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Ecological Modelling 221 (2010) 1194–1208

Contents lists available at ScienceDirect

Ecological Modelling

journa l homepage: www.e lsev ier .com/ locate /eco lmodel

ssesment of the response of a shallow macrotidal estuary to changes inydrological and wastewater inputs through numerical modelling

ndrés Garcíaa,∗, José A. Juanesa,1, César Álvareza,1, José A. Revillaa,1, Raúl Medinab,2

Submarine Outfall and Environmental Hydraulics Group, Institute of Environmental Hydraulics, University of Cantabria, Avda. de los Castros s/n, 39005 Santander, SpainOcean and Coastal Research Group, Institute of Environmental Hydraulics, University of Cantabria, Avda. de los Castros s/n, 39005 Santander, Spain

r t i c l e i n f o

rticle history:eceived 16 June 2009eceived in revised form0 November 2009ccepted 31 December 2009vailable online 4 February 2010

eywords:utrophicationhallow estuary

a b s t r a c t

The aim of this study was to investigate the response to short-term changes in river freshwater dischargesand in nutrients loadings (mainly from the treatment of urban wastewater), of the shallow macrotidalUrdaibai estuary (north of Spain), by using numerical tools. A two-dimensional hydrodynamic modeland a water quality model were applied to the estuary, in order to better use it as a prediction tool inthe study of the effects of variations in hydrodynamic conditions and in waste water inputs. The modelwas calibrated and verified using data measured under different hydrological conditions (spring andsummer). A model calibration was carried out with field data measured during the summer, while themodel validation was conducted for spring conditions. The calibration process allowed the model param-eter definition, while the model validation permitted the verification of the calibrated parameters under

odellinghytoplanktonastewater discharge

rdaibai

different environmental conditions. The model results were in reasonable agreement with field mea-surements, in both the calibration and the validation phases. The model showed a significant decreasein phytoplankton concentration with river input increase. A study on the effects of nutrient input reduc-tion from the Gernika Waste Water Treatment Plant (WWTP) was conducted. It showed a decrease inphytoplankton concentration with decreasing levels of nutrient discharge. This reduction was more pro-nounced in conjunction with the highest river discharge. In that case, a 50% decrease for the elimination

as ob

of the WWTP discharge w

. Introduction

Estuaries receive inorganic nutrients and organic matter fromhe land and represent important systems where terrestrial nutri-nts and organic matter are processed before entering the oceanOrtega et al., 2005). As a result, the ecology and biodiversity ofstuarine waters in many parts of the world are under the threat ofncreasing anthropogenic nutrient inputs (Nixon, 1995).

Phytoplankton growth is limited by several environmental fac-ors, including light, temperature and nutrients. Among theseactors, only nutrients can be controlled; hence, they have been the

ocus of most efforts to control algal blooms responsible of wateruality deterioration (Na and Park, 2006). Knowledge of the nutri-nt assimilation capacity of estuarine ecosystems is essential forater quality management and rehabilitation. The main adverse

∗ Corresponding author. Tel.: +34 942201704; fax: +34 942201714.E-mail addresses: garciagan@unican.es (A. García), juanesj@unican.es

J.A. Juanes), alvarezc@unican.es (C. Álvarez), revillaj@unican.es (J.A. Revilla),edinar@unican.es (R. Medina).1 Tel.: +34 942201704.2 Tel.: +34 942201810.

304-3800/$ – see front matter © 2010 Elsevier B.V. All rights reserved.oi:10.1016/j.ecolmodel.2009.12.027

served.© 2010 Elsevier B.V. All rights reserved.

effects of uncontrolled eutrophication are depression of oxygen lev-els and the massive occurrence of harmful algal species. All this hasnegative effects on water quality and food webs. Thus, manage-ment of aquatic ecosystems has traditionally focused on reducingnutrient input.

However, besides nutrient control strategies, hydrologicalconditions may have an impact on the response of estuarine ecosys-tems to eutrophication (Chan et al., 2002). Water mass circulationin estuaries is predominantly controlled by fresh water discharges,tidal circulation and atmospheric forces. The time scale of physicalprocesses ranges from hours to seasons and large spatial gradientsaffect the hydrographic conditions and nutrient distribution (Dyer,1997).

The high number of factors influencing eutrophication processeshas led to consider the use of complex mathematical tools forstudying and predicting their evolution. Water quality models areessential tools to evaluate the impact of human activities in estu-arine ecosystems.

Shallow estuaries are characterized by the presence of wide tidalflats which yield a considerable variability in the water domain.This feature increases the difficulty of eutrophication modellingand restricts the application of many water quality models thathave been successfully validated in environments in which water

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A. García et al. / Ecological M

omain changes have no substantial effects, such as deep estuariesnd bays, coastal areas or enclosed seas.

The objective of this study was to develop a quantitative under-tanding of the Urdaibai estuary response to hydrological inputsnd wastewater loading. The Urdaibai estuary is a shallow uniqueabitat for many species. It receives urban inputs, especially fromhe village of Gernika, which cause its environmental degradationCortazar et al., 2008). The estuarine ecosystem is also highly vari-ble. During spring and summer, intense phytoplankton bloomssually develop in the upper reaches of the estuary (Madariaga,002).

