基于SSP-RCP情景的2015-2100年未来全球1公里分辨率土地数据集

人类活动的持续加强和气候变化的日益加剧，共同影响着全球土地覆被的变化。基于情景的土地变化模拟作为一种能够评估潜在土地变化影响的有效工具，已获得广泛应用。然而，当前全球尺度的未来土地预测产品普遍存在分辨率低、情景设置不合理、地类覆盖少等缺陷。为此，我们研发了2015-2100年期间1公里分辨率的全球土地预测数据集系列，采用了IPCC最新发布的社会经济与气候变化耦合情景SSP-RCP。
该系列实际包含两个子数据集：1）包含7种地类的1公里全球土地利用/覆被（LULC）数据集；2）包含20种地类的1公里全球植物功能型（PFT）土地数据集。LULC数据集的生成融合了CMIP6官方数据集提供的"自上而下"的土地需求约束，以及元胞自动机（CA）实现的"自下而上"的空间模拟。官方数据源的土地需求确保了不同SSP-RCP情景下土地变化轨迹的权威性，而CA模型在全球不同区域的分别执行则有效体现了空间异质性。基于LULC数据集，我们进一步根据气候数据将其地类细分为20种类型。验证结果表明，我们的全球土地模拟达到了令人满意的精度，各情景中不同地类的空间变化恰当地反映了相应情景的核心发展路径。我们相信，该数据集凭借其在分辨率、情景设置和地类分类方面的优势，能为气候模型等气候研究领域提供更强有力的数据支撑。

Intensifying human activities and accelerating climate change jointly drive global land cover changes. Scenario-based land change modeling, as an effective tool for assessing the impacts of potential land changes, has been widely adopted. However, existing global-scale future land projection products generally suffer from drawbacks such as low spatial resolution, unreasonable scenario design, and limited land cover category coverage. To address these issues, we developed a series of global land projection datasets spanning 2015–2100 at 1 km spatial resolution, adopting the newly released IPCC-coupled socioeconomic and climate change scenarios SSP-RCP. This series actually consists of two sub-datasets: 1) A 1 km global Land Use/Land Cover (LULC) dataset with 7 land categories; 2) A 1 km global Plant Functional Type (PFT) land dataset with 20 land categories. The LULC dataset was generated by integrating the "top-down" land demand constraints provided by official CMIP6 datasets and the "bottom-up" spatial simulation implemented via Cellular Automata (CA). The land demand from official data sources guarantees the authority of land change trajectories under different SSP-RCP scenarios, while the separate execution of CA models across global regions effectively captures spatial heterogeneity. Based on the LULC dataset, we further subdivided the land categories into 20 types using climate data. Validation results demonstrate that our global land simulation achieves satisfactory accuracy, with the spatial changes of different land categories in each scenario properly reflecting the core development pathways of the corresponding scenarios. We believe that this dataset, with its advantages in spatial resolution, scenario design, and land category classification, can provide more robust data support for climate research fields such as climate modeling.

搜集汇总

背景与挑战

背景概述

该数据集是基于IPCC SSP-RCP耦合情景的2015-2100年未来全球土地投影数据，分辨率为1公里，包含两个子集：7种土地类型的LULC数据集和20种土地类型的PFT数据集。它通过整合CMIP6官方土地需求数据和FLUS模型进行空间模拟，解决了现有全球土地投影产品分辨率低、情景不适用和土地类型少的问题，为气候研究提供了高精度数据支持。