Biogeochemical Processes and Buffering Capacity Concurrently Affect Acidification in a Seasonally Hypoxic Coastal Marine Basin

Coastal areas are impacted by multiple natural and anthropogenic processes and experience stronger pH fluctuations than the open ocean. These variations can weaken or intensify the ocean acidification signal induced by increasing atmospheric pCO 2. The development of eutrophication-induced hypoxia intensifies coastal acidification, since the CO 2 produced during respiration decreases the buffering capacity in any hypoxic bottom water. To assess the combined ecosystem impacts of acidification and hypoxia, we quantified the seasonal variation in pH and oxygen dynamics in the water column of a seasonally stratified coastal basin (Lake Grevelingen, the Netherlands). Monthly water-column chemistry measurements were complemented with estimates of primary production and respiration using O 2 light–dark incubations, in addition to sediment–water fluxes of dissolved inorganic carbon (DIC) and total alkalinity (TA). The resulting data set was used to set up a proton budget on a seasonal scale. Temperature-induced seasonal stratification combined with a high community respiration was responsible for the depletion of oxygen in the bottom water in summer. The surface water showed strong seasonal variation in process rates (primary production, CO 2 air–sea exchange), but relatively small seasonal pH fluctuations (0.46 units on the total hydrogen ion scale). In contrast, the bottom water showed less seasonality in biogeochemical rates (respiration, sediment– water exchange), but stronger pH fluctuations (0.60 units). This marked difference in pH dynamics could be attributed to a substantial reduction in the acid–base buffering capacity of the hypoxic bottom water in the summer period. Our results highlight the importance of acid–base buffering in the pH dynamics of coastal systems and illustrate the increasing vulnerability of hypoxic, CO 2-rich waters to any acidifying process.


Introduction
The absorption of anthropogenic carbon dioxide (CO 2 ) has decreased the average pH of open ocean surface water by circa 0.1 unit since the Industrial Revolution (Orr et al., 2005). In coastal areas, the problem of ocean acidification is more complex, as seawater pH is influenced by various natural and anthropogenic processes other than CO 2 5 uptake (Borges and Gypens, 2010;Duarte et al., 2013;Hagens et al., 2014). As a result, the signal of CO 2 -induced acidification may not be readily discernable in coastal systems, as time series of pH show high variations at diurnal, seasonal and decadal time scales (e.g., Hofmann et al., 2011;Wootton and Pfister, 2012). One major anthropogenic process impacting coastal pH is eutrophication (Borges and Gypens, 2010;10 Provoost et al., 2010;Cai et al., 2011). Enhanced inputs of nutrients lead to higher rates of both primary production and respiration (Nixon, 1995), thereby increasing the variability in pH on both the diurnal (Schulz and Riebesell, 2013) and seasonal scale (Omstedt et al., 2009). Moreover, when primary production and respiration are not balanced, they can lead to longer-term changes in pH at rates which can strongly exceed In the 21st century, seawater buffering capacity is expected to decline as a result of increasing CO 2 and the subsequent decrease in pH (Egleston et al., 2010;Hofmann et al., 2010a;Hagens et al., 2014). As a result, one would predict a greater seasonal pH variability (Frankignoulle, 1994;Egleston et al., 2010) and a more pronounced diurnal pH variability in highly productive coastal environments (Schulz and Riebesell, 2013;10 Shaw et al., 2013), which may additionally be modified by ecosystem feedbacks (Jury et al., 2013). In seasonal hypoxic systems, model analysis predicts more pronounced fluctuations in bottom-water pH (Sunda and Cai, 2012). However, detailed studies of the effects of seasonal hypoxia on pH buffering and dynamics are currently lacking.
Here we present a detailed study of the pH dynamics and acid-base buffering capac- 15 ity in a temperate coastal basin with seasonal hypoxia (Lake Grevelingen). We quantify the impact of individual processes, i.e., primary production, community respiration, sediment effluxes and CO 2 air-sea exchange, on pH. From this, we construct a proton budget that attributes proton production or consumption to these processes. Our aim is to quantify seasonal changes in the acid-base buffering capacity and elucidate their struction of two dams. The Grevelingen estuary was closed off on the landward side in 1964 and on the seaward side in 1971. This isolation led to a freshening of the system, with vast changes in water chemistry and biology (Bannink et al., 1984). To counteract these water quality problems, a sluice extending vertically between 3 and 11 m depth was constructed on the seaward side in 1978 (Pieters et al., 1985). Exchange with saline North Sea water has dominated the water budget since, resulting in the lake approaching coastal salinity (29-32) and an estimated basin-wide water residence time of 229 days (Meijers and Groot, 2007). Upon intrusion, the denser North Sea water forms a distinct subsurface layer, which is then laterally transported into the lake. Yet it has been found that opening the sluice hardly affects water-column mixing (Nolte 15 et al., 2008) and the water quality problems sustain. Monthly monitoring carried out by the executive arm of the Dutch Ministry of Infrastructure and the Environment revealed that the main gully of Lake Grevelingen has experienced seasonal stratification and hypoxia since the start of the measurements in 1978, though differing in extent and intensity annually (Wetsteyn, 2011). 20 Throughout 2012, we performed monthly sampling campaigns onboard the R/V Luctor examining water column chemistry, biogeochemical rates and sediment-water exchange. Sampling occurred in the Den Osse Basin (maximum water depth 34 m; Fig. 1b), a basin located in the main gully of Lake Grevelingen. Two sills surround the basin at water depths of 10 and 20 m at the landward and seaward side, respectively. Introduction 3.898 • E). Each campaign, water-column sampling was performed at station S1. Discrete water-column samples were collected with a 12 L Niskin bottle at eight different 5 depths (1, 3, 6, 10, 15, 20, 25 and 32 m) to assess the carbonate system parameters (pH, partial pressure of CO 2 (pCO 2 ), total alkalinity (TA) and DIC), concentrations of O 2 , hydrogen sulphide (H 2 S), dissolved organic carbon (DOC) and nutrients, and rates of community metabolism. All water samples were collected from the Niskin bottle with gas-tight Tygon tubing. A YSI6600 CTD probe was used to record depth profiles of 10 temperature (T ), salinity (S), pressure (p) and chlorophyll a (chl a). To determine sediment-water exchange fluxes, intact, undisturbed sediment cores (6 cm Ø) were retrieved with a UWITEC gravity corer in March, May, August and November 2012 at the three stations S1, S2 and S3. Sampling usually took place mid-morning to minimise the influence of diurnal variability in determining the seasonal trend. The exact dates 15 and times of sampling are provided in the Supplement.

Stratification-related parameters
From T , S and p the water density ρ w (kg m −3 ) was calculated according to Feistel (2008) using the package AquaEnv (Hofmann et al., 2010b) in the open-source programming framework R. Subsequently, the density anomaly σ T (kg m −3 ) was defined 20 by subtracting 1000 kg m −3 from the calculated value of ρ w . Water density profiles were also used to calculate the stratification parameter φ (J m −3 ), which represents the amount of energy required to fully homogenise the water column through vertical mixing (Simpson, 1981): Here, h is the total height of the water column (m), z is depth (m), g is gravitational acceleration (m s −2 ), and ρ av is the average water-column density (kg m −3 ). Samples for the determination of [O 2 ] were drawn from the Niskin bottle into volumecalibrated clear borosilicate biochemical oxygen demand (BOD) bottles of circa 120 mL (Schott). O 2 concentrations were measured using an automated Winkler titration pro-5 cedure with potentiometric end-point detection (Mettler Toledo DL50 titrator and a platinum redox electrode). Reagents and standardisations were as described by Knap et al. (1994).
During summer months we examined the presence of H 2 S in the bottom water. Water samples were collected in 60 mL glass serum bottles, which were allowed to overflow 10 and promptly closed with a gas-tight rubber stopper and screw cap. To trap the H 2 S as zinc sulphide, 1.2 mL of 2 % zinc acetate solution was injected through the rubber stopper into the sample using a glass syringe and needle. A second needle was inserted simultaneously through the rubber stopper to release the overpressure. The sample was stored upside down at 4 • C until analysis. Spectrophotometric estimation 15 of H 2 S (Strickland and Parsons, 1972) was conducted by adding 1.5 mL of sample and 0.120 mL of an acidified solution of phenylenediamine and ferric chloride to a disposable cuvette. The cuvette was closed immediately thereafter to prevent the escape of H 2 S and was allowed to react for a minimum of 30 min before the absorbance at 670 nm was measured. For calibration, a 2 mmol L −1 sulphide solution was prepared, for which 20 the exact concentration was determined by iodometric titration.

