Simulating the Spring Phytoplankton Bloom in the Humber Plume, UK

PII:

Marine Pollution Bulletin Vol. 37, Nos. 3±7, pp. 295±305, 1998 Ó 1999 Elsevier Science Ltd. All rights reserved Printed in Great Britain 0025-326X/99 $ ± see front matter S0025-326X(98)00174-X

Simulating the Spring Phytoplankton Bloom in the Humber Plume, UK J. I. ALLEN*, R. M. H. HOWLAND, N. BLOOMER and R. J. UNCLES Centre for Coastal Marine Science, Plymouth Marine Laboratory, Prospect Place, West Hoe, Plymouth, Devon PL1 3DH, UK During the spring of 1996, continuously monitoring ¯uorometers were deployed in the Humber plume during the spring phytoplankton bloom. The most striking feature of these records is the sharp increase and decrease in the chlorophyll-a concentration marking the onset and end of the spring bloom. A coupled physical±biogeochemical water column model has been used to successfully hindcast the observed spring bloom. Sensitivity analysis demonstrates that the onset of primary production occurs when the euphotic layer depth is >15% of the depth of the water column. The magnitude and duration of the simulated bloom is controlled by silicate limitation for diatoms and by grazing pressure for autotrophic ¯agellates. The implications of these results for the forecasting of phytoplankton blooms are discussed. Ó 1999 Elsevier Science Ltd. All rights reserved Primary production in turbid coastal waters is controlled by the balance between available nutrients (land-derived and through benthic/pelagic recycling processes), and light limitation in the water column due to seasonal variations in incident solar radiation and suspended sediment loads. Such waters are characterised by a thin euphotic zone and a thick aphotic zone. The determining factor for phytoplankton growth is the relative periods spent in light and dark by the algae and the consequent balance between photosynthesis and respiration. If water mixing exceeds a critical depth (e.g. Grobbelar, 1990), photosynthesis cannot compensate for losses due to respiration and the phytoplankton biomass does not increase. Due to the relative thinness of the euphotic zone, the mixing depth is often assumed to be the major factor in controlling primary production in turbid waters. This has major advantages for phytoplankton, if the light regime is favourable for photosynthesis there is often high nutrient availability, especially in the spring. The estuarine plume of the river Humber is a good example of such a coastal zone (Fig. 1). It is vertically well-mixed due e€ects of wind and tide and is characterised by high nutrient and suspended sediment concentrations (Morris et al., 1995).

*Corresponding author.

The behaviour of the plume ecosystem over a seasonal cycle can be split into two periods. A dormant period occurs between October and April. Inhibition of phytoplankton growth by high SPM loads in the well-mixed waters of the Humber/Wash region (Fichez et al., 1992; Morris et al., 1995) limits the biological activity. The biology becomes active from May onwards and the spring bloom is triggered when the euphotic layer occupies a signi®cant part of the water column. In wellmixed light-limited, nutrient-rich waters we would expect the ecosystem to be characterised by new production, large phytoplankton and grazing by herbivorous mesozooplankton (Ryther, 1969). One of the main objectives of the Land Ocean Interaction Study (LOIS) is `To characterise the key physical and biogeochemical processes that govern ... the functioning of coastal ecosystems, with particular reference to the e€ects of variations in sediment supply and inputs of pollution' (LOIS, 1994). As a part of this programme, continuously monitoring instruments were deployed in the vicinity of the Humber plume. Each one monitored temperature, ¯uorescence, suspended sediment and salinity during the period of the spring bloom. These data represent a unique record of both the development of the spring bloom in 1996 and of the physical variables that in¯uence it, and provide key forcing functions and validation for the modelling study that follows. In this paper we are concerned with determining the controls on primary production in the late spring/early summer in the vicinity of the Humber plume. The hypotheses we wish to test with the model are: · Is the controlling factor on the timing of the spring bloom the ratio of the euphotic depth to the depth of the water column in turbid waters? · Does the bloom crash because of nutrient limitation, or is it grazed out? · Is the qualitative behaviour of the modelled ecosystem consistent with marine ecosystem theory? To demonstrate these controls we have adapted and run an existing coupled biogeochemical water column model, in order to hindcast the observed bloom at two sites in the Humber plume during the spring of 1996. 295

Marine Pollution Bulletin

Sensitivity analysis has been performed to identify the controls on the onset and end of the observed bloom.

