WL | delft hydraulics

WL | delft hydraulics

Development of Delft3D-ECO

Z4524

Research Report

WL | Delft Hydraulics

Prepared for:

Calibration for a tropical stratified reservoir

December 2007

Prepared for:

WL | Delft Hydraulics

Development of Delft3D-ECO

Johannes Smits (WL | Delft Hydraulics)

Research Report

December 2007

Calibration for a tropical stratified reservoir

Development of Delft3D-ECO Z4524 December 2007Calibration for a tropical stratified reservoir

WL | Delft Hydraulics i

Contents

1 Introduction ..........................................................................................................1

1.1 Background................................................................................................1

1.2 Objectives ..................................................................................................1

1.3 Project organisation....................................................................................2

1.4 About this report ........................................................................................2

2 Description of ECO ..............................................................................................32.1 The structure..............................................................................................3

2.2 Processes and formulations ........................................................................42.2.1 The phytoplankton module BLOOM..............................................42.2.2 The water and sediment quality module .........................................8

2.3 Process coefficients..................................................................................15

3 The Upper Peirce Reservoir ...............................................................................17

3.1 Reservoir and catchment ..........................................................................17

3.2 Observed water quality.............................................................................18

4 Methodology .......................................................................................................254.1 Starting points ..........................................................................................25

4.2 The calibration.........................................................................................26

5 The Upper Peirce Reservoir water quality model..............................................295.1 Model input .............................................................................................29

5.1.1 Computational grid and bathymetry .............................................295.1.2 Flow fields, dispersion, inflows and outflows...............................305.1.3 Loads ..........................................................................................325.1.4 Initial conditions..........................................................................345.1.5 Meteorological forcing ................................................................36

5.2 Simulated and observed concentrations ....................................................375.2.1 The water column........................................................................385.2.2 The sediment ...............................................................................50

5.3 Nutrient mass balances.............................................................................56

6 Conclusions and discussion ................................................................................59

7 References ...........................................................................................................63

December 2007 Z4524 Development of Delft3D-ECOCalibration for a tropical stratified reservoir

i i WL | Delft Hydraulics

Appendices

A Input parameters for water and sediment quality processes ............................ 65

B Input parameters for phytoplankton................................................................. 69


WL | Delft Hydraulics 1

1 Introduction

1.1 Background

Delft3D-ECO is a specific configuration of substances and processes selected from theprocess library of the DELWAQ water quality modelling framework for eutrophicationstudies. In 2000 Delft Hydraulics started a R&D programme for the improvement ofsediment-water interaction modelling and process formulation in ECO. The programmeresulted in a far reaching redesign of key processes that allows for the explicit simulation ofthe composition of both water compartments and sediment layers. The number and thicknessof the sediment layers can be determined by the user. The new model structure, indicated asDELWAQ-G, operates on the basis of a more generic set of process formulations. For eachwater compartment or sediment layer the local transient conditions determine if a processactually proceeds, and which kinetics and coefficients are applying. The dissolved oxygenconcentration is an important ruling condition. Whereas mass transport in the water columnis calculated in the core algorithm of DELWAQ, vertical mass transport in water andsediment is taken care of a dedicated set of process routines that are part of the processlibrary.

To allow more comprehensive sediment-water interaction modeling DELWAQ has beenextended with a new microphytobenthos submodel, with a sediment consolidation anderosion submodel, and with processes for sulphate, dissolved and particulate sulphide andmethane (Delft Hydraulics, 2004a and 2003a/b). A link has been established betweenmicrophytobenthos biomass, benthic grazing and sediment stability.

DELWAQ-G was first implemented and calibrated on the basis of a research application forthe Wadden Sea (Delft Hydraulics, 2006a). This application concerns the Generic EstuarineEcological Model (GEM), the development of which started in 1995 on the initiative ofRijkswaterstaat RIKZ (Delft Hydraulics, 1997a). Since then GEM (e.g. ECO) has graduallybeen improved and extended to become a rather comprehensive modelling framework foreutrophication and primary production in estuaries and coastal waters (Delft Hydraulics,2002 and 2003b). It has been used for various investigations concerning the eutrophicationof the Dutch North Sea, the Wadden Sea and Lake Veere in the Scheldt Delta.

1.2 Objectives

Delft3D-ECO as based on DELWAQ-G has been calibrated for a marine/estuarine system.The objectives of the present research are:

to calibrate ECO for a tropical fresh water system;to obtain a more generic set of process coefficients for the water quality processesincluded in the model;to evaluate the performance of ECO as based on DELWAQ-G;to formulate recommendations for further development; andto enhance insight in the development of water quality in tropical reservoirs.


2 WL | Delft Hydraulics

In view of an ongoing consultancy project an appropriate case has been found in the UpperPeirce Reservoir (UPR), a tropical stratified reservoir located in Singapore. Hydrodynamicinput, meteorological forcing and loads of relevant substances are available from theongoing “Marina Reservoir” project (Delft Hydraulics, 2006a and 2007).

1.3 Project organisation

The calibration of ECO for UPR has been performed by the following team. Jan van Beekcompiled model input, carried out and processed the simulations, and improved the softwarecode in co-operation with Johannes Smits. Hans Los provided input coefficients forphytoplankton, and advised with regard to the calibration of those parameters. JohannesSmits determined the input coefficients of water and sediment quality processes, performedthe calibration and co-ordinated the project.

1.4 About this report

ECO is briefly described in Chapter 2. Extensive descriptions of the process formulationsare available in other reports. The process coefficients resulting from the calibration areincluded in the tables of Appendices A and B. Chapter 3 contains a concise description ofUpper Peirce Reservoir and the available water quality data.

Chapter 4 describes the starting points and the approach of the calibration of ECO for UPR.The results are presented in Chapter 5 with regard to predicted concentrations and the massbalances of the main nutrients, nitrogen and phosphorus.

Chapter 6 discusses the conclusions, and provides recommendations for the furtherdevelopment of ECO. References are listed in Chapter 7.



2 Description of ECO

2.1 The structure

ECO is basically a eutrophication model. ECO simulates the concentrations of nutrients(N,P,Si), organic matter, dissolved oxygen, and a number of “auxiliary” substances, as wellas the growth and species composition of phytoplankton in surface water. Figure 2.1provides a schematic overview of the ecosystem components and interactions as describedin the most comprehensive form of ECO. The arrows indicate conversion processes. Algae,being primary producers, take up nutrients from water to produce cell material, includingammonium, nitrate, phosphate and (in case of diatoms) silicate. The photosysthesis by algae,which requires light, leads to the production of oxygen (DO). Carbon dioxide which is alsoconsumed and produced is not included in ECO, since this nutrient is hardly ever limitingalgae growth.

Figure 2.1: Schematic overview of the ecosystem components and interactions in ECO.

Phytoplankton biomass becomes organic detritus when algae die. The opal silicate skeletonsfrom diatoms slowly dissolve. A substantial fraction of algae biomass may be consumed bygrazers, predominantly zooplankton in the water column in case of fresh water systems.Benthic grazers (filter feeders, etc.) have therefore not been included in the present modelfor UPR. Grazing by zooplankton is included in the mortality rate.

When light reaches the sediment, microphytobenthos (benthic algae, mostly diatoms) maygrow on the sediment. Given the substantial turbidity of the water in Singaporean reservoirsand their considerable depths microphytobenthos can be present in only very lowconcentrations. Microphytobenthos is therefore not included in the present model for UPR.



Detritus arises from primary production as well as from organic matter discharged by man.Parts of the detritus and opal settle on the sediment, where it gets incorporated into the topsediment layer due to the activity of burrowing and bioturbating benthic organisms. Thebacterial decomposition of detritus proceeds in the sediment. The dissolution of opalcontinues. The nutrients produced are partially removed (N) or precipitated (P) in thesediment and partially released into the overlying water column (N, P, Si). In conjunctionwith sediment accretion, nutrients are also buried in the deeper sediment, where they arestored more or less permanently. Dissolved oxygen for the oxidation of organic matter iswithdrawn from the water column, which creates a sediment oxygen demand.

ECO simulates mass transport due to advection and dispersion in the water column and thesediment on the basis of imposed flow fields. All transport and water quality processes havebeen formulated in such a way that mass is conserved for all substances.

2.2 Processes and formulations

2.2.1 The phytoplankton module BLOOM

Main features

The BLOOM module in Delft3D-ECO allows for modelling species competition andadaptation of species to limiting nutrients and light (Los et al. 1984; Los and Brinkman,1988; Los and Bokhorst, 1997; Los and Wijsman, 2007; Delft Hydraulics, 2002a and2006a). Linear programming is used as an optimisation technique to determine the optimumdistribution of nutrients and light over the different species, where maximum net growth ofthe total phytoplankton is reached. By optimising on maximum net growth a combination ofspecies groups is selected that use the limiting resources most efficiently and/or have thehighest net growth rate. Respiration associated with primary production is included in thenet maximum primary production rate. During the optimisation process several constraintslimit the number of possible solutions:

growth constraint: the biomass increase of any of the species groups cannot exceed themaximum net growth rate (production minus respiration) at actual temperature and lightintensity. The relation between light intensity and growth efficiency (as a fraction of themaximum net growth rate) is defined by a table, based on laboratory studies;mortality constraint: the mortality rate of any of the species groups cannot exceed themaximum mortality rate at actual temperature and salinity;nutrient constraints: the total uptake of any of the nutrients cannot exceed the amount ofthese nutrients available;light constraint: the total extinction of light by phytoplankton cannot exceed thethreshold level where the light intensity becomes insufficient to maintain net growth forany of the selected species groups;nutrient constraints. the total uptake of each of the nutrients (N, P, Si) must not exceedthe availability. The total available amount of a nutrient is defined as the sum ofdissolved inorganic nutrient at the beginning of the time step and the amount of thenutrient in phytoplankton.



In the models used for the Marina Reservoir study, the mortality due to grazing has beenincluded in the overall mortality rate for phytoplankton. Nutrient concentrations, underwater light regime and temperature are provided by ECO’s water quality module. Organicdetritus resulting from the mortality of algae in BLOOM is transferred to the water qualitymodule.

To account for adaptation to environmental conditions by phytoplankton the followingdifferent phenotypes can be distinguished for every species group in the model:

1. one phenotype adapted to light limitation, with relatively high growth rates and highN/C and P/C ratio;

2. one phenotype adapted to nitrogen limitation, with typically lower internal N/C ratioand lower maximum growth rates; and

3. one phenotype adapted to phosphorus limitation, with typically lower internal P/C ratioand lower maximum growth rates.

The different phenotypes of a species group are modelled as separate variables withdifferent parameter settings for e.g. growth rates, settling velocities, respiration rates etc.When the conditions in the water change, the biomass from one phenotype can beinstantaneously converted to another phenotype of the same species group.

In the present model five fresh water species groups are included:

1. diatoms;2. green algae;3. microcystis;4. oscillatoria; and5. aphanizomenon (representing Raphrodiopsis).

Species 3-5 are blue-green algae (cyanobacteria). Microcystis takes advantage of itscapacity to buoy. BLOOM is can also include a nitrogen fixing species, defined as anadditional Aphanizomenon phenotype. However, this type is not included in the presentmodel because nitrogen fixer properties are not well known, and because concurrentphosphorus and light limitation in those reservoir will prevent nitrogen fixation most of thetime.

For almost all species (groups) a light limited, a nitrogen limited and a phosphorus limitedtype is included in the model. Diatoms only have light limited and phosphorus limited types.

Formulations

Algal blooms usually consist of various species of phytoplankton belonging to differenttaxonomic or functional groups such as diatoms, greens and blue-greens. They havedifferent requirements for resources (e.g. nutrients, light) and they have different ecologicalproperties. The phytoplankton module BLOOM is based upon the principle of resourcecompetition between different species (Los and Wijsman, 2007). A model species can be agroup of species (e.g. diatoms), an individual biological species or a specific (pheno-)type of



species (e.g. N, P or light limited types). Since types differ with respect to all characteristicsincluded in the model, a shift between types not only implies a shift in nutrientstochiometry, but also in other characteristics such as the growth, mortality, sedimentationand respiration rates and in the carbon to chlorophyll ratio.

The model considers the growth rate and the requirements for all potentially limitingenvironmental factors to determine the optimum combination of model species using thelinear programming method. Nutrient concentrations, light energy and temperature areprovided by the water quality model (see below). Starting from the algae biomassespredicted in the previous timestep, the optimization procedure distributes the availableresources among all selected algae species yielding a new composition of algae species(type) concentrations. Each distinct model species (type) is denoted by the index k. TheBLOOM model identifies the concentration of biomass, Bk, of each algae type k that can besupported in the aquatic environment characterized by light conditions and nutrientconcentrations. It can be demonstrated that finding the best adapted types at any moment intime is equivalent to maximizing the total rate of primary production given a number ofenvironmental conditions (constraints). Defining the temperature dependent gross growthconstant Pgk (day-1), the objective of the model thus is to:

Maximize k Pgk Bk (2.1)

For each algae species (type), the requirements for nitrogen, phosphorus and silica (onlyused by diatoms) are specified by coefficients nik, the fraction of nutrient i per unit biomassconcentration of algae species (type) k. In the case BLOOM coupled to a water qualitymodel, the total readily available concentration, Ci (g.m-3) of each nutrient in the watercolumn equals the amount in the total living biomass of algae, k(nikBk), plus that dissolvedin the water, wi. These mass balance constraints apply for each nutrient i:

k (nik Bk ) + wi = Ci (2.2)

Algae absorb light for photosynthesis and growth. Energy becomes limiting through self-shading when the total light absorption consisting of a non-algal part and an algal part,exceeds the maximum at which growth is just balanced by respiration and mortality. Foreach algae species (type) k there exists a specific extinction value Kk

max (m-1) at which this isthe case. The light intensity can also be too high, which means the total extinction is too low(photo-inhibition) for growth. This specific extinction value is Kk

min. The ranges betweenKk

min and Kkmax differ for different algal species (types) k, because each one of them is

characterized by a different set of model coefficients. Among others a different lightresponse curve for growth is used for each species in the model in the form of a table,through which a curve is fitted which is integrated numerically to account for diurnalvariations in light intensities over depth due to mixing and in time. Letting Kk (m3/m/gdry)represent the specific light absorbing extinction constant for living material of algae species(type) k, the total extinction due to all living algae is:

KL = k( Kk Bk ) (2.3)



Added to this must be the extinction caused by dead cells, KD and the contribution of allother fractions such as inorganic suspended matter and humic substances to the extinction ofthe water, KW (m-1). Hence:

Kkmin KL + KD + KW Kk

max (2.4)

If the total extinction is not within the range for an algae species (type) k, its concentrationBk will be zero.

