Data underlying the publication: Water footprints and crop water use of 175 individual crops for 1990–2019 simulated with a global crop model
收藏DataCite Commons2025-05-19 更新2025-06-14 收录
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<strong>Overview</strong>Here we provide outputs of the global simulation of water footprints of crops with a process-based gridded crop model ACEA (see model description in the references). The model is based on FAO’s AquaCrop and covers 175 widely-grown crops in the 1990–2019 period at a 5 arcminute resolution (10 x 10 km). We partition water footprints into green (water from precipitation) and blue (from irrigation or capillary rise) and differentiate between rainfed and irrigated production systems. The outputs cover several variables, including unit water footprints (expressed in m<sup>3</sup> t<sup>-1</sup> yr<sup>-1</sup>), water footprints of crop production (m<sup>3</sup> yr<sup>-1</sup>), and crop water use (mm yr<sup>-1</sup>). For more information on methods, input data, validation, and uncertainties, please refer to the corresponding data descriptor paper published in Nature Scientific Data: doi.org/10.1038/s41597-024-03051-3<br>This dataset includes global gridded datasets (NetCDF4 files) and summary datasheets for national and global values (CSV files). For more information on each provided file, please refer to <em>readme.pdf</em><br><strong>Citation</strong>To cite the dataset, please refer to doi.org/10.1038/s41597-024-03051-3<br><strong>Version History</strong>2025-05-16 (v3): new files added to provide <strong>global gridded datasets for all individual crops & years</strong>: ><em> unit_wf_175_crops_annual_1990_2019.zip </em>– unit water footprints per crop in 1990–2019 for all combinations of production systems (rainfed, irrigated, total) with water types (green, blue, total) ><em> cwu_175_crops_annual_1990_2019.zip </em>– same but for crop water use ><em> harvested_area_175_crops_annual_1990_2019.zip </em>– rainfed, irrigated, and total harvested areas per crop in 1990–2019 ><em> production_175_crops_annual_1990_2019.zip </em>– same but for crop production ><em> classification_of_175_crops.csv </em>– crop classification for user convenience, based on FAOSTAT<br>2024-02-28 (v2): new files added to provide <strong>representative data for the current state of water footprints</strong>: ><em> global_wf_175_crops_average_2010_2019.csv </em>– global average values per crop over 2010–2019 ><em> national_wf_175_crops_average_2010_2019.csv </em>– same but for individual countries <br>2023-08-16 (v1): dataset uploaded for the first time (as described in the data descriptor paper)
<strong>概述</strong>:本数据集提供基于过程的网格化作物模型ACEA(详见参考文献中的模型说明)所产出的全球作物水足迹模拟结果。该模型以联合国粮食及农业组织(Food and Agriculture Organization of the United Nations, FAO)的AquaCrop为基础,以5弧分(约10×10千米)的分辨率覆盖1990–2019年间广泛种植的175种作物。我们将水足迹划分为绿水(来源于降水)与蓝水(来源于灌溉或毛管上升水)两类,并区分雨养与灌溉两种生产系统。数据集输出包含多项变量,包括单位水足迹(单位:m³ t⁻¹ yr⁻¹)、作物生产总水足迹(单位:m³ yr⁻¹)以及作物用水量(单位:mm yr⁻¹)。如需了解方法、输入数据、验证与不确定性的更多细节,请参阅发表于《Nature Scientific Data》的对应数据描述论文:doi.org/10.1038/s41597-024-03051-3。
本数据集包含全球网格化数据集(NetCDF4格式文件)以及用于国家与全球尺度数值汇总的数据表(CSV格式文件)。如需了解各提供文件的详细信息,请参阅<em>readme.pdf</em>
<strong>引用方式</strong>:如需引用本数据集,请参考doi.org/10.1038/s41597-024-03051-3
<strong>版本历史</strong>:
2025-05-16(v3):新增文件以提供<strong>全球所有单种作物与年份的网格化数据集</strong>:
> <em>unit_wf_175_crops_annual_1990_2019.zip</em>——1990–2019年间所有生产系统(雨养、灌溉、总计)结合水类型(绿水、蓝水、总计)组合下的单作物单位水足迹数据;
> <em>cwu_175_crops_annual_1990_2019.zip</em>——同上,但对应作物用水量数据;
> <em>harvested_area_175_crops_annual_1990_2019.zip</em>——1990–2019年间各作物的雨养、灌溉与总收获面积数据;
> <em>production_175_crops_annual_1990_2019.zip</em>——同上,但对应作物产量数据;
> <em>classification_of_175_crops.csv</em>——基于FAOSTAT编制的作物分类表,方便用户使用。
2024-02-28(v2):新增文件以提供<strong>当前水足迹现状的代表性数据</strong>:
> <em>global_wf_175_crops_average_2010_2019.csv</em>——2010–2019年间各作物的全球平均水足迹数值;
> <em>national_wf_175_crops_average_2010_2019.csv</em>——同上,但对应单个国家的数值。
2023-08-16(v1):首次上传本数据集(与数据描述论文中的描述一致)
提供机构:
4TU.ResearchData
创建时间:
2025-05-19
搜集汇总
数据集介绍

背景与挑战
背景概述
该数据集通过全球作物模型ACEA模拟了1990-2019年175种作物的水足迹和作物用水,空间分辨率为5弧分钟,区分绿水和蓝水来源以及雨养和灌溉系统。数据包括全球网格化的单位水足迹、作物用水、收获面积和产量等变量,并提供国家和全球汇总的CSV文件,适用于农业水资源管理和环境评估研究。
以上内容由遇见数据集搜集并总结生成



