one- and three-dimensional biogeochemical simulations of ...fig. 2. schematic representation of the...

Ecological Modelling 174 (2004) 143–160

One- and three-dimensional biogeochemicalsimulations of two differing reservoirs

J.R. Romero∗, J.P. Antenucci, J. ImbergerCentre for Water Research, University of Western Australia, Crawley, WA 6009, Australia

Abstract

A one-dimensional hydrodynamic model was coupled to an aquatic ecological model and applied to two differing reservoirswith one set of parameters. Simulations over 2 years of small (area= 5 km2) and shallow (average depth= 9 m) ProspectReservoir (1988–1990), and large (volume= 2 km3, area= 82 km2, length= 50 km) and deep (maximum depth= 90 m) LakeBurragorang (1992–1994) were validated with field data. The dominant fate processes of the nitrogen and phosphorus cyclesin the water column of these reservoirs were identified with model output. CAEDYM was then coupled to a three-dimensionalhydrodynamic model (ELCOM) to simulate a flood underflow through Lake Burragorang. Advective transport was the dom-inant mechanism that caused biogeochemical variations during the flood, though settling was important for riverine-derivedparticulates. Validation of ecological models with both one- and three-dimensional hydrodynamics drivers and across multipleaquatic settings is advocated as an additional approach to limit ranges of biogeochemical parameters and assess ecologicalrepresentations of lacustrine systems.© 2004 Elsevier B.V. All rights reserved.

Keywords:Reservoirs; Biogeochemical modeling; Nitrogen; Phosphorus

1. Introduction

Application of numerical water quality modelsto evaluate aquatic management strategies is widelyused for lakes, reservoirs, rivers, estuaries, and coastalzones. A range of modeling approaches is availableto evaluate aquatic ecosystems as summarized inJørgensen and Bendoricchio (2001). Here, we con-sider the ‘reductionist/analytical’ approach with ageneral plankton model. In contrast to other modelingapproaches that capture the dominant processes witha few parameters, the aim of these general planktonmodels is to model the major biogeochemical pro-cesses. For such general plankton models, a goal is to

∗ Corresponding author. Tel.:+61-8-9380-1685;fax: +61-8-9380-7115.

E-mail address:[email protected] (J.R. Romero).

establish parameter values that are independent of theaquatic setting, and to identify those parameters thatare site-specific.

Such approaches have been considered in the pastin lakes and reservoirs with one-dimensional (1D)frameworks such as CE-QUAL-R1 (USCE, 1995),MINILAKE ( Riley and Stefan, 1988), and DYRESM-WQ (Hamilton and Schladow, 1997), and with multi-dimensional models such as WASP (Ambrose et al.,1993). These 1D water quality models (CE-QUAL-R1, MINILAKE, DYRESM-WQ) were generallyintegrated into the architecture of an existing verticalmixing model. In the case of the multi-dimensionalWASP modeling system, output from a prior hydro-dynamic simulation is needed to provide transport forbiogeochemical model runs. Desktop computationalpower is now sufficient to evaluate general plank-ton models in a ‘multi-system’ (several lakes and/or

0304-3800/$ – see front matter © 2004 Elsevier B.V. All rights reserved.doi:10.1016/j.ecolmodel.2004.01.005

144 J.R. Romero et al. / Ecological Modelling 174 (2004) 143–160

reservoirs) validation framework to bound parametervalues. Further, coupling of biogeochemical modelswith both 1D and 3D hydrodynamic drivers allowsfor evaluation of time and space scale dependenciesof parameters.

In this study a general plankton model was cou-pled to a 1D vertical mixing model and applied totwo differing reservoirs with a single parameter set.This 1D configuration was used to evaluate the sea-sonal dynamics and to quantify the primary fluxes ofnitrogen and phosphorus in two differing reservoirs.The general plankton model was also coupled to athree-dimensional (3D) hydrodynamic model to eval-uate spatial and temporal biogeochemical distributionsduring a flood in one of the reservoirs. The aim of thisstudy was to evaluate whether a simple ecological con-figuration and one set of biogeochemical parameterscould be coupled to both 1D and 3D hydrodynamicdrivers to adequately simulate the nutrient cycles intwo differing reservoirs.

2. Study sites

Both reservoirs are located in (Prospect Reser-voir) or near (Lake Burragorang) Sydney, Aus-tralia (Fig. 1A). Prospect Reservoir is a moderatelysized (5.25 km2) reservoir with two primary inflows(Pipeline and Upper Canal) along the western marginand four outlets around the perimeter (Fig. 1B). Priorto 1996 both of the inflows discharged directly intoProspect Reservoir. Selective withdrawal from LakeBurragorang is the source of the Pipeline waters.Those of the Upper Canal derive from reservoirs to thesouth of Sydney. The average residence time was lowat ca. 1 month over the 2 years of this study (Febru-ary 1988 to February 1990). Past modeling studiesof Prospect Reservoir have been conducted to predictthe effects of diversion of inflows from the reservoirand the cessation of withdrawals (Hamilton, 1999;Hamilton et al., 1995; Schladow and Hamilton, 1995).

Lake Burragorang is a large reservoir (volume of2 km3, length of 60 km) (Fig. 1C) with a water balancethat is dominated by occasional floods and consistentwithdrawals for Sydney supply. The seven primary in-flows into Lake Burragorang are gauged with most ofthe river confluences further than 30 km from the dam.The long, narrow and deep morphology of the reser-

voir results in the development of horizontal gradientsof dissolved oxygen because of the greater sedimentarea to hypolimnetic volume ratio in the upper reaches(Romero and Imberger, 1999).

