Prepared for: WL | Delft Hydraulics
Development of Delft3D-ECO
Calibration for a tropical stratified reservoir
Research Report
December 2007
Z4524 WL | delft hydraulics
Prepared for: WL | Delft Hydraulics
Development of Delft3D-ECO
Calibration for a tropical stratified reservoir
Johannes Smits (WL | Delft Hydraulics)
Research Report
December 2007
Development of Delft3D-ECO Z4524 December 2007 Calibration for a tropical stratified reservoir
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 ...... 3 2.1 The structure...... 3 2.2 Processes and formulations ...... 4 2.2.1 The phytoplankton module BLOOM...... 4 2.2.2 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 ...... 25 4.1 Starting points...... 25 4.2 The calibration...... 26 5 The Upper Peirce Reservoir water quality model...... 29 5.1 Model input ...... 29 5.1.1 Computational grid and bathymetry ...... 29 5.1.2 Flow fields, dispersion, inflows and outflows...... 30 5.1.3 Loads ...... 32 5.1.4 Initial conditions...... 34 5.1.5 Meteorological forcing ...... 36 5.2 Simulated and observed concentrations ...... 37 5.2.1 The water column...... 38 5.2.2 The sediment ...... 50 5.3 Nutrient mass balances...... 56 6 Conclusions and discussion ...... 59 7 References...... 63
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Appendices A Input parameters for water and sediment quality processes ...... 65 B Input parameters for phytoplankton...... 69
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1 Introduction
1.1 Background
Delft3D-ECO is a specific configuration of substances and processes selected from the process library of the DELWAQ water quality modelling framework for eutrophication studies. In 2000 Delft Hydraulics started a R&D programme for the improvement of sediment-water interaction modelling and process formulation in ECO. The programme resulted in a far reaching redesign of key processes that allows for the explicit simulation of the composition of both water compartments and sediment layers. The number and thickness of the sediment layers can be determined by the user. The new model structure, indicated as DELWAQ-G, operates on the basis of a more generic set of process formulations. For each water compartment or sediment layer the local transient conditions determine if a process actually proceeds, and which kinetics and coefficients are applying. The dissolved oxygen concentration is an important ruling condition. Whereas mass transport in the water column is calculated in the core algorithm of DELWAQ, vertical mass transport in water and sediment is taken care of a dedicated set of process routines that are part of the process library.
To allow more comprehensive sediment-water interaction modeling DELWAQ has been extended with a new microphytobenthos submodel, with a sediment consolidation and erosion submodel, and with processes for sulphate, dissolved and particulate sulphide and methane (Delft Hydraulics, 2004a and 2003a/b). A link has been established between microphytobenthos biomass, benthic grazing and sediment stability.
DELWAQ-G was first implemented and calibrated on the basis of a research application for the Wadden Sea (Delft Hydraulics, 2006a). This application concerns the Generic Estuarine Ecological Model (GEM), the development of which started in 1995 on the initiative of Rijkswaterstaat RIKZ (Delft Hydraulics, 1997a). Since then GEM (e.g. ECO) has gradually been improved and extended to become a rather comprehensive modelling framework for eutrophication and primary production in estuaries and coastal waters (Delft Hydraulics, 2002 and 2003b). It has been used for various investigations concerning the eutrophication of 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:
x to calibrate ECO for a tropical fresh water system; x to obtain a more generic set of process coefficients for the water quality processes included in the model; x to evaluate the performance of ECO as based on DELWAQ-G; x to formulate recommendations for further development; and x to enhance insight in the development of water quality in tropical reservoirs.
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In view of an ongoing consultancy project an appropriate case has been found in the Upper Peirce Reservoir (UPR), a tropical stratified reservoir located in Singapore. Hydrodynamic input, meteorological forcing and loads of relevant substances are available from the ongoing “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 Beek compiled model input, carried out and processed the simulations, and improved the software code in co-operation with Johannes Smits. Hans Los provided input coefficients for phytoplankton, and advised with regard to the calibration of those parameters. Johannes Smits determined the input coefficients of water and sediment quality processes, performed the calibration and co-ordinated the project.
1.4 About this report
ECO is briefly described in Chapter 2. Extensive descriptions of the process formulations are available in other reports. The process coefficients resulting from the calibration are included in the tables of Appendices A and B. Chapter 3 contains a concise description of Upper 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 mass balances of the main nutrients, nitrogen and phosphorus.
Chapter 6 discusses the conclusions, and provides recommendations for the further development of ECO. References are listed in Chapter 7.
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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 well as the growth and species composition of phytoplankton in surface water. Figure 2.1 provides a schematic overview of the ecosystem components and interactions as described in the most comprehensive form of ECO. The arrows indicate conversion processes. Algae, being primary producers, take up nutrients from water to produce cell material, including ammonium, 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 also consumed and produced is not included in ECO, since this nutrient is hardly ever limiting algae 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 skeletons from diatoms slowly dissolve. A substantial fraction of algae biomass may be consumed by grazers, 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 model for UPR. Grazing by zooplankton is included in the mortality rate.
When light reaches the sediment, microphytobenthos (benthic algae, mostly diatoms) may grow on the sediment. Given the substantial turbidity of the water in Singaporean reservoirs and their considerable depths microphytobenthos can be present in only very low concentrations. Microphytobenthos is therefore not included in the present model for UPR.
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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 top sediment layer due to the activity of burrowing and bioturbating benthic organisms. The bacterial decomposition of detritus proceeds in the sediment. The dissolution of opal continues. The nutrients produced are partially removed (N) or precipitated (P) in the sediment and partially released into the overlying water column (N, P, Si). In conjunction with sediment accretion, nutrients are also buried in the deeper sediment, where they are stored more or less permanently. Dissolved oxygen for the oxidation of organic matter is withdrawn from the water column, which creates a sediment oxygen demand.
ECO simulates mass transport due to advection and dispersion in the water column and the sediment on the basis of imposed flow fields. All transport and water quality processes have been 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 and adaptation 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 and 2006a). Linear programming is used as an optimisation technique to determine the optimum distribution of nutrients and light over the different species, where maximum net growth of the total phytoplankton is reached. By optimising on maximum net growth a combination of species groups is selected that use the limiting resources most efficiently and/or have the highest net growth rate. Respiration associated with primary production is included in the net maximum primary production rate. During the optimisation process several constraints limit the number of possible solutions: x growth constraint: the biomass increase of any of the species groups cannot exceed the maximum net growth rate (production minus respiration) at actual temperature and light intensity. The relation between light intensity and growth efficiency (as a fraction of the maximum net growth rate) is defined by a table, based on laboratory studies; x mortality constraint: the mortality rate of any of the species groups cannot exceed the maximum mortality rate at actual temperature and salinity; x nutrient constraints: the total uptake of any of the nutrients cannot exceed the amount of these nutrients available; x light constraint: the total extinction of light by phytoplankton cannot exceed the threshold level where the light intensity becomes insufficient to maintain net growth for any of the selected species groups; x nutrient constraints. the total uptake of each of the nutrients (N, P, Si) must not exceed the availability. The total available amount of a nutrient is defined as the sum of dissolved inorganic nutrient at the beginning of the time step and the amount of the nutrient in phytoplankton.
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In the models used for the Marina Reservoir study, the mortality due to grazing has been included in the overall mortality rate for phytoplankton. Nutrient concentrations, under water light regime and temperature are provided by ECO’s water quality module. Organic detritus resulting from the mortality of algae in BLOOM is transferred to the water quality module.
To account for adaptation to environmental conditions by phytoplankton the following different phenotypes can be distinguished for every species group in the model:
1. one phenotype adapted to light limitation, with relatively high growth rates and high N/C and P/C ratio; 2. one phenotype adapted to nitrogen limitation, with typically lower internal N/C ratio and lower maximum growth rates; and 3. one phenotype adapted to phosphorus limitation, with typically lower internal P/C ratio and lower maximum growth rates.
The different phenotypes of a species group are modelled as separate variables with different 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 be instantaneously 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; and 5. aphanizomenon (representing Raphrodiopsis).
Species 3-5 are blue-green algae (cyanobacteria). Microcystis takes advantage of its capacity to buoy. BLOOM is can also include a nitrogen fixing species, defined as an additional Aphanizomenon phenotype. However, this type is not included in the present model because nitrogen fixer properties are not well known, and because concurrent phosphorus and light limitation in those reservoir will prevent nitrogen fixation most of the time.
For almost all species (groups) a light limited, a nitrogen limited and a phosphorus limited type 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 different taxonomic or functional groups such as diatoms, greens and blue-greens. They have different requirements for resources (e.g. nutrients, light) and they have different ecological properties. The phytoplankton module BLOOM is based upon the principle of resource competition between different species (Los and Wijsman, 2007). A model species can be a group of species (e.g. diatoms), an individual biological species or a specific (pheno-)type of
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species (e.g. N, P or light limited types). Since types differ with respect to all characteristics included in the model, a shift between types not only implies a shift in nutrient stochiometry, but also in other characteristics such as the growth, mortality, sedimentation and respiration rates and in the carbon to chlorophyll ratio.
