Connectivity between estuaries influences nutrient transport, cycling and water quality

    Connectivity between estuaries influences nutrient transport, cycling and water quality Karen Wild-Allen, John Andrewartha PII: DOI: Reference:

S0304-4203(16)30060-3 doi: 10.1016/j.marchem.2016.05.011 MARCHE 3375

To appear in:

Marine Chemistry

Received date: Revised date: Accepted date:

29 September 2015 25 May 2016 26 May 2016

Please cite this article as: Wild-Allen, Karen, Andrewartha, John, Connectivity between estuaries influences nutrient transport, cycling and water quality, Marine Chemistry (2016), doi: 10.1016/j.marchem.2016.05.011

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Karen Wild-Allen* & John Andrewartha

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CSIRO Marine & Atmospheric Research, Hobart, Tasmania

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Connectivity between estuaries influences nutrient transport, cycling and water quality

Abstract

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Water quality in coastal and estuarine systems is dictated by nutrient supply and circulation. However, convoluted coastlines, intermittent river discharge, complex currents, and variable nutrient inputs and transformations, confound interpretation of sparse water quality observations. In high use regions with competing human activities, this system complexity precludes identification of factors causing poor water quality, which can lead to conflict between users. High resolution 3D hydrodynamic and biogeochemical models can simulate the dynamics of a region and demonstrate nutrient pathways in convoluted systems. Here, we use a 3D coupled hydrodynamic, sediment and biogeochemical model to investigate the fine-scale (<100 m; <1 day resolution) nutrient dynamics of temperate coastal waters in southeast Tasmania, Australia. The fate and transport of anthropogenic nutrients between the Huon estuary, the D’Entrecasteaux Channel and the Derwent estuary is of concern and we use the model to explore the regional contribution and connectivity of marine, catchment and anthropogenic nutrient loads. Model skill in the year 2009 is assessed against water quality observations throughout the region and nutrient budgets are evaluated for each sub-region. Results show seasonal variation in estuarine flow with persistent anthropogenic nutrient sources augmenting phytoplankton production, and reducing water quality in summer months. Nutrientenriched Huon Estuary and D’Entrecasteaux Channel waters are flushed north to the mouth of the Derwent Estuary and then south into the open ocean, with greatest flux in winter. This demonstrates that water quality in the Derwent is not compromised by nutrient influx from the adjacent waters. By using fine-scale 3D biogeochemical models, such as demonstrated here, we can quantify nutrient transport pathways in coastal waters and resolve conflicts between users to support integrated marine management. Keywords: nitrogen cycle, environment management, pollution, numerical models Region: Australia, Tasmania, Huon Estuary, D’Entrecasteaux Channel, Derwent Estuary

Introduction Anthropogenic nutrient enrichment of coastal waters is of concern for resource managers tasked with delivery of improving water quality (Cloern 2001; Rabalais & Nixon 2002). Nutrients enter coastal waters via a diverse range of mechanisms including catchment run off, atmospheric deposition, groundwater seepage, wastewater and effluent discharge. Quantifying these various nutrients loads and managing their anthropogenic component is a huge challenge. In many regions water quality monitoring programs deliver data sets for quantitative assessment and report cards, which synthesise results (Hunter et al., 2013; Sherwood et al., 2016; Tango & Batuik 2015). The

ACCEPTED MANUSCRIPT trajectory of change is often due to changes in multiple source and sink terms, which are difficult if not impossible to identify from monitoring data (Ward et al., 1986; Van Beusekom et al., 2009).

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The transfer of dissolved and particulate material in coastal waters is subject to local tidal currents and residual circulations which are often well understood in specific regions (eg. Ji et al., 2011; Kärnä et al., 2015). Field studies using dye release have been used to investigate the dispersal of passive tracers from discharge locations (Ledwell et al., 2004), although increasingly hydrodynamic models are employed to investigate the transport and fate of passive tracers (Stashchuk et al., 2014; Hong et al., 2010). Hydrodynamic models have also been applied in ecological studies of population connectivity by constraining the trajectories of larval stages (Condie et al., 2011; Berry et al., 2012).

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Investigations into the transfer of nutrients in estuaries and coastal waters is complicated by their non-conservative behaviour, with often rapid assimilation and cycling of nutrients through organic biomass and detritus. Biogeochemical field studies require considerable resources (e.g. Martin et al., 2007; Falco et al., 2010; Fellman et al., 2011; Yan et al., 2012; Li et al., 2013). More recently biogeochemical models have been used in a number of studies to explore nutrient cycling and fluxes in individual estuaries (Wild-Allen et al., 2010, 2013; Arndt et al., 2011; Fan & Song 2014) and for scenario simulation to explore alternative management strategies (Lacroix et al., 2007a; Gypens et al., 2013; Skerratt et al., 2013). We are not aware of any previous study that has investigated the connectivity of nutrient sources between estuaries.

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Nutrient enrichment from fish farms in the rural Huon Estuary and D’Entrecasteaux Channel in southeast Tasmania, Australia, is well documented (Wild-Allen et al., 2010; Volkman et al., 2009; Ross & Macleod 2013; Parsons 2012). The adjacent urbanized Derwent Estuary is also well studied with generally improving water quality following significant industrial contamination (Jones et al., 2003; Green & Coughanowr 2003). Analysis of monitoring data from the mouth of the Derwent Estuary detected a recent decline in water quality and an increase in ammonia concentration (Whitehead et al., 2010). Previous modelling studies showed the residual circulation in the D’Entrecasteaux Channel to be from south to north (Herzfeld & Andrewartha 2010) and raised concerns that fish farm waste might be swept into the Derwent Estuary compromising water quality. In this study we demonstrate how a 3D coupled hydrodynamic and biogeochemical model can be used to investigate the transfer of anthropogenic nutrients between two temperate micro-tidal salt wedge estuaries linked by an oligotrophic marine channel. The model is implemented over a new larger domain that fully encompasses the Huon Estuary, the D’Entrecasteaux Channel and the Derwent Estuary in southeast Tasmania, Australia. A new hind-cast simulation of 2009 is forced with marine, river and anthropogenic nutrient loads, validated against observations, and used to compute nitrogen fluxes and budgets for each waterway.

