GULF OF FINLAND YEAR 2014 - ASSESSMENT

GOF Year 2014 Team

Contents 1 General features of hydrography in the Gulf of Finland ...... 3 1.1 Data used in this assessment ...... 4 1.2 Drivers of the physics of the Gulf of Finland ...... 4 1.2.1 Weather and climate ...... 5 1.2.2 Precipitation ...... 8 1.2.3 River runoff ...... 9 1.2.4 Ice conditions in the Gulf of Finland in 1996 - 2014 ...... 12 1.2.5 Sea level ...... 15 1.3 Monitoring and indicators of hydrography ...... 18 1.4 Gulf of Finland assessment period 1996 - 2014 in perspective ...... 18 2 Changes in stratification ...... 19 3 Halocline development ...... 21 4 Near bottom salinity changes ...... 21 5 Deep-water oxygen conditions ...... 24 6 Dynamic features of the Gulf of Finland ...... 27 7 Wave climate in the Gulf of Finland 2014 ...... 29 8 Conclusions ...... 30 9 Nutrient loading and eutrophication ...... 31 9.1 Eutrophication (vs. nutrient loading) in the Baltic Sea and in the Gulf of Finland in recent decades (short literature overview) ...... 31 10 Distribution and trends of N and P ...... 33 10.1 Introduction ...... 34 10.2 Data providers ...... 34 10.3 DIN and DIP as trophic indices ...... 34 10.4 Spatial approach ...... 35 10.5 Spatial distributions of DIN and DIP ...... 36 10.5.1 East-West section: offshore regions“Offshore Middle”and“Offshore West” 36 10.5.2 East-West section:“Finnish Coast Outer” and “Finnish Coast Eastern” ...... 36 10.5.3 East-West section: “Estonian Coast Inner”and “Narva Bay” ...... 36 10.5.4 North-South section: “Estonian Coast Inner” and “Estonian Coast Outer” 37

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10.5.5 North-South section: “Finnish Coast Inner” and “Finnish Coast Outer” 37 10.6 The current DIN and DIP fields ...... 38 10.7 Long-term trends of DIN and DIP ...... 38 10.8 DIN and DIP: relation to the existing environmental targets ...... 41 10.9 DIN/DIP ratio...... 42 11 Distribution and trends of chlorophyll a and surface blooms ...... 54 11.1 Material and methods ...... 54 11.2 Spatial distributions of chlorophyll a in late summers ...... 56 11.3 Trends...... 57 11.3.1 Chlorophyll trends ...... 57 11.4 Trends in algal bloom indices ...... 59 11.5 Areal comparison of chlorophyll a relative to its environmental targets 59 11.6 References...... 72 12 Oxygen indicator assessment using results from an autonomous profiling buoy ...... 74 12.1 Introduction ...... 74 12.2 Autonomous profiling buoy ...... 75 12.3 Method ...... 75 12.4 References...... 76

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1 General features of hydrography in the Gulf of Finland

Responsible: Pekka Alenius (FMI) Co-authors: Kai Myrberg (SYKE), Oleg Korneev (RSHU), Urmas Lips (MSI TUT)

This hydrographical assessment of the Gulf of Finland describes the state and development of the basic physical (hydrographic in oceanographic meaning) features of the Gulf of Finland in 1996 – 2014 including the Gulf of Finland Years 1996 and 2014. Special emphasis is given to the conditions in 2014.

The Gulf of Finland is topographically a direct continuation of the Baltic Sea proper. There are no physical boundaries that separate the gulf from the rest of the Baltic Sea. The Gulf of Finland is defined to begin from the line between city of Hanko in Finland and Osmussaar island in Estonia. This definition gives the Gulf a surface area of roughly 30.000 km2. The average depth of the Gulf is 35 m and the water volume around 1040 km3. The largest depth is over 110 m in the South-Western Gulf.

The geographical nature of the gulf is very different on its southern coast and northern cost. On the southern side there are some larger islands, but on the northern side the coastal waters are full of smaller islands. In the Eastern Gulf, there are some large islands as well. The islands and archipelago have great influence on the dynamics of the Gulf as do also the overall topography and shapes of the coastlines. Especially the big islands and half-peninsula near Tallinn, Estonia have an important effect of the conditions in the central western Gulf of Finland.

The eastern end of the Gulf of Finland receives the largest single freshwater inflow to the Baltic Sea from the river Neva. There are also some other large rivers flowing to the gulf, but their runoffs are much smaller than that of Neva.

The physical properties of the water masses change continuously along the gulf. The long-term mean circulation is anti-cyclonic with eastward flow at the southern coast and westward flow in the northern gulf. This simplified description is based on the prevailing wind direction that is from southwest and on the earth rotation that favours this type of circulation. The true circulation is much more variable and depends on the prevailing wind conditions. The stability of the current, which means the ratio between vector mean speed and scalar average speed, varies throughout the year and is generally less than 50%. Thus we can say, that though there is an long term average anti-clockwise transport of a couple of cm/s, the substances released to the gulf may spread very widely along the gulf in longer time periods.

Modelling studies (Andrejev & Myrberg, 2013) have indicated that the water exchange in the western gulf is larger than in the big eastern basin of the gulf. This means that the age of the water particles in a certain spot is larger in the

3 eastern basin than in the western gulf. This again suggests that the changes in hydrographic features are more rapid in the western gulf than in the eastern basin. One hydrographic feature that is of great importance to the biology and environmental conditions of the gulf is the horizontal extent and vertical thickness of the anoxic bottom boundary layer. This anoxic water comes from the Baltic Sea Proper and extends to the east as long as the depth is large enough. The vertical overturning in the spring and autumn may mix the waters down to the bottom if the density gradient is not too big in the bottom layer. There are a lot of areas in the western gulf where the depth is such that the anoxic saline water layer is rather thin and may remain completely unobserved if no bottom samples are taken. The layer thickness is often less than 5 meters and thus it remains below the standard CTD observations. 1.1 Data used in this assessment

We have mostly used the official Gulf of Finland Year dataset in this assessment. The dataset includes bottle data from 1995 till 2014 and CTD data from 2014. Years 1996 and 2014 were Gulf of Finland theme years and therefore there is rather much data from those years. We chose 1996 to be a reference year and compare that to 2014 and analyse the variability and changes between those years.

As background data we have used different datasets of NAO-data (NOAA National Weather Service Climate Prediction Center, http://www.cpc.ncep.noaa.gov/products/precip/CWlink/pna/nao.shtml), ice data from FMI Marine Services, weather and sea level data from FMI weather stations along the Finnish coast of the Gulf of Finland and SST data from FMI’s Harmaja weather station in front of Helsinki. The FMI weather station datasets are time series with one-hour time steps.

River discharge data of river Kymi is got from Finnish Environment administrations database Hertta in their Oiva-system , discharges to the sub- basins of the Baltic Sea from HELCOM Baltic Sea Environmental Fact Sheets 2014, Total and regional runoff to the Baltic Sea (http://www.helcom.fi/baltic- sea-trends/environment-fact-sheets/hydrography/total-and-regional-runoff-to- the-baltic-sea/), modelled runoff data from SMHI (E-HYPE model data, http://www.smhi.se/en/2.575/hydrology/european-hydrological-predictions- for-the-environment-1.12711 and data downloadable from http://e- hypeweb.smhi.se/timeseries/) and runoff of rivers Neva, Luga and Narva from NorthWest HYDROMET of Russia. 1.2 Drivers of the physics of the Gulf of Finland

The dynamics of the Gulf of Finland is driven by two variable main factors, the prevailing atmospheric forcing and the fresh water runoff to the gulf. Static drivers are the coastlines and bathymetry of the gulf. In this chapter we briefly look at the variable drivers and their changes in 1996 – 2014.

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1.2.1 Weather and climate

Weather conditions in the Baltic Sea and in the Gulf of Finland are much controlled by low-pressures coming from the North-Atlantic Ocean. The weather conditions are often described by using different North Atlantic Oscillation, NAO, indexes defined as air pressure difference between Azores and Iceland. A specific Baltic Sea Index (?) has also been developed, but it seems to be difficult to find as a time series. Thus we still stick to different NAO indexes as the simple descriptor of large-scale weather conditions.

Positive NAO means stronger storms in the North Europe and negative NAO means fewer and weaker winter storms.

Seasonal NAO indexes show large variations in the time period between and including the Gulf of Finland years 1996 and 2014.

Figure 1. Seasonal NAO indexes for the period 1996 – 2014.

The strong storms that may force major saline water inflows to the Baltic Sea have traditionally been thought to be winter storms. Therefore the wintertime NAO (djfm) that describes the NAO in December – March may be used to indicate the intensity of saline water exchange between the North Sea and Baltic Sea. The possible effects of the major saline water inflows should to be seen in the western Gulf of Finland deep water after six months or more from the inflow event in the Danish Straits.

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Figure 2. Winter NAO index in 1996 – 2014.

Wind conditions in the Gulf of Finland are typically such that low-pressures come from the North Atlantic to the Baltic Sea and to the Gulf of Finland. Southwest winds are prevailing (Figs ). The wind direction distribution is even more focused the heavier the winds are. Thus the heavy winds blow usually almost along the gulf from southwest to northeast.

Figure 3. The wind rose from Helsinki lighthouse between Helsinki and Tallinn in the central Gulf of Finland. The data is from years 2003-2015.

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Figure 4. The wind roses of summer and winter times from Helsinki lighthouse between Helsinki and Tallinn in the central Gulf of Finland. The data is from years 2003-2015.

Figure 5. The wind direction distribution at Helsinki lighthouse. The solid black curve shows the overall distribution od winds, the grey line shows the distribution of winds where speed is >= 5 m/s and the red curve shows the distribution of winds with speed >= 10 m/s.

To describe the weather conditions by air and seawater temperature in the Gulf of Finland we used data from Harmaja just outside Helsinki. The weather station has excellent time series including that of the sea surface temperature. We consider that Harmaja data gives good overall indication of the nature of the years in the study period.

