modeling the mass fluxes and transformations of nutrients ...magan/amce6082010/paperpool2010... ·...

Journal of Marine Systems 78 (2009) 146–167

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Journal of Marine Systems

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Modeling the mass fluxes and transformations of nutrients in thePearl River Delta, China

Jiatang Hu, Shiyu Li ⁎School of Environmental Science and Engineering, Sun-Yat Sen University, Guangzhou, 510275, China

⁎ Corresponding author. Tel.: +86 20 84113620; fax:E-mail addresses: [email protected] (J. Hu), eeslsy

0924-7963/$ – see front matter © 2009 Elsevier B.V. Adoi:10.1016/j.jmarsys.2009.05.001

a b s t r a c t
a r t i c l e i n f o
Article history:Received 5 November 2008Received in revised form 9 April 2009Accepted 4 May 2009Available online 8 May 2009

Regional index terms:ChinaSouth China SeaPearl River Delta

Keywords:Biochemical oxygen demandNutrientsPhysical–biological modelFluxesBiogeochemical cycleEstuarine dynamics

Over recent years, accelerated anthropogenic nutrient discharges have exerted great pressure on the waterquality management in the Pearl River Delta (PRD), China. There is a concern about the eutrophicationprocesses and hypoxia in this region. A better understanding of the origins and transport of nutrients isrequired before accurate prediction of impacts of nutrients on eutrophication and hypoxia in the PRD can beanticipated. Therefore a coupled physical–biological model is developed to simulate the fluxes andtransformations of nutrients in the PRD. The coupled model combines a one-dimensional model for the rivernetwork (called the RNPRD) and a three-dimensional model for the Pearl River Estuary (PRE), which are bothphysical–biological models. The model is calibrated and validated to different sets of field data. The modelresults of water surface elevation, discharges, salinity, suspended sediment and water quality variables are inreasonable agreement with the observational data, suggesting that the model is robust enough to capture thephysical and biogeochemical dynamics in the PRD. Also, the fluxes and transformations of carbonaceousbiochemical oxygen demand (CBOD), ammonia nitrogen (NH3), nitrate plus nitrite nitrogen (NO23) andinorganic phosphorus (IP) in July 1999 (wet season) are explored and discussed. Results show that theRNPRD act as a source for NO23, but a sink for CBOD, NH3 and IP that consumes 50%, 37% and 11% of theirexternal loads, respectively. The riverine fluxes of nutrients exported from the RNPRD to the PRE aregenerally controlled by high river discharge and significantly contributed by upstream inputs. The riverinefluxes are the largest inputs of nutrients to the PRE. The PRE also behaves as a source for NO23, but a sink forCBOD, NH3 and IP that consumes 90%, 80% and 16% of their external loads, respectively. The estuarine fluxesof nutrients exported from the PRE to the South China Sea are significantly contributed by the external andinternal sources of nutrients in the PRE. In the RNPRD, the transformations of CBOD, NH3 (also NO23) and IPare dominated by carbonaceous oxidation, nitrification and deposition, respectively. Regarding the PRE,carbonaceous oxidation, nitrification and phytoplankton uptake are identified as the dominant processeswith respect to CBOD, NH3 (also NO23) and IP. Unlike the RNPRD, the phytoplankton dynamics and internalsources of nutrients play an important role in the nutrient budgets in the PRE. Also, seasonal variations of thenutrient budgets in the PRD are discussed. Model results indicate that the dry season and wet season have asimilar feature in terms of transformations of nutrients, but show significant seasonal variations in terms ofnutrient fluxes. At the same time, the PRE is compared to the Changjiang and Mississippi Rivers with regardto differences in nutrient inputs between these similar river-dominated systems.

© 2009 Elsevier B.V. All rights reserved.

1. Introduction

The Pearl River Delta (PRD) is a very complicated large-scaleestuarine system in China (Fig. 1a). It consists of a tidal river network(called the River-network in the PRD) and an estuary (called the PearlRiver Estuary). In recent years, the PRD region has become one of themost densely populated and economically developed regions in China.Consequently, the water body of the PRD receives a high load of

+86 20 [email protected] (S. Li).

ll rights reserved.

anthropogenic nutrients from increased agricultural activities (Nellerand Lam, 1994), fish dike farming (Ruddle and Zhong, 1988) andsewage effluents (Hills et al., 1998). This increase in nutrients is likelyto result in serious environmental issues, such as eutrophication,harmful red tides and hypoxia. The water quality has been extensivelyexamined in the Pearl River Estuary (PRE), indicating that the estuaryexhibits some symptoms of eutrophication and low dissolved oxygen(Yin et al., 2001; Huang et al., 2003; Tang et al., 2003; Yin et al., 2004a,b; Dai et al., 2006; Harrison et al., 2008). Furthermore, the River-network in the PRD (RNPRD), which is mainly comprised of theXijiang River, Beijiang River and Dongjiang River (Fig.1a), is the largestand most complicated tidal river network system in China. A large

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http://dx.doi.org/10.1016/j.jmarsys.2009.05.001

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Fig. 1. Maps showing (a) the Pearl River Delta (PRD) coastline, bottom topography, major rivers, major cities and monitoring stations in the river network (RNPRD), and (b) longitudinalcirculation in the RNPRD, the Pearl River Estuary (PRE) and the South China Sea (SCS).

147J. Hu, S. Li / Journal of Marine Systems 78 (2009) 146–167

amount of nutrients from these rivers and wastewater discharges inthe PRD transports through multiple river channels in the RNPRD,passes to the PRE through eight river outlets (Fig. 1a), and ultimatelytransports to the South China Sea (SCS). The transport of nutrients is

controlled by multiple forcing mechanisms including complicatedtopography, river discharges, monsoon winds, tides and coastalcurrents (Wong et al., 2003a,b; Dong et al., 2004; Mao et al., 2004),in association with complex biogeochemical processes (Cai et al.,

148 J. Hu, S. Li / Journal of Marine Systems 78 (2009) 146–167

2004). These processes can result in major transformations in theamount and chemical nature of nutrients (Tappin, 2002; Tappin et al.,2003). Therefore it is important to provide an improved quantitativeunderstanding of the fluxes and transformations of nutrients in thePRD, in order to assess their potential impacts on thewater quality andhence provide guidelines in settling goals of pollutant reduction toachieve water quality standards.

In recent decades, numerical modeling techniques have beenwidelyapplied to estuaries such as the Humber estuarine system (e.g., Tappinet al., 2003) and the Chesapeake Bay (e.g., Cerco and Cole, 1993; Xu andHood, 2006) to simulate both physical and biogeochemical processesand study the interactionsbetween them. The complexity of the coupledphysical–biological models ranges from one-dimensional (1-D) (e.g.,Tappin et al., 2003) to fully three-dimensional (3-D)models (e.g., Cercoand Cole, 1993; Zheng et al., 2004; Xu and Hood, 2006). When suchnumerical models are applied to the estuaries they can provide a betterestimation on the fluxes of nutrients associatedwith their transfers andtransformations (Jay et al., 1997). Some well-established models havebeen applied to calculate mass balance for nutrients and suspendedparticles in coastal river-dominated systems (as the PRD) such as theNorthern Adriatic Sea (e.g., Spillman et al., 2007), the Ringkøbing Fjord(e.g., Håkanson et al., 2007) and the Humber estuarine system (e.g.,Tappin et al., 2003). Results from these studies indicate a close couplingof physical and biogeochemical processes over a range of space and timescales, which demonstrates the importance of representing estuarineprocesses in dynamic process-based physical–biological models. Therehave been many dynamic models developed to describe chemical andbiological transfers and transformations in the Pearl River Delta (PRD)(e.g., Guan et al., 2001a,b; Jos et al., 2007), in addition to water andsuspended sediments (e.g., Chen et al., 1999; Wong et al., 2003a,b; Huand Li, 2008). However, previous studies have mainly focused on thehydrodynamic features and water quality variations in the PRD. Little isknown about the fluxes, transformations and ultimate fate of nutrientswithin the PRD. Furthermore, themajority of modeling work in the pasthas focused on the river network (RNPRD) or the Pearl River Estuary(PRE) separately, with few studies (e.g., Hu and Li, 2008; Jos et al.,2007) integrating these two regions as an entity. Since the RNPRD iscomprised of narrow and shallow river channels and primarily domi-nated by river discharges (Fig. 1b), the physical and biogeochemicalprocesses in the RNPRD are simulated by a cross-sectinoally integrated1-D model. Regarding the PRE, it is a complex system where severaldifferent circulation regimes coexist and various types of fronts formbetween the circulation regimes such as the river plume front andcoastal temperature front (Wong et al., 2003a). The value of using 3-Dmodels for the PRE has been clearly demonstrated previously (Wonget al., 2003a,b; Hu and Li, 2008), in which the 3-D circulation in thePRE with respect to the combined forcing of multiple mechanisms isextensively explored and discussed. As the RNPRD and the PRE areaffecting each other and closely interrelated (Fig. 1), it is essentialto integrate them as an entity. This necessitates application of a 1-D and3-D coupledmodel, which has the advantage over previous studies thatonly included the PRE or the RNPRD and neglected the dynamicvariations of material exchange between the RNPRD and the PREthrough the eight river outlets (e.g. Guan et al., 2001a,b; Wong et al.,2003a, b). In addition, a 1-D and 3-D coupled model has the advantageover a fully 3-D model for the whole area in terms of computationalefficiency and model grid generation. Overall, the major objectives ofthis study are:

• to propose a 1-D (for the RNPRD) and 3-D (for the PRE) coupledmodel, which includes physical and biogeochemical processes;observations are used to evaluate the model performance;

• to simulate the fluxes of nutrients (nitrogen, phosphorus and CBOD)passing through the RNPRD, the PRE, and the SCS, respectively;

• to construct the nutrient budgets for the entire study area, andquantify the contributions from external and internal sources of

nutrients to their budgets, and characterize the key biogeochemicalprocesses in the transfers and transformations of nutrients; theinternal sources of nutrients are defined as internal nutrient inputsfrom primary production, benthic releases, conversion from ammo-nia nitrogen via nitrification, and generation of inorganic nutrientsfrom organic matter via mineralization or bacterial decomposition.