3. Description of models

The Dynamic Reservoir Simulation Model (DYR-ESM) is a one-dimensional hydrodynamic modelthat simulates the temperature, salinity, and densityin lakes and reservoirs. The model is based on aLagrangian architecture that models the lake as hor-izontal layers of uniform properties (i.e. temperatureand salinity) (Fig. 2). The layers move vertically, ex-panding and contracting in response to mass fluxesand mixing processes. The model explicitly simulatesfundamental mixing mechanisms in stratified lakesand reservoirs (Imberger and Patterson, 1981, 1990).A new version of DYRESM has recently been com-pleted with updates to the mixing parameterisationsand a upgrade to the model architecture (Gal et al.,2003) to allow coupling to an aquatic ecologicalmodel (see the subsequent text). The updated versionof DYRESM has been successfully applied to simu-late the seasonal thermal evolution of lake Kinneretover 3 years (Gal et al., 2003).

The Estuary and Lake Computer Model (EL-COM) is a 3D hydrodynamic model (Fig. 2) that hasbeen applied to investigate internal waves in lakes(Hodges et al., 2000) and floods in reservoirs (Romeroand Imberger, 2003). ELCOM is based on the un-steady Reynolds-averaged, hydrostatic, Boussinesq,Navier–Stokes, and scalar transport equations withan eddy-viscosity approximation for the horizontalturbulence correlations (Hodges et al., 2000). Themodel solves the unsteady Reynolds-averaged equa-tions using a semi-implicit method with quadraticEuler–Lagrange discretization of momentum advec-tion (Cassulli and Cheng, 1992) and a conservativeULTIMATE QUICKEST approach for scalar trans-port (Leonard, 1991). A one-dimensional mixed-layermodel (Imberger and Patterson, 1990; Spigel et al.,1986) is included for turbulence closure of verticalReynolds stress terms, and thus the turbulent fluxes(Hodges et al., 2000; Laval et al., 2003).

Either of these two hydrodynamic models (DYR-ESM, ELCOM) can be readily coupled to the Com-

J.R. Romero et al. / Ecological Modelling 174 (2004) 143–160 145

Fig. 1. (A) Location of Sydney region in Australia. (B) Bathymetry and locations of inflows, outflows, the meteorological station, andthe sampling station in Prospect Reservoir. (C) Shoreline, river confluences, and sampling stations in Lake Burragorang. (D) Straightenedbathymetry of Lake Burragorang for 3D simulations and corresponding sampling station locations.


Fig. 2. Schematic representation of the modeled nitrogen, phosphorus, and dissolved oxygen processes in CAEDYM. The (∗) symbolrepresents dissolved oxygen consumption from nitrification. Fluxes between the water column, sediment, and atmosphere are depicted aswell as nutrient and dissolved oxygen fluxes from inflows and outflows. Instantaneous atmospheric losses of nitrogen from denitrificationin the water column were modeled. The difference in discretization between the 1D and 3D models is highlighted in the lower portion ofthe diagram.

putational Aquatic Ecosystems Dynamic Model(CAEDYM). CAEDYM contains process descrip-tions for primary production, secondary production,nutrient cycling, and oxygen dynamics (Griffin et al.,2001; Romero and Imberger, 2003). State variables(Table 1) and ecological parameterizations (Table 2)of CAEDYM are similar to those of DYRESM-WQ(Hamilton and Schladow, 1997). CAEDYM was con-figured to simulate the dynamics of phosphorus, nitro-gen, dissolved oxygen, and algae (Fig. 2) with one set

of parameter values (Table 3) for the three cases of thisstudy. CAEDYM has been applied to the winter floodin Lake Burragorang of this study previously (Romeroand Imberger, 2003), and zooplankton–phytoplanktoninteractions in the Swan River Estuary of WesternAustralia (Griffin et al., 2001).

During a simulation, ecological processes (Fig. 2,Table 2) are updated by CAEDYM after each EL-COM time step (i.e. transport, mixing, meteorology,inflows, and outflows). Filterable reactive phospho-


Table 1State variables simulated by CAEDYM

Variable Common name Process description

DO Dissolved oxygen Algal production/respiration, organic decomposition, nitrification,surface exchange, sediment oxygen demand

FRP Filterable reactive phosphorus Algal uptake, organic mineralization, sediment fluxPOP Particulate phosphorus= TP− IP − FRP where TP is

total P and IP is algal internal PMineralization, settling, algal mortality/excretion

NH4+ Ammonium Algal uptake, nitrification, organic mineralization, sediment flux

NO3− Nitrate Algal uptake, nitrification, denitrification, sediment flux

PON Particulate nitrogen= TN − IN − NO3 − NH4 whereTN is total N and IN is algal internal N

Mineralization, settling, algal mortality/excretion

AD Diatoms Growth, respiration, settling, resuspensionAG Greens Growth, respiration, settling, resuspensionAB Blue-greens Growth, respiration, settling, resuspensionADmass Areal mass of diatoms on the sediments Settling, resuspensionAGmass Areal mass of green algae on the sediments Settling, resuspensionABmass Areal mass of blue-green algae on the sediments Settling, resuspension

Water temperatures (T) and currents modeled by the hydrodynamic models DYRESM and ELCOM. In the table above a list of modeledmechanisms for each state variable is provided. All state variables also have inflow and withdrawal fluxes. The units of the state variablesare: DO, mg DO l−1; FRP/POP, mg P l−1; NH4

