New Slovenia - WIT Press · 2014. 5. 13. · account, while thermohaline forcing is usually...

Hydrodynamic and pollutant transport models

applied to coastal seas

R.Rajar, A. Sirca

University of Ljubljana, Faculty of Civil Engineering and Geodesy,

Slovenia

Abstract

Role of mathematical models and of field measurements in solving water quality problems isdescribed. When simulating phenomena in different space and time scales there are someimportant differences, regarding forcing factors, time averaging and measuring technique. Twocase studies are presented: (a) Modelling of mercury cycling in Trieste Bay. 2D Hydrodynamicmodel Is used together with a sedimentation model and with a mercury mass transport model.Simulation of several bio-chemical processes as methylation, demethylation, reduction andsedimentation is included, (b) Long term simulations of transport-dispersion of radioactivepollutants in Japan sea is described. Thermohaline forcing, wind forcing, and inflow currents aretaken into account. Results are in good agreement with measurements.

1 Modelling in Different Space and Time Scales

It is well known, that mathematical models are an extremely efficient tool when savingenvironmental problems related to water quality. The models aim at understanding the basicprocesses involved and the response of the system to different forcing. With simulations ofdifferent possible solutions the models can finally be used as a management tool.

Cheng and Smith [1] state that for a fixed cost, the computing power increases five to tentimes every five years. This promises that in the near future sophisticated 2D and 3D waterquality models will be widely used on personal computers like today zero- and one-dimensionalmodels are used.

Water quality modelling is needed in environments with very different space scales fromsmall bays and marinas with dimensions of several hundreds of meters, through larger coastalseas to global ocean areas of the dimensions of several thousands of kilometers. Time scale isusually connected with space scale, although it also depends on the type of pollutant and itsbiological and chemical growth/decay characteristics. Fig 1. presents the space time diagramwith six cases of model applications and Table I. gives some details of the cases.

All these six cases, so different in their space and time scales, were simulated by thesame model. This model named LMT3D, developed at the University of Ljubljana, has alreadybeen described, (Rajar [6], Rajar et al [8]) so only some basic characteristics are given here.

Transactions on Ecology and the Environment vol 9, © 1996 WIT Press, www.witpress.com, ISSN 1743-3541

252 Environmental Problems in Coastal Regions

Global

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Satelite imageryTracer dipersion

RA Tracers Flow meters, Floaters

0.01 0.1 1 10 100VELOCITY cm/s

Figure 1. a) Space-Time diagram with ranges, covered marine models (adapted fromRobinson 1990). b) Measuring technique, used for velocity measurements in different ranges

The model is composed from hydrodynamic (HD) sub-model and mass-transport (MT)sub-model. For some cases also sedimentation sub-model has been used and for simulation ofmercury cycling (Case 4, Section 2) a special mass-transport model has been developed.

The HD sub-model is three-dimensional, non linear and baroclinic. For problems wheredepth-averaging of all quantities is acceptable a 2D version of the model (LMT2D) is used. In themass-transport sub-model, the advection-diffusion equation for any pollutant, nutrient or otherquantity is used. In some cases the quantity is taken as conservative, in others several biologicalor chemical decay/growth processes are taken into account.

The HD sub-model is based on the finite volume (FV) numerical method. The MT sub-model is developed in two forms: for transport-dispersion of pollutants over large regions thesame FV numerical method is applied. But for simulation of pollutant dispersion from pointsources, a sub-model applying Lagrangian based Particle tracking method (PTM) was developed(Sirca, [11]). This method is free from numerical diffusion (the FV methods always suffer fromthis disadvantage) and is very well applicable for the simulation of decay/growth processes as acertain amount of the contaminant can be "attached" to each particle and the time developmentof its "mass" is simulated.