The model considers the growth rate and the requirements for all potentially limiting environmental factors to determine the optimum combination of model species using the linear programming method. Nutrient concentrations, light energy and temperature are provided by the water quality model (see below). Starting from the algae biomasses predicted in the previous timestep, the optimization procedure distributes the available resources 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. The
BLOOM model identifies the concentration of biomass, Bk, of each algae type k that can be supported in the aquatic environment characterized by light conditions and nutrient concentrations. It can be demonstrated that finding the best adapted types at any moment in time is equivalent to maximizing the total rate of primary production given a number of environmental conditions (constraints). Defining the temperature dependent gross growth constant Pgk (day-1), the objective of the model thus is to:
Maximize 6k Pgk Bk (2.1)
For each algae species (type), the requirements for nitrogen, phosphorus and silica (only used by diatoms) are specified by coefficients nik, the fraction of nutrient i per unit biomass concentration of algae species (type) k. In the case BLOOM coupled to a water quality -3 model, the total readily available concentration, Ci (g.m ) of each nutrient in the water column equals the amount in the total living biomass of algae, 6k(nikBk), plus that dissolved in the water, wi. These mass balance constraints apply for each nutrient i:
6k (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. For max -1 each algae species (type) k there exists a specific extinction value Kk (m ) at which this is the case. The light intensity can also be too high, which means the total extinction is too low min (photo-inhibition) for growth. This specific extinction value is Kk . The ranges between min max Kk and Kk 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 light response 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 diurnal 3 variations in light intensities over depth due to mixing and in time. Letting Kk (m /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 = 6k( Kk Bk ) (2.3)
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Added to this must be the extinction caused by dead cells, KD and the contribution of all other fractions such as inorganic suspended matter and humic substances to the extinction of the water, KW (m-1). Hence:
min max Kk d KL + KD + KW d Kk (2.4)
If the total extinction is not within the range for an algae species (type) k, its concentration
Bk will be zero.
It may be impossible to achieve the biomass level at which either light or some nutrient gets limiting within a single time-step of the model. To account for this situation, a constraint to delimit the maximum biomass increase within the time-interval is considered during the optimization procedure. Assuming that losses will be low during the exponential growth phase of a phytoplankton species, mortality is ignored in the computation of this growth constraint. For all algae species (types) k the maximum possible biomass concentration, max -3 Bk (gdry.m ), at the end of the time interval 't (days) depends on the initial biomass o -3 max -1 concentration, Bk , (gdry.m ), the maximum gross production rate Pgk (day ), the -1 respiration rate constant, Rk, (day ), and the time and depth averaged production efficiency max factor, Ek. Using the temperature dependent net production rate constant, Pnk (= Pgk .Ek – Rk) (day-1), for each algae species (type) k:
max o Bk = Bk exp{ Pnk 't } (2.5)
As in the case of growth, the mortality of each algae species is also constrained to prevent a complete removal within a single time-step. The minimum biomass value of a species is min obtained when there is no production, but only mortality. The minimum biomass, Bk (gdry.m-3), of species (type) k at the end of time interval 't depends on the initial biomass, o -3 Bk (gdry.m ), 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 into account:
min o Bk = Bk exp{– Mk't } (2.6)
In biological terms the competition in the BLOOM model is governed according to the following principle. The algal types defined in the input compete with each other for all potentially limiting resources taking the existing biomass into account. The outcome of the competition for a potentially limiting resource is determined by the ratio between the gross growth rate constant and the requirement for that resource. Hence species with very high growth rates may outcompete more efficient, but slowly growing species, or very efficient species may outcompete species with a higher potential growth rate but a much higher requirement for that particular resource. In practice this means that opportunistic species with high growth rate usually dominate when total available nutrients are low and the average light intensity is high, whereas efficient species with lower potential growth rates and lower resource requirements dominate when total available nutrient levels are high and the average light intensity is low (high level of self-shading).
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2.2.2 The water and sediment quality module
Nutrient processes
Nutrients are subjected to a number of microbial and chemical conversion processes, which are quite different for nitrogen, phosphorus and silicon. The rates of all these processes increase with increasing temperature, and therefore proceed much slower in winter than in summer in temperate water systems. Rates are rather constant in tropical water systems, that have little seasonal variation in temperature. The processes included in ECO reflected by the arrows in Figure 2.2. As can be seen, conversion takes place in the water column as well as in 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 sessile bacteria, that need readily available organic substrates. This implies that nitrification proceeds most actively at and in the oxidising top sediment layer.
When oxygen is not available, bacteria resort to nitrate for the decomposition of organic matter (Vanderborght et al., 1977). This process of denitrification usually only proceeds in the sediment, but will also proceed in anoxic water. Denitrification turns nitrate into elementary nitrogen, implying that is no longer available to algae as a nutrient. Nitrogen fixation into ammonium is not included in the models of the Marina Reservoir study as argued in section 2.2.1.
Figure 2.2: Schematic overview of the microbial and chemical conversion of nutrients in ECO.
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Phosphate (PO4), largely present as ortho-phosphate, adsorbs onto sediment particles, in particular onto the surface of various metaloxides, predominantly iron oxihydroxides (Stumm and Morgan, 1996). The adsorption is a reversible equilibrium process, meaning that when algae would deplete phosphate the adsorbed phosphate will desorb. When adsorbed to suspended sediment, phosphate settles to the sediment. The adsorption capacity of sediment depends on the presence of oxygen. Under anoxic conditions the capacity becomes 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 water increases greatly.
In the sediment several phosphate minerals may precipitate at the supersaturation of the pore water (Santschi et al, 1990; Stumm and Morgan, 1996). Ironphosphate (vivianite; VIVP) is only stable under anoxic conditions, whereas calcium carbonate phosphates (apatites; APATP) are unconditionally stable. The precipitation of apatites may lead to more or less permanent storage of phosphate in the sediment, implying that this phosphate is no longer available to algae.
Opal silicate (OPAL) is produced from dissolved silicate (Si) by diatoms, that strengthen their cell walls with silicate skeletons. When diatom cells have died, the skeleton remains start to dissolve and settle on the sediment (processes not included in Fig. 2.2). The physical-chemical dissolution process continues in the sediment, since pore water is generally 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. The microbial decomposition of organic matter into its basic inorganic components such as carbon dioxide, ammonium and phosphate is called mineralisation. The rate of mineralisation increases with increasing temperature (Delft Hydraulics, 1980). As mentioned before, carbon dioxide in not included in the model.
During the decomposition process the organic matter is gradually converted into material that 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 both the difference in degradability of the numerous chemical components in detritus and the (bio)chemical conversion of readily degradable components into less readily degradable components. Eventually, refractory organic matter results, that is subjected to very slow decomposition. Particulate refractory organic matter accumulates in the sediment, whereas dissolved refractory organic matter will spread through the water column and will gradually be decomposed due to speeding up of the process by photo-oxidation.
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The conversion and decomposition processes of organic matter are described according to the following scheme:
ŕőőőőőőőő! POC1 + O2 ! CO2 Algae C, Waste C
śőőőőőőőő! 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 dissolved fraction. In the above scheme given for carbon the decomposition rate decreases in the downward direction and to the right. POC3 and DOC are very slowly decomposing (refractory) particulate and dissolved organic matter fractions. Similar schemes concern the conversion 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). These electron-acceptors include dissolved oxygen (DO; OXY), nitrate (NO3) and sulphate (SO4). Denitrification, the conversion of nitrate into elementary nitrogen, can only take place when oxygen is depleted. Sulphate reduction occurs only when both oxygen and nitrate are lacking. Denitrification and sulphate reduction usually only take place in the anoxic sediment. The sulphide produced in dissolved and particulate form (SUD and SUP) is turned into sulphate again as soon as it meets oxygen.
When even sulphate gets depleted bacteria resort to methanogenesis to decompose organic matter. At places where oxygen or sulphate are still available, bacteria will oxidize methane into carbon dioxide. When supersaturated methane will escape to the atmosphere.
The consumption of oxygen may lead to undersaturation of the water with respect to oxygen. Under- or supersaturation will be counteracted by the exchange of oxygen with the atmosphere. The rate of reaeration water from the atmosphere is dependent on the windspeed and the temperature. The saturation concentration is determined by temperature and salinity (Wanninkhof, 1992). Seawater has approximately 25% lower oxygen saturation concentrations than fresh water, which implies that generally oxygen concentrations are lower in seawater.
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Processes and formulations
The water and sediment quality processes include:
x decomposition and conversion of four fractions of organic matter (detritus); x consumption of electron acceptors at the decomposition of organic matter, oxygen consumption, denitrification, sulphate reduction and methanogenesis; x reaeration of dissolved oxygen; x nitrification; x adsorption of phosphate on suspended sediment; x precipitation and dissolution of phosphate in vivianite (redox sensitive) and in an apatite like mineral; x dissolution of opal silicate; x oxidation of sulphide; x precipitation and dissolution of sulphide in minerals; x oxidation, ebullition and volatilisation of methane; x settling and resuspension of organic matter, inorganic particulate phosphorus (adsorbed, precipitated), suspended sediment; x advection in the sediment due to seepage, settling and resuspension combined with burial into or digging from the inactive deep sediment; x dispersion in the sediment due to bioturbation, bio-irrigation, flow induced turbulence and molecular diffusion; and x extinction of light.
From literature it is known that artificial aeration devices can transfer substantial quantities of oxygen to surface water with reduced oxygen concentrations. Nevertheless, the direct transfer of oxygen from air bubbles to the water column is ignored in the model, since the current process library of Delft3D-ECO does not contain a process aeration by bubble aerators.
The water and sediment quality processes have been formulated on the basis of the following concepts (Delft Hydraulics, 2002a and 2006a).
For the decomposition and conversion of organic matter components first-order kinetics:
R f uu kC (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 and electron-acceptor affinity, and for the organic nutrients to relatively enhanced mineralisation. In the case of faecal coli bacteria f is a mortality enhancing function of salinity and UV-light regime.
