finer spatial resolutions.

摘要

土地利用预测对于气候模型预测土地利用变化对地球系统的影响至关重要。然而,现有的全球土地利用预测的空间分辨率(例如,土地利用协调(LUH2)数据集中为0.25°×0.25°)仍然过于粗糙,无法驱动区域气候模型并在区域和地方尺度上评估缓解效果。为了生成具有共享社会经济路径和代表性浓度路径(SSPs-RCPs)最新综合情景的高分辨率土地利用产品,用于中国的各种区域气候研究,我们首先使用基于从LUH2数据集中提取的土地利用需求轨迹的新开发的未来土地利用模拟(FLUS)模型进行土地利用模拟。在此基础上,生成了一套新的基于植物功能类型(PFT)分类的土地利用预测,时间分辨率为5年,空间分辨率为5公里,涵盖中国2015年至2100年的八个SSP-RCP情景。结果表明,不同SSP-RCP情景下土地利用动态的差异受到全球假设和国家政策的共同影响。此外,随着空间分辨率的提高,本研究生成的数据能够充分描述土地利用分布的细节,并更好地捕捉区域尺度上不同土地利用类型的空间异质性。我们强调,这些PFT水平的新土地利用预测在降低具有更精细空间分辨率的区域气候模型模拟中的不确定性方面具有很大潜力。

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