It may be impossible to achieve the biomass level at which either light or some nutrient getslimiting within a single time-step of the model. To account for this situation, a constraint todelimit the maximum biomass increase within the time-interval is considered during theoptimization procedure. Assuming that losses will be low during the exponential growthphase of a phytoplankton species, mortality is ignored in the computation of this growthconstraint. For all algae species (types) k the maximum possible biomass concentration,Bk

max (gdry.m-3), at the end of the time interval t (days) depends on the initial biomassconcentration, Bk

o, (gdry.m-3), the maximum gross production rate Pgkmax (day-1), the

respiration rate constant, Rk, (day-1), and the time and depth averaged production efficiencyfactor, Ek. Using the temperature dependent net production rate constant, Pnk (= Pgk

max.Ek –Rk) (day-1), for each algae species (type) k:

Bkmax = Bk

o exp{ Pnk t } (2.5)

As in the case of growth, the mortality of each algae species is also constrained to prevent acomplete removal within a single time-step. The minimum biomass value of a species isobtained when there is no production, but only mortality. The minimum biomass, Bk

min

(gdry.m-3), of species (type) k at the end of time interval t depends on the initial biomass,Bk

o (gdry.m-3), of type k and the temperature dependent specific mortality rate constant, Mk

(day-1) of species (type) k, in which the effect of grazing by zooplankton can be taken intoaccount:

Bkmin = Bk

o exp{– Mk t } (2.6)

In biological terms the competition in the BLOOM model is governed according to thefollowing principle. The algal types defined in the input compete with each other for allpotentially limiting resources taking the existing biomass into account. The outcome of thecompetition for a potentially limiting resource is determined by the ratio between the grossgrowth rate constant and the requirement for that resource. Hence species with very highgrowth rates may outcompete more efficient, but slowly growing species, or very efficientspecies may outcompete species with a higher potential growth rate but a much higherrequirement for that particular resource. In practice this means that opportunistic specieswith high growth rate usually dominate when total available nutrients are low and theaverage light intensity is high, whereas efficient species with lower potential growth ratesand lower resource requirements dominate when total available nutrient levels are high andthe average light intensity is low (high level of self-shading).



2.2.2 The water and sediment quality module

Nutrient processes

Nutrients are subjected to a number of microbial and chemical conversion processes, whichare quite different for nitrogen, phosphorus and silicon. The rates of all these processesincrease with increasing temperature, and therefore proceed much slower in winter than insummer in temperate water systems. Rates are rather constant in tropical water systems, thathave little seasonal variation in temperature. The processes included in ECO reflected by thearrows in Figure 2.2. As can be seen, conversion takes place in the water column as well asin the sediment. Different processes dominate in the water column and in the sediment.

Ammonium (NH4) is oxidised into nitrate (NO3) in a microbial process called nitrification,which requires oxygen (Vanderborght et al., 1977). Nitrifiers are predominantly sessilebacteria, that need readily available organic substrates. This implies that nitrificationproceeds most actively at and in the oxidising top sediment layer.

When oxygen is not available, bacteria resort to nitrate for the decomposition of organicmatter (Vanderborght et al., 1977). This process of denitrification usually only proceeds inthe sediment, but will also proceed in anoxic water. Denitrification turns nitrate intoelementary nitrogen, implying that is no longer available to algae as a nutrient. Nitrogenfixation into ammonium is not included in the models of the Marina Reservoir study asargued in section 2.2.1.

Figure 2.2: Schematic overview of the microbial and chemical conversion of nutrients in ECO.



Phosphate (PO4), largely present as ortho-phosphate, adsorbs onto sediment particles, inparticular onto the surface of various metaloxides, predominantly iron oxihydroxides(Stumm and Morgan, 1996). The adsorption is a reversible equilibrium process, meaningthat when algae would deplete phosphate the adsorbed phosphate will desorb. Whenadsorbed to suspended sediment, phosphate settles to the sediment. The adsorption capacityof sediment depends on the presence of oxygen. Under anoxic conditions the capacitybecomes far smaller due to the reduction of metaloxides. When top sediment gets anoxic,the adsorption capacity collapses and the return flux of phosphate to the overlying waterincreases greatly.

In the sediment several phosphate minerals may precipitate at the supersaturation of the porewater (Santschi et al, 1990; Stumm and Morgan, 1996). Ironphosphate (vivianite; VIVP) isonly stable under anoxic conditions, whereas calcium carbonate phosphates (apatites;APATP) are unconditionally stable. The precipitation of apatites may lead to more or lesspermanent storage of phosphate in the sediment, implying that this phosphate is no longeravailable to algae.

Opal silicate (OPAL) is produced from dissolved silicate (Si) by diatoms, that strengthentheir cell walls with silicate skeletons. When diatom cells have died, the skeleton remainsstart to dissolve and settle on the sediment (processes not included in Fig. 2.2). Thephysical-chemical dissolution process continues in the sediment, since pore water isgenerally undersaturated with respect to opal silicate (Berner 1974).

Organic matter and electron-acceptor processes

Algal detritus or waste organic matter discharged by man is decomposed by bacteria. Themicrobial decomposition of organic matter into its basic inorganic components such ascarbon dioxide, ammonium and phosphate is called mineralisation. The rate ofmineralisation increases with increasing temperature (Delft Hydraulics, 1980). Asmentioned before, carbon dioxide in not included in the model.

During the decomposition process the organic matter is gradually converted into materialthat is more resistant to microbial breakdown (Westrich and Berner, 1984). In other words,the decomposition rate decreases at the increase of the age of detritus. This is caused by boththe difference in degradability of the numerous chemical components in detritus and the(bio)chemical conversion of readily degradable components into less readily degradablecomponents. Eventually, refractory organic matter results, that is subjected to very slowdecomposition. Particulate refractory organic matter accumulates in the sediment, whereasdissolved refractory organic matter will spread through the water column and will graduallybe decomposed due to speeding up of the process by photo-oxidation.


1 0 WL | Delft Hydraulics

The conversion and decomposition processes of organic matter are described according tothe following scheme:

POC1 + O2 CO2

Algae C, Waste C

POC2 + O2 CO2 + DOC + O2 CO2

POC3 + O2 CO2

Algal detritus Particulate Dissolved and organic waste organic matter organic matter

Four organic matter fractions are simulated, three particulate fractions and one dissolvedfraction. In the above scheme given for carbon the decomposition rate decreases in thedownward direction and to the right. POC3 and DOC are very slowly decomposing(refractory) particulate and dissolved organic matter fractions. Similar schemes concern theconversion and mineralisation of organic nitrogen (PON1-3, DON) and phosphorus (POP1-3, DOP), in which carbon dioxide (CO2) is replaced by ammonia (NH3) or phosphate(H3PO4).

Bacteria consume electron-acceptors to oxidize organic matter (Santschi et al, 1990). Theseelectron-acceptors include dissolved oxygen (DO; OXY), nitrate (NO3) and sulphate (SO4).Denitrification, the conversion of nitrate into elementary nitrogen, can only take place whenoxygen is depleted. Sulphate reduction occurs only when both oxygen and nitrate arelacking. Denitrification and sulphate reduction usually only take place in the anoxicsediment. The sulphide produced in dissolved and particulate form (SUD and SUP) is turnedinto sulphate again as soon as it meets oxygen.

When even sulphate gets depleted bacteria resort to methanogenesis to decompose organicmatter. At places where oxygen or sulphate are still available, bacteria will oxidize methaneinto carbon dioxide. When supersaturated methane will escape to the atmosphere.

The consumption of oxygen may lead to undersaturation of the water with respect tooxygen. Under- or supersaturation will be counteracted by the exchange of oxygen with theatmosphere. The rate of reaeration water from the atmosphere is dependent on thewindspeed and the temperature. The saturation concentration is determined by temperatureand salinity (Wanninkhof, 1992). Seawater has approximately 25% lower oxygen saturationconcentrations than fresh water, which implies that generally oxygen concentrations arelower in seawater.


WL | Delft Hydraulics 1 1

Processes and formulations

The water and sediment quality processes include:

decomposition and conversion of four fractions of organic matter (detritus);consumption of electron acceptors at the decomposition of organic matter, oxygenconsumption, denitrification, sulphate reduction and methanogenesis;reaeration of dissolved oxygen;nitrification;adsorption of phosphate on suspended sediment;precipitation and dissolution of phosphate in vivianite (redox sensitive) and in an apatitelike mineral;dissolution of opal silicate;oxidation of sulphide;precipitation and dissolution of sulphide in minerals;oxidation, ebullition and volatilisation of methane;settling and resuspension of organic matter, inorganic particulate phosphorus (adsorbed,precipitated), suspended sediment;advection in the sediment due to seepage, settling and resuspension combined with burialinto or digging from the inactive deep sediment;dispersion in the sediment due to bioturbation, bio-irrigation, flow induced turbulenceand molecular diffusion; andextinction of light.

From literature it is known that artificial aeration devices can transfer substantial quantitiesof oxygen to surface water with reduced oxygen concentrations. Nevertheless, the directtransfer of oxygen from air bubbles to the water column is ignored in the model, since thecurrent process library of Delft3D-ECO does not contain a process aeration by bubbleaerators.

The water and sediment quality processes have been formulated on the basis of thefollowing concepts (Delft Hydraulics, 2002a and 2006a).

For the decomposition and conversion of organic matter components first-order kinetics:

R f k C (2.7)

with:C = concentration (g.m-3)f = function of various rate determining factors (-)k = process rate (d-1)

R = process flux (g.m-3.d-1)

In case of organic matter, f refers to limiting factors with regard to nutrient availability andelectron-acceptor affinity, and for the organic nutrients to relatively enhancedmineralisation. In the case of faecal coli bacteria f is a mortality enhancing function ofsalinity and UV-light regime.



For the oxidation of sulphide and the oxidation driven dissolution of vivianite, second-orderkinetics:

oR k C C (2.8)

with:Co = dissolved oxygen concentration (g.m-3.d-1)

For nitrification and the oxidation of methane, second order limitation/saturation Michaelis-Menten kinetics:

1 2

1 1 2 2

C CR kC Ks C Ks

(2.9)

with:C1 or 2 = concentration of limiting substance 1 or limiting substance 2 (g.m-3)Ks1 or 2 = half-saturation conc. for limiting substance 1 or limiting substance 2 (g.m-3)

For the consumption of electron-acceptors, limitation/inhibition Michaelis-Menten kinetics:

1l i

l l i i

C CR f kC Ks C Ks

(2.10)

with:Cl = concentration of limiting substance (g.m-3)Ci = concentration of inhibiting substance (g.m-3)f = fraction factor, multiplied with a stochiometric constant (-)Ksl = half-saturation concentration for the limiting substance (g.m-3)Ksi = half-saturation concentration for the inhibiting substance (g.m-3)

For the adsorption of phosphate, Langmuir kinetics:

aa

d

Ce OHKaC Ca

(2.11)

( )a aR k Ce C (2.12)

with:a = stochiometric reaction constant (-)Ca = free adsorbent concentration (mole.l-1)Cea = adsorbed substance concentration in equilibrium (mole.l-1)Ca = actual adsorbed substance concentration (mole.l-1)Cd = dissolved substance concentration (moleP.l-1)k = sorption rate (d-1)Ka = adsorption equilibrium constant (molea-1.la-1)OH = hydroxyl concentration (mole.l-1)



For the precipitation and/or dissolution of phosphate minerals, sulphide mineral and opal,and for reaeration, volatilisation and ebullition, saturation kinetics:

( )d dRp kp C Ce (2.13)

p d dRd kd C Ce C (2.14)

with:Cd = actual dissolved substance concentration (g.m-3)Ced = equilibrium dissolved substance concentration at saturated solution (g.m-3)Cp = precipitated substance concentration (g.m-3)

kp = precipitation reaction rate (d-1)kd = dissolution reaction rate (d-1)Rp = precipitation flux (g.m-3.d-1)Rd = dissolution flux (g.m-3.d-1)

Process rates are generally temperature dependent according to:

20 ( 20)Tk k kt (2.15)

with: k = process rate (d-1 or g.m-3.d-1); k20 reference rate at 20 oC

kt = temperature coefficient (-)T = temperature (oC)

Settling and resuspension of substances follow from Parteniades-Krone formulations. Theformulation for settling is:

/Rs f s C H (2.16)

0.0 , 1c

f max (2.17)

with: = shear stress inhibition/limitation function (-)

H = depth of the water layer (m)Rs = settling flux (g.m-3.d-1)s = settling velocity (m.d-1)

= actual shear stress (Pa)c = critical shear stress for settling (Pa)

The shear stress is derived from the flow velocity calculated from volume and flow rate.