Several previous studies have been carried out in order to char-cterize the short-term variability of phytoplankton in the Urdaibaistuary from field observations. However, scarce modelling wassed due to the complex geometry of the estuary and the highariability of river flow and nutrient fluxes.

For this study, we set up and implemented a two-dimensionalater quality model for the estuary. Here, we present the cali-

ration and validation of the model based on data measured atifferent meteorological and hydrodynamic conditions. Once cal-

brated, the model was used to study the effect of variations inydrodynamic conditions on estuarine water quality. The modelas also applied to analyse future scenarios of nutrient input reduc-

ion by the Gernika WWTP. The results and conclusions of thesetudies are described.

. Study area

The Urdaibai Estuary is a meso-macrotidal temperate estuaryocated in the Basque Country (43◦22′N, 2◦40′W), north of SpainFig. 1). An important geomorphological feature of the estuary ists shallowness (on average 2.6 m deep in the main channel). Itas a drainage area of 149 km2, being about 12.5 km long, from

ts mouth to the city of Gernika, and with a maximum width of.2 km.

The estuary receives inputs from several rivers and it is well-ixed to partially mixed depending on river discharge and tidal

ange. The strongest salinity gradient occurs generally within lesshan 4 km of the upper estuary, where salinity oscillates betweenear zero and more than 30 psu. The estuary passes from a well-ixed state during periods of low river discharge and spring tides,

o partially mixed during neap tides or freshets. Thus, the estuarys river dominated during freshets but tidally dominated duringummer, when river discharges tend to be low (Ruiz et al., 1994).

Water residence time is less than 1 day on average in the lowerstuary due to the large tidal amplitude. Conversely, in the upperstuary it experiences drastic changes depending on river flow,scillating from 1 day to more than 3 months during prolongedry periods (Revilla et al., 2002).

The Oka River basin has a drainage area of 67 km2, with theain stream covering a distance of 25 km. The average rainfall is

ver 1400 mm per year and the overall runoff coefficient is 0.64.ther rivers entering the estuary are the Mape and the Golako. Therainage area of the latter is 34 km2, while the basin drainage ofhe Mape River covers an area of 20 km2.

The estuary is surrounded by relatively broad tidal flats alongts lower reaches and by salt-marshes in its middle part. These geo-

orphological features determine the estuarine ecosystem, whichan vary considerably in volume and flushing rates, strongly affect-ng its biological and chemical properties (Madariaga, 2002).

The catchment area is essentially rural, and industrial activitiesre mainly concentrated in the town of Gernika, which with a pop-lation of 16255 inhabitants, is located in the upper estuary. As aonsequence, estuarine waters receive the discharge of a domes-ic sewage treatment plant effluent near Gernika that introduces

ing 221 (2010) 1194–1208 1195

a considerable amount of nutrients (mainly ammonia and phos-phate) to the estuary, causing eutrophication (Fraile et al., 1992).

3. Model description

The model reproduces estuarine water movements during atidal cycle using short time steps from hydrodynamic model results(velocity components, water surface level) obtained by previouslyrunning a two-dimensional hydrodynamic model. This workingscheme allows a larger time step to be selected in order to modeleutrophication than that used for the velocity field calculation. Butin the case of shallow estuaries, this methodology has the disad-vantage that during hydrodynamic data interpolation inside thewater quality model there is a loss of information about the realinstant in which wetting and drying of the tidal flat occur. To avoidthis problem, caused by using hydrodynamic results stored sepa-rately in time, a subroutine has been included in the model thatallows water surface level to be computed in the entire estuar-ine domain from two consecutive water level recordings providedby the hydrodynamic model (García et al., 2002). In this way theexact instant in which shallow areas get wet or dry is accuratelycomputed.

3.1. The hydrodynamic model

The hydrodynamic and transport models used in this worksolve the two-dimensional vertically integrated hydrodynamic andtransport equations. The numerical computation is carried out on aspatial domain that represents the entire estuary through a finite-difference grid. The system of equations is expressed in Cartesiancoordinates (x increasing eastward and y increasing northward)and following the hydrostatic assumption and Boussinesq approx-imation.

The hydrodynamic model uses the so-called alternating direc-tion implicit technique (ADI), to integrate the depth-averaged massand momentum equations in the space–time domain, which areexpressed as:

∂H

∂t+ ∂(UH)

∂x+ ∂(VH)

∂y= 0 (1)

∂(UH)∂t

+ ∂(U2H)∂x

+ ∂(UVH)∂y

= fVH − gH∂�

∂x− gH2

2�0

∂�0

∂x

+ 1�0

[�xz(�) − �xz(−h)

]+ H�e

[∂2U

∂x2+ ∂2U

∂y2

]+ 2H

∂�e

∂x

∂U

∂x

+ H∂�e

∂y

[∂U

∂y+ ∂V

∂x

](2)