Carbonate system parameters
For the determination of TA, two separate samples were collected in 50 mL centrifuge tubes. To determine the contribution of suspended particulate matter to TA, one sample was left unfiltered, while the other was filtered through a 0.45 µm nylon membrane 25 syringe filter (Kim et al., 2006). TA was determined using the standard operating procedure for open cell potentiometric titration SOP 3b) Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | mostated reaction vessel (T = 25 • C) and combination pH glass electrode (Metrohm 6.0259.100). TA values were calculated by a non-linear least-squares fit to the titration data in a custom-made script in R. Quality assurance involved regular analysis of Certified Reference Materials (CRM) obtained from the Scripps Institution of Oceanography (A.G. Dickson,batches 116 and 122). The relative SD of the procedure was less than 5 0.2 % or 5 µmol kg −1 (n = 10). Samples for DIC analysis were collected in 10 mL headspace vials, left to overflow and poisoned with 10 µL of a saturated mercuric chloride (HgCl 2 ) solution. DIC analysis was performed using an AS-C3 analyser (Apollo SciTech) which consists of an acidification unit in combination with a LICOR LI-7000 CO 2 /H 2 O Gas Analyser. Quality assurance involved carrying out three replicate measurements of each sample and regular analysis of CRM. The accuracy and precision of the system are 0.15 % or 3 µmol kg −1 .
Water for pCO 2 analysis was collected in 50 mL glass serum bottles from the Niskin bottle with Tygon tubing, left to overflow, poisoned with 50 µL of saturated HgCl 2 and 15 sealed with butyl stoppers and aluminium caps. Samples were analysed within 3 weeks of collection by the headspace technique (Weiss, 1981) using gas chromatography (GC) with a methaniser and flame ionisation detection (GC-FID, SRI 8610C). The GC-FID was calibrated with pure N 2 and three CO 2 : N 2 standards with a CO 2 molar fraction of 404, 1018, 3961 ppmv (Air Liquide Belgium). Headspace equilibration was done 20 overnight in a thermostated bath, and temperature was recorded and typically within 3 • C of in situ temperature. pCO 2 data were corrected to in situ temperature. Samples were collected in duplicate and the relative SD of duplicate analysis averaged ±0.8 % (n = 90) Samples for the determination of pH were collected in 100 mL glass bottles. pH 25 measurements were done immediately after collection at in situ temperature using a glass/reference electrode cell (Metrohm 6.0259.100) following standard procedures SOP 6a ibration. The temperature difference between buffers and samples never exceeded 2 • C. pH values are expressed on the total hydrogen ion scale (pH T ).

Community metabolism
Net community respiration (NCP), gross primary production (GPP) and community respiration (CR) were determined using the oxygen light-dark method (Riley, 1939;5 Gazeau et al., 2005a). Samples were drawn from the Niskin bottle into similar BOD bottles as described in Sect.
p max is the maximum GPP norm (mmol O 2 (µg chl a) −1 h −1 ), I and I opt are the measured and optimum irradiance, respectively (both in µE m −2 s −1 ) and ω is a dimensionless 5 indicator of the relative magnitude of photoinhibition. Downwelling light as a function of water depth was measured using a LI-COR LI-193SA spherical quantum sensor connected to a LI-COR LI-1000 data logger. A separate LICOR LI-190 quantum sensor on the roof of the research vessel connected to this data logger was used to correct for changes in incident irradiance. Light penetration depth (LPD; 1 % of surface irradiance) was quantified by calculating the light attenuation coefficient using the Lambert-Beer extinction model. To additionally assess water-column transparency, Secchi disc depth was measured and corrected for solar altitude (Verschuur, 1997). In contrast to the measurements of downwelling irradiance, which were only taken mid-morning, Secchi depths were also determined in the after- 15 noon. Although Secchi depths cannot directly be translated into LPD estimates, they do give an indication of the seasonal and diurnal variability in subsurface light climate.
Hourly averaged measurements of incident irradiance were obtained with a LI-COR LI-190SA quantum sensor from the roof of NIOZ-Yerseke, located about 31 km from the sampling site ( These GPP values were integrated over time to determine volumetric GPP on the day of sampling (mmol O 2 m −3 d −1 ). A similar procedure using measured hourly incident irradiance was followed to calculate volumetric GPP on the days in between sampling days. Parameters of the Eilers-Peeters fit were kept constant in the monthly time interval around the day of sampling, while [chl a] depth profiles and the light attenuation co-5 efficient were linearly interpolated between time points. These daily GPP values were integrated over time to estimate annual GPP (mmol O 2 m −3 yr −1 ). Rates of volumetric CR (mmol O 2 m −3 h −1 ) were converted to daily values (mmol O 2 m −3 d −1 ) by multiplying them by 24 h. An annual estimate for CR (mmol O 2 m −3 yr −1 ) was calculated through linear interpolation of the daily CR values 10 obtained on each sampling day. Finally, CR and GPP were converted from O 2 to carbon (C) units. For CR, a respiratory coefficient (RQ) of 1 was used. For GPP, the production coefficient (PQ) was based on the use of ammonium (NH + 4 ) or nitrate (NO − 3 ) during primary production. Assuming Redfield ratios, when NH + 4 is taken up, this results in an O 2 : C ratio of 1 : 1, hence a PQ of 1. Alternatively, when the algae use NO − 3 , this 15 leads to an O 2 : C ratio of 138 : 106 and a PQ of 1.3. Since the utilisation of NH + 4 is energetically more favourable than that of NO − 3 , the former is the preferred form of dissolved inorganic nitrogen taken up during primary production (e.g., MacIsaac and Dugdale, 1972 concentration is subsequently measured with a non-dispersive infrared detector (Middelburg and Herman, 2007). Nutrient and DOC data can be found in the Supplement.

