Metalimnetic oxygen minima alter the vertical profiles of carbon dioxide and methane in a managed freshwater reservoir

McClure, Ryan P.; Hamre, Kathleen D.; Niederlehner, B. R.; Munger, Zackary W.; Chen, Shengyang; Lofton, Mary E.; Schreiber, Madeline E.; Carey, Cayelan C.

doi:10.1016/j.scitotenv.2018.04.255

Cited by 36 publications

(53 citation statements)

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“…We evaluated the ability of the forecasting system to predict the day that fall turnover was first observed at the reservoir. Following McClure et al [ 2018 ], we defined fall turnover as the first day in autumn when water temperatures at 1 m and 8 m depths are within 1℃. In the forecasts prior to turnover, we calculated the proportion of ensemble members that predicted the 1 m and 8 m water temperatures to be ≤1℃ different on each day in the forecast.…”

Section: Methodsmentioning

confidence: 99%

A near-term iterative forecasting system successfully predicts reservoir hydrodynamics and partitions uncertainty in real time

Thomas¹,

Figueiredo

Daneshmand

et al. 2020

Preprint

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We created a near-term iterative lake water temperature forecasting system that uses 13 sensors, data assimilation, and hydrodynamic modeling 14 15 • FLARE quantifies the uncertainty in each daily forecast and provides an open-source, 16 generalizable system for water quality forecasting 17 18• 16-day forecasted temperatures were within 0.91℃ over 100 days in a reservoir case Abstract 22 Freshwater ecosystems are experiencing greater variability due to human activities, necessitating 23 new tools to anticipate future water quality. In response, we developed and operationalized a 24 near-term iterative water temperature forecasting system (FLARE -Forecasting Lake And 25 Reservoir Ecosystems) that is generalizable for lakes and reservoirs. FLARE is composed of: 26 water quality and meteorology sensors that wirelessly stream data, a data assimilation algorithm 27 that uses sensor observations to update predictions from a hydrodynamic model and calibrate 28 model parameters, and an ensemble-based forecasting algorithm to generate forecasts that 29 include uncertainty. Importantly, FLARE quantifies the contribution of different sources of 30 uncertainty (parameters, driver data, initial conditions, and process) to each daily forecast of 31 water temperature at multiple depths. We applied FLARE to a temperate reservoir during a 100-32 day period that encompassed stratified and mixed thermal conditions and found that daily 33 forecasted water temperatures were on average within 0.91℃ at all depths of the reservoir over a 34 16-day forecast horizon. FLARE successfully predicted the onset of fall turnover eight days in 35 advance, and identified meteorology driver data and downscaling as the dominant sources of 36 forecast uncertainty. Overall, FLARE provides an open-source and easily-generalizable system 37 for water quality forecasting for lakes and reservoirs to improve management. 39Lake Model, Water temperature 41 45 freshwater ecosystems, which have been more degraded than any other ecosystem on the planet 46 [Millennium Ecosystem Assessment 2005], are seeking new tools to anticipate future change and 47 ensure clean water for drinking, fisheries, irrigation, industry, and recreation [Brookes et al. 48 2014]. 49 In response to this need, near-term iterative ecological forecasting has emerged as a 50 solution to provide stakeholders, managers, and policy-makers crucial information about future 51 ecosystem conditions [Clark et al. 2001, Dietze et al. 2018, Luo et al. 2011. Here, we define a 52 near-term iterative forecast as a projection of future ecosystem states with fully-specified 53 uncertainties, generated from predictive models that can be constantly updated with new data as 54 they become available [Clark et al. 2001]. Importantly, a near-term iterative forecast is not 55 created from merely one ecosystem simulation, but an ensemble of simulations that enable 56 quantification of the uncertainty in the forecast contributed by different sources [i.e., parameters, 57 driver data, initi...

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Section: Methodsmentioning

confidence: 99%

A near-term iterative forecasting system successfully predicts reservoir hydrodynamics and partitions uncertainty in real time

Thomas¹,

Figueiredo

Daneshmand

et al. 2020

Preprint

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“…The gas samples were analyzed using a gas chromatograph coupled with a flame ionization detector within 24 hr of collection (following McClure et al, ). We determined the ebullition rate as follows:

italicDaily {CH}_{4} italicebullition rate ()(, mg {CH}_{4} m^{- 2} {day}^{- 1}) = \frac{V_{gas} (][, {italicCH}_{4})}{()(, T_{normald}) A_{F}},

where V gas is the volume of gas in the trap (L), [ CH 4 ] is the CH 4 concentration of the gas (mg CH 4 L −1 ), T d is the duration of time the trap was deployed (in days), and A F is the cross‐sectional area of the funnel (0.26 m 2 ).…”