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For the oxidation of sulphide and the oxidation driven dissolution of vivianite, second-order kinetics:
R kCCuuo (2.8)
with: -3 -1 Co = dissolved oxygen concentration (g.m .d )
For nitrification and the oxidation of methane, second order limitation/saturation Michaelis- Menten kinetics:
CC Rk uu12 (2.9) C1122 Ks C Ks
with: -3 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 )
For the consumption of electron-acceptors, limitation/inhibition Michaelis-Menten kinetics:
CC§· R fkuuli u¨¸1 (2.10) Cll Ks©¹ C ii Ks
with: -3 Cl = concentration of limiting substance (g.m ) -3 Ci = concentration of inhibiting substance (g.m ) f =fraction factor, multiplied with a stochiometric constant (-) -3 Ksl = half-saturation concentration for the limiting substance (g.m ) -3 Ksi = half-saturation concentration for the inhibiting substance (g.m )
For the adsorption of phosphate, Langmuir kinetics:
Ceu OH a Ka a (2.11) Cd u Ca
R kCeCu()aa (2.12)
with: a = stochiometric reaction constant (-) Ca = free adsorbent concentration (mole.l-1) -1 Cea = adsorbed substance concentration in equilibrium (mole.l ) -1 Ca = actual adsorbed substance concentration (mole.l ) -1 Cd = dissolved substance concentration (moleP.l ) k = sorption rate (d-1) Ka = adsorption equilibrium constant (molea-1.la-1) OH = hydroxyl concentration (mole.l-1)
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For the precipitation and/or dissolution of phosphate minerals, sulphide mineral and opal, and for reaeration, volatilisation and ebullition, saturation kinetics:
Rp kpu() Cdd Ce (2.13)
RdkdCCeC uup dd (2.14)
with: -3 Cd = actual dissolved substance concentration (g.m ) -3 Ced = equilibrium dissolved substance concentration at saturated solution (g.m ) 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:
k k20u kt (T 20) (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. The formulation for settling is:
Rs fW uu sCH/ (2.16)
§·§·W f max 0.0,1 (2.17) W ¨¸¨¸ ©¹©¹W c
with: IIJ = 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) W = actual shear stress (Pa)
Wc = 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 suspended sediment, whereas the settling of phytoplankton and detritus is independent. Burial in the inactive sediment is equal to the settling flux, since the sediment layers and their porosities are fixed.
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The formulation for resuspension is:
Rr fW u rH/ (2.18)
§·§·W f max 0.0,1 (2.19) W ¨¸¨¸ ©¹©¹W c with: IIJ = shear stress inhibition/limitation function (-) r = resuspension rate (g.m-2.d-1) Rr = resuspension flux (g.m-3.d-1) W = actual shear stress (Pa)
Wc = critical shear stress for resuspension (Pa)
The resuspension rate of sediment (IM1-3) is to be provided to the model. The resuspension fluxes of other particulate components are calculated proportional to the concentrations in the sediment. Digging from the inactive sediment is equal to the resuspension flux, since the sediment layers and their porosities are fixed.
Dispersion in the sediment is formulated according to Fick’s Law, which requires dispersion coefficients for the dispersion of particulate substances due to bioturbation and the dispersion of dissolved substances due to bio-irrigation, flow induced turbulence and molecular diffusion.
The light intensity is an exponential attenuation function of depth times the total extinction coefficient according to the law of Lambert-Beer. The total extinction coefficient is calculated as 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 to the Poole-Atkins relation:
SD apa / et (2.21)
with:
apa = Poole-Atkins constant (-) SD = Secchi depth (m)
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2.3 Process coefficients
ECO requires values for a large set of process coefficients. BLOOM requires a substantial subset 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 settling velocities. The coefficients have been listed in Appendix B. The coefficients are generic and most of them have been determined during many previous model applications.
The major part of the coefficients of the water and sediment quality module concerns process rate constants (k, s, r) and temperature coefficients (kt). Other coefficients concern half-saturation constants (Ks), specific limiting factors, stochiometric reaction constants,
specific extinction coefficients (e), critical threshold concentrations and shear stresses (IJc).
Almost all coefficients and their preferred values and ranges have been listed in Appendix A. Many of these coefficients have either fixed values or preferred values that have been determined by extensive calibrations for many different water systems. Consequently, the values 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 and resuspension velocities.
The temperature dependencies of the process rates (input values for 20 oC) are not included in 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 to option 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 primary production by phytoplankton are:
x for phytoplankton biomass, species specific values varying from 0.1875 to 0.4 (g.m-3)-1.m-1 (Table B.3); x for the particulate detritus fractions POC1-3 0.2 (g.m-3)-1.m-1; x for the dissolved detritus fraction DOC 0.675 (g.m-3)-1.m-1; x for inorganic suspended sediment fractions IM1 and IM3 0.018, for fraction IM2 0.009 (g.m-3)-1.m-1; x for water with various dissolved inorganic components 0.08 m-1.
The Poole-Atkins constant SecchiExt1 has its nominal value 1.7.
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3 The Upper Peirce Reservoir
3.1 Reservoir and catchment
The Upper Peirce Reservoir (UPR) is located just northwest of the “Marina” catchment of downtown Singapore (Fig. 3.1). The contours of UPR are displayed in Figure 3.2. The catchments 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 a pipe connection, the reservoir receives water from Tebrau in Johor (Malaysia). Water is withdrawn 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 reservoir are 35.6 106 m3, 310 ha and 11.5 m. The maximum depth of UPR near the dam on the eastern 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 is taken 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 decreases below 2.5 mg/L, and proceeds until it is restored to 3 mg/L.
BD
TPY
WL KLR
GLR
MR9 HD MR8 MR7 M DT MR3 a MR2 C r h i a n n a MR4 n e MR1 MR5 l MR6 barrage
Figure 3.1 The location of Upper Peirce Reservoir. Indicated are Upper Peirce Reservoir, Lower Peirce Reservoir, MacRitchie Reservoir and the “Marina” catchment of downtown Singapore, with Marina Bay and tributaries.
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Ɣ RUPJ
Ɣ 1 03 I Ɣ 2/RUPE
Ɣ 3 01 Ɣ 5 Ɣ 4RUPB
04
02
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 available for 1995-2006. Data analysis showed little difference in water quality in horizontal directions in the reservoir. The analysis then focussed on the data for central sampling point RUPB.
UPR is artificially aerated, which has large effects on water quality. The aeration causes increased mixing, leading to an absence or reduction of vertical temperature and concentration gradients. Aeration in UPR started in May 1990 and was continuous until October 2003. After this date aeration starts only when the dissolved oxygen concentration decreases 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 Reservoir between 1995 and 2003 (range 25.2-30.2 oC; Fig. 3.3), although the Reservoir does become weakly thermally stratified during warmer periods. When the reduced aeration scheme sets in after October 2003, the degree of thermal stratification becomes stronger. The mean temperature difference between surface (depth 0.1 m) and bottom (depth 21 m) waters over the whole period is 0.19 oC, with a maximum difference of 1.5 oC (Jan. 2006). Between 2004 and 2006, the mean difference in surface and bottom water temperature was approximately 0.4 oC.
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0 A
5
10 Depth (m)Depth 15
20 0 B
5
10 Depth (m) 15
20 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
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.
0 A 5
10 Depth (m) 15
20 0 B 5
10 Depth (m) Depth 15
20 2004 2005 2006
Figure 3.4 Water column temperature (A oC,) and dissolved oxygen concentration (B, mg/L) at Site 5, Upper Peirce Reservoir, Jan. 2004 to Dec. 2006.
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Surface Bottom 31.0 31.0 Temp Temp 30.0 30.0 C) C) o 29.0 o 29.0
28.0 28.0
27.0 27.0 mean Temp. ( Temp. mean ( Temp. mean 26.0 26.0
25.0 25.0 10.0 J FMAMJ J ASOND 10.0 J FMAMJ J ASOND DO DO 8.0 8.0
6.0 6.0
4.0 4.0
2.0 2.0 mean concentration (mg/l) concentration mean (mg/l) concentration mean 0.0 0.0 J FMAMJ J ASOND J FMAMJ J ASOND
Figure 3.5 Mean monthly temperature and dissolved oxygen (DO) concentrations measured in the surface and bottom 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 date concentrations of dissolved oxygen are similar throughout the water column or show weak vertical gradients. Between 2004 and 2006, the maximal vertical concentration differences are much larger, approximately 5 mg/L as a rule. The bottom waters below a depth of 17 meters 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 slight increase of 1 mg/L between 2004 and 2006 while bottom concentrations have declined by over 1 mg/L over the whole period, probably caused by slightly increased stratification. Mean water column temperatures show little seasonal variability (< 2 oC), with lower temperatures recorded in January (Fig. 3.5).
Mean annual concentrations of NO3-N have shown a decrease in both the surface and bottom waters between 1999 and 2006, while mean annual NH4-N concentrations have remained largely invariant over this time (Fig. 3.6). Mean surface concentrations of NH4-N and NO3-N between 1995 and 2006 at Site B are 0.09 and 0.27 mg/L, respectively. This is lower than the mean concentrations in the bottom waters at the same site (0.12 and 0.29 mg/L, NH4-N and NO3-N, respectively).
Concentrations of SRP appear to have increased slightly between 2003 and 2006, although measurements collected are often below the analysis detection limit (0.02 mg/L) making it difficult to observe actual trends (Fig. 3.6).
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Surface Bottom 0.60 0.60 NO3-N NO3-N 0.50 0.50
0.40 0.40
0.30 0.30
0.20 0.20
0.10 0.10 mean concentration (mg/l) concentration mean mean concentration (mg/l) concentration mean 0.00 0.00 0.251994 1999 2004 0.251994 1999 2004 NH4-N NH4-N 0.20 0.20
0.15 0.15
0.10 0.10
0.05 0.05 mean concentration (mg/l) mean concentration (mg/l) mean concentration 0.00 0.00 0.051994 1999 2004 0.051994 1999 2004 PO4-P PO4-P 0.04 0.04
0.03 0.03
0.02 0.02
0.01 0.01 mean concentration (mg/l) mean concentration (mg/l) mean concentration 0.00 0.00 1994 1999 2004 1994 1999 2004
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, 1995 to 2006. Bars represent standard error.
