Modelling nitrogen and phosphorus dynamics in a mesocosm pelagic ecosystem in Laizhou Bay in China

J. Ocean Univ. China (Oceanic and Coastal Sea Research) DOI 10.1007/s11802-009-0133-6 ISSN 1672-5182, 2009 8 (2): 133-140 http://www.ouc.edu.cn/xbywb/ E-mail:[email protected]

Modelling Nitrogen and Phosphorus Dynamics in a Mesocosm Pelagic Ecosystem in Laizhou Bay in China

LI Keqiang1), 2), 3), 4), *, WANG Xiulin1), 2), 3), HAN Xiurong1), 2), 3), SHI Xiaoyong1), 2), 3), and CHEN Hu5)

1) College of Chemistry and Chemical Engineering, Ocean University of China, Qingdao 266100, P. R.China 2) Key Laboratory of Marine Chemistry Theory and Technology, Ministry of Education, Ocean University of

China, Qingdao 266100, P. R. China 3) Marine Pollution Eco-Chemistry Laboratory, Ocean University of China, Qingdao 266100, P. R. China 4) National Oceanography Center, Southampton, University of Southampton, Southampton SO14 3ZE, UK. 5) Qingdao Land of State Power Environment Engineering CO. LTD, Qingdao 266071, P. R. China

(Received September 25, 2008; revised December 9, 2008; accepted February 11, 2009)

Abstract A model of nitrogen and phosphorus dynamics in mesocosm experiments was established on the basis of a summary and synthesis of the existing models. The established model comprised seven state variables (DIN, PO4-P, DON, DOP, phyto-plankton, zooplankton and detritus) and five modules – phytoplankton, zooplankton, dissolved inorganic nutrients, dissolved or-ganic nutrients and detritus. Comparison with the in situ experimental data in Laizhou Bay at the end of August 2002 showed that this model could properly simulate the variations of DIN, PO4-P, and phytoplankton biomass in a mesocosm pelagic ecosystem. It was found that not only the model structure but also the parameters adopted were fit for simulation. The sensitivity of the main state variables to the parameter change was assessed by sensitivity analysis. All these results are useful for studying the control mechanism of biogeochemical cycling of nutrients in Laizhou Bay and other Chinese coastal waters.

Key words Laizhou Bay; nitrogen and phosphorus; dynamic model; mesocosm

1 Introduction Simulation by a numerical model is one of the tools

used to study the dynamic processes of nitrogen and phosphorus in marine ecosystems. Modelling nitrogen and phosphorus dynamics in marine ecosystems has in-creased significantly since the 1940s (Riley, 1946). And models concerning the ecological aspects of China’s seas have been developed and improved since the 1990s (Tang et al., 2005; Gao et al., 2000), including the stud-ies in Jiaozhou Bay (Cui and Zhu, 2001; Wu et al., 1999; Yu et al., 1999), the Bohai Sea (Gao et al., 1998; Wei et al., 2004), the Yellow Sea (Tian et al., 2003), the East China Sea (Cui et al., 1997), the Changjiang River Estu-ary (Qiao et al., 2001), the Zhujiang River Estuary (Guan et al., 2001a, b), Dapeng Bay (Xia et al., 1997) and the Taiwan Strait (Hong and Shang, 1994; Shang and Hong, 1995). However, the parameters in these models were mostly taken from the literature for other sea areas, which impedes the development of the ecological dynamic modeling in China. Because of the basic geographical and ecosystem differences among most dynamic models for * Corresponding author. Tel: 0086-532-82032479-804

E-mail: [email protected]

different marine areas, the dynamic processes and pa-rameters should be established and determined through in situ experiments (Tang and Su, 2000). With its controlla-bility, similarity to the actual marine environment, and possibility of continuous observation (Lu, 1987), a mesocosm is often used to model nutrient dynamics and determine the dynamic parameters (Baretta-Bekker et al., 1994; Grice and Reeve, 1982).

In this paper, a model of nitrogen and phosphorus dy-namics of a mesocosm pelagic ecosystem is established according to the European Regional Seas Ecosystem Model (ERSEM) (Baretta et al., 1995; Ebenhöh et al., 1997), and based on the in situ experimental data in Laizhou Bay at the end of August 2002.