The annual course of the sea surface temperature at Harmaja in 1996 was very different than in 2014. The previous Gulf of Finland year was cool until mid- August and then mostly average except the middle of October. The present Gulf of Finland year was warm in the spring, then variable and average in the autumn.

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Figure 6. Annual course of sea surface temperature (SST) at Harmaja in 1996 (blue) and in 2014 (red) in comparison to minimum and maximum values of the period 1996 – 2014.

We used the traditional definitions of the seasons to the air temperature data from Harmaja to get the variability of the season length. The winter is defined to be the season when the air-temperature is below zero whole the time. Summer is the season when air temperature is above 10 degrees and spring and autumn are between. We see that there are no clear trends within our study period.

1.2.2 Precipitation

For precipitation we used the data from Helsinki, Kaisaniemi weather station from where there is a good time series of monthly precipitation. We calculated the annual precipitations and their anomalies in comparison to the average of the study period. Both Gulf of Finland Years (1996 and 2014) were slightly drier than the long-term mean.

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Figure 7. Annual precipitation anomaly at Helsinki in 1996 – 2014.

1.2.3 River runoff

The GoF is estimated to receive 112 km3/a river water (Soomere et al 2009). The data from HELCOM fact sheets (Total and regional runoff to the Baltic Sea Baltic Sea Environment Fact Sheets 2014, Kronsell and Andersson 2014) gives the same average annual runoff for the period 1996 – 2013. Here it should be noticed that partly the data behind these estimates comes from model simulations and may have inaccuracies.

The eastern end of the Gulf of Finland receives the largest single freshwater inflow to the Baltic Sea from the river Neva. From 1996 to 2014 Neva had an average discharge of 2432 m3/s and the range of variation was from 861 to 3650 m3/s. The annual minimum discharge is in winter (Fig. ) and maximum in spring.

Figure 8. Monthly average discharge of river Neva.

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Other major rivers that flow to the Gulf of Finland are Kymi, Narva and Luga. Average discharge of Kymi in our study period was 304 m3/s with range of variation 87 - 743 m3/s. Average discharge of Narva was 398 m3/s and range of variation 131 - 949 m3/s. Luga is smaller with averga discharge of 104 m3/s and range of variation 14 - 634 m3/s.

The average discharge of all these four rivers together is about 100 km3/a, which is roughly 89% of the total river runoff to the gulf. Neva alone contributes about 67% of the river runoffs to the Gulf of Finland.

Figure 9. The runoff of rivers Neva, Narva, Kymi and Luga to the Gulf of Finland and their sum. Runoff.

It is interesting to compare the Gulf of Finland year runoff data to the estimates of HELCOM where modelled runoffs are used. We can notice that the general picture is similar, but some discrepancies exist.

Figure 10. Total river runoff to the Gulf of Finland given in HELCOM (thick solid line) and the sum runoff of the rivers Neva, Narva, Kymi and Luga.

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The annual average runoff to the GoF is about 11% of its volume. Thus the runoff has to be considered in modelling the dynamics and conditions of the gulf. Because the runoffs are not available in real time or even not in near-real time, the attempts has been done to use such rivers from where there is runoff data quickly available, like river Kymi, as a descriptor of the total runoff. However, the temporal behaviour of the discharges differ between rivers so much (see Fig.) that this is hardly possible with any better accuracy than got by using hydrological model results.

Figure 11. Monthly mean discharge of river Nave versus that of river Kymi in 1960 to 2014.

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Figure 12. Anomaly of the monthly mean runoff of river Neva in comparison to the monthly mean between 1996 and 2014.

The general behaviour of river runoff during our study period has been towards larger runoff, but there is not linear trend. The runoff had a minimum in 2003 and it has increased slowly since then. The time variations in the monthly mean runoffs are large and this should affect salinity variations, too.

1.2.4 Ice conditions in the Gulf of Finland in 1996 - 2014

Annual maximum ice extent (Fig. ) is a simple parameter that is often used to describe the severity of the winter. It may also be used in estimating variability of the winters. The national ice services around the Baltic Sea have agreed together to classify the winters into four main severity categories on the basis of data from winters 1960/61 – 2009/10. The winter is mild if the annual maximum ice extent is under 115 000 km2 and severe if the annual maximum ice extent is over 230 000 km2. Winters that have annual maximum ice extent over 345 000 km2 are extremely severe.

In this scale the first Gulf of Finland year was second hardest winter in our study period and winter 2014 was the second mildest winter in this period. However, there was no significant trend during this time period, but the winters were variable. There were four severe winters (in the order of severity, largest first) 2011, 1996, 2010 and 2003 and five mild winters (in the order from mildest), 2008, 2000, 2014, 2002 and 2009. Ten of the 19 study years had average winters.

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Figure 13. The maximum annual ice extent of the Baltic Sea in 1996 – 2014. The horizontal lines classify the winters to mild, normal, severe and extremely severe.

1.2.4.1 Ice conditions in the Gulf of Finland 2014

The course of the ice winter of 2014 is shown in Fig. . In such a mild winter only the eastern GoF freezes and the Estonian coast remains ice-free during the whole winter. However, the ice is still an issue for St Petersburg and for the Russian oil harbours in and near the Vyborg bay.

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Figure 14. The weekly progression of the ice conditions in the Gulf of Finland from 13th January to 7th April 2014.

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1.2.5 Sea level

In the Baltic Sea the sea level is interesting question because there are two different processes affecting to opposite directions. The land upheaval, that is largest in the Quark causes the mean sea level to sink in reference to land, where as the global sea level rise tends to rise the mean sea level. At present these processes almost compensate each other in the Gulf of Finland as seen from annual mean sea levels given in the reference level of the sea level stations (Fig. ).

Figure 15. Annual averages of sea level along the Finnish coast of the Gulf of Finland relative to the reference points of the sea level stations.

Figure 16. Annual sea level standard deviation along the Finnish coast of the Gulf of Finland. The variability increases from west to east.

The variability of the sea level may be used as an indicator of weather variability. We see that the standard deviation of the sea level along the Finnish coast behave similarly from west to east (from Hanko to Hamina) and characterises the conditions of the different years. We may conclude that the weather varies

15 continuously with time and no systematic changes are present in our study period.

Figure 17. Sea level in Helsinki in 1996 in comparison to the statistics from 1904 – 2014. The reference is the theoretical mean sea level.

Figure 18. Sea level in Helsinki in 2014 in comparison to the statistics from 1904 – 2014. The reference is the theoretical mean sea level.

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Figure 19. Daily average sea level at Hanko in 1996 – 2014 referenced to theoretical mean sea level.

Figure 20. Daily average sea level at Helsinki in 1996 – 2014 referenced to theoretical mean sea level.

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Figure 21. Daily average sea level at Hamina in 1996 – 2014 referenced to theoretical mean sea level.

1.3 Monitoring and indicators of hydrography

Typical indicators of hydrography have been temperature and salinity. Because the annual cycle of temperature is strong, deep-water temperature has sometimes been used as indicator of longer-term changes. In this sense the deep water is considered to be water layers that are not affected by the seasonal overturning of the upper layer. This means the layer that is below the dicothermal layer, or in other word below the old winter water layer. It should be kept in mind that the thermal memory of the upper layer is reset in the overturning events.

Salinity changes are also affected by the overturning but in lesser extent than for the temperature. Thus both upper layer and deeper layer salinities are interesting.

1.4 Gulf of Finland assessment period 1996 - 2014 in perspective

This assessment examines the changes in the physical state of the Gulf of Finland in the two Gulf of Finland years and between them. Its is important to put this study period into some perspective against longer time period if possible.

Temperature and salinity have varied up and down in 70 meters depth in the central Gulf of Finland between Helsinki and Tallinn after 1962 whole the time in shorter time scales. There is also a longer period of variation so that especially the salinity had a degreasing trend from 1976 till 1993, which is related to the absence of the major saline water inflows to the Baltic Sea. The salinity increased from 1993 till 2006 and decreased again after till 2013. During these general changes there were, however, large short-term variations. These are caused by the internal dynamics of the gulf. Because there is continuous longitudinal gradient and because the layers oscillate, the salinity and

18 temperature values vary considerably at fixed positions. These higher frequency variations make the assessing of the state of the gulf difficult if there are not enough observations available.

From the data we also see how dependent the physical conditions in the Gulf of Finland are from the large-scale conditions in the Baltic Sea Proper.

Figure 22. Temperature (lower curve) and salinity (upper curve) at 70 m depth at station LL7 in the middle of the Gulf of Finland between Helsinki and Tallinn.

2 Changes in stratification

The density stratification is determined by the vertical distribution of temperature and salinity. In winter the temperature is near to freezing point in the surface layer and in summer there is a warm upper layer below which there is a strong thermocline.

The salinity stratification is usually different in winter in comparison to the summer. The difference of surface and bottom salinities is 1.5 – 2 in winter and about 4 in winter. The mixing in autumn and winter breaks the stratification and helps to renew even the deep waters of the GoF, because the gulf is generally rather shallow. In summer the stratification develops and vertical mixing is hindered and then the stronger salinity stratification develops. The more saline water from the Baltic Sea proper can then form a noticeable halocline somewhere at depths greater than 60 m.

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Figure 23. Salinity stratification in the middle of the Gulf of Finland (LL7) in winter (left) and in summer (right).

Figure 24: Time series of thermocline depth in LL7 station in August

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Figure 25: Time series of salinity variations in LL7 station 1991-2014

3 Halocline development

The Gulf of Finland has positive fresh water balance, which is mainly affected by fresh water inflow from the rivers, fresh water outflow and salty water inflow from the Northern Baltic Proper. The river Neva brings fresh water to the eastern Gulf of Finland where there is no permanent halocline. Also, in the shallow coastal areas the halocline is missing. In these areas strong winds are able to mix the whole water column.

4 Near bottom salinity changes

The near bottom salinity is an important parameter, because it determines the deep layer density stratification.

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Figure 26. Salinity variations at 60 m depth at stations LL12, LL9, LL7, LL5 and LL3A along the centreline of the Gulf of Finland.

Figure 27. Temperature variations at 60 m depth at stations LL12, LL9, LL7, LL5 and LL3A along the centreline of the Gulf of Finland.