Environmental Problems in Coastal Regions 253

Case123456

RegionMarina KoperPiran BayTrieste BayTrieste BayN. AdriaticJapan Sea

Dimensions300x700 m4x5 km20x25 km20x25 km130x1 80km900x1 800km

PurposeWater exchangeHD circulationOil SpillMercury cyclingPollutant TDTD of RA pollutants

Forcing FactorsTide, WindWmd.River inflowWind, real timeWmd.an.meanWindThermoh., Wind, 0

Table I. Cases, simulated by the model LMT3D

There are some important differences in the applications of the model for different space-time scales:

/.Forcing factors. In the modelling process it is first necessary to determine the relativeimportance of different forcing factors (FF) in order to take into account the most important ones.The FF differ depending on the space-time scale.

For small bays, harbours and marinas (e.g. Case 1) tide is usually the most important FF,wind can be of the same or smaller importance. Sometimes river inflow must also be taken intoaccount, while thermohaline forcing is usually negligible. For coastal seas wind is usually thestrongest FF (Cases 2,3,4 and 5), tide can have an important influence, and in some cases alsothermohaline forcing. For large oceans thermohaline forcing is prevalent, while wind is usuallythe second by importance (Case 6, Sec. 3)

2. Time averaging Short term processes in small bays or marinas, which must besimulated over several tidal cycles or several days can be executed in "real time" simulations.For long term processes in large scale environment this cannot be done due to extremely longcomputational time. There are several approximations possible.

The most commonly used methodology is to determine first quasy steady HD velocityfields either for monthly averages or for seasonal or annual mean. The mass transport sub-model is then used to simulate the relevant processes over several months, seasons or years,where the mentioned velocity fields are taken into account. In the case of simulation of transport-dispersion of radioactive pollutants in the Japan Sea (Case 6) the simulations had to be doneover several decades. We have first used thermohaline and wind forcing for each of the fourseasons of the year. The computations were carried out so long as to reach a quasy steadystate. So only four HD simulations had to be made. With four repeating velocity fields duringeach year we have executed simulation of RA dispersion over 10 and 20 years with MT sub-model, using the Particle tracking method. As simulations with MT sub-model are for an order ofmagnitude faster as HD simulations, this is usually quite feasible.

3. Measuring technique. As the models should be verified and calibrated, fieldmeasurements are indispensable. HD models can be calibrated directly on the basis ofmeasured velocities, but as most HD models include also a transport-dispersion module, theycan also be calibrated indirectly via measured concentrations of different parameters such asinduced tracers, chlorophyll, total suspended matter, radionuclides, etc. The tracer should be asconservative as possible, otherwise an additional unknown - the decay constant - is introducedinto the calibration.

This second technique is especially useful in cases with large space and time scaleswhere the velocities are usually very small and it is impossible to measure them directly. Fig. 1b.shows some of the most common measuring techniques for phenomena of different space andtime scales as a function of the magnitude of the measured velocity. As the bottom circulation indeep seas and oceans is very slow, measurements of velocities are usually very difficult. In suchcases a measuring technique with chemical and radioactive tracer can be used (Tsunogai et al.,[14] where the distribution of the tracer shows the age of different water masses (which can be



several decades or even several centuries). This technique, combined with modelling, can giveinformation on the circulation of water masses and contaminants.

In Case 1 the model was used to determine efficient water exchange in a small marina(Rajar, Sajovic [9]). The 3D oil-spill model for the Trieste bay has been presented at COASTAL95 (Rajar et al [8]). Cases 4 and 6 are presented more in detail in the following.

2 Case Study 4: Modelling of Mercury Cycling in Trieste Bay.

Trieste bay is a shallow, up to 25 m deep bay in the East side of the Northernmost part of theAdriatic. Its dimensions are 25 by 25 km approx. The only important inflow is the Soca river,discharging its waters into the NE comer of the bay. Its mean annual discharge is 150 m3/s.

During the last decade measurements in Trieste bay have shown increasedconcentrations of mercury compounds in water and in marine organisms. Concentration ofdissolved mercury in water was about two times greater than in other regions of the Adriatic andconcentrations in marine organisms were several times greater than typical values in theMediterranean. In the sediments the concentrations are two or three orders of magnitude greaterthan in the middle and southern Adriatic. The source of such increased concentrations ofmercury can only be the mercury mine of Idrija, situated at the Idrijca river, a tributary of the Socariver. Although the mercury mine is presently being closed, there are a lot of mercurycontaminated sediments in Idrijca and Soca rivers and also in the sediments of Trieste bay.These sediments are still an important source of mercury contamination.

