Water quality assessment using integrated modeling ... - Springer Link

Environ Model Assess (2006) 11:315–332 DOI 10.1007/s10666-006-9045-7

ORIGINAL PAPER

Water quality assessment using integrated modeling and monitoring in Narva Bay, Gulf of Finland Gennadi Lessin & Urmas Raudsepp

Received: 25 July 2005 / Accepted: 18 February 2006 / Published online: 20 October 2006 # Springer Science + Business Media, Inc. 2006

Abstract A coupled three-dimensional hydrodynamic–ecological model was used for the assessment of water quality in Narva Bay during one biologically active season. Narva Bay is located in the south-eastern Gulf of Finland. Narva River with a catchment_s area covering part of Russia and Estonia discharges water and nutrients to Narva Bay. The ecological model includes phytoplankton carbon, nitrogen and phosphorus, chlorophyll a, zooplankton, detritus carbon, nitrogen and phosphorus, inorganic nitrogen, inorganic phosphorus and dissolved oxygen as state variables. Both the hydrodynamic and ecosystem models were validated using a limited number of measurements. The hydrodynamic model validation included comparison of time series of currents and temperature and salinity profiles. The ecological model results were compared with the monitoring data of phytoplankton biomass, total nitrogen and phosphorus and dissolved oxygen. The comparison of hydrodynamic parameters, phytoplankton biomass, surface layer total phosphorus and dissolved oxygen and near-bottom layer total nitrogen was reasonable. Time series of spatially mean values and standard deviations of selected parameters were calculated for the whole Narva Bay. Combining model results and monitoring data, the characteristic concentrations of phytoplankton biomass, total nitrogen and phosphorus and near-bottom dissolved oxygen were estimated. Phytoplankton biomass and total phosphorus showed seasonal variations of 0.6–1.1 and

G. Lessin : U. Raudsepp (*) Marine Systems Institute, Tallinn University of Technology, Akadeemia Rd. 21b, 12618 Tallinn, Estonia e-mail: [email protected]

0.022–0.032 mg/l, respectively, during spring bloom, 0.1–0.3 and 0.015–0.025 mg/l in summer and 0.2–0.6 and 0.017–0.035 mg/l during autumn bloom. Total nitrogen and near-bottom oxygen concentrations were rather steady, being 0.25–0.35 and 2–6 mg/l, respectively. The total nitrogen and phosphorus concentrations show that according to the classification of Estonian coastal waters, Narva Bay water belongs to a good water quality class. Keywords ecological modeling . water quality . model validation . Narva Bay . Gulf of Finland

1. Introduction The EU water framework directive states that water body types should be characterized according to their hydromorphological, physicochemical and biological quality elements. Type-specific conditions may be established either using spatial data or based on modelling, or may be derived from a combination of these methods [9]. Usually, coastal water quality is determined from data obtained in the frame of monitoring programs [2, 4, 21]. The monitoring network comprises limited number of sampling stations, which are visited either on regular or irregular basis. Time interval between measurements varies in general between bimonthly and once per season. The monitoring stations are chosen so that they are representative for particular coastal sea area. As a rule, one station spatially covers relatively homogeneous region of the sea. Temporal coverage is chosen in order to capture basic variations of water properties. However, no matter how much

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effort is put into monitoring network design, there is still a chance that important variations of water quality parameters are not resolved. Numerical modelling can be used for water quality evaluation with detailed spatial and temporal resolution [12, 13, 15, 25]. The ability of the model to predict marine ecosystem dynamics depends crucially on reliability of model parameterizations of biological processes [17]. A number of parameters are poorly known. Model is an approximation of natural processes, and therefore, model results alone may not adequately describe the actual quality of seawater. For this reason, integrated use of measurements and modelling for assessment of ecological state of coastal waters has increased in recent years [5, 20, 26]. Simultaneous implementation of these two methods allows developing vigorous tool for water quality assessment and prediction. The aim of this study is to assess the water quality in Narva Bay (figure 1) during one biologically active season. For this purpose, results of a coupled hydrodynamic–ecological model MIKE 3 (developed by Danish Hydraulics Institute) are combined with the measurement data. The same model has been used for numerical modelling of phytoplankton biomass in Danish coastal waters [7]. The Narva Bay is defined as an area in the Gulf of Finland bounded by the coastline in the south and in the east, with its northern and western boundaries falling within the respective coordinates of 59-550 N and 26-350 E [21]. Narva Bay is an open bay with an intensive water exchange with the main Gulf of Finland. The Narva River, which is located in the south-eastern part of the bay, has an average runoff of 14.3 km3/year and loads of total nitrogen (TN) and total phosphorus (TP) of 26,400 and 750 t/year, respectively [24].