Sediment fluxes
To determine DIC and TA fluxes across the sediment-water interface, we used shipboard closed-chamber incubations. Upon sediment core retrieval, the water level was 5 adjusted to circa 18-20 cm above the sediment surface. To mimic in situ conditions, the overlying water was replaced with ambient bottom water prior to the start of the incubations, using a gas-tight tube and ensuring minimal disturbance of the sediment-water interface. Immediately thereafter, the cores were sealed with gas-tight polyoxymethylene lids and transferred to a temperature controlled-container set at in situ temperature. The core lids contained two sampling ports on opposite sides and a central stirrer to ensure that the overlying water remained well mixed. Incubations were done in triplicate and the incubation time was determined in such a way that during incubation the concentration change of DIC would remain linear. As a result, incubation times varied from 6 (at S1 during summer) to 65 h (at S3 during winter). 15 Throughout the incubation, water samples (∼ 7 mL) for DIC analysis were collected from each core five times at regular time intervals in glass syringes via one of the sampling ports. Concurrently, an equal amount of ambient bottom water was added through a replacement tube attached to the other sampling port. Ca. 5 mL of the sample was transferred to a headspace vial, poisoned with 5 µL of a saturated HgCl 2 solution 20 and stored submerged at 4 • C. These samples were analysed as described in Sect. 2.3. The subsampling volume of 7 mL was less than 5 % of the water mass, so no correction factor was applied to account for dilution. DIC fluxes (mmol m −2 d −1 ) were calculated from the change in concentration, taking into account the enclosed sediment area and overlying water volume: ∆t is the change in DIC in the overlying water vs. time (mmol m −3 d −1 ), which was calculated from the five data points by linear regression, V ow is the volume of the overlying water (m 3 ) and A is the sediment surface area (m 2 ). To determine TA fluxes, no subsampling was performed. Instead, the fluxes were calculated from the difference in TA between the beginning and end of the incubation, accounting for enclosed sed-5 iment area and overlying water volume. TA samples were collected and analysed as described in Sect. 2.3.

Carbonate system calculations
The measurement of four carbonate system parameters implies that we can check the internal consistency of the carbonate system (see Appendix A). For the rest of this paper, we use DIC and pH T for the carbonate system calculations. This has been suggested to be the best choice when systems other than the open ocean are studied and measurements of TA may be difficult to interpret (Dickson, 2010; see also Appendix A). All calculations were performed using the R package AquaEnv. The main advantage of AquaEnv is that it has the possibility to include acid-base systems other than the carbonate and borate system, which is especially important in highly productive and hypoxic waters. Furthermore, it provides a suite of output parameters necessary to compute the individual impact of a process on pH, such as the acid-base buffering capacity. As equilibrium constants for the carbonate system we used those of Mehrbach et al. (1973) as refitted by Dickson and Millero (1987), which were calculated from CTD-20 derived T , S and p using CO2SYS (Pierrot et al., 2006). For the other equilibrium constants (borate, phosphate, ammonia, silicate, nitrite, nitrate and the auto-dissociation of water) we chose the default settings of AquaEnv. CO 2 air-sea exchange (mmol C m −2 d −1 ) on the day of sampling was estimated using the gradient between atmospheric pCO 2 (pCO 2, atm ) and the calculated seawater pCO 2 at 1 m depth (both in atm): Here, k (m d −1 ) is the gas transfer velocity, which was calculated from wind speed according to Wanninkhof (1992), normalised to a Schmidt number of 660. Dailyaveraged wind speed at Wilhelminadorp (51.527 • N, 3.884 • E, measured at 10 m above the surface) was obtained from the Royal Netherlands Meteorological Institute (http://www.knmi.nl). The quantity α is the solubility of CO 2 in seawater (Henry's con-5 stant; mmol m −3 atm −1 ) and was calculated according to Weiss (1974). For pCO 2, atm we used monthly mean values measured at Mace Head (53.326 • N, 9.899 • W) as obtained from the National Oceanic and Atmospheric Administration Climate Monitoring and Diagnostics Laboratory air sampling network (http://www.cmdl.noaa.gov/). To calculate CO 2 air-sea exchange on the days in between sampling days, we used dailyaveraged wind speed and linear interpolation of the other parameters.

Acid-base buffering capacity and proton cycling
The acid-base buffering capacity plays a crucial role in the pH dynamics of natural waters. Many different formulations of this buffering capacity exist (Frankignoulle, 1994;Egleston et al., 2010). However, a recent theoretical analysis (Hofmann et al., 2008) 15 has shown that, for natural waters, it is most adequately defined as the change in TA associated with a certain change in [H + ], thereby keeping all other total concentrations (e.g., DIC, total borate) constant: Hence, when the acid-base buffering capacity of the water is high, one will observe only 20 a small change in [H + ] for a given change in TA. It should be noted that β is intrinsically different from the well-known Revelle factor (Revelle and Suess, 1957;Sundquist et al., 1979) which quantifies the CO 2 buffering capacity of seawater, i.e., the capacity of the coupled ocean-atmosphere system to counteract changes in atmospheric CO 2 . In this study, β was calculated according to Hofmann et al. (2008) and subsequently 25 used to quantify the effect of several processes on pH individually as described in 15841 Introduction  Hofmann et al. (2010a). Briefly, each chemical reaction takes place at a certain rate and with a certain stoichiometry, e.g., aerobic respiration can be described as where γ N and γ P are the ratios of nitrogen (N) and phosphorous (P) to carbon (C) in organic matter, respectively. At first sight, this reaction equation does not seem to pro-5 duce any protons. However, the CO 2 (as carbonic acid, H 2 CO 3 ), ammonia (NH 3 ) and phosphoric acid (H 3 PO 4 ) formed will immediately dissociate into other forms at a ratio similar to their occurrence at ambient pH. As a result, protons are produced during aerobic respiration, despite the fact they are absent in Reaction (R1). The amount of protons produced is termed the stoichiometric coefficient for the proton (ν x H + ) or pro-10 ton release rate. This coefficient is process-specific and, for aerobic respiration, equals c 2 + 2c 3 − γ N n 1 + γ P (p 2 + 2p 3 + 3p 4 ) (Hofmann et al., 2010a; Table 1). Here, c 2 and c 3 are the ratios of bicarbonate (HCO − 3 ) and carbonate (CO 2− 3 ) to DIC, n 1 is the ratio of NH + 4 to total ammonia, and p 2 , p 3 and p 4 are the ratios of dihydrogen phosphate (H 2 PO − 4 ), monohydrogen phosphate (HPO 2− 4 ) and PO 3− 4 to total phosphate, respec-15 tively. As these ratios depend on the ambient pH, so does the value of ν x H + . In natural systems, the vast majority of protons produced during a biogeochemical process according to ν x H + is consumed through immediate acid-base reactions, thereby neutralising their acidifying effect. The extent to which this attenuation occurs is controlled by the acid-base buffering capacity of the system. Hence, the net change in 20 [H + ] due to a certain process x (µmol kg −1 d −1 ) is the product of the process rate (R x ; µmol kg −1 d −1 ) and the stoichiometric coefficient for the proton of that reaction (ν x H + ), divided by β: BGD 11,2014 Acidification in a seasonally hypoxic coastal basin The total net change in [H + ] over time is simply the sum of the effects of all relevant processes, as they occur simultaneously: A straightforward way to express the vulnerability of a system to changes in pH is to look at the proton turnover time (Hofmann et al., 2010a). For this we first need to define 5 the proton cycling intensity, which is the sum of all proton-producing (or consuming) processes. When dividing the ambient [H + ] by the proton cycling intensity, the proton turnover time (τ H + ) can be estimated. The smaller the proton turnover time, the more susceptible the system is to changes in pH. In a system that is in steady state, i.e., the final change in [H + ] is zero, the proton cycling intensity is the same irrespective 10 of whether the sum of the proton producing or consuming processes is used for its calculation. In a natural system like the Den Osse Basin this is not the case, meaning that total H + production and total H + consumption are not equal. Here, we use the smaller of the two for the calculation of the proton cycling intensity. As a result, the calculated turnover times should be regarded as maximal values. change of DIC and TA and vertical water column mixing, taking into account the effects of S and T changes (Hofmann et al., 2008(Hofmann et al., , 2009. We divided the vertical of the basin into eight depth layers, whereby the eight sampling depths represented the midpoint of each layer. Using the bathymetry of the lake, for each box we calculated the total volume of water in the layer, the area at the upper and lower boundary (planar BGD 11,2014 Acidification in a seasonally hypoxic coastal basin using the following equation (Regnier et al., 1997): Here, k max is the maximum nitrification rate constant (3 × 10 −4 mmol m −3 s −1 ) and q 10 , which is set at 2, is the factor of change in rate for a change in temperature of 10 • C.
CO 2 air-sea exchange rates were converted to mmol m −3 d −1 by first multiplying them with the total surface area of the Den Osse Basin (m 2 ) and then dividing them by 10 the volume of the uppermost box (m 3 ), assuming that CO 2 sea-air exchange only directly affects the proton budget of this box. Similarly, DIC and TA sediment fluxes (mmol m −2 d −1 ) were multiplied by the corresponding sediment area of the basin (m 2 ) and then divided by the volume of the box corresponding to their measurement depth (m 3 ). To ensure mass conservation, vertical TA and DIC transport rates (mmol d −1 ) 15 were computed by multiplying the difference in mass between two consecutive boxes (mmol), i.e., the product of concentration and volume, with a mixing coefficient ζ (d −1 ) that was calculated based on the entrainment function by Pieters et al. (1985), multiplied by the volume of water below the pycnocline. Then, the transport rates were converted to mmol m −3 d −1 by dividing them by the volume of the corresponding box. 20 Finally, all rates (expressed in mmol m is represented as the closure term of the budget, which is needed because some of the proton-producing and consuming processes are unknown or have not been measured. This budget closure term includes the effect of lateral transport induced by wind and/or water entering Lake Grevelingen through the seaward sluice, which could not be quantified due to a lack of hydrodynamic data.

