Surface layer salinity gradients and flow patterns in the archipelago coast of SW Finland, northern

38 Pages·2017·1.14 MB·English

Surface layer salinity gradients and flow patterns in the
archipelago coast of SW Finland, northern Baltic Sea
Tapio Suominen, Harri Tolvanen, Risto Kalliola
To cite this version:
Tapio Suominen, Harri Tolvanen, Risto Kalliola. Surface layer salinity gradients and flow patterns in
the archipelago coast of SW Finland, northern Baltic Sea. Marine Environmental Research, 2010, 69
(4), pp.216. ￿10.1016/j.marenvres.2009.10.009￿. ￿hal-00564778￿
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Accepted Manuscript
Surface layer salinity gradients and flow patterns in the archipelago coast of SW
Finland, northern Baltic Sea
Tapio Suominen, Harri Tolvanen, Risto Kalliola
PII: S0141-1136(09)00135-4
DOI: 10.1016/j.marenvres.2009.10.009
Reference: MERE 3384
To appear in: Marine Environmental Research
Received Date: 27 May 2009
Revised Date: 17 September 2009
Accepted Date: 12 October 2009
Please cite this article as: Suominen, T., Tolvanen, H., Kalliola, R., Surface layer salinity gradients and flow patterns
in the archipelago coast of SW Finland, northern Baltic Sea, Marine Environmental Research (2009), doi: 10.1016/
j.marenvres.2009.10.009
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ACCEPTED MANUSCRIPT
1 Surface layer salinity gradients and flow patterns in the archipelago coast of SW
2 Finland, northern Baltic Sea
3
4
5 Tapio Suominen*, Harri Tolvanen, Risto Kalliola
6 Department of Geography, 20014 University of Turku, Finland
7
8
9 Abstract
10 (cid:1)
11 The highly fragmented Archipelago Sea in the northern Baltic Sea forms part of a sill
12 area between two large sea basins. In addition to the water exchange between the
13 basins, its waters are influenced by runoff, and thus the sea area has both sill and
14 estuarine characteristics. We studied surface layer salinity gradients and their
15 applicability in defining water exchange patterns through and within the region. A
16 broad scale salinity pattern was detected during two sequential years. The spreading
17 of fresh water in the spring was succeeded by a gradual increase in salinity during
18 the summer. Long term data revealed a non-seasonal salinity fluctuation and
19 diminished salinity stratification in the central and northern parts of the study area.
20 We concluded that temporally unrepresentative mean values of salinity alone are
21 inadequate for the purposes of coastal management in this region. In addition, both
22 the range of variation and persistence of the conditions define the character of the
23 transitional and coastal waters.
24
25 Keywords: Archipelago; Baltic Sea; estuaries; GIS; interpolation; monitoring; salinity
26
27 1. Introduction
28
29 The horizontal and vertical gradations of the water properties are characteristic
30 features of the Baltic Sea. The basin has a positive water balance, whose major
31 components are the in- and outflows through the Danish straits (Fig. 1), river runoffs
32 and net precipitation (von Storch and Omsted, 2008). The occurrence and intensity of
33 the water exchange through the straits control much of the physical, chemical and
34 eventual biological processes in the Baltic Sea (HELCOM, 2009). Further, the sea
35 basin is divided by sills into multiple large sub-basins, which complicates the

* Corresponding author. Tel: [phone]; Fax: [phone]. E-mail address: [email protected]
1
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1 distribution of the Atlantic waters, along with a weak anti-clockwise surface layer
2 circulation (e.g. Alenius et al., 1998; Stigebrandt, 2001; Maslowski and Walczowski,
3 2002; Myrberg and Andrejev, 2006).
