<table><caption>Table 3 Integrated chlorophyll a concentrations, planktonic gross primary production (GPP), community respiration (CR) and net community production (NCP) estimated on two surveys during each of the EUBAL cruises.</caption><thead><tr><th rowspan="2">Station</th><th rowspan="2">Date</th><th rowspan="2">Integrated chlorophyll a</th><th>GPP</th><th>CR</th><th>NCP</th></tr><tr><th>mmol O<sub>2</sub> m<sup>-2</sup> d<sup>-1</sup></th><th></th><th></th></tr></thead><tbody><tr><td>Posidonia Bahia</td><td>07/03/2002</td><td>16</td><td>44</td><td>-33</td><td>11</td></tr><tr><td>Cap Enderrocat</td><td>“</td><td>8</td><td>93</td><td>-45</td><td>48</td></tr><tr><td>4</td><td>“</td><td>3</td><td>20</td><td>-27</td><td>-7</td></tr><tr><td>8</td><td>“</td><td>15</td><td>84</td><td>-35</td><td>49</td></tr><tr><td>18</td><td>“</td><td>15</td><td>90</td><td>-89</td><td>1</td></tr><tr><td>19</td><td>“</td><td>14</td><td>78</td><td>-18</td><td>60</td></tr><tr><td>20</td><td>“</td><td>8</td><td>45</td><td>-16</td><td>29</td></tr><tr><td>22</td><td>“</td><td>15</td><td>86</td><td>-67</td><td>19</td></tr><tr><td>23</td><td>“</td><td>15</td><td>89</td><td>-49</td><td>40</td></tr><tr><td>26</td><td>“</td><td>16</td><td>94</td><td>-74</td><td>20</td></tr><tr><td>Mean (± SD)</td><td>07/03/2002</td><td>13 ± 4</td><td>74 ± 25</td><td>-47 ± 24</td><td>27 ± 29</td></tr><tr><td>Posidonia Bahia</td><td>11/03/2002</td><td>7</td><td>42</td><td>-33</td><td>9</td></tr><tr><td>Cap Enderrocat</td><td>“</td><td>30</td><td>175</td><td>-45</td><td>130</td></tr><tr><td>4</td><td>“</td><td>9</td><td>53</td><td>-27</td><td>26</td></tr><tr><td>8</td><td>“</td><td>35</td><td>204</td><td>-35</td><td>169</td></tr><tr><td>18</td><td>“</td><td>19</td><td>109</td><td>-89</td><td>20</td></tr><tr><td>19</td><td>“</td><td>12</td><td>72</td><td>-18</td><td>54</td></tr><tr><td>20</td><td>“</td><td>15</td><td>88</td><td>-16</td><td>72</td></tr><tr><td>22</td><td>“</td><td>24</td><td>140</td><td>-67</td><td>73</td></tr><tr><td>23</td><td>“</td><td>22</td><td>128</td><td>-49</td><td>79</td></tr><tr><td>26</td><td>“</td><td>16</td><td>92</td><td>-61</td><td>31</td></tr><tr><td>Mean (± SD)</td><td>11/03/2002</td><td>19 ± 9</td><td>112 ± 50</td><td>-47 ± 24</td><td>65 ± 48</td></tr><tr><td>Posidonia Bahia</td><td>21/06/2002</td><td>2.1</td><td>41</td><td>-45</td><td>-4</td></tr><tr><td>Cap Enderrocat</td><td>“</td><td>4.2</td><td>81</td><td>-75</td><td>6</td></tr><tr><td>4</td><td>“</td><td>0.5</td><td>9</td><td>-59</td><td>-50</td></tr><tr><td>8</td><td>“</td><td>0.8</td><td>16</td><td>-107</td><td>-91</td></tr><tr><td>18</td><td>“</td><td>0.5</td><td>12</td><td>-23</td><td>-11</td></tr><tr><td>19</td><td>“</td><td>4.1</td><td>80</td><td>-41</td><td>39</td></tr><tr><td>20</td><td>“</td><td>0.9</td><td>18</td><td>-37</td><td>-19</td></tr><tr><td>22</td><td>“</td><td>1</td><td>20</td><td>-28</td><td>-8</td></tr><tr><td>23</td><td>“</td><td>0.1</td><td>1</td><td>-16</td><td>-15</td></tr><tr><td>26</td><td>“</td><td>0.1</td><td>3</td><td>-17</td><td>-14</td></tr><tr><td>Mean (± SD)</td><td>21/06/2002</td><td>1.3 ± 1.5</td><td>26 ± 29</td><td>-43 ± 28</td><td>-17 ± 33</td></tr><tr><td>Posidonia Bahia</td><td>26/06/2002</td><td>2.1</td><td>40</td><td>-45</td><td>-5</td></tr><tr><td>Cap Enderrocat</td><td>“</td><td>7.1</td><td>138</td><td>-75</td><td>63</td></tr><tr><td>4</td><td>“</td><td>0.5</td><td>10</td><td>-59</td><td>-49</td></tr><tr><td>8</td><td>“</td><td>0.8</td><td>15</td><td>-107</td><td>-92</td></tr><tr><td>18</td><td>“</td><td>0.3</td><td>6</td><td>-19</td><td>-13</td></tr><tr><td>19</td><td>“</td><td>8.5</td><td>165</td><td>-41</td><td>125</td></tr><tr><td>20</td><td>“</td><td>1.2</td><td>24</td><td>-37</td><td>-14</td></tr><tr><td>22</td><td>“</td><td>0.6</td><td>15</td><td>-25</td><td>-10</td></tr><tr><td>23</td><td>“</td><td>0.7</td><td>13</td><td>-24</td><td>-11</td></tr><tr><td>26</td><td>“</td><td>0.4</td><td>8</td><td>-21</td><td>-13</td></tr><tr><td>Mean (± SD)</td><td>26/06/2002</td><td>2 ± 2.9</td><td>40 ± 56</td><td>-43 ± 28</td><td>-3 ± 56</td></tr><tr><td>EUBAL-I Mean (± SD)</td><td>March 2002</td><td>16 ± 7</td><td>93 ± 43</td><td>-47 ± 24</td><td>46 ± 41</td></tr><tr><td>EUBAL-II Mean (± SD)</td><td>June 2002</td><td>1.7 ± 2.3</td><td>33 ± 44</td><td>-43 ± 27</td><td>-10 ± 45</td></tr></tbody></table>
<table><caption>Table 4 Mean (± SE) benthic net community production (NCP) in March (EUBAL-I) and June 2002 (EUBAL-II).</caption><thead><tr><th>Cruise</th><th>Community</th><th>Depth (m)</th><th>NCP ± SE mmol O<sub>2</sub> m<sup>-2</sup> d<sup>-1</sup></th></tr></thead><tbody><tr><td rowspan="4">EUBAL-I</td><td rowspan="2">P. oceanica</td><td>7</td><td>29.7 ± 10.8</td></tr><tr><td>15</td><td>17.1 ± 3.3</td></tr><tr><td rowspan="2">Sediment</td><td>7</td><td>-0.3 ± 0.2</td></tr><tr><td>15</td><td>-0.4 ± 0.1</td></tr><tr><td rowspan="5">EUBAL-II</td><td rowspan="4">P. oceanica</td><td>4</td><td>44.2 ± 13.6</td></tr><tr><td>7</td><td>12.0 ± 11.0</td></tr><tr><td>15</td><td>24.6 ± 3.9</td></tr><tr><td>22</td><td>19.1 ± 11.1</td></tr><tr><td rowspan="4">Sediment</td><td>4</td><td>5.7 ± 0.5</td></tr><tr><td>7</td><td>-4.0 ± 0.4</td></tr><tr><td>15</td><td>-3.7 ± 0.3</td></tr><tr><td>22</td><td>-6.4 ± 0.4</td></tr></tbody></table>
<table><caption>Table 5 Average ± SD of AOU, pCO₂, pCO₂ normalized to a constant temperature (pCO₂(19°C)), DIC₃₇, wind speed (u), the air-sea gradient of pCO₂ (ΔpCO₂), in the surface waters, and atmospheric CO₂ flux computed using the gas transfer velocity given by Wanninkhof and McGillis (1999) (F), over the *Posidonia* meadow and in the rest of the bay, on the surveys carried out during the EUBAL-I and -II cruises. Data were interpolated using the Kriging procedure on a grid of 0.0034°E x 0.0025°N and averaged.</caption><thead><tr><th></th><th>AOU<br>µmol kg<sup>-1</sup></th><th>pCO<sub>2</sub><br>ppm</th><th>pCO<sub>2</sub>(19°C)<br>ppm</th><th>DIC<sub>37</sub><br>mmol kg<sup>-1</sup></th><th>ΔpCO<sub>2</sub><br>ppm</th><th>u<br>m s<sup>-1</sup></th><th>F<br>mmol C m<sup>-2</sup> d<sup>-1</sup></th></tr></thead><tbody><tr><td colspan="8"><em>Bay (excluding Posidonia meadow)</em></td></tr><tr><td>01/03/2002</td><td>-4.6 ± 2.3</td><td>324 ± 3</td><td>386 ± 4</td><td>2.142 ± 0.002</td><td>-46 ± 16</td><td>3.9 ± 1.6</td><td>-1.0 ± 0.9</td></tr><tr><td>04/03/2002</td><td>-10.5 ± 1.8</td><td>320 ± 2</td><td>384 ± 3</td><td>2.140 ± 0.002</td><td>-64 ± 1</td><td>6.9 ± 1.8</td><td>-5.8 ± 4.5</td></tr><tr><td>07/03/2002</td><td>-2.4 ± 0.6</td><td>320 ± 1</td><td>388 ± 2</td><td>2.144 ± 0.001</td><td>-54 ± 2</td><td>4.5 ± 2.2</td><td>-1.9 ± 2.3</td></tr><tr><td>09/03/2002</td><td>-0.8 ± 0.9</td><td>320 ± 1</td><td>386 ± 3</td><td>2.143 ± 0.001</td><td>-57 ± 3</td><td>2.9 ± 0.9</td><td>-0.4 ± 0.3</td></tr><tr><td>11/03/2002</td><td>-3.2 ± 0.5</td><td>320 ± 1</td><td>381 ± 2</td><td>2.140 ± 0.001</td><td>-55 ± 4</td><td>3.3 ± 0.9</td><td>-0.5 ± 0.4</td></tr><tr><td>12/03/2002</td><td>-4.2 ± 0.9</td><td>321 ± 2</td><td>380 ± 2</td><td>2.140 ± 0.001</td><td>-53 ± 7</td><td>3.3 ± 1.0</td><td>-0.5 ± 0.5</td></tr><tr><td>19/06/2002</td><td>-15.8 ± 3.5</td><td>420 ± 7</td><td>363 ± 7</td><td>2.121 ± 0.004</td><td>50 ± 12</td><td>4.7 ± 2.1</td><td>1.7 ± 1.4</td></tr><tr><td>21/06/2002</td><td>-13.3 ± 3.3</td><td>426 ± 10</td><td>361 ± 7</td><td>2.120 ± 0.004</td><td>52 ± 18</td><td>4.1 ± 2.2</td><td>1.6 ± 2.0</td></tr><tr><td>25/06/2002</td><td>-10.0 ± 2.4</td><td>442 ± 10</td><td>361 ± 5</td><td>2.118 ± 0.003</td><td>82 ± 10</td><td>6.3 ± 1.4</td><td>5.9 ± 3.4</td></tr><tr><td>26/06/2002</td><td>-10.5 ± 4.0</td><td>444 ± 11</td><td>358 ± 8</td><td>2.116 ± 0.005</td><td>77 ± 20</td><td>3.6 ± 1.2</td><td>1.1 ± 1.1</td></tr><tr><td>27/06/2002</td><td>-10.8 ± 1.1</td><td>443 ± 3</td><td>360 ± 1</td><td>2.119 ± 0.001</td><td>83 ± 3</td><td>5.4 ± 1.4</td><td>3.6 ± 2.5</td></tr><tr><td colspan="8"><em>Posidonia meadow</em></td></tr><tr><td>01/03/2002</td><td>-6.1 ± 2.4</td><td>323 ± 2</td><td>384 ± 3</td><td>2.141 ± 0.002</td><td>-56 ± 17</td><td>3.9 ± 1.6</td><td>-1.0 ± 0.9</td></tr><tr><td>04/03/2002</td><td>-14.8 ± 2.7</td><td>316 ± 3</td><td>379 ± 4</td><td>2.138 ± 0.002</td><td>-63 ± 1</td><td>6.9 ± 1.8</td><td>-5.7 ± 4.5</td></tr><tr><td>07/03/2002</td><td>-2.0 ± 1.6</td><td>323 ± 5</td><td>395 ± 6</td><td>2.147 ± 0.003</td><td>-54 ± 4</td><td>4.5 ± 2.2</td><td>-1.9 ± 2.4</td></tr><tr><td>09/03/2002</td><td>0.0 ± 0.6</td><td>323 ± 8</td><td>394 ± 10</td><td>2.147 ± 0.005</td><td>-58 ± 9</td><td>2.9 ± 0.9</td><td>-0.4 ± 0.4</td></tr><tr><td>11/03/2002</td><td>-2.6 ± 0.6</td><td>320 ± 1</td><td>383 ± 2</td><td>2.141 ± 0.001</td><td>-64 ± 8</td><td>3.3 ± 0.9</td><td>-0.6 ± 0.5</td></tr><tr><td>12/03/2002</td><td>-4.2 ± 1.1</td><td>321 ± 3</td><td>379 ± 2</td><td>2.140 ± 0.001</td><td>-59 ± 12</td><td>3.3 ± 1.0</td><td>-0.6 ± 0.5</td></tr></tbody></table>
<table><caption>Table 5 continued</caption><thead><tr><th></th><th>AOU<br>µmol kg<sup>-1</sup></th><th>pCO<sub>2</sub><br>ppm</th><th>pCO<sub>2</sub>(19°C)<br>ppm</th><th>DIC<sub>37</sub><br>mmol kg<sup>-1</sup></th><th>ΔpCO<sub>2</sub><br>ppm</th><th>u<br>m s<sup>-1</sup></th><th>F<br>mmol C m<sup>-2</sup> d<sup>-1</sup></th></tr></thead><tbody><tr><td>19/06/2002</td><td>-24.0 ± 5.8</td><td>405 ± 17</td><td>346 ± 15</td><td>2.110 ± 0.010</td><td>40 ± 21</td><td>4.7 ± 2.1</td><td>1.4 ± 1.1</td></tr><tr><td>21/06/2002</td><td>-18.0 ± 4.7</td><td>420 ± 18</td><td>351 ± 13</td><td>2.114 ± 0.009</td><td>45 ± 31</td><td>4.1 ± 2.2</td><td>1.3 ± 1.7</td></tr><tr><td>25/06/2002</td><td>-14.1 ± 3.9</td><td>433 ± 14</td><td>351 ± 10</td><td>2.111 ± 0.007</td><td>74 ± 15</td><td>6.3 ± 1.4</td><td>5.3 ± 3.1</td></tr><tr><td>26/06/2002</td><td>-12.6 ± 5.8</td><td>445 ± 18</td><td>354 ± 12</td><td>2.114 ± 0.008</td><td>79 ± 31</td><td>3.6 ± 1.2</td><td>1.1 ± 1.1</td></tr><tr><td>27/06/2002</td><td>-12.7 ± 2.2</td><td>449 ± 7</td><td>358 ± 5</td><td>2.117 ± 0.003</td><td>87 ± 11</td><td>5.4 ± 1.4</td><td>3.7 ± 2.6</td></tr></tbody></table>
Table 6 Net ecosystem production (NEP) and the photosynthetic quotient (PQ), over the *Posidonia oceanica* meadow during the EUBAL-II cruise. DIC₃₇ and O₂ data were interpolated as explained in legend of Table 5. Top half of the table corresponds to NEP computed from the observed surface distributions of DIC₃₇ and O₂. Lower half of the table corresponds to NEP computed from the surface distributions of DIC₃₇ and O₂ that were adjusted to account for their vertical gradients (see text for details). The uncertainty on NEP was computed assuming an error on tₜotal of ± 1 d. The uncertainty on PQ corresponds to SE on the slope of the linear regression of NEP₀₂ vs. NEPₔₘ.

<table><thead><tr><th></th><th>NEP<sub>DIC</sub><br>mmol C m<sup>-2</sup> d<sup>-1</sup></th><th>NEP<sub>O2</sub><br>mmol O<sub>2</sub> m<sup>-2</sup> d<sup>-1</sup></th><th>PQ</th></tr></thead><tbody><tr><td colspan="4">Based on observed surface distribution</td></tr><tr><td>19/06/2002</td><td>41 ± 3</td><td>31 ± 3</td><td rowspan="5">0.93 ± 0.18 (r<sup>2</sup> = 0.926)</td></tr><tr><td>21/06/2002</td><td>25 ± 2</td><td>10 ± 1</td></tr><tr><td>25/06/2002</td><td>27 ± 2</td><td>9 ± 1</td></tr><tr><td>26/06/2002</td><td>9 ± 1</td><td>2 ± 1</td></tr><tr><td>27/06/2002</td><td>7 ± 1</td><td>-5 ± 1</td></tr><tr><td colspan="4">Based on adjusted surface distribution to account for vertical gradients</td></tr><tr><td>19/06/2002</td><td>59±4</td><td>77 ± 7</td><td rowspan="5">0.92 ± 0.18 (r<sup>2</sup> = 0.902)</td></tr><tr><td>21/06/2002</td><td>42 ± 4</td><td>56 ± 3</td></tr><tr><td>25/06/2002</td><td>45 ± 2</td><td>54 ± 3</td></tr><tr><td>26/06/2002</td><td>27 ± 2</td><td>48 ± 2</td></tr><tr><td>27/06/2002</td><td>25 ± 3</td><td>40 ± 1</td></tr></tbody></table>
<table><caption>Table 7 Day-time net community production (NCP<sub>d</sub>) from the EUBAL-II cruise based on O<sub>2</sub> incubations in both planktonic and benthic compartments and based on the day-time DIC evolution. NCP<sub>d</sub> is computed from the slope of the linear regression of water column averaged DIC values (the error estimate on NCP<sub>d</sub> is based on SE on the slope).</caption><thead><tr><th rowspan="3"></th><th colspan="3">O<sub>2</sub> incubations</th><th>DIC evolution</th></tr><tr><th>Planktonic NCP<sub>d</sub></th><th>Benthic NCP<sub>d</sub></th><th>NEP<sub>d</sub></th><th>NEP<sub>d</sub></th></tr><tr><th colspan="3">mmol O<sub>2</sub> m<sup>-2</sup> d<sup>-1</sup></th><th>mmol C m<sup>-2</sup> d<sup>-1</sup></th></tr></thead><tbody><tr><td>Posidonia</td><td>2.9</td><td>3.7</td><td>6.6</td><td>15.0 ± 2.1</td></tr><tr><td>Cap Enderrocat</td><td>-0.5</td><td>4.1</td><td>3.6</td><td>9.2 ± 5.0</td></tr></tbody></table>
Figure captions

Figure 1. Map of the Bay of Palma showing the bathymetry (dotted lines), the distribution of the *Posidonia oceanica* meadow (green area, adapted from Rey and Diaz Del Rio 1984), the four reference stations where incubations for metabolic processes were carried out (red stars) and stations where vertical profiles were carried out (blue circles).

Figure 2. Monthly net community production (NCP) in the benthic and planktonic compartment, and the combined net ecosystem production at the Posidonia station (cf. Fig. 1).

Figure 3. Velocity distribution during EUBAL-I at 14 m for A) 01 March 2002, B) 03 March 2002 and C) 12 March 2002; and EUBAL-II at 10 m D) for 25 June 2002.

Figure 4. Salinity distribution during EUBAL-I at 2 m for A) 01 March 2002, B) 03 March 2002; C) 12 March 2002.

Figure 5. Depth profiles of planktonic daily CR and GPP (mmol O₂ m⁻³ d⁻¹) measured at four stations in the Bay of Palma during A) EUBAL-I and B) EUBAL-II cruises (cf. Fig. 1).

Figure 6. A) Linear relationships between integrated gross primary production (GPP) and integrated chlorophyll *a* concentrations (Chl *a*) at four stations (cf. Fig. 1) during EUBAL-I and II cruises. B) Linear relationship between GPP and respiration (CR) integrated in the water column above the *Posidonia oceanica* meadow at four stations during the EUBAL-II cruise.

Figure 7. A) AOU (µmol kg⁻¹) versus pCO₂ normalized to a constant temperature (pCO₂(19°C), ppm) in surface waters from all the surveys during the EUBAL-I and II cruises. The range of variation of AOU and pCO₂(19°C) at the Dyfamed station for the March 1998-99 and June 1998-99 periods (Copin-Montégut and Bégovic 2002; Bégovic and Copin-Montégut 2002) are indicated by the horizontal and vertical lines. B) TA (mmol kg⁻¹) versus salinity in surface waters during the EUBAL-I and II cruises. Dotted line corresponds to the regression function reported by Copin-Montégut (1993) for the Alboran Sea (TA = -1.072 (± 0.016) + 0.09485 (± 0.0004) x salinity) and the solid line corresponds to the regression function based on the EUBAL-I and II cruises (TA = -0.346 (± 0.007) + 0.0756 (± 0.003) x salinity; r² = 0.869; n = 119; p < 0.0001). The error bar on the lower left corner of the plot corresponds to the estimated accuracy on TA measurements (B).
Figure 8. Surface distributions of pCO₂ normalized to a constant temperature (pCO₂(19°C), ppm) and AOU (µmol kg⁻¹) obtained on 3 March 2002 (EUBAL-I cruise) and 19 June 2002 (EUBAL-II cruise).

Figure 9. NEP<sub>DIC</sub> (A; mmol C m<sup>-2</sup> h<sup>-1</sup>) and NEP<sub>O₂</sub> (B; mmol O₂ m<sup>-2</sup> h<sup>-1</sup>) over the *Posidonia oceanica* meadow versus surface irradiance (I₀; µmol m<sup>-2</sup> s<sup>-1</sup>) during the EUBAL-II cruise. NEP<sub>DIC</sub> and NEP<sub>O₂</sub> data were adjusted linearly to 12:00 Universal Time (UT; all surveys ended in mid-afternoon, ranging from 14:30 to 16:45 UT). Surface irradiance was averaged from dawn to 12:00 UT.

Figure 10. Vertical profiles of DIC normalized to a constant salinity (DIC<sub>37</sub> in mmol kg<sup>-1</sup>) and O<sub>2</sub> (µmol kg<sup>-1</sup>) at four reference stations (cf. Fig. 1).

Figure 11. A) Variations of water column averaged DIC normalized to a constant salinity (DIC<sub>37</sub> in mmol kg<sup>-1</sup>) during 24 h at Posidonia (20-21 June), Bahia (22-23 June) and Cap Enderrocat (24-25 June; cf. Fig. 1). B) Variations during the 24 h cycles of the difference of DIC (ΔDIC in µmol kg<sup>-1</sup>) between the average value from the top half of the water column and the average value from the bottom half of the water column. DIC was computed from the pCO<sub>2</sub> measurements (vertical resolution of 1 m), TA values computed from the linear regression against salinity (legend of Fig. 7) and salinity and temperature profiles from CTD casts contemporary to the pCO<sub>2</sub> profiles.
<figure><img src="image_17.png" /><figcaption>Fig. 1</figcaption></figure>
<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>
<figure><img src="image_20.png" /><figcaption>Fig. 3</figcaption></figure>
<figure><img src="image_21.png" /><figcaption>Fig. 4</figcaption></figure>
<figure><img src="image_22.png" /><figcaption>Fig. 5</figcaption></figure>
<figure><img src="image_23.png" /></figure>

<figure><img src="image_24.png" /><figcaption>Fig. 6</figcaption></figure>
<figure><img src="image_25.png" /></figure>

<figure><img src="image_26.png" /><figcaption>Fig. 7</figcaption></figure>
<figure><img src="image_27.png" /><figcaption>Fig. 8</figcaption></figure>
<figure><img src="image_28.png" /><figcaption>Fig. 9</figcaption></figure>
<figure><img src="image_29.png" /><figcaption>Fig. 10</figcaption></figure>
<figure><img src="image_30.png" /><figcaption>Fig. 11</figcaption></figure>
3.2 Coefficient d'échange
dans les estuaires

Borges A. V., Delille B., Schiettecatte L. -S., Gazeau F., Abril G. & Frankignoulle M.
Gas transfer velocities of CO₂ in three European estuaries (Randers Fjord, Scheldt and
Thames) (2004)

Limnology and Oceanography 49(5).
# Gas transfer velocities of CO₂ in three European estuaries (Randers Fjord, Scheldt, and Thames)

Alberto Vieira Borges,<fnref n="1" /> Bruno Delille, and Laure-Sophie Schiettecatte

Université de Liège, MARE, Unité d'Océanographie Chimique, Institut de Physique (B5), B-4000 Liège, Belgium

Frédéric Gazeau

Université de Liège, MARE, Unité d'Océanographie Chimique, Institut de Physique (B5), B-4000 Liège, Belgium;
Laboratoire Océanographique de Villefranche, ESA 7076 CNRS-UPMC, BP 28, F-06234 Villefranche-sur-mer, France

Gwenaël Abril

Université de Bordeaux 1, Département de Géologie et Océanographie, CNRS-UMR EPOC 5805, Avenue des Facultés, F-33405 Talence, France

Michel Frankignoulle

Université de Liège, MARE, Unité d'Océanographie Chimique, Institut de Physique (B5), B-4000 Liège, Belgium

## Abstract

We measured the flux of CO₂ across the air-water interface using the floating chamber method in three European estuaries with contrasting physical characteristics (Randers Fjord, Scheldt, and Thames). We computed the gas transfer velocity of CO₂ (k) from the CO₂ flux and concomitant measurements of the air-water gradient of the partial pressure of CO₂ (pCO₂). There was a significant linear relationship between k and wind speed for each of the three estuaries. The differences of the y-intercept and the slope between the three sites are related to differences in the contribution of tidal currents to water turbulence at the interface and fetch limitation. The contribution to k from turbulence generated by tidal currents is negligible in microtidal estuaries such as Randers Fjord but is substantial, at low to moderate wind speeds, in macrotidal estuaries such as the Scheldt and the Thames. Our results clearly show that in estuaries a simple parameterization of k as a function of wind speed is site specific and strongly suggest that the y-intercept of the linear relationship is mostly influenced by the contribution of tidal currents, whereas the slope is influenced by fetch limitation. This implies that substantial errors in flux computations are incurred if generic relationships of the gas transfer velocity as a function of wind speed are employed in estuarine environments for the purpose of biogas air-water flux budgets and ecosystem metabolic studies.

