Modeling eutrophication kinetics in reservoir microcosms

War. Res. Vol. 31, No. 10, pp. 2511-2519 1997

Publishedby ElsevierScienceLtd Printed in Great Britain 0043-1354/97 $17.00+ 0.00

Pergamon

PII: S0043-1354(97)00082-1

MODELING EUTROPHICATION KINETICS IN RESERVOIR MICROCOSMS PILAR H E R N A N D E Z I*, ROBERT B. AMBROSE JR 2, D A N I E L PRATS 3, E D U A R D O F E R R A N D I S 4 and J. C. ASENSI 3 ~Department af Environmental Sciences and Natural Resources, University of Alicante, 03080 Alicante, Spain; 2U.S. Environmental Protection Agency, Athens, Georgia 30605-2700, U.S.A.; 3Department of Chemical Engineering, University of Alicante, Spain and 4Department of Statistics, University of Alicante, Spain (Received July 1996; accepted in revised form March 1997)

A~tract--This study addresses the question of how a general seasonal eutrophication model, WASP5, can handle daily phytoplankton and nutrient dynamics in perturbed microcosms for 1- to 2-week periods of time. It is intended to explore both the interpretative and the predictive capabilities of conventional kinetic formulations. The general method adopted in this study is to first apply EUTRO5, a component of WASP5, to a well-behaved microcosm, calibrating the parameter values and reformulating equations if necessary. Next, the calibrated model is subjected to testing in other microcosm experiments, with altered parameter va'tues if necessary. Model performance is further explored through sensitivity analyses to try to "explain" the observed kinetics in the experiments, and a two-way ANOVA is finally applied to the simulated vs observed evolution curves of the three main variables, i.e. chlorophyll a, soluble-P and ammonia-N in all experiments and microcosms studied. Published by Elsevier Science Ltd Key words--microcosms, modeling, eutrophication, freshwater phytoplankton

INTRODUCTION

When an isolated phytoplankton community in balance with its environment is presented with higher nutrient concentrations, exponential growth modified by temperature and light is expected. As one of the nutrients becomes limiting, and as light declines due to self-shading, the growth rate should slow and fall below the respiration and death rates, resulting in declining phytoplankton levels. Eventually, nutrient recycling and increased light should let growth rates increase again and reach equilibrium with respiration and death at lower phytoplankton levels. Mathematical relationships have been developed over many years to describe this dynamic behavior in a general way. These relationships have been coded into many models, and parameterized and calibrated to specific situations. The Water Quality Simulation Program, WASP5 (Ambrose et al., 1987), is widely used to investigate dissolved oxygen, eutrophication, and toxicant problems in surface waters. The modeling system includes the programs TOXI5 for investigating toxicant dynamics and EUTRO5 for investigating interactions among dissolved oxygen, nutrients, and phytoplankton. EUTRO5 reflects conventional mod*Author to whom all correspondence should be addressed [Fax: + 34 6 590 3464, E-mail: Pilar.Hernandez(d!aitana.cpd.ua.es].

cling practice for waste load allocation of point and non-point sources of nutrients and organic carbonaceous wastes in rivers, reservoirs, and some estuaries. Recently published applications of EUTRO5 include the Mississippi River (Lung and Larson, 1995) and the inner shelf of the Gulf of Mexico (Bierman et al., 1994). The kinetic interactions in EUTRO5 are intended to simulate seasonal changes in a mixed phytoplankton community in response to unsteady nutrient loads and changing light, temperature, and flow conditions. The equations in EUTRO5, properly parameterized, should be valid for short-term dynamics of specific phytoplankton species. This study addresses the question of how a general seasonal eutrophication model can handle daily phytoplankton and nutrient dynamics in perturbed microcosms for 1- to 2-week periods of time. It is intended to explore both the interpretative and the predictive capabilities of conventional kinetic formulations. THE STUDY SITE

This study deployed a series of "outdoor microcosms" in the Amadorio Reservoir, which is located in the province of Alicante, southeastern Spain. The reservoir covers a surface area of 102.7 ha and contains a maximum capacity of 16 × 106 mL With an average inflow of 0.3 m3/s, the average

