Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, China.
Institute of Future Cities, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR.
Sci Data. 2022 Mar 30;9(1):125. doi: 10.1038/s41597-022-01208-6.
This study presents a global land projection dataset with a 1-km resolution that comprises 20 land types for 2015-2100, adopting the latest IPCC coupling socioeconomic and climate change scenarios, SSP-RCP. This dataset was produced by combining the top-down land demand constraints afforded by the CMIP6 official dataset and a bottom-up spatial simulation executed via cellular automata. Based on the climate data, we further subdivided the simulation products' land types into 20 plant functional types (PFTs), which well meets the needs of climate models for input data. The results show that our global land simulation yields a satisfactory accuracy (Kappa = 0.864, OA = 0.929 and FoM = 0.102). Furthermore, our dataset well fits the latest climate research based on the SSP-RCP scenarios. Particularly, due to the advantages of fine resolution, latest scenarios and numerous land types, our dataset provides powerful data support for environmental impact assessment and climate research, including but not limited to climate models.
本研究提出了一个全球土地预估数据集，分辨率为 1 公里，涵盖 2015-2100 年的 20 种土地类型，采用了最新的 IPCC 耦合社会经济和气候变化情景，即 SSP-RCP。该数据集是通过结合 CMIP6 官方数据集提供的自上而下的土地需求约束和通过元胞自动机执行的自下而上的空间模拟而生成的。基于气候数据，我们进一步将模拟产品的土地类型细分为 20 种植物功能类型（PFTs），这很好地满足了气候模型对输入数据的需求。结果表明，我们的全球土地模拟具有令人满意的精度（Kappa=0.864，OA=0.929 和 FoM=0.102）。此外，我们的数据集很好地符合基于 SSP-RCP 情景的最新气候研究。特别是，由于具有精细分辨率、最新情景和众多土地类型的优势，我们的数据集为环境影响评估和气候研究提供了强大的数据支持，包括但不限于气候模型。