Based on organic carbon flux budgets, the overall picture of the net ecosystem metabolism in the coastal ocean is that temperate open continental shelves (bordered by a continental margin) are net autotrophic (net exporters of carbon and thus potential sinks for atmospheric CO₂) while near-shore systems influenced by anthropogenic and/or terrestrial organic carbon inputs, in particular temperate estuaries, are net heterotrophic (e.g., Smith and Mackenzie 1987; Gattuso et

al. 1998). This picture has recently been confirmed by direct measurements of the air-water gradient of pCO₂ with a sufficient temporal and spatial resolution to allow the annual integration of the computed air-water CO₂ fluxes. Temperate open continental shelves are net sinks for atmospheric CO₂ (e.g., Tsunogai et al. 1999; Frankignoulle and Borges 2001a; Borges and Frankignoulle 2002a; DeGranpre et al. 2002), while temperate estuaries are net sources of CO₂ to the atmosphere (e.g. Frankignoulle et al. 1998; Cai et al. 2000; Raymond et al. 2000; Borges and Frankignoulle 2002b). Although the surface area of estuaries is globally about 20 times smaller than that of open continental shelves, the air-water fluxes of CO₂ in the temperate estuaries so far studied are about two orders of magnitude higher (about +100 mmol m⁻² d⁻<fnref n="1" />) than over temperate open continental shelves (about -5 mmol m⁻² d⁻<fnref n="1" />). A global integration of the CO₂ fluxes in these two systems is not possible at present time because of the lack of adequate data coverage. Indeed, few data are available at subtropical and tropical latitudes in both estuaries and continental shelves. The recent work by Cai et al. (2003) in the U.S. South Atlantic Bight shows that, unlike temperate continental shelves, subtropical continental shelves could in general be sources of CO₂. However, regional comparisons strongly suggest that CO₂ fluxes in es-

## Acknowledgments

We thank the crew of the R. V. *Belgica* for full collaboration during the Scheldt and Thames cruises, Niels Iversen for welcome on the Randers Fjord, Management Unit of the North Sea Mathematical Models for providing thermosalinograph and meteorological data during the Scheldt and Thames cruises, Renzo Biondo, Emile Libert, and Jean-Marie Théate for invaluable technical support, and an anonymous reviewer and J. N. Kremer for constructive comments on a previous version of the paper. This work was funded by the European Union through the BIOGEST (ENV4-CT96-0213) and EUROTROPH (EVK3-CT-2000-00040) projects, and by the Fonds National de la Recherche Scientifique (FRFC 2.4545.02) where A.V.B and M.F. are, respectively, a postdoctoral researcher and a senior research associate. This is MARE contribution 043.

<footnote n="1">¹ Corresponding author (Alberto.Borges@ulg.ac.be).</footnote>
<table><caption>Table 1. Basic data of the three studied sites. The surface area, length, and width are for the region of salinity mixing (the tidal freshwater region is excluded). The average estuary depth is at low tide and for the region of salinity mixing; however, note that measurements were made in the navigation channel.</caption><thead><tr><th></th><th>Randers Fjord</th><th>Thames</th><th>Scheldt</th></tr></thead><tbody><tr><td>Type</td><td>microtidal</td><td>macrotidal</td><td>macrotidal</td></tr><tr><td>Catchment area (10<sup>3</sup> km<sup>2</sup>)</td><td>3.3</td><td>14</td><td>21</td></tr><tr><td>Surface area (km<sup>2</sup>)</td><td>22</td><td>215</td><td>268</td></tr><tr><td>Length (km)</td><td>27</td><td>85</td><td>75</td></tr><tr><td>Width (km)</td><td>1.0</td><td>2.1</td><td>3.3</td></tr><tr><td>Average depth (m)</td><td>2</td><td>8</td><td>10</td></tr><tr><td>Navigation channel depth (m)</td><td>7</td><td>12</td><td>13</td></tr><tr><td>Tidal amplitude (m)</td><td>0.1–0.2</td><td>3–6</td><td>2–5</td></tr><tr><td>Fresh water discharge (km<sup>3</sup> yr<sup>-1</sup>)</td><td>1.2</td><td>9.5</td><td>3.8</td></tr><tr><td>Residence time (days)</td><td>5–10</td><td>20–40</td><td>30–90</td></tr></tbody></table>

tuaries could be very significant. For instance, the integrated emission of atmospheric CO₂ from Europe's estuaries (30 to 60 × 10⁹ kg C yr⁻¹; Frankignoulle et al. 1998) is of the same order of magnitude as the integrated sink over the European open continental shelf (90 to 170 × 10⁹ kg C yr⁻¹, Frankignoulle and Borges 2001a). Thus, more data of pCO₂ in estuaries and continental shelves are needed worldwide to allow the computation and global integration of the related air-water CO₂ fluxes. This also requires a better constraint on the formulation of the gas transfer velocity, which is the subject of a long-lived debate that at present time seems unresolved in both open oceanic waters (e.g., Liss and Merlivat 1986; Wanninkhof 1992; Jacobs et al. 1999; Wanninkhof and McGillis 1999; Nightingale et al. 2000; McGillis et al. 2001) and estuarine environments (e.g., Raymond and Cole 2001; Kremer et al. 2003a). Finally, the computation of gas exchange is also critical in the study of ecosystem metabolism based on the open-water method using O₂ and dissolved inorganic carbon measurements (e.g., Smith and Key 1975; Kremer et al. 2003a).

The flux of CO₂ across the air-water interface can be computed according to

$$F = \varepsilon k \alpha \Delta p \text{CO}_2 \quad (1)$$

where α is the solubility coefficient of CO₂, ΔpCO₂ is the air-water gradient of pCO₂, k is the gas transfer velocity of CO₂ (also referred to as piston velocity), and ε is the chemical enhancement factor of gas exchange.

In both open oceanic and coastal environments, highly precise and accurate methods to measure ΔpCO₂ are available; thus, the largest uncertainty in the computation of F comes from the k term (α is straightforwardly computed from salinity and water temperature, and the contribution from ε is usually negligible, except under very low turbulent conditions, see, e.g., Wanninkhof 1992 for open oceanic waters and Raymond and Cole 2001 for estuarine environments). Based on numerous theoretical, laboratory, and field studies, it is well established that k depends on a variety of variables such as capillary and breaking waves, boundary layer stability, air bubbles, surfactant surface films, evaporation/condensation, and precipitation, but the most important one is turbulence at the air-water interface (in the case of sparingly soluble gases such as CO₂ the critical variable is turbulence in the liquid phase). In open oceanic waters,

the gas transfer velocity of CO₂ is usually parameterized as a function of wind speed because wind stress is the main generator of turbulence in these systems.

In a recent review that compiles available measurements of k based on different methodologies in various estuaries, Raymond and Cole (2001) suggested that the parameterization of k as a function of wind speed could be significantly different in estuarine environments from those developed in open oceanic waters (higher values of k in estuaries for the same wind speed). To contribute to the debate, we analyze in the present paper a reasonably large data set of k values, based on the floating chamber method, in three European estuaries with contrasting physical characteristics (Randers Fjord, Denmark; Scheldt, Belgium/the Netherlands; and Thames, United Kingdom).

## Materials and methods

Pertinent characteristics of the three studied estuaries are summarized in Table 1. During the Thames and the Scheldt cruises, sampling was carried out to cover the full salinity range, typically by steps of 2.5 of salinity. During the Scheldt cruise of November 2002, four stations (51.13°N 4.31°E, 51.23°N 4.40°E, 51.41°N 4.04°E, 51.39°N 4.21°E) were occupied for 24 h and flux measurements were carried out approximately every 10 min during daytime (10 h). During the Randers Fjord cruises, three stations (56.46°N 10.04°E, 56.62°N 10.23°E, 56.61°N 10.30°E) were occupied for 24 h and flux measurements were carried out hourly.

During the Thames cruises and most of the Scheldt cruises (Table 2), pCO₂ was computed from the measurements of pH and total alkalinity (TAlk) sampled from a Niskin bottle in subsurface water. During the three most recent Scheldt cruises (Table 2), pCO₂ was measured directly (1-min recording interval) with an infrared gas analyzer (IRGA, Licor Li-6262) in air equilibrated with subsurface water (pumped from a depth of 2.5 m), using the equilibrator described by Frankignoulle et al. (2001). During the Randers Fjord cruises, pCO₂ was measured directly by equilibration with the floating equilibrator system (FES) described by Frankignoulle et al. (2003). In brief, the FES is a buoy containing an equilibrator, an IRGA, water and air temperature probes, an anemometer, and a data logger (1-min recording interval),
CO₂ gas transfer velocity in three estuaries

<table><caption>Table 2. Sampling dates and range of variables (salinity, water pCO₂ [ppm]; atmospheric pCO₂ [ppm]; air-water CO₂ fluxes [mmol m⁻² day⁻¹]) during the cruises carried out in the three studied estuaries. *n*, total number of air-water CO₂ flux measurements.*</caption><thead><tr><th></th><th>Salinity</th><th>pCO<sub>2water</sub></th><th>pCO<sub>2air</sub></th><th>Air-water CO<sub>2</sub> flux</th><th>n</th><th>n*</th></tr></thead><tbody><tr><td colspan="7"><strong>Randers Fjord</strong></td></tr><tr><td>25 Apr–29 Apr 2001</td><td>2–19</td><td>206–1011 (FES)</td><td>370–400</td><td>–36–65</td><td>27</td><td>10</td></tr><tr><td>20 Aug–28 Aug 2001</td><td>0–21</td><td>370–3910 (FES)</td><td>355–435</td><td>0–290</td><td>66</td><td>46</td></tr><tr><td colspan="7"><strong>Scheldt</strong></td></tr><tr><td>27 Nov–29 Nov 1995</td><td>1–29</td><td>605–6800 (pH/TAlk)</td><td>362–455</td><td>63–850</td><td>36</td><td>36</td></tr><tr><td>08 Jul–12 Jul 1996</td><td>1–30</td><td>472–7170 (pH/TAlk)</td><td>365–401</td><td>43–1465</td><td>48</td><td>45</td></tr><tr><td>09 Dec–13 Dec 1996</td><td>0–30</td><td>608–5632 (pH/TAlk)</td><td>349–390</td><td>23–740</td><td>42</td><td>42</td></tr><tr><td>25 May–28 May 1998</td><td>1–30</td><td>299–7718 (pH/TAlk)</td><td>392–460</td><td>–21–767</td><td>34</td><td>22</td></tr><tr><td>05 Oct–08 Oct 1998</td><td>0–27</td><td>956–7741 (pH/TAlk)</td><td>414–458</td><td>104–2270</td><td>34</td><td>34</td></tr><tr><td>04 Jul–06 Jul 2000</td><td>1–31</td><td>523–8351 (Eq)</td><td>387–445</td><td>15–1178</td><td>24</td><td>21</td></tr><tr><td>06 Nov–08 Nov 2000</td><td>0–20</td><td>614–5512 (Eq)</td><td>401–455</td><td>92–1328</td><td>12</td><td>10</td></tr><tr><td>06 Nov–12 Nov 2002</td><td>0–17</td><td>993–7553 (Eq)</td><td>368–422</td><td>66–2028</td><td>112</td><td>112</td></tr><tr><td colspan="7"><strong>Thames</strong></td></tr><tr><td>11 Sep–18 Sep 1996</td><td>2–35</td><td>458–4617 (pH/TAlk)</td><td>404–457</td><td>41–1728</td><td>58</td><td>55</td></tr><tr><td>16 Feb–18 Feb 1999</td><td>0–33</td><td>281–3025 (pH/TAlk)</td><td>400–460</td><td>0–1587</td><td>34</td><td>26</td></tr></tbody></table>

powered by four 12-volt batteries and a solar panel, providing an autonomy up to 30 h. For a detailed description of the pH and TAlk measurement methods, the computations of pCO₂ from pH and TAlk and the calibration procedure of the IRGA refer to Frankignoulle and Borges (2001b). Note that the measurements of pCO₂ by equilibration and the computed values of pCO₂ from pH and TAlk are consistent within ±1.5% (Frankignoulle and Borges 2001b).

The air-water CO₂ fluxes were measured with the floating chamber method described by Frankignoulle (1988) from a drifting rubber boat to avoid the interference of water turbulence within the chamber created by passing water current observed in earlier measurements carried out from a fixed point (Frankignoulle unpubl. data). The chamber is a plastic right circular cone (top radius = 49 cm; bottom radius = 57 cm; height = 28 cm) mounted on a float and connected to a closed air circuit with an air pump (30 L min⁻¹) and an IRGA, both powered with a 1-volt battery. The IRGA was calibrated daily using pure nitrogen (Air Liquide Belgium) and a gas mixture with a CO₂ molar fraction of 351 ppm (Air Liquide Belgium). The readings of pCO₂ in the chamber were written down every 30 s during 5 min in the Scheldt and Thames estuaries and during 10 min in the Randers Fjord because in the latter the flux signal was weaker than in the former two estuaries (*see Table 2*). The flux was computed from the slope of the linear regression of pCO₂ against time (r² usually ≥ 0.99) according to Frankignoulle (1988). The uncertainty of the flux computation due to SE on the regression slope is on average ±3%.

The floating chamber technique has been dismissed by several workers (Liss and Merlivat 1986; Raymond and Cole 2001), and one of the critiques of this technique is that the chamber covers the water surface and eliminates wind stress. However, for sparingly soluble gases such as CO₂, gas transfer is controlled by turbulence in the liquid phase. Thus, if

the floating chamber does not disrupt the underlying water turbulence, then the corresponding gas transfer measurements should be reasonable estimates of those from the undisturbed surface. The disturbance of the floating chamber on the surface wind boundary layer was tested experimentally by Kremer et al. (2003b). They measured O₂ fluxes using a floating chamber with an adjustable speed fan to generate air turbulence and using a control floating chamber in parallel. Under moderate wind conditions, the additional air turbulence from the fan only increased the fluxes by 2% to 12% compared to the control chamber. Kremer et al. (2003b) also report a series of experiments comparing the floating chamber technique with mass balance approaches of O₂, ²²²Rn, and ³He in various experimental settings (laboratory tanks, outdoor tanks, mesocosms, and lakes). Fluxes based on the floating chamber technique agreed with the other direct methods within 10% to 30%. However, two publications report large discrepancies between the floating chamber technique and other approaches (Belanger and Korzum 1991; Matthews et al. 2003) that, in our opinion, highlight the limits of the method rather than dismiss it altogether. Belanger and Korzum (1991) compared O₂ evasion rates from pools by a mass balance approach and floating chamber measurements. They concluded that the floating chamber measurements were biased by changes of temperature and pressure during the experiments. However, the duration of these measurements was several hours and temperature and pressure changes are not expected to interfere during very short deployments of the floating chamber (such as in our case). Matthews et al. (2003) compared *k* estimates in a small sheltered boreal reservoir, based on floating chamber and SF₆ evasion techniques. However, during their experiment, wind speeds were extremely low, on average 0.2 m s⁻¹ and never exceeding 0.5 m s⁻¹. As noted by Kremer et al. (2003b), the fluxes measured in nearly motionless wa-

<footnote>* *n*, number of flux measurements for an absolute air-water pCO₂ gradient > 200 ppm; FES = pCO₂ measurements carried out with the floating equilibrator system; Eq = pCO₂ measurements carried out with an equilibrator from the subsurface water supply of the *R.V. Belgica*; pH/TAlk = pCO₂ computed from the measurements of pH and TAlk sampled from a Niskin bottle in subsurface waters (*see Materials and methods for details*).</footnote>
ters with a floating chamber should be taken with caution. Also, estuarine environments (such as in our case) are much more turbulent because of tidal currents than the reservoir studied by Matthews et al. (2003). Finally, an indirect validation of this technique is given by Frankignoulle et al. (1996), who showed that floating chamber measurements over several coral reef systems give *k* estimates that fall between those based on the empirical formulations of Liss and Merlivat (1986) and Wanninkhof (1992).

The gas transfer velocity of CO₂ was computed from the CO₂ flux and ΔpCO₂ measurements (atmospheric pCO₂ was measured and recorded at the start of each flux measurement), using the CO₂ solubility coefficient formulated by Weiss (1974) and normalized to a Schmidt number (Sc) of 600 (*k*<sub>600</sub>), assuming a dependency of the gas transfer velocity proportional to Sc<sup>-0.5</sup>. Estuaries are highly turbulent systems (see Discussion), and the dependency of *k* proportional to Sc<sup>-2/3</sup> usually applied in open oceanic waters for the smooth surface regime, i.e., at wind speeds below 3 m s<sup>-1</sup> (e.g., Liss and Merlivat 1986), probably does not hold true. The Schmidt number was computed for a given salinity from the formulations for salinity 0 and 35 given by Wanninkhof (1992) and assuming that Sc varies linearly with salinity.

During the Thames and Scheldt cruises, wind speed was measured at 18 m height with a Friedrichs 4034.000 BG cup anemometer and recorded every 10 s. During the Randers Fjord cruises, wind speed measurements at 2 m height from a Young 03002VP cup anemometer were recorded every 60 s. The winds speeds were referenced to a height of 10 m (*u*<sub>10</sub>) according to Smith (1988) using concomitant air and water temperature measurements and were averaged for the period of each flux measurement. Water current speeds in subsurface waters were measured with an Aanderaa RCM7 and were recorded every minute during the Randers Fjord cruises and the November 2002 Scheldt cruise and were averaged for the period of each flux measurement.

## Results

During all cruises and in the three estuaries, the full gradient of salinity was sampled (Table 2), except during the November 2000 Scheldt cruise because of bad weather conditions and during the November 2002 Scheldt cruise because of a different sampling strategy (see Materials and methods). The range of water pCO₂ values spans one order of magnitude and is highest in the Scheldt estuary, although variable from one cruise to another. Oversaturation of CO₂ with respect to atmospheric equilibrium is observed in all three estuaries, although significant undersaturations are observed on some occasions, systematically in the high-salinity region of the estuary (Randers Fjord in April, Scheldt in May, and Thames in February) (Table 2). The range of CO₂ air-water fluxes spans two orders of magnitude and is smaller in the Randers Fjord, while the Scheldt and the Thames show similar ranges. The atmospheric pCO₂ values are above typical global average values, as observed in other near-shore coastal systems (e.g., Bakker et al. 1996; Borges and Frankignoulle 2001). Indeed, atmospheric pCO₂ values in the Randers Fjord, Scheldt, and Thames are on average 3 (±14

<figure><img src="image_31.png" /><figcaption>Fig. 1. Theoretical error (±%) on the computation of the gas transfer velocity of CO₂ (*k*<sub>600</sub>) as a function of the air-water gradient of CO₂ (ΔpCO₂ in ppm), assuming a constant uncertainty on ΔpCO₂ of ±3%.</figcaption></figure>

SD), 27 (±22 SD), and 42 (±11 SD) parts per million (ppm) above the “uncontaminated” values from Weather Station Mike (66.00°N 2.00°E), representative of the open North Sea waters (from the National Oceanic and Atmospheric Administration Climate Monitoring and Diagnostics Laboratory air samples network, available on the internet at http://www.cmdl.noaa.gov/). The individual atmospheric pCO₂ values for each cruise were compared with the corresponding monthly value at Weather Station Mike, where from 1995 to 2002 the annual mean increased from 361 to 373 ppm.

The *k*<sub>600</sub> data sets were filtered before further analysis because as ΔpCO₂ values approach zero, the computation of *k*<sub>600</sub> becomes more sensitive to error. This was investigated by assuming a reasonable error on ΔpCO₂ of ±3% and then assessing the corresponding error on the computation of *k*<sub>600</sub> (Fig. 1). An absolute value of ΔpCO₂ equal to 200 ppm was chosen as the threshold value below which the *k*<sub>600</sub> data were rejected because it corresponds to a good compromise between an acceptable error on the *k*<sub>600</sub> computation (below ±10%, Fig. 1) and maintains a fairly large number of filtered variables. After this filtering, the remaining *k*<sub>600</sub> data sets correspond to 60%, 88%, and 94% of the original data sets for the Randers Fjord, Thames, and Scheldt, respectively (Table 2). The *k*<sub>600</sub> data were averaged over wind speed bins of 2 m s<sup>-1</sup>, a common practice in gas transfer velocity studies (e.g., Cole and Caraco 1998; Fairall et al. 2000; McGillis et al. 2001), but one that changes the statistical power of regression and hypothesis testing. A rather large interval of wind speed bins was chosen because the data sets for the Randers Fjord and Thames are small compared to the one for the Scheldt (Table 2).

Figure 2 shows unbinned *k*<sub>600</sub> versus wind speed in the Randers Fjord, the Scheldt, and the Thames. In the three estuaries, a distinct increasing trend of *k*<sub>600</sub> values with wind
<figure><img src="image_32.png" /></figure>

Fig. 2. Gas transfer velocity of CO₂ (k₆₀₀, cm h⁻¹) as a function of wind speed at 10 m height (u₁₀, m s⁻¹) in the three studied estuaries and three published relationships. The error bars on the top left corner of the plot correspond, for each of the three estuaries, to the average uncertainty on k₆₀₀ estimated using the individual standard error on the slope of the regression of pCO₂ in the floating chamber against time (from which the CO₂ flux was computed; see the Materials and methods section) and assuming an error on ΔpCO₂ of ±3%. The estimated uncertainty on k₆₀₀ varies with wind speed; in the Thames and Scheldt it ranges from about ±1 to ±4 cm h⁻¹ at, respectively, low and high wind speeds; in the Randers Fjord it ranges from about ±0.1 to ±1.4 cm h⁻¹ at, respectively, low and high wind speeds. The solid bold line corresponds to model 1 regression functions (Table 3). The Raymond and Cole (2001) relationship (k₆₀₀ = 1.91 exp [0.35u₁₀]) is based on a compilation of published k₆₀₀ values in various rivers and estuaries and obtained using different methodologies (floating chamber, natural tracers [CFC, ²²²Rn] and purposeful tracer [SF₆]). The Carini et al. (1996) relationship (k₆₀₀ = 0.045 + 2.0277u₁₀) is based on a SF₆ release experiment in the Parker River estuary. The Marino and Howarth

<figure><img src="image_33.png" /></figure>

Fig. 3. Gas transfer velocity of CO₂ (k₆₀₀, cm h⁻¹) as a function of wind speed at 10 m height (u₁₀, m s⁻¹) in the three studied estuaries and three published relationships. The data were averaged over wind speed bins of 2 m s⁻¹. Standard deviations are shown by the horizontal and vertical dotted lines for the bin averages of u₁₀ and k₆₀₀, respectively. The error bars on the top left corner of the plot correspond, for each of the three estuaries, to the average uncertainty on the k₆₀₀ (refer to legend of Fig. 2 for details). The long-dashed line corresponds to the Raymond and Cole (2001) relationship, the short-dashed line corresponds to the Carini et al. (1996) relationship, and the solid line corresponds to the Marino and Howarth (1993) relationship (refer to legend of Fig. 2 for details).

speed is observed, although in the macrotidal Scheldt and Thames estuaries, data show higher scatter than the micro-tidal Randers Fjord. Note that the average estimated uncertainty on the k₆₀₀ values (error bars on top left corner of plots in Fig. 2) is lower than the data scatter. For wind speeds below 6 m s⁻¹, the k₆₀₀ values in the Randers Fjord follow published parameterizations in estuaries of k as a function of wind speed. For wind speed above 6 m s⁻¹, the k₆₀₀ values in the Randers roughly follow the Carini et al. (1996) pa-

←
(1993) relationship (k₆₀₀ = 0.94 exp [1.09 + 0.249u₁₀]) is based on floating chamber oxygen measurements in the tidal freshwater portion of the Hudson River estuary. The latter two relationships were developed for oxygen and are expressed as k₆₀₀ using the Schmidt number formulations given by Wanninkhof (1992) and assuming a dependency of the gas transfer velocity proportional to Sc⁻⁰.⁵.
<table><caption>Table 3. Linear regression functions between the gas transfer velocity of CO₂ ($k_{600}$, cm h⁻¹) and wind speed at 10-m height ($u_{10}$, m s⁻¹) in the three studied estuaries, based on unbinned and bin-averaged data ($k_{600}$ data were averaged over wind speed bins of 2 m s⁻¹).*</caption><thead><tr><th></th><th><i>k</i><sub>600</sub> = a (±SE) + b (±SE)<i>u</i><sub>10</sub></th><th><i>r</i><sup>2</sup></th><th><i>p</i></th><th><i>n</i></th></tr></thead><tbody><tr><td><strong>Unbinned data</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Scheldt</td><td><i>k</i><sub>600</sub> = 3.8(±1.0) + 3.45(±0.19)<i>u</i><sub>10</sub></td><td>0.519</td><td>&lt;0.0001</td><td>322</td></tr><tr><td>Thames</td><td><i>k</i><sub>600</sub> = 9.7(±3.2) + 3.64(±0.45)<i>u</i><sub>10</sub></td><td>0.471</td><td>&lt;0.0001</td><td>76</td></tr><tr><td>Randers Fjord</td><td><i>k</i><sub>600</sub> = 1.2(±0.7) + 2.30(±0.11)<i>u</i><sub>10</sub></td><td>0.897</td><td>&lt;0.0001</td><td>56</td></tr><tr><td><strong>Bin-averaged data</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Scheldt</td><td><i>k</i><sub>600</sub> = 3.4(±2.4) + 3.60(±0.35)<i>u</i><sub>10</sub></td><td>0.963</td><td>0.0005</td><td>6</td></tr><tr><td>Thames</td><td><i>k</i><sub>600</sub> = 10.2(±2.4) + 3.62(±0.35)<i>u</i><sub>10</sub></td><td>0.965</td><td>0.0005</td><td>6</td></tr><tr><td>Randers Fjord</td><td><i>k</i><sub>600</sub> = 0.9(±1.5) + 2.30(±0.22)<i>u</i><sub>10</sub></td><td>0.965</td><td>0.0004</td><td>6</td></tr></tbody></table>

parameterization. For wind speeds below 8 m s⁻¹, the lowest $k_{600}$ values in the Scheldt and Thames for a given wind speed fall on the lines from published parameterizations. However, the highest $k_{600}$ values at a given wind speed are about eight times higher than the observed minimal values. This suggests that at a given wind speed, a process other than wind stress increases $k_{600}$ in the Scheldt and Thames. Figure 3 shows the averaged $k_{600}$ over wind speed bins of 2 m s⁻¹ versus wind speed in the three estuaries. The binned $k_{600}$ values are highly variable from one estuary to another, and at a given wind speed the highest values are observed in the Thames and the lowest in the Randers Fjord. The ratio of binned $k_{600}$ values between the estuaries varies with wind speed; at low wind speeds, $k_{600}$ is about eight times higher and at high wind speeds about two times higher in the Thames than in the Randers Fjord.