Higher concentrations of NH4-N and SRP concentration in the bottom waters relative to the rest of the water column occur primarily during stratification events. This can partially be attributed to enhanced rates of sediment nutrient release rates during periods of low dissolved oxygen concentrations, coinciding with stratification.
There is little data available to describe historic trends in TP and TN concentrations in the Reservoir. For TN, where regular monthly measurements do exist, values are often at the high detection limit of 1 mg/L. Between 2004 and 2006, mean surface TP and TN concentrations, based on monthly measurements, were 0.042 and 1.32 mg/L, respectively. In the bottom waters at the same site, mean TP and TN concentrations were slightly higher at 0.05 and 1.34 mg/L, respectively (Fig. 3.7). These differences are statistically not significant.
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0.6 TP Surface 0.5 Mid Bottom 0.4
0.3 TP(mg/l) 0.2
0.1
0 4.51995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 4 TN Surface Mid 3.5 Bottom 3 2.5 2 TN (mg/l) 1.5 1 0.5 0 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
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), and lowest in February (c. 0.2 mg/L). Ammonium concentrations also show some degree of seasonal variation, with higher concentrations around March and June, while trends in SRP concentrations are less defined due to concentrations being generally below the the detection limit..
Spatial differences in surface nutrient concentrations between sites also appear to be relatively minor. For NO3-N and SRP, mean differences between measured samples at Sites B and E were 0.003 and 0.001 mg/L, respectively, over the whole period (1995-2006). For
NH4-N, mean differences in concentration were 0.035 mg/L.
While concentrations of chlorophyll-a in the surface waters of the Upper Peirce Reservoir are increasing until 1998, a decrease appears to occur as of 1999. Chlorophyll-a appears to be lower over the period 2004-2006 (mean 28.9 ug/L) than in the period 1998-2000 (mean 40.6 ug/L). A similar trend is observed in secchi depth measurements, with higher water column 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 over the 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 in the monthly measurements. Variation may be caused mainly by variations in nutrient loads, stratification and solar radiation.
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100.0 3.0 90.0 Chlorophyll-a & Secchi depth 80.0 2.5
70.0 2.0 60.0 50.0 1.5 40.0 Chl-a (ug/l) Chl-a 1.0 30.0 20.0 (m) depth Secchi Chl-a 0.5 10.0 S.Disk 0.0 0.0 1000001995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Algal Biovolume Raphrodiopsis Microcystis Total )
-1 10000 ml -3
um 1000 4
100
10 Biovolume (10
1 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
Figure 3.8 Surface water chlorophyll-a (Chl-a) concentrations and secchi depth, and estimates of phytoplankton biovolume, at Site B, Upper Peirce Reservoir, 1995 to 2006. Note that the phytoplankton counts are presented on a logarithmic scale.
Phytoplankton cell counts are dominated mainly by green algae (chlorophytes) and diatoms species. However, on the basis of biovolume (biomass) the phytoplankton community is equally dominated by diatoms and green algae (chlorophytes) for most of the time. (Due to the uncertainty in the biovolume of a green algae cell, the contribution of green algae may be overestimated.) Blue-green algae (cyanobacteria) are important too, and dominate occasionally, especially Microcystis (Fig. 3.8).
The changing nutrient concentrations as of 1999 may have resulted in changing growth limiting factors for phytoplankton. Until 2002 the algae are probably mainly limited by phosphorus. As of 2003 nitrogen may have become the second main limiting factor. The significant changes as of 1999, the decrease of nitrate and chlorophyll-a and the slight increase of phosphate, may be related to a changed loading of UPR with nutrients. Two relevant facts can be mentioned, that support this hypothesis. The inflow of Tebrau water has been minimised, and is now just enough to maintain maximal water level. It remains uncertain when this change took place. Furthermore, until April 2001 the filter sludge produced by the Chestnut Avenue drinking water plant was stored at a dump site near the inlet for Tebrau water. Excess water enriched with nutrients from the sludge freely flowed into UPR. The inflow of nutrients from the dump site may still be occurring, but probably at lower concentrations.
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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 therefore been decided to start the calibration of ECO for Upper Peirce Reservoir with the coefficient values that have been derived for previous calibrations, and to try to deviate as little as possible from these values.
However, it seems unlikely that coefficients, in which underlying processes and parameters such as bacterial activity are implicit, can be extrapolated within a temperature range of 30 oC on the basis of only one temperature dependency function. Tropical water systems are different from moderate climate waters in many respects. Temperatures are far higher and much more constant, which has led to the development of aquatic ecosystems with weak stratification, very small seasonal variation and much higher process rates, resulting in different plant, animal and bacterial species and different biotic-abiotic interactions. The consequence is that models developed for moderate climate systems can not be applied to tropical systems simply by extrapolation.
The species composition and the coefficients for BLOOM have been based on the setting for tropical fresh water systems as derived for Lake Victoria (Delft Hydraulics, 1999). In this set most coefficients have the same values as derived during the extensive calibrations of BLOOM for fresh water systems in areas with moderate climate (Los and Brinkman, 1988; Delft Hydraulics, 1995). The gross production rates and their temperature dependencies had been modified to better reproduce species composition and primary production for a temperature range of 20-30 oC.
The species composition for UPR has been verified on the basis of observed species composition (section 3.2). Raphrodiopsis and Aphanizomenon are both filamentous cyanobacteria species with similar properties. Since model coefficients are not available for Raphrodiopsis it was decided to consider Aphanizomenon as being representative for this species.
The water quality process coefficients have been based on the values determined in the first calibration 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 of ECO and its precursor DBS (Delft Hydraulics, 2004a, 2002a, 1997b, 1995, 1994 and 1980). The temperature coefficients for the decomposition of organic matter and the consumption of electron-acceptors kt_Dec and TcOxCon have been raised from 1.047 to 1.07. The settling velocities 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 velocities in the reservoir the resuspension rate was set equal to zero.
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Ting and Appan (1996) measured iron in sediment taken from Kranji Reservoir and MacRitchie 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 iron content of fine cohesive sediment can be estimated. This iron content is in both reservoirs roughly 5% DW, similar as found for sediment in other surface water systems around the world. 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 low value 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 entirely consists of fine cohesive sediment (IM1), whereas according to data provided by Ting and Appan (1996) the coarser sediment fraction (>63 µm) having a very low iron content may be up to 40%.
Another finding of Ting and Appan (1996) is that apatite occurs only in (very) small quantities in the sediment of Kranji Reservoir and MacRitchie Reservoir, which may be expected for a slightly acidic environment. For this reason it was decided to decrease the ratio 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 be present.
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 loads and 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 at the end of this long simulation was taken as the initial composition for the actual calibration runs, each spanning three more cycles. This delivers a “dynamic steady state” for sediment composition, in which the conversion fluxes in the sediment are in balance with the sediment-water exchange and burial fluxes.
In an early stage of the calibration it appeared that the hydrodynamic model produced too little vertical mixing leading to the underprediction of dissolved oxygen in the lower water layers. An additional vertical dispersion coefficient of 1.5 10-4 m2/s was imposed on the water 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 for phytoplankton, and consequently to a strongly overpredicted phytoplankton biomass (chlorophyll-a). The increase of denitrification and phosphate accumulation in the sediment was required.
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The adsorption capacity of the sediment at reducing conditions was increased by bringing the oxidised iron fraction in reducing sediment up from 15% to 30% (fr_Feox = 0.3). The precipitation rate of vivianite was increased a factor 5. These modifications reduced the mobility of phosphorus in the sediment, and consequently the return flux of phosphate to the water column.
The ammonium and nitrate concentrations in the water column were brought down by increasing the nitrification and denitrification rates, and by reduction of the total-N and nitrate loads from Tebrau water with 10%. The latter is justified from the significant uncertainty in the loads (see section 5.3.3). The nitrification rates RcNit20 for the water column and for the sediment were increased, respectively from 0.05 to 0.1 gN.m-3.d-1 and from 20 to 40 gN.m-3.d-1. Further enhancement of nitrification was established by decreasing the half saturation concentration KsAmNit from 1 to 0.4 gN.m-3. Accelerating nitrification in the sediment also leads to the acceleration of denitrification, because more nitrate becomes available in the upper sediment layers. Additionally, the half saturation concentration KsNiDen for denitrification was also reduced from 1 to 0.4 gN.m-3, leading to a higher contribution of denitrification in the mineralisation of organic matter.
In this stage of the calibration, it appeared that the predicted nutrient concentrations, the nitrate 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 the conclusion that the data for total-N and dissolved phosphate for 2006 did not lead to representative loads for this year. The 2006 load for nitrogen was 15% higher than the 2004-5 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 phosphate concentrations in UPR in 2006 tend to be higher than in 2004-5. Moreover, a relatively large fraction of phosphate may have been adsorbed on inorganic suspended sediment in Tebrau water in 2006, which does not show up in the dissolved phosphate measurements. The average suspended matter concentration in 2006 is almost three times higher than the average concentration in 2004-5. It was decided to adjust the total-N and total-P concentrations for 2006 to the average concentrations for 2004-2005. This took away the structural deviation of the predicted concentrations to a large extent.
The Secchi depth appeared overpredicted, and consequently light limitation was underpredicted. In order to improve the model in these respects the extinction coefficients of the organic detritus components have been raised with a factor 2.5 resulting in the values presented in section 3.4. Additionally, the DOC concentrations were reduced by diminishing the conversion fraction dt_dtdr from 0.05 to 0.025, and by raising the decomposition rate of DOC at 20 oC k_DOCdec20 a from 0.00035 to 0.0008 d-1. These changes are justified because of the more optimal conditions for dissolved organic matter decomposition in tropical water systems compared to moderate climate water systems (constant high insolation and temperature).
