水质
环境科学
质量(理念)
分辨率(逻辑)
水资源管理
计算机科学
生态学
生物
人工智能
哲学
认识论
作者
Shengji Luan,Huixiao Pan,Ruoque Shen,Xiaosheng Xia,Hongtao Duan,Wenping Yuan,Jing Wei
标识
DOI:10.1038/s41597-025-04915-y
摘要
Water quality parameters (pH, dissolved oxygen (DO), total nitrogen (TN, includes both organic nitrogen and inorganic nitrogen), total phosphorus (TP), permanganate index (CODMn), turbidity (Tur), electrical conductivity (EC), and dissolved organic carbon (DOC)) are important to evaluate the ecological health of lakes and reservoirs. In this research, we developed a monthly dataset of these key water quality parameters from 2000 to 2023 for nearly 180,000 lakes and reservoirs across China, using the random forest (RF) models. These RF models took into account the impacts of climate, soil properties, and anthropogenic activities within basins of studied lakes and reservoirs, and effectively captured the spatial and temporal variations of their water quality parameters with correlation coefficients (R2) ranging from 0.65 to 0.76. Interestingly, an increase in Tur and EC was observed during this period, while pH, DO, and other parameters showed minimal fluctuations. This dataset is of significant value for further evaluating the ecological, environmental, and climatic functions of aquatic ecosystems.
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