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For Review Purposes Only/Aux fins d'examen seulement
Ecological response of an intermittently open lagoon to
entrance opening regimes and catchment loads: a numerical
study
Jason D. Everett∗,a,b, Mark E. Bairdc, Iain M. Suthersa,b
aEvolution & Ecology Research Centre, School of Biological Earth and Environmental Sciences,
University of New South Wales, Sydney NSW 2052, AustraliabSydney Institute of Marine Science, Mosman NSW 2088, Australia
cCoastal and Regional Oceanography Laboratory, School of Mathematics and Statistics, University
of NSW, Sydney NSW 2052, Australia
Abstract
Ecological model behaviour of an intermittently closed and open lake or lagoon
(ICOLL) is analysed in order to assess the response to altered estuary forcings (catch-
ment loads, opening regimes and lake depth), initial conditions and model parame-
ters. Due to the low nutrient loads currently entering Smiths Lake NSW Australia,
changes in the opening regime do not exhibit a strong estuarine response, however
the system is sensitive to increased water depth and catchment loads. The water
depth of the lagoon is shown to drive a switch from a seagrass to an algal dominated
system at average depths greater than 7 m. Elevated loads (10×) of phosphorus
entering the estuary increase the productivity of phytoplankton which is limited by
phosphorus at lower loads. Moderate nutrient enrichment stimulates the produc-
tion of seagrass, which is generally nitrogen limited and catchment loads are able
to double without a decline in seagrass biomass. At elevated catchment loads, light
limitation of seagrass occurs due to the increased plankton biomass and total sus-
pended solids, and the system switches from seagrass dominated to algal dominated
at loads above 5 mg N m−2 d−1 and 0.09 mg P m−2 d−1. Increasing nutrient loads
∗Corresponding AuthorEmail address: [email protected] (Jason D. Everett)
Preprint submitted to Marine and Freshwater Research March 25, 2010
For Review Purposes Only/Aux fins d'examen seulement
also increase the amplitude and decrease the frequency of predator-prey cycles. In
order to interpret the model output the sensitivity of different parameters and ini-
tial conditions are examined. The model showed non-linear response to variations
in some parameters with the feeding efficiency of small and large zooplankton the
most sensitive. For small perturbations in the initial conditions of dissolved inorganic
nitrogen, phytoplankton and zooplankton biomass, ensemble members initially di-
verge, before converging. Periods of lake opening accelerate trajectory convergence
with longer openings having the greatest effect. All simulations within the ensemble
converge to the same stable oscillation within 2 open/closed phases.
Key words: eutrophication, light attenuation, opening cycle, ICOLL, plankton,
seagrass, Australia, NSW, Smiths Lake
Introduction1
Eutrophication is a growing problem in lakes and rivers caused by the excessive2
inputs of nitrogen and phosphorus (Carpenter et al., 1998). Land clearing and urban-3
isation can lead to eutrophication in estuarine waters as a result of increased nutrient4
and sediment loads entering the estuary from the catchment (Harris, 2001). Biogeo-5
chemical modelling is an important tool which can be used to assess system change6
and assist with management of potentially impacted systems (Webster and Harris,7
2004). Nutrient exports from south-eastern Australian catchments into estuaries are8
generally considered small when compared to the Northern Hemisphere, resulting in9
oligotrophic conditions in most estuaries and coastal lagoons (Harris, 2001). Loads10
are highly variable due to differing levels of urbanisation and catchment flows. Total11
nitrogen (TN) and phosphorus (TP) loads from 24 catchments around Sydney have12
been shown to vary by three orders of magnitude with TN and TP increasing with13
population density (Harris, 2001).14
Intermittently Closed and Open Lakes or Lagoons (ICOLLs) are a common type15
of estuary in south-eastern Australia. Of the 134 estuaries in New South Wales16
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(NSW), 67 (50%) are classified as intermittently open estuaries (Roy et al., 2001).17
ICOLLs are characterised by low freshwater inflow, leading to sand barriers (berms)18
forming across the entrance preventing mixing with the ocean. Increased residence19
time and lack of flushing makes ICOLLs particularly susceptible to pollution events20
such as increases in nutrient and sediment loads (Cloern, 2001; Lloret et al., 2008), yet21
there are far fewer studies of ICOLLs, than permanently open estuaries (Schallenberg22
et al., 2010). Following a rise in the water level, the sand barriers are intermittently23
breached. The timing and frequency of the entrance opening is related to factors24
such as the size of the catchment, rainfall, evaporation, the height of the berm25
and creek or river inputs (Roy et al., 2001). Additionally, marine currents, wave26
activity, weather patterns and the strength of the initial breakout will determine how27
long the estuary remains open to the sea. As each of these processes are variable28
and opening regimes are rarely predictable, ICOLLs seldom reach any long-term29
steady state (Roy et al., 2001). As an added complication, 72% of the estuaries30
in NSW are now artificially opened when they reach a predefined ’trigger’ height31
(DIPNR, 2004). Reasons for artificial opening include flood prevention strategies32
and flushing in order to minimise pollution and eutrophication. However, opening33
events have previously resulted in unintended ecological consequences (Schallenberg34
et al., 2010) such as de-oxygenation, nutrient enrichment and increased chlorophyll35
a concentrations (Twomey and Thompson, 2001; Gobler et al., 2005; Becker et al.,36
2009).37
Unlike permanently open estuaries which have regular tidal flushing, the opening38
regimes of ICOLLs are often unpredictable. There are many estuary-specific char-39
acteristics such as lake inflow, depth and catchment loads which will influence the40
ecology of the system after opening (Cloern, 2001). Understanding the interaction41
of physical and ecological processes within an ICOLL needs to be understood before42
decisions are made about altering the opening regimes. Biogeochemical modelling43
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studies of ICOLLs, such as this one, allow the examination of the ecological impacts44
during altered opening regimes. The model configuration used in this study empha-45
sises the two different phases of the ICOLL cycle - open and closed - and the different46
hydrological processes which occur.47
The ecological model examined in this study was configured for Smiths Lake and48
was built using a combination of physical and physiological limits to the processes49
of nutrient uptake, light capture by phytoplankton and predator prey interactions50
(Everett et al., 2007). The model outputs were assessed against field data and shown51
to capture the important ecological dynamics of Smiths Lake. A cost function was52
used to compare the field and model data, normalised by the standard deviation53
of the field data, to assess the fit, as per the methods of Moll (2000). The fit for54
salinity (0.20) and dissolved inorganic nitrogen (DIN, 0.79) were considered very55
good (<1 standard deviation), while dissolved inorganic phosphorus (DIP, 1.49) and56
chlorophyll a (1.01) were considered good fits (1-2 standard deviations, Everett et al.,57
2007; Moll, 2000). The simulated DIN and DIP concentrations were consistent with58
observations for much of the open-closed cycle. At high lake levels, DIN and DIP are59
underestimated. The concentration of water column DIN and DIP remains relatively60
constant over the course of the model simulation. Both observations and model data61
show very low concentrations of water column DIP (Everett et al., 2007).62
Smiths Lake has a largely forested catchment with a small population and low63
levels of urbanisation (Webb McKeown & Associates Pty Ltd, 1998). Population64
pressure and increasing urbanisation will result in increased nutrient exports from65
the catchment. Changing rainfall patterns in south-eastern Australia (Hennessy66
et al., 2004; CSIRO and Bureau of Meteorology, 2007) will also alter the length and67
frequency of opening regimes and lake depths. As a result, there is a need for an68
analysis of the response of the state variables to changing estuary forcings such as69
nutrient loads and opening regimes.70
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Simple forcings models have previously been used to simulate the response of71
coastal lagoons to changing nitrogen loads and water residence times (Webster and72
Harris, 2004). Their study focussed on the response of primary producers to increas-73
ing and decreasing nitrogen loads, and how the response is altered by changes in74
the flushing rate of the lagoon. The model showed that the highly episodic catch-75
ment nutrient loading regimes increases the vulnerability of the lagoon (Webster and76
Harris, 2004). When compared with steady loading regimes, episodic loads caused77
phytoplankton blooms at lower catchment loads.78
The behaviour of the ecological model can be used to explore the response of79
a broader range of ICOLLs to changing nutrient loads and opening regimes. The80
inorganic nitrogen load for Smiths Lake has been calculated as 0.5 mg DIN m−281
d−1 from field data (Everett, 2007) and the total nitrogen load calculated as 5 mg82
TN m−2 d−1 from modelled data (CSIRO, 2003). This value is low when compared83
with other eastern Australian estuaries due mainly to Smiths Lake’s largely forested84
catchment and small catchment to lake area ratio. Other estuaries in eastern Aus-85
tralia have much higher loads. Lake Macquarie (permanently open) and Narrabeen86
Lakes (ICOLL) for example, have an estimated 5% forested catchment and loads of87
70 mg DIN m−2 d−1. Turros and Coila Lakes (ICOLLs), which have catchments that88
are 40% forested have loads of 20 mg DIN m−2 d−1 (CSIRO, 2003).89
In this study, numerical experiments are used to examine the biological response90
of an estuarine model to changes in estuary forcings (opening regimes, catchment91
loads, lake depth), initial conditions and model parameters. The range of forcing92