4
5 Figure 1.
6
7 Surface water salinity in the Baltic Sea decreases from ~9 ‰ in the Arkona basin
8 close to the entrance area to almost freshwater in the northern parts of the Gulf of
9 Bothnia (Bock, 1971; Rodhe, 1998). In general, the less saline waters flow
10 southwards in the surface layer, while the inflowing saline and dense water
11 penetrates into the deeper layers. This results in a permanent stratification with the
12 halocline at a depth of 60–80 metres in the largest basin of the Baltic Sea, the Baltic
13 Proper. The gradient of the surface salinity is rather even in the open sea, with the
14 highest variations occurring in the sill areas between the sub-basins (Rodhe, 1998;
15 Stigebrandt, 2001). The mean surface salinity fluctuations of the Baltic are related to
16 the fresh water input and show an approximate 1 ‰ variation over several decades
17 with no long-term trends (Winsor et al., 2001; Fonselius and Valderrama, 2003). In
18 the northern Baltic Sea salinity exhibits a seasonal cycle in near shore areas as in
19 spring the snowmelt runoff diffuses from the mainland. In summer the water is
20 temperature-stratified, while during spring, autumn and in mild winters the water
21 column shows strong vertical circulation above the halocline.
22
23 In the coastal and estuarine regions of the northern Baltic Sea, the complex
24 bathymetry associated with different geomorphic forms sets strong prerequisites
25 upon coastal circulations, resulting in highly variable chemical and physical
26 properties of seawater over space and time (e.g. Kirkkala et al., 1998; Hänninen et
27 al., 2000; Weckström et al., 2002; Erkkilä and Kalliola, 2004). These kinds of
28 transitional changes are ecologically significant with manifold implications for the
29 living environment, fisheries and environmental planning (Anon., 2003; Schernewski
30 and Wielgat, 2004; Nordic Council of Ministers, 2006). One of the most fragmented
31 coastal areas is the Archipelago Sea between mainland Finland and the island of
32 Åland, forming the eastern part of the sill between the Baltic Proper and the Gulf of
33 Bothnia (Fig. 1). The western side of this sill, formed by the Åland Sea between
34 Åland and Sweden, is relatively deep and wide, whereas the Archipelago Sea is a
35 unique coastal area with a mosaic of islands.
36
2
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1 The net water exchange through the Archipelago Sea is estimated to be low
2 compared to the Åland Sea (Kullenberg, 1981; Omsted et al., 2004). The baroclinic
3 flows combined with river runoffs and net precipitation are of major importance
4 considering the water exchange between the Baltic Proper and the large gulfs as a
5 whole (Omsted and Axell, 2003). Water exchange through and within the Archipelago
6 Sea is further mixed by estuarine circulation with wind-driven surface currents.
7 Islands and underwater sills form numerous local sea basins at various scales,
8 resulting in a complex transitional system where the fresh water runoff mixes with the
9 brackish sea water of the adjacent main basins.
10
11 The intermediate osmotic pressure of the brackish water of the Baltic Sea does not
12 correspond to either a purely marine or limnic environment, with many of the aquatic
13 organisms occurring at the edge of their ecological amplitude. Thus, the horizontal
14 and vertical salinity gradients of the Baltic Sea strongly influence the species
15 composition and abundance of flora and fauna (e.g. Remane and Schlieper, 1972;
16 Bäck et al., 1992; Lappalainen et al., 2000; Hänninen et al., 2003; Gasi(cid:1)nait(cid:2) et al.,
17 2005). Generally, the lowest biodiversity in the Baltic Sea is reported to occur in
18 conditions where the salinity ranges from 5 to 7 (von Storch and Omsted, 2008),
19 corresponding to the conditions prevailing in the Archipelago Sea.
20
21 The aim of this paper is to provide detailed quantitative information about the surface
22 salinity gradients and their temporal fluctuations in the Archipelago Sea –
23 phenomena that create relevant dynamic ecological thresholds in the area. Further,
24 we introduce an interpolation method modified for the archipelagial environment and
25 discuss the applicability of salinity monitoring data in determining long term flow
26 properties through the sill area of the Archipelago Sea.
27
28 We identified three main issues concerning the surface layer salinity: gradients,
29 persistence and the magnitude of fluctuations. These issues were studied through
30 three approaches. First, the general surface salinity patterns between the mainland
31 and Åland Island were outlined using salinity data from different sources. The four
32 salinity raster maps show the salinity gradients in July-August in 2007 and 2008.