2. Materials and methods

2.1. Model description

2.1.1. Physical and suspended sediment transport modelA 1-D model (called Riv1D) has been developed for the river

network (RNPRD) (Hu and Li, 2008). The model has been expandedand refined in recent years. To date, it can be used to simulate thehydrodynamics, salinity distribution, suspended sediment (SS) trans-port and water quality processes in well-mixed rivers and shallowestuaries. The hydrodynamic module of Riv1D is based on the solutionof Saint Venant equations of mass and momentum conservation.These equations are solved through a Preissmann implicit scheme andan iterative approach. For water quality aspects, Riv1D solves thegoverning advection–diffusion equation, with additional source andsink terms to account for external loads and transformations of waterquality constituents. A control volume scheme has been applied tosolve the advection–diffusion equation. Modules for salinity and SSdynamics are also incorporated within Riv1D, using the same modelstructure and computational framework as the water quality module.

The governing hydrodynamic equations (Cunge et al., 1980) areshown as follows:

1BAQAx

+AZAt

= qL ð1Þ

AQAt

+A

AxQ2

A

!+ gA

AZAx

+ gQ jQ jAC2

S R= 0 ð2Þ

where Q is the cross-section averaged discharge; B is the watersurface width; Z is the water surface elevation; qL is the lateral inflow;A is the cross-section area; g is the gravity acceleration; CS is the Chezyresistance coefficient; and R is the hydraulic radius.

The 1-D SS transport model is based on the principle of massconservation. The governing equation can be expressed by

A AC1ð ÞAt

+A QC1ð Þ

Ax− A

AxAEx

AC1

Ax

� �− Sc − Wc = 0 ð3Þ

where C1 is the SS concentration (SSC); Ex is the coefficient oflongitudinal dispersion; Sc is the internal sources and sinks; andWc isthe external inputs from point sources, non-point sources, fall-lineloads and atmospheric input. Fall-line loads are classified as thoseintroduced from rivers, representing the amount of nutrients enteringthe study region from upland areas. For SS aspects, Sc representssediment deposition and resuspension processes.

A fully 3-D estuarine and coastal ocean model coupled with asediment transport module (Blumberg, 2002), namely, ECOMSED, isused to simulate the physical and cohesive sediment dynamics in thePearl River Estuary (PRE) and its adjacent coastal waters. Thehydrodynamic module of ECOMSED solves the Navier–Stokes equa-tions for a water body with a free surface, under Boussinesq andhydrostatic approximations. It incorporates the Mellor and Yamada's(1982) level 2.5 turbulent closure sub-model, which was modified byGalperin et al. (1988). The prognostic variables in the hydrodynamicmodule are the free surface elevation, three components of velocity,turbulence kinetic energy, turbulence macroscale, temperature andsalinity. The transport and fate of cohesive sediments can be simulatedwith the sediment transport module of ECOMSED. Cohesive sediment

Fig. 2. Schematic of water quality model used for the Pearl River Delta (PRD), a revised version of Fig. 3 in Zheng et al. (2004).


dynamics inherent in the sediment module include sedimenttransport, deposition and resuspension. Themechanisms of resuspen-sion and deposition depend upon the bottom shear stress induced atthe sediment–water interface. A detailed description of the hydro-dynamic and sediment modules can be found in literature (Blumberg,2002; Chen and Wang, 2008).

2.1.2. Water quality modelThe 3-D water quality model is based on the Row-Column AESOP

(Advanced Ecological Systems Modeling Program) developed byHydroQual (Fitzpartick, 2004). The Row-Column AESOP (RCA) consti-tutes a complex of five interacting systems: phytoplankton dynamics,nitrogen cycle, phosphorus cycle, carbon cycle, and dissolved oxygenbalance. RCA originally incorporates twenty-six state variables. Thegoverning equation for each state variable can be described as follows:

ACAt

+ uACAx

+ vACAy

+ wACAz

=A

AxAh

ACAx

� �+

A

AyAh

ACAy

� �

+A

AzKh

ACAz

� �+ S + W0

ð4Þ

where C is the concentration of a water quality state variable; u, v,w arethe water velocity components in the x, y and z directions, respectively;Ah and Kh are the horizontal and vertical diffusion coefficients; S is theinternal sources and sinks of the water quality state variable;W0 is theexternal loads from point sources, non-point sources, fall-line loads andatmospheric input of the water quality state variable.

Multiple formsof nutrients and organic carbon aremodeled as a statevariable in RCA (Fitzpartick, 2004). It is difficult to apply the original RCAto the Pearl River Delta because of the problem of data availability inChina. Therefore modifications are made to RCA in view of datalimitations in this study. First, the modified model includes eight statevariables rather than twenty-six. New variables are phytoplanktoncarbon (PHYT), organic phosphorus (OP), inorganic phosphorus (IP),

and organic nitrogen (ON), ammonia nitrogen (NH3), nitrate plus nitritenitrogen (NO23), carbonaceous biochemical oxygen demand (CBOD)and dissolved oxygen (DO). Two available groups of phytoplankton,Green and Diatoms, are simulated in the model, representing riverineand marine algae, respectively. Two phases of IP are considered,including ortho-phosphate (OPO4) and particle-sorbed phosphate(PO4SS). The kinetic processes in the modified model generally followwater quality analysis simulation program (called WASP5; Ambroseet al., 1993; Zheng et al., 2004), as shown in Fig. 2. Second, the effect ofSSC on the light intensity for phytoplankton growth is consideredbecause high sediment loads in estuaries can lead to rapid lightattenuation in estuarine waters, which limits primary production.Meanwhile, the model includes the process of adsorption–desorptionbetween SS andNH3, in addition to the sorption between SS and OPO4. Alinear isotherm is adopted to model the distribution over dissolved andsorbed phases for NH3, while the Langmuir equilibrium isotherm isselected for IP. The formulation of equilibrium adsorption contentregarding OPO4 and NH3 is shown in Table 1.

The kinetic processes and water quality state variables in thewaterquality module of Riv1D are the same as those in the modified RCAmodel, i.e., five systems and eight state variables are included inRiv1D. The governing equation of each water quality state variable canbe written as Eq. (3), with different sources or sink terms to accountfor pollutant loads and specific decay and transformations ofnutrients. The equations for the water quality model are summarizedin Table 1. A brief description of the kinetic processes is given below.

2.1.2.1. Phytoplankton kinetics. The growth rate of phytoplankton is acomplicated function of nutrients availability, solar radiation and am-bientwater temperature. The specific growth rate is represented as themultiplication of maximum phytoplankton growth rate and limitationfunctions of each factor. Mechanisms that contribute to the phyto-plankton loss mainly include endogenous respiration, grazing by zoo-plankton and settling to bottom sediment.


2.1.2.2. Phosphorus cycle. In the phosphorus system kinetics, themain source of OP is recycled from the phytoplankton biomass poolin the water column through endogenous respiration and predatorygrazing. OP is converted to IP at a temperature-dependent miner-alization rate, and its particulate fraction will sink and deposit on thebottom sediment. Available IP is utilized by phytoplankton for growth.Three internal sources for IP are (1) recycled from the phytoplanktonbiomass pool in the water column, (2) converted from OP viamineralization or bacterial decomposition, and (3) released fromthe bottom sediment. In addition, OPO4 interacts with PO4SS via anadsorption–desorption process. PO4SS will sink and deposit on thebenthic sediment.

2.1.2.3. Nitrogen cycle. The nitrogen kinetics are basically similar tothe phosphorus kinetics. Nitrogen is recycled from the phytoplanktonbiomass pool in the water column to the dissolved and particulate ONpools and to the NH3 pool. Particulate ONwill settle and deposit on thebenthic sediment. ON is converted to NH3 at a temperature-dependent mineralization rate, and NH3 is then nitrified at atemperature- and oxygen-dependent rate. Under low DO condition,NO23 can be denitrified at a temperature-dependent rate. NH3 andNO23 are consumed by phytoplankton uptake. NH3 is the preferredform of inorganic nitrogen for phytoplankton growth. Also, thebenthic nutrient fluxes are internal sources for NH3 and NO23. Inaddition, particulate NH3 adsorbed on SS will sink and deposit on thebottom sediment.

2.1.2.4. CBOD kinetics. The internal source of CBOD is recycled fromphytoplankton respiration and death. The internal sinks for CBODinclude carbonaceous oxidation and settling of particulate carbona-ceous material to the benthic sediment. The denitrifcation reactionalso provides a sink for CBOD under low DO condition.

2.1.2.5. DO balance. DO is one of the most important water qualityindicators (Zheng et al., 2004) as the availability of oxygen controls alllife (in particular fish) in thewater. In addition, DO is closely related toeutrophication and hypoxia which are increasing threat to coastalsystems. The sources of DO considered in themodel are reaeration andphytoplankton photosynthesis. The sinks of DO include phytoplank-ton respiration, nitrification, oxidation of CBOD, and sediment oxygendemand (SOD).

