The formation of a metalimnetic oxygen minimum exemplifies how ecosystem dynamics shape biogeochemical processes: A modelling study

Mi, Chenxi; Shatwell, Tom; Ma, Jun; Wentzky, Valerie; Boehrer, Bertram; Xu, Yaqian; Rinke, Karsten

doi:10.1016/j.watres.2020.115701

Cited by 41 publications

(27 citation statements)

References 48 publications

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“…Additionally, expanding the model description of sediment dynamics may lead to improvements in oxygen and nutrient simulations. Compared to recent applications of the GLM-AED2 model in Lake Mendota, USA, (Ladwig et al 2021) and of the CE-QUAL-W2 model in Rappbode Reservoir, Germany, (Mi et al 2020), the nutrient fit statistics of the present study are similar.…”

Section: Model Validationsupporting

confidence: 67%

Drivers of phytoplankton responses to summer storms in a stratified lake: a modelling study

Mesman¹,

Ayala²,

Goyette³

et al. 2022

Preprint

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Extreme wind events affect lake phytoplankton amongst others by deepening the mixed layer and increasing internal nutrient loading. Both increases and decreases of phytoplankton biomass after storms have been observed, but the precise mechanisms driving these responses remain poorly understood or quantified. In this study, we coupled a one-dimensional physical model to a biogeochemical model to investigate the factors regulating short-term phytoplankton responses to summer storms, now and under expected high-temperature conditions. We simulated physical, chemical and biological dynamics in Lake Erken, Sweden, to take advantage of a long-term time series that we used to calibrate our model. We found that wind storms could increase or decrease the phytoplankton concentration one week after the storm, depending on antecedent lake physical and chemical conditions. Storms had little effect on phytoplankton biomass if the mixed layer was deep prior to storm exposure. Higher incoming shortwave radiation and hypolimnetic nutrient concentration boosted growth, whereas higher surface water temperatures decreased phytoplankton concentration after storms. Medium-intensity wind speeds resulted in more phytoplankton biomass after storms than high-intensity wind. Simulations under a future climate scenario did not show marked differences in the way wind affects phytoplankton growth following storms. Our study shows that storm impacts on lake phytoplankton are complex and likely to vary as a function of local environmental conditions.

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Section: Model Validationsupporting

confidence: 67%

Drivers of phytoplankton responses to summer storms in a stratified lake: a modelling study

Mesman¹,

Ayala²,

Goyette³

et al. 2022

Preprint

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“…This high value of SOD was scaled by the water temperature using an Arrhenius multiplier, effectively reducing it to a value between 1 to 1.5 g m -3 d -1 of maximum oxygen depletion by the sediment sink in the hypolimnion. A recent modeling study investigating the formation of metalimnetic oxygen minima in a drinking water reservoir by Mi et al (2020) confirmed that such high maximum SOD values are typical for many lakes.…”

Section: Oxygen Depletion Ratesmentioning

confidence: 83%

“…The manual calibration was done to ensure that the model was not overoptimized with unrealistic parameter combinations of the biological parameters. This calibration approach was done in accordance with other aquatic ecosystem modeling studies (Fenocchi et al, 2019;Mi et al, 2020), that did not apply computational optimization to water quality models.…”

Section: Glm-aed2mentioning

confidence: 99%

Lake thermal structure drives inter-annual variability in summer anoxia dynamics in a eutrophic lake over 37 years

Ladwig¹,

Hanson²,

Dugan³

et al. 2020

Preprint

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Abstract. The concentration of oxygen is fundamental to lake water quality and ecosystem functioning through its control over habitat availability for organisms, redox reactions, and recycling of organic material. In many eutrophic lakes, oxygen depletion in the bottom layer (hypolimnion) occurs annually during summer stratification. The temporal and spatial extent of summer hypolimnetic anoxia is determined by interactions between the lake and its external drivers (e.g., catchment characteristics/nutrient loads, meteorology), as well as internal feedback mechanisms (e.g., organic matter recycling, phytoplankton blooms). How these drivers interact to control the evolution of lake anoxia over decadal time scales will determine, in part, the future lake water quality. In this study, we used a vertical one-dimensional hydrodynamic-ecological model (GLM-AED2) coupled with a calibrated hydrological catchment model (PIHM-Lake) to simulate the thermal and water quality dynamics of the eutrophic Lake Mendota (USA) over a 37-year period. The calibration and validation of the lake model consisted of a global sensitivity evaluation as well as the application of an evolutionary optimization algorithm to improve the fit between observed and simulated data. By quantifying stability indices (Schmidt Stability, Birgean Work, stored internal heat), we identified spring mixing and summer stratification periods, and quantified the energy required for stratification and mixing. To qualify which external and internal factors were most important in driving the inter-annual variation in summer anoxia, we applied a random-forest classifier and multiple linear regression to modeled ecosystem variables (e.g., stratification onset and offset, ice duration, gross primary production.) Lake Mendota exhibited prolonged hypolimnetic anoxia each summer, lasting between 50–60 days. The summer heat budget, as well as the timing of thermal stratification, were the most important predictors of the spatial and temporal extent of summer anoxia periods in Lake Mendota. An earlier onset of thermal stratification in combination with a higher vertical stability strongly affected the duration and spatial extent of summer anoxia. As the heat budget depended primarily on external meteorological conditions, the spatial and temporal extent of summer anoxia in Lake Mendota is likely to increase in the near future as a result of projected climate change in the region.