2 Mesocosm Experiment Mesocosm experiments were performed in the coastal

area of Laizhou Bay (37˚36΄N, 120˚19΄E) from 25 to 30 August 2002. The experiments were mainly designed to examine the dynamic processes of nutrients. The meso-cosms were 2.5 m3 polyethylene bags mounted on a float-ing steel frame, with no sediment at the bottom (Fig.1). The conditions in the bags were kept as similar as possi-ble to the actual condition. At the beginning of the ex-periment, NaNO3, KH2PO4 and NaSiO3 were added into

LI et al. / J. Oean Univ. China (Oceanic and Coastal Sea Research) 2009 8: 133-140

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Fig.1 Schematic view of mesocosm.

the bags at the concentrations of 32 mmol m-3, 2 mmol·m-3 and 32 mmol m-3 in mesocosm 1 and 32 mmol·m-3, 1.5

mmol m-3 and 32 mmol m-3 in mesocosm 2 respectively. Sampling was performed at 8: 00 and 18: 00 daily when complete mixing was reached. Water temperature, salinity, nutrients, phytoplankton and chlorophyll-a were deter-mined respectively, and the sampling and analyzing methods were according to ‘the Specification for Ocean- ographic Survey’ (GB/T 12763.4-1991; GB/T 12763.6-

1991). During the experimental period, the ‘Photosyn-thetically Active Radiation’ (PAR) below the water sur-face averaged 89.8 W m-2±14.9 W m-2, the water tempera-ture averaged 26.6℃± 0.5℃, and the salinity averaged 33.37± 0.10. The dominant species of phytoplankton were Pseudonitzschia sp. at the earlier stage, and Chaetoceros lorenzianus and Chaetoceros curvisetus at the later stage; the dominant species of zooplankton was Oithouna sp. for the two mesocosm experiments.

3 The Model Because there is no matter exchange through the

mesocosm bag, or between water and sediment, the mesocosm ecosystem can be regarded as a matter- con-servation system, neglecting the matter exchange be-tween the water and the atmosphere. Thus the bio- transport and bio-transformation processes of nitrogen and phosphorus by phytoplankton and zooplankton will be the major processes in the mesocosm ecosystem (GESAMP, 1991).

3.1 Premises As the mesocosm ecosystem in Laizhou Bay is a typi-

cal pelagic ecosystem with very complex processes, for convenience, only the major dynamic processes are con-sidered with some details simplified. Two premises are assumed: 1) each of the phytoplankton and zooplankton

in the system is represented by one state variable, without distinction of species, size, growth phase, etc., and 2) the processes of absorption and metabolization of nitrogen and phosphorus follow the Redfield ratio (16:1), without distinction of ammonium-nitrogen, nitrite-nitrogen and nitrate-nitrogen.

Previous simulations with models revealed that ex-plicit parameterization of bacteria (Fasham et al., 1990) aggravated the fit of simulated processes to the in situ data. Although bacteria play an essential role in the bio-degradation of nutrients and detritus in the marine eco-system (Azam et al., 1983; Ducklow, 1983), we found that the model can be tuned more easily when the com-plex bacterial activity is modeled implicitly.

3.2 Logical Structure and Dynamic Equations To understand how nitrogen and phosphorus transport

and transformation take place in a pelagic ecosystem, we are more concerned about the types of inorganic, organic and particulate nutrients. We choose a box model with seven state variables, i. e., inorganic nitrogen (DIN), phos-

phate (PO4-P), dissolved organic nitrogen (DON), dis-solved organic phosphorus (DOP), phytoplankton (PPT), zooplankton (ZPT) and detritus (DPT) (Fig.2).

Fig.2 Concept model for nitrogen and phosphorus transport and transformation in mesocosm. 1. photo-synthesis; 2. phytoplankton exudation; 3. zooplankton grazing; 4. phytoplankton mortality; 5 and 6. zooplank-ton excretion; 7. zooplankton grazing, mortality and excretion; 8 and 9. detritus breakdown by microorgan-ism; 10. dissolved organic nutrients remineralization by microorganism; 11. detritus sinking.

