4 Dissolved Oxygen

4.1 Contributors

Matthew R. Hipsey and Peisheng Huang

4.2 Overview

Dissolved Oxygen (\(DO\) or \(O_2\)) is considered to be one of the most important indicators of aquatic ecosystem conditions and is commonly used in models of water quality. Eutrophication and climate change pressures are exacerbating issues of hypoxia and anoxia in waters, ranging from small wetlands, lakes, and the coastal ocean.

\(O_2\) dynamics respond to processes of atmospheric exchange, sediment oxygen demand, microbial respiration during organic matter mineralisation and nitrification, photosynthetic oxygen production and respiratory oxygen consumption, chemical oxygen demand, and respiration by other biotic components such as seagrass and bivalves.

The \(\mathrm{AED}\) oxygen module is suitable for a wide range of waterbody types, from ponds and wetlands, to lakes estuaries and the coastal ocean.

4.3 Model Description

This module supports one state variable to capture the oxygen concentration, \(O_2\). The module is a low-level module that supports the two core processes of air-water exchange, \(\check{f}_{atm}^{O_2}\), and sediment-water exchange, \(\hat{f}_{sed}^{O_2}\), and is designed to be linked to by other modules that interact with oxygen. The dynamics of \(O_2\) can therefore be summarised as:

\[\begin{eqnarray} \frac{D}{Dt}O_2 = \color{darkgray}{ \mathbb{M} + \mathcal{S} } \quad &+& \overbrace{\check{f}_{atm}^{O_2}+\hat{f}_{sed}^{O_2}}^\text{aed_oxygen} \\ \tag{4.1} &-& \color{brown}{ f_{min}^{DOC} - f_{nitrf}^{NH_4} - f_{ch4ox}^{CH_4} - f_{h2sox}^{H_2S} - f_{feox}^{FeII} } \\ \nonumber &+& \color{brown}{ f_{gpp}^{PHY} - f_{rsp}^{PHY} - f_{rsp}^{ZOO} + \hat{f}_{gpp}^{MAC} - \hat{f}_{rsp}^{MAC}} \\ \nonumber &+& \color{brown}{f_{gpp}^{MAG} - f_{rsp}^{MAG} - \hat{f}_{rsp}^{BIV} } \\ \nonumber \end{eqnarray}\]

where \(\mathbb{M}\) and \(\mathcal{S}\) refer to water mixing and boundary source terms, respectively, and the coloured \(\color{brown}{f}\) terms reflect the optionally configurable contributions from other modules; these include the breakdown of \(DOC\) by aerobic heterotrophic bacteria to \(CO_2\), whereby a stoichiometrically equivalent amount of oxygen is removed, chemical oxidation reactions, such as nitrification or sulfide oxidation, photosynthetic oxygen production and respiratory consumption by phytoplankton, and also oxygen consumed and produced by any benthic biological groups.

