Decadal changes in water transparency of lakes in the middle and lower reaches of the Yangtze River: Trends and implications

In the past few decades, freshwater lakes in the middle and lower reaches of the Yangtze River have experienced significant ecological degradation, with many lakes transitioning from oligotrophic to eutrophic states. In response to this issue, China has implemented some ecological restoration measur...

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Bibliographic Details
Main Authors: Zhou Wang, Fei Xiao, Miaomiao Chen, Jiahuan Luo, Shuhui Cao, Qi Feng
Format: Article
Language:English
Published: Elsevier 2025-08-01
Series:Ecological Indicators
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Online Access:http://www.sciencedirect.com/science/article/pii/S1470160X25007009
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Summary:In the past few decades, freshwater lakes in the middle and lower reaches of the Yangtze River have experienced significant ecological degradation, with many lakes transitioning from oligotrophic to eutrophic states. In response to this issue, China has implemented some ecological restoration measures in the Yangtze River basin to improve water quality, but the results of those measures on water transparency are unsure. This study integrates long-term Landsat satellite imagery with a machine learning model to estimate Secchi disk depth (Zsd), a critical indicator of lake transparency and ecological health. The model achieved an average absolute error of 13.4 cm, a root mean square error of 17.9 cm, and a coefficient of determination (R2) of 0.712.From 2013 to 2023, Zsd maps revealed significant seasonal fluctuations in transparency. Among the 24 monitored lakes, one exhibited an upward trend, while 11 showed declining trends. The analysis also identified a positive correlation between Zsd and water levels in Poyang and Dongting Lakes, suggesting that higher water levels contribute to higher transparency. However, short-term ecological measures, such as returning embankments to lakes, did not result in measurable improvements in Zsd. This study highlights the importance of systematic monitoring of lake transparency to assess ecosystem health. It demonstrates the potential of integrating remote sensing and machine learning for effective water management and provides a foundation for evaluating the long-term outcomes of ecological restoration policies.
ISSN:1470-160X