Data Collection Three instrumentation packages, Fig. 1 each comprising an ÔAquapackÕ, Chelsea Instruments 11993 (for the measurement of salinity, temperature, depth and ¯uorescence) and a Chelsea Instruments ÔAlphatrackerÕ (for measurement of transmittance) were deployed at sites in the Humber plume and the approaches to the Wash. The positions of the sites are shown in Fig. 1 and Table 1. The instruments, with their battery packs, were mounted around a central stainless steel pillar 80 cm high, and protected by a stainless steel cage which was attached to the central pillar at the top and bottom. The packages were deployed at 5 m depth between a surface buoy and an anchor clump from a local ®shing vessel, the M.V. Challenge. The instruments were programmed to record 60 readings at 3 s intervals every hour. During the period of the deployment, 19 April±26 July 1996, the instruments were recovered once for servicing and the downloading of data. The ÔdownÕ time for this operation was approximately 24 h. The instruments functioned perfectly throughout the deployments and 100% data recovery was achieved.

Description of the Model The model used in this study is the highly portable, coupled, physical biogeochemical water column model described in Allen et al. (1997). It is a synthesis of the European Regional Seas Ecosystem Model (ERSEM), which describes the biogeochemistry (Baretta et al., 1995) and a one dimensional version of the Princeton Ocean Model (Blumberg and Mellor, 1980) which provides the background physical information. The portability of the model comes from the generic nature of ERSEM, whereby the chemical and biological processes included are non site speci®c. All site-dependence comes from the physical environment that they are placed within. The water column model can be used in a dynamic system such as the Humber plume, because modelled ecosystem behaviour has been previously demonstrated to be relatively unin¯uenced by lateral transports of nutrients. (Ruardij et al., 1997; Allen, 1997). ERSEM The ecosystem described in ERSEM is considered to be a series of interacting physical, chemical and biological processes which together exhibit a coherent system behaviour and is reviewed by Baretta et al. (1995). State variables have been chosen in order to keep the 1 Chelsea Instruments Ltd, 2/3 Central Avenue, East Molesey, Surrey, KT8 0QX, UK.

296

Fig. 1 Map of the Humber plume indicating the position of the moorings and the meteorological monitoring station at Hemsby.

TABLE 1 Di€erences between sites M1 and M2.

Latitude Longitude Depth DZ Extinction Coe€ xeps$

Units

M1

M2

m m mÿ1

53 15ÕN 00 39ÕE 20.0 1.0 0.1

53 21ÕN 00 29ÕE 15.0 0.75 0.1

model relatively simple without omitting any component that exerts a signi®cant in¯uence upon the energy balance of the system. ERSEM uses a ÔfunctionalÕ group approach to describe the ecosystem whereby biota are grouped together according to their trophic level (subdivided according to size classes or feeding method). The dynamics of biological functional groups are described by both physiological (ingestion, respiration, excretion and egestion) and population processes (growth, migration and mortality). A full description of the ERSEM model including equations and parameters is beyond the scope of this paper. Detailed descriptions of the biological submodels can be found for, phytoplankton in Varela et al. (1995) and Ebenh oh et al. (1997), functional groups related to the microbial food web in Baretta-Bekker et al. (1995; 1997), mesozooplankton in Broekhuizen et al. (1995), ®sh in Bryant et al. (1995) and benthic fauna in Ebenh oh et al. (1995) and Blackford (1997). The chemical dynamics of nitrogen, phosphate, silicate and oxygen are coupled to the biologically driven carbon dynamics. A schematic diagram of the pelagic food web is shown in Fig. 2. The phytoplankton pool is described by four functional groups based on size and trophic position (Ebenh oh et al., 1997): 1. diatoms (P1), size class 20±200 lm, eaten by micro and mesozooplankton; 2. autotrophic ¯agellates (P2), size class 2±20 lm, eaten by micro and mesozooplankton; 3. picoplankton (P3), size class 0.2±2 lm, eaten by heterotrophic nano¯agellates;

Volume 37/Numbers 3±7/March±July 1998

Fig. 2 Schematic diagram of the pelagic foodweb of the ERSEM model.