The $k_{600}$ was parameterized as a function of wind speed by linear regression in each of the three studied estuaries (Table 3). Although various parameterization functions have been used in literature (linear, e.g., Liss and Merlivat 1986; Carini et al. 1996; Kremer et al. 2003a; power law, e.g., Hartman and Hammond 1985; Wanninkhof 1992; Cole and Caraco 1998; Jacobs et al. 1999; Wanninkhof and McGillis 1999; Nightingale et al. 2000; McGillis et al. 2001; exponential, e.g., Marino and Howarth 1993; Raymond and Cole 2001; Kremer et al. 2003a), the linear model is clearly appropriate for Randers Fjord, and for the Scheldt and Thames estuaries the linear approximation is the best first approximation especially considering the scatter. Moreover, in wind tunnel experiments, *k* has been shown to vary linearly with wind speed between 2 and 13 m s⁻¹ (Broecker and Siems 1984), the range of our data. In wind tunnel experiments, at wind speeds above 13 m s⁻¹, the slope of *k* versus wind speed increased because of presence of air bubbles from breaking waves. Kremer et al. (2003a) also showed that a linear relationship between *k* and wind speed provides the best data fit in Sage Lot Pond and Childs River estuaries (Waquoit Bay).

The model 2 regression functions of binned $k_{600}$ and model 1 regression functions of unbinned $k_{600}$ versus wind speed

are highly significant in the three studied estuaries (Table 3). The slopes and y-intercepts of unbinned and binned $k_{600}$ versus wind speed relationships in the three estuaries (model 1 and 2 functions, respectively) are not significantly different (Table 3). The slopes of the linear regression functions are similar in the Scheldt and the Thames and significantly higher than the one in the Randers Fjord. The y-intercept of the linear regression function in the Thames is higher than those of the Randers Fjord and the Scheldt.

## Discussion

The differences of the y-intercept and slope of the linear regressions of $k_{600}$ as a function of wind speed described in the previous section for the three studied estuaries are discussed in relation to the potential contribution of water currents to water turbulence and fetch limitation.

*Contribution of water currents to* $k_{600}$—In the macrotidal Scheldt and Thames estuaries $k_{600}$ values showed high scatter, and at wind speeds below 8 m s⁻¹ most $k_{600}$ values are above the values predicted by published parameterizations (Fig. 2). In contrast, in the microtidal Randers Fjord $k_{600}$ values showed much lesser scatter. This strongly suggests that, in the Scheldt and Thames, tidal currents, in addition to wind stress, significantly contribute to $k_{600}$ and induce high variability depending on the tidal phase (maximum ebb or flow and tidal slacks).

In streams, the interaction of the gravity flow and bottom topography generates turbulence because of bottom shear that is frequently considered to be the main factor controlling the gas transfer velocity of sparingly soluble gases in these sheltered systems where wind is usually very low. This has led to various parameterizations of the gas transfer velocity as a function of water current and depth based on empirical or conceptual approaches, reviewed by Bansal (1973) and more recently by Melching and Flores (1999) and Gualtieri et al. (2002). In estuaries, the tidal current can be as high as the gravity flow in streams and, thus, could in theory contribute significantly to $k_{600}$. To our best knowledge, in estu-

<footnote>* These relationships are only valid for $u_{10}$ spanning the range of values between 0 and 11 m s⁻¹. For unbinned data, the regression function was computed with model 1 least squares fit. For bin-averaged data, the regression function was computed with model 2 functional fit. For the Scheldt and Thames regression functions, the slopes are not statistically different ($p$ = 0.6299 for unbinned data; $p$ = 0.9675 for bin-averaged data) but the y-intercepts are statistically different ($p$ < 0.0001 for unbinned data; $p$ = 0.0016 for bin-averaged data). The slope of the regression function of the Randers Fjord is statistically different from those of the Scheldt ($p$ = 0.0016 for unbinned data; $p$ = 0.0099 for bin-averaged data) and the Thames ($p$ = 0.0086 for unbinned data; $p$ = 0.0090 for bin-averaged data).</footnote>
aries, this has been investigated in the field by Hartman and Hammond (1984) in San Francisco Bay using floating chamber ²²²Rn measurements and by Zappa et al. (2003) in Plum Island Sound using the gradient flux technique. Hartman and Hammond (1984) found no distinct evidence for the contribution of water currents to the gas transfer velocity. However, in this study water currents were estimated from tide tables and not actually measured. Moreover, the sampling period for each flux measurement was rather long (1 h) in comparison with the time-scale characteristic of tidal current variability. Zappa et al. (2003) carried out four k measurements under low winds (1.9 m s⁻¹) during half a tidal cycle and found a significant enhancement of k (up to 10 cm h⁻¹) related to water currents measured with an acoustic Doppler current profiler (ranging from 10 to 80 cm s⁻¹).

Water currents measurements concomitant to flux measurements were obtained during the two Randers Fjord cruises and the November 2002 Scheldt cruise. Although water currents are expected to contribute to water turbulence whatever the wind speed, their effect is best identified if the contribution to turbulence from wind stress is low ($u_{10} < 4$ m s⁻¹). In the Randers Fjord, water current concomitant to flux measurements at wind speeds below 4 m s⁻¹ ranged from 1 to 38 cm s⁻¹ (on average 11 ± 12 SD cm s⁻¹). So it is possible to compare quantitatively water currents to the corresponding $k_{600}$ values. In contrast, during the November 2002 Scheldt cruise, current speeds were systematically high (ranging between 66 and 107 cm s⁻¹, on average 92 ± 15 SD cm s⁻¹) during the flux measurements carried out at wind speeds below 4 m s⁻¹. Hence, it is not possible to directly compare $k_{600}$ and water currents.

The $k_{600}$ data set of the Randers Fjord was filtered by rejecting data for wind speeds above 4 m s⁻¹ and for nil water currents (Fig. 4). Coincidentally, the $k_{600}$ data are grouped for two wind speed ranges between 0 and 1 m s⁻¹ and between 2 and 4 m s⁻¹ (Fig. 4A). Even at these low wind speeds, wind stress contributes to $k_{600}$, as shown by the comparison of the data group for wind speeds below 1 m s⁻¹ (filled squares in Fig. 4A) and the data group for wind speed above 2 m s⁻¹ and water currents below 10 cm s⁻¹ (open squares in Fig. 4A). Thus, the groups of $k_{600}$ data for the two wind speed ranges are treated separately in relation to water currents (Fig. 4B). For the data group for wind speed below 1 m s⁻¹, the range of water currents is low (1 to 8 cm s⁻¹) and no relationship between $k_{600}$ and water current is apparent. However, for the data group of wind speeds between 2 and 4 m s⁻¹, the range of water currents is high (1 to 38 cm s⁻¹) and $k_{600}$ is well related to water current (Fig. 4B). This clearly shows that water currents contribute to $k_{600}$.

In the Scheldt, a different approach than in the Randers Fjord was used; the contribution of water current to $k_{600}$ was estimated based on the frequently referenced conceptual relationship of O'Connor and Dobbins (1958) that gives the oxygen reaeration rate ($R$ in d⁻¹) according to

$$R = 0.439 w^{0.5} h^{-1.5} \quad (2)$$

where $w$ is the water current in centimeters per second and $h$ is the depth in meters.

The oxygen reaeration rate given in Eq. 2 can be expressed as the gas transfer velocity ($k = Rh$) and normalized

<figure><img src="image_34.png" /><figcaption>Fig. 4. The gas transfer velocity of CO₂ ($k_{600}$, cm h⁻¹) in the Randers Fjord as a function of (A) wind speed at 10 m height ($u_{10}$, m s⁻¹) less than 4 m s⁻¹ and (B) non-nil water current (w, cm s⁻¹). The error bars on top left corner of the plots correspond to the average uncertainty on $k_{600}$ (refer to legend of Fig. 2 for details). Filled squares correspond to data below a wind speed of 1 m s⁻¹. For clarity in the data interpretation and discussion, data for wind speeds above 2 m s⁻¹ were separated for water currents above (crossed squares) and below (open squares) 10 cm s⁻¹. The short-dashed line corresponds to the $k_{600}$ predicted from the conceptual relationship of O'Connor and Dobbins (1958) to which was added 6.1 cm h⁻¹ (a depth 7 m was used in the computation, corresponding to the average value in the navigation channel, where measurements were carried out). The value of 6.1 cm h⁻¹ is the average value of the two $k_{600}$ values observed at the lowest (nearly zero) water currents and roughly accounts for the effect of wind speed on $k_{600}$ evident from panel A. The solid line corresponds to the model 1 linear regression ($k_{600} = 6.3 [\pm 1.1 \text{ SE}] + 0.13 [\pm 0.03 \text{ SE}] w$, $r^2 = 0.732$, $p = 0.0033$, $n = 9$) between all the observed $k_{600}$ for the wind speed range between 2 and 4 m s⁻¹ and water current. The long-dashed line corresponds to a power law function ($k_{600} = 1.87 [\pm 0.03 \text{ SE}] w^{0.5} h^{-0.5}$, $r^2 = 0.725$, $n = 9$) that accounts for $w$ and depth ($h$, m) in the same fashion as the O'Connor and Dobbins (1958) relationship ($k_{600} = 1.719 w^{0.5} h^{-0.5}$), based on all the observed $k_{600}$ for the wind speed range between 2 and 4 m s⁻¹.</figcaption></figure>
to a Schmidt number of 600 using the formulations given by Wanninkhof (1992), assuming a dependency of k proportional to Sc⁻⁰.⁵, with the result that

$$k_{600current} = 1.719w^{0.5}h^{-0.5} \quad (3)$$

where kₖₐ₀₀ₐₘₐₗₐ is the gas transfer velocity of CO₂ in centimeters per hour, w is the water current in centimeters per second, and h is the depth in meters.

From the water current measurements concomitant to those of the CO₂ flux, the contribution of water current to the gas transfer velocity of CO₂ (kₖₐ₀₀ₐₘₐₗₐ) was computed according to Eq. 3 and was removed from the observed kₖₐ₀₀ (kₖₐ₀₀ₐₗₐₘₐₗₐ). This gives in theory the contribution to kₖₐ₀₀ of wind speed alone (kₖₐ₀₀ₐₗₐ = kₖₐ₀₀ₐₗₐ - kₖₐ₀₀ₐₘₐₗₐ), assuming that both contributions to water turbulence are additive. At low wind speeds, kₖₐ₀₀ₐₗₐ is about three times lower than kₖₐ₀₀ₐₗₐ, which suggests that the contribution of water currents to water turbulence is substantial when wind stress is low (Fig. 5A). At high wind speeds, kₖₐ₀₀ₐₗₐ is about 1.1 times lower than kₖₐ₀₀ₐₗₐ, in agreement with the theoretical analysis of Cerco (1989) that shows that the relative contribution of water currents to the gas transfer velocity decreases with increasing wind.

The y-intercept of the model 2 regression function of kₖₐ₀₀ₐₗₐ against wind speed for the Scheldt is negative but close to zero (Table 4). This is most probably related to the poor constraint on the linear regression at low wind because only two measurements were obtained for wind speeds below 2 m s⁻¹. The other possible explanation is that the conceptual relationship of O'Connor and Dobbins (1958) overestimates the contribution of water currents to the gas transfer velocity. However, in the Randers Fjord, the curve of kₖₐ₀₀ as a function of w predicted by the O'Connor and Dobbins (1958) relationship (short-dashed line in Fig. 4B; Eq. 3) is close to the regression line of the observed kₖₐ₀₀ as a function of w (solid line in Fig. 4B; kₖₐ₀₀ = 6.3 + 0.13w) (Fig. 4B).

A power law function that accounts for w and h in the same fashion as the O'Connor and Dobbins (1958) relationship (short-dashed line in Fig. 4B; Eq. 3) was established from the observed kₖₐ₀₀ and w values (long-dashed line in Fig. 4B; kₖₐ₀₀ = 1.87w⁰.⁵h⁻⁰.⁵), and both relationships are very similar (Fig. 4B). Zappa et al. (2003) also showed that the O'Connor and Dobbins (1958) relationship gives a good approximation of four k measurements based on the gradient flux technique during a tidal cycle in Plum Island Sound estuary under low wind conditions. Our results and those of Zappa et al. (2003) strongly suggest that the O'Connor and Dobbins (1958) relationship gives a fairly adequate estimation of the contribution of water currents to the gas transfer velocity in estuarine environments.

The same computations as those outlined above were carried out for the Randers Fjord (Fig. 5B and Table 4), and the y-intercepts of the linear regression function of kₖₐ₀₀ₐₗₐ and kₖₐ₀₀ₐₗₐ against wind speed are not significantly different. This suggests that the overall contribution of water currents to kₖₐ₀₀ is negligible in the microtidal Randers Fjord. Indeed, 78% of the observed water currents are below 10 cm s⁻¹ (Fig. 6A) and, thus, the high water currents in Fig. 4B are exceptional values. Moreover, for water currents ranging

<figure><img src="image_35.png" /><figcaption>Fig. 5. Gas transfer velocity of CO₂ (kₖₐ₀₀, cm h⁻¹) as a function of wind speed at 10 m height (u₁₀, m s⁻¹) for (A) the November 2002 Scheldt cruise and (B) for the two Randers Fjord cruises. The data were averaged over wind speed bins of 2 m s⁻¹. Standard deviations are shown by the horizontal and vertical dotted lines for the bin averages of u₁₀ and kₖₐ₀₀, respectively. The error bars on the top left corner of the plots correspond, for each of the two estuaries, to the average uncertainty on kₖₐ₀₀ (refer to legend of Fig. 2 for details). The open symbols correspond to the observed kₖₐ₀₀. The filled symbols correspond to kₖₐ₀₀ from which the contribution of water currents was removed. The contribution of water currents to kₖₐ₀₀ was estimated from the conceptual relationship of O'Connor and Dobbins (1958), using water current measurements concomitant to the CO₂ flux measurements, and it was removed from individual kₖₐ₀₀ estimates before the data were bin averaged. Solid lines correspond to the model 2 regression functions developed in Table 4. The long-dashed line corresponds to the Raymond and Cole (2001) relationship, and the short-dashed line corresponds to the Carini et al. (1996) relationship (refer to legend of Fig. 2 for details).</figcaption></figure>

from 0 to 10 cm s⁻¹, the expected increase of kₖₐ₀₀ is on average only of a factor of about 1.2, based on the linear regression function in Fig. 4B. Also, the kₖₐ₀₀ data of the Randers Fjord follow closely the relationship of Carini et al. (1996) for Parker River estuary (Figs. 2, 3, and 5B) that is also characterized by low tidal currents according to Raymond and Cole (2001).
CO₂ gas transfer velocity in three estuaries

<table><caption>Table 4. Regression functions between the gas transfer velocity of CO₂ ($k_{600}$, cm h⁻¹) and wind speed at 10-m height ($u_{10}$, m s⁻¹) in the Randers Fjord and the Scheldt (only the data from the November 2002 cruise).*</caption><thead><tr><th></th><th>k<sub>600</sub> = a(±SE) + b(±SE)u<sub>10</sub></th><th>r<sup>2</sup></th><th>p</th><th>n</th></tr></thead><tbody><tr><td colspan="5"><strong>Observed k<sub>600</sub>: unbinned data</strong></td></tr><tr><td>Scheldt</td><td>k<sub>600</sub> = 4.7(±2.2) + 2.76(±0.33)u<sub>10</sub></td><td>0.897</td><td>&lt;0.0001</td><td>112</td></tr><tr><td>Randers Fjord</td><td>k<sub>600</sub> = 1.2(±0.7) + 2.30(±0.11)u<sub>10</sub></td><td>0.395</td><td>&lt;0.0001</td><td>56</td></tr><tr><td colspan="5"><strong>k<sub>600</sub> without the contribution from water currents: unbinned data</strong></td></tr><tr><td>Scheldt</td><td>k<sub>600</sub> = -0.8(±2.3) + 3.16(±0.34)u<sub>10</sub></td><td>0.898</td><td>&lt;0.0001</td><td>112</td></tr><tr><td>Randers Fjord</td><td>k<sub>600</sub> = 0.1(±0.6) + 2.26(±0.10)u<sub>10</sub></td><td>0.439</td><td>&lt;0.0001</td><td>56</td></tr><tr><td colspan="5"><strong>Observed k<sub>600</sub>: binned data</strong></td></tr><tr><td>Scheldt</td><td>k<sub>600</sub> = 3.4(±0.8) + 2.92(±0.14)u<sub>10</sub></td><td>0.993</td><td>0.0005</td><td>5</td></tr><tr><td>Randers Fjord</td><td>k<sub>600</sub> = 0.9(±1.5) + 2.30(±0.22)u<sub>10</sub></td><td>0.963</td><td>0.0002</td><td>6</td></tr><tr><td colspan="5"><strong>k<sub>600</sub> without the contribution from water currents: binned data</strong></td></tr><tr><td>Scheldt</td><td>k<sub>600</sub> = -2.7(±1.2) + 3.41(±0.21)u<sub>10</sub></td><td>0.989</td><td>0.0006</td><td>5</td></tr><tr><td>Randers Fjord</td><td>k<sub>600</sub> = -0.3(±1.6) + 2.28(±0.24)u<sub>10</sub></td><td>0.959</td><td>0.0005</td><td>6</td></tr></tbody></table>

In contrast, 67% of the observed water currents in the Scheldt estuary are above 10 cm s⁻¹, and the range of variation of the observed water currents is higher than in the Randers Fjord (Fig. 6B). This supports the idea that the difference in the y-intercept of the regression functions of $k_{600}$ against wind speed between the Randers Fjord and the Scheldt (Table 3) is related to the contribution of water currents to $k_{600}$ that is substantial in the Scheldt and negligible in the Randers Fjord. This is also consistent with the low y-intercept of the regression function of $k$ as a function of wind speed reported by Kremer et al. (2003a) for Childs River and Sage Lot Pond estuaries (respectively, 1.9 and 0.8 cm h⁻¹ normalized to a Sc of 600) that are close to the value in Randers Fjord. Indeed, Waquoit Bay is characterized by a low tidal amplitude (<0.5 m); thus, tidal currents can be assumed to be low in Childs River and Sage Lot Pond estuaries and, according to Kremer et al. (2003a), have a negligible effect on $k$. The high y-intercept of the regression functions of $k_{600}$ against wind speed of the Thames (Table 3) is also assumed to be related to strong tidal currents, although no water current measurements are available to verify this hypothesis. The high tidal amplitude in the Thames (Table 1) suggests that tidal currents should be at least as strong as in the Scheldt.

*Fetch limitation?*—In addition to the differences in y-intercepts, the regression functions of $k_{600}$ against wind speed of the Scheldt and the Thames have higher slopes compared to the one of the Randers Fjord (Table 3). This is not related to water currents because their contribution to $k_{600}$ tends on the contrary to slightly decrease the slope (Fig. 5A; Table 4). This difference could be related to fetch limitation. Fetch is the distance over which the wind blows without significant

deviation and determines (for a given wind speed) the intensity of water turbulence and wave height. The effect of fetch on $k$ has been shown in wind tunnel experiments (Wanninkhof and Bliven 1991). Hartman and Hammond (1984) suggested fetch limitation to explain the differences of $k$ values on the different sides of San Francisco Bay. Kremer et al. (2003a) have hypothesized that the lower slopes of $k$-wind linear relationships in Sage Lot Pond and Childs River compared to other estuaries could be related to fetch limitation. Also, Wanninkhof (1992) hypothesized that the difference between the slope of the linear regression functions of $k_{600}$ (based on SF₆ evasion experiments) against wind speed in various lakes is related to their surface area.

Among the three studied estuaries, the Randers Fjord is shorter and narrower and has a smaller surface area than the Scheldt and Thames (Table 1). Thus, stronger fetch limitation could explain the lower slope of the linear regression function of $k_{600}$ against wind in the Randers Fjord compared to the other two estuaries. In Fig. 7, the slopes of the regression functions of the three studied estuaries plus those investigated by Kremer et al. (2003a) are plotted on a semilogarithmic scale against their respective estuarine surface area. This clearly shows a significant effect of fetch limitation on $k$ that induces a decrease of the slope of the $k$ versus wind speed regression functions with increasing fetch limitation. The nonlinearity of the relationship suggests that the effect of fetch limitation is disproportionately stronger in small estuaries (<30 km²). However, Fig. 7 should be interpreted with caution since besides the estuarine surface area, fetch limitation is expected to depend on the shape of the estuary (funnel, oval, narrow or wide linear channel) and on the relation between the direction of prevailing winds and the direction of main axis of the estuary (parallel or across).

<footnote>* For unbinned data, the regression function was computed with model 1 least-squares fit. For bin-averaged data, the regression function was computed with model 2 functional fit. The contribution of water currents to $k_{600}$ was estimated from the conceptual relationship of O'Connor and Dobbins (1958), using water current measurements concomitant to those of air-water CO₂ fluxes. This contribution was removed from the observed individual $k_{600}$, and data were then averaged over wind speed bins of 2 m s⁻¹ and the model 2 regression functions against $u_{10}$ were recomputed (lower half of table). In the Scheldt, for the regression functions of the observed $k_{600}$ and the $k_{600}$ without the contribution from water currents, the slopes are not statistically different ($p$ = 0.3881 for unbinned data; $p$ = 0.1059 for binned data) but the y-intercepts are statistically different ($p$ = 0.0052 for unbinned data; $p$ = 0.0028 for binned data). In the Randers Fjord, for the regression functions of the observed $k_{600}$ and the $k_{600}$ without the contribution from water currents, the slopes ($p$ = 0.8015 for unbinned data; $p$ = 0.9164 for binned data) and the y-intercepts ($p$ = 0.0133 for unbinned data; $p$ = 0.2263 for binned data) are not statistically different.</footnote>
<figure><img src="image_36.png" /></figure>

Fig. 6. Frequency distribution of water current (w, cm s⁻¹) measurements by (A) intervals of 5 cm s⁻¹ in the Randers Fjord (total number of observations = 7,909) and by (B) intervals of 10 cm s⁻¹ in the Scheldt (total number of observations = 2,278). During both Randers Fjord cruises, data were recorded every minute at three stations (56.46°N 10.04°E, 56.62°N 10.23°E, 56.61°N 10.30°E) that were occupied during 24 h. Data at the upstream station (56.46°N 10.04°E) were lost because of equipment failure during the April 2001 cruise. During the November 2002 Scheldt cruise, data were recorded every minute at four stations (51.13°N 4.31°E, 51.23°N 4.40°E, 51.41°N 4.04°E, 51.39°N 4.21°E) that were occupied during 24 h. Mean current speeds are 8 (±12 SD) and 44 (±40 SD) cm s⁻¹ in the Randers Fjord and the Scheldt, respectively.

When compared at the European regional level, the integrated air-water CO₂ fluxes in estuaries and open continental shelf are of the same order of magnitude but opposite in direction. Thus, more air-water CO₂ flux estimates are needed in worldwide estuaries to allow an evaluation of their significance in the CO₂ flux budget of the overall coastal ocean. The critical factor in the computation of the air-water CO₂ flux is the large uncertainty on the formulation of the gas transfer velocity. Based on a fairly large data set of air-water CO₂ fluxes, measured using the floating chamber method, in three European estuaries (Randers Fjord, Scheldt,

<figure><img src="image_37.png" /></figure>

Fig. 7. Slope of the model 1 regression functions of $k_{600}$ versus wind speed (Table 3) from the Thames (T), Scheldt (S), Randers Fjord (RF), Childs River (CR), and Sage Lot Pond (SLP) versus the logarithm of the estuarine surface area. The data from Childs River (1.3 km²) and Sage Lot Pond (3.3 km²) from Kremer et al. (2003a) were normalized to a Schmidt number of 600 using the formulations given by Wanninkhof (1992) and assuming a dependency of the gas transfer velocity proportional to Sc⁻⁰.⁵. Solid line corresponds to model 1 regression function (slope = 1.14 (±0.09 SE) + 0.99 (±0.05 SE) log (surface area), r² = 0.991, p = 0.0003, n = 5).

and Thames), significant regression functions between $k_{600}$ and wind speed were established. Based on these and in accordance with the conclusions of Kremer et al. (2003a), it appears that the formulation of $k_{600}$ as a function of wind speed is site specific in estuarine environments. This implies that substantial errors in flux computations are incurred if generic k-wind relationships are employed in estuarine environments for the purpose of biogas air-water flux budgets and ecosystem metabolic studies. From one estuary to another, the differences in the y-intercepts of the linear relationships are due to tidal currents, whereas the differences in the slopes of the regression functions are related to fetch limitation. The contribution of tidal currents to $k_{600}$ is significant in macrotidal estuaries such as the Scheldt and Thames but seems negligible in microtidal estuaries such as Randers Fjord, Childs River, and Sage Lot Pond. Based on our results and in accordance with those of Zappa et al. (2003), the O'Connor and Dobbins (1958) relationship originally developed for streams is appropriate to estimate the contribution of waters currents to k in estuarine environments. Finally, we suggest that in estuarine environments future research efforts should concentrate in the development of a physically more rigorous and probably multivariable formulation of the gas transfer velocity (including at least wind stress and water current effects on turbulence at the air-water interface), rather than a simple empirical formulation as a function of wind speed. To assist such theoretical
work, more data on air–water CO₂ fluxes are also needed. Natural or purposeful tracer approaches give gas transfer velocity estimates that are on a time scale (day to week) that is larger than the one characteristic of tidal currents (minutes to hours). To resolve gas transfer velocity variability on short time scales, besides the floating chamber method, micro-meteorological methods seem adequate. The earlier controversial drawbacks of these methods (Broecker et al. 1986) seem to a large extent resolved (Fairall et al. 2000; McGillis et al. 2001), and estuaries provide ideal settings for their application (low ship motion, high signal-to-noise ratio, i.e., very large fluxes and ΔpCO₂).
3.3 Le fjord de Randers

Gazeau F., Borges A. V., Barrón C., Duarte C. M., Iversen N., Middelburg J. J., Pizay M. -D., Frankignoulle M. & Gattuso J. -P.

