AA

HJa

b

a

ARRAA

KENKKC

1

rsToccetlc“aa

ar

0h

Ecological Modelling 265 (2013) 99– 113

Contents lists available at SciVerse ScienceDirect

Ecological Modelling

jo ur nal ho me page: www.elsev ier .com/ locate /eco lmodel

n advanced tool for eutrophication modeling in coastal lagoons:pplication to the Victoria lagoon in the north of Spain

ala Zouitena,∗, César Álvarez Díaza,b, Andrés García Gómeza,b,osé Antonio Revilla Cortezóna, Javier García Albab

University of Cantabria, E.T.S.I. Caminos Canales y Puertos, Avda. de los Castros s/n, 39005 Santander, SpainEnvironmental Hydraulics Institute “IH Cantabria”, C/Isabel Torres n◦ 15, Parque Científico y Tecnológico de Cantabria, 39011 Santander, Spain

r t i c l e i n f o

rticle history:eceived 21 November 2012eceived in revised form 31 May 2013ccepted 4 June 2013vailable online 7 July 2013

eywords:utrophicationumerical modelinetics of phytoplankton

a b s t r a c t

A mathematical eutrophication model, EnvHydrEM (Environmental Hydraulics Institute EutrophicationModel), was developed to be applied specifically to coastal lagoons. This model takes into consider-ation 19 state variables, including phytoplankton, carbon (total inorganic carbon and sediment carbon),phosphorus (organic phosphorus and phosphate), nitrogen (organic nitrogen, ammonia and nitrate), sil-ica (available dissolved and particulate biogenic silica), dissolved oxygen, carbonaceous organic matter,zooplankton, bacterioplankton, detritus, iron (total and ferrous iron) and manganese (total manganeseand manganous ion). EnvHydrEM also describes all possible interactions between the considered vari-ables, showing biological and physicochemical processes that can occur in this type of aquatic systems.These are usually characterized by a series of peculiar aspects which result mainly from the complex

inetics of nutrientsoastal lagoons

interaction between inland and marine waters, as well as from a low hydrodynamic renewal rate. Toprovide an example, the EnvHydrEM model was applied to the study of the eutrophication process inthe Victoria lagoon, in northern Spain. In this case study, the proposed model has proved its ability toreproduce the chlorophyll-a concentration trends in the water body. The study also concluded that theVictoria lagoon is a mesotrophic aquatic media in which silica is the most critical factor for the analysisof its eutrophication state, due mainly to the presence of diatoms.

© 2013 Elsevier B.V. All rights reserved.

. Introduction

Population growth and economic development, observed inecent decades throughout the world, are the main factors respon-ible for many of the environmental changes currently taking place.hese pressures greatly influence aquatic systems and are the resultf multiple human factors (urban, agricultural and industrial dis-harges, sediment accumulation, modification of aquatic systemharacteristics, etc.) which can cause a marked increase in the nutri-nt inputs to the aquatic system. In several parts of the world,his forced enrichment of nitrates (mainly from the agriculturaland washing), ammonia and phosphates (abundant in urban dis-harges) has created an imbalance in aquatic systems known as

cultural eutrophication”, manifested by a large algal productionnd often followed by a decrease in dissolved oxygen, both of whichre harmful to wildlife (Carlson and Simpson, 1996; Rivera, 2002).

∗ Corresponding author. Tel.: +34 942201616x1144; fax: +34 942266361.E-mail addresses: zouitenh@unican.es, h.zouiten@yahoo.fr (H. Zouiten),

lvarezc@unican.es (C.Á. Díaz), garciagan@unican.es (A.G. Gómez),evillaj@unican.es (J.A.R. Cortezón), garciajav@unican.es (J.G. Alba).

304-3800/$ – see front matter © 2013 Elsevier B.V. All rights reserved.ttp://dx.doi.org/10.1016/j.ecolmodel.2013.06.009

Eutrophication can also have a natural origin, especially in sys-tems with low hydrodynamic renewal. Traditionally the field oflimnology has used the terms eutrophic and oligotrophic environ-ments to designate abundance or deficiency of organisms, organicmatter or nutrients. In this regard, eutrophic systems are thosein which the nutrient availability is able sustain a high biomassand, conversely, pristine oligotrophic systems are those in whichthe low availability of these substances limits the development ofbiological activity. Originally, the terms eutrophic and oligotrophicwere used to describe the type of environments from a qualitativepoint of view. Subsequently, other scales were developed basedon phytoplankton abundance in the system, in order to assess thisphenomenon from a quantitative approach. The scientific commu-nity has since been accepted that the “eutrophic degree” of a waterbody is quantified as the average annual concentration of chloro-phyll in that environment (OCDE, 1982; Ryding and Rast, 1992).Vollenweider (1976) was the first author who proposed measuringthe eutrophic degree of an ecosystem by means of the chlorophyll

concentration, a parameter which, in itself, is associated to theincrease in the water nutrient concentration.

Therefore, the evaluation of the trophic state of a waterbody expresses the relationship between nutrient availability and

1 l Mod

petIgt

tavsCttMcnti

lcrmtatateivs

otaimpe

acapteatt

iiagt

ttdamauN

00 H. Zouiten et al. / Ecologica

hytoplankton growth within the system and, consequently, theutrophication process depends mainly on the geological setting ofhe aquatic system and the received nutrient loads (Rivera, 2002).t is affected not only by the contribution of nutrients such as nitro-en and phosphorus, but also by factors such as temperature, pH,urbidity, etc. (Carlson and Simpson, 1996).

Lakes, with very slow renewal rates, were historically the firsto raise scientific interest toward the development of methodsnd tools to analyze their environmental restoration. Among thearious types of lakes we find coastal lagoons, which are tran-itional elements, consisting of both fresh and coastal waters.oastal lagoons occupy approximately 14% of the coastal area ofhe planet, being more numerous in the middle latitudes with lowides and in sand accumulation areas (Mitsch and Gosselink, 1993;

uhammetoglu and Soyupak, 2000). These systems are defined asommon land formations located along the edges of most conti-ents. They are characterized by having restricted connections withhe ocean and long water flushing times, being ephemeral systemsn the geological time scale (Kjerfve and Magill, 1989).

From a biological perspective, the productivity of some coastalagoons is the highest recorded in nature (Knoppers, 1994), due toontinental inputs, the ocean influence, and high sediment inputates. This makes them systems which encourage the establish-ent of large populations of birds, mammals and fish, making

hem major tourist attractions (Day et al., 1989). The fragile bal-nce that often exists between the extreme aquatic dynamics andhe biota is, however, highly vulnerable to human activities andlso to nature itself, since coastal lagoons are periodically exposedo perturbations such as floods and seawater intrusions (Costanzat al., 1993; Kjerfve, 1994). Furthermore, as they are usually locatedn the lower basin areas and affected by human activities, as pre-iously mentioned, their biota are usually submitted to permanenttress.

With this, and given that eutrophication is one of the most seri-us problems that threatens the ecological life of coastal lagoons,here is no doubt that the trophic state analysis of these types ofquatic systems is indispensable. However, due to the peculiar-ty of these water bodies, a much more specific eutrophic analysis

ust be applied, taking into consideration the different types ofrocesses and interactions that can contribute to the coastal lagoonutrophication phenomena.

Moreover, the environmental management of water quality in system requires the use of accurate predictive tools in order toonsider the physical, chemical and biological aquatic processesnd also its environmental conditions. The complex nature of theserocesses and their interactions highlights the need for more effec-ive numerical models to study management strategies for thesecosystems. These models should consider all relevant processesnd their interactions and, of course, must be able to adapt to theime and space scales in which the effects of eutrophication relatedo physical, chemical and biological processes take place.

The application of numerical models has played a significant rolen understanding the causes and mechanisms of water pollution,n general, and eutrophication, in particular. Using these tools hasllowed the establishment of better resource management strate-ies and water control, so as to preserve or restore the quality ofhese complex systems.

In this regard, many models have been used for the eutrophica-ion analysis of water bodies in the current state of the art. Amonghe most commonly used, the following can be mentioned: one-imensional models such as QUAL-2K (Chapra and Pelletier, 2003)nd AQUATOX (Mäkynen, 2009; Park et al., 2008); two-dimensional

odels such as CE-QUAL-W2 (Kuo et al., 2006; Liu et al., 2009)

nd T2D8 (Garcı́a et al., 2010). There are also models which can besed in one, two or three dimensions, such as WASP (Hajda andovotny, 1996; Kellershohn and Tsanis, 1999), CAEDYM (Hipsey

elling 265 (2013) 99– 113

et al., 2006) or CE-QUAL-ICM (Cerco and Cole, 1995) as well as three-dimensional models such as MOHID (Trancoso et al., 2009), Delft3D(Hulail, 2010; Gu et al., 2012), EFDC (Park et al., 1995) and ROMS(Hedström, 2009). Among others, some of these water quality mod-els, their considered parameters, the possible aquatic systems fortheir application and their limitations are shown in Table 1.

As shown in this table, most of these models are not specific,so they can be applied to any aquatic system and to various envi-ronmental problems. Although many of them have already beenapplied to analyze the eutrophication process in coastal lagoons,such as WASP (Ekdal et al., 2011) and MOHID (Saraiva et al.,2007), most of these models do not take into consideration allthe processes involved somehow in the eutrophication of thistype of aquatic systems, such as metal reactions and zooplanktondynamics. The main objective of this paper is therefore to developa mathematical eutrophication model to be used specifically incoastal lagoons, which includes these processes as well as thoseprocesses which are considered to be critical and the interactionsthat characterize this phenomenon in these ecosystems, includ-ing the reactions of metals such as iron and manganese that havebeen considered in this kind of systems by various authors such asCancela Da Fonseca et al. (2001) and Pereira et al. (2009). More-over, this model should take into consideration all the associatedpeculiarities of this type of aquatic systems, which result mainlyfrom the complex interaction between inland and marine waters,as well as from its low hydrodynamic renewal. Finally, the proposedmodel is applied to study the eutrophication process in the Victorialagoon, in the north of Spain.