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“…Meteorological forcing data were obtained from the second phase of the North American Land Data Assimilation System (NLDAS-2; Xia et al, 2012). The data from the grid cell were centered at 43 • 6 3.4128 N, 89 • 24 37.1124 W. The NLDAS-2 grid cells have 1/8th-degree spacing and data are at an hourly resolution from 1 January 1979 to present (Mitchell, 2004). Meteorological parameters used in this study included wind speed, air temperature, specific humidity, surface pressure, surface downward short-and longwave radiation, and total precipitation, which were used primarily as boundary data for GLM-AED2.…”

Section: Driver Data Acquisitionmentioning

confidence: 99%

Lake thermal structure drives interannual variability in summer anoxia dynamics in a eutrophic lake over 37 years

Ladwig

Hanson

Dugan

et al. 2021

Hydrol. Earth Syst. Sci.

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Abstract. The concentration of oxygen is fundamental to lake water quality and ecosystem functioning through its control over habitat availability for organisms, redox reactions, and recycling of organic material. In many eutrophic lakes, oxygen depletion in the bottom layer (hypolimnion) occurs annually during summer stratification. The temporal and spatial extent of summer hypolimnetic anoxia is determined by interactions between the lake and its external drivers (e.g., catchment characteristics, nutrient loads, meteorology) as well as internal feedback mechanisms (e.g., organic matter recycling, phytoplankton blooms). How these drivers interact to control the evolution of lake anoxia over decadal timescales will determine, in part, the future lake water quality. In this study, we used a vertical one-dimensional hydrodynamic–ecological model (GLM-AED2) coupled with a calibrated hydrological catchment model (PIHM-Lake) to simulate the thermal and water quality dynamics of the eutrophic Lake Mendota (USA) over a 37 year period. The calibration and validation of the lake model consisted of a global sensitivity evaluation as well as the application of an optimization algorithm to improve the fit between observed and simulated data. We calculated stability indices (Schmidt stability, Birgean work, stored internal heat), identified spring mixing and summer stratification periods, and quantified the energy required for stratification and mixing. To qualify which external and internal factors were most important in driving the interannual variation in summer anoxia, we applied a random-forest classifier and multiple linear regressions to modeled ecosystem variables (e.g., stratification onset and offset, ice duration, gross primary production). Lake Mendota exhibited prolonged hypolimnetic anoxia each summer, lasting between 50–60 d. The summer heat budget, the timing of thermal stratification, and the gross primary production in the epilimnion prior to summer stratification were the most important predictors of the spatial and temporal extent of summer anoxia periods in Lake Mendota. Interannual variability in anoxia was largely driven by physical factors: earlier onset of thermal stratification in combination with a higher vertical stability strongly affected the duration and spatial extent of summer anoxia. A measured step change upward in summer anoxia in 2010 was unexplained by the GLM-AED2 model. Although the cause remains unknown, possible factors include invasion by the predacious zooplankton Bythotrephes longimanus. As the heat budget depended primarily on external meteorological conditions, the spatial and temporal extent of summer anoxia in Lake Mendota is likely to increase in the near future as a result of projected climate change in the region.

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The formation of a metalimnetic oxygen minimum exemplifies how ecosystem dynamics shape biogeochemical processes: A modelling study

Cited by 41 publications

References 48 publications

Drivers of phytoplankton responses to summer storms in a stratified lake: a modelling study

Drivers of phytoplankton responses to summer storms in a stratified lake: a modelling study

Lake thermal structure drives inter-annual variability in summer anoxia dynamics in a eutrophic lake over 37 years

Lake thermal structure drives interannual variability in summer anoxia dynamics in a eutrophic lake over 37 years

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