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The overall primary production in the model was now at the right level, but the phytoplankton species composition needed improvement, since phytoplankton was entirely dominated by green algae. In order to raise diatom biomass relative to green algae biomass the P/C-ratios of the N- and P-types of green algae have been increased circa 3%, making green algae slightly less competitive with regard to phosphorus limitation. In an iterative process Microcystis was made more competitative for light/growth limitation (at concurrent phosphorus limitation) by raising its Pmax circa 25% and decreasing its P/C-ratio circa 15%. This resulted in a situation, in which diatoms and green algae have more or less equal contributions to yearly average phytoplankton biomass, in line with measurement data. Microcystis grows more frequently, but still develops too low biomass.
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5 The Upper Peirce Reservoir water quality model
5.1 Model input
5.1.1 Computational grid and bathymetry
The bathymetry of the Upper Peirce Reservoir relative to maximal water level is displayed in Figure 5.1 This bathymetry is based on the computational grid for the hydrodynamic model of the reservoir (Delft Hydraulics, 2007). The grid for the water quality model in Figure 5.2 is a 4x4 aggregated version of the hydrodynamic grid. Considering the weak horizontal water quality gradients, aggregation has resulted a rather coarse grid, that distinguishes only large sections of the main water body and the various branches. The 19 layers of the hydrodynamic grid have been aggregated into 7 layers in the water quality model. The layer thickness varies as follows from top to bottom: 4x2 and 3x4.
Figure 5.1 The bathymetry of Upper Peirce Reservoir.
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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 for the sediment is added to the grid for the water column. The layer structure from the sediment-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 for 2004-2006 (Delft Hydraulics, 2007). The fields are basically driven by hourly wind, thermal stratification, artificial aeration, daily inflows and outflows. The aeration induced vertical flow is taken into account as a function of the capacity of the aerators. For pragmatic reasons aeration is continuous in FLOW, whereas it is intermittent in reality. In view of the turbulence 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 one major point source inflow to the Upper Pierce Reservoir. In addition there are several non- point discharges to the Reservoir. The inflows concern: x discharge of raw drinking water from Tebrau (Malaysia); x runoff from the catchment; and x rainfall on the reservoir.
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Figure 5.3 The water balances of Upper Peirce Reservoir for 2004 and 2006.
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The outflows concern: x withdrawal of raw drinking water for Chestnut Avenue WW; x withdrawal to MacRitchie Reservoir; x withdrawal for the irrigation of the SICC Golf Course; x spill to Lower Peirce Reservoir; and x evaporation from the reservoir.
The water balances for 2004 and 2006, representative for the present situation, are depicted in Figure 5.3. The Tebrau water discharge is dominant among the inflows, and the withdrawal for Chestnut Avenue WW is dominant among the outflows. The water balance for 2005 with similar characteristics is presented in Delft Hydraulics (2007).
5.1.3 Loads
The loads result from: x discharge of raw drinking water from Tebrau (Malaysia, via Chestnut Avenue WW); x runoff from the Upper Peirce catchment due to wet and dry deposition; and x wet and dry deposition on the reservoir.
The various loads of nitrogen and phosphorus are presented in Table 5.1. The load from Tebrau water is clearly dominant for nitrogen as well as phosphorus. Yet, the atmospheric loads partially entering the reservoir through runoff are substantial.
The loads from Tebrau water have been calculated by multiplication of the daily flow rates with observed concentrations for 2004-2006. Monthly observed parameters include total nitrogen, 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 and phosphate), implying a substantial uncertainty in the calculated loads from Tebrau water. Daily data are available for ammonium and total organic carbon. With regard to phosphorus only dissolved PO4 was available, which necessitated the estimation of total-P proportional to PO4. The organic-P fraction can be estimated from TOC. The fraction of phosphate adsorbed to suspended sediment has been ignored. During model calibration it was decided (see also section 5.5.1): x to use the 2004-2006 average concentration for total-N and nitrate-N in stead of monthly values, because this resulted in much better reproduction of the nitrate concentrations observed for Upper Peirce Reservoir; x to reduce total-N and nitrate-N concentrations with 10% to avoid overprediction of ammonium and nitrate; x to use the 2004-2005 average concentration for PO4-P for 2006, because observed PO4 was much lower for this year than for the other two years, whereas total-N was similar for all three years; x to calculate total-P as 1.4 x dissolved PO4-P in agreement with an estimated organic-P fraction; and x to use yearly average data for TSS, considering the large variation of the monthly data.
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Table 5.1 The nutrient loads on Upper Peirce Reservoir for 2004-2006.
Source Total-N % N-load Total-P % P-load (kg/day) (kg/day) Tebrau water 197.8 85.4 15.05 86.5 catchment runoff 8.6 3.7 0.60 3.5 wet deposition 17.6 7.6 0.68 3.9 dry deposition 7.5 3.3 1.07 6.1 Total 231.5 100 17.4 100
The monthly varying concentrations used to calculate the daily loads for the model result in the following average concentrations:
x TOC is 4.72 mgC/L (observed; converted into POC/N/P1-3 and DOC/N/P) x Total-N is 2.71 mgN/L (0.9 x observed total-N) x NO3 is 0.97 mgN/L (0.9 x observed nitrate N) x NH4 is 1.28 mgN/L (calculated as total-N minus organic N and nitrate N; in agreement with the average observed concentration of 1.3 mgN/L) x Total-P is 0.206 mgP/L (1.4 x observed PO4-P) x PO4 is 0.15 mgP/L (dissolved plus adsorbed; calculated as total-P minus organic P; in agreement with the average observed concentration of 0.147 mgN/L) x Salinity is 0.015 psu (calculated as 2 x Cl) x SO4 is 2.1 mgS/L (observed) x 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 of POC1, POC2 and POC3 are respectively 45%, 45% and 10% of TOC-DOC (expert estimate). 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 available have been estimated as constant concentrations. This includes dissolved oxygen (saturation at 30 oC; 7 mg/L), silicate (10 mgSi/L) and feacal coli bacteria (FCB; 57000/100 mL). The silicate 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 a direct load on the surface of the reservoir and as an indirect load by means of runoff and groundwater inflow from the catchment of the reservoir (Table 5.1). Atmospheric deposition is the only source of nutrients in runoff and groundwater inflow. The wet atmospheric loads have been calculated by multiplication of average nutrient contents in rain, rainfall (2.79 m/year in 2004-2006) and surface area (catchment 738 ha; reservoir 320 ha). The dry atmospheric loads are fixed percentages of the wet loads. For the quantification of the loads from the catchment, it is assumed that maximal retention of 75% occurs in the catchment for
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both nitrogen and phosphorus (Schreiber et al., 2003). Only 25% of N and P deposited from the atmosphere in the catchment ends up in the reservoir. All loads resulting from atmospheric deposition have been imposed on the model proportional to observed daily rainfall for 2004-2006.
The following two sets of measurement data are available for the quantification of these atmospheric loads: x PUB data of total-N, ammonium, nitrate and phosphate in rainfall (2005-2006); and x NUS data of total-N, ammonium, nitrate and phosphate in rainfall and dry deposition rates (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 it is the most comprehensive dataset, especially generated for the present modelling work. Moreover, the two datasets are very similar as to the average nitrogen and phosphorus contents in rain. The NUS data deliver mean contents in rain water of 0.65 mgN/L and 0.025 mgP/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 NUS investigation, which is in line with mean literature data (average = 8.8 kg/ha/yr; Delft Hydraulics, 2006b). With regard to nitrogen it is assumed that 60% is discharged into the reservoir 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. The dissolved silicate and sulphate loads have been calculated as the product of the runoff flow rate 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 at the start of a simulation. Since 2004 is the first simulation year, the concentrations observed for January 2004 have been used to quantify initial conditions for the water column. The quantification involves conversion of observed parameters to model substances such as the various organic C, N and P fractions. Concentration fractions of the organic C components as 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 the sediment and pore water in Upper Peirce Reservoir. The following initial sediment composition was derived from sediment data for Marina Bay: x total organic carbon TOC = 15206 gC/m3 (POC1 = 1%, POC2 = 3%, POC3 = 96%, DOC = 4 mgC/L) x total nitrogen TN = 793 gN/m3 (in PON1-3 according to N/C1=0.12, N/C2=0.1 and N/C3=0.05)
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x total phosphorus TP = 230 gP/m3 (80 in POP1-3 according to P/C1=0.015, P/C2=0.012 and P/C3=0.005; 74 each in AAP and VVIP and 0 in APATP) x opal silicate OPAL = 2500 gSi/m3 (expert estimate) x particulate sulphide SUP = 20 gS/m3 (expert estimate) x inorganic sediment IM1 = 587000 g/m3 (according to porosity = 0.75)
Ting and Appan (1996) investigated the phosphorus content and speciation in sediment from Kranji Reservoir and MacRitchie Reservoir. The sediment conditions in UPR is probably in between the conditions of these two reservoirs, more resembling the conditions in MacRitchie Reservoir, being the least loaded reservoir, than the conditions in the Kranji Reservoir. For three sediment samples taken from each of the reservoirs Ting and Appan found average total-P concentrations of 1.76 gP/kgDW for Kranji Reservoir and 0.3 gP/kgDW for MacRitchie Reservoir. They also found very high water contents (80-89 %WW) that may have been caused by the sampling technique, surface dredging representative 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 MacRitchie Reservoir, the in-situ porosity of the upper 20 cm layer in this reservoir is more likely 0.75 as has been found for Marina Bay. On the basis of this porosity the bulk total-P concentration 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 230 g/m3 for Marina Bay is probably too low.
