Land-use intensity alters both the source and fate of CO2 within eight sub-tropical estuaries

Journal Pre-proofs Land-use intensity alters both the source and fate of CO2 within eight subtropical estuaries Naomi S. Wells, Damien Maher, Peisheng Huang, Dirk V. Erler, Paul Maxwell, Matthew R. Hipsey, Bradley D. Eyre PII: DOI: Reference:

S0016-7037(19)30628-3 https://doi.org/10.1016/j.gca.2019.09.042 GCA 11466

To appear in:

Geochimica et Cosmochimica Acta

Received Date: Revised Date: Accepted Date:

14 January 2019 10 September 2019 26 September 2019

Please cite this article as: Wells, N.S., Maher, D., Huang, P., Erler, D.V., Maxwell, P., Hipsey, M.R., Eyre, B.D., Land-use intensity alters both the source and fate of CO2 within eight sub-tropical estuaries, Geochimica et Cosmochimica Acta (2019), doi: https://doi.org/10.1016/j.gca.2019.09.042

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Land-use intensity alters both the source and fate of CO2 within eight sub-tropical estuaries

Naomi S. Wells1*, Damien Maher1,2, Peisheng Huang3, Dirk V. Erler1, Paul Maxwell4, Matthew R. Hipsey3, Bradley D. Eyre1

1

Centre for Coastal Biogeochemistry, School of Environment, Science & Engineering, Southern

Cross University, PO Box 157, Lismore, 2480 NSW, Australia 2

Southern Cross Geoscience, Southern Cross University, PO Box 157, Lismore, 2480 NSW,

Australia 3

Aquatic Ecodynamics, UWA School of Agriculture and Environment, University of Western

Australia, Crawley, 6009 WA, Australia 4

Healthy Land & Water, Brisbane, QLD, Australia

*Corresponding author: [email protected]

Submitted to Geochemica et Cosmochimica Acta

1

ABSTRACT Combined pressures from inland agricultural intensification and coastal development are dramatically altering estuaries’ structure and function. Despite the established global significance of estuarine carbon (C) cycling, the impact of growing anthropogenic stress on coastal C inputs and exports is unclear. To address this gap, we evaluated the magnitude and drivers of estuary C fluxes in eight sub-tropical estuaries at Low (n = 3), Moderate (n = 2), and High (n = 3) levels of nutrient enrichment. We measured changes in the concentration and isotopic composition (δ13C) of the major C pools (organic and inorganic) and gaseous product of C turnover (CO2) over wet and dry seasons. Over both sampling periods estuaries classified Moderate and High emitted far more CO2 (37 ± 10 mmol m-2 d-1) than those classified Low (6.3 ± 4 mmol m-2 d-1). However, estuaries with both high nutrients and high turbidity produced less CO2, and thus exported more DIC, than expected from hydrodynamics (freshwater flushing time). Differences in estuary phytoplankton biomass (Chla concentrations) corresponded with differences in the biological CO2 production (respiration) rates estimated from δ13C-DIC variations, although respiration rates were higher than predicted based on hydrodynamics (surface area / discharge) in high nutrient, low turbidity systems. Together these findings demonstrate that land-use intensification can alter both the source and the production of estuary CO2, and suggest that the direction of this shift can depend on ancillary factors like turbidity as well as nutrient enrichment. Evidence that human alterations to coastal ecosystems can shift the balance between DIC downstream export and CO2 emissions outside of the range predicted by hydrodynamic factors like residence time, surface area, and discharge has implications for global C models.

Keywords: Australia, δ13C, land-use change, eutrophication, dissolved organic carbon, dissolved inorganic carbon

2

1. Introduction Estuaries can act as either filters or exporters of terrestrial nutrients and carbon (C) to the ocean. Their location between C-exporting rivers and C-producing marine systems makes both estuary C storage (in biomass and sediments) and emission (as the greenhouse gas carbon dioxide, CO2) globally significant (Bauer et al., 2013; Ward et al., 2017). However, simultaneous coastal urban development and inland agricultural intensification could be altering the balance between estuary C storage and emission (Cloern, 2001; Cloern et al., 2016). Estuaries are increasingly subjected to obstructed freshwater flows, marine channel dredging, sediment load increases, and excess inputs of nutrients like nitrogen (N) and phosphorus (P) (Bauer et al., 2013). Although landuse intensification is broadly acknowledged to alter the source (and thus quality) of organic matter (OM) entering estuaries, the effect of such changes on internal C cycling and the associated CO2 emissions remains unclear (Raymond and Hamilton, 2018). In the absence of human disturbance, an estuary’s C balance is broadly defined by freshwater inputs of dissolved and particulate organic C (DOC, POC) plus inputs of inorganic C (DIC) from both bedrock dissolution and upstream respiration (Bauer et al., 2013; Cai, 2011; Middelburg and Herman, 2007; Raymond and Hamilton, 2018). Land-use intensity can directly alter this balance by changing both C source quantity and C quality. For example, cultivated soils leach more (and different) DOC and wastewater treatment plants (WWTPs) discharge both DOC and DIC (Alshboul et al., 2016; Lambert et al., 2017; Raymond and Hamilton, 2018). Simultaneously, increased N and P inputs indirectly alter estuary C quantity and quality by enhancing internal biological production (eutrophication), which increases biomass (POC) and DOC and drives the internal respiration and consumption of DIC (Borges and Abril, 2011; Cotovicz Jr et al., 2015). Physical alterations (channelization, dredging, damming) can also alter discharge dynamics, thus affecting transit times of C within the estuary; changes in estuarine hydrology therefore indirectly affect the amount and quality of C as higher flows move more DOC downstream and slower flows promote photodegradation, molecular breakdown, and recycling, which alters its quality (Poff et al., 2007; 3

Raymond et al., 2016). These competing influences make it difficult to identify global trends in either the direction, magnitude, or pathway through which human activities affect estuary C budgets (Bauer et al., 2013; Raymond and Hamilton, 2018; Ward et al., 2017). Human alterations to estuary C turnover also impact the direction and magnitude of estuary C off-gassing, and thus their C footprints. New field-deployable instruments make collecting high temporal/spatial resolution data on dissolved CO2 and C stable isotope ratios possible (Maher et al., 2013b; Webb et al., 2016). The increasingly detailed data on coastal greenhouse gas dynamics enabled by these instruments have shown that more impacted estuaries evade more CO2 (David et al., 2018; Ruiz-Halpern et al., 2015). Increased fluxes are variably attributed to: WWTPs discharging DIC (Akhand et al., 2016; Hu et al., 2017), urban storm drains discharging DIC (Alshboul et al., 2016; Wang et al., 2017a), agricultural DOC leaching enhancing C degradation (Borges et al., 2018), and excess N enhancing respiration and anoxia (Vivanco et al., 2015). Yet not all impacted estuaries emit more CO2 (Cotovicz Jr et al., 2015; Tanner et al., 2017). Furthermore, relatively pristine ecosystems produce some of the highest measured CO2 fluxes (Call et al., 2019; Rosentreter et al., 2018). This variability demonstrates the need to move beyond ‘black box’ approaches that focus on accounting for fluxes in and out of systems without assessing their biological and hydrological drivers. Evidence that terrestrial land-use intensity functionally changes estuarine C cycling is scarce, particularly in the tropics where high baseline C metabolism could mask anthropogenic effects (Bouillon et al., 2012). Carbon isotopic composition (δ13C) can be a useful tool for decoding shifts in C processes and sources (Bird et al., 1992; Campeau et al., 2017; Magozzi et al., 2017). This is based on knowledge that biology predictably alters δ13C: respiration produces CO2 with δ13C similar to that of the source OM (Min et al., 2016), and differences in dominant photosynthetic pathways in terrestrial v. marine environments produce divergent δ13C signatures that can be used to trace them through estuaries (Bouillon et al., 2007; Watanabe and Kuwae, 2015) (Fig. 1). Here we used this knowledge of δ13C dynamics to redress the scientific gap in understanding 4

the direction and magnitude of human development on coastal CO2 emissions. We hypothesized that land-use driven alterations to C input quality and quantity will alter the production and fate of CO2 in coastal environments. We tested this hypothesis by carrying out seasonal surveys of the size and δ13C composition of the major C pools (DIC and DOC) and respiration product (CO2) in eight subtropical estuaries subjected to varying degrees of anthropogenic alterations.