The settling of adsorbed substances and minerals is linked to the settling of suspendedsediment, whereas the settling of phytoplankton and detritus is independent. Burial in theinactive sediment is equal to the settling flux, since the sediment layers and their porositiesare fixed.



The formulation for resuspension is:

/Rr f r H (2.18)

0.0 , 1c

f max (2.19)

with: = shear stress inhibition/limitation function (-)

r = resuspension rate (g.m-2.d-1)Rr = resuspension flux (g.m-3.d-1)

= actual shear stress (Pa)c = critical shear stress for resuspension (Pa)

The resuspension rate of sediment (IM1-3) is to be provided to the model. The resuspensionfluxes of other particulate components are calculated proportional to the concentrations inthe sediment. Digging from the inactive sediment is equal to the resuspension flux, since thesediment layers and their porosities are fixed.

Dispersion in the sediment is formulated according to Fick’s Law, which requires dispersioncoefficients for the dispersion of particulate substances due to bioturbation and thedispersion of dissolved substances due to bio-irrigation, flow induced turbulence andmolecular diffusion.

The light intensity is an exponential attenuation function of depth times the total extinctioncoefficient according to the law of Lambert-Beer. The total extinction coefficient is calculatedas the sum of five contributions:

et eat edt est eot eb (2.20)

with:eat = overall extinction coefficient of algae biomass (m-1)eb = background extinction coefficient (m-1)edt = overall extinction coefficient of detritus (m-1)est = overall extinction coefficient of suspended inorganic matter (m-1)eot = overall extinction coefficient of other substances (m-1)et = total extinction coefficient (m-1)

The Secchi depth (transparency) is derived from the total extinction coefficient according tothe Poole-Atkins relation:

/paSD a et (2.21)

with:apa = Poole-Atkins constant (-)

SD = Secchi depth (m)



2.3 Process coefficients

ECO requires values for a large set of process coefficients. BLOOM requires a substantialsubset of algae species specific coefficients, such as growth, respiration and mortality rates,N/C, P/C, Si/C and chlorophyll/C ratios, specific extinction coefficients and settlingvelocities. The coefficients have been listed in Appendix B. The coefficients are generic andmost of them have been determined during many previous model applications.

The major part of the coefficients of the water and sediment quality module concernsprocess rate constants (k, s, r) and temperature coefficients (kt). Other coefficients concernhalf-saturation constants (Ks), specific limiting factors, stochiometric reaction constants,specific extinction coefficients (e), critical threshold concentrations and shear stresses ( c).

Almost all coefficients and their preferred values and ranges have been listed in AppendixA. Many of these coefficients have either fixed values or preferred values that have beendetermined by extensive calibrations for many different water systems. Consequently, thevalues are generic and model calibration can usually be restricted to just a few coefficients,such as the decomposition rate constants, the nitrification rate constant and the settling andresuspension velocities.

The temperature dependencies of the process rates (input values for 20 oC) are not includedin the Tables, but are the same for all processes. All temperature coefficients are 1.07.

The reaeration of oxygen through the water-atmosphere interface is simulated according tooption 9 for lakes. The reaeration rate constant KLRear at 20 oC is equal to 4.0 m.d-1.

Specific light extinction coefficients used to simulate underwater light regime for primaryproduction by phytoplankton are:

for phytoplankton biomass, species specific values varying from 0.1875 to 0.4 (g.m-3)-1.m-1 (Table B.3);

for the particulate detritus fractions POC1-3 0.2 (g.m-3)-1.m-1;for the dissolved detritus fraction DOC 0.675 (g.m-3)-1.m-1;for inorganic suspended sediment fractions IM1 and IM3 0.018,

for fraction IM2 0.009 (g.m-3)-1.m-1;for water with various dissolved inorganic components 0.08 m-1.

The Poole-Atkins constant SecchiExt1 has its nominal value 1.7.



3 The Upper Peirce Reservoir

3.1 Reservoir and catchment

The Upper Peirce Reservoir (UPR) is located just northwest of the “Marina” catchment ofdowntown Singapore (Fig. 3.1). The contours of UPR are displayed in Figure 3.2. Thecatchments of the reservoir is rather small, namely 738 ha, and is mainly covered in forest.UPR spills into Lower Peirce Reservoir (LPR), which in turn spills into Kallang River. Via apipe connection, the reservoir receives water from Tebrau in Johor (Malaysia). Water iswithdrawn from the reservoir for drinking water production at Chestnut Avenue Waterworks(CAWW), and for supply to MacRitchie Reservoir.

The full capacity volume and the pertinent surface area and average depth of the reservoirare 35.6 106 m3, 310 ha and 11.5 m. The maximum depth of UPR near the dam on theeastern side is approximately 19 m. The average residence time is approximately 340 days.

Two aeration points are located on either side of the control tower, from which raw water istaken in for CAWW. Aeration started in May 1990 and was continuous until October 2003.After this date aeration only starts when the dissolved oxygen concentration decreasesbelow 2.5 mg/L, and proceeds until it is restored to 3 mg/L.

Marina

Channel

MR6MR5

MR4

MR3MR2

MR7

MR8MR9

DT

HD

WL

TPY

BD

KLR

GLR

MR1

barrage

Figure 3.1 The location of Upper Peirce Reservoir. Indicated are Upper Peirce Reservoir, Lower PeirceReservoir, MacRitchie Reservoir and the “Marina” catchment of downtown Singapore,with Marina Bay and tributaries.



RUPJ

I

02

01

03

04

12/RUPE

3

4RUPB

5

RUPJ

I

02

01

03

04

12/RUPE

3

4RUPB

5

Figure 3.2 The contours of Upper Peirce Reservoir, sampling stations, inflows (I=Tebrau water) and outflows (O1=Chestnut Avenue WW, O2=MacRitchie Reservoir, O3=Lower Peirce Reservoir, O4=SICC Golf Course) .

3.2 Observed water quality

All available water quality data have been produced and provided by Public Utilities Board(Singapore). The sampling stations are indicated in Figure 3.2. Monthly data are availablefor 1995-2006. Data analysis showed little difference in water quality in horizontaldirections in the reservoir. The analysis then focussed on the data for central sampling pointRUPB.

UPR is artificially aerated, which has large effects on water quality. The aeration causesincreased mixing, leading to an absence or reduction of vertical temperature andconcentration gradients. Aeration in UPR started in May 1990 and was continuous untilOctober 2003. After this date aeration starts only when the dissolved oxygen concentrationdecreases below 2.5 mg/L, and proceeds until it is restored to 3 mg/L.

There is little variability in water column temperatures in the Upper Peirce Reservoirbetween 1995 and 2003 (range 25.2-30.2 oC; Fig. 3.3), although the Reservoir does becomeweakly thermally stratified during warmer periods. When the reduced aeration scheme setsin after October 2003, the degree of thermal stratification becomes stronger. The meantemperature difference between surface (depth 0.1 m) and bottom (depth 21 m) waters overthe whole period is 0.19 oC, with a maximum difference of 1.5 oC (Jan. 2006). Between2004 and 2006, the mean difference in surface and bottom water temperature wasapproximately 0.4 oC.



1995 1996 1997 1998 1999 2000 2001 2002 2003 200420

15

10

5

020

15

10

5

0

Dep

th (m

)D

epth

(m)

A

B

1995 1996 1997 1998 1999 2000 2001 2002 2003 200420

15

10

5

0

20

15

10

5

020

15

10

5

0

20

15

10

5

0

Dep

th (m

)D

epth

(m)

A

B

Figure 3.3 Water column temperature (A, oC,) and dissolved oxygen concentration (B, mg/L) at Site 5,Upper Peirce Reservoir, Jan. 1995 to Dec. 2004.

20

15

10

5

0

20

15

10

5

0

2004 2005 2006

Dep

th (m

)D

epth

(m)

A

B

20

15

10

5

0

20

15

10

5

0

20

15

10

5

0

20

15

10

5

0

2004 2005 2006

Dep

th (m

)D

epth

(m)

A

B

Figure 3.4 Water column temperature (A oC,) and dissolved oxygen concentration (B, mg/L) at Site 5, UpperPeirce Reservoir, Jan. 2004 to Dec. 2006.



Temp

25.0

26.0

27.0

28.0

29.0

30.0

31.0

J F M A M J J A S O N D

mea

n Te

mp.

(o C

)

DO

0.0

2.0

4.0

6.0

8.0

10.0


mea

n co

ncen

tratio

n (m

g/l)

Temp

25.0

26.0

27.0

28.0

29.0

30.0

31.0


mea

n Te

mp.

(o C

)

DO

0.0

2.0

4.0

6.0

8.0

10.0


mea

n co

ncen

tratio

n (m

g/l)

Surface Bottom

Temp

25.0

26.0

27.0

28.0

29.0

30.0

31.0


mea

n Te

mp.

(o C

)

DO

0.0

2.0

4.0

6.0

8.0

10.0


mea

n co

ncen

tratio

n (m

g/l)

Temp

25.0

26.0

27.0

28.0

29.0

30.0

31.0


mea

n Te

mp.

(o C

)

DO

0.0

2.0

4.0

6.0

8.0

10.0


mea

n co

ncen

tratio

n (m

g/l)

Surface Bottom

Figure 3.5 Mean monthly temperature and dissolved oxygen (DO) concentrations measured in the surface andbottom waters at Site B, Upper Peirce Reservoir, 1995 to 2006. Bars represent standard error.

The lack of pronounced stratification until October 2003 also means that until this dateconcentrations of dissolved oxygen are similar throughout the water column or show weakvertical gradients. Between 2004 and 2006, the maximal vertical concentration differencesare much larger, approximately 5 mg/L as a rule. The bottom waters below a depth of 17meters do become almost anoxic at times (Fig. 3.4).

Mean annual surface temperatures were slightly lower in 1996 (26.5 oC) than in other years(mean 29.4 oC). Mean annual surface dissolved oxygen concentrations have shown a slightincrease of 1 mg/L between 2004 and 2006 while bottom concentrations have declined byover 1 mg/L over the whole period, probably caused by slightly increased stratification.Mean water column temperatures show little seasonal variability (< 2 oC), with lowertemperatures recorded in January (Fig. 3.5).

Mean annual concentrations of NO3-N have shown a decrease in both the surface andbottom waters between 1999 and 2006, while mean annual NH4-N concentrations haveremained largely invariant over this time (Fig. 3.6). Mean surface concentrations of NH4-Nand NO3-N between 1995 and 2006 at Site B are 0.09 and 0.27 mg/L, respectively. This islower than the mean concentrations in the bottom waters at the same site (0.12 and 0.29mg/L, NH4-N and NO3-N, respectively).

Concentrations of SRP appear to have increased slightly between 2003 and 2006, althoughmeasurements collected are often below the analysis detection limit (0.02 mg/L) making itdifficult to observe actual trends (Fig. 3.6).



Surface Bottom

NO3-N

0.00

0.10

0.20

0.30

0.40

0.50

0.60

1994 1999 2004

mea

n co

ncen

tratio

n (m

g/l)

NH4-N

0.00

0.05

0.10

0.15

0.20

0.25

1994 1999 2004

mea

n co

ncen

tratio

n (m

g/l)

NO3-N

0.00

0.10

0.20

0.30

0.40

0.50

0.60

1994 1999 2004

mea

n co

ncen

tratio

n (m

g/l)

NH4-N

0.00

0.05

0.10

0.15

0.20

0.25

1994 1999 2004

mea

n co

ncen

tratio

n (m

g/l)

PO4-P

0.00

0.01

0.02

0.03

0.04

0.05

1994 1999 2004

mea

n co

ncen

tratio

n (m

g/l)PO4-P

0.00

0.01

0.02

0.03

0.04

0.05

1994 1999 2004

mea

n co

ncen

tratio

n (m

g/l)

Surface Bottom

NO3-N

0.00

0.10

0.20

0.30

0.40

0.50

0.60

1994 1999 2004

mea

n co

ncen

tratio

n (m

g/l)

NH4-N

0.00

0.05

0.10

0.15

0.20

0.25

1994 1999 2004

mea

n co

ncen

tratio

n (m

g/l)

NO3-N

0.00

0.10

0.20

0.30

0.40

0.50

0.60

1994 1999 2004

mea

n co

ncen

tratio

n (m

g/l)

NH4-N

0.00

0.05

0.10

0.15

0.20

0.25

1994 1999 2004

mea

n co

ncen

tratio

n (m

g/l)

PO4-P

0.00

0.01

0.02

0.03

0.04

0.05

1994 1999 2004

mea

n co

ncen

tratio

n (m

g/l)PO4-P

0.00

0.01

0.02

0.03

0.04

0.05

1994 1999 2004

mea

n co

ncen

tratio

n (m

g/l)

Figure 3.6 Mean annual nitrate (NO3-N), ammonium (NH4-N) and soluble reactive phosphorus (PO4-P)concentrations measured in the surface and bottom waters at Site B, Upper Peirce Reservoir, 1995to 2006. Bars represent standard error.

Higher concentrations of NH4-N and SRP concentration in the bottom waters relative to therest of the water column occur primarily during stratification events. This can partially beattributed to enhanced rates of sediment nutrient release rates during periods of lowdissolved oxygen concentrations, coinciding with stratification.

There is little data available to describe historic trends in TP and TN concentrations in theReservoir. For TN, where regular monthly measurements do exist, values are often at thehigh detection limit of 1 mg/L. Between 2004 and 2006, mean surface TP and TNconcentrations, based on monthly measurements, were 0.042 and 1.32 mg/L, respectively. Inthe bottom waters at the same site, mean TP and TN concentrations were slightly higher at0.05 and 1.34 mg/L, respectively (Fig. 3.7). These differences are statistically notsignificant.