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Figure 28: Near bottom salinity measured during the years 1991-2014 from deep stations: F62, F56, SLJP5, GF1, GF2

Figure 29: Near bottom salinity during the years 1996 (above) and 2014 (below)

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5 Deep-water oxygen conditions

The strong stratification isolates deep waters from the surface layer in summer. Thus, the vertical mixing is hindered and the deep-water oxygen conditions are mainly related to the horizontal advection. The latter may act so that often more saline water from the deep layers of the Baltic Sea Proper bring oxygen-poor saline water into the western Gulf of Finland. In areas where autumn and spring mixing reaches the bottom, the oxygen content renews twice a year and in deeper areas the poor oxygen conditions may remain longer times. This is seen in the fact that generally the deep-water oxygen content is lower in summer than in winter.

The Gulf of Finland year datasets show that both in coastal and open sea GoF there is clear overall salinity dependence of the oxygen content in deep water. However there is also a lot of variability in that meaning that the oxygen content may be very different at the same salinity.

Figure 30. Oxygen content versus salinity in deep water at a coastal station near Helsinki.

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Figure 31. Oxygen content versus day of the year in deep water at a coastal station near Helsinki. The big dots are data from 2014.

Figure 32. Oxygen content at 90 m depth at LL7 relative to the salinity.

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Figure 33. Oxygen content at 60 m depth at LL7 relative to the salinity.

Figure 34. Oxygen content versus day of the year in deep water at an open sea station LL7 between Helsinki and Tallinn. The big dots are data from 2014.

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Figure 35. Oxygen content of the deep-water (70 m depth) of the western Gulf of Finland in autumn 2014.

Figure 36. Near bottom oxygen content in the western Gulf of Finland in autumn 2014. NOTE! The bottom depth is different at each station and the oxygen content is from 5 m above the bottom at each station.

6 Dynamic features of the Gulf of Finland

CTD-data from relatively dense station network in the Gulf of Finland can be used to describe the dynamic scales on the gulf. We calculated the first internal Rossby radius from the CTD-profiles taken from R/V Aranda in 2013 and 2014 in a dense grid in the western Gulf of Finland. In the calculations the whole density profile down to the bottom should be taken into account.

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However, the profiles measured with the large CTD/Rosette-system are usually stopped five (5) meters above the bottom. In the Baltic Sea there are often situations where there is a pronounced secondary halocline very near to the bottom (less than 5 m) depending on the depth of the station and on the water masses that come from deeper more saline areas. To overcome this problem we use a secondary small portable CTD (CastAway) which is attached under the CTD/Rosette system with a 5 m long rope. This small CTD then can measure the profile practically down to the bottom (to only 10 – 20 cm above the bottom). Unfortunately the small CTD can only be used at stations where the bottom depth is less than 100 meters, but usually that is enough.

Figure 37. First internal Rossby radius of deformation in the western Gulf of Finland in autumn 2014 calculated from CTD-profiles from stations less than 100 m deep. The profiles reach the bottom.

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Figure 38. First internal Rossby radius of deformation in the western Gulf of Finland in autumn 2014 calculated from CTD-profiles that reach depths 5 m above the bottom.

We can notice that the length of the Rossby radius is quite much related to the depth of the basin. Thus it is 3 – 6 km in the southern gulf and less than 3 km in the northern gulf. The figures show that there is a slight difference between the Rossby radius values depending on how complete the profiles are. The profiles with data down to the bottom give larger Rossby radius than those that need to be extrapolated to the bottom.

7 Wave climate in the Gulf of Finland 2014 H. Pettersson

The winter 2013 - 2014 was rather mild and the wave buoy in the Northern Baltic Proper could be kept anchored through the season. Due to the growth of the sea ice coverage the wave buoy off Helsinki was recovered 22nd January and redeployed 1st April.

The wave climate in the middle parts of the Gulf of Finland showed similar behaviour than in the Northern Baltic Proper. January was calmer than usually, followed by a spring and early summer typical for the season. From July to December, the clearly calmer and slightly rougher than usual months alternated. The highest significant wave heights remained well below the highest measured values in these two locations. The highest significant wave height in the measurement period 2014 was 3.1 m at station Helsinki, recorded on 3rd December, while in the Northern Baltic Proper the highest significant wave height reached 6.7 metres on 10th December. These were also the only times the significant wave height exceeded 3 and 6 metres, respectively.

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Figure 39. The monthly means (left panels) and maxima (right panels) of significant wave height at stations Helsinki (59 57.0' N, 025 14.1'E) and Northern Baltic Proper (59 15.0' N, 021 00.0' E). The year 2014 is in red and the long-term statistics in blue. The year in the legend means the beginning of the measurements, but due to the ice coverage, instrument failures etc. all months do not include the whole time period.

8 Conclusions

The general conclusion of the hydrographic features of the Gulf of Finland is that the conditions are very time and space variable. There seems to be no big trends in the parameters between the Gulf of Finland years 1996 and 2014. We can state that the two Gulf of Finland years were very different to each other and direct comparisons of these two without taking into account the variability between these years could lead to wrong conclusions.

The dynamic scales of the gulf are rather small which means that spatial resolution of observations should be high in order to resolve the relevant

30 processes. The time variability with strong annual cycles in temperature and mixing suggests that monitoring with dense temporal resolution and regular observations should be done.

The Gulf of Finland is by nature a vulnerable sea area. Natural conditions lead easily to oxygen depletion in the deep waters and thus there are all reasons to keep on watch the environment and keep the human impact as small as possible.

9 Nutrient loading and eutrophication

Coordinator: Heikki Pitkänen, SYKE

9.1 Eutrophication (vs. nutrient loading) in the Baltic Sea and in the Gulf of Finland in recent decades (short literature overview)

Heikki Pitkänen (SYKE), Tatjana Eremina (RSHU, Urmas Lips (MSI)

The Gulf of Finland is sensitive to eutrophication due to both natural and anthropogenic reasons. This is caused by salinity stratification, the free sub- halocline connection with the Baltic Proper and the fact that the surface area specific loading of nutrients critical for eutrophication - phosphorus and nitrogen - is higher for the Gulf than for most other sub-basins of the Baltic Sea (Pitkänen et al. 2008). Noticeable eutrophication related phenomena are e.g. cyanobacterial blooms, increased turbidity, oxygen deficits/ accelerated release of benthic nutrients, and extensive growth of filamentous algae.

Assessing the Gulf as only a one sub-basin is in many respects an oversimplification. In addition to variations between coastal water and the open sea, there are large differences in the basic physical and geomorphological conditions between the more marine western Gulf, and the estuarine eastern Gulf, strongly affected by fresh water, especially from the River Neva, the largest river entering the Baltic Sea. Additionally, most of the external nutrient loading enters the eastern Gulf of Finland. Thus, a detailed analysis on the eutrophication and its controlling factors require an assessment, which follows a suitable and well formulated sub-area division regarding both coastal and open sea waters.

The external nutrient loading into the Gulf has substantially decreased since the late 1980, when annual loads were assessed as about 10 000 tons of phosphorus (P) and 200 000 tons of nitrogen (N) (Kiirikki et al. 2003). For the year 2010 the corresponding loads were estimated as 6 400 tons of phosphorus and 122 000 tons of nitrogen (HELCOM 2013). The loading of both nutrients decreased in all three countries especially during the late 1980s/early 1990s due to water

31 protection measures, and in Russia and Estonia also due to decreases in agricultural and industrial production (Lääne et al. 2002). In recent years considerable decreases in phosphorus loading have taken place due to effective improvement of municipal waste water treatment in St. Petersburg and measures which finished phosphorus leaching from the waste stack of a fertilizer plant by the River Luga (HELCOM 2012, Vodokanal 2015).

An important factor controlling the trophic status of the Gulf of Finland is the exchange of water and nutrients between the Gulf and the Baltic Proper. For nitrogen the net flux is clearly from the Gulf to the Baltic Proper, while for phosphorus the annual net flux can be also towards the Gulf (Savchuk 2005, HELCOM 2009). The Input of saline deep water affects the nutrient balance of the Gulf also indirectly via affecting the strength of halocline and near-bottom oxygen conditions, and thus controlling the amounts of benthic nutrient release.

Another factor, which should be taking into account for trophic status assessment, is climate change. In the Baltic Sea, water temperature increased by up to 1 ˚C per decade between 1990 and 2009. It is projected that near the end of this century, summer sea-surface temperature will be about 2 ˚C higher in the southern parts of the Baltic Sea and about 4 ˚C higher in the northern parts than present. At present, it is not clear how climate change will influence productivity and sub-sequent eutrophication signals in the Baltic Sea and it is likely that impacts will vary in different sub-basins (HELCOM 2013c). Studying the effects of climate change on deep-water oxygen in the eastern Gulf of Finland showed that the deterioration of the oxygen regime in the late XX - early XXI century, was mainly due to large-scale changes of atmospheric processes in the Northern Hemisphere (Eremina et.al, 2012). Also the results of the new climate change assessment (HELCOM 2013c) indicate that runoff is related to temperature and that the warming will be associated with reduced runoff in southern parts of the Baltic Sea catchment area and greater runoff in the northern regions. This can change the nutrients loading significantly.

Long-term development of the summertime trophic status of the Gulf did not follow the decreased loading in the late 1980s and the 1990s. However, the decrease was evident in the observed spring time phytoplankton biomasses, measured as chlorophyll-a, in the coastal western Gulf of Finland, which could be connected to decreased inorganic nitrogen concentrations and decreased loading of N (Raateoja et al. 2005). The similar development probably took place also more widely in the Gulf (Pitkänen et al. 2008).

Despite the decreased nutrient loading, summertime chrophyll concentrations have shown an increase in the 1990s and the early 2000s due to accelerated sediment release of nutrients (“internal loading”) caused by occasional extensive oxygen deficiencies in the mid-1990s and after that. The development has been connected to intensified sediment releases of phosphorus, which led to intensified production of N-fixing cyanobacteria (Raateoja et al 2005, Pitkänen et al. 2008). The present eutrophication status of the Gulf is below good, and mostly classified as bad, both in coastal and open sea areas (HELCOM 2010).