values used in this study allow insights into the future behaviour of Smiths Lake93
under altered conditions, while also being used as a tool to understand other ICOLLs.94
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For Review Purposes Only/Aux fins d'examen seulement
Methods95
A 1-box ecological model of an intermittently open lagoon (Everett et al., 2007)96
is investigated. The sensitivity of the model to estuary forcings, initial values of the97
state variables and model parameters is examined. The sensitivity results presented98
here are for a 1-box configuration with an idealised lake height, flushing regime and99
solar radiation. A brief overview of the model is presented in the following section.100
For a more detailed description see Everett et al. (2007).101
Transport Model102
The transport model is reduced to a 1-box configuration for this study. The model103
is forced by evaporation (E ), rainfall (R) and tidal exchange (TE ). The equation for104
the change in concentration of a tracer is given by:105
dC
dt=CcrRI
V︸ ︷︷ ︸
Runoff
−CE
V︸ ︷︷ ︸
Evaporation
+CocTE
V︸ ︷︷ ︸
T idal Exchange
−C dV
dt
V︸ ︷︷ ︸
Dilution
(1)
where V is volume (m3), E is evaporation (m3 s−1) and is negative, Ccr is the106
concentration of creek flow into the model (mol m−3), RI is the flow of water off107
the catchment (m3 s−1), Coc is the concentration of the ocean (mol m−3), dVdt
is the108
change in volume over time (m3 s−1) and TE is the tidal exchange when open to the109
ocean (m3 s−1).110
Ecological Model111
The ecological model contains 17 state variables (Table 1). With the exception112
of state variables relating to phosphorus and total suspended solids, nitrogen is the113
basic currency of the state variables. The autotrophs (phytoplankton, epiphytes114
and seagrass) gain all their nutrients from the water column, with the exception of115
seagrass which is also able to draw nutrients from the sediment. Dissolved inorganic116
nitrogen, unflocculated phosphorus, and total suspended solids enter the system from117
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the catchment. Total suspended solids and flocculated and unflocculated phosphorus118
sink out of the water column into the sediment. Depending on water column and119
sediment pore water concentrations, DIN and DIP are able to diffuse back into the120
water column from the sediment. Small and large zooplankton grow by feeding on121
small and large phytoplankton respectively.122
In the model, light is attenuated through the water column, epiphytes and sea-123
grass sequentially. The light model is adapted from Baird et al. (2003). The propor-124
tion of the downward solar radiation (mol photon m−2 s−1) available as photosyn-125
thetically available radiation (PAR) is assumed to be 43% (Fasham et al., 1990).126
The realised growth rate (µx) of each autotroph is determined by the minimum of127
the physical limit to the supply of nitrogen, phosphorus and light, and the maximum128
metabolic growth rate of an organism:129
µx = (µnitrogen, µphosphorus, µlight, µmax) (2)
The four autotrophs in the model have fixed internal stoichiometric requirements.130
Phytoplankton (small and large) and epiphytic microalgae adopt the Redfield ratio131
(C:N:P) of 106:16:1 (Redfield, 1958), while seagrass adopt a seagrass-specific ratio132
of 474:21:1 (Duarte, 1990). The light requirements of all the autotrophs are fixed at133
10 photons per carbon atom (Kirk, 1994).134
Growth rates of phytoplankton, epiphytic microalgae and seagrass135
Phytoplankton are divided into two size classes - small (PS) and large (PL).136
Phytoplankton cells obtain nutrients from the surrounding water column and absorb137
light as a function of the average available light in the water column and their138
absorption cross section. The maximum rate (s−1) at which a phytoplankton cell139
can obtain nutrients through diffusion from the water column is given by:140
µPS,N =ψPSDNDIN
mPS,N
(3)
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where ψPS is the diffusion shape factor (m cell−1), DN is the molecular diffusivity141
of nitrogen (m2 s−1), and mPS,N is the nitrogen cell content of small phytoplankton142
cells (mol N cell−1). Phosphorus uptake is calculated in a similar manner. The143
diffusion shape factor (ψ) in this study is for a sphere and is calculated as 4πr,144
where r is the radius of the cell (m).145
The light-limited growth rate of small phytoplankton (and similarly for large146
phytoplankton) is given by:147
µPS,I =IAV aAPS
mPS,I
(4)
where IAV is the average light field in the water column (mol photon m−2 s−1),aA148
is the absorption cross-section of the cell (cell−1 m2), and mPS,I is the required moles149
of photons per phytoplankton cell (mol photon cell−1).150
In the model, seagrass grows on the benthos and the epiphytes grow on both the151
surface of the seagrass and the benthos. Both seagrass and epiphytes absorb water152
column nutrients through the same effective diffusive boundary layer which occurs153
along their surface. Nutrient uptake is divided between both epiphytes and seagrass154
by a ratio termed the seagrass uptake fraction (η). In the model, seagrass are able to155
extract nutrients from both the water column and the sediment. The zooplankton156
biomass is not modelled explicitly here, but is implied to be proportional to the157
aboveground biomass. Priority in nutrient uptake is given to the water column,158
however nutrient limitation in the seagrass only occurs when the combined sediment159
and water column nutrients do not meet demand.160
The nitrogen limited growth rate for epiphytes is given as:161
µEP,N =DN
BL(1 − η)DIN
1
EP(5)
where DN is molecular diffusivity of nitrogen, BL is the boundary layer thick-162
ness, η is the seagrass uptake fraction and DIN is the water column concentration163
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For Review Purposes Only/Aux fins d'examen seulement
of dissolved inorganic nitrogen. Phosphorus limited growth rate is calculated in a164
similar manner.165
When formulating seagrass dynamics, fluxes are calculated per m2. As a result,166
to convert the water column uptake to a growth rate requires division of the uptake167
rate by the biomass. The nitrogen (and similarly for phosphorus) limited uptake168
rate (s−1) for seagrass is the sum of the water column and the sediment uptake and169
is given as:170
µSG,N =DN
BLηDIN
1
SG︸ ︷︷ ︸
water column
+µmax
SG DINsedconc
kSG,N︸ ︷︷ ︸
sediment
(6)
where µmaxSG is the maximum growth rate of seagrass (s−1), DINsedconc is the171
sediment concentration of nitrogen and kSG,N is the half saturation constant for172
nitrogen uptake in seagrass.173
Light is available to the epiphytes after it passes through the water column, and174
to the seagrass after passing through the epiphytic layer. The light limited growth175
rate for epiphytes and seagrass respectively is given as:176
µEP,I = (Ibot − IbelowEP )16
1060
1
EP(7)
µSG,I = (IbelowEP − IbelowSG)21
4740
1
SG(8)
where IBOT is PAR at the bottom of the water column, IbelowEP and IbelowSG is177
the light below the epiphytes and seagrass layers respectively, 16:1060 and 21:4740178
is based on the Redfield and Duarte Ratio (Duarte, 1990; Redfield, 1958) and a 10:1179
photon:carbon ratio (Kirk, 1994).180
A linear mortality is applied to epiphytes (ζEP ) and a quadratic mortality is181
applied to seagrass (ζSG) as a closure term for the benthic autotrophs. A resorption182
coefficient (ωSG) is appended to the mortality term of the seagrass to represent the183
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detainment of nutrients as carbon is lost through blade death (Hemminga et al.,184
1999). When seagrass die, the remaining nutrients after resorption breakdown into185
refractory detritus (RD) before breaking down further and being released into the186
water column as DIN and DIP .187
Zooplankton Grazing188
In the model small zooplankton graze on small phytoplankton as shown below189
(and similarly large zooplankton graze on large phytoplankton):190
µZS =µmax
ZS PS
KZS + PS(9)
where µmaxZS is the maximum growth rate of small zooplankton (s−1) and KZS is191
the half saturation constant of small zooplankton growth (mol N m−3). A quadratic192
mortality for zooplankton is selected on the assumption that the biomass of zoo-193
plankton will be proportional to their predator.194
Initial Conditions195
The model is run for a period of one closed and one open cycle to allow the196
model to reach a quasi steady-state before commencing the model simulations. The197
initial conditions of the spin-up are set from the ocean boundary conditions for water198
column state variables. The initial biomass of epiphytes and seagrass is based upon199
the literature (Duarte, 1990, 1999) and the mapped seagrass coverage in Smiths Lake200
(West et al., 1985). Initial conditions for sediment state variables are derived from201
Baird et al. (2003), Murray and Parslow (1997) and Smith and Heggie (2003). The202
values at the end of this ’spin-up’ are used as the initial conditions for the model203
(Table 1).204
Lake Boundary Conditions205
Nutrients (DIN and Punfloc) and TSSunfloc enter the lake through runoff from the206
catchment. Nutrients (DIN and DIP ) and plankton (PS, PL, ZS and ZL) enter207
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from the ocean when the lake is open. Nutrient concentrations are interpolated from208
field sampling in the creeks (catchment) and at Seal Rocks Beach (oceanic). The209
boundary conditions for DIN and Punfloc from the catchment are 1.08×10−2 mol N210
m−3 and 9.03×10−5 mol P m−3 respectively. The boundary condition of TSSunfloc211
from the catchment is 0.1 kg TSS m−3 (Baird et al., 2003). Oceanic boundary212
conditions are found in Table 1.213
Idealised Configuration214
In this study, the transport model is used in an idealised single box configuration.215
During a control simulation, the lake height increases linearly from 0.4 m to 2.0216
m Australian Height Datum (AHD) over 10 months which corresponds to average217
depths of 1.9 - 3.5 m (Fig. 1). After 10 months, the ICOLL opens to the ocean218
and drains for 48 hours to a lake height of 0.4 m AHD. A sinusoid tidal exchange219
is imposed for 2 months, with amplitude of 0.05 m. This cycle is repeated until a220
steady oscillation is reached (< 10 years).221
The depths and sinusoidal amplitude used in the model are chosen to be represen-222