+/NO3−/PON, mg N l−1; Ai, mg chla l−1; Aimass, g chlam−2.

rus (FRP) processes include algal uptake, sedimentfluxes, and organic matter mineralization. Phyto-plankton uptake of dissolved inorganic nitrogen wasanalogous to FRP except for a preference factor(PN) for NH4

+ over NO3−, and the additional pro-

cesses of nitrification and denitrification. Sources ofparticulate organic nitrogen (PON) and phosphorus(POP) included inflows, algal mortality and excre-tion, and settling from higher layers, while miner-alization and settling to lower layers were losses.Because shear stresses have not been validated withDYRESM, no resuspension of detritus was modeled.However, algal resuspension was simulated throughapplication of a small critical shear stress (Table 3)to ensure that phytoplankton at the sediment–waterinterface in shallow regions remained in suspension.The bed shear stress was computed with currentsfrom the hydrodynamic drivers, and CAEDYM es-timates of the orbital velocities from surface waves(Romero et al., 2003). The oscillatory currents gen-erated by surface waves only influence the upperseveral meters of the water column. Hence, any phy-toplankton that settled onto the sediments of theupper several meters of the water column were sim-ulated to resuspend because of surface waves duringmoderate winds. Simulated dissolved oxygen (DO)processes included exchange across the air–water

interface, photosynthetic production, phytoplank-ton respiration, organic matter decomposition, andnitrification.

CAEDYM was configured with three phyto-plankton groups (diatom, chlorophyte, cyanophyte)representative of Prospect Reservoir and Lake Bur-ragorang (Fig. 2, Table 1). CAEDYM tracks nitrogenand phosphorus in algae (IP, IN), organic matter(POP, PON), and dissolved inorganic forms avail-able for algal uptake (FRP, NH4+, NO3

−) to ensuremass balance of nutrients. In this application, con-stant internal nutrient to algal biomass ratios (YP:chla,YN:chla) were modeled, though CAEDYM can alsobe configured to simulate dynamic nutrient to chlaratios. All of the nutrient state variables are explic-itly tracked to ensure mass balance, regardless ofwhether dynamic or static internal nutrients are mod-eled. PON and POP were modeled as one detritalparticle with the same parameters for decompositionand settling (Table 3). In short, the simplest nutrientcycle (nutrient→ algae → detritus → nutrient inFig. 2) is examined as an ecological representationof the meso-oligotrophic reservoirs considered in thisstudy. Many of the model parameters in this applica-tion were ‘fixed’ from previous studies though severalhad to be calibrated over the three model settingsconsidered here (Table 3).


Table 2A summary of the differential equations for the nitrogen, phosphorus, dissolved oxygen, and algal cycles in CAEDYM

Nitrogen

∂NO3−

∂t= −

D,G,B∑i

µi(1 − PNi)YN:chlaAi

︸︷︷︸algal NO3 uptake

+{knf(T)

DO

Kn + DONH4

+}

︸︷︷︸nitrification

−{kdf(T)

Kd

Kd + DONO3

−}

︸︷︷︸denitrification

∂NH4+

∂t= −

D,G,B∑i

µiPNiYN:chlaAi

︸︷︷︸algal NH4 uptake

−{knf(T)

DO

Kn + DONH4

+}

︸︷︷︸nitrification

+{kANKON + kONDO

KON + DOf(T)PON

}︸︷︷︸

organic matter mineralization

+ f(T)SNH4KDOS/(KDOS + DO)

h︸︷︷︸dissolved sediment flux

∂PON

∂t= vN

�zPON︸︷︷︸

settling

−{[

kANKON + kONDO

KON + DO

]f(T)PON

}︸︷︷︸


+D,G,B∑

i

krf(T)YN:chlaAi

︸︷︷︸mortality and excretion

PNi =[

NH4+NO3

−

(NH4+ + KNi)(NO3

− + KNi)

] [NH4

+KNi

(NH4+ + NO3

−)(NO3− + KNi)

]

Phosphorus

∂FRP

∂t= −

D,G,B∑i

µiYP:chlaAi

︸︷︷︸algal FRP uptake

+{[

kAPKOP + kOPDO

KOP + DO

]f(T)POP

}︸︷︷︸


+ f(T)SPKDOS/(KDOS + DO)

h︸︷︷︸dissolved sediment flux

∂POP

∂t= vP

�zPOP︸︷︷︸

settling

−{[

kAPKOP + kOPDO

KOP + DO

]f(T)POP

}︸︷︷︸


+D,G,B∑

i

krf(T)YP:chlaAi

︸︷︷︸mortality and excretion

Dissolved oxygen

∂DO

∂t= ka(Oa − DOw)︸︷︷︸

air–water flux

+D,G,B∑

i

{µi(1 − kp) − krif(T)}YO:CYC:chlaAi

︸︷︷︸algal oxygen consumption/production

− f(CDO)︸︷︷︸mineral

− knf(T)DO

Kn + DONH4

+YO:N︸︷︷︸nitrification

− f(T)FSODDO/(DO + KSOD)

h︸︷︷︸sediment oxygen demand

Phytoplankton groupAi where i = D for diatoms,i = G for green algae andi = B for blue-green algae

