5 Inorganic Carbon

5.1 Contributors

Peisheng Huang, Kamilla Kurucz, and Matthew R. Hipsey

5.2 Overview

Understanding carbon cycling is central to understanding food webs and how aquatic communities are structured and supported (Dodds and Whiles, 2020). Exchange of carbon between aquatic systems and the atmosphere is also fundamental in influencing climate (Borges and Abril, 2011; e.g. Cai, 2011). Accordingly, there are a wide range of applications that require simulation of carbon species.

The AEDAED library can be used to simulate both the organic and inorganic pools of carbon. As carbon is a core building block of an aquatic ecosystem, aed_carbon (CARCAR) is designed as a low-level module for managing inorganic carbon pools, and is able to be linked to by higher level modules associated with organic matter cycling, primary production, and other biotic groups. In particular, organic carbon is the currency of energy exchange through food webs, and the processes for modelling organic carbon are described in aed_organic_matter. This chapter presents the forms of inorganic carbon and fluxes of carbon in the aquatic environment and how they are modelled in the aed_carbon module.

5.3 Model Description

The core variables included in the aed_carbon module are Dissolved Inorganic Carbon (DIC) and methane (CH4).

The dynamics for DIC is captured in the model when simDIC = .true.. The rate of change of DIC concentration as it moves with the water is calculated from local effects associated with air-water exchange (ˇfCO2atmˇfCO2atm), oxidation of CH4, (fCH4oxfCH4ox), and sediment-water exchange (ˆfDICsed^fDICsed), which is summarized as:

DDtDIC=M+S+aed_carbonˇfCO2atm+fCH4ox+ˆfDICsed+fDOCminfPHYgpp+fPHYrsp+fZOOrspˆfMACgpp+ˆfMACrspfMAGgppfMAGrsp+ˆfBIVrspDDtDIC=M+S+aed_carbonˇfCO2atm+fCH4ox+^fDICsed+fDOCminfPHYgpp+fPHYrsp+fZOOrsp^fMACgpp+^fMACrspfMAGgppfMAGrsp+^fBIVrsp(5.1)

where MM and SS refer to water mixing and boundary source terms, respectively, and the coloured ff terms reflect DIC fluxes computed by other (optionally) linked modules such as modules of organic matter (OMGOMG), phytoplankton (PHYPHY) or zooplankton (ZOOZOO).

The dynamics for CH4 is captured in the model when simCH4 = .true.. CH4 is formed during the decomposition of organic material by microbial methanogenesis (e.g. Cicerone and Oremland, 1988), which requires strictly anaerobic conditions and is therefore usually restricted to be deep within aquatic sediment. Inputs to the water therefore are from sediment CH4 production, which can enter the water column either as a dissolved flux or via ebullition (bubble release), or from external sources such as wastewater discharges. Once in the water, dissolved CH4 is the balance between inputs from the sediment and losses from air-water exchange and CH4 oxidation to DIC.

The rate of change of CH4 concentration as it moves with the water is therefore calculated as:

DDtCH4=M+S+aed_carbonˇfCH4atmfCH4ox+ˆfCH4sed+fCH4bubbledisDDtCH4=M+S+aed_carbonˇfCH4atmfCH4ox+^fCH4sed+fCH4bubbledis(5.2)

where ˇfCH4atmˇfCH4atm is the air-water exchange of dissolved methane and ˆfCH4sed^fCH4sed is the sediment diffusive release flux. Bubbles moving through the water column after release from the sediment can also dissolve, denoted fCH4bubbledisfCH4bubbledis.

The details of how each flux relevant to DIC and CH4 is calculated, and the associated parameter settings, are described in the next sections. For details on how to model organic carbon refer to the aed_organic_matter model.

5.3.1 Process Descriptions

5.3.1.1 Carbonate buffering and pH

In most circum-neutral waters the carbonate buffer system controls the pH conditions within the water. When CO2 within the atmosphere (CO2(gas)CO2(gas)) is dissolved in water, it can exist in a variety of forms. Initially, carbon dioxide will dissolve, CO2(aq)CO2(aq), which can then form carbonic acid, H2CO3H2CO3, and dissociate into bicarbonate, HCO3HCO3, and carbonate, CO23CO23, ions.