2.2. Dynamic integration of the 1-D and 3-D model

The 1-D model (Riv1D) and 3-D model (ECOMSED-RCA) are dy-namically coupled to create a single model in order to perform anoverall simulation on the dynamics of the Pearl River Delta (PRD). Inthe 1-D and 3-D coupled model, quantities of the hydrodynamic andwater quality state variables are exchanged across coupling cross-sections (locations of the eight river outlets, see Fig. 1a) at each timestep. For the hydrodynamic modeling, the 1-D model sends itssimulated discharge at the coupling cross-sections to the 3-Dmodel asits upstream boundary condition. As a feedback, the 3-D model sendsits simulated water surface elevation at the coupling cross-sectionsto the 1-D model as its downstream boundary condition. For thewater quality modeling, simulated mass fluxes (volume flux multi-plied by concentrations) of the state variables are exchanged across

[−139.34411+1.575701�105�T−1−6.642308�107�T−2+1.243800�1010�T−3\kern-34.35pc\

Notes to Table 1a Ambrose et al. (1993).b Steele (1962).c Fitzpartick (2004).d Di Toro (1978).e Chao et al. (2007).

the coupling cross-sections at each time step. The exchanges aredetermined by the instantaneous direction of flow through the cou-pling cross-sections. The simulated mass fluxes from the 3-Dmodel are transferred to the 1-D model when the flow is directedlandwards (i.e. from the Pear River Estuary to the river network),whereas the simulated mass fluxes from the 1-D model are trans-ferred to the 3-D model when the flow is directed seawards. Theapproach for coupling the 1-D and 3-D model is illustrated in Fig. 3.The major challenge of coupling the 1-D and 3-D model is toreproduce the realistic physical and biogeochemical processes in the1-D domain and 3-D domain, in addition to depicting the materialexchange between these two regions. The coupling approach shownin Fig. 3 has been applied successfully to the PRD in previous study(Hu and Li, 2008).

2.3. Model set-up

2.3.1. Model designFig. 4a shows the model domain for the river network (RNPRD)

and model grids for the Pearl River Estuary (PRE) and its adjacentshelf. The RNPRD is discretized into 299 reaches and 1726 cross-sections, with five upstream boundaries (Fig. 1a) including Gaoyao atthe Xijiang River, Shijiao at the Beijiang River, Boluo at the DongjiangRiver, Laoyagang at the Liuxi River and Shizui at the Tanjiang River. Theeight river outlets, including Humen, Jiaomen, Hongqili, Hengmen,Modaomen, Jitimen, Hutiaomen and Yamen, are specified as theexchange cross-sections in the 1-D and 3-D coupled model. The firstfour outlets are defined as the eastern four river outlets (Fig. 4b),while the last four are defined as thewestern four river outlets. The 3-Dpart of the coupled model is configured to cover the PRE and itsadjacent coastal waters. An orthogonal curvilinear grid is designedto resolve the complex coastline, with a total of 183 by 186 points.There are 6 equidistant sigma layers in the vertical to representthe irregular bottom topography. The time steps are 40 s for thehydrodynamic and SS models, and 120 s for the water quality model.The 1-D model and the 3-D model have the same time steps.

For the hydrodynamic modeling, the real-time freshwater dis-charges with zero salinity are introduced for the upstream boundariesin the RNPRD. Some small waterways in the RNPRD are not included inthe 1-D model because their topography data is not available. Waterfrom these waterways is treated as lateral inflows. However, theircontributions to the RNPRD are ignored since there are no observa-tions of water discharges for them. The 3-D part of the coupled modelis driven by four dominant tidal constituents (M2, S2, K1 and O1) at theopen sea boundaries, with a uniform salinity boundary condition (Joset al., 2007). Winds are specified in the model using hourly observeddata from the Taipa Grande Station (Fig. 1a) provided by the MacaoMeteorological and Geophysical Bureau. In the model domain,southwesterly winds dominate during summer and northeasterlywinds dominate during winter. For the SS andwater quality modeling,measured data of the monitoring stations located at the upstreamboundaries or nearest to the upstream boundaries have been used toassign boundary conditions in the 1-D part of the coupled model. Inaddition, the boundary conditions at the open sea boundaries arederived from data collected in the 1990s at the open sea stations (Joset al., 2007). Initial conditions are derived by spin-up simulationswhich run for 30 days. The initial conditions are replaced by results at

lower25pt{{−8.621949�1011�T−4−Sa(1.7674�10−2−10.754�T−1+2140.7�T−2) ]

Table 1Summary of equations for the water quality model.

Variables Description/formulation

PHYT Phytoplankton carbon (mg C l−1)OP Organic phosphorus (mg P l−1)IP Inorganic phosphorus (mg P l−1)ON Organic nitrogen (mg N l−1)NH3 Ammonia nitrogen (mg N l−1)NO23 Nitrate plus nitrite nitrogen (mg N l−1)CBOD Carbonaceous biochemical oxygen demand (mg O2 l−1)DO Dissolved oxygen (mg O2 l−1)H Water column depth or thickness of the water segment (m)I0 Incident light intensity at the segment surface (ly day−1)T Water temperature (°C)Sa Salinity (ppt)Pchl Phytoplankton chlorophyll a concentration (µg l−1,

computed based on PHYT and the ratio of chlorophyll a to carbon)SSC Suspend sediment concentration (mg SS l−1)C0 Initial IP concentration in the solution (mg P l−1)

Phytoplankton kineticsKinetic term Sp=(Gp−Dp) PHYTGrowth rate Gp=Gpmax· fN(N)· fI(I)· fT(T)

Nutrient limitation functiona fN Nð Þ = min NH3 + NO23KmN + NH3 + NO23 ;

OPO4KmP + OPO4

� �Light limitation functiona, b fI Ið Þ = 2:718

KeHexp − I0

Ise−KeH

� �− exp − I0

Is

� �h i

Temperature dependencyc fT Tð Þ =exp −Kb1 Topt−T

� �2� �when TVTopt

exp −Kb2 Topt−T� �2� �

when T N Topt

8<:

Light attenuation coefficienta, d Ke=Kebase+0.0088Pchl+0.054Pchl0.67+0.052SSC

Loss rate Dp = KPRΘT − 20ð ÞPR + VsP

H + KgrzΘ T − 20ð Þgrz

Phosphorus cycleKinetic term of OP SOP = apcDpfOPPHYT − Km1Θ

T − 20ð Þm1

PHYTKmPc + PHYT

� �OP − VsOP 1 − fDOPð Þ

H OP

Kinetic term of IP SIP = apcDp 1− fOPð ÞPHYT − apcGpPHYT + Km1ΘT − 20ð Þm1

PHYTKmPc + PHYT

� �OP

−VsIP 1− fDIPð ÞH

IP +BOPO4

HEquilibrium adsorption content for phosphoruse Ne = KadpCd

1 + KadpCdNem

Concentration of phosphorus in dissolved phaseeCd = 1

2 C0 − 1Kadp

− SSC � Nem

� �+

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiC0 + 1

Kadp−SSC � Nem

� �2+ 4SSC�Nem

Kadp

r" #

Fraction of dissolved IPe fDIP = CdC0

Nitrogen cycleKinetic term of ON SON = ancDpfONPHYT − Km2Θ

T − 20ð Þm2

PHYTKmPc + PHYT

� �ON − VsON 1 − fDONð Þ

H ON

Kinetic term of NH3 SNH3 = ancDp 1− fONð ÞPHYT − ancGpPNH4PHYT + Km2ΘT − 20ð Þm2

PHYTKmPc + PHYT

� �ON

−KniΘT − 20ð Þni

DOKnitr + DO

� �NH3 −VsNH 1− fDNH4ð Þ

HNH3 +

BNH3

H

Kinetic term of NO23 SNO23 = − ancGp 1− PNH4ð ÞPHYT + KniΘT − 20ð Þni

DOKnitr + DO

� �NH3

− KdnΘT − 20ð Þdn

KNO3

KNO3 + DO

� �NO23 +

BNO23

H

Ammonium preference factor PNH4 = NH3 �NO23KmN + NH3ð Þ KmN + NO23ð Þ + NH3 �KmN

NH3 + NO23ð Þ KmN + NO23ð ÞDistribution over dissolved and sorbed phases for NH3

c fDNH4 = 11 + KadnSSC

CBOD kinetics

Kinetic term of CBOD SCBOD = aoc KPRΘT − 20ð ÞPR + KgrzΘ T − 20ð Þ

grz

� �PHYT − KdcΘ

T − 20ð Þdc

DOKBOD + DO

� �CBOD

−VsCBOD 1− fDCBODð ÞH

CBOD − 54� 3214

� KdnΘT − 20ð Þdn

KNO3

KNO3 + DO

� �NO23

DO balance

Kinetic term of DO SDO = KaΘ T − 20ð Þa Cs − DOð Þ + Gp

3212

+4814

anc 1− PNH4ð Þ

PHYT

−3212

KPRΘT − 20ð ÞPR PHYT − KdcΘ

T − 20ð Þdc

DOKBOD + DO

� �CBOD

−6414

KniΘT − 20ð Þni

DOKnitr + DO

� �NH3 − SOD

HΘ T − 20ð ÞSOD

DO saturation concentrationc Cs = exp ½−139:34411 + 1:575701 � 105 � T−1 − 6:642308 � 107 � T−2 + 1:243800 � 1010 � T−3

− 8:621949 � 1011 � T−4−Sa 1:7674 � 10−2 − 10:754 � T−1 + 2140:7 � T−2� ��


Fig. 3. Approach for coupling the 1-D and 3-D model. Qi(1), Zi(1) and Fi

(1) represent simulated discharge, water surface elevation and mass flux (volume flux multiplied byconcentration of state variables) at the ith coupling cross-section from 1-D model, respectively, while Qi

(3), Zi(3) and Fi(3) are those from 3-D model.


Day 30 and the 1-D and 3-D coupled model runs again. Theseprocesses are repeated for 4 times when the difference between thefinal results and previous results of nutrient budgets is within 1%.