Environmental settings
Over the year 2012, the surface water temperature at Den Osse ranged from 1.99 to 21.03 • C, while bottom water temperature showed a substantially smaller variation (1.47-16.86 • C; Fig. 3a). The surface water was colder than the bottom water in January, while the reverse was true between February and April. However, the temperature difference between surface and bottom water of Den Osse remained within 1 • C.
Warming of the surface water in late spring rapidly increased the difference between surface and bottom water to 9.3 • C in May. This gradient persisted, albeit with decreasing magnitude, until August. The thermocline, which was located between 10-15 m in 15 May, deepened to 15-20 m in June. In July and August, on the contrary, temperature continuously decreased with depth. In September, the temperature depth profile was almost homogeneous, while in November and December, surface waters were again cooler than bottom waters. Salinity (Fig. 3b) increased with water depth at all months, but the depth of the halo-20 cline and the magnitude of the salinity gradient varied considerably over the year. This salinity gradient resulted from denser, more saline North Sea water that sank when entering Lake Grevelingen. Variations in the sluice operation, and resulting changes in North Sea exchange volumes, could therefore explain the observed month-to-month variability in salinity depth profiles. surface (30.08) and bottom (32.21) water salinity was found in March. Lower in-and outflow volumes, resulting from strict water level regulations in spring and early summer (Wetsteyn, 2011), led to a lower salinity throughout the water column between April and June. In July and August, a small (∼ 0.2) but noticeable decrease in salinity was recorded from 15-20 m onward, suggesting the intrusion of a different water 5 mass. Precipitation did not appear to exert a major control on the salinity distribution, as there was no correlation between mean water-column salinity and monthly rainfall as calculated from daily-integrated rainfall data obtained from the Royal Netherlands Meteorological Institute (http://www.knmi.nl) measured at Wilhelminadorp. Similar to temperature, the difference in density anomaly (σ T ; Fig. 3c) between sur-10 face and deep water was highest in May. This density gradient was sustained until August, indicating strong water-column stratification during this period. The depth of the pycnocline decreased from ca. 15 m in May and June to ca. 10 m in July and August. This corresponded to a weakening of the stratification as indicated by the stratification parameter φ, which dropped from 3.34 J m −3 in May to 2.09 J m −3 in August 15 (Fig. 3e). This weakening in stratification was presumably due to the delayed warming of bottom water compared to surface water. A week before sampling in September, weather conditions were stormy (maximum daily-averaged wind speed of 7.0 m s −1 ), which most likely disrupted stratification and led to ventilation of the bottom water. The resemblance in the spatio-temporal patterns of T , S and σ T indicates that the water- August the bottom water was fully depleted of O 2 , [H 2 S] remained below the detection limit (5 µM), indicating the absence of euxinia. From September onwards, water-column mixing restored high O 2 concentrations throughout the water column. Lake Grevelingen surface water is generally characterised by high water transparency and deep light penetration (Fig. 3e). LPD was 9.4 m in March and slightly 5 increased to 10.6 m in May. Between June and August, during a period of high primary production (see Sect. 3.3.1), LPD decreased until 5.8 m. From September onwards, the surface water turned more transparent again. Accordingly, LPD increased up to 12.6 m in November, after which it stabilised at a value of 12.0 m in December. The Secchi disc data generally confirm the observed temporal pattern in the LPD, as is shown by the 10 significant correlation between morning Secchi depths and LPD (r 2 = 0.86; P < 0.001). Secchi disc depth was on average ∼ 80 % of LPD and, similar to LPD, was highest in November and lowest in July. Additionally, the Secchi depths indicate that diurnal variations in light penetration may exist. Especially in July, during an intense dinoflagellate bloom (see Sect. 3.3.1), light penetrated much deeper into the water column in the 15 morning than in the afternoon (Secchi disc depths of 2.9 and 0.9 m, respectively). The difference between morning and afternoon Secchi disc depth was much smaller in August (3.3 and 2.5 m) and virtually absent in November (8.5 and 8.4 m).