tative of the average lake cycle and flushing rates of Smiths Lake observed over the223
last 10 years (Everett, 2007). Due to intermittent rainfall, there is a large variability224
in open/closed phases. On average the lake stays open for 62 days (from lake height225
data courtesy of Manly Hydraulics Laboratory). Depending on the height difference226
between Smiths Lake and the ocean, it takes 36-48 hours to drain and begin flushing227
with the ocean.228
A constant solar radiation is maintained throughout the simulations to prevent229
any seasonal effects influencing the sensitivity analysis. The solar radiation in the230
model is 95 W m−2 and is derived from the yearly mean of averaged 6 hourly, 2◦231
shortwave downward solar radiation, linearly interpolated in space to Smiths Lake232
(152.519E, 32.393S - data courtesy of the NOAA-CIRES Climate Diagnostic Centre).233
The control simulations in the following sections refer to a model run with no changes234
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made to estuary forcings, initial conditions or parameters.235
Estuary Forcing236
Catchment Load : In order to examine the response of the model to varying catch-237
ment loads of dissolved inorganic nitrogen (DIN), dissolved inorganic phosphorus238
(DIP ) and total suspended solids (TSS), the loads are changed by 0.1×, 0.5×, 2.0×,239
10× and 100× the control values. This is undertaken in seven different combinations240
of DIN , DIP , TSS, DIN+DIP , DIN+TSS, DIP+TSS and DIN+DIP+TSS.241
The control values for DIN , DIP and TSS are 0.5 mg N m−2 d−1, 0.009 mg P m−2242
d−1 and 0.033 g TSS m−2 d−1. The inflow concentrations for DIN and DIP are243
determined from sampling of five creeks entering Smiths Lake. The value for TSS244
is based upon Baird et al. (2003).245
Opening Regimes : The current study varies the duration of the entire open/close246
cycle and the duration of the open phase to examine the effect on plankton and247
seagrass dynamics within Smiths Lake. The open/closed cycle duration is the total248
time between two successive closed phases (i.e. one complete open and closed cycle)249
and is undertaken at 12, 18, 24, 36, 48 and 60 months. The open duration is the250
total time the lake is open to the ocean and is undertaken by 7, 14, 30 and 60 days,251
making a total of 20 simulations (5 cycles × 4 open phases)252
Lake Depth: In order to examine the sensitivity of the model to lake depth,253
simulations are run with depths, when open to the ocean, of 0.4 m AHD (control254
simulation - representative of Smiths Lake), 2.5, 5 and 10 m AHD. These heights255
(AHD) correspond to average lake depths over the whole simulation of 2.5, 4.5 7.0256
and 12.0 m. During these simulations the same rate of lake rise is maintained, hence257
these simulations do not alter the catchment inflow.258
Initial Condition Sensitivity259
The initial conditions for DIN , PS, PL, ZS and ZL are randomly varied and260
the effect on the model is examined for three ensembles of ten members each. The261
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three ensembles have open durations of two months, one month and two weeks. The262
initial conditions are varied based on a uniform random distribution with a mean263
of zero and a maximum of no more than the concentration of DIN (4×10−4 mol N264
m−3), which is the state variable with the lowest concentration. This formulation is265
used to prevent the initial conditions of state variables from taking negative values,266
while also keeping the total mass constant.267
Parameter Sensitivity268
A sensitivity analysis of all model parameters is undertaken by varying each269
parameter by 10%, 25% and 50% and analysing the change in each state variable270
(Table 2). Results for the 14 most sensitive parameters are presented here. The271
results from the 5th year of the simulation (by which time the model had reached272
a steady oscillation) are analysed for changes in the state variables, by comparison273
with the control simulation. Results of the parameter sensitivity are compared with274
the 11-box model of Smiths Lake Everett et al. (2007). The standardised sensitivity275
of each state variable to each parameter (Murray and Parslow, 1997) is calculated276
as:277
Normalised Sensitivity =V (1.1p) − V (0.9p)
V (p)0.2(10)
where V (1.1p) is the mean value of the state variable V when parameter p is278
increased by 10% and V (0.9p) is the mean value when the parameter is decreased279
by 10%. V (p) is the mean value of the state variable when there is no change in the280
parameter. If the normalised sensitivity is close to 1, the change in V is proportional281
to the change in p. If the sensitivity is close to 2, V is proportional to p2.282
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Results283
Catchment Load Response284
Plankton Dynamics : Phosphorus is the limiting nutrient for the growth of small285
and large phytoplankton in the control simulations. Simulations run at 0.1× and286
2× the control phosphorus load result in only a small deviation from the values287
of PS and PL during the control simulations (Fig. 2A,B). There is no change in288
the overall shape of the oscillation. When the phosphorus load is increased by 10×289
and 100×, there is an increase in the frequency and amplitude of the oscillations290
(Fig. 2A,B). There is an associated increase in the yearly productivity of PS and291
PL from 19.3 and 10.8 mmol m−3 yr−1 at 2× DIP loads to 34.7 and 26.3 mmol292
m−3 yr−1 respectively at 10× DIP loads (Fig. 2C,D). The change in oscillation293
structure corresponds with a switch in the growth limitation of PS (Fig. 3A,C) and294
PL (Fig. 3B,D) from predominately phosphorus limited to nitrogen and maximum295
growth rate limited. In both 2× and 10× simulation (Fig. 3) PS and PL are out of296
phase, with the growth of PS limited by nitrogen when its biomass is increasing and297
by its maximum growth rate when the biomass is decreasing. The opposite is true298
of PL, which is limited by its maximum growth rate as its biomass is increasing and299
by nitrogen when its biomass is decreasing.300
Seagrass: Seagrass is nitrogen limited during the control simulations and the301
addition of 10× and 100× DIN load increases the seagrass biomass (Fig. 4A). These302
scenarios show no discernible difference in the light attenuation of phytoplankton and303
light availability for seagrass (Fig. 4C,D)304
The addition of 10× DIN +DIP and DIN +TSS load show little change in the305
seagrass light availability, however the addition of DIP allows the phytoplankton306
biomass, and therefore phytoplankton light attenuation, to increase (Fig. 4G). The307
addition of DIN allows the seagrass to obtain a higher biomass (Fig. 4E,I). The308
addition of DIN+DIP loads results in a small decrease in seagrass light availability309
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(Fig. 4H). At higher loads the SG light availability oscillates, coinciding with the310
oscillation of phytoplankton biomass and light attenuation. The addition of 100×311
DIN + DIP and DIN + TSS loads (Fig. 4E,I) results in an increase in biomass312
during the shallower open phases when seagrass is maximum growth rate limited313
(not shown). There is a reduction in biomass during the closed phases (Fig. 4E),314
when seagrass becomes light limited (not shown). This is a result of decreased315
light availability to seagrass (Fig. 4H) due to an increase in the phytoplankton light316
attenuation (Fig. 4G).317
The simultaneous addition of DIN +DIP + TSS at 100× control loads results318
in a permanently lower seagrass biomass of 0.014 mol N m−2 (Fig. 4M) due to the319
reduction in light availability at the bottom of the water column (Fig. 4P).320
Opening Regimes321
There is an increasing DIN concentration in simulations with short open/close322
cycle durations (Fig. 5A). The highest mean DIN concentrations occur during the323
1 year cycle, and are consistent across all open phases. The highest mean DIP324
values occur at an open/closed cycle duration of 5 years and an open duration of 60325
days (Fig. 5B). PS and PL both increase in biomass as the open duration increases326
(Fig. 5C,D). The highest biomass of PS occurs during simulations with a long open327
duration (60 days) and long open/closed cycle duration (5 years), while the highest328
biomass of PL occurs during simulations with long open phases (30-60 days) and329
short open/closed cycle duration (1 year). The highest biomass of ZS and ZL330
occurs during simulations with long open phases (60 days) (Fig. 5E,F). The highest331
mean biomass of SG is 0.037 eutrophication N m−2 and occurs during the 5 year332
open/closed cycle (Fig. 5G). The duration of the open phase does not substantially333
affect the mean SG biomass.334
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Lake Depth335
For simulations with an average lake depth of greater than 7 m, there is a re-336
duction of up to 97% in seagrass biomass when compared to the control simulations337
(Fig. 6H). At depths of 4.5 and 7 m, there is no discernible difference in SG biomass338
from the 2.5 m control simulations. In simulations with depths greater than 7 m,339
PS and PL more limited by their maximum growth rate as opposed to phospho-340
rus limitation, when compared to simulations with depths of 2.5 and 4.5 m (Fig. 7341
Col 1,2). At average depths of greater than 7 m, there is a more sudden shift in342
seagrass and epiphytic growth limitation from phosphorus and nitrogen respectively,343
to light limitation (Fig. 7 Col 3,4). This results in a reduced growth rate for sea-344
grass (Fig. 8F) which in turn allows more nutrients to be available for increased345
phytoplankton growth (Fig. 8A,B).346
Initial Conditions347
An ensemble of 10 members with different initial conditions of each of the state348
variables shows large standard deviations during the first year (Fig. 9A). However,349
the mean standard deviation of state variables (DIN , DIP , PS, PS, ZS and ZL)350
is reduced almost 10 fold between the first (9.6×10−5) and the second closed phase351
(1.4×10−6). The 2 month long open phase acts to accelerate the trajectory conver-352
gence of the model. The model is subsequently run for simulations with shorter open353
phases (1 month and 2 weeks). These simulations take longer to converge but by the354
end of the second open phase the mean standard deviation is 2.4×10−6 and 3.7×10−5355