It is well known that mercury compounds, especially methyl-mercury (MeHg) are verytoxic. MeHg has bioacumulating properties and concentrations are especially elevated at the endof food chain, that is in predator fish, which is an important human food. Measuredconcentrations in fish were in some cases over the allowed limits prescribed by WHO (0.5 jag/g).As possible future deterioration of water quality in the Bay, with temporary anoxia, may causeincreased release of mercury from sediments, this may even worsen the present conditions.

All this dictated a three-year research project on modelling of mercury cycling in theTrieste Bay with the following main goals:

(1) To determine spatial distribution of mercury compounds in water, biota and sedimentsof the Bay. (2) Simulation of transport of mercury forms in the Bay with basic transformationprocesses. The aim of this item is better understanding of bio-chemical processes in present andfuture conditions. (3) To determine the importance of mercury contamination from the Soca river.

It would also be very valuable to give indications how to diminish mercury concentration inedible fish, but as not enough measurements are available in this research, this step will becarried out in the continuation of the project.

As the modelling of mercury processes demand a thorough calibration of many modelparameters, numerous measurements of mercury forms in water, suspended solids, planktonand in sediments are being performed during the project.

Model descriptionThe integrated model of mercury cycling is composed of three sub-models:

a. Hydrodynamic sub-model is a two-dimensional, depth-averaged model LMT2Ddeveloped at University of Ljubljana, FGG. Although a three-dimensional model has also beendeveloped, it was decided that relating to the available information on the complex mercuryprocesses and on the available measured data, the 3D model would be of too high andunnecessary order of complexity. But it is planned to extend the research to 3D modelling in thecontinuation of the project.



b. Sedimentation sub-model is a 2D commercial software developed at Danish HydraulicInstitute. It is the sc. Mud Transport (MT) module of the MIKE 21 HD model for simulation oftransport-dispersion and sedimentation of suspended load,

c. Mercury sub-model The model is called STATRIM (SJAtionary IRIeste bay Mercurymodel, Sirca [13]). In the first phase it allows only stationary simulations.

Figure 2. Conceptual diagram of mercury sub-model. Adapted from Harris [3]

Fig. 2 shows the basic processes simulated in the model. Two mercury compounds aresimulated: inorganic mercury- Hgll and methyl-mercury- MeHg. Each of them can be in threeforms: (1) dissolved in water, (2) participate (adsorbed to suspended particles) and (3) adsorbedto plankton. Two transport equations are used, one for the entire quantity of each mercurycompound:

ox, *oy ox. ox, aoy cy(1)

u and v are depth-averaged velocity components, h- depth, O depth averagedconcentration , K- dispersion coefficient (constant over space) and SRC is source/sink termexpressing transformations of mercury compounds.

Determination of distribution of the two mercury forms in three compartments (water,particles and plankton) is still possible with the sc. partitioning coefficients (F), defined as follows:

1 -plan

SUM(i) SUM(i) SUM(i)

with (2)

and (3)

index i represents either Hgll or MeHg, Cp*t and Cpi* are concentrations of inorganicparticles and of organic particles (plankton) in water with units (g/m3 d.w.). Kp«t(i) and Kpun(i) in(m3/g) are relations between concentration of the mercury compound in suspended matter or inplankton (ng/g d.w.) and the concentration of dissolved compound (ng/m3).

With the above relations it is possible to express the total amount of a compound in thewater column Hg«c.(i) with the concentration of the dissolved compound



(4)

This sub-model is in fact a combination of the transport-dispersion module of the modelLMT2D and a very simplified version of a mercury model of R. Harris (Harris [3]). As the latter isa compartment (zero-dimensional) model for modelling of mercury cycling in lakes, it assumestotal mixing of all quantities in the water body under question. The combination with 2D modelLMT2D enables to account for the spatial distribution of all the quantities and processes in theTrieste Bay. As the model was developed for fresh water lakes, some processes are different inmarine environment, which was taken into account.