Using an extraction scheme Ting and Appan (1996) found for MacRitchie Reservoir that the iron 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 regard to the P-speciation in the sediment of the Peirce Reservoirs these data indicate that:
x 37-77% may be associated with organic matter (poly-phosphate); x 23-65% may be associated with iron (adsorbed P and vivianite-P); and x 1-8% 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 assumed that 35% is organic matter associated, 65% is iron associated, equally divided among the adsorbed and vivianite fractions, and 0% is calcium associated.
Starting from the initial sediment composition a representative sediment composition was then developed by restarting the model for a number of year simulations, using the calculated sediment composition at the end of a year as starting point for the next year. The procedure was continued for 10 years until a “dynamic steady state” was established.
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5.1.5 Meteorological forcing
The meteorological forcing includes: x water temperature; x windspeed; and x solar radiation.
Vertical differences in water temperature are usually less than 1 oC. Seasonal differences may amount to 2 oC. The temperature has therefore been imposed as a time series of the monthly mean observed temperatures (1995-2006), that do not vary over depth. These values 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, 1995 to 2006. Error bars represent standard error.
300 Sol. Rad. 250
200
150
100
50
0 0 5 10 15 20 25 30 35 40 45 50
Figure 5.5 Weekly average solar radiation (W/m2) for 2003, measured at Changi Airport (NEA).
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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 at Changi meteorological station. The reaeration coefficient adopts its lower limit at this windspeed.
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 fit of simulated and observed water quality. Water quality model simulations have much less spatial and temporal variability than observed water quality, mainly because the forcing of models is much less variable than in reality and because observed water quality depends on patchiness. It is therefore inevitable to perform and assess calibration by ignoring observed variability to a certain degree. In view of this the following facts must be noticed:
x The loads imposed on the Upper Peirce Reservoir model have been determined on the basis of limited monthly or even yearly data. x The meteorological forcing of the model is based on a constant windspeed and on solar radiation data for 2003. This meteorological forcing has been applied for all simulated years (2004-2006). x Vertical mixing in the reservoir is driven by artificial aeration. For pragmatic reasons vertical mixing occurs continuously in the model, whereas in reality aeration is discontinuous.
Consequently, the forcing of the models lacks most of the diurnal, seasonal and yearly variation that occurs in the real world. Moreover, the calculated loads may not be very accurate.
In view of all this the calibration of the water quality model for Upper Peirce Reservoir focussed on an as good as possible reproduction of observed:
x average levels and characteristic variability of the concentrations of the relevant substances, in particular nutrient components (N and P; no data for Si), phytoplankton biomass (chlorophyll-a), total organic carbon and dissolved oxygen; x vertical concentration gradients, especially those of dissolved oxygen; and x phytoplankton species composition.
The performance of the calibrated model is to be judged accordingly.
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5.2.1 The water column
Figures 5.6-18 present a rather complete picture of the simulated and observed water quality including simulated phytoplankton biomass and species composition for location RUPB in the deepest part of Upper Peirce Reservoir for 2004-2006 (Fig. 5.2). From the data analysis as well as the simulations it appeared that horizontal gradients are generally small. The observations 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 of 1 mgN/L (Fig. 5.6). Observed total-N is almost constantly at the detection limit, whereas simulated total-N varies between 0.45 and 0.75 mgN/L. Simulated ammonium agrees well with the observations, although sometimes peaks are missed (Fig. 5.7). Nitrate agrees with the measurements in the upper layer most of the time, but is somewhat overpredicted in the lower layer (Fig. 5.8). Very low nitrate concentrations have been measured in the top layer during 4 short periods, pointing at nitrogen limitation of phytoplankton growth during these periods. The model does not reproduce the low concentrations, implying that nitrogen limitation does not occur in the model.
Simulated total-P agrees with the observations in the sense that the averages are approximately 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 phytoplankton biomass (chlorophyll-a; see below). Patchiness in the reservoir with regard to particulate components is another possible explanation. Simulated and observed ortho-phosphate agree quite well considering the extremely low concentrations, although observed peaks are missed (Fig. 5.10). The low concentrations indicate that phosphorus limits phytoplankton growth almost permanently.
Figure 5.16 shows that dissolved silicate varies strongly depending on the blooming of diatoms, which is much stronger in 2004 than in the other years. Silicate is far from being depleted and therefore not growth limiting for phytoplankton. However, no observations are available to confirm this.
Although average observed and simulated chlorophyll-a are more or less equal, they show significant differences as to the variation over time (Fig. 5.11). Chlorophyll-a is clearly underpredicted in the first half of 2004, which might be caused by too strong phosphorus limitation. 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 only one of those species in the simulation at a time, whereas in reality all species are more or less continuously present. Varying from 20% to 5% of phytoplankton biomass in the top layer and from 10% to 50% in the second layer, Microcystis is realistically predicted. The observations indicate a 10-50% biomass share of this blue-green algae species. Mismatches of observed and simulated species composition may lead to over- or underprediction of chlorophyll-a. Light limited diatoms have the highest chlorophyll/carbon ratio, 30% higher ratio than phosphorus limited green algae.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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 growth limitation. It is therefore very important to have correct light extinction in the model. A strong 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 is composed 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 slow production. The distribution among the fractions can be seen in Figure 5.13. The rather good agreement of simulated and observed TOC is proof of primary production and detritus settling and decomposition being in balance.
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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.
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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 upper and middle layers (Fig. 5.17). The possible causes are discussed in Chapter 6. The fact that observed vertical gradients of dissolved oxygen and nutrients are reproduced by the model indicates that vertical mixing is taken into account adequately.
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Figure 5.18 shows that simulated and observed sulphate, one of the electron-acceptors for the decomposition of organic matter, agree quite well. This is due to including a sulphate load from catchment runoff.
Not shown in the graphs are the simulation results for suspended sediment (IM1) and adsorbed phosphate (AAP). The concentrations of these substances are very low. Suspended sediment IM1 is 1.2 on the average and varies from 0.5 to 2.5 mg/L. AAP is higher in the lower 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 is depicted in Figures 5.19-34. The graphs show the development of concentrations over time in 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 the chemical diagenesis of the sediment. Figure 5.19 shows that the highest concentrations of TOC 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 mainly consists of very slowly decomposing POC3. The pore water concentration of DOC is maximal 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 are presented in graphs 5.23-26. Dissolved oxygen penetrates only the upper 2 mm of the sediment. Nitrate and sulphate do not get deeper than 4 mm due to denitrification and sulphate reduction. Methane saturates at 2 cm depth. All methane produced below this depth escapes the sediment in gas bubbles.
Figures 5.27-24 present the concentrations of the nutrients. The steepness of the vertical gradients near the sediment-water interface is a measure for the magnitude of the diffusive return flux to the overlying water. Ammonium develops the highest concentrations in the lower 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 of vivianite. Total-P is very constant over depth, but increases slowly with time, approximately 1.2% per year. Some 8% of total-P is tied up in vivianite, 25% in organic matter, and the rest is adsorbed-P. The total-P concentration of approximately 1290 mgP/kgDW is exactly equal to the one observation (CAWT, 2007). This is much higher than measured for the sediment in MacRitchie Reservoir, but much lower than measured for the sediment in Kranji Reservoir (Ting and Appan, 1996; CAWT, 2007). Silicate saturates at a depth of 2 cm. Opal silicate has highest concentrations in the lower layers, which points at opal not being in steady-state yet due to very slow dissolution. The initial concentration has been too high.
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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.
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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.
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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.
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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.
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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.
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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 of ammonium and nitrate from the sediment to the overlying water. Burial is the removal of substances into the inactive sediment below the simulated layers resulting from net settling of sediment. Storage is the difference between initial and final composition in a water or sediment compartment. Negative storage indicates lower concentrations at the end of the simulation. In a “dynamic steady-state” storage should be approximately equal to zero, very small compared to the mass fluxes. The exception is the accumulation of phosphorus in the sediment, 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 be deduced: x Denitrification is 74.1% of the total external nitrogen load. Outflow of nitrogen is 25.4% and burial only 0.5%. The storage of nitrogen is negligibly small. x Outflow of phosphorus is 24.6% of the total external phosphorus load. Storage of phosphorus in the sediment is 50.2%, and burial 25.2%. 75.4% of the external load accumulates in the sediment. x The internal load of nitrogen is 1.69 times the total external load. The internal load of phosphorus is 2.01 times the total external load.
The latter finding indicates the great importance of the return fluxes for the primary production of phytoplankton and the water quality in Upper Peirce Reservoir. This is to be expected considering the long average residence time of water in the reservoir, namely 340 days.
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.
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Total-N Atmospheric deposition Denitrification 2.9 0.0
Tebrau load 23.3 Storage Outflow -0.04 6.9 Catchment Runoff 1.0 Internal load Water 45.9 Sediment Denitrification 66.1 20.1 Settling Storage -0.01
0.1 Burial
Total-P Atmospheric deposition 0.20
Tebrau load 1.77 Storage Outflow 0.002 0.50 Runoff 0.07 Internal load Water 4.11 Sediment
5.64 Settling Storage 1.02
0.51 Burial
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.
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6 Conclusions and discussion
Water quality in the tropical Upper Peirce Reservoir
The simulation of the water quality in the weakly stratified, artificially aerated, tropical Upper Peirce Reservoir (UPR) with Delft3D-ECO allows for the following conclusions. UPR is moderately eutrophic. Phosphorus, nitrogen and light are limiting factors for algae growth. Light (or energy) limitation is established as a combination of maximal growth in the upper water layers and mortality in the deeper water layers. Denitrification in the sediment is the dominant sink for nitrogen, approximately 20 gN/m2/year. 74% of the external load is denitrified. Accumulation in the sediment is the dominant sink for phosphorus in both reservoirs. The accumulation rate is 1.53 gP/m2/year. 75% of the external load accumulates in the sediment. The strong retention/removal of nutrients in the reservoir is due to the long hydraulic residence time of 340 days.