CO2(aq)CO2(gas)CO2(aq)+H2OH2CO3H2CO3H++HCO3HCO3H++CO23CO2(aq)CO2(gas)CO2(aq)+H2OH2CO3H2CO3H++HCO3HCO3H++CO23

Figure 5.1: Bjerrum plot of DIC speciation.

The sum of the concentrations of all these forms is the inorganic carbon concentration (DIC), and the composition of these forms depends on acidity (pH). The total DICDIC state variable is the sum of carbonate species in a water parcel, which is conservative with respect to hydrodynamics:

DIC=[CO23]+[HCO3]+[CO2]+[H2CO3].DIC=[CO23]+[HCO3]+[CO2]+[H2CO3].(5.3)

To capture the interaction between carbonate species that make up DIC, each cell is treated as a closed system, as in a titration, where fluxes into and out of the system are specified, such as fluxes due to respiration, dissolved sediment fluxes, or atmospheric exchange. The changes due to these fluxes alter the CO2(aq)CO2(aq) concentration without changing the alkalinity, allowing for the speciation and pH to be recomputed at the end of the time-step.

aed_carbon provides three options for modelling the bicarbonate equilibrium by switching the CO2_model options:

The users can choose the aed_geochemistry module, or adopt the widely used CO2SYS code (Lewis and Wallace, 1998) or Butler (1982) code to calculate the partial pressure of CO2 (pCO2) and bicarbonate system. These options all lead to creation of state variables for DIC and pH, and their concentrations need to be specified in the boundary conditions. The use of each option is described in below sections.

5.3.1.1.1 co2_model = 0

This option is selected if the user wishes to defer the speciation, pCO2 and pH calculation to the geochemistry model in aed_geochemistry. In this option, aed_carbon creates the DIC and pH state variables, will compute the atmospheric exchange and sediment fluxes, but not update the speciation, pCO2 or pH.

When using this approach, users must pay attention to correctly configure the aed_geochemistry model to link to DIC, pCO2 and pH within the aed_carbon module.

To resolve carbonate buffering that module can be setup with the following components, and species:

X{CO23,H+,H2O}C{CO2,HCO3,H2CO3,CO23,H+,OH}X{CO23,H+,H2O}C{CO2,HCO3,H2CO3,CO23,H+,OH}

This will be solved each time the geochemical module is run. Fluxes of CO2CO2 between the atmosphere and sediment or from bioloigcal process will also be applied to the DICDIC variable and therefore dynamically affect pHpH.

For users interested in simulating calcite precipitation, then this may also be achieved by configuring the geochemistry module, and enabling Calcite as a simulated mineral component.

5.3.1.1.2 co2_model = 1

This option adopts the CO2SYS code (Lewis and Wallace, 1998), in which the bicarbonate system was modelled from DIC (mmol m-3) and total alkalinity (TA, mmol m-3), with water temperature (T, degrees), salinity (S, PSU) and water pressure (P, dbar) derived from the coupled hydrodynamic model, and DIC concentration derived from the aed_carbon model.

There are multiple options for TA calculation methods in this code depending on the data availability and environment of the study site, which can be switched by the alk_mode option:

Θcaralk_code={0,carbonate alkalinity only1,dependance on salinity25dependance on salinity and DICΘcaralk_code=0,carbonate alkalinity only1,dependance on salinity25dependance on salinity and DIC

For freshwaters, there is a simple freshwater alkalinity option (alk_mode = 0), whereby TA is assumed to be dominated by the carbonate alkalinity. In this option, TA is calculated from the carbonate species according to:

TA=[H+]+[HCO3]+2[CO23]+[OH]TA=[H+]+[HCO3]+2[CO23]+[OH](5.4)

For hardwater lakes, estuaries or coastal systems, then other ions contribute significantly to the total alkalinity and the assumption above is no longer valid. However, the component ions needed for an alkalinity calculation are not always commonly available or included in the model and so approximations are needed to estimate TA. TA is a quasi-conservative parameter and it has been a popular practice in coastal modelling to predict TA from salinity (Alin et al., 2012; e.g. Lee et al., 2006; Takahashi et al., 2014), or from combination of salinity and DIC concentration (Jones et al., 2016; Kim and Lee, 2009). Accordingly, aed_carbon supports a few options to model the TA in coastal waters.