2. Study area

Victoria (43◦28′15′′N, 3◦30′43′′W) is a lagoon that, together withother marshes, forms the most important group of northern wet-lands of the Iberian Peninsula, which has been declared a naturalreserve by the Spanish Government and included in internationalconventions on aquatic birds conservation such as the Ramsar Con-vention (Fig. 1).

Despite its being a marsh, Victoria lagoon is an example ofa freshwater coastal lagoon which periodically becomes salinebecause of its connection with the sea. It is characterized by a highbiological diversity. This wetland covers an area of 61 ha, of which39.5 ha are marsh and 21.5 ha are dunes. The strong pressure ofVictoria marsh occupation has led, since the mid-nineteenth cen-tury, to a reduction of the floodplain surface. It is estimated thatthe actual estuary surface is approximately 25% of the original one,already present during the Flandrian transgression. This trend hasslowed down in recent years after having been declared as a pro-tected area.

The Victoria marsh is surrounded by housing blocks and alsoby the mountain of Brusco, therefore the lagoon often receivesrainwater via small rivers. On the other hand, the farming activi-ties around the lagoon are considered very few, so they are verylow levels of nutrients such as organic phosphorus (<6 �g L−1),phosphates (<7 �g L−1), organic nitrogen (<300 �g L−1), ammonia(<10 �g L−1) and nitrates (<0.2 �g L−1) that usually reach the lagoonwaters.

As example of the hydrologic characteristics of this area, Fig. 2represents the daily evolution of the runoff flow that has been reg-istered in the Laredo rain gauge placed around 20 km away fromthe lagoon, and that corresponds to the Victoria lagoon data, fromthe 1stof January 2009 to the 30th of December 2012. In this figure,

it can be observed that the most important flows have been regis-tered in the autumn periods, while in the summer, the runoff flowswere very low, except for July 2011, in which the runoff flow hasreached 1.2 m3 s−1 approximately.

H. Zouiten et al. / Ecological Modelling 265 (2013) 99– 113 101

Table 1Review of some water quality models.

Model Parameters Aquatic systems Limitations

QUAL2K Phytoplankton, dissolved oxygen, CBOD (organic nitrogen,ammonia, nitrate, organic phosphorus, phosphate), totalinorganic carbon, inorganic suspended solids, detritus,pathogens, bottom algae

Rivers and permanent regimeconditions

Limited in one-dimensional simulationin rivers

AQUATOX Phytoplankton, periphyton and submerged aquatic vegetation,planktonic and benthic invertebrates, fish, nutrients, dissolvedoxygen, organic sediments, organic toxic chemicals

Vertical stratification in lakes,reservoirs, lakes, rivers and streams

Inorganic pollutants are not considered

CE-QUAL-W2 Algae (phytoplankton, etc.), epiphyte, periphyton, dissolvedoxygen, nutrients, organic matter, carbon, BOD5, bacteria,dissolved and suspended solids

Stratified water bodies, reservoirs,estuaries, lakes and rivers

Zooplankton, metals and sedimentoxygen demand are not included

More focused on dissolved oxygenprocesses

WASP Phytoplankton, nutrients, dissolved oxygen, CBOD, fecalcoliforms, sediments and trace chemicals

All kinds of water bodies: estuaries,lakes, rivers, etc.

Metals reactions are not considered

CAEDYM Algae (phytoplankton, etc.), zooplankton, nutrients, organiccarbon, dissolved oxygen, BOD, benthic oxygen demand,metals, fish, jelly fish, invertebrates, silica, suspended solids

Lakes, marshes, wetlands, rivers,estuaries and reservoirs

Metals, toxics and sedimentcompartment are not considered

CE-QUAL-ICM Algae, dissolved oxygen, COD, carbon, nutrients, silica, organicmatter, sediment oxygen demand, detritus, total active metal,conservative tracers, micro and mesozooplankton, epiphytes,submerged aquatic vegetation, benthic compartments

Dimensional aquatic system(reservoirs, lakes, rivers, etc.) andsystems with mixed dimensions (riverdischarged into estuary, etc.)

Bacteria compartment is notconsidered

MOHID Phytoplankton, zooplankton, nutrients, dissolved silica,biogenic silica, organic matter in pelagic phase, organic matterin benthic phase, pelagic bacteria, benthic bacteria, dissolvedoxygen

Estuaries, oceans and rivers Metals are not included

EFDC Cyanobacteria, algae, diatoms, green algae, dissolved oxygen,particulate refractory and non-refractory organic carbon,dissolved organic carbon, nutrients, available dissolved silica,biogenic particulate silica, chemical oxygen demand, activetotal metal, fecal coliforms

Reservoirs, estuaries, lakes, oceans andrivers

Zooplankton and detritus are notincluded

ROMS Several groups of phytoplankton including diatomaceous,coccolithophores, phytoplankton chlorophyll, zooplankton,nitrite, ammonia, organic matter, silica, small and large

Large aquatic systems: estuaries andoceans

Its application is limited to largeecosystems

Alriv

cTbaosw

t

detritus, ironT2D8 Phytoplankton, ammonia, nitrate, phosphate, dissolved

oxygen, dissolved BOD, suspended BOD, sediment BOD

Furthermore, the lagoon communicates with the sea through aonstruction limiting the channel, which crosses the dunes and therengandín beach. This channel is crossed by a sixteenth centuryridge, which has three semicircular openings, each one spanning

distance of 3 m. The bottom level of the channel in the centralpening is +4.06 m above the mean sea level and +3.94 m at the

ides. Also the channel crosses a newly created bridge with a 10 midth.

The connection of the lagoon with the sea is conditioned byhe opening and closing situation of the channel. When rainfall

Fig. 1. Location of V

l kinds of systems: estuaries, lakes,ers, etc.

Zooplankton, detritus, silica, andmetals are not considered

is abundant, the freshwater flowing into the sea causes the duneerosion, while in periods of climate stability, marine dynamics arepredominant and originate sediment accumulation on the beach,closing the channel. The length and width are conditioned by stormevents that typically affect them, because when these events occur,the coastline reaches new heights, causing that part of the chan-

nel to be submerged and the water amount moving through it toincrease. This leads to the erosion of the margins and an incrementin the width of the channel. During recent years, the mentionedconnection has been limited, so that the lagoon behaves as a

ictoria lagoon.

102 H. Zouiten et al. / Ecological Modelling 265 (2013) 99– 113

he Vic

wno

mctbdpa

3

3

mcbcta

Fginibt(aaedzd

tto

t

Fig. 2. Daily runoff flow registered in t

etland confined, mostly fed by fresh water coming from conti-ental springs, with minimal contributions of sea water, which onlyccasionally enter the system.

The Victoria lagoon has a problem of eutrophication attributedore to a natural process favored by its hydromorphological

haracteristics than to the existence of a significant amount of con-amination. On this subject, some field campaigns were conductedy the Government of Cantabria during the 2009–2012 period. Theata of these campaigns show that the chlorophyll and organichosphorus concentrations increase significantly between springnd summer as shown in Table 2.

. Eutrophication modeling

.1. Model description

In the proposed eutrophication model, “EnvHydrEM” (Environ-ental Hydraulics Institute Eutrophication Model), processes with

lear relevance for eutrophication study in coastal lagoons haveeen included. However, the majority of those processes are notonsidered in detail in the most currently existing mathematicalools, in spite of constituting a great scientific contribution for thenalysis of such phenomena.

In this regard, the model considers a total of 19 variables (seeig. 3), including phytoplankton (C1) as a driving process, total inor-anic carbon (C2), sediment carbon (C3), organic phosphorus (C4),norganic phosphorus (C5), organic nitrogen (C6), ammonia (C7),itrate (C8), available dissolved silica (C9), particulate biogenic sil-

ca (C10), dissolved oxygen (C11), organic matter or carbonaceousiochemical oxygen demand CBOD (C12), zooplankton (C13), bac-erioplankton (C14), detritus (C15), total iron (C16) and ferrous ironC17), total manganese (C18) and manganous ion (C19). This modellso analyzes a total of 72 interactions between the considered vari-bles, including the following 16 processes: respiration, uptake,xcretion, sedimentation, oxidation, mineralization, nitrification,enitrification, photosynthesis, resuspension, grazing, reminerali-ation, predation, reaeration, sediment oxygen demand (SOD) andeath.

The EnvHydrEM model is coupled to a hydrodynamic modelhrough the transport equation, which integrates the advection and

he diffusion properties of the flow, as well as the basic processesccurring in the water column (García et al., 2010).

Clearly many of the considered processes have already been por-rayed in other models with similar characteristics. Among others,

toria lagoon between 2009 and 2012.

we will mention as an example the WASP model (Wool et al., 2001)in which some of the processes involved have been considered,including the decomposition of dead phytoplankton which intro-duces organic phosphorus into the system, the uptake of inorganicphosphorus by the phytoplankton for its growth, the organic nitro-gen mineralization to ammonia, the oxygen uptake for the organicmatter oxidation or the denitrification process which contributesto the reduction of organic matter concentration, among others. Inthe CAEDYM model (Hipsey et al., 2006), for example, the trans-formation of inorganic phosphorus mineralization to phosphate aswell as iron and manganese resuspension and sedimentation pro-cesses were analyzed. While in the EFDC model (Park et al., 1995)surface reaeration, algae respiration, organic matter oxidation anddetritus remineralization processes which contribute to increasethe total inorganic carbon concentration (TIC) in the water columnare studied.

However, an important effort has been carried out in order toadapt the developed eutrophication model to the conditions thatoften characterize coastal lagoons. This has been reflected in a moreprecise definition of some important specific processes in suchwater bodies in comparison with those which are usually consid-ered for eutrophication analysis in aquatic environments in general.Among these processes, the following can be mentioned:

- The simultaneous consideration of carbon, phosphorus, nitrogen,silica, iron and manganese as nutrients for phytoplankton growth(3rd term of Eq. (20)) (Boyd et al., 2007; De Baar et al., 2005;Thomann and Mueller, 1987).