The processes of mercury transformations are described with 8 equations, four for Hglland four for MeHg. Elementary mercury Hg(0) is not explicitly taken into account, it onlyrepresent intermediate non stable volatile form, which exits into the atmosphere.

One of the most important processes is sedimentation, which is for Hgll expressed by

AHglU = - v̂ • Hgn „, • F̂ (HgH) A,,,, (5)

where Aceii is surface of the computational cell. Intensity of sedimentation is expressed bymean settling velocity Vsett. This is one of important parameters of the model, its value wasobtained from measurements. Processes of methylation, demethylation and reduction areexpressed with equations of similar form as eq. (5) where the settling velocity is replaced byrespective factors which should be obtained by calibration.

Simulations. Case 4.1 is a simulation of the basic case for annual mean conditions onhydrodynamics, sedimentation and mercury inflow from the Soca River. Case 4.2 is ahypothetical case where mercury input from the Soca river is reduced to 10% of the presentquantity to find out the influence of mercury pollution from the river.

Hydrodynamics. The main aim of the study was to determine the time-mean, quasysteady-state fluxes and distribution of mercury in the Bay. Mercury is entering the Bay mainlyfrom the Soca river and partly from atmosphere. An extensive study was carried out to provethat steady-state hydrodynamic conditions can cause approximately the same transport-dispersion of mercury as the realistic, unsteady forcing during one or several years (Sirca andRajar, [12]). This is a typical example of previously mentioned problem of time-averaging forlong term ecological simulations. By HD simulations it had been determined that the main forcingfactor in Trieste Bay is wind. The "representative" wind was determined: 1.61 m/s and direction73°. As the model is 2D, it was not possible to take into account the influence of stratification,caused by fresh water inflow. Tidal forcing is an order of magnitude smaller and was neglected.

Because of all the uncertainties in input data and calibration parameters the selectednumerical grid was relatively coarse, Dx=Dy=1800 m. The resulting representative velocity fieldis shown in Fig. 3. It was tested with simulations in a denser numerical grid (Dx=Dy=600 m) thatthe main features of the circulation are well reproduced also with the coarser grid. As theboundary conditions at the open boundary are usually not quite reliable, the computational filedwas extended for about 70% to reduce its influence to minimum.

Sedimentation. Finally assumed concentration of suspended sediments in the Soca riverwas 50g/m3, only one fraction was taken into account with mean settling velocity of 1.16 * 10-5m/s and critical velocity for deposition being 0.007 m/s. The dispersion coefficient was K= 5m2/s. These values were partly obtained by measurements and calibration and partly byrecommendations for the applications of the model MIKE 21 - MT.

Mercury sub-model. Basic data for Case 4.1 were: Mercury input from the Soca river wasobtained from concentration of suspended matter (50 g/m3) and from concentration of Hgll inparticulate matter (7.52 jig/m3) resulting in 374.5 jig/m3. As there were no measurements ofmethyl-mercury in the Soca river available, a value of 1.5 pg/m3 was assumed, a typical value



for moderately polluted rivers. Input from the atmosphere was estimated to be 0.0274 |ig/m2/dayfor Hgll and 0.0003 ̂g/m2/day for MeHg.

SCALE5 CM/S

3600 m

Figure 3. Velocity field in Trieste Bay, simulation of Case 4 (or mean annual conditions.

Model Verification and Results. Fig. 3 shows the hydrodynamic circulation for meanannual conditions which are characterized with "representative" wind. The flow near the mouth ofthe Soca river is along the N coast towards W approx. In this region the velocities are greater asin the other parts of the Bay due to very small depths (order of 5 m).

