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适用于1.5°C气候的1公里分辨率全球土地系统地图。

Global land system maps at 1 km resolution for 1.5 °C climate.

作者信息

Gao Yifan, McJeon Haewon, Ou Yang, Chen Li, Lv Jiaying, Fang Delin, Wang Yuanhui, Ye Sijing, Song Changqing, Gao Peichao

机构信息

State Key Laboratory of Earth Surface Processes and Hazards Risk Governance, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China.

Graduate School of Green Growth & Sustainability, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.

出版信息

Sci Data. 2025 Apr 22;12(1):672. doi: 10.1038/s41597-025-04991-0.

Abstract

Climate pledges are a key pathway for achieving temperature control but also exert profound cascading effects on the global Earth system. Evaluating such cascading effects often requires land change forecast products. However, current forecast products are all based on shared socioeconomic pathways (SSPs) and representative concentration pathways (RCPs). We proposed a comprehensive approach to generating 2100 land system maps under the 1.5 °C climate scenario and the baseline scenario by harmonizing the Global Change Analysis Model (GCAM), Globeland30, and the Land-N2N model. The maps exhibit a spatial resolution of 1 km and consist of 30 land system types, reflecting local high, medium, and low densities of land cover types. Additionally, we evaluated the Land-N2N using the kappa coefficient and figure of merit (FoM). The average kappa coefficient and FoM values across all the basins were 83.14% and 8.48%, respectively, demonstrating the reliability of the Land-N2N model in simulating land system changes. The dataset provides finer resolution quantitative support for global climate change mitigation and essential data for related research.

摘要

气候承诺是实现温度控制的关键途径,但也会对全球地球系统产生深远的级联效应。评估此类级联效应通常需要土地变化预测产品。然而,目前的预测产品均基于共享社会经济路径(SSP)和代表性浓度路径(RCP)。我们提出了一种综合方法,通过协调全球变化分析模型(GCAM)、Globeland30和Land-N2N模型,生成1.5°C气候情景和基线情景下的2100年土地系统地图。这些地图的空间分辨率为1公里,由30种土地系统类型组成,反映了当地土地覆盖类型的高、中、低密度。此外,我们使用kappa系数和品质因数(FoM)对Land-N2N进行了评估。所有流域的平均kappa系数和FoM值分别为83.14%和8.48%,这表明Land-N2N模型在模拟土地系统变化方面的可靠性。该数据集为全球气候变化缓解提供了更高分辨率的定量支持,并为相关研究提供了重要数据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2e0/12015323/ccdd831ab7c6/41597_2025_4991_Fig1_HTML.jpg

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