ACCEPTED MANUSCRIPT Methods Figure 1: Map of southeast Tasmania, Australia, showing model domain within dashed lines, depth contours, major rivers and point source loads

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Model description

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The CSIRO Environmental Modelling Suite (EMS) comprises a fully coupled hydrodynamic, sediment and biogeochemical model (Herzfeld & Waring 2015; Margvelashvili 2009; Wild-Allen et al., 2010, 2013) and was implemented on 3D curvilinear grid encompassing the Huon Estuary, the D’Entrecasteaux Channel and the Derwent Estuary (Fig. 1). Hydrodynamic Model

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Within EMS we use the Sparse Hydrodynamic Ocean Code (Herzfeld 2006; Herzfeld et al., 2005; Herzfeld & Andrewartha 2010), which is a finite difference primitive equation hydrodynamic model based on the 3D equations of momentum, continuity and conservation of heat and salt, that uses the hydrostatic and Boussinesq assumptions (Herzfeld & Waring 2015). SHOC has a free surface and uses mode splitting to separate the two- and three-dimensional modes. Constant values of horizontal viscosity (Ah = 40 m2s-1) and horizontal diffusivity (Kh = 0.1 m2s-1) were used in the model, while a k-epsilon scheme was employed to calculate the vertical viscosity and diffusivity. The ‘ultimate quickest’ advection scheme (Leonard, 1991) was implemented for tracers and a simple fetch based wave scheme was used to simulate wave enhanced seabed stress and augment sediment resuspension in shallow water. Sediment Model

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The EMS sediment transport model (Margvelashvili, 2009; Margvelashvili et al., 2005, 2008) solves advection-diffusion equations for the mass conservation of sediments and biogeochemical particles. Particles sink through the water column and are deposited to, or resuspended from, the bed depending on local bottom friction. Critical friction thresholds are determined from the log-profile of currents and bottom roughness (Ariathurai & Krone 1976) scaled by ripple height (Grant and Madsen, 1986). Diffusive exchange of particles between the water and sediment bed is also included. Biogeochemical Model The biogeochemical model cycles carbon, nitrogen, phosphorous and dissolved oxygen through dissolved and particulate organic and inorganic forms and has been described in (Wild-Allen et al., (2010, 2013) and online (CSIRO Coastal Environmental Modelling Team 2015). The model has multiple phytoplankton (small, large, dinoflagellate, microphytobenthos), zooplankton (small, large), macrophyte (seagrass, macroalgae), detritus (pelagic, benthic, refractory) and dissolved nutrient (inorganic, organic) components. The temporal evolution of each biogeochemical component is the sum of conservative advection, diffusion and sinking, plus non-conservative biogeochemical rate processes. Biogeochemical dissolved substances were advected and diffused in a similar fashion to temperature and salinity and particulate components sank and were resuspended by the same formulation as sediment particles. At each ecological time step, non-conservative biogeochemical rate processes such as growth, nutrient uptake, grazing and mortality are integrated within the

ACCEPTED MANUSCRIPT ecological module which returns updated concentrations to the hydrodynamic and sediment models via an interface routine.

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The model includes the main processes contributing to nitrogen cycling in the region which are: phytoplankton, zooplankton and macrophyte growth and mortality, detrital remineralisation, nitrification and denitrification. It is sufficient for the purpose of simulating the cycling of nitrogen through dissolved and particulate phases and subsequent transport of these components throughout the model domain. Specific algorithms and parameters for each nitrogen cycling process are detailed in the Appendix. Process model parameters were largely based on Wild-Allen et al., (2010 & 2013), which were originally sourced from regional observations, previous modelling studies and literature and are detailed in the Appendix. During model calibration poorly constrained parameters (such as phytoplankton mean cell size, maximum growth rate and proportion of each functional group) were varied within literature values to determine a parameter set which produced model results most consistent with observations.

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Model Implementation

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Whilst the focus of this study was on the cycling and transport of nitrogen, the dynamics of carbon, phosphorous and oxygen were also simulated to accurately resolve the in water light field, any potential phosphorous limitation of autotrophic growth (none found) and to define the background oxygen environment. As oxygen concentration controls the rate of ammonia nitrification and denitrification, algorithms and parameters resolving the oxygen field are also detailed in the Appendix.

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The hydrodynamic model was applied on a curvilinear orthogonal grid in the horizontal, with a resolution of <100 m in the upper estuaries, increasing to 1 km in the marine channel. The vertical z coordinate grid has 25 layers increasing from 0.5 m at the surface to 10 m in water > 30 m. The model was nested into regional and intermediate scale ocean models (described in Baird et al., 2013; Jones et al., 2012), which provided temperature, salinity, sea level and velocity conditions at the marine boundaries. It was forced locally with flow data from the Derwent, Huon and several smaller rivers (Jordan, North-West Bay, Esperance), and meteorology (wind speed and direction, air temperature, pressure, humidity and cloud cover) from the Australian Bureau of Meteorology’s operational atmospheric model (ACCESS-A, http://www.bom.gov.au/nwp/doc/access/NWPData.shtml). Incident short wave solar radiation was calculated from latitude, local time and date and adjusted for cloud cover. The sediment and biogeochemical models were implemented on the same model grid with an additional 3 layers of bed sediment (5, 15, 200 mm) comprising sand, silt, mud and biogeochemical particles and initialized with concentrations derived from September 2008 Derwent Estuary Program observations (Whitehead et al., 2010) made throughout the region. At the marine boundary the model was forced with an upstream condition (Herzfeld & Waring 2015) such that out-flowing concentrations were determined by the model whilst in-flowing concentrations were specified from the interpolated seasonal cycle of surface and bottom water observations (Whitehead et al., 2010; Ross & Macleod 2013). River sediment, and biogeochemical loads were estimated from observations scaled against flow (similar to Wild-Allen et al., 2010, 2013) for the major Huon and Derwent Rivers, and minor Jordan River, Northwest Bay Rivulet and Esperance River. Anthropogenic nutrient load from fish farms, treated sewerage and industry effluent were interpolated from

ACCEPTED MANUSCRIPT monthly feed (converted to estimated dissolved and particulate waste load assuming full consumption of feed pellets (Wild-Allen et al., 2010)) and effluent discharge data respectively (data supplied by the Tasmanian Department of Primary Industry, Parks, Water and the Environment, TasWater, Norske Skog and Nystar Zinc smelter). Discharge locations are shown in Figure 1.