∂Ai

∂t=

µi︸︷︷︸growth

− krif(T)︸︷︷︸respiration

+ vi

�z︸︷︷︸settling

Ai + αA(τ − τcA)/τrAimass/(Aimass+ Kimass)

h︸︷︷︸resuspension

µi = fi(T)µmaximin[f(I), f(N), f(P)] fi(T) = θT−20 + θk(T−a) + b

f(P) = FRP

FRP+ KPif(N) = NH4

+ + NO3−

NH4+ + NO3

− + KNi

f(I) = 1 − exp

(− I

IK

)f(T) = θT−20

The rates of change of these state variables from transport and mixing are simulated by the hydrodynamic drivers (1D DYRESM, 3DCAEDYM), and are not listed here. Algal groups include diatoms, chlorophytes (green algae), and cyanophytes (blue-green algae), whichwere configured with constant internal nutrient to chla ratios. The variableh is the layer (1D) or grid cell (3D) thickness above thesediments and�z is the layer or grid cell thickness in the water column. DOw is the dissolved oxygen in the surface layer. The termf(CDO) represents dissolved oxygen consumption from organic matter decomposition (DO sub-model). The organic carbon sub-model isnot described here because it is beyond the scope of this study but is detailed inRomero et al. (2003). PNi is the NH4 preference factorof algal groupi over NO3. τr is a reference shear stress set to 1 N m−2.


Table 3Nitrogen and phosphorus parameters used in the simulations with all rates at 20◦C and temperature multipliers (θ) set to 1.08 (Ambroseet al., 1988), except for algal temperature functions

Parameter Value Description References/remarks

DOOa Equation Equivalent DO at air–water interface (mg DO l−1) Riley and Skirrow (1975)ka Equation Transfer coefficient from equation Wanninkhof (1992)KDOS 0.5 DO 1/2 saturation constant for nutrient sediment

fluxes (mg DO l−1)Pickering (1994)

FSOD 0.3 Static DO consumption by sediments (g DO m2 perday)

Pickering (1994)

KSOD 5.0 DO 1/2 saturation constant for sediment oxygendemand (mg DO l−1)

Calibrated

FRP, POPYP:chla 0.3 Fixed algal P to chla ratio (mg P (mg chla)−1) Stoichiometry relationkOP 0.010 Aerobic POP mineralization rate (per day) Schladow and Hamilton (1997)kAP 0.003 Anaerobic POP mineralization rate (per day) 30% of aerobic rate, askAN

vPa −0.1 Settling velocity of POP (m per day) Calibrated

SP 0.0005 Maximum FRP sediment flux (g P m−2 per day) Pickering (1994)KOP 5.0 DO 1/2 saturation constant for POP mineralization

(mg DO l−1)Calibrated

NO3, NH4, PONYN:chla 9.0 Fixed algal N to chla ratio (mg N (mg chla)−1) Stoichiometry relationkON 0.010 Aerobic PON mineralization rate (d−1) As kOP

kAN 0.003 Anaerobic PON mineralization rate (per day) AskAP

vNb −0.1 Settling velocity of PON (m per day) AsvP

SNH4 0.01 Maximum NH4 sediment flux (g N m−2 per day) Pickering (1994)YO:N 3.43 Nitrification stoichiometry ratio of DO to N

(mg DO (mg N)−1)Stoichiometry relation

kn 0.02 Nitrification rate (per day) Hamilton and Schladow (1997)Kn 2.0 DO 1/2 saturation constant for nitrification (mg DO l−1) Calibratedkd 0.01 Denitrification rate (per day) CalibratedKd 0.5 DO 1/2 saturation constant for denitrification

(mg DO l−1)As SP

KON 5.0 DO 1/2 saturation constant for PON mineralization(mg DO l−1)

As KOP

Diatoms (D), green algae (G), blue-green algae (B)YO:C 2.67 Photosynthetic stoichiometry ratio of DO to C

(mg DO (mg C)−1)Stoichiometry relation

YC:chla 40 Ratio of C to chla (mg C (mg chla)−1) Griffin et al. (2001)kp 0.014 Fraction of algal DO lost to photosynthetic respiration Ambrose et al. (1993)µmaxD,G,B 1.3, 0.8, 1.1 Maximum growth rates of algae (per day) USCE (1995)krD,G,B 0.14, 0.09, 0.07 Algal respiration, mortality, and excretion (per day) Schladow and Hamilton (1997)KPD,G,B 0.007, 0.005, 0.008 P 1/2 saturation constant for algal uptake (mg P l−1) 0.002–0.05 (USCE, 1995),

calibratedKND,G,B 0.06, 0.06, 0.03 N 1/2 saturation constant for algal uptake (mg N l−1) Schladow and Hamilton (1997)IKD,G,B 60c, 100, 130 Light 1/2 saturation constant for algal limitation

(�E m−2 s−1)Hamilton and Schladow (1997)

vD,G,B −0.05,−0.02,−0.01 Algal settling velocities (m per day) CalibratedTSTD,G,B 16, 20, 20 Standard temperature for algal growth (◦C) Griffin et al. (2001)TOTD,G,B 20, 28, 30 Optimum temperature for algal growth (◦C) Griffin et al. (2001)TMTD,G,B 29, 35, 39 Maximum temperature for algal growth (◦C) Griffin et al. (2001)kD,G,B 2.4, 3.2, 2.2 Exponent parameter 1 for algal temperature function ComputedaD,G,B 24.7, 30.2, 30.1 Exponent parameter 2 for algal temperature function Computed


Table 3 (Continued)

Parameter Value Description References/remarks

bD,G,B 0.20, 0.08, 0.18 Offset parameter for algal temperature function ComputedτCD,G,B 0.001 Critical shear stress for algal resuspension (N m−2) Low critical shear stress

ensures algal resuspension inthe upper water column

αD,G,B 0.000008 Resuspension rate of algae (g chlam−2 s−1) High so rate not limitingKmassD,G,B 0.00001 1/2 saturation constant of available phytoplankton

mass on sediments for algal resuspension (g chlam−2)Low, so all available algaeresuspended