Loc.T1T2T3T4T13T14T15D6A4A20

TotaIHa(M/m3)

Meas.2.98.35.011.8------

Slm.1250505.00.1-----•

Sim.250101.00.2------

ParticulateMa)

Meas.2.17.64.611.13.53.27.6---

Sim.1248492.00.11004015---

Sim.2509.80.40.2208.03.0---

Dissolvedfag/m3)

Meas.223.53.11.34.14.05.03.01.91.4

Sim.285.01.00.16.55.03.06.02.53.0

Planktonfa

Meas.---.0.08

--.0.70.4

1/9)Sim.---.2.0---

1.01.0

Table 2. Verification of model STA TRIM: comparison of simulation results for total Hg atseveral locations (see Fig. 4b) with measurements of three authors: T1-T4- Olivotti, [5]; T13-T15- Ferrara andMaserti, [2]; D6, A4, A20 - Horvat[4]

Table 2 shows model verification: comparison of measurements from several publishedpapers and simulated results for Hgll. For dissolved and plankton Hg the results can be



considered good, they are of the same order of magnitude as measured ones. For particulate Hgsimulations of Case 4.1 cannot be directly compared with measurements. The simulation ismade for mean annual conditions, which include also some floods which always have very highconcentrations of suspended matter, resulting in relatively high mean annual concentrations. Asthe measurements were executed mainly at very low discharges in the Soca river, theconcentration of suspended matter was low and this results also in low input of mercury with thesuspended particles. (The dissolved mercury concentration is not so strongly dependent on thewater discharge). With an estimation that the average concentration of suspended matter in theSoca river during measurements was only 10 g/m3 (20% of the mean annual concentration) weget much better agreement with measurements (results under "Sim.2" in Table 2).

holmes on 0 00] ug m3

Figure 4. Simulated distribution of mercury compounds in Trieste bay a) Case 4.1 - Hgll total;b) Case 4.1 - Hgll dissolved; c) Case 4.1 - MeHg total; d) Case 4.2 (10% input ofHg) MeHgtotal



There are some important differences between measurements and simulations. At pointT1, which is very close to the Soca river mouth (see Fig 4b ) the simulated values are too high.This is because of too rough discretisation of numerical grid, which was proved by testsimulations with the denser grid. On the other hand at locations T3 and T4 the simulated valuesare too low. It seems that the steady state simulations with "representative" wind cannotreproduce exactly the over-the-year unsteady phenomena. The simulated HD circulation doesnot allow for any pollutant from the Soca river to enter the southern part of the Bay. This is notquite realistic, as during the year there are periods of NW or N wind, which is not very strong andhence does not influence much the "representative " wind, but surely causes some transport ofpollutants also to the southern part of the bay.

In spite of the mentioned deficiencies the overall verification of the model of such acomplex phenomenon can be regarded satisfactory.

Fig. 4 a,b, and c show the distribution of Hgll total, Hgll dissolved and MeHg total for thebasic case 4.1 - mean annual conditions. Due to HD circulation all forms of mercury from theSoca river which do not settle down are transported along N coast of the Bay towards W - partlyout of the bay. Fig. 4.d shows the distribution of MeHg total for Case 4.2 with only 10% of basicinput from the Soca river. Basically reduction of mercury input from the Soca river would meanalmost proportional reduction of maximum mercury concentrations in the bay.

Inflow with Soca RiverInput from AtmosphereSedimentationMethylationDemethylationReductionNet Outflow from Bay

Quantity in BayDissolved PartParticular PartPlankton PartConcentr. in Bay

Hg total (g/day)*## âa#%if/&? r̂ u?%f-%%?/ A?/.f%;f//.«P ff/#4W-02 AAWTW-2.7 AW- /:/&/%;45.3kg#f%7%J%7/%4.85ng/m3

Hgll (g/day)+4855+16.0-3988-1.8--2.7-87845.0kg#J%737%72%4.81 ng/m3

MeHg(g/day)+19f&J-13+1.8-0.2--8.00.3kg52%30.9%/7/%0.04ng/m3

Table 3. Overall balance of mercury forms in Trieste Bay, obtained by simulation of Case 4.1

Table 3. shows the mass balance of mercury forms from simulation of Case 4.1. with allthe processes included. An important information is obtained: 82 % of the mercury settles down,almost 18 % exits from the bay with currents and only 0.06 % is "lost" due to reduction anddemethylation. It was expected that sedimentation is of first order of importance, but theinfluence of hydrodynamic circulation is of the same order, although of smaller magnitude.