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We present the results from January 2009 to December 2009 which allowed the model to stabilise for the initial three month period (September to December 2008).

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Model Validation

The purpose of our model was to quantify the seasonal circulation and flux of nitrogen between the Huon Estuary, D’Entrecasteaux Channel and Derwent Estuary. Model validation was achieved by evaluating the performance of the model against 3 specific criteria (Rykiel, 1996):

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1. The model must reproduce the correct magnitude and timing of the annual cycle in temperature and salinity compared with observations. 2. The model must conserve mass (carbon, nitrogen, phosphorous) as material is transformed through various forms; mass must also be conserved during advection and diffusion and during sinking and resuspension. 3. The model must reproduce the correct magnitude and timing of the annual cycle in dissolved inorganic nitrogen and phytoplankton chlorophyll compared with observations.

skill  1 

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For quantitative comparison of model and observations the model was sampled at a number (N) of equivalent times (t), locations and depths. The correlation between model and observations was assessed by calculation of the coefficient of determination (R2) and, in addition, the performance of the model was evaluated using the definition proposed by Willmott (1982) which does not heavily penalise small errors in phase:

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A Willmott skill score (WSS) of 1 indicates model and measurements agree perfectly, whilst 0 indicates total disagreement. However when comparing model results with observations it is important to remember the mis-match in scale (1000’s L in model grid vs. <1 L sample volume) and recognise that in regions of strong spatial gradients, small-scale patchiness will confound results. Model bias was calculated as:

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so that a value close to 0 indicates good agreement between model and observations. During 2009 water quality observations were made at 12 sites in the Derwent Estuary (Whitehead et al., 2010) and 15 sites in the D’Entrecasteaux Channel and Huon Estuary (Ross & Macleod 2013); a moored temperature and salinity sensor (Timms et al., 2009) was also deployed off the CSIRO wharf located in the mid Derwent Estuary (Fig. 2A). At each site, samples were analysed for surface

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chlorophyll and surface and bottom water temperature, salinity and dissolved inorganic nitrogen (DIN = nitrate + nitrite + ammonium) (Whitehead et al., 2010; Ross & Macleod 2013). Due to sensor calibration issues (S. Riley, Aquenal, pers. comm., Jan 2015) salinity data were only available for the Derwent Estuary. Willmott skill scores were computed for each time series of data at each station, and summarised for sub-regions of the model.

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Results Model Validation

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1. Hydrodynamic Model Performance

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The hydrodynamic model reproduced the observed annual cycle of temperature and salinity at a mooring site in the mid Derwent Estuary (Fig. 2A & B) and at monitoring stations throughout the region (Fig. 3). At the mooring site surface temperature was well captured with an R2 value of 0.93, a Willmott skill of 0.95 and a small bias of 0.33 °C as simulated summer and autumn temperatures were slightly higher than observed. Throughout the year observed temperatures were very well simulated throughout the Derwent and Huon Estuaries and well simulated in the D’Entrecasteaux Channel (Fig. 3), resulting in an overall R2 of 0.89, Willmott skill of 0.87 and small positive bias of 0.20 °C for the whole region (Table 1). The observed spatial gradients in temperature throughout the region were generally well simulated although simulated summer temperatures were slightly higher than observed in the lower Derwent Estuary and northern D’Entrecasteaux Channel (Fig. 4).

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The model performance for surface salinity at the mooring site, was lower (R2 0.02; WSS 0.48), although the model did capture the major fresh water events observed in winter and spring (Fig. 2B). At other stations in the mid Derwent the model performance was better (Fig. 3) likely due to the more consistent position of the surface freshwater plume which typically favours the eastern shore, but can be temporarily swept west by local wind forcing. The mean model R2 for salinity in the Derwent was 0.31 and Willmott skill 0.52; there was also a small negative bias (Table 1). Given the large gradients in salinity in the mid estuary, which were generally well captured (Fig. 4), the mismatch between salinity modelled on a scale of ~150 m and observations at specific points is relatively small. We are confident that in general the hydrodynamic model is able to simulate the observed regional hydrodynamics, as evidenced by the model agreement with observed temperature and salinity, with sufficient skill to host the biogeochemical model. 2. Conservation of Mass During computation the biogeochemical model was processed in columns corresponding to the model grid. At the start and end of each ecological time step the total mass of carbon, nitrogen and phosphorus in the water column, epi-benthos and sediment across all biogeochemical model components was summed and differenced to confirm conservation of mass. During advection and diffusion dissolved and particulate substances were redistributed throughout the model grid, and non-conservative gains and losses at the open boundaries were integrated to confirm conservation

ACCEPTED MANUSCRIPT of mass. The fully coupled hydrodynamic and biogeochemical model simulation achieved mass balance for carbon, nitrogen and phosphorous in all biogeochemical model processes in the pelagic, epi-benthic and sediment layers.

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3. Nitrogen and Chlorophyll Model Performance

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The biogeochemical model simulated the observed annual cycle of phytoplankton chlorophyll and dissolved inorganic nitrogen at stations throughout the region (Fig. 2A & C). Statistics (Table 1) show the model performed well for DIN (R2 0.55, WSS 0.69, regional average of all station time series), although there was a negative bias of -16.1 mg m-3. Surface DIN was very well simulated in the Derwent Estuary and well simulated at most of the other stations (Fig. 3 & 4). The tendency to under predict DIN was evident in the D’Entrecasteaux Channel in winter and the Derwent and Huon Estuaries in late summer and autumn. In 2009 there was a low oxygen event in the upper Derwent Estuary resulting in high concentrations of ammonia (Whitehead et al., 2010); this was not captured by the model.

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In general phytoplankton chlorophyll was less well simulated by the model compared to observations, with average R2 of 0.15 and WSS of 0.42 for all station time series and negligible net bias (Table 1). The magnitude and timing of the spring phytoplankton bloom in the Huon Estuary, however, was well captured (Fig. 3). In the D’Entrecasteaux Channel the spring bloom was simulated ~ 1 month earlier than observed, which resulted in poorer model performance and partially explains the winter drawdown of DIN in the model c.f. observations in this region (Fig. 4A). In the Derwent Estuary the model underestimated the surface chlorophyll concentration in Autumn and late Spring by ~1 mg m-3, although there was evidence of a weak annual cycle. Regional gradients in surface chlorophyll in summer were generally well reproduced (Fig. 4B).