respectively (Fig. 9B,C). Convergence within two phases illustrates the importance356
of steady states in the model behaviour and provides confidence that using the 5th357
year is a meaningful comparison of these states.358
Parameter Sensitivity359
The sensitivity of the parameters is investigated using a normalised sensitivity360
derived from a power law relationship. The feeding efficiency of ZS (βZS) and ZL361
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For Review Purposes Only/Aux fins d'examen seulement
(βZL) and the mortality of ZS (ζZS) and EP (ζEP ) are the most sensitive parameters362
in this configuration of the model (Table 3). DIP , phytoplankton, zooplankton and363
epiphytes are relatively sensitive to the radii of PS and PL when compared to364
the sensitivity of many other parameters in the model. The nine most sensitive365
parameters are investigated further by running the same simulations with changes366
in the value of the parameters of ± 25 and 50% (Table 4).367
The non-linearity of the model is illustrated by changes in the normalised sensitiv-368
ity as the percentage change of the parameter is increased. State variable-parameter369
combinations with changes of more than 0.1 between simulations are considered to370
have different sensitivities at different magnitudes of permutation and are highlighted371
in Table 4. The parameters which exhibited the greatest change in normalised sen-372
sitivity when changed by 25 and 50% are rPL, ζEP , βZS and βZL. The control373
parameter values for rPL and ζEP , are 1 µm and 0.05 d−1 respectively (Table 2) with374
a range (±50%) of 0.5-1.5 µm and 0.25-0.75 d−1 respectively. The control parameter375
values for βZS and βZL are 0.31 and 0.34 respectively (Table 2) with a range (±50%)376
of 15.5-46.5 and 17-51 respectively. The model responds in a relatively linear manner377
to the other parameters tested (Table 2).378
Discussion379
In this paper we have undertaken numerical experiments with changes in estuarine380
forcings, parameter values and initial conditions from an assessed configuration of381
Smiths Lake, Australia. It is not possible to co-vary all these factors simultaneously.382
As an example we have not simultaneously co-varied opening regimes and catchment383
loads. By examining each estuarine response in isolation, we gain less complicated,384
yet revealing, insights into the behaviour of intermittently open estuaries, while not385
deviating significantly from the assessed model.386
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Catchment Load Response387
Plankton Dynamics : The responses of plankton communities and biomass to388
eutrophication in coastal lagoons are not well documented (Rissik et al., 2009). Phy-389
toplankton and zooplankton biomass in intermittently open estuaries is generally low390
during times of little freshwater influx and therefore small nutrient loads (Grange391
and Allanson, 1995; Froneman et al., 2000). Plankton biomass was higher in estuar-392
ies following freshwater pulses and their accompanying nutrient loads (Wooldridge,393
1999; Froneman et al., 2000). Similar responses are seen in this study with ele-394
vated loads (10×) of phosphorus entering the estuary increasing the productivity395
of phytoplankton which is limited by phosphorus at lower loads. This increase in396
productivity allows the biomass of PS and PL to turn over more quickly and hence397
decrease the period of oscillation and increase the amplitude (Fig. 2). The high398
availability of non-limiting DIN and light, combined with a high growth rate com-399
pared with other state variables allows phytoplankton to quickly take advantage of400
any additional phosphorus which becomes available in the water column. Similarly401
Edwards and Brindley (1996) found that under increasing nutrient loads in an NPZ402
model produced stable predator-prey (limit) cycles of greater amplitude.403
This model produced strong predator-prey cycles that are typical of numerical404
models (Edwards and Brindley, 1996). At high loads (10× and 100×), the oscillations405
of PS and PL are out of phase. PS is nitrogen limited when the biomass is increasing406
and maximum growth rate limited when it is decreasing, while PL is the opposite.407
Even though the maximum biomass increases throughout the closed phase, the switch408
in growth limitation always occurs at the same place on the curve rather than at409
the same water column DIN:DIP ratio. The switch between nitrogen and maximum410
growth rate limitation occurs at the same time in alternate cycles of PS and PL.411
That is, when PS changes from nitrogen limited to maximum growth rate limited,412
PL changes from maximum growth rate limited to nitrogen limited. This dynamic413
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is driven by PL, which is able to draw down the most DIN from the water column414
(as a result of its higher biomass), resulting in PS becoming nitrogen limited. The415
switch in PS growth limitation to nitrogen limitation corresponds with the lowest416
point of DIN concentration (not shown).417
Seagrass: The modelled seagrass biomass is relatively insensitive to changes in418
the catchment loads of up to 2×. Up to these loads the biomass of seagrass stays419
relatively constant and is not influenced by changes in light availability. A 10×420
increase in catchment loads of DIN (even in the presence of 10× increase in DIP421
and/or TSS) doubled the seagrass biomass to 0.11 mol N m−2 at the end of the five422
year simulation when compared to the 2× simulation (0.05 mol N m−2). Under these423
conditions there is only a small increase in light attenuation by the phytoplankton424
and non-phytoplankton component of the water column. These simulations illustrate425
the importance of light for seagrass growth.426
The addition of 100× the limiting nutrient for seagrass (nitrogen) allowed seagrass427
to attain a higher biomass, however the addition of 100× loads of phosphorus (the428
limiting nutrient for phytoplankton), led to a bloom in phytoplankton which resulted429
in reduced light availability to the seagrass (Fig. 4). The same occurred when the430
levels of TSS are increased 100×, which also increased the light attenuation of the431
water column.432
In both these light-limiting scenarios the seagrass is able to recover during the433
open phases when the lake level is low and flushing occurs with cleaner oceanic434
water. In contrast, the addition of DIN +DIP +TSS at 100× levels resulted in the435
collapse of the seagrass to 0.014 mol N m−2, with very little recovery during the 2436
months that the lake is open to the ocean. In this scenario, the light attenuation of437
the water column (phytoplankton and non-phytoplankton components) is increased438
beyond what could be flushed out of the estuary during the open phase, and the439
estuary switches to an alternative state (Scheffer and Carpenter, 2003).440
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Benthic macrophytes, such as seagrass, are extremely important in the function-441
ing of coastal lagoons. Benthic macrophytes are able to intercept nutrients from the442
water column, which is often sufficient to maintain reduced nutrient concentrations in443
the overlying water column (Grall and Chauvaud, 2002). As the macrophytes become444
stressed from increased nutrient and sediment loads, long-term retention of organic445
matter will decline, and be replaced by algal blooms and unattached ephemeral al-446
gae (McGlathery et al., 2007; Duarte, 1995; Valiela et al., 1997). Shallow lagoons,447
such as Smiths Lake, are at greater risk as there is a smaller volume of water into448
which to absorb the nutrient loads, but also, the photic zone generally reaches to the449
benthos, and so the macrophytes macrophytes play a larger role in controlling the450
overlying water column than in deeper systems (McGlathery et al., 2007). Seagrass is451
especially important during closed phases of ICOLL’s, when phytoplankton blooms452
cannot be flushed out. If seagrass dominates the primary production of the lagoon,453
and nutrients will be bound by plant tissue, rather than by phytoplankton, slowing454
the nutrient release back into the water column (McGlathery et al., 2007).455
Moderate nutrient enrichment stimulates the production of the benthic macro-456
phytes in this study. This converges to the well-documented response at higher457
loads where benthic macrophytes reduce their distribution and production within458
the ecosystem through light limitation (Borum and Sand-Jensen, 1996; Smith and459
Schindler, 2009). Due to the shallow depths of Smiths Lake, and the low catchment460
loads (even at 10×), light limitation only becomes growth limiting for SG after 180461
days of a 1 year closed phase. The reduction in biomass is small, and is less than462
the increase during the first half of the cycle. Hence, the overall change in seagrass463
biomass is positive, even in cycles with light limitation.464
Opening Regimes465
In Smiths Lake, PS and PL are generally growth limited by phosphorus, and466
SG is growth limited by nitrogen (Everett et al., 2007). Changes in the nutrient467
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For Review Purposes Only/Aux fins d'examen seulement
availability of DIN and DIP drive changes in the biomass of the seagrass and468
phytoplankton respectively. Due to the availability of DIN in the water column469
throughout the closed phases, a longer closed phase results in an increased biomass of470
SG (Fig. 5). The ocean provides an additional source of DIP to supplement the low471
DIP in the lake. Therefore, the longer the lake is open to the ocean, the higher the472
mean biomass of phosphorus-limited phytoplankton. Similar responses of increased473
phytoplankton biomass during opening events have occurred in Australia (Twomey474
and Thompson, 2001) and the USA (Gobler et al., 2005), and were attributed to475
oceanic nutrient addition and sediment nutrient release respectively. The longer the476
cycle duration is, the longer the lake must stay open to the ocean to maintain the477
same biomass of DIP , PS and EP (Fig. 5B,C,G). A reduction in the length of the478
open phase, for a given cycle duration, results in a reduced average biomass of DIP ,479
PS and EP . Within this study however, these increases in biomass do not increase480
the phytoplankton biomass enough to significantly decrease the light availability to481
