High Resolution Water Quality Dataset of Chinese Lakes and Reservoirs from 2000 to 2023

Water quality parameters (pH, dissolved oxygen (DO), total nitrogen (TN, includes both organic nitrogen and inorganic nitrogen), total phosphorus (TP), permanganate index (COD<sub>Mn</sub>), 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 (R<sup>2</sup>) 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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Publication Details

Journal
Scientific Data
Published
2025-04-04
DOI
https://doi.org/10.1038/s41597-025-04915-y
Citations
9
Primary Topic
Hydrology and Watershed Management Studies
Type
article
Field-Weighted Citation Impact
7.35

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article

High Resolution Water Quality Dataset of Chinese Lakes and Reservoirs from 2000 to 2023

Hongtao Duan, Shengji Luan, Ruoque Shen, Xiaosheng Xia et al.
9 citations
Scientific Data
Hydrology and Watershed Management Studies
7.35
article

High Resolution Water Quality Dataset of Chinese Lakes and Reservoirs from 2000 to 2023

Hongtao Duan, Shengji Luan, Ruoque Shen, Xiaosheng Xia, Wenping Yuan, Jing Wei, Huixiao Pan
article en
9 citations

Abstract

Water quality parameters (pH, dissolved oxygen (DO), total nitrogen (TN, includes both organic nitrogen and inorganic nitrogen), total phosphorus (TP), permanganate index (COD<sub>Mn</sub>), 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 (R<sup>2</sup>) 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.

Scientific DataVol. 12(1)
Sun Yat-sen University (CN), Chinese Academy of Sciences (CN), Peking University (CN), Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) (CN), Nanjing Institute of Geography and Limnology (CN)
National Natural Science Foundation of China
Clean water and sanitation
Openalex Percentile: Top 3%
Hydrology and Watershed Management Studies
7.35
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