4. inedible phytoplankton (P4), size class 20±200 lm, not grazed. Phytoplankton are also grazed benthic suspension feeders. The potential carbon assimilation rate is not dependent on the nutrient limitation, due to intracellular N and P shortage, nor external nutrient concentrations. Instead, in the case of intracellular nutrient shortage, the unutilised assimilation products are excreted as dissolved organic carbon. The uptake and loss of carbon (C) by a phytoplankton class is described by the following word equation: oC ˆ …Assimilation - Excretion - Respiration - Lysis ot Sedimentation†C - Grazing; …1† where the assimilation of carbon is a function of temperature and light limitation and the loss terms due to excretion, lysis and sedimentation are enhanced when the plankton are nutrient-limited. Thus the production of dissolved organic carbon (DOC) is enhanced under these conditions. The uptake of nutrients (NO3 , NH4 and PO4 ) has been decoupled from the carbon assimilation processes by including dynamic nutrient kinetics according to Droop (1974) and Nyholm (1977). Nutrient uptake is dependent on the external nutrient concentrations and on the level of intercellular storage. The microbial food web contains bacteria, heterotrophic ¯agellates and microzooplankton, each with dynamically varying C:N:P ratios. Bacteria act to consume DOC, decompose detritus and can compete for inorganic nutrients with phytoplankton. DOC is not explicitly represented in the model, and the production is assumed to be immediately consumed by bacteria. Heterotrophic ¯agellates feed on bacteria and picoplankton, are grazed by microzooplankton and exhibit within-group consumption. Microzooplankton consume diatoms, autotrophic and heterotrophic ¯agellates, are grazed by mesozooplankton and exhibit within-group consumption. Mesozooplankton consume diatoms, au-

totrophic ¯agellates and microzooplankton and exhibit within-group consumption. The benthic submodel contains a food web model capable of describing nutrient and carbon cycling via both aerobic and anaerobic bacterial pathways, bioturbation/ bioirrigation and the vertical transport in the sediment of particulate matter due to the activity of benthic biota. Benthic nutrient dynamics are described separately (Ruardij and van Raaphorst 1995). In this model the vertical positions of the oxygen and sulphide horizons, nutrient pro®les and the resultant ¯ux to or from the sediment are determined. These processes are strongly dependent on the benthic community structure and activity. The benthic±pelagic coupling is described by the inputs of settling organic detritus into the benthos and di€usional nutrient ¯uxes into and out of the sediment. Physical Model The physical model is one-dimensional version of the Princeton Ocean Model and calculates physical conditions throughout the depth of the water column. The equations of motion in the two horizontal directions are given by ou o ou ˆ Px ‡ fv ‡ KM ; ot oz oz

…2†

ov o ov ˆ Py ÿ fu ‡ KM : …3† ot oz oz The ®rst term on the right-hand side is the horizontal pressure gradient due to barotropic tidal currents, the second term is due to the inertial forcing by the EarthÕs rotation where f is the coriolis parameter and the third describes the transport of momentum through the water column via frictional coupling between the layers, via an eddy di€usion coecient KM . Density changes in the water column occur via surface heating and cooling or via changes in the surface salinity with the vertical turbulent transfer of heat or salinity controlled by oF o oF ˆ KH ; …4† ot oz oz where F is temperature or salinity and the KH is the coecient of vertical eddy di€usivity. There is no ¯ux of heat or salt through the seabed. The same equation is used to describe the vertical mixing of biogeochemical variables. The surface heat ¯ux used in the model was calculated using the methods of Edinger et al. (1968) from incident solar radiation, wind speed, and dew point temperature. The concept of Ôequilibrium temperatureÕ is used to determine the heat ¯ux into the water surface. The heat ¯ux Q is given by Q ˆ K…TE ÿ TS †;

…5†

where K is a heat exchange coecient, TE is the equilibrium temperature and TS is the surface temperature of the water. It can be shown empirically that a good approximation to TE is 297

Marine Pollution Bulletin

Hs ; …6† K where TD is the dew point temperature and Hs the rate of solar radiation per unit area. James (1977) gives TE ˆ TD ‡

K ˆ 4:5 ‡ 0:05Ts ‡ …b ‡ 0:47†f …W †;

…7:1†

where b ˆ 0:35 ‡ 0:015TM ‡ 0:0012TM2 ;

…7:2†

where TM ˆ 0:5…Ts ‡ TD †

…7:3†

ÿ2

ÿ1

and K is in W m °C , Ts , TM , and TD in °C. f(W) is given by the following empirical formulae f …W † ˆ 9:2 ‡ 0:46W 2 ;