The parameters (a, b, and k) for the algal growth temperature function were determined so that maximum productivity occurred atTO

(optimum temperature), zero productivity was simulated at the lethal temperature ofTM (maximum temperature), and the power relationproportional toθT −20 was modeled belowTS (the standard temperature). Higher PON and POP settling rates (vP and vN) for the 3Dsimulations are footnoted. ‘Calibrated’ parameters in italics were varied during simulations to provide good validation results for bothreservoirs. All other parameters were assigned values based on previous studies and a literature review.

a PON settling velocity set to−2 m per day for the 3D flood simulation.b POP settling velocity set to−5 m per day for the 3D flood simulation.c Ritchey and Romero (unpublished data).

4. Simulation inputs

Meteorology from both reservoirs was not availableover the simulation period, so measurements of short-

Fig. 3. (A) Gauged daily discharge from Warragamba Pipeline and Upper Canal, and total withdrawals of Prospect Reservoir from 1988to 1990. Measured daily to weekly (B) temperature, (C) FRP, (D) TP, (E) NH4

+, (F) NO3−, and (G) TN.

wave radiation, net longwave radiation, air tempera-ture, and vapour pressure from on-lake stations dur-ing 2002–2003 were used. Daily wind run and rainfallwere available from the shoreline of Prospect Reser-


voir during 1988–1990 and nearby weather stationsfor Lake Burragorang during 1992–1994, and servedas inputs.

In Prospect Reservoir from February 1988 to Febru-ary 1990, both the Upper Canal (200 ML per day)and the Pipeline (1000 ML per day) discharged di-rectly into the reservoir (Fig. 3A). Both of these in-flows were sampled frequently (daily to weekly) fortemperature, dissolved oxygen, and nutrients (Fig. 3).Temperature (Fig. 3C) and phosphorus levels (Fig. 3Cand D) in the Pipeline were higher in the second yearbecause withdrawals were extracted from the met-alimnion (1989–1990) rather than the hypolimnion(1988–1989) of Lake Burragorang. The high NH4 ofthe Upper Canal was from chloramination prior to dis-charge into Prospect Reservoir (Fig. 3E).

From July 1992 to July 1994 drought conditionsin Lake Burragorang led to a low average inflow of500 ML per day (Fig. 4A) relative to the long-term3-year average of ca. 4000 ML per day from 1960 to

Fig. 4. Daily discharge from July 1992 to July 1994 of (A) two of the primary inflows (Wollondilly and Kowmung Rivers) into LakeBurragorang and (B) withdrawals at the dam. (C) Measured water temperatures at the Wollondilly gauging station, and representativeestimates of nutrient inputs for the Wollondilly and Kowmung Rivers of (D) FRP, (E) TP, (F) NH4

+, (G) NO3−, and (H) TN.

2002. The residence time over the 2 years of the sim-ulation period was 8 years, substantially longer than 1month of Prospect Reservoir. Withdrawals of 1000 MLper day at 30–50 m below the surface corresponded tothe upper hypolimnion during seasonal thermal strat-ification (Fig. 4B). Inflow data for the rivers nearthe confluence with Lake Burragorang is temporallysparse because of the remote and rugged nature of thelower catchments. Inflow biogeochemistry was esti-mated from correlations with discharge and water tem-peratures for the seven rivers (Fig. 4C–G). No sub-stantial floods occurred over these two drought years,so details of these inflow estimates are not providedhere.

A well-monitored flood event from late June tomid-July 1997 in Lake Burragorang served as thethird case of this study for a 3D simulation withELCOM–CAEDYM. A detailed description of thefield study and validation of ELCOM–CAEDYM forthis flood is given elsewhere (Romero and Imberger,


Fig. 5. (A) Gauged discharge of the major rivers during the 1997 flood event and examples of the measurements from the WollondillyRiver station including (B) river temperature, (C) FRP, and (D) PON(= TN − NO−

3 − NH+4 ). Lines on FRP and PON plots are based on

regression relations as a function of discharge.

2003). Approximately 90% of the discharge duringthis flood event was from the Wollondilly River,which had a gauging station and automatic watersampler. Hence the total fluxes of water (Fig. 5A),heat (Fig. 5B), and nutrients (Fig. 5C and D) into thereservoir were well measured for this flood event.

5. Results and discussion

5.1. 1D simulation of Prospect Reservoir

The stratification and biogeochemical dynamics ofProspect Reservoir were largely controlled by exter-nal forcing from the inflows (Warragamba Pipelineand Upper Canal with 80 and 20% of the discharge,respectively) and withdrawals (supply to the city ofSydney) that led to a low residence time (ca. 30 days).DYRESM–CAEDYM reproduced thermal stratifica-tion well over the 2 years (Fig. 6A) with hypolimnetictemperatures maintained at ca. 15◦C by the War-ragamba Pipeline inflows (Fig. 3B). Constant Pipelinetemperatures occurred over the 2 years because thesource of these inflows was withdrawals from the up-per hypolimnion of Lake Burragorang (consistent tem-

perature) with subsequent transport via the Pipeline(prevention of atmospheric heating). In contrast, theUpper Canal inflows had strong temperature seasonal-ity (Fig. 3B), therefore these inputs inserted above thehypolimnion during thermal stratification (Fig. 6A).High DO in both inflows (not shown) maintainedoxic conditions in both the surface and bottom watersin correspondence with field profiles (Fig. 6B). Thesimulated total phosphorus (TP= FRP+ POP+ IP,where IP = YP:chla chla) and FRP reproduced thefield data, except the modeled FRP was too low inthe summer mixed layer (Fig. 6C and D). Variationsof FRP and TP in the reservoir tracked external phos-phorus inputs of the two major inflows (Fig. 3C andD), which again highlighted the dominance of exter-nal forcing on Prospect Reservoir. NH4