- The zooplankton and fish respiration and excretion processes,leading to total inorganic carbon liberation in the media (3rd, 4th,6th and 7th terms of Eq. (2)) (Tetra Tech Inc., 1980).

- The detritus remineralization, which increases the organic phos-phorus and organic nitrogen concentrations in the media (4thterms of Eqs. (4) and (6)) (Cole and Buchak, 1995).

- The zooplankton excretion that also participates in the ammoniaconcentration increasing in the water column (3rd term of Eq. (7))(Ganf and Blazka, 1974).

- The available dissolved silica resuspension (1st term of Eq. (9))and particulate biogenic silica sedimentation (last term of Eq.

(10)) processes (Tkalich and Sundarambal, 2003).

- The reduction of dissolved oxygen concentration in the media asconsequence of zooplankton and fish respiration processes (4thand 5th terms of Eq. (11)) (Kitazawa and Kumagai, 2005).

H. Zouiten et al. / Ecological Modelling 265 (2013) 99– 113 103

Table 2Chlorophyll and nutrient concentrations, in �g L−1, registered in the Victoria marsh between May 2009 and November 2012.

Date Chlorophyll-a Organic phosphorus Inorganic phosphorus Organic nitrogen Ammonia Nitrate

01/05/2009 4.1 7.6 7.4 400 71 9201/08/2009 11.3 99 75 2240 19 0.901/11/2009 1.3 80 79 980 119 123301/02/2010 0.7 10 15 340 21 21701/05/2010 3.2 6.7 22.2 450 74 4301/08/2010 15.1 47 66.5 1200 11 0.201/11/2010 6.1 68 65.8 1500 691 32201/02/2011 0.8 78 38.9 870 44.5 80901/04/2011 12.8 29 25.2 1000 21.8 7101/07/2011 2.5 284 391.8 1200 67.8 301/11/2011 2.1 343 278.2 1100 204.3 28.601/02/2012 3.5 47 50.3 2800 113 2227.401/05/2012 3.6 106 93 2500 120.7 1656.601/07/2012 15.4 130 128 400 144 69

5

-

-

-

-

-

mk

FD

01/11/2012 13.7 420 31

The increase of carbonaceous organic matter concentration in thewater column as a result of phytoplankton excretion (6th term ofEq. (12)), and its diminution for the bacterioplankton growth (lastterm of Eq. (12)) (Eppley and Sloan, 1965; Lancelot, 1979).

The zooplankton mortality via predation (4th term of Eq. (13))(Christoffersen et al., 1993; Kishi et al., 2007).

The uptake of carbonaceous organic matter, organic nitrogen andammonia as substrates for bacterioplankton growth (Eq. (24)).

The bacterial mortality as a source of detritus to the ecosystem(3rd term of Eq. (15)).

The iron and manganese uptake processes by phytoplankton forits growth (last terms of Eqs. (16)–(19)) (De Baar et al., 2005).

The interactions of the nineteen state variables of EnvHydrEModel are described mathematically, in terms of eutrophication

inetics, by a set of equations regrouped in Table 3.

ig. 3. Processes and interactions considered in the EnvHydrEM model. Porg: organic phosO: dissolved oxygen; Fe: includes total iron and ferrous iron; Mn: includes total mangan

1300 10 91

In this table we can see that F is the fish average concentration(mg L−1), CO2 and Csub are the carbon dioxide and substrate con-centrations (mg L−1), respectively, H and hsedi are the water columndepth (m) and the thick sediment layer (m), respectively, A is thewater surface (m2), T and S are the water temperature (◦C) andsalinity (PSU), respectively, kfd is the fraction of silica available forpredation, fdp is the diatom percentage in the total phytoplankton,fb is the fraction of bacteria attached to particles and fpod, fpid, fnodand fD are the fractions of dissolved organic phosphorus, dissolvedinorganic phosphorus, dissolved organic nitrogen and dissolvedcarbonaceous organic matter, respectively.

As seen in Table 3, algal growth is described by a first-orderkinetic expression where the first-order growth rate is defined as

the difference between the growth, Gp, and the death, Dp, rates.The growth depends on the light, temperature and nutrient factors.Assuming the multiplicative approach proposed by Chapra (1997),

phorus; Norg: organic nitrogen; TIC: total inorganic carbon; Csed: sediment carbon;ese and manganous ion.

104 H. Zouiten et al. / Ecological Modelling 265 (2013) 99– 113

Table 3The EnvHydrEM model interactions.

Eq. no. Variables Interaction equations

(1) Phytoplankton ∂C1∂t

=(

Gp − Kpr �pr(T−20) − Cg�(T−20)

z C13 − Db − VsfitoH

)C1

(2) Total inorganic carbon

∂C2

∂t= fdcKrmindet �(T−20)

detC15 + KCsed

�(T−20)sed

C3 + KrF �(T−20)F

F++fzrcaczKzr �(T−20)

zr C13 + fprcacpKpr �(T−20)pr C1 + KeF F + fzecaczGzC13 − acpGpC1+

+fmocKD�(T−20)D

XDBOC12 + KaA(CO2s − CO2)

(3) Sediment carbon ∂C3∂t

= acpVsfitoC1 − �sedihsedi

C3 − KCsed�(T−20)

sedC3

(4) Organic phosphorus

∂C4

∂t= fpoapcDpC1 + fzrpapzDzC13 + fzepapzGzC13 + fdpoKrmindet �(T−20)

detC15−

−KminP �(T−20)minP

XPRC C4 −Vspo

(1 − fpod

)H

C4

(5) Inorganic phosphorus ∂C5∂t

= fpiapcDpC1 + KminP �(T−20)minP

XPRC C4 − apcGpC1 − Vspi(1−fpid)H C5 + Spi

H

(6) Organic nitrogen

∂C6

∂t= fnoancDpC1 + fzrnanzDzC13 + fzenanzGzC13 + fdnoKrmindet �(T−20)

detC15−

−KminN�(T−20)minN

XNRC C6 − Vsno (1 − fnod)H

C6 − GB.mtC6

kmBN + C6

(7) Ammonia

∂C7

∂t= fnaancDp C1 + KminN�(T−20)

minNXNRC C6 + fNH3 anzGzC13 − ancGpPNH+

4C1−

−Knitri�(T−20)nitri

XNIT C7 − GB.mtC7

kmBN + C7+

SNH+4

H

(8) Nitrate ∂C8∂t

= −ancGp

(1 − PNH+

4

)C1 + Knitri�

(T−20)nitri

XNIT C7 − Kdenitri�(T−20)denitri

XDENIT C8 + SNO3H

(9) Available dissolved silica ∂C9∂t

= SSiH + asc

(fsidkfd Dp − Gp

)fdpC1 − aex (kdisC9 − C10)

(10) Particulate biogenic silica ∂C10∂t

= fsipasc

(1 − kfd

)Dpf dpC1 + aex (kdisC9 − C10) − VsSi

H C10

(11) Dissolved oxygen

∂C11

∂t= Ka�(T−20)

Ka (Cs − C11) + Gp ×(

3212

+ 4814

1412

(1 − PNH+

4

)× C1

)− 32

12Kpr �(T−20)

pr C1−

−Kzr �zr aocC13 − aFOKrF �(T−20)F

F − KD�(T−20)D

XDBOC12−− 64

16Knitri�

(T−20)nitri

XNIT C7 − OSD

H�(T−20)

SOD

(12) DBOC

∂C12

∂t= fmoaocDpC1 + fzmoaocDzC13 − KD�(T−20)

DXDBOC12 − Vsd (1 − fD)

HC12−

− 54

3214

kdenitri �(T−20)denitri

XDENIT C8 + acpGpC1 − GB.mtC12

kmBC + C12

(13) Zooplankton ∂C13∂t

=(

azazpkmpg

Cfito+kmpgCg�(T−20)

z C1 − Kzr �(T−20)zr − anzGz − Kzp�(T−20)

z F)

C13

(14) Bacterioplankton ∂C14∂t

=[

GB.mtCsub

kmB+Csub− (0.8 + 0.02 S) �T−20

DB− ̨ I0

keH (1 − e−keH) − fbVsPB

H

]C14

(15) Detritus

∂C15

∂t= fdDpC1 + fzdDzC13 + DBC14 + fzedGzC13 + fmodKD�(T−20)

DXDBOC12−

−Krmindet �(T−20)det

C15 − Vsdet

HC15

(16) Total iron ∂C16∂t

= f RSFFeT

+ f DSF

Fe2+ + VsFeH (C16 − C17) − GpaFecC1C16

(17) Ferrous iron ∂C17∂t

= KFeR�(T−20)FeR

kFeRkFeR+C11

(C16 − C17) − KFeO�(T−20)FeO

C11kFeO+C11

C17 + f DSF

Fe2+ − GpaFecC1C17

C18∂t

=C19∂t

=

te

G

w

I

�ipssaa

tz

(18) Total manganese ∂

(19) Manganous ion ∂

he phytoplankton growth rate can be described as the followingquation:

p = Gmax �Gp(T−20) 2.718

keH

⎛⎝e

−I0Is

e−keH

− e−I0Is

⎞⎠

× min

⎧⎪⎨⎪⎩

C2

kmC + C2,

PID

kmP + PID,

NID

kmN + NID,

SID

kmSi + SID,

C16

kmFe + C16,

C18

kmMn + C18

⎫⎪⎬⎪⎭ (20)

here

0 = Im (1 − 0.071 Nb) (21)

Gp is the temperature coefficient for the bacterioplankton mortal-ty; PID, NID and SID are the concentrations of dissolved inorganichosphorus, dissolved inorganic nitrogen and dissolved inorganicilica, respectively; kmC, kmP, kmN, kmSi, kmFe and kmMn are the half-aturation constants (see Table 4); I0 and Im are the light intensityt water surface and the average luminous intensity, respectively;

nd Nb is the cloudiness.

