Journal
SCIENCE OF THE TOTAL ENVIRONMENT
Volume 647, Issue -, Pages 1518-1530Publisher
ELSEVIER
DOI: 10.1016/j.scitotenv.2018.08.031
Keywords
Climate change; Artificial neural network model; Deep-water lake; Eutrophication; Non-point pollution
Categories
Funding
- University of Chinese Academy of Innovation Program for Undergraduate Students [20170650004]
- Institute of Geo-chemistry Chinese Academy of Sciences
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Climate change-related temperature increases and sea level rise have a significant impact on the atmosphere, hydrosphere, biosphere, and anthroposphere. Impacts on ecosystems will mostly occur over the long term and short-term effects may consequently attract comparatively less attention from researchers and decision-makers. In this study, we investigate eight meteorological factors and eleven water quality indicators of deep-water lakes in the Yunnan-Guizhou Plateau in southwestern China. A robust proxy model based on a seven-year dataset (2010-2016) was established to predict the effects of climate change on water quality in Hongfeng Lake over the coming years. Perturbation analysis revealed that global warming has a more significant effect on chlorophyll a levels than on total phosphorus or total nitrogen in the lake area, and that external nutrient loading is a key factor aggravating eutrophication. Non-point source pollution induced by heavy precipitation will likely lead to an increase in total nitrogen and the lake may become more phosphorus-restricted. Reducing external inputs and controlling endogenous releases will help alleviate eutrophication. (C) 2018 Elsevier B.V. All rights reserved.
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