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Intensified benthic nutrient release doesn’t bring totally new nutrients in the water-sediment system, but circulates “old” earlier sedimented nutrients. Thus it can’t be directly compared with external nutrient loading from the catchment and via atmosphere bringing totally new nutrients into the system. However, sediment nutrient release keeps the nutrient content of water mass on an elevated level, and thus increases algal production, and partly hides the positive effects of nutrient load reductions.

Because the status of the Gulf is affected both by external loading and internal processes, and additionally internal processes are also affected by external loading, an important but also a very challenging task will be to distinguish the roles of external loading and internal processes in the development of the status of eutrophication of the Gulf. This concerns especially the most recent years, when positive signs in the development of summertime trophic status have emerged.

References

HELCOM 2009.Eutrophication in the Baltic Sea.BSAP No. 115 b. 148 p.

HELCOM, 2010.Ecosystem Health of the Baltic Sea 2003–2007: HELCOM Initial Holistic Assessment. Balt. Sea Environ. Proc. No. 122.68 p.

HELCOM 2012.Final report of the HELCOM assignment "Building capacity within environmental monitoring to produce pollution load data from different sources for e.g. HELCOM pollution load compilations".The Finnish Environment Institute (SYKE) and Institute of Limnology, Russian Academy of Sciences (ILRAS).36 p.

HELCOM 2013.Updated Fifth Baltic Sea Pollution Load Compilation (PLC-5.5) – An Extended Summary.Balt. SeaEnviron. Proc. No. XXX

Kiirikki, M., Rantanen, P.,Varjopuro, R., Leppänen, A., Hiltunen, M., Pitkänen, H., Ekholm, P., Moukhametshina, E., Inkala, A., Kuosa, H., & Sarkkula, J. 2003. Cost effective water protection in the Culf of Finland.The Finnish Environment, no. 632.55 p.

10 Distribution and trends of N and P

Mika Raateoja, Pirkko Kauppila, Seppo Kaitala, Jan-Erik Bruun (Finnish Environment Institute) Tatjana Eremina, Alexandra Ershova, Eugenija Lange (Russian State Hydrometeorological University) Urmas Lips (Marine Systems Institute)

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10.1 Introduction Marine primary production happens within the frames set by the chemical environment, and the nutrient inventories are the most direct and convenient metrics describing the trophic status of an aquatic system.In this section, we describe the past changes in the trophic status of the GOF using dissolved inorganic nutrient regimes. We adopt different angles to the issue; the spatial approach, the current situation, and the long-term trends. 10.2 Data providers The assessment of distribution and trends of dissolved inorganic nitrogen and phosphorus (more specifically NO2-N+NO3-N and PO4-P, hereby DIN and DIP) was based on the GOF2014 dataset supplemented by the 2014-15 trilateral data. The data providers include (in alphabetical order): City of Helsinki Environment Centre; Estonian Marine Institute; Finnish Environment Institute; Marine Systems Institute; North-West Interregional Territorial Administration for Hydrometeorology and Environmental Monitoring; Russian State Hydrometeorological University; South-East Finland Centre for Economic Development, Transport and the Environment; and Uusimaa Centre for Economic Development, Transport and the Environment. 10.3 DIN and DIP as trophic indices Our conclusions are based on the wintertime surface layer data of DIN and DIP (December-March, upper 10 m).

We chose to use the wintertime nutrient inventories as these inventories are at their annual peak at that time due to the nutrient regeneration after the growth season and subsequent introduction of these regenerated nutrients into the surface layer. Traditionally, wintertime data refers to the months January- February but as the vernal bloom period will not start until in April in the GOF (Raateoja et al., 2011) we thought of including March into the dataset. The inclusion of December to represent the wintertime situation, in turn, depends on the advance of the vertical mixing processes late in the year. We tested the feasibility of December and March for inclusion into our dataset. We calculated average values for January-February (upper 10 m) and arrived at 13.9 and 0.87 µmol/l for DIN and DIP, respectively (n = 1125 and 1095). When compared to these January-February average values, December and March averages for DIN were 54 and 29 % higher, respectively. For DIP, December and March averages were 6 % higher and 9 % lower, respectively. Hence, it seemed reasonable to include both of these months to represent the wintertime situation.

We were actually not dealing here with years, but rather, winters. So, there were times that we linked the December observations to wintertime. For instance, the observations in December 1999 may have been included in the data of the winter 2000. These instances are mentioned separately.

Choosing all the data in the upper 10 m of the water column was a necessity. We expected that the making of the assessment based solely on 1 m observations 34 would not provide with enough data for any solid conclusions (n = 556 for DIN and 566 for DIP). We were also aware that the inclusion of the 5 and 10 m data could triple the observation count due to the countries’ commitment to the HELCOM guidelines(HELCOM, 2014). The 1-m average for DIN and DIP (December-March) was 11.0 and 0.75 µmol/l, respectively. The 5 and 10 m averages were 87 and 81 % of that for DIN, and 111 and 112 % of that for DIP. At a first glance, the stretching of the part of the water column that was chosen as the basis for the assessment led to changes in the representative DIN and DIP levels. The depth-wise differences in DIN were caused by the region “FIN Coast In” where the stations situated in the inner archipelago; some of the stations were shallower than 10 m, and they all were situated in the estuaries.KYVY-9 is located near the western outlet of River Kymijoki,UUS-16 in a semi-enclosed fjord-like basin subject to River Mustionjoki, Vanhankaupunginselkä 4 near to the outlet of River Vantaanjoki, and Virolahti 291 near to the outlet of River Virojoki. These stations are subject to surface river plumes high in DIN and DIP excluding the stations affected by River Kymijoki, which is poor in DIP (Pitkänen et al. 1986). So, the depth-wise variation in the data could be ignored. 10.4 Spatial approach We employed a spatial approach where the GOF was divided into regions on a geographical and topographical basis (Fig. not yet drawn, Table 1). The regions are as follows:  Estonian Coast Inner (ECI) represents the inner Estonian coastal area. In spite of being called “Inner”, ECI possesses much of the characteristics of the offshore areas: it has a similar salinity range due to the open coastal topography and the typical current pattern of the GOF.  Estonian Coast Outer (ECO) represents the outer Estonian coastal area. ECO is fully comparable to offshore areas, and includes actually the deepest stations in the GOF.  Finnish Coast Inner (FCI) represents the inner Finnish coastal area. Surrounded by the scattered archipelago, and subject to a considerable riverine impact, FCI represents the least marine environment here.  Finnish Coast Outer (FCO) represents the tension zone between the Finnish archipelago and the offshore area. The stations are located near to the outer islets, and represent quite well the offshore environment. KYVY-1 is an exception: it is located off the River Kymijoki’s outlet and is subject to a considerable riverine impact. This way, it lies somewhere between FCI and FCO, but is more conveniently included in FCO.  Finnish Coast Eastern (FCE) represents the tension zone between the Finnish archipelago the offshore area east of Gogland Island. In this study, this region is most affected by the nutrient loading pattern of the Eastern GOF.  Narva Bay is the shallowest (albeit open coast) region under study within the Southern seaboard, and as such is cannot be considered as a true offshore region.  Offshore Middle (OM) represents the offshore area of the middle GOF, having salinity about 5.  Offshore West (OW) representing the offshore area of the western GOF, having salinity about 6.  Neva Bay is the shallowest region which is located between the River Neva outlet and Kotlin Island, and separated from the rest of the GOF by the St. Petersburg Dam.

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 Easternmost GOF (EGOF) covers two subareas, namely the shallow-water area, which lies east of the transect Shepelevsky cape – Flotsky cape, and the deep-water area, which situates itself between this line and Gogland Island. The inner part of the latter, having a western limit along the transect Berezovye - Seskar Islands, is under the impact of the Neva river flow.

The regional representative values for DIN and DIP are averages of data met the following criteria: i) temporal: since the year 1996, ii) vertical: the upper 10 m data, iii) spatial: all the stations in the region. For the Russian territorial waters, there is no coherent long-term wintertime data available, and hence, the summertime data will be used instead.

10.5 Spatial distributions of DIN and DIP

10.5.1 East-West section: offshore regions“Offshore Middle”and“Offshore West” In general, the DIN and DIP concentrations tended to increase towards the east. This tendency was more obvious for DIP (region wise averages0.67 and 0.86 µmol/l for OW and OM) than for DIN (6.8 and 7.6 µmol/l for OW and OM).The observed discrepancies between Estonian and Finnish monitoring data obscured this pattern (Fig. 10.1).

If the station averages were compared nation-wise we would arrive in clear west-east gradients of about 100 nautical miles:  FIN: LL12

10.5.2 East-West section:“Finnish Coast Outer” and “Finnish Coast Eastern” The eastward increasing pattern in DIN and DIP holds true also for the Finnish outer coastal waters. FCO, covering the area from Hanko peninsula to Helsinki, had its average values of 8.8 and 0.90 µmol/l for DIN and DIP while FCE, locating north-east of Gogland, had the corresponding averages of 12.5 and 1.13 µmol/l. The eastward cascade of stations UUS-23 <39A< KYVY-11 had increasing trends for DIN and DIP, too (6.7 to 10.2, and 0.83 to 1.11 µmol/l for DIN and DIP) due to the traditionally high level of nutrient loading into the GOF. The station KYVY-1 situates closest to the coast of all the stations in FCO and is subject to a pronounced impact of River Kymijoki. This is reflected in its average DIN of 13.1µmol/l. By contrast, the average DIP there is reduced by the DIP-poor riverine waters (Pitkänen et al. 1986).

10.5.3 East-West section: “Estonian Coast Inner”and “Narva Bay” ECI was split up into groups according to the DIP levels; the western group of stations (23A, PW, PE), covering the coast from Osmussaar to Paldiski, had DIP averages ≤ 0.60 µmol/l while the eastern group (2, 3, 57A, 18A), situating off

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Tallinn and in Kolga Bay, had averages > 0.75 µmol/l. The station network in Narva Bay had the average DIP > 0.80 µmol/l with one exception. Put together, the above-mentioned trend holds true in the southern seaboard, too.