We express the state variables of DIN and DON in ni-trogen molarity units, PO4-P and DOP in phosphorus molarity units, PPT, ZPT and DPT in weight units and fluxes in nitrogen molarity units. These variables repre-sent the major interacting components within a marine pelagic ecosystem. And there are five modules in the model, which are phytoplankton, zooplankton, dissolved inorganic nutrients, dissolved organic nutrients and de-


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tritus modules. They mainly include the processes of phytoplankton growth, mortality and exudation (Skogen and Søiland, 1998; Ebenhöh et al., 1997; Varela et al., 1995; Baretta et al., 1988), zooplankton grazing, mortal-ity and excretion (Baretta-Bekker et al., 1995; Broek-huizen et al., 1995), dissolved organic nutrients reminer-alization (Varela et al., 1995) and detritus breakdown and sinking (Ebenhöh et al., 1995). The equations are given as follows:

PPT PPT_G PPT_E PPT_D PPTd d ( )C t Cμ μ μ= − − −

PPT_Z ZPTCμ , (1)

ZPT PPT_Z PPT_Z DPT_Z DPT_Zd d (C t δ δμ μ= + −

ZPT_D ZPT_N ZPT)Cμ μ− , (2)

DIN PPT_G PPT ZPT_N ZPT_N ZPTd /dC t C r Cμ μ= − + +

DON_B DONCμ , (3)

4PO P PPT_G PPT ZPT_N ZPT_N ZPT N/Pd /d ( )C t C r C rμ μ− = − + +

DOP_B DOPCμ , (4)

DON PPT_E PPT ZPT_N ZPT_N ZPTd /d (1 )C t C r Cμ μ= + − +

DPT_B DPT DON_B DONC Cμ μ− , (5)

DOPd /dC t = PPT_E PPT ZPT_N ZPT_N ZPT[ (1 )C r Cμ μ+ − +

DPT_B DPT N/P DOP_B DOP]C r Cμ μ− , (6)

DPT ZPT_D PPT_Z PPT_Zd /d [ (1 )C t δμ μ= + − −

DPT_Z DPT_Z ZPT PPT_D PPT]C Cδ μ μ+ −

DPT_B DPT_S DPT( )Cμ μ+ . (7)

3.3 Process Description The photosynthesis rate is a multiplicative function of

respective effects of temperature, available light and nu-trients. The temperature effect is exponential (Eppley, 1972) when the inhibitory effect of high temperature is neglected, which is true for the northern China seas. Steele’s function is used to describe the light control of photosynthesis. The solar radiation that reaches the sea surface has a wide range of wavelengths. For the primary production in the mesocosm ecosystem, light in the range of wavelengths from 400 to 700 nm is normally consid-ered, which is about 43% of the total solar radiation (Zhou et al., 1984), and is defined as ‘Photosynthetically Active Radiation’ (PAR). This range of irradiance pro-vides the predominant source of energy for autotrophic organisms. The strength of solar radiation penetrating the sea surface is influenced by the ellipticity of the earth’s orbit, absorption of atmospheric clouds, the echo from sea surface and the solar altitude (Dobson and Smith,

1988). Average irradiance is used in the model without considering its daily change. The irradiance just below the water surface decreases with depth according to an exponential equation (Ebenhöh et al., 1997) at a rate de-pending on the light extinction coefficient, which is mainly influenced by chlorophyll self shading in the mesocosm ecosystem (Riley, 1956). Nutrient limitation follows the Michaelis-Menten equation; the limitation is proportional to the concentration at low nutrient levels and approaches a constant value of unity at high nutrient levels. The total nutrient limitation can follow the minimum law, the mul-tiplication law or the resistance law (Schnoor, 1996); in this study the resistance law was used.

There are metabolizing processes during photosynthe-sis, such as respiration and exudation. The former has a positive correlation with growth rate (Laws and Caperon, 1976), and the latter has a certain proportional relation-ship with the phytoplankton biomass. For convenience, in this model the phytoplankton metabolizing is taken as a process depending on light irradiance, temperature and nutrient, in which light irradiance is a decisive factor (Zlotnik and Dubinsky, 1989).

