Exploring the spatiotemporal water quality variations and their influencing factors in a large floodplain lake in China. (August 2020)

• Record Type: Journal Article
• Authors: Li, Bing; Wan, Rongrong; Yang, Guishan; Wang, Shigang; Wagner, Paul D.
• Is Part Of: Ecological indicators. Volume 115(2020)
• Publication Date: 2020-08
• Subjects: Fuzzy matter-element -- Random forest -- Water quality deteriorate -- Hydrological conditions -- Poyang Lake; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2020.106454
• Languages: English
• ISSNs: 1470-160X

Abstract

Highlights: The FME model was used to integrate water quality parameters into water quality rank values in Poyang Lake. Remarkable water quality deterioration trend could be observed during both wet season and dry season. Poyang Lake showed remarkable poor water quality in the east part of the lake. Water level fluctuations played important role in water quality variations in Poyang Lake. Abstract: Poyang Lake is the largest freshwater lake in China. It is characterized by dramatic hydrodynamic fluctuations which result in complex water quality variations. To explore the water quality status we used the fuzzy matter-element evaluation (FME) model to integrate key water quality indicators into a rank value. Monthly monitoring data from 18 sampling sites in the main lake and in the inflowing rivers collected during June 2010 to December 2015 were used to evaluate the water quality variations at spatial, seasonal and annual scales in the Poyang Lake. The results showed that the water quality status was generally the best in the wet summer season and the poorest in dry winter season. In addition, two considerably different spatial water status clusters were identified. A significant trend of water quality deterioration was observed during dry winter season for high pollution cluster and during summer for both clusters the lake from 2010 to 2015, which makes the lake more vulnerable to algal blooms. Random forest (RF) model showed that hydrological conditions together with water quality of inflow rivers yield 54% and 51% of the variation in water quality variations for low- and high-polluted clusters, respectively. Importance analysis based on RF indicated that hydrological conditions contributed the most to water quality variations in Poyang Lake, followed by the pollutants from adjacent sub-basins. This study provides new insights into when and where to control pollution sources of Poyang Lake.