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The biogeochemical model captured the essential dynamics of the observed annual cycle in DIN and surface chlorophyll concentration sufficient to provide insight to study the connectivity and fate of nitrogen within the region. There were insufficient observations to fully validate the simulation of plankton group composition, detrital remineralisation and denitrification. Whilst results from these elements of the model appear realistic and are consistent with our understanding of the system, they should be treated as a hypothesis until validated against observations.

ACCEPTED MANUSCRIPT Figure 2: A, Monitoring stations and mooring (star) in the Derwent Estuary (north), the Huon Estuary (west) and the D’Entrecasteaux Channel. B, surface temperature and salinity at the mooring site. C, Modelled and observed chlorophyll (left) and dissolved inorganic nitrogen (right); regional means and standard deviations shown.

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Table 1: Mean model R2 correlation (number of observations), Willmott skill score and bias for stations in each waterway [*the mean temperature and salinity score for the Derwent included the mooring station, however these continuous data are in addition to the number of observations shown].

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Figure 3: (A) Spatial distribution of Willmott score, to assess model performance against surface observations and (B) pointwise correlation of all data for (from left to right) temperature, salinity, nitrogen and chlorophyll; the mean value for all stations is also shown, higher values indicate better model performance.

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Figure 4: Spatial distribution of modelled surface temperature, salinity, nitrogen and chlorophyll (from left to right) with observed values overlain, for (A) 18 August 2009 (winter) and (B) 15 December 2009 (summer).

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Residual Circulation

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The modelled annual mean surface current (Fig. 5) was southward, down both the Huon and Derwent Estuaries, driven by buoyant river input of fresh water. In the D’Entrecasteaux Channel, and also Ralphs Bay off the lower Derwent Estuary surface currents were generally weaker, influenced by local winds and estuarine plumes. The southern D’Entrecasteaux Channel had a complex surface circulation with part of the southward flow along the western shore deflected left (by winds and the Coriolis effect) into the South Bruny Island Bays, and then northward. In the central D’Entrecasteaux Channel a coherent cyclonic circulation formed in the large shallow bay, whilst to the north mean flows were weak and variable in direction.

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In bottom waters annual mean currents (Fig. 5) were weaker than surface currents and flow was predominantly from south to north. Deep water entered the southern D’Entrecasteaux Channel and Huon Estuary and continued north over shallow bathymetry (<15 m) into the mid D’Entrecasteaux. In the Derwent Estuary deep water flowed north to the upper reaches of the estuary and was also deflected into Ralphs Bay to the east and the D’Entrecasteaux Channel to the west. The bottom water circulation in the north D’Entrecasteaux ventilated the side bays and moved south to the mid Channel. The residual circulation intensified during periods of elevated river flow in winter. In 2009 strong northward flow through the D’Entrecasteaux Channel occurred during a period of high Huon River flow in July (Fig. 5). Figure 5: Annual mean surface (left) and bottom (middle) current. July mean surface current and salinity (right). Greatest northward flow through the D’Entrecasteaux Channel occurs during periods of peak Huon River flow.

Annual Variation in Nitrogen Supply The total influx of nitrogen into the region (6753 tN y-1) varied throughout the year from about 300 tN month-1 in January-February to more than 3 times that value in August (Fig. 6). Anthropogenic point source nutrient loads (fish farms, treated sewage and industry) provided the greatest annual input of nitrogen to the region in 2009 (2599 tN y-1; 38%; Table 2) with greatest supply in August-

ACCEPTED MANUSCRIPT November and December (Fig. 6). Fish farms contributed most of the point source nitrogen (78%), followed by treated wastewater (18%) and industry discharge (4%). Variation in anthropogenic nitrogen load was primarily due to fish farm nitrogen release which varied with the salmon growth and harvest cycle (peak load August-October, lowest input February-March).

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Marine nitrogen supply accounted for 37% (2479 tN y-1) of the total nitrogen input to the region with 90% of this supplied at the south D’Entrecasteaux Channel boundary. Marine nitrogen supply varied throughout the year with peak flux delivered in August (451 tN month-1) and lowest flux delivered in January (31 tN month-1) due primarily to the natural annual variation in shelf nitrogen concentration and the intensity of the circulation through the region. The remaining nitrogen supply (25%; 1674 tN y-1) to the region was delivered by rivers, dominated by the major Derwent (55%) and Huon rivers (41%). River nitrogen supply was greatest in winter (479 tN in August) and least in summer (33 tN in February).

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Nitrogen removal from the region by denitrification accounted for 54% (-3818 tN y-1) and export across the marine boundary 46% (-3072 tN y-1) of the total flux. Denitrification varied throughout the year with greatest removal occurring in November (-435 tN month-1) and lowest removal in February (-249 tN month-1). Marine export was mostly (88%) across the south Derwent marine boundary with peak flux in August (629 tN month-1) and lowest flux in February (50 tN month-1).

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Whilst the monthly supply and loss of nitrogen from the region was broadly balanced, small differences in the timing of fluxes resulted in a net accumulation of nitrogen in March – June and August. In September – December and January there was a net loss of nitrogen from the region so that the total nitrogen stored in the system was drawn down.

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Figure 6: Nitrogen flux in (+ve) and out (-ve) of the whole region. Point source loads include fish farms, sewerage and industry discharge.

Annual Nitrogen Budgets

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Monthly nitrogen fluxes were summed to provide annual nitrogen budgets for the region (Fig. 7). In 2009 the Huon Estuary accumulated 160 tN over the year, whilst the D’Entrecasteaux Channel lost 179 tN; the Derwent Estuary and the region integrated as a whole were close to balanced (accumulated 6 tN and 43 tN respectively). In the Huon Estuary the largest nitrogen influx was from the Huon River (58%) followed by fish farms (34%) and marine influx (7%) (Table 2, Fig. 7A). The Huon catchment drains large areas of natural wilderness with low nutrient soils, eucalypt forest and heath (Butler 2006). Huon River nitrogen loads comprised 73% refractory dissolved organic nitrogen (DON) which was unavailable to modelled phytoplankton. DON has a breakdown rate of >6 months which exceeds the flushing time of the estuary and is therefore considered unlikely to contribute to detrimental local nitrogen enrichment. Fish farm waste comprised labile ammonia and particulate detritus with a breakdown rate of 5 days. The supply of fish farm waste in the nitrogen depleted summer months enhanced natural phytoplankton production in the estuary, particularly as this coincided with low river discharge, optically clearer waters in the estuary and peak summer insolation. Loss of nitrogen from the Huon Estuary was due to export into the adjoining D’Entrecasteaux Channel (57%) and denitrification (43%). The circulation in the Estuary was predominantly upestuary in deep water and down-estuary in surface waters driven by Huon River discharge. Export was greatest in winter and was efficient in removing surface nitrogen from the Estuary. Sediment denitrification rates (maximum in November; 61 tN month-1) were modulated by ambient nitrogen

ACCEPTED MANUSCRIPT and dissolved oxygen concentrations and temperature. In the Huon Estuary the greatest denitrification flux was found at depth in the mid and lower estuary (Fig. 8).

