2010 IEEE/OES US/EU Baltic International Symposium

(BALTIC) August 25, 26, 27 2010 - Riga, Latvia

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Operational observations methods during offshore sand mining – case study in Tallinn Bay, the

southern Gulf of Finland

Getli Haran, Urmas Raudsepp, Victor Alari, Rivo Uiboupin, Liis Sipelgas and Ants Erm Marine Systems Institute at Tallinn University of Technology

Akadeemia tee 21, 12618 Tallinn, Estonia getli.jervan@phys.sea.ee

Abstract- Offshore sand mining is increasing activity nowadays. Environmental impact of sand mining appears usually through the transport of suspended particulate matter (SPM) away from the actual mining area. Satellite remote sensing is efficient tool for the operational monitoring of SPM distribution during harbor dredging, but problematic in the case of sand mining, as SPM remains mainly below the water surface. We used satellite remote sensing, in-situ measurements of optical properties of seawater and combined wave, hydrodynamic and sediment transport numerical modeling for assessment of the area affected by sand mining in Tallinn Bay, the southern Gulf of Finland. Sand mining took place from October 2008 to April 2009 with short breaks that were random in time. Vertical profiles of spectral attenuation and absorption coefficients by spectrometer AC Spectra, underwater light field and albedo by radiometer Ramses-ACC-VIS were measured in situ. In satellite remote sensing MODIS images with 250 m spatial resolution were used for the qualitative estimation of the surface area that was affected by sand mining. Nested 2D hydrodynamic model and wave model SWAN with 400 m spatial resolution at mining site gave input fields of currents and bottom shear stresses to the Lagrangian type particle transport model.

While in-situ measurements and satellite remote sensing give snapshot about the SPM distribution numerical modeling enables to have dynamics of the ongoing process. In-situ measurements showed that the concentrations of SPM were the highest at the mining operation. The thickness of the elevated SPM layer was about 6 m. Satellite remote sensing showed minor or no signal of elevated SPM concentrations on the water surface in comparison to surrounding area. Model result show clearly that eastward transport of SPM prevailed during the sand mining activities. The SPM covered larger area during autumn than during winter and spring. This can be attributed to the stronger winds that forced higher waves and stronger currents. Wave activity is responsible for keeping the SPM in suspension, which favors the transport of SPM away from the mining site. In the environmental point of view, the most affected area remains in the radius of 1 km from the mining site. In conclusion, the use of satellite remote sensing and in-situ measurements can be misleading when considering environmental impact assessment caused by SPM.

I. INTRODUCTION

Offshore sand mining is an increasing activity nowadays [1], [2], [3]. Environmental impact of sand mining appears usually via the disturbance of the habitat and the transport of suspended particulate matter (SPM) away from the actual mining area. Satellite remote sensing is efficient tool for the operational monitoring of SPM distribution during harbor dredging [4], [5], [6], [7], but may be problematic in the case of sand mining, as SPM remains mainly below the water surface. In this case study observational methods for monitoring the concentration and transport of suspended matter during offshore sand mining are presented. The source of the suspended matter is located near Naissaar Island in Tallinn Bay, the southern Gulf of Finalnd (Fig 1) and the suspended particulate matter was released to the water column from 01 October 2008 until 14 April 2009. We used satellite remote sensing, in-situ measurements of optical properties of seawater and combined wave, hydrodynamic and sediment transport numerical modeling for assessment of the area affected by sand mining in Tallinn Bay, the southern Gulf of Finland.

II. DATA AND METHODOLOGY

A. Field measurements The observation period during offshore sand mining was from 01 October 2008 to 14 April 2009. It is divided into autumn

period (from 01 October to 02 December 2008) and winter-spring period (from 03 December 2008 to 14 April 2009). During the study period two field measurements campaigns were performed. First study was on 30 October 2008 and second on 21 February 2009. Both were carried out in two dredging areas in Tallinn Bay, the southern Gulf of Finland. Dredging area I is situated south from Naissaar Island, dredging area II is situated south – east from the Naissaar Island. During the first survey the measurements were performed in 5 stations in the first dredging area and in 7 stations in the second area (Fig 2). During the second survey there were four and three stations respectively (Fig 3). The closest stations to the dredging point were station H5 and S6. In the dredging area the measurements were carried out with the horizontal step of 500 m downwind from the dredging point. Underwater light field and albedo by radiometer Ramses-ACC-VIS, vertical profiles of spectral attenuation and absorption coefficients by spectrometer AC Spectra were measured and water samples from the surface water were collected.

