A cellular automata downscaling based 1 km global land use datasets (2010–2100)

Global climate and environmental change studies require detailed land-use and land-cover (LULC) information about the past, present, and future. In this paper, we discuss a methodology for downscaling coarse-resolution (i.e., half-degree) future land use scenarios to finer (i.e., 1 km) resolutions at the global scale using a grid-based spatially explicit cellular automata (CA) model. We account for spatial heterogeneity from topography, climate, soils, and socioeconomic variables. The model uses a global 30 m land cover map (2010) as the base input, a variety of biogeographic and socioeconomic variables, and an empirical analysis to downscale coarse-resolution land use information (specifically urban, crop and pasture). The output of this model offers the most current and finest-scale future LULC dynamics from 2010 to 2100 (with four representative concentration pathway (RCP) scenarios—RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5) at a 1 km resolution within a globally consistent framework. The data are freely available for download, and will enable researchers to study the impacts of LULC change at the local scale.摘要全球气候与环境变化研究需要详细的关于过去、现在和未来的土地利用信息。本研究探讨了通过降尺度的方法来生成1 km分辨率2010-2100年全球土地利用数据集的方法 。主要的输入数据为30米分辨率的2010年全球地表覆盖产品(FROM-GLC) ,同时结合0.5度的全球土地利用数据(LUH)和不同分辨率的地理空间异质性要素(包括地形、气候、土壤和社会经济条件等) ,通过元胞自动机模型来生成未来4种RCP情景的1 km土地利用的动态比例产品。

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