∂(VH)∂t

+ ∂(UVH)∂x

+ ∂(V2H)∂y

= −fUH − gH∂�

∂y− gH2

2�0

∂�0

∂y

+ 1�0

[�yz(�) − �yz(−h)

]+ H�e

[∂2V

∂x2+ ∂2V

∂y2

]+ 2H

∂�e

∂y

∂V

∂y

+ H∂�e

∂x

[∂U

∂y+ ∂V

∂x

](3)

where � is the water level (m), g is the gravitational acceleration

(m s−2), H = h + � is the total water depth (m), h is the undisturbedwater depth (m), U and V (m s−1) are the vertical integrated veloc-ities, �0 is the average water density, �e is the horizontal eddyviscosity, �iz(�) and �iz(−h) are the wind and bed stresses and f isthe Coriolis parameter. The bottom shear stress is represented as a

1196 A. García et al. / Ecological Modelling 221 (2010) 1194–1208

dynam

qf

To

C

w

f

Fig. 1. Study area and location of hydro

uadratic function of velocity parameterized in terms of the Chezyriction coefficient (C):

1�H

�iz(−h) = gUi

√U2 + V2

C2H(4)

he Chezy friction term can be expressed as a variable dependentn depth, using the following formula:

= Mh1/6 (5)

here M is the inverse of the Manning friction coefficient.

Salinity and temperature variations along the estuary are solved

rom the depth-averaged transport equation for these variables:

∂(HS)∂t

+ ∂(UHS)∂x

+ ∂(VHS)∂y

= ∂

∂x

(HDx

∂S

∂x

)+ ∂

∂y

(HDy

∂S

∂y

)(6)

ic and water quality sampling stations.

∂(HT)∂t

+ ∂(UHT)∂x

+ ∂(VHT)∂y

= ∂

∂x

(HDx

∂T

∂x

)+ ∂

∂y

(HDy

∂T

∂y

)

(7)

where S is the salinity (psu), T is the water temperature (◦C) andDx and Dy are the diffusivities in x and y. Density is calculated fromspatially variable salinity and temperature through the expressionsuggested by UNESCO (1981).

We chose a time interval of 5 s as time step for the hydro-

dynamic model in order to assure stable conditions. At openboundaries we prescribed the water level and freshwater flow,while at the open sea boundary, we prescribed tidal elevationfrom the Andersen–Grenoble version 95.1 model (Le Provost,2001).

A. García et al. / Ecological Modell

3

tTnddvr

tto

wai

wbgem

G

1

G

w2i

dependent on both temperature and salinity. To compute the oxy-

Fig. 2. Variables of the water quality model.

.2. The water quality model

The water quality model solves the system of differential equa-ions that describe the main chemical and biological interactions.he variables of the model are the following (see Fig. 2): ammo-ia (C1), nitrate (C2), phosphate (C3), phytoplankton biomass (C4),issolved BOD (C5), suspended BOD (C6), sediment BOD (C7) andissolved oxygen (C8). The spatial and temporal evolution of theseariables is influenced by external factors, such as incident solaradiation, or urban and freshwater discharges.

The water quality model is coupled to the hydrodynamic modelhrough the transport equation, which integrates the advection andhe diffusion properties of the flow, as well as the basic processesccurring in the estuary water column:

∂(HCi)∂t

+ ∂(UHCi)∂x

+ ∂(VHCi)∂y

= ∂

∂x

(HDx

∂Ci

∂x

)

+ ∂

∂y

(HDy

∂Ci

∂y

)+ RiH (8)

here Ci is the depth-averaged concentration for water quality (i),nd Ri describes the chemical reaction terms, corresponding to thenteraction equations for state variables.

Algal growth is described by a first-order kinetic expressionhere the first-order growth rate is defined as the difference

etween the growth (Gp) and the death (Dp) rates. Phytoplanktonrowth (Gp) is a function of light G(I), temperature G(T) and nutri-nts G(N), assuming the classical approach that these effects areultiplicative (Chapra, 1997):

p = G(T)G(I)G(N) (9)

The effect of temperature is expressed as (Thomann and Mueller,987):

T−20

(T) = Gmax�Gmax(10)

ere Gmax is the maximum daily growth rate of phytoplankton at0 ◦C (day−1) under optimal light and nutrient conditions and �Gmax

s the temperature coefficient.

ing 221 (2010) 1194–1208 1197

The penetration of incoming solar radiation is described by theLambert–Beer equation:

Iz = I0 exp(−Kez) (11)

where Iz is the light intensity at depth z (ly day−1) calculated fromlight surface intensity (I0) and the extinction coefficient (Ke). DailyI0, issued by the National Meteorological Institute from the mete-orological station G057 located at Mungia, was used in the model.The total light attenuation coefficient is determined by the effect ofwater and chlorophyll a, and can be expressed for the Urdaibai estu-ary as a function of suspended organic matter and phytoplanktonbiomass as follows:

Ke = 0.36948 + 0.99577(

C6

2.7+ C4

)(12)

The light-limitation function of Steele and Baird (Thomann andMueller, 1987) is used in the model. Vertically averaged G(I) over agiven water depth is integrated as:

G(I) = 2.718KeH

[exp(−˛1) − exp(−˛0)] (13)

where

˛1 = I0Is

exp(−KeH) (14)

˛0 = I0Is

(15)

being Is the saturating light intensity calculated as the weightedaverage of light intensity for the previous 3 days (Chau and Jin,1998).