The same kind of kinetic occurs with zooplankton and bac-erioplankton dynamics, that the first-order growth rate forooplankton, for example, is defined as the difference between its

f RSFMnT

+ f DSF

Mn2+ + VsMnH (C18 − C19) − GpaMncC1C18

KMnR �(T−20)MnR

kMnRkMnR+C11

(C18 − C19) − KMnO�(T−20)MnO

C11kMnO+C11

C19 + f DSF

Mn2+ − GpaMncC1C19

growth, Gz, and death, Dz, rates. The corresponding equation for thezooplankton growth rate is written as follows:

Gz = az azpkmpg

C1 + kmpgCg�(T−20)

z C1 (22)

while the bacterioplankton growth rate, GB, is expressed in thefollowing form:

GB = GB. maxexp0.092(T−27) Csub

kmB + Csub(23)

where

Csub

kmB + Csub= C12

kmBC + C12+ C6 + C7

kmBN + C6 + C7(24)

On the other hand, within anoxic water column, nitrogenappears mostly in the form of nitrates. Indeed, nitrates and nitritesare reduced to ammonium and this is then converted to glu-cosamine by glutamate dehydrogenase enzyme (Hu et al., 2006).These differences make ammonia the preferred element for phy-toplankton in comparison with nitrite and nitrate. The preferential

uptake term is described as

PNH+4

= C7

kmN + C8

(C8

kmN + C7+ kmN

C7 + C8

)(25)

H. Zouiten et al. / Ecological Modelling 265 (2013) 99– 113 105

Table 4Parameters and values used in the EnvHydrEM model.

Parameter Description Value Unit References

Gmax Maximum growth rate of phytoplankton at 20 ◦C 2.8 day−1 Bruce et al. (2006)�Gp Temperature coefficient for phytoplankton growth 1.066 – Chau and Jin (1998), Thomann and Mueller (1987)Ke Light attenuation coefficient 5 m−1 Wool et al. (2001)Is Saturation light intensity 300 ly day−1 Chao et al. (2010), Di Toro et al. (1977), Farnsworth-Lee

and Lawrence (2000), Wool et al. (2001)kmC Half-saturation constant for carbon uptake 0.006 mg C L−1 Chen and Durbin (1994)kmN Half-saturation constant for nitrogen uptake 0.02 mg N L−1 Bárcena et al. (2012), Chau and Jin (1998), Rigosi et al.

(2011), Thomann and Mueller (1987)kmP Half-saturation constant for phosphorus uptake 0.001 mg P L−1 Bruce et al. (2006), Chau and Jin (1998), Del Barrio et al.

(2012), Park et al. (2005), Rigosi et al. (2011), Thomann andMueller (1987), Wool et al. (2001), Zheng et al. (2004)

kmSi Half-saturation constant for silica uptake 0.05 mg Si L−1 Aumont (2004), Park et al. (2005)kmFe Half-saturation constant for iron uptake 6.7 × 10−5 mg Fe L−1 Lancelot et al. (2000)kmMn Half-saturation constant for manganese uptake 7.6 × 10−5 mg Mn L−1 Knauer et al. (1999)Kpr Endogenous respiration rate of phytoplankton 0.1 day−1 Del Barrio et al. (2012), Di Toro et al. (1977), Edwards

(2001), Faure et al. (2010), Missaghi and Hondzo (2010),Thomann and Mueller (1987)

�pr Temperature coefficient for phytoplankton respiration 1.05 – Missaghi and Hondzo (2010)Cg Grazing rate for herbivorous zooplankton 0.4 L mg−1 C day−1 Del Barrio et al. (2012), Faure et al. (2010)�z Zooplankton temperature coefficient for grazing 1.05 – Lessin and Raudsepp (2006)Db Phytoplankton loss rate due to parasitism 0.01 day−1 Wool et al. (2001)Vsfito Phytoplankton settling velocity 1.0 m day−1 Chau and Jin (1998), Farnsworth-Lee and Lawrence (2000),

Lee et al. (1991), Schladow et al. (2007)Kr.min.det Detritus remineralization rate 0.1 day−1 Edwards (2001)�det Temperature coefficient for detritus remineralization 1.05 – Drago et al. (2001)KCsed

Sediment carbon oxidation rate 0.1 day−1 Chau and Jin (1998)�sed Temperature coefficient for sediment carbon oxidation 1.05 – Carlson and Simpson (1996), Chao et al. (2010)KrF Fish respiration rate 0.2 day−1 Hipsey et al. (2004)�F Temperature coefficient for fish respiration 0.05 – Megrey et al. (2007)KeF Excretion rates of fish 0.05 day−1 Hipsey et al. (2004)Kzr Zooplankton respiration rate 0.02 day−1 Chau and Jin (1998)�zr Temperature coefficient for zooplankton respiration 1.05 – Bruce et al. (2006)acz Carbon/zooplankton ratio 0.4 mg C mg−1 DW Farnsworth-Lee and Lawrence (2000)az Conversion efficiency for zooplankton 0.65 mg C mg−1 C Di Toro et al. (1975)azp Carbon/chlorophyll in zooplankton ratio 50 mg C mg−1 chl-a Di Toro et al. (1975)kmpg Half saturation constant of herbivorous phytoplankton

biomass0.06 mg chl-a L−1 Di Toro et al. (1975)

acp Carbon/phytoplankton ratio 30 mg C mg−1 chl-a Mitsch and Gosselink (1993), Wool et al. (2001)KD Oxidation rate of carbonaceous organic matter 0.2 day−1 Chau and Jin (1998), Zheng et al. (2004)�D Temperature coefficient for carbonaceous organic matter

oxidation1.05 – Estrada and Diaz (2010)

kDBO Half saturation constant for oxygen limitation ofcarbonaceous organic matter oxidation

0.5 mg O2 L−1 Estrada and Diaz (2010), Zheng et al. (2004)

Ka Reaeration coefficient 1.0 day−1 Martín Monerris (1998)apc Phosphorus/carbon in phytoplankton ratio 0.02 mg P mg−1 C Muhammetoglu and Soyupak (2000), Tkalich et al. (2002),

Wool et al. (2001)apz Phosphorus/carbon in zooplankton ratio 0.01 mg P mg−1 C Bruce et al. (2006), Lessin and Raudsepp (2006)Kminp Organic phosphorus mineralization rate 0.02 day−1 Bruce et al. (2006), Muhammetoglu and Soyupak (2000),

Wool et al. (2001)�minp Temperature coefficient for organic phosphorus

mineralization1.08 – Muhammetoglu and Soyupak (2000), Wool et al. (2001)

kmpc Half saturation constant for phytoplankton limitation ofphosphorus mineralization

1 mg C L−1 Martín Monerris (1998), Tkalich et al. (2002), Wool et al.(2001)

Vspo Settling velocity of particulate organic phosphorus 0.3 m day−1 Chau and Jin (1998)Vspi Settling velocity of particulate inorganic phosphorus 18 m day−1 Martín Monerris (1998)Spi Sediment inorganic phosphorus release rate 0 mg P m−2 day−1

anc Nitrogen/carbon in phytoplankton ratio 0.25 mg N mg−1 C Muhammetoglu and Soyupak (2000), Tkalich et al. (2002),Wool et al. (2001)

anz Nitrogen/carbon in zooplankton ratio 0.1 mg N mg−1 C Nguyen et al. (2010)KminN Organic nitrogen mineralization rate 0.02 day−1 Bruce et al. (2006)�minN Temperature coefficient for organic nitrogen

mineralization1.08 – Muhammetoglu and Soyupak (2000), Schladow et al.

(2007), Wool et al. (2001)kmNc Half saturation constant for phytoplankton limitation of

nitrogen mineralization1 mg C L−1 Martín Monerris (1998)

Vsno Settling velocity of particulate organic nitrogen 0.3 m day−1 Chau and Jin (1998)Knitri Ammonia nitrification rate 0.1 day−1 Bruce et al. (2006), Lee et al. (1991), Wool et al. (2001)�nitri Temperature coefficient for ammonia nitrification 1.08 – Bruce et al. (2006), Cerco and Cole (1995), Muhammetoglu

and Soyupak (2000), Wool et al. (2001)knit Half saturation constant for oxygen limitation of

nitrification2 mg O2 L−1 Chau and Jin (1998), Wool et al. (2001)

Sni Sediment ammonia release rate 0 mg N m−2 day−1

Kdenitri Denitrification rate 0.09 day−1 Chau and Jin (1998), Muhammetoglu and Soyupak (2000),Wool et al. (2001)

�denitri Temperature coefficient for denitrification 1.045 – Bruce et al. (2006), Muhammetoglu and Soyupak (2000),Nguyen et al. (2010), Wool et al. (2001)

106 H. Zouiten et al. / Ecological Modelling 265 (2013) 99– 113

Table 4 (Continued)

Parameter Description Value Unit References

kNO3 Half saturation constant for denitrification 0.1 mg O2 L−1 Chau and Jin (1998), Martín Monerris (1998),Muhammetoglu and Soyupak (2000), Nguyen et al. (2010),Wool et al. (2001)

SNO3 Sediment nitrate release rate 0 mg N m−2 day−1

VsSi Settling velocity of particulate biogenic silica 0.1 m day−1 Kim and Cerco (2003)asc Silica/carbon in phytoplankton ratio 0.8 mg Si mg−1 C Kim and Cerco (2003), Tetra Tech Inc. (1980)aex Exchange coefficient of dissolved and particulate silica 0.5 – Tkalich and Sundarambal (2003)kdis Partition coefficient of sorbed versus dissolved available

silica0.1 – Kim and Cerco (2003)