…7:4† ÿ1

where W is the wind speed in m s . Hence Q can be calculated given a knowledge of W, TD , Hs and Ts (from the model). A Mellor Yamada 2.5 turbulence closure model (Mellor and Yamada, 1982) is used to calculate the vertical temperature, salinity, diffusion and momentum structure of the water column. The 2.5 turbulence closure model characterises the turbulence using equations for the turbulent kinetic energy q2 /2 and a turbulent macro scale k. The vertical mixing coecients are de®ned as …KM ; KH † ˆ kq…SM ; SH †:

…8†

SM and SH are stability factors which are a function of the Richardson number which depends on strati®cation. The turbulence length scale and turbulent kinetic energy are calculated from turbulence transport equations of the form oF ˆ …diffusion of F † ot ‡ …shear and buoyancy production of F † ‡ …dissipation of F †; 2

…9†

2

where F is either q /2 or q k The model has been set up at two sites M1 and M2 (Fig. 1). The modelled water column is divided into 20 layers in the pelagic, open to the air at the surface (layer 1) and with a benthic module below layer 20. Forcing functions Hourly wind speed and solar radiation data was obtained from a meteorological station at Hemsby for the period of the simulations. The location of this station is indicated in Fig. 1. The hourly wind speeds and daily average solar radiation are over the period of the simulation are illustrated in Fig. 3. Seasonal mean dew point temperatures for the southern North Sea are described using the following function and parameter values adapted from Sharples and Tett (1994). TD ˆ a1 ‡ 0:5a2 …1 ÿ cos……t ÿ a3 †2p=365††; 298

…10†

Fig. 3 Hourly wind speeds (m sÿ1 ) and daily averaged solar radiation (W mÿ2 dayÿ1 ) measured at Hemsby during the period of the simulations.

where a1 ˆ 8.85, a2 ˆ 5.07 and a3 ˆ 60 and t is the time in days. The variation in the attenuation of light in the modelled water column was modi®ed using the observed attenuation data converted to suspended particulate matter (SPM) from the buoy-mounted tranmissometer. These data are shown in Fig. 4. The trends in the SPM are similar to those observed in the NERC North Sea Project measurements in this region, when values of >50 g mÿ3 were measured in April and <10 g mÿ3 in May and June (Lowry et al. 1992). Initial conditions and parameters Table 1 describes the variations in the model set-up at both sites. The simulations run from the end of April (day 120) to late July (day 200). In the absence of suitable ®eld data, the biological model has been initialised using simulation results from Humber plume ecosystem model of Allen (1997), which used ERSEM to describe the biology. The open boundaries of this model are de®ned by latitude 54°00Õ N in the north and longitude 1°48Õ E to the east and the grid size is approximately 4.5 by 4.5 km. A uniform ®eld of all state variables was applied to the model domain. The model was then spun up to a quasi steady state by applying the river forcing, seaward boundary conditions and the ecology. The initial conditions for the simulations in this paper were taken from the grid cell containing the model site on day 120 of the quasi-steady state simulation.

Volume 37/Numbers 3±7/March±July 1998

heterotrophic submodels are given in Tables 2 and 3, because they have been slightly modi®ed from those of ERSEM v11. The half saturation constant for silicate uptake by diatoms has been lowered to 0.3 (Varela et al., 1995) to enhance the consumption of silicate. To obtain the best data ®t, the grazing pressure of heterotrophic ¯agellates and microzooplankton on phytoplankton, has been altered by modifying the half saturation constants for uptake of carbon. The values chosen are within the range of those used previously (Baretta-Bekker et al., 1995; 1997). The sensitivity of the model to these changes is discussed later. Standard parameters sets for ERSEM v11 are given in Radford (1996), and have been used in every other part of the model.

Results All of the simulations shown in this paper are at 5 m depth, which is coincident with the sampling depth of the forcing and validation data. Because of the wellmixed nature of the system the 5 m depth is representative of the whole water column. A comparison of the simulated and observed temperatures are shown in Figs. 5a and 6a. As expected, the system is being heated and observed temperatures increase fairly linearly from about 7.5°C at the end of April to 15°C in late July at M1 and 7.5°C to 17°C at M2. During the period of the bloom the temperatures increase markedly as the bloom begins, reaching a maxima when the bloom peaks and then falling slightly

Fig. 4 Daily averaged Suspended Particulate Matter (g mÿ3 ) at M1 and M2.