As a conclusion it can be said that the verification of the model STATRIM is satisfactory,and the model gave some interesting information on mercury processes. The results andreliability of the model could be further improved by the following: (1) 3D modelling, including theeffects of vertical distribution of all parameters and with the influence of stratification (2) Densernumerical grid (3) More detailed measurements for calibration of several model parameters (4)Better definition of "mean annual" conditions. The most accurate method would be real-timesimulation of unsteady wind forcing over the year. This would though demand very longcomputational time.



3 Case Study 6: Modelling of Transport of Radioactive Pollutants in Japan Sea.

Although in the following example the environment is more oceanic than coastal, thesame model and similar methodology has been used with some important differences. The caseis presented with the aim of comparison of modelling in different space and time scales.

Problem. Japan Sea is a large, almost enclosed sea, between the Japanese islands,Sakhalin island, Siberian coast and Korea. Its length is about 1800 km, its width 900 km (seeFig. 5) and the maximum depth is about 3900 m.

A large amount of radioactive (RA) waste has been deposited at the sea bottom (depthof about 3500 m) at several locations, mainly at station No. 9, off Vladivostok (see Fig. 5b).Although several measurements of RA pollution in this Sea have shown no increase ofradioactivity over the natural values, concern exists that there may be some leakage of the RApollutants from the dumped waste. The question is where they might be transported by currentsand dispersion, and especially in what time they would reach the surface layers, where theymight contaminate sea food. Therefore a research on measurements and modelling of transportand dispersion of RA pollutants was executed at the International Atomic Energy Agency(I.A.E.A) in Monaco.

JAPAN SEAsummer circulation

Soya SCALE:Stratt

15 cm/s

Fig. 5. Surface circulation in the Japan sea a) Observed (from Yoon[15]), b) Simulated

Forcing factors. As the RA waste was deposited on the seabed, the bottom currents arethe most important for their transport and dispersion. It was determined that the thermohalineforcing, mainly due to important temperature and salinity differences between the Northern andsouthern part of the Sea is the most important forcing factor. Relatively strong NE wind (meanannual wind being 7m/s) was also taken into account, although its influence is for an order of



magnitude smaller. The inflow / outflow surface currents, although not important for the bottomcirculation, were also included into the modelling to simulate in a proper manner the surfacecurrents over the Japan Sea, which was used for the model verification.

Methodology. There were two possible methodologies to simulate the influence ofthermohaline forcing: either to simulate directly the impact of atmospheric heating and coolingthroughout the year on water temperature and salinity, and through it on the circulation or to usemeasured data of temperature and salinity distribution and to force with it the HD circulation. Thefirst methodology is usually called prognost/cmode, the second one diagnostic'mode.

Results. Three-dimensional (3D) diagnostic simulations of the circulation for four seasons ofthe year were executed. Fig 5b presents the simulated HD field for the surface layer for summerconditions. For comparison in Fig. 5a (from Yoon, [15]) the global pattern of the surface currentsafter measurements and observations is presented. The simulated currents are very near to theobserved ones. Simulated velocities in the bottom layers are up to 4 cm/s, in spring even up to10 cm/s which is an order of magnitude greater than obtained with wind forcing only.

