Wang Minglei, Shi Wenjiao
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China.
Sci Data. 2024 Dec 18;11(1):1373. doi: 10.1038/s41597-024-04185-0.
Accurately quantifying agricultural water use is essential for protecting agricultural systems from the risk of water scarcity and promoting sustainable water management. While previous studies have innovatively provided spatially explicit analyses or datasets, they tend to have relatively coarse resolution (~8.3 km), and inadequately considered precise localization parameters. Here, we produced annual blue and green water use for 15 main crops with a resolution of 1 km for the years 1991-2019 in China. Firstly, we estimated the yearly crop blue and green water use at the site scale by incorporating more localized input parameters using a dynamic water balance model. Then, the random forest model was combined with site-scale simulation results to generate spatial predictions of blue and green water for each crop from 1991 to 2019. The resulting maps showed a high correlation with locally observed values at field stations (R = 0.95), statistics (R = 0.77), and exhibited some strengths compared with existing datasets that covered various scales. This dataset can play a key role in devising sustainable water management strategies.
准确量化农业用水对于保护农业系统免受水资源短缺风险和促进可持续水资源管理至关重要。虽然先前的研究创新性地提供了空间明确的分析或数据集，但它们的分辨率往往相对较粗（约8.3公里），并且对精确的定位参数考虑不足。在此，我们针对1991 - 2019年中国15种主要作物生成了分辨率为1公里的年度蓝水和绿水用水量。首先，我们通过使用动态水平衡模型纳入更多本地化输入参数，在站点尺度上估算了年度作物蓝水和绿水用水量。然后，将随机森林模型与站点尺度模拟结果相结合，以生成1991年至2019年每种作物蓝水和绿水的空间预测。所得地图与实地观测站的局部观测值（R = 0.95）、统计数据（R = 0.77）显示出高度相关性，并且与涵盖各种尺度的现有数据集相比展现出一些优势。该数据集在制定可持续水资源管理策略中可发挥关键作用。