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The nitrogen input to the D’Entrecasteaux Channel (Table 2, Fig. 7B) was dominated by the marine flux (52%) with 91% entering the water way from the south predominantly in deep water. Fish farms contributed 35% of the nitrogen load to the Channel with the remaining 13% delivered from the Huon Estuary. The marine and estuarine nitrogen load to the channel was greatest in winter (August) and least in summer (January), whilst fish farm nitrogen was supplied more evenly throughout the year.

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Nitrogen loss from the D’Entrecasteaux Channel was 51% by marine export, with the majority of this leaving the Channel to the north (85%). This export flux was greatest in winter in surface waters modulated by the residual circulation. Denitrification accounted for 47% of nitrogen loss from the Channel with the remaining 1% entering the Huon Estuary. Denitrification was greatest in November in the deep water channel in the northern basin (271 tN month-1).

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In the Derwent Estuary nitrogen fluxes into the estuary were of greater magnitude, but in similar proportions to the Huon with 56% supplied by the Derwent River, 35% from treated sewerage and industry discharge and 6% from marine influx (Table 2, Fig. 7C). The Derwent River catchment includes higher urbanisation and a greater area of farmed land (Edgar et al., 1999; Jones et al., 2003) resulting in greater nitrogen supply to the Estuary for similar freshwater discharge. River nitrogen load comprised 65% refractory DON with peak influx in winter; treated wastewater contained labile nitrogen with year round supply. The influx of wastewater in summer enhanced phytoplankton production in the Estuary by mitigating nitrogen limitation.

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Loss of nitrogen from the Derwent Estuary was by marine flux (51%) and denitrification (49%). The marine outflow was mostly in the surface water modulated by the estuarine circulation which was driven by the inflow of fresh Derwent river water into the upper estuary. Marine export from the Derwent was greatest in winter, during periods of highest river flow. Denitrification in the Derwent was high in the upper estuary and in deep water in the lower estuary with greatest efflux in October (94 tN month-1). Locations that favoured denitrification had elevated nitrogen and dissolved oxygen concentrations suitable for nitrification of ammonia and denitrification.

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For the whole region nitrogen influx was split between anthropogenic point source loads (38%), marine influx (37%) and rivers (25%) (Table 2, Fig. 7D). Anthropogenic nitrogen sources were labile in composition and supplied relatively evenly throughout the year. The marine influx to the region was dominated by the influx of nitrogen from the south into the D’Entrecasteaux Channel (90% of the flux). The Derwent River provided 55% of the river nitrogen load to the region with the Huon supplying 41%; the other minor rivers made up the remaining 4%. Nitrogen loss from the region was split between denitrification 54% and marine export 46%. Denitrification was greatest in the D’Entrecasteaux Channel (62% in the largest area) and least in the Huon (12% in the smallest area).

ACCEPTED MANUSCRIPT Figure 7: Annual nitrogen budgets for the A) Huon Estuary, B) D’Entrecasteaux Channel, C) Derwent Estuary, D) Whole Region. Marine/estuarine fluxes are noted in black, fish farm loads in blue, treated sewerage in red, industry discharge in green, river loads in brown and denitrification in orange. -2 -1

Figure 8: Annual denitrification flux (kgN m y ). -1

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Table 2: Annual influx and loss of nitrogen (tN y ) and % contributions from marine, river and point source discharge for each waterway and the whole region.

Connectivity between basins

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There was a large flux of total nitrogen northward out of the D’Entrecasteaux Channel which was greatest in winter and spring (July – October 2009) (Fig. 9A & C). This was temporally matched with the discharge of total nitrogen from the Derwent Estuary. The net result was a strong outflow of total nitrogen into Storm Bay with peak flux in winter and spring (July – October 2009).

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The residual circulation for this region in winter (Fig. 9B) shows the northward surface current leaves the D’Entrecasteaux Channel and is deflected south by the out flow from the Derwent Estuary. During this change in direction the current weakens and compresses the outflow from the Derwent Estuary along the eastern shoreline.

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In winter the flux of total nitrogen leaving the D’Entrecasteaux Channel included 22-54% DIN (Fig. 9D); at other times of the year the flux was mostly plankton biomass with detrital N becoming increasingly important in late autumn (May – June). The Derwent export included DIN in June – September plus high concentrations of DON from the river Derwent water. In other seasons DIN and phytoplankton were swept into the estuary, whilst detritus and zooplankton were generally exported. Losses due to denitrification, evaluated for the small transition area, were small. -1

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Figure 9: A) Total nitrogen transport between waterways (tN y ); B) mean surface currents in July; C) summary of fluxes in transition zone; D) monthly composition of northern D’Entrecasteaux Channel nitrogen flux.