pH T , DIC, TA, pCO 2 20
In January, pH T showed little variation with depth, with an average value of 8.04 (Fig. 4a). From February to April, pH T increased throughout the water column, though the increase was faster at the surface than at depth, up to a maximum of 8.36 in the surface water in April. From June onward, stratification augmented the difference between surface and bottom water pH T . In August, this difference had increased to 0.69 25 units. The sharp decrease in pH T with depth during this month coincided with the declining trend seen for [O 2 ] (Fig. 3d) gradient coincided with the pycnocline depth. In June, DIC increased strongly (by 100-200 µmol kg −1 ) below the pycnocline, while in July and August, a strong drawdown in DIC occurred above the pycnocline, concurrent with an intense dinoflagellate bloom (see Sect. 3.3.1). In combination with the persisting stratification, this resulted in a DIC gradient of 600 µmol kg −1 . After the disruption of the stratification, the gradient 20 between surface and bottom water DIC was strongly reduced, and decreased further from 144 to 47 µmol kg −1 between September and December. Concomitantly, the average DIC increased from 2146 to 2201 µmol kg −1 , although the month October was characterised by overall slightly lower DIC (average value of 2123 µmol kg −1 ). Surfacewater DIC variation over the year (453 µmol kg −1 ) was somewhat higher than in the 25 bottom water (361 µmol kg −1 ). TA (Fig. 4c) generally showed more temporal than spatial variability. Therefore, variations in TA with depth were usually much smaller compared to DIC. In January and February, TA was fairly constant with depth (average value of 2404 µmol kg −1 ), with the (195 µmol kg −1 ), as a result of high bottom-water TA, and in August (306 µmol kg −1 ), mainly due to the strong drawdown in surface-water TA. Because of this, average watercolumn TA in June was much higher (2520 µmol kg −1 ) than in August (2366 µmol kg −1 ).
The low surface-water TA persisted until November, while TA below 10 m depth was much less variable. Similar to DIC, the month October was characterised by overall 10 lower TA. There was little difference between surface and bottom-water variation in TA over the entire year (372 and 337 µmol kg −1 , respectively).
The pattern of pCO 2 (Fig. 4d) was inversely proportional to that of pH T . January was characterised by little variation with depth and an average pCO 2 (404 ppmv) close to pCO 2, atm (396 ppmv). In February, low T throughout the water column led to a draw-15 down of pCO 2 which continued until April, albeit with larger magnitude in the surface compared to the bottom water. The onset of stratification in May led to a build-up of CO 2 resulting from organic matter degradation in the bottom water. Maximum bottom-water pCO 2 (1399 ppmv) was found in August and, as expected, co-occurred with the period of most intense hypoxia (Fig. 3d). While in May and June, pCO 2 increased throughout 20 the water column, in July and August, a substantial drawdown in surface-water pCO 2 was observed coinciding with an increase in [O 2 ], which is indicative of high autotrophic activity. Water-column ventilation disrupted the surface-to-bottom pCO 2 gradient from September onwards. Mean water-column pCO 2 decreased from 584 to 490 ppmv between September and December, although pCO 2 values were slightly higher in Novem- 25 ber, especially in the bottom water (601 ppmv on average). Note that, in contrast to January, the average water-column pCO 2 in December was much higher than pCO 2, atm (398 ppmv). Similar to pH T , pCO 2 variation over the year was higher in the bottom water (1099 ppmv) than in the surface water (375 ppmv). We investigated the correlation between the different carbonate system parameters and O 2 by calculating coefficients of determination and testing their significance using the package Stats in R. In line with our visual observations, we found a strong correlation between pH T and pCO 2 (r 2 = 0.89, P < 0.001) and weak to moderate correlations between pH T and O 2 (r 2 = 0.68, P < 0.001), pCO 2 and O 2 (r 2 = 0.70, P < 0.001), and 5 DIC and TA (r 2 = 0.56, P < 0.001). DIC does not appear to be correlated with pH T (r 2 = 0.18, P < 0.001), pCO 2 (r 2 = 0.17, P < 0.001) or O 2 (r 2 = 0.21, P < 0.001). Finally, TA could not statistically significant be correlated to pH T (r 2 = 0.01, P = 0.278), pCO 2 (r 2 = 0.01, P = 0.384) or O 2 (r 2 = 0.04, P = 0.066).

Acid-base buffering capacity 10
The acid-base buffering capacity generally showed a similar spatio-temporal pattern as pH T and the inverse of the pCO 2 pattern (Fig. 4e). In January, β had an average value of 22 967 and hardly varied with depth. From February to April, the buffering capacity increased throughout the water column, with a faster increase in the surface compared to the bottom water and a maximum of 82 557 in the surface water in April. In May   15 and June, the acid-base buffering capacity showed an overall decline. In contrast to pH T , the onset of stratification did not lead to a direct amplification of the difference between surface and bottom water β. July was characterised by a sharp increase in surface-water β, coinciding with the decrease in DIC, and a decrease in bottom-water β, a trend that was further intensified in August. During this period of strongest hy-20 poxia, surface-water β (71 454) was an order of magnitude higher than bottom-water β (6802). Between September and December, i.e., after bottom-water ventilation, the buffering capacity did not show any substantial variations with depth. Over the course of the year, surface-water β varied a factor 2 more than bottom-water β.
To further elucidate what controls the acid-base buffering capacity, we calculated Introduction

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Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | the relative contribution of the borate system to the total buffering capacity was higher than in the anoxic, poorly-buffered bottom water (24 and 17 %, respectively), while the reverse holds for the carbonate system (73 vs. 81 %). Acid-base systems other than the carbonate and borate system contributed most to the buffering capacity in the anoxic bottom water, due to the accumulation of NH showed it consisted mainly of the dinoflagellate Prorocentrum micans. Measured volumetric rates of GPP ranged from 0.0-150.7 mmol C m −3 d −1 (Fig. 5a), while volumetric CR ranged from 0.0-31.5 mmol C m −3 d −1 (Fig. 5b) confirmed by the rate measurements. Hence, it presumably represented sinking algal biomass that was being degraded. The fact that the chl a peak at ca. 10 m depth in May was not preceded by a surface water chl a peak of equal magnitude could mean that part of the algal biomass may not have formed in situ, but was imported with North Sea water. As an alternative explanation, there was a relatively long period between supporting the idea of a bloom between sampling dates.

10
To assess the metabolic balance in the surface water, we averaged the volumetric GPP and CR in the photic zone. This analysis reveals that in summer, from June to September, volumetric GPP was higher than CR above the light penetration depth. Before and after this period, average photic zone CR was higher than GPP. This is another indication that a significant part of the organic carbon respired within the sur-15 face water layer was not produced in situ, emphasising the potential importance of lateral input of detrital matter at the field site. Yearly integrated GPP averaged over the photic zone was estimated to be 2494 mmol C m −3 yr −1 , which amounts to an average of 6.8 mmol C m −3 d −1 . Annual depth-weighted photic zone CR was slightly higher than GPP, i.e., 2852 mmol C m −3 yr −1 or 7.8 mmol C m −3 d −1 . Depth-weighted volumetric CR 20 below the photic zone, which annual rate was approximated at 2232 mmol C m −3 yr −1 or 6.1 mmol C m −3 d −1 , was lower than average photic zone CR except for February and December.

CO 2 air-sea exchange
For most of 2012, the surface water (1 m) of the Den Osse Basin was undersaturated 25 with respect to pCO 2, atm , which led to CO 2 uptake from the atmosphere (Fig. 6a). In January, surface-water pCO 2 was very close to pCO 2, atm , resulting in a very small influx. From February to April, surface-water pCO 2 steadily declined to a value of 15852 Introduction 199 ppmv in April. This brought about an increasingly larger gradient and a CO 2 uptake that was highest in April (21.4 mmol C m −2 d −1 ). Surface-water pCO 2 increased in late spring until a value of 350 ppmv in June, after which it declined to 202 ppmv in August. Water-column ventilation from September onward brought CO 2 -rich bottom water to the surface, leading to a surface-water pCO 2 value exceeding that of the atmosphere 5 and inducing strong outgassing of CO 2 towards the atmosphere. Outgassing continued until the end of 2012, albeit with a smaller magnitude due to a decrease in surface water pCO 2 to 411 ppmv in December.
Although the direction of the CO 2 air-sea flux is solely determined by the saturation state of surface water with respect to pCO 2, atm , its magnitude is also influenced by 10 the gas transfer velocity k, which is parameterised as a function of wind speed. Dailyaveraged wind speed over 2012 varied between 1.5 and 14.5 m s −1 , with an average of 4.6 m s −1 . With the exception of January, February, April and December, our samples were taken on days with wind speeds below average (see Supplement). We interpolated the CO 2 sea-air flux as described in Sect. 2.6 (red dotted line in Fig. 6a). When