�Ka Temperature coefficient of reaeration 1.024 – Thomann and Mueller (1987), Zheng et al. (2004)aoc Oxygen/carbon ratio 2.65 mg O2 mg−1 C Lee et al. (1991)aFO Carbon in fish/oxygen ratio 2.67 mg C mg−1 O Hipsey et al. (2004)OSD Sediment oxygen demand 50 mg O2 m−2 day−1 Berger (1994)�SOD Temperature coefficient for sediment oxygen demand 1.08 – Estrada and Diaz (2010), Zheng et al. (2004)Kzp Mortality rate of zooplankton due to predation 0.02 day−1 Berger (1994), Wool et al. (2001)VsD Settling velocity of carbonaceous organic matter 0.03 m day−1 Estrada and Diaz (2010)GB.max Maximum growth rate of bacterioplankton 2 day−1 Bissett et al. (1999)kmBC Half saturation constant for carbon uptake by

bacterioplankton0.3 mg C L−1 Zhao et al. (2009)

kmBN Half saturation constant for nitrogen uptake bybacterioplankton

0.01 mg N L−1 Faure et al. (2010)

DB Bacterioplankton mortality rate 0.09 day−1 Zhao et al. (2009)VsPB Settling velocity of bacterioplankton particles 0.4 m day−1 Chapra (1997)Vsdet Settling velocity of detritus 0.8 m day−1 Edwards (2001)KFeR Maximum reduction rate of Fe3+ at 20 ◦C 0.03 day−1 Garcia-Webb (2003), Hipsey et al. (2004)�FeR Temperature coefficient for Fe3+ reduction 1.05 – Hipsey et al. (2004)kFeR Half saturation constant for Fe3+ reduction 0.5 mg L−1 Hipsey et al. (2004)KFeO Maximum oxidation rate of iron at 20 ◦C 0.06 day−1 Hipsey et al. (2004)�FeO Temperature coefficient for iron oxidation 1.05 – Hipsey et al. (2004)kFeO Half saturation constant for iron oxidation 2 mg L−1 Garcia-Webb (2003), Hipsey et al. (2004)aFeC Iron/carbon in phytoplankton ratio 9.27 10−4 mg Fe mg−1 C Lancelot et al. (2000)SFe Iron release rate 10 mg m−2 day−1 Garcia-Webb (2003)kOD-Fe Half saturation coefficient that regulates the iron release

rate according to the dissolved oxygen in the layer abovethe sediments

0.04 mg L−1 Hipsey et al. (2004)

kpH-Fe Half saturation coefficient that regulates the iron releaserate according to pH in the layer above the sediments

7 – Garcia-Webb (2003)

VsFe Settling velocity of iron 8.64 m day−1 Hipsey et al. (2006)˛Fe Resuspension rate constant of iron 864 mg m−2 day−1 Garcia-Webb (2003)�ref Reference shear stress 1 N m−2 Hipsey et al. (2004), Rubio López (2009)�CFe Critical shear stress for particulate iron 0.04 N m−2 Hipsey et al. (2004)KMnR Maximum reduction rate of Mn3+ at 20 ◦C 0.03 day−1 Hipsey et al. (2004)�MnR Temperature coefficient for Mn3+ reduction 1.05 – Hipsey et al. (2004)kMnR Half saturation constant for Mn3+ reduction 0.5 mg L−1 Hipsey et al. (2004)KMnO Maximum oxidation rate of manganese at 20 ◦C 0.1 day−1 Hipsey et al. (2004)�MnO Temperature coefficient for manganese oxidation 1.05 – Hipsey et al. (2004)kMnO Half saturation constant for manganese oxidation 0.5 mg L−1 Hipsey et al. (2004)aMnC Manganese/carbon in phytoplankton ratio 2.75 10−5 mg Mn mg−1 C Schoemann et al. (2001)SMn Manganese release rate 10 mg m−2 day−1 Garcia-Webb (2003)kOD-Mn Half saturation coefficient that regulates the manganese

release rate according to the dissolved oxygen in the layerabove the sediments

0.4 mg L−1 Hipsey et al. (2004)

kpH-Mn Half saturation coefficient that regulates the manganeserelease rate according to pH in the layer above thesediments

7 – Garcia-Webb (2003)

VsMn Settling velocity of manganese 8.64 m day−1 Hipsey et al. (2004)˛Mn Resuspension rate constant of manganese 864 mg m−2 day−1 Garcia-Webb (2003)�CMn Critical shear stress for particulate manganese 0.04 N m−2 Hipsey et al. (2004)� Sediment accumulation rate 0.001 day−1 Chau and Jin (1998)

pr

X

a

X

sedi

Furthermore, the limitation of phytoplankton on the organichosphorus and organic nitrogen mineralization, XPRC and XNRCespectively, is described as follows:

C1

PRC =

kmPC + C1(26)

nd

NRC = C1

kmNC + C1(27)

While the limitation of oxygen on the carbonaceous organicmatter oxidation, XDBO, nitrification, XNIT, and denitrification, XDENIT,is expressed as

XDBO = C11

kDBO + C11(28)

XNIT = C11 (29)

knit + C11

XDENIT = kNO3

kNO3 + C11(30)

l Modelling 265 (2013) 99– 113 107

ad

f

f

a

f

f

lpddsmtoafbirmoztaptof

f

3

u2mBle

tasIioa

1eo

Table 5The selected parameters and values used for the model calibration in the Victorialagoon.

Parameter Studied values

Gmax (day−1) 2.4, 2.8Ke (m−1) 3.0, 5.0Kpr (day−1) 0.1, 0.2VsFe (m day−1) 0.5, 1.0

H. Zouiten et al. / Ecologica

Moreover, the resuspension rates of iron and manganese, f RSFFeT

nd f RSFMnT , and their sediment liberation rates, f DSF

Fe2+ and f DSFMn2+ , are

escribed in the following form

RSFFeT = ˛Fe

[� − �CFe

�ref

]1H

(31)

RSFMnT = ˛Mn

[� − �CMn

�ref

]1H

(32)

nd

DSFFe2+ = SFe

[kOD−Fe

kOD−Fe + C11+

∣∣pH − 7∣∣

kpH−Fe +∣∣pH − 7

∣∣]

1H

(33)

DSFMn2+ = SMn

[kOD−Mn

kOD−Mn + C11+

∣∣pH − 7∣∣

kpH−Mn +∣∣pH − 7

∣∣]

1H

(34)

As can be seen in Fig. 3, the phytoplankton mortality processeads to the increase of substance concentrations such as organichosphorus, inorganic phosphorus, organic nitrogen, ammonia,issolved silica, particulate silica, carbonaceous organic matter andetritus. The terms fpo, fpi, fno, fna, fsid, fsip, fmo and fd are the corre-ponding fractions of the dead phytoplankton that generate theseentioned variables, respectively. The dead zooplankton also con-

ributes to increasing the concentrations of organic phosphorus,rganic nitrogen, carbonaceous organic matter and detritus in thequatic system, which is reflected by the fractions fzrp, fzrn, fzmo, and

zd, respectively. The inorganic carbon concentration in the waterody can increase due to processes such as, the detritus remineral-

zation (fdc), the zooplankton respiration (fzrc), the phytoplanktonespiration (fprc), the zooplankton excretion (fzec) and the organicatter oxidation (fmoc). Similarly, the organic form concentrations

f phosphorus and nitrogen may also increase as a result of theooplankton excretion and the detritus remineralization processes,hrough obtained fractions fzep, fdpo, fzen and fdno, respectively, andlso the ammonia concentration that can increase due to the zoo-lankton excretion process (fNH3 ). Finally, it can be observed thathe detritus can also be obtained from zooplankton excretion andrganic matter oxidation processes, which is reflected in fzed and

mod fractions, respectively.The model parameters and their bibliographic values selected

or this application case are shown in Table 4.

.2. Model calibration

To carry out this study, two hydrodynamic models have beensed: a two-dimensional hydrodynamic model, H2D (Sámano et al.,012), for the tidal velocities study and a quasi three-dimensionalodel, H2DZ (García et al., 2010), for the wind currents analysis.

oth models have proved to offer good approximations to waterevels and velocity currents in shallow aquatic systems (Bárcenat al., 2012; García et al., 2010; Sámano et al., 2012).

Before the model calibration and validation, the sensitivity ofhe model parameters must be analyzed. In this regard, with theim of selecting the most influential of the parameters, an initialtudy was carried out which considered different simulation cases.n each one of these cases, the value of these parameters was variedndividually, maintaining the rest of them fixed. The case in whichne of the parameters has the values shown in Table 4 was used as

reference.

After analyzing over 100 cases, the results revealed that only

0 out of all the parameters demonstrated to significantly influ-nce the model results, these being: the maximum growth ratef phytoplankton (Gmax), the light attenuation coefficient (Ke), the

kmN (mg N L−1) 0.01, 0.02kmP (mg P L−1) 0.001, 0.02

saturation light intensity (Is), the endogenous respiration rate ofphytoplankton (Kpr), the grazing rate for herbivorous zooplankton(Cg), the mortality rate due to parasitism phytoplankton (DB), thephytoplankton settling velocity (Vsfito), the half saturation constantfor nitrogen uptake (kmN), the half saturation constant for phos-phorus uptake (kmP) and the half saturation constant for nitrogenuptake by bacterioplankton (kmBN).

Furthermore, six of these parameters proved to dominate overthe rest of them: the maximum growth rate of phytoplankton(Gmax), the light attenuation coefficient (Ke), the endogenous res-piration rate of phytoplankton (Kpr), the phytoplankton settlingvelocity (Vsfito), the half saturation constant for nitrogen uptake(kmN) and the half saturation constant for phosphorus uptake (kmP).

As a result of this sensitivity study, the EnvHydrEM model wascalibrated using the field data provided by the Water FrameworkDirective (see Table 2) and corresponding to a period of time of sixmonths (from the 1st of May to the 1st of November of 2009). Thecalibration consisted of varying the values of the six parametersshown to be more sensitive, considering two values for each one(Table 5). By combining these values, a total of 64 cases study wereobtained.

The results of this study showed that the value combina-tion which best reproduced the real data of the chlorophyll-aconcentration evolution (Fig. 4) corresponds to the case whereGmax = 2.4 day−1, Ke = 3.0 m−1, Kpr = 0.1 day−1, Vsfito = 1.0 m day,KmN = 0.02 mg N L−1 and KmP = 0.001 mg P L−1 adopting, for the restof the parameters, the values shown in Table 4.