All of the individual nutrient limitations are computed by aMichaelis–Menten-type expression and the minimum value is cho-sen for G(N) (Chao et al., 2007):

G(N) = min{

(C1 + C2)KmN + (C1 + C2)

,C3

KmP + C3

}(16)

The preferential uptake of ammonia as compared to nitrate forphytoplankton growth kinetics is described as:

PNH3 = C1

[C2

(KmN + C1)(KmN + C2)+ KmN

(C1 + C2)(KmN + C2)

](17)

Phytoplankton mortality is described as the sum of phyto-plankton endogenous respiration and zooplankton grazing. Theendogenous respiration rate is expressed as proportional to algalbiomass and the carbon/chlorophyll (CCHL) ratio:

kr = 37.85 CCHL C4 (18)

The CCHL ratio (mg C/mg Chl-a) is considered a variable whichis affected by light, temperature and algae (Cloern et al., 1995):

CCHL = 0.003 + 0.0154exp(0.05 T) exp(−0.059Iav)G(N) (19)

where Iav is the depth-averaged daily irradiance.The effect of temperature over endogenous respiration is given

by:

kr(T) = kr(20 ◦C)�T−20r (20)

where kr is the endogenous respiration rate and �r is a temperaturecoefficient.

In the water column, biological oxidation of NH3 to NO3 anddenitrification are all considered a function of temperature anddissolved oxygen (Chau and Jin, 1998).

The dissolved oxygen concentration at saturation is considered

gen saturation value we used the following equation (Lopes et al.,2008):

Cs = 14.652 − 0.0841S + T[0.0026S − 0.41022 + T ˇ(S, T)

](21)

1198 A. García et al. / Ecological Modelling 221 (2010) 1194–1208

Table 1Water quality model interactions.

Variable Interaction equation

Ammonia-N dC1dt

= Ydkd�(T−20)d

C5 + Ysks�(T−20)s C6 + Ybkb�(T−20)

bC7 + (kr �(T−20)

r + kdm)anc(1 − fon)C4 − G(T)G(I)G(N)PNH3 ancC4 − kn�(T−20)n

[C8

(C8+knit )

]C1

Nitrate-N dC2dt

= kn�(T−20)n

[C8

(C8+knit )

]C1 − G(T)G(I)G(N)(1 − PNH3 )ancC4 − kdn�(T−20)

dn

[kNO

(C8+kNO)

]C2

Phosphate-P dC3dt

= Yd2kd�(T−20)

dC5 + Ys2 ks�

(T−20)s C6 + Yb2

kb�(T−20)b

C7 + apc(kr �(T−20)r + kdm)(1 − fop)C4 − G(T)G(I)G(N)apcC4

Phytoplankton-C dC4dt

=[

G(T)G(I)G(N) − (kr �(T−20)r + kdm) − Vph

H

]C4

Dissolved BOD dC5dt

= −kd�(T−20)d

C5

Suspended BOD dC6dt

= VrH C7 + (kr �(T−20)

r + kdm)(ancfon + apcfop)C4 − VsH C6 − ks�

(T−20)s C6

Settled BOD dC7dt

= VsH C6 − Vr

H C7 − kb�(T−20)b

C7 + VphH C4

PNH3 )

w

ˇ

pm

aapt(bsctt

ot

iAm

4

4

stobn

dssaFeWtm

td

Dissolved oxygen dC8dt

= ka�(T−20)a (Cs − C8) + G(T)G(I)G(N)

[3212 + 48

14 anc(1 −Ybkb�(T−20)

bC7) − SOD

H �(T−20)SOD

ith

(S, T) = 0.007991 − 0.0000374S − 0.000077774T (22)

The re-aeration kinetic constant ka is influenced by water tem-erature, flow characteristics (flow velocity and water depth) andeteorological conditions (wind).The developed model allows Eq. (8) to be solved by using

n explicit finite-difference scheme based on the split operatorpproach, in which advection and diffusion processes are com-uted independently for each time step. This approach facilitateshe use of different numerical methods for solving each processKomatsu et al., 1997). Hence, advective transport is computedy an upwind scheme while diffusion is described by a centredcheme. In a further step, changes in water quality concentrationaused by reaction processes are computed. The time step for theransport model was 30 s, in order to assure stable solutions forransport equations.

The whole system of equations which describe the interactionsf 8 state variables is presented in Table 1. The symbols along withhe units used for the model are listed in Table 2.

The discharge of the Gernika WWTP is treated as a point-sourcenput of organic matter and nutrients into the water quality model.t the discharge cell, a mass balance assuming complete mixing isade, due to its small water depth.

. Model calibration

.1. Field data

A model calibration may be defined as an operation by whichpecific values, distributions, or a range of variations are given tohe floating free model parameters, so that the model results fitptimally to a set of field observations (Lopes et al., 2008). To cali-rate the model, both hydrodynamic and water quality data wereeeded.