Sediment fluxes
In all months, sediment DIC fluxes were highest at S1 (Fig. 6b). Since S1 is lo-20 cated at the deepest point of the Den Osse Basin, it receives the highest input of organic matter through both sinking and lateral transport. S2 and S3 showed similar DIC fluxes throughout the year, with the exception of November, when the flux at S2 (18.6 ± 2.9 mmol m −2 d −1 ) exceeded that of S3 (10.7 ± 2.3 mmol m −2 d −1 ). In August, DIC fluxes at S2 and S3 were substantially higher than in the other months. During this month, the amount of organic matter sinking through the water column may have been high as a result of a peak in primary production in the preceding month.
BGD 11,2014 Acidification in a seasonally hypoxic coastal basin The sediment TA fluxes generally showed much more site-specific variability, making it difficult to identify any spatial or temporal patterns. TA fluxes in March show a clear spatial variability, with the highest flux at S1 (45.0 ± 19.0 mmol m −2 d −1 ), followed by S2 (13.1 ± 2.7 mmol m −2 d −1 ) and S3 (4.9 ± 1.3 mmol m −2 d −1 ). May and August did not display any difference between stations or months, with fluxes varying from 10.4 ± 12.7 5 to 25.3±19.3 mmol m −2 d −1 . In November, TA fluxes at S2 (0.3±5.2 mmol m −2 d −1 ) and S3 (1.2 ± 3.9 mmol m −2 d −1 ) were similar and very small, while S1 showed an uptake rather than release of TA (−10.1±9.9 mmol m −2 d −1 ), likely because of re-oxidation processes that consume TA. Beggiatoa spp. were abundant in these sediments in November (Seitaj et al., 2014a) and their activity may generate a decrease in surface-sediment 10 TA (Sayama et al., 2005). For most of the year, the ratio of sediment DIC to TA flux was higher than 1, meaning that more DIC than TA was released from the sediments. Only in March at S1 and S2, the efflux of TA was higher than that of DIC. Because of the sedimentary uptake of TA at S1 in November, the corresponding DIC : TA was negative.

Community metabolism
In 2012, Lake Grevelingen experienced a major phytoplankton bloom in summer (July), a minor bloom in early spring (March), both with completely different dynamics, and a potential third bloom in late spring (April). The minor March bloom is reflected in 20 a slightly elevated surface water [chl a] and pH T , no obvious peak in GPP, but a small peak in CR. The major peak in CR in May, accompanied by a chl a peak at 10 m depth, could result from the early spring bloom, as we might not have captured its full extent, or the potential late spring bloom (see Sect. 3.3.1). However, it most likely represents laterally transported degrading Phaeocystis globosa, the haptophyte that makes up Highest P. globosa cell counts have been found between mid-April and mid-May, corresponding to the timing of the CR peak, at the mouth of the Eastern Scheldt (51.602 • N, 3.721 • E) between 1990 and 2010 (Wetsteyn, 2011), and off the Belgian coast between 1989and 1999(Lancelot et al., 2007. Moreover, the years with high P. globosa cell counts at the mouth of the Eastern Scheldt coincided with a large area of low-oxygen 5 water in the entire Lake Grevelingen (Peperzak and Poelman, 2008;Wetsteyn, 2011), highlighting the connection between P. globosa blooms and O 2 consumption in the lake. The high CR in May combined with the onset of stratification led to a rapid decline in bottom water [O 2 ]. The major dinoflagellate bloom in July was short but very intense in terms of GPP and [chl a] and appeared to contribute to the sharp increase 10 in hypoxic water volume between June and August. Sinking P. micans from this bloom was degraded, which is reflected in higher CR in July and August compared to June, and the products of this degradation were trapped in the water below the pycnocline, as is indicated by elevated DIC levels. However, the higher CR in July and August and subsequent decline in [O 2 ] may also result from higher water temperatures (Fig. 3a), 15 resulting in faster degradation of allochtonous organic matter. The drawdown of bottomwater O 2 is, however, not due to CR alone. The fact that [O 2 ] declines with depth at all months indicates that sediment oxygen uptake may be an important process affecting water-column [O 2 ]. Indeed, substantial sediment O 2 uptake was found to take place year-round with rates up to 61 mmol m −2 d −1 at S1 (Seitaj et al., 2014b). 20 Our depth-weighted, annually averaged CR of 7.8 mmol C m −3 d −1 in the photic zone and 6.1 mmol C m −3 d −1 below the LPD are similar to estimates from the nearby located Western Scheldt, where volumetric annually averaged CR ranged from 4.7-19.1 mmol C m −3 d −1 , with a volume-weighted mean value of 6.6 mmol C m −3 d −1 (Gazeau et al., 2005b). In the mesohaline part of the sea-25 sonally hypoxic Chesapeake Bay, summertime surface-water CR was found to vary between 9.8-53.0 mmol C m Recent modelling studies and previous measurement campaigns have presented lake-wide estimates of GPP ranging from 100 g C m −2 yr −1 (Nienhuis and Huis in't Veld, 1984) to 572 g C m −2 yr −1 (Meijers and Groot, 2007). When integrating annual volumet-5 ric GPP over the depth of the photic zone, we arrive at an estimate of GPP for the Den Osse Basin of 176 g C m −2 yr −1 in 2012. Given the different methods used and time periods considered, our estimate of GPP is consistent with these previous studies. In comparison with other coastal systems in the Netherlands, GPP in the Den Osse Basin is somewhat lower than that in the adjacent Eastern Scheldt (

Proton cycling due to GPP and CR
The fluctuations in pH T as shown in Fig. 4a result from the balance between rates and 15 stoichiometry of proton-producing and consuming processes, mediated by the acidbase buffering capacity of the water. Taking into account that variations in the stoichiometric coefficient for the proton are relatively minor (Table 1) compared to changes in process rates (Figs. 5 and 6) and acid-base buffering capacity (Fig. 4e), we will focus our discussion mainly on the latter two.

20
Any biogeochemical process will either consume or produce protons based on its stoichiometry, as the reaction always proceeds in the forward direction. The signs of ν x H + in Table 1  [H + ] depends on the ratio of TA to DIC flux entering the water mass. When the flux of TA exceeds that of DIC, protons are consumed. On the contrary, when DIC fluxes are higher than TA fluxes, the net effect is an increase in [H + ]. Considering the magnitude of the seasonal variability in the various process rates measured at Den Osse, they must significantly impact H + dynamics. 5 Aside from this, the spatio-temporal variations in buffering capacity (Fig. 4e) also exert a major control on the proton cycling in this basin. Taking the month of August as an example, β decreases by one order of magnitude when going from surface to bottom water. When the rate of a certain process does not change with depth, then the number of protons produced or consumed by this process per kg of water is one order 10 of magnitude higher in the bottom water than in the surface water (see Eq. 7). This indicates that, in August, the bottom water is much more prone to changes in pH than the surface water. The fact that the contribution of acid-base systems other than the carbonate and borate system to β is highest in the anoxic bottom water is in line with previous work (e.g., Ben-Yaakov, 1973;Soetaert et al., 2007). However, their small 15 contribution in the Den Osse Basin contrasts with results from the Eastern Gotland basin in the Baltic Sea. Here, generation of TA during remineralisation under anoxic conditions by denitrification, sulphate reduction and the release of NH + 4 and PO 3− 4 , and the resultant increase in buffering capacity, was found to contribute significantly to the observed changes in pH (Edman and Omstedt, 2013). 20 To understand how variations in both process rates and acid-base buffering capacity control proton cycling in the Den Osse Basin, we used Eq. (7) to calculate the change in [H + ] (µmol kg −1 d −1 ) due to GPP at 1 m depth and CR at 1 and 25 m depth. This analysis reveals that it is the interplay between GPP (Fig. 7d) and β (Fig. 7b) (Fig. 7c), which had a higher magnitude in August (−1.31) in comparison with September (−0.92), due to a switch from NO − 3 to NH + 4 uptake (Sect. 2.4). Thus, the relatively high proton consumption in September was driven by the lower surface-water buffering capacity, which is a factor 3.7 smaller in September compared to August (71 454  in the surface layer (Fig. 7e), we see that when GPP was higher than CR, the decrease in [H + ] due to GPP was also higher than the increase in [H + ] due to CR. This can simply be explained by the fact that β was the same for both processes (Fig. 7b), and the effect of ν GPP H + was only minor (Fig. 7c) at 1 and 25 m depth can be identified (Fig. 7e). With the exception of February, October and December, volumetric CR was higher at 1 m depth than at 25 m depth (Fig. 7d). Thus, the higher d[H + ] CR dt in June and August at 25 m compared to 1 m depth was solely driven by the lower acidbase buffering capacity of the bottom water (Fig. 7b). In July, on the contrary, CR at 15 1 m depth was so much higher than at 25 m depth (30.8 vs. 2.9 µmol kg −1 d −1 ), that this compensated for the lower buffering capacity at depth (65 373 vs. 10 025) and led to a higher surface-water d[H + ] CR dt . Again, this highlights that the magnitudes of both CR and β play a role in determining the actual change in pH.
In summary, in the Den Osse surface water we observe relatively small pH fluctu-20 ations (Fig. 7a), despite high variability in the balance between GPP and CR. In the bottom water, CR is much more constant, yet pH variability is much higher. Assuming these processes are the main biogeochemical processes producing or consuming H + on a seasonal scale, this shows that seasonal changes in the acid-base buffering capacity play a major role in pH dynamics. Thus, our calculations clearly demonstrate that 25 we cannot use only measured process rates to estimate the effect of a certain process on pH. Rather, it is the combined effect of variability in process rates and buffering x H + , that determines the change in pH induced by a certain process.