2. Methods 2.1 Site descriptions Surveys were carried out in eight estuaries in SE Queensland (QLD), Australia in March 2016 (wet season) and October 2016 (dry season) (Fig. 2). Mean air temperatures range from 16°C in May – October (dry season) to 27°C in November – April (wet season), when the majority of the 1,000 mm of annual precipitation falls. Estuaries were selected for geographic similarity and contrasting land-use intensity (Table 1). They were classified as either ‘Low’ (Noosa River Estuary, ‘Noosa’; Mooloolah River Estuary, ‘Mooloolah’; Nerang River Estuary, ‘Nerang’), ‘Moderate’ (Maroochy River Estuary, ‘Maroochy’; Pine River Estuary, ‘Pine’), or ‘High’ (Caboolture River Estuary, ‘Caboolture’; Brisbane River Estuary, ‘Brisbane’; Logan/Albert Rivers Estuary, ‘Logan/Albert’) degradation status based on total N and total P loads: Low estuaries received ~0.5 kg P km-2 d-1 and ~1 kg N km-2 d-1, Moderate estuaries received ~5 kg P km-2 d-1 and ~10 kg N km-2 d-1, and High estuaries received ~ 30 kg P km-2 d-1 and ~70 kg N km-2 d-1 (Wells et al., 2018), which corresponded with differences in surface water nutrient concentrations (Appendix B: Table B1). Regional geology is dominated by Quaternary alluvial deposits underlain by Triassic/Jurassic sandstones (Landsborough series) with some granites and volcanoclastic intrusions, and does not contain significant carbonate deposits (Australian-Government, 2016).

2.2 Sampling Samples were collected from a boat driven at ~7 km h-1 through the centre of each estuary. 5

Each transect started at high tide at the mouth (salinity ~36) and continued upstream until freshwater was reached (salinity <1; n = 4), or the estuary became too shallow (<0.5 m depth; n = 4). A sounder recorded water depth (h) every ~5 min, and water was continuously pumped from ~20 cm below the surface and then split to either, 1) air-water gas equilibrators, 2) a sampling bucket to measure water chemistry, or, 3) outflow for collection of grab samples for DOC and DIC analysis. Two calibrated sondes (Hydrolab DS5X and HL4 in wet season; Manta, YSI, and DS5X in dry season) measuring salinity, temperature, pH, and dissolved oxygen (DO) every 5 min were placed in a bucket receiving continuously pumped estuary water. This ensured that water chemistry and dissolved gas data aligned. Water samples were collected every ~2 salinity changes, as determined via real-time salinity monitoring of sondes. Samples for DIC, DOC, δ13C-DIC, and δ13C-DOC were passed through 0.45 µm cellulose acetate (Millipore) syringe-tip filters into precombusted 40 ml vials, killed with 0.1 ml 1% HgCl2, sealed with Teflon septa, and placed on ice until they could be stored at 4°C until analysis. Dissolved CO2 (concentration and δ13C) were measured every 1 s during transects using online laser ring-down spectroscopy (Picarro G2201-i and Picarro G2308 for back-up concentration measurements). Briefly, water from ~20 cm below the surface was sparged through two showerhead exchangers (Durridge) and passed through a Drierite column before entering the gas analysers (Maher et al., 2013b). Instruments were calibrated using two CO2 gasses (0 and 1,000 ppm), which showed accuracy of ±1 ppm for CO2 concentrations and ±0.5‰ for δ13C-CO2 (Maher et al., 2015). Surface water 222Rn concentrations were also measured as a qualitative groundwater tracer (Table 1, data from Wells et al. (2018)]. The timing offsets for CO2 and 222Rn equilibration defined by Webb et al. (2016) were applied. Following each transect atmospheric air was run through the system for 10 – 20 min. This provided site-specific values and accounted for any instrument variation, but did not fully account for possible variations in air concentrations over transects’ lengths. Due to instrument error, δ13C-CO2 was not measured in the Albert branch of the Logan/Albert in the dry season. Note that the breadth of this study made it necessary to narrow our 6

consideration of estuary C balances to air-water exchange (CO2) and discharge to the ocean (DIC, DOC), without C burial or POC. Methane is another C respiration product, but is not considered here as it typically is a small component of the estuary C balance.

2.3 Chemical analyses DOC and DIC concentrations and δ13C values were measured via continuous flow wet oxidation on an Aurora 1030W TOC analyser coupled to a Thermo Delta V Plus IRMS (Centre for Coastal Biogeochemistry). Precision for DOC and DIC concentrations was 3%. Precision for δ13C was ± 0.1‰ (DIC) and 0.3‰ (DOC) (Oakes et al., 2010; St-Jean, 2003). Values of δ13C-DOC were measured in all estuaries in the dry season, but only Noosa, Maroochy, and Brisbane in the wet season. There is no DIC data for wet season Noosa.

2.4 Data analysis After testing for normality, relationships between variables were established with Pearsons correlation (SPSS v.22). Differences between estuaries or land-use classes between seasons were tested using a two-way ANOVA with Sidak post-hoc (SPSS v.22). Analyses were performed on data collected only from salinities ≥10 to ensure that the inability to reach freshwater in half of the estuaries did not bias interpretation. All values are reported as mean ± SD. Significance is defined as p<0.05.

2.4.1 Hydrology The freshwater discharge (Q, l d-1) into each estuary was obtained using upstream gauging station data for Nerang and validated catchment models for Noosa, Maroochy, Mooloolah, Caboolture, Pine, Brisbane, Logan/Albert (BMT WBM, 2017). Note that the modelled values include fluxes from ungauged basins, which were not included in previously published flushing times (FT; days needed for Q to replace the freshwater volume) for these systems (Wells et al., 7

2018; Wells and Eyre, 2019). Seasonal FTs were calculated based on the measured salinity gradients and the mean Q for the 30 days preceding sampling (Eq. 1): (1)

𝑚

𝐹𝑇 = ∑𝑓𝑤(𝑓𝑓𝑤 × 𝑉𝑚 +…𝑓𝑓𝑤 × 𝑉𝑓𝑤)/𝑄

where FT is computed as the sum of the freshwater fraction (ffw, the measured salinity as a fraction of the salinity difference between marine and freshwater ends) for each water volume increment (V = measured depth, h, × width, estimated from satellite images, × distance travelled per 1 min) between the marine (Vm) and freshwater (Vfw) extents. This approach slightly overestimates V by assuming constant h over segment width.