+, NO3−, and

total nitrogen (TN= NH4+ + NO3

− + PON+ IN,where IN= YN:chla chla) also followed the observedseasonal patterns (Fig. 6E–G). Though reservoirNO3

− levels (Fig. 6E) tracked the concentrations ofthe two inflows (Fig. 3F), the high NH4

+ of the Up-per Canal (Fig. 3E) was substantially diluted upondischarge into the reservoir (Fig. 6F).

The phytoplankton biomass (as indexed by chla)was in the range of observations, but was overesti-


Fig. 6. Comparison between field data (symbols) and 1D simulation (lines) of Prospect Reservoir at 2 m (∗, thin line) and 17 m (�, boldline) from 1988 to 1990 for (A) temperature, (B) dissolved oxygen, (C) FRP, (D) TP, (E) NO3

−, (F) NH4+, (G) TN, and (H) chla.

mated during the spring bloom of the second year(algal growth too high) and underestimated in themixed layer during summer stratification (modeledFRP too low) (Fig. 6H). As in other temperate reser-voirs, diatoms are a dominant group in the winterand spring phytoplankton assemblage of ProspectReservoir (Romero, unpublished data). Often a springdiatom bloom is silica (Si) limited, an element notconsidered in this modeling study. However, reac-tive Si levels in the surface waters were in excessof 3 mg SiO2 l−1 from 1988 to 1990 (Romero, un-published data), which precludes Si limitation of thespring diatom bloom. In summary, the algal, nitro-gen, and phosphorus state variables tracked the ob-served seasonal variations with the 1D configuration(DYRESM–CAEDYM). Next the same parameter setwas applied to a simulation of two drought years forthe much larger Lake Burragorang system.

5.2. 1D simulation of Lake Burragorang

The simulation of the 1992–1994 drought repro-duced the observed seasonal patterns of temperature,dissolved oxygen, and nutrients at the surface andbottom of Lake Burragorang (Fig. 7). Thermal strat-ification was reproduced well except in the surfacewaters during autumnal cooling (Fig. 7A), perhapsbecause of the application of the 2002–2003 meteo-rological forcing (except for wind speed and rainfall)to the 1992–1994 period. Simulated dissolved oxygenobservations in the bottom and surface waters werereproduced well (Fig. 7B) with the sediment param-eters from Prospect Reservoir. No observations wereavailable to evaluate FRP over the simulation period,which generally remained low (ca. 0.002 mg P l−1) inthe surface waters except during winter mixing (Fig.7C). Simulated TP was greater than observations


Fig. 7. Comparison between field data (symbols) and 1D simulation (lines) of Lake Burragorang at 3 m (∗, thin line) and 75 m (�, boldline) from July 1992 to July 1994 for (A) temperature, (B) dissolved oxygen, (C) FRP (no field data), (D) TP, (E) NO3

−, (F) NH4+, (G)

TN, and (H) chla.

(Fig. 7D), possibly from overestimation of externalnutrient loading during several small flood events. Hy-polimnetic NO3

− was simulated well with a decreaseeach year during holomixis and relatively constantlevels through seasonal stratification (Fig. 7E). Sur-face NO3

− decreased during seasonal stratificationfrom algal uptake similar to field data. Ammoniumwas reproduced well at the surface though concen-trations were overestimated in the bottom waters(Fig. 7F). Denitrification in the bottom waters of asmall eutrophic lake was hypothesized to maintainconstant hypolimnetic levels of dissolved inorganicnitrogen even with sources from the sediments andorganic matter decomposition during seasonal ther-mal stratification (Krivtsov et al., 2001). Sedimentfluxes of NH4

+ were low in both of the reservoirsof this study (seeFig. 8E), so modeled organic de-

composition rates (the major source of NH4+, see

Fig. 8E) may have been too high in the bottom wa-ters of Lake Burragorang. The inter-annual reductionof TN from 1992/1993 to 1993/1994 was capturedby the simulation (Fig. 7G), but the modeled levelswere lower than observations during the second year.Uncertainties in the external TN loading from the in-flows during small flood events may account for thesediscrepancies between modeled and observed levels.

Simulated chla at the surface (3 m) was greaterthan the field data, but had similar seasonal patterns(Fig. 7H). Diatoms dominate the spring phytoplank-ton bloom in Lake Burragorang (Romero, unpublisheddata), so that inclusion of Si dynamics into CAEDYMmay reduce the algal biomass peak via limitation bythis third macronutrient. Diatom blooms not only leadto a depletion of Si in the surface waters, but also


Fig. 8. (A) Cumulative change of FRP at 2 m during the 1D simulation of Prospect Reservoir. The total change (mg l−1 per simulation)over the model runs of 730 days for Prospect Reservoir at 2 and 17 m, and Lake Burragorang at 3 and 75 m are illustrated for eachprocess for the state variables of (B) FRP, (C) POP, (D) NO3

−, (E) NH4+, and (F) PON. Processes depicted include algal uptake (uptak),

organic matter mineralization (miner), dissolved sediment flux (sedms), algal excretion and mortality into the organic matter pool (ex/mo),nitrification (nitri), denitrification (denit), and vertical mixing (hydro).

of phosphorus levels through sedimentation, therebydecreasing phosphorus availability and phytoplanktonconcentrations in the summer (Krivtsov et al., 2000,2001). Next, biogeochemical rate output is used toidentify the dominant mechanisms of the nitrogen andphosphorus cycles.