The hydrodynamic model was calibrated and validated withata measured between January 1998 and February 1998 at sixtations distributed along the estuary. The collected data includedurface elevation, velocities and freshwater inputs from the Okand Mape rivers. The location of the sampling stations is shown inig. 1. Two tide gauge stations were located at the middle part of thestuary (Murueta shipyards) and at the inner part (near the GernikaWTP), respectively, during a time period of 15 days. A tidal dis-

ortion is observed as the tide moves landwards. This distortion isore pronounced during spring tides at low tide.Velocity measurements were carried out at four different loca-

ions (stations H1, H2, H5 and H7) distributed along the estuaryuring neap and spring tides. Maximum velocities were registered

]C4 − 32

12 kr �(T−20)r C4 − 64

14 kn�(T−20)n

[C8

(C8+kn)

]C1 − (Ydkd�(T−20)

dC5 + Ysks�

(T−20)s C6 +

during spring tides near the mouth of the estuary with valuesover 1 m s−1. In the upper estuary maximum velocities were about0.8 m s−1. The velocity field during spring tides was more than twicethe magnitude found during neap tides.

Time series of water quality variables were measured for a4-day period in May and July 1999, at four locations in the estu-ary (stations WQ1, WQ3, WQ5 and WQ7), which are indicated inFig. 1. Vertical profiles of salinity, temperature, dissolved oxygenand chlorophyll a were taken. The sampling carried out in May wasintended to evaluate the estuary recovery after a period of heavyrain. That of July, in contrast, was conducted during a relatively dryperiod in order to see the daily changes in water quality associatedwith changes in radiation.

In May, salinity remained low and constant at the uppermostsite during the study period. In contrast, station WQ5 experi-enced strong changes in water salinity, which varied in the surfacefrom 1.1 psu, on the first day, to 12.5 psu on the last one. Tem-perature experienced drastic changes along the estuary, mainly atstation WQ5 which ranged from 16 to 21 ◦C. Oxygen concentrationsremained near saturation level during the sampling period. Tem-perature experienced a significant increase at the inner stations,while it remained constant in the sea area. Chlorophyll a valueswere low, both in the river and at the upper estuary. The averageOka River discharge at the Muxika gauging station in this samplingperiod was less than 0.5 m3 s−1, and the tidal range varied between2.0 and 2.7 m.

During the summer the main physical feature was the decreasein the oxygen concentration from the mouth towards the upperestuary. Salinity and temperature remained rather constant. Inthe upper estuary, the water column stratification caused by lowriver discharges enhanced phytoplankton growth and planktonmetabolism, leading to anoxic conditions for most of the water col-umn. The concentration of chlorophyll a was typical of the summer,with peaks at station WQ8, and the minimum values in the sea area.At station WQ1 values lower than 2 �g l−1 were observed whileat station WQ8, the daily changes in the concentration of chloro-phyll, which ranged between 14.8 and 50.2 �g l−1, were due to theeffect of solar radiation. The average Oka River discharge during thisperiod was about 0.13 m3 s−1 and the tidal range varied between1.7 and 2.0 m.

The spatio-temporal variability in the estuary hydrographicproperties during the two surveys is summarized in Table 3. Table 4shows the nutrient concentrations measured at the mouth of the

main rivers flowing into the estuary.

Benthic fluxes of dissolved oxygen were derived from datacollected in five sampling stations along the estuary by Ortegaet al. (2005). The sediment oxygen demand (SOD) varied from1.5 gO2 m−2 day−1 at the mouth of the estuary to 8.4 gO2 m−2 day−1

A. García et al. / Ecological Modelling 221 (2010) 1194–1208 1199

Fig. 3. Sea surface elevation from model results (continuous line) and from the tide gauge located at the Murueta shipyards and at the Gernika WWTP.

Fig. 4. Depth-averaged velocities measured (black dots) and modelled (continuous line) during neap tides.

1200 A. García et al. / Ecological Modelling 221 (2010) 1194–1208

Table 2Coefficients and constants used in the model.

Parameter Description Value Units

Fr d Fraction of dissolved BOD 0.5 –kd BOD dissolved degradation rate 0.4 day−1

ks BOD suspended degradation rate 0.4 day−1

kb BOD settled degradation rate 0.2 day−1

�d Temperature coefficient for dissolved BOD degradation 1.047 –�s Temperature coefficient for suspended BOD degradation 1.047 –�b Temperature coefficient for settled BOD degradation 1.047 –Vr Resuspension rate of particulate BOD 0.1 m day−1

Vs Settling rate of particulate BOD 0.2 m day−1

Yd Yield factor for release of ammonia by ammonification of dissolved BOD 0.1 g NH3-N/g BODYs Yield factor for release of ammonia by ammonification of suspended BOD 0.1 g NH3-N/g BODYb Yield factor for release of ammonia by ammonification of settled BOD 0.1 g NH3-N/g BODYd2 Yield factor for release of phosphate from dissolved BOD 0.01 g PO4-P/g BODYs2 Yield factor for release of phosphate from suspended BOD 0.01 g PO4-P/g BODYb2 Yield factor for release of phosphate from settled BOD 0.01 g PO4-P/g BODanc Nitrogen/carbon ratio 0.15 mg N/mg Capc Phosphorus/carbon ratio 0.009 mg P/mg Ckn Nitrification rate 0.05 day−1

