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Drivers of Spatial and Temporal Dynamics in Water Turbidity of China Yangtze River Basin
The sustainable development of the water environment in the Yangtze River basin has become a critical issue in China. Turbidity is a comprehensive element for water quality monitoring. In this study, the baseline of water turbidity in the Yangtze River was constructed using 36 years of Landsat images from 1986 to 2021. The spatial and temporal dynamics of turbidity and its driving factors were explored. The results show that (i) the proposed Landsat-based turbidity model performs well, with a correlation coefficient (R2) of 0.68 and a Root Mean Square Error (RMSE) of 7.83 NTU for the whole basin. (ii) The turbidity level in the Yangtze River basin is spatially high in the upper reaches (41.7 NTU), low in the middle reaches (30.9 NTU), and higher in the lower reaches (37.6 NTU). The river turbidity level (60.1 NTU) is higher than the turbidity in lakes and reservoirs (29.6 NTU). The turbidity in the Yangtze River basin shows a decreasing trend from 1986 to 2021, with the most significant decrease in the mainstream of the Yangtze River. Seasonally, the mean turbidity in the Yangtze River basin shows a “low in summer and high in winter” trend, but opposite trends were revealed for the first time in rivers and lakes, such as Dongting Lake, Poyang Lake, and Taihu Lake, etc. (iii) Natural factors, including precipitation and natural vegetation cover (woodlands, grasslands, and shrubs) could explain 58% of the turbidity variations, while human activities including impervious surfaces, cropland, and barren land are lower impact. Annual precipitation was negatively correlated with water turbidity, while cropland and barren land showed a significant positive correlation. The study is of great practical value for the sustainable development of the water environment in the Yangtze River basin and provides a reference for remote sensing monitoring of the water environment in inland water bodies.
Drivers of Spatial and Temporal Dynamics in Water Turbidity of China Yangtze River Basin
The sustainable development of the water environment in the Yangtze River basin has become a critical issue in China. Turbidity is a comprehensive element for water quality monitoring. In this study, the baseline of water turbidity in the Yangtze River was constructed using 36 years of Landsat images from 1986 to 2021. The spatial and temporal dynamics of turbidity and its driving factors were explored. The results show that (i) the proposed Landsat-based turbidity model performs well, with a correlation coefficient (R2) of 0.68 and a Root Mean Square Error (RMSE) of 7.83 NTU for the whole basin. (ii) The turbidity level in the Yangtze River basin is spatially high in the upper reaches (41.7 NTU), low in the middle reaches (30.9 NTU), and higher in the lower reaches (37.6 NTU). The river turbidity level (60.1 NTU) is higher than the turbidity in lakes and reservoirs (29.6 NTU). The turbidity in the Yangtze River basin shows a decreasing trend from 1986 to 2021, with the most significant decrease in the mainstream of the Yangtze River. Seasonally, the mean turbidity in the Yangtze River basin shows a “low in summer and high in winter” trend, but opposite trends were revealed for the first time in rivers and lakes, such as Dongting Lake, Poyang Lake, and Taihu Lake, etc. (iii) Natural factors, including precipitation and natural vegetation cover (woodlands, grasslands, and shrubs) could explain 58% of the turbidity variations, while human activities including impervious surfaces, cropland, and barren land are lower impact. Annual precipitation was negatively correlated with water turbidity, while cropland and barren land showed a significant positive correlation. The study is of great practical value for the sustainable development of the water environment in the Yangtze River basin and provides a reference for remote sensing monitoring of the water environment in inland water bodies.
Drivers of Spatial and Temporal Dynamics in Water Turbidity of China Yangtze River Basin
Jian Li (author) / Chunlin Xia (author)
2023
Article (Journal)
Electronic Resource
Unknown
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