2.4.2 Gas fluxes To ease processing, CO2 and δ13C-CO2 data were block-averaged to 1 min intervals (n = 5629). Instruments outputs were then converted to µM CO2 based on the partial pressure (µatm) and solubility coefficients proposed by Weiss (1974) (K0, in mol atm-1 l-1).. Water-air CO2 evasion was then calculated as the difference between dissolved (Cw) and atmospheric (Cair) partial pressure (Eq. 2): (2)

𝐹 = 𝑘𝐾0(𝐶𝑤 ― 𝐶𝑎𝑖𝑟)

where k is the gas transfer velocity in cm h-1 corrected for the Schmidt number (Sc), which was corrected for the temperature and salinity at each measurement interval as per Wanninkhof (2014), i.e., k = k600 × (Sc/600)-0.5. Post-transect air measurements were used as Cair. The uncertainty inherent in converting dissolved concentrations to atmospheric fluxes was accounted for by solving Eq. 2 using four different k parameterisations: one based solely on wind speed (U10) (Carini et al., 1996), two that also incorporate current velocity (v) and h (Ho et al., 2016; Rosentreter et al., 2017), and one that also includes estuary surface area (A) (Abril et al., 2009). At each water sampling point U10 was measured with an on-board weather station and h recorded; v was generated for each point with a TUFLOW-FV 3D hydrodynamic model (see BMT WBM (2017) for validation details). See Appendix Table B2 for complete parameterisations. 8

Eq. 2 outputs were also used to calculate whole-estuary CO2 fluxes using an angular distance approach to interpolate between measurement points over A, which was calculated from satellite imagery (GoogleEarth, accessed 04/2017), using a grid size resolution based on the distance between sampling points (SAGA 2.1.3). To best express the uncertainty about the impact of physical factors (U10, v, and h) on gas evasion, grid F values were summed over A for each k parameterisation and then averaged. Thus the reported FCO2 values represent the mean ± SD of gas evasion based on known variations in estuarine k values. Values were normalised to A to produce comparable FCO2 values for each estuary within each season. Note that the marine-freshwater transects mean that any lateral inputs from mangroves and salt marshes around the estuary mouth are included in FCO2. Riverine contributions to FCO2 were estimated as per Eq. 3 (Borges and Abril, 2011): (3)

%𝑅𝑖𝑣𝑒𝑟𝐷𝐼𝐶 = 100 × (𝑄 × 𝐶𝑓𝑤)/(𝐹𝐶𝑂2)

where FCO2 in mol C estuary-1 d-1 is compared to the riverine influx in mol d-1 (Q × Cfw, where Cfw is the freshwater concentration) to calculate the proportion of the CO2 flux attributable to riverine loads (%RiverDIC, unit-less). Here Cfw was defined as riverine DIC excess (Abril et al., 2000), estimated from measured DIC, pCO2, and pH using CO2SYS (Pierrot et al., 2006), parameterised to the total pH scale (mol/kg-SW) and carbonic acid constants from (Dickson and Millero, 1987), to account for subsequent salinity and alkalinity – driven changes to the DIC-CO2 equilibrium.

2.4.3 Isotope data The isotopic composition of the DIC pool can help untangle unaccounted for source mixing. Despite the higher density of δ13C-CO2 data available here, we focus on δ13C-DIC dynamics to avoid the convoluting abiotic factors partitioning 13C between the gaseous, aqueous, and dissolved forms (Polsenaere and Abril, 2012; Zhang et al., 1995). This approach is based on the knowledge that, without significant lateral / groundwater sources or internal production, estuary δ13C-DIC composition reflects conservative freshwater - marine mixing (Eq. 4): 9

(4)

𝛿𝑥 = 𝛿𝑓𝑤𝑓𝑓𝑤 + 𝛿𝑚𝑓𝑚 1 = 𝑓𝑚 + 𝑓𝑓𝑤 𝑓𝑚 =

𝑆𝑥 ― 𝑆𝑓𝑤 𝑆𝑚 ― 𝑆𝑓𝑤

where salinity (S) at location x defines the relative contribution of freshwater (ffw) and marine (fm) end-members. These fractions predict the δ13C-DIC composition at x (δx) as mixing between the measured δ13C-DIC in the freshwater (δfw) and marine (δm) ends. Underpinning Eq. 4 with S accounts for any non-linear dilution caused by variations in h and width over transects. We expanded this approach to account for evasion effects on the residual δ13C-DIC (Campeau et al., 2017) using measured FCO2 (Eq. 2) and established fractionation effects (εev) (Eq. 5): (5)

𝛿𝑚𝑖𝑥(𝑥) = 𝑓𝑢𝑝(𝛿𝑚𝑖𝑥(𝑥 ― 1) + 𝜀𝑒𝑣ln (𝑓𝑒𝑣)) + 𝑓𝑚2𝛿𝑚2 1 = 𝑓𝑢𝑝 + 𝑓𝑚2 𝑓𝑚2 = 𝑓𝑚(𝑥) ― 𝑓𝑚(𝑥 ― 1)

where the δ13C-DIC value at x (δmix) is calculated over the sampled transect by applying εev (-8‰ at 25°C; (Zhang et al., 1995)] to the DIC in the fraction of water originating upstream (fup) based on the relative proportion of the DIC pool evaded over the length between the two locations (fev) based on the change in fm (Eq. 4) and measured FCO2 (Eq. 6): (6)

𝑓𝑒𝑣(𝑥) = 𝑓𝑒𝑣(𝑥 ― 1) +

𝐹𝐶𝑂2

(

)/𝐿𝑥 ― 1

𝐶 ∙ 1000

where fev at x includes the fraction calculated upstream from x (fev(x-1)) and the ratio of FCO2 (mmol m-2 d-1; Eq. 2) to the measured DIC concentrations (C; mM) normalised to the distance between x and x-1 with an assumed channel width of 1 m (Lx-1; m). To solve this equation all inputs and outputs were normalised to d-1. This provides a FCO2:DIC ratio without forcing uniform dynamics outside of the sampled area, and constrains DIC to the top 1 m of the water column that would be consistently affected by evasion within the 24 h period in all scenarios. Differences between measured δ13C-DIC (δ13CDIC) and δmix (Eq. 5) values were integrated 10

over estuary length to determine net δ13C deviation for each estuary × season (δ13Coffset) (Eq. 7): (7)

𝑚

𝛿13𝐶𝑜𝑓𝑓𝑠𝑒𝑡 = ∫𝑥 = 𝑓𝑤(𝛿13𝐶𝐷𝐼𝐶(𝑥) ― 𝛿𝑚𝑖𝑥(𝑥))

where δ13Coffset values greater than analytical precision indicate non-conservative transport. Significant Eq. 7 outputs could then be used to constrain potential C sources and processes driving the offset.

3. Results 3.1 Estuary overviews Light penetration was lowest in High estuaries (Brisbane and Logan/Albert < Caboolture) and highest in Mooloolah and Nerang (Table 1). Estuary Chla was higher in wet than dry seasons, and highest in Mooloolah and Nerang and lowest in Logan/Albert, Caboolture, and Pine (Table 1). Freshwater Q was higher in the wet than the dry season, and consistently higher in Logan/Albert and Brisbane than the other estuaries (Appendix Fig. A2). Noosa had the longest FT and Mooloolah the shortest (Table 1). Transect salinities were generally lower in the wet than the dry season (Fig. 3), and water temperatures declined from 27°C to 22°C (Appendix Table B2). Estuaries were ~100% saturated in DO (Appendix Table B1).