For DIN, the trend was not so obvious. Narva had somewhat higher average than ECI (6.7 and 6.1 µmol/l, respectively), but when looking at the stations, the average seemed to be more affected by the actual point along the coast than the west-east location. For instance, the highest DIN level in ECI and Narva were observed at 57A and N8, respectively. The former is located in Tallinn Bay, and the latter situates off the River Narva outlet.

10.5.4 North-South section: “Estonian Coast Inner” and “Estonian Coast Outer” ECI and ECO were located approximately at the same longitudal interval and thus was a solid pair for the coast-offshore comparison. The groups had only a moderate difference in the average DIN and DIP values (5.2 and 6.1 µmol/l, and 0.69 and 0.71 µmol/l, respectively, with lower values representing the offshore area). More precisely,  the Osmussaar pair (23, 23A) had clear differences; 23A (ECI) had lower DIP and higher DIN level than its offshore counterpart, being probably a manifestation of waters coming from Väinameri (Urmas, is this so?)  the Tallinn transect (19, 2, 57A) had clear increasing trends towards the coast, as expected. DIN and DIP presented the trends of 4.9 to 8.3 µmol/l, and 0.63 to 0.77 µmol/l, respectively, simply due to the anthropogenic impact  the Kolga pair (18, 18A) had no difference in DIN but some in DIP

10.5.5 North-South section: “Finnish Coast Inner” and “Finnish Coast Outer” The stations in FCI were all located in the complex and sheltered inner archipelago and subject to at times pronounced riverine impact. The huge difference observed in DIN levels between FCI and FCO was thus expected (the average DIN values of 8.8 and 63.0 µmol/l with the lower value representing the offshore area).

The station Vanhankaupunginselkä off Helsinki represents the least marine condition in this study, being in the inner estuary of the clayish River Vantaanjoki with DIN and DIP averages of 134.0 and 1.51 µmol/l, respectively. The inner coastal areas off Helsinki belonged to the highly-loaded coastal areas in Finland in the 1960s and 1970s, and have suffered from internal loading since then (e.g. Varmo et al. 1989, Kauppila et al. 2005). The station KYVY-9, in turn, receives waters from the large and DIP-poor River Kymijoki. As a result, KYVY-9 had the lowest average DIP level of all the Finnish stations.

The riverine impact rich in DIN but poor in DIP is clearly seen in the following station pairs  KYVY-9 and KYVY-1 situate 6 nautical miles apart in the vicinity of the River Kymijoki’s western outlet. The former situates itself in the inner archipelago while the latter in the tension zone to the offshore area. The gradually-increasing impact of GOF’s water rises up the DIP average from 0.37to 0.89 µmol/l when moving off the coast. At the same time, the average DIN decreases from 19.4 to 13.1 µmol/l

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 UUS-16 and UUS-23 represent the pair of a semi-closed inland bay and the offshore condition, being 16 nautical miles apart. Here, DIP average rises up from 0.50 to 0.83 µmol/l when moving off the coast. At the same time, the average DIN decreases from 28.8 to 6.7 µmol/l

In short, the DIN and DIP level seemed to increase eastwards and towards to the coasts, except in the Finnish coast where a substantial fresh water impact decreased the DIP level in the FCI. 10.6 The current DIN and DIP fields The description of the current nutrient fields are based on the data collected in the Estonian, Finnish and Russian territorial waters (excluding the shallow water area of the GOF) in the winters 2014-15. Furthermore, the summertime (August 2014) near-bottom data in the Russian part of GOF was used. Here, we did not employ any regional approach, but used station data as such.

Like in the regional approach, the wintertime surface-layer DIN showed an increasing pattern towards to the east (Fig. 10.2). Much of the western and middle GOF had DIN values < 8 µmol/l with somewhat higher values in the Finnish waters. The level 10 µmol/l was reached in the area east of Gogland Island and the next step-up (> 12 µmol/l) took place when entering the area east of Moshchny Island. The highest values were observed in the northern seaboard off Primorsk.

The wintertime surface-layer DIP presented an entirely different pattern. A level of 1.0 µmol/l seemed to be the barrier that divided the GOF into regions in this respect. The western GOF had DIP levels below that while the offshore and southern areas of the middle GOF had levels above that. The offshore areas east of Gogland Island had typically levels below that.

In general, spatial distribution of summertime deep-water DIN and DIP in the Russian territorial waters followed similar features (Fig. 10.3); the highest concentrations were observed within or at the brink of the shallow-water area where estuarine effects, i.e., the interaction of the Neva river waters with more saline waters, are pronounced. Especially for DIP but also for DIN, high values were observed in the deep water area of the EGOF, east of Gogland Island. Here, a greater depth causes a stronger vertical density gradient, and hence, oxygen deprivation. For DIP, that leads to internal loading.

10.7 Long-term trends of DIN and DIP In this temporal study we are dealing with winters, not years. Also the regional approach was employed (see above).

By pooling a number of stations into one mold, even from relatively small areas as the regions used here, we accept to deal with the varying trophic conditions of the pooled stations. The station-wise averages naturally varied within the regions; for all data, the highest station-wise average value was 60 and 30 % higher than the lowest station-wise average value for DIN and DIP, respectively.

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Consequently, for trend analyses, all the data within the region had to be normalized in order to exclude the impact of the quantitative inter-station differences on the conclusions. The following approach was used for both DIN and DIP:

The approach brings along a new way of thinking where single observations do not any more deviate from the station-wise average by units of µmol/l, but rather, by units of standard deviation (SD) of the sample population. This approach helps us to get rid of the impact of trophic differences of the stations within the region; the higher the quantitative level of the station, the larger SD we will meet. We also benefit from this approach in the handling of inner coastal stations which typically has been challenging due to a large temporal variation in the data. Now, the division of the data with SD compensates this effect at least partly. We chose the average value of the standardized DIN and DIP for each region and assessment year as our metrics. From now on, these will be denoted as DINst and DIPst, respectively.

10.7.1.1 DIP In addition to the DIP load from the catchment, the internal DIP load shapes the ambient DIP inventory of the GOF (Fig. 4). We thus expected that the temporal DIP trend could be characterized by a fluctuation that may not escort closely the changes of the DIP load from the catchment. Large recurring fluctuations indeed occurred. If we choose to explain the observed fluctuation with the changes in the intensity of the internal loading, we have to take into account that these values represent the wintertime. So, the physicochemical conditions favoring internal loading have had to take place in the previous year.

Looking at the entire monitoring period, the DIP pattern is characterized by a slight increasing trend superimposed by fluctuations spanning 2 to 4 year (Fig. 5). No region presented a clear trend that would have lasted throughout the assessment period. Various phases did emerge. The period 2000 to 2005 was characterized by marked fluctuation in the metrics, the period 2006 to 2010 by generally low metrics, and the period 2011-14 by generally high metrics.

FCE, FCO,OW, and OM (in the offshore area and northern part of the GOF) reminded much each other with respect to temporal variation in the DIP stock. They presented dual behavior for DIP in 1996-2006 where years of high and low DIPst shifted with gradually-stretching amplitude.

ECI, ECO, and Narva (in the southern part of the GOF) formed their own category where this dualism could not be verified, but perhaps due to insufficient data. This group seemed to shift into a new elevated DIPst level in 2009 to 2011.

FCI is topographically and hydrographically separated by the other regions, and expectedly, presented a pattern that was not in line with any other region. FCI is a collection of stations whose chemical characteristics largely reflect their catchment properties, and hence, is not a harmonious region according to any

39 criterion. Its inclusion into this assessment could well be questioned, but it is here to show that it does not share any trophic gradients observed in the other parts of the GOF.

All the regions exhibited an abrupt step up in DIPst in 2010 to 2011. This was not an exceptional phenomenon, but the southern regions continued to show high DIPst at least up to 2015 that is the end of our dataset. At that time, the trend was to decrease, though.

10.7.1.2 DIN Looking at the entire assessment period, the DIN pattern is characterized by a slight increasing trend starting at the turn of the millennium superimposed by fluctuations spanning 2 to 4 years (Fig 5).

The northern and offshore regions FCE, FCO, OM, and OW presented clearest the pattern similar to the DIP; a clear fluctuating DINst pattern throughout the assessment period.

The southern regions ECI, ECO, and Narva showed fluctuations that surpassed the ones of the northern and offshore regions but, again, the actual pattern was left unclear due to the shortage of data.

FCI had its unique fingerprint having only in 1996 to 2002 a pattern that resembled the others.

All the regions except FCI exhibited an abrupt step up in DINst in 2011 to 2012. The more-than-just-temporary change in the DINst level in the southern regions is noteworthy.

To conclude, both DIN and DIP was described by a fluctuating pattern with a variable time-span, and a moderate increasing trend when the entire assessment period was looked upon. The latest observed step-up in the DIN and DIP levels in 2010-12was more consistent in the southern than in offshore and northern regions. This step-up was not however any unique phenomenon per se. Rather, it was a continuation of the pattern that has taken place throughout the entire assessment period. The amplitude seems to have gotten larger, though.

10.7.1.3 Summertime DIN and DIP in the Eastern GOF The Russian monitoring program lacks the continuous wintertime monitoring. The most appropriate way to assess DIN and DIP trends there is to use the summertime (August) monitoring data. As regeneration processes control the surface-layer inorganic nutrient quota in the summertime we employed the near-bottom observations for the long-term inspection.

Alike in the western GOF, the long-term DIP dynamics in its easternmost part demonstrated a large inter-annual fluctuation, only this time without any clear trend stretching over the monitored period (Fig. 6). The temporary peaks of DIP in 2003, 2006, and 2010 were related to oxygen deprivation near to the seafloor. In 2003 and 2010, hypoxic conditions were extremely severe. In the shallow area, a slight increasing DIP trend as of 2008 was observed, while in the deep

40 part of the GOF, no such trend was observed. The long-term DIN pattern showed a constant and significant increase after 2006 due the invasion of the non- indigenous species Marenzelleria arctia and their bioirrigation and bioturbation activity (Maximov et al. 2014, Fig. 7). This step-up seems to have resulted in at least a quasi-constant change in the nutrient regime.