^_./'

Jir

Fig. 6. Dispersion of radioactive pollutants from dumping site No.9 after 6 years of simulationwith winter and summer velocity fields, a) In Layer 12 (depth of 100-180 m) b) In vertical cross-section

Fig. 6 shows the results of simulations of transport-dispersion of RA pollutants from themain dumping site by the particle tracking method. Fig. 6a indicates that the RA pollutants wouldreach surface layers in the Northern part of the Sea. In the vertical cross section (Fig. 6b) it isshown, that after 6 years the pollutants will be approaching the surface waters. This indicatesthat the turnover time of the Japan sea is of the order of magnitude of 10 years._ As manyauthors (eg. Tsunogai et al [14]) are describing the turnover time to be about 100 years this is avery surprising result. Although this fact is still being verified, besides the proper simulation ofsurface circulation there is another confirmation of the model results. Measurements of bottom



currents, executed by Takematsu (personal communication 1995) during a period of 10 months,show that the bottom currents (at depth of 3000 m) are of the same order of magnitude as thesimulated ones. They attain maximum values of 8 cm/s in spring,

This case is a typical example of prognosis of future - possible events, where modelling isthe best- if not the only- possible approach.

Acknowledgements

This paper is based on research work mainly funded by the Slovene Ministry of Science andTechnology. The study of the Japan sea was carried out at the International Atomic EnergyAgency during the authors one year work at the Agency.

References

1. Cheng, R.T. and Smith, P.E., 1990. A Survey of 3D Numerical Estuarine Models. In:Spaulding, Ml (Editor), Estuarine and Coastal Modeling. Proc. Conf., ASCE Public., N.Y., 1-15.

2. Ferrara, R., Maserti, B. (1992). Mercury concentration in water, Particulate Matter,Plankton and sediment of the Adriatic Sea. Marine Chemistry, No. 38, pp. 237-249.

3. Harris, R., 1991: A Mechanistic Model to Examine Mercury in Aquatic Systems, Thesis,Master of Engineering., McMasters University, Hamilton, Ontario, Canada.

4. Horvat, M. (1995), Personal Communication.5. Olivetti, R. (1992). Risanamento delta baia di Panzano - Studia di fattibilita (Studio

integrativo per I'approfondimento delle conoscenze relative all'inquinamento da Mercurio. Techn.Report, Regione Autonoma Friuli-Venezia Giulia.

6. Rajar, R., 1989. Three-Dimensional Modelling of Currents in the Northern Adriatic Sea.Proc. of the XXIII Congress of the Intern. Association for Hydraulic Research, Ottawa.

7. Rajar, R., 1992. Application of the Three-Dimensional Model to Slovenian Coastal Sea.In: Brebia, C.A. and Wrobel, L.C. (Editors), Computer Modelling of Seas and Coastal Regions.Proc. Conf., Southampton.

8. Rajar, R, Cetina, D. and Zagar, D., 1995. Three-Dimensional Modelling of Oil Spill in theAdriatic, In: Wrobel. L.C. and Brebbia, C. (Editors) COASTAL 95, Proceedings of the Int.Conference, Can-Cun, Mexico.

9. Rajar, R., Sajovic, A., 1995. Modelling of Circulation and Water Exchange in Marinas,Proc. of the Int. Conference Water Pollution 95, Porto Carras, Greece.

10. Robinson, I.S., 1990. Applications of Remotely Sensed Image Data to Marine Modeling.In: Davies, A.M. (Ed.) Modeling Marine Systems, CRC Press, Boca Raton, U.S. pp. 141-180.

11. Sirca, A., 1992. Modelling of Pollutant Transport with the Particle Tracking Method,Thesis, M.Sc. University of Ljubljana, (in Slovene)

12. Sirca, A., Rajar, R. (1995). Evaluation of the effect of wind for long-term hydrodynamicand transport simulations. Sent for publication to Estuarine, Coastal and Shelf Science.

13. Sirca, A. (1996). Modelling of Hydrodynamics and of Transport of Mercury Compounds,Thesis Ph. D., FGG, University of Ljubljana, Slovenia.

14. Tsunogai, S., Watanabe, Y.W,. Harada, K., Watanabe, S., Saito, S., Nakajima, M.,1993. Dynamics of the Japan Sea Deep Water Studied with Chemical and Radiochemical Tracers,Deep Ocean Circulation, Physical and Chemical Aspects, Elsevier, pp. 105-119.

15. Yoon, J.H. (1995): Numerical Model of the Ocean Miniature "Japan sea", TechnicalReport, Kyushu University.


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