The first alk_mode option (Θcaralk_code=1Θcaralk_code=1) approximates the TA from salinity as:

TA=a+bSTA=a+bS(5.5)

where aa and bb are constant coefficients, with pre-defined values of a=1627.4a=1627.4 and b=22.176b=22.176 derived from a study case in the Caboolture River estuary. Other remaining alk_mode options (Θcaralk_code=25Θcaralk_code=25) approximate the TA from salinity and DIC concentration with a quadratic polynomial relationship as:

TA=p00+p10 S+p01 DIC+p20 S2+p11 S DIC+p02 DIC2TA=p00+p10 S+p01 DIC+p20 S2+p11 S DIC+p02 DIC2(5.6)

where p00p00, p10p10, p01p01, p20p20, p11p11, p02p02 are constant coefficients. For this method the available options include:

  • alk_mode=2: constant coefficients derived from all field work in Noosa, Maroochy, and Brisbane estuaries in Australia;
  • alk_mode=3: constant coefficients derived from field work in Noosa River;
  • alk_mode=4: constant coefficients derived from field work in Maroochy River;
  • alk_mode=5: constant coefficients derived from field work in Brisbane River;

The constant coefficients in these options are provided in Table 5.1. A case study using alk_mode=5 in Brisbane River is given in Section 5.5.1. Users may wish to implement tailored alkalinity relationships as additional alk_mode options for specific study sites and use-cases.

Table 5.1: Constant coefficients in alk_mode options 2 - 5.
alk_mode p00p00 p10p10 p01p01 p20p20 p11p11 p02p02
alk_mode=2 334.40 -2.123 0.7194 0.2259 0.0007654 0.0000496
alk_mode=3 -258.80 34.590 0.9923 0.8186 -0.0310100 0.0001045
alk_mode=4 -47.51 -17.210 1.3200 0.1439 0.0122400 -0.0002055
alk_mode=5 357.80 -2.095 0.6931 0.2244 0.0007714 0.0000563
5.3.1.1.3 co2_model = 2

This option adopts the approach of Butler (1982) to model the carbonate system in freshwater environment. In a closed carbonate system, the TA, DIC and pH are related by:

TA=DICKa1[H+]+2Ka1Ka2[H+]2+Ka1[H+]+Ka1Ka2+Kw[H+][H+]TA=DICKa1[H+]+2Ka1Ka2[H+]2+Ka1[H+]+Ka1Ka2+Kw[H+][H+](5.7)

where KwKw is the ion product of water, Ka1Ka1 is the first acidity constant, Ka2 is the second acidity constant. pH is calculated from:

pH=log10[H+]

and DIC is defined as:

DIC=[CO23]+[HCO3]+[CO2]+[H2CO3]

where all concentrations are in (mmolC/m3). Note that the concentration of hydrated carbon dioxide, [H2CO3], is considered to be negligible and is therefore omitted.

The conversion between the carbon dioxide partial pressure, pCO2 and carbon dioxide concentration (molL1) is given by:

[CO2]=KHpCO2

where KH is the activity coefficient.

The ionic strength of the water impacts the dissociation constants. Ionic strength, I, is typically defined as :

I=0.5([Na+]+[H+]+[HCO3]+4[CO23]+[H+]/Kw).

Rather than model this explicitly, the ionic strength is entered into the model as a constant, and is not assumed to vary significantly in space or time for a given system. From the ionic strength, an activity coefficient is defined as:

f=(I1/21+I1/20.2I)(298TK)2/3.

The dissociation constants are then computed following: log10(Ki+1H)=log10(K0H)bIlog10(Ki+1w)=log10(K0w)+flog10(Ki+1a1)=log10(K0a1)+f+bIlog10(Ki+1a2)=log10(K0a2)+2f where b=0.105 .

For this system, if two of the three components (i.e. TA, pH or DIC) are known the third can be determined. In AED, pH and DIC are tracked as state variables, and at each time step TA is also computed and avalable via a diagnostic output.