Proton budget for the Den Osse Basin
To further elucidate the driving mechanisms of pH fluctuations, we calculated full proton budgets for each of the four seasons in 2012. Figure 8 shows these budgets for 1 5 and 25 m depth; the budgets for the other depths can be found in the Supplement. This calculation illustrates that of all the measured processes, GPP and CR generally had the highest contribution to proton cycling intensity in 2012. CR always dominated the total proton production between 4.5-17.5 m and was usually a major contributing process above and below this interval. In the surface water GPP accounted for 38.1-100 % 10 of H + consumption, but also deeper in the photic zone GPP still accounted for a significant part of the proton removal (2.8-34.1 % between 4.5-8 m depth). CO 2 air-sea exchange usually played a minor role in the surface-water proton cycling, apart from November when outgassing of CO 2 was high, and ∼ 62 % of the total proton consumption in the surface water was due to this process. In March, CO 2 air-sea exchange 15 contributed 15.4 % to the budget, while in May and August, its influence was less than 6 %. The effect of vertical mixing was even less pronounced, as it only accounted for 0.05-14.4 % of the proton cycling intensity throughout the water column.
With the exception of March, the net result of the TA and DIC fluxes from the sediment was the dominant contributor to the total H + production in the bottom layer 20 (72.6-99.4 %). Higher up in the basin, its contribution ranged from 2.9-49.2 %. In March, the net result of the sediment flux at S1 was a contribution of 27.1 % to the total proton consumption, while at S2 and S3, its effect on the budget was less than 10 %. dt , at the depths where these processes took place. Thus, as was the case for another coastal system (Hofmann et al., 2009) . As a result, the budget closure term dominated the proton cycling intensity, with the exception of the surface water in March and November. Its 5 contribution ranged from 29.1-100 % of the total H + production or consumption, the latter depending on the sign of the budget closure term. Since was mostly positive, the processes making up the closure term generally had to decrease [H + ], i.e., remove protons from the basin. Taking into account both its order of magnitude and direction of change, we calculated that lateral transport may have accounted for the 10 budget closure term. The average inflow in Lake Grevelingen through the seaward sluice in 2012 was 221 m 3 s −1 and took place for 9.9 h d −1 (calculated based on sluice water levels measured at 10 min intervals, P. Lievense, personal communication, 2013). Meijers and Groot (2007) showed that 30.2 % of the water entering Lake Grevelingen through the sluice remains in the lake for a longer period of time and is not directly 15 transported back during the consecutive period of outflow. This means that, per day, 24 × 10 5 m 3 of North Sea water enters Lake Grevelingen. Assuming that all of this water eventually reaches the Den Osse Basin and taking into account the total volume of this basin (655 × 10 5 m 3 ), this means that the inflow of the seaward sluice can fully replenish the Den Osse Basin in 30 days. The average density of the water in the basin 20 in 2012 was 1023.7 kg m −3 . If we assume that the pH of the inflowing water was 8.2, or [H + ] was 6.31 × 10 −3 µmol kg −1 , then the proton flux entering the Den Osse Basin was 1.55×10 7 µmol d −1 . Dividing this by the total volume of the Den Osse Basin, which may be a valid assumption if stratification is absent, and correcting for density, led to a proton flux of 2.11 × 10 −4 µmol kg −1 d −1 into the entire basin. This is in the same or- 25 der of magnitude as the closure term, which, e.g., for the surface water in May was −1.68 × 10 −4 µmol kg −1 d −1 . Note, however, that the net proton flux will be smaller as protons are also leaving the basin with outflowing water. Additionally, both from the . This is in line 5 with the negative sign of the budget closure term for most months.
Over the course of the year, proton turnover time (τ H + ) varied substantially. In March (42.2 days) and November (36.1 days), the linearly interpolated and depth-averaged τ H + in the basin was much higher than in May (18.5 days) and August (14.6 days). For each month, different driving factors explain these patterns. The proton turnover 10 time is linearly correlated with both ambient [H + ] and β, and inversely correlated to the process rates. The high average value of τ H + in March is mostly explained by a high buffering capacity in combination with low biogeochemical activity. The decrease in May resulted from a significant increase in biogeochemical and physical process rates, since both the average [H + ] and β were higher compared to March. In August, on the 15 contrary, average β decreased a factor 2.6 while average [H + ] increased a factor 2.7, thereby almost cancelling out each other's effect on τ H + . The higher turnover time in November, finally, was mostly driven by low process rates in combination with a relatively high average [H + ]. To summarise, the proton turnover time in the Den Osse Basin is a complex combination of variability in process rates and buffering capacity, but also 20 depends on the ambient pH. The calculation of τ H + takes into account the buffering effect by immediate acidbase reactions that attenuate the net change in [H + ]. When the proton turnover time is divided by β, one calculates the gross proton turnover time, i.e., the turnover time before buffering (Hofmann et al., 2010a) buffering reactions in active natural systems are extremely important in modulating the net change in [H + ], and again highlights that pH dynamics in these settings cannot be studied by measuring process rates alone.