Common features in interannual variance are high values of DIP in 2003, 2006 and 2010 years which are related to hypoxia or anoxia conditions especially for 2003 and 2010 when hypoxia was extremely vast. Low values of DIP were observed in 2007-2008 and 2012.

10.8 DIN and DIP: relation to the existing environmental targets EU (2010) stipulated the criteria for good environmental status to its marine areas. The descriptor 5’s definition was that human-induced eutrophication is minimized, especially adverse effects thereof, such as losses in biodiversity, ecosystem degradation, harmful algal blooms and oxygen deficiency in bottom waters. The criterium5.1.defined the nutrient levels being close to natural levels. Consequently, (HELCOM, 2013); HELCOM (2014) listed, based on mathematical models, the target values for its eutrophication core indicators “concentration of dissolved inorganic nitrogen” and “concentration of dissolved inorganic phosphorus”. These indicators were built in similar way to our approach except the months December-February were used instead of December-March. For the open GOF, the target values were 3.8 and 0.59 µmol/l for DIN and DIP, respectively. The model approach was not applicable for coastal waters, and hence, target values for these areas are lacking.

We chose to reflect the DIN and DIP trends of three stations in the GOF onto these targets.  LL12 situates in the western brink of the GOF, and as an offshore station represent the baseline, i.e., the lowest nutrient content met in the GOF (similar stations in this study were, e.g., H1 and 23, but these had no such long datasets)  LL7/LL7S in the middle of the GOF has the longest time series available. It represents well the GOF in general  KYVY-11 situates at the near-to-offshore area in the middle GOF, having the highest nutrient content in the Finnish territorial waters (the stations in the inner archipelago not counted here)  UUS-23 is comparable to KYVY-11 with regard to its location in the outskirts of the archipelago. Hence, their differences in the nutrient regime reflect the east-west trophic gradient Together these three stations cover well the trophic gradient in the studied part of the GOF.

For DIN, LL7/LL7S has not met the GES target level since the mid-1970s (Fig. 8). LL12 has done that also after this time, but only occasionally. UUS-23 and KYVY- 11 have not met the target during the monitored period which was expected as the target concerns the offshore area. UUS-23 has clearly lower general DIN level indicating the east-west trophic gradient.

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For DIP, all of the stations have met the GES target at some point of time. KYVY- 11 did this in the late 1960s, LL7/LL7S and UUS-23 have done this occasionally but not after 2001. LL12 meets the target because in the chemical sense LL12 is not part of the GOF. If we compare LL12 to the target values of the Northern Baltic Proper (2.9 and 0.25 µmol/l for DIN and DIP, respectively) it would not have reached the targets at any time. 10.9 DIN/DIP ratio The molar DIN/DIP ratio shows no concise trend during the monitored period (Fig. 9). The ratio ranges from 7 to 13 showing temporal fluctuation similar to the patterns of DIN and DIP, more precisely similar to that of DIN. The southern regions ECI, ECO, and Narva have the ratio somewhat lower (6 to 9) than have the offshore and northern regions (8 to 12).

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Table 1. The sub-regions and stations in the GOF: the stations, temporal data coverage, the numbers of observation for DIP and DIN, and the representative averages for the entire time period. The data is surface wintertime data (upper 10 m, months December-March). * LL7 and LL7S form a pair of stations; LL7 is the deep hydrographic station (100 m) and LL7S is the shallower (77 m) benthic station. The distance between the two is less than 1 nm.

Surface Average Average Count Depth salinity Count of of of Subregion / Station Time period of (m) in PSU DIN DIN DIP DIP (approx.) (µmol/l) (µmol/l) EST Coast In 10-44 5.8 1999 2015 217 6.1 214 0.71 2 44 5.9 1999 2015 41 5.8 44 0.77 3 40 5.5 1999 2015 36 5.3 36 0.82 PE 21 6.0 1999 2014 30 5.6 27 0.60 18A 46 5.3 1999 2014 33 5.3 33 0.80 23A 25 6.1 1999 2013 20 6.3 20 0.51 57A 10 5.9 1999 2015 27 8.3 27 0.77 PW 24 5.9 1999 2014 30 6.5 27 0.55 EST Coast Out 85-102 5.6 1999 2015 126 5.2 118 0.69 17 102 5.3 1999 2015 42 5.4 39 0.77 18 96 5.1 1999 2014 33 5.5 31 0.70 19 85 5.9 1999 2015 30 4.9 30 0.63 23 90 6.3 1999 2015 21 4.7 18 0.59 FIN Coast East 48-65 3.9 1996 2015 189 12.5 189 1.13 KYVY-11 (Haapasaari) 65 4.2 1996 2015 61 10.2 61 1.11 KYVY-8A (Huovari) 48 3.7 1996 2014 128 13.6 128 1.14 FIN Coast In 4-37 1.6 1996 2014 258 63.0 258 0.86 KYVY-9 (Ahvenkoskenlahti) 16 0.9 1996 2014 21 19.4 21 0.37 UUS-16 (Pohjapitäjänlahti 92) 37 1.6 1996 2014 129 28.8 129 0.50 VANHANKAUPUNGINSELKÄ 4 4 1.4 1996 2014 87 134.0 87 1.51 VIROLAHTI 291 6 3.2 2003 2014 21 22.1 21 0.87 FIN Coast Out 28-60 5.1 1996 2015 390 8.8 395 0.90 39A 42 5.2 1996 2015 46 9.7 49 0.90 KYVY-1 (Ängsön) 28 3.5 1996 2010 76 13.1 76 0.89 UUS-10A (Länsi-Tonttu) 53 5.3 1996 2014 109 8.5 109 1.04 UUS-23 (Längden) 60 5.9 1996 2015 159 6.7 161 0.83 Narva Bay 8-38 4.3 1999 2015 174 6.7 176 0.86 15 25 4.6 1999 2015 33 6.0 33 0.78 38 8 4.4 1999 2015 24 7.1 25 0.92 12C 13 4.6 1999 2015 34 6.4 36 0.87 G 8 4.9 1999 2015 21 7.3 22 0.85 N12 38 4.5 1999 2015 33 6.1 31 0.84 N8 13 3.2 1999 2015 29 7.9 29 0.89 OffshoreMiddle 68-100 5.2 1996 2015 297 7.6 307 0.86 14 75 5.1 1999 2015 39 5.5 37 0.79 LL3A 68 4.8 1996 2015 54 10.0 60 0.94 LL5 70 5.2 1997 2015 50 8.7 55 0.91 LL7/LL7S 100/77 5.3 1997 2015 82 8.2 88 0.86 F1 75 4.9 1999 2015 33 6.1 30 0.79 F3 80 5.4 1999 2015 39 4.9 37 0.76 Offshore West 69-82 6.0 1997 2015 143 6.8 155 0.67 LL12 82 6.2 1997 2015 61 6.6 67 0.60

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LL9 69 5.7 1997 2015 61 7.8 67 0.77 H1 80 6.2 1999 2015 21 4.4 21 0.59 Grand Total 4-102 4.1 1996 2015 1794 15.8 1812 0.85

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Fig. 10.1. DIN determined at the stations that belong to either Estonian (F3, H1) or Finnish (LL12, LL7/LL7S) monitoring programme. In OW, the stations H1 and LL12 are less than 2 nm apart but their data averages differed by 2.2 µmol/l. In OM, the stations F3 and LL7/LL7S are less than 1 nm apart but their data averages differed by 3.3µmol/l.There seemed to have been an issue of analytical performance which, evidently, has now been rectified.

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Fig. 10.2. The spatial distribution of 1– m observations of DIN (above) and DIP (below) in the years 2014– 15 as contour images. The dots show the locations of the stations (Mika: the final image will take into account the GOF’s shoreline).

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Fig. 10.3. Spatial distribution of summer DIP and DIN (mol/l, below) in the near-bottom layer of the EGOF in 2014.

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Fig. 10.4. The deep-water DIP concentration as a function the O2concentration at LL7/LL7S, data compiled from the years 1969–2015. The sampling took place within the lowest 2 m of the water column. The relation is quite clear; the lower the O2level, the higher the DIP level. The relation is linear at higher O2levels suggesting the role of the Baltic Proper deep water advection that brings along waters poor in O2 and rich in DIP into the GOF. The relation shifts to an exponential one at lower O2levels suggesting the additive impact of the internal DIP load commencing at severe O2 conditions.

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Fig. 10.5. The temporal trends (as winters) of the standardized DIN (above) and DIP (below) values.Here, the regionalaverages values werefurther averaged to form two groups according to the regions’ temporal patterns: those stations were pooled whose temporal patterns mimicked each other.

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II+IIIa (bottom) Median = Distance W eighted Least Squares 8

7

6

5

4

mol/l

μ

, 3

DIP 2

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-1 2001 2003 2005 2007 2009 2011 2013

IIIb (bottom) Median = Distance W eighted Least Squares 8 Median 25%-75% 7 Non-Outlier Range Outliers 6 Extremes

5

mol/l

μ 4

, 3

DIP

2

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0 2001 2003 2005 2007 2009 2011 2013

Fig. 10.6. Long-term trends for the near-bottom summertime DIP for shallow (above) and deep-water (below) subareas in the EGOF.

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II+IIIa (bottom) Median = Distance W eighted Least Squares 20 18 Median 25%-75% Non-Outlier Range 16 Outliers Extremes 14 12 10

mol/l

μ

, 8

DIN 6 4 2 0 -2 2001 2003 2005 2007 2009 2011 2013

IIIb (bottom) Median = Distance W eighted Least Squares 20 18 Median 25%-75% Non-Outlier Range 16 Outliers Extremes 14 12

mol/l 10

μ

, 8

DIN 6 4 2 0 -2 2001 2003 2005 2007 2009 2011 2013

Fig. 10.7. Long-term trends for the near-bottom summertime DIN for shallow (above) and deep-water (below) subareas in the EGOF

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Fig. 10.8.The DIN and DIP trends (as winters) for LL12, LL7/LL7S, UUS-23, and KYVY-11.The upper 1 m data from December-March is used. The GES target values are also shown.