4.3.1 Process Descriptions

Oxygen solubility & atmospheric exchange

Atmospheric exchange is typically modelled based on Wanninkhof (1992) and the flux equation of Schlungbaum (1976) for the open water, and on Ho et al. (2011) for estuarine environments. The instantaneous flux at the air-water interface, \(\mathcal{F}_{atm}^{O_2}\), depends on the oxygen gradient between the air and water and the gas transfer or piston velocity, \(k_{piston}\) \((m/s)\) : \[\begin{equation} \mathcal{F}_{atm}^{O_2} = k_{piston}^{O_2} \left({O_2}_{air} - {O_2}_{s}\right) \tag{4.2} \end{equation}\] where \({O_2}_{s}\) is the oxygen concentration in the surface waters near the air-water interface and \(C_{air}\) is the concentration of oxygen in the air phase near the interface (\(mmol\) \(O_2/m^3\)). A positive flux represents input of oxygen from the atmosphere to the water. \({O_2}_{air}\) is dependent on temperature, \(T\), salinity, \(S\) and atmospheric pressure, \(p\), as given by: \[\begin{eqnarray} {O_2}_{air}\left[T,S,p \right] &=& \tfrac{32}{1000} \: 1.42763 \: \Phi_{p}^{atm} \\ &\times&\exp \left\{ -173.4292+249.6339 \left[\tfrac{100}{T_K}\right] + 143.3483 \ln \left[ \tfrac{T_K}{100}\right] - 21.8492 \left[\tfrac{T_K}{100}\right] \right. \nonumber \\ &+& \left. S\left(-0.033096 +0.014259 \left[\tfrac{T_K}{100}\right] -0.0017 \left[\tfrac{T_K}{100}\right]^2 \right) \right\} \tag{4.3} \end{eqnarray}\] where \(T_K\) is temperature in degrees Kelvin, salinity is expressed as parts per thousand and atmospheric pressure is in kPa. The pressure correction function, \(\Phi_{p}^{atm}\) varies between one and zero for the surface and high altitudes respectively: \[\begin{equation} \Phi_{p}^{atm}[p]=\frac{p_H}{p_{SL}}\left[1-\frac{p_{vap}}{p_H}\right]/\left[1-\frac{p_{vap}}{p_{SL}}\right]. \tag{4.4} \end{equation}\] where \(p_H\) and \(p_{SL}\) is the pressure at the altitude of the simulation domain (\(H\)) and at sea level, respectively.

As \(\mathrm{AED}\) can be applied across many types of environments, spanning the ocean to small lakes and flowing waters, users can select from a range of \(k_{piston}\) options, or implement their own, to capture this process. The options of \(k\) methods are listed in the Gas Transfer section of Generic utilities & Functions chapter.

Once the exchange flux is computed it is applied to the surface cells of the simulated domain, and the change in the \(O_2\) concentration in the surface layer is:

\[\begin{equation} \check{f}_{atm}^{O_2} = \frac{\mathcal{F}_{atm}^{O_2}}{\Delta z_{surf}} \tag{4.5} \end{equation}\] where \(\Delta z_{surf}\) is the surface layer thickness.

Sediment oxygen demand

Modelling sediment oxygen demand (\(SOD\)) can take a variety of forms. The simplest form in \(\mathrm{AED}\) is the “static” model whereby \(SOD\) varies as a function of the overlying water temperature and bottom water dissolved oxygen concentration: \[\begin{equation} \mathcal{F}_{sed}^{O_2}[T,O_2] = F_{sed}^{oxy} \: \theta_{sod}^{T-20} \: \Phi_{sod}^{oxy}\left[O_2\right] \tag{4.6} \end{equation}\] where \(F_{sed}^{oxy}\) is the maximum oxygen flux across the sediment-water interface at \(20^{\circ}C\). The oxygen limitation is computed based on:

\[\begin{equation} \Phi_{sod}^{oxy}\left[O_2\right]=\frac{O_2}{K_{sod}^{oxy}+O_2} \tag{4.7} \end{equation}\]

where \(K_{sod}^{oxy}\) is the half-saturation constant controlling the sediment oxygen demand. Once the exchange flux is computed it is applied to the bottom cells of the simulated domain, and the change in the \(O_2\) concentration in the bottom layer is:

\[\begin{equation} \hat{f}_{sed}^{O_2} = \frac{\mathcal{F}_{sed}^{O_2}}{\Delta z_{bot}} \tag{4.8} \end{equation}\] where \(\Delta z_{bot}\) is the bottom layer thickness.

Spatially variable sediment fluxes: The approach described above is the most simple and default method for capturing oxygen fluxes into the sediment, and is referred to as the static model. This approach can be extended to allow for spatial variability in \(F_{sed}^{oxy}\) by engaging the link to the aed_sedflux module, where the host models support multiple benthic cells or zones. In this case, users input spatially discrete values of \(F_{sed}^{oxy}\).