Conclusions
The Den Osse Basin experiences temperature-induced seasonal stratification that, 5 combined with high oxygen consumption, results in the development of hypoxic bottom water with higher DIC and pCO 2 and lower pH T . The strong correlation between pH T and pCO 2 in 2012 and their moderate correlations with O 2 suggest a link between GPP, CR and pH T , which was further investigated in a detailed proton study. Volumetric GPP showed a major peak in July, while CR was highest in late spring. Although 10 atmospheric CO 2 was taken up for most of the year, the relatively strong outgassing after the termination of stratification resulted in the Den Osse Basin being only a weak sink for CO 2 . Sediment DIC fluxes were highest at the deepest point of the basin and were generally higher than TA fluxes. The calculated proton budgets clearly show that it is the combination between 15 changes in process rates and changes in buffering capacity that determines the net proton change of the system. Which of these two dominates, depends on the season, depth and the process considered. However, it appears that variations in the process rates control the general pattern of proton cycling, while the buffering capacity dampens its signal with varying intensity. In 2012, this became especially apparent during 20 the period of summer hypoxia, when the decrease in buffering capacity with depth led to a much shorter proton turnover time at depth compared with the surface. Of the process rates considered, the balance between primary production and respiration had the biggest impact on proton cycling. on the proton balance was mostly negligible, horizontal exchange appeared to exert a major control on the proton budget of the basin. This work highlights that process rates, buffering capacity and ambient pH are all essential compartments when determining the vulnerability of a system to changes in pH. By calculating one of the first proton budgets originating from measurements, this 5 study shows the certainties and uncertainties therein.
Appendix A: Overdetermination of carbonate system A1 Contribution of particles and organic alkalinity to TA In oceanic research, samples for the determination of TA are typically not filtered before measurements (e.g., Dickson et al., 2007). In an open ocean setting concentrations of suspended matter are usually low and its effect on TA may therefore be neglected. However, in highly productive regions, such as coastal regions, high concentrations of particulate organic matter and calcium carbonate (CaCO 3 ) particles are often found. In an incubation experiment, Kim et al. (2006) showed that the titration of surface sites on phytoplankton and bacterial cells can add significantly to the measured TA. By filtering 15 seawater upon collection, particulates are removed and the contribution of particulate organic matter (POM) and CaCO 3 particles to TA can be ignored.
We assessed the contribution of suspended particulate matter (SPM) to TA by calculating the difference between TA measured on unfiltered and filtered (0.45 µm) seawater. This difference ∆TA, which could indicate the contribution of SPM to TA, is not 20 significantly different from zero (t = 0.1281, df = 190, P = 0.898; two-sided Student's t test calculated using the package Stats in R), neither does it not show a clear pattern with TA ( Fig. A1; blue triangles), pH T or SPM (results not shown). Additionally, the outliers in this data set could not unequivocally be correlated to events such as the phytoplankton bloom in July or high CR in May. Introduction Additionally, dissolved organic matter (DOM) may contribute to TA, as DOM is comprised of several compounds that have acid-base groups attached to them. The bulk of terrestrial-derived DOM consists of humic and fulvic acids and their contribution to estuarine TA and acid-base properties were described by Cai et al. (1998). In general, the contribution of DOM-associated acid-base groups to TA can be assessed using 5 a chemical model set up by Oliver et al. (1983), which was calibrated for natural waters by Driscoll et al. (1989). However, the calibration performed by these authors was done on freshwater lakes with maximum pH < 7.5. Thus, their parameterisation might not be directly applicable to saline waters including Lake Grevelingen, where the majority of DOM is derived from phytoplankton. Both in incubation experiments (Hernández-Ayon et al., 2007;Kim and Lee, 2009;Koeve and Oschlies, 2012) and in biologically active natural waters (Hernández-Ayon et al., 2007;Muller and Bleie, 2008) it has been shown that DOM resulting from phytoplankton may contribute significantly to seawater TA. The contribution of DOM to TA relies on two factors: the density of acid-base functional groups within the organic matter compounds and their associated pK a values.
Both of these factors depend on DOM quality and source material, and neither of them is well known for marine DOM. To highlight this complexity, the increase in TA per unit increase of DOM in phytoplankton culture experiments appears to be species-specific (Kim and Lee, 2009).
In theory, one would expect that TA calculated from DIC (and total concentrations of 20 borate, ammonia, phosphate and other inorganic species contributing to TA) represents the inorganic, aqueous fraction of TA. When TA is measured directly using a filtered seawater sample, one implicitly includes TA derived from dissolved organic acids and bases. We evaluated the contribution of DOM to the total alkalinity by: (1) comparing TA calculated from pH and DIC with TA determined from filtered (0.45 µm) seawater; and 25 (2) applying the formulation of Driscoll et al. (1989)  Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | df = 187, P = 0.965). However, Fig. A1 shows that, in general, the difference between TA measured on filtered samples and TA calculated from DIC and pH (red squares), is positive in the lower range of TA values. A positive difference might indicate that DOM-associated acid-base groups increase TA. On the contrary, a negative difference was found in the higher range of TA values, indicating that DOM-associated groups 5 decrease the acid neutralisation capacity of the water. When these data were plotted as a function of pH T or DOC, no pattern was observed (results not shown). Similar to the difference between TA measured on unfiltered and filtered seawater, we found no correlation between the outliers in this data set and biogeochemical process rates. The contribution of organic alkalinity to TA as calculated with the model calibrated by Driscoll et al. (1989) did not show any systematic variability and ranged between 16 and 32 µmol kg −1 , with DOC ranging between 119 and 237 µmol kg −1 (see Supplement). Its pattern did not resemble the difference between TA measured from filtered samples and calculated TA, indicating that the model could not explain the current results. In the range of pH values observed at Den Osse, the operational pK value de-15 rived from the Driscoll et al. (1989) model, which is an average representative of various DOM-associated acid-base groups, ranged between 5.91 and 6.06, indicating that the majority of these groups were present in their basic form. However, this operational pK value is significantly lower than the pK a of organic acids associated with phytoplankton, which was found to be above 7 (Hernández-Ayon et al., 2007), indicating that the 20 fraction of organic acid-base groups present in their basic form may be smaller. This would thus decrease the calculated contribution of DOC to TA. Additionally, the fraction of DOC that is releasing bases during phytoplankton blooms is unknown but may be higher than the 14 % calibrated by Driscoll et al. (1989), which would mean that their model underestimates organic alkalinity in coastal systems. Introduction

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Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | nation. Which two parameters can best be measured to describe the carbonate system is subject of an ongoing debate. Dickson et al. (2007) suggest that, if possible, it is always better to measure a parameter rather than calculate it from other parameters, since there are limitations to the accuracy of the carbonate system prediction when certain combinations of parameters are used. For instance, in a compilation of incubation 5 studies it was found that calculating pCO 2 from DIC and TA tends to underestimate pCO 2 at high levels (i.e., ∼ 1000 ppmv) by up to 30 %, for, as yet, unknown reasons (Hoppe et al., 2012). In 2012, pCO 2 calculated from DIC and pH T ranged between 189-1407 ppmv in the Den Osse Basin. To check whether this natural system also showed internal incon-10 sistencies, and to further highlight the complexity of an overdetermined system, we compared pCO 2 values calculated with different combinations of TA, DIC and pH T with measured pCO 2 values (Fig. A2). For each combination of parameters, we assessed their agreement with measured pCO 2 by calculating the sum of squared differences. This calculation showed that using pH T and DIC provides the best agreement between 15 measured and calculated pCO 2 . The highest sum of squares was found when using DIC with either filtered or unfiltered TA, which is another indication for the uncertainties introduced when using this combination of carbonate system parameters in non-open ocean settings. Another feature in Fig. A2 is that calculated pCO 2 values are generally lower than measured values, as indicated by a positive ∆pCO 2 . Only 20 in the higher range of measured pCO 2 (> ca. 1000 ppmv) and when TA is used as a starting parameter, is the calculated pCO 2 mostly higher than the measured pCO 2 . A closer look at these data reveals that all samples below the pycnocline in August show higher calculated than measured pCO 2 when DIC and either of the TA measurements are used as the parameter combination. These differences range between 3 and 25 299 ppmv (0-21.4 %) and are generally higher when unfiltered TA samples are used. Furthermore, the two points where TA calculated from pH and DIC is highest (2593 and 2629 µmol kg −1 ; Fig. A1), which are the samples taken at 25 and 32 m depth in July, also show a higher calculated than measured pCO 2 when DIC and unfiltered TA are Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | used as parameter combination (differences of 185 and 169 ppmv or 20.6 and 17.6 %, respectively).

A3 Concluding remarks
To summarise, these results suggest that, especially in hypoxic natural waters, TA may not unequivocally be chosen as one of the two parameters necessary to quantify the 5 carbonate system. Additionally, the Den Osse data set cannot be used to draw any clear conclusions on the effect of DOM and SPM on TA. This conclusion supports our choice of using pH T and DIC for the carbonate system calculations.