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residence time is 1.58 years. The maximum volume is rarely reached, since the region is semi-arid, with 408 mm annual average rainfall and an average annual temperature of 22°C. While the maximum depth near the dam is 50 m, the mean depth is 10 m. Amadorio Reservoir isfed by two main tributaries, River Sella and River Amadorio, and a temporary stream, Arroyo del Querenquel. Because its waters are used primarily for agriculture and domestic supply, the quality must be properly protected. The observed sewage flow is lower than 3 liters/s resulting in a maximum concentration of total phosphorus that is always lower than 0.01 mg/liter, which leads to the classification of this reservoir as mesotrophic with a eutrophic potential (Prats et al., 1992). M O D E L DESCRIPTION

dissolved oxygen in the aquatic environment. EUTRO5 simulates the transport and transformation reactions of up to eight state variables, illustrated in Fig. I. These variables can be considered as four interacting systems: phytoplankton kinetics, the phosphorus cycle, the nitrogen cycle, and the dissolved oxygen balance. The general WASP5 mass balance equation is solved for each state variable. To this general equation, the EUTRO5 sub-routines add specific transformation processes to customize the general mass balance for the eight state variables in the water column and benthos. In this study, benthos is not included. As general information for the reader, the complete WASP5 water column nutrient equations are presented. Some terms were zeroed out when applied to the experimental microcosms. Phytoplankton kinetics

Several physical-chemical processes can affect the transport and interaction among the nutrients, phytoplankton, organic carbonaceous material, and

Phytoplankton kinetics assume a central role in eutrophication, affecting all other systems. The reaction term of phytoplankton is the difference

C2

NO3-N

c.,

non-gving Cl

NH4-N

Cs

Ca l ~

CBOD

DIP

Cs non4Mng OP

c.

KEY:

DO

Settling

1 Fig. 1. EUTRO5 state variable interactions.

Modeling eutrophication kinetics between the growth rate of phytoplankton and their death and settling rates: 0C,

Ot - (Gp - Op - k,,)C4

(1)

where C4 is the phytoplankton carbon (mg carbon/ liter), Gp is the growth rate constant (per day), Dp is the death plus respiration rate constant (per day), and k~ is the settling rate constant (per day). The rates and quantities refer to the total mixed phytoplankton community. The balance between the growth rate and the death rate (together with the transport, settling, and mixing) determines the rate at which phytoplankton mass is created. A simple measure of total biomass that is characteristic of all phytoplankton, chlorophyll a, is used as the aggregated variable. For internal computational purposes, however, EUTRO5 uses phytoplankton carbon as a measure of algal biomass. The growth rate expresses the rate of production of biomass as a function of temperature, light, and nutrients. The specific growth rate, Gp, is related to k~¢, the maximum 20°C growth rate at optimum light and nutrients, via the following equation: Gpl = klcXRTXRIXRN

(2)

where XRT is the temperature adjustment factor (dimensionless), XR~ is the light limitation factor as a function of l , f , D , and K~ (dimensionless), XRN is the nutrient limitation factor as a function of dissolved inorganic phosphorus and nitrogen (DIP and DIN) (dimensionless), T i:~ the ambient water temperature (°C), I is the incident solar radiation (ly/day), f is the fraction day between sunrise and sunset (unitless), D is the depth of the water column (m), K~ is the total light extinction coefficient (per m), DIP is the dissolved inorganic: phosphorus (orthophosphate) available for growth (mg/liter), and DIN is the dissolved inorganic nitrogen (ammonia plus nitrate) available for growth (mg/liter). The maximum growth rate constant is adjusted throughout the simulation for ambient temperature, light, and nutrient conditions. Water temperature has a direct effect on the phytoplankton growth rate. The selected maximum growth rate is temperature-corrected using temporally and spatially variable water column temperatures according to the following expression: XRT = o r - 20

(3)

where O¢ is the temperature coefficient (unitless). In the natural environment, the light intensity to which the phytoplankton are exposed is not uniformly distributed with depth at the optimum value. Modeling frameworks developed by Di Toro et al. (1971) and by Smith (1980), extending upon a light curve analysis formulated by Steele (1962), account for both the effects ,of supersaturating light intensities and light attenuation through the water column. The instantaneous depth-averaged growth rate reduction

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developed by Di Toro is obtained by integrating the specific growth rate over depth:

XR, = e_E_f K~D

leAp{ I.xp, "O't exp(/7)1 ,4, where/, is the average incident light intensity during daylight hours just below the surface, assumed to average 0.9 l / f (ly/day), /, is the saturating light intensity of phytoplankton (ly/day), Ke is the light extinction coefficient, computed from the sum of the non-algal light attenuation, Kc, and the phytoplankton self-shading attenuation, K0shd (per m). K~hd is calculated by: K,shd = 0.0088 (Chla) + 0.054 (Chla) °67

(5)

where Chla is the phytoplankton chlorophyll concentration (gg/liter). The term Is, the temperature-dependent light saturation parameter, is an unknown in the Di Toro light formulation and must be determined via the calibration-verification process. The effects of various nutrient concentrations on the growth of phytoplankton is modeled using Monod growth kinetics. At an adequate level of substrate concentration, the growth rate proceeds at the saturated rate for the ambient temperature and light conditions present. At low substrate concentration, however, the growth rate becomes linearly proportional to substrate concentration. Because both nitrogen and phosphorus are considered in this framework, the Michaelis-Menten expression is evaluated for the dissolved inorganic forms of both nutrients and the minimum value is chosen to reduce the saturated growth rate: ( DIN DIP XRN = Min ~KmN~ E)IN' Km~~ h i P ]

(6)

where Kms is the half-saturation constant for inorganic nitrogen (mg/liter), and K~p is the half-saturation constant for inorganic phosphorus (rag/liter). Numerous mechanisms contribute to the biomass reduction rate of phytoplankton: endogenous respiration, grazing by herbivorous zooplankton, and parasitization. If the respiration rate of the phytoplankton as a whole is greater than the growth rate, there is a net loss of phytoplankton carbon or biomass. The endogenous respiration rate is temperature dependent and is determined by: kR(T) = kR(20°C)O~r- 2o)

(7)

where kR (20°C) is the endogenous respiration rate at 20°C (per day), kR(T) is the temperature corrected rate (per day), and OR is the temperature coefficient (dimensionless). The total biomass reduction rate for

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the phytoplankton is expressed as: D = kR(T) + ko + k~Z(t)

(8)

where D is the biomass reduction rate (per day), kD is the death rate, representing the effect of parasitization and toxic materials (per day), kc is the grazing rate on phytoplankton per unit zooplankton population (liters/mg-C per day), and Z(t) is the herbivorous zooplankton population grazing on phytoplankton (mg-C/liter). The settling effect of phytoplankton on biomass reduction is not considered here, since manual agitation is performed every sampling day during the experimental periods. The nutrient uptake kinetics are expressed by their corresponding mass balance equations much in the same way as is done for the phytoplankton biomass. Once their stoichiometric ratios have been determined, phosphorus and nitrogen are expressed as unit carbon biomass. The constant stoichiometry is a simplification, as ratios can be expected to vary among species and in response to different nutrient regimes. The phosphorus cycle

Three phosphorus variables are modeled: phytoplankton phosphorus, organic phosphorus, and inorganic (orthophosphate) phosphorus. Organic phosphorus can be divided into particulate and dissolved concentrations by user-specified dissolved fractions. Inorganic phosphorus also can be divided into particulate and dissolved concentrations by user-specified dissolved fractions, reflecting sorption. The phosphorus equations for phytoplankton P, inorganic P, and organic P are summarized below: Vs4 ~(C4a~) St - G~a~C4 - D~ar, C4 - --~a~C4

plankton. EUTRO5 uses a saturating recycle mechanism that slows the recycle rate, ks3, if the phytoplankton population is small, but does not permit the rate to increase continuously as phytoplankton increase. The assumption is that at higher population levels, recycle kinetics proceed at the maximum first order rate. The default value for Kmpc of 0, which causes mineralization to proceed at its first-order rate at all phytoplankton levels, was used for these experiments, and it was assumed that the sorption of inorganic phosphorus to suspended particulate matter and the settling loss of phosphorus in these experiments was zero. The nitrogen cycle

Four nitrogen variables are modeled: phytoplankton nitrogen, organic nitrogen, ammonium nitrogen, and nitrate nitrogen. A summary of the equations is given below: /)s4 S(C4anc)St- GpancC4 - OpancC4 - -'~ancC4