2. Model description 2.1. Hydrodynamic model The basic equations of the hydrodynamic model consist of a mass conservation equation for compressible fluid, non-linear momentum equations in the three main directions, a conservation equation for salinity and temperature and the equation of the state of seawater [22]. The simulations were performed with hydrostatic model version. The Smagorinsky formulation was used for horizontal eddy viscosity, while k–( formulation was used for vertical turbulent closure model [3, 23]. The horizontal

Environ Model Assess (2006) 11:315–332

non-dimensional calibration parameter was set to 0.4. The eddy diffusivities for temperature and salinity were determined as proportional to the eddy viscosity. The non-dimensional proportionality coefficients were taken as 0.1. Wind stress was calculated from the quadratic law with the constant wind drag coefficient Cw = 0.0016. Heat exchange between the atmosphere and the sea was calculated on the basis of sensible and latent heat flux and the net short- and long-wave radiation. No heat and salt exchange through the sea bottom occurs. The lateral boundary conditions involve no slip for velocity and insulation for temperature and salinity on side-walls. A quadratic drag law was used for bottom stress. The effect of external sources or sinks (rivers) was included in a model. 2.2. Ecological model The ecological model includes 11 interdependent state variables: phytoplankton carbon, nitrogen and phosphorus, chlorophyll a (Chl a), zooplankton, detritus carbon, nitrogen and phosphorus, inorganic nitrogen, inorganic phosphorus and dissolved oxygen (DO). In addition to the state variables, a number of derived variables including TN and TP concentrations are calculated. The model implements two phytoplankton groups: diatoms and green algae. They have different growth rates and temperature dependencies for growth. These two groups appear in consecutive order in the model. First, diatoms are simulated. Then, on the 130th day of the year, the dominance is shifted to green algae. The ecological model describes the relation between available inorganic nutrients (nitrogen and phosphorus) and the following phytoplankton growth. The nutrient supply depends on the land-based load and the transport into the area through model open boundaries. The nutrient dynamics in phytoplankton are expressed by internal pools of nitrogen and phosphorus; therefore, the growth can take place even though the nutrient concentrations in the surrounding water are very low [18]. Phytoplankton biomass decreases due to zooplankton grazing, sedimentation and natural mortality. Both phytoplankton and zooplankton extinction as well as zooplankton excretion all lead to the formation of detritus. The bulk of detritus is degraded to inorganic nutrients in surface waters and becomes available for phytoplankton production. The rest of detritus continues to settle to the deeper water layers and eventually to the bottom, where it is either remineralized or buried in the sediment.


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Figure 1 The Baltic Sea area (a) and the model domain covering central and eastern parts of the Gulf of Finland (b). Limits of Narva Bay and the location of monitoring station N12 are shown. Small arrow points at Narva River mouth

Chlorophyll a simulation is included in the model. Chl a production, settling and death rates are closely related to corresponding rates for phytoplankton. However, phytoplankton carbon to Chl a ratio varies during the year depending on the nutritional status of phytoplankton and on the light adaptation. Ecological model includes simulation of DO mass balance. Oxygen budget is regulated by a number of ecological processes including production of phytoplankton, consumption by mineralization and respiration. At the water surface, oxygen exchange between water and air takes place. A brief mathematical description of the ecological model is presented in Appendix. Lists of model variables and functions (table 1) and parameter values

(table 2) are also given. Further mathematical formulation and description of the model can be found in [6] and [8].

3. Model set-up The modelling domain covers central and eastern Gulf of Finland (figure 1b). Western open boundary of the model was set at about the longitude of Tallinn and Helsinki. The set-up applies a 1,500 1,500 m horizontal grid based on the Universal Transverse Mercator coordinate system. Vertical resolution of the model is 2 m. The set-up specifications make it possible to use a model time step of 200 s without

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Table 1 Variables and functions of the model. Notation

Description

PC PN PP Chl ZC DC DN DP IN IP DO rd f(I) IK I T f1(T) f2(T)

Phytoplankton carbon Phytoplankton nitrogen Phytoplankton phosphorus Chlorophyll a Zooplankton carbon Detritus carbon Detritus nitrogen Detritus phosphorus Inorganic nitrogen Inorganic phosphorus Dissolved oxygen Relative day length Light function for phytoplankton growth Light saturation Actual irradiance Water temperature Temperature function for phytoplankton growth Temperature function for grazing rate by zooplankton Temperature dependence function for detritus mineralization Temperature function for mineralization in sediments Temperature function for carbon mineralization in sediment Nutrient dependence function for phytoplankton growth Nutrient dependence function for phytoplankton cease Nitrogen dependence function Phosphorus dependence function Nutrient dependence function for chlorophyll a production Phytoplankton dependence function for grazing Oxygen dependence function for zooplankton grazing Oxygen dependence function for detritus mineralization Oxygen dependence function for mineralization in sediments Oxygen saturation concentration Salinity

f3(T) f4(T) f5(T) f1(N,P) f2(N,P) f(N) f(P) fchl(N) f(PC) f(DO) f1(DO) f2(DO) CS S

stability problems. The modelling period from 1 April to 1 October 2001 was chosen, which spans the biologically active season. Land-based freshwater, inorganic phosphorus and inorganic nitrogen sources consist of river discharge from major rivers of the Gulf of Finland and some smaller Estonian rivers. The river input data from Russia and Finland (Neva, Koskenkylanjoki, Kumijoki, Ma¨ ntsa¨ lanjoki, Porvoonjoki, Virojoki, Vantaa,