33 Second, the succession of the salinity gradients was followed from late spring to
34 early autumn to study the intra-annual persistence of salinity in greater detail in the
35 north eastern Archipelago Sea. Third, to attain an inter-annual perspective, salinity
36 time series data from three intensively monitored stations representing the southern,
37 eastern and northern Archipelago Sea were used.
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1
2 2. Material and methods
3
4 2.1 Physical geography of the study area
5
6 Archipelago coasts are typical in the northern Baltic Sea (Frisén et al., 2005). The
7 Archipelago Sea in SW Finland consists of 25 000 islands larger than 500 m2 and
8 14 400 km of shoreline in an area of approximately 10 000 km2 (Granö et al., 1999)
9 (Fig. 1). The area is structured by fragmented bedrock that has a relative elevation
10 range of about two hundred metres. The bedrock base is partially covered with till,
11 glaciofluvial deposits and marine sediments. The deepest basins in bedrock faults
12 provide channels for water currents through and within the area. The mean depth of
13 the Archipelago Sea is estimated to be only 23 m. The depth is typically ranging from
14 0 to 50 metres, but some deeps and fault lines exceed 100 m. The Åland Sea in the
15 west is an approximately 40 km wide strait between the Archipelago of Stockholm
16 and the island of Åland. Its maximum depth is 301 m, but there is a sill at a depth of
17 70 metres at the southern end of the channel. In the Archipelago Sea, the highest
18 surface-layer water temperatures (~20 °C) occur near the mainland in August, while
19 the mean annual period of permanent ice cover extends to 100 days (Seinä and
20 Peltola, 1991). According to the meteorological data from the island of Utö (close to
21 the observation station S in Fig. 1), December is the windiest month with an average
22 wind speed of 8.6 m s-1, while May, June and July show the lowest average wind
23 speeds (Drebs et al., 2002).
24
25 2.2 Description of the salinity data
26
27 2.2.1 The Archipelago Sea in July-August in 2007 and 2008
28
29 The Southwest Finland Regional Environment Centre (SFREC) collects annual water
30 quality data from 61 stations in the eastern part of the Archipelago Sea (see Fig. 1,
31 Table 1), comprising three sampling visits in the period between July and August. At
32 most stations only the surface layer is sampled, and salinity is rarely measured, since
33 the analyses are focused on indications of eutrophication. However, extended
34 surface-layer (1 m) conductivity sampling was carried out from 27 and 56 stations
35 respectively, in 2007 and 2008. Conductivity was analysed by the laboratory of the
36 Water Protection Association of Southwest Finland, according to the standard SFS-
37 EN 27888. The conductivity values were compensated to 25º C and the laboratory
4
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1 results were given in mS m-1 to an accuracy of three significant figures. The
2 algorithms presented by Fotonoff and Millard Jr. (1983) were used to convert
3 conductivity into a practical salinity scale (PSS).
4
5 The Environmental Agency of Åland (referred to as ÅL) measures salinity in the
6 western part of the Archipelago Sea, synchronising their sampling regime with
7 SFREC to the same weeks each year. Salinity samples are collected from a depth of
8 one metre at all stations. In this study we used the 38 stations on the eastern side of
9 Åland Island (Fig. 1, Table 1). Conductivity was measured according to the standard
10 SFS-EN 27888 using a WTW inoLab Multi Level 1 instrument, which converts
11 conductivity to salinity. These results were given in per mil to an accuracy of two
12 significant figures.