Net ecosystem metabolism in a micro-tidal estuary (Randers Fjord, Denmark): evaluation of methods and interannual variability (soumis)

Marine Ecology Progress Series.
Net ecosystem metabolism in a micro-tidal estuary
(Randers Fjord, Denmark): evaluation of methods and
interannual variability

Frédéric Gazeau<fnref n="1" />,<fnref n="2" />,* , Alberto Vieira Borges<fnref n="2" />, Cristina Barrón<fnref n="3" />, Carlos M. Duarte<fnref n="3" />, Niels Iversen<fnref n="4" />, Jack J. Middelburg<fnref n="5" />, Bruno Delille<fnref n="2" />, Marie-Dominique Pizay<fnref n="1" />, Michel Frankignoulle<fnref n="2" /> & Jean-Pierre Gattuso<fnref n="1" />

Submitted to MEPS

<footnote n="1">¹ Laboratoire d'Océanographie de Villefranche, CNRS-Université de Paris 6, B. P. 28, F-06234 Villefranche-sur-mer Cedex - France</footnote>

<footnote n="2">² Unité d'Océanographie Chimique (B5), MARE, Université de Liège, B-4000 Sart Tilman - Belgium</footnote>

<footnote n="3">³ IMEDEA (CSIC-UIB), C/Miquel Marquès 21, 07190 Esporles - Spain</footnote>

<footnote n="4">⁴ Aalborg Universitet (AU), Sohngnardsholmsvej 57, DK-Aalborg - Denmark</footnote>

<footnote n="5">⁵ Netherlands Institute of Ecology (NIOO-KNAW), Postbus 140, 4400 AC Yerseke - The Netherlands</footnote>

<footnote>* corresponding author: gazeau@obs-vlfr.fr, phone: +33493763843, fax: +33493763834</footnote>
ABSTRACT

The metabolic performance of an estuary (Randers Fjord, Denmark) has been assessed based on two field campaigns in April and August 2001 and a number of approaches: (1) the O₂ incubation method, (2) the Response Surface Difference (RSD) method, (3) dissolved inorganic carbon (DIC) budgets and (4) Land-Ocean Interaction in the Coastal Zone (LOICZ) budgets.

Although each method has its own associated limitations and uncertainties, all approaches converged to consistent metabolic estimates, both in sign and magnitude and reveal that this system is near metabolic balance in spring and net heterotrophic in summer. In this shallow estuary (mean depth: 1.6 m), the benthic compartment was very active and represented 70 and 40% of the total gross primary production in April and August, respectively. Net ecosystem production (NEP) rates measured during this study are in the range of previously reported rates in estuaries. A long-term study of the metabolism of this ecosystem based on the LOICZ approach revealed a significant increase of annual NEP from 1991 to 2002.

KEYWORDS

ECOSYSTEM METABOLISM, ESTUARIES, PRIMARY PRODUCTION, RESPIRATION, RANDERS FJORD, OXYGEN, CARBON DYNAMICS
## INTRODUCTION

The coastal zone, covering an area of 26 x 10⁶ km² (7% of the surface of the global ocean), is one of the most biogeochemically active regions of the biosphere (Gattuso et al. 1998). Although it comprises 30-50% of calcium carbonate and about 80% of organic carbon accumulation of the whole ocean (Wollast & Mackenzie 1989, Morse & Mackenzie 1990, Wollast 1991, Milliman 1993, Smith & Hollibaugh 1993, Wollast 1998), there is no consensus whether the coastal ocean acts as a net source or sink for atmospheric CO₂. Two major reasons can explain this lack of consensus: (1) the net community production (NCP) and net calcification of the coastal zone are still poorly documented and (2) the wide physiographic and environmental diversity of coastal ecosystems complicates any upscaling procedure (Gazeau et al. in press).

Estuaries are particularly complex cases of coastal ecosystems, as they are inherently variable and dynamic with large temporal (from daily to seasonal timescales) and spatial gradients (Heip et al. 1995). Thus, to estimate the metabolic status of such systems a fully integrated method both in space and time is required. An ecosystem is deemed net autotrophic when production of organic matter by primary producers exceeds the consumption of this matter by heterotroph organisms. Such systems are potentially net sinks for atmospheric CO₂ although import of high partial pressure of CO₂ (pCO₂) such as in productive upwelling areas or estuaries can result in the release of CO₂ to the atmosphere. On the contrary, an ecosystem is heterotrophic when consumption exceeds primary production, leading generally to high pCO₂ and low oxygen (O₂) concentrations in the water column.

A wide range of techniques have been used to estimate the trophic status of coastal ecosystems. Each technique relies on one or several assumptions and covers different spatial and temporal scales. For instance, metabolic processes rates measurements based on incubation methods (mainly O₂ and ¹⁴C) or open water O₂ measurements (Odum 1956, Kemp & Boynton 1980, Howarth et al. 1992, Caffrey 2004) have been used on local scales in numerous studies but their extrapolation to larger areas remains problematic.

The direct measurement of CO₂ fluxes across the air-water interface has been only recently applied at large spatial and temporal scales in a number of coastal
ecosystems (Frankignoulle et al. 1998, Tsunogai et al. 1999, Cai et al. 2000, Raymond et al. 2000, Frankignoulle & Borges 2001b, Borges & Frankignoulle 2002).

Within the IGBP program, the Land Ocean Interaction in the Coastal Zone (LOICZ) stoichiometry budgeting approach has been applied to more than 170 sites and can provide system scale estimates (Gordon et al. 1996).

The EUROTROPH project (http://www.ulg.ac.be/oceanbio/eurotroph/) aims at determining the trophic status of three European coastal ecosystems (Randers Fjord, Scheldt estuary and Bay of Palma) using simultaneously different available techniques and to compare the estimates at several time scales. Here, we estimate, using a variety of methods, the metabolic status of the Randers Fjord (Denmark), during two field campaigns in 2001. The following methods were used and compared: integration and extrapolation of changes in O₂ during incubations, the RSD (Response Surface Difference) method, dissolved inorganic carbon (DIC) and LOICZ budgets. Moreover, the LOICZ budgeting procedure was applied on an annual scale from 1991 to 2002, based on an historical dataset collected by the Danish National Environmental Research Institute (NERI). To our knowledge, this is the first systematic comparison of these four approaches to assess the metabolic balance of a coastal ecosystem.
## Study area

Randers Fjord is the longest Danish estuary located on the East coast (Fig. 1). The river and fjord drain an area of 3260 km² and receive treated sewage water from 600,000 inhabitants. The estuary is 27 km long and covers an area of 23 km². The main freshwater input comes from the river Gudenå, which drains 80% of the catchment area and enters the innermost part of Randers Fjord (Nielsen et al. 2001). The mean annual water residence time within the estuary is ~13 d (Nielsen et al. 2001). A pycnocline is present throughout the year in almost the entire estuary (Nielsen et al. 1993). The top-layer of sediment varies from soft-mud with a high organic content (10-14%) in the inner part to fine/medium-sized silt with low organic content (1-2%) in the outer area (Nielsen et al. 2001). Seasonal anoxic conditions in bottom waters have been recorded in the innermost part of the estuary during summer (Sømod et al. 1999). In the 1970's, regulatory measures were implemented in order to decrease the loading of nutrients and organic carbon from point sources and cities. Additionally, villages with more than 200 inhabitants were required to build sewage treatment plants with C, N and P removal capabilities.

## Oxygen incubations

Planktonic gross primary production (GPP) and community respiration (CR) were measured at several stations in the estuary (Fig. 1) during two cruises in April-May and August 2001 hereafter referred to Randers 1 and Randers 2, respectively. Samples were incubated *in situ* at 4 depths (5 replicates at each depth) from sunrise to sunset in both transparent and dark 60 ml biological oxygen demand (BOD) bottles. O₂ concentrations were measured before and after incubation using an automated Winkler titration technique with a potentiometric end-point detection. Analyses were performed with an Orion® redox electrode (9778-SC) and a custom built titrator. Reagents and standardizations were similar to those described by Knap et al. (1996). Hourly planktonic CR (expressed as a negative value) and net community production during the day (NCP<sub>d</sub>) were estimated by regressing O₂ in the dark and transparent bottles against
time. Hourly planktonic GPP was calculated as the difference between NCP<sub>d</sub> and hourly CR.

Triplicate sediment cores (8 cm in diameter) were collected with a box corer at 6 stations along the estuary in the navigation channel. Additionally, 2 sets of triplicate sediment cores were collected manually at 3 stations located on the shallow banks (Fig. 1). For each sampling station, cores were incubated in the laboratory at *in situ* temperature under darkness for around 6 h and one set of bank cores was incubated under *in situ* light conditions for 3 to 6 h. Before the start of the incubations, the overlying water was removed and cores were filled with *in situ* water. Cores were closed with airtight lids and the overlying water was stirred during the whole incubation period. Water samples were collected with acid-washed syringes before and after incubation and O<sub>2</sub> concentrations were determined by the Winkler method using potentiometric end-point detection and a Mettler® titrator (DL21). Hourly benthic CR and GPP rates were calculated with the same method as that for the planktonic compartment outlined above.

At each station and incubation depth, samples were taken for chlorophyll *a* and nutrient analysis. For chlorophyll *a*, water was filtered through GF/F filters that were stored frozen until extraction and analysis by high-performance liquid chromatography (Barranguet et al. 1997). Automated colorimetric techniques were used for orthophosphate, nitrate, nitrite and ammonium (Van den Meersche et al. 2004).

Light penetration in the water column was measured using a LI-COR quantum LI-1935A and a LI-1400 datalogger twice during each incubation period. Surface irradiance was measured every 15 min during the first cruise using a LI-COR cosine corrected quantum sensor (LI-1925A) and a LI-1400 datalogger. Hourly surface irradiance data during the second cruise were provided by the Danish Institute of Agricultural Sciences at Folum, about 25 km from the city of Randers.

Benthic and planktonic primary production is strongly dependent on the available light intensity. Strong variations in surface light irradiance were observed during both campaigns (see Table 1) and a correction was therefore applied to compare results obtained under different light conditions. During both cruises, for each station, relationships between planktonic hourly GPP rates normalized per unit of chlorophyll *a*
(GPP<sub>B</sub>, mmol O<sub>2</sub> mg Chl *a* h<sup>-1</sup>) and daily light intensities (*I*, µmol m<sup>-2</sup> s<sup>-1</sup>) were established using the model of Platt et al. (1980).

$$GPP_B = GPP_{Bmax} [1 - \exp(-I/I_k)] \quad (1)$$

where GPP<sub>Bmax</sub> is the maximal GPP rate normalized per unit of chlorophyll *a* (mmol O<sub>2</sub> mg Chl *a* h<sup>-1</sup>), *I* is the irradiance at each incubation depth (µmol m<sup>-2</sup> s<sup>-1</sup>) and *I*<sub>k</sub> is the saturation irradiance value (µmol m<sup>-2</sup> s<sup>-1</sup>).

As samples for benthic GPP measurements were taken at only one depth and incubations performed under a 0.2 m water height, such procedure was not applicable. Gargas (1970) estimated saturation irradiance (*I*<sub>k</sub>) on an annual scale in sediments of Nivå bay (Oresund) and found values around 100 and 300 µmol m<sup>-2</sup> s<sup>-1</sup> in April and August respectively. Thus, for each station, we fixed this parameter and calculated GPP<sub>max</sub> (mmol O<sub>2</sub> m<sup>-2</sup> h<sup>-1</sup>) values to fit our data using eq. (1) although benthic GPP rates were expressed in mmol O<sub>2</sub> m<sup>-2</sup> d<sup>-1</sup> and not normalized per unit of chlorophyll *a*. Planktonic and benthic hourly GPP values were recalculated for each station assuming a mean daily surface irradiance for each campaign (486.3 and 486.2 µmol m<sup>-2</sup> s<sup>-1</sup> during Randers 1 and 2, respectively). Daily planktonic and benthic CR rates were calculated by multiplying the hourly rates by 24. Daily GPP rates were multiplied by the daylight duration (14 h and 15 h during Randers 1 and 2, respectively) to estimate daily planktonic and benthic GPP.

The fjord was divided in 2 boxes and 3 depth layers were considered (0-2 m, 0-4 m and 0-7 m, the latter corresponding to the navigation channel). The surface area and the percentage covered by each depth layer were estimated for each box using a digitized map (INT 1381) and Arcview 8.3 software package. Planktonic metabolic rates were integrated and both benthic and planktonic rates were extrapolated according to these bathymetric profiles. Benthic GPP was assumed to be nil in the deep inner box of the estuary that mainly consists of the navigation channel (7 m deep) with a negligible surface area covered by banks.

Planktonic and benthic daily NCP rates were calculated as the sum of daily GPP and CR and the net ecosystem production (NEP) of each box was estimated by summing NCP rates in both compartments. As metabolic rates estimates in each box correspond to a mean value between several stations, associated standard errors (SE) are mentioned hereafter.
Carbonate dynamics and NEP estimates

During the Randers 1 cruise, pCO₂, total alkalinity (TA) and O₂ data were obtained at two stations that were occupied during 24 h (Sta. 2 and 4, respectively on 25-26 and 28-29 April 2001) and along a transect aboard the *Genetica* from the city of Randers to the mouth of the estuary (30 April 2001). During the Randers 2 cruise, the same variables were obtained at 3 stations occupied during 24 h (Sta. 2, 4 and 6, respectively on 23-24, 27-28 and 20-21 August 2001) and along 2 transects aboard the *Tyrfing* from the city of Randers to the mouth of the estuary on 30 and 31 August 2001. Samples for TA and O₂ were collected hourly during the 24 h cycles and each 2 salinity units during the shipboard transects. Samples for TA determination were filtered on GF/F filters.

pCO₂ was measured by equilibration using the Floating Equilibrator System (FES) described by Frankignoulle et al. (2003). Briefly, the FES is a buoy containing an equilibrator, a non-dispersive infra-red gas analyser (IRGA), water and air temperature probes, an anemometer, a data logger (1 min recording interval) as well as air and water pumps. During the 24 h cycles, the FES was deployed and anchored before 12:00 UT. For the shipboard transects, it was adapted for underway measurements from the subsurface water supply of the ship. The IRGA (LI-COR 6262) was calibrated daily using pure nitrogen and 3 gas standards with a CO₂ molar fraction of 365, 810 and 4000 ppm. All gases were supplied by Air Liquide, Belgium. The estimated accuracy of the pCO₂ measurements is ± 3 ppm. TA was measured by Gran electro-titration on 100 ml samples with a reproducibility of ± 2 µmol kg⁻¹ and an estimated accuracy of ± 3 µmol kg⁻¹. DIC was computed from pCO₂ and TA using the thermodynamic constants of Mehrbach et al. (1973); the accuracy is ± 5 µmol kg⁻¹ (for further details refer to Frankignoulle & Borges 2001a). O₂ was measured by the Winkler method using a potentiometric end-point determination, with an estimated accuracy of ± 2 µmol kg⁻¹.

Apparent oxygen utilisation (AOU) was calculated using the O₂ saturation computed according to Benson and Krause (1984). Salinity was measured with a portable salinometer (Orion® 125) with a precision of ± 0.1.
NEP estimates at estuarine scale

A mass balance of DIC in the mixed layer was constructed using estimates of fluvial DIC input, output of DIC to the Baltic Sea and air-water flux of CO₂. NEP was computed to balance the budget. The fluvial DIC input (FI<sub>DIC</sub> in mmol m⁻² d⁻¹) was computed according to:

$$ FI_{DIC} = \frac{\rho DIC_{0} Q}{S} \qquad (2) $$

where DIC₀ is the mean value of DIC measured at Sta. 6 (mmol kg⁻¹), ρ is water density (kg m⁻³), Q is the fresh water flow (m³ d⁻¹) and S is the surface area of the Fjord (m²). The output of DIC to the Baltic Sea (OBS<sub>DIC</sub> in mmol m⁻² d⁻¹) was computed according to:

$$ OBS_{DIC} = \frac{\rho DIC_{AZE} Q}{S} \qquad (3) $$

where DIC<sub>AZE</sub> is the Apparent Zero End-member of DIC (mmol kg⁻¹), ρ is water density (kg m⁻³), Q is the fresh water flow (m³ d⁻¹) and S is the surface area of the Fjord (m²). DIC<sub>AZE</sub> is the intercept at zero salinity of a line tangent to the observed curve of DIC against salinity in the lower estuary (Kaul & Froelich 1984). The intercept at zero salinity was computed by linear regression from DIC data at salinities above 9, since DIC shows a relatively conservative behaviour in the lower estuary.

The air-water CO₂ flux was computed according to:

$$ F = \alpha k \Delta pCO_2 \qquad (4) $$

where F is the air-water CO₂ flux (mmol m⁻² d⁻¹), α is the CO₂ solubility coefficient (mmol m⁻³ ppm⁻¹), k is the gas transfer velocity (m d⁻¹) and ΔpCO₂ is air-water gradient of CO₂ (pCO₂air- pCO₂water in ppm). A positive flux corresponds to a transfer of CO₂ from the atmosphere to the water (Notice that our convention differs from that in air-sea exchange literature).

The gas transfer velocity was computed according to a parameterization as a function of wind speed established from floating chamber interfacial CO₂ flux measurements carried out during the two cruises (Borges et al. in press):

$$ k_{600} = 0.3 + 0.536u_{10} \qquad (5) $$

where k<sub>600</sub> is the gas transfer velocity of CO₂ normalized to a Schmidt number (Sc) of 600 (m d⁻¹) and u<sub>10</sub> is wind speed referenced at a height of 10 m (m s⁻¹).
$k_{600}$ was converted to *in situ* temperature conditions assuming a k dependency proportional to Sc⁻⁰.⁵. Sc was computed for a given salinity from the formulations for salinity 0 and 35 given by Wanninkhof (1992), and, assuming that Sc varies linearly with salinity.

Uncertainty on the DIC fluvial input was estimated assuming an analytical error on DIC₀ of ± 0.005 mmol kg⁻¹ and an error on Q of ± 5%. Uncertainty on the DIC output to the Baltic Sea was estimated from the standard error on the y-intercept of the regression line of DIC versus salinity (DIC<sub>AZE</sub>) and assuming an error on Q of ± 5%. Uncertainty on the CO₂ atmospheric flux was computed assuming an error on ΔpCO₂ of ± 3 ppm and an error on u₁₀ of ± 10%.

## NEP estimates at daily scale

The rates of metabolic processes in the mixed layer were computed from the DIC and AOU time courses during the 24 h cycles according to the following equation (example for DIC):

$$ \Delta DIC = h \rho \frac{(DIC_{(2)} - \frac{F}{h\rho}\Delta t) - DIC_{(1)}}{\Delta t} \quad (6) $$

where ΔDIC is the hourly DIC variation (mmol C m⁻² h⁻¹), DIC<sub>(1)</sub> is DIC (mmol kg⁻¹) at time step 1 (t₁), DIC<sub>(2)</sub> is DIC at time step 2 (t₂), h is the mixed layer depth (2.5 m), ρ is water density (kg m⁻³), Δt is t₂-t₁ (h), F is the atmospheric CO₂ flux (mmol m⁻² h⁻¹) computed according to eq. (4) and (5) from ΔpCO₂ at t₁ and the average u₁₀ between t₁ and t₂.

The average of day-time ΔDIC or ΔAOU values correspond to GPP + CR and the average of night-time ΔDIC or ΔAOU values correspond to CR. The integration at daily scale of hourly GPP and CR rates is the same as outlined for O₂ incubation method. The DIC and AOU data were fitted with polynomial curves to remove the variability related to analytical errors and potential variability related to water mass advection. SE associated with ΔDIC an ΔAOU averages were estimated and are mentioned hereafter.
## Response surface difference (RSD) method

The RSD method developed by Swaney et al. (1999) is derived from the *in situ* diel O₂ approach applied to flowing waters. It is based on the comparison of O₂ concentrations at an initial time and station with O₂ concentrations at a second station downstream, after the time interval estimated for water to flow from the upstream to the downstream station (Odum 1956). In tidal estuarine systems, this procedure is difficult to apply because of the intrusion of marine waters and potentially subsequent water column stratification. Swaney et al. (1999) used in their method salinity as a conservative tracer to provide information on mixing and advection. This procedure is based on the hypothesis that the concentration of O₂ in an estuary varies linearly with depth, salinity and time, following the equation:

$$O_2 = b_0 + b_z z + b_s Sal + b_t t + \varepsilon \quad (7)$$

where O₂ is the observed O₂ concentration (mol m⁻³), bᵢ is the rate of O₂ concentration variation associated with a unit change in each of the independent variables (z: depth, Sal: salinity and t: time), and ε represents the random component of O₂ concentration variation not explained by the independent variables.

Rates of metabolic parameters (NEP, GPP and CR) are estimated from the change of O₂ over time (bᵢ), corrected for air-water O₂ exchange. The latter was estimated using the same procedure than the one used for CO₂ air-water exchange (see above). During the Randers 1 cruise, 8 stations were investigated along the estuary (Sta. 1A, 2, 2A, 3, 4, 5, 5A and 6; see Fig. 1) at midday, sunset (1 May 2001) and sunrise (2 May 2001). Seven stations were investigated during Randers 2 (Sta. 2, 2A, 3, 4, 5, 5A and 6; see Fig. 1) at sunset (19 August 2001), sunrise, midday and sunset (20 August 2001). At each station, vertical profiles of O₂, temperature and salinity were made using an YSI 556 multiprobe. The O₂ sensor was calibrated in water-saturated air before each transect. Wind speed was measured with a hand-held anemometer.

SE associated with this method result mainly from the uncertainty of the regression of O₂ over time (eq. 7) and from averaging wind speeds values (i.e. uncertainty in air-water exchange).
## LOICZ budgeting procedure

Stoichiometrically-linked water-salt-nutrient budgets were constructed using data collected during the two cruises as described by Gordon et al. (1996). A 2 box-2 layer model was considered, as strong vertical stratifications were observed in the fjord. The inner box covers the area from the freshwater zone (Sta. 6) to Sta. 5 and the outer part from Sta. 5 to the mouth of the estuary (Sta. 1A; see Fig. 1). A depth of the mixed layer of 2.5 m was used as outlined above. Data of freshwater flows were provided by the National Environmental Research Institute, Department of Freshwater Ecology, Denmark. Vertical profiles of salinity and nutrients (DIN and DIP) were measured as described above. Sta. 6 and 1 data were used as riverine and marine end-members respectively.

NEP ($p-r$ in the LOICZ terminology) is calculated from the non-conservative fluxes of DIP in each box, assuming that these fluxes are only related to biological activity and following the equation:

$$NEP = -\Delta DIP \cdot (C:P)_{part} \qquad (8)$$

where NEP is the net ecosystem production in mmol C m⁻² d⁻¹, ΔDIP is the non-conservative flux of DIP in mmol P m⁻² d⁻¹ and $(C:P)_{part}$ is the particulate C:P ratio. A value of 106:1 was used.

Budgets of DIN were also computed and the difference between nitrogen fixation and denitrification ($n_{fix-denit}$ in the LOICZ terminology) was calculated from the equation:

$$n_{fix-denit} = \Delta DIN - \Delta DIP \cdot (N:P)_{part} \qquad (9)$$

where $n_{fix-denit}$ is in mmol N m⁻² d⁻¹, ΔDIN and ΔDIP are the non-conservative fluxes of nitrogen and phosphorus in mmol m⁻² d⁻¹ and $(N:P)_{part}$ is the particulate N:P ratio. A value of 16:1 was used.

Annual LOICZ budgets were also calculated for the years 1991 to 2002 using data of salinity, orthophosphate, nitrate, nitrite and ammonium concentrations extracted from the MADS database (National Environmental Research Institute, Denmark, http://www.dmu.dk/1_viden/2_Miljoe-tilstand/3_vand/4_mads/default_en.asp). Data for 2 stations in the estuary (corresponding to Sta. 2 and 5 in Fig. 1) and 1 station in the Kattegat (Sta. 1; see Fig. 1) were used. Data of freshwater flow and concentrations of
DIP and DIN in the river for the same period were provided by the National Environmental Research Institute, Department of Freshwater Ecology, Silkeborg, Denmark.
## RESULTS

### Oxygen incubations

Environmental characteristics at the sampling stations during both cruises are presented in Table 1 and Fig. 2. In April, the mean water temperature was 8.1 ± 1.0°C; it was lowest in the freshwater area (Sta. 6; 6.6°C) and highest at the mouth of the estuary (Sta. 1A; 9.9°C). In August, lowest water temperatures were observed in the mid-estuary (Sta. 4 and 4A) while highest values were observed in the inner area (Sta. 5), the mean value during this cruise was 18.5 ± 1.2°C. The water column was stratified during both cruises especially in the inner part of the estuary. In April, the light attenuation coefficient (K) was highest in the inner estuary where relatively high suspended particulate matter (SPM) concentrations were measured. In August, the most turbid waters were located in the mid-estuary. Large changes of mean surface irradiance were observed during both cruises. The total range was 183.5 to 737.8 µmol m⁻² s⁻¹.

