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Analysis of the temporal and spatial evolution of turbidity in Tonle Sap Lake and its influencing factors.
Zou, Tianle; Yang, Kun; Pan, Meie; Zhu, Yanhui; Zhang, Yang; Su, Danni.
Afiliación
  • Zou T; Faculty of Geography, Yunnan Normal University, Kunming 650500, China.
  • Yang K; Faculty of Geography, Yunnan Normal University, Kunming 650500, China; The Engineering Research Centre of GIS Technology in Western China, Ministry of Education of China, Yunnan Normal University, Kunming. Electronic address: kmdcynu@163.com.
  • Pan M; Faculty of Geography, Yunnan Normal University, Kunming 650500, China; The Engineering Research Centre of GIS Technology in Western China, Ministry of Education of China, Yunnan Normal University, Kunming. Electronic address: pme123@126.com.
  • Zhu Y; Faculty of Geography, Yunnan Normal University, Kunming 650500, China; The Engineering Research Centre of GIS Technology in Western China, Ministry of Education of China, Yunnan Normal University, Kunming.
  • Zhang Y; Faculty of Geography, Yunnan Normal University, Kunming 650500, China.
  • Su D; Faculty of Geography, Yunnan Normal University, Kunming 650500, China.
Sci Total Environ ; 943: 173618, 2024 Sep 15.
Article en En | MEDLINE | ID: mdl-38852871
ABSTRACT
Turbidity is a crucial indicator of water quality. The European Commission's Copernicus Land Monitoring Service Platform provides free turbidity data for large lakes to monitor the global water quality of lakes. However, the data were missing from April 2012 to April 2016, severely limiting long-term analysis. Based on MODIS and turbidity data, Random Forest and XGBoost models are used to invert Tonle Sap Lake's turbidity. Random Forest outperformed the XGBoost model. Based on Random Forest model, missing data were filled in to construct long-term series data of Tonle Sap Lake turbidity (2004-2021). Trend, persistence and correlation analyses were conducted to reveal spatiotemporal characteristics and driving mechanism of turbidity. The results showed that (1) spatially, the average annual, monthly, and seasonal turbidity was higher in the north but lower in the south, with regions of higher turbidity exhibiting more significant changes; (2) temporally, the annual turbidity mean was 53.99 NTU and showed an increasing trend. Monthly, turbidity values were higher from March to August and lower from September to February, with the highest and lowest recorded in June and November at 110.06 and 5.82 NTU, respectively. Seasonally, turbidity was higher in spring and summer compared to autumn and winter, with mean turbidity values of 84.16, 93.47, 15.33 and 23.21 NTU, respectively; (3) In terms of sustainability, the Hurst exponent for annual turbidity was 0.23, indicating a reverse trend in the near future; (4) Dam construction's impact on turbidity was not significant. Compared with natural factors (permanent wetlands, grasslands, lake surface water temperature, and remote sensing ecological index), human activities (barren, urban and built-up lands, croplands and population density) had a more significant impact on turbidity. Turbidity was highly correlated with croplands (r = 0.76), followed by population density (r = 0.71), and urban and built-up lands (r = 0.69).
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Sci Total Environ Año: 2024 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Sci Total Environ Año: 2024 Tipo del documento: Article País de afiliación: China