Discussion

The demonstration of model performance against observations is a critical step in the pathway to model application and its ability to provide science insight, with social license (Rykiel 1996). Whilst quantitative metrics for model performance have been applied to hydrodynamic models for some time (e.g. Warner et al., 2005; Ralston et al., 2010), rigorous assessment of biogeochemical models has only recently become more common (Allen et al., 2007; Lacroix et al., 2007b; Stow et al., 2009, Arndt et al., 2011). In this study we calculated the coefficient of determination (R2) and the Willmott skill metric (Willmott 1982), similar to Davies et al., 2014, who worked on a coastal biogeochemical model in the US Pacific Northwest. Although the Willmott skill metric has been criticised (Ralston et al., 2010) we recognise an advantage in this metric in that it does not heavily penalise slight errors in phase, which can commonly occur in prognostic models of dynamic systems. Krause et al., 2005 critically review a number of model assessment statistics and counsel that the selection of appropriate metrics should be guided by the context of the study and the models intended use. Whilst our comparative model skill scores were poorer than those of Davies et al., 2014, [temperature 0.87 c.f. 0.97, nitrate 0.69 c.f. 0.93, chlorophyll 0.42 c.f. 0.73], our model bias

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was smaller [temperature 0.20 °C c.f. 0.78 °C, nitrate 1.2 µM c.f. 5.2 µM, chlorophyll -0.09 µgL-1 c.f. 2.7 µgL-1]. This suggests our model was better at reproducing the mean conditions, but not quite so good at reproducing the temporal trend; although it should be noted that Davis et al., (2014) had access to double the number of observations in their analysis, that spanned more than 3 times our range in nitrate and chlorophyll concentrations. Observations in our region were comparatively sparse and limited to a small set of model state variables and time and space scales (Whitehead et al., 2010; Ross & Macleod 2013). Model results for other properties and scales, whilst consistent with our understanding of the system dynamics, remain a hypothesis until validated with observations.

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Our simulated circulation for the Huon and D’Entrecasteaux region was consistent with the findings of Herzfeld & Andrewartha (2010) with a general south to northward circulation through the D’Entrecasteaux Channel intensifying in winter. Huon river flow was 21% greater in 2002 with elevated flow in June-July, (in addition to August), leading to comparatively stronger outflow and estuarine circulation (Herzfeld & Andrewartha 2010). In both studies the D’Entrecasteaux Channel behaved as a ROFI (Region of Fresh Water Influence) regime with buoyant surface water deflected north due to Coriolis (Simpson 1997). Our simulated circulation in the Derwent Estuary was similar to that modelled by Wild-Allen et al., 2013. In both studies bottom waters were drawn upstream by the estuarine circulation to a salt wedge structure in the mid estuary, and the buoyant surface outflow generally tracked the eastern shore. Peak Derwent River flow occurred in August in 2009 (this study), and October in 2003 (Wild-Allen et al., 2013) with concurrent intensification of the estuarine circulation.

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Circulation through the Huon Estuary, the D’Entrecasteaux Channel and the Derwent Estuary is primary modulated by the seasonal influx of fresh water from the major Huon and Derwent rivers. Inter-annual variation in river flow (Parsons 2012) can therefore have a significant impact on the ventilation of the region. Flow from the Huon catchment is largely unmodified (DPIW 2009) such that a wet year would have an immediate impact by intensifying the circulation through the Estuary and D’Entrecasteaux Channel. Derwent river flow is heavily modified by controlled dam release for hydroelectric energy generation (Eriksen et al., 2011) which modulates flow during periods of extreme rain or drought. The regional circulation is also influenced by the prevailing conditions on the Tasmanian shelf that are impacted in summer by the East Australian Current and in winter by the Zeehan Current (Ridgeway 2007; Kelly et al., 2015). The East Australia Current brings warm nutrient depleted water down the east coast of Tasmania in summer with increasing southward extent in recent years (Ridgeway & Hill 2009; Hill et al., 2008). The Zeehan Current is an extension of the Leeuwin Current that brings warm tropical waters down the west coast of Tasmania in winter (Cresswell 2000; Buchanan et al., 2013) particularly during the cooling phase of the El Niño Southern Oscillation (La Niña). Seasonal and inter-annual variability in the extent of these boundary currents modulates the water mass composition of the southern Tasmanian Shelf and its associated nutrient supply to inshore waters (Harris et al., 1987; Wild-Allen & Rayner 2014). Marine supply is often the dominant source of nutrients to coastal waters (e.g. Jickells 1998; Davis et al., 2014) with seasonally high influx in temperate latitudes in winter (Mann & Lazier 2006). Estuaries typically receive a large proportion of their nutrients from fluvial sources, and depending

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on the estuary size and flushing regime, these nutrients may be transformed within the estuary or exported to coastal waters (Arndt et al., 2011; Jickells et al., 2014). In our study we found fluxes of nitrogen between waterways were dominated by particulate nitrogen comprising plankton and detrital material; dissolved inorganic nitrogen was only present in winter when nutrient supply exceeded biological assimilation. In the Scheldt Estuary, Arndt et al., (2011) found phytoplankton growth was limited by the supply of phosphate and silicate in summer resulting in persistent discharge of nitrogen that fuelled Phaeocycstis blooms in adjacent coastal waters. In our system nitrogen constrained phytoplankton growth in summer, whilst phosphate concentrations in the outer estuaries and marine channel were in excess of Redfield (Wild-Allen & Rayner 2014; Thompson et al., 2005).

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The nitrogen mean composition of Sewerage Treatment Plant discharge was 69 % ammonium, 19 % nitrate and 12 % labile detritus (TasWater pers. comms.), whilst fish farm waste was assumed to comprise 85.5% ammonium and 14.5% labile detritus (Skretting 2003, Wild-Allen et al., 2010). STP treatment nitrifies a fraction of the ammonium to nitrate therefore reducing its net biological oxygen demand in the receiving water; fish farm waste however has a greater potential to drawdown oxygen via nitrification. As modelled autotrophs take up both ammonia and nitrate, STP and fish farm waste have similar potential to enhance primary production (modulated by ambient light, temperature and load). Fish farm nitrogen input is 4 times greater than STP load into the region (Table 2) therefore the potential for anthropogenic enrichment of autotrophic production by fish farms is much greater. If autotrophs were to take up ammonia in preference to nitrate then we might expect more rapid assimilation of fish farm waste and given similar flushing, potentially more local assimilation of fish farm waste in comparison to STP discharge locations.