13
14 Table 1.
15
16 2.2.2 The 2007 summer season in the NE Archipelago Sea
17
18 To obtain spatially and temporally representative salinity data covering the period
19 from late spring to early autumn, we carried out a sampling program in the north-
20 eastern part of the Archipelago Sea. The sampling regime consists of 22 stations
21 (Fig. 1, Table 1), a subset of the stations sampled by SFREC. In the sampling
22 network design, we prioritised relatively open sea areas and paid special attention to
23 long and deep straits and their crossings, i.e. flow channels to, from and within the
24 area. At each station, three parallel profiles were measured in a constellation of an
25 equilateral triangle with sides of 300 metres. The measurements were made every
26 third week from mid-May to early October in 2007. One exception to this schedule
27 was made to synchronise our data with the SFREC routine monitoring schedule
28 during weeks 29 and 31 in July. Each station was visited eight times.
29
30 The field measurements were made using a multi-parameter sonde (YSI 6600 V2),
31 equipped with sensors for conductivity and temperature (sensor model YSI 6560),
32 and pressure. The readings were recorded at six sampling depths (1, 2, 4, 6, 8 and
33 10 metres) at each of the three parallel profiles, whose mean values were used. At
34 two stations where the depth was less than 10 metres the measurements were made
35 at one metre intervals until two metres from the sea floor.
36
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1 To intercalibrate the sonde data with the monitoring program data, water samples
2 were collected from 7 stations during the weeks 29–40. A Limnos water sampler was
3 used to collect 5–10 litres of water to a plastic container, from which the laboratory
4 sample bottles were filled. The sonde was subsequently immersed into the container
5 and readings recorded for 1–2 minutes to get a control reading for the calibration.
6 The conductivity values measured in the field were compensated to 25º C by an
7 equation provided by the manufacturer; (cid:1) = (cid:1)/(1+TC(T-25)) where (cid:1) is the
25º C 25º C
8 specific conductance compensated to 25º C, (cid:1) is the measured conductance, TC is
9 temperature coefficient 0.0191 and T is the sample temperature at the time of
10 measurement (YSI 2007). The specific conductance values (cid:1) were calibrated to
25º C
11 correspond with the laboratory values with a linear regression model. The field
12 measurements of conductivity showed a good linear correspondence with the
13 laboratory results (r2=0.98, n=48). However, although the calibrations with distilled
14 water and the standard solutions differed only marginally from the nominal values,
15 the field readings were typically 20–25 mS m-1 higher than the laboratory results.
16 According to the manufacturer, the accuracy of the conductivity sensor is ±0.5 % of
17 the reading. The algorithms presented by Fotonoff and Millard Jr. (1983) were used
18 to convert the inter-calibrated conductivity to PSS. The reference conductivity was
19 analysed by the laboratory of the Water Protection Association of Southwest Finland
20 according to the standard SFS-EN 27888. An exceptionally high conductivity value at
21 one of the stations in week 26 was assigned as an outlier, and data are missing from
22 the southernmost station in week 37. In these two cases the mean conductivity
23 values from the preceding and subsequent week were used instead.
24
25 2.2.3 Long term observations in 1999–2008
26
27 The Finnish Environment Institute (SYKE) has 14 intensively sampled monitoring
28 stations in Finnish coastal waters, of which three, i.e. KORP 200, NAU 2361 and
29 BRÄNDÖ 100, are located in the Archipelago Sea. They are referred to here as the
30 southern (S), eastern (E) and northern (N) station, respectively (Fig. 1, Table 1).
31 These stations are sampled nominally 20 times annually, but due to weather
32 conditions some data are missing, especially during winter periods. The longest time-
33 series, from 1983 to present, is available from station E (depth 52 metres). The time-
34 series at stations S (78 m) and N (33 m) start in 1999 and 2000, respectively. In this
35 study, we used data extending from 1999 (2000 at station N) to 2008. We used
36 sample means collected from 1, 5 and 10 metre depths as the surface layer value.
37 This range was found to be homogeneous and by using the mean of the surface
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1 layer, we minimised the effects of individual erroneous values obtained as a result of
2 incorrect sampling, analysis or registration. The near-bottom salinities were sampled
3 approximately one metre above the sea floor. Here, salinity was analysed in the
4 laboratory of SYKE with a Guideline Autosal 8400B salinometer and the laboratory
5 results were given as per mil to an accuracy of three significant figures.
