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共享社会经济路径下2020 - 2100年印度河流域的网格化人口结构数据集

Gridded Population Structure Datasets for the Indus River Basin under Shared Socioeconomic Pathways 2020-2100.

作者信息

Haider Barira, Jing Cheng, Su Buda, Hussain Muhammad Asad, Ashraf Arshad, Huang Jinlong, Wang Yanjun, Jiang Tong

机构信息

State Key Laboratory of Climate System Prediction and Risk Management, Nanjing University of Information Science & Technology, Nanjing, 210044, China.

Key Laboratory for Climate Risk and Urban-Rural Smart Governance, School of Geography, Jiangsu Second Normal University, Nanjing, 210013, China.

出版信息

Sci Data. 2025 Jul 10;12(1):1188. doi: 10.1038/s41597-025-05537-0.

Abstract

The Indus River Basin is one of the most densely populated transboundary river basins in the world and is the region with the most serious water disputes. Given the current population's rapid growth, inclusive and high-resolution datasets are urgently needed to assess how this growth will affect the distribution of resources and the sustainability of the environment. Here we present a population gridded dataset for the Indus River Basin with a resolution of 2.5 arc-minutes (~5 km). Based on the historical population distribution and the provincial (state) demographic parameters of the four countries, Afghanistan, China, India, and Pakistan, we projected the population size and structure (age, sex) changes under the Shared Socioeconomic Pathways (SSP1-5) for the period of 2020-2100 on each grid in the Indus River Basin. The dataset was well verified by comparing it with the observed population gridded dataset in 62,140 grids in the basin. The dataset can be useful sources for further research in resource management, sustainable development initiatives, and assessment of climate change impact.

摘要

印度河流域是世界上人口最密集的跨境流域之一,也是水资源争端最严重的地区。鉴于当前人口的快速增长,迫切需要具有包容性和高分辨率的数据集,以评估这种增长将如何影响资源分配和环境可持续性。在此,我们展示了一个印度河流域人口网格化数据集,分辨率为2.5弧分(约5公里)。基于阿富汗、中国、印度和巴基斯坦这四个国家的历史人口分布和省级(邦)人口统计参数,我们预测了2020年至2100年期间印度河流域每个网格在共享社会经济路径(SSP1 - 5)下的人口规模和结构(年龄、性别)变化。通过将该数据集与流域内62140个网格的实测人口网格化数据集进行比较,对其进行了充分验证。该数据集可为资源管理、可持续发展倡议及气候变化影响评估等进一步研究提供有用的数据来源。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f875/12246191/faf32012ddf5/41597_2025_5537_Fig1_HTML.jpg

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