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Whilst marine and river nitrogen supplies had a strong annual cycle, anthropogenic nitrogen supply was persistent throughout the year. Conley et al., 2009 notes that exhaustion of the supply of nitrogen typically limits the spring bloom, but where nitrogen fixers sustained production into summer they contributed to the ‘vicious circle’ of eutrophication *sinking of organic matter, oxygen depletion and hypoxia]. In our system anthropogenic nitrogen supply in summer plays a similar role to nitrogen fixers by prolonging phytoplankton blooms and, in localised areas, contributing to poor water quality (Whitehead et al., 2010; Ross & Macleod 2013). Chen et al., (2012) observed an increase in nutrient loading to the Yangtze River Estuary over the past 30 years resulting in increasing levels of total nitrogen and phosphorous. Between 2002-3 and 2009 the estimated anthropogenic nitrogen load has increased [Huon Estuary 86 %; D’Entrecasteaux Channel 162 %; Derwent Estuary 1 %] predominantly due to expansion in the fish farming industry (Wild-Allen et al., 2010, 2013, this study). Since 2009 the fish farming industry has continued to expand coincident with declining water quality in some localised areas (Ross & Macleod 2013). In our model nitrogen losses were dominated by denitrification. Whilst regional observations are sparse Abell et al., (2013) confirmed through microbial analysis that denitrification was the primary pathway for N2 efflux in Derwent Estuary sediments. Modelled denitrification ranged from 5-79 µmol N2 m-2h-1 (5 – 95 percentile) throughout the Derwent Estuary with an annual mean of 36 µmol N2 m-2h-1 which is within the range reported by Banks et al., (2012, 2013) for 3 sites in the estuary. They report denitrification rates ranging from 2 – 100 µmol N2 m-2h-1 with spatial and temporal variability due to sediment heterogeneity in grain size, porosity, nitrogen load, oxygen respiration, bioturbation and irrigation by benthic organisms. Simulated denitrification rates throughout the

ACCEPTED MANUSCRIPT region are comparable to rates in Port Philip Bay (Heggie et al., 1999) and similarly critical in mitigating anthropogenic nitrogen load.

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Jickells et al., (2014) found denitrification fluxes were strongly correlated with estuarine area in small estuaries (<80 km2) and proposed a metric for the classification of estuaries as the ratio of estuarine area to river flow. The Huon and Derwent Estuaries were classified (Huon = 60/96 = 0.63; Derwent = 71/103 = 0.69) similar to 31 estuarine systems in the UK (Jickells et al., 2014). For the Huon and Derwent Estuaries denitrification accounted for 64% and 83% respectively of the river nitrogen load, however in our system the river load comprised a significant fraction of refractory nitrogen with slow breakdown rate, therefore our denitrification flux more likely mediated the labile anthropogenic nitrogen load.

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Annual nitrogen budgets for the region suggest near steady state, although it should be noted that as the model has limited capacity to bury or retain significant loads of nitrogen it may underestimate retention of particulate nitrogen in the system. Similar budgets were calculated for the waterways in 2002-3 (Wild-Allen et al., 2010, 2013) and indicate some evolution of the system to 2009. In 2009 the anthropogenic load to the Huon Estuary and D’Entrecasteaux Channel was significantly higher (2042 c.f. 843 tN y-1 in 2002) due to expansion in the fish farming industry. This was accompanied by a reduction in river load (62% of 2002) primarily due to reduced flow in 2009, and a reduction in the marine input (63% of 2002) primarily due to the associated relaxation in the estuarine circulation. The total input of nitrogen to the Huon Estuary and D’Entrecasteaux Channel in 2009 was therefore 752 tN y-1 less than in 2002 as the increased anthropogenic load was more than offset by the reduced marine and river influx.

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Marine export from the D’Entrecasteaux Channel in 2009 was less (64% of 2002) than in 2002 due to relaxation in the circulation, but this was offset by more than twice as much denitrification. Jickells et al., (2014) note the denitrification potential for a system depends on both its area and flow (Huon 2002 area/flow = 60/120 = 0.5). Our results suggest the reduced flow in 2009 c.f. 2002 resulted in a comparatively longer residence time and therefore a greater system wide denitrification flux. For the Huon Estuary and D’Entrecasteaux Channel elevated Huon River flow would likely deliver more catchment nitrogen into the system and intensify the estuarine and ROFI circulation through the D’Entrecasteaux Channel thus bringing more marine nitrogen into the system from the south. This would be concurrent with an increase in the export flux of nitrogen to the north and a decrease in the system wide residence time and hence denitrification flux. The unregulated Huon River can generate floods throughout the year subject to precipitation in the catchment (DPIWE 2009). The system is therefore vulnerable to natural variability. In the Derwent Estuary river flow in 2003 (107 m3s-1) was similar to 2009, however the marine influx was much larger in 2003 with a small net import of marine nitrogen (96 tN y-1) (Wild-Allen et al., 2013); in 2009 there was a net export of 719 tN y-1 across the marine boundary due primarily to a reduction in local denitrification rate (47% of 2002). This reduction in denitrification was independent of estuarine flow, but more likely reflects changes in nitrogen assimilation and recycling in the estuary. In 2003 the Derwent CDOM attenuation of fresh water was set to 1.0 m-1, however in the regional Huon-D’Entrecasteaux-Derwent model this attenuation coefficient increased to 4.4 m-1 to better represent dark humic laden water entering the southern catchments (Clementson et al., 2004). The relatively opaque water simulated in the estuary in 2009 limited phytoplankton

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assimilation of nitrogen into biomass with a corresponding reduction in detritus for sediment recycling and denitrification. Given clearer Derwent River water it is likely that phytoplankton biomass, detrital deposition to the sediment, nitrogen remineralisation and denitrification would be much greater. Whilst the index of Jickells et al., (2014) is useful, we recommend updating the index to account for estuarine attenuation of light, which can limit assimilation and cycling of nutrients in some systems.

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Connectivity and nitrogen flux within the region is predominantly between the Huon Estuary and the D’Entrecasteaux Channel and to the north from the D’Entrecasteaux Channel and Derwent Estuary into Storm Bay. Fluxes vary throughout the year but in 2009 they appeared synchronised with peak northward flux from the D’Entrecasteaux Channel matched by peak Derwent Estuary discharge. In this situation water from the locally shallower D’Entrecasteaux Channel is entrained with the outflowing Derwent Estuary water, with the influx into the Derwent Estuary limited to deeper waters. In other years high flow events from the unregulated Huon catchment occur throughout the year, whilst the Derwent River discharge is managed more consistently (Parsons 2012; Erikson et al., 2011). We can imagine a situation then when elevated Huon River flow, intensified estuarine and ROFI circulation would discharge more nitrogen at the entrance to the Derwent Estuary. Should this be a period of low Derwent River flow, then the excursion of nitrogen into the entrance of the Derwent might be greater. This flux could be further enhanced should it comprise particulate nitrogen that might sink into the bottom inflowing circulation or sediment and be recycled within the estuary.