SC7 St

Dr,a.~fo.C4 --

(12)

k7107rl - 20

SC, = Dva,~(1 -fo,)C4 + k7~OTrt-2° St

(14)

(9)

SC3 -- Dpap~(l -fop) St C, + k,3Or-2°( K,p~+-~4)Cs - Gpa~C4

(10)

- k2oO 0-2°( KNo,

kKNo, + C'4]

SC8 St -- Dpav~C°P

C4 - k830r3- 2°(-g"~pC~ m P c-~ +

v~(1-

(11)

For every milligram of phytoplankton carbon produced, ap~ mg of inorganic phosphorus is taken up. As phytoplankton respire and die, biomass is recycled to nonliving organic and inorganic matter. For every milligram of phytoplankton carbon consumed or lost, aw mg of phosphorus is released. A fraction fop is organic, while (1-fop) is in the inorganic form and readily available for uptake by other viable algal cells. Non-living organic phosphorus must undergo mineralization or bacterial decomposition into inorganic phosphorus before utilization by phyto-

(15)

For every milligram of phytoplankton carbon produced, a.c milligram of inorganic nitrogen is taken up. Both ammonia and nitrate are available for uptake and use in cell growth by phytoplankton; however, for physiological reasons, the preferred form is ammonia nitrogen. The ammonia preference term, PNH3, is calculated internally using an empirically-derived relationship that approaches 1 as NH4-N greatly exceeds KmN, and that approaches 0 as NH~-N approaches 0 in the presence of N O r N . As phytoplankton respire and die, living organic material is recycled to non-living organic and inorganic matter. For every milligram of phytoplankton carbon consumed or lost, a.¢ mg of nitrogen is released. During phytoplankton respiration and death, a fraction of the cellular nitrogenfo, is organic,

Modeling eutrophication kinetics

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Table 1. Summary of experiments Exl~yriment 4: 8-28 April 1992. T = 14--20°C Microcosm 1: Natural water + inorganic nutrients Microcosm 2: Natural water + 1% raw sewage Microcosm 3: Natural water + 1% raw sewage Exl~'.riment 5: 1-15 July 1992. T= 25--25.4°C Microcosm 1: Natural water + 1% raw sewage Microcosm 2: Natural water + 1% P-containing detergent + synthetic sewage Microcosm 3: Natural water + 1% P-free detergent + synthetic sewage Experiment 7: 31 May-28 June 1993. T= 23-25°C l~[icrocosm 1: Natural water + 1% raw sewage l~[icrocosm 2: Natural water + 1% P-containing detergent + synthetic sewage I~[icrocosm 3: Natural water + 1% P-free detergent + synthetic sewage Experiment 8: 3 Nov.-21 Dec. 1993. T= 16.5-12.3°C Microcosm I: Natural water + 1% raw sewage Microcosm 2: Natural water + 1% P-containing detergent + synthetic sewage Microcosm 3: Natural water + 1% P-free detergent + synthetic sewage

while (1 - f o , ) is in the inorganic form o f a m m o n i a nitrogen. Mineralization o f non-living organic nitrogen follows the same type o f kinetics as explained for p h o s p h o r u s with a h a l f s a t u r a t i o n c o n s t a n t Kmpoo f 0, which causes mineralization to proceed at its first-order rate at all p h y t o p l a n k t o n levels. A settling velocity of zero was assumed for these experiments as well as nitrification a n d denitrification rates, since the oxygen levels detected along the water c o l u m n are high a n d the period o f enclosure is not t o o long.

METHODS

Field procedures The evaluation of natural ecosystem behavior by means of enclosing a part of these systems in "outdoor microcosms" has been greatly developed in the last 20 years (Hill et al., 1994). Tile outdoor microcosms used in this study were vertical plastic tubes of l-m diameter and 5-m depth enclosing just under 5 m 3 of water with its indigenous planktonic community, At the bottom, a valve that allows water to enter the cohtmn during placement is closed when the microcosm reaches the vertical position to assure the hermeticity. The upper part of the microcosms is opened to the air and fastened to a buoy system made of PVC pipes. Three microcosms were settled in Amadorio reservoir in an area close to the dam where the water level is always high enough and where the turbulence is expected to be minimal. During a 3-year research project conducted by the University of Alicante, a set of eight field assays was performed to evaluate the impact of urban wastewater on the state of eutrophication in Amadorio Reservoir. Experiments were conducted with treated and untreated wastewater, and with different types of phosphorus-containing and phosphorus-free detergents. The design of the foar experiments selected for the present study is summarized in Table 1. "Raw sewage" is waste water sampled at the intake of the Alicante Sewage Treatment Plant. "Synthetic sewage" refers to a mixture of human metabolic wastes (urine + feces) diluted with tap water and laundry effluent made out of several known detergents at a regularly laundry concentration, heated and mixed at a temperature of 60°C. The microcosms were spiked with the loads by means of a flexible pipe hose ';-m long. Starting from the bottom, several spiral movements were executed before reaching the surface in order to obtain a homogeneous loading distribution along the: enclosed water column.