O₂ saturation was higher in April than in August (Fig. 2). The lowest saturation values were measured during both cruises in the inner estuary with a minimal value in August at the bottom of Sta. 6 (36.8%). O₂ super-saturations were only measured in the Kattegat (Sta. 1) in April. Chlorophyll *a* concentrations were similar in the outer estuary during Randers 1 and 2 with values lower than 3 mg m⁻³. In the brackish area, values were higher in April than in August with a maximal concentration in surface at Sta. 5 (21 mg m⁻³).

The DIN concentrations were higher in April than in August with a maximal value in the freshwater area (135 mmol N m⁻³). It generally decreased seaward, except in August when surface concentration was higher in the inner part of the estuary (Sta. 5) than in the river, suggesting a DIN release in this area. The DIP concentrations were higher in August than in April and were highest in the mid-estuary (2.9 mmol P m⁻³; Sta. 4).

Rates of planktonic community metabolism (GPP and CR) are shown in Fig. 3. It should be noted that in April incubations were not performed in the Kattegat (Sta. 1). Similar CR volumetric rates were observed in surface waters during Randers 1 and 2 ranging from -1.2 to -11 mmol O₂ m⁻³ d⁻¹, while higher rates were generally measured in bottom waters during the second cruise. Stronger variations were observed for GPP
volumetric rates during both cruises. In April, the highest rates were measured in the
freshwater and inner parts of the estuary (max: 44 mmol O₂ m⁻³ d⁻¹ at Sta. 5 at 0.5 m)
while low values were observed in the outer and marine parts. In August, Sta. 4 and 4A
presented high GPP volumetric rates (max: 80 mmol O₂ m⁻³ d⁻¹ at Sta. 4a at 0.5 m).

It must be stressed that, in August, metabolic rates measured at Sta. 4 and 4a were not
used to calculate the integrated value for the outer estuary given in Table 2. In fact,
these stations were investigated following a precipitation event on 26 August 2001
which strongly modified the environmental characteristics: surface temperature was 2°C
lower (Table 1) and DIP concentrations were almost twice the observed concentrations
at stations sampled before that date. Thus, only results obtained at Sta. 2 were upscaled
to the whole outer estuary in August.

In April, benthic GPP was highest in the banks along the outer part of the estuary (72 ± 32 mmol O₂ m⁻² d⁻¹) while the highest planktonic GPP was measured in the inner area (46 mmol O₂ m⁻² d⁻¹). For the whole estuary, benthic GPP represents almost 70% of the organic matter production during this period, but benthic CR was of the same order of magnitude and the benthic compartment was net heterotrophic in April. In contrast, the planktonic compartment was net autotrophic in April. Consequently NEP estimates reveal a slight net heterotrophy in the estuary in April although not significantly different from 0 (1 ± 30 mmol O₂ m⁻² d⁻¹).

In August, planktonic GPP was higher than in April and CR rates more than
doubled in the inner area. The planktonic compartment was net autotrophic (5 mmol O₂
m⁻² d⁻¹). Benthic GPP decreased by half between the two campaigns and benthic CR
was similar during both cruises. As a result, the benthic NCP was lower in August than
in April (-20 ± 16 vs. -5 ± 28 mmol O₂ m⁻² d⁻¹). The summertime NEP of the whole
estuary was -15 ± 16 mmol O₂ m⁻² d⁻¹.

Carbonate dynamics and NEP estimates

Carbonate dynamics

The distribution of pCO₂, DIC and TA during both Randers cruises is shown as a
function of salinity in Fig. 4. pCO₂ was significantly lower in the inner estuary in April
than in August (Fig. 4A). The large variability of pCO₂ (2243-3906 ppm) at salinity 0.2
in August was measured during a 24 h cycle. It is related to diel alternation between
photosynthesis and respiration and is discussed in more detail below. TA and DIC show
strong non conservative behaviour against salinity during both cruises, with a distinct
increase at salinities from 0 to 9 (Fig. 4B and C).

NEP estimates at estuarine scale

The air-water CO₂ fluxes exhibited strong spatial variations during both cruises.
The inner part of the estuary was a source of CO₂ during both cruises, -54 ± 31 and -146
± 18 mmol C m⁻² d⁻¹ during, respectively, in April and August. The outer part of the
estuary was a CO₂ sink in spring and a source in summer (+10 ± 23 and -7 ± 27 mmol C
m⁻² d⁻¹).

The strong precipitation event which prevented to use the O₂ incubation data
obtained during Randers 2 (see above) also prevented the use of DIC and pCO₂ values
obtained at Sta. 4 as well as at the end of the cruise (transect onboard the *Tyrfing* on 30-
31 August 2001). The mixed layer DIC budget of the Randers Fjord during both cruises
is shown in Table 3. A careful error analysis shows that this approach to compute NEP
is prone to very large uncertainty. NEP computed using this method reveals a higher net
heterotrophy during the second cruise with values of -10 ± 36 and -47 ± 15 mmol C m⁻²
d⁻¹ in April and August, respectively.

NEP estimates at daily scale

During the 24 h cycles at Sta. 2 and 4 in April and August, the variations of DIC and AOU were related to the tidal advection of water masses of different salinities (not shown) and cannot be used to estimate the rates of metabolic processes. During the 24 h cycle at Sta. 6 (20-21 August, Randers 2) salinity was constant at 0.2 and water flow was always downstream. The time-course of DIC and AOU clearly follows the evolution expected from the diel succession of photosynthesis and respiration (Fig. 5).

High values of metabolic rates with strong associated errors were estimated and
a positive NEP value was found using both methods (Fig. 5).
**Response surface difference (RSD) method**

The estimates of time-dependent rates of oxygen change ($b_t$) resulting from eq. (7) are compiled in Table 4. During both campaigns, $b_t$ estimates were significantly different from 0 although $r^2$ values were higher in April than in August. Stronger $O_2$ decrease overnight (i.e. higher respiration) and lower $O_2$ increase during daylight (i.e. lower NCPd) were estimated in August than during the first cruise.

As the water column was always undersaturated in term of $O_2$, fluxes across the air-water interface were directed from the atmosphere to the water with highest transfer velocities ($k$) in April due to stronger wind speeds. Nevertheless, because of lower water column $O_2$ saturation during the second cruise (< 80% vs. > 90% during Randers 1), higher $O_2$ fluxes across the interface were estimated. Computation of metabolic parameters yields a GPP of 143 ± 33 mmol $O_2$ m⁻² d⁻¹ and a CR of -123 ± 24 mmol $O_2$ m⁻² d⁻¹ in April. NEP value during this cruise is estimated to 20 ± 57 mmol $O_2$ m⁻² d⁻¹, which due to the high cumulated error, is not significantly different from 0. In August, GPP, CR and NEP were estimated to 128 ± 33, -191 ± 28 and -63 ± 61 mmol $O_2$ m⁻² d⁻¹.

**LOICZ budgets**

Salinity, nutrients concentrations (DIP and DIN), water residence time, non-conservative fluxes of DIP and DIN, as well as derived NEP and ($n_{fix-denit}$) values are shown in Table 5 for each box budgeted. As seen above, DIP concentrations were higher during both campaigns in the inner deep compartment than in the inner upper compartment. Concentrations of DIN and DIP revealed antagonistic changes between the two cruises with higher DIP and lower DIN in summer than in spring. Freshwater flows averaged over each period of investigation were significantly higher in April than in August (3.1 vs. 1.9 x 10⁶ m³ d⁻¹; not shown in Table 6). During both campaigns, the water residence time ($τ$) was highest in the inner deep compartment with a maximal value in April (44 d) due to a high surface freshwater flow inhibiting vertical mixing. Non-conservative fluxes of DIP were extremely low in spring with a whole estuary net release of 0.007 mmol P m⁻² d⁻¹. It is noteworthy that during this period the upper compartments were net sinks and the deeper compartments were net sources of DIP. Consequently, NEP calculations led to a net heterotrophy in deep sections and a net
autotrophy in surface. In April, the estuary was roughly in metabolic balance (NEP = -0.8 mmol C m⁻² d⁻¹). Moreover, nₐₗₐ₆₅ₐ₆₄ values of -0.6 and -0.2 mmol N m⁻² d⁻¹ were calculated in the outer and inner estuary respectively, leading to a whole estuary rate of -0.3 mmol N m⁻² d⁻¹. In August, a higher DIP release is observed (0.26 mmol P m⁻² d⁻¹) leading to a strong net heterotrophy (-27 mmol C m⁻² d⁻¹). Results of nₐₗₐ₆₅ₐ₆₄ computations revealed a net DIN fixation in the inner part and a net DIN denitrification in the outer part (5 vs. -10 mmol N m⁻² d⁻¹).

LOICZ annual budgets from 1991 to 2002 are presented in Fig. 6. A significant increase of the NEP is observed during this period, although annual net autotrophic conditions were only met once in 2000. According to these results, the Randers Fjord seems to act as a sink for organic matter and a source of atmospheric CO₂ on an annual scale. Results of nₐₗₐ₆₅ₐ₆₄ computations showed an annual net DIN fixation in the estuary during all the considered period.
DISCUSSION

One of the goals of this study was to estimate ecosystem metabolism in an estuary using different approaches. Results obtained using each method are summarized in Table 6. Data should be interpreted with caution as the different methods do not estimate NEP in the estuary at the same spatial and temporal scales. The LOICZ and DIC budgets as well as the O₂ incubation estimates are based on local estimates (either metabolic rates or concentrations) which were upscaled to the whole estuary. The DIC budget provides estimates of NEP for the mixed layer only. The RSD method is based on several transects along the estuary at different periods of the day, it therefore provides estimates of NEP integrated across space and time.

Moreover, the different methods do not provide estimates expressed in the same unit, a conversion factor is needed to express the O₂-based rates in carbon units. In August, using results from the AOU and DIC diel cycles at Sta. 6 (Fig. 5), a gross photosynthetic quotient (PQ) and a respiratory quotient (RQ) of around 1.26 and 1, respectively, were calculated. This value of PQ is within the range of 1.0-1.36 given by Williams and Robertson (1991). A RQ of 1 corresponding to the oxidation of carbohydrates (Richardson 1929) is commonly used to convert mineralization rates into carbon units in estuaries (Hopkinson & Smith in press).

All approaches provided consistent metabolic estimates, both in sign and magnitude. Estimates of NEP in April suggest a slight heterotrophy but the errors associated with each method do not allow us to draw a statistically unambiguous conclusion on the metabolic status of the estuary at this time of the year. In August, estimates based on the O₂ incubations and LOICZ budgets were very similar (-28 and -27 mmol C m⁻² d⁻¹) while DIC budgets provided a slightly lower NEP (-47 mmol C m⁻² d⁻¹). In contrast to April, the RSD method provides a lower NEP (-89 mmol C m⁻² d⁻¹) than the other methods. It is noteworthy that the results from Sta. 4 were removed from our calculations using the O₂ incubation, LOICZ and DIC methods while this station was sampled during the RSD transects. In order to compare consistently the results from the different methods, we estimated NEP using the RSD method without considering Sta. 4 profiles. The estimated NEP reaches a value of -36 ± 76 mmol C m⁻² d⁻¹, closer to the values obtained using the other methods. Therefore, we believe that NEP results based on O₂ incubation, LOICZ and DIC methods are perhaps underestimated in August
because the rates or concentrations from only one station (Sta. 2) were extrapolated to
the whole outer estuary. The value of -89 mmol C m⁻² d⁻¹ based on the RSD method
(using the full dataset) is closer to reality as it covers the entire estuary.

The metabolic rates estimated for the whole estuary obtained with the open water (RSD) and incubation approaches differ dramatically. The values of GPP and CR resulting from the RSD method are about twice higher than the ones estimated from the O₂ incubations method during both cruises. This discrepancy between open system and bottle incubations measurements has already been reported in several studies (Pomeroy 1960, Bender & Jordan 1970, Kemp & Boynton 1980, Howarth et al. 1992). Usually, open water techniques estimates provide estimates 1.5 to 3 times higher than incubation methods. These discrepancies can have several causes. First, the RSD method considers a linear relationship between oxygen, salinity, depth and time. Applied to our data, these regressions yield r² of around 0.6 and 0.3 in April and August respectively. This is an important source of uncertainty. It should be stressed that this method was developed for the partially mixed Hudson River while the Randers Fjord, as outlined above, is almost permanently stratified. Second, incubations eliminate the effects of mixing and turbulence which may have strong effects on both primary production and respiration (Kemp & Boynton 1980, Howarth et al. 1992).

The data collected in August at St. 6 allow the comparison of open water and incubation approaches for the estimation of metabolic rates at daily scale. NCP values were similar (Table 6) but GPP and CR estimates based on AOU/DIC 24 h cycles are one order of magnitude higher than those from the O₂ incubations. The discrepancy may be mainly due to the presence of extensive freshwater marshes upstream of Sta. 6, leading to a large contribution of benthic GPP and CR to the daily oscillations of DIC and AOU in the mixed layer during the 24 h cycle.

An important source of uncertainty in the RSD method as well as in the DIC budgets is the parameterization of the gas transfer velocity to compute the air-water O₂ and CO₂ fluxes, respectively. We recomputed the DIC budgets using the air-water CO₂ flux calculated from the gas transfer velocity parameterization of Raymond and Cole (2001) based on a compilation of gas tracer experiments in various estuaries. The corresponding NEP estimates are -12 ± 36 and -38 ± 12 mmol C m⁻² d⁻¹ for April and August, respectively. This corresponds to a change of -20% and +20% compared to the
data shown in Table 6 which were estimated using the formulation of Borges et al. (in press). Moreover, O₂ fluxes across the air-water interface (FO₂) represented between 17 and 70% of the observed O₂ variation with time (b_t), and thus is an important source of error in the computation of metabolic rates using the RSD method. This illustrates that the evaluation of the gas transfer velocity is important in ecosystem metabolic studies based on the open water approach, especially in estuaries where parameterisations as a function of wind speed only have recently been shown to be site specific (Kremer et al. 2003, Borges et al. in press).

As mentioned above, the DIC budgeting procedure only gives an estimation of the mixed layer NEP. Estimating the NEP of the mixed layer based on the LOICZ approach yields to a value of 0.3 and -45 mmol C m⁻² d⁻¹ in April and August respectively, close to the ones estimated by the DIC budgets. The Apparent zero end-member (AZE) method used for the DIC budgets has been criticized (e.g. Regnier & Steefel 1999) because if there is a large and rapid change of freshwater DIC during a time interval shorter than the flushing time of the estuary, then a non-conservative behaviour can appear in the profile of DIC versus salinity that is unrelated to a real removal or production term. This holds true for macrotidal estuaries where the flushing time is of the order of months, but the Randers Fjord is characterized by a very short water residence time (~5 d in April and August).

The LOICZ procedure assumes that all non-conservative DIP fluxes in a system are only related to ecosystem gross primary production and respiration (Gordon et al. 1996). This assumption may not be valid in systems receiving high loads of SPM because adsorption and desorption of phosphorus to and from these particles (the so-called phosphorus buffer mechanism) can occur along the salinity gradient (Sharp et al. 1982, Edmond et al. 1985, Froelich 1988, Fox 1990, Zwolsman 1994). SPM concentrations in the Randers Fjord are much lower than those reported in the latter studies with values ranging from 1.5 to 43.1 g m⁻³ in April and 0.6 to 12.1 g m⁻³ in August. Thus, abiotic non-conservative fluxes are unlikely to play a significant role in this system.

Finally, another source of uncertainty for both DIC and LOICZ budgets is lateral inputs of DIP and DIC. A significant modification of water column characteristics was evidenced in August following a strong precipitation event while no concomitant
freshwater flow increase was observed. This suggests that lateral inputs can be punctual
sources of DIP and DIC variability in this system. Unfortunately, no data were available
to us to test this hypothesis on larger time scales.

Planktonic GPP is highly variable in estuaries and mostly depends on physical
parameters governing light availability such as residence time, turbidity and vertical
mixing rather than on nutrient concentrations (Boynton et al. 1982, Heip et al. 1995). In
European estuaries (see review of Gazeau et al. in press), planktonic GPP was found to
range between 1.7 (Ems-Dollard estuary; Van Es 1977) and 153.3 mmol C m⁻² d⁻¹ in the
highly productive Urdaibaï estuary (Revilla et al. 2002). The production of organic
matter by chemoautotrophic bacteria such as nitrifiers, which are reported to be very
active in eutrophic estuaries (Heip et al. 1995), was not measured during our study.
Ignoring this oxygen consuming process may have lead to an overestimation of CR.
Nitrification is unlikely to be a significant process in the water column because
ammonium concentrations were low during both cruises (3.5 and 2.7 mmol m⁻³ in April
and August respectively).

In the extensive review of Hopkinson & Smith (in press), planktonic CR rates in estuaries were found to range between -1.7 in the Gulf of Finland to -84 mmol C m⁻³ d⁻¹ in the Fly River delta with a mean value of -19.6 mmol C m⁻³ d⁻¹. Rates measured during this study (-1.2 to -11.2 mmol C m⁻³ d⁻¹ with a mean value of -5.8 mmol C m⁻³ d⁻¹) are thus in the lower end of the range reported by Hopkinson & Smith (in press).

In this shallow estuary, the benthic compartment plays a significant role on the
production and mineralization of organic matter. Benthic GPP was 53 and 23 mmol C
m⁻² d⁻¹ in April and August, respectively or 70 and 40% of the total GPP. This is not
surprising since the shallowness of the estuary (average depth: 1.6 m) and the relatively
low SPM concentrations (mean of 4 to 9 g m⁻³) enable benthic primary production on a
large portion of the estuary. These rates of benthic GPP are within the range of 16-72
mmol C m⁻² d⁻¹ given by Van Es (1982) and Colijn and de Jonge (1984) for intertidal
flats in the Ems-Dollard estuary. Benthic CR amounted to -58 and -43 mmol C m⁻² d⁻¹,
within the range from -30.2 to -62.9 mmol C m⁻² d⁻¹ in Norsminde Fjord (Denmark;
Andersen & Kristensen 1988). Benthic CR rates measured during this study (-17 to -69
mmol C m⁻² d⁻¹) fall in the range of -3 to -115 mmol C m⁻² d⁻¹ of published benthic CR rates compiled by Hopkinson & Smith (in press).

In a recent paper, Caffrey (2004) reported annual NEP estimates based on the *in situ* diel O₂ method for 42 estuarine sites in the US. All sites are heterotrophic in term of carbon with NEP ranging from -11 to -278 mmol C m⁻² d⁻¹ and a mean value of -110 ± 66 mmol C m⁻² d⁻¹. Using the O₂ incubation method, Smith & Kemp (1995) estimated a NEP in Chesapeake Bay ranging from -6 to 65 mmol C m⁻² d⁻¹. Raymond et al. (2000) estimated an annual NEP in the York River estuary based on a DIC budget of -23 mmol C m⁻² d⁻¹. Smith & Hollibaugh (1997) used a LOICZ budget to estimate the NEP in Tomales Bay and found an annual rate of -12 mmol C m⁻² d⁻¹. NEP rates measured in the Randers Fjord using four methods during the two cruises fall within the range of these previous studies.

Averaging CO₂ fluxes estimated during the two cruises leads to a rough estimate of the annual emission of CO₂ from the Randers Fjord of -6 mmol C m⁻² d⁻¹. Although this is a minimal estimate since the spring phytoplanktonic bloom does not last half of the year, it is below the range of CO₂ emission from nine macro-tidal European estuaries (-86 to -208 mmol C m⁻² d⁻¹; Frankignoulle et al. 1998). This is probably related to the shorter water residence time in micro-tidal estuaries, such as the Randers Fjord, than in macro-tidal estuaries. This leads to a less efficient consumption of allochthonous organic carbon during the transit through the estuary and a lower generation of CO₂. Also, due to the stratified nature of micro-tidal estuaries (as opposed to the generally well mixed macro-tidal estuaries), the CO₂ produced by degradation processes below the pycnocline are not immediately available for exchange with the atmosphere. This could also explain the fact that a net sink for atmospheric CO₂ was observed in April while all approaches suggest that the estuary was either heterotrophic or nearly balanced. This illustrates that the metabolic status and air-water CO₂ fluxes can be temporally decoupled by physical features.

As mentioned before, all LOICZ budgets for the years 1991 to 2002 suggest that nitrogen fixation is higher than denitrification. This result seems unrealistic since the estuary receives high loads of DIN and the mean annual rate of benthic denitrification is 2.7 mmol N m⁻² d⁻¹ or 281 tN yr⁻¹ (Nielsen et al. 2001). Our budgets only take into
account DIN inputs from the River Gudenå while Nielsen et al. (2001) also considered
the relatively minor river Allingå as well as waste water discharge. The difference
between their DIN input value and the one we used for the same year (1995) is
calculated to 670 tN yr⁻¹ and therefore can explain the artificial net DIN fixation given
by our budget. Adding this input to our budget yields to a value of -0.5 mmol N m⁻² d⁻¹.
This value is in the range of previously reported rates for sites with “Steve Smith’s
ranking ≥ 2” (-5.2 to 2.6 mmol N m⁻² d⁻¹) and roughly corresponds to the median rate (-
0.56 mmol N m⁻² d⁻¹) from the LOICZ environmental database (downloaded on 23
March 2004 from http://hercules.kgs.ukans.edu/hexacoral/envirodata/main.htm).

There is also a difference of 31.5 tP yr⁻¹ between the DIP input used in our budget for
the year 1995 and the one used by Nielsen et al. (2001). Adding this extra input to the
LOICZ budget leads to a higher NEP (-7 vs. -18 mmol C m⁻² d⁻¹).

Unfortunately, nutrient loading from the river Allingå and from point sources such as factories and sewage treatment plants are not available to us for the whole investigated period (1991-2002). Therefore, we believe that these annual LOICZ budgets provide significantly underestimates of both DIP and DIN net retention in the estuary and must be viewed with caution. Nevertheless, if the underestimations over the investigated period are assumed constant, then Fig. 6 clearly shows an evolution of the metabolic balance of the Randers Fjord toward higher NEP in the 1990's. (from -21 mmol C m⁻² d⁻¹ in 1991 to -9 mmol C m⁻² d⁻¹). These annual values fall within the range of reported NEP values estimated using the LOICZ budgeting procedure in 70 coastal sites with “Steve Smith's ranking ≥ 2” (-42 and 32 mmol C m⁻² d⁻¹; downloaded on 20 May 2004 from the LOICZ environmental database http://hercules.kgs.ukans.edu/hexacoral/envirodata/main.htm).

In the catchment area of the Randers Fjord, the construction of sewage treatment
plants took place in the 1970's and 1980's. Andersen (1999) pointed out that, since
1974, phosphorus and organic matter loading from waste water treatment plants have
been reduced by around 90% and nitrogen loading by about 50%. In the estuary itself,
phosphorus decrease has been delayed due to the release of phosphorus from the
sediment in lakes in the catchment area (Nielsen et al. 1993).