TP

0

0.1

0.2

0.3

0.4

0.5

0.6

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

TP (m

g/l)

SurfaceMidBottom

TN

00.5

11.5

22.5

33.5

44.5

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

TN (m

g/l)

SurfaceMidBottom

TP

0

0.1

0.2

0.3

0.4

0.5

0.6

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

TP (m

g/l)

SurfaceMidBottom

TN

00.5

11.5

22.5

33.5

44.5

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

TN (m

g/l)

SurfaceMidBottom

Figure 3.7 Total phosphorus (TP) and total nitrogen (TN) concentrations measured at Site B, Upper Peirce Reservoir, 1995 to 2006.

Seasonal trends in surface and bottom nutrient concentrations are weak. For NO3-N,concentrations are highest in July in both the surface and bottom waters (c. 0.4 mg/L), andlowest in February (c. 0.2 mg/L). Ammonium concentrations also show some degree ofseasonal variation, with higher concentrations around March and June, while trends in SRPconcentrations are less defined due to concentrations being generally below the the detectionlimit..

Spatial differences in surface nutrient concentrations between sites also appear to berelatively minor. For NO3-N and SRP, mean differences between measured samples at SitesB and E were 0.003 and 0.001 mg/L, respectively, over the whole period (1995-2006). ForNH4-N, mean differences in concentration were 0.035 mg/L.

While concentrations of chlorophyll-a in the surface waters of the Upper Peirce Reservoirare increasing until 1998, a decrease appears to occur as of 1999. Chlorophyll-a appears tobe lower over the period 2004-2006 (mean 28.9 ug/L) than in the period 1998-2000 (mean40.6 ug/L). A similar trend is observed in secchi depth measurements, with higher watercolumn transparency recorded during periods of low chlorophyll-a concentration (Fig. 3.8).There is little seasonal variability in concentrations of chlorophyll-a and secchi depth overthe whole period. Mean monthly measurements of chlorophyll-a are highest in November(mean 39 ug/L) and lowest in March (26 ug/L) although there is no clear seasonal trend inthe monthly measurements. Variation may be caused mainly by variations in nutrient loads,stratification and solar radiation.



Chlorophyll-a & Secchi depth

0.010.020.030.040.050.060.070.080.090.0

100.0

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Chl

-a (u

g/l)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Secc

hi d

epth

(m)

Chl-aS.Disk

Algal Biovolume

1

10

100

1000

10000

100000

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Bio

volu

me

(104 u

m-3

ml-1

)

Raphrodiopsis Microcystis Total

Chlorophyll-a & Secchi depth

0.010.020.030.040.050.060.070.080.090.0

100.0

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Chl

-a (u

g/l)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Secc

hi d

epth

(m)

Chl-aS.Disk

Algal Biovolume

1

10

100

1000

10000

100000

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Bio

volu

me

(104 u

m-3

ml-1

)

Raphrodiopsis Microcystis Total

Figure 3.8 Surface water chlorophyll-a (Chl-a) concentrations and secchi depth, and estimates of phytoplanktonbiovolume, at Site B, Upper Peirce Reservoir, 1995 to 2006. Note that the phytoplankton counts arepresented on a logarithmic scale.

Phytoplankton cell counts are dominated mainly by green algae (chlorophytes) and diatomsspecies. However, on the basis of biovolume (biomass) the phytoplankton community isequally dominated by diatoms and green algae (chlorophytes) for most of the time. (Due tothe uncertainty in the biovolume of a green algae cell, the contribution of green algae maybe overestimated.) Blue-green algae (cyanobacteria) are important too, and dominateoccasionally, especially Microcystis (Fig. 3.8).

The changing nutrient concentrations as of 1999 may have resulted in changing growthlimiting factors for phytoplankton. Until 2002 the algae are probably mainly limited byphosphorus. As of 2003 nitrogen may have become the second main limiting factor. Thesignificant changes as of 1999, the decrease of nitrate and chlorophyll-a and the slightincrease of phosphate, may be related to a changed loading of UPR with nutrients. Tworelevant facts can be mentioned, that support this hypothesis. The inflow of Tebrau waterhas been minimised, and is now just enough to maintain maximal water level. It remainsuncertain when this change took place. Furthermore, until April 2001 the filter sludgeproduced by the Chestnut Avenue drinking water plant was stored at a dump site near theinlet for Tebrau water. Excess water enriched with nutrients from the sludge freely flowedinto UPR. The inflow of nutrients from the dump site may still be occurring, but probably atlower concentrations.



4 Methodology

4.1 Starting points

The water quality process formulations and coefficients in ECO are thought to be generic,and therefore, in principle, applicable to all fresh surface water systems. It has thereforebeen decided to start the calibration of ECO for Upper Peirce Reservoir with the coefficientvalues that have been derived for previous calibrations, and to try to deviate as little aspossible from these values.

However, it seems unlikely that coefficients, in which underlying processes and parameterssuch as bacterial activity are implicit, can be extrapolated within a temperature range of 30oC on the basis of only one temperature dependency function. Tropical water systems aredifferent from moderate climate waters in many respects. Temperatures are far higher andmuch more constant, which has led to the development of aquatic ecosystems with weakstratification, very small seasonal variation and much higher process rates, resulting indifferent plant, animal and bacterial species and different biotic-abiotic interactions. Theconsequence is that models developed for moderate climate systems can not be applied totropical systems simply by extrapolation.

The species composition and the coefficients for BLOOM have been based on the setting fortropical fresh water systems as derived for Lake Victoria (Delft Hydraulics, 1999). In thisset most coefficients have the same values as derived during the extensive calibrations ofBLOOM for fresh water systems in areas with moderate climate (Los and Brinkman, 1988;Delft Hydraulics, 1995). The gross production rates and their temperature dependencies hadbeen modified to better reproduce species composition and primary production for atemperature range of 20-30 oC.

The species composition for UPR has been verified on the basis of observed speciescomposition (section 3.2). Raphrodiopsis and Aphanizomenon are both filamentouscyanobacteria species with similar properties. Since model coefficients are not available forRaphrodiopsis it was decided to consider Aphanizomenon as being representative for thisspecies.

The water quality process coefficients have been based on the values determined in the firstcalibration of ECO-DELWAQ-G (e.g. GEM) for the Wadden Sea (Delft Hydraulics, 2006a).Starting values for this calibration had been derived from the many previous calibrations ofECO and its precursor DBS (Delft Hydraulics, 2004a, 2002a, 1997b, 1995, 1994 and 1980).The temperature coefficients for the decomposition of organic matter and the consumptionof electron-acceptors kt_Dec and TcOxCon have been raised from 1.047 to 1.07. The settlingvelocities of detritus and inorganic particulate substances were adjusted to 1.0 m.day-1,representative for deep, slightly stratified fresh water systems. Given the low flow velocitiesin the reservoir the resuspension rate was set equal to zero.



Ting and Appan (1996) measured iron in sediment taken from Kranji Reservoir andMacRitchie Reservoir. They found average iron contents of respectively 3.47% and 2.95%DW. After correction for the organic matter and the >63 µm sediment fraction, the ironcontent of fine cohesive sediment can be estimated. This iron content is in both reservoirsroughly 5% DW, similar as found for sediment in other surface water systems around theworld. The iron contents fr_FeIM1 and fr_FeIM2 have both been set equal to 0.05.

The oxidised iron fraction in reducing sediment was set at 15% (fr_Feox = 0.15), a lowvalue in line with the strongly reducing conditions in the sediment of Singaporean reservoirs(very low sulphate content). Moreover, it has been assumed that the sediment entirelyconsists of fine cohesive sediment (IM1), whereas according to data provided by Ting andAppan (1996) the coarser sediment fraction (>63 µm) having a very low iron content maybe up to 40%.

Another finding of Ting and Appan (1996) is that apatite occurs only in (very) smallquantities in the sediment of Kranji Reservoir and MacRitchie Reservoir, which may beexpected for a slightly acidic environment. For this reason it was decided to decrease theratio of the precipitation rates of apatite and vivianite (RatAPandVP) from 2 to 0.00001,effectively leading to a situation in which only very small quantities of apatite can bepresent.

In line with the pH data for the Upper Peirce Reservoir, the pH has been set equal to 7.0.

4.2 The calibration

The calibration started with the stabilisation of sediment quality as resulting from the loadsand the water quality processes by running the model for a simulation period of nine years,three cycles of three years (2004-2006). The water and sediment composition resulting atthe end of this long simulation was taken as the initial composition for the actual calibrationruns, each spanning three more cycles. This delivers a “dynamic steady state” for sedimentcomposition, in which the conversion fluxes in the sediment are in balance with thesediment-water exchange and burial fluxes.

In an early stage of the calibration it appeared that the hydrodynamic model produced toolittle vertical mixing leading to the underprediction of dissolved oxygen in the lower waterlayers. An additional vertical dispersion coefficient of 1.5 10-4 m2/s was imposed on thewater quality model to arrive at correct simulation of vertical concentration gradients.

The second important finding was the overprediction of the nutrients in the water column,leading to strongly overpredicted nutrient concentrations, the absence of P-limitation forphytoplankton, and consequently to a strongly overpredicted phytoplankton biomass(chlorophyll-a). The increase of denitrification and phosphate accumulation in the sedimentwas required.



The adsorption capacity of the sediment at reducing conditions was increased by bringingthe oxidised iron fraction in reducing sediment up from 15% to 30% (fr_Feox = 0.3). Theprecipitation rate of vivianite was increased a factor 5. These modifications reduced themobility of phosphorus in the sediment, and consequently the return flux of phosphate to thewater column.

The ammonium and nitrate concentrations in the water column were brought down byincreasing the nitrification and denitrification rates, and by reduction of the total-N andnitrate loads from Tebrau water with 10%. The latter is justified from the significantuncertainty in the loads (see section 5.3.3). The nitrification rates RcNit20 for the watercolumn and for the sediment were increased, respectively from 0.05 to 0.1 gN.m-3.d-1 andfrom 20 to 40 gN.m-3.d-1. Further enhancement of nitrification was established by decreasingthe half saturation concentration KsAmNit from 1 to 0.4 gN.m-3. Accelerating nitrification inthe sediment also leads to the acceleration of denitrification, because more nitrate becomesavailable in the upper sediment layers. Additionally, the half saturation concentrationKsNiDen for denitrification was also reduced from 1 to 0.4 gN.m-3, leading to a highercontribution of denitrification in the mineralisation of organic matter.

In this stage of the calibration, it appeared that the predicted nutrient concentrations, thenitrate concentration in particular, deviated structurally from the observations for 2006,whereas the measurement data were much better reproduced for the other years (2004-2005). The concentrations observed in Tebrau water were scrutinised again, which led to theconclusion that the data for total-N and dissolved phosphate for 2006 did not lead torepresentative loads for this year. The 2006 load for nitrogen was 15% higher than the2004-5 load, while nitrate concentrations in UPR in 2006 are much lower than in 2004-5.The 2006 load for phosphorus was 35% lower than the 2004-5 load, while phosphateconcentrations in UPR in 2006 tend to be higher than in 2004-5. Moreover, a relatively largefraction of phosphate may have been adsorbed on inorganic suspended sediment in Tebrauwater in 2006, which does not show up in the dissolved phosphate measurements. Theaverage suspended matter concentration in 2006 is almost three times higher than theaverage concentration in 2004-5. It was decided to adjust the total-N and total-Pconcentrations for 2006 to the average concentrations for 2004-2005. This took away thestructural deviation of the predicted concentrations to a large extent.

The Secchi depth appeared overpredicted, and consequently light limitation wasunderpredicted. In order to improve the model in these respects the extinction coefficients ofthe organic detritus components have been raised with a factor 2.5 resulting in the valuespresented in section 3.4. Additionally, the DOC concentrations were reduced by diminishingthe conversion fraction dt_dtdr from 0.05 to 0.025, and by raising the decomposition rate ofDOC at 20 oC k_DOCdec20 a from 0.00035 to 0.0008 d-1. These changes are justifiedbecause of the more optimal conditions for dissolved organic matter decomposition intropical water systems compared to moderate climate water systems (constant highinsolation and temperature).



The overall primary production in the model was now at the right level, but thephytoplankton species composition needed improvement, since phytoplankton was entirelydominated by green algae. In order to raise diatom biomass relative to green algae biomassthe P/C-ratios of the N- and P-types of green algae have been increased circa 3%, makinggreen algae slightly less competitive with regard to phosphorus limitation. In an iterativeprocess Microcystis was made more competitative for light/growth limitation (at concurrentphosphorus limitation) by raising its Pmax circa 25% and decreasing its P/C-ratio circa15%. This resulted in a situation, in which diatoms and green algae have more or less equalcontributions to yearly average phytoplankton biomass, in line with measurement data.Microcystis grows more frequently, but still develops too low biomass.



5 The Upper Peirce Reservoir water qualitymodel

5.1 Model input

5.1.1 Computational grid and bathymetry

The bathymetry of the Upper Peirce Reservoir relative to maximal water level is displayedin Figure 5.1 This bathymetry is based on the computational grid for the hydrodynamicmodel of the reservoir (Delft Hydraulics, 2007). The grid for the water quality model inFigure 5.2 is a 4x4 aggregated version of the hydrodynamic grid. Considering the weakhorizontal water quality gradients, aggregation has resulted a rather coarse grid, thatdistinguishes only large sections of the main water body and the various branches. The 19layers of the hydrodynamic grid have been aggregated into 7 layers in the water qualitymodel. The layer thickness varies as follows from top to bottom: 4x2 and 3x4.

Figure 5.1 The bathymetry of Upper Peirce Reservoir.



Figure 5.2 The computational grid of Upper Peirce Reservoir.

The active sediment layer below the water column is 20 cm thick. A computational grid forthe sediment is added to the grid for the water column. The layer structure from thesediment-water interface to the interface with the inactive sediment is as follows: 0.1, 0.2,0.3, 0.6, 1.8, 7.0, 10.0 cm.