the SG. As a result SG maintains a large biomass at all opening regimes. This482
is mainly due to the severe phosphorus limitation of phytoplankton which prevents483
significant blooms, and the low TSS loads due to the highly forested catchment.484
At the present loads entering Smiths Lake there is no decline in SG biomass485
or blooms or PS or PL as a result of changes in the lake cycle duration or open486
phase duration. Schallenberg et al. (2010) highlights that the relationship between487
opening regimes and eutrophication depends on multiple factors specific to each la-488
goon. Longer residence times generally favour phytoplankton dominance (McGlath-489
ery et al., 2007; Valiela et al., 1997), however due to the nutrient limitation within490
Smiths Lake, increased residence time does not have as large an impact as is expected.491
Contrary to initial expectations, the biomass of SG is not decreased by an increased492
residence time and an accumulation of nutrients (Fig. 5). Because phytoplankton493
and SG are limited by two different nutrients, changes in their biomass rarely occur494
21
For Review Purposes Only/Aux fins d'examen seulement
simultaneously. The opening phases are driving DIP availability through oceanic495
enrichment, and DIN accumulation during the closed phases is driving DIN avail-496
ability. If changes in the stoichiometry of the catchment loads were to occur (not497
considered in this study), it is expected that residence time will have a much more498
substantial influence.499
Lake Depth500
Water depth is shown to be important as it drives a switch from a seagrass501
dominated system to an algal dominated system at average depths greater than 7 m502
(Fig. 6). As would be expected due to the reduced light penetration at these increased503
depths, the biomass of seagrass is reduced as the system becomes dominated by504
plankton. The increased frequency of oscillations of phytoplankton and zooplankton505
in simulations with greater depths is a result of their increased growth rate due506
to the availability of nutrients that occurs with the decline of seagrass biomass.507
Interestingly, the epiphytes also reduce in biomass becoming light limited at depths508
greater than 7 m (Fig. 7). This is despite the fact that they require less light than509
seagrass per carbon, and the same light as phytoplankton (Redfield, 1958; Kirk,510
1994; Duarte, 1990). Phytoplankton do not become light limited as they have first511
access to the light and absorb light at the average light availability through the entire512
water column, whereas epiphytes are at the bottom of the water column. Sensitivity513
analysis of depth changes of this extent (>7 m) are an important consideration,514
allowing the model formulation to be applicable for use in other estuaries.515
Assimilative Capacity of Smiths Lake516
Based upon the results of the catchment load scenarios, Smiths Lake can assim-517
ilate more than the current nutrient and sediment loads without large changes in518
the ecosystem. If it is assumed that any catchment load increases will equally affect519
DIN , DIP and TSS, the catchment loads are able to double without either a detri-520
mental effect to seagrass, or forcing a switch to an alternative plankton-dominated521
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For Review Purposes Only/Aux fins d'examen seulement
system. At loads of 10×, there is a change in oscillation frequency and size; however522
there is a further increase in seagrass biomass suggesting the increase in plankton523
biomass is not fundamentally changing the ecosystem. At loads of 100× however,524
there is a definite switch to a plankton dominated system, and large declines in sea-525
grass biomass. There is also an associated build-up in water column nutrients. As526
nutrients are entering the lake in the same DIN:DIP ratios, the plankton remains527
growth limited by phosphorus, however the seagrass becomes light limited. Smiths528
Lake’s assimilative capacity is in part determined by its small catchment area to lake529
area ratio of 2:1 (Haines et al., 2006), its predominately forested catchment (Webb530
McKeown & Associates Pty Ltd, 1998) and its large biomass of seagrass which is531
likely to play an important role in intercepting water column nutrients and enhanc-532
ing the lagoons resistance to eutrophication (McQuatters-Gollop et al., 2009). If533
Smiths Lake loads increase to levels similar to more urbanised estuaries (10-100×),534
simulations suggest that the system would switch to an alternate state, from seagrass535
dominated to microalgal dominated. As a result residence time and opening/closing536
cycles would play a greater role in determining ecosystem state.537
Parameter Sensitivity538
In order to interpret the model output, it is important to also consider the sensi-539
tivity of different parameters. A sensitivity analysis provides a measure of the sensi-540
tivity of changes in model parameters with respect to changes in the state variables541
of interest (Jorgensen and Bendoricchio, 2001). Sensitivities are usually examined542
at a few different levels which are decided upon based on prior knowledge about543
the parameter. If a parameter or forcing is found to be sensitive, more effort can544
be put into making sure the parameter is accurate. The parameters to which the545
model state variables are most sensitive are the feeding efficiency of ZS (βZS) and546
ZL (βZL) and the mortality of ZS (ζZS) and EP (ζEP ). For the majority of state547
variable-parameter combinations, the normalised sensitivity for the state variables548
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is small (< 1) up to a ± 50% change in parameter values. The non-linearity of549
βZS and βZL at parameter changes of ± 25 and 50% is shown by a large change550
in the normalised sensitivity to phytoplankton biomass (Table 4). The normalised551
sensitivity for βZS increased from -1.28 to -1.78 for PS and βZL increased from -1.15552
to -1.35 for PL. There is a relatively large degree of uncertainty surrounding the553
parameter values for feeding efficiency (βZS = 0.31 and βZL = 0.34), however, the554
values chosen in this study are extracted from a compilation of 27 field studies on 33555
different species (Hansen et al., 1997). The range of values for βZS and βZL in those556
studies is 0.10-0.42 and 0.23-0.45 respectively. By varying these model parameters557
by up to ± 50%, the sensitivity for the entire range of possible values from Hansen558
et al. (1997) is captured. Within the study there was one exception with a value of559
0.10 which fell below the minimum value for βZS. This value could be considered an560
outlier, given its distance of more than two standard deviations from the mean.561
The predators of zooplankton are not represented explicitly in this model but their562
dynamics are approximated by the closure term. The closure terms can significantly563
affect the dynamics of an ecological model (Edwards and Yool, 2000; Steele and Hen-564
derson, 1992). In this study a quadratic mortality is implemented which represents565
grazing from a predator whose biomass is proportional to that of the zooplankton566
(Edwards and Yool, 2000). Nevertheless, the quadratic form of model closure does567
not necessarily increase stability or reduce oscillatory behaviour (Edwards and Yool,568
2000). Therefore it is not surprising that the quadratic mortality coefficients of ZS569
and ZL are the most sensitive parameters in the model (For further discussion of the570
relative costs and benefits of different closure terms, refer to Steele and Henderson571
(1992) and Edwards and Yool (2000)).572
The sensitivity of ζZS and ζZL is greatly reduced in the simulations presented in573
this paper when compared to their sensitivity in the 11-box model of Smiths Lake574
(Everett et al., 2007). The difference is that the current study utilises a 1-box model575
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For Review Purposes Only/Aux fins d'examen seulement
with idealised physical configuration (idealised inflow and incoming solar radiation).576
Due to the quadratic formulation of the mortality term any values for zooplankton577
biomass in the 11-box configuration which are above the mean value, would have578
been magnified, leading it to appear more sensitive than in the 1-box configuration.579
Therefore, the sensitivity of ζZS and ζZL may be underestimated in this and other580
studies which smooth out the patchiness of zooplankton biomass.581
Zooplankton has been shown to be the most poorly simulated state variable in582
aquatic biogeochemical models (Arhonditsis and Brett, 2004). One suggested reason583
for this is the need to convert observed zooplankton data from units of individuals584
per volume to units of carbon or nitrogen per volume. A second possible reason is585
that entire zooplankton life-histories need to be fully simulated to achieve accurate586
representation (Arhonditsis and Brett, 2004). A possible shortcoming of this model587
is that it contains both these potential sources of error. Of the 27 field studies588
mentioned above (Hansen et al., 1997), the feeding efficiencies of only two studies589
are a direct measure of nitrogen efficiency, the modelled variable in this study. In all590
other cases, the values are given in units of volume, dry weight or carbon content591
(Hansen et al., 1997). Also, in this study zooplankton growth is exponential, which is592
characteristic of micro-zooplankton, rather than age structured, as is characteristic593
of macro-zooplankton (Baird and Suthers, 2007). Therefore, life histories are not594
explicitly modelled in this study, apart from the two different size classes - small and595
large zooplankton.596
Summary597
A previously developed and assessed model of Smiths Lake, Australia, is used598
to investigate the ecosystem response to changes in estuarine forcings and biologi-599
cal parameters for a broader range of idealised ICOLLs. There is little change in600
estuarine response for small changes in depth and catchment loads due to the rela-601
tively small loads and low light attenuation under current Smiths Lake conditions.602
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Larger load increases, as would be applicable in other estuaries, exhibit a switch in603
estuarine condition resulting in a decline in seagrass biomass and large changes in604
predator-prey cycles.605