The sensitivity of the model to the initial conditions for dissolved inorganic nutrients is discussed later. The parameters sets used in the phytoplankton and

TABLE 2 Parameters of the phytoplankton functional groups. P1 is the diatoms; P2 is the autotrophic ¯agellates; P3 is the picoalgae, P4 is the inedible phytoplankton. The parameter names used follow the nomenclature described in Blackford and Radford (1995). P1

P2

P3

P4

Environmental e€ects Characteristic Q10

q10ST$

2.0

2.0

2.0

2.0

Uptake Max. speci®c upt at 10°C

sumST$

2.5

2.7

4.0

1.5

Loss rates Excreted fraction of uptake Nutrient-lysis rate Nutrient-lysis rate under Si limitation

pu_eaST$ sdoSTs$ sdoSts$

0.05 0.05 0.1

0.2 0.05 ±

0.2 0.05 ±

0.2 0.05 ±

Respiration Rest respiration at 10°C Activity respiration

srsST$ pu_raST$

0.15 0.1

0.1 0.25

0.1 0.25

0.1 0.25

Nutrient dynamics Min N/C ratio (mol gCÿ1 ) Min P/C ratio Red®eld N/C ratio Red®eld P/C ratio Multiplic fact min N/C ratio Multiplic fact min P/C ratio Multiplic fact max. N/C ratio Multiplic fact max. P/C ratio Max. Si/C ratio Anity for NO3 Anity for NH4 Anity for P Half value of SiO4 lim

qn1ST$ qp1ST$ qnRST$ qpRST$ xqcSTn$ xqcSTp$ xqnST$ xqpST$ qsSTc$ quSTn3$ quSTn4$ quStp$ chSTs$

0.0067 0.4288Eÿ3 0.0126 0.886Eÿ3 0.55 0.55 2 4 0.03 0.0025 0.0025 0.0025 0.3

0.0067 0.4288Eÿ3 0.0126 0.886Eÿ3 0.55 0.55 2 3 ± 0.0025 0.0025 0.0025 ±

0.0067 0.4288Eÿ3 0.0126 0.886Eÿ3 0.55 0.55 2 2 ± 0.0025 0.0 0.0025 ±

0.0067 0.4288Eÿ3 0.0126 0.886Eÿ3 0.55 0.55 2 2 ± 0.0025 0.0 0.0025 ±

299

Marine Pollution Bulletin TABLE 3 Parameters of the microzooplankton functional groups and bacteria. Z5 ˆ microzooplankton, Z6 ˆ heterotrophic ¯agellates and B1 ˆ bacteria. The parameters that have been changed from the value in ERSEM v11 (Radford, 1996) are indicated in bold italics. The parameter names follow the nomenclature described in Blackford and Radford (1995). Parameter Environmental e€ects Characteristic Q10 Half oxygen saturation Uptake Half saturation value Max. spec uptake rate 10°C Availability of P1 for ST Availability of P2 for ST Availability of P3 for ST Availability of Z5 for ST Availability of Z6 for ST Availability of B1 for ST Selectivity

q10ST$ chrSTo$ chuSTc$ sumST$ suP1_ST$ suP2_ST$ suP3_ST$ suZ5_ST$ suZ6_ST$ suB1_ST$ minfoodST$

Z5

Z6

B1

2.0 7.8125

2.0 7.8125

4.0 7.8125

100 1.2 0.75 1.0 0.0 1.0 1.0 0.0 30

300 5.0 0.0 0.0 1.0 0.0 0.2 1.0 100

± 8.38 ± ± ± ± ± ± ±

Loss rates Assimilation eciency Assimilation eciency at low temp Excreted fraction of uptake

puST$ puSTo$ pu_eaST$

0.5 ± 0.5

0.5 ± 0.5

0.5 0.2 ±

Excretion Fraction of excretion production to DOM

pe_R1ST$

0.5

0.5

±

Mortality Oxygen dependent mortality rate Temperature independent mortality

sdSTo$ sdST$

0.25 0.05

0.25 0.05

± 0.0

Respiration Rest respiration at 10°C

srsST$

0.02

0.02

0.01

Nutrient Dynamics Max. N/C ratio Max. P/C ratio

qnSTc$ qpSTc$

0.01 0.001

0.01 0.001

0.02084 0.002084

Fig. 5 Observed daily averaged (....) and simulated (A): (a) temperature and (b) chlorophyll-a at M1.

300

Fig. 6 Observed daily averaged (....) and simulated (A): (a) temperature and (b) chlorophyll-a at M2.