5.1.2 Flow fields, dispersion, inflows and outflows

The imposed 3D flow and dispersion fields have been simulated with Delft3D-FLOW for2004-2006 (Delft Hydraulics, 2007). The fields are basically driven by hourly wind, thermalstratification, artificial aeration, daily inflows and outflows. The aeration induced verticalflow is taken into account as a function of the capacity of the aerators. For pragmaticreasons aeration is continuous in FLOW, whereas it is intermittent in reality. In view of theturbulence caused by artificial aeration an additional vertical dispersion coefficient of 10-4

m2/s is imposed on the water quality model.

The locations of specific inflows and outflows are indicated in Figure 3.2. There is onemajor point source inflow to the Upper Pierce Reservoir. In addition there are several non-point discharges to the Reservoir. The inflows concern:

discharge of raw drinking water from Tebrau (Malaysia);runoff from the catchment; andrainfall on the reservoir.



Figure 5.3 The water balances of Upper Peirce Reservoir for 2004 and 2006.



The outflows concern:

withdrawal of raw drinking water for Chestnut Avenue WW;withdrawal to MacRitchie Reservoir;withdrawal for the irrigation of the SICC Golf Course;spill to Lower Peirce Reservoir; andevaporation from the reservoir.

The water balances for 2004 and 2006, representative for the present situation, are depictedin Figure 5.3. The Tebrau water discharge is dominant among the inflows, and thewithdrawal for Chestnut Avenue WW is dominant among the outflows. The water balancefor 2005 with similar characteristics is presented in Delft Hydraulics (2007).

5.1.3 Loads

The loads result from:

discharge of raw drinking water from Tebrau (Malaysia, via Chestnut Avenue WW);runoff from the Upper Peirce catchment due to wet and dry deposition; andwet and dry deposition on the reservoir.

The various loads of nitrogen and phosphorus are presented in Table 5.1. The load fromTebrau water is clearly dominant for nitrogen as well as phosphorus. Yet, the atmosphericloads partially entering the reservoir through runoff are substantial.

The loads from Tebrau water have been calculated by multiplication of the daily flow rateswith observed concentrations for 2004-2006. Monthly observed parameters include totalnitrogen, nitrate, ortho-phosphate, chloride, sulphate and total suspended solids (TSS).These data show large standard variations (circa 50% of the means of total-N, nitrate andphosphate), implying a substantial uncertainty in the calculated loads from Tebrau water.Daily data are available for ammonium and total organic carbon. With regard to phosphorusonly dissolved PO4 was available, which necessitated the estimation of total-P proportionalto PO4. The organic-P fraction can be estimated from TOC. The fraction of phosphateadsorbed to suspended sediment has been ignored. During model calibration it was decided(see also section 5.5.1):

to use the 2004-2006 average concentration for total-N and nitrate-N in stead ofmonthly values, because this resulted in much better reproduction of the nitrateconcentrations observed for Upper Peirce Reservoir;to reduce total-N and nitrate-N concentrations with 10% to avoid overprediction ofammonium and nitrate;to use the 2004-2005 average concentration for PO4-P for 2006, because observed PO4was much lower for this year than for the other two years, whereas total-N was similarfor all three years;to calculate total-P as 1.4 x dissolved PO4-P in agreement with an estimated organic-Pfraction; andto use yearly average data for TSS, considering the large variation of the monthly data.



Table 5.1 The nutrient loads on Upper Peirce Reservoir for 2004-2006.

Source Total-N(kg/day)

% N-load Total-P(kg/day)

% P-load

Tebrau water

catchment runoff

wet deposition

dry deposition

197.8

8.6

17.6

7.5

85.4

3.7

7.6

3.3

15.05

0.60

0.68

1.07

86.5

3.5

3.9

6.1

Total 231.5 100 17.4 100

The monthly varying concentrations used to calculate the daily loads for the model result inthe following average concentrations:

TOC is 4.72 mgC/L (observed; converted into POC/N/P1-3 and DOC/N/P)Total-N is 2.71 mgN/L (0.9 x observed total-N)NO3 is 0.97 mgN/L (0.9 x observed nitrate N)NH4 is 1.28 mgN/L (calculated as total-N minus organic N and nitrate N; in agreementwith the average observed concentration of 1.3 mgN/L)Total-P is 0.206 mgP/L (1.4 x observed PO4-P)PO4 is 0.15 mgP/L (dissolved plus adsorbed; calculated as total-P minus organic P; inagreement with the average observed concentration of 0.147 mgN/L)Salinity is 0.015 psu (calculated as 2 x Cl)SO4 is 2.1 mgS/L (observed)IM1 is 47.6 mg/L (calculated as TSS minus 2.5 x TOC)

It has been estimated that DOC = 0.5 mgC/L. The estimated concentration fractions ofPOC1, POC2 and POC3 are respectively 45%, 45% and 10% of TOC-DOC (expertestimate). The regular N/C and P/C ratios in the model are used to calculate PON1-3, DON,POP1-3 and DOP.

The concentrations of substances in Tebrau water for which measurements are not availablehave been estimated as constant concentrations. This includes dissolved oxygen (saturationat 30 oC; 7 mg/L), silicate (10 mgSi/L) and feacal coli bacteria (FCB; 57000/100 mL). Thesilicate content being quite elevated as is typical tropical inland waters is rather uncertain.

The nutrient loads on the reservoir from wet and dry atmospheric deposition occur as adirect load on the surface of the reservoir and as an indirect load by means of runoff andgroundwater inflow from the catchment of the reservoir (Table 5.1). Atmospheric depositionis the only source of nutrients in runoff and groundwater inflow. The wet atmospheric loadshave been calculated by multiplication of average nutrient contents in rain, rainfall (2.79m/year in 2004-2006) and surface area (catchment 738 ha; reservoir 320 ha). The dryatmospheric loads are fixed percentages of the wet loads. For the quantification of the loadsfrom the catchment, it is assumed that maximal retention of 75% occurs in the catchment for



both nitrogen and phosphorus (Schreiber et al., 2003). Only 25% of N and P deposited fromthe atmosphere in the catchment ends up in the reservoir. All loads resulting fromatmospheric deposition have been imposed on the model proportional to observed dailyrainfall for 2004-2006.

The following two sets of measurement data are available for the quantification of theseatmospheric loads:

PUB data of total-N, ammonium, nitrate and phosphate in rainfall (2005-2006); andNUS data of total-N, ammonium, nitrate and phosphate in rainfall and dry depositionrates (April-July 2007, for three sampling sites in the Marina catchment).

The NUS data have been used to quantify the atmospheric loads on the reservoirs, because itis the most comprehensive dataset, especially generated for the present modelling work.Moreover, the two datasets are very similar as to the average nitrogen and phosphoruscontents in rain. The NUS data deliver mean contents in rain water of 0.65 mgN/L and 0.025mgP/L, the PUB data mean contents of 0.6 mgN/L and 0.03 mgP/L.

The dry N-deposition is equal to 43% of wet N-deposition according to the NUSinvestigation, which is in line with mean literature data (average = 8.8 kg/ha/yr; DelftHydraulics, 2006b). With regard to nitrogen it is assumed that 60% is discharged into thereservoir as nitrate and 40% as ammonium, according to the ratio in rainwater. The dry P-deposition is equal to 157% of wet P-deposition, which is also in line with literature data(average = 1.1 kgP/ha/yr; Delft Hydraulics, 2006b).

The runoff loads from the catchment have only been considered for nitrogen, phosphorus,silicate and sulphate. Loads of other substances have been ignored as being very small. Thedissolved silicate and sulphate loads have been calculated as the product of the runoff flowrate and constant concentrations, estimated at 10 mgSi/L and 4 mgS/L.

5.1.4 Initial conditions

The initial conditions concern the concentrations of all simulated substances in the model atthe start of a simulation. Since 2004 is the first simulation year, the concentrations observedfor January 2004 have been used to quantify initial conditions for the water column. Thequantification involves conversion of observed parameters to model substances such as thevarious organic C, N and P fractions. Concentration fractions of the organic C componentsas well as N/C and P/C ratios are used for the conversion.

Except for one total-P measurement, no data are available with respect to the quality of thesediment and pore water in Upper Peirce Reservoir. The following initial sedimentcomposition was derived from sediment data for Marina Bay:

total organic carbon TOC = 15206 gC/m3 (POC1 = 1%, POC2 = 3%, POC3 = 96%,DOC = 4 mgC/L)total nitrogen TN = 793 gN/m3 (in PON1-3 according to N/C1=0.12, N/C2=0.1 andN/C3=0.05)



total phosphorus TP = 230 gP/m3 (80 in POP1-3 according to P/C1=0.015, P/C2=0.012and P/C3=0.005; 74 each in AAP and VVIP and 0 in APATP)opal silicate OPAL = 2500 gSi/m3 (expert estimate)particulate sulphide SUP = 20 gS/m3 (expert estimate)inorganic sediment IM1 = 587000 g/m3 (according to porosity = 0.75)

Ting and Appan (1996) investigated the phosphorus content and speciation in sediment fromKranji Reservoir and MacRitchie Reservoir. The sediment conditions in UPR is probably inbetween the conditions of these two reservoirs, more resembling the conditions inMacRitchie Reservoir, being the least loaded reservoir, than the conditions in the KranjiReservoir. For three sediment samples taken from each of the reservoirs Ting and Appanfound average total-P concentrations of 1.76 gP/kgDW for Kranji Reservoir and 0.3gP/kgDW for MacRitchie Reservoir. They also found very high water contents (80-89%WW) that may have been caused by the sampling technique, surface dredgingrepresentative of the upper 5 cm, implying porosities of respectively 0.95 and 0.92.Considering the >63 µm grain size fraction of 40% in the sediment from MacRitchieReservoir, the in-situ porosity of the upper 20 cm layer in this reservoir is more likely 0.75as has been found for Marina Bay. On the basis of this porosity the bulk total-Pconcentration in sediment would be 187 gP/m3 for MacRitchie Reservoir, and 1098 gP/m3

for Kranji Reservoir. Considering the higher P-loads in UPR, the expert estimate of 230g/m3 for Marina Bay is probably too low.

Using an extraction scheme Ting and Appan (1996) found for MacRitchie Reservoir that theiron and aluminum associated P-fraction is approximately 23% and calcium associated P-fraction less than 1% and the residual P-fraction, mostly associated with organic matter,77%. For Kranji Reservoir the percentages are respectively 65%, 8% and 37%. With regardto the P-speciation in the sediment of the Peirce Reservoirs these data indicate that:

37-77% may be associated with organic matter (poly-phosphate);23-65% may be associated with iron (adsorbed P and vivianite-P); and1-8% may be associated with calcium (apatite-P).

The small fraction associated with calcite may be caused by the slightly acidic conditions(pH < 7) in Singaporean Reservoirs. In the initial sediment composition it has been assumedthat 35% is organic matter associated, 65% is iron associated, equally divided among theadsorbed and vivianite fractions, and 0% is calcium associated.

Starting from the initial sediment composition a representative sediment composition wasthen developed by restarting the model for a number of year simulations, using thecalculated sediment composition at the end of a year as starting point for the next year. Theprocedure was continued for 10 years until a “dynamic steady state” was established.



5.1.5 Meteorological forcing

The meteorological forcing includes:

water temperature;windspeed; andsolar radiation.

Vertical differences in water temperature are usually less than 1 oC. Seasonal differencesmay amount to 2 oC. The temperature has therefore been imposed as a time series of themonthly mean observed temperatures (1995-2006), that do not vary over depth. Thesevalues vary over time from 27.5 to 29.5 oC (Fig. 5.4).

Figure 5.4 Mean monthly temperature measured in the surface waters at Site B, Upper Peirce Reservoir, 1995to 2006. Error bars represent standard error.

0

50

100

150

200

250

300

0 5 10 15 20 25 30 35 40 45 50

Sol. Rad.

Figure 5.5 Weekly average solar radiation (W/m2) for 2003, measured at Changi Airport (NEA).



Representative windspeed data are not available for the Peirce Reservoirs (Delft Hydraulics,2007c). Since the windspeed is generally low and not subjected to a clear seasonal variation,it has been imposed as a constant windspeed of 2 m/s which is the average windspeed atChangi meteorological station. The reaeration coefficient adopts its lower limit at thiswindspeed.

Daily solar radiation data (W/m2) measured at Changi station are available for 2001-2003.The weekly average daily radiation of 2003 has been used as forcing for 2004-6 (Fig. 5.5).

5.2 Simulated and observed concentrations

For the calibration of a water quality model criteria are needed to assess the goodness of fitof simulated and observed water quality. Water quality model simulations have much lessspatial and temporal variability than observed water quality, mainly because the forcing ofmodels is much less variable than in reality and because observed water quality depends onpatchiness. It is therefore inevitable to perform and assess calibration by ignoring observedvariability to a certain degree. In view of this the following facts must be noticed:

The loads imposed on the Upper Peirce Reservoir model have been determined on thebasis of limited monthly or even yearly data.The meteorological forcing of the model is based on a constant windspeed and on solarradiation data for 2003. This meteorological forcing has been applied for all simulatedyears (2004-2006).Vertical mixing in the reservoir is driven by artificial aeration. For pragmatic reasonsvertical mixing occurs continuously in the model, whereas in reality aeration isdiscontinuous.

Consequently, the forcing of the models lacks most of the diurnal, seasonal and yearlyvariation that occurs in the real world. Moreover, the calculated loads may not be veryaccurate.