Although each method investigated during this study has its own limitations and
associated uncertainties, consistent NEP estimates in this estuary have been produced
during two field campaigns by all methods and showed that the Randers Fjord was near
metabolic balance in spring and net heterotrophic in summer. As incubation techniques,
as well as *in situ* diel methods such as RSD remain hard to apply on large spatial and
temporal scales, this result suggests that indirect methods such as LOICZ or DIC
budgets may be alternative confident ways to estimate the metabolic status of a coastal
ecosystem. Within the frame of the EUROTROPH project, the LOICZ budgeting
procedure will be applied to the Scheldt estuary in order to assess the validity of this
method in very turbid areas.
Acknowledgements

This research was supported by the European Union in the framework of the
EUROTROPH project (contract # EVK3-CT-2000-00040), and by a
CNRS/CGRI/FNRS cooperation. We gratefully acknowledge S. V. Smith for his help
concerning the implementation of the LOICZ budgeting procedure. We would like to
thank the skippers and crews of R. V. *Genetica* and *Tyrfing*, R. Biondo, J. -M. Théate
and E. Libert for technical support and D. Conley, K. Nielsen and N. Ovesen for
providing river flows data and riverine nutrient concentrations. Irradiance data were
collected by the Danish Institute of Agricultural Sciences (Folum, Denmark). AVB and
MF are, respectively, a post-doctoral and a senior research associate at the FNRS. CB
was funded by a scholarship from the government of the Balearic Islands. This is
MARE contribution XXX and NIOO publication XXX.
<table><caption>Table 1 Date, geographical location and characteristics of incubated stations during both cruises. I₀ is the average irradiance, K is the light attenuation coefficient and SPM is the concentration of suspended particulate matter during each incubation period.</caption><thead><tr><th>Sta.</th><th>Date</th><th>Longitude<br>°E</th><th>Latitude<br>°N</th><th>Depth<br>(m)</th><th>Sal</th><th>Temp<br>(°C)</th><th>I<sub>0</sub><br>(µmol m<sup>-2</sup> s<sup>-1</sup>)</th><th>K<br>(m<sup>-1</sup>)</th><th>SPM<br>(g m<sup>-3</sup>)</th></tr></thead><tbody><tr><td rowspan="12">Randers 1</td><td rowspan="4">1</td><td rowspan="4">25/04</td><td rowspan="4">10.44</td><td>0.5</td><td>21.4</td><td>7.0</td><td rowspan="4">205.3</td><td rowspan="4">-</td><td>1.9</td></tr><tr><td>1.5</td><td>21.4</td><td>7.0</td><td>1.5</td></tr><tr><td>3</td><td>21.4</td><td>7.0</td><td>3.7</td></tr><tr><td>7</td><td>21.4</td><td>7.0</td><td>1.9</td></tr><tr><td rowspan="4">1a</td><td rowspan="4">1/05</td><td rowspan="4">10.32</td><td>0.5</td><td>12.1</td><td>9.9</td><td rowspan="4">737.8</td><td rowspan="4">0.72</td><td>5.2</td></tr><tr><td>1.5</td><td>12.3</td><td>9.9</td><td>3.9</td></tr><tr><td>3</td><td>12.5</td><td>9.9</td><td>4.0</td></tr><tr><td>6</td><td>19.6</td><td>9.0</td><td>5.3</td></tr><tr><td rowspan="4">2</td><td rowspan="4">26/04</td><td rowspan="4">10.30</td><td>0.5</td><td>6.7</td><td>8.5</td><td rowspan="4">472.2</td><td rowspan="4">0.60</td><td>5.3</td></tr><tr><td>1.5</td><td>6.8</td><td>8.5</td><td>5.3</td></tr><tr><td>3</td><td>6.9</td><td>8.5</td><td>3.9</td></tr><tr><td>7</td><td>29.5</td><td>7.7</td><td>6.5</td></tr><tr><td rowspan="4">2a</td><td rowspan="4">28/04</td><td rowspan="4">10.26</td><td>0.5</td><td>4.9</td><td>8.8</td><td rowspan="4">454.9</td><td rowspan="4">0.64</td><td>43.1</td></tr><tr><td>1.5</td><td>5.3</td><td>8.9</td><td>5.2</td></tr><tr><td>3</td><td>14.5</td><td>8.5</td><td>5.8</td></tr><tr><td>6</td><td>19.2</td><td>8.1</td><td>3.0</td></tr><tr><td rowspan="4">5</td><td rowspan="4">29/04</td><td rowspan="4">10.21</td><td>0.5</td><td>1.4</td><td>9.1</td><td rowspan="4">678.2</td><td rowspan="4">1.13</td><td>9.7</td></tr><tr><td>1.5</td><td>2.5</td><td>9.0</td><td>12.4</td></tr><tr><td>3</td><td>14.2</td><td>8.3</td><td>8.0</td></tr><tr><td>6</td><td>15.9</td><td>8.2</td><td>35.3</td></tr><tr><td rowspan="4">6</td><td rowspan="4">22/04</td><td rowspan="4">10.04</td><td>0.5</td><td>0.2</td><td>7.0</td><td rowspan="4">723.2</td><td rowspan="4">1.28</td><td>10.2</td></tr><tr><td>1</td><td>0.2</td><td>7.0</td><td>9.9</td></tr><tr><td>1.5</td><td>0.2</td><td>7.0</td><td>9.6</td></tr><tr><td>4</td><td>8.2</td><td>6.6</td><td>9.5</td></tr><tr><td rowspan="12">Randers 2</td><td rowspan="4">1</td><td rowspan="4">22/08</td><td rowspan="4">10.44</td><td>0.5</td><td>22.0</td><td>18.7</td><td rowspan="4">551.5</td><td rowspan="4">0.22</td><td>0.57</td></tr><tr><td>3</td><td>23.0</td><td>18.4</td><td>0.57</td></tr><tr><td>5</td><td>23.1</td><td>18.4</td><td>0.91</td></tr><tr><td>7</td><td>28.1</td><td>15.9</td><td>12.10</td></tr><tr><td rowspan="4">2</td><td rowspan="4">24/08</td><td rowspan="4">10.30</td><td>0.5</td><td>16.3</td><td>19.8</td><td rowspan="4">529.4</td><td rowspan="4">0.67</td><td>2.58</td></tr><tr><td>1.5</td><td>18.3</td><td>19.8</td><td>3.20</td></tr><tr><td>3</td><td>19.8</td><td>19.8</td><td>2.49</td></tr><tr><td>7</td><td>21.8</td><td>19.6</td><td>2.37</td></tr><tr><td rowspan="4">4</td><td rowspan="4">28/08</td><td rowspan="4">10.23</td><td>0.5</td><td>11.3</td><td>17.4</td><td rowspan="4">502.8</td><td rowspan="4">1.35</td><td>6.34</td></tr><tr><td>1.5</td><td>11.6</td><td>17.4</td><td>6.10</td></tr><tr><td>3</td><td>13.6</td><td>17.8</td><td>7.34</td></tr><tr><td>5</td><td>19.8</td><td>18.1</td><td>5.05</td></tr><tr><td rowspan="4">4a</td><td rowspan="4">30/08</td><td rowspan="4">10.23</td><td>0.5</td><td>6.9</td><td>17.2</td><td rowspan="4">508.9</td><td rowspan="4">1.30</td><td>5.34</td></tr><tr><td>1.5</td><td>8.0</td><td>17.6</td><td>5.08</td></tr><tr><td>3</td><td>13.2</td><td>17.1</td><td>4.53</td></tr></tbody></table>
<table><caption>Table 1 continued</caption><thead><tr><th>Sta.</th><th>Date</th><th>Longitude<br>°E</th><th>Latitude<br>°N</th><th>Depth<br>(m)</th><th>Sal</th><th>Temp<br>(°C)</th><th>I<sub>0</sub><br>(µmol m<sup>-2</sup> s<sup>-1</sup>)</th><th>K<br>(m<sup>-1</sup>)</th><th>SPM<br>(g m<sup>-3</sup>)</th></tr></thead><tbody><tr><td rowspan="6">Randers 1</td><td rowspan="3">5</td><td rowspan="3">26/08</td><td rowspan="3">10.21</td><td>6</td><td>20.2</td><td>16.6</td><td rowspan="3">183.5</td><td rowspan="3">0.89</td><td>9.42</td></tr><tr><td>0.5</td><td>2.7</td><td>19.9</td><td>3.58</td></tr><tr><td>1.5</td><td>8.7</td><td>20.3</td><td>3.82</td></tr><tr><td rowspan="3">6</td><td rowspan="3">21/08</td><td rowspan="3">10.04</td><td rowspan="3">56.46</td><td>3</td><td>13.5</td><td>20.2</td><td>4.61</td></tr><tr><td>6</td><td>16.1</td><td>20.0</td><td>8.20</td></tr><tr><td>0.5</td><td>0.2</td><td>18.8</td><td>4.10</td></tr><tr><td rowspan="3"></td><td rowspan="3"></td><td rowspan="3"></td><td rowspan="3"></td><td>1</td><td>0.2</td><td>18.8</td><td>4.46</td></tr><tr><td>1.5</td><td>0.3</td><td>18.8</td><td>4.11</td></tr><tr><td>3.5</td><td>11.4</td><td>18.0</td><td>4.86</td></tr></tbody></table>
<table><caption>Table 2 Integrated metabolic rates based on oxygen incubations during both cruises. Planktonic volumetric rates are depth-integrated. All rates are expressed in mmol O₂ m⁻² d⁻¹. Numbers in parenthesis refer to the standard error associated with the rates averaging between stations (± SE). See Fig. 1 for definition of inner and outer areas.</caption><thead><tr><th rowspan="2"></th><th rowspan="2">Area<br>(km²)</th><th colspan="3">Plankton</th><th colspan="3">Benthos</th><th>Whole ecosystem</th></tr><tr><th>GPP</th><th>CR</th><th>NCP</th><th>GPP</th><th>CR</th><th>NCP</th><th>NEP</th></tr></thead><tbody><tr><td colspan="9"><strong>April 2001</strong></td></tr><tr><td>River</td><td>-</td><td>34</td><td>-25</td><td>9</td><td>0</td><td>-20</td><td>-20</td><td><strong>-11</strong></td></tr><tr><td>Inner</td><td>6.3</td><td>46</td><td>-25</td><td>21</td><td>0</td><td>-26</td><td>-26</td><td><strong>-5</strong></td></tr><tr><td>Outer</td><td>16.7</td><td>17 (± 1)</td><td>-17 (± 1)</td><td>0 (± 2)</td><td>72 (± 32)</td><td>-69 (± 7)</td><td>3 (± 39)</td><td><strong>3 (± 41)</strong></td></tr><tr><td>Whole estuary</td><td>23</td><td>25 (± 1)</td><td>-19 (± 1)</td><td>6 (± 2)</td><td>53 (± 23)</td><td>-58 (± 5)</td><td>-5 (± 28)</td><td><strong>1 (± 30)</strong></td></tr><tr><td>Kattegat</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td colspan="9"><strong>August 2001</strong></td></tr><tr><td>River</td><td>-</td><td>44</td><td>-28</td><td>16</td><td>0</td><td>-29</td><td>-29</td><td><strong>-13</strong></td></tr><tr><td>Inner</td><td>6.3</td><td>70</td><td>-67</td><td>3</td><td>0</td><td>-17</td><td>-17</td><td><strong>-14</strong></td></tr><tr><td>Outer</td><td>16.7</td><td>26</td><td>-21</td><td>5</td><td>32 (± 12)</td><td>-53 (± 12)</td><td>-21 (± 24)</td><td><strong>-16 (± 24)</strong></td></tr><tr><td>Whole estuary</td><td>23</td><td>38</td><td>-33</td><td>5</td><td>23 (± 8)</td><td>-43 (± 8)</td><td>-20 (± 16)</td><td><strong>-15 (± 16)</strong></td></tr><tr><td>Kattegat</td><td>-</td><td>62</td><td>-31</td><td>31</td><td>0</td><td>-22</td><td>-22</td><td><strong>9</strong></td></tr></tbody></table>
<table><caption>Table 3 Dissolved inorganic carbon (DIC) budget for the mixed layer of the Randers Fjord in April and August. Net ecosystem production (NEP) was computed to balance the budget. Positive CO₂ atmospheric flux corresponds to a transfer of CO₂ from the atmosphere to the water. Numbers in parenthesis refer to associated standard errors (± SE).</caption><thead><tr><th>mmol C m<sup>-2</sup> d<sup>-1</sup></th><th>24-30 April</th><th>20-24 August</th></tr></thead><tbody><tr><td>DIC fluvial input</td><td>283 (± 14)<sup>a</sup></td><td>166 (± 9)<sup>b</sup></td></tr><tr><td>DIC output to the Baltic Sea</td><td>-298 (± 21)<sup>c</sup></td><td>-195 (± 13)<sup>d</sup></td></tr><tr><td>CO<sub>2</sub> atmospheric flux</td><td>5 (± 1)<sup>e</sup></td><td>-17 (± 7)<sup>f</sup></td></tr><tr><td>NEP</td><td><strong>-10 (± 36)</strong></td><td><strong>-47 (± 15)</strong></td></tr></tbody></table>

<footnote><sup>a</sup> DIC₀ = 2.075 ± 0.005 mmol kg⁻¹; <sup>b</sup> DIC₀ = 2.063 ± 0.005 mmol kg⁻¹; <sup>c</sup> DIC<sub>AZE</sub> = 2.205 ± 0.044 mmol kg⁻¹; <sup>d</sup> DIC<sub>AZE</sub> = 2.389 ± 0.027 mmol kg⁻¹; <sup>e</sup> ΔpCO₂ = 29 ppm; <sup>f</sup> ΔpCO₂ = -348 ppm.</footnote>
<table><caption>Table 4 Nighttime and daytime parameters used for and resulting from the response surface difference (RSD) method, and derived metabolic parameters. O₂ sat: oxygen saturation (%); bᵢ: volumetric O₂ rate of change (mmol O₂ m⁻³ h⁻¹); r²: determination coefficient; p: probability value; wind speed (m s⁻¹); kO₂: gas transfer velocities (m h⁻¹); FO₂: fluxes across the air-water interface (mmol O₂ m⁻² h⁻¹); GPP: gross primary production; CR: community respiration; NEP: net ecosystem production. All metabolic rates are expressed in mmol O₂ m⁻² d⁻¹. Numbers in parenthesis (± SE) refer to the standard errors associated with the calculation of each parameter.</caption><thead><tr><th>Date</th><th>O₂ sat<br>(± SE)</th><th>bᵢ estimate<br>(± SE)</th><th>r²</th><th>p</th><th>Wind speed<br>(± SE)</th><th>kO₂<br>(± SE)</th><th>FO₂<br>(± SE)</th><th>GPP<br>(± SE)</th><th>CR<br>(± SE)</th><th>NEP<br>(± SE)</th></tr></thead><tbody><tr><td colspan="11">April (Sta. 1A – 6)</td></tr><tr><td rowspan="2">Nighttime</td><td>94<br>(± 2)</td><td>-2.747<br>(± 0.2465)</td><td rowspan="2">0.62</td><td rowspan="2">&lt; 0.0001</td><td>5.4<br>(± 0.9)</td><td>0.11<br>(± 0.02)</td><td>0.7<br>(± 0.6)</td><td rowspan="2"><b>143</b><br>(± 33)</td><td rowspan="2">-123<br>(± 24)</td><td rowspan="2"><b>20</b><br>(± 57)</td></tr><tr><td>97<br>(± 1)</td><td>3.869<br>(± 0.4574)</td><td>7.1<br>(± 0.7)</td><td>0.14<br>(± 0.01)</td><td>1.1<br>(± 0.7)</td></tr><tr><td colspan="11">August (Sta. 2 – 6)</td></tr><tr><td rowspan="2">Nighttime</td><td>78<br>(± 3)</td><td>-3.561<br>(± 0.5544)</td><td rowspan="2">0.35</td><td rowspan="2">&lt; 0.0001</td><td>1.9<br>(± 0.6)</td><td>0.06<br>(± 0.01)</td><td>2.3<br>(± 0.3)</td><td rowspan="2"><b>128</b><br>(± 33)</td><td rowspan="2">-191<br>(± 28)</td><td rowspan="2">-63<br>(± 61)</td></tr><tr><td>79<br>(± 3)</td><td>1.601<br>(± 0.3983)</td><td>1.6<br>(± 0.3)</td><td>0.05<br>(± 0.01)</td><td>2<br>(± 0.4)</td></tr></tbody></table>
<table><caption>Table 5 Surface area, salinity and nutrient concentrations (dissolved inorganic phosphorus, DIP and dissolved inorganic nitrogen, DIN) in each budgeted box (see Fig. 1). Computed water residence times (τ), non conservative fluxes of DIP and DIN (ΔDIP and ΔDIN) as well as net ecosystem production (NEP) and net difference between DIN fixation and denitrification (n<sub>fix-denit</sub>).</caption><thead><tr><th>Compartment</th><th>Area (km²)</th><th>Sal</th><th>DIP (mmol m⁻³)</th><th>DIN (mmol m⁻³)</th><th>τ (d)</th><th>ΔDIP (mmol P m⁻² d⁻¹)</th><th>ΔDIN (mmol N m⁻² d⁻¹)</th><th>NEP (mmol C m⁻² d⁻¹)</th><th>n<sub>fix-denit</sub> (mmol N m⁻² d⁻¹)</th></tr></thead><tbody><tr><td colspan="10"><strong>Randers 1</strong></td></tr><tr><td>River</td><td>-</td><td>0.2</td><td>0.20</td><td>129</td><td></td><td>-</td><td>-</td><td></td><td>-</td></tr><tr><td>Inner upper</td><td>6.3</td><td>2.0</td><td>0.20</td><td>110</td><td>8</td><td>-0.006</td><td>-0.6</td><td>0.62</td><td>-0.47</td></tr><tr><td>Inner deep</td><td>6.3</td><td>15.1</td><td>0.29</td><td>45</td><td>44</td><td>0.009</td><td>0.0</td><td>-0.93</td><td>-0.15</td></tr><tr><td>Outer upper</td><td>16.7</td><td>8.4</td><td>0.18</td><td>70</td><td>7</td><td>-0.001</td><td>-0.1</td><td>0.09</td><td>-0.07</td></tr><tr><td>Outer deep</td><td>3.9</td><td>20.7</td><td>0.14</td><td>34</td><td>10</td><td>0.043</td><td>0.3</td><td>-4.53</td><td>-0.42</td></tr><tr><td>Kattegat</td><td>-</td><td>21.4</td><td>0.05</td><td>2</td><td></td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td><strong>Inner</strong></td><td><strong>6.3</strong></td><td></td><td></td><td></td><td></td><td><strong>0.003</strong></td><td><strong>-0.6</strong></td><td><strong>-0.3</strong></td><td><strong>-0.6</strong></td></tr><tr><td><strong>Outer</strong></td><td><strong>16.7</strong></td><td></td><td></td><td></td><td></td><td><strong>0.009</strong></td><td><strong>0.0</strong></td><td><strong>-1.0</strong></td><td><strong>-0.2</strong></td></tr><tr><td><strong>Whole estuary</strong></td><td><strong>23</strong></td><td></td><td></td><td></td><td></td><td><strong>0.007</strong></td><td><strong>-0.2</strong></td><td><strong>-0.8</strong></td><td><strong>-0.3</strong></td></tr><tr><td colspan="10"><strong>Randers 2</strong></td></tr><tr><td>River</td><td>-</td><td>0.2</td><td>1.46</td><td>59</td><td></td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Inner upper</td><td>6.3</td><td>5.7</td><td>1.83</td><td>67</td><td>9</td><td>-0.017</td><td>9.3</td><td>1.78</td><td>9.52</td></tr><tr><td>Inner deep</td><td>6.3</td><td>14.8</td><td>2.53</td><td>27</td><td>17</td><td>0.232</td><td>-1.1</td><td>-24.63</td><td>-4.85</td></tr><tr><td>Outer upper</td><td>16.7</td><td>18.1</td><td>1.98</td><td>13</td><td>3</td><td>0.638</td><td>-5.2</td><td>-67.60</td><td>-15.36</td></tr><tr><td>Outer deep</td><td>3.9</td><td>21.8</td><td>0.75</td><td>7</td><td>2</td><td>-1.556</td><td>-4.2</td><td>164.98</td><td>20.73</td></tr><tr><td>Kattegat</td><td>-</td><td>28.1</td><td>0.49</td><td>2</td><td></td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td><strong>Inner</strong></td><td><strong>6.3</strong></td><td></td><td></td><td></td><td></td><td><strong>0.216</strong></td><td><strong>8.1</strong></td><td><strong>-23</strong></td><td><strong>5</strong></td></tr><tr><td><strong>Outer</strong></td><td><strong>16.7</strong></td><td></td><td></td><td></td><td></td><td><strong>0.271</strong></td><td><strong>-6.1</strong></td><td><strong>-29</strong></td><td><strong>-10</strong></td></tr><tr><td><strong>Whole estuary</strong></td><td><strong>23</strong></td><td></td><td></td><td></td><td></td><td><strong>0.256</strong></td><td><strong>-2.2</strong></td><td><strong>-27</strong></td><td><strong>-6</strong></td></tr></tbody></table>
<table><caption>Table 6 Areal metabolic rates estimates during both cruises based on the four different methods. All metabolic parameters are in mmol C m⁻² d⁻¹. Gross primary production (GPP) rates based on oxygen (O₂ incubations and RSD method) were converted in carbon unit assuming a photosynthetic quotient (PQ) of 1.3 while a respiratory quotient (RQ) of 1 was applied to convert community respiration (CR) rates. Net ecosystem production (NEP) is the sum of GPP and CR. Number in parenthesis represents the standard error (±SE) associated with each parameter estimation.</caption><thead><tr><th></th><th colspan="3">April</th><th colspan="3">August</th></tr><tr><th></th><th>GPP</th><th>CR</th><th>NEP</th><th>GPP</th><th>CR</th><th>NEP</th></tr></thead><tbody><tr><td colspan="7">Whole estuary estimates</td></tr><tr><td>O₂ incubations</td><td>62 (± 19)</td><td>-77 (± 6)</td><td>-15 (± 25)</td><td>49 (± 8)</td><td>-76 (± 8)</td><td>-28 (± 16)</td></tr><tr><td>RSD</td><td>114 (± 26)</td><td>-123 (± 24)</td><td>-13 (± 50)</td><td>102 (± 27)</td><td>-191 (± 28)</td><td>-89 (± 55)</td></tr><tr><td>LOICZ budget</td><td>-</td><td>-</td><td>-1</td><td>-</td><td>-</td><td>-27</td></tr><tr><td colspan="7">Mixed layer estimates</td></tr><tr><td>DIC budget</td><td>-</td><td>-</td><td>-10 (± 36)</td><td>-</td><td>-</td><td>-47 (± 15)</td></tr><tr><td colspan="7">River estimates (mixed layer)</td></tr><tr><td>O₂ incubations</td><td></td><td></td><td></td><td>34</td><td>-12</td><td>22</td></tr><tr><td>DIC/AOU cycle</td><td></td><td></td><td></td><td>312 (± 54)</td><td>-301 (± 38)</td><td>11 (± 92)</td></tr></tbody></table>
## Figure captions

Fig. 1. Map of the Randers Fjord and location of sampling stations in the navigation channel (open circles) and on the banks (full squares). Planktonic oxygen incubations were performed at Sta. 1, 1a, 2, 2a, 5 and 6 in April and at stations 1, 2, 4, 4a, 5 and 6 in August. Benthic oxygen incubations were performed in the navigation channel at Sta. 1, 2, 3, 4 and 5 and on the 3 bank stations during both cruises. See text for stations sampled for the Response Surface Difference (RSD) method as well as for dissolved inorganic carbon (DIC) and Land-Ocean in the Coastal Zone (LOICZ) budgets.

Fig. 2. Vertical profiles of oxygen saturation and of the concentrations of chlorophyll *a* (*Chl* *a*), dissolved inorganic nitrogen (DIN) and dissolved inorganic phosphorus (DIP) at each station where incubation of the planktonic community was carried out.

Fig. 3. Planktonic volumetric gross primary production (GPP) and community respiration (CR) rates measured in April (A) and August (B). Rates are in mmol O₂ m⁻³ d⁻¹.

Fig. 4. Distribution along the salinity gradient of the partial pressure of CO₂ (pCO₂; ppm), dissolved inorganic carbon (DIC; mmol kg⁻¹) and total alkalinity (TA; mmol kg⁻¹) compiled from the 24 h cycle measurements and ship-born transects during the Randers 1 and 2 cruises (24-30 April and 20-31 August 2001).

Fig. 5. Time-course of dissolved inorganic carbon (DIC; mmol kg⁻¹) and apparent oxygen utilisation (AOU; µmol kg⁻¹) during a 24 h cycle at Sta. 6 during the Randers 2 cruise (20-21 August 2001). Time is expressed in UT. Gross primary production (GPP), community respiration (CR) and net ecosystem production (NEP) rates computed from these 24 h cycles are expressed in mmol C m⁻² d⁻¹ (DIC cycle) and mmol O₂ m⁻² d⁻¹ (AOU cycle). Numbers in parenthesis refer to associated standard errors (± SE).
Fig. 6. Annual net ecosystem production (NEP) and n<sub>fix-denit</sub> rates computed using the LOICZ budgeting procedure from 1991 to 2002.
<figure><img src="image_38.png" /><figcaption>Fig. 1 – Gazeau et al.</figcaption></figure>
<figure><img src="image_39.png" /><figcaption>Fig. 2 – Gazeau et al.</figcaption></figure>
<figure><img src="image_40.png" /><figcaption>Fig. 3 – Gazeau et al.</figcaption></figure>
<figure><img src="image_41.png" /><figcaption>Fig. 4 – Gazeau et al.</figcaption></figure>
<figure><img src="image_42.png" /><figcaption>Fig. 5 – Gazeau et al.</figcaption></figure>
<figure><img src="image_43.png" /><figcaption>Fig. 6 – Gazeau et al.</figcaption></figure>
3.4 L'estuaire de l'Escaut

Gazeau F., Gattuso J. -P., Middelburg J. J., Barrón C., Duarte C. M., Schiettecatte L. -S., Brion N., Pizay M. -D., Frankignoulle M. & Borges A. V.

Planctonic and whole-system metabolism in a nutrient-rich estuary (The Scheldt Estuary)
(soumis)

Limnology and Oceanography.
PLANKTONIC AND WHOLE SYSTEM METABOLISM IN A NUTRIENT-RICH ESTUARY (THE SCHELDT ESTUARY)

Frédéric Gazeau<sup>1,2,*</sup>, Jean-Pierre Gattuso<sup>2</sup>, Jack J. Middelburg<sup>3</sup>, Cristina Barrón<sup>4</sup>, Carlos M. Duarte<sup>4</sup>, Laure-Sophie Schiettecatte<sup>1</sup>, Natacha Brion<sup>5</sup>, Marie-Dominique Pizay<sup>2</sup>, Michel Frankignoulle<sup>1</sup> and Alberto Vieira Borges<sup>1</sup>

<sup>1</sup> Unité d'Océanographie Chimique (B5), MARE, Université de Liège, B-4000 Sart Tilman - Belgium

<sup>2</sup> Laboratoire d'Océanographie de Villefranche, CNRS-Université de Paris 6, B. P. 28, F-06234 Villefranche-sur-mer Cedex - France

<sup>3</sup> Netherlands Institute of Ecology (NIOO-KNAW), Postbus 140, 4400 AC Yerseke - The Netherlands

<sup>4</sup> IMEDEA (CSIC-UIB), C/Miquel Marquès 21, 07190 Esporles - Spain

<sup>5</sup> Department of Analytical and Environmental Chemistry, Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussels - Belgium

Submitted to Limnology and Oceanography

<footnote>* corresponding author: gazeau@obs-vlfr.fr, phone: +33493763843, fax: +33493763834</footnote>
Acknowledgements

We thank the skipper and crew of the *RV Luctor* and *Belgica* for their hospitality, J. Peene, J. Sinke and J. Nieuwenhuize for analytical and logistic support, R. Wollast for fruitful discussions, K. Soetaert and S. V. Smith for their invaluable help during implementation of the LOICZ procedure, F. Ronday and H. Malcorps for providing wind speed data at Antwerpen, J. -P. Vanderborght for providing water currents and bathymetry data, and the Management Unit of the North Sea Mathematical Models for providing thermosalinograph data during the *Belgica* cruises. This research was supported by the European Union (EUROTROPH project; EVK3-CT-2000-00040), by the Netherlands Organisation for Scientific Research, by a CNRS/CGRI/FNRS cooperation, by the Belgian Federal Office for Scientific, Technical and Cultural Affairs (contract # EV/12/20C) and the Fonds National de la Recherche Scientifique (contract # 2.4545.02) where AVB and MF are, respectively a post-doctoral researcher and senior research associate. CB was funded by a scholarship from the government of the Balearic Islands. This is MARE contribution XXX and NIOO publication XXX.
Abstract

Planktonic gross primary production (GPP), community respiration (CR) and nitrification were measured monthly in the Scheldt estuary by the oxygen incubation method from January to December 2003 as well as in November 2002 and April 2003 with concomitant benthic subtidal CR measurements. Planktonic GPP rates were high in the freshwater area (92 mmol C m⁻² d⁻¹) and decreased seaward (17-34 mmol C m⁻² d⁻¹). Planktonic CR was significantly higher than subtidal benthic CR, was highest in the brackish area (max: -313 mmol C m⁻² d⁻¹ in October) and appears to be mainly driven by inputs of allochthonous organic matter rather than by local production. A significant decrease of nitrification is observed since the 1990’s but this process still represents up to 31% of total organic matter production in the inner estuary.