Mustionjoki) were compiled relying on the database of a monthly time resolution [24]. The longterm monthly mean values were calculated as the sample means for each month using all available data for a particular source and month over the period 1970–1990. Data of Estonian rivers (Narva, Pu¨hajo˜gi, Purtse, Kunda, Seljajo˜gi, Loobu, Valgejo˜gi, Pudisoo, Ja¨gala) were compiled from the observations in 2001. In total, the model set-up includes 17 rivers. 3.1. Hydrodynamic parameters The initial temperature and salinity fields for 1 April 2001 were prepared based on a very limited number of TS casts. In total, 10 CTD profiles collected by Estonian Marine Institute and four stations with TS values at standard depth provided by Finnish Environmental Institute were available. The measurements are distributed unevenly over the model domain. Narva Bay, Tallinn Bay and Finnish coastal area are covered with data, while measurements are absent from the central and eastern Gulf of Finland. Besides, the temperature and salinity data were collected on 24 April 2001, while the model simulation started on 1 April 2001. Air–sea heat exchange is rather intensive in spring, so that the temperature of the surface layer of the Gulf of Finland changes relatively rapidly, which may result in an overestimated surface layer temperature. Due to very limited number of available measurements, the initial temperature distribution was assumed horizontally uniform. Vertical temperature stratification was prepared by averaging over 10 measured temperature profiles. Longitudinal salinity drops from about 6 psu in the central Gulf of Finland to 0 psu in Neva Bay. Therefore, initial salinity profiles were resampled to the model vertical grid. Then, the salinity values were horizontally interpolated to a model grid using bilinear interpolation method. Air pressure, air temperature and wind fields were taken from HIRLAM at 3-h intervals. Prescribed sea level, temperature and salinity distributions were applied at the open boundary. The modelled sea level between Tallinn and Helsinki was used to force the water exchange through the open boundary. Initial time series consisted of sea-level values at five locations separated by 16,668 m and at 4-h interval. The values were interpolated to the model grid. The modelled sea level compared with sea-level data from Helsinki mareograph with reasonable accuracy, except for the mean (not shown). The modelled sea level was 46 cm higher in average. This value was subtracted from modelled sea level, and mean sea level was set to zero in the model.


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Table 2 Parameter values. Parameter

Description

Unit

Value

2

Maximum growth coefficient at 20-C

dayj1

FC ! Di Dg

Correction factor for dark reaction Light saturation intensity for algae at 20-C Temperature parameter for light saturation Temperature coefficient for phytoplankton growth

n.u. E/m2/day n.u. n.u.

PNmin PNmax PPmin PPmax KC 2d 2s Us Vkn Vkp Chlmin Chlmax

Minimum internal nitrogen content Maximum internal nitrogen content in algae Minimum internal phosphorus content in algae Maximum internal phosphorus content in algae Half-saturation constant for phosphorus in phytoplankton Maximum death rate of phytoplankton Sedimentation rate of phytoplankton, h < 2 m Sedimentation velocity of phytoplankton, h > 2 m The uptake rate constant for nitrogen The uptake rate constant for phosphorus Coefficient for minimum chlorophyll a production Coefficient for maximum chlorophyll a production in the absence of nutrient limitation Growth efficiency parameter for zooplankton Maximum grazing rate constant at 20-C Temperature coefficient for grazing rate 0th order grazing rate dependency on phytoplankton biomass 1st order grazing rate dependency on phytoplankton biomass Half-saturation concentration for DO, zooplankton grazing Proportionality constant for zooplankton respiration Death rate constant important at concentrations below 1 g/m3 Death rate constant important at high concentrations Fraction of dead phytoplankton undergoing immediate mineralization Settling rate of detritus at low water depth Sedimentation velocity for detritus Maximum detritus mineralization rate at 20-C Temperature coefficient for detritus mineralization Half-saturation concentration for DO, detritus mineralization Proportionality factor for nitrogen mineralization in sediment at 20-C Proportionality factor for phosphorus mineralization in sediment at 20-C Temperature coefficient for mineralization of sediment Temperature coefficient for carbon mineralization rate in sediment Nitrogen release rate under anoxic conditions Phosphorus release rate under anoxic conditions Half-saturation concentration for DO, sediment mineralization Proportionality factor at 20-C (oxidized condition) Nitrogen content of zooplankton Phosphorus content of zooplankton Oxygen-to-carbon ratio at production Reaeration rate

gN/gC gN/gC gP/gC gP/gC gP/gC dayj1 dayj1 m dayj1 gN/gC/day gP/gC/day (E/m2/day)j1 (E/m2/day)j1

0.8 (diatoms) 1.4 (green algae) 1.3 15 1.04 1.2 (diatoms) 1.07 (green algae) 0.07 0.17 0.002 0.03 0.2 0.005 0.1 0.1 1 0.5 0.04 1.1

gC/gC dayj1 n.u. n.u. m3/g g/m3 n.u. dayj1 {dayj1I(g/m3)j1} n.u. dayj1 m dayj1 dayj1 n.u. g/m3 n.u. n.u. n.u. n.u. g/m2 dayj1 g/m2 dayj1 g/m3 n.u. gN/gC gP/gC gO2/gC dayj1

0.28 1.5 1.05 3 25 2 0.3 0.05 6 0.5 0.2 0.2 0.015 1.11 2 1 1 1.01 1.1 0.01 0.01 2 1 0.07 0.002 3.5 1.5