In view of all this the calibration of the water quality model for Upper Peirce Reservoirfocussed on an as good as possible reproduction of observed:

average levels and characteristic variability of the concentrations of the relevantsubstances, in particular nutrient components (N and P; no data for Si), phytoplanktonbiomass (chlorophyll-a), total organic carbon and dissolved oxygen;vertical concentration gradients, especially those of dissolved oxygen; andphytoplankton species composition.

The performance of the calibrated model is to be judged accordingly.



5.2.1 The water column

Figures 5.6-18 present a rather complete picture of the simulated and observed water qualityincluding simulated phytoplankton biomass and species composition for location RUPB inthe deepest part of Upper Peirce Reservoir for 2004-2006 (Fig. 5.2). From the data analysisas well as the simulations it appeared that horizontal gradients are generally small. Theobservations made at RUPB are therefore representative for the whole reservoir.

The comparison of simulated and observed total-N is hampered by a high detection limit of1 mgN/L (Fig. 5.6). Observed total-N is almost constantly at the detection limit, whereassimulated total-N varies between 0.45 and 0.75 mgN/L. Simulated ammonium agrees wellwith the observations, although sometimes peaks are missed (Fig. 5.7). Nitrate agrees withthe measurements in the upper layer most of the time, but is somewhat overpredicted in thelower layer (Fig. 5.8). Very low nitrate concentrations have been measured in the top layerduring 4 short periods, pointing at nitrogen limitation of phytoplankton growth during theseperiods. The model does not reproduce the low concentrations, implying that nitrogenlimitation does not occur in the model.

Simulated total-P agrees with the observations in the sense that the averages areapproximately equal (Fig. 5.9). The observed variation over time is not at all reproduced,which is at least partially related to the mismatch of simulated and observed phytoplanktonbiomass (chlorophyll-a; see below). Patchiness in the reservoir with regard to particulatecomponents is another possible explanation. Simulated and observed ortho-phosphate agreequite well considering the extremely low concentrations, although observed peaks aremissed (Fig. 5.10). The low concentrations indicate that phosphorus limits phytoplanktongrowth almost permanently.

Figure 5.16 shows that dissolved silicate varies strongly depending on the blooming ofdiatoms, which is much stronger in 2004 than in the other years. Silicate is far from beingdepleted and therefore not growth limiting for phytoplankton. However, no observations areavailable to confirm this.

Although average observed and simulated chlorophyll-a are more or less equal, they showsignificant differences as to the variation over time (Fig. 5.11). Chlorophyll-a is clearlyunderpredicted in the first half of 2004, which might be caused by too strong phosphoruslimitation. The model produces light limited diatoms and phosphorus limited green algae.Time average diatoms and green algae are present in more or less equal quantities Fig.5.14), in line with observed species composition. However, the model tends to have onlyone of those species in the simulation at a time, whereas in reality all species are more orless continuously present. Varying from 20% to 5% of phytoplankton biomass in the toplayer and from 10% to 50% in the second layer, Microcystis is realistically predicted. Theobservations indicate a 10-50% biomass share of this blue-green algae species. Mismatchesof observed and simulated species composition may lead to over- or underprediction ofchlorophyll-a. Light limited diatoms have the highest chlorophyll/carbon ratio, 30% higherratio than phosphorus limited green algae.



Figure 5.6 Simulated and observed total nitrogen (mgN/L) in Upper Peirce Reservoir at location RUPB near the surface, at mid depth and near the bottom for 2004-2006.



Figure 5.7 Simulated and observed ammonium (mgN/L) in Upper Peirce Reservoir at location RUPB near the surface, at mid depth and near the bottom for 2004-2006.



Figure 5.8 Simulated and observed nitrate (mgN/L) in Upper Peirce Reservoir at location RUPB near the surface, at mid depth and near the bottom for 2004-2006.



Figure 5.9 Simulated and observed total phosphorus (mgP/L) in Upper Peirce Reservoir at location RUPB near the surface, at mid depth and near the bottom for 2004-2006.



Figure 5.10 Simulated and observed ortho-phosphate (mgP/L) in Upper Peirce Reservoir at location RUPB near the surface, at mid depth and near the bottom for 2004-2006.



Figure 5.11 Simulated and observed chlorophyll-a (µg/L) in Upper Peirce Reservoir at location RUPB near the surface, at mid depth and near the bottom for 2004-2006.



Figure 5.12 Simulated and observed total total organic carbon (mgC/L) in Upper Peirce Reservoir at location RUPB near the surface, at mid depth and near the bottom for 2004-2006.



Figure 5.13 Simulated TOC, PhytC, POC1, POC2, POC3, DOC (mgC/L) in Upper Peirce Reservoir at location RUPB in the surface layer for 2004-2006.

Figure 5.14 Simulated biomasses of diatoms, green algae and Microcystis (mgC/L) in Upper Peirce Reservoir at location RUPB in the surface layer and the second layer for 2004-2006.



Figure 5.15 Simulated and observed Secchi depth (m) in Upper Peirce Reservoir at location RUPB for 2004-2006.

Figure 5.16 Simulated dissolved silicate (mgSi/L) in Upper Peirce Reservoir at location RUPB near the surface, at mid depth and near the bottom for 2004-2006.

The algae are continuously light limited due to vertical mixing driven by artificial aeration.Because of large mixing the light limitation induces both mortality and maximal growthlimitation. It is therefore very important to have correct light extinction in the model. Astrong indication for correct light limitation is accurate prediction of the Secchi depth.Figure 5.15 shows that simulated and observed Secchi depths agree very well on average.

Figure 5.12 shows observed and simulated organic matter (TOC), which in the model iscomposed of phytoplankton biomass (Phyt), three particulate detritus fractions (POC1-3)and dissolved organic matter (DOC). Phyt and POC2 deliver the largest contributions.POC1 is low because of rapid decomposition, POC3 and DOC are low because of slowproduction. The distribution among the fractions can be seen in Figure 5.13. The rather goodagreement of simulated and observed TOC is proof of primary production and detritussettling and decomposition being in balance.



Figure 5.17 Simulated and observed dissolved oxygen (mgO2/L) in Upper Peirce Reservoir at location RUPB near the surface, at mid depth and near the bottom for 2004-2006.



Figure 5.18 Simulated and observed sulphate (mgS/L) in Upper Peirce Reservoir at location RUPB near the surface, at mid depth and near the bottom for 2004-2006.

Simulated dissolved oxygen shows some structural overprediction, especially in the upperand middle layers (Fig. 5.17). The possible causes are discussed in Chapter 6. The fact thatobserved vertical gradients of dissolved oxygen and nutrients are reproduced by the modelindicates that vertical mixing is taken into account adequately.



Figure 5.18 shows that simulated and observed sulphate, one of the electron-acceptors forthe decomposition of organic matter, agree quite well. This is due to including a sulphateload from catchment runoff.

Not shown in the graphs are the simulation results for suspended sediment (IM1) andadsorbed phosphate (AAP). The concentrations of these substances are very low. Suspendedsediment IM1 is 1.2 on the average and varies from 0.5 to 2.5 mg/L. AAP is higher in thelower layer than in the surface layer, in line with the higher phosphate concentrations.Maxima occasionally exceed 0.0015 mgP/L.

5.2.2 The sediment

The simulated composition of bottom sediment and pore water (upper 20 cm) at RUPB isdepicted in Figures 5.19-34. The graphs show the development of concentrations over timein seven sediment layers. The top layer 1 is 1 mm thick, the lower layer 10 cm thick.Vertical concentration gradients can be seen along imaginary vertical lines.

The decomposition of organic matter settled on and incorporated in the sediment drives thechemical diagenesis of the sediment. Figure 5.19 shows that the highest concentrations ofTOC are found near the surface of the sediment. TOC is decomposed on its way down.Rapidly decomposing POC1 is only significantly present in the upper three layers (6 mm),slowly decomposing POC2 in the upper 4 layers (12 mm) (Fig. 5.20-21). TOC mainlyconsists of very slowly decomposing POC3. The pore water concentration of DOC ismaximal at a depth of 3 cm in the sediment (1 mgC/L; Fig. 5.22).

The concentrations of the electron-acceptors oxygen, nitrate, sulphate and methane arepresented in graphs 5.23-26. Dissolved oxygen penetrates only the upper 2 mm of thesediment. Nitrate and sulphate do not get deeper than 4 mm due to denitrification andsulphate reduction. Methane saturates at 2 cm depth. All methane produced below this depthescapes the sediment in gas bubbles.

Figures 5.27-24 present the concentrations of the nutrients. The steepness of the verticalgradients near the sediment-water interface is a measure for the magnitude of the diffusivereturn flux to the overlying water. Ammonium develops the highest concentrations in thelower layer, and shows a relatively weak gradient in the upper 3 cm due to nitrification.Total-N decreases with depth proportionally to organic matter.The concentration of dissolved phosphate is constrained by the adsorption on sediment.Ortho-phosphate develops lower concentrations in the lower layers due to the formation ofvivianite. Total-P is very constant over depth, but increases slowly with time, approximately1.2% per year. Some 8% of total-P is tied up in vivianite, 25% in organic matter, and the restis adsorbed-P. The total-P concentration of approximately 1290 mgP/kgDW is exactly equalto the one observation (CAWT, 2007). This is much higher than measured for the sedimentin MacRitchie Reservoir, but much lower than measured for the sediment in KranjiReservoir (Ting and Appan, 1996; CAWT, 2007).Silicate saturates at a depth of 2 cm. Opal silicate has highest concentrations in the lowerlayers, which points at opal not being in steady-state yet due to very slow dissolution. Theinitial concentration has been too high.



Figure 5.19 Simulated total organic carbon (mgC/L) in sediment layers 1(top) – 7 in Upper Peirce Reservoir at location RUPB for 2004-2006.

Figure .20 Simulated readily degradable organic carbon (POC1, mgC/L) in sediment layers 1(top) – 7 in Upper Peirce Reservoir at location RUPB for 2004-2006.

Figure 5.21 Simulated slowly degradable organic carbon (POC2, mgC/L) in sediment layers 1(top) – 7 in Upper Peirce Reservoir at location RUPB for 2004-2006.



Figure 5.22 Simulated dissolved organic carbon (DOC, mgC/L) in sediment layers 1(top) – 7 in Upper Peirce Reservoir at location RUPB for 2004-2006.

Figure 5.23 Simulated dissolved oxygen (mgO2/L) in sediment layers 1(top) – 7 in Upper Peirce Reservoir at location RUPB for 2004-2006.

Figure 5.24 Simulated nitrate (mgN/L) in sediment layers 1(top) – 7 in Upper Peirce Reservoir at location RUPB for 2004-2006.



Figure 5.25 Simulated sulphate (mgS/L) in sediment layers 1(top) – 7 in Upper Peirce Reservoir at location RUPB for 2004-2006.

Figure 5.26 Simulated methane (mgC/L) in sediment layers 1(top) – 7 in Upper Peirce Reservoir at location RUPB for 2004-2006.

Figure 5.27 Simulated ammonium (mgN/L) in sediment layers 1(top) – 7 in Upper Peirce Reservoir at location RUPB for 2004-2006.



Figure 5.28 Simulated total nitrogen (mgN/L) in sediment layers 1(top) – 7 in Upper Peirce Reservoir at location RUPB for 2004-2006.

Figure 5.29 Simulated dissolved ortho-phosphate (mgP/L) in sediment layers 1(top) – 7 in Upper Peirce Reservoir at location RUPB for 2004-2006.

Figure 5.30 Simulated total phosphorus (mgP/L) in sediment layers 1(top) – 7 in Upper Peirce Reservoir at location RUPB for 2004-2006.



Figure 5.31 Simulated adsorbed phosphate (mgP/L) in sediment layers 1(top) – 7 in Upper Peirce Reservoir at location RUPB for 2004-2006.

Figure 5.32 Simulated vivianite-P (mgP/L) in sediment layers 1(top) – 7 in Upper Peirce Reservoir at location RUPB for 2004-2006.

Figure 5.33 Simulated dissolved silicate (mgSi/L) in sediment layers 1(top) – 7 in Upper Peirce Reservoir at location RUPB for 2004-2006.



Figure 5.34 Simulated opal silicate (mgSi/L) in sediment layers 1(top) – 7 in Upper Peirce Reservoir at location RUPB for 2004-2006.

5.3 Nutrient mass balances

The yearly average mass balances of nitrogen and phosphorus are depicted in Figure 5.35.Most of the mass fluxes are obvious. The internal load is the diffusive return flux ofammonium and nitrate from the sediment to the overlying water. Burial is the removal ofsubstances into the inactive sediment below the simulated layers resulting from net settlingof sediment. Storage is the difference between initial and final composition in a water orsediment compartment. Negative storage indicates lower concentrations at the end of thesimulation. In a “dynamic steady-state” storage should be approximately equal to zero, verysmall compared to the mass fluxes. The exception is the accumulation of phosphorus in thesediment, that may be a large part of the load.

From the mass fluxes expressed in g N or g P per m2 per year, the following findings can bededuced:

Denitrification is 74.1% of the total external nitrogen load. Outflow of nitrogen is25.4% and burial only 0.5%. The storage of nitrogen is negligibly small.Outflow of phosphorus is 24.6% of the total external phosphorus load. Storage ofphosphorus in the sediment is 50.2%, and burial 25.2%. 75.4% of the external loadaccumulates in the sediment.The internal load of nitrogen is 1.69 times the total external load. The internal load ofphosphorus is 2.01 times the total external load.

The latter finding indicates the great importance of the return fluxes for the primaryproduction of phytoplankton and the water quality in Upper Peirce Reservoir. This is to beexpected considering the long average residence time of water in the reservoir, namely 340days.

Denitrification does not occur in the water column due to permanently oxic conditions.Dissolved oxygen in the lower water layer does not decrease below 3 mg/L in the model.