As shown in the above figure, the evolution curve of chlorophyll-a concentration obtained from the model results is in agreementwith the measured data. This allows the authors to concludethat the model adequately reproduces the evolution tendencyof chlorophyll-a concentration between the 1st of May and the1st of November of 2009. Also, it can be observed that thechlorophyll-a concentration increases above its monthly averagevalue (∼5 �g L−1) between May and August, reaching maximumvalues (11.3 �g L−1) and attaining mesotrophic levels over theperiod that coincides with the increase of the temperature andthe light intensity of the environment, thereby favoring the phy-toplankton mass growth in the aquatic system. Moreover, it canbe seen that between August and November, when the mentionedparameters decrease, the chlorophyll-a concentration reverts backdown again, reaching an oligotrophic situation.

4. Results and discussion

Once calibrated, the EnvHydrEM model was used to study theevolution of the chlorophyll-a concentration within the Victorialagoon between the 1st of February 2010 and the 1st of February2013 (Fig. 5).

As shown in this figure, the evolution curve obtained from themodel results is in agreement with the measured data, which

confirms the above conclusion about the capacity of the devel-oped model to effectively reproduce the evolution tendency ofchlorophyll-a concentration between 2010 and 2013.

108 H. Zouiten et al. / Ecological Modelling 265 (2013) 99– 113

n, from

ci2ucT(

lec

a2Iid

Fig. 4. Evolution of chlorophyll-a concentration in Victoria lagoo

It can also be observed that the chlorophyll-a concentrationonsiderably increases between spring and summer periods, reach-ng its maximum value (about 15 �g L−1 approximately) in August010, and coming back down from summer seasons. However,nlike the other years, for the year 2011, the chlorophyll-a con-entration has decreased just with the beginning of the summer.his is due, principally, to the rain event registered in July 2011Fig. 2).

Moreover, the application of the EnvHydrEM model to theagoon has yielded results on the distribution in the system and thevolution over time of chlorophyll-a, nutrients and other substanceoncentrations.

As an example of this, Fig. 6 shows the evolution of chlorophyll- concentration every ten days between the 5th of May and the4th of June during 2009 and their distribution within the lagoon.

n this figure, we can observe that the chlorophyll-a concentrationncreases throughout this period of time, in accordance with theata results.

Fig. 5. Evolution of chlorophyll-a concentration in Victoria lagoon, from th

the 1st of May to the 1st of November 2009 (calibration curve).

From these results, it can be deduced that the chlorophyll-aconcentration in Victoria increases between spring and summer2009, reaching a value of 13.5 �g L−1 approximately. This increaseis due to the weather conditions that often characterize this timeof year, in terms of temperature and solar radiation, considered themost favorable for phytoplankton growth in the media (Thomannand Mueller, 1987) and, also to the nutrients availability that arenecessary for this growth, as shown above in Eq. (20).

Moreover, it can be seen that in the channel, which connectsthe lagoon to the sea, chlorophyll-a concentrations are typicallylower than those observed in the lagoon. This is due principally tothe effect of sea water inlet to the lagoon, allowing internal waterrenewal, which reduces the chlorophyll concentrations within thelagoon.

Meanwhile, Fig. 7 represents the evolution and distribution

of zooplankton concentration in the lagoon. As can be observed,this variable shows also a gradual increase in the middle, as thechlorophyll-a concentration.

e 1st of February 2009 to the 1st of February 2013 (validation curve).

H. Zouiten et al. / Ecological Modelling 265 (2013) 99– 113 109

Fig. 6. Evolution of chlorophyll-a concentration, in mg L−1, in Victoria lagoon between the 5th of May and the 24th of June 2009.

Fig. 7. Evolution of zooplankton concentration, in mg L−1, in Victoria lagoon between the 5th of May and the 24th of June 2009.

110 H. Zouiten et al. / Ecological Modelling 265 (2013) 99– 113

Fig. 8. Evolution of nitrate concentration, in mg L−1, in Victoria lagoon between the 5th of May and the 24th of June 2009.

wpp(T

stocginwt(a

ogbpbefatpatt

It can be observed that the zooplankton concentration in theater column increases with the growth of phytoplankton, aerfectly logical behavior given that the zooplankton needs phyto-lankton for its feed and growth, a process known as herbivorismDi Toro et al., 1975), as shown above in the first term of Eq. (13) inable 3.

Figs. 8–10 show the evolution of nitrate, dissolved availableilica and ferrous iron concentrations, respectively, and their dis-ribution within the lake between the 5th of May and the 24thf June, 2009. In these figures, it can be seen that, in contrast tohlorophyll-a, the concentrations of these substances decrease pro-ressively within the lagoon. These results suggest that the increasen phytoplankton concentration is associated with a decrease in theutrient concentration such as nitrate, silica, iron, etc. within theater body, mainly due to the uptake of these substances by phy-

oplankton, which uses them for its growth, according to Eqs. (8)1st term) (9) (3rd term) and (17) (last term), respectively, shownbove in Table 3.

However, the results and also the data show that concentrationsf some substances such as organic phosphorus, organic nitro-en and phosphate increase with time, a phenomenon that cane explained mainly due to the fact that in the eutrophicationrocess, it assumed that for every milligram of phytoplankton car-on produced, apc milligrams of phosphorus are released (Hipseyt al., 2006). It also admitted that, the increase of the grazing rateor herbivorous zooplankton has a similar effect on the associ-ted phosphorus concentration in the system. It is also consideredhat a fraction of phytoplankton phosphorus remains as organic

hosphorus while another part is directly converted to bioavail-ble inorganic phosphorus (Di Toro et al., 1975). It is importanto also mention the bio-physicochemical processes of eutrophica-ion which often favors the liberation of these substances into the

system, for example, zooplankton excretion and detritus reminer-alization, which generate organic phosphorus and nitrogen in thewater column.

From the obtained results, the authors can conclude that for itsgrowth, phytoplankton assimilates other substances such as silica.These results are confirmed by the data obtained from field cam-paigns carried out by the Environmental Hydraulics Institute (IHCantabria) in the Victoria lagoon in 2009, which showed the pres-ence of diatoms in the environment. These types of algae constantlyneed silica for the development of their siliceous skeleton or frus-tules, thus the presence of silica in this water body turns out to beessential for the phytoplankton growth within it.

Furthermore, from the available data concerning the nutrientconcentrations in the Victoria lagoon, it was observed that the phos-phorus concentration is clearly inferior to that of nitrogen. In thisregard, according to the limiting factor concept, it can be assumedthat the limiting nutrient for the phytoplankton growth in the Vic-toria lagoon is phosphorus. This result is completely logical giventhe nature of diffuse sources that characterize water inputs in thissystem. According to Thomann and Mueller (1987), in these sourcesthe ratio nitrogen/phosphorus (N/P) is usually much higher than10, marking the predominance of nitrogen to phosphorus, whichis then considered to be the limiting nutrient. Moreover, given thepresence of diatoms in Victoria and the absolute importance of sil-ica for the growth process of this type of algae, it can be assumedthat silica is a co-limiting nutrient to phytoplankton growth in thisaquatic system.

Finally, based on the limits set by the (OCDE, 1982) for the water

bodies trophic classification, and being that the annual averagechlorophyll-a concentration is 5 �g L−1, approximately, reaching amaximum of 13.5 �g L−1, as stated above, it can be concluded thatthe Victoria lagoon is a mesotrophic system.

H. Zouiten et al. / Ecological Modelling 265 (2013) 99– 113 111

Fig. 9. Evolution of dissolved available silica concentration, in mg L−1, in Victoria lagoon between the 5th of May and the 24th of June 2009.

Fig. 10. Evolution of ferrous iron concentration, in mg L−1, in Victoria lagoon between the 5th of May and the 24th of June2009.

1 l Mod

5

liVscee

icwttbbcdcitsec

eEscdTpm

A

ittfiS(

R

A

B

B

B

B

B

C

12 H. Zouiten et al. / Ecologica

. Conclusion

From the application of the EnvHydrEM model on Victoriaagoon the authors can conclude that this eutrophication models able to reproduce the chlorophyll-a concentration trends inictoria lagoon. Therefore, the EnvHydrEM model can be con-idered a very useful tool for the eutrophication analysis inoastal lagoons as peculiar systems while considering the differ-nt bio-physicochemical processes that generally characterize thisnvironmental phenomenon in aquatic systems.

On the other hand, it was concluded that the Victoria lagoons a mesotrophic aquatic system in which silica is the most criti-al factor for the analysis of its eutrophication state. Moreover, itas deduced that the increase of chlorophyll-a concentration in

he system is often accompanied by an increase in the zooplank-on concentration, due mainly to the necessity of phytoplanktony zooplankton that consumes it for its growth, through the her-ivorism process. At the same time, the study found that theoncentrations of some nutrients such as nitrate and phosphateecrease due to their uptake by phytoplankton. Also it was con-luded that the concentration of organic phosphorus and nitrogenncrease with chlorophyll-a concentration increasing as a result ofhe simple kinetics of eutrophication and also as a consequence ofome processes such as zooplankton excretion or detritus remin-ralization that usually generate these substances in the waterolumn.

In summary, although the Victoria lagoon does not reach theutrophication state, this study shows the applicability of thenvHydrEM model as an important mathematical tool, which con-iders processes and interactions that have not been reflected inlassical eutrophication models. Also the ability of this model toescribe the trophic status changes in lagoons has been shown.herefore, its application to coastal lagoons with eutrophicationroblems may constitute an interesting tool for the planning ofanagement strategies in such aquatic systems.

cknowledgments

The authors would like to express their gratitude to the Span-sh Agency for International Development Cooperation (AECID)hough the scholarship offered to develop this work. As well,he study described in this paper is part of a research projectnanced by the VI National Plan(2008e2011) for Research incience & Technological Innovation of the Spanish GovernmentProject CGL2009-10620).

eferences

umont, O., 2004. PISCES biogeochemical model. In: Scientific Manual. Laboratoired’Océanographie Dynamique et de Climatologie.