2.3.2. Pollutant loads in the water quality modelIn addition to the inflows from upstream boundaries, there are two

types of pollutant loads included in the water quality model: point-source loads and non-point-source loads. Both the point and non-point-source loads from the Pearl River Delta (PRD) region wereestimated by the South China Institute of Environmental Sciences (Joset al., 2007). Parameters included in the pollution loads are BOD5, NH3,Total Kjeldahl Nitrogen (TKN) and total phosphorus (TP). Thepollutant loads from Macao and Hong Kong (Fig. 1a) are providedby the Environment Council of Macao Special Administrative Regionand the Environment Protection Department of Hong Kong SpecialAdministrative Region, respectively. The pollution loads for themodeled water quality state variables are converted from BOD5,NH3, TKN (ON+NH3) and TP (OP+IP) based on the stoichiometricratios in organic waster materials (San Deigo-McGlone et al., 2000).The external loads for BOD5, NH3, total nitrogen (TN) and TP in thewetseason and dry season are summarized in Fig. 5, showing a significantseasonal fluctuation. The wet (rainy) season is defined as April–September and the dry season as October–March (as Cai et al., 2004).The external loads in the wet season are much larger than those in thedry season, which arises from high inflows from the upstreamboundaries and non-point-source loads in the wet season.

2.3.3. Parameters used in the water quality modelThe parameters used in the water quality model are specified

based on observations in the PRE and scientific literature (Ambroseet al., 1993; Guan et al., 2001a; Fitzpartick, 2004; Zheng et al., 2004; Luet al., 2005; Shi et al., 2005; Zhan et al., 2005; Chao et al., 2007; Joset al., 2007). They are fine-tuned within ranges recommended byliterature to produce the best fit between simulated values and fielddata. The required parameters and their descriptions in the waterquality model are listed in Table 2. Although the water quality modelincorporates a sediment diagenesis process model (Fitzpartick, 2004)to simulate processes in the benthic sediment, the sediment processmodel is not used because no data is available to support thisactivation. Instead, constant values are specified for benthic nutrientfluxes and SOD based on previous measurements (Guan et al., 2001a;Zhan et al., 2005). Future study is needed to represent these processesmore accurately.

2.4. Field data used for model calibration and validation

Three simulation periods are selected for model calibration andvalidation purpose, including January 1999 (dry season), July 1999(wet season) and February 2001 (dry season). In all cases thesimulation period is 30 days, which means that two spring tide/neaptide periods are covered. However, there is only 10 days or less offield data available in each simulation period due to data limitation.Two surveys were conducted by the Pearl River Water Resources

Commission on July 16–24, 1999 and February 7–16, 2001. Thesesurveys produced sets of continuous and synchronous data in the rivernetwork (RNPRD). The observational data include hourly watersurface elevation, discharge and SSC at fifty-nine stations (five stationslocated at the upstream boundaries). Salinity is available for February7–16, 2001. Locations of the time-series stations in the RNPRD areshown in Fig. 1a. In the Pearl River Estuary (PRE), an investigationwascarried out by Pearl River Estuary Pollution Project on July 17–27, 1999(Wong et al., 2003a), yielding sets of hydrodynamic and water qualityparameters including current speed and direction, salinity, DO,chlorophyll a, nitrate, nitrite, NH3, OPO4, SSC and chemical oxygendemand. Fig. 4b shows the locations of the survey and time-seriesstations in this summer cruise. Also, water surface elevation obtainedfrom five tidal gauge stations (Fig. 4b) is used for the calibration andvalidation of the hydrodynamic model. Water quality observations inthe wet season and dry season for the years 1998, 1999 and 2000 areused for the calibration and validation of the water quality model.These observations are available for seventy-one locations in theRNPRD (Fig. 4a). They are actually averaged from several samplestaken at unknown time during the specific season. Detailed datasetsfor the model calibration and validation are shown in Table 3.

3. Results and discussion

3.1. Model calibration and validation

A set of calibration and validation statistics are used to quantitativelyassess the performance of the coupled model. Wherever possible, wesuggest potential reasons for deficiencies and possiblemeans for furtherimprovement of the model performance. The correlation coefficients(r2) of the model results and observations, the mean errors and therelative errors of the model results are summarized in Table 4.

3.1.1. Model calibration

3.1.1.1. Physical and suspended sediment transport model. The coupledmodel was run for February 2001 for calibration of the hydrodynamicand SS models. Result shows that the simulated water surfaceelevation, discharge, salinity and SSC are in reasonable agreementwith the observational values (Table 4). The correlation coefficientsfor water surface elevation and discharge are greater than 0.90, andthose for salinity and SSC are 0.82 and 0.56, respectively. The largererror for SSC, relative to the hydrodynamic variables, may be mainlyattributed to the simplified parameterization of processes controllingSS dynamics, e.g., a constant critical shear stress and resuspension rateare specified in the model. Also, these processes are too complex to befully understood based on current studies.

As the eight river outlets are the exchange cross-sections in thecoupled model, they are the important locations to check if the modelis capable of capturing the physical–biogeochemical dynamics in theentire domain and depicting the material exchange between the rivernetwork (RNPRD) and the Pearl River Estuary (PRE). Results of tworepresentative river outlets including Humen andModaomen (Fig. 1a)

Fig. 4.Maps showing (a) model domain of the 1-Dmodel and survey stations for water quality observations in the river network (RNPRD), and (b) model grids of the 3-Dmodel andmonitoring stations in the Pearl River Estuary (PRE). Blue empty triangles ( ) denote locations of the survey stations for water quality observations in the RNPRD. Data are availablefor seventy-one stations. Red triangles ( ) and blued solid diamonds ( ) are locations of survey stations and time-series stations in the PRE for the summer cruise in July 1999,respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)


Fig. 5. Overview of pollutant loads in the wet season (April–September) and dry season (October–March). The ‘PRD DS’ and ‘PRD PS’ terms represent non-point-source loads andpoint-source loads from the Pearl River Delta (PRD), respectively. The ‘HK+Macau’ term represents pollutant loads from Hong Kong and Macao combined. The ‘upstream’ termrepresents pollutant loads from five upstream boundaries in the river network (RNPRD), evaluated on the basis of observed nutrient concentrations and river discharges in July 1999(wet season) and January 1999 (dry season), respectively.


are shown in Fig. 6a–b. These plots reveal that the model results ofwater surface elevation, discharge and SSC compare reasonably wellwith the observational values.

3.1.1.2. Water quality model. The coupled model was run for January1999 for calibration of the water quality model. Fig. 6c shows thesimulated and observed NH3, CBOD, TN and TP in the Guangzhou–Foshan (GF series), Zhongshan–Shunde (ZS series) and Jiangmen–Kaiping (JK series) regions (Fig. 4a). Since the observational data werecollected at different times of the years, they are shown with modelresults of monthly-minimum, monthly-averaged and monthly-max-imum concentrations. The model results of nutrient and CBOD followthe general patterns shown by the observational data, which showstrong spatial gradients for the water quality components, with highconcentrations in the Guangzhou–Foshan and Jiangmen–Kaipingregions and low concentrations in the Zhongshan–Shunde region.The model reproduces these gradients well. It can be noticed that onlylimited field data are available for TP. Insofar as field data are available,there is a reasonable agreement with the model results. However, themodel tends to overestimate NH3 and underestimate CBOD in somestations in the Guangzhou–Foshan region (e.g., GF9–GF12). The localpollutant loads from non-point sources in the dry season are apotential cause for this problem, rather than point-source loads andpollutant kinetic processes, which would affect the results in the wetseason. Thus, a better representation on the non-point-source loads isneeded to further optimize the water quality model. In general, themodel results agree reasonably with the observational data in terms ofboth values and distribution patterns.

3.1.2. Model validation

3.1.2.1. Physical and suspended sediment transport model. For modelvalidation, the coupledmodel was run for July 1999 using the same set ofparameters as used in the calibrationprocess. The simulatedwater surfaceelevation, discharge, salinity and SSC agree well with the observationaldata, with correlation coefficients greater than 0.65 (Table 4). Thesimulated SSC in the surface layer in the Pearl River Estuary (PRE) is

compared to a satellite remote sensing image of SS (Fig. 7). The modelresult follows the observed spatial distribution quite closely.

Fig. 8a and b shows the simulated and observed water surfaceelevation, discharge and SSC at Humen andModaomen. The simulatedresults generally follow the observed patterns. However, there are alsosome significant discrepancies between the simulated SSC andobservational values at Modaomen. The coupled model is able toreproduce the mean values of SS at Modaomen, but fails to capture itshigh variability of SS. This phenomenon is not readily explained, butmay be linked to the high variability of settling of SS promoted byflocculation (not included in the 1-D model), or related to thesimplified parameterization of deposition and resuspension pro-cesses. Fig. 8c shows the simulated water and SS fluxes through theeight river outlets calculated from July 16 to July 24, 1999. Goodagreements are found for both water and SS fluxes.

3.1.2.2. Water quality model. It appears that the coupled model has atendency to overestimate dissolved inorganic nitrogen (DIN) and OPO4,which corresponds to the underestimation of chlorophyll a (Table 4).Also, the model has a tendency to underestimate CBOD and over-estimate DO. The correlation coefficients of the model results andobservational values arehighest forDO (0.68) and lowest for chlorophylla (0.41). It is difficult to simulate the OPO4 and chlorophyll a distributionaccurately because their variability is driven by many processes (seesubsection 2.1.2; Table 1) and there are many uncertainties associatedwith theseprocesses. Consequently, the correlation coefficients forOPO4

and chlorophyll a are relatively smaller.Satisfactory agreement can be found for the simulated and ob-

served NH3, CBOD, TN and TP in the Guangzhou–Foshan, Zhongshan–Shunde and Jiangmen–Kaiping regions in the wet season (Fig. 9a).These plots reveal that themodel is capable of reproducing reasonablepatterns and acceptable magnitudes for the water quality compo-nents. Fig. 9b and c shows the comparisons between the simulatedresults and observations of CBOD, OPO4, NO23 and DO at the time-series stations C1 and C2 as well as the survey stations in the PearlRiver Estuary (PRE) (Fig. 4b). With some exception, the model resultsfollow the observed patterns well in space and time. However, themodel tends to overestimate or underestimate the nutrients and DO in

Table 2Parameters and constants for the water quality model.

