High Resolution Water Quality Dataset of Chinese Lakes and Reservoirs from 2000 to 2023
Abstract 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 th...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-04915-y |
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| author | Shilong Luan Huixiao Pan Ruoque Shen Xiaosheng Xia Hongtao Duan Wenping Yuan Jing Wei |
| author_facet | Shilong Luan Huixiao Pan Ruoque Shen Xiaosheng Xia Hongtao Duan Wenping Yuan Jing Wei |
| author_sort | Shilong Luan |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-f5cef055343f4c36bd2c9e2c12441cff |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-f5cef055343f4c36bd2c9e2c12441cff2025-08-20T04:01:25ZengNature PortfolioScientific Data2052-44632025-04-0112111510.1038/s41597-025-04915-yHigh Resolution Water Quality Dataset of Chinese Lakes and Reservoirs from 2000 to 2023Shilong Luan0Huixiao Pan1Ruoque Shen2Xiaosheng Xia3Hongtao Duan4Wenping Yuan5Jing Wei6School of Atmospheric Sciences, Sun Yat-sen UniversitySchool of Atmospheric Sciences, Sun Yat-sen UniversitySchool of Atmospheric Sciences, Sun Yat-sen UniversitySchool of Atmospheric Sciences, Sun Yat-sen UniversityKey Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of SciencesInstitute of Carbon Neutrality, Sino-French Institute for Earth System Science. College of Urban and Environmental Sciences, Peking UniversitySchool of Atmospheric Sciences, Sun Yat-sen UniversityAbstract 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.https://doi.org/10.1038/s41597-025-04915-y |
| spellingShingle | Shilong Luan Huixiao Pan Ruoque Shen Xiaosheng Xia Hongtao Duan Wenping Yuan Jing Wei High Resolution Water Quality Dataset of Chinese Lakes and Reservoirs from 2000 to 2023 Scientific Data |
| title | High Resolution Water Quality Dataset of Chinese Lakes and Reservoirs from 2000 to 2023 |
| title_full | High Resolution Water Quality Dataset of Chinese Lakes and Reservoirs from 2000 to 2023 |
| title_fullStr | High Resolution Water Quality Dataset of Chinese Lakes and Reservoirs from 2000 to 2023 |
| title_full_unstemmed | High Resolution Water Quality Dataset of Chinese Lakes and Reservoirs from 2000 to 2023 |
| title_short | High Resolution Water Quality Dataset of Chinese Lakes and Reservoirs from 2000 to 2023 |
| title_sort | high resolution water quality dataset of chinese lakes and reservoirs from 2000 to 2023 |
| url | https://doi.org/10.1038/s41597-025-04915-y |
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