�n Temperature coefficient for nitrification 1.088 –kdn Denitrification rate 0.1 day−1

�dn Temperature coefficient for denitrification 1.05 –knit Half saturation constant of oxygen for nitrification 2.0 gO2 m−3

kNO Half saturation constant for oxygen limitation denitrification 0.1 gO2 m−3

fon Fraction of dead algae recycled to organic nitrogen 0.5 –fop Fraction of dead algae recycled to organic phosphorus 0.5 –�SOD Temperature coefficient for SOD 1.09 –Gmax Maximum specific growth rate of phytoplankton 2.0 day−1

�Gmax Temperature coefficient for growth rate of phytoplankton 1.067 –kmN Half saturation constant for uptake of inorganic nitrogen 0.025 mg N l−1

kmP Half saturation constant for uptake of inorganic phosphorus 0.001 mg N l−1

kr Endogenous respiration rate of phytoplankton 0.1 day−1

�r Temperature coefficient for endogenous respiration 1.045 –kdm Maximum grazing rate by zooplankton 0.1 day−1

Vph Settling speed of phytoplankton 0.1 m day−1

�a Temperature coefficient for re-aeration 1.024 –

Fig. 5. Depth-averaged velocities measured (black dots) and modelled (continuous line) during spring tides.

A. García et al. / Ecological Modelling 221 (2010) 1194–1208 1201

Fig. 6. Time series of depth-averaged salinity (�, measured; —, modelled) and temperature (�, measured; - - -, modelled) during spring conditions.

Fig. 7. Time series of depth-averaged salinity (�, measured; —, modelled) and temperature (�, measured; - - -, modelled) during summer conditions.

1202 A. García et al. / Ecological Modelling 221 (2010) 1194–1208

Fig. 8. Depth-averaged dissolved oxygen (�, measured; — modelled) and phytoplankton-C (�, measured; - - -, modelled) during summer conditions.

Fig. 9. Depth-averaged ammonia (�, measured; —, modelled) and phosphate (�, measured; - - -, modelled) during summer conditions.

A. García et al. / Ecological Modelling 221 (2010) 1194–1208 1203

and ph

aca

amo

4

faaCM

wi

TM

Fig. 10. Depth-averaged dissolved oxygen (�, measured; —, modelled)

t a location near the Gernika WWTP. Different SODs wereonsidered for the whole estuary taking into account the bed char-cteristics and the distance to measuring points.

The discharge flow of the Gernika WWTP ranged between 1560nd 6990 m3 day−1 with an average value of 3970 m3 day−1, aean 5-day BOD of 60.6 mgO2 l−1, and an average concentration

f ammonium higher than 52 mg NH4+ l−1.

.2. Hydrodynamic model calibration

The model calibration was performed adjusting the bottomriction coefficient for the entire estuary. In this study the bestdjustment between model results and field observations waschieved through bottom roughness parameterized from a variable

hezy coefficient dependent on water depth. A friction coefficientof 45 and an eddy viscosity of 1 m2 s−1 were obtained.Fig. 3 shows the comparison between computed and observed

ater surface elevation time series for the two stations locatedn the estuary. The model results are in general in good agree-

able 3ean values (±STD) of hydrodynamic and water quality parameters at each sampling sta

Survey Station Variable

Salinity (psu) Temperature (◦C) Dissolved

Spring WQ1 34.2 ± 0.7 17.3 ± 0.5 8.1 ± 0.3WQ3 25.3 ± 3.6 18.8 ± 1.0 7.4 ± 0.3WQ5 11.2 ± 5.0 19.0 ± 1.8 7.0 ± 0.5WQ8 0.1 ± 0.0 18.6 ± 1.6 9.1 ± 1.4

Summer WQ1 34.7 ± 0.2 22.0 ± 0.4 7.2 ± 0.3WQ3 31.4 ± 0.6 22.8 ± 0.4 5.5 ± 0.2WQ5 24.5 ± 1.8 23.3 ± 0.5 3.4 ± 0.6WQ8 14.9 ± 3.3 23.1 ± 0.4 2.6 ± 2.0

ytoplankton-C (�, measured; - - -, modelled) during spring conditions.

ment with measured data but small deviations are observed duringspring tides, mainly at the shipyards of Murueta. However, thisdiscrepancy which was due to the complexity inherent in the geo-metric representation of the estuary and the processes of tidal wavedeformation occurring along it, was not significant.

Modelled time series of velocities occurring along the estuaryfor four different locations were compared with measurementstaken during neap tide (Fig. 4) and spring tide (Fig. 5). The com-puted velocities were generally quite close to the measured values.The largest differences were observed at stations 1 and 2, whichwere located at the upper site of the estuary. Therefore, it can beconcluded that the hydraulic model reproduced water movementsatisfactorily throughout the estuary.