Parameter Description Value Unit

Gpmax Maximum phytoplankton growth rate 2.0a day−1

Is Optimal light intensity 250.0b ly day−1

Topt Optimum temperature 25.0a °CKb1 Temperature correction effect on growth rate

below Topt

0.004b (°C)−2

Kb2 Temperature correction effect on growth rateabove Topt

0.006b (°C)−2

KPR Phytoplankton respiration rate 0.01c day−1

Kgrz Mortality rate due to grazing 0.04a day−1

apc Ratio of phosphorus to carbon 0.025a Unitlessanc Ratio of nitrogen to carbon 0.25a Unitlessaoc Ratio of oxygen to carbon 32/12a UnitlessKebase Background light attenuation coefficient 0.5c m−1

Km1 OP mineralization rate 0.22a day−1

Km2 ON mineralization rate 0.075a day−1

Kni Nitrification rate 0.1a day−1

Kdn Denitrification rate 0.09a day−1

Kdc CBOD oxidation rate 0.2a day−1

Ka Reaeration rate 2.0b day−1

SOD Sediment oxygen demand 1.5–2.5d gO2m−2day−1

VsP Settling velocity of PHYT 0.5a m day−1

VsOP Settling velocity of particulate OP 0.5a m day−1

VsIP Settling velocity of sorbed IP 0.5a m day−1

VsON Settling velocity of particulate ON 0.5a m day−1

VsNH Settling velocity of sorbed NH3 0.5a m day−1

VsCBOD Settling velocity of CBOD 0.5a m day−1

ΘPR Temperature coefficient for phytoplanktonrespiration rate

1.045a Unitless

Θgrz Temperature coefficient for zooplanktongrazing rate

1.000a Unitless

Θm1 Temperature coefficient for OP mineralization rate 1.080a UnitlessΘm2 Temperature coefficient for ON mineralization rate 1.080a UnitlessΘni Temperature coefficient for nitrification rate 1.080a UnitlessΘdn Temperature coefficient for denitrification rate 1.080a UnitlessΘdc Temperature coefficient for CBOD oxidation rate 1.047a UnitlessΘa Temperature coefficient for reaeration rate 1.028a UnitlessΘSOD Temperature coefficient for SOD 1.080a UnitlessKmN Half saturation constant for nitrogen uptake 0.025a mg N l−1

KmP Half saturation constant for phosphorus uptake 0.001a mg P l−1

KmPc Half saturation constant for phytoplanktonlimitation

1.0a mg C l−1

Knitr Half saturation concentration for oxygenlimitation of nitrification

1.0a mg O2 l−1

KNO3 Half saturation concentration for oxygenlimitation of denitrification

0.1a mg O2 l−1

KBOD Half saturation concentration for oxygenlimitation of CBOD oxidation

0.5a mg O2 l−1

fOP Fraction of dead and respired phytoplanktonrecycled to the OP pool

0.65e Unitless

fON Fraction of dead and respired phytoplanktonrecycled to the ON pool

0.65e Unitless

fDOP Fraction of dissolved OP 0.5f UnitlessfDIP fraction of dissolved IP Table 1 UnitlessfDON Fraction of dissolved ON 0.5f UnitlessfDNH4 Fraction of dissolved NH3 Table 1 UnitlessfCBOD Fraction of dissolved CBOD 0.5a UnitlessBNH3 Bottom NH3 flux 14.4g mg m−2 day−1

BNO23 Bottom NO23 flux −1.65g mg m−2 day−1

BOPO4 Bottom OPO4 flux 0.062g mg m−2 day−1

Kadp Ratio of adsorption and desorption ratecoefficient for phosphorus

8.25h l mg−1 SS

Nem Maximum concentration of phosphorus insolid phase

0.015h mg P mg−1 SS

Kadn Partition coefficient for sorbed NH3 548.3i l kg−1 SS

a Ambrose et al. (1993).b Fitzpartick (2004).c Jos et al. (2007).d Guan et al. (2001a).e Zheng et al. (2004).f Chao et al. (2007).g Zhan et al. (2005).h Lu et al. (2005).i Shi et al. (2005).

Table 3Calibration and validation dataset for the 1-D and 3-D coupled model.

Data period Domain Models to becalibrated/validated

Purpose

February 7–16, 2001 1-D Hydrodynamic, SS CalibrationDry season for the years

1998, 1999 and 20001-D Water quality Calibration

July 16–24, 1999 1-D Hydrodynamic, SS ValidationJuly 17–27, 1999 3-D Hydrodynamic, SS, water quality ValidationWet season for the years

1998, 1999 and 20001-D Water quality Validation

Note the wet season is defined as April–September and the dry season as October–March. SS: Suspended sediment.


the bottom layer (Fig. 9b). This discrepancy is probably due to theoversimplified parameterization of benthic processes in the PRE, i.e. auniform and constant benthic nutrient fluxes and SOD are assumed inthe model, which can not reflect the realistic benthic processes. Asediment diagenesis process model is certainly required for furtherimprovement of the model performance, and more detailed informa-tion are needed to support the implementation of the sedimentdiagenesis model. Fig. 9c shows the simulated CBOD in the surfacelayer reaches to high concentrations in the upper PRE (due to theinflows directly from the eight river outlet), but declines with distanceto offshore. This CBOD pattern is consistent with the observationaldata. A similar pattern is also observed for NO23, and again, it is wellreproduced by the model.

Through comparisons with observations we show that the coupledmodel is robust enough to generate reasonable results in both wetseason and dry season, although there are also some discrepanciesbetween themodel results and observations. In general, the results arequite encouraging.

3.2. Fluxes and transformation processes of nutrients in the wet season

July 1999, a representative wet season with moderately high riverdischarge (~22,840 m3 s−1) and occurrence of hypoxia (Yin et al.,2004b), is selected as the period for further analysis on the fluxesand transformations of nutrients in the Pearl River Delta (PRD). Theflux estimates (residual fluxes) are determined by averaging thenutrient fluxes (volume flux multiplied by nutrient concentrations)calculated at every time step over 30 days (~58 M2 tidal cycles). Toclarify the nutrient fluxes introduced from or exported to differentregions, we define fluxes introduced from the upstream boundaries,fluxes passing through the eight river outlets and fluxes exportedfrom the Pearl River Estuary (PRE) to the South China Sea (SCS) as

Table 4Calibration and validation statistics for the 1-D and 3-D coupled model.

Simulation period Variables Domain Mean error Relativeerror (%)

r2

February 2001(dry season forcalibration)

Water surface elevation 1-D −0.03 m −7.5 0.95Water surface elevation 3-D 0.06 m 9.5 0.91Discharge 1-D 55.9 m3 s−1 10.9 0.96Salinity 1-D −0.08 PSU −18.6 0.82SSC 1-D 1.2 mg l−1 22.7 0.56

July 1999(wet season forvalidation)

Water surface elevation 1-D −0.10 m −7.7 0.98Water surface elevation 3-D −0.03 m −9.3 0.92Discharge 1-D 203.8 m3 s−1 8.6 0.97Salinity 3-D −2.7 PSU −26.4 0.94SSC 1-D −20.0 mg l−1 −28.1 0.77SSC 3-D −3.4 mg l−1 −38.6 0.67Chlorophyll a 3-D −0.0004 µg l−1 −39.3 0.41DIN (NH3+NO23) 3-D 0.13 mg l−1 29.3 0.67OPO4 3-D 0.01 mg l−1 32.5 0.48CBOD 3-D −0.04 mg l−1 −19.1 0.57DO 3-D 0.67 mg l−1 25.1 0.68

Fig. 6. Comparisons of simulated and observed results for calibration: (a) water surface elevation and discharge, (b) SSC at two representative river outlets (Humen and Modaomen) inFebruary 2001 (dry season), and (c) NH3, CBOD, TN and TP in the Guangzhou–Foshan region (GF series), Zhongshan–Shunde region (ZS series) and Jiangmen–Kaiping region (JK series) inJanuary 1999 (dry season); observational data are available for the dry season for the years 1998 (blue circles), 1999 (pink squares) and 2000 (green triangles). Locations of the surveystations in the river network (RNPRD) are shown in Fig. 4a. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)


the upstream fluxes, the riverine fluxes and the estuarine fluxes,respectively. The exchange cross-section between the PRE and theSCS is shown in Fig. 1a. At the same time, the pollutant loads frompoint sources and non-point sources are defined as waste loads inthis section in order to distinguish them from the upstream fluxes.There are thirty-one kinetic terms related to the transformations ofnutrients (Table 1). The effective reaction term for each water qualitycomponent is determined by averaging each kinetic term calculatedat every time step over 30 days. The nutrient budgets areconstructed based on the estimated fluxes and effective reaction

terms. In this section, we will focus on the fluxes and budgets forCBOD, NH3, NO23 and IP for the river network (RNPRD) and the PREcombined in July 1999 (wet season), which are shown in Fig. 10,together with information on the relative importance of differentsources and sinks. It should be noted that the results do notdistinguish between terrestrial inputs and primary productivitysources (or aquatic inputs). Besides, the budgets are not completelyconservative because the simulation considers only one month andthe final conditions can not return to the initial conditions over thisshort period.