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The objective of this paper was to demonstrate how a 3D coupled hydrodynamic and biogeochemical model could be used to investigate the transfer of anthropogenic nutrients between coastal waterways. We have shown that such a model, validated against observations, can be used with confidence to investigate observations, elucidate nutrient transformations and fluxes and compute budgets for system understanding. Specifically for the Derwent Estuary we have found a plausible explanation of the observed increase in nitrogen found by Whitehead et al., (2010) in 2009. Modelled circulation and nitrogen fluxes demonstrate a small northward excursion of nitrogen rich surface water from the D’Entrecasteaux Channel prior to entrainment and flushing of Derwent Estuary water south into Storm Bay.

Conclusions The Huon Estuary, D’Entrecasteaux Channel and Derwent Estuary are connected by a south to northward residual circulation. Fish farm nitrogen moves northward from the D’Entrecasteaux Channel into the lower Derwent Estuary, and is then flushed south, by the surface outflow of the Derwent Estuary into Storm Bay. The circulation and flux of nitrogen is modulated by seasonal variation in river flow, marine flushing and anthropogenic nitrogen load. In 2009 the highest flux of nitrogen from the D’Entrecasteaux Channel north into the entrance of the Derwent Estuary occurred in August coincident with peak discharge from the Derwent Estuary. In other years a mis-match in the timing of elevated Huon and Derwent River discharge could result in a greater flux of nitrogen from the D’Entrecasteaux Channel into the lower Derwent Estuary. Nitrogen budgets for the three waterways suggest that denitrification is a critical process in maintaining the health of the system and we recommend that this is validated with observations. In this study we have demonstrated

ACCEPTED MANUSCRIPT how a fine-scale 3D biogeochemical model can be used to resolve and quantify complex nutrient transport pathways between adjacent coastal waterways.

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Acknowledgements

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Derwent Estuary Program for water quality data & catchment loads; the Tasmanian Salmonoid Growers Association for access to water quality & feed data; Hydro Tasmania for Derwent River flow; TasWater, Norske Skog & Nystar for anthropogenic loads. This research was partially funded by the Fisheries Research and Development Corporation (FRDC) on behalf of the Australian Government through INFORMD Stage 2: Rick-based tools supporting consultation, planning and adaptive management for aquaculture and other multiple-uses of the coastal waters of southern Tasmania (project no. 2012/024).

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Ridgway K.R. 2007. Seasonal circulation around Tasmania: an interface between eastern and western boundary dynamics. J. Geophys. Res., 112:C10016. http://dx.doi.org/10.1029/2006JC003898

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Tables

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Table 1: Mean model R2 correlation (number of observations), Willmott skill score and bias for stations in each waterway [*the mean temperature and salinity score for the Derwent included the mooring station, however these continuous data are in addition to the number of observations shown].

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Table 2: Annual influx and loss of nitrogen (tN/y) and % contributions from marine, river and point source discharge for each waterway and the whole region.

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Figure 1: Map of southeast Tasmania, Australia, showing model domain within dashed lines, depth contours, major rivers (+) and point source loads: fish farms *, sewerage treatment plant outfalls ( ) and industry outfalls (▪).

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Figure 2: A, Monitoring stations and mooring (star) in the Derwent Estuary (north), the Huon Estuary (west) and the D’Entrecasteaux Channel. B, surface temperature and salinity at the mooring site. C, Modelled and observed chlorophyll (left) and dissolved inorganic nitrogen (right); regional means and standard deviations shown. Figure 3: (A) Spatial distribution of Willmott score, to assess model performance against surface observations and (B) pointwise correlation of all data for (from left to right) temperature, salinity, nitrogen and chlorophyll; the mean value for all stations is also shown, higher values indicate better model performance. Figure 4: Spatial distribution of modelled surface temperature, salinity, nitrogen and chlorophyll (from left to right) with observed values overlain, for (A) 18 August 2009 (winter) and (B) 15 December 2009 (summer). Figure 5: Annual mean surface (left) and bottom (middle) current. July mean surface current and salinity (right). Greatest northward flow through the D’Entrecasteaux Channel occurs during periods of peak Huon River flow. Figure 6: Nitrogen flux in (+ve) and out (-ve) of the whole region. Point source loads include fish farms, sewerage and industry discharge.

ACCEPTED MANUSCRIPT Figure 7: Annual nitrogen budgets for the A) Huon Estuary, B) D’Entrecasteaux Channel, C) Derwent Estuary, D) Whole Region. Figure 8: Annual denitrification flux (kgN m-2y-1).

Appendix

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BGC model equations and parameters

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Figure 9: A) Total nitrogen transport between waterways (tN/y); B) mean surface currents in July; C) summary of fluxes in transition zone; D) monthly composition of northern D’Entrecasteaux Channel nitrogen flux.

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0.59 (264) 0.05 (132)

0.89

0.83

0.47 0.59

0.65 0.39

0.37 °C

0.97 °C

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0.20 (96) 0.34 (48)

0.93 (286*) 0.31 (286*) 0.63 (288) 0.17 (144)

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R2 Correlation: Temperature Salinity DIN Chlorophyll Willmott Score: Temperature Salinity DIN Chlorophyll Bias: Temperature Salinity DIN

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0.89 (646*) 0.55 (648) 0.15 (324)

0.92 0.52 0.80 0.39

0.87

-0.60 °C -1.62 -11.9 mg/m3

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Table 1: Mean model R2 correlation (number of observations), Willmott skill score and bias for stations in each waterway [*the mean temperature and salinity score for the Derwent included the mooring station, however these continuous data are in addition to the number of observations shown].

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4676

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% Marine

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% Major River/Estuary

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Table 2: Annual influx and loss of nitrogen (tN/y) and % contributions from marine, river and point source discharge for each waterway and the whole region.

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Highlights 3D biogeochemical model resolves nutrient transport pathways in coastal waters Marine, river and anthropogenic nitrogen fluxes into coastal waterways quantified Seasonal nitrogen input, composition and export including denitrification quantified New nitrogen budgets for D’Entrecasteaux Channel, Huon and Derwent Estuaries Nutrient rich Huon Estuary and D’Entrecasteaux Channel water flushed into Storm Bay