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Fig. 10.9. The molar DIN/DIP ratio. Here, the regional averages values were further averaged to form two groups according to the regions’ temporal patterns: those stations were pooled whose temporal patterns mimicked each other.

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EU, 2010. Commission Decision of 1 September 2010 on criteria and methodological standards on good environmental status of marine waters. 2010/477/EU. Union, E.

HELCOM, 2013. HELCOM core indicators: Final report of the HELCOM CORESET project. Balt. Sea Env. Proc. Commission, B. S. E. P.

HELCOM, 2014. Eutrophication status of the Baltic Sea 2007– 2011 – A concise thematic assessment. Balt. Sea Env. Proc., 143.

HELCOM, 2014. Manual for Marine Monitoring in the COMBINE Programme of HELCOM. Baltic Marine Environment Protection Commission (Helsinki Commission).

Raateoja, M., Kuosa, H., Hällfors, S., 2011. Fate of excess phosphorus in the Baltic Sea: A real driving force for cyanobacterial blooms? J. Sea Res. 65, 315– 321.

11 Distribution and trends of chlorophyll a and surface blooms

Pirkko Kauppila, Saku Anttila, Jenni Attila, Jan-Erik Bruun, Tatjana Eremina*, Alexandra Ershova*, Seppo Kaitala, Kari Kallio, Seppo Knuuttila, Inga Lips **, Urmas Lips**, Alexey Maximov*** & Mika Raateoja SYKE RSHU* MSI** ZIN***

Phytoplankton chlorophyll a is a commonly used indicator of algal biomass in surface waters. It is included in the core indicators of HELCOM, and is used in the status assessments of the Baltic Sea (HELCOM 2013a, 2014a). Additionally, the EU Member States are using chlorophyll a (Chl) as a proxy measure for phytoplankton biomass in their ecological classification. Our aim here is to assess eutrophication status and tendencies in the GOF based on information on chlorophyll a from in situ monitoring data, Alg@line data and remote sensing data. Similar to the trend analyses in Chapter 2.2, we used normalized Chl values in order to exclude the impact of the quantitative inter-station differences on the conclusions.

11.1 Material and methods

The datasets on Chl samples originate from Finnish, Estonian and Russian institutes and administrations listed in Chapter 2.2. The data consisted of 56

54 sampling stations of the GOF visited annually between June and September since 1996 (Table 1). Altogether 48 RSHU stations in the eastern GOF were visited in summers 2003-2014 (Fig. 11.1), and the dataset comprising ca. 20 Chl samples per year were treated separately. For spatial distribution, we averaged the chlorophyll (Chl) results for the summer period 2010-2014 and used the areal division presented in Chapter 2.2. As for trend analyses, we used the data series originated from the period 1996/1999-2014.

Fig. 11.1. Areal division of the eastern GOF (Pitkänen 1991) and RSHU monitoring stations in the eastern GOF. Sub-areas: (I) Neva Bay inside the barrier, (II) inner Neva estuary, (IIIa) outer Neva estuary, (IIIb) and offshore East.

Basically, the three countries followed the HELCOM COMBINE manual in their sampling and analyzing of Chl (HELCOM 2014b). However, in the coastal waters of Finland, Chl samples are taken from composite sample, the thickness of the sampled water column representing twice the Secchi depth. Additionally, Helsinki City takes composite sample from the upper surface water column between 0 and 4 m deep. In Russia, integrated samples are collected by a bathometer within a surface water layer down up to three Secchi depths, and acetone are used for extraction of Chl instead of ethanol (GOST 17.1.4.02-90).

The Alg@line Ferrybox system provides in real-time information on the water quality with high-frequency automated sampling onboard the merchant ship of Silja Serenade on the GOF. Alg@line devices measure in situ fluorescence, which is a proxy for Chl along with other water quality parameters. The depth of the inlet is ca. 5 m below the surface with a spatial resolution of 200 m. The system includes a sequence water sampler storing 24 water samples along the route. The Alg@line data cover the years of 1992-2014.

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The satellite instruments involved MERIS (MEdium Resolution Imaging Spectrometer), which provides so far the best functionality for the estimation of parameters related to Chl and surface accumulations of phytoplankton due to improvement of spatial resolution to 300 m. The satellite data for chlorophyll products cover the open water period between 2005 and 2011. As for surface accumulations of blue-green algae, the satellite data from MERIS and MODIS instruments were available for the summers of 2003-2014. The interpretations on surface accumulation characteristics of summertime algae blooms have led to a development of new cyanobacteria surface accumulations indicator (Anttila et al. manuscript). The method is under development and basically combines information on the seasonal volume (intensity), length of the algal surface accumulation period and bloom severity into a seasonal index-value, i.e. Cyanobacteria Surface Accumulations index (CSA index).For the indicator, target conditions are derived by using independent satellite based time series on algae accumulations from the Baltic Sea by Kahru and Elmgren (2014). The smaller the CSA index, the worse the status, and vice versa. 11.2 Spatial distributions of chlorophyll a in late summers

In the late summers of 2010-2014, the average Chl ranged between 3 and 5 µg l-1 in offshore areas, increasing towards the outer coasts up to the level of 6 µg l-1(Fig. 11.1a). In the innermost bays, Chl exceeded the level of 10 µg l-1. In general, elevated levels could be recorded in the estuarial waters off Helsinki and Porvoo in the northern coast, in Narva Bay in the southern coast and Neva estuary in the eastern GOF. These areas are most directly exposed to anthropogenic nutrient loading. Moreover, in the complex archipelago, the slow water exchange allows phytoplankton to harvest nutrients efficiently to its growth.

During recent summers, the level of Chl in the eastern open GOF has lowered to 2-4 µg l-1, although highly elevated concentrations exceeding10 µg l-1 have still be recorded in the easternmost shallow waters. For instance in July 2013, the maximum value of Chl around 20 µg l-1 was observed at the boundary of shallow inner Neva estuary and the deeper outer estuary where concentrations gradually decreased towards the western boundary of the eastern GOF down to the minimum of 1.4 µg l-1. In the easternmost and north-eastern GOF, reduction in Chl level could be related to the lately-reduced DIP loading from St. Petersburg and via River Luga. Moreover, Marenzelleria-induced changes in nutrient cycling resulted in an increase of N/P ratio and mitigation of nitrogen-fixing cyanobacterial blooms (see Chapter 1.2).

The satellite Chl data, produced by means of MERIS instrument, revealed great spatial and inter-annual variation in the GOF. In the midsummer of 2011, surface values of Chl calculated as geometric means were greatest along the Finnish inner coast, the Vyborg Bay, the inner Neva estuary and Luga Bay (Fig. 11.2). By contrast, between 2004 and 2008, Chl values were most elevated in a vast water area of the easternmost Gulf of Finland but also high in the whole GOF. The elevated Chl in summers 2007 and 2008 was partly related the inflow of more saline waters through Danish Straits in the turn of 2006 and 2007, and phosphorus-rich waters ending up the GOF thereafter.

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The satellite data on surface accumulations of algae provided a somewhat different picture on the status of N/P ratio in the Baltic Sea (Fig. 11.3). Surface accumulations of filamentous blue-green algae have been a regular phenomenon in the GOF in middle and late summer conditions since the mid-1900s (e.g. Kahru et al. 1994). In general, the strength and intensity in summertime surface accumulations of blue green algae depends on nutrient amounts and ratios, hydrographical and weather conditions. The amounts of nutrients after vernal bloom and upwellings of near-bottom waters carrying phosphorus abundantly up to the productive surface layer control accumulations of blue green algae. For example in summer 1997, accumulations in the GOF were most extensive and prolonged ever recorded (Rantajärvi 1998), the phenomenon which could be related to release of phosphorus from the sediment during extensive oxygen deficit in summer 1996 (Pitkänen and Välipakka 1997). Moreover, calm and warm weather conditions favored the occurrences of the accumulations in the summer 1997.

During the summers 2000s, algal situation in the GOF varied a lot. For example, in 2010 the situation was exceptionally good, mainly due to an average good near-bottom oxygen conditions restraining internal loading: Possible surface accumulations were observed on relatively restricted water areas in the western GOF and in the southern coast from Luga Bay to Neva Bay. At the same time, the southern Baltic Sea suffered from extensive surface accumulations. Surface accumulations of algae were more extensive in summers 2009, 2011 and 2012. By contrast, in 2014 they concentrated in the Baltic proper, Archipelago Sea and the western parts of the GOF. As a whole, in deep water areas poorer oxygen conditions and higher nutrient concentrations are significantly influenced by the intermittent inflow of low-oxygen deep water from the Baltic proper into the GOF. Instead, in shallow and highly eutrophic coastal waters strong summertime temperature stratification prevents oxygen from accessing the sea floor, which together with huge amount of organic material reinforces release of nutrients, leading eventually to acceleration of eutrophication. However, the wintertime DIN/DIP ratios did not seem to have a clear and consistent influence either on the distributions and amounts of surface algal accumulations or on the satellite interpretations of Chl in summer conditions (compare Fig. 5 in Chapter 2.2). 11.3 Trends

11.3.1 Chlorophyll trends

Here we describe tendencies in the standardized values of Chl between June and September 1996 - 2014 using the region division presented in Chapter 2.2, starting from the Estonian coast, moving there to Neva Bay and onwards to the Finnish coast. In the end, Chl tendencies in the open GOF were described moving from east to west. With standardized vales we excluded the impact of the quantitative inter-station differences on the conclusions, similar to the cases of nutrient trends (see Chapter 2.2).

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The sub-regions in the southern GOF (ECI, ECO and Narva Bay) resembled each other in terms of temporal variation in the standardized Chl values, which were mostly consistent with each other (Fig. 11.4). No clear trends could be detected. In ECI and Narva Bay, a clear minimum was observed in 1996. In all sub-regions, Chl peaked in 2002 and 2008.