3.2. DOC and DIC Increasing salinity correlated with decreasing DOC concentrations (p<0.001, r = -0.70; Fig. 3). In both seasons, Maroochy and Caboolture had the highest DOC, and Pine and Nerang the lowest (Fig. 3). However, there was no significant difference in DOC concentrations between the Low, Moderate, and High classes. Independent of seasonal salinity shifts, DOC concentrations were higher in Noosa, Maroochy, Caboolture, Logan/Albert, and Pine (but not Mooloolah, Nerang, or Brisbane) in the wet season (Fig. 3). Wet season salinity explained >90% of DOC concentration changes in all Low (Noosa, Mooloolah, Nerang) and some Moderate (Maroochy) and High (Caboolture, Brisbane, and the Logan branch of the Logan/Albert) estuaries, but this relationship 11

did not persist into the dry season. Estuary DIC concentrations were higher in the dry than the wet season (Fig. 3), with a mean shift from 2,000 ± 300 µM to 1,800 ± 200 µM across the eight estuaries. Concentrations were highest in Logan/Albert, Pine, and Brisbane, and lowest in Noosa (p<0.001, F = 3.9) (Fig. 3). This corresponded to a decrease from High (2,000 ± 300 µM) to Moderate (1,800 ± 300 µM) to Low (1,700 ± 300 µM) estuaries (p<0.001). Salinity explained >90% of variations in DIC concentrations in two Low (Mooloolah and Nerang) and one Moderate (Maroochy) estuary in both seasons, plus dry season Noosa and Caboolture (Fig. 3). The DIC concentration v. salinity slopes ranged from 14 in Mooloolah (wet) to 45 in Noosa (dry). Accordingly, the relative concentrations of DOC to DIC were decreased from the wet to the dry season in all estuaries: the DOC:DIC molar ratios at the freshwater ends decreased from 0.33 ± 0.2 (wet) to 0.19 ± 0.1 (dry).

3.3 CO2 Within-estuary CO2 concentrations increased with decreasing salinity (for all estuaries and seasons: p<0.001, r = -0.63). Dissolved CO2 decreased approximately continuously over salinity in four estuaries (Nerang, Mooloolah, Pine, and Brisbane), but peaked at intermediate salinities (1 – 20) in Maroochy, Noosa, Logan/Albert, and Caboolture (Fig. 4). Concentrations of CO2 differed between estuaries, with the highest concentrations in Maroochy and the lowest in Noosa. Estuary CO2 decreased from Moderate (54 ± 40 µM) to High (41 ± 20 µM) to Low (25 ± 20 µM) systems (F = 190, p<0.001), and was higher in the wet than dry season in all systems except Nerang (Fig. 4). In the dry season upper Noosa and Caboolture were under-saturated in CO2 (Fig. 4). In two estuaries (Maroochy and Mooloolah) CO2 concentration were significantly higher in the wet than the dry season, while in Nerang concentrations were higher in the dry than the wet season (Fig. 4). For gas evasion, k values were somewhat higher for wet (11 ± 4 cm h-1) than dry (7.2 ± 4 cm h-1) season samplings (p<0.001). The Ho et al. (2016) parameterisation yielded the highest k600 values (4 – 32 cm h-1) and the Rosentreter et al. (2017) parameterisation the lowest (3.6 – 16 cm h-1) 12

(p<0.001; Appendix Table B2). Overall FCO2, which was based on mean ± SD from the four k parameterisation, decreased from Moderate (39 ± 20 mmol m-2 d-1) to High (36 ± 10 mmol m-2 d-1) to Low (6.3 ± 4 mmol m-2 d-1) estuaries, and tended to be higher in the dry season than the wet (p<0.001, Fig. 5). Values of %RiverDIC tended to be >100% in the wet season (Fig. 5). Although two of three High estuaries had %RiverDIC consistently >100%, %RiverDIC did not consistently differ between classes.

3.4 δ13C Brisbane and Logan/Albert had higher δ13C-DOC values than the other seven estuaries (Fig. 3). There were no clear seasonal differences in estuary δ13C-DOC (Fig. 3), although δ13C-DOC values tended to decrease towards the estuary mouth in the wet season but not the dry season (Appendix Fig. A3). Mean estuary δ13C-DIC values ranged from -6.7 ± 2 ‰ (Caboolture) to -3.2 ± 3 ‰ (Noosa), and values were overall higher in the dry than wet season (Fig. 3). Surface water δ13C-DIC became less negative as salinity increased (all estuaries and seasons: p<0.001, r = 0.86) (Fig. 6). Estuary δ13C-CO2 values became more negative with decreasing salinity (all estuaries and seasons: p<0.001, r = 0.36) (Fig. 6). Estuary δ13C-CO2 values were highest in Noosa, Nerang, and Mooloolah (-11 ± 2 ‰), and decreased from Low (-11 ± 2 ‰) to Moderate (-14 ± 3 ‰) to High (15 ± 2 ‰) estuaries (F = 800, p<0.001,). Estuary δ13C-CO2 values were overall lower in the wet than the dry season (F = 28; p<0.001).

4. Discussion Estuary FCO2 values were somewhat lower than the 57 mmol m-2 d-1 reported median for global estuaries (Borges and Abril, 2011). Combined with the water chemistry patterns over salinity, which are typical for river-dominated estuaries (Bauer et al., 2013; Borges and Abril, 2011; Cloern et al., 2017), this makes the eight studied systems a reasonably representative gradient for identifying land-use driven changes to estuary C cycling. However, the drivers of potential C 13

balance shifts between the Low, Moderate, and High estuaries are not clear from the concentration differences alone. For instance, higher DIC concentrations could come from either new inputs (liming, leached soil CO2) or in-situ respiration driven by new nutrient inputs (Cloern et al., 2016; Raymond and Hamilton, 2018). The subsequent sections use δ13C and hydrologic data to evaluate the roles of, 1) external sources (rivers, groundwater, WWTPs), and, 2) internal C cycling (respiration), in linking land-use intensity to estuary C dynamics.

4.1 Estuary C sources Changes in the source and/or amount of C reaching estuaries could drive the increased DIC and CO2 concentrations over land-use intensities. Rivers draining agricultural catchments may carry higher DOC and DIC loads (Raymond and Hamilton, 2018; Stanley et al., 2012), while groundwater can convey disproportionately high terrestrial C loads to estuaries (Santos et al., 2012). So in addition to collecting data on riverine inputs, we used 222Rn data to establish that three of the systems (Low: Nerang; Moderate: Maroochy and Pine) had higher groundwater inputs (Table 1). However, decreasing 222Rn concentrations from freshwater to marine reaches within the estuaries made it difficult to untangle ‘groundwater’ from ‘freshwater’ effects (Fig. 6). Dams upstream in Nerang, Pine, and Caboolture (Table 1) could also alter both terrestrial C inputs and internal C production (Davis et al., 2012; Giling et al., 2015; Voss et al., 2017). The WWTPs in the Moderate and High estuaries could discharge DIC (2000 – 5000 µmol kg-1) and DOC (400 – 900 µmol kg-1) rich effluent (Alshboul et al., 2016; Yang et al., 2018). While human activities can affect geogenic C inputs (Raymond and Hamilton, 2018), here the lack of bedrock carbonates and consistent surface water pH (Table 1) made it reasonable to rule out major ‘chemical’ C differences.