The grazing follows the Michaelis-Menten formulation with modifications (Fasham et al., 1990), whilst there is a threshold for overgrazing (Radach and Moll, 1993). As-similation is a constant fraction of the ingested food, whilst the non-assimilated fraction is rejected as feces (Butler et al., 1969). There are dissolved inorganic nutri-ent and dissolved organic nutrient in the excretion, which have some relationship with zooplankton biomass and size (Peters, 1983; Huntley and Boyd, 1984); in this model, for convenience, only zooplankton biomass is considered (Wen and Peters, 1994). Remineralization of organic nutrients and detritus are described by a simple temperature-dependent formula, without detailing the different chemical steps and microorganism species (Cha-

pelle et al., 1994; Jones and Henderson, 1986). Similarly, phytoplankton and zooplankton mortality are functions of temperature (Eppley, 1972; Jørgensen et al., 1991). The mathematical formulae are summarized in Table 1.

3.4 Model Parameters Model parameters are essential elements in numerical

modelling (Ye, 1997; Schartau et al., 2001). The reliabil-ity of parameters plays a pivotal role in ensuring the ra-tionality and accuracy of the results (Gao et al., 1999; Schartau et al., 2001). It is impossible to determine all the parameters in field and laboratory experiments, therefore how to choose and assimilate parameters is very important (Harmon and Challenor, 1997). The initial values of the parameters in this experiment are mainly determined in field and laboratory experiments, whilst some are taken from the literature. Furthermore, the pa-rameters are optimized by modelling and sensitivity analysis for the mesocosm experiments in Laizhou Bay. There are about 20 parameters found to be fit for Laizhou Bay. The parameters used in the model for the mesocosm pelagic ecosystem are listed in Table 2.


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Table 1 Internal equations in modelling nitrogen and phosphorus transport and transformation

Symbol Equation Definition Source

HI PAR 0

1 e dH zI z

Hκ−∫

Average light irradiance at depth h Ebenhöh et al., 1997

κ 2 / 30 1 Chl/PN PPT 2 Chl/PN PPT( ) ( )r C r Cκ κ κ+ + Phytoplankton extinction coefficient Riley, 1956

PPT_Gμ PPT_G PPT-G PPT-G PPT-G( ) ( ) ( )k f T f I f N Phytoplankton photosynthesis rate Baretta et al., 1988

PPT-G( )f T PPT_Ge Γ T Temperature effect on phytoplankton Eppley, 1972

PPT-G( )f I H

opt(1 )

H

opt

eIII

I

−

Light effect on phytoplankton Steele, 1962

PPT-G( )f N

4

4

4

4

PO PDIN

DIN N PO P P

PO PDIN

DIN N PO P P

CCC Ks C Ks

CCC Ks C Ks

−

−

−

−

+ +

++ +

Nutrient limitation of phytoplankton Schnoor, 1996

PPT_Eμ PPT_E PPT_Gr μ Phytoplankton metabolic rate Laws and Caperon, 1976

PPT_Er optH

opt opt

0.24 1.20.1210 10

II

I I−

+− −

Ratio of phytoplankton exudates to photosynthesis Zlotnik and Dubinsky, 1989

PPT_Dμ PPT_D( )PPT_De Γ Tk Phytoplankton mortality rate Eppley, 1972

PPT_Zμ *

*PPT PPTPPT_Z PPT PPT*

PPT PPT PPT*

PPT PPT0

C Ck C CC C Ks

C C

⎧ − ≥⎪ − +⎨⎪ <⎩

Grazing rate of zooplankton on phytoplankton

Fasham et al., 1990; Radach and Moll, 1993

DPT_Zμ DPTDPT_Z

DPT DPT

CkC Ks+

Grazing rate of zooplankton on detritus Fasham et al., 1990

ZPT_Dμ ZPT_DZPT_De Γ Tk Zooplankton mortality rate Jørgensen et al., 1991

ZPT_Nμ ZPT_NZPT_NeΓ Tk Zooplankton excretion rate Wen and Peters, 1994

DPT_Bμ DPT_BDPT_BeΓ Tk Detritus biodegradation rate Jones and Henderson, 1986

DPT_Sμ DPT_SvH

Detritus sedimentation rate Fasham et al., 1990

DON_Bμ DON_BDON_BeΓ Tk Biodegradation rate of dissolved organic nitrogen Chapelle et al., 1994