Total-N Atmospheric deposition Denitrification2.9 0.0

Tebrau load23.3 Storage Outflow

-0.04 6.9Catchment Runoff1.0

Internal loadWater 45.9Sediment

Denitrification66.1 20.1Settling Storage

-0.01

0.1Burial

Total-P Atmospheric deposition0.20

Tebrau load1.77 Storage Outflow

0.002 0.50Runoff0.07

Internal loadWater 4.11Sediment

5.64Settling Storage

1.02

0.51Burial

Figure 5.35 The average yearly mass balances of total nitrogen and total phosphorus for Upper Peirce Reservoir for 2004-2006. The fluxes are expressed in g N or kg P per m2 per year.



6 Conclusions and discussion

Water quality in the tropical Upper Peirce Reservoir

The simulation of the water quality in the weakly stratified, artificially aerated, tropicalUpper Peirce Reservoir (UPR) with Delft3D-ECO allows for the following conclusions.UPR is moderately eutrophic. Phosphorus, nitrogen and light are limiting factors for algaegrowth. Light (or energy) limitation is established as a combination of maximal growth inthe upper water layers and mortality in the deeper water layers. Denitrification in thesediment is the dominant sink for nitrogen, approximately 20 gN/m2/year. 74% of theexternal load is denitrified. Accumulation in the sediment is the dominant sink forphosphorus in both reservoirs. The accumulation rate is 1.53 gP/m2/year. 75% of theexternal load accumulates in the sediment. The strong retention/removal of nutrients in thereservoir is due to the long hydraulic residence time of 340 days.

Since the temperature in UPR is continuously high, water quality processes may proceeddifferently compared to fresh water systems in moderate climate areas. Temperaturedependency is probably different for the ranges 0-20 oC and 20-30 oC, which has beenexpressed in the model by means of a higher (maximal) temperature coefficient for thedecomposition of organic matter at 20-30 oC. Nitrification and denitrification proceedprobably at a higher level than in moderate climate waters, which may find its cause in morestable, better developed bacteria populations, in the water column as well as in the sediment.

Due to strongly reducing conditions in the sediment of tropical reservoirs the adsorptioncapacity for phosphate is low compared to the adsorption capacity of sediment in moderateclimate water systems. Apatite-like minerals may hardly be formed due to rather acidicconditions in the sediment.

The decomposition of refractory dissolved organic matter (DOC) may proceed significantlyfaster in tropical surface water, because of enhanced photo-oxidation.

Development of the UPR water quality model

The development of the UPR water quality model has met several problems, that affect thequality of this model. The amount and quality of the data available to quantify the loads ofthe nutrients and organic matter on the reservoirs and to describe the water quality in thereservoirs is minimal. Especially scarce are the data on total-N and total-P in the inflows andthe reservoirs. Detection limits for nutrients, total-N in particular, were too high.

The meteorological forcing (windspeed, solar radiation) had to be quantified with limiteddata. Local windspeed is unknown, a constant average windspeed was deduced from ChangiAirport data. Solar radiation for 2003 had to be used for 2004-2006, which partially explainsmismatches of predicted and observed phytoplankton biomass (chlorophyll-a) with regard toseasonal variability.



Continuous vertical mixing due to artificial aeration has been imposed on the model forpragmatic reasons, whereas in reality aeration and mixing are intermittent, triggered by theoxygen concentration dropping below 2.5 mg/L. Vertical mixing affects concentrationgradients and primary production.

The water quality data for the reservoirs point at a transition in water quality immediatelyprior to the simulated years 2004-2006, most probably due to reduction of the nutrient loads.The transition towards lower nutrient concentrations may have continued during thesimulation period, which implies that the initial nutrient concentrations in the sedimentgenerated with the model may be somewhat too low. However, the nutrient return loadsfrom the sediment are not underestimated because compensation is found in the processcoefficients for nitrification, denitrification and phosphate adsorption.

Model performance

In view of the above the performance of the UPR water quality model is assessed asfollows. The average levels and variability of the concentrations of the relevant substances,in particular nutrient components (N, P), phytoplankton biomass (chlorophyll-a), totalorganic carbon and dissolved oxygen can be reproduced rather well with ECO. Verticalconcentration gradients in water and sediment as well as sediment-water exchange fluxesare realistically simulated.

Often the model does not reproduce peaks and dips, in particular for total-P. Causes are atleast partially found in unrepresentative and/or smoothed forcing. Patchiness of observedwater quality and inaccurate measurement may be additional causes.

Dissolved oxygen tends to be overpredicted in the upper water layer of Upper PeirceReservoir. This may be due to:

imposing a constant windspeed in stead of hourly windspeeds;ignoring of the oxygen in- and outputs caused by artificial aeration;ignoring of additional oxygen demands caused by leaves from trees falling into thereservoirs, and by the remains of drowned vegetation in and at the sediment; andoverpredicted methane ebullition into the atmosphere (currently 15-20% of the oxygendemand, which seems realistic).

The prediction of phytoplankton species composition is quite realistic. The observations ofspecies composition indicate the continuous presence of diatoms, green algae andMicrocystis. The main observed limiting factors for algal growth (P and energy/light) arealso active in the model. The model tends to produce only one or two algae species at atime, and create long periods with only one dominant limiting factor for algae growth. Thisis because the forcing functions in both models are rather constant or show small variability.Consequently, algae in the model are confronted with slowly and rather weakly varyingconditions. The continuous presence of diatoms, green algae and Microcystis would requiremuch stronger and much more frequently varying conditions.



Numerical aspects in ECO

During the calibration a discrepancy was discovered between parameters that are evaluatedat each 5 minutes transport time step and the parameters that are computed only at each 6hour BLOOM time step. Particulate substances of the former group are subjected to settling.Consequently, the sum of the biomasses of algae species types, computed at the transporttime step, is smaller than the total algae biomass computed at the BLOOM time step.Similar discrepancies arise for the total organic nutrients concentrations and Secchi depth.Mass balances are not affected, but the discrepancies cause a problem at the interpretation ofconcentrations. Options to take the problem away need to be investigated.

Furthermore, an inaccuracy has been encountered in the mass balance for particulatesubstances in the sediment. It appeared that very small upward and downward transportfluxes that arise due to keeping the porosity constant are not fully accounted for. The fluxesmay be set equal to zero at rounding off for single precision computing. The inaccuracy canbe substantial, in particular for the phosphorus components, and needs to be taken away.

The inaccuracy in the mass balance is dependent on the concentrations imposed on thelower boundary in the sediment. These boundary concentrations are used to compute fluxesacross the boundary to the lower sediment layer. Especially for particulate substances theseboundary concentrations should be equal to the concentrations in the lower layer. In thecurrent model set-up with input defined boundary concentrations large differences may arisebetween the concentrations at either side of the boundary. The model needs to be providedwith an option to take the concentration of the lower sediment layer for the computation ofthe upward fluxes across the boundary.

Recommendations

The delicate balance between the various limiting factors for algal primary production (N, P,light, maximal growth and maximal mortality) and the effects on algae species competitionin artificially aerated but still slightly stratified tropical reservoirs are not wel understood.Research is needed for a better understanding of:

the adjustment of phytoplankton species properties to conditions in stratified reservoirs;the interaction of phytoplankton species with limiting factors and vertical mixing;the occurrence and importance of nitrogen fixation (not included in the presentapplication of ECO); andhow phytoplankton interactions and forcing functions can be improved in ECO.

To allow for more comprehensive simulation of dissolved oxygen ECO needs to beextended with a process routine for artificial aeration.



In further model development and calibration special attention should be given to thefollowing issues:

algae species competition under stratified conditions;phosphorus species in the sediment and phosphorus return fluxes; anddissolved oxygen as affected by artificial aeration and methane ebullition;mass balances of particulate substances in the sediment;boundary concentrations for the sediment; anddiscrepancy of parameters computed at different time steps for BLOOM and transport.



7 References

Berner, R.A., 1980. Early diagenesis – a theoretical approach. Princeton University Press, Princeton, New York, 241 pp.Boudreau, B.P., 1997,

Diagenetic Models and their Implementation, Modelling Transport and Reactions in Aquatic Systems.Springer, Berlin, pp. 414.

Delft Hydraulics, 2007.Marina Reservoir Study. Hydrodynamic modelling. Research Report Z4265.10/20/30 (Firmijn Zijl andDaniel Twigt).

Delft Hydraulics, 2006a.GEM, generic ecological model for estuaries. Research report Z2845 (Johannes Smits; draft report).

Delft Hydraulics, 2006b.Marina Reservoir Study. Estimation of pollutant loads to the Marina Reservoir. Part 1: Average annualloads. Q4277.30 (David Burger, Marnix van der Vat).

Delft Hydraulics, 2004a.Sediment-water exchange of substances, Modelling of interactions between organisms and sediment.Research report Z2645/Q2935 (J.G.C. Smits, J.K.L. van Beek, T. van Kessel).

Delft Hydraulics, 2003a.Sediment-water exchange of substances, Sediment diagenesis and quality modelling, Phase 3. Researchreport Q2935.30 (J.G.C. Smits).

Delft Hydraulics, 2002a.GEM documentation and user manual. Documentation report Z3197 (A.N. Blauw, J.G.C. Smits).

Delft Hydraulics, 2002b.Sediment-water exchange of substances, Diagenesis modelling, Phase 2. Research report Q2935.30 (J.G.C.Smits).

Delft Hydraulics, HydroQual, IHE Delft, 1999.Preparation of a preliminary Lake Victoria Physical processes and water quality model. Report T2274.

Delft Hydraulics, 1997a.GEM, a Generic Ecological Model for estuaries. Model documentation T2087 (J.G.C. Smits, M. Bokhorst,A.G. Brinkman, P.M.J. Herman, P. Ruardij, H.L.A. Sonneveldt, M.W.M. van der Tol).

Delft Hydraulics, 1997b.Trial of DB-SWITCH on peatlakes Geerplas and Nannewijd. Research report T1697 (in Dutch: J.G.C. Smits).

Delft Hydraulics, 1995.Application of DBS to Lake Volkerak-Zoom. Research report T1440 (in Dutch: J.G.C. Smits).

Delft Hydraulics, 1994.Switch, a model for sediment-water exchange of nutrients; Part 3: Reformulation and recalibration for LakeVeluwe. Research report T584 (J.G.C. Smits).

Delft Hydraulics, 1991.Switch, a model for sediment-water exchange of nutrients; Part 1: Formulation; Part 2: Calibration/Applicationfor Lake Veluwe. Research report T542/T584 (J.G.C. Smits).

Delft Hydraulics, 1980.Microbial decomposition of organic matter and nutrient regeneration in natural waters and sediments. Reporton a literature study R1310-5 (J.G.C. Smits).

DiToro, D.M., 2001.Sediment Flux Modeling. John Wiley & Sons, Inc. Publication, New York.

Los, F.J. and J.W.M. Wijsman, 2007.Application of a validated primary production model (BLOOM) as ascreening tool for marine, coastal andtransitional waters. Journal of Marine Systems 64: 201-215.

Los, F.J. and Bokhorst, M., 1997.Trend analysis Dutch coastal zone. In: New Challenges for North Sea Research. Zentrum for Meeres- undKlimaforschung, University of Hamburg, 161-175.

Los, F.J., and Brinkman J.J., 1988.Phytoplankton modelling by means of optimization: A 10-year experience with BLOOM II. Verh. Internat.Verein. Limnol., 23:790-795.

Los, F.J., Smits, J.G.C. and De Rooij, N.M., 1984.Application of an Algal Bloom Model (BLOOM II) to combat eutrophication. Verh. Internat. Verein.Limnol., 22:917-923.

Santschi, P., P.Höhener, G. Benoit, and M. Buchholtz-ten Brink, 1990. Chemical processes at the sediment water-interface. Mar. Chem. 30: 269-315.



Schreiber et al., 2003. Harmonised inventory of point and diffuse emissions of niotrogen and phosphorus for a transboundary river basin. Environmental Research of the Federal Ministry of the Environment, Nature Conservation and Nuclear Safety, Germany. Water Research Project. Research Report 200 22 232, August 2003. Mar. Chem. 30: 269-315.Smits, J.G.C., and D.T. van der Molen, 1993.

Application of SWITCH, a model for sediment-water exchange of nutrients, to Lake Veluwe in theNetherlands. Hydrobiologia 253: 281-300.

Stumm, W. and J.J. Morgan, 1996. Aquatic chemistry, Chemical Equilibria and Rates in Natural Water . (third edition 1996, first edition 1981). John Wiley &Sons, Inc., New York.Ting, Ding-Sie and Adhityan Appan, 1996.

General characteristics and fractions of phosphorus in aquatic sediments of two tropical reservoirs.Wat. Sci. Tech. 34: 53-59.

Vanderborght, J.P., R. Wollast, and G. Billen, 1977. Kinetic models of diagenesis in disturbed sediments: Part II. Nitrogen diagenesis. Limnol. Oceanogr. 22: 794-803.Wanninkhof, R., 1992.

Relationship between wind and gas exchange over the ocean. Journal of Geophysical Research 97 (C5): 7373-7382.

Westrich, J.T., and R.A. Berner, 1984. The role of sedimentary organic matter in bacterial sulfate reduction: The G model tested. Limnol. Oceanogr. 29 (2): 236-249.Wetzel, R.G., 1983. Limnology. 2nd edition. Saunders College Publishing. New York.Yan Swee Ling, Tiew King Nyau and Chan Chow Teing, 1993. Artificial destratification through aeration in Upper Peirce Reservoir – Its effects on water quality and chemical costs in treatment. PUB RGD Journal. Issue No. 5: 32-49



A Input parameters for water and sedimentquality processes

Table A.1 Input parameters for the decomposition of organic matter, electron-acceptor consumption andmethanogenesis in ECO.