5.3. Seasonal biogeochemical fluxes

CAEDYM allows one layer (i.e. depth) to be nomi-nated during 1D simulations to output the incrementalchange to state variables from each fate mechanismduring every time step. This process-based output at3 and 17 m for Prospect Reservoir and 3 and 75 m forLake Burragorang was used to identify the dominantbiogeochemical flux paths in the surface and bottomwaters of these two systems.

As the same parameter set was used in both sim-ulations of these reservoirs, a comparison of nutrientfluxes may provide insight into reservoir biogeochem-

ical cycling. For example, FRP at 2 m below the sur-face in Prospect Reservoir was simulated to have algaluptake as the only loss balanced by mineralization oforganic matter and hydrodynamics (Fig. 8A). Hydro-dynamics includes fluxes from inflows and outflowsin addition to transport and mixing. If these results areexpressed as the cumulative change in nutrient concen-tration over the duration of the simulation (2 years, 730days), then comparisons between reservoirs (Prospectand Burragorang) and depths (surface and bottom wa-ters) can identify the dominant fate mechanisms. Algaluptake was the only loss of FRP in the surface watersof both reservoirs, which was balanced by replenish-ment through transport (i.e. vertical mixing) and insitu POP mineralization (Fig. 8B). In the bottom wa-ters POP mineralization was the dominant source bal-anced primarily by dilution during winter holomixis.In the surface waters, algal mortality and excretion wasthe dominant source of POP and mineralization wasa major loss mechanism in both reservoirs (Fig. 8C).


However, settling losses of POP from the surface wa-ters of Lake Burragorang were large though substan-tial replenishment occurred during winter holomixis(vertical transport to the surface). In contrast, POP inProspect Reservoir was much more influenced by thehydrodynamics (inflow and outflows in particular).

Nitrogen cycling in these two systems was similarto phosphorus, though the additional mechanisms ofnitrification and denitrification also contributed sub-stantial fluxes. NO3− at the surface of both reservoirsdecreased primarily from algal uptake and to a lesserdegree denitrification, whereas nitrification was theprimary source (Fig. 8D). PON mineralization was theprimary source of NH4+ in the surface waters, whereasnitrification and algal uptake were the primary losses(Fig. 8E). The dominant flux paths for PON cycling(Fig. 8F) were as POP (Fig. 8C). Because oxygen lev-els did not approach anoxia in the bottom waters, dis-solved NH4

+ (Fig. 8E) and FRP (Fig. 8B) fluxes werenegligible from the sediments.

In general, the process-based rates for nitrogencycling in Prospect Reservoir were similar to LakeBurragorang with a few exceptions. Nitrificationwas substantially greater in Prospect Reservoir be-cause of the high NH4+ loading from the UpperCanal. Settling losses from the surface waters andsubsequent replenishment during winter holomixiswas greater in Lake Burragorang because of greaterdependence on internal cycling during the droughtconditions (low inflow, long residence time). Lastly,inflows (i.e. hydrodynamics) increased PON and POPin Prospect Reservoir, whereas in Lake Burragorangconsistent withdrawals decreased particulate nutrientlevels.

5.4. 3D simulation of a winter flood through LakeBurragorang

As in many water supply reservoirs, flood eventsprovide most of the water into Lake Burragorang,which can cause short-term (weeks) degradation inwater quality. Because physical processes such astransport, mixing, and settling largely control spatialand temporal distributions of nutrients during floods;accurate modeling of such events requires 3D sim-ulation. Next a 3D simulation of a moderate-sizedflood with the same parameter set as the prior 1Dsimulations is described.

The June 27–28, 1997 flood resulted in a cool nu-trient laden underflow that traversed the reservoir inca. 7 days (Fig. 9, left panels). The underflow clearlydominated the biogeochemistry during and severalweeks after the event. The cool well-oxygenated un-derflow displaced the pre-flood hypolimnion upwards,which resulted in a mid-depth anoxic region. Highlevels of nutrients from the underflow remained as a‘pool’ ‘trapped’ in the profundal regions of the reser-voir near the dam. Particle settling was evident fromthe large decrease of PON from the gauging station(Fig. 5C) to the mid-reservoir site with a subsequentmodest decrease to the dam (Fig. 9). Such winterunderflow events, if cool and turbid, may cause per-sistent stratification through the winter months (Ferrisand Tyler, 1992).

Because of the complex bathymetry of Lake Bur-ragorang (Fig. 1C), model idealizations (Fig. 1D) ofthe actual reservoir morphometry were used to im-prove simulations and reduce run times. The ideal-ization involves ‘straightening’ the basin morphologyso that the Cartesian grid is aligned with the stream-wise and cross-stream axes (Romero and Imberger,2003). A comparison with field profiles at two stations(DWA2 and DWA27 inFig. 1C and D) served to vali-date the accuracy of the modeled underflow movementand biogeochemical concentrations.

The only modifications to the biogeochemical pa-rameters from the previous 1D simulations were in-creases to the settling velocities of POP and PON(Table 2). Floods convey particles with different char-acteristics into Lake Burragorang than internal particlesources such as algal mortality. The 1D drought simu-lation compared well with field data because the riverdischarge was low over the 2 years from 1992 to 1994,and thus did not input large loads of riverine PON. Inorder to model flood events, at least two organic par-ticle size classes are needed to account for differentialsettling between external (riverine) and internal (algaldetritus) sources of particulate nutrients.