The carbonate speciation for a given value of pH and DIC is then estimated from: [CO23]=DICKa1Ka2[H+]2+Ka1[H+]+Ka1Ka2,[HCO3]=DICKa1[H+][H+]2+Ka1[H+]+Ka1Ka2,[CO2]=DIC[H+]2[H+]2+Ka1[H+]+Ka1Ka2.

Using this framework, the inorganic carbon model co2_model = 2 operates as follows:

  • from initial field measurements of TA and pH, calculate DIC using Equation (5.7)
  • calculate initial carbonate speciation using Equation (5.14) – Equation (5.16)
  • calculate partial pressure of CO2 using Equation (5.10)
  • start timestep loop
  • calculate atmospheric CO2 flux using Equation (5.28)
  • calculate internal CO2 fluxes from phytoplankton photosynthesis/respiration, zooplankton respiration and bacterial respiration
  • calculate new DIC using Equation (5.9)
  • assuming TA constant, as CO2 does not change the alkalinity directly, calculate new pH by iteratively solving Equation (5.7)
  • calculate new carbonate speciation using Equation (5.14) – Equation (5.16)
  • calculate new partial pressure of CO2 using Equation (5.10)
  • update new TA and DIC concentration estimates

5.3.1.2 Sediment exchange

The exchange fluxes of DIC and CH4 between the sediment and overlaying water are modelled as a function of the overlying water temperature and bottom water dissolved oxygen concentration:

FDICsed[T,O2]=FDICsedθT20sedDICϕDICO2[O2] FCH4sed[T,O2]=FCH4sedθT20sedCH4ϕCH4O2[O2]

where the parameters FDICsed and FCH4sed are the maximum flux rate across the sediment-water interface at 20°C, the θsedDIC and θsedCH4 are the Arrhenius temperature multiplier for temperature control. The last item is the oxygen mediation of the fluxes.

Oxygen mediation of the inorganic carbon fluxes: The sediment flux can be changed by bottom water O2 concentration. If the aed_oxygen module is correctly linked to the carbon module then this setting will switch on automatically. At low O2 concentrations, the amount of DIC fluxing out of the sediment is decreased and at high O2 concentrations, it is set close to the FDICsed, as shown in Equation (5.17). This is a convenient simplification that can be tuned within this module, rather than a more complicated full set of biogeochemical reactions. The half-saturation oxygen parameter KO2sedDIC can be used to tune the DIC flux dependence on bottom water O2. A similar function can be used to tune the CH4 flux using the parameter KO2sedCH4, as shown in (5.20). At high O2 concentrations, CH4 flux decreases, and at low O2 concentrations, the flux is close to the parameter FCH4sed.

ΦDICO2[O2]=O2O2+KO2sedDIC ΦCH4O2[O2]=KO2sedCH4O2+KO2sedCH4

Once the exchange fluxes are computed it is applied to the bottom cells of the simulated domain, and the changes in the DIC and CH4 concentrations in the bottom layer are:

ˆfDICsed=FDICsedΔzbot ˆfCH4sed=FCH4sedΔzbot

where Δzbot is the bottom layer thickness.

Advanced options: The approach described above is the most simple and default method for capturing inorganic carbon fluxes from the sediment, and is referred to as the static model. This approach can be extended to allow for spatial variability in FDICsed and FCH4sed, 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 FDICsed and FCH4sed.

Where dynamic rates of DIC and CH4 are required to flux to/from the sediment (e.g. in response to episodic loading of organic material to the sediment, or for assessment of long-term changes in C loading), then the above expressions (Eq (5.17) and (5.18)) are replaced instead with dynamically calculated variables in aed_seddiagenesis, via a link created with the aed_sedflux module.

Methane ebullition

Methane bubbles are formed when anoxic methane production in the sediments exceeds the rate of oxidation and diffusion. As the bubbles overcome sediment tension, they are released to the water column. The sediment bubble flux is greatly depth-dependent and is enhanced by increasing temperatures (DelSontro et al., 2010). Moreover, it has been observed that falling water levels and decreasing hydrostatic pressure also result in increased ebullition rates (Casper et al., 2000). The ebullition flux is formulated mathematically as follows:

ˆfCH4bubsed[T,Δz]=FCH4bubsedθT20sedch4bubΦCH4bubΔz[Δz]1Δz

The sediment bubble flux FCH4bubsed can be set as a constant or depth-dependent flux. By setting up a link to the aed_sedflux module (Fsed_ebb_variable = 'SDF_Fsed_ch4_ebb'), different sediment bubble flux rates can be implemented in each sediment zone, accounting for the spatial variability of ebullition. ΦCH4bubΔz[Δz] accounts for the effect of water level fluctuations (z) on the sediment bubble flux and is calculated by adopting a site-specific regression equation from Lake Kinneret derived by Ostrovsky et al. (2012).