Acknowledgements606
This work comprises part of JDE’s PhD and was funded by Australian Research607
Council (ARC) Linkage grant (LP0349257). MEB was supported by ARC Discovery608
Project DP0557618. The authors wish to thank Gerard Tuckerman and Great Lakes609
Council for their support. We also thank Peter Scanes, Geoff Coade, Ed Czobik610
and the Department of Environment and Climate Change (NSW) for processing611
the nutrient samples. The Manly Hydraulics Laboratory provided lake height data,612
the Department of Water and Energy provided bathymetry data and NOAA-CIRES613
Climate Diagnostic Center provided the solar radiation data. The authors would614
also like to thank the following colleagues for assistance in collecting the field data:615
A. Everett, E. Gale, S. Moore, K. Wright, L. Carson, T. Mullaney, R. Piola and M.616
Taylor.617
References618
Arhonditsis, G. B. and Brett, M. T. (2004). Evaluation of the current state of619
mechanistic aquatic biogeochemical modelling. Marine Ecology Progress Series620
271, 13–26621
Baird, M. E. and Suthers, I. M. (2007). A size-resolved pelagic ecosystem model.622
Ecological Modelling 203, 185–203623
Baird, M. E., Walker, S. J., Wallace, B. B., Webster, I. T., and Parslow, J. S. (2003).624
The use of mechanistic descriptions of algal growth and zooplankton grazing in625
an estuarine eutrophication model. Estuarine, Coastal and Shelf Science 56(3-4),626
685–695627
26
For Review Purposes Only/Aux fins d'examen seulement
Becker, A., Laurenson, L. J., and Bishop, K. (2009). Artificial mouth opening fos-628
ters anoxic conditions that kill small estuarine fish. Estuarine, Coastal and Shelf629
Science 82(4), 566 – 572. doi:10.1016/j.ecss.2009.02.016630
Borum, J. and Sand-Jensen, K. (1996). Is total primary production in shallow coastal631
marine waters stimulated by nitrogen loading? Oikos 76(2), 406–410632
Carpenter, S. R., Caraco, N. F., Correll, D. L., Howarth, R. W., Sharpley, A. N.,633
and Smith, V. H. (1998). Nonpoint pollution of surface waters with phosphorus634
and nitrogen. Ecological Applications 8(3), 559–568635
Cloern, J. (2001). Our evolving conceptual model of the coastal eutrophication636
problem. Marine Ecology Progress Series 210, 223–253637
CSIRO (2003). Simple Estuarine Response Model II638
http://www.per.marine.csiro.au/serm2/index.htm Accessed: 12th March 2007639
CSIRO and Bureau of Meteorology (2007). Climate change in Australia: Technical640
report 2007. Tech. rep., CSIRO641
DIPNR (2004). Discussion Paper: ICOLL Management Guidelines. Tech. rep.,642
Department of Infrastructure, Planning and Natural Resources, N.S.W.643
Duarte, C. (1990). Seagrass nutrient content. Marine Ecology Progress Series 67(2),644
201 – 207645
Duarte, C. (1995). Submerged aquatic vegetation in relation to different nutrient646
regimes. Ophelia 41, 87–112647
Duarte, C. (1999). Seagrass ecology at the turn of the millennium: challenges for648
the new century. Aquatic Botany 65(1-4), 7–20649
Edwards, A. and Yool, A. (2000). The role of higher predation in plankton population650
models. Journal of Plankton Research 22(6), 1085–1112651
27
For Review Purposes Only/Aux fins d'examen seulement
Edwards, A. J. and Brindley, J. (1996). Oscillatory behaviour in a three-component652
plankton population model. Dynamics and Stability of Systems 11, 347–370653
Everett, J. D. (2007). Biogeochemical Dynamics of an Intermittently Open Estuary:654
A Field and Modelling Study. Ph.D. thesis, University of New South Wales, Sydney655
Everett, J. D., Baird, M. E., and Suthers, I. M. (2007). Nutrient and plankton656
dynamics in an intermittently closed/open lagoon, Smiths Lake, south-eastern657
Australia: An ecological model. Estuarine, Coastal and Shelf Science 72, 690–702658
Fasham, M., Ducklow, H., and McKelvie, S. (1990). A nitrogen-based model of659
plankton dynamics in the oceanic mixed layer. Journal of Marine Research 48(3),660
591–639661
Froneman, P. W., Pakhomov, E. A., Perissinotto, R., and McQuaid, C. D. (2000).662
Zooplankton structure and grazing in the atlantic sector of the southern ocean663
in late austral summer 1993 - part 2. biochemical zonation. Deep-Sea Research I664
47(9), 1687–1702665
Gobler, C. J., Cullison, L. A., Koch, F., Harder, T., and Krause, J. (2005). Influence666
of freshwater flow, ocean exchange and seasonal cycles on phytoplankton - nutrient667
dynamics in a temporarily open estuary. Estuarine Coastal and Shelf Science 65,668
275–288669
Grall, J. and Chauvaud, L. (2002). Marine eutrophication and benthos: the need670
for new approaches and concepts. Global Change Biology 8, 813–830. doi:671
10.1046/j.1365-2486.2002.00519.x672
Grange, N. and Allanson, B. (1995). The influence of freshwater inflow on the nature,673
amount and distribution of seston in estuaries of the eastern cape, south africa.674
Estuarine, Coastal and Shelf Science 40, 403–420675
28
For Review Purposes Only/Aux fins d'examen seulement
Haines, P., Tomlinson, R., and Thom, B. (2006). Morphometric assessment of inter-676
mittently open/closed coastal lagoons in New South Wales, Australia. Estuarine,677
Coastal and Shelf Science 67(1-2), 321–332678
Hansen, P., Bjørnsen, P., and Hansen, B. (1997). Zooplankton grazing and growth:679
Scaling within the 2-2,000 µm body size range. Limnology and Oceanography680
42(4), 687–704681
Harris, G. (2001). Biogeochemistry of nitrogen and phosphorus in Australian catch-682
ments, rivers and estuaries: effects of land use and flow regulation and comparisons683
with global patterns. Marine & Freshwater Research 52(1), 139 – 149684
Hemminga, M., Marba, N., and Stapel, J. (1999). Leaf nutrient resorption, leaf685
lifespan and the retention of nutrients in seagrass systems. Aquatic Botany 65,686
141–158687
Hennessy, K., Page, C., McInnes, K., Jones, R., Bathols, J., Collins, D., and Jones,688
D. (2004). Climate Change in New South Wales. Part 1: Past climate variability689
and projected changes in average climate. Tech. rep., New South Wales Greenhouse690
Office691
Jorgensen, S. and Bendoricchio, G. (2001). Fundamentals of Ecological Modelling692
(Elsevier Science Ltd, Oxford, UK), 3rd edn.693
Kirk, J. (1994). Light and photosynthesis in aquatic ecosystems. 509 pp. (Cambridge694
University Press), 2nd edn.695
Lloret, J., Marın, A., and Marın-Guirao, L. (2008). Is coastal lagoon eutrophication696
likely to be aggravated by global climate change? Estuarine, Coastal and Shelf697
Science 78(2), 403–412698
29
For Review Purposes Only/Aux fins d'examen seulement
McGlathery, K., Sundback, K., and Anderson, I. (2007). Eutrophication in shallow699
coastal bays and lagoons: The role of plants in the coastal filter. Marine Ecology700
Progress Series 348, 1–18701
McQuatters-Gollop, A., Gilbert, A., Mee, L., Vermaat, J., Artioli, Y., Humborg, C.,702
and Wulff, F. (2009). How well do ecosystem indicators communicate the effects of703
anthropogenic eutrophication? Estuarine Coastal and Shelf Science 82, 583–596.704
doi:10.1016/j.ecss.2009.02.017705
Moll, A. (2000). Assessment of three-dimensional physical-biological ecoham1 simu-706
lations by quantified validation for the north sea with ices and ersem data. ICES707
Journal of Marine Science 57(4), 1060–1068708
Murray, A. G. and Parslow, J. S. (1997). Port Phillip Bay integrated model: Final709
report. Tech. Rep. Technical Report 44, CSIRO Division of Marine Resources,710
Canberra711
Redfield, A. (1958). The biological control of chemical factors in the environment.712
American Scientist 46, 205–221713
Rissik, D., Ho Shon, E., Newell, B., Baird, M., and Suthers, I. (2009). Plankton714
dynamics due to rainfall, eutrophication, dilution, grazing and assimilation in an715
urbanized coastal lagoon. Estuarine, Coastal and Shelf Science 84, 99–107. doi:716
10.1016/j.ecss.2009.06.009717
Roy, P., Williams, R., Jones, A., Yassini, I., Gibbs, P., Coates, B., West, R., Scanes,718
P., Hudson, J., and Nichol, S. (2001). Structure and function of south-east Aus-719
tralian estuaries. Estuarine, Coastal and Shelf Science 53(3), 351–384720
Schallenberg, M., Larned, S., Hayward, S., and Arbuckle, C. (2010). Contrasting721
effects of managed opening regimes on water quality in two intermittently closed722
30
For Review Purposes Only/Aux fins d'examen seulement
and open coastal lakes. Estuarine, Coastal and Shelf Science 86(4), 587 – 597.723
doi:DOI: 10.1016/j.ecss.2009.11.001724
Scheffer, M. and Carpenter, S. (2003). Catastrophic regime shifts in ecosystems:725
linking theory to observation. Trends in Ecology & Evolution 18, 648–656. doi:726
10.1016/j.tree.2003.09.002727
Smith, C. and Heggie, D. (2003). Benthic Nutrient Fluxes in Smiths Lake, NSW.728
Tech. Rep. Record 2003/16, Geoscience Australia729
Smith, V. H. and Schindler, D. W. (2009). Eutrophication science: where do730
we go from here? Trends in Ecology & Evolution 24(4), 201–207. doi:731
10.1016/j.tree.2008.11.009732
Steele, J. and Henderson, E. (1992). The role of predation in plankton models.733
Journal of Plankton Research 14, 157–172734
Twomey, L. and Thompson, P. (2001). Nutrient limitation of phytoplankton in a735
seasonally open bar-built estuary: Wilson Inlet, Western Australia. Journal of736
Phycology 37, 16–29737
Valiela, I., McClelland, J., Hauxwell, J., Behr, P. J., Hersh, D., and Foreman, K.738
(1997). Macroalgal blooms in shallow estuaries: Controls and ecophysiological and739
ecosystem consequences. Limnology and Oceanography 42(5), 1105–1118740
Webb McKeown & Associates Pty Ltd (1998). Smiths Lake Estuary Process Study.741
Tech. rep., Prepared for Great Lakes Council, N.S.W. Australia742
Webster, I. T. and Harris, G. P. (2004). Anthropogenic impacts on the ecosystems743
of coastal lagoons: modelling fundamental biogeochemical processes and manage-744
ment implications. Marine & Freshwater Research 55(1), 67–78745
31
For Review Purposes Only/Aux fins d'examen seulement
West, R. J., Thorogood, C., Walford, T., and Williams, R. J. (1985). An estuar-746
ine inventory for New South Wales, Australia (New South Wales, Department of747
Agriculture, Sydney)748
Wooldridge, T. (1999). Estuarine zooplankton community structure and dynam-749
ics. In B. Allanson and D. Baird, eds., Estuaries of South Africa, pp. 141–166750
(Cambridge University Press)751
32
For Review Purposes Only/Aux fins d'examen seulement
Table 1: The 17 state variables used in the ecological model. The initial conditions and ocean boundary conditions and their symbol and
units are also shown.
