Which aspects of economic development are most closely linked to Dongting Lake’s water quality?

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Lakes face the challenge of alleviating water-quality deterioration associated with rapid socioeconomic development. This makes understanding the influence of socioeconomic development on water quality urgent. Although previous studies have examined this relationship, they have often overlooked the theoretical basis and selection process for both socioeconomic and water-quality factors.

This observational watershed analysis used a conceptual model to select candidate indicators. The researchers proposed a newly developed method introducing a monthly correction coefficient to evaluate socioeconomic indicators, alongside seasonal-trend decomposition (STL) using locally weighted scatterplot smoothing (LOESS). They also used the steady-state transformation index (RSI) to further select water-quality indicators. Canonical correlation analysis (CCA), together with measured and ideal values of water-quality indicators, was then used to quantify the assessed degree of influence of socioeconomic systems and their key factors on water quality.

The methodology was applied to the Dongting Lake basin and its four sub-watersheds—the Xiang, Zi, Yuan and Li River watersheds—because these water bodies had been seriously affected by human activities in recent years.

The socioeconomic system and water quality were highly correlated, with marked temporal and spatial characteristics. The average annual assessed degree of socioeconomic influence on water quality was moderate in the Dongting Lake basin and its four sub-watersheds; the Xiang River watershed had a higher degree of influence than the other watersheds. From 2004 to 2017, the assessed degree of influence decreased in the Dongting Lake basin and the Xiang and Li River watersheds, but increased in the Yuan and Zi River watersheds.

The main socioeconomic factors linked to Dongting Lake’s water quality were water consumption (WC), percentage of cultivated land area (CA), total nitrogen (TN) input from agricultural non-point sources (A_TN), and human response factors such as industrial wastewater compliance discharge rate (WR) and sewage treatment (ST).

The researchers recommended prioritizing an efficient water-use plan, control of agricultural non-point-source pollution and improvements in sewage-treatment capacity to improve Dongting Lake’s water quality. They concluded that the study clarified the relationship between socioeconomic drivers and water quality and provided a reliable tool for watershed-management decision makers.