6
7 2.3 Analysis methods
8
9 2.3.1 The inverse path distance weighted interpolation
10
11 The archipelago conditions require sophisticated interpolation analyses, because the
12 islands and straits restrict free water flow, resulting in an anisotropic distance
13 configuration (see Little et al., 1997; Dunn and Ridgway, 2002; Løland and Høst,
14 2003; Krivoruchko and Gribov, 2004). We applied a procedure based on the inverse
15 distance weighted (IDW) method (e.g. Longley et. al., 2001; Chang, 2002) to
16 interpolate salinity values for the entire study area. The IDW weights the values of
17 sample points linearly according to the inverse distance from the known data point to
18 the raster cell to be estimated. The influence of the distance could be adjusted by
19 raising the distance to a power and by limiting it to a given maximum value. Instead
20 of using Euclidean distances, we calculated path distances along the water surface
21 from each sampling point and named the method as inverse path distance weighted
22 (IPDW). This calculation was made by applying a cost raster surface, in which the
23 water areas have a value of 1, with land areas assigned a high value to prevent the
24 path from crossing land surfaces. The script was written with Python programming
25 language within ArcGIS and utilises GIS functions introduced in this environment.
26 The execution of the script is described in Table 2, with some of the intermediate and
27 resulting raster surfaces demonstrated in Figure 2.
28
29 Table 2.
30
31 Figure 2.
32
33 The cost raster originates from the shoreline vector data (1:20 000) of the National
34 Land Survey of Finland, which was converted to raster format with a cell size of 100
35 metres. The cells whose centre points lie on land were assigned with the cost value
36 10 000, and other cells with the cost value 1. Thus, the cost raster allows unrestricted
37 connectivity through narrow straits (<200 m), which does not correspond to the real
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1 water exchange potential in such cases. To simulate a more realistic situation where
2 the flow in shallow shores and narrow channels is limited, the cells retaining the cost
3 value 10 000 were buffered by adding an extra row of cells around them.
4
5 The error assessment of the model was done with the UTU data (University of Turku,
6 Table 1), which was sampled from 22 stations during the summer of 2007 (see
7 section 2.2.2). The test was performed by omitting one station at time, interpolating a
8 salinity raster surface with this incomplete set of known data points, extracting the
9 interpolated value at the location of the omitted station, and by comparing the
10 deviations of observed and extracted salinity from the observed mean salinity of the
11 corresponding week (Fig. 3). This was done for each of the eight sampling rounds.
12 However, all stations could not be used in this procedure: the stations at the outer
13 limit of the interpolation area were excluded since they would have been extrapolated
14 instead of interpolated. Thus, only 12 out of 22 stations were used. Since one
15 measurement was regarded as an outlier, the total number of comparisons of
16 modelled and measured salinity values was 95.
17
18 We compared the effect of the inverse path distance weighted interpolation (IPDW)
19 against standard IDW by performing the above mentioned test with two cost
20 surfaces. In addition to the cost raster described above, the test was performed with
21 a constant cost raster in which all the cell values were set to value 1. This surface
22 ignores the effect of land and the inverse path distance weighted simulation performs
23 like a standard IDW. In both calculations, the influence of the stations were limited to
24 a maximum distance of 20 km and the effect of the stations were set to decrease
25 linearly according the increasing distance.
26
27 Figure 3.
28
29 The comparison revealed only minor differences in accuracy between the different
30 interpolation methods. In both cases, the coefficient of determinations (Fig. 3) were r2
31 = 0.81, while the mean deviations of the modelled from the observed salinities were
32 also similar, i.e. MD = 0.04. This similarity occurs partly because the stations have
33 initially been selected to represent open sea areas and they are usually connected
34 through straight and wide waterbodies. The main benefits of the IPDW would occur in
35 more complex and sheltered areas. The differences between IDW and IPDW
36 methods were apparent in the straits and their ends (Fig. 4). The IPDW method was
37 used to model continuous raster surfaces on the surface salinity of the Archipelago
8