DOP_Bμ DON_BDOP_BeΓ Tk Biodegradation rate of dissolved organic phos-

phorus Chapelle et al., 1994

4 Results and Discussion 4.1 Model Results

The model was implemented on the platform of soft-ware Modelmaker 4.0 (Cherwell Scientific Ltd.). The equations were solved by a standard Runge-Kutta method, the integration time step used was 4 h, whilst the running time step was typically one day. Simulated variations of DIN, PO4-P and chlorophyll a (Chl-a) in the mesocosm 1 ecosystem in August 2002 are presented in Figs.3a–c (lines), which match the experimental values (open cir-cles) quite well, with good correlation (R= 0.8 ± 0.1) and significant differences (P<0.05). The simulated high concentration of phytoplankton in the mesocosm 1 eco-system indicates a bloom after 2 d with associated nutri-ent uptake (Fig.3c). Due to heavy grazing by zooplankton and very low nutrient availability, the phytoplankton concentration rapidly decreases subsequently. Similarly, the simulated variations of DON and DOP also match the experimental values reasonably well (Figs.3d and e).

Because of lack of experimental data to judge the results for zooplankton and detritus, only the concentration of particulate nitrogen (PN) composed of phytoplankton, zooplankton and detritus was simulated. The result shows that the simulated concentration of PN agrees with the experimental data reasonably well (Fig.3f), implying some degree of rationality in the concentration simulation of zooplankton and detritus.

4.2 Model Validation To check the reliability of the model, the concentration

variations of DIN, PO4-P, Chl-a, DON, DOP and PN were simulated (lines) and compared with the mesocosm 2 experimental data (open circles) in August 2002, with good correlation (R= 0.7 ± 0.2) and significant differences (P<0.05) (Fig.4). The pattern characteristic of state vari-ables was the same as the mesocosm 1’s. Similarly, the simulated concentration of phytoplankton in the meso-cosm 2 ecosystem increases rapidly earlier and decreases rapidly later (Fig.4). The results show that not only the simulated variations of the concentrations of DIN, PO4-P


137

Fig.3 Simulation results (lines) of variations of state variables and mesocosm experiment data (open circles) in the mesocosm 1 for Laizhou Bay.

Table 2 List of parameters

Name Symbol Value Unit Source Depth of mesocosm H 3 m − Water self extinction coefficient† κ0 0.4 m-1 Fasham et al., 1990 Chlorophyll self shading coefficient κ1 0.0088 m-1 (mg Chl-a)-1 Riley, 1956 Chlorophyll self shading coefficient κ2 0.054 m-1 (mg Chl-a)-2/3 Riley, 1956 Optimal light irradiance† Iopt 90 W·m-2 Yu et al., 1999 Constant for phytoplankton growth rate† kPPT_G 2.0 d-1 Eppley, 1972 Constant for phytoplankton mortality rate† kPPT_D 0.025 d-1 Eppley, 1972 Constant for zooplankton mortality rate† kZPT_D 0.025 d-1 Jørgensen et al., 1991 Constant for zooplankton excretion rate kZPT_N 0.1 d-1 Wen and Peters, 1994 Constant for zooplankton grazing rate on phytoplankton† kPPT_Z 0.5 d-1 Radach and Moll, 1993 Constant for zooplankton grazing rate on detritus† kDPT_Z 0.6 d-1 Fasham et al., 1990 Constant for dissolved organic nitrogen biodegradation rate† kDON_B 0.01 d-1 Chapelle et al., 1994 Constant for dissolved organic phosphorus biodegradation

rate† kDOP_B 0.25 d-1 Chapelle et al., 1994

Constant for detritus biodegradation rate† kDPT_B 0.05 d-1 Jones and Henderson, 1986