500 600 700 800 900Distance (km)

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Estonia

Figure 1. Study area. Coordinate system UTM-34.

Figure 2. Measurement stations 30 November2008.

N-stations

Figure 3. Measurement stations 21 February 2009.

B. Laboratory measurements The concentrations of SPM from surface and bottom water samples were determined using dry weight method in laboratory.

C. In situ measurements of inherent and apparent optical properties in water column Inherent optical properties absorption (a) and attenuation (c) coefficients were measured with WetLabs in situ spectrometer AC

Spectra that enables to measure the spectra of light attenuation and absorption regardless of the external light conditions. The corresponding spectral scattering (b) coefficient was calculated as follows:

( ) ( ) ( )λλλ acb −= , (1)

where the λ is corresponding wavelength.

Our previous studies have shown that in Estonian coastal sea the scattering coefficient at 550 nm is well correlated with the SPM concentration [8]. Thus, we used scattering coefficient at 550 nm to describe the vertical profiles SPM concentration. The absorption coefficient at wavelength 402 nm is indicative of colored dissolved organic matter (CDOM) concentration in water in the Estonian coastal sea [9].

Underwater downwelling irradiance Ed(λ,z) was measured to describe the underwater light field. A set of two underwater radiometers Ramses-ACC-VIS (Trios GmbH Germany) positioned on the same frame at the elevations 0.5 m and 1 m from the sea bed was used. It allowed measuring the spectral profiles of underwater irradiation Ed(λ,z) as well as the depth profiles and time variation of diffuse attenuation coefficient from 320 nm to 950 nm.

The diffuse attenuation coefficient [10]

( ) ( ) ( )( )

dzzdE

zEzE

dzdzK d

ddd

λ

λλλ

,

,,,

1ln == , (2)

characterizes the attenuation rate of the downwelling irradiance at a certain depth z. The larger this coefficient, the less light penetrates to the deeper layers. An approximate average value of Kd,λ in the water layer between the sensors can be calculated as

( )( )ud

ld

ul

uld zE

zEzz

zzK

λ

λλ

,

,, ln1

2 −=⎟

⎠⎞

⎜⎝⎛ + (3)

where Ed,λ(z) is measured at the depths zu (upper sensor) and zl (lower sensor). The theoretical basis of resuspension investigation by means of an optical set is described in particular detail by [11], [12] and [13].

D. Satellite data MODIS reflectance data from band 1 (620-670 nm) can be used for monitoring the suspended matter on surface water during

the dredging operations [4]. The monitoring of SPM using satellite remote sensing depends on the cloud coverage over the sea. During the study period there were only four days in March 2009 when satellite images were available.

E. Description of circulation model The two dimensional circulation model based on hydrodynamic equations for a shallow sea was used. The model has been

earlier applied for different regions of the Estonian coastal sea [4]. The model consists of vertically integrated motion equations

xx

bx

w Gh

Fh

Fx

gfvyuv

xuu

tu +−+

∂∂−=−

∂∂+

∂∂+

∂∂ η , y

yb

yw G

hF

hF

ygfu

yvv

xvu

tv +−+

∂∂−=+

∂∂+

∂∂+

∂∂ η , (4)

and a continuity equation

0=∂∂+

∂∂+

∂∂

yvh

xuh

tη , (5)

where (u,v) are the vertically averaged velocities in the water column in the Cartesian coordinates, ( )y

wx

w FF , are the kinematic

wind stresses, ( )yb

xb FF , are the bottom friction stresses, ( )yx GG , are the horizontal turbulent viscosities on the (x,y) directions,

f is the Coriolis parameter, g is the acceleration due to gravity, η is the sea surface elevation (deviation from the equilibrium depth) and h(x,y) is the depth.