4.1.2 DOC The differences in δ13C-DOC composition between the estuaries suggests a change in OM sources over the sampled gradient, despite consistency in DOC quantity. This is because δ13C-DOC 14

reflects its origin, e.g., inputs from ecosystems like mangroves or soils, where C3 photosynthesis produces an isotopically depleted C pool of ~ -28‰ (Bouillon et al., 2007; Middelburg and Herman, 2007). Thus, in the sampled estuaries, the terrestrial DOC in the rivers mixing with isotopically similar ‘marine’ DOC from the mangrove dominated mouths could reasonably produce the observed within-estuary δ13C-DOC consistency. Similarly, the less negative δ13C-DOC in Logan/Albert and Brisbane could be due to inputs from inland sugarcane production, as sugarcane is a C4 photosynthesising crop that exports C of ~ -18‰ δ13C (Bird et al., 1992; Smith and Epstein, 1971). Yet Pine and Maroochy, which have comparable inland sugarcane production, did not express these lower values. The clearer expression of the terrestrial C4 signature in Brisbane and Logan/Albert must then reflect, 1) faster decomposition of the labile C4 organic matter in the less turbid Moderate estuaries (Middelburg and Herman, 2007; Wynn and Bird, 2007), 2) higher internal DOC production or mineralisation masking the sugarcane signatures in the Moderate estuaries (Geeraert et al., 2016), and/or, 3) distinct molecular DOC composition in Logan/Albert and Brisbane (McCarthy et al., 2004). The δ13C-DOC data provide clear evidence that OM differed between the estuaries, despite no detected quantity change, fitting previous findings that land-use alters organic C (Fuß et al., 2017; Looman et al., 2019) and N (Wells and Eyre, 2019) lability.

4.1.3 DIC (including CO2) Hydrodynamics can regulate the relative importance of terrestrial DIC in estuaries: with short FTs and high Qs riverine inputs dominate the DIC pool, while longer FTs and lower Qs allow more internal CO2 production (Borges et al., 2006; Raymond et al., 2016). Here the higher riverine DIC contributions to FCO2 in the wet v. dry season broadly fits this expectation (Fig. 7a). The three estuaries with the clearest groundwater input signatures (Nerang, Pine, Maroochy) also fit this ‘kinetic limitation’ DIC model, suggesting minimal groundwater DIC input. Yet %RiverDIC was higher than FT predicted in Logan/Albert and Brisbane, which had minimal groundwater inputs and should have the highest net internal CO2 production due to their relatively large size (Hotchkiss et 15

al., 2015). And, despite relatively large WWTP discharge into some of the estuaries (Table 1), this produced no detectable C ‘hot-spots’, with neither DOC, DIC, nor CO2 concentrations increasing around discharge points (Fig. 2, Fig. 3, Fig. 4). A more nuanced approach is therefore needed to assess differences in estuary DIC sources. Differences between δx and measured δ13C-DIC are caused by either alternative source mixing or internal biological activity (Fig. 1). Assuming that sampling the outgoing tide produced pseudo steady-state conditions, δ13Coffset could be due to, 1) mixing with DIC from groundwater and WWTPs, or, 2) biological CO2 production (see 4.2.2). For (1), a mixing model was used to estimate the flux of ‘new’ DIC needed to resolve δ13Coffset for estuaries with known WWTP (n = 5) or groundwater (n = 8) inputs (Eq. 8): (8)

𝛿13𝐶𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑 = 𝛿13𝐶𝑚𝑖𝑥𝑓𝑚𝑖𝑥 + 𝛿13𝐶𝑛𝑒𝑤𝑓𝑛𝑒𝑤 1 = 𝑓𝑛𝑒𝑤 + 𝑓𝑚𝑖𝑥

which was solved using iterative fnew solutions for δ13Cnew values typical of WWTPs, -9‰ and 10‰, (Yang et al., 2018) and coastal groundwater, -14‰ and -18‰ (Maher et al., 2013a; Santos et al., 2012) until residual δ13Coffset ≤ analytical uncertainty. Eq. 8 solutions indicated that external sources contribute between 0.005% (Brisbane wet season) and 44% (Caboolture dry season) of estuary DIC fluxes (Table 2). The plausibility of the estimated WWTP inputs were tested by calculating their associated DIC flux based on fnew and the known estuary WWTP discharges over FT (Table 1) and comparing outputs (DICWWTP) to the 2 – 5 mM range reported for discharge from similar tertiary treatment plants (Yang et al., 2018). The calculated WWTP DIC concentrations for Brisbane and dry season Pine and Maroochy indicate that WWTP inputs could explain 100% of their δ13Coffset, while all other estuary × seasons would require WWTP discharge to contain implausible ~10 mM DIC concentrations (Table 2). However, constraining groundwater DIC sources is more complex (Makings et al., 2014; Marx et al., 2018). While estuary 222Rn concentrations provided insight into groundwater’s relative importance, regional variations in both groundwater 222Rn concentrations and sediment 222Rn release create too 16

much uncertainty for quantitative groundwater-surface water flux calculations with this data (Makings et al., 2014; Schubert and Paschke, 2015). Instead we constrained the potential for significant groundwater DIC inputs by calculating the amount of DIC groundwater would need to supply in order to satisfy δ13Coffset (DICGW) from fnew and measured DIC concentrations, and compared the resultant DICGW:222Rn ratio to the regional ratio of ~40 (Jeffrey et al., 2018; Maher et al., 2013a). Although the DICGW:222Rn ratios in the three groundwater-rich estuaries fell within the expected regional range, ratios for Caboolture, Noosa, dry season Logan/Albert, and wet season Maroochy and Pine were implausibly high (Table 2). So although groundwater supplying estuary DIC cannot be fully ruled out with the available data, neither groundwater nor WWTPs appear to drive the observed DIC variations between estuaries, seasons, and land-use classes.

4.2 Internal C processing As external DIC sources could not fully explain the observed estuary C dynamics, we next evaluated the role of within-estuary C breakdown (respiration). Note we do not differentiate between estuary-produced C and catchment (e.g., mangrove) produced C laterally transported to the estuary (Maher et al., 2018; Pfeiffer-Herbert et al., 2016).

4.2.2 Respiration Heterotrophy (CO2 production > O2 production) can increase in response to both nutrient loads driving biological growth (eutrophication) and enhanced terrestrial DOC breakdown (Lapierre et al., 2013; Van Dam et al., 2018). Here evidence that, 1) DIC dynamics in two of the estuaries (Brisbane, Logan/Albert) were poorly predicted by inputs (%RiverDIC) × hydrology (FT) dynamics, and, 2) eight of the 15 estuary × seasons were net CO2 producers, suggest that DIC production (respiration) affected the FCO2 differences between land-use classes (Fig. 7a). Based on knowledge that respired CO2 (and thus DIC) has a comparable δ13C composition to the source DOC pool we used δ13Coffset values (Eq. 7) to calculate total, rather than net (Fig. 7a), respiration rates (Eq. 9): 17

(9)

