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全球每月部门用水量 2010-2100 年 0.5°分辨率在不同的未来情景下。

Global monthly sectoral water use for 2010-2100 at 0.5° resolution across alternative futures.

机构信息

Joint Global Change Research Institute, Pacific Northwest National Laboratory, 5825 University Research Ct., Suite 3500, College Park, MD, 20740, USA.

Department of Forest and Wildlife Ecology, College of Agriculture & Life Sciences, University of Wisconsin - Madison, Russell Labs, 1630 Linden Drive, Madison, WI, 53706, USA.

出版信息

Sci Data. 2023 Apr 11;10(1):201. doi: 10.1038/s41597-023-02086-2.

Abstract

Water usage is closely linked with societal goals that are both local and global in scale, such as sustainable development and economic growth. It is therefore of value, particularly for long-term planning, to understand how future sectoral water usage could evolve on a global scale at fine resolution. Additionally, future water usage could be strongly shaped by global forces, such as socioeconomic and climate change, and the multi-sector dynamic interactions those forces create. We generate a novel global gridded monthly sectoral water withdrawal and consumption dataset at 0.5° resolution for 2010-2100 for a diverse range of 75 scenarios. The scenarios are harmonized with the five Shared Socioeconomic Pathways (SSPs) and four Representative Concentration Pathways (RCPs) scenarios to support its usage in studies evaluating the implications of uncertain human and earth system change for future global and regional dynamics. To generate the data, we couple the Global Change Analysis Model (GCAM) with a land use spatial downscaling model (Demeter), a global hydrologic framework (Xanthos), and a water withdrawal downscaling model (Tethys).

摘要

水资源利用与地方和全球规模的社会目标密切相关,如可持续发展和经济增长。因此,了解未来全球范围内各部门水资源利用如何在精细分辨率下演变具有重要价值,特别是对于长期规划而言。此外,未来的水资源利用可能会受到全球力量的强烈影响,如社会经济和气候变化,以及这些力量所产生的多部门动态相互作用。我们生成了一个新颖的全球网格化月度部门水资源提取和消费数据集,分辨率为 0.5°,涵盖了 75 种情景,时间范围为 2010-2100 年。这些情景与五个共享社会经济途径(SSPs)和四个代表性浓度途径(RCPs)情景相协调,以支持在评估不确定的人类和地球系统变化对未来全球和区域动态的影响的研究中使用。为了生成数据,我们将全球变化分析模型(GCAM)与土地利用空间降尺度模型(Demeter)、全球水文框架(Xanthos)和水资源提取降尺度模型(Tethys)耦合在一起。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/36fa/10090044/2bbb5710be49/41597_2023_2086_Fig1_HTML.jpg

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