The calibration procedure for the dispersion coefficient was

performed against the observed salinity for the four stations ofthe estuary. Figs. 6 and 7 show the comparison between mod-elled and observed salinity and temperature. Model salinity resultscompared well with field measurements. A constant dispersioncoefficient of 0.5 m2 s−1 was obtained.

tion.

oxygen (mg l−1) Chl-a (�g l−1) PO43− (mg l−1) NH4

+ (mg l−1)

1.60 ± 0.77 0.31 ± 0.15 0.06 ± 0.022.44 ± 1.10 0.32 ± 0.11 0.14 ± 0.093.10 ± 2.61 0.24 ± 0.06 0.46 ± 0.140.90 ± 0.30 0.37 ± 0.32 1.36 ± 0.79

1.13 ± 0.33 0.43 ± 0.22 0.05 ± 0.012.57 ± 1.17 0.60 ± 0.12 0.30 ± 0.092.98 ± 0.56 1.20 ± 0.08 1.50 ± 0.19

29.93 ± 17.50 2.66 ± 0.50 4.92 ± 2.17

1204 A. García et al. / Ecological Modelling 221 (2010) 1194–1208

Table 4Mean values (±STD) of water quality parameters in the main rivers.

River Survey NO3− (mg l−1) PO4

3− (mg l−1) NH4+ (mg l−1) Chl-a (�g l−1)

Oka Spring 4.00 ± 0.61 – – 0.84 ± 0.17Summer 4.86 ± 0.52 0.16 ± 0.02 0.49 ± 0.36 0.23 ± 0.10

Golako Spring 3.26 ± 0.55 – – –Summer 2.16 ± 0.25 0.12 ± 0.05 0.57 ± 0.24 –

4

f

aFaanwoetFciCtT

Mape Spring 1.57 ± 0.65Summer 1.64 ± 0.37

.3. Water quality model calibration and validation

The calibration procedure for the water quality model was per-ormed against data measured along the 4-day survey in July 1999.

Water quality comparisons between field data and model resultsre presented for dissolved oxygen and phytoplankton in Fig. 8.ig. 9 shows the comparison of nutrient concentrations measurednd modelled at stations WQ1 to WQ8. The modelled curves shown important amplitude variation with tidal oscillations that couldot be measured during the field survey since only one daily sampleas taken at each station. This type of oscillations has been found in

ther mesotidal estuaries, such as the Ria de Aveiro lagoon (Lopest al., 2008). These fluctuations of the amplitude of the concentra-ion made it more difficult to compare model to data. As is shown inigs. 8 and 9, the model reproduces the general trend of data. Con-erning dissolved oxygen, the mean relative error is lower than 10%

n stations WQ1 to WQ5. The model overestimates phytoplankton-

concentrations for stations WQ3 and WQ5 and underestimateshe phytoplankton-C concentrations for stations WQ1 and WQ8.he mean relative error is of the order of 20%. In the case of nutri-

Fig. 11. Depth-averaged ammonia (�, measured; —, modelled) and ph

– – –0.12 ± 0.05 0.50 ± 0.18 –

ents, the model reproduces the order of magnitude of data, eventhough the estimated relative error is of the order or greater than20%. The calibrated parameter values are presented in Table 2.

Thus, the model could be used as a predictive tool for com-puting algal growth dynamics and other water quality processesaffecting dissolved oxygen concentrations in shallow water bodies.Nevertheless, we developed a validation process using the parame-ters obtained considering the data acquired during May 1999. Thisprocess focused on verifying the model applicability to differentenvironmental conditions. The comparison between computed andobserved dissolved oxygen and phytoplankton levels is presentedin Fig. 10. Fig. 11 shows the same comparison but for nutrient lev-els. In these figures it can be observed that the model reproducesthe general trend of observed values and the order of magnitude ofdata. The mean relative error is lower than 10% for dissolved oxygenand phosphate concentrations. Concerning phytoplankton-C and

ammonia concentrations, the mean relative error is of the order orgreater than 20%. The model underestimates the phytoplankton-Cconcentration for station WQ1 and overestimates the phytoplank-ton levels in the innermost stations.

osphate (�, measured; - - -, modelled) during spring conditions.

A. García et al. / Ecological Modelling 221 (2010) 1194–1208 1205

Fig. 12. Model results at the Gernika WWTP discharge.

Fig. 13. Time series showing the effect of the hydrodynamic input variability on depth-averaged phytoplankton levels in spring time.

1206 A. García et al. / Ecological Modelling 221 (2010) 1194–1208

t vari

clo

5c

(ao

Fig. 14. Time series showing the effect of the hydrodynamic inpu

The model results for both periods at the Gernika WWTP dis-harge are presented in Fig. 12. High nutrient levels as well asow dissolved oxygen concentrations in summer conditions werebtained as a consequence of urban waste water inputs.

. Modelling of the estuary response to environmentalhanges

Previous studies carried out in the Urdaibai estuarine ecosystemMadariaga and Orive, 1989) showed that phytoplankton biomassnd production in the upper estuary was low during seasonal peri-ds of high river flow and short residence time, but highly increased

Fig. 15. Comparison of mean phytoplankton levels at stations WQ3, WQ5 a

ability on depth-averaged phytoplankton levels in summer time.

during the summer season, when lower river flow and higher resi-dence time occurred.