0 = 𝛿13𝐶𝑜𝑓𝑓𝑠𝑒𝑡2(1 ― 𝑓𝑅) + 𝛿13𝐶𝑅𝑓𝑅 𝛿13𝐶𝑅 = 𝛿13𝐶𝑂𝑀 ― 𝜀𝑅

where δ13Coffset2 (δ13C-DIC that cannot be accounted for by freshwater-marine mixing) is explained by newly respired DIC based on its fractional input to the whole-estuary DIC pool (fR) and isotopic composition (δ13CR) less the isotopic fractionation during respiration (εR, which can range from 0‰ (Mueller et al., 2014) to -4‰ (Werth and Kuzyakov, 2010)]. Here δ13CR is defined by the δ13C of source OM (δ13COM) as mean–SD and mean+SD of estuary δ13C-DOC. Iteratively solving Eq. 9 for fR indicated that respiration accounted for between 5% (Brisbane) and 50% (Nerang, Mooloolah) of estuary DIC, and was proportionally more important in Low and Moderate estuaries (Table 2). The relatively low fR in the High estuaries raises the possibility that these systems are less productive than currently modelled, e.g., metabolism accounts for ~50% of FCO2 at subtropical latitudes (Laruelle et al., 2017; Najjar et al., 2018). To test what drove these shifts estuary daily respiration rates (DICR) were estimated using fR values and the DIC mass in the simplified sampled channel (1 m deep × 1 m wide, see Eq. 7) (Table 2). Notably, despite assumed channel homogeneity, uncertainty about whether bulk δ13C-DOC values accurately represents δ13COM (Min et al., 2016), and εR variability, the calculated DICR broadly aligned with those reported for nearby estuaries (benthic: ~10,000 µmol m-2 d-1 (Maher and Eyre, 2011), pelagic: 9 – 52 µmol C m-2 d-1 (Maher and Eyre, 2012)]. These respiration estimates enabled us to scrutinize the roles of hydrodynamics and biology in driving DIC variations. Calculated DICR generally fit the hydrodynamic (A/Q) model Laruelle et al. (2017) developed to predict estuary respiration (Fig. 7b). Specifically, systems with small A/Q, such as the otherwise dissimilar Mooloolah and Logan/Albert, favour DICR, while systems with large A/Q like Noosa and Nerang have DICR close to equilibrium. Yet DICR in Pine, Maroochy, and Caboolture were higher than predicted by A/Q constraints. The positive relationships between surface water Chla and DICR, which hints that the δ13C approach underestimates DICR relative to direct productivity measurements (Eyre et al., 2011), makes it difficult to attribute this deviation to 18

methodological uncertainty (Fig. 7c). Indeed, the variability of Chla concentrations and contrasting DICR methods (Cloern et al., 2014; Eyre et al., 2011) make a tight fit with previous measurements implausible. Groundwater DIC inputs could account for some excess DICR in dry season Pine and Maroochy, but not wet season Pine or dry season Caboolture (Table 2, Fig. 7b). The implication that DICR above A/Q predicted values was at least partially biology-driven is supported by the high nutrient loads and light availability of the three ‘outlier’ estuaries (Table 1). This contrasts sharply with the high nutrient, low light estuaries (Brisbane and Logan/Albert), which exported, rather than produced, more DIC than predicted based on hydrodynamics (Fig. 7a). The turbidity in Logan/Albert and Brisbane (Hossain et al., 2004) make it likely that light, rather than hydrodynamics, limits their biological activity, and thus CO2 production. Contrasting productivity between these five estuaries also fits with evidence that terrestrial δ13C-DOC signatures were better retained in Logan/Albert and Brisbane. Critically, DIC in the Low estuaries were reasonably predicted by each of the established approaches for linking inputs, hydrodynamics, and internal production (Fig. 7). These findings reveal how models without land-use factors may fail to predict DIC dynamics in modified estuaries.

4.3 Implications for C budgets Evidence that human activities alter estuary C fluxes through both, 1) direct ‘new’ C inputs, and, 2) non-linear changes in biological cycling, has implications for global C budgets. Our findings suggest that interactions between nutrient enrichment and light (turbidity) will determine whether intensifying land-use causes estuaries to either overshoot (high nutrients + normal light) or undershoot (high nutrients + low light) the CO2 emissions predicted by standard hydrodynamic models. This supports Cloern (2001)’s proposal that increasing habitat degradation causes estuaries to shift from nutrient-limited to light-limited systems. Furthermore, our findings suggest that, instead of converting C inputs to CO2, highly impacted systems can instead export disproportionately high C loads offshore. This adds complexity to previous proposals that impacted 19

waterways export more C due to greater terrestrial C inputs (Regnier et al., 2013). Accounting for such ‘excess’ exported DIC suggested here will significantly affect the C footprint of highly impacted systems. For instance, degassing Brisbane’s excess DIC export would increase the system’s 100-year global warming potential from 5.7 ± 1 Gg C y-1 to 8.7 ± 2 Gg C y-1. Evidence that catchment soil erosion (increased turbidity) not only carries particulate C offshore but also inhibits the biological processing of this C during its transport through estuaries highlights the importance of moving beyond traditional hydrologic boundaries in order to accurately compute ‘C footprints’ (Gounand et al., 2018; Wang et al., 2017b). As human impacts on coastal environments increase, information on light availability, phytoplankton biomass, and/or inorganic nutrient inputs are therefore critical for predicting CO2 dynamics beyond what is controlled solely by coastal hydrodynamics.

20

Acknowledgements Thanks to Leslie Beardman, Vera Sandel, Mustefa Yasin Reshid, Paul Kelly, Kenji Chen, Judith Rosentreter, and Jessica Rieckenberg for field work assistance. Iain Alexander and Matheus Carvalho de Carvalho assisted with laboratory analyses. James Udy (Healthy Land & Water) helped identify study sites. Research was funded by ARC Linkage Grant LP150100519. DM received additional support from ARC projects DP180101285 and DE1500100581. Author contributions: BDE conceived the study. BDE, DVE, DM, and MH designed the study. PH and MH contributed hydrological data and interpretation. NSW carried out the study, analysed the samples and data, and wrote the manuscript, with input from all of the co-authors.

Research data Data is available at 10.6084/m9.figshare.9792092, as well as from Southern Cross University: https://epubs.scu.edu.au/data_collections/26/.

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respiration (3). A small (<1%) proportion of C is also transported as CH4, which is not shown here.

Figure 2 Map of the study area, which is located in SE Queensland, Australia. Eight estuaries, classified as either Low, Moderate, or High land-use intensity, were sampled: Noosa (a; Low), Maroochy (b; Moderate), Mooloolah (c; Low), Caboolture (d; High), Pine (e; Moderate), Brisbane (f; High), Logan/Albert (g; High), and Nerang (h; Low). Background images from GoogleEarth (accessed 07/05/2019).

Figure 3 Concentrations of DOC (top row) and DIC (bottom row) over the salinity gradient in eight subtropical SE QLD (Australia) estuaries in a wet season (a, c) and dry season (b, d). Estuaries spanned a gradient from High (red triangle) to Moderate (green squares) to Low (blue circles) impact, based on land-use and nutrient status. Two-way ANOVA results for impact × season for samples at salinities ≥ 10 are shown in insets in (b) and (d), with significant effects in bold.

Figure 4 Dissolved CO2 concentrations over salinity in eight SE Queensland estuaries in the wet season (Mar-2016) and dry season (Oct-2016). Estuaries were classified as Low (blue; a,b), Moderate (green; c,d), or High (red; e,g) degradation level. The Oxley Creek and North Pine tributaries, as well as near-estuary dams, are indicated (see Fig. 2 for locations of WWTPs). The CO2 µM range corresponds to a ~400 – 2,500 µatm: grey shading indicates ≤ 100% saturation (± 1 µM for seasonal and site differences). Note that lines represent the same estuary distance in both seasons, despite shorter salinity gradients in some estuaries in the dry season.

Figure 5 Air-water fluxes of CO2 (FCO2), normalised to surface area (m-2) as a function of the percentage of estuary FCO2 attributable to riverine inputs (%RiverDIC; Eq. 3) from eight estuaries in SE Queensland in the wet (filled shapes) and dry (open shapes) seasons. Grey shading indicates %RiverDIC values <100%. Estuaries were classified as either Low (blue circles: Noosa, 27

Mooloolah, Nerang), Moderate (green squares: Maroochy, Pine), or High (red triangles: Caboolture, Brisbane, Logan/Albert) levels of degradation. Error bars indicate the ±SD of values calculated using the four different k600 parameterisations (Appendix Table A2). Note x-axis scale change at 100%.