Chl-a / n ratio† rChl/PN 1.6 mg Chl-a mmol

N-1 Yu et al., 1999

N / p ratio in phytoplankton and zooplankton rN/P 16 mol N mol P-1 Redfield et al., 1963

Inorganic nutrient fraction of the excretion of zooplankton rZPT_N 0.75 Carpenter and Capone, 1983

Half-aturation constant for n-limitation† KsN 8 mmol N m-3 Zhang et al., 2002 Half-saturation constant for p-limitation† KsP 2 mmol P m-3 Zhang et al., 2002 Half-saturation constant for detritus-limitation KsDPT 0.7 mmol N m-3 Fasham et al., 1990 Half-saturation constant for ingestion KsPPT 0.6 mmol N m-3 Radach and Moll, 1993 Threshold for overgrazing on phytoplankton *

PPTC 0.12 mmol N m-3 Radach and Moll, 1993 Temperature coefficient for phytoplankton growth ΓPPT_G 0.065 ℃-1 Eppley, 1972 Temperature coefficient for phytoplankton mortality ΓPPT_D 0.065 ℃-1 Eppley, 1972 Temperature coefficient for zooplankton mortality† ΓZPT_D 0.05 ℃-1 Jørgensen et al., 1991 Temperature coefficient for zooplankton excretion ΓZPT_N 0.027 ℃-1 Wen and Peters, 1994 Temperature coefficient for dissolved organic nutrient bio-

degradation† ΓDON_B 0.05 ℃-1 Chapelle et al., 1994

Temperature coefficient for detritus biodegradation† ΓDPT_B 0.05 ℃-1 Jones and Henderson, 1986Assimilation efficiency for phytoplankton† δPPT_ Z 0.8 Chapelle et al., 1994 Assimilation efficiency for detritus† δDPT_Z 0.7 Savchuk, 2002 Detritus sinking speed† νDPT_S 0.5 m d-1 Fasham et al., 1990 Note: † indicates an optimized parameter by mesocosm.


138

Fig.4 Simulation results (lines) of variations of state variables and mesocosm experiment data (open circles) in the mesocosm 2 for Laizhou Bay.

and Chl-a match experiment data quite well, the simu-lated variations of the concentrations of DON, DOP and PN also match the data reasonably well.

In addition, the simulation results indicate that the model is stable and can reflect the key dynamic processes of nitrogen and phosphorus in the mesocosm pelagic ecosystem for Laizhou Bay. The nitrogen and phosphorus transferred from water to plankton by photosynthesis and grazing, and return to water by respiration, exudation and mineralization. At the same time, the results illustrate the rationality of the structure of the dynamic equations and of the model parameters.

4.3 Model Sensitivity Analysis The coefficient of variation (CV) is a simple standard

measure of uncertainty of state variables (Hakanson and Peters, 1995; Hakanson, 2000) and has been used to ana-

lyze the sensitivity of the state variables of DIN, PO4-P and Chl-a to the major parameters in the model. Sensitiv-ity analysis involves the study, by Monte Carlo Analysis (Harmon and Challenor, 1997), of how an alteration of one parameter in a model influences a given state vari-able, whilst everything else is kept constant. Here part of the parameters in the model have been changed 100 times. And it has been assumed that there exists a typical, characteristic mean value for each of the parameters and a given uncertainty for this value given by a standard deviation, which has been set to 50% of the mean as a default assumption. When the CV-value exceeds 0.5, the state variable is regarded as most sensitive to the pa-rameter analyzed; when the CV-value is intervenient 0.1 and 0.5, the state variable is regarded as relatively sensi-tive; when the CV-value is less than 0.1, the state vari-able is regarded as un-sensitive.