Planktonic net community production (NCP) was most of the time negative in the estuary with values ranging from -300 to 165 mmol C m⁻² d⁻¹. Whole estuary net ecosystem production (NEP) was investigated from June to December 2003 using the Land-Ocean Interaction in the Coastal Zone (LOICZ) budgeting procedure applied to dissolved inorganic phosphorus and carbon (DIP and DIC). While DIC budgets provided consistent NEP estimates with in situ measurements, DIP budgets failed to provide realistic estimates in the inner estuary where abiotic processes account for more than 50% of the DIP non-conservative flux. The same procedure applied to dissolved inorganic nitrogen (DIN) revealed that denitrification in this estuary did not significantly change since the 1980’s.
Estuaries are highly dynamic systems with large seasonal and spatial gradients of biogeochemical compounds and processes (Heip et al. 1995). Linking land to the ocean, they are often greatly influenced by human activities, including enhanced organic matter and nutrient loadings. The balance between organic matter and nutrient loading is critical in determining the balance between autotrophy and heterotrophy (Kemp et al. 1997; Eyre and Mckee 2002).

An ecosystem is autotrophic when production of organic matter by primary producers exceeds the consumption of this matter by the overall community. Such systems are potentially net sinks for atmospheric CO₂ although air-sea CO₂ flux is also controlled by external inputs of dissolved inorganic carbon (DIC; upwelling, river inputs...), calcium carbonate (CaCO₃) precipitation and temperature thermodynamic effects. In contrast, an ecosystem is heterotrophic when organic matter consumption exceeds primary production, leading generally to high CO₂ partial pressures (pCO₂) and low oxygen (O₂) concentrations in the water column. Primary production is light-limited in many estuaries due to high turbidity driving these systems toward heterotrophy (Smith and Hollibaugh 1993; Gattuso et al. 1998).

Estuaries are generally oversaturated in CO₂ with respect to atmospheric equilibrium, with CO₂ fluxes ranging from 10 to 660 mmol C m⁻² d⁻¹ (Abril and Borges in press). Such emissions of CO₂ can have a significant impact on regional CO₂ budgets. For instance, European estuaries emit between 5 and 10% of present anthropogenic CO₂ emissions for Western Europe (Frankignoulle et al. 1998).

The Scheldt estuary (Belgium and The Netherlands; Fig. 1) is one of the most nutrient-rich estuaries in Europe (Wollast 1988), as a result of urban waste water drainage and runoff from agriculture. Indeed, the Scheldt river, its tributaries and the estuary drain a highly populated and industrialized area and therefore are subject to strong anthropogenic disturbances. It is one of the most studied coastal systems in Western Europe, and although several biogeochemical models or budgets have been reported and provide estimates of its metabolic status (Soetaert and Herman 1995b; Hellings et al. 2001; Vanderborght et al. 2002), no direct measurements have been carried out in the past.

A wide range of techniques have been used to estimate the trophic status of coastal sites. Each of them relies on one or several assumptions and covers different spatial and temporal scales. For instance, metabolic processes rates measurements based on incubation methods (mainly O₂ and ¹⁴C) or open water O₂ measurements (Odum 1956; Kemp and Boynton 1980;
Howarth et al. 1992; Caffrey 2004) have been used in numerous studies. Within the IGBP program, the Land Ocean Interaction in the Coastal Zone (LOICZ; Gordon et al. 1996) stoichiometric budgeting approach has been applied to more than 200 coastal sites. This latter approach presents the capacity (1) to provide system scale estimates and (2) to be easily implemented as it is based on usually well studied parameters. As recommended by Gordon et al. (1996), dissolved inorganic phosphorus (DIP) is the main parameter used to compute net ecosystem production rates using the LOICZ budgeting procedure. Nevertheless, in turbid estuaries, processes of adsorption and desorption to and from suspended particles and/or sediment (Froelich 1988) can introduce a bias in the calculations. The use of DIC to compute the NEP suffers from the difficulty to estimate the other processes, besides primary production and respiration, which are likely to have an impact on DIC in coastal systems, such as CO₂ fluxes at the air-sea interface and CaCO₃ precipitation and dissolution (Gordon et al. 1996).

In this paper, we report, compare and discuss the metabolic status of the Scheldt estuary based on in situ measurements collected from November 2002 to December 2003 and on the LOICZ budgeting procedure applied to both DIP and DIC.

Finally, consecutive to strong summer depletions of O₂ and the severe associated fish and invertebrates mortality at the end of the 1970's (Heip 1988), the construction of wastewater treatment plants was initiated in the drainage basin of the Scheldt estuary. This resulted in a significant improvement of water quality in the river, its tributaries and the estuary over the past 20 years (Billen et al. submitted; Soetaert et al. submitted). The results collected in 2003 are compared to previous estimates of biogeochemical processes and their control by these environmental changes is discussed.
## Material and methods

**Study area** - The river Scheldt, with a catchment area of 19500 km² (Heip 1989), is the most important freshwater source. Tidal exchange is, on average, 200 times higher than the freshwater input (Wollast 1988) and consequently the freshwater residence time is long, from 70 d in the upstream part of the estuary to 10-15 d in the most seaward area (Soetaert and Herman 1995a). Salinity ranges from about 0.5 at Temse to about 28 at the mouth of the estuary (Fig. 1). Due to strong tidal currents (up to 1.5 m s⁻¹), the water column is well mixed throughout the estuary (Wollast 1988). Turbidity is high in the entire upper estuary and phytoplankton is light limited rather than nutrient limited (Van Spaendonk et al. 1993; Kromkamp and Peene 1995). Intensive bacterial growth has been estimated and is among the highest reported in the literature (Goosen et al. 1995) and fuelled mainly by external subsidies (Boschker et al., 2004). These high bacterial production rates coupled with the long freshwater residence time lead to O₂ depletion and a large efflux of CO₂ to the atmosphere in the upper and middle estuary (Frankignoulle et al. 1996; Frankignoulle et al. 1998).

**Planktonic metabolism** - Planktonic gross primary production (GPP), community respiration (CR) and nitrification were measured at five and six stations in November 2002 and April 2003, respectively (Fig. 1A and Table 1) and monthly from January to December 2003 at five fixed salinities (±1): 0, 2, 10, 18 and 25. Figure 1A shows the average position of the stations corresponding to each salinity from the monthly surveys. Surface water samples (at least 3 replicates) were incubated in a 5-compartment on-deck incubator for at least 6 h in transparent 60 ml biological oxygen demand (BOD) bottles. Samples were kept at in situ temperature by flowing water and irradiance was controlled in each compartment with filters of shading capacity of 0, 81, 87, 92 and 100%. In order to avoid sedimentation of particulate material in samples, bottles were fixed on a rotating device (1 rpm).

Concentrations of dissolved O₂ were measured before and after incubation using an automated Winkler titration technique with a potentiometric end-point detection. Analyses were performed with an Orion redox electrode (9778-SC) and a custom built titrator from November 2002 to June 2003 and with a Mettler Toledo DL50 titrator and a Pt redox electrode for the rest of the study period. Reagents and standardizations were similar to those described by Knap et al. (1996). The O₂ consumption due to CR and nitrification (both expressed as negative values) was
partitioned by incubating samples, in the dark compartment, with and without addition of the nitrification inhibitors N-serve (Nitrapyrine; 5 mg l⁻¹) and chlorate (10 mmol l⁻¹). In each compartment, hourly net community production during daylight (NCPd) was estimated by regressing O₂ against time. Hourly planktonic GPP rates for each irradiance were calculated as:

$$GPP = NCPd - (CR + \text{nitrification}) \qquad (1)$$

Light attenuation in the water column was measured using LI-COR LI-193SA Spherical Quantum Sensor which was connected to a LI-COR LI-1400 datalogger. Surface irradiance data were obtained from the Netherlands Institute of Ecology (Yerseke, The Netherlands) using a LI-COR LI-190SA quantum sensor coupled to a LI-COR LI-1000 data logger.

The relationship of Platt et al. (1980) was fitted on the GPP data collected at the five irradiance levels (GPP/I or PI curves). Because of the duration of the incubations (usually less than 10 h) and to correct for the daily variability of surface irradiance, hourly GPP rates were recalculated using a mean daily surface irradiance for each month.

Hourly rates of CR and nitrification were converted to daily rates assuming no variation during the 24 h. Hourly rates of GPP were multiplied by the average day length for each month to estimate daily rates. Daily NCP was then calculated as the sum of daily GPP, CR and nitrification. The volumetric rates of CR and nitrification in November 2002 and April 2003 were depth-integrated at each station assuming no vertical variation and using PI curves to integrate GPP rates over the euphotic zone (0.1% of the surface irradiance). The volumetric rates measured during the monthly surveys were depth-integrated using a bathymetric study of the estuary carried out with the Arcview 8.3 software package. The estuary was divided in five boxes corresponding to the sampled salinities and each metabolic parameter was integrated with regard to the depth gradient in each box.

These oxygen based values were converted into carbon units by using a photosynthetic quotient (PQ) of 1.3, a respiratory quotient (RQ) of 1 and a nitrification O₂:C ratio of -14. This latter conversion factor was estimated from simultaneous N-serve-sensitive ¹⁴C bicarbonate nitrification measurements (N. Brion, unpublished data) on four occasions in 2003.

At 17 fixed stations (hereafter referred to monitoring stations; Fig. 1B), samples were taken for nutrient and pigments analysis. For pigments, water was filtered through GF/F filters which were stored frozen pending extraction and analysis by high-performance liquid chromatography (Barranguet et al. 1998). Automated colorimetric techniques, as described by
Strickland and Parsons (1972), were used to measure concentrations of dissolved inorganic phosphorus (DIP), nitrate + nitrite ($NO_x$), ammonium ($NH_4$) and silicates (Si). Hereafter, dissolved inorganic nitrogen (DIN) refers to the sum of $NH_4$ and $NO_x$.

Total alkalinity (TA) and pH were measured at each of the five incubation stations from January to May as well as at each of the 17 monitoring stations from June to December. TA was measured by Gran electro-titration on 50 ml GF/F filtered samples with a reproducibility of ± 2 µmol kg⁻¹ and an estimated accuracy of ± 3 µmol kg⁻¹. pH was measured using a combined electrode (Metrohm) with a reproducibility of ± 0.004 pH units. Frankignoulle and Borges (2001) provide details on the pH calibration procedure and on $pCO_2$ and DIC computation from the pH and TA measurements. During the November 2002 and April 2003 cruises, surface $pCO_2$ was measured continuously by equilibration as described by Frankignoulle and Borges (2001). Surface $O_2$ was also measured continuously with an Oxyguard polarographic electrode calibrated against discrete Winkler samples.

*Benthic metabolism* - Triplicate sediment cores (5 cm in diameter) were collected with a box corer at 8 stations along the estuary in November 2002 and April 2003 (Table 1). Sediment cores, from each sampling station, were incubated in the laboratory at in situ temperature under darkness for about 6 h. Before starting the incubations, the overlying water was removed and cores were filled with collected bottom water. Cores were closed with airtight lids and overlying water was stirred during the whole incubation period. Before and after incubation, water samples were collected with acid-washed syringes and $O_2$ concentrations were determined by the Winkler method using a Mettler titrator (DL21). Daily benthic CR was calculated with the same method as the one described above for the planktonic compartment.

*LOICZ budgeting procedure* - Biogeochemical modelling according to Gordon et al. (1996) was applied to the Scheldt estuary based on salinity, DIP, DIN, DIC and $pCO_2$ data obtained at each monitoring station (Fig. 1B) from June to December 2003. As the water is well-mixed along the estuary throughout the year, a 3 box-1 layer model was considered: the inner, middle and outer estuary (Fig. 1A and B). Monitoring stations located near the freshwater tidal zone and stations located in the river plume (Fig. 1B) were used as the riverine and marine end-members respectively. The river plume data of DIC, DIN and DIP were collected during 5 cruises from June to December 2003 (Carbon, nitrogen and phosphorus cycling in the North Sea, CANOPY
project; N. Brion, unpublished data). DIC was computed from measurements of TA and pCO₂, measured as described above. Colorimetric techniques were used for NH₄ and DIP while NOₓ was measured using an AutoAnalyser (Technicon; Strickland and Parsons 1972).

Freshwater flow was provided by the Ministerie van de Vlaamse Gemeenschap, Belgium. Soetaert et al. (submitted) estimated that 32% of the total freshwater flow to the Scheldt estuary comes from lateral sources (12% from the Gent-Terneuzen channel, 10% from the Antwerpen harbour and 10% from diffuse sources and small canals). Wind speed data at Hansweert and Hoofdplaat were provided by Hydro Meteo Centrum Zeeland (http://www.hmcz.nl/). Precipitation data at Vlissingen (near mouth of the estuary) and Stabroek (near Antwerp) were provided by the Royal Netherlands Meteorological Institute and the Belgian Royal Meteorological Institute, respectively. Data of DIN and DIP in the Gent-Terneuzen channel (station Zelzate; 51.2° N 3.82°E) as well as at several stations in the Antwerpen harbour were provided by the Flemish Environment Agency, Belgium (http://www.vmm.be). DIC concentrations at these stations were estimated using an empirical DIC vs. O₂sat relationship established during the study period in the Scheldt estuary:

$$DIC = -21 O_{2sat} + 4738 \quad (r^2 = 0.88; P < 0.001) \quad (2)$$

where DIC is expressed in mmol m⁻³ and O₂sat is the oxygen saturation (%).

No data of DIP, DIN and DIC loadings from diffuse sources and small canals were available for the year 2003. Representing 10% of the total water input, this source of nutrient was neglected in the present budget.

The net ecosystem production (NEP; p-r in the LOICZ terminology) was calculated from the non-conservative fluxes of DIP in each box, assuming that these fluxes are only related to biological activity, following the equation:

$$NEP = -\Delta DIP \times (C:P)_{part} \quad (3)$$

where NEP is expressed in mmol C m⁻² d⁻¹, ΔDIP is the non-conservative flux of DIP (mmol P m⁻² d⁻¹) and (C:P)ₚart is the particulate C:P ratio (a value of 106:1 was used).

NEP was also computed from a DIC budget (Gordon et al. 1996). The total non-conservative flux of DIC (ΔDICₜ) is computed according to:

$$\Delta DIC_t = \Delta DIC_o + \Delta DIC_g + \Delta DIC_c \quad (4)$$

where ΔDIC_o, ΔDIC_g and ΔDIC_c are variations of DIC related to organic matter production (included nitrification) and mineralization (ΔDIC_o equals -NEP), flux of CO₂ across the air-water
interface and precipitation or dissolution of CaCO$_3$, respectively. No information on this latter process is available, to our knowledge, in the saline estuary. Nevertheless, Hellings et al. (1999).reported that, in two intertidal areas located in the freshwater tidal estuary, the contribution of calcite dissolution to the DIC pool was relatively small. Therefore, this process was assumed negligible in the present study.

The air-water CO$_2$ flux was computed according to:

$$F = \alpha k \Delta pCO_2 \quad (5)$$

where F is the air-water CO$_2$ flux (mmol m$^{-2}$ d$^{-1}$), α is the CO$_2$ solubility coefficient (mmol m$^{-3}$ ppm$^{-1}$), k is the gas transfer velocity (m d$^{-1}$) and ΔpCO$_2$ is air-water gradient of CO$_2$ (pCO$_{2water}$ - pCO$_{2air}$, in ppm). Then, as a convention, a positive flux corresponds to a transfer of CO$_2$ from the water to the atmosphere. Therefore, in equation (4), ΔDIC$_g$ equals −F.

To compute ΔpCO$_2$, we used monthly values of atmospheric CO$_2$ molar fraction from Weather Station Mike (66.00°N 2.00°E), representative of the open North Sea waters (from the National Oceanic and Atmospheric Administration / Climate Monitoring and Diagnostics Laboratory air samples network, available from the internet at http://www.cmdl.noaa.gov/) to which was added a value of 27 ppm that corresponds to the average observed increase of the atmospheric signal in the Scheldt (Borges et al. 2004a). The CO$_2$ molar fraction values were converted to partial pressure values in wet air using the procedure described in DOE (1994).

k was computed according to a parametrization as a function of wind speed, water current and depth established from 295 floating chamber interfacial CO$_2$ flux measurements carried out during the November 2002 and April 2003 cruises (Borges et al. 2004b):

$$k_{600(B)} = 0.24 + 0.4126w^{0.5}h^{-0.5} + 0.619 u_{10} \quad (6)$$

where k$_{600(B)}$ is the gas transfer velocity of CO$_2$ normalized to a Schmidt number (Sc) of 600 in m d$^{-1}$, w is the water current (cm s$^{-1}$), h is the depth (m) and u$_{10}$ is the wind speed (m s$^{-1}$) referenced at a height of 10 m.

k was also estimated using k-wind parametrization based on the compilation of k measurements in various estuaries derived from the mass balance of naturally occurring and purposeful tracers (Raymond and Cole 2001):

$$k_{600(R\&C)} = 0.38 \exp (0.3 u_{10}) \quad (7)$$

Monthly averages of k$_{600(B)}$ and k$_{600(C&R)}$ were computed from hourly wind speed measurements and modelled water currents (as described in Borges et al. 2004b) at three
reference stations representative of the three boxes (Fig. 1B). The k<sub>600(B)</sub> and k<sub>600(C&R)</sub> values converted to in situ temperature conditions assuming a k dependency proportional to Sc<sup>-0.5</sup>. Sc was computed for a given salinity from the formulations for salinity 0 and 35 given by Wanninkhof (1992), and assuming that Sc varies linearly with salinity.

Budgets of DIN were also computed and the difference between DIN fixation and denitrification (n<sub>fix-denit</sub> in the LOICZ terminology) was calculated, assuming that this latter corresponds to the fraction of the non-conservative DIN flux which is not associated to production and consumption of organic matter, from the equation:

$$n_{\text{fix-denit}} = \Delta\text{DIN} - \Delta\text{DIC}_o \times (\text{N:C})_{\text{part}} \quad (8)$$

where ΔDIN is the non-conservative flux of nitrogen in mmol N m<sup>-2</sup> d<sup>-1</sup>, ΔDIC<sub>o</sub> is equal to –NEP (see above, mmol C m<sup>-2</sup> d<sup>-1</sup>) and (N:C)<sub>part</sub> is the particulate N:C ratio (a value of 16:106 was used).
## Results

### Partial pressure of CO₂ and O₂ saturation during the November 2002 and April 2003 cruises
pCO₂ and O₂ saturation values recorded during surveys aboard the RV Belgica are shown against salinity in Figure 2. Very high pCO₂ values were recorded in the inner part of the estuary during both cruises with maximal values of 7821 and 6729 ppm in November 2002 and April 2003 respectively. These high pCO₂ values are well correlated in the inner estuary with low O₂ saturation values (1 and 5% in November and April respectively). pCO₂ values were always above atmospheric equilibrium in November 2002, while in April 2003, CO₂ was under-satured in the river plume area (minimal pCO₂ value of 198 ppm). O₂ super-saturations were measured in this area during both cruises with highest values in April (110 and 122 % in November and April respectively). It must be noted that the O₂ saturation increased significantly in the outer estuary and adjacent plume area from the start to the end of the April cruise. In November, a higher variability was observed in the upper estuary both for O₂ and pCO₂, especially around salinity 4 where distinctly lower pCO₂ and higher O₂ values than the general trend along the salinity gradient were observed (arrows in Figure 2). These data were observed at low tide during a 24 h cycle carried out at fixed station close to the extensive Saeftinghe salt marshes (located between stations 6 and 7 in Fig. 1A).

### Community metabolism during the November 2002 and April 2003 cruises
Higher planktonic GPP were measured in April than in November with values increasing seaward and a maximal rate in the plume area (83.9 mmol O₂ m⁻² d⁻¹; Fig. 3). The lowest GPP were measured during both cruises in the turbid inner estuary with minimal rates around salinity 3. High rates of planktonic CR and nitrification were found in the inner part with maximal values in November at salinity 3 where planktonic nitrification consumed approximately the same amount of O₂ as planktonic CR (-273 vs. -323 mmol O₂ m⁻² d⁻¹). The rates of nitrification strongly decreased seaward, during both cruises, to reach minimal values of -1.6 and -2.1 mmol O₂ m⁻² d⁻¹ in November 2002 and April 2003 respectively. Planktonic CR was higher in April than in November in the middle and marine parts of the estuary despite lower water column temperatures. It should be noted that significantly higher GPP as well as particulate and dissolved organic matter concentrations were measured in April (data not shown).
Benthic CR was significantly lower than planktonic CR, with a maximal rate (-53 mmol O₂ m⁻² d⁻¹) in November and April at salinity 0.4 and 0.7 respectively. As for planktonic CR, benthic CR strongly decreased seaward during both cruises.

*Planktonic metabolism over an annual cycle* - pCO₂, along the estuary, was lowest in winter and increased until the end of summer to reach a maximal value of 7784 ppm at salinity 2 in September (Fig. 4A). pCO₂ was always highest at the freshwater station except in summer when values strongly decreased to reach a minimal value in August (4781 ppm). Values were always above atmospheric equilibrium except in May at salinity 18.

O₂ saturation was higher in winter than in spring-summer, especially in the brackish part (salinity 2; Fig. 4B). The lowest O₂ saturations were measured, almost throughout the year, at salinity 2 than in the freshwater part. In summer, as noted for pCO₂, a strong increase of O₂ saturation was observed in the freshwater area. O₂ super-saturations were only measured in the marine part of the estuary (salinity 18) in May.

Since no sampling for nutrient and pigments concentrations were carried out at the freshwater incubated station (salinity 0), freshwater concentrations presented in the following section were collected at the less saline station during each monitoring survey (salinity between 0 and 1). Higher nutrient concentrations were measured in the freshwater part, dilution with low nutrient marine waters being observed seaward (Fig. 5). A large peak of NH₄ was observed in May at salinity 0 (308 mmol N m⁻³; Fig. 5A). NH₄ concentration was highest in spring in the brackish part then decreased to reach minimal values in summer. Seaward, the highest concentrations were measured in winter, then concentrations decreased in early spring and were almost constant throughout the rest of the year. NOₓ concentration was also highest in the freshwater and brackish parts with maximal values in winter (458 mmol N m⁻³ at salinity 0 in December; Fig. 5B) and minimal ones in summer (261 mmol N m⁻³ at salinity 2 in July). As for NH₄, seasonal changes were less marked in the mesohaline and marine parts of the estuary. DIP concentration was highest in the freshwater part during most of the year and decreased seaward (Fig. 5C). The seasonal cycle exhibited minimal values in winter - spring and maximal ones in summer - fall. As for the other nutrients, Si concentration was generally higher in the freshwater and brackish parts, except in August when it dropped to very low values (8.5 mmol Si m⁻³ in August at salinity 2; Fig. 5D).
The highest concentrations of chlorophyll *a* (chl *a*) were found in the freshwater area with maximal values in summer-fall (maximum in October: 110 mg m⁻³; Fig. 6A). In the brackish part, a small peak was observed in June (30 mg m⁻³) followed by a larger peak in September (73 mg m⁻³). In the mesohaline and marine parts, a unique peak was observed in May (maximum: 30 mg m⁻³ at salinity 18). High concentrations of phaeopigments were measured in spring in the freshwater area (maximum: 51 mg m⁻³ in May; Fig. 6B), then values decreased in summer. In the brackish part, a peak was observed in May and highest concentrations were measured in late summer. Low phaeopigments concentrations were measured throughout the year in the mesohaline and marine part.

Integrated planktonic GPP, CR and nitrification rates are presented Figure 7. Planktonic GPP was high in the freshwater area in summer with a maximal value of 272 mmol C m⁻² d⁻¹ in August (Fig. 7A). Two peaks were observed in the brackish area (salinity 2): in June (50 mmol C m⁻² d⁻¹) and August (102 mmol C m⁻² d⁻¹). Seaward, the highest rates were observed in May and July at salinity 18 and 25 respectively. Averaged over the duration of the study (± SD), planktonic GPP rates were 92 (± 111), 25 (± 31), 17 (± 20), 30 (± 39) and 34 (± 41) mmol C m⁻² d⁻¹ at salinity 0, 2, 10, 18 and 25 respectively. Nitrification was always highest in the brackish part with a maximal value of 20 mmol C m⁻² d⁻¹ in May (Fig. 7B) and was related to high NH₄ concentrations (Fig. 5) in the freshwater area. A second peak was observed in October although no increase in NH₄ was observed at this time. Low nitrification activities were observed seaward with a minimal value in October at salinity 18 (0.2 mmol C m⁻² d⁻¹). On average, annual nitrification represented 5, 31, 10, 4 and 3% of total organic production at salinity 0, 2, 10, 18 and 25. Planktonic CR was high especially in the freshwater and brackish parts (Fig. 7C). Maximal values were observed in May and October, well related to high phaeopigments (Fig. 6) and total organic carbon concentrations (data not shown). The highest rate was measured in October at salinity 2 (-313 mmol C m⁻² d⁻¹). Planktonic NCP was negative during most of the year with lowest values in the brackish part (minimum in October: -300 mmol C m⁻² d⁻¹; Fig. 7D). Positive NCP values were recorded in summer in the freshwater area (maximum in August; 165 mmol C m⁻² d⁻¹). Seaward, positive NCP were also measured in April, July and August.