árcena, J.F., García, A., Gómez, A.G., Álvarez, C., Juanes, J.A., Revilla, J.A., 2012. Spatialand temporal flushing time approach in estuaries influenced by river and tide:an application in Suances Estuary (northern Spain). Estuar. Coast Shelf Sci. 112,40–51.

erger, C.J., 1994. Water Quality Modeling of the Tualatin River. Technical Report.EWR-OO1-94. Portland State University.

issett, W.P., Walsh, J.J., Dieterle, D.A., Carder, K.L., 1999. Carbon cycling in the upperwaters of the Sargasso Sea: I. Numerical simulation of differential carbon andnitrogen fluxes. Deep Sea Res. Part I Oceanogr. Res. Pap. 46, 205–269.

oyd, P.W., Jickells, T., Law, C.S., Blain, S., Boyle, E.A., Buesseler, K.O., Coale, K.H.,Cullen, J.J., De Baar, H.J.W., Follows, M., Harvey, M., Lancelot, C., Levasseur, M.,Owens, N.P.J., Pollard, R., Rivkin, R.B., Sarmiento, J., Schoemann, V., Smetacek, V.,Takeda, S., Tsuda, A., Turner, S., Watson, A.J., 2007. Mesoscale iron enrichmentexperiments 1993–2005: synthesis and future directions. Science 31, 612–617,315.

ruce, L.C., Hamilton, D., Imberger, J., Gal, G., Gophend, M., Zohary, T., Hambright,

K.D., 2006. A numerical simulation of the role of zooplankton in C, N and P cyclingin Lake Kinneret, Israel. Ecol. Modell. 193, 412–436.

ancela Da Fonseca, L., Bernardo, J.M., Costa, A.M., Falcão, M., Vale, C., 2001. Seasonalchemical changes and eutrophication of a land-locked coastal lagoon (St. André,Sw Portugal). Bol. Mus. Mun. Funchal. 6, 167–183.

elling 265 (2013) 99– 113

Carlson, R.E., Simpson, J., 1996. A Coordinator’s Guide to Volunteer Lake MonitoringMethods. North American Lake Management Society, Madison, WI.

Cerco, C.F., Cole, T., 1995. User’s Guide to the CE-QUAL-ICM: Three-DimensionalEutrophication Model. Technical Report EL-95e1.5. U.S. Army Corps of Engineers.Vicksburg, MS.

Chao, X., Jia, Y., Shields Jr., F.D., Wang, S.S.Y., Cooper, C.M., 2010. Three-dimensionalnumerical simulation of water quality and sediment-associated processes withapplication to a Mississippi Delta lake. J. Environ. Manage. 91, 1456–1466.

Chapra S.C., Pelletier G.J., 2003. QUAL2K: A Modeling Framework for Simulating Riverand Stream Water Quality. Documentation and Users Manual Version 2.04. Civiland Environmental Engineering Dept, Tufts University, Medford, MA.

Chapra, S.C., 1997. Surface Water Quality Modeling. McGraw-Hill Series in WaterResources and Environmental Engineering, McGraw-Hill, New York.

Chau, K.W., Jin, H., 1998. Eutrophication model for a coastal bay in Hong Kong. J.Environ. Eng. 62, 628–638.

Chen, C.Y., Durbin, E.G., 1994. Effects of pH on the growth and carbon uptake ofmarine phytoplankton. Mar. Ecol. Prog. Ser. 109, 83–94.

Christoffersen, K., Riemann, B., Klysner, A., Sondergaard, M., 1993. Potential role offish predation and natural populations of zooplankton in structuring a planktoncommunity in eutrophic lake water. Limnol. Oceanogr. 38 (3), 561–573.

Cole T., Buchak E. 1995. CE-QUAL-W2: A Two-Dimensional, Laterally Averaged,Hydrodynamic and Water Quality Model. Technical Report. v2. 0EL-95-May1995. Waterways Experiments Station. Vicksburg, MS.

Costanza, R., Wainger, L., Folke, C., Maler, K.G., 1993. Modeling complex ecologicaleconomic-systems to ward an evolutionary, dynamic understanding of peopleand nature. Biosciences 43, 545–555.

Day Jr., J.W., Hall, C.H.S., Kemp, W., Yanez-Arancibia, A., 1989. Estuarine Ecology.Wiley, New York.

De Baar, H.J.W., Boyd, P.W., Coale, K.H., Landry, M.R., Tsuda, A., Assmy, P., Bakker,D.C.E., Bozec, Y., Barber, R.T., Brzezinski, M.A., Buesseler, K.O., Boyé, M., Croot,P.L., Gervais, F., Gorbunov, M.Y., Harrison, P.J., Hiscock, W.T., Laan, P., Lancelot,C., Law, C.S., Levasseur, M., Marchetti, A., Millero, F.J., Nishioka, J., Nojiri, Y., VanOijen, T., Riebesell, U., Rijkenberg, M.J.A., Saito, H., Takeda, S., Timmermans, K.R.,Veldhuis, M.J.W., Waite, A.M., Wong, C.S., 2005. Synthesis of iron fertilizationexperiments: from the iron age in the age of enlightenment. J. Geophys. Res.110, 1–24.

Del Barrio, P., García, A., García, J., Álvarez, C., Revilla, J.A., 2012. A model for describ-ing the eutrophication in a heavily regulated coastal lagoon: application to theAlbufera of Valencia (Spain). J. Environ. Manage. 112, 340–352.

Di Toro, D.M., Thomann, R.V., O‘Connor, D.J., Mancini, J.L., 1977. Estuarine phyto-plankton biomass models verification analyses and preliminary applications. In:Goldberg, et al. (Eds.), The Sea Ideas and Observations on Progress in the Studyof the Seas, vol. 6. Wiley-Interscience (John Wiley & Sons), New York, London,Sydney, Toronto, Chap 25.

Di Toro, D.M., O’Connor, D.J., Thomann, R.V., Mancini, J.L., 1975.Phytoplankton–zooplankton–nutrient interaction model for Western Lake Erie.systems analysis and simulation. Ecology III, 423–474.

Drago, M., Cescon, B., Iovenitti, L., 2001. A three-dimensional numerical model foreutrophication and pollutant transport. Ecol. Modell. 145, 17–34.

Edwards, A.M., 2001. Adding detritus to a nutrient–phytoplankton–zooplanktonmodel: a dynamical-systems approach. J. Plankton Res. 23 (4), 389–413.

Ekdal, A., Tanik, A., Ambrose Jr., R.B., 2011. Data inventory and analysis for theapplication of WASP in a coastal lagoon. J. Coastal Res. (SPEC. ISSUE 64) (64),885–889.

Eppley, R.W., Sloan, P.R., 1965. Carbon balance experiments with marine phyto-plankton. Can. J. Fish Aquat. Sci. 22, 1083–1097.

Estrada, V., Diaz, M.S., 2010. Global sensitivity analysis in the development of firstprinciple-based eutrophication models. Environ. Modell. Softw. 25, 1539–1551.

Farnsworth-Lee, L.A., Lawrence, A.B., 2000. Conceptual model of aquatic plant decayand ammonia toxicity for shallow lakes. J. Environ. Eng. 199.

Faure, V., Pinazo, C., Torréton, J.P., Jacquet, S., 2010. Modelling the spatial and tem-poral variability of the SW lagoon of New Caledonia I: A new biogeochemicalmodel based on microbial loop recycling. Mar. Pollut. Bull. 61, 465–479.

Ganf, G.G., Blazka, P., 1974. Oxygen uptake, ammonia and phosphate excretionby zooplankton of a shallow equatorial lake (Lake George, Uganda). Limnol.Oceanogr. 19, 313–325.

García, A., Sámano, M.L., Juanes, J.A., Medina, R., Revilla, J.A., Álvarez, C., 2010. Assess-ment of the effects of a port expansion on algae appearance in a costal baythrough mathematical modeling: application to San Lorenzo Bay (North Spain).Ecol. Modell. 21, 1413–1426.

Garcia-Webb, J., 2003. A Numerical Study of the Water Quality in Wilson Inlet. FinalYear Project Dissertation. Centre for Water Research. The University of Western.Australia.

Gu, J., Li, W.T., Huang, J., Han, B., 2012. Numerical study on the impact of the sewagedrainage on water quality of Shanghai water sources. Adv. Mater. Res. 102,1020–1026.

Hajda, P., Novotny, V., 1996. Modelling impact of urban and upstream nonpointsources on eutrophication of the Milwaukee River. Water Sci. Technol. 32 (4-5),153–158.

Hedström, K.S., 2009. DRAFT Technical Manual for a Coupled Sea-Ice/Ocean Circula-tion Model. v3. Alaska. Department of the Interior Minerals Management Service

Anchorage. Arctic Region Supercomputing Center University of Alaska FairbanksU.S.

