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

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2025-04-04 DOI:10.1038/s41597-025-04915-y

Shilong Luan, Huixiao Pan, Ruoque Shen, Xiaosheng Xia, Hongtao Duan, Wenping Yuan, Jing Wei

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Abstract

Water quality parameters (pH, dissolved oxygen (DO), total nitrogen (TN, includes both organic nitrogen and inorganic nitrogen), total phosphorus (TP), permanganate index (COD_Mn), 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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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.