The friction at the bottom is calculated using quadratic relationship from the flow speed

,, vuCFuuCF Dy

bDx

b == (6)

where DC (= 2.5×10-3) is the bottom friction coefficient and, u is the current velocity. The bottom friction coefficient is taken as a constant, since reliable data on the sea bottom irregularities are lacking. The terms of horizontal turbulence are calculated using the constant eddy viscosity coefficient HA (= 50 m2 s-1):

⎟⎟⎠

⎞⎜⎜⎝

⎛∂∂+

∂∂=⎟⎟

⎠

⎞⎜⎜⎝

⎛∂∂+

∂∂= 2

2

2

2

2

2

2

2

,yv

xvAG

yu

xuAG H

yH

x . (7)

A triple-nested circulation model was used for the simulation of currents. The coarse grid model covers the whole Baltic Sea

with a spatially constant grid size of 2x2 km. The digital topography is taken from [14]. The model for the Tallinn Bay region has a grid size of 400x400 m, whereas the boundary conditions for water transport are obtained from the whole Baltic Sea model.

Hydrodynamic model forcing was obtained from the atmospheric model HIRLAM (High Resolution Limited Area Model) with horizontal resolution of 11 km. Wind velocity components were interpolated to both model grids. During the study period the wind speed was mainly 2-10 m s-1. A strong storm passed the study area on 23 November when the maximum NNE wind speed was 25 m s-1.

F. Description of wave model The SWAN wave model was implemented to describe wave conditions in the dredging area. The SWAN model is a third

generation phase averaged spectral wave model developed at Delft University of Technology [15]. In SWAN, the waves are described with the two-dimensional wave action density spectrum, whereas the evolution of the action density N is governed by the time dependent wave action balance equation, which reads:

( )σθσ

θσ totg

SNcNcNUc

tN =

∂∂

+∂

∂+∇⋅++

∂∂ . (8)

The first term represents the local rate of change of action density; the second term denotes the propagation of wave energy in two

dimensional geographical space, with gc being the group velocity and U the ambient current. This term can be recast in Cartesian, spherical or curve-linear coordinates. The third term represents the effect of shifting of the radian frequency due to variations in depth and mean currents. The fourth term represents the depth-induced and current-induced refraction. The quantities σc and θc are the propagation velocities in spectral space ( )θσ , , with σ and θ representing the radian frequency

and propagation direction respectively. The right-hand side contains the source term totS that represents all physical processes

that generate, dissipate or redistribute wave energy. In shallow water, six processes contribute to totS :

dbbotwcnlnlwindtot SSSSSSS +++++= 43 . (9)

These terms denote the energy input by wind ( windS ), the nonlinear transfer of wave energy through three-wave ( 3nlS ) and four-

wave interactions ( 4nlS ), and the decay of waves due to whitecapping ( wcS ), bottom friction ( botS ) and depth-induced wave

breaking ( dbS ) respectively. Extensive details on the formulations of these processes can be found for example in [16]. For the present calculations with SWAN, the same bottom topography and meteorological forcing as in the circulation model

was used. The third-generation model in respect to wind-input, quadruplet interactions and whitecapping was used. Triads, bottom friction and depth-induced breaking were also activated. For wind-input and whitecapping dissipation, the formulation by [17] was used; in case of wind-input, only the exponential growth term was activated. The quadruplet interaction was approximated using the Discrete Interaction Approximation (DIA). The breaking of waves is controlled by the ratio of maximum individual wave height over the depth and is set to be 0.73. Semi-empirical expression for bottom friction (see [18]) was also activated.

G. Description of particle transport model The Lagrangian type particle transport model was used for calculation of SPM distribution. The particles were released to the

water column from a point source and approximately 1% of the mining material was assumed to be re-suspended. The time step of the particle transport model is 10 min. In water column the particles were dispersed randomly

,))()(()()( 111 ttvtvtxtx ndispnadvnn Δ++= −−− (10)

where )( ntx is location of particle at time step tn, n is time step number, tΔ is time step interval, advv is advection velocity from

numerical hydrodynamic model and dispv is dispersion velocity at particle location. Dispersion velocity that takes implicitly into account turbulence and wave action is parameterized as:

[ ] ,1

1−= Rvv advdisp (11)

where [ ]11−R is random number between -1 and 1 from uniform distribution.