ΦCH4bubΔz[Δz]=cnexp{εreg(ˉzz)}

Where cn is a normalising constant, εreg is the exponential factor from the regression equation.

The methane bubbles released from the sediments either dissolve in the water column or are directly emitted to the atmosphere. The dissolution rate of methane bubbles can be set constant, or depth-dependent by splitting the water column into two layers and assigning different dissolution rates above (ch4_bub_disf1) and below (ch4_bub_disf2) the splitting depth (ch4_bub_disp). In stratified lakes, this functionality can be used to set different dissolution rates in the epilimnion and hypolimnion (Rdiss[z]) to account for the effect of different thermal layers on dissolution rates.

fCH4bubdis=Rdiss[z] ˆfCH4bubsed

Methane oxidation

Aerobic oxidation is mediated by methanotrophs and is the most significant sink for dissolved methane. The metabolic rate of methanotrophs increases with increasing temperatures, hence aerobic oxidation is temperature sensitive. The aerobic oxidation rate can be described according to Michealis-Menten Kinetics as follows: (Schmid et al., 2017)

fCH4ox= RCH4oxmax Φch4oxO2[O2]θT20ox[CH4]

where RCH4oxmax is the maximum reaction rate of methane oxidation at 20˚C (/day), Φch4oxO2[O2] is the oxygen mediation of the oxidation rate, θox is the temperature multiplier for methane oxidation, T the water temperature, and [CH4] is the methane concentration in the water.

The methane oxidation rate can be changed by water O2 concentration. At low O2 concentrations, the amount of CH4 oxidated to DIC decreased and at high O2 concentrations, it is set close to the RCH4oxmax, as shown in Equation (5.26):

ΦCH4oxO2[O2]=O2O2+KO2CH4ox

where O2 is the dissolved oxygen concentration, KO2CH4ox is the half saturation oxygen concentration for methane oxidation.

Air-water exchange

Carbon dioxide and methane transfer occurs across the air-water interface based on the gas solubility, the amount of turbulence at the interface and the concentration gradient between the air and water, using widely used approach introduced by Wanninkhof (1992). The air-water exchange rate of CO2 is modelled as:

FCO2atm= kCO2600 Υ0CO2(pCOw2pCOa2)

where the pCOw2 and pCOa2 are the partial pressure of CO2 in the water surface and the air (in unit of atm), respectively; Υ0CO2 is the solubility of CO2, and kCO2600 is the air-water gas transfer velocity for CO2. As 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 gas exchange piston velocity models and Schmidt models, or implement their own, to capture this process. The options are listed in the Gas Transfer section of the Generic utilities & Functions chapter. The switches of the piston and Schmidt models are set via the use of Θpistongas and Θschmidtgas, respectively (see option details in Generic utilities & Functions). The exchange flux can then be added to or subtracted from the surface cells of an aquatic system, and the change in the CO2 concentration in the surface layer is:

ˇfCO2atm=FCO2atmΔZsurf

where Δzsurf is the surface layer thickness.

5.3.3 Feedbacks to the Host Model

The inorganic carbon module has no feedbacks to the host hydrodynamic model.

5.3.4 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 5.2. The diagnostic outputs able to be output are summarised in Table 5.3.