VC 2Z DZ k1 k2 MDO KR kd1 kd2 VM 2l Ud 2m DD MDO1 KSN KSP DM DC NREL PREL MDO2 KMSC VZN VZP VO KRA

Temperature and salinity distributions at the open boundary were prepared based on three or four measured temperature and salinity profiles. The measurements were performed once per month in April, May and September and twice per month in June, July

and August. The measured values were interpolated onto a model grid on the open boundary. Examples of temperature and salinity distributions on the open boundary in April and September are presented in figure 2. The salinity distributions (figure 2b for April

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Figure 2 Temperature and salinity distributions at the model open boundary. Temperature (a) and salinity (b) in April, temperature (c) and salinity (d) in September

and figure 2d for September) show high salinity at the Estonian coast and low salinity at the Finnish coast in accordance to prevailing cyclonic water circulation in the Gulf of Finland [1, 19]. Seasonal thermocline is absent in April (figure 2a). In general, water column is well mixed down to the permanent halocline in the open sea, and inverse temperature layer extends to the bottom. Incline of the isotherms towards Estonian coast coincides with the shape of the isohalines

indicating inflow of more saline and warmer deep water to the Gulf of Finland. Some indication of the warming up in the shallow coastal area is seen at the Estonian coast. Temperature distribution in September (figure 2c) shows that water column was warmed up during summer. The upper layer temperature is still high. Well-defined upper mixed layer and seasonal thermocline is absent in interpolated distribution due to too few measurements.


3.2. Ecological parameters The initial concentrations of carbon, Chl a and zooplankton were derived from measurements. A very limited amount of data allowed using only homogeneous concentrations for those variables. The initial concentrations of 0.2 mg/l were prescribed for phytoplankton carbon and 0.01 mg/l for Chl a. Thus, initial phytoplankton C/Chl a ratio is set to 20. Usually, this ratio varies between 22 and 154. The species composition, extent and variability of light adaptation, age and nutritional state of the cells all affect these ratios [27, 28]. However, somewhat smaller value was selected to fit the measurement data. The growth rates of 0.8 and 1.4 dayj1 were selected for diatoms and green algae, respectively. Respective temperature dependencies for growth were 1.2 and 1.07. Initial concentration of zooplankton was 0.01 mg/l. The initial distribution of detritus carbon, nitrogen and phosphorus, inorganic nitrogen and phosphorus and DO were prepared based on a limited amount of measurement data. The data were interpolated onto the model grid using objective analysis. The boundary conditions for ecological state variables were prepared based on data from the same measurement sites as temperature and salinity, except that water samples were collected at standard depth. All the data were interpolated onto the model grid at the open boundary. Only distributions of TN and TP for April are presented in figure 3a and b and for September in figure 3c and d, since these parameters are discussed in detail in the following sections. Distributions at the upper 0–10 m were compared with the 20-year monthly average, minimum and maximum values at station F3 in the central part of the Gulf of Finland, close to the model open boundary [10]. In April, TN distribution showed higher concentrations in the upper 10-m layer (around 0.3 mg/l) due to already initialized phytoplankton bloom. However, these values are close to the long-term minimum in this area. Concentrations were somewhat lower in the interstitial layers, where spatial variability was also small. Near-bottom TN content was slightly higher near the Estonian coast, which received nitrogen with inflowing water. Concentration of TP was 0.02–0.03 mg/l in the upper 10-m layer in April. These values are between the 20-year minimum and average values for the area [10]. Phosphorus concentration increased towards the bottom. In deeper layers, TP concentration is higher near the Finnish coast than at the Estonian coast. This indicates that near-bottom phosphorus is mainly of local origin and not brought to the gulf with

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the inflowing water from the western Gulf of Finland. In September, TN distribution showed high concentrations in the upper layer of about 10–15 m and close to the bottom. There, the TN concentrations were similar to the values in April. TN concentrations in the upper layer were between long-term average and maximum values [10]. In September, concentration of TP was higher at the Estonian coast than at the Finnish coast, which is opposite to the distribution of phosphorus in April. In the upper layer (0–10 m), TP concentration of 0.02 mg/l was between the 20-year average and maximum value for this month [10]. In general, the concentrations of TN and TP in the upper layer of the open boundary were lower than the average 20-year values in April, but higher in September.

4. Model validation 4.1. Hydrodynamic model Hydrodynamic factors may essentially affect spatiotemporal distribution of biochemical fields mainly through transport and mixing processes. Therefore, the performance of hydrodynamic model was checked first. For that, modelled currents were compared with measured ones at a single point in Narva Bay. The measurements consisted of a time series of current meter data at 7-m depth in about 40-m water. The location of mooring station (59-32.80 N; 27-40.50 E) was selected relatively far from the coast and river mouth, so that minor direct effect of Narva River outflow was expected. Time series of currents from 18 to 31 July were used for comparison. The current speed and direction were recorded at 10-min interval. Hourly mean east and north velocity components were calculated from raw data to remove short-term fluctuations. Also, simulated currents were stored at hourly interval. Inertial oscillations dominate in measured and simulated currents (figure 4a and b). The model has reproduced the amplitude reasonably well. In certain periods, even the phases coincide, e.g., from 28 to 31 July. Also, low-frequency currents compare rather well, which is essential for horizontal transport of biochemical parameters. Modelled east velocity component (alongshore) matches measured values better than north velocity component (cross-shore). Both the simulated and measured east velocity components show mean flow to the east, but simulated north velocity component indicates weak southward flow, while the measurements show weak northward flow. Hence, on average, the modelled transport is to the