Dynamically varying sediment flux rates: Where dynamic rates of \(O_2\) are required to flux to the sediment (e.g. in response to episodic loading of organic material to the sediment, or for assessment of long-term changes in sediment quality), then the above expressions may be replaced instead with dynamically calculated variables in aed_seddiagenesis via a link created with the aed_sedflux module.

4.3.3 Feedbacks to the Host Model

The oxygen module has no feedbacks to the host hydrodynamic model.

4.3.4 Variable Summary

The default variables created by this module, and the optionally required linked variables needed for full functionality of this module are summarised in Table 4.1. The diagnostic outputs able to be output are summarised in Table 4.2.

State variables

Table 4.1: Oxygen - state variables
AED name Symbol Description Unit Type Typical Range Comments
aed_oxygen
OXY_oxy \[\mathbf{O_2}\] dissolved oxygen concentration \[\small{mmol\:O_2/m^3}\] pelagic 0 - 500 .
Dependent variables
SDF_Fsed_oxy \[\mathbf{F}_{sed}^{oxy}\] sediment \(O_2\) flux \[\small{mmol \:O_2/m^2/s}\] benthic -300 - 300 read and output as /day, but internally used as /s

Diagnostics

Table 4.2: Oxygen - diagnostic variables
AED name Symbol Description Unit Type Typical Range Comments
diag_level = 0+
OXY_sat \[\mathbf{O_2^{sat}}\] oxygen saturation \[\small{\%}\] pelagic 0-200 .
diag_level = 2+
OXY_sed_oxy \[\mathbf{F}_{sed}^{oxy}\] \(O_2\) exchange across sediment-water interface \[\small{mmol\: O_2/m^2/d}\] benthic 0 - 50 .
OXY_atm_oxy_flux \[\mathbf{F}_{atm}^{oxy}\] \(O_2\) exchange across atm-water interface \[\small{mmol \:O_2/m^2/d}\] surface 0 - 100 .
diag_level = 9+
OXY_atm_oxy_exch3d \[\mathbf{\check{f}}_{atm}^{oxy}\] \(O_2\) exchange across atm-water interface \[\small{mmol \:O_2/m^3/d}\] surface 0 - 1 .


4.3.5 Parameter Summary

The parameters and settings used by this module are summarised in Table 4.3.

Table 4.3: Oxygen module parameters and configuration options
AED name Symbol Description Unit Type Typical Range Comments
Initialisation
oxy_initial \[O_2|_{t=0}\] initial \(O_2\) concentration \[\small{mmol\: O_2/m^3}\] float 0 - 5000 can be overwritten by initial condition files
oxy_min \[O_2\rfloor_{min}\] minimum \(O_2\) concentration \[\small{mmol\: O_2/m^3}\] float 0 optional limitier
oxy_max \[O_2\rceil^{max}\] maximum \(O_2\) concentration \[\small{mmol\: O_2/m^3}\] float 600 optional limitier
Sediment exchange
Fsed_oxy \[F_{sed}^{oxy}\] sediment \(O_2\) flux at 20C \[\small{mmol\: O_2/m^2/d}\] float -300 - 300 .
Ksed_oxy \[K_{sod}^{O_2}\] half-saturation oxygen conc. controlling \(O_2\) flux \[\small{mmol\: O_2/m^3}\] float 20 - 100 .
theta_sed_oxy \[\theta_{sed}^{oxy}\] Arrhenius temperature multiplier for sediment \(O_2\) flux \[\small{-}\] float 1.0 - 1.2 .
Fsed_oxy_variable variable name to link to for spatially resolved sediment zones \[\small{-}\] string SDF_Fsed_oxy optional link to enable spatially resolved fluxes
Atmospheric exchange
oxy_piston_model \[\Theta_{oxy}^{piston}\] selection of air/water \(O_2\) flux velocity method \[\small{-}\] integer 1 - 9 see options in the Gas Transfer section
altitude \[H\] altitude of site above sea level \[\small{m}\] float 0 - 4000 defaults to 0; Eq (4.4) used for \(H \gt 1\)


4.4 Setup & Configuration

An example aed.nml parameter specification block for the aed_oxygen module is shown below:

&aed_oxygen
   oxy_initial       = 250.0 
   oxy_min           =   0 
   oxy_max           = 500 
   Fsed_oxy          = -40.0
   Ksed_oxy          = 100.0
   theta_sed_oxy     =   1.08
!  Fsed_oxy_variable = 'SDF_Fsed_oxy'
!  altitude          = 1000  
 /

Note that when users link Fsed_oxy_variable then Fsed_oxy is not required as this parameter will be set for each each sediment zone from values input via the aed_sedflux module. The entries oxy_min and oxy_max are optional.

4.5 Case Studies & Examples

4.5.1 Case Study: Swan-Canning Estuary

The Swan-Canning Estuary in Western Australia experiences hypoxia and anoxia due to the strong salt-wedge that forms within the system, and the high sediment oxygen demand. Huang et al. (2018) used the aed_oxygen model with the TUFLOW-FV hydrodynamic model to capture the large spatio-temporal variability in oxygen distribution.


Figure 4.1: Map of the Swan-Canning Estuary in Western Australia.


Using spatial zones with different values of Fsed_oxy, the simulations show very high accuracy in resolving the “dome” of low oxygen associated with the salt-wedge.


Cross-section plots comparing modelled and field salinity (psu, left) and $O_2$ (mg/L, right) for Sep-Dec 2008 within the Swan River. The field plots are based on contouring approx. 20 profile data locations.

Figure 4.2: Cross-section plots comparing modelled and field salinity (psu, left) and \(O_2\) (mg/L, right) for Sep-Dec 2008 within the Swan River. The field plots are based on contouring approx. 20 profile data locations.

The results of the model were integrated over the length of the modelled domain to demonstrate the model’s performance in capturing the system-scale areal extent of hypoxia (\(O_2<4\:mg/L\)) and anoxia (\(O_2<2\:mg/L\)). Huang et al. (2018) also used the validated model to explore different remediation options based on artificial oxygenation inputs.

The total area of anoxia (<2 mg O2 / L) and hypoxia (<4 mg O2 / L) within the estuary for a) 2008 and b) 2010, comparing the model (black line) and spatially interpolated weekly profile data (shaded region).

Figure 4.3: The total area of anoxia (<2 mg O2 / L) and hypoxia (<4 mg O2 / L) within the estuary for a) 2008 and b) 2010, comparing the model (black line) and spatially interpolated weekly profile data (shaded region).

4.5.2 Publications

Author/Year Paper Title Description

Bruce et al. (2014)

Hydrodynamic controls on oxygen dynamics in a riverine salt wedge estuary, the Yarra River estuary, Australia.

NA

Bruce et al. (2015)

A model of oxygen and nitrogen biogeochemical response to hydrodynamic regimes in the Yarra River estuary.

NA

Snortheim et al. (2017)

Meteorological drivers of hypolimnetic anoxia in a eutrophic, north temperate lake.

NA

Huang et al. (2018)

Assessing artificial oxygenation in a riverine salt-wedge estuary with a three-dimensional finite-volume model.

NA

Huang et al. (2019)

An integrated modelling system for water quality forecasting in an urban eutrophic estuary: The Swan-Canning Estuary Virtual Observatory.

NA

Ladwig et al. (2021)

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

NA

Carey et al. (2022)

Anoxia decreases the magnitude of the carbon, nitrogen, and phosphorus sink in freshwaters.

NA

Farrell et al. (2020)

Ecosystem-scale nutrient cycling responses to increasing air temperatures vary with lake trophic state.

NA

Ward et al. (2022)

Differential responses of maximum and median chlorophyll-a to air temperature and nutrient load in a 31-year oligotrophic lake simulation.