State variables

Table 5.2: Carbon - state variables
AED name Symbol Description Unit Type Typical Range Comments
aed_carbon
CAR_dic DIC dissolved inorganic carbon concentration mmolC/m3 pelagic 0 - 5000 simDIC activated by setting dic_initial >9999
CAR_ch4 CH4 dissolved methane concentration mmolC/m3 pelagic 0 - 200 simDIC activated by setting ch4_initial >9999
CAR_pH pH pH value pelagic 7 - 9 activated when simDIC=T
Dependent variables
OXY_oxy O2 dissolved oxygen concentration mmolO2/m3 pelagic 0 - 500 optional for methane oxidation, via methane_reactant_variable
SDF_Fsed_dic Fdicsed dissolved sediment CO2 flux mmolC/m2/s benthic 0 - 300 required for sediment zones; internally used as /s
SDF_Fsed_ch4 Fch4sed dissolved sediment CH4 flux mmolC/m2/s benthic NA required for sediment zones; internally used as /s
SDF_Fsed_ch4_ebb Fch4ebbsed sediment CH4 bubble release rate mmolC/m2/s benthic NA required for sediment zones; internally used as /s

Diagnostics

Table 5.3: Carbon - diagnostics
AED name Symbol Description Unit Type Typical Range Comments
diag_level = 0+
CAR_pco2 pCO2 partial pressure of CO2 in water atm pelagic 0 - 5000
diag_level = 2+
CAR_ch4ox fch4ox methane oxidation rate mmolC/m3/d pelagic 0 - 100
CAR_sed_dic Fdicsed CO2 exchange across sediment-water interface mmolC/m2/d benthic 0 - 50
CAR_sed_ch4 Fch4sed CH4 exchange across sediment-water interface mmolC/m2/d benthic 0 - 1
CAR_atm_co2_flux Fdicatm CO2 exchange across atm-water interface mmolC/m2/d surface 0 - 100
CAR_atm_ch4_flux Fch4atm CH4 exchange across atm-water interface mmolC/m2/d surface 0 - 1
CAR_atm_ch4_ebb_flux Fch4bubatm CH4 bubble flux across air-water interface mmolC/m2/d surface NA
CAR_sed_ch4_ebb Fch4bubsed CH4 bubble flux (ebullition) across sediment-water interface mmolC/m2/d benthic NA
CAR_sed_ch4_ebb_3d ˆfCH4bubblesed CH4 bubble flux (ebullition) volume flux mmolC/m3/d pelagic NA
CAR_ch4_ebb_df fCH4bubbledis CH4 bubble dissolution mmolC/m3/d pelagic 0 - 1

5.3.5 Parameter Summary

The parameters and settings used by this module are summarised in Table 5.4.

Table 5.4: Carbon - parameters
AED name Symbol Description Unit Type Typical Range Comments
Initialisation
dic_initial DIC|t=0 initial DIC cooncentration mmolC/m3 float 0 - 5000 can be overwritten by initial condition files
pH_initial pH|t=0 initial pH values float 7 - 9 can be overwritten by initial condition files
ch4_initial CH4|t=0 initial CH4 values mmolC/m3 float 0 - 200 can be overwritten by initial condition files
Carbonate buffering
co2_model Θco2 selection of pCO2 model algorithms integer 0, 1, 2 1: CO2SYS; 2: Butler; 0: aed_geochem
alk_model ΘTA selection of total alkalinity model algorithms integer 0,1,2,3,4,5 empirical relations developed for Queensland Rivers
ionic I average ionic strength of the water column meq float NA
Atmospheric exchange
atm_co2 pCOa2 atmospheric CO2 concentration atm float 350450×106
atm_ch4 pCHa4 atmospheric CH4 concentration atm float 12×106
co2_piston_model Θpistonco2 selection of air-water CO2 flux velocity method integer 1 - 9 see options in the Gas Transfer section
ch4_piston_model Θpistonch4 selection of air-water CH4 flux velocity method integer 1 - 9 see options in the Gas Transfer section
Methane oxidation
Rch4ox Rch4ox maximum reaction rate of CH4 oxidation @ 20C /d float 0 - 1
Kch4ox KO2ch4ox half-saturation O2 conc. for CH4 oxidation mmolO2/m3 float 0.1 - 10
vTch4ox θch4ox Arrhenius temperature multiplier for CH4 oxidation float 1 - 1.1
methane_reactant_variable O2 state variable to be consumed during CH4 oxidation string OXY_oxy
Sediment exchange
Fsed_dic Fdicsed sediment CO2 flux @ 20C mmolC/m2/d float 0 - 1
Ksed_dic KO2seddic half-saturation oxygen conc. controlling CO2 flux mmolO2/m3 float 20 - 100
theta_sed_dic θdicsed Arrhenius temperature multiplier for sediment CO2 flux float 1 - 1.1
Fsed_dic_variable Fdicsed variable name to link to for spatially resolved sediment zones float SDF_Fsed_dic
Fsed_ch4 Fch4sed sediment CH4 flux @ 20C mmolC/m2/d float 0 - 1
Ksed_ch4 KO2sedch4 half-saturation oxygen conc. controlling CH4 flux mmolO2/m3 float 10 - 50
theta_sed_ch4 θch4sed Arrhenius temperature multiplier for sediment CH4 flux float 1 - 1.1
Fsed_ch4_variable Fch4sed variable name to link to for spatially resolved sediment zones mmolC/m2/d float SDF_Fsed_ch4
Sediment ebullition
ebb_model Θebullitionch4 option to activate CH4 ebullition logical 0 , 1 0: no ebullition (default); 1: simple release model
Fsed_ebb_variable Fch4ebbsed variable name to link to for spatially resolved sediment zones float SDF_Fsed_ch4_ebb
ch4_bub_aLL ˉz mean water depth m float 1 - 100
ch4_bub_cLL cn normalising constant float 0.5 - 0.7 Lake Kinneret specific value
ch4_bub_kLL ϵreg exponential factor from the depth-ebullition regression relation float -0.8 - -0.9 Lake Kinneret specific value
ch4_bub_disf1 kshallowdis bubble dissolution fraction (surface) float 0 - 1
ch4_bub_disf2 kdeepdis bubble dissolution fraction (deep) float 0 - 1
ch4_bub_disdp zdepthdis bubble dissolution fraction depth interface m float 1 - 100