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Figure 3 Total nitrogen and phosphorus distributions at the model open boundary. Total nitrogen (a) and total phosphorus (b) in April; total nitrogen (c) and total phosphorus (d) in September

east and slightly onshore, while measured currents are to the east and offshore. A snapshot of modelled vertical salinity and temperature stratification in comparison to measured profiles is shown in figure 4c and d, respectively. Temperature and salinity profiles were measured close to the mooring station on 25 July. Measurements represent a single temperature and salinity realization at particular location. Daily mean temperature and salinity profiles calculated from the model results are presented. In general, the stratification is weaker in the model than

measured. Upper mixed layer is thicker, salinity higher and temperature lower in the model than in the measurements. Underestimated vertical stratification may cause overestimated vertical heat, salt and nutrient flux in the model. Simulated lower layer salinity and temperature match better with the measurements. 4.2. Ecological model The model results were compared with measurements from a monitoring station N12 (59-38.0N, 27-26.9E) in


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the central Narva Bay. Phytoplankton biomass integrated over upper 10-m layer, DO, TN and TP concentrations on the water surface and in the benthic layer were compared. Several model runs with different set of parameters were performed. Only the results

from the best fit between the model results and measured data are presented. Modelled phytoplankton showed reasonable seasonal behaviour (figure 5a). The spring bloom started in early April and reached its peak at the end of the

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Figure 5 Modeled (line) and observed (dots) phytoplankton biomass integrated over upper 10-m layer (a), surface total nitrogen (b), near-bottom total nitrogen (c), surface total phosphorus (d), near-bottom total phosphorus (e), surface dissolved oxygen (f) and nearbottom dissolved oxygen (g) at monitoring station N12 in Narva Bay

month, when concentrations were slightly higher than 0.8 mg/l. There is the second peak in the middle of May caused by horizontal transport of phytoplankton from the coastal area. Biomass decreased to minimum in summer with concentrations less than 0.2 mg/l. There is an evidence of small increase of biomass in September. Measurements of phytoplankton biomass with reasonable temporal resolution are lacking, especially during spring. The only measurement does not match the modelled values possibly due to ice cover in Narva Bay until the end of April. Modelled summer values and measurements compare reasonably well. Simulated surface TN concentration showed decreasing trend from April (about 0.35 mg/l) until August (about 0.1 mg/l). This can be explained by sedimentation of phytoplankton during and after the

spring bloom, when inorganic nitrogen incorporated into phytoplankton cells is transported to the deeper layers (figure 5b). The measurements showed rather uniform TN concentration of about 0.35 mg/l. The model does not take into account dissolved organic matter in seawater, which is important in the estuarine areas, where part of it can be of riverine origin [23]. Modelled TN consists of the sum of phytoplankton nitrogen, detrital nitrogen and inorganic nitrogen. Near the bottom, model results match well with the measurements except for a single point in midSeptember (figure 5c). Model did not show any temporal increase in near-bottom TN concentrations since organic matter is remineralized before it reaches the bottom. On average, surface concentration of TP is underestimated about 0.01 mg/l by the model compared to


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measurements (figure 5d). The concentrations decreased from 0.025 mg/l in April to a level below 0.02 mg/l in summer. The decrease in spring coincides with the onset of spring bloom, when inorganic phosphorus is taken up by phytoplankton and transported to deeper water layers by sedimentation. There is a

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slight increase in concentration starting in late August. In the benthic layer, measured values were considerably higher than modelled except in spring (figure 5e). The source of discrepancy is an underestimation of inorganic phosphorus concentration in near-bottom layer by the model. Phosphorus remineralization rate

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is inversely proportional to near-bottom oxygen concentration [14]. Modelled oxygen concentration in near-bottom layer is overestimated as compared to measurements, which results in too low phosphorus remineralization rate. It is also possible for the estuaries that higher phosphorus values exist due to resuspension [11]. DO concentrations were reasonably simulated in the surface layer with higher values in spring that correspond to enhanced oxygen production by blooming phytoplankton (figure 5f). As the bloom started to cease, oxygen concentration began to decrease until midsummer, when equilibrium state was reached. There is slight increase of DO in September that corresponds to small increase in phytoplankton biomass. Modelled values were higher than measured by approximately 1 mg/l during summer and autumn. Model overestimated DO concentrations in the benthic layer (figure 5g). The reason is too low detritus remineralization rate specified in the model, so that more oxygen is left near the bottom than detected by measurements.

5. Water quality assessment Surface layer phytoplankton biomass, surface TN and TP and near-bottom oxygen concentrations were analysed in order to assess water quality in Narva Bay. Time series of spatially mean values and standard deviations (SDs) of selected parameters were calculated for the whole Narva Bay. From measurements, it is common to determine phytoplankton, TN and TP concentrations from mixed water samples taken at 0-, 5- and 10-m depth. For consistency with the sampling methods, mean concentrations of these parameters were calculated from model layers corresponding to 0-, 5- and 10-m depth. Near-bottom DO content is determined from water samples taken at 1 m above seabed. Usually, sampling stations where DO concentrations are measured are not located in the shallow part of the sea. Therefore, mean near-bottom oxygen concentrations were calculated over the area deeper than 20 m. Spatial mean phytoplankton biomass reached about 0.85 mg/l in Narva Bay during spring bloom (figure 6a). Standard deviation was about 0.2–0.25 mg/l during intensive bloom phase. Relatively low standard deviation to mean biomass ratio shows that spring bloom is rather uniform in Narva Bay. Taking into account mean phytoplankton biomass and standard deviation, we can estimate that characteristic phytoplankton biomass ranged from 0.6 to 1.1 mg/l. Comparison of mean concentrations with modelled values at station