Fig. 7. Comparison of (a) the simulated SSC and (b) that from remote sensing image in the wet season.


3.2.1. Fluxes and transformation processes of CBODIn theRNPRD, the external loads of CBODare estimated as4256 t d−1

(Fig. 10a). The upstream flux dominates the external loads, with 69%of the total. For individual rivers, CBOD loads from the Xijiang River(Fig. 1a), which are primarily contributed by waste loads and intenseerosion processes in the upstream drainage area of the Xijiang River(Gao et al., 2002), account for 93% of the upstream flux. Waste loads inthe RNPRD, significantly influenced by the point sources, are alsoimportant sources for CBOD. Among the waste loads, the non-point-source loads contribute 24% to the total, which reflects the significanceof non-point-sources pollution in the wet season compared to that inthe dry season (Fig. 5). Fig. 10a shows that the RNPRD acts as a sinkfor CBOD that consumes 50% of the external loads. The loss of CBODfrom the simulated system is dominated by carbonaceous oxidationwhich is also the most important sink for DO. The oxidation of CBODremoves 36% of the external loads and contributes 71% to its total loss.Anothermajor loss of CBOD is via deposition on the benthic sediment, at578 t d−1 (~14% of the external loads). As such, the denitrificationprocess also provides a sink for CBOD, but this reaction is insignificant incomparison with the oxidation and deposition processes. In relation tothe cumulative CBOD in the benthic sediment, its ultimate fateassociated with benthic diagenesis processes remains unclear. Inaddition, the cumulative CBOD is available for resuspension when thecurrent is strong enough to erode the sediment during flood periods orstorm events. More CBOD will be produced and the subsequentdecomposition can cause water quality deterioration and hypoxia inthe RNRPD. A further research on the diagenesis processes andresuspension of CBOD is required to address these important questions.Regarding the internal primary source for CBOD, it comes from therecycle of detrital phytoplankton carbon due to endogenous respi-ration and zooplankton predation. This contribution, however, is quitetrivial because primary productivity in the RNPRD is strongly inhibiteddue to high turbidity associated with high sediment loads in the wetseason (Fig. 8c). The turbidity leads to reduced light penetration, lowphytoplankton biomass and low primary production (Yin et al., 2000).Model results show that the chlorophyll a concentrations in theRNPRD are generally less than 0.1 µg l−1, and the net primary pro-ductivity is less than 0.1 g C m−2 d−1 in July 1999. This low primaryproductivity (b0.1 g C m−2 d−1) was also reported by Yin et al. (2000).

The simulated riverine flux of CBOD shows the river network(RNPRD) to be a net exporter of CBOD to the Pearl River Estuary (PRE).The riverine flux has a relatively high value, indicating the residencetime of CBOD in the RNPRD is short in thewet season. Based on Yin et al.(2000), the water residence time in the PRE is about 2.08–4.91 days in

the wet season. CBOD in the water column can not complete itstransformationprocesses due to such short residence time. FromFig.10ait can be observed that the riverine flux of CBOD appears to follow thedistribution pattern of the water flux (Fig. 8c) quite closely. Thisphenomenon suggests that the riverine flux is strongly controlled byhigh river discharge and significantly contributed by the upstreamflux which is the most important external source in the RNPRD asdiscussed above. For individual river outlets, the flux through Modao-men dominates the riverine flux, with 28% of the total. Jiaomen, Humenand Hengmen are the other major receiving river outlets for CBOD.The proportions of the fluxes through Jiaomen, Humen and Hengmento the riverine flux are 21%, 17% and 14%, respectively. As for theeastern four river outlets, its flux is much larger than that through thewestern four river outlets. This is mainly attributed to the higher riverdischarge through the eastern four river outlets (~61% of the totalriver discharge) relative to that through the western four river outlets.

In the PRE, the external loads of CBOD, at 2839 t d−1, arecontributed by the riverine flux and waste loads discharged in thePRE. The riverine flux is the largest input of CBOD to the PRE,accounting for 81% of the external loads in the PRE. In contrary to theRNPRD, the dynamics of phytoplankton play an important role in thePRE. The internal source for CBOD recycled from algal death in the PRE,at 1243 t d−1, is simulated to be at least twoorders ofmagnitudehigherthan in the RNPRD. It is approximately equivalent to 44% of the externalloads. Fig. 11 shows the simulated surface SSC and chlorophyll adistributions in the PRE. It can be observed that the concentrations ofchlorophyll a at the inner part of the PRE (close to the RNPRD) are lowbecause of the turbidity induced by high sediment loads from the eightriver outlets. At the outer part of the PRE, water transparency increasesdue to the substantial decreases in SSC, and as a result, the con-centrations of chlorophyll a increase. These phenomena are exploredand discussed in more detail in literature (Yin et al., 2000; Yin et al.,2001; Yin et al., 2004a,b; Harrison et al., 2008). The amount of CBODrecycled from algal death at the outer part of the PRE, at 1173 t d−1, issimulated to be approximately 15–16-fold greater than that at theinner part of the PRE. However, the internal source for CBOD isexceeded by large consumptions via oxidation, denitrification anddeposition processes. The PRE thus behaves as a sink for CBOD thatconsumes 90% of the external loads. The oxidation process dominatesthe simulated CBODbehavior, resulting in a loss at 2608 t d−1 (~94% ofthe external loads). The loss of CBOD via denitrification is simulated tobe roughly equivalent to the loss via deposition, at 575 t d−1 (~20% ofthe external loads). As most of CBOD is depleted in the water column,only a small amount of CBOD, at 288 t d−1, is exported from the PRE to

Fig. 8. Comparisons of model results and observational data in July 1999 (for validation). Data include (a) water surface elevation and discharge, (b) SSC at Humen and Modaomen,and (c) water and SS fluxes through the eight river outlets during July 16 and July 24, 1999. G1–G8 represents the eight river outlets (Fig. 1a): Humen, Jiaomen, Hongqili, Hengmen,Modaomen, Jitimen, Hutiaomen and Yamen, respectively.


the South China Sea (SCS). The estuarinefluxof CBODmay be primarilymarine original, as most of the terrestrial CBOD is depleted inside thePRE and the contribution from algae death is very significant. Thisfeature is supported by the studies of Jia and Peng (2003) and Hu et al.(2006). They both stated that the terrestrial derived organic mattercontributed significantly to the bottom sediment at the inner part ofthe PRE, while organic matter in the sediment at the outer part of thePRE and adjacent shelf were mainly marine original.

3.2.2. Fluxes and transformation processes of ammonia nitrogenThe fluxes and budgets of NH3 for the river network (RNPRD) and

the Pearl River Estuary (PRE) combined are summarized in Fig. 10b.The external loads of NH3 in the RNPRD are estimated as 712 t d−1. Theupstream flux provides 57% to the external loads, with the XijiangRiver being the most important source (~90% of the upstream flux).Percentage contribution of the waster loads of NH3 to the externalloads has increased compared to that of CBOD. Source apportionment

of the waster loads is found as follows: 71% for the point sources and29% for the non-point sources. In the RNPRD, nitrification is identifiedas the dominant process in the transformations of NH3, leading to aloss at 276 t d−1 (~39% of the external loads) which accounts for 99%of its removal. This process also contributes significantly to the highNO23 concentrations and low DO saturations in the water columnboth simulated and observed in thewet season. Deposition of particle-sorbed NH3 also provides a sink for NH3, but this process isinsignificant in comparison with the nitrification process. AlthoughNH3 is the preferred form of inorganic nitrogen for phytoplanktongrowth, the loss of riverine NH3 via phytoplankton uptake is trivialsince the primary productivity in the RNPRD is strongly inhibited dueto high turbidity. The internal sources for NH3 are quite small relativeto the loss via nitrification. Conclusively, the RNPRD behaves as a sinkfor NH3 that consumes 37% of the external loads.

The proportion of the simulated riverine flux of NH3 to the externalloads in the RNPRD (~63%) is higher than that of CBOD (~54%), which

Fig. 9. Comparisons of simulated and observed results for validation: (a) NH3, CBOD, TN and TP in the Guangzhou–Foshan region, Zhongshan–Shunde region and Jiangmen–Kaipingregion in July 1999 (wet season); observational data are available for thewet season for the years 1998 (blue circles),1999 (pink squares) and 2000 (green triangles), (b) CBOD, OPO4,NO23 and DO for different layers at the time-series stations C1 and C2, and (c) CBOD, DO, NO23 and OPO4 for the surface layer at the survey stations in the Pearl River Estuary (PRE) inJuly 1999. The number in bottom panel denotes the survey station number (Fig. 4b). (For interpretation of the references to color in this figure legend, the reader is referred to theweb version of this article.)


is mainly attributed to the higher percentage contribution of thewasteloads of NH3 (~43%) relative to that of CBOD (~31%). The riverine fluxof NH3 behaves in a similar manner to that of CBOD, except the NH3

flux through Humen exceeds Jiaomen. This difference is related to the

fact that most of the waste loads discharged from Dongguan, Foshanand Guangzhou (the city with the largest waste discharge, see Fig. 1a)are direct to the upstream area of Humen, and hence lead to aremarkable increase in the NH3 flux through Humen. The proportion


of the flux through Humen to the riverine flux has increased to 21%.Like CBOD, the eastern four river outlets are the major receiving riveroutlets for NH3, transferring 61% of the riverine flux to the PRE.