Sampling and analysis Samples were taken at regular 2-day intervals throughout the 15- to 40-day experimental periods, always after agitation with a 0.4-m-diameter holed plastic disk. Temperature, dissolved oxygen, and Secchi depth were measured in situ with portable devices. Three l-liter bottles were taken from each microcosm and from the reservoir itself. These samples were kept in shade and at low temperature until arriving at the laboratory. One bottle was preserved in acid Lugol's solution for phytoplankton counting under an inverted microscope. The other two bottles were used for nutrient analysis: P, N, Si, and pigment analyses Chla, b, c following the directions given by the APHA Standard Methods for Water and Wastewater Analysis (1989). Nitrogen was analyzed as NH4-N, NOrN, and NO2-N; phosphorus was analyzed as OPO4 (or DIP) and as total P; and silicon was analyzed as SiO3. Statistical methods After calibration and modifications of the model, validation was achieved through a two-way ANOVA, a comparison between empirical and simulated curves for all the microcosms, experiments, and variables. This method was selected for its good ability to compare experimental curves (Cuadras and S~ichez, 1988). The standardized residuals supplied by the two-way ANOVA applied to the curves were found to give a very good fit to the Standard Normal Distribution. The authors chose 0.05 as the critical value for significance level over which the goodness of fit is accepted. RESULTS AND DISCUSSION F r o m the eight experiments conducted d u r i n g 3 years, the model was applied to the four summarized in Table 1. They were conducted in different seasons, giving a great variability of e n v i r o n m e n t a l conditions a n d species composition for the model to simulate. Calibration M i c r o c o s m 1 from experiment 4 was chosen for the first calibration because it c o n t a i n e d natural water with a k n o w n c o n c e n t r a t i o n of inorganic nutrients a n d exhibited a regular evolution in all the variables studies (Figs 2 a n d 3). Three model kinetic parameters were o b t a i n e d directly from experimental data: the m a x i m u m growth rate (k~c), the c a r b o n to chlorophyll ratio (CCHL), a n d the p h o s p h o r u s to c a r b o n ratio (PCRB). The remaining kinetic parameters, as

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P. Hernandez et al. Table 2. Parameters used in calibration process kt~ 3.8~ per day k,T 1.04 LGHTSW 1.0 CCHL 80~ /s~ 500 ly/day Km~ 0.09 mg/liter K~p 0.003 rag/liter k,R 0.150 per day k,er 1.045 k,D 0.06 per day PCRB 0.06 ~ NCRB 0.60 .f,. 1.0 ,fo~ 0.9 ~From empirical data.

indicated in Table 2, were calibrated after several simulations using various combinations of values (always within accepted and published ranges). Table 2 shows the parameters used and Fig. 2 gives the best-fit curves for the evolution in time of the three main variables--Chla, SoI.P DIP, and NH4N--in microcosm 1 of experiment 4 at a first calibration step. For C h l a and for NH4-N, the simulated values are close to those experimentally obtained. For DIP, however, the experimental data revealed more rapidly decreasing concentrations that approached 0 in 5 days; the model was unable to reproduce this rapid decline. EUTRO5 eutrophication routines follow Monod kinetics for nutrient uptake. Modeled phytoplankton growth depends on the nutrient concentrations in the medium and is coupled with its depletion. Some more mechanistic models (Lehman et al., 1975; Jorgensen, 1976, 1981; Bierman et al., 1980; Park et al., 1980) take into consideration not only the "external" nutrient concentration in the surrounding medium, but also the "internal" concentration within the cells (Caperon, 1968; Droop, 1968). As has been observed in laboratory cultures as well as in the field, nutrient uptake is not instantly transformed into cell growth