Figure 6 The δ13C composition of DIC and CO2 over salinity in the wet (filled shapes) and dry (open shapes) seasons in eight estuaries in SE Queensland, Australia. Estuary Surface water 222Rn data from (Wells et al., 2018) are also shown (crosses). Estuaries were classified by anthropogenic impact level as Low (top row: Noosa, a; Mooloolah, b; Nerang, c), Moderate (middle row: Maroochy, d; Pine, e), or High (bottom row: Caboolture, f; Logan/Albert, g; Brisbane, h). Grey bars indicate the known δ13C composition of potential sources. Lines through the δ13C-DIC data (squares) indicate expected values based on conservative mixing.

Figure 7 DIC dynamics in eight SE Queensland estuaries classified as High (red triangles), Moderate (green squares), or Low (blue circles) levels of degradation, from measurements over the salinity gradient during one wet season (filled shapes) and one dry season (open shapes) sampling. Data is plotted relative to three proposed models of estuary DIC dynamics: (a) hydrodynamics (flushing time, FT) predict the proportion of DIC inputs evaded v discharged downstream (Borges et al., 2006); (b) estuary hydrodynamics (surface area, A, over discharge, Q) predicts within-system net DIC production (Laruelle et al., 2017); and, (c) pelagic primary producer abundance (Chla) predicts DIC respiration rates, DICR (Eyre et al., 2011). The ‘net CO2 producing’ estuaries (%RiverDIC < 100%) are indicated with grey shading in (a) and (c). Estuary DICR (b, c) were estimated δ13C-DIC values, as detailed in Table 2. Dashed lines in (a), (b), and (c) show previously established relationships between parameters. The solid line in (c) represents the best-fit relationship (95% CI = dotted lines) for ‘net CO2 producing’ systems. Note different DICR units in (b) and (c). 28

29

Table 1 Potential constraints on C inputs to eight SE Queensland with Low (Noosa, Mooloolah, Nerang), Moderate (Maroochy, Pine), or High (Caboolture, Brisbane, Logan/Albert) land-use intensity. Freshwater inputs come from rivers (surface water Q over 2016 from WBM (2017)], which can be altered by upstream dams, the freshwater flushing time (FT, mean of dry and wet season values for Eq. 1), or groundwater (qualitatively constrained by 222Rn concentrations, measured during 2016 estuary surveys (Wells et al., 2018)]. Local bedrock, listed here as per 2016 Australian Government data https://www.bioregionalassessments.gov.au/data/21-22-data-analysis-clarence-moreton-bioregion), constrains geogenic DIC inputs. Phytoplankton (Chla) abundance and light availability (Secchi depth), which constrain within-estuary biological C cycling, were measured monthly at 4 – 8 locations per estuary from 2015 – 2016 by Healthy Land & Water. All numerical values represent the mean (SD). Additional biogenic sources include WWTP loads, reported biweekly over 2016, and agricultural run-off, indicated here by the proportion of land-use cover in the catchment (data from Healthy Land & Water). Superscript letters indicate significant differences between estuaries (ANOVA and Sidak post-hoc tests). Estuary

Hydrology Dams?

Geogenic C

Internal biogenic C

Q (m3 s-1)

FT (days)

Groundwater (222Rn, dpm m-3)

Bedrock type

Chla (μg l-1)

a

b

c

Secchi (m)

d

External biogenic C WWTP (106 l d-1)

Agricultural (% land cover)

Noosa

-

0.77 (2)

370 (300)

590 (300)

Sandstone, basalt

1.4 (1)

0.85 (0.6)

0

33

Mooloolah

-

0.57 (2)

5.2 (0.2)

650 (400)

Sandstone, basalt

2.4 (3)

1.5 (0.4)

0

26

Nerang

Y

0.58 (2)

92 (40)

4300 (6000)

Phyllite, sandstone

4.7 (6)

1.0 (0.4)

0

25

Maroochy

-

5.1 (20)

19 (4)

2900 (2000)

Sandstone, basalt

5.3 (6)

1.0 (0.4)

34 (6)

24

Pine

Y

1.7 (10)

12 (3)

3200 (3000)

Rhyolite, mafites

6.2 (6)

1.0 (0.3)

27 (5)

26

Caboolture

-

2.7 (20)

27 (5)

1300 (700)

Rhyolite, mafites

8.7 (9)

0.66 (0.4)

17 (2)

46

Brisbane

Y

13 (20)

56 (10)

1000 (600)

Phyllite, sandstone

3.7 (6)

0.32 (0.3)

79 (70)

67

Logan/Albert

-

1.8 (5)

34 (0.6)

910 (500)

Phyllite, sandstone

6.2 (8)

0.24 (0.3)

25 (20)

73

Land-use intensity class

Low

Moderate

High

a p<0.001, F = 40 (Mooloolah, Nerang, Noosa < Caboolture, Pine, Logan/Albert, Maroochy < Brisbane) b p<0.001, F = 17 (Brisbane, Caboolture, Logan/Albert, Mooloolah, Noosa < Maroochy, Pine, Nerang) c p<0.001, F = 43 (Mooloolah, Noosa < Brisbane < Maroochy, Nerang < Pine, Logan/Albert < Caboolture) d p<0.001, F = 150 (Brisbane, Logan/Albert < Caboolture < Noosa < Maroochy < Pine < Mooloolah, Nerang) 30

Table 2 Offsets from conservative DIC mixing (δ13Coffset) in estuaries classed as Low (Noosa, Mooloolah, Nerang), Moderate (Maroochy, Pine) or High (Caboolture, Brisbane, LA) land-use intensities were calculated DIC concentrations, δ13C-DIC, and FCO2 measured over wet season (Mar-2016) and dry season (Oct-2016) transects. Values were used to evaluate mixing (Scenario 1) drivers of δ13Coffset by calculating, 1) the fraction of the estuary DIC pool that would need to originate from WWTPs (fWWTP, δ13C = -10‰, -9‰) and then using the reported WWTP discharge to estimate DIC concentration the WWTPs would need to discharge to balance δ13Coffset (DICWWTP), and, 2) the relative groundwater contribution (fGW, δ13C = -18‰, 14‰) to the estuary DIC pool and then comparing the resultant groundwater DIC concentration (DICGW) to measured 222Rn concentrations. Plausible solutions* are in bold font. Respiration was evaluated as the biological drivers of δ13Coffset (Scenario 2), where the residual δ13Coffset was used to calculate respiration’s relative contribution to the estuary DIC pool (fR) and then estimate aerial rates (μmol CO2 m-2 d-1) assuming respired CO2 reflects measured δ13C-DOC values plus reported εR values (0‰, -4‰). Missing Noosa wet season δ13C-DIC data is indicated with an ‘x’; ‘n.s.’ indicates either that data was available but yielded no significant results or that evaluations were not carried out due to lack of potential WWTP sources. Estuary

δ13Coffset δ13CDIC/km

Noosa

Scenario 1: External source mixing

Scenario 2: Internal Processing

fWWTP %

mM DICWWTP *

fGW %

µmol DICGW / dpm 222Rn **

fR %

Respiration μmol CO2 m-2 d-1

Wet

x

x

x

x

x

x

x

Dry

+0.13

n.s.

n.s.

n.s.

n.s.