Volume 37/Numbers 3±7/March±July 1998

when the bloom crashes at both sites. This corresponds with the highest values of solar radiation (Fig. 3) and hence sea surface heating. The model mimics the general trends and magnitude of the temperature data very well at both sites, but fails to reproduce the perturbation in the temperature signal at the time of the spring bloom. Figures 5b and 6b show the results obtained by in situ continuous ¯uorometeric measurement of chlorophyll-a during spring 1996 at both stations along with the simulated chlorophyll-a. The most striking feature of these records is the sharp increase and decrease in the chlorophyll-a concentration marking the onset and end of the spring bloom. The bloom starts at the same time at both sites, however the magnitude of the bloom at M2 is larger. The model reproduces the general trends in observed chlorophyll trace very well up to the end of the bloom. After this point model and observations diverge rapidly. In both cases timing of the bloom is correct. The magnitude of the bloom is correct at both sites, but the duration of the bloom is underestimated at M2.

bloom range between 0.01 and 0.1 m2 sÿ1 which means that the water column is well mixed throughout. The modelled phytoplankton succession at M1 is shown in Fig. 8. Prior to the bloom phytoplankon, biomass is low and dominated by picoplankton. The modelled bloom is composed of two phytoplankton peaks. Diatoms grow ®rst, reaching a maxima around day 160, rapidly followed by autotrophic ¯agellates which peak around day 170. The bloom ends by day 180. Inedible phytoplankton dominate the plankton biomass after the bloom, indicating grazer control on growth. Modelled nutrient concentrations are shown in Fig. 9 and exhibit similar values and trends to observations of nutrients in the region (Lowry et al., 1992; Morris et al., 1995). Nutrients are rapidly depleted as the spring bloom occurs. Nutrient concentrations are then maintained by in situ benthic and pelagic recycling. Phytoplankton uptake of nitrogen and phosphate over the period of the bloom is 170% of the initial dissolved inorganic nutrient content of the model (Table 4). The excess nutrients are supplied by benthic recycling (16% of N and 19% of P uptake)

Discussion What controls the modelled bloom at M1? To ascertain the behaviour and what controls the modelled spring bloom we consider the simulation of M1 in more detail. Similar arguments apply at M2. Figure 7, illustrates how light-limitation controls the growth rate of phytoplankton. It is a plot of the net growth rate of diatoms against the ratio of the euphotic depth (ZEU ) to the depth of the water column (Z). The net growth rate is the net primary production of diatom divided by the biomass of diatoms. The critical value of ZEU :Z is between 0.15 and 0.2, above which the net growth of diatoms is positive. This compares well with previous values from the literature of 0.17 and 0.2 (Grobbelar, 1990; Fichez et al., 1992). Similar values were obtained for the other phytoplankton groups. The simulated values of KM and KH at the beginning of the

Fig. 7 Plot of net diatom growth rate (dayÿ1 ) against ZEU :Z M1. ZEU is de®ned as the 1% light penetration depth as calculated using Beers law and includes the e€ects of attenuance by SPM and phytoplankton.

Fig. 8 Simulated phytoplankton succession at M1, where diatoms (A), autotrophic ¯agellates ( ± ± ), picoplankton (:::::::: ) and inedible ¯agellates (± ±). Units are mg C mÿ3 .

Fig. 9 Simulated nutrient usage at M1, where silicate (A), nitrate ( ± ± ), ammonia (:::::::: ) and phosphate (± ±). Units are mmol mÿ3 .

301

Marine Pollution Bulletin TABLE 4 Uptake and recycling of nitrogen and phosphate during the modelled bloom period day 120±180. mmol mÿ2