(Droop, 1968; Caperon and Meyer, 1972; Eppley and Renger, 1974). In fact, phytoplankton cells are able to remove nutrients from the water and store them without showing any simultaneous growth (Marra et al., 1989). Given this evidence, it was decided to modify the EUTRO5 eutrophication kinetics to allow phytoplankton cells to remove DIP at the more rapid, experimentally observed rate, while at the same time predicting the Chla peak as observed on day 7. The modification adopted is based on the concept of "luxury uptake" or "cell quota" (Droop, 1968; Caperon, 1969; Rhee, 1978), whereby phosphorus uptake is governed by external DIP concentrations (C3 in the model), but phytoplankton growth is governed by phosphorus concentrations already incorporated into the intracellular nutrient pool, C3~ (in mg/litre) or C~t (in mg-P/mg-C). This internal phosphorus (phyt-P) is a new state variable that is constrained by user-specified minimum and maximum concentrations per unit phytoplankton, C3,MN and CnMx (in mg/P/mg-C). Internal inorganic phosphorous changes as a function of uptake and loss: 0C~,

0t - UPTAKEa - LOSSp

(16)

Uptake is calculated by UPTAKEp (17) where UpMxis the maximum relative uptake rate per unit biomass carbon (per day) and KmPe is a dimensionless half-saturation constant expressing the effect of the amount of available nutrient per unit biomass carbon on relative growth. The first term in brackets gives the influence of internal Phyt-P on uptake, which varies between 0 when C~, = C3,ux and

zo]

30

25

!::1

,O015

j

~x

~'°I \ ', \xx.

1°5

5-." 0~

..........

°o

Time (days)

2

,

e "-"d ~ ........ "¢o'"""i'2

o

Time (days) b

~

,

~

~ .... ~b

Time (days)

c

Fig. 2. Comparison between model (dashed line) and experimental data (solid line). Experiment 4 microcosm 1. a: chlorophyll a, b: dissolved Inorganic P, c: ammonium-N. First calibration.

~

Modeling eutrophication kinetics

2517

]

25

4

i-

\

~o

c

.........

0

, .........

G

, .........

10

, .........

15

Time (days)

, .........

20

,

2~'

...................................... ~........ ~ Time {days)

c

..............

~..,.'.,.~:'.-..7.7.:'r

dl

8

Time (days)

.....

12

16

Fig. 3. Comparison between model and experimental data. Experiment 4, microcosm 1. After modification of phosphorus kinetics and recalibration for nitrogen dynamics. 1 when C'a, = C3,MN. The second term in brackets gives the influence of external DIP on uptake, which varies between 0 as Ca approaches 0 and 1 as Ca greatly exceeds Kmn C4. Loss of internal phosphorus is calculated from "death" which includes respiration, grazing, and settling, although in the present case, settling is not considered: LOSSp = (Dp + D ) ' G ,

(18)

The phosphorus limitation factor for phytoplankton growth is expressed as a function of internal cell phosphorus: LIMp - C3n- C3UMN C;,

(19)

This model requires four new parameters plus initial conditions for internal phosphorus. The initial conditions for internal phosphorus are expressed per unit biomass carbon, and converted internally to milligram per liter units. From existing publications and model calibration, values for the new parameters were derived and are shown in Table 2. Parameter values for C31Mx, C,MN, and UpMx were taken from Morrison et al. (1987), while values for Cau/C4 and KMpExPwere calibral;ed. With this new modification in the model, a better fit to the three evolution curves was achieved. Validation testing The calibrated model was applied with the same parameters to data from other microcosms and other experimental periods. Recalibration of parameters during this phase is noted and explained. The resulting correspondence with experimental data was assessed both visually and then statistically, through use of a two-way ANOVA.