15 – 12

1,100 – 930

Mooloolah Wet

-1.4

n.s.

n.s.

15 – 26

490 (240 – 840)

57 – 49

40,000 – 35,000

Dry

-0.31

n.s.

n.s.

1.6 – 2.1

55 (28 – 95)

8.9 – 7.4

790 – 660

Wet

-0.080

n.s.

n.s.

0.8 – 1.1

6.9 (2.9 – 16)

19 – 16

1,900 – 1,600

Dry

-0.39

n.s.

n.s.

2.2 – 2.9

9.1 (3.3 – 34)

55 – 50

180 – 170

Wet

-3.8

53 – 64

32 (25 – 43)

24 – 32

140 (68 – 250)

39 – 31

5,500 – 4,500

Dry

-1.3

14 – 16

4.6 (3.7 – 6.0) 7.4 – 9.7

56 (28 – 103)

15 – 12

4,300 – 3,600

Wet

-1.3

18 – 20

8.4 – 11

91 (43 – 180)

24 – 20

8,600 – 7,200

Dry

-0.36

4.0 – 4.6

4.1 (3.2 – 5.2) 2.1 – 2.8

14 (5.6 – 35)

12 – 9.9

2,000 – 1,700

Caboolture Wet

-1.2

16 – 18

12 (9.6 – 14)

7.8 – 18

120 (69 – 180)

19 – 17

6,200 – 5,400

Dry

-3.1

45 – 52

24 (19 – 29)

21 – 28

430 (240 – 680)

31 – 27

5,800 – 5,000

Wet

-0.020

0.10 – 0.085

0.12 (0.056 – 1.1)

0.005 – 0.009

0.16 (0.079 – 0.26)

4.5 – 3.4

5,900 – 4,500

Nerang

Maroochy

Pine

Brisbane

26 (21 – 34)

31

LA

Dry

-0.19

2.0 – 2.2

2.4 (1.2 – 22)

1.1 – 1.4

23 (13 – 35)

6.5 – 5.5

6,200 – 5,300

Wet

-1.2

16 – 20

8.4 (14 – 130)

7.9 – 10

190 (130 – 340)

24 – 20

48,000 – 40,000

Dry

-0.047

6.4 – 6.8

17 (5.2 – 47)

2.6 – 3.0

62 (49 – 130)

11 – 8.9

23,000 – 19,000

* Literature values show that DICWWTP should be 2 - 5 mM (Yang et al., 2018) ** Local groundwater has µmol DIC / dpm 222Rn values of ~41 (Jeffrey et al., 2018; Maher et al., 2013a)

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Figures Figure 1

Figure 1 Rivers and groundwater (GW) carry terrestrial C, often with a unique δ13C signature (δterrestrial) to estuaries, where it mixes with tidally transported marine C and its δ13C signature (δmarine). This mixing is affected by the salinity-driven shifts in alkalinity that shift the balance between minerals (HCO3) and CO2 gasses within the estuary DIC pool, as well as by physical evasion of CO2 gas from the water column to the atmosphere (1). Within the estuary the quantity, form, and isotopic composition of terrestrial and marine C can be modified as photosynthesis converts CO2 into new OC (2), which is subsequently converted back to CO2 via abiotic and biotic respiration (3). A small (<1%) proportion of C is also transported as CH4, which is not shown here. Photo credit: Kylie Maguire.

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Figure 2

Figure 2 Map of the study area, which is located in SE Queensland, Australia. Eight estuaries, classified as either Low, Moderate, or High land-use intensity, were sampled: Noosa (a; Low), Maroochy (b; Moderate), Mooloolah (c; Low), Caboolture (d; High), Pine (e; Moderate), Brisbane (f; High), Logan/Albert (g; High), and Nerang (h; Low). Background images from GoogleEarth (accessed 07/05/2019).

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Figure 3

Figure 3 Concentrations of DOC (top row) and DIC (bottom row) over the salinity gradient in eight subtropical SE QLD (Australia) estuaries in a wet season (a, c) and dry season (b,d). Estuaries spanned a gradient from High (red triangle) to Moderate (green squares) to Low (blue circles) impact, based on land-use and nutrient status. Two-way ANOVA results for impact × season for samples at salinities ≥ 10 are shown in insets in (b) and (d), with significant effects in bold.

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Figure 4

Figure 4 Dissolved CO2 concentrations over salinity in eight SE QLD (Australia) estuaries in the wet season (Mar-2016) and dry season (Oct-2016). Estuaries were classified as Low (blue; a,b), Moderate (green; c,d), or High (red; e,g) degradation level. The Oxley Creek and North Pine tributaries, as well as near-estuary dams, are indicated (see Fig. 2 for locations of WWTPs). The CO2 µM range corresponds to a ~400 – 2,500 µatm: grey shading indicates ≤ 100% saturation (± 1 µM for seasonal and site differences). Note that lines represent the same estuary distance in both seasons, despite shorter salinity gradients in some estuaries in the dry season.

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Figure 5

Figure 5 Air-water fluxes of CO2 (FCO2), normalised to surface area (m-2) as a function of the percentage of estuary FCO2 attributable to riverine inputs (%RiverDIC; Eq. 3) from eight estuaries in SE QLD in the wet (filled shapes) and dry (open shapes) seasons. Grey shading indicates %RiverDIC values <100%. Estuaries were classified as either Low (blue circles: Noosa, Mooloolah, Nerang), Moderate (green squares: Maroochy, Pine), or High (red triangles: Caboolture, Brisbane, Logan/Albert) levels of degradation. Error bars indicate the ±SD of values calculated using the four different k600 parameterisations (Appendix Table A2). Note x-axis scale change at 100%.

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Figure 6

Figure 6 The δ13C composition of DIC and CO2 over salinity in the wet (filled shapes) and dry (open shapes) seasons in eight estuaries in SE QLD, Australia. Estuary Surface water 222Rn concentrations from (Wells et al., 2018) are also shown (crosses). Estuaries were classified by anthropogenic impact level as Low (top row: Noosa, a; Mooloolah, b; Nerang, c), Moderate (middle row: Maroochy, d; Pine, e), or High (bottom row: Caboolture, f; Logan/Albert, g; Brisbane, h). Grey bars indicate the known δ13C composition of potential sources. Lines through the δ13C-DIC data (squares) indicate expected values based on conservative mixing. Figure 7

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Figure 7 DIC dynamics in eight SE QLD estuaries classified as High (red triangles), Moderate (green squares), or Low (blue circles) levels of degradation, from measurements over the salinity gradient during one wet season (filled shapes) and one dry season (open shapes) sampling. Data is plotted relative to three proposed models of estuary DIC dynamics: (a) hydrodynamics (flushing time, FT) predict the proportion of DIC inputs evaded v discharged downstream (Borges et al., 2006); (b) estuary hydrodynamics (surface area, A, over discharge, Q) predicts within-system net DIC production (Laruelle et al., 2017); and, (c) pelagic primary producer abundance (Chla) predicts DIC respiration rates, DICR (Eyre et al., 2011). The ‘net CO2 producing’ estuaries (%RiverDIC < 100%) are indicated with grey shading in (a) and (c). Estuary DICR (b, c) were estimated δ13C-DIC values, as detailed in Table 2. Dashed lines in (a), (b), and (c) show previously established relationships between parameters. The solid line in (c) represents the best-fit relationship (95% CI = dotted lines) for ‘net CO2 producing’ systems. Note different DICR units in (b) and (c).

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