Name Process / Definition of parameter Source1 PreferredValue

Range Units

Decomposition of organic matter

ku_dFdec20kl_dFdec20ku_dSdec20kl_dSdec20k_dprdec20k_DOCdec20au_dNfal_dNfau_dNsal_dNsa_dNprau_dPfal_dPfau_dPsal_dPsa_dPprb_nib_sub_dtsb_dtprb_dtdr

max. first-order min. rate fast decomp. detr. at 20o Cmin. first-order min. rate fast decomp. detr. at 20o Cmax. first-order min. rate slow dec. detr. at 20o Cmin. first-order min. rate slow dec. detr. at 20o Cfirst-order min. rate part. refractory detr. at 20o Cfirst-order min. rate diss. refractory detr. at 20o Cmax. stoch. constant nitrogen in fast dec. detritusmin. stoch. constant nitrogen in fast dec. detritusmax. stoch. constant nitrogen in slow dec. detritusmin. stoch. constant nitrogen in slow dec. detritusstoch. constant nitrogen in refractory detritusmax. stoch. constant phosphorus in fast dec. detritusmin. stoch. constant phosphorus in fast dec. detritusmax. stoch. constant phosphorus in slow dec. detritusmin. stoch. constant phosphorus in slow dec. detritusstoch. constant phosphorus in refractory detritusattenuation constant for nitrate as electron acceptorattenuation constant for sulphate as electron acceptorconv. fraction fast dec. detr. into slow dec. detr.conv. fraction slow dec. detr. into part. refr. detr.conv. fraction slow dec. detr. into diss. refr. detr.

2/32/3

1/4/51/4/51/4/51/4/52/32/32/32/31/52/32/32/32/3144

1/41/41/4

0.18 0.12 0.015 0.015 0.00035 0.0008 0.15 0.10 0.12 0.08 0.05 0.020 0.013 0.015 0.010 0.005 1.0 1.0 1.0 0.95 0.025

0.3-0.120.2-0.060.1-0.010.05-0.0050.003-0.00030.003-0.00030.18-0.120.12-0.080.18-0.120.12-0.080.05-0.0150.025-0.0150.015-0.0080.020-0.0120.012-0.0080.005-0.0021.0-0.81.0-0.51.5-0.51.5-0.50.2-0.02

d-1

d-1

d-1

d-1

d-1

d-1

gN.gC-1

gN.gC-1

gN.gC-1

gN.gC-1

gN.gC-1

gP.gC-1

gP.gC-1

gP.gC-1

gP.gC-1

gP.gC-1

-----

Consumption of electron-acceptors, methanogenesis

KsOxConKsNiDenKsSuRedKsOxDenInhKsNiRedInhKsSuMetInhCoxDenInhCoxRedInhCoxMetInhCniMetInbRedFacDenRedFacRedRedFacMetCTBactAc

half saturation constant for oxygen limitationhalf saturation constant for nitrate limitationhalf saturation constant for sulphate limitationhalf sat. const. for DO inhibition of denitrificationhalf sat. const. for nitrate inhib. of sulph. reductionhalf sat. const. for sulphate inhib. of methanogenesiscritical diss. oxygen conc. inhibition of denitrific.2

critical diss. oxygen conc. inhibition of sulphate red.critical diss. oxygen conc. inhib. of methanogenesiscritical nitrate conc. inhibition of methanogenesisreduction factor for denitr. below crit. temperaturereduction factor for sulph. red. below crit. temp.reduction factor for methanogen. below crit. temp.critically low temp. for specific bacterial activity

4/54/54/54/54/54/54444444½

1.0 0.4 2.0 0.1 0.05 1.0 1.0/5.0 0.05 0.02 0.05 0.2 0.2 0.2 3.0

2.0-0.32.0-0.33.0-1.00.5-0.050.2-0.031.0-0.051.0-5.00.1-0.010.1-0.010.1-0.010.5-0.00.5-0.00.5-0.04.0-2.0

gO2.m-3

gN.m-3

gS.m-3

gO2.m-3

gN.m-3

gS.m-3

gO2.m-3

gO2.m-3

gO2.m-3

gN.m-3

---oC

1) 1=SWITCH (Delft Hydraulics, 1994 en 1997b), 2=ECO (MARE, 2002), 3=DBS (Delft Hydraulics, 1995),4=expert estimate, 5=literature

2) low value in water column, high value in sediment



Table A.2 Input parameters for processes of ammonium, sulphide and methane in ECO.


Range Units

Nitrification

RcNit20RcNit20KsAmNitKsOxNitRc0NitoxCoxNitRc0NitTCTNit

MM nitrification rate in waterMM nitrification rate in sedimenthalf saturation constant for ammonium limitationhalf saturation constant for oxygen limitationzeroth order nitrification rate at negative DOcritical DO concentration for nitrificationzeroth order nitrification rate at low temperaturecritically low temperature for nitrification

4/555544

1/41/2

0.1 50.0 0.4 0.5 0.0 0.0 0.0 3.0

0.15-0.0160.0-5.00.2-1.00.25-1.325.0-0.00.0- -1.05.0-0.04.0-2.0

gN.m-3.d-1

gN.m-3.d-1

gO2.m-3

gN.m-3

gN.m-3.d-1

gO2.m-3

gN.m-3.d-1

oC

Oxidation, precip., dissolution, speciation sulphide

RcSox20Rc0SoxCoxSUDDisSEqFeSRcDisS20RcPrecS20pKhspKh2spH

pseudo second-order sulphide oxid. rate at 20 oCzeroth order sulphide oxidation ratecritical dissolved oxygen concentrationeq. dis. free sulph. conc. for amorph. iron sulphidedissolution reaction rateprecipitation reaction rateneg. log. of eq. constant for HS- (see directives!)neg. log. of eq. constant for H2S (see directives!)acidity in the water columnacidity in the sediment

544

4/5445555

10.0 0.0 0.0 0.4 10-10

2.0 106

106

-14.0 -7.1 7.0 7.0

0.02-10.010.0-0.00.0- -1.05.0-0.2 10-9

??--9.0-7.08.0-7.0

gO2-1.m3.d-1

gS.m-3b.d-1

gO2.m-3

mole.l-1

d-1

d-1

-log(l.mole-1)-log(l.mole-1)--

Oxidation, ebullition, volatilisation of methane

RcMetOx20Rc0MetOxRcMetSu20Rc0MetSuCoxMetCsuMetKsMetKsOxMetKsSuMetCTMetOxfScEbulKLVolCH4AtmPrCH4SaturCH4

MM-rate for methane oxid. with oxygen at 20 oCzeroth order methane oxid. rate with oxygenMM-rate for methane oxid. with sulphate at 20 oCzeroth order methane oxid. rate with sulphatecritical DO concentration for methane oxidationcritical sulphate conc. for methane oxidationhalf saturation constant for methane consumptionhalf saturation constant for DO consumptionhalf saturation constant for sulphate consumptioncritically low temperature for methanogenesisscaling factor for the methane ebullition rateair-water transfer coefficient for methane (option 9)atmospheric methane pressuresaturation conc. of methane relative to atmosphere

444444444

1/24

2/3/444

0.4 0.0 0.1 0.0 0.5 0.0 0.5 1.0 1.0 3.0 1.0 1.0 0.0 0.0

0.5-0.050.05-0.00.3-0.000.05-0.02.0-0.02.0-0.01.0-0.23.0-1.03.0-1.04.0-2.01.0-0.02.0-0.10.1-0.0calculated

gC.m-3.d-1

gC.m-3.d-1

gC.m-3.d-1

gC.m-3.d-1

gO2.m-3

gS.m-3

gC.m-3

gO2.m-3

gS.m-3

oC--atmgO2.m-3

1) 1=SWITCH (Delft Hydraulics, 1994 en 1997b), 2=ECO (MARE, 2002), 3=DBS (Delft Hydraulics, 1995), 4=tentative estimate, 5=literature



Table A.3 Input parameters for processes of phosphate and silicate in ECO.


Range Units

Adsorption, precipitation, dissolution of phosphate

KadsP_20

a_OH-PO4pHfr_FeIM1fr_FeoxCc_oxPsorRCAdPO4AAPRcPrecP20RcDissP20EqVIVDisPRatAPandVPRcDisAP20EqAPATDisP

molar adsorption equilibrium constant sedimentmolar adsorption equilibrium constant waterstochiometric reaction constant for pH-dependencypH 9 (variable in water, 7 in sediment)fraction reactive iron in susp. inorg. matter IM1fraction ox. iron (III) in the reactive iron fractioncritical DO concentration for iron reductionsorption reaction ratevivianite precipitation reaction ratevivianite dissolution reaction rateequilibrium diss. phosphate conc. for vivianiteratio of apatite and vivianite precipitation ratesapatite dissolution reaction rateequilibrium diss. phosphate conc. for apatite

52/5566

1/4/51/445

1/41/4/5

545

1000.0 1000.0 0.2 var./7 0.05 0.3 0.5 10.0 0.05 0.025 0.05 1.0 0.0025 0.05

1000-1002000-5000.3-0.17-90.055-0.0350.8-0.12.0-0.110.0-1.00.1-0.0010.05-0.0020.15-0.052.0-0.10.01-0.0010.15-0.05

(mole.l-1)a-1

(mole.l-1)a-1

--gFe.gDW-1

-gO2.m-3

d-1

d-1

m3.gO2-1.d-1

gP.m-3

-m3.gP-1.d-1

gP.m-3

Dissolution of opal silicate

Ceq_DisSiRcDisSi20

equilibrium dissolved silicate concentrationdissolution reaction rate

55

17.0 0.002

22.0-11.00.01-0.001

gSi.m-3

m3.gSi-1.d-1

1) 1=SWITCH (Delft Hydraulics,1994 en 1997b), 2=ECO (MARE, 2002), 3=DBS (Delft Hydraulics, 1995), 4=tentative estimate, 5=literature, 6=measurements.

Table A.4 Input parameters for bioturbation and bio-irrigation in ECO, dispersion of solid and dissolved substances.


Range Units

TurCoefSgm dispersion coefficient solids in sedimentat lower side of sediment layerin top layerthickness uniform top layer, depth function2

10.02.0 10-6

0.021-10 10-6

0.005-0.025

m2.d-1

m2.d-1

m

DifCoefSgm dispersion coefficient solutes in sed.at lower side of sediment layer3

in top layerthickness uniform top layer

15 0.5 10-4

3.0 10-4

0.020.1-5.0 10-4

0.005-0.025

m2.d-1

m2.d-1

m

1) 1=SWITCH (Delft Hydraulics, 1994 en 1997b), 5=literature on molecular diffusion.2) Uniform value for top layer, linear decay between this depth and total layer thickness.3) The actual coefficient on the lower boundary is equal to zero to avoid transport across this boundary.



B Input parameters for phytoplankton

Table B.1: Specific extinction coefficients and stochiometric ratios of fresh water phytoplankton types defined in BLOOM for the various reservoir models.

Algal type Ext[m2/gC]

N/C[g/g]

P/C[g/g]

Si/C[g/g]

Chla/C[g/g]

Vsed[m/d]

Diatoms-E 0.2700 0.210 0.0180 0.6600 0.040 1.0Diatoms-P 0.1875 0.188 0.0113 0.5500 0.025 1.5Greens-E 0.2250 0.275 0.0238 0.0018 0.033 0.5Greens-N 0.1875 0.175 0.0155 0.0018 0.025 1.0Greens-P 0.1875 0.200 0.0129 0.0018 0.025 1.0Aphanizomenon-E 0.4500 0.220 0.0125 0.0018 0.033 0.0Aphanizomenon-N 0.4000 0.125 0.0125 0.0018 0.025 0.0Aphanizomenon-P 0.4000 0.170 0.0088 0.0018 0.025 0.0Microcystis-E 0.4000 0.225 0.0250 0.0018 0.025 0.0Microcystis-N 0.2875 0.113 0.0230 0.0018 0.017 0.0Microcystis-P 0.2875 0.175 0.0200 0.0018 0.017 0.0Oscillatoria-E 0.4000 0.225 0.0188 0.0018 0.033 0.0Oscillatoria-N 0.2875 0.125 0.0138 0.0018 0.020 0.0Oscillatoria-P 0.2875 0.150 0.0113 0.0018 0.020 0.0

Table B.2: Specific rates and temperature coefficients for growth, mortality (natural mortality plus grazing) and respiration of fresh water phytoplankton types defined in BLOOM for the various reservoir models.

Algal type Pmax[1/d]

ktp,i[-]

M[1/d]

ktm,i[-]

R[1/d]

ktr,i[-]

Diatoms-E 0.500 1.060 0.035 1.080 0.048 1.072Diatoms-P 0.370 1.060 0.045 1.085 0.048 1.072Greens-E 0.431 1.059 0.035 1.080 0.048 1.072Greens-N 0.315 1.071 0.045 1.085 0.048 1.072Greens-P 0.315 1.071 0.045 1.085 0.048 1.072Aphanizomenon-E 0.270 1.063 0.035 1.080 0.012 1.072Aphanizomenon-N 0.230 1.063 0.045 1.085 0.012 1.072Aphanizomenon-P 0.230 1.063 0.045 1.085 0.012 1.072Microcystis-E 0.260 1.077 0.035 1.080 0.012 1.072Microcystis-N 0.160 1.085 0.045 1.085 0.012 1.072Microcystis-P 0.160 1.085 0.045 1.085 0.012 1.072Oscillatoria-E 0.285 1.059 0.035 1.080 0.012 1.072Oscillatoria-N 0.215 1.059 0.045 1.085 0.012 1.072Oscillatoria-P 0.215 1.059 0.045 1.085 0.012 1.072

WL | delft hydraulics

Documents

Transcript of WL | delft hydraulics