State Variable Symbol Initial Conditions Ocean Boundary Condition Units
Dissolved Inorganic Nitrogen DIN 7.3 × 10−4 9.5 × 10−4 mol N m−3
Dissolved Inorganic Phosphorus DIP 5.1 × 10−6 1.0 × 10−4 mol P m−3
Small Phytoplankton PS 1.1 × 10−4 1.6 × 10−4 mol N m−3
Large Phytoplankton PL 3.3 × 10−4 2.1 × 10−4 mol N m−3
Small Zooplankton ZS 9.8 × 10−4 1.5 × 10−4 mol N m−3
Large Zooplankton ZL 1.1 × 10−3 1.5 × 10−4 mol N m−3
Epiphytic and Benthic Microalgae EP 8.4 × 10−5 0 mol N m−2
Seagrass SG 6.0 × 10−2 0 mol N m−2
Refractory Detritus RD 21 × 10−2 0 mol N m−2
Sediment Dissolved Inorganic Nitrogen DINsed 3.3 × 10−2 0 mol N m−2
Unflocculated Phosphorus Punfloc 2.7 × 10−7 0 mol P m−3
Flocculated Phosphorus Pfloc 2.0 × 10−8 0 mol P m−3
Sediment Dissolved Inorganic Phosphorus DIPsed 1.8 × 10−3 0 mol P m−2
Unflocculated Sediment Phosphorus Psed unfloc 3.1 0 mol P m−2
Flocculated Sediment Phosphorus Psed floc 5.6 0 mol P m−2
Unflocculated Total Suspended Solids TSSunfloc 5.2 × 10−3 0 kg TSS m−3
Flocculated Total Suspended Solids TSSfloc 2.2 × 10−5 0 kg TSS m−3
33
For Review Purposes Only/Aux fins d'examen seulement
Table 2: Parameter values for the ecological model. The ranges tested in the sensitivity analysis are shown in column 2 and 3. N/T =
Not Tested.
Parameter Value 10% Range 50% Range
Cell radius of PS rPS = 2.5 × 10−6 m 2.25-2.75 × 10−6 1.25-3.75 × 10−6
Cell radius of PL rPL = 1.0 × 10−5 m 0.9-1.1 × 10−6 0.5-1.5 × 10−6
Cell radius of ZS rZS = 4.0 × 10−6 m 3.6-4.4 × 10−6 N/T
Cell radius of ZL rZL = 1.0 × 10−3 m 0.9-1.1 × 10−3 N/T
Cell radius EP rEP = 5.0 × 10−6 m 4.5-5.5 × 10−6 N/T
Mortality rate of ZS ζZS = 94.78 (mol N m−3)−1 d−1 85.30-104.26 47.39-142.17
Mortality rate of ZL ζZL = 18.03 (mol N m−3)−1 d−1 16.23-19.83 9.02-27.05
Mortality rate of EP ζEP = 0.05 d−1 0.045-0.055 0.025-0.075
Mortality rate of SG ζSG = 4.22 (mol N m−2)−1 d−1 (Duarte, 1990) 3.80-4.64 2.11-6.33
Rate of RD Breakdown RD = 0.1 d−1 (Murray and Parslow, 1997) 0.09-0.11 N/T
Feeding efficiency of ZS βZS = 0.31 (Hansen et al., 1997) 0.28-0.34 0.155-0.465
Feeding efficiency of ZL βZL = 0.34 (Hansen et al., 1997) 0.31-0.37 0.17-0.51
Sinking rate of Pfloc w Pfloc = 5 d−1 (Baird et al., 2003) 4.5-5.5 N/T
Sinking rate of Punflocc w Punfloc = 5 d−1 (Baird et al., 2003) 4.5-5.5 N/T
34
For Review Purposes Only/Aux fins d'examen seulement
Table 3: Normalised sensitivity when parameters are varied by 10%. The rows represent the
parameters changed, and the columns the state variables examined. The values in bold are the
parameters with a normalised sensitivity greater than |0.4|.
DIN DIP PS PL ZS ZL EP SG
rPS -0.04 -0.43 0.68 -0.52 0.64 -0.62 -0.44 -0.01
rPL -0.04 -0.52 -0.59 0.57 -0.58 0.71 -0.53 -0.01
rZS 0 0 0 0 0 0 0 0
rZL 0 0 0 0 0 0 0 0
rEP 0 0 0 0 0 0 0 0
ζZS -0.01 -0.15 1.05 -0.14 0.01 -0.22 -0.15 0
ζZL -0.01 -0.17 -0.2 0.52 -0.18 0.06 -0.17 -0.02
ζEP 0 0 0 0 0 0 -1.01 0
ζSG 0 0 0 0 0 0 0 -0.41
rRD 0 0 0 0 0 0 0 0.4
sedxch -0.05 0.03 0.04 0.04 0.03 0.04 0.03 -0.01
βZS 0.01 0.18 -1.28 0.18 0 0.25 0.18 -0.01
βZL 0.02 0.24 0.24 -1.11 0.26 0.09 0.25 0.02
w Pfloc 0 0 0 0 0 0 0 0
w Punfloc 0.01 0 -0.01 -0.01 -0.01 -0.01 0 0
35
For Review Purposes Only/Aux fins d'examen seulement
Table 4: Table of normalised sensitivity at the 25 (and 50% - in brackets) levels for the 8 most sensitive parameters from Table 3. Bold
represents the parameters whose sensitivity changed by more than 0.1 from 10 to 25% or 25 to 50%.
DIN DIP PS PL ZS ZL EP SG
rPS 0.04 (-0.04) -0.48 (-0.50) 0.65 (0.63) -0.53 (-0.50) 0.63 (0.62) -0.58 (-0.58) -0.49 (-0.51) -0.01 (0)
rPL -0.04 (-0.39) -0.47 (-0.48) -0.57 (-0.58) 0.62 (0.61) -0.54 (-0.56) 0.61 (0.65) -0.47 (-0.48) -0.01 (-0.01)
ζZS -0.01 (-0.01) -0.15 (-0.18) 1.03 (1.02) -0.12 (-0.11) 0 (0) -0.22 (-0.21) -0.15 (-0.18) 0 (-0.01)
ζZL -0.01 (0) -0.14 (-0.09) -0.14 (-0.11) 0.48 (0.49) -0.16 (-0.11) 0.03 (-0.05) -0.14 (-0.09) -0.02 (-0.02)
ζEP 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) -1.07 (-1.33) 0 (0)
ζSG 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) -0.42 (-0.47)
βZS 0.01 (0.03) 0.18 (0.3) -1.28 (-1.78) 0.14 (0.25) 0 (0.09) 0.27 (0.22) 0.18 (0.30) -0.01 (0.01)
βZL 0.02 (0.02) 0.25 (0.30) 0.25 (0.31) -1.15 (-1.35) 0.27 (0.33) 0.10 (0.11) 0.25 (0.30) 0.03 (0.02)
36
For Review Purposes Only/Aux fins d'examen seulement
0 50 100 150 200 250 300 3501.8
2
2.2
2.4
2.6
2.8
3
3.2
3.4
3.6
Open Phase
Days
Ave
rage
Dep
th (
m)
1.8
1.9
2
Av.