Phytoplankton uptake

Heterotrophic recycling

Benthic e‚ux

Original nutrient content

Excess nutrient uptake

Nitrogen Phosphate

436.78 27.94

158.72 9.24

69.30 5.39

260 16

176.78 11.94

and the microbial loop (36% of N and 33% of P uptake). This indicates that the regeneration of mineral nutrients via predation provides an important feedback of material through the microbial loop and helps to sustain the primary production (Azam et al., 1983). Bacterial uptake of nutrients does not occur in this simulation. This is due to the nutrient-rich environment which means that the heterotrophic bacterial pool has low internal C:N and C:P ratios and therefore does not need to compete for nutrients with phytoplankton to maintain its internal nutrient ratios. Silicate is depleted by day 160, which coincides with the peak of the diatom bloom. The nutrient-limitation on diatom growth is illustrated in Fig. 10. Silicate becomes limiting about day 160, which is the point when the modelled diatom bloom begins to die o€ and autotrophic ¯agellates take over the bloom. Phosphate and nitrogen limitation does not occur in the model for any of the phytoplankton groups. This may be a consequence of the rapid recycling of nutrients through the microbial loop. The modelled bloom is grazed out before nitrogen or phosphate limitation can occur. This e€ect has been observed o€ the Dutch coast in similar nutrient-rich turbid waters (Klein and van Buuren, 1992). Figure 11 shows the grazing pressure exerted by various modelled heterotrophs on individual phytoplankton groups. Grazing pressure becomes signi®cant around day 170 and reaches a maximum around day 180, the point at which the modelled bloom ends. The ¯agellate bloom is primarily grazed out by microzooplankton. Modelled microzooplankton grazing rates are

Fig. 10 Simulated silicate limitation (dimensionless) of diatom growth at M1.

302

consistent with observed grazing rates for UK coastal waters (Burkill et al., 1987), which indicate that 30±65% of the phytoplankton standing stock is grazed out. Using the ecosystem classi®cation scheme of Legendre and Rassoulzadegan (1995), the modelled ecosystem is showing a multivorous behaviour whereby, herbivorous and microbial grazing both have signi®cant roles in controlling phytoplankton biomass.

Fig. 11 Simulated daily grazing rates of heterotrophic on phytoplankton, where microzooplankton on diatoms (A), microzooplankton on autotrophic ¯agellates (-: -: -: -), mesozooplankton on diatoms (:::::: ), mesozooplankton on autotrophic ¯agellates ( ± ± ) and heterotrophic ¯agellates on picoplankton (± ±).

Fig. 12 E€ect of varying background extinction coecient on the magnitude and timing of the modelled chlorophyll peak at M1. Xeps$ ˆ 0.05 (-
-), xeps$ ˆ 0.1, (A), xesp$ ˆ 0.15, (:::::::: ). Units are mg Chl-a mÿ3 .

Volume 37/Numbers 3±7/March±July 1998

Fig. 13 E€ects of varying the initial conditions for nutrients on the magnitude and timing of the modelled chlorophyll peak at M1. N ˆ 6.5, P ˆ 0.4, (-
-), N ˆ 13.0, P ˆ 0.8, (A), N ˆ 18.5, P ˆ 1.2, (:::::::: ). Units are mg Chl-a mÿ3 .

Fig. 14 E€ects of varying grazing pressure on the magnitude and timing of the modelled chlorophyll peak at M1. chuZ5$ ˆ 50, chuZ6$ ˆ 150, (-
-), chuZ5$ ˆ 100, chuZ6$ ˆ 300, (A), chuZ5$ ˆ 150, chuZ6$ ˆ 450, (:::::::: ). Units are mg Chl-a mÿ3 .

Sensitivity analysis The simulations presented appear to be sensitive to light-limitation, nutrient concentrations and grazing pressure. In this section we explore how the model behaviour is in¯uenced by changes in these variables. To vary the light-limitation in the model, we have altered to background extinction coecient, xeps$ by +/ÿ50%. The e€ects of these changes on the modelled chlorophyll-a are shown in Fig. 12. As might be expected, reducing xeps$ brings the bloom forward in time and increasing xeps$ pushes it back. The magnitude of the chlorophyll bloom remains roughly the same. As the light-limitation increases, the chlorophyll curve becomes shallower as the bloom begins and resembles more closely the observed trace. The initial conditions for nitrogen and phosphate have been varied by +/ÿ50%. The variations in the chlorophyll-a peaks are shown in Fig. 13. The variation in nutrient concentrations has very little di€erence on the modelled chlorophyll trace while growth is light limited. The three model runs only deviate when the grazing pressure starts to become signi®cant (day 170). This is a consequence of the Droop±Nyholm uptake kinetics employed by the model and the fact that carbon assimilation is not nutrient limited. The primary production is slightly enhanced by increasing nutrient availability (Table 5). However nutrient uptake increases signi®cantly, which illustrates the decoupling of the carbon and nutrient dynamics in the model and the

