Sammendrag
Understanding controls of dissolved oxygen (DO) concentrations in reservoirs is important as they are important for fisheries and a significant driver of greenhouse gas emissions. The latter is of global significance as IPCC inventories now require greenhouse gas emissions from artificial reservoirs to be included. Declines in dissolved oxygen (DO) concentrations in lakes and reservoirs have been linked to climate change and human activity. However, these effects can vary widely in any given region under various meteorological conditions. There is a clear need to know how changes in weather patterns affect DO in reservoirs by changing internal processes. Based on a six-year (2016–2021) high-frequency (twice a week) dataset from a shallow urban reservoir (Xinglinwan Reservoir) in subtropical China, the long-term (six years) and short-term (8–72-h) drivers of DO concentrations in surface waters were evaluated. Over the past six years, the concentration of DO has gradually decreased in the reservoir from 2016 to 2021. Multivariate adaptive regression spline (MARS) models were developed to identify the key factors explaining variability in DO and partial least squares path models (PLS-PM) were used to explore the short-term relationships between DO and environmental variables in rainy and dry (non-rain) periods, separately. We identified three key drivers operating on different time scales. First, the long-term decline of DO in Xinglinwan Reservoir from 2016 to 2021 was best explained by anthropogenic nutrient inputs. Second, rainy periods prior to sampling reduced DO concentrations indirectly by affecting the algal biomass and nutrient concentrations. This effect varied in complexity with the duration of the rainfall period. Third, water temperature best explained DO concentrations during dry periods, while wind reduced DO by reducing algal biomass. We conclude that anthropogenic nutrient and organic matter inputs drive long-term oxygen declines in urban subtropical reservoirs, while meteorological factors determine short-term variability in DO concentrations.
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