N12 shows that in terms of intensity and timing, the latter represents spring bloom in Narva Bay rather well. However, values from a point location can be influenced by short-term variability caused by advective transport, e.g., the second peak at N12 in the middle of May. Termination of phytoplankton bloom led to simultaneous decrease of spatial variability of phytoplankton biomass in Narva Bay. Mean biomass was about 0.2 mg/l in summer. A weak summer bloom with increased spatial variability can be identified in the record. It started after the first week of July and disappeared by the end of the month. Characteristic phytoplankton biomass ranged from 0.1 to 0.4 mg/l during summer bloom. Autumn phytoplankton bloom with increased spatial variability started at the end of August and lasted for the rest of modelling period. The range of phytoplankton biomass was 0.4 T 0.2 mg/l. High standard deviation of phytoplankton biomass compared to mean values during summer and autumn blooms shows that phytoplankton concentrations were rather variable in Narva Bay. The modelled and measured biomass values at N12 belong to the lower part of characteristic phytoplankton biomass range. Mean TN concentration showed continuous decrease from about 0.3 mg/l in the first half of April to about 0.12 mg/l in the beginning of August (figure 6b). Afterwards, concentration started to increase slightly and reached values of about 0.15 mg/l by the end of the modelling period. SD was about 0.05 mg/l during spring bloom and about 0.03 mg/l during the rest of the period. In comparison with data, the model underestimated TN concentration after spring bloom. Average TN concentration in the upper layer calculated from the measurements was 0.32 mg/l in summer and early autumn, which gives the offset of about 0.2 mg/l. Taking into account average TN concentration from measurements, model offset and standard deviation, the characteristic values of TN for Narva Bay range from 0.25 to 0.35 mg/l (modelled mean T SD) in spring and from 0.29 to 0.35 (mean from data T SD from model) in summer and early autumn. Comparing the 20-year (1979–1999) average data for the central Gulf of Finland [10] and model results shows that the latter values were close to the long-term minimum. It must be noted that 20-year data had broad variability range (up to 0.28 mg/l for TN and up to 0.03 mg/l for TP). Measured values were slightly lower than the longterm average in spring and well matched with average summer and autumn values. Once again, modelled TN concentrations at station N12 were comparable to spatial mean in spring and lower than mean for the rest of the period.


327

1.2

a) Mean phytoplankton

1

mg/l

0.8 0.6 0.4 0.2 0 04/01

05/01

06/01

07/01

08/01

09/01

10/01

09/01

10/01

0.35

mg/l

0.3

b) Mean total nitrogen

0.25 0.2 0.15 0.1 0.05 04/01

05/01

06/01

07/01

08/01

0.04

c) Mean total phosphorus

0.035

mg/l

0.03 0.025 0.02 0.015 0.01 04/01

05/01

06/01

07/01

08/01

09/01

10/01

14

d) Mean dissolved oxygen

12

mg/l

10 8 6 4 2 04/01

05/01

06/01

07/01 Date

08/01

09/01

10/01

Figure 6 Time series of phytoplankton (a), total nitrogen (b), total phosphorus (c) and dissolved oxygen (d) concentrations in Narva Bay: spatial mean (bold), mean T SD (dashed), modeled (thin) and measured (dots) at monitoring station N12

Spatial mean TP concentration decreased steadily from about 0.025 mg/l in April to 0.02 mg/l in August, and then increased again to the spring values in September (figure 6c). Spatial variability of TP was high during the periods of increasing spatial mean TP

concentrations, which match the periods of phytoplankton blooms. During spring and summer blooms, the standard deviation of TP was about 0.005 mg/l and reached values close to 0.01 mg/l during autumn bloom. In general, characteristic TP concentrations range from

328

0.022 to 0.032 mg/l in spring then drop monotonically to values between 0.015 and 0.025 in the second half of summer and increase to 0.017–0.035 mg/l in early autumn. Compared to the 20-year mean TP concentrations for the central Gulf of Finland, the modelled values match well the long-term average, while some of the measurement data were slightly higher. The modelled TN concentrations at station N12 were comparable to spatial mean in spring, but lower than mean for the rest of the period. Spatial mean near-bottom oxygen concentration was relatively steady over the modelling period being about 7 mg/l on average (figure 6d). The SD of oxygen concentration was 2 mg/l on average. The mean concentration was higher than average before the onset of spring bloom and after the bloom when oxygen concentration raised to 9 mg/l. High oxygen concentrations were accompanied with higher spatial variability. During these periods, the standard deviation increased from 2 to 3 mg/l. The modelled oxygen concentrations at N12 match the spatial mean rather well. There were some events when oxygen concentration at N12 differed from spatial mean up to 2 mg/l. Both the mean near-bottom oxygen concentration and the values at station N12 were overestimated by the model. Model offset of 3 mg/l could be estimated. Taking into account the modelled mean oxygen concentration of about 7 mg/l, the offset and standard deviation, the characteristic oxygen content for the near-bottom layers of Narva Bay ranged from 2 to 6 mg/l. In that respect, it is possible that hypoxic condition can be reached in Narva Bay occasionally. Simultaneous behaviour of phytoplankton, TN, TP and oxygen concentrations indicates that after the spring-bloom period, increase and high level of phytoplankton concentration coincide with the periods of increasing and higher TN and TP concentrations. Usually, these periods match also with drop and lower levels of near-bottom oxygen concentrations in Narva Bay.