relative insensitivity of the modelled bloom to the initial nutrient conditions. Once the bloom has ®nished, the initial conditions have a important in¯uence on the modelled phytoplankton biomass. Variation in the grazing pressure also has a signi®cant e€ect on the modelled chlorophyll-a concentration. In this case we have varied the half saturation constants for feeding of microzooplankton and heterotrophic ¯agellates by +/ÿ50%. The results of these simulations are shown in Fig. 14. The changes only take e€ect after day 165 when grazing pressure starts to limit phytoplankton growth. The duration of the bloom is reduced by a week with higher grazing pressure and lasts a week longer when the grazing pressure is lowered. Consequently the primary production increases as the grazing pressure reduces (Table 6.). However low grazing pressure inhibits the pelagic recycling of nitrogen due to the reduction in predation and hence nitrogen recycling. This is illustrated in Table 6, where in spite of having increased primary production, the nitrogen uptake is less than that in the standard run.

Conclusions The model presented here is capable of hindcasting the observed spring phytoplankton bloom in the Humber plume, but it fails to reproduce the observations once the bloom has collapsed. The timing of the onset of the spring bloom is determined by the ratio of the eu-

TABLE 5 E€ect of varying the initial conditions of dissolved nitrogen and phosphate on primary production and nutrient uptake during the modelled bloom period (day 120±180). mmol mÿ3 Standard run N ˆ 13.0; P ˆ 0.8 + 50% N ˆ 18.5; P ˆ 1.2 ÿ 50% N ˆ 6.5; P ˆ 0.4

Net primary production g mÿ2

Phytoplankton nitrogen uptake

Phytoplankton phosphate uptake

22.26 24.23 19.59

436.78 517.46 257.78

27.94 38.06 15.92

303

Marine Pollution Bulletin TABLE 6 E€ect of varying the grazing pressure on primary production and nutrient uptake during the modelled bloom period (day 120±180). mmol mÿ2 Standard run chuZ5$ ˆ 100 chuZ6$ ˆ 300 + 50% chuZ5$ ˆ 150 chuZ6$ ˆ 450 ÿ 50% chuZ5$ ˆ 50 chuZ6$ ˆ 150

Net primary production g mÿ2

Phytoplankton nitrogen uptake

Phytoplankton phosphate uptake

22.26 16.62 27.15

436.78 349.79 420.26

27.94 27.28 24.81

photic depth to the depth of the water column. The model suggests the ZEU :Z ratio of about 0.2 is the transition value for predicting the onset of the bloom which is consistent with empirical determinations. Diatom growth is limited by the availability of silicate. Light-limitation, due to high SPM concentrations, prevents modelled autotrophic ¯agellates from growing fast enough to deplete the available dissolved inorganic nutrients before heterotrophic grazing becomes signi®cant and grazes the bloom out. The modelled ecosystem is multivourous, the shape and extent of the bloom being determined by grazing pressure. The failure of the model to reproduce the observed chlorophyll-a after the spring bloom may be caused by, incorrect parameterisation of nutrient recycling mechanisms and or the lack of a mechanism for the lateral transport of particulate material from the model. Empirical data is required to force, parameterise and validate models. The results presented here demonstrate the value of high frequency data to the ecosystem modeller. We have shown that given such data to provide physical forcing, we can reproduce the observed spring bloom in the Humber plume, thus demonstrating that the ERSEM model has the potential to forecast marine phytoplankton growth. In this case the key factor in determining the onset of the spring bloom is the light-limitation of plankton growth. The timing of the bloom is simulated correctly, because we can force the model with accurate details of the changes in the availability of light for photosynthesis. To further develop the system for the purpose of forecasting, we need to address the failure of the model to reproduce the observations after the bloom has occurred. There are two approaches, both of which will require detailed data sets. The ®rst involves reparameterising or restructuring the modelled nutrient cycling in the light of experimental process studies. The second is to develop data assimilation methods, which in an optimal way merge information about the dynamics contained in a model with the information about the current state of the system contained in a set of measurements. This work forms part of the CCMS, Plymouth Marine LaboratoryÕs contribution to the Land Ocean Interaction Study, Community Research Project of the NERC (LOIS Publication No. 550) Allen, J. I., Blackford, J. C. and Radford, P. J. (1998) A 1-D vertically resolved modelling study of the ecosystem dynamics of the middle and southern Adriatic sea. Journal of Marine Systems. (in press). Allen, J. I. (1997) A modelling study of the ecosystem dynamics of the Humber Plume UK. Journal of Sea Research 38, 333±360.

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