CNSW 1.0: Prefectural Reconstruction of China's Surface Water Resources Using Machine Learning Methods

A comprehensive and long-term dataset of prefectural surface water resources is crucial for effective water resource management in China. However, there has been a significant gap in the availability of such datasets, with no existing datasets providing comprehensive long-term coverage. To address this gap, we have developed CNSW 1.0, the first long-term (2000-2020) dataset of prefectural surface water resources in China. Utilizing surface water resource data from official water resource bulletins, we employed 14 advanced machine learning models to reconstruct the CNSW 1.0 dataset. The resulting dataset exhibits high accuracy, with an R² of 0.978 for total surface water resources and acceptable level of bias across China. CNSW 1.0 not only outperforms existing datasets like CNRD v1.0, GRUN, and ISIMIP in terms of simulation accuracy and spatial distribution but also fills a critical gap in water resource data for China. This dataset is expected to be an invaluable tool for developing more informed water resource management strategies at the administrative level in China, particularly in the context of climate change.
The CNSW 1.0 encompasses surface water resource data for 341 prefectural-level administrative units in mainland China over a period from 2000 to 2020, comprising a total of 16 surface water datasets. The data are stored in both CSV and shapefile (SHP) formats. The naming convention for annual spatial distribution shapefiles follows the structure "CNSW_1.0_YYY_ZZZZ.shp," where "YYY" represents the model’s name, and "ZZZZ" denotes the year. The naming convention for multi-year average spatial distribution shapefiles is "CNSW_1.0_mean_YYY.shp," and for the spatial evolution trend shapefiles, it is "CNSW_1.0_trend_YYY.shp." A total of 368 shapefiles are provided, which approximately take up 7.2GB of disk space. Additional information, such as region, province, and prefecture area, is also stored in the files.