*Whole system metabolism* - Characteristics of the modelled boxes (surface area, mean depth and volume) as well as mean salinity, pCO₂ and DIP, DIN, DIC concentrations are presented in Table 2. The whole estuary acts as a net source of DIP (0.16 mmol P m⁻² d⁻¹ or 462 tP yr⁻¹; Table
3) and as a net sink of DIN (3.3 mmol N m⁻² d⁻¹ or 4420 tN yr⁻¹). Almost 7.5 mmol C m⁻² d⁻¹ or 8530 tC yr⁻¹ of DIC are also lost in the Scheldt estuary. NEP was estimated using three different approaches: LOICZ budgets based on DIP, DIC and in situ measurements.

NEP computations based on DIP budgets lead to an integrated negative NEP of -17 mmol C m⁻² d⁻¹. It should be noted that these DIP budgets revealed an autotrophic status in the inner part with a NEP of 1 mmol C m⁻² d⁻¹.

NEP computations based on DIC budgets lead to integrated negative NEP of respectively -52 and -132 mmol C m⁻² d⁻¹, using the parametrizations of Raymond and Cole (2001) and Borges et al. (2004b) to estimate CO₂ fluxes across the air-water interface. CO₂ release to the atmosphere of 60 and 140 mmol C m⁻² d⁻¹ (68000 vs. 158000 tC yr⁻¹) were estimated using these two formulations respectively.

NEP based on in situ incubations was estimated using planktonic metabolism results detailed in the previous section and published benthic metabolic rates (Middelburg et al. 1996; Barranguet et al. 1998). From June to December, our results suggested that the planktonic compartment was strongly heterotrophic with values of -103, -14 and -18 mmol C m⁻² d⁻¹ in the inner, middle and outer estuary respectively. Planktonic GPP, CR and nitrification rates in the inner area were estimated by averaging results obtained at salinities 2 and 10, in the middle and outer areas by using results obtained at salinity 18 and 25 respectively. Benthic CR estimated by Middelburg et al. (1996) at six intertidal stations in the area considered in the present study and based on CO₂ and CH₄ fluxes were used to estimate the rate of benthic mineralization in each box. Data of benthic CR collected during the November 2002 and April 2003 cruises (see above) showed that benthic CR at subtidal stations were almost 10-fold lower than planktonic CR, therefore we assumed that benthic mineralization only occurs on intertidal flats. The percentage of area covered by these flats was estimated by a bathymetry study in each box (40, 23 and 17% in the inner, middle and outer box respectively). Multiplying intertidal benthic CR rates measured by Middelburg et al. (1996) by the intertidal flats surface area leads to a benthic CR of -30, -16 and -12 mmol C m⁻² d⁻¹ in the inner, middle and outer estuary respectively. The same procedure was applied for benthic GPP estimated by Barranguet et al. (1998). Summing all these metabolic parameters leads to an estimation of the NEP in the estuary from June to December of -44 mmol C m⁻² d⁻¹.
Nfix-denit values of -20, -10 and -8 mmol N m⁻² d⁻¹ were calculated in the inner, middle and outer estuary respectively. These values were computed using NEP estimates based on Raymond and Cole (2001) formulation.
Discussion

Surface water pCO₂ did not change significantly since the period 1993-1996 (Frankignoulle et al. 1998). In 2003, pCO₂ ranged from 349 (May, outer estuary) to more than 10,000 ppm (or more than 25 times the atmospheric value; June, salinity 0). pCO₂ was generally higher in the freshwater part and decreased along the salinity gradient due to dilution with low pCO₂ marine waters and degassing at the air-water interface. O₂ concentrations did not follow a similar pattern as lower values were generally found in the inner estuary than in the freshwater zone. This discrepancy between pCO₂ and O₂ can be attributed to high nitrification rates found in the inner estuary as this process consumes more O₂ than it produces CO₂. Although O₂ conditions improved in the Scheldt estuary since the 1970's (Soetaert et al. submitted) due to a decrease of nitrification (see below), O₂ saturation was below 15% from May to November 2003 in the brackish part.

The O₂ and pCO₂ variability observed in November 2002 during a 24 h cycle near the extensive Saeftinghe salt marshes suggests that benthic processes on the salt marshes lead at high tide to a decrease of pCO₂ and an increase of O₂ due to net oxygen production, carbon fixation and enhanced air-sea water exchange. At low tide this water mass enriched in O₂ and impoverished in CO₂ mixes with the adjacent water of the channel, which explains the deviations observed from the general trends along the salinity gradient (Fig. 2). However, this is probably a transient feature that rapidly fades due to mixing since it has never been observed before from an available large data-set (31 cruises carried out from 1993 to 2000; Frankignoulle et al. 1996; Frankignoulle et al. 1998; Frankignoulle and Borges 2001; Borges et al. 2004a; Borges et al. 2004b; Boschker et al. in press).

Planktonic GPP rates measured in the present study are close to previous rates estimated in the 1990's by Kromkamp and Peene (1995) in the salinity mixing zone and by Muylaert (1999) in the tidal river (Fig. 8), showing no clear evolution over this time period. High rates were observed in the freshwater area (92 mmol C m⁻² d⁻¹), decreasing towards the brackish part of the estuary (17-25 mmol C m⁻² d⁻¹) to rise again towards the marine area (30-34 mmol C m⁻² d⁻¹). As mentioned by van Spaendonk et al. (1993) and Kromkamp et al. (1995), nutrient concentrations in the Scheldt are high enough not to limit phytoplankton growth, although a significant decrease of silicates was observed in the freshwater and brackish parts in August and in the marine part in May coinciding with high GPP rates. In the freshwater and inner part of the
estuary, GPP was highest in summer when irradiance and water temperature were also highest. Chl *a* and phaeopigments data showed that high loads of riverine phytoplankton were imported from March to May and degraded in the upper estuary when salinity increases. In summer, high chl *a* concentrations were still observed but phaeopigments concentrations dropped to low values indicating a much more active phytoplanktonic community in agreement with higher GPP. This is consistent with the findings of Muylaert et al. (2000) who studied the distribution and dynamics of phytoplankton communities in the inner and freshwater parts of the Scheldt estuary and found that salinity, irradiance and temperature were the main driving forces. Planktonic GPP rates measured in this estuary are in the range of published estuarine rates reviewed by Heip et al. (1995) and Gazeau et al. (2004) for European estuaries.

Planktonic CR rates were generally highest in the brackish area with maximal rates observed in May and October (-258 and -313 mmol C m⁻² d⁻¹). These high rates are related to enhanced total organic carbon inputs during these 2 months (data not shown). Freshwater flow alone cannot explain these elevated organic matter loads, but daily precipitation data at Vlissingen (51.27 °N; 3.36 °E) and at Stabroek (51.19 °N; 4.21 °E) reveal that highest precipitations occurred in May and October (data not shown). Therefore, these peaks of planktonic CR might be explained by an increase of lateral organic matter inputs by water runoff inside the estuary and show that planktonic CR in the Scheldt estuary is mainly driven by inputs of allochtonous organic matter rather than by local production, consistent with the compound specific isotope data evidence presented by Boschker et al. (in press).

Hopkinson and Smith (in press) reviewed planktonic and benthic CR rates in 22 estuaries around the world and report planktonic CR between -1.7 in the Gulf of Finland to -84 mmol C m⁻³ d⁻¹ in the Fly River delta with an arithmetic mean value of -17.8 mmol C m⁻³ d⁻¹. In the Scheldt estuary, we measured annually averaged planktonic CR rates ranging from -4.7 in the outer estuary to -19.1 mmol C m⁻³ d⁻¹ in the inner part with a mean value in the whole estuary of -6.6 mmol C m⁻³ d⁻¹. This mean rate is close to the ones measured by Witek et al. (1999) in the Gulf of Gdánsk and Rudek and Cloern (1996) in San Francisco Bay.

Nitrification is maximal in the brackish part of the estuary and represents more than 30% of the total organic matter production in this area. In 1997-1998, de Bie et al. (2002) reported a mean rate over the whole estuary of 1.1 (range: 0 to 7.5) mmol C m⁻³ d⁻¹ while we estimated a rate of 0.5 (range: 0 to 2.2) mmol C m⁻³ d⁻¹. Over the same time period, NH₄ concentrations in the
estuary decreased from 90 to 60 mmol N m⁻³. Therefore, it seems that the decrease in NH₄
loading in the Scheldt estuary induced a significant decrease of nitrification and led to improved
O₂ conditions as reported by Soetaert et al. (submitted). Nevertheless, rates of nitrification
measured in the Scheldt estuary in 2003 are still higher than or similar to the ones reported by
Owens (1986) in the Tamar estuary (max: 0.8 mmol C m⁻³ d⁻¹), Berounsky and Nixon (1993) in
Narragansett Bay (0 to 1.9 mmol C m⁻³ d⁻¹) and Brion et al. (2000) in the Seine river estuary (0.2
to 1.9 mmol C m⁻³ d⁻¹).

We report the first measurements of planktonic NCP in the Scheldt estuary which was
only estimated through modelling by Soetaert and Herman (1995b). Rates measured along the
salinity gradient in 2003 follow the same trend but measured data are significantly higher than
those estimated by these authors (Fig. 8). Their model was calibrated on data gathered in the
period 1980-1985 but it is difficult to conclude whether this difference results from an increase of
the planktonic NCP over 20 years or from differences in methodology.

Estimates of NEP in the Scheldt estuary from June to December 2003 based on the
LOICZ modelling procedure applied to DIC and using the parametrization of Raymond and Cole
(2001) to estimate air-sea CO₂ fluxes, are consistent with in situ direct measurements for the
same period (-52 vs. -44 mmol C m⁻² d⁻¹). CO₂ fluxes over the investigated time-period were
estimated using this relationship to 149, 56 and 20 mmol C m⁻² d⁻¹ in the inner, middle and outer
area respectively. Hellings et al. (2001), in their DIC budget, estimated CO₂ fluxes in the inner
area using the parametrization of Wanninkhof (1992) to compute *k* values from wind speed data
and found an annual CO₂ efflux significantly lower than the one reported in the present study
using the parametrization of Raymond and Cole (2001): 71 vs.149 mmol C m⁻² d⁻¹. Moreover,
these authors estimated the NEP in this zone to -73 mmol C m⁻² d⁻¹, a value significantly higher
than the one calculated in this study. Integrated over the entire estuary, a CO₂ flux to the
atmosphere of 186 tC d⁻¹ is estimated, lower than the estimate of 456 tC d⁻¹ given by
Frankignoulle et al. (1998). Vanderborght et al. (2002) estimated a CO₂ flux to the atmosphere
for July 1996 of 225 tC d⁻¹ using the coupled, networked, transport-reaction algorithm for strong
tidal estuaries (CONTRASTE) model. This value is close to the one estimated in the present
study for July 2003 (214 tC d⁻¹).

As mentioned above, NEP estimates based on the LOICZ budgeting procedure applied to
DIC using the gas transfer velocity parametrization of Raymond and Cole (2001) is consistent
with the NEP estimated from incubations while the use of Borges et al. (2004b) parametrization
leads to a lower estimate. However, it does not allow to conclude on the validity of these
parametrizations of the gas transfer velocity. Indeed, an error analysis on the NEP estimate
associated to the computations of CO2 fluxes was carried out assuming reasonable experimental
errors on ΔpCO2 and k600 of, respectively, ± 3% and ± 5 cm h-1. The overall resulting error on
NEP is ± 165, ± 33 and ± 12 mmol C m-2 d-1 for, respectively, the upper, middle and lower
estuary. The error estimate on NEP is, as expected, strongly dependent on the spatial variations of
ΔpCO2. Thus, the lower bound of the NEP estimate based on the DIC budget using the Raymond
and Cole (2001) parametrization (-103 mmol C m-2 d-1) overlaps the higher bound of NEP
estimate based on the DIC budget using the Borges et al. (2004b) parametrization (-71 mmol C
m-2 d-1). It is also possible that both DIC budgets underestimate the NEP. Some DIC input terms
have been neglected or under-estimated. For instance, the lateral sources of DIC could be
underestimated since they were computed from a relationship between O2 and DIC established in
the main channel of the estuary. Also, DIC diffuse inputs were not accounted for in the budget
although diffusive inputs of total organic carbon had a major influence on metabolic processes in
May and October during precipitation periods (see above). Finally, CaCO3 dissolution was
neglected in the budget, since to our best knowledge this process has not been quantified in the
mixing region of the Scheldt, and we cannot exclude the possibility that this process could be
significant. If both DIC budgest underestimate the NEP, the NEP value derived using the Borges
et al. (2004b) gas transfer velocity parametrization could be consistent with the value from
incubation measurements.

The annual NEP estimated in the Scheldt estuary by means of compiled in situ
measurements (-42 mmol C m⁻² d⁻¹) is in the lower range of previously reported LOICZ based
rates with “Steve Smith’s ranking ≥ 2” (-42 and 32 mmol C m⁻² d⁻¹; downloaded on 20 May 2004
from
the
LOICZ
environmental
database
http://hercules.kgs.ukans.edu/hexacoral/envirodata/main.htm).

In a recent paper, Caffrey (2004) reported annual NEP estimates based on the in situ diel O2 method for 42 estuarine sites in the US. All sites are heterotrophic in term of carbon with NEP ranging from -11 to -278 mmol C m⁻² d⁻¹ and a mean value of -110 ± 66 mmol C m⁻² d⁻¹, lower than the one estimated in this study. It must be stressed that the in situ diel O2 method does not take into account the O2 consumption due to the nitrification process, which is likely to be an
important process in estuarine systems, and can therefore underestimate NEP rates. The in situ
diel O₂ method is also highly sensitive to the estimation of the exchange of O₂ across the air-
water surface. This is particularly critical in estuaries where a simple parametrization of the gas
transfer velocity as a function of wind speed has been shown to be site specific (Kremer et al.
2003; Borges et al. 2004a).

The LOICZ modelling procedure applied to DIP concentrations leads to a significant overestimation of NEP especially in the inner part of the estuary. It should be stressed that this method assumes that all non-conservative fluxes of DIP can be attributed to net uptake into organic matter during primary production or release from organic matter by respiration. The primary shortcoming of this method is that systems with high amounts of suspended mineral material may show evidence for DIP adsorption onto particulate materials or desorption from them (2001). High suspended matter concentrations are encountered in the inner Scheldt estuary (average over the investigated period: 74 ± 55 g m⁻³) and processes of DIP sorption on these particles are likely (Zwolsman 1994). As the same procedure applied to DIC gave results very consistent with in situ measurements (see hereafter), we attributed the difference to DIP particle exchange processes and estimated the rate of net DIP sorption in the inner, middle and outer estuary to 1.3, 0.2 and 0.1 mmol P m⁻² d⁻¹. Therefore, more than 50% of the non-conservative DIP flux in the inner area can be attributed to this net adsorption process. Unfortunately, no such estimates were found in the literature for comparative purposes.

Finally, the LOICZ approach applied to DIN from June to December 2003 provided an estimation of nfix-denit of -20, -10 and -8 mmol N m⁻² d⁻¹ in the inner, middle and outer estuary respectively, for a mean value in the whole estuary of -11 mmol N m⁻² d⁻¹ and, as NEP, far below the range (-5.2 to 2.6 mmol N m⁻² d⁻¹) reported in the LOICZ environmental database. Assuming no N₂ fixation in this highly eutrophied estuary, a denitrification rate of -11 mmol N m⁻² d⁻¹ or -14900 tN yr⁻¹ is estimated, lower than the rate reported by Billen et al. (1985) for the 1970's (-20000 tN yr⁻¹) but consistent with the one provided by Soetaert and Herman (1995c) for the 1980's (-15000 tN yr⁻¹). As mentioned by Soetaert et al. (submitted), it seems therefore that denitrification decreased from the 1970's to the 1980's in response to improved aeration, and decrease of NH₄ loadings and nitrification rates. Our results suggest that denitrification did not decrease significantly since the 1980's. Middelburg et al. (1995) reported denitrification rates on an intertidal flat in the Scheldt (station Doel) and found an annual average of -1.7 mmol N m⁻² d⁻¹
for 1991. This is almost one order of magnitude lower than the rate estimated in this study for both the planktonic and benthic compartments. Since, according to Soetaert et al. (submitted), there is no evidence of water column denitrification since the beginning of the 1990's although DIN removal significantly increased during the last ten years, it is suggested that benthic denitrification increased significantly between 1991 and 2003. This may explain the discrepancy between our results and those of Middelburg et al. (1995) measured in 1991.

Finally, DIC, DIN and DIP inputs from lateral sources represented 18, 33 and 35% of the total inputs in the estuary. Inputs from diffuse sources as well as from small canals were not integrated in our models. Although representing only 10% of the total freshwater input in the estuary (Soetaert et al. submitted), this may have introduce a bias in the budgets output. We already discussed the limitation of the LOICZ budgeting procedure applied to DIP in this turbid estuary as well as the sensitivity to the parametrization of the gas transfer velocity when applied to DIC, it should be also mentioned that this procedure requires a perfect knowledge on the numerous potential sources of nutrients which are not always easy to sample.

This study confirmed that, due to high inputs of allochtonous organic matter, residence time and turbidity, the Scheldt estuary is a strongly heterotrophic system. Phytoplankton production is not nutrient-limited. Despite a significant decrease of nutrient inputs over the last decade, GPP values estimated in 2003 are very similar along the salinity gradient to rates measured in the 1990's. In contrast, a significant decrease of nitrification was observed in the past 6 years and is correlated with a decrease of the ammonium loading. Nevertheless, this process is still important and represents more than 30% around the maximum turbidity zone. As no previous direct CR or NEP estimates are available to compare with, no conclusion on the evolution of these parameters can be drawn.

Several approaches were tested to estimate the NEP in this estuary. We showed that the LOICZ method based on non-conservative DIP fluxes cannot be applied in this turbid system, as abiotic processes such as sorption and desorption from and to suspended particles and/or sediment can have a strong impact on these fluxes. The LOICZ method based on DIC using the parametrization of the gas transfer velocity of Raymond and Cole (2001) yielded NEP estimates consistent with estimates from incubations. Nevertheless, the present study also highlights that this method was subject to a relatively large uncertainty mainly related to the errors associated to the CO₂ fluxes estimates as well as the unknown influence of CaCO₃ on the carbon cycle in this
system. Finally, the LOICZ approach applied to DIN suggests that the denitrification process did not change significantly since the 1980's despite a strong improvement of O₂ conditions in the estuary
<table><caption>Table 1. Sampling stations location and characteristics during the November 2002 and April 2003 cruises.</caption><thead><tr><th>Cruise</th><th>Station number</th><th>Station name</th><th>Benthic/Planktonic</th><th>Lat (°N)</th><th>Long (°E)</th><th>Bottom depth (m)</th><th>Salinity</th><th>Temperature (°C)</th></tr></thead><tbody><tr><td rowspan="11">November 2002</td><td>1</td><td>Oostende</td><td>Both</td><td>51.25</td><td>2.85</td><td>11</td><td>34</td><td>12.5</td></tr><tr><td>2</td><td>Vlissingen</td><td>Benthic</td><td>51.40</td><td>3.60</td><td>17.9</td><td>27.6</td><td>11</td></tr><tr><td>3</td><td>Terneuzen</td><td>Benthic</td><td>51.35</td><td>3.81</td><td>15.5</td><td>23.1</td><td>11</td></tr><tr><td>5</td><td>Hansweert</td><td>Both</td><td>51.41</td><td>4.04</td><td>16.6</td><td>19.7</td><td>11</td></tr><tr><td>7</td><td>Prosperpolder</td><td>Both</td><td>51.39</td><td>4.21</td><td>15.5</td><td>5.9</td><td>11.7</td></tr><tr><td>8</td><td>Doel</td><td>Benthic</td><td>51.35</td><td>4.25</td><td>14.4</td><td>4.9</td><td>12.4</td></tr><tr><td>10</td><td>Antwerpen</td><td>Both</td><td>51.23</td><td>4.40</td><td>14.8</td><td>1.9</td><td>12.2</td></tr><tr><td>11</td><td>Ruppel</td><td>Both</td><td>51.13</td><td>4.31</td><td>14.9</td><td>0.5</td><td>12</td></tr><tr><td rowspan="11">April 2003</td><td>1</td><td>Oostende</td><td>Both</td><td>51.23</td><td>2.84</td><td>11.6</td><td>30.3</td><td>7.8</td></tr><tr><td>2</td><td>Vlissingen</td><td>Benthic</td><td>51.40</td><td>3.60</td><td>22.4</td><td>25.40</td><td>7.7</td></tr><tr><td>3</td><td>Terneuzen</td><td>Benthic</td><td>51.35</td><td>3.81</td><td>11.5</td><td>20.55</td><td>8.2</td></tr><tr><td>4</td><td>Hoek van Ossenise</td><td>Planktonic</td><td>51.38</td><td>3.93</td><td>17</td><td>20.88</td><td>8</td></tr><tr><td>5</td><td>Hansweert</td><td>Benthic</td><td>51.41</td><td>4.04</td><td>15.3</td><td>18.43</td><td>8.1</td></tr><tr><td>6</td><td>Overloop</td><td>Planktonic</td><td>51.37</td><td>4.09</td><td>16.5</td><td>12.92</td><td>8.7</td></tr><tr><td>7</td><td>Prosperpolder</td><td>Benthic</td><td>51.39</td><td>4.21</td><td>14.2</td><td>12.00</td><td>9.3</td></tr><tr><td>8</td><td>Doel</td><td>Both</td><td>51.35</td><td>4.25</td><td>16.3</td><td>7.5</td><td>10.3</td></tr><tr><td>9</td><td>Oesterweel</td><td>Planktonic</td><td>51.24</td><td>4.37</td><td>14.6</td><td>2.88</td><td>10.8</td></tr><tr><td>10</td><td>Antwerpen</td><td>Benthic</td><td>51.23</td><td>4.40</td><td>12.5</td><td>2.28</td><td>10.7</td></tr><tr><td>11</td><td>Ruppel</td><td>Both</td><td>51.12</td><td>4.30</td><td>15.1</td><td>1</td><td>11.1</td></tr></tbody></table>
<table><caption>Table 2. Surface area, mean depth and volume of each estuarine box budgeted using the LOICZ procedure (in bold). Salinity and other environmental parameters in each budgeted box (in bold) as well as for the lateral inputs and in the freshwater and river plume areas. DIP, DIN, DIC and pCO₂ are respectively dissolved inorganic phosphorus, dissolved inorganic nitrogen, dissolved inorganic carbon and partial pressure of CO₂. Each value is an average of several stations (Fig. 1B) from June to December 2003 and numbers in parenthesis refer to the associated standard deviation (± SD).</caption><thead><tr><th>Compartment</th><th>Freshwater flow (m³ d⁻¹)</th><th>Area (km²)</th><th>Mean depth (m)</th><th>Volume (km³)</th><th>Sal</th><th>DIP (mmol m⁻³)</th><th>DIN (mmol m⁻³)</th><th>DIC (mmol m⁻³)</th><th>pCO₂ (ppm)</th></tr></thead><tbody><tr><td>River</td><td>5.62 x 10⁶</td><td>-</td><td>-</td><td>-</td><td>1.1 ± 0.5</td><td>7.18 ± 0.51</td><td>397 ± 79</td><td>4670 ± 115</td><td>6185 ± 501</td></tr><tr><td>Gent-terneuzen channel</td><td>9.9 x 10⁵</td><td>-</td><td>-</td><td>-</td><td>14</td><td>17.10</td><td>535</td><td>3314</td><td>-</td></tr><tr><td>Antwerpen harbour</td><td>8.26 x 10⁵</td><td>-</td><td>-</td><td>-</td><td>4</td><td>5.58</td><td>295</td><td>3030</td><td>-</td></tr><tr><td><strong>Inner</strong></td><td></td><td><strong>54</strong></td><td><strong>8.8</strong></td><td><strong>475</strong></td><td><strong>7.9 ± 2.1</strong></td><td><strong>6.37 ± 0.60</strong></td><td><strong>298 ± 64</strong></td><td><strong>3898 ± 113</strong></td><td><strong>3850 ± 690</strong></td></tr><tr><td><strong>Middle</strong></td><td></td><td><strong>93</strong></td><td><strong>9.5</strong></td><td><strong>884</strong></td><td><strong>20.9 ± 2</strong></td><td><strong>5.24 ± 0.59</strong></td><td><strong>136 ± 21</strong></td><td><strong>3039 ± 97</strong></td><td><strong>1137 ± 319</strong></td></tr><tr><td><strong>Outer</strong></td><td></td><td><strong>113</strong></td><td><strong>12.9</strong></td><td><strong>1455</strong></td><td><strong>28.6 ± 1.1</strong></td><td><strong>2.88 ± 0.59</strong></td><td><strong>55 ± 10</strong></td><td><strong>2550 ± 73</strong></td><td><strong>682 ± 176</strong></td></tr><tr><td>River plume</td><td></td><td>-</td><td>-</td><td>-</td><td>32.3 ± 0.5</td><td>1.43 ± 0.33</td><td>19 ± 16</td><td>2333 ± 181</td><td>570 ± 136</td></tr><tr><td><strong>Whole estuary</strong></td><td></td><td><strong>260</strong></td><td><strong>9.7</strong></td><td><strong>2814</strong></td><td>-</td><td><strong>4.21 ± 1.6</strong></td><td><strong>121 ± 110</strong></td><td><strong>2931 ± 578</strong></td><td><strong>1360 ± 1496</strong></td></tr></tbody></table>