The external loads of NH3 in the PRE are estimated as 577 t d−1, ofwhich 78% are contributed by the riverine flux. The interactions

Fig. 10.Monthly-average fluxes and budgets for (a) CBOD, (b) NH3, (c) NO23, and (d) IP for thseason). Data in square brackets are ratios of waste loads, effective reaction terms and fluxenegative values represent losses. Regarding the riverine fluxes, data in square brackets arebrackets are proportions of the nutrient fluxes through the eastern four river outlets (G1–G

between multiple mechanisms for NH3 appear to be more compli-cated in the PRE than in the RNPRD. The simulated NH3 behavior in thePRE is controlled under the combined effects of phytoplanktonkinetics, nitrification, ON mineralization and benthic processes. Theactive dynamics of phytoplankton play an important role in the

e river network (RNPRD) and the Pearl River Estuary (PRE) combined in July 1999 (wets of nutrients to the external loads in each system. Positive values represent inputs andratios with respect to the RNPRD (the former) and the PRE (the later). Data in round4) to the riverine fluxes.

Fig. 10 (continued).


transformations of NH3 in the water column, resulting in a net lossequivalent to 15% of the external loads in the PRE. Duringphytoplankton growth, NH3 is utilized by phytoplankton for growth,at 132 t d−1 (~23% of the external loads). The large consumption ofNH3 via phytoplankton uptake corresponds to the increased primaryproduction in the PRE which also leads to a significant loss of NO23 at

107 t d−1. During phytoplankton death, NH3 is recycled from thephytoplankton biomass pool in the water column, at 47 t d−1 (~8% ofthe external loads). The benthic NH3 flux and conversion from ON viamineralization also provide significant contributions to the NH3

budget, together at 340 t d−1 (~59% of the external loads). However,these sources for NH3 are exceeded by large consumptions mainly via

Fig. 11. Simulated surface (a) SSC and (b) chlorophyll a distributions in the Pearl River Estuary (PRE) in July 1999 (wet season, monthly-average).


nitrification and phytoplankton uptake. Thus the PRE behaves as a sinkfor NH3 that consumes 80% of the external loads. The loss of NH3 viadeposition is trivial, indicating that the sorption process between SSand NH3 is so insignificant that it can be ignored in the transforma-tions of NH3. The major loss of NH3 is via nitrification which issimulated to be larger than the external loads in the PRE (Fig. 10b).Ultimately, only 121 t d−1 of NH3 are exported from the PRE to the SCSdue to the high depletion of NH3 in the PRE. The proportion of thesimulated estuarine flux of NH3 (~21%) is higher than that of CBOD

(~10%), presumably due to the significant contributions from theinternal sources of NH3.

3.2.3. Fluxes and transformation processes of nitrate plus nitrite nitrogenFrom Fig. 10c it can be observed that the upstream flux of NO23

dominates the external loads of this constituent in the river network(RNPRD) which are estimated as 1975 t d−1. NO23 loads from theXijiang River provide 92% to the upstream flux. With respect to theinternal sources for NO23, the conversion from NH3 via nitrification is


the leading input for NO23, which is simulated to be equivalent to 14%of the external loads in the RNPRD. The major loss of NO23 is viadenitrification, at 14 t d−1 (~1% of the external loads); although thisprocess plays a minor role in terms of NO23 budget. The internal sinksof NO23 such as phytoplankton uptake and denitrification are trivialrelative to its generation from NH3 nitrification. As a consequence, theRNPRD acts as a source for NO23 and all of the NO23 loads added tothe RNPRD are lost to the PRE. It can be concluded that NO23 behavesquasi-conservatively in the water column of the RNPRD.

The simulated riverine flux of NO23 is particularly high under thecombined contribution from nitrification and external loads of NO23. Itshows a similar distribution pattern to that of NH3. Approximately 68% ofthe riverine flux of NO23 is exported from three major river outletsincluding Modaomen, Humen and Jiaomen. The proportions to theriverine flux are 25% forModaomen, 23% for Humen and 20% for Jiaomen.The high NO23 flux through Humen is significantly contributed by theconversion from NH3 via nitrification, which implies an intense nitrifica-tion rate within this region (Harrison et al., 2008). The eastern four riveroutlets remain the major receiving outlets in terms of nutrient fluxes. Inaddition, the riverine flux of NO23 accounts for 84% of the riverine flux ofDIN (~2713 t d−1), indicating that NO23 is the major phase of DIN.

The riverinefluxwith thewaste loads adds up to a total external inputof 2268 t d−1 of NO23 to the Pearl River Estuary (PRE) which isdominated by the former.Muchalike theRNPRD, thePRE acts as a sourcefor NO23 due to the significant internal compensation from NH3

nitrification which is simulated to be equivalent to 32% of the externalloads in the PRE. Regarding the internal sinks for NO23, about 5% of theexternal loads (~107 t d−1) are utilized via phytoplankton uptake, whileanother 9% of the external loads (~201 t d−1) is consumed via deni-trification. These two processes contribute 95% of the removal of NO23.In general, the internal sinks of NO23appear to be relatively small, whichindicates the importance of nitrification in the maintenance of NO23. Asa result, a particularly high fluxof NO23 (~2709 t d−1) is exported to theSouth China Sea (SCS). Also, the proportion of the simulated estuarineflux of NO23 to the estuarine flux of DIN (~2830 t d−1) is over 96%, avaluemuchhigher than that for the riverinefluxofNO23. All of theNO23loads added to the PRE are lost to the SCS. This suggests the quasi-conservative behavior of NO23 in the water column of the PRE and theaccumulation of NO23 in the SCS.

3.2.4. Fluxes and transformation processes of inorganic phosphorusIn the river network (RNPRD), the external loads of IP are estimated

as 103 t d−1 (Fig. 10d). Source apportionment of the external loads isfound as follows: 79% for the upstreamflux (ofwhich 94% is contributedby the Xijiang River), and 21% for the waste loads (of which 70% iscontributed by the point sources). From Fig. 10d it can be observed thatthe RNPRD acts as a sink for IP that consumes 11% of the external loads.Deposition of PO4SS is thedominant process in the transformations of IP,and this process leads to a loss of IP at 12 t d−1 which accounts for morethan 99% of its removal. This indicates that the behavior of IP is greatlyinfluenced by SS which exhibits an intense adsorption on OPO4. Similarto DIN in the RNPRD, the loss of riverine IP via phytoplankton uptake istrivial. It suggests that phytoplanktonproduction in theRNPRD ismostlylimited by light due to high turbidity, rather than nutrients availability.This phenomenon can also be reflected by the simulated SSC andchlorophyll a distributions at the inner part of the PRE (close to theRNPRD), as shown in Fig. 11.

The simulated riverine flux of IP is particularly high because of thelowconsumption in theRNRPD. It showsa similardistributionpattern tothose for CBOD, NH3 and NO23, with Modaomen, Jiaomen, Humen andHengmen being the major receiving river outlets. One difference is thatthe IP flux through Humen exceeds Modaomen, and it becomes thelargest one out of the eight river outlets. This is because Modaomen aswell as Jiaomen carries a higher SS load relative to Humen (Fig. 8c), andthus in the regions of Modaomen and Jiaomen, IP experiences asignificant loss via deposition ascribed to the intense adsorption ofOPO4

by SS. The proportions to the riverine flux are 25% for Humen, 24% forModaomen, 20% for Jiaomen and 12% for Hengmen. The proportion ofthe flux through the eastern four river outlets (~64%) is relatively high,which is associated with its large river discharge and further enhancedby the increasedproportionof theflux throughHumen. In relation to thedifferent phases of IP in the water column, the riverine fluxes for OPO4

and PO4SS are estimated as 44 and 52 t d−1, respectively. The latteraccounts for 57% of the riverine flux of IP, indicating that a large amountof IP transports in the form of sorbed phase. It is clear that both theriverine fluxes for OPO4 and PO4SS are so important that none of themcanbe ignoredor individually represent the riverinefluxof IP. Also, sincemore OPO4 will be desorbed from the riverine PO4SS due to eliminationof sorption capacity of SS in the Pearl River Estuary (PRE), it isinappropriate to regard the riverine flux of OPO4 as the only source foravailable IP that is exported from the RNPRD to the PRE.

In the PRE, the riverine flux dominates the external loads of IP, with91% of the total (~105 t d−1). The internal sources for IP are ofimportance to the IP budget. The amounts of IP recycled from the algaedeath and converted from OP via mineralization are estimated as 6 and18 t d−1, respectively. Spurredby the increasedprimaryproductivity, theloss of IP is dominated by phytoplankton uptake which is estimated as30 t d−1 (~28% of the external loads). Another major loss of IP is viadeposition on the benthic sediment, at 11 t d−1 (~10% of the externalloads). This serves to emphasize the significant influence of SS on thebehavior of IP. Overall, the PRE acts as a sink for IP that consumes 16% ofthe external loads in the PRE. The simulated estuarine flux of IP has arelatively highvalue, estimated as92 t d−1 (~88%of the external loads inthePRE). It canbe found theestuarineflux is significantly contributedbythe internal sources of IP. In addition, the estuarine fluxes for OPO4 andPO4SS are estimated as 92 and 1 t d−1, respectively. The former oneaccounts for 99% of the estuarine flux of IP, suggesting that OPO4 is themajor phase of IP transporting in the PRE.