Since the temperature in UPR is continuously high, water quality processes may proceed differently compared to fresh water systems in moderate climate areas. Temperature dependency is probably different for the ranges 0-20 oC and 20-30 oC, which has been expressed in the model by means of a higher (maximal) temperature coefficient for the decomposition of organic matter at 20-30 oC. Nitrification and denitrification proceed probably at a higher level than in moderate climate waters, which may find its cause in more stable, 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 adsorption capacity for phosphate is low compared to the adsorption capacity of sediment in moderate climate water systems. Apatite-like minerals may hardly be formed due to rather acidic conditions in the sediment.
The decomposition of refractory dissolved organic matter (DOC) may proceed significantly faster 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 the quality of this model. The amount and quality of the data available to quantify the loads of the nutrients and organic matter on the reservoirs and to describe the water quality in the reservoirs is minimal. Especially scarce are the data on total-N and total-P in the inflows and the reservoirs. Detection limits for nutrients, total-N in particular, were too high.
The meteorological forcing (windspeed, solar radiation) had to be quantified with limited data. Local windspeed is unknown, a constant average windspeed was deduced from Changi Airport data. Solar radiation for 2003 had to be used for 2004-2006, which partially explains mismatches of predicted and observed phytoplankton biomass (chlorophyll-a) with regard to seasonal variability.
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Continuous vertical mixing due to artificial aeration has been imposed on the model for pragmatic reasons, whereas in reality aeration and mixing are intermittent, triggered by the oxygen concentration dropping below 2.5 mg/L. Vertical mixing affects concentration gradients and primary production.
The water quality data for the reservoirs point at a transition in water quality immediately prior 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 the simulation period, which implies that the initial nutrient concentrations in the sediment generated with the model may be somewhat too low. However, the nutrient return loads from the sediment are not underestimated because compensation is found in the process coefficients for nitrification, denitrification and phosphate adsorption.
Model performance
In view of the above the performance of the UPR water quality model is assessed as follows. The average levels and variability of the concentrations of the relevant substances, in particular nutrient components (N, P), phytoplankton biomass (chlorophyll-a), total organic carbon and dissolved oxygen can be reproduced rather well with ECO. Vertical concentration gradients in water and sediment as well as sediment-water exchange fluxes are realistically simulated.
Often the model does not reproduce peaks and dips, in particular for total-P. Causes are at least partially found in unrepresentative and/or smoothed forcing. Patchiness of observed water quality and inaccurate measurement may be additional causes.
Dissolved oxygen tends to be overpredicted in the upper water layer of Upper Peirce Reservoir. This may be due to: x imposing a constant windspeed in stead of hourly windspeeds; x ignoring of the oxygen in- and outputs caused by artificial aeration; x ignoring of additional oxygen demands caused by leaves from trees falling into the reservoirs, and by the remains of drowned vegetation in and at the sediment; and x overpredicted methane ebullition into the atmosphere (currently 15-20% of the oxygen demand, which seems realistic).
The prediction of phytoplankton species composition is quite realistic. The observations of species composition indicate the continuous presence of diatoms, green algae and Microcystis. The main observed limiting factors for algal growth (P and energy/light) are also active in the model. The model tends to produce only one or two algae species at a time, and create long periods with only one dominant limiting factor for algae growth. This is 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 varying conditions. The continuous presence of diatoms, green algae and Microcystis would require much stronger and much more frequently varying conditions.
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Numerical aspects in ECO
During the calibration a discrepancy was discovered between parameters that are evaluated at each 5 minutes transport time step and the parameters that are computed only at each 6 hour 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 transport time 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 of concentrations. Options to take the problem away need to be investigated.
Furthermore, an inaccuracy has been encountered in the mass balance for particulate substances in the sediment. It appeared that very small upward and downward transport fluxes that arise due to keeping the porosity constant are not fully accounted for. The fluxes may be set equal to zero at rounding off for single precision computing. The inaccuracy can be 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 the lower boundary in the sediment. These boundary concentrations are used to compute fluxes across the boundary to the lower sediment layer. Especially for particulate substances these boundary concentrations should be equal to the concentrations in the lower layer. In the current model set-up with input defined boundary concentrations large differences may arise between the concentrations at either side of the boundary. The model needs to be provided with an option to take the concentration of the lower sediment layer for the computation of the 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 competition in artificially aerated but still slightly stratified tropical reservoirs are not wel understood. Research is needed for a better understanding of:
x the adjustment of phytoplankton species properties to conditions in stratified reservoirs; x the interaction of phytoplankton species with limiting factors and vertical mixing; x the occurrence and importance of nitrogen fixation (not included in the present application of ECO); and x how phytoplankton interactions and forcing functions can be improved in ECO.
To allow for more comprehensive simulation of dissolved oxygen ECO needs to be extended with a process routine for artificial aeration.
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In further model development and calibration special attention should be given to the following issues: x algae species competition under stratified conditions; x phosphorus species in the sediment and phosphorus return fluxes; and x dissolved oxygen as affected by artificial aeration and methane ebullition; x mass balances of particulate substances in the sediment; x boundary concentrations for the sediment; and x discrepancy of parameters computed at different time steps for BLOOM and transport.
62 WL | Delft Hydraulics Development of Delft3D-ECO Z4524 December 2007 Calibration for a tropical stratified reservoir
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 and Daniel 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 annual loads. 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. Research report 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 Lake Veluwe. 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/Application for 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. Report on 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 and transitional 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- und Klimaforschung, 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.
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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 the Netherlands. 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
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A Input parameters for water and sediment quality processes
Table A.1 Input parameters for the decomposition of organic matter, electron-acceptor consumption and methanogenesis in ECO.
Name Process / Definition of parameter Source1 Preferred Range Units Value
Decomposition of organic matter
ku_dFdec20 max. first-order min. rate fast decomp. detr. at 20o C 2/3 0.18 0.3-0.12 d-1 kl_dFdec20 min. first-order min. rate fast decomp. detr. at 20o C 2/3 0.12 0.2-0.06 d-1 ku_dSdec20 max. first-order min. rate slow dec. detr. at 20o C 1/4/5 0.015 0.1-0.01 d-1 kl_dSdec20 min. first-order min. rate slow dec. detr. at 20o C 1/4/5 0.015 0.05-0.005 d-1 k_dprdec20 first-order min. rate part. refractory detr. at 20o C 1/4/5 0.00035 0.003-0.0003 d-1 k_DOCdec20 first-order min. rate diss. refractory detr. at 20o C 1/4/5 0.0008 0.003-0.0003 d-1 au_dNf max. stoch. constant nitrogen in fast dec. detritus 2/3 0.15 0.18-0.12 gN.gC-1 al_dNf min. stoch. constant nitrogen in fast dec. detritus 2/3 0.10 0.12-0.08 gN.gC-1 au_dNs max. stoch. constant nitrogen in slow dec. detritus 2/3 0.12 0.18-0.12 gN.gC-1 al_dNs min. stoch. constant nitrogen in slow dec. detritus 2/3 0.08 0.12-0.08 gN.gC-1 a_dNpr stoch. constant nitrogen in refractory detritus 1/5 0.05 0.05-0.015 gN.gC-1 au_dPf max. stoch. constant phosphorus in fast dec. detritus 2/3 0.020 0.025-0.015 gP.gC-1 al_dPf min. stoch. constant phosphorus in fast dec. detritus 2/3 0.013 0.015-0.008 gP.gC-1 au_dPs max. stoch. constant phosphorus in slow dec. detritus 2/3 0.015 0.020-0.012 gP.gC-1 al_dPs min. stoch. constant phosphorus in slow dec. detritus 2/3 0.010 0.012-0.008 gP.gC-1 a_dPpr stoch. constant phosphorus in refractory detritus 1 0.005 0.005-0.002 gP.gC-1 b_ni attenuation constant for nitrate as electron acceptor 4 1.0 1.0-0.8 - b_su attenuation constant for sulphate as electron acceptor 4 1.0 1.0-0.5 - b_dts conv. fraction fast dec. detr. into slow dec. detr. 1/4 1.0 1.5-0.5 - b_dtpr conv. fraction slow dec. detr. into part. refr. detr. 1/4 0.95 1.5-0.5 - b_dtdr conv. fraction slow dec. detr. into diss. refr. detr. 1/4 0.025 0.2-0.02 -
Consumption of electron-acceptors, methanogenesis
-3 KsOxCon half saturation constant for oxygen limitation 4/5 1.0 2.0-0.3 gO2.m KsNiDen half saturation constant for nitrate limitation 4/5 0.4 2.0-0.3 gN.m-3 KsSuRed half saturation constant for sulphate limitation 4/5 2.0 3.0-1.0 gS.m-3 -3 KsOxDenInh half sat. const. for DO inhibition of denitrification 4/5 0.1 0.5-0.05 gO2.m KsNiRedInh half sat. const. for nitrate inhib. of sulph. reduction 4/5 0.05 0.2-0.03 gN.m-3 KsSuMetInh half sat. const. for sulphate inhib. of methanogenesis 4/5 1.0 1.0-0.05 gS.m-3 2 -3 CoxDenInh critical diss. oxygen conc. inhibition of denitrific. 4 1.0/5.0 1.0-5.0 gO2.m -3 CoxRedInh critical diss. oxygen conc. inhibition of sulphate red. 4 0.05 0.1-0.01 gO2.m -3 CoxMetInh critical diss. oxygen conc. inhib. of methanogenesis 4 0.02 0.1-0.01 gO2.m CniMetInb critical nitrate conc. inhibition of methanogenesis 4 0.05 0.1-0.01 gN.m-3 RedFacDen reduction factor for denitr. below crit. temperature 4 0.2 0.5-0.0 - RedFacRed reduction factor for sulph. red. below crit. temp. 4 0.2 0.5-0.0 - RedFacMet reduction factor for methanogen. below crit. temp. 4 0.2 0.5-0.0 - CTBactAc critically low temp. for specific bacterial activity ½ 3.0 4.0-2.0 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
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Table A.2 Input parameters for processes of ammonium, sulphide and methane in ECO.