Hipsey, M.R., Romero, J.R., Antenucci, J.P., Hamilton, D., 2006. Computational AquaticEcosystem Dynamics Model: CAEDYM v2. Science Manual v2.3. University ofWestern Australia.

l Mod

H

H

H

K

K

K

K

K

K

K

K

K

L

L

L

L

L

M

M

M

M

M

M

H. Zouiten et al. / Ecologica

ipsey, M.R., Romero, J.R., Antenucci, J.P., Hamilton, D.P., 2004. Computationalaquatic ecosystem dynamics model CAEDYM v2. In: Science Manual. Centre forWater Research, The University of Western Australia, Crawley, Australia, WP1387.1 MH.

u, H., Chen, W., Shi, Y., Cong, W., 2006. Nitrate and phosphate supplementation toincrease toxin production by the marine dinoflagellate Alexandrium tamarense.Mar. Pollut. Bull. 52, 756–760.

ulail, F., 2010. Water quality description and modelling for South of Kuwait: Az-Zour area Al- 2010ICCCE. International Conference on Chemistry and ChemicalEngineering. Proceedings. art. no. 5560418, pp. 346–349.

ellershohn, D.A., Tsanis, I.K., 1999. 3D eutrophication modeling of Hamilton har-bour: analysis of remedial options. J. Great Lakes Res. 25 (1), 3–25.

ishi, M.J., Kashiwai, M., Ware, D.M., Megrey, B.A., Eslinger, D.L., Werner, F.E.,Noguchi-Aita, M., Azumaya, T., Fujii, M., Hashimoto, S., Huang, D., Iizumi, H.,Ishida, Y., Kang, S., Kantakov, G.A., Kim, H.C., Komatsu, K., Navrotsky, V.V., LanSmith, S., Tadokoro, K., Tsuda, A., Yamamura, O., Yamanaka, Y., Yokouchi, K.,Yoshie, N., Zhang, J., Zuenko, Y.I., Zvalinsky, V.I., 2007. NEMURO-a lower trophiclevel model for the North Pacific marine ecosystem. Ecol. Modell. 202, 12–25.

im, S.C., Cerco, C.F., 2003. Hydrodynamic and Eutrophication Model of the ChesterRiver Estuary and the Eastern Bay Estuary. Draft Report, December 2003.

itazawa, D., Kumagai, M. Numerical simulation on seasonal variation in dissolvedoxygen tension in Lake Biwa. In: Proceedings of the Second Joint Japan/KoreaWorkshop on Marine Environmental Engineering, 2005. pp. 71–91.

jerfve, B., 1994. Coastal Lagoons Processes. Elsevier Science Publ., Amsterdam.Oceanography Series, pp. 577, 60.

jerfve, B., Magill, K.E., 1989. Geographic and hydrodynamic characteristics of shal-low coastal lagoons. Mar. Geol. 88, 187–199.

nauer, K., Jabusch, T., Sigg, L., 1999. Manganese uptake and Mn(II) oxidation by thealga Scenedesmus subspicatus. Aquat. Sci. 61, 44–58.

noppers, B, 1994. Aquatic primary production in coastal lagoons. In: Kjerfve B.(Ed.) Coastal Lagoon Processes, Elsevier Oceanography Series, vol. 60. Elsevier,Amsterdam, pp. 243–286.

uo, J.T., Lung, W.S., Yang, C.P., Liu, W.C., Yang, M.D., Tang, T.S., 2006. Eutrophicationmodelling of reservoirs in Taiwan. Environ. Modell. Softw. 21, 829–844.

ancelot, C., Hannon, E., Becquevort, S., Veth, C., De Baar, H.J.W., 2000. Modelingphytoplankton blooms and carbon export production in the Southern Ocean:dominant controls by light and iron in the Atlantic sector in Austral spring 1992.Deep Sea Res. Part I Oceanogr. Res. Pap. 47, 1621–1662.

ancelot, C., 1979. Gross excretion rates of natural marine phytoplankton and het-erotrophic uptake of excreted products in the southern north sea, as determinedby short-term kinetics. Mar. Ecol. Prog. Ser. 1, 179–186.

ee, J.H.W., Wu, R.S.S., Cheung, Y.K., 1991. Forecasting of dissolved oxygen in marinefish culture zone. J. Environ. Eng., 816–833, ASCE.117.6:.

essin, G., Raudsepp, U., 2006. Water quality assessment using integrated model-ing and monitoring in Narva Bay, Gulf of Finland. Environ. Model Assess. 11,315–332.

iu, W.C., Chen, W.B., Kimura, N., 2009. Impact of phosphorus load reduction onwater quality in a stratified reservoir-eutrophication modeling study. Environ.Monit. Assess. 159, 393–406.

äkynen, A., 2009. AQUATOX—Ecological Risk Assessment Model—A Tool for ImpactAssessment for Waters. Report. Department of Biological and EnvironmentalScience, University of Jyväskylä.

artín Monerris, M. (University of Valencia). 1998. Modelación de la calidad enaguas superficiales. Aplicación al caso de la Albufera de Valencia,. PhD Thesis.

egrey, B.A., Rose, K.A., Klumb, R.A., Hay, D.E., Werner, F.E., Eslinger, D.L.,Smithg, S.L., 2007. A bioenergetics-based population dynamics model ofPacific herring (Clupea harengus pallasi) coupled to a lower trophic levelnutrient–phytoplankton–zooplankton model: description, calibration, and sen-sitivity analysis. Ecol. Modell. 202, 144–164.

issaghi, S., Hondzo, M., 2010. Evaluation and application of a three-dimensionalwater quality model in a shallow lake with complex morphometry. Ecol. Modell.

221, 1512–1525.

itsch, W.J., Gosselink, J.G., 1993. Wetlands, second ed. Van Nostrand Reinhold Co,New York.

uhammetoglu, A., Soyupak, S., 2000. A three-dimensional water quality-macrophyte interaction model for shallow lakes. Ecol. Modell. 133, 161–180.

elling 265 (2013) 99– 113 113

Nguyen, D.T., Harada, M., Hiramatsu, K., 2010. Evaluation of the water-qualitydynamics in a eutrophic agricultural pond by using a one-box ecosystem modelconsidering several algal groups. Paddy Water Environ. 8, 301–318.

OCDE Cooperation and Economic Development Organization, 1982. Eutrophicationof Waters—Monitoring Assessment and Control. Paris.

Park, R.A., Clough, J.S., Wellman, M.C., 2008. AQUATOX: modeling environmentalfate and ecological effects in aquatic ecosystems. Ecol. Modell. 213, 1–15.

Park, K., Jung, H.S., Kim, H.S., Ahn, S.M., 2005. Three-dimensional hydrodynamic-eutrophication model (HEM-3D): application to Kwang-Yang Bay, Korea. Mar.Environ. Res. 60, 171–193.

Park, K., Kuo, A.Y., Shen, J., Hamrick, J.M., 1995. A three-dimensional hydrodynamic-eutrophication model (HEM-3D): description of water quality and sedimentprocess submodels (EFDC water quality model). In: Special Report in AppliedMarine Science and Ocean Engineering. School of Marine Science Virginia, Insti-tute of Marine Science, College of William and Mary Gloucester Point, VA 23062,pp. 327.

Pereira, P., De Pablo, H., Vale, C., Rosa-Santos, F., Cesário, R., 2009. Metal and nutrientdynamics in a eutrophic coastal lagoon (Óbidos, Portugal): the importance ofobservations at different time scales. Environ. Monit. Assess. 158, 405–418.

Rigosi, A., Marcé, R., Escot, C., Rueda, F.J., 2011. A calibration strategy for dynamicsuccession models including several phytoplankton groups. Environ. Modell.Softw. 26, 697–710.

Rivera, D., 2002 Modelación del oxígeno disuelto en lagos estratificados incorpo-rando los efectos del viento. Memory Report. Department of Civil Engineering,Faculty of Engineering, University of Concepción.

Rubio López, C. 2009. Análisis y estimación de la evolución térmica del embalse deAmadorio con un modelo hidrodinámico. Master Thesis. University of Valencia.

Ryding, S.O., Rast, W., 1992. El control de la eutrofización en lagos y pantano, EditionsPirámide. UNESCO, Madrid.

Sámano, M.L., Bárcena, J.F., García, A., Gómez, A.G., Álvarez, C., Revilla, J.A., 2012.Flushing time as a descriptor for heavily modified water bodies classificationand management: application to the Huelva Harbour. J. Environ. Manage. 107,37–44.

Saraiva, S., Pina, P., Martins, F., Santos, M., Braunschweig, F., Neves, R., 2007. Mod-elling the influence of nutrient loads on Portuguese estuaries. Hydrobiologia 587(1), 5–18.

Schladow, S.G., Fleenor, W.E., Bombardelli, F.A., Chung, E.G., 2007. Quantifying Sedi-ment Resuspension Linkages to Nutrient Enrichment in the Existing and FutureSalton Sea. In: UC Water Resources Center Technical Completion Report. ProjectNo. W-998. December 2007.

Schoemann, V., Wollast, R., Chou, L., Lancelot, C., 2001. Effects of photosynthesis onthe accumulation of Mn and Fe by Phaeocystis colonies. Limnol. Oceanogr. 46(5), 1065–1076.

Tetra Tech Inc., 1980. Methodology for Evaluation of Multiple Power Plant CoolingSystem Effects. Volume V. Methodology Application to Prototype—Cayuga Lake.Report EPRI EA-1111. Electric Power Research Institute.

Thomann, R.V., Mueller, J.A., 1987. Principles of surface water quality modeling andcontrol. Harper Collins Publishers Inc., New York.

Tkalich, P., Sundarambal, P., 2003. Eutrophication modeling for Singapore waters.Sing. Mar. & Port. J. 122–136.

Tkalich, P., Pang, W.C., Sundarambal, P. Hydrodynamics and eutrophication mod-elling for Singapore straits. In: Proceedings of The Seventh Workshop onOcean Models for the APEC Region (WOM-7), 30 September–2 October 2002,Singapore. Vol. 5, pp. 1–9.

Trancoso, A.R., Braunschweig, F., Leitão, P.C., Obermann, M., Neves, R., 2009. Anadvanced modelling tool for simulating complex river systems. Sci. Total Envi-ron. 407, 3004–3016.

Vollenweider, R.A., 1976. Advances in defining critical loading levels for phosphorusin lake eutrophication. Mem. Ist. Ital. Idrobiol. 33, 53–83.

Wool, T.A., Ambrose, R.B., Martin, J.L., Comer, E.A., 2001. Water Quality AnalysisSimulation Program (WASP). Report 652. US Environmental Protection Agency,

Atlanta, GA.

Zhao, H., Li, Q., Yue, X., Tao, J., 2009. Ecohydrodynamic Modelling of DissolvedOrganic Carbon Cycle. IEEE 978-1-4244-2902-8/09.

Zheng, L., Chen, C., Zhang, F.Y., 2004. Development of water quality model in theSatilla River estuary, Georgia. Ecol. Modell. 178, 457–482.