5.4 Setup & Configuration

An example aed.nml parameter specification block for the aed_carbon module that is only modelling DIC is shown below:

&aed_carbon
 dic_initial       = 1000.
 pH_initial        = 7.5
 ch4_initial       = -9999       ! disables CH4
! Carbonate buffering
 co2_model         = 1
 alk_model         = 5
! Atmospheric exchange
 atmco2            = 0.000380
 co2_piston_model  = 1
! Sediment respiration
 Fsed_dic          = 10.0        ! replaced by linked SDF var
 Ksed_dic          = 100.
 theta_sed_dic     = 1.08
 Fsed_dic_variable = 'SDF_Fsed_dic'
/


An example aed.nml parameter specification block for the aed_carbon module that includes all options is shown below:

&aed_carbon
  !-- DIC and pH --!
   dic_initial               = 91
   Fsed_dic                  =  0.001
   Ksed_dic                  = 53.44356
   theta_sed_dic             =  1.08
  !Fsed_dic_variable         = 'SDF_Fsed_dic'
   pH_initial                =  6.2
   atm_co2                   =  4e-04 
   co2_model                 =  1
   alk_mode                  =  1
   ionic                     =  0.1
   co2_piston_model          =  1
  !-- CH4 (dissolved) --!
   ch4_initial               =  5
   Rch4ox                    =  0.1
   Kch4ox                    =  0.2
   vTch4ox                   =  1.2
   Ksed_ch4                  =  3.437
   theta_sed_ch4             =  1.2
   methane_reactant_variable = 'OXY_oxy'
   atm_ch4                   =  1.76e-6
   ch4_piston_model          =  1
  !Fsed_ch4_variable         = 'SDF_Fsed_ch4'
  !-- CH4 (bubbles) --!
   ebb_model                 =  1
   Fsed_ch4_ebb              =  0.0
  !Fsed_ebb_variable         = 'SDF_Fsed_ch4_ebb'
   ch4_bub_aLL               = 42.95127
   ch4_bub_cLL               =  0.634
   ch4_bub_kLL               = -0.8247
   ch4_bub_disdp             = 20
   ch4_bub_disf1             =  0.33
   ch4_bub_disf2             =  0.07
/


5.5 Case Studies & Examples

5.5.1 Case Study : South-east Queensland estuaries

The aed_carbon model was applied to three estuaries of the Moreton Bay region in south-east Queensland, Australia. The river and estuaries were monitored to measure dissolved inorganic carbon and methane as part of a campaign in March 2016 to understand controls on greenhouse gas emissions.


