Effects of Long-Term Increases in Water Temperature and Stratification on Large Artificial Water-Source Lakes in South Korea

Abstract

Long-term changes in air and water temperatures and the resulted stratification phenomena were observed for Soyang Lake (SY), Paldang Lake (PD), Chungju Lake (CJ), and Daecheong Lake (DC) in South Korea. Non-parametric seasonal Kendall and Mann-Kendall tests, Sen slope estimator, and potential energy anomaly (PEA) were applied. The lake surface water temperatures (LSWTs) of SY and DC increased at the same rate (0.125 °C/y), followed by those of CJ (0.071 °C/y) and PD (0.06 °C/y). Seasonally, the LSWT increase rates for all lakes, except PD, were 2–3 times higher than the air temperature increase rates. The lake stratification intensity order was similar to those of the LSWT increases and correlations. SY and DC displayed significant correlations between LSWT (0.99) and PEA (0.91). Thus, the LSWT significantly affected stratification when the water temperature increased. PD demonstrated the lowest correlation between LSWT and PEA. Inflow, outflow, rainfall, wind speed, and retention time were significantly correlated, which varied within and between lakes depending on lake topographical, hydraulic, and hydrological factors. Thus, hydraulic problems and nutrients should be managed to minimize their effects on lake water quality and aquatic ecosystems because lake cyanobacteria can increase as localized water temperatures increase.

The Spatiotemporal Eutrophication Status and Trends in the Paldang Reservoir, Republic of Korea

“…This suggests that site PD5 could have a significant impact on EOR, indicating the need for regular monitoring. The trends observed at individual reservoir sites may not always accurately reflect the characteristics of the entire reservoir [24]. Although no trends were observed in TSI KO at site PD1, TSI KO (Chl-a) showed a decreasing trend with a slope value of −0.11 mg/m 3 /year.…”

“…The SMK test is a non-parametric statistical method that computes Kendall's tau rank correlation coefficient for monthly and seasonal data. This helps to statistically validate correlations among water quality data and identify tendencies, whether positive, zero, or negative [24]. The SMK test in this study was conducted using the "seasonal" package in R software.…”

“…However, despite various regulations and investments, an increase in COD concentration, eutrophication, and algal blooms remains an issue [23]. This is because various factors, such as rainfall, ongoing land use, climate change, human activities, and hydrological conditions, affect the water quality of the PDR [24]. Managing and conserving freshwater resources that encompass various aspects, such as the PDR, are important for a sustainable environment and society.…”