Panda Manas Ranjan, Kim Yeonjoo
Department of Civil & Environmental Engineering, Yonsei University, Seoul, South Korea.
Sci Data. 2024 Dec 5;11(1):1331. doi: 10.1038/s41597-024-04148-5.
Spatially distributed industrial water use (IWU) data are essential for effective region-specific water resource management. Such data are often scarce in underdeveloped and developing countries. We propose a random forest regression model to predict IWU at a spatial resolution of 0.5° by combining socioeconomic, climatic, and geographical datasets. These datasets included nighttime light (NL), global power plants, country-wise IWU, elevation data (DEM), gross domestic product (GDP), road density (RD), cropland (CRP), wetland (WLND), population (POP), precipitation (PCP), temperature (TEMP), wet days (WET) per year, and potential evapotranspiration (PET). The results show that RD, CRP, POP, GDP, DEM, and TEMP were the most influential variables. We assessed the accuracy of the global IWU map using published and observed datasets from various sources for the major industrialized countries such as the USA and China from 2000 to 2015. The predicted global map shows a reasonable distribution of grid-wise values for highly industrialized countries and data-scarce regions. Thus, fine-resolution maps can support local planning and decision-making for large basins worldwide.
空间分布的工业用水（IWU）数据对于有效的区域特定水资源管理至关重要。在欠发达国家和发展中国家，此类数据往往很稀缺。我们提出了一种随机森林回归模型，通过结合社会经济、气候和地理数据集，以0.5°的空间分辨率预测工业用水。这些数据集包括夜间灯光（NL）、全球发电厂、国家层面的工业用水、海拔数据（DEM）、国内生产总值（GDP）、道路密度（RD）、农田（CRP）、湿地（WLND）、人口（POP）、降水量（PCP）、温度（TEMP）、每年的湿日数（WET）以及潜在蒸散量（PET）。结果表明，道路密度、农田、人口、国内生产总值、海拔数据和温度是最具影响力的变量。我们使用来自各种来源的已发表和观测数据集，对2000年至2015年期间美国和中国等主要工业化国家的全球工业用水地图的准确性进行了评估。预测的全球地图显示了高度工业化国家和数据稀缺地区的网格值的合理分布。因此，高分辨率地图可以支持全球大型流域的地方规划和决策。
