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Biblioteca Scenarios of land cover in Karst area of Southwestern China

Scenarios of land cover in Karst area of Southwestern China

Scenarios of land cover in Karst area of Southwestern China

Resource information

Date of publication
Dezembro 2015
Resource Language
ISBN / Resource ID
AGRIS:US201500213619
Pages
6407-6420

The method of surface modeling of land cover scenarios (SMLCS) has been improved to simulate the scenarios of land cover in the karst areas of southwestern China. On the basis of the observation monthly climatic data collected from 782 weather stations of China during the period from 1981 to 2010, the climatic scenarios data of RCP26, RCP45 and RCP85 scenarios released by CMIP5, and the land cover current data of China in 2010, the land cover scenarios of southwestern China were respectively simulated. The average total accuracy and Kappa index of SMLCS are 90.25 and 87.96 %, respectively. The results show that there would be a very apparent similar variety on the spatial distribution pattern of land cover in the karst areas of southwestern China under all the three scenarios during the period from 2010 to 2100, but there would have the different change rate. In general, the change rate of land cover type under RCP85 scenario would be the fastest, then under RCP45 scenario, and under RCP26 would be the slowest. From 2010 to 2100, deciduous coniferous forest, deciduous broadleaf forest, grassland, cropland, nival area, and desert and bare rock would have a gradual decrease trend, while evergreen coniferous forest, evergreen broadleaf forests, mixed forest, scrublands, wetlands, construction built-up land, and water bodies body would gradually increase in karst areas of southwestern China, in which wetland would have the fastest increase rate (5.28 % per decade on average), and desert and bare rock would decrease with the fastest rate (2.34 % per decade on average).

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Authors and Publishers

Author(s), editor(s), contributor(s)

Fan, Zemeng
Li, Jing
Yue, Tianxiang
Zhou, Xun
Lan, Anjun

Publisher(s)
Data Provider
Geographical focus