6. Conclusions A coupled 3D hydrodynamic–ecological model was used for the assessment of water quality in Narva Bay, the Gulf of Finland. Simulated and measured currents compared reasonably well over a 2-week period that was used for comparison. Ecological model validation was performed for phytoplankton biomass, total nitrogen and phosphorus and near-bottom oxygen concen-


trations relying on a limited number of measurements in the central Narva Bay. Model results and measurements showed reasonable comparison for all these parameters during spring bloom. After the spring bloom, simulated and measured phytoplankton biomass and total phosphorus matched satisfactory. Total nitrogen was not correctly simulated, being about 0.2 mg/l lower than measured. Also, the model overestimated near-bottom oxygen concentrations about 3 mg/l on average. Instead of single-point measurements, water quality in Narva Bay was assessed using time series of spatial means calculated over the predefined bay area. Three periods of different ranges of water quality parameters can be identified in Narva Bay. The spring-bloom period is characterized by high phytoplankton biomass between 0.6 and 1.1 mg/l and corresponding TN and TP concentrations of 0.25–0.35 and 0.022–0.032 mg/l, respectively. The next period is bloom termination from May to the end of June. Then, phytoplankton biomass and TP concentrations decreased to characteristic values in the range 0.1–0.3 and 0.015–0.025 mg/l, respectively. The TN concentrations remained similar to spring values (0.29–0.35 mg/l). Spatial variability of these parameters decreased during this period. The third period is autumn phytoplankton bloom (late August and September), which is characterized by higher mean values and spatial variability. Phytoplankton biomass ranged between 0.2 and 0.6 mg/l; TN and TP concentrations were in the range 0.29–0.35 and 0.017–0.035 mg/l, respectively. Comparison of the characteristic TN and TP concentrations with corresponding values presented for the classification of Estonian coastal waters [16] shows that Narva Bay water belongs to a good water class (TN = 0.28–0.42 mg/l, TP = 0.019–0.034 mg/l [16]). Near-bottom dissolved oxygen concentrations were rather steady (2–6 mg/l), not showing any drastic changes during the entire modelling period. Simultaneous analysis of modelling and measurements indicates that presence of hypoxic conditions is possible in deep areas of Narva Bay. Model results showed that monitoring station N12 could be considered as a representative station for assessment of ecological conditions in Narva Bay. However, some caution must be paid when water quality is assessed relying on data from a single location.

Acknowledgements This work was partially supported by Estonian Science Foundation research grant No. 5596 and Danish EPA project EISEMM. The authors wish to thank Anja Friis-Christensen from Danish Hydraulic Institute for her assistance in ecological modelling.


Appendix. Mathematical description of the ecological model

329

where

f2 ðN; PÞ ¼ 1=2 fPNmax =ðPN =PC Þ þ PPmax =ðPP =PC Þg Ecological model equations Phytoplankton death: 1. Phytoplankton carbon mass balance: dPC ¼ prPC grPC sePC þ sePn1 C dePC dt Phytoplankton production:

prPC ¼ f ðI Þ f1 ðT Þ f1 ðN; PÞ FC rd;

dePC ¼ d f2 ðN; PÞ PC : 2. Mass balance of phytoplankton nitrogen: dPN n1 ¼ unPN grPN sePN þ sePN dePN dt Uptake of inorganic nitrogen:

where f ðI Þ ¼

2 2 Vkn IN PC unPN ¼ min 4 max 4 Mineralization þ external load prPC PNmax

I=IK I < IK 1 I IK ðT20Þ

IK ¼ Q i

;

f1 ðN; PÞ ¼

1 f ðN Þ

Vkn IN PC under non limiting conditions: prPC PNmax

unPN ¼ min

f1 ðT Þ ¼ QðgT20Þ ;

under limiting conditions;

Grazing on phytoplankton nitrogen:

2 ; þ f ð1PÞ

grPN ¼ grPC ðPN =PC Þ: Sedimentation of phytoplankton nitrogen:

f ðN Þ ¼

f ðPÞ ¼

PN =PC PNmin ; PNmax PNmin

ðPP =PC PPmin Þ ðKC þ PP =PC PPmin Þ

sePN ¼ sePC ðPN =PC Þ:

Death of phytoplankton nitrogen: ðPPmax PPmin Þ : ðKC þ PPmax PPmin Þ

3. Mass balance of phytoplankton phosphorus:

Grazing on phytoplankton:

f ðP1 Þ f ðD ÞZ

grPC ¼ z f2 ðT Þ

O

C;

C

where f 2 ðT Þ ¼

dePN ¼ dePC ðPN =PC Þ:

dPP n1 ¼ unPP grPP sePP þ sePP dePP dt Uptake of inorganic phosphorus:

QðzT20Þ ;

f ðPC Þ ¼ 1 þ eðk1 k2 PC Þ ;

2 2 Vkp IP PC unPP ¼ min 4 max 4 Mineralization þ external prPC PPmax

f ðD O Þ ¼

unPP ¼ min

D2O D2O þ MDO

:

load under limiting conditions;

Vkp IP PC under nonlimiting conditions: prPC PPmax

Grazing on phytoplankton phosphorus: Sedimentation of phytoplankton:

grPP ¼ grPC ðPP =PC Þ:

sePC ¼ s f2 ðN; PÞ PC in shallow water ðh < 2 mÞ;

sePC ¼ Us =h f2 ðN; PÞ PC in deep water ðh > 2 mÞ;

Sedimentation of phytoplankton phosphorus:

sePP ¼ sePC ðPP =PC Þ:

330


Death of phytoplankton phosphorus:

Sedimentation of detritus:

dePP ¼ dePC ðPP =PC Þ:

seDC ¼ l DC in shallow water ðh < 2 mÞ;

4. Chl a mass balance

seDC ¼ Ud =hDC in deep water ðh > 2 mÞ:

dChl ¼ prChl deChl seChl þ seChln1 dt Production of chlorophyll: prChl ¼ ðChlmin =IK Þexpðfchl ðN ÞÞprPC ;

Mineralization of detritus: reDC ¼ m f3 ðT Þf1 ðDO ÞDC ; where ðT20Þ

f3 ðT Þ ¼ QD

;

where fchl ðN Þ ¼ Chlmax fðPN =PC PNmin Þ=ðPNmax PNmin Þg: Death of chlorophyll: deChl ¼ ðdePC þ grPC ÞðChl=PC Þ: Sedimentation of chlorophyll: seChl ¼ sePC ðChl=PC Þ 5. Zooplankton mass balance dZC ¼ prZC deZC reZC ekZC dt Production of zooplankton: prZC ¼ VC grPC : Death of zooplankton: deZC ¼ kd1 ZC þ kd2 ZC2 : Zooplankton respiration: reZC ¼ KR grPC : Zooplankton excretion: ekZC ¼ grPC prZC reZC : 6. Detritus carbon mass balance dDC ¼ ð1 VM ÞdePC þ ekZC þ deZC seDC dt n1

þ seDC reDC

f1 ðDO Þ ¼ D2O

D2O þ MDO1 :

7. Mass balance of detritus nitrogen dDN ¼ ð1 VM ÞdePN þ ekZN þ deZN seDN dt n1

þ seDN reDN Excretion of zooplankton nitrogen: ekZN ¼ VZN ekZC : Input of nitrogen from dead zooplankton: deZN ¼ VZN deZC : Sedimentation of zooplankton nitrogen: seDN ¼ seDC DN =DC : 8. Mass balance of detritus phosphorus dDP ¼ ð1 VM ÞdePP þ ekZP þ deZP seDP dt n1

þ seDP reDP Excretion of zooplankton phosphorus: ekZP ¼ VZP ekZC : Input of phosphorus from dead zooplankton: deZP ¼ VZP deZC : Sedimentation of zooplankton phosphorus: seDP ¼ seDC DP =DC :


331

9. Mass balance of inorganic nitrogen

11. Dissolved oxygen mass balance:

dIN ¼ reDN þ reZN þ reSN þ VM dePN unPN dt

dDO ¼ odPC odZC odDC odSC Vm Vo dt dePC þ rear

Respiration by zooplankton: reZN ¼ VZN reZC : Detritus remineralization: reDN ¼ reDC DN =DC : Remineralization from sediment: reSN ¼ KSN f4 ðT Þf2 ðDO Þ ðseDN þ sePN Þ under oxygenated conditions; reSN ¼ NREL =h under anoxic conditions;

Oxygen production: odPC ¼ Vo prPC : Oxygen consumption due to respiration: odZC ¼ Vo reZC Oxygen consumption due to mineralization: odDC ¼ Vo reDC Sediment oxygen demand: odSC ¼ Vo reSC ;

where ðT20Þ

f4 ðT Þ ¼ QM

;

f2 ðDO Þ ¼ DO =ðDO þ MDO2 Þ: 10. Mass balance of inorganic phosphorus dIP ¼ reDP þ reZP þ reSP þ VM dePP unPP dt Respiration by zooplankton: reZP ¼ VZP reZC :

Remineralization of carbon in sediments: reSC ¼ KMSC f5 ðT Þf2 ðDO ÞðsePC þ seDC Þ; where ðT20Þ

f5 ðT Þ ¼ QC

:

Reaeration: rear ¼ KRA ðCS DO Þ; where

Detritus remineralization: reDP ¼ reDC DP =DC :

CS ¼ 14:652 0:0841S þ T f0:00256S 0:41022 þ T ð0:007991 0:0000374S 0:000077774TÞg

Remineralization from sediment: reSP ¼ KSP f4 ðT Þf2 ðDO Þ

Note: Index (n j 1) denotes input from the above layer.

ðseDP þ sePP Þ under oxygenated conditions; reSP ¼ PREL =h under anoxic conditions; where ðT20Þ

f4 ðT Þ ¼ QM

;

f2 ðDO Þ ¼ DO =ðDO þ MDO2 Þ:

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