Meteorological drivers of hypolimnetic anoxia in a eutrophic, north temperate lake
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Abstract
Oxygen concentration is both an indicator and driver of water quality in lakes. Decreases in oxygen concentration leads to altered ecosystem function as well as harmful consequences for aquatic biota, such as fishes. The responses of oxygen dynamics in lakes to climate-related drivers, such as temperature and wind speed, are well documented for lake surface waters. However, much less is known about how the oxic environment of bottom waters, especially the timing and magnitude of anoxia in eutrophic lakes, responds to changes in climate drivers. Understanding how important ecosystem states, such as hypolimnetic anoxia, may respond to differing climate scenarios requires a model that couples physical-biological conditions and sufficiently captures the density stratification that leads to strong oxygen gradients. Here, we analyzed the effects of changes in three important meteorological drivers (air temperature, wind speed, and relative humidity) on hypolimnetic anoxia in a eutrophic, north temperate lake using the anoxic factor as an index that captures both the temporal and spatial extent of anoxia. Air temperature and relative humidity were found to have a positive correlation with anoxic factor, while wind speed had a negative correlation. Air temperature was found to have the greatest potential impact of the three drivers on the anoxic factor, followed by wind speed and then relative humidity. Across the scenarios of climate variability, variation in the simulated anoxic factor was primarily due to changes in the timing of onset and decay of stratification. Given the potential for future changes in climate, especially increases in air temperature, this study provides important insight into how these changes will alter lake water quality.
Publication type | Article |
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Publication Subtype | Journal Article |
Title | Meteorological drivers of hypolimnetic anoxia in a eutrophic, north temperate lake |
Series title | Ecological Modelling |
DOI | 10.1016/j.ecolmodel.2016.10.014 |
Volume | 343 |
Year Published | 2017 |
Language | English |
Publisher | Elsevier |
Contributing office(s) | Office of Water Information |
Description | 15 p. |
First page | 39 |
Last page | 53 |
Google Analytic Metrics | Metrics page |