1 Name Process / Definition of parameter Source Preferred Range Units Value
Nitrification
RcNit20 MM nitrification rate in water 4/5 0.1 0.15-0.01 gN.m-3.d-1 RcNit20 MM nitrification rate in sediment 5 50.0 60.0-5.0 gN.m-3.d-1 -3 KsAmNit half saturation constant for ammonium limitation 5 0.4 0.2-1.0 gO2.m KsOxNit half saturation constant for oxygen limitation 5 0.5 0.25-1.3 gN.m-3 Rc0Nitox zeroth order nitrification rate at negative DO 4 0.0 25.0-0.0 gN.m-3.d-1 -3 CoxNit critical DO concentration for nitrification 4 0.0 0.0- -1.0 gO2.m Rc0NitT zeroth order nitrification rate at low temperature 1/4 0.0 5.0-0.0 gN.m-3.d-1 CTNit critically low temperature for nitrification 1/2 3.0 4.0-2.0 oC
Oxidation, precip., dissolution, speciation sulphide
o -1 3 -1 RcSox20 pseudo second-order sulphide oxid. rate at 20 C 5 10.0 0.02-10.0 gO2 .m .d Rc0Sox zeroth order sulphide oxidation rate 4 0.0 10.0-0.0 gS.m-3b.d-1 -3 CoxSUD critical dissolved oxygen concentration 4 0.0 0.0- -1.0 gO2.m DisSEqFeS eq. dis. free sulph. conc. for amorph. iron sulphide 4/5 0.4 10-10 5.0-0.2 10-9 mole.l-1 RcDisS20 dissolution reaction rate 4 2.0 106 ? d-1 RcPrecS20 precipitation reaction rate 4 106 ? d-1 pKhs neg. log. of eq. constant for HS- (see directives!) 5 -14.0 - -log(l.mole-1) -1 pKh2s neg. log. of eq. constant for H2S (see directives!) 5 -7.1 - -log(l.mole ) pH acidity in the water column 5 7.0 9.0-7.0 - acidity in the sediment 5 7.0 8.0-7.0 -
Oxidation, ebullition, volatilisation of methane
RcMetOx20 MM-rate for methane oxid. with oxygen at 20 oC 4 0.4 0.5-0.05 gC.m-3.d-1 Rc0MetOx zeroth order methane oxid. rate with oxygen 4 0.0 0.05-0.0 gC.m-3.d-1 RcMetSu20 MM-rate for methane oxid. with sulphate at 20 oC 4 0.1 0.3-0.00 gC.m-3.d-1 Rc0MetSu zeroth order methane oxid. rate with sulphate 4 0.0 0.05-0.0 gC.m-3.d-1 -3 CoxMet critical DO concentration for methane oxidation 4 0.5 2.0-0.0 gO2.m CsuMet critical sulphate conc. for methane oxidation 4 0.0 2.0-0.0 gS.m-3 KsMet half saturation constant for methane consumption 4 0.5 1.0-0.2 gC.m-3 -3 KsOxMet half saturation constant for DO consumption 4 1.0 3.0-1.0 gO2.m KsSuMet half saturation constant for sulphate consumption 4 1.0 3.0-1.0 gS.m-3 CTMetOx critically low temperature for methanogenesis 1/2 3.0 4.0-2.0 oC fScEbul scaling factor for the methane ebullition rate 4 1.0 1.0-0.0 - KLVolCH4 air-water transfer coefficient for methane (option 9) 2/3/4 1.0 2.0-0.1 - AtmPrCH4 atmospheric methane pressure 4 0.0 0.1-0.0 atm -3 SaturCH4 saturation conc. of methane relative to atmosphere 4 0.0 calculated gO2.m
1) 1=SWITCH (Delft Hydraulics, 1994 en 1997b), 2=ECO (MARE, 2002), 3=DBS (Delft Hydraulics, 1995), 4=tentative estimate, 5=literature
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Table A.3 Input parameters for processes of phosphate and silicate in ECO.
1 Name Process / Definition of parameter Source Preferred Range Units Value
Adsorption, precipitation, dissolution of phosphate
KadsP_20 molar adsorption equilibrium constant sediment 5 1000.0 1000-100 (mole.l-1)a-1 molar adsorption equilibrium constant water 2/5 1000.0 2000-500 (mole.l-1)a-1 a_OH-PO4 stochiometric reaction constant for pH-dependency 5 0.2 0.3-0.1 - pH pH 9 (variable in water, 7 in sediment) 6 var./7 7-9 - fr_FeIM1 fraction reactive iron in susp. inorg. matter IM1 6 0.05 0.055-0.035 gFe.gDW-1 fr_Feox fraction ox. iron (III) in the reactive iron fraction 1/4/5 0.3 0.8-0.1 - -3 Cc_oxPsor critical DO concentration for iron reduction 1/4 0.5 2.0-0.1 gO2.m RCAdPO4AAP sorption reaction rate 4 10.0 10.0-1.0 d-1 RcPrecP20 vivianite precipitation reaction rate 5 0.05 0.1-0.001 d-1 3 -1 -1 RcDissP20 vivianite dissolution reaction rate 1/4 0.025 0.05-0.002 m .gO2 .d EqVIVDisP equilibrium diss. phosphate conc. for vivianite 1/4/5 0.05 0.15-0.05 gP.m-3 RatAPandVP ratio of apatite and vivianite precipitation rates 5 1.0 2.0-0.1 - RcDisAP20 apatite dissolution reaction rate 4 0.0025 0.01-0.001 m3.gP-1.d-1 EqAPATDisP equilibrium diss. phosphate conc. for apatite 5 0.05 0.15-0.05 gP.m-3
Dissolution of opal silicate
Ceq_DisSi equilibrium dissolved silicate concentration 5 17.0 22.0-11.0 gSi.m-3 RcDisSi20 dissolution reaction rate 5 0.002 0.01-0.001 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.
1 Name Process / Definition of parameter Source Preferred Range Units Value
TurCoefSgm dispersion coefficient solids in sediment 1 at lower side of sediment layer 0.0 m2.d-1 in top layer 2.0 10-6 1-10 10-6 m2.d-1 thickness uniform top layer, depth function2 0.02 0.005-0.025 m
DifCoefSgm dispersion coefficient solutes in sed. 1 at lower side of sediment layer3 5 0.5 10-4 m2.d-1 in top layer 3.0 10-4 0.1-5.0 10-4 m2.d-1 thickness uniform top layer 0.02 0.005-0.025 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.
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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 N/C P/C Si/C Chla/C Vsed [m2/gC] [g/g] [g/g] [g/g] [g/g] [m/d] Diatoms-E 0.2700 0.210 0.0180 0.6600 0.040 1.0 Diatoms-P 0.1875 0.188 0.0113 0.5500 0.025 1.5 Greens-E 0.2250 0.275 0.0238 0.0018 0.033 0.5 Greens-N 0.1875 0.175 0.0155 0.0018 0.025 1.0 Greens-P 0.1875 0.200 0.0129 0.0018 0.025 1.0 Aphanizomenon-E 0.4500 0.220 0.0125 0.0018 0.033 0.0 Aphanizomenon-N 0.4000 0.125 0.0125 0.0018 0.025 0.0 Aphanizomenon-P 0.4000 0.170 0.0088 0.0018 0.025 0.0 Microcystis-E 0.4000 0.225 0.0250 0.0018 0.025 0.0 Microcystis-N 0.2875 0.113 0.0230 0.0018 0.017 0.0 Microcystis-P 0.2875 0.175 0.0200 0.0018 0.017 0.0 Oscillatoria-E 0.4000 0.225 0.0188 0.0018 0.033 0.0 Oscillatoria-N 0.2875 0.125 0.0138 0.0018 0.020 0.0 Oscillatoria-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 ktp,i M ktm,i R ktr,i [1/d] [-] [1/d] [-] [1/d] [-] Diatoms-E 0.500 1.060 0.035 1.080 0.048 1.072 Diatoms-P 0.370 1.060 0.045 1.085 0.048 1.072 Greens-E 0.431 1.059 0.035 1.080 0.048 1.072 Greens-N 0.315 1.071 0.045 1.085 0.048 1.072 Greens-P 0.315 1.071 0.045 1.085 0.048 1.072 Aphanizomenon-E 0.270 1.063 0.035 1.080 0.012 1.072 Aphanizomenon-N 0.230 1.063 0.045 1.085 0.012 1.072 Aphanizomenon-P 0.230 1.063 0.045 1.085 0.012 1.072 Microcystis-E 0.260 1.077 0.035 1.080 0.012 1.072 Microcystis-N 0.160 1.085 0.045 1.085 0.012 1.072 Microcystis-P 0.160 1.085 0.045 1.085 0.012 1.072 Oscillatoria-E 0.285 1.059 0.035 1.080 0.012 1.072 Oscillatoria-N 0.215 1.059 0.045 1.085 0.012 1.072 Oscillatoria-P 0.215 1.059 0.045 1.085 0.012 1.072
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