As has been mentioned before, two field surveys were carriedout during the year 1999 considering different environmental con-ditions. The first one was conducted at the end of May, trying torepresent spring conditions after an increase in freshwater dis-charges. The second one was carried out at the end of July, undersummer conditions. The comparison between the two periods

shows that the inner zone of the estuary had a higher concentra-tion of oxygen when salinity was very low, whereas when salinityincreased the oxygen concentration decreased. In the same way,station 5, also in the Gernika channel, only went into hypoxicconditions when salinity increased. The measured data reflected

nd WQ8 for several pollutant loads of the Gernika WWTP discharge.

A. García et al. / Ecological Modelling 221 (2010) 1194–1208 1207

Fig. 16. Comparison between the phytoplankton biomass concentration levels computed in the current situation and in the hypothetical scenario of eliminating the dischargeof the Gernika WWTP during spring conditions (—, without WWTP discharge; - - -, current situation).

Fig. 17. Comparison between the phytoplankton biomass concentration levels computed in the current situation and in the hypothetical scenario of eliminating the dischargeof the Gernika WWTP during summer conditions (—, without WWTP discharge; - - -, current situation).

1 odel

aim

owvFarttwo

rwpttoiitr

lttqbdcslcOzcFt(ttpf

6

e(tdwfiem

208 A. García et al. / Ecological M

significant decrease in the phytoplankton concentration as thenput of fresh water from the river Oka increased, reaching the

inimum level after a maximum peak in river discharge.To analyse the influence of the hydrodynamic input variability

n changes in the phytoplankton level, water quality simulationsere extended to a 15-day period. In this way the effect of tidal

ariations on phytoplankton dynamics was taken into account.igs. 13 and 14 show the phytoplankton concentrations obtainedt stations WQ1, WQ5 and WQ8 for spring and summer conditions,espectively. These results highlight the influence that stems fromhe periodic oscillation of the astronomical tide, i.e., the concentra-ion levels of phytoplankton are higher during periods coincidingith neap tides, with a gradual reduction as the tide rises to periods

f spring tides.The relationship between oxygen concentration and salinity

eflects the impact of water residence time on productivity andater quality in the estuary. Productivity can be estimated fromhytoplankton biomass, which in previous studies has been showno be highly correlated with phytoplankton and bacterial produc-ion and respiration rates in the community. On the other hand,xygen concentration is a good indicator of water quality. Thenverse relationship between productivity and water quality in thenner estuary makes it necessary to apply mathematical modelso assist in making decisions about river flow management andeduction of waste water loads.

Several simulations were run considering different lower pol-utant load levels for the Gernika WWTP discharge with respecto the current situation. Also, a scenario of total suppression ofhe waste water discharge was analyzed. In such situations, wateruality indicator variables, especially nutrients and phytoplanktoniomass, were simulated. Fig. 15 presents the model results for theifferent input levels studied. The curves shown in this figure indi-ate the mean 15-day phytoplankton-C concentrations obtained attations WQ3, WQ5 and WQ8 as a function of the WWTP dischargeevel. It can be observed that the reduction in the nutrient inputaused a lower concentration of phytoplankton within the system.bviously, the lowest phytoplankton levels were associated to theero discharge. This phenomenon was more pronounced for springonditions, that corresponded to higher river discharges (see alsoig. 16), with a decrease of over 50% in the phytoplankton concen-ration in stations WQ3 and WQ8. In July, this reduction was smallerFig. 17), due to nutrient concentration decrease was partly neu-ralized by the improvement in the environmental conditions forhe development of phytoplankton, with respect to those of May. Ahytoplankton concentration decrease of around 20% was obtainedor water quality stations WQ3 and WQ8.

. Conclusions

A two-dimensional mathematical model for computingutrophication changes in the shallow macrotidal Urdaibai estuaryBasque Country), considering the water domain variability relatedo wetting and drying phenomena of the shallowest areas, was

eveloped and applied. The model was calibrated and verified bothith hydrodynamic and water quality data measured in severaleld surveys. Water quality data collected in May characterizedstuarine conditions after a period of strong rainfall, while dataeasured in July reflected dry weather conditions.

ling 221 (2010) 1194–1208

The reduction in the magnitude of the currents within the estu-ary due to a decrease in river discharges and tidal range led to anincrease in available nutrients and therefore a significant increasein phytoplankton biomass, especially in the innermost stations.This could increase their potential risk of eutrophication.

The results of modelling considering several reduced input lev-els from the Gernika WWTP, concluded that there was a decreasein the phytoplankton concentration in the estuary. The maximumdecrease was obtained with the “elimination of the WWTP dis-charge” scenario. This reduction was more pronounced in spring,when the weather and the increased river discharge were morefavourable to this decline. In such conditions a decrease of about50% in phytoplankton concentrations was predicted. This phe-nomenon was not so clear under summer conditions.

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