Figure 5.2: Map of the three estuaries of the Moreton Bay region in south-east Queensland, Australia.


Using the AED model coupled with the 3-D model TUFLOW-FV, the dynamics of the estuaries were simulated for the year 2016 and compared against data from the intensive field campaigns. An example of the model capturing the different gradients in DIC and CH4 in the three systems is shown in Figure 5.3.


Trasect plots comparing modelled and field data for DOC (uM, 1st row), DIC (uM, 2nd row), pCO2 (uATM, 3rd row) and pCH4 (uATM, 4th row) for three estuaries in south-east Queensland: a) Brisbane river, b) Maroochy estuary, and c) Noosa estuary. Data presented for March 2016, collected by Southern Cross University Centre for Coastal Biogeochemistry.

Figure 5.3: Trasect plots comparing modelled and field data for DOC (uM, 1st row), DIC (uM, 2nd row), pCO2 (uATM, 3rd row) and pCH4 (uATM, 4th row) for three estuaries in south-east Queensland: a) Brisbane river, b) Maroochy estuary, and c) Noosa estuary. Data presented for March 2016, collected by Southern Cross University Centre for Coastal Biogeochemistry.


5.5.2 Case Study : Lake Kinneret

The aed_carbon model was applied to Lake Kinneret, a deep subtropical lake in Israel. Using the AED model coupled with the General Lake Model, the dynamics of Lake Kinneret (Figure 5.4) were simulated from 01/01/1998 until 23/02/2002.


Figure 5.4: Map of Lake Kinneret, Israel.


Dissolved methane concentrations were calibrated using the glmtools R package and were compared against observations (Figure 5.5). Ebullition was simulated using a site-specific algorithm and the sources and sinks of both dissolved methane and methane bubbles are presented on Figure 5.6.


The observed vs. simulated dissolved methane concentrations (mmol/m^3^) at 1 m, 10 m, 20 m, 30 m, 35 m and 38-39 m depths in Lake Kinneret. The rhombus symbols represent the observed and the solid lines represent the simulated methane concentrations. Data consisting of dissolved methane concentrations in Lake Kinneret was extracted from @schmid2017, using WebPlotDigitizer (http://arohatgi.info/WebPlotDigitizer).

Figure 5.5: The observed vs. simulated dissolved methane concentrations (mmol/m3) at 1 m, 10 m, 20 m, 30 m, 35 m and 38-39 m depths in Lake Kinneret. The rhombus symbols represent the observed and the solid lines represent the simulated methane concentrations. Data consisting of dissolved methane concentrations in Lake Kinneret was extracted from Schmid et al. (2017), using WebPlotDigitizer (http://arohatgi.info/WebPlotDigitizer).


The sources and sinks of methane (mol 10^6^) in Lake Kinneret. The sources and sinks of dissolved methane (stacked) are presented in the first figure and include the dissolution of bubbles (“dissolution”), sediment diffusive flux (“sed_flux”), aerobic oxidation (“oxid”) and gas exchange at the water-air interface on the secondary axis (“sed_flux”). The sources and sinks of ebullition (stacked) in Lake Kinneret are presented in the second figure and include the sediment bubble flux (“sed_ebb”), the dissolution of bubbles (“dissolution”) and the atmospheric ebullition flux (“atm_ebb”).

Figure 5.6: The sources and sinks of methane (mol 106) in Lake Kinneret. The sources and sinks of dissolved methane (stacked) are presented in the first figure and include the dissolution of bubbles (“dissolution”), sediment diffusive flux (“sed_flux”), aerobic oxidation (“oxid”) and gas exchange at the water-air interface on the secondary axis (“sed_flux”). The sources and sinks of ebullition (stacked) in Lake Kinneret are presented in the second figure and include the sediment bubble flux (“sed_ebb”), the dissolution of bubbles (“dissolution”) and the atmospheric ebullition flux (“atm_ebb”).


5.5.3 Publications

Author/Year Paper Title Description

Huang et al. (2022)

A modelling approach for addressing sensitivity and uncertainty of estuarine greenhouse gas (CO2 and CH4) dynamics.

NA

Huang et al. (2022)

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