3.3. Seasonal variations of nutrient budgets in the Pearl River Delta

As shown in Fig. 5, the external loads of nutrients exhibit significantseasonal variations in terms of upstream fluxes and non-point-sourceloads. Fig. 12 is meant to provide an insight into the nutrient budgets inJanuary 1999 (dry season), and to ascertain if the river network (RNPRD)and the Pearl River Estuary (PRE) are sinks or sources for nutrients in thedry season. Model results show that the transformations of nutrients inthe dry season are generally in a similar manner to those in the wetseason (Fig. 10), in terms of the key biogeochemical processes fornutrients and the contribution to the nutrient budgets from phyto-plankton dynamics and internal sources of nutrients. For instance,carbonaceous oxidation andnitrification are still the dominant processesin the transformations of CBOD and NH3, respectively, in the RNPRD andthePRE in thedry season.Also, theRNPRDand thePRE appear to be sinksfor CBOD, NH3 and IP, and sources for NO23 in the dry seasonwhen theRNPRD and the PRE have a common feature of retaining a highproportion of nutrient inputs. This implies that in the dry season as wellas the wet season, a great amount of nutrients from terrestrial inputs orprimary production have been consumed through a series of intensebiogeochemical processes, e.g., phytoplankton uptake, settling to theseafloor and nitrification. As a result, eutrophication and its conse-quences (e.g., harmful algal blooms and hypoxia in bottomwaters) mayoccur. These impacts are an increasing threat to coastal ecosystems andare believed to be associatedwith high nutrient loads delivered by riversto the PRD and its coastal waters. Regarding a potential hypoxia, modelresults show that consumption of DO in bottom waters is linked to theoxidation process of CBOD and ammonia nitrification, in addition to SODfuelled by cumulative organic matter in the benthic sediment fromprimary production along with terrestrial inputs.

By comparingwith thewet season, themagnitudeof internal sourcesfor nutrients in thedry season ismuch smaller, but their contributions tothe nutrient budgets are more significant, because the external loads in


the dry season are of less importance due to low river dischargecondition (~2620 m3 s−1). In the dry season, the external loads ofnutrients are generally contributed by thewaste loads in the RNPRD andthe PRE since the upstream fluxes and the riverine fluxes are relativelysmall (Fig. 12). The proportions of the simulated riverine fluxes ofnutrients to the external loads have remarkably decreased due to a

Fig. 12.Monthly-average fluxes and budgets for (a) CBOD, (b) NH3, (c) NO23, and (d) IP for t(dry season).

longer residence time. From Figs. 10 and 11 it is clear that the seasonalvariations of nutrient fluxes are very significant. The upstream fluxes ofnutrients in thewet season are 12–27-folds greater than those in the dryseason, while the simulated riverine fluxes and estuarine fluxes are 5–11-folds and 1–5-folds larger, respectively. This indicates that most ofthe annual nutrients occur in the wet season, which is closely linked to

he river network (RNPRD) and the Pearl River Estuary (PRE) combined in January 1999

Fig. 12 (continued).


the water discharge volume. It is estimated that about 80% of thefreshwater discharge occurs in the wet season (April–September) andonly 20% in the dry season (October–March) (Cai et al., 2004).

3.4. Comparison of nutrient inputs among the Pearl River Estuary,Changjiang and Mississippi Rivers

The Pearl River Estuary (PRE) and similar river-dominated systemssuch as the Changjiang and Mississippi Rivers have a common feature

of being highly dynamic and complicated environments with a closecoupling between riverine nutrients, net productivity and hypoxia(Spillman et al., 2007; Rabouille et al., 2008), therefore comparison ofnutrients inputs among these large river systems will be instructivefor further studies of the PRE in terms of eutrophication processes andhypoxia. Freshwater discharge, riverine DIN and DIP inputs, andnutrientmolar ratios in the PRE, the Changjiang andMississippi Riversare shown in Table 5. To facilitate the comparison, we estimate theannual value of nutrient inputs in the PRE by averaging the results

Table 5Freshwater discharge, riverine nutrient input and nutrient ratios in three river-dominantsystems.

System Data period Discharge(103 m3 s−1)

Nutrient input(t d−1)

DIN:DIP(mol:mol)

DIN DIP

Pearl River Estuarya July 1999 23 2713 44 137January 1999 3 348 10 77Annual⁎ 13 1531 27 126

Changjiang Riverb Annual 27 1306 45 65Mississippi Riverb Annual 16 1955 133 33

a This work. Annual ⁎ is estimated by averaging monthly results obtained in July 1999(wet season) and January 1999 (dry season).

b Dagg et al. (2004).


obtained in July 1999 (wet season) and January 1999 (dry season). Theannual values of riverine DIN and DIP fluxes in the PRE, estimated as1531 and 27 t d−1, respectively, are similar to the values of 1074 and25 t d−1 estimated by Cai et al. (2004). Among these rivers, theMississippi has the largest inputs of DIN and DIP, but it has a lowerDIN:DIP ratio due to its relatively high DIP input. Nitrogen limitation ismore frequent in the Mississippi River due to efficient recycling ofphosphorus and loss of nitrogen through denitrifcation (Rabalaiset al., 2002). Riverine DIN input for the PRE and the Changjiang issimilar, although the Changjiang discharge is two times larger.However, the PRE has a relatively low DIP input, resulting in a highDIN:DIP ratio over 126, a value much higher than other two riversystems. The seasonal variation of the riverine DIN flux is clearly moresignificant than the riverine DIP flux in the PRE (Table 5), thusresulting in a significant seasonal variation in nutrient ratio. This ismainly ascribed to the influence by the upstream fluxes of DIN and DIPin association with their biogeochemical processes in the river net-work, e.g., loss of DIN via phytoplankton uptake and denitrification isquite trivial (see subsection 3.2 and 3.3). Response of the ecosystem inthe PRE to seasonal and annual variations in eutrophication pressuresis an intriguing aspect of the problems that need to be addressed inthe future. The DIN:DIP ratio is particularly high in the wet seasonwhen the DIN input is very large and phosphorus is generally believedto be the main limiting nutrient for phytoplankton growth (Yin et al.,2000; 2004a; Harrison et al., 2008), although light also limits primaryproduction (Fig. 11). This feature of phosphorus limitation in the PREis similar to the Changjiang River where the DIN:DIP ratio is muchhigher than the Redfield ratio (16:1), but in contrast to the MississippiRiver where nitrogen appears to be the more common limiting nu-trient (Lohrenz et al., 2008).

4. Conclusions

A 1-D and 3-D coupled model, which includes physical andbiogeochemical processes, is used to simulate the fluxes andtransformations of nutrients in the Pearl River Delta (PRD). Themodel has been calibrated and validated to available field datacollected in February 2001, January 1999 and July 1999. It isparamount to see the model results of physical and water qualitystate variables are in reasonable agreement with the observationaldata, suggesting that the model is sufficiently robust to capture thephysical and biogeochemical dynamics in the PRD. At the same time,the fluxes and budgets for CBOD, NH3, NO23 and IP in July 1999 (arepresentative wet season) are presented in this study. Results showthat the river network (RNPRD) behaves as a source for NO23, but asink for CBOD, NH3 and IP that consumes 50%, 37% and 11% of theexternal loads for CBOD, NH3 and IP, respectively. The simulatedriverine fluxes of nutrients exported from the RNPRD to the Pearl RiverEstuary (PRE) have relatively high values due to the short residencetime in the RNPRD. The riverine fluxes are generally controlled by high

river discharge and significantly contributed by the upstream fluxes.In addition, the riverine fluxes are the largest inputs to the PRE, with78–100% of the external loads in the PRE. The PRE also acts as a sourcefor NO23 and a sink for CBOD, NH3 and IP that consumes 90%, 80% and16% of the external loads for CBOD, NH3 and IP, respectively. Thesimulated estuarine fluxes exported from the PRE to the SCS aresignificantly contributed by the internal sources of nutrients, inaddition to the external loads in the PRE.

Regarding the transformation processes of nutrients in the RNPRD,carbonaceous oxidation, nitrification and deposition are identified asthe dominant processeswith respect to CBOD, NH3 (also NO23) and IP.The phytoplankton dynamics play a minor role in the transformationsof nutrients as phytoplankton growth is strongly limited due to highturbidity associated with high sediment loads in the wet season. Theinternal sources of nutrients are also trivial. With respect to the PRE,the interactions between multiple mechanisms for nutrients are morecomplicated than in the RNPRD. The transformations of CBOD, NH3

(also NO23) and IP are dominated by carbonaceous oxidation,nitrification and phytoplankton uptake, respectively. Different fromthe RNPRD, the phytoplankton dynamics in the PRE appear to play animportant role in the transformations of nutrients due to theincreased primary production. Furthermore, the nutrient budgets inthe PRE are significantly contributed by the internal sources ofnutrients, in associationwith the external loads. At the same time, thenutrient budgets in January 1999 (dry season) are discussed.Conclusively, the transformations of nutrients in the dry season aregenerally in a similar manner to those in the wet season, while thenutrient fluxes show significant seasonal variations.

It should be noted that the nutrient budgets in this study areconstructed based on the results in one month (July 1999) due to datalimitations. July 1999 is a representative wet season in terms ofhydrodynamic conditions. In this sense, the nutrient budgets arerepresentative for the wet season. Also, eutrophication and occur-rence of hypoxia are reported and widely studied in this period (e.g.,Yin et al., 2001, 2004a,b; Harrison et al., 2008). This work wouldprovide a basis for heuristic studies of the PRD estuarine system interms of understanding of the severity of eutrophication, variation ofprimary production, and formation, occurrence and intensity ofhypoxia. Long-term simulations (e.g., one year or more) are certainlyrequired to perform a better estimation on the nutrient budgets in thePRD in the future.

Acknowledgements

Wewould like to thank the Macao Meteorological and GeophysicalBureau for providing us the wind data. We also acknowledge thecontribution of information on pollutant loads from the EnvironmentCouncil of Macao Special Administrative Region, Environment Protec-tion Department of Hong Kong Special Administrative Region, and theSouth China Institute of Environmental Sciences. In addition, we alsowant to thank the Pearl River Estuary Pollution Project and the PearlRiver Water Resources Commission for sharing the field data used formodel calibration and validation. This research was financiallysupported by the 908 Project of Guangdong (GD908-02-03).

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