The data sets first selected for validation were microcosms 2 and 3 from experiment 4. The model output matched very well the empirical data for the evolution of Chla and phosphorus, but simulated a faster NH4-N decline than experimentally observed. Sensitivity tests indicated that a readjustment of parameters related to nitrogen recycling was necessary to obtain a better fit for NH4-N. The model was recalibrated to allow a higher death rate (0.16 per day) and higher recycling fractions returning phytoplankton nitrogen and phytoplankton phosphorus to NH4-N (0.2 per day) during respiration and death. The results much better fit the three data sets from microcosms 1, 2 and 3. In Fig. 3, the three curves from microcosm 1 are shown. Experiment 5 was also tested using the same parameters. The fit for the three main variables-Chla, NH4-N, and DIP--was not quite acceptable. A deeper study of the data revealed that the species composition blooming in this experiment was different from experiment 4. A switch was observed from Cyclotella (round diatoms in experiment 4) to small Chlorococcales in experiment 5. Following a literature review, three parameters were changed - the half saturation constant for nitrogen uptake (KmN = 0.12mg/liter), the nitrogen to carbon ratio (NCRB = 0.5, and the carbon to chlorophyll ratio (CCHL = 40) (Jorgensen et al., 1991). After these changes, the fit was much better. Experiment 7 was well simulated by the model with the original parameters, except for a slight increase in nutrients during the last days of experimentation. This was interpreted as rapid nutrient recycling, probably due to the higher water temperature and, again, the species composition, which included very rapidly growing small Chloroccales and large Oocystaceae undergoing high mortality due to a massive fungal infection. These two characteristics of the phytoplankton could have promoted the faster

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P. Hernandez et al.

nutrient recycling that is observed at the experiment's end. Mineralization rates for organic phosphorus (0.005 per day) and for organic nitrogen (0.02 per day) as well as their temperature-dependent functions (ks3x and k7~x= 1.05) were added at this point to better simulate this phenomenon. These mineralization parameters were afterwards added to the previous data sets and simulations were re-run. Results were virtually unchanged. Experiment 8 was very different from the previous experiments, because it was carried out in late fall, with much lower temperature, weaker light intensity, and different species composition. The model was allowed to run with mineralization rates as specified in experiment 7. A lower light intensity value of 350 ly/day was specified, reflecting the season. The model consistently underpredicted the phytoplankton levels during this period, reflecting an underprediction of growth or an overprediction of death. In a second run, a better fit in experimental data was obtained by reducing the death rate constant from 0.16 to 0.04 per day and by increasing mineralization rates for phosphorus to 0.05 per day and for nitrogen to 0.15 per day. The statistical validation through the two-way ANOVA resulted in acceptable performance for all the variables and microcosms except for phosphorus in microcosm l of experiment 5 and Chla in microcosm 2 of experiment 8, which are in both cases overpredicted by the model. In the first case, the model simulates an earlier limitation of growth by nitrogen which stops the phosphorus consumption at the last part of the experiment, but this limitation is not observed in the empirical data. In the case of experiment 8, the longer enclosure period should be considered. A significant amount of periphyton attachment to the plastic walls was observed and this extra biomass may interfere with the dynamics of the phytoplankton by taking up part of the recycled nutrients at the last phase of the experiment. CONCLUSIONS The rapid short-term dynamic processes of nutrient uptake and phytoplankton growth can be simulated by a modified version of WASP5 and other similar models that include algal internal phosphorus as a state variable. At present, however, the model must be considered a descriptive rather than a predictive tool for simulating short-term dynamics. Factors not directly modeled, such as species composition, affect the parameterization significantly. If enough experiments could be run, it may be possible to derive consistent sets of parameter values to describe the main species or groups. The WASP5 model has demonstrated flexibility as an analysis and interpretative tool, considering that it had to simulate evolution of parameters in pulse-type experiments within an enclosed system.

The absence of dilution or diffusion within the main water body allows the increased nutrient loads to be expressed rapidly by enhanced biomass production and, depending on the temperature, also rapid biomass decline and nutrient recycling. It was a difficult task for a model originally designed to simulate longer-term processes (seasonal) to approach these short-term dynamics. Following the modification done to the phosphorus uptake kinetics (equations 16-19), however, WASP5 was able to achieve acceptable simulations of enclosed systems. Acknowledgements--This study has been supported by CEEP (Centre Europ6en d'Etudes de Polyphosphates) and by a doctoral grant from Education and ScienceCouncil of Generalitat Valenciana, Spain. The first author thanks also the U.S. Environmental Protection Agency at Athens, Georgia, the facility director, Dr Rosemarie C. Russo, and all staff members for assistance provided in the development of the modeling part of the study.

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