III. RESULTS AND DISCUSSION

A. SPM concentration and optical parameters Concentrations of SPM determined from surface water samples varied between 0.6 and 1.4 g m-3 during the first survey on 30

November 2008 (Fig 4a). Only in the closest sampling station to the mining point the concentration was higher, 2.8 g m-3. The SPM concentration from bottom water is shown on (Fig 4b). On figure is seen that near the mining site the SPM concentration was up to 9.5 g m-3 but in stations 500 m away from the mining site the concentration of SPM was already 1.2 g m-3. During the second survey on 21 February 2009 the highest concentration of SPM on surface water was 3.4 g m-3 (Fig 4c) in station just next to the mining operations. Also, in the bottom water the highest concentration, 7.2 g m-3 (fig 4d), was measured near the mining site. Background SPM concentrations in the surface and bottom layer were about 1 g m-3 and between 1-2 g m-3, respectively, which is in the same level as during the survey in autumn.

24.5 24.52 24.54 24.56 24.58 24.6 24.62 24.64

59.48

59.5

59.52

59.54

59.56

59.58

59.60.811

11.2

1 1.4

0.62.81.21.20.8

0.5

1.3

2.1

2.9

3.7

4.5

5.3

6.1

6.9

7.7

Naissaar

24.5 24.52 24.54 24.56 24.58 24.6 24.62 24.64

59.48

59.5

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59.56

59.58

59.60.520.420.71

0.490.87

0.34 0.55

1.29.51.21.51.1

00.511.522.533.544.555.566.577.588.599.5

Naissaar

24.5 24.52 24.54 24.56 24.58 24.6 24.6259.5

59.52

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10.8

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1

1.23.4

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6

Naissaar

24.5 24.52 24.54 24.56 24.58 24.6 24.6259.5

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1

17.2

2

0.5

1

1.5

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3

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4

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5

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6

Naissaar

Figure 4. The SPM concentration 30 October 2008 on the surface (a) and in the bottom (b). 21 February 2009 on the surface (c) and in the bottom (d). Color

scale: concentration in g m-3. The scattering coefficient at 550 nm was used to describe the vertical profiles of SPM in stations closest to the mining site

(station H5 on 30 October 2008 and station S6 on 21 February 2009) (Fig 5). The absorption coefficient at 402 nm was used to describe CDOM vertical profiles near the dredging site. On 30 October 2008 the values of a(402) and b(550) started to increase at 8 m depth showing the rapid increase of SPM and CDOM at that depth (figure 5a). On 21 February 2009 the values of a(402) and b(550) started to increase between the depths 10-12m (figure 5b). Both cases confirm that higher impact of mining operations as release of sediments into the water column is near the bottom.

0,00

2,00

4,00

6,00

8,00

10,00

12,00

14,00

16,00

0,00 0,50 1,00 1,50 2,00 2,50 3,00b (550); a(402), m-1

dept

h, m

a(402)

b(550)

0

2

4

6

8

10

12

14

16

18

20

0 5 10 15 20 25 30a(402); b(550), m-1

dept

h, m

a(402)b(550)

Figure 5. The vertical profiles of scattering coefficient at 550 nm and absorption coefficient at 402 nm. On 30 October 2008 in station H5 (a) and 21 February

2009 in station S6 (b).

a) b)

d) c)

a) b)

With the increase of depth and particles in the water column underwater light field grows weaker and less light penetrates into the water column and the corresponding diffuse attenuation coefficient increases. From the data measured during the second survey station S6 (closest to the mining point) had significantly higher diffuse attenuation coefficient compared to other stations (0.69 m-1 in station S6 and 0.2-0.24 m-1 in all other stations). Form figure 6 it is also visible that the station S6 was optically inhomogeneous. At depths 7-10 m the diffuse attenuation coefficient was considerably higher (2.3 m-1). The comparison with attenuation and absorption spectrums of AC Spectra shows that the near bottom layer of very high turbidity was not reached by underwater radiometer measurements. The high turbidity layer was between 12 to 17 m, but Kd was measured down to 10-m depth.