NA

Ward et al. (2020) Snortheim et al. (2017) Farrell et al. (2020) Ladwig et al. (2021) Bruce et al. (2015) Bruce et al. (2014) Carey et al. (2022) Huang et al. (2019)

References

Bruce, L.C., Cook, P.L.M., Teakle, I., Hipsey, M.R., 2014. Hydrodynamic controls on oxygen dynamics in a riverine salt wedge estuary, the Yarra River estuary, Australia, Hydrology and Earth System Sciences 18, 1397–1411. https://doi.org/10.5194/hess-18-1397-2014
Bruce, L., Cook, P., Hipsey, M., 2015. A model of oxygen and nitrogen biogeochemical response to hydrodynamic regimes in the Yarra River estuary, in: IMACS World Congress and MODSIM International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand Inc.(MSSANZ), pp. 524–530.
Carey, C.C., Hanson, P.C., Thomas, R.Q., Gerling, A.B., Hounshell, A.G., Lewis, A.S.L., Lofton, M.E., McClure, R.P., Wander, H.L., Woelmer, W.M., Niederlehner, B.R., Schreiber, M.E., 2022. Anoxia decreases the magnitude of the carbon, nitrogen, and phosphorus sink in freshwaters, Global Change Biology. https://doi.org/10.1111/gcb.16228
Farrell, K.J., Ward, N.K., Krinos, A.I., Hanson, P.C., Daneshmand, V., Figueiredo, R.J., Carey, C.C., 2020. Ecosystem-scale nutrient cycling responses to increasing air temperatures vary with lake trophic state, Ecological Modelling 430, 109134. https://doi.org/10.1016/j.ecolmodel.2020.109134
Ho, D.T., Wanninkhof, R., Schlosser, P., Ullman, D.S., Hebert, D., Sullivan, K.F., 2011. Toward a universal relationship between wind speed and gas exchange: Gas transfer velocities measured with 3 He/SF 6 during the Southern Ocean Gas Exchange Experiment, Journal of Geophysical Research 116. https://doi.org/10.1029/2010jc006854
Huang, P., Kilminster, K., Larsen, S., Hipsey, M.R., 2018. Assessing artificial oxygenation in a riverine salt-wedge estuary with a three-dimensional finite-volume model, Ecological Engineering 118, 111–125. https://doi.org/10.1016/j.ecoleng.2018.04.020
Huang, P., Trayler, K., Wang, B., Saeed, A., Oldham, C.E., Busch, B., Hipsey, M.R., 2019. An integrated modelling system for water quality forecasting in an urban eutrophic estuary: The swan-canning estuary virtual observatory, Journal of Marine Systems 199, 103218. https://doi.org/10.1016/j.jmarsys.2019.103218
Ladwig, R., Hanson, P.C., Dugan, H.A., Carey, C.C., Zhang, Y., Shu, L., Duffy, C.J., Cobourn, K.M., 2021. Lake thermal structure drives interannual variability in summer anoxia dynamics in a eutrophic lake over 37 years, Hydrology and Earth System Sciences 25, 1009–1032. https://doi.org/10.5194/hess-25-1009-2021
Schlungbaum, G., 1976. Chemical Oceanography, 2nd edition. ed, Internationale revue der gesamten hydrobiologie und hydrographie. London; New York: Academic Press. https://doi.org/10.1002/iroh.19760610308
Snortheim, C.A., Hanson, P.C., McMahon, K.D., Read, J.S., Carey, C.C., Dugan, H.A., 2017. Meteorological drivers of hypolimnetic anoxia in a eutrophic, north temperate lake, Ecological Modelling 343, 39–53. https://doi.org/10.1016/j.ecolmodel.2016.10.014
Wanninkhof, R., 1992. Relationship between wind speed and gas exchange over the ocean, Journal of Geophysical Research 97, 7373. https://doi.org/10.1029/92jc00188
Ward, N.K., Steele, B.G., Weathers, K.C., Cottingham, K.L., Ewing, H.A., Hanson, P.C., Carey, C.C., 2020. Differential responses of maximum versus median chlorophyll‐ a to air temperature and nutrient loads in an oligotrophic lake over 31 years, Water Resources Research. https://doi.org/10.1029/2020wr027296