Section: Methodsmentioning

confidence: 99%

“…FCR is a small (surface area = 0.119 km 2 ) and shallow (Z max = 9.3 m, Z mean = 4.0 m) reservoir with one primary inflow stream from an upstream reservoir (Figure ). FCR is dimictic and thermally stratified between late April and mid‐October (Gerling et al, ; McClure et al, ). The reservoir was constructed in 1898 and is owned and managed by the Western Virginia Water Authority.…”

Section: Methodsmentioning

confidence: 99%

“…We used temperature data from the surface and SWI loggers to determine the day of fall turnover in the reservoir. Because shallow upstream sites were expected to exhibit isothermal water column temperatures earlier than the deeper stratified sites, we determined fall turnover as the date that the temperature loggers at the SWI on transect T4 (Figure ) were <1 °C different from the temperature at the water surface, following McClure et al ().…”

Section: Methodsmentioning

confidence: 99%

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The Magnitude and Drivers of Methane Ebullition and Diffusion Vary on a Longitudinal Gradient in a Small Freshwater Reservoir

et al. 2020

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Key Points CH4 ebullition rates decreased by 98% and CH4 diffusion decreased by 32% on a longitudinal gradient from reservoir inflow to dam Ebullition was driven by physical variables upstream and phytoplankton downstream; diffusion was most related to phytoplankton Despite large variation in CH4 emissions longitudinally, time series models well captured the dynamics of emissions from a small reservoir

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“…The previous studies have preliminarily summarized the formation of MOM mainly due to the following reasons: (1) Higher DO consumption in the metalimnion was mainly attributed to the enhanced decomposition of dead algae or respiration of algae beyond their photosynthetic activity in the metalimnion [15]; (2) the interflow of DO depleted or depleting water also has been claimed to be an important influencing factor for MOM formation [17]; (3) MOM has been related to oxygen depletion at the side wall in some situation, which was advected into the water body of the reservoir on isopycnal water layer [18]; (4) finally, vertical gradient in the water column due to the retardation of diapycnal transfer and vertical exchange led to the reoxygenation difficulty [19]. Although those studies have reported MOM phenomenon in different areas and stated the reasons, the characteristics including the MOM formation mechanisms, the developed thickness and ecological risk of this phenomenon in reservoirs are of great difference with regard to hydrological and climatic conditions [10,15,20,21,22,23]. As far as we known, few comprehensive studies have been published regarding MOM in Chinese deep canyon-shaped drinking water source reservoir, therefore the reason and effect of anoxic area in middle water layer in deep canyon-shaped reservoir are still need to be examined.…”

Section: Introductionmentioning

confidence: 99%

S-type Dissolved Oxygen Distribution along Water Depth in a Canyon-shaped and Algae Blooming Water Source Reservoir: Reasons and Control

Huang

Yang

Wen

et al. 2019

IJERPH

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Dissolved oxygen (DO) is a crucial indicator of water quality. DO usually shows a monotonic decrease along water depth during thermal stratification in reservoir, whereas metalimnetic oxygen minimum (MOM) is observed in some cases. Although MOM phenomena have been reported in different areas, the characteristics of different reservoirs are greatly different, and few comprehensive studies have been published regarding MOM in Chinese drinking water source reservoirs. The DO distribution along water depth was determined and the detailed reasons were clarified by two-years of field monitoring. In addition the effect of water lifting aerators (WLAs) on DO improvement was investigated in the Lijiahe Reservoir in Northwest China. A typical S-type DO distribution with two anaerobic water layers, below the epilimnion (10–25 m water depth) and above the sediment (bottom water), was observed derived from the decomposition of dead algae or organic matter and the restriction of DO vertical exchange. Moreover, after WLAs’ operation since 10 June 2018, the water body was completely mixed and DO was rich and uniform along water depth by eliminating the water stratification and inhibiting algae growth. The deep understanding of the DO distribution in a deep canyon-shaped reservoir and the technical support for reservoir restoration are meaningful for optimizing reservoir management.

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Metalimnetic oxygen minima alter the vertical profiles of carbon dioxide and methane in a managed freshwater reservoir

Cited by 36 publications

References 36 publications

A near-term iterative forecasting system successfully predicts reservoir hydrodynamics and partitions uncertainty in real time

A near-term iterative forecasting system successfully predicts reservoir hydrodynamics and partitions uncertainty in real time

The Magnitude and Drivers of Methane Ebullition and Diffusion Vary on a Longitudinal Gradient in a Small Freshwater Reservoir

S-type Dissolved Oxygen Distribution along Water Depth in a Canyon-shaped and Algae Blooming Water Source Reservoir: Reasons and Control

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