0

2

4

6

8

10

12

0 0,5 1 1,5 2 2,5 3 3,5

Kd, m-1de

pth,

m

Figure 6. Diffuse attenuation coefficient in station S6 on 21 February 2009.

B. Satellite remote sensing results We analyzed MODIS reflectance images from March 2003 (figure 7). Altogether 4 cloud free images were obtained and

processed with ENVI software. The red color on images indicates higher reflectance caused by increased amount of particles in water. On images is seen that higher reflectance was near the coastal area but at the coast of Nassaare Island the reflectance was low. From satellite images we could not define the increased amount of SPM near the mining site.

08 March 2009

19 March 2009

22 Marc 2009

25 March 2009

Figure 7. Satellite images from March 2009.

C. Numerical modeling of SPM Numerical models were implemented for two periods: from 01 October to 02 December 2008 and from 03 December 2008 to

14 April 2009. For assessing the area affected by sand mining, the amounts of material dredged in the study period were used. Daily amounts of dredged material differed significantly: from 1 000 to 14 000 m3 a day, 7 300 m3 on the average. The model does not take into account the natural background of SPM. It also does not take into account the possibility that some material will be removed from the water column during later dredging. Fig. 8 shows the relative extent of SPM impact at the end of both study periods. The distribution does not show the SPM on the surface but SPM in the water column and settled to the bottom because measurements showed that on the surface the concentration of SPM is significantly lower than in the water column and

does not exceed the natural background. During the autumn period the SPM covered larger area than during winter-spring period. This can be attributed to the stronger winds that forced stronger currents (Fig 9) and higher waves (Fig 10).

The spatial distribution of average current speed was similar in both periods, but the current speed was significantly higher during the second period. The current speed in both periods was lower in the deeper areas of the sea and increased in the shallow areas. The spatial distribution of significant wave height was also similar during both periods and the significant wave height decreased towards the coast.

During the first modeling period significantly greater sea area was affected by the sand mining. During the second modeling period the sea area affected by the dredging is mainly the area which is under direct influence of the works. The amount of SPM that spread outside the dredging area is marginal. The area affected depends also on the wind speed and direction. When southerly, south-westerly and westerly winds dominate then the main influence is to the east from dredging areas. During the storm event on 23 November, when the NNE wind speed was up to 25 m s-1, the influence shifted to south-west from dredging area. At the end of study period moderate winds dominate and only marginal amount of dredged material was carried away from the dredging area.

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685 690 695 700 705 710 715Distance (km)

6590

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m)

Figure 8. Relative extent of SPM impact released into the water until 02 December 2008 (left) and until 14 April 2009 (right). Coordinate system UTM-34.

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m)

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tanc

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m)

00.11357911131517192123

Figure 9. Average modeled current speed: autumn (left) and winter-spring (right). Coordinate system UTM-34. Color scale: current speed in cm s-1.

685 695 705 715Distance (km)

6590

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6620D

ista

nce

(km

)

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6590

6600

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6620

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tanc

e (k

m)

00.10.20.30.40.50.60.70.80.911.11.2

Figure 10. Average modeled significant wave height: autumn (left) and winter-spring (right). Coordinate system UTM-34. Color scale: significant wave height in m.

IV. CONCLUSIONS

Three methods, in-situ measurements and laboratory analyses of water samples, remote sensing and numerical modeling were used for monitoring the SPM distribution released into the water during offshore sand mining. The in-situ measurements and numerical modeling results indicate that SPM remains mainly below the surface. Therefore remote sensing is not efficient for monitoring the SPM concentrations in that case. From four available remote sensing images no higher SPM concentrations were detectible. Long cloudy periods also make the use of remote sensing problematic. The measurements and model results also showed that the concentrations of SPM were highest at the mining point. The area covered with SPM depends on wind conditions. During autumn, when winds were stronger, larger area was covered than in winter and spring. The results show that the SPM distribution depends on dredging amounts and on the wind forced waves and currents. This case study indicates that remote sensing and in-situ measurements might not give sufficient information for environmental impact assessment caused by SPM.

ACKNOWLEDGMENT

This study was financially supported thorough Estonian Science Foundation grant Nr. 7000 83 and EMP 53. REFERENCES

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