Combining multivariate statistical techniques and random forests model to assess and diagnose the trophic status of Poyang Lake in China |
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| Institution: | 1. Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China;2. University of Chinese Academy of Sciences, Beijing 100049, China;3. Department of Hydrology and Water Resources Management, Institute for Natural Resource Conservation, Kiel University, 24118 Kiel, Germany;1. Center for the Study of Regional Development, Jawaharlal Nehru University, New Delhi, India;2. Dept. of Geography and Environment Management, Vidyasagar University, Midnapore, West Bengal, India;3. The Centre for International Politics, Organization and Disarmament, Jawaharlal Nehru University, New Delhi, India;1. CAS Key Lab on Reservoir Environment, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China;2. Chongqing School, University of Chinese Academy of Sciences, Chongqing 400714, China;3. Key Laboratory of Eco-environments in Three Gorges Reservoir Region of the Ministry of Education, School of Life Science, Southwest University, Chongqing 400715, China;4. Department of Geography and Environmental Science, University of Reading, Whiteknights Reading RG6 6AB, UK;1. GIS Technology Research Center of Resource and Environment in Western China, Ministry of Education, Yunnan Normal University, Yunnan 650500, China;2. School of Tourism and Geographical Science, Yunnan Normal University, Yunnan 650500, China;3. School of Information Science and Technology, Yunnan Normal University, Yunnan 650500, China;4. Dean’s Office, Yunnan Normal University, Yunnan 650500, China |
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| Abstract: | Floodplain lakes are valuable to humans because of their various functions. An emerging public concern on lake eutrophication has heightened the need to assess and predict the trophic status in floodplain lakes, particularly for those with high spatial heterogeneity. In this study, combined multivariate statistical techniques and random forests model were used to characterize the water quality and trophic status of Poyang Lake. By classifying and characterizing seasonal water samples comprising 11 water quality parameters collected from 13 sampling sites in Poyang Lake between 2008 and 2014, the dataset was divided into the central and northern lake groups, which corresponded to lentic and lotic regions in Poyang Lake, respectively. The spatial water quality variations and underlying patterns were investigated by performing discriminant analysis and principal component analysis (PCA). Lastly, random forests (RF) were used to predict the chlorophyll a (Chl-a) variations of the central and northern lakes. The PCA results indicated that the water quality of the central and northern areas of the lake was controlled by different environmental variables and underlying pollutant sources. The RF model outperformed the artificial neural network and linear regression and was robust with strong predictive capabilities. It was determined that the most important predictors of the Chl-a variations in the northern lake were water temperature (T) and water level, whereas transparency, T, and water level were the most efficient predictors in the central lake. The RF model can also be applied to trophic prediction in other large lakes with considerable spatial variations. This study will have implications on water quality management and eutrophication prevention in floodplain lakes with high spatial heterogeneity. |
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| Keywords: | Water quality PCA/FA Random forests Poyang lake |
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