Comparisons of isopleths of T, DO, and PON be-tween field and simulation profiles at mid-reservoir(DWA27 at 20 km from the dam) and near dam(DWA2 at 0.5 km from the dam) stations illustratethat ELCOM–CAEDYM reproduced the event well(Fig. 9, right panels). In particular, the upward dis-placement of the anoxic hypolimnion and the tempo-ral and spatial evolution of PON were captured. Next,


Fig. 9. Field data (left panels) compared to model output (right panels) at two stations (DWA27 and DWA2,Fig. 1) for temperature, DO,and PON during the 1997 flood event.


Fig. 10. The two dominant mechanisms for changes to the state variables of (A) NO3 (hydrodynamics and nitrification) (B) FRP(hydrodynamics and mineralization), and (C) POP (hydrodynamics and settling) during the 1997 flood simulation at 10 m above thesediment–water interface at DWA27 are represented as the cumulative change over the model run.

quantification of the most important processes is ad-dressed with the biogeochemical rate output from the3D simulation.

Similar to the 1D simulations, a time series grid cellcan be nominated through the CAEDYM user inter-face to output the rate of change of state variables fromfate mechanisms during 3D simulations. The time se-ries grid cell was configured at 10 m above the bottomat the mid-reservoir station (ca. DWA27). The hydro-dynamics (in particular advective transport) was theprimary mechanism that caused variations of NO3

−,FRP, and POP during the flood (Fig. 10). The nextdominant processes for NO3

− and FRP was nitrifica-tion and mineralization, respectively, though substan-tially less important than advective transport. For POP,settling contributed to lower concentrations within thehead of the underflow as it passed the time series gridcell. In summary, during physical perturbations suchas floods, the hydrodynamics cause most of the spa-tial and temporal variations of state variables. Henceaccurate hydrodynamic numerical modeling is a pre-cursor to confident simulation of the nutrient cyclingduring and after such events.

6. Conclusions

There are advantages and disadvantages to usingcomplex mechanistic biogeochemical models such asCAEDYM relative to other modeling approaches. Byincorporating most of the dominant mechanisms intoa general plankton model, biogeochemical fluxes canbe quantified. By extension, this may provide insight

into the development of suitable amelioration strate-gies for poor water quality settings. A major disad-vantage to these complex models is that a sensitivityanalysis over the entire parameter space is impossible.Establishment of generic parameter values for gen-eral plankton models has remained elusive. The appli-cation of CAEDYM with one parameter set coupledto the 1D hydrodynamics model, DYRESM, to thesetwo differing reservoirs provided the following con-clusions.

(1) A simple ecological configuration (nutrients, par-ticulate organic matter, and phytoplankton) withone parameter set is sufficient to capture mostof the seasonal and interannual vertical variabil-ity of nitrogen and phosphorus levels in thesemeso-oligotrophic reservoirs. However, improvedparameter estimation for algal growth, particlesettling, particle resuspension, and dissolved sed-iment fluxes is needed. Incorporation of the Sibiogeochemical cycle is also needed to capture thespring diatom bloom and sedimentation dynamicsthat causes a depression in summer phosphorusconcentrations (Krivtsov et al., 2000, 2001).

(2) ‘Particle closure’, whereby the same parametervalues for settling and decomposition of par-ticulate organic nutrients are prescribed, yieldsagreement with field observations for these tworeservoirs under 1D conditions.

(3) The settling velocities of the particulate organicmatter (i.e. detritus) (Table 3) to simulate the sim-ple nutrient cycle considered here (Fig. 1) weremuch lower than field measurements (USCE,1995). This indicates that an additional state vari-


able for ‘dissolved organic matter’ is needed inCAEDYM.

Application of ELCOM–CAEDYM with the sameparameter set to a flood in Lake Burragorang high-lighted the importance of accurate hydrodynamicmodeling to reproduce temporal and spatial distribu-tions of biogeochemistry during such perturbations.This application also identified a shortcoming ofCAEDYM where only one particle size class for par-ticulate organic matter is currently configurable. Inthe case of floods, the characteristics of particulateorganic matter (i.e. density, diameter, decompositionrates) from riverine sources are substantially differentfrom internal sources such as phytoplankton mortality.

The use of a ‘multi-system’ and ‘multi-hydrodyna-mic driver’ validation approach provided insight intoappropriate ecological representation of lacustrinesystems and parameter values of biogeochemicalprocesses. Improved ecological representation ofCAEDYM to allow for several types of particulateand dissolved organic matter and improved shearstress estimates from DYRESM and ELCOM is un-derway. Future cross-system validation studies willbe extended to other lakes and reservoirs to furtherbound suitable biogeochemical parameter values overa range of lacustrine settings.

Acknowledgements

Amir Deen (Sydney Catchment Authority, SCA)and Robert Craig (SCA) provided data and coordi-nated financial support. Assistance in model appli-cations was provided by Ben Hodges (Universityof Texas, Austin) and Chris Dallimore (CWR) withELCOM; David Hamilton (CWR) and Matt Hipsey(CWR) with CAEDYM; and Alan Imerito (CWR)with DYRESM. This research has been fundedthrough a Sydney Catchment Authority project. Fiveanonymous reviewers and the editor of this special is-sue (Vladimir Krivtsov) are gratefully acknowledgedfor valuable suggestions and comments to improvethe manuscript.

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