From Table 3, one can note that from these presuppo-sitions for the model the DIN predictions are most sensi-

tive to the choice of the values for kPPT_G, ΓPPT_G, ΓPPT_D, Iopt, kPPT_Z, kDOP_B and KsP, and the PO4-P predictions are most sensitive to the choice of the values for kPPT_G, ΓPPT_D and KsP. The Chl-a predictions are only relatively sensitive to the choice of the values for kPPT_D, ΓPPT_D, kZPT_N, kPPT_Z, δPPT_ Z, kDON_B, kDOP_B and ΓDON_B. The re-sults show that the dominant transport and transformation processes for nitrogen and phosphorus are the processes related to the growth of phytoplankton and zooplankton, while water temperature and light irradiance are the ma-jor influencing factors of ecological processes.

Table 3 Sensitivity of DIN, PO4-P and phytoplankton biomass to the parameter change

Coefficients of variationParameter Mean Range DIN PO4-P BChl-a

Iopt 90 45−135 0.74 0.12 0.03kPPT_G 2 1−3 0.58 0.22 0.01kPPT_D 0.025 0.0125−0.0375 0.32 0.38 0.23kZPT_D 0.025 0.0125−0.0375 0.05 0.05 0.03kZPT_N 0.1 0.05−0.15 0.13 0.08 0.17kPPT_Z 0.5 0.25−0.75 0.98 1.09 0.47kDPT_Z 0.6 0.3−0.9 0.18 0.18 0.10kDON_B 0.01 0.005−0.015 0.40 0.33 0.14kDOP_B 0.25 0.125−0.375 0.74 0.48 0.13kDPT_B 0.05 0.025−0.075 0.08 0.07 0.01rZPT_N 0.75 0.375−1.125 0.18 0.02 0.02KsN 8 4−12 0.44 0.38 0.04KsP 2 1−3 0.70 0.60 0.08ΓPPT_G 0.065 0.0325−0.0975 1.25 0.26 0.03ΓPPT_D 0.05 0.025−0.075 0.80 0.92 0.30ΓZPT_D 0.05 0.025−0.075 0.07 0.04 0.07ΓZPT_N 0.027 0.0135−0.0405 0.08 0.09 0.02ΓDON_B 0.05 0.025−0.075 0.32 0.32 0.13ΓDPT_B 0.05 0.025−0.075 0.09 0.09 0.02δPPT_ Z 0.8 0.4−1.2 0.25 0.026 0.14δDPT_Z 0.7 0.35−1.05 0.18 0.18 0.10


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5 Conclusion On the basis of the analysis of transport and transfor-

mation processes of nitrogen and phosphorus, a model for the nitrogen and phosphorus dynamics of mesocosm pelagic ecosystem has been constructed, in which seven state variables: DIN, PO4-P, DON, DOP, phytoplankton, zooplankton and detritus, are included. By using the sen-sitivity analysis of the state variables, we have discussed the dominant transport and transformation processes for nitrogen and phosphorus, related to the growth of phyto-plankton and zooplankton.

The dynamic processes of nitrogen and phosphorus in the mesocosm pelagic ecosystem are successfully simu-lated based on the mesocosm 1 experimental data, and the reliability of the model is checked with the mesocosm 2 experimental data of simulation for Laizhou Bay. The simulated results of the state variables obtained by the model match the mesocosm 1 and 2 experimental data quite well, which shows that not only the logical struc-ture of the model but also the parameters adopted are fit for the Laizhou Bay simulation using the in situ data of August 2002. All these are necessary for studying the control mechanism of biogeochemical cycling of nutri-ents in Laizhou Bay and other Chinese coastal waters.

Acknowledgements The study was supported by the the State Ocean Ad-

ministration of China ‘908’ Foundation (No. 908-02-02-

03), the State Ocean Administration of China Foundation (No. 200805065), the Scientific Research Launching Foundation for Introduced Talents of Ocean University of China, and the Scientific and Technical Projects of Shandong Province on Environmental Protection (No. [2006]050). The irradiance data in this paper were ob-tained from the U. S. National Centers for Environmental Predication (NCEP) and National Centers for Atmos-pheric Research (NCAR) from their Web site: http://www.

cdc.noaa.gov/. We gratefully acknowledge their support.

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Modelling nitrogen and phosphorus dynamics in a mesocosm pelagic ecosystem in Laizhou Bay in China