Dep
. (m
)Open Phase
Days
Figure 1: Plot of the average lake depth (m) of the idealised model formulation against time
(days). The same cycle is repeated each year. The average depth of the lake is taken from
lake depth measurements and is different to the Australian Height Datum (AHD), which
is the reference datum for altitude measurement in Australia. The inserted box shows the
sinusoidal oscillation which occurs during the open phase.
37
For Review Purposes Only/Aux fins d'examen seulement
0
0.5
1
1.5
2x 10
−3
A) Biomass of PS
mol
N m
−3
0
0.5
1
1.5
2x 10
−3
B) Biomass of PL
mol
N m
−3
0 100 200 300 400 500 600 7000
0.5
1
1.5x 10
−6
C) Productivity of PS
mol
N m
−3 d
−1
Days0 100 200 300 400 500 600 700
0
0.5
1
1.5x 10
−6
D) Productivity of PL
mol
N m
−3 d
−1
Days
2x 10x Control
Figure 2: Biomass of A) PS, B) PL (mol N m−3), productivity of C) PS, D) PL (mol N
m−3 d−1) in response to changes to the phosphorus load entering the estuary. The lines
represent the changes to the phosphorus load of 2×, 10× and the Control). The grey
shaded areas represent the time the lake is open to the ocean.
38
For Review Purposes Only/Aux fins d'examen seulement
0
0.5
1
1.5
2x 10
−3 PS Biomass and Growth Limitation
A) 2× DIP Catchment Loads
mol
N m
−3
0
0.5
1
1.5
2x 10
−3PL Biomass and Growth Limitation
B) 2× DIP Catchment Loads
mol
N m
−3
0 100 200 300 400 500 600 7000
0.5
1
1.5
2x 10
−3
C) 10× DIP Catchment Loads
mol
N m
−3
Days0 100 200 300 400 500 600 700
0
0.5
1
1.5
2x 10
−3
D) 10× DIP Catchment Loads
mol
N m
−3
Days
Nitrogen Limited Phosphorus Limited Maximum GR Limited
Figure 3: Growth limitation of PS (A, C) and PL (B, D) in response to changes to the
phosphorus load entering the estuary. In plots A/B (2× increase in DIP Catchment Loads)
and C/D (10× increase in DIP Catchment Loads), the line shows the biomass and the
colour of the line represents the process that limits growth (nitrogen uptake, phosphorus
uptake, light capture or maximum metabolic rate). The grey shaded areas represent the
time the lake is open to the ocean.
39
For Review Purposes Only/Aux fins d'examen seulement
0
0.2
Seagrass Biomass (mol N m−2)
A)
DIN
0
1
2
Non−Phytoplankton Light Attenuation (m−1)
B)
0
1
2
3
Phytoplankton Light Attenuation (m−1)
C)
0
1
2x 10
−4
Light Availability for Seagrass (mol photons m−2 s−1)
D)
0
0.2
E)
DIN
+D
IP
0
1
2F)
0
1
2
3G)
0
1
2x 10
−4
H)
0
0.2
I)
DIN
+T
SS
0
1
2J)
0
1
2
3K)
0
1
2x 10
−4
L)
0 200 400 6000
0.2
Days
M)
DIN
+D
IP+
TSS
0 200 400 6000
1
2
Days
N)
0 200 400 6000
1
2
3
Days
O)
0 200 400 6000
1
2x 10
−4
Days
P)
0.1x 2x 10x 100x Control
Figure 4: Sensitivity of seagrass to changing catchment loads. Columns give seagrass
biomass (mol N m−2), water column light attenuation (m−1), phytoplankton light attenu-
ation (m−1) and seagrass light availability (mol photon m−2 s−1). Rows show catchment
load simulations of DIN , DIN + DIP , DIN + TSS and DIN + DIP + TSS. The grey
shaded areas represent the time the lake is open to the ocean.
40
For Review Purposes Only/Aux fins d'examen seulement
121824364860
A) DIN (mol N m−3)
Cyc
le D
urat
ion
(mth
s)
2
4
6
x 10−4 B) DIP (mol N m−3)
3.2
3.43.63.8
4x 10
−6
121824364860
C) PS (mol N m−3)
Cyc
le D
urat
ion
(mth
s)
1.3
1.4
1.5
1.6
x 10−4 D) PL (mol N m−3)
1
1.5
2
x 10−4
121824364860
E) ZS (mol N m−3)
Cyc
le D
urat
ion
(mth
s)
6
7
8x 10
−4 F) ZL (mol N m−3)
5
5.5
6
x 10−4
7 14 30 60121824364860
G) EP (mol N m−2)
Open Duration (d)
Cyc
le D
urat
ion
(mth
s)
4.24.44.64.855.2
x 10−5
7 14 30 60
H) SG (mol N m−2)
Open Duration (d)
0.04750.0480.04850.0490.0495
Figure 5: The contours represent the mean biomass of the state variables at a lake opening
height of 3.5 m. The mean value is calculated from the final closed phase. The white
crosses show the location of the mean simulation values used to create the contours
41
For Review Purposes Only/Aux fins d'examen seulement
0
1
2x 10
−3 A) DIN
mol
N m
−3
0
1
2x 10
−5B) DIP
mol
P m
−3
0
0.5
1x 10
−3 C) PS
mol
N m
−3
0
1
2x 10
−3D) PL
mol
N m
−3
0
1
2x 10
−3 E) ZS
mol
N m
−3
0
1
2x 10
−3F) ZL
mol
N m
−3
0 100 200 300 400 500 600 7000
1x 10
−4 G) EP
mol
N m
−2
Days0 100 200 300 400 500 600 700
0
0.05H) SG
mol
N m
−2
Days
2.5 m (control) 4.5 m 7.0 m 12.0 m
Figure 6: The biomass of DIN , DIP , PS, PL, ZS, ZL, EP and SG for simulations as
the lake depth is changed. The lines represent the changes in estuary depth from 2.5, 4.5,
7 and 12 m. The grey shaded areas represent the time the lake is open to the ocean.
42
For Review Purposes Only/Aux fins d'examen seulement
0
0.5
1x 10
−3
PS (mol N m−3)
2.5
m
0
1
2x 10
−3
PL (mol N m−3)
0
1x 10
−4
EP (mol N m−2)
0
0.05
SG (mol N m−2)
0
0.5
1x 10
−3
4.5
m
0
1
2x 10
−3
0
1x 10
−4
0
0.05
0
0.5
1x 10
−3
7.0
m
0
1
2x 10
−3
0
1x 10
−4
0
0.05
0 200 400 6000
0.5
1x 10
−3
Days
12 m
0 200 400 6000
1
2x 10
−3
Days0 200 400 600
0
1x 10
−4
Days0 200 400 600
0
0.05
Days
Nitrogen Limited Phosphorus Limited Light Limited Maximum Growth Rate Limited
Figure 7: Biomass of PS, PL, EP and SG (columns 1-4 respectively) coloured by process
limiting growth rate. Depth simulations of 2.5, 4.5, 7 and 12 m are plotted across the rows.
The line shows the biomass and the colour represents the growth limitation options of
nitrogen, phosphorus, light and maximum metabolic rate. The grey shaded areas represent
the time the lake is open to the ocean.
43
For Review Purposes Only/Aux fins d'examen seulement
0
0.5
1x 10
−8 A) PS
mol
N m
−3 s
−1
0
1
2
x 10−9B) PL
mol
N m
−3 s
−1
0
1
2
x 10−9 C) ZS
mol
N m
−3 s
−1
0
0.5
1x 10
−9D) ZL
mol
N m
−3 s
−1
0 100 200 300 400 500 600 7000
2
4
6x 10
−11 E) EP
mol
N m
−2 s
−1
Days0 100 200 300 400 500 600 700
0
0.5
1
1.5
2x 10
−8F) SG
mol
N m
−2 s
−1
Days
2.5 m (control) 4.5 m 7.0 m 12.0 m
Figure 8: The realised growth rate for PS, PL, ZS, ZL, EP and SG as the lake depth is
changed. The colours represent the changes in estuary depth from 2.5, 4.5, 7 and 12 m.
The grey shaded areas represent the time the lake is open to the ocean.
44
For Review Purposes Only/Aux fins d'examen seulement
0
0.5
1
1.5x 10
−3
Sta
ndar
d D
evia
tion A) 2 Month Open Phase
DINDIPPSPLZSZL
0
0.5
1
1.5x 10
−3
Sta
ndar
d D
evia
tion B) 1 Month Open Phase
DINDIPPSPLZSZL
0 100 200 300 400 500 600 700 800 900 10000
0.5
1
1.5x 10
−3
Sta
ndar
d D
evia
tion
Days
C) 2 Week Open Phase
DINDIPPSPLZSZL
Figure 9: The standard deviation of state variable biomass for 10 ensemble members for
the first 3 years of the simulation. The plots represent A) 2 week open phase, B) 1 month
open phase and C) 2 month open phase. The lines represent the 6 state variables of DIN ,
DIP , PS, PL, ZS and ZL. The grey shaded areas represent the time the lake is open to
the ocean.
45