The variation in Chl in Luga Bay, Koporye Bays and in Inner Neva Estuary was in many cases also consistent with each other (Figs. 4 and 5). The decrease in Chl since the early 2000s could be explained both by natural and human-induced reasons. The former was related to the greater water flows from River Neva increasing dilution of nutrients in the estuary, the fact which could also be indicated by lowered salinity. The latter covered the intense dredging works in the Neva Bay in 2006-2007 and the recent intensified wastewater treatment purification in St. Petersburg. In the South Inner Estuary Chl has steeply dropped from its elevated level in the turn of the millennium. However, Chl in the North Inner Estuary have been highly variable over the 2000s despite its decreasing tendency. As a whole, consistency could also be observed between the eastern GOF and the Estonian Coast so that Chl in the entire southern coast of the GOF and the Neva estuary showed high values in 2002 and 2008, and low values in 2007; the decline in Chl in 2007 relating to the intense dredging works in the Neva Bay ("Marine Facade" construction)

In the north-eastern GOF (Vyborg Bay, Offshore East Rus, FIN Coast East), Chl variation showed some similarities, but consistency between these sub-regions was not as clear as above (Figs. 11.4). In any rate, decreasing tendencies were evident, and the low values coincided in 2010 and 2014. However, in 1999 Chl was considerably high only off Vyborg Bay and in Offshore East within the Russian territory, while Chl in FIN Coast east rose only slightly over its average. Overall, variation was greater off Vyborg Bay and Offshore East than in FIN Coast East. Moreover, similarities could not only be detected within the NE GOF but also between the SE GOF: high values of Chl in 1999 and low values in 2010, 2014 coincided.

In its entirety, the mid-summer Chl in the eastern GOF demonstrated a clear downward trend after 2004 and 2005, and this decrease was more pronounced in the shallow-water area of the inner and outer Neva estuaries than its extensive offshore area (Figs. 11.4 and 11.5). During the period of 2009-2014, Chl fell down to its minimum and has remained virtually constant in eastern GOF after 2009. The reason is that the runoff of the Neva River influences strongly the Neva estuary. Excluding the summer 2005, the period 2000-2007 was characterized by low water flows, whereas in 2008-2013 high-water flows dominated resulting in the "dilution" of nutrient concentrations. Additionally, nitrogen and mostly phosphorus discharges from St. Petersburg have decreased by 1.4 and 5.7 times, in resp., in 2013 from their levels in 2002 (http://www.gof2014exhibition.net...). The reduction has resulted in the decrease in the loading of DIN and DIP since 2009, too. At the same time the ratio of DIN:DIP in sewage has increased during the last decade from 5-9 in the period of 2003-2008 to 14-21 in the period of 2009-2014. The change in ratio of

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DIN:DIP had a negative effect on the development of nitrogen-fixing cyanobacteria.

In the northern GOF (FIN Coast Out, FIN Coast In) consistency could be detected but variation in the standardized Chl values remained small (Fig. 11.4). Instead, variation in Chl in the Offshore West and Offshore Middle was clearly observable and consistent with each other. Furthermore, Chl has decreased in recent years and peaked in 1999 similar to the NE, E and SE GOF. Nevertheless, more variation could be found in station-specific analyses within FIN Coast In when considering the long-term time series since the late 1970s (data not shown here). In River Vantaanjoki estuary east of Helsinki, Chl values have dropped steeply after the closing of the local municipal treatment plants by 1987. In River Virojoki estuary, a decrease in the 2000s could be explained by reduction of nutrient inputs from fish farms whereas in the innermost Pojo Bay the decline resulted mainly from the reduction of waste water loading. The River Porvoonjoki estuary was the only one where Chl has recently increased, mainly due to release of phosphorus from the bottom sediment but also due to increase in TP inputs from the river.

Comparing the corresponding results of DIN and DIP some similarities with summertime Chl could be found, especially within FIN Coast east (FCI) and Offshore West (OW) (Fig. 11.4, cf. Fig. 11.5). In FCI, wintertime DIP was high in 2004 and 2005 and low during 2012-2014, which matched with summertime Chl. In OW, DIP was high in 1999, 2000, 2002 and 2005 and low in 2013 and 2014, which also matched with summertime Chl within this sub-region. The lack of wintertime data on inorganic nutrients prevented correspondent comparison of DIP and Chl in the easternmost part of the GOF.

11.4 Trends in algal bloom indices

Remote sensing data: Trend of summertime algal bloom index In the GOF (Saku) (Fig. 6) (target jätetään pois ja kuvataan tässä raportissa vain vaihtelua)

Remote sensing data &Algaline data: Long term trends of spring bloom index (similar toFig. 7) - Region: from station Kasuuni off Helsinki to the mouth of GOF - Spring periods 1992-2014 - Setsi ja Jennituottavat - Fleming-Lehtinen&Kaitala 2006

11.5 Areal comparison of chlorophyll a relative to its environmental targets

The Water Framework Directive (WFD) aims to maintain surface waters at least at the status of good or the restore them where necessary to that level, by 2015. EU Marine Strategy Framework Directive (MSFD) integrates all pressures and

59 impacts with the purpose of achieving good environmental status by 2021. In the open sea of the GOF, the Chl targets to achieve good environmental status (GES boundary) are based on the criteria determined by HELCOM (EU 2010, HELCOM 2013b), whereas in the Estonian and Finnish coastal waters, the criteria have been established nationally to comply with the WFD requirements (Lips 2004, Aroviita et al. 2012). The definition of summer period varies a bit due to differences in the implementation process of the directives. In Estonian coastal waters and open GOF, the summer season is between June and September, whereas in Finnish coastal waters it is between July and the first week of September. Here, we use the summer season of June to September for all sub- regions. Additionally, sampling and analytical methods between the tree countries differ to some extent from each other.

As a whole, GOF did not achieve good ecological and environmental status based on average Chl during the three six-year periods of 1996-2001, 2002-2007 and 2008-2014 (Fig. 11.8). In the western and middle offshore waters, the average Chl has decreased since 1996, but in 2008-2014 it was still nearly a double compared to its target (2 µg l-1). In the offshore waters of the eastern GOF (region IIIb), the average Chl has been close to the target of 2 µg l-1 since 2009 (Fig. 11.5b), but the results are not quite comparable with the Chl measured in the middle and western open GOF due to the differences in the sampling and analytical methods (cf. Chapter 2.3.1). Overall, the results of Chl in the open GOF were basically in line with the status assessed for the wintertime inorganic nutrients in that the targets have not been reached yet.

Estonian coastal typology includes coastal waters in the western GOF and Narva Bay, corresponding here the sub-regions of Estonian Coast In (ECI) and Narva Bay. Instead, Estonian Coast Out (ECO), locating inside Estonian territorial waters limit, are not covered inside the coastal zone according to the WFD. Thus, ECO represents offshore waters, wherefore we used the target values defined by HELCOM. In ECI, the target (2.7 µg l-1) has not achieved and it has gone more far away when the level of Chl increased in 2008-2014. Also in Narva Bay, the target (3.7 µg l-1) has not been reached despite the recent decline of the Chl level. In ECO, summertime Chl has varied a lot: in a couple of years they have been close to the target (2 µg l-1) and then more than a double compared to it.

In Finnish Coast Out (FCO), the pattern was similar to that of offshore waters: recent Chl levels have decreased but the target values (2.3 µg l-1 in the western part and elsewhere 2.5 µg l-1) have not been reached. In Finnish easternmost archipelago, the decline in Chl could partly be related to an average better near bottom oxygen conditions, restraining release of nutrients from the sea floor. In Finnish Coast In (FCI), the target values (3.0 µg l-1 in the western part and elsewhere 3.5 µg l-1) and seemed to be more far away compared to the situation in FCO. However, the monitoring stations included in this study do not represent the entire innermost archipelago as they are mostly located in the innermost estuaries. However, based on the recent ecological classification results in Finland, the status in the coastal GOF was mostly satisfactory and in the innermost water bodies even poor but the status exhibited improvement in the easternmost archipelago where it was assessed lately as moderate

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(http://www.ymparisto.fi/pintavesientila). Summertime total phosphorus and nitrogen, used in Finnish ecological classification for coastal waters, supported the status assessment made based on the biological quality elements.

Table 11.1. The sub-regions and stations included in Chl analyses in the GOF with information on total depth of the station, time period of monitoring, number of samples and average concentration of Chl in productive surface layer (below 10 m) in June to September.

Subregion / Station Total depth Time period Count of Chl Average Chl (m) µg l-1 EST Coast In - PE 21 2007-2014 19 4.1 - 18A 46 2007-2014 10 4.8 - 23A 25 2007-2013 17 3.1 - 2Est 44 1996-2014 112 4.3 - 3Est 40 1996-2014 58 4.0 - 57A 10 1996-2014 104 5.3 - PW 24 2007-2014 19 3.9 EST Coast out - 17 102 2007-2014 12 4.4 - 18 96 2007-2014 12 4.2 - 23 90 2007-2014 7 3.9 - 19Est 85 2007-2014 7 4.6 - 23B 2 5.9 - E80 7 3.2 - F62 7 3.6 - GF1 7 3.7 Narva Bay - 38 8 1996-2014 84 5.4 - 12C 13 2006-2014 54 4.4 - N12 38 1996-2014 45 4.6 - N8 13 1996-2014 90 5.9 - 15 5 3.5 - G 5 3.1 Luga Bay &Koporye Bay - 3K 13 1999-2014 17 4.8 - 6K 26 1999-2014 15 5.3 - 6L 28 1999-2014 14 3.9 - 18L 10 1999-2014 16 4.2 Neva BaySouth - 1 29 2000-2014 16 5.2 - 24 21 1999-2014 17 6.6 - 26 7 1999-2014 16 8.8 Neva BayNorth - 20 12 1999-2014 16 8.7 - 21 14 1999-2014 17 9.8 - 22 10-20 1999-2014 16 7.2 - 19Rus 10 1999-2013 17 10.4 Vyborg Bay - A 30 1999-2014 17 5.0 FIN coast East - Kyvy-8A Huovari 48 1996-2013 200 7.0 - Kyvy-11 Haapasaari / 65 1996-2013 199 5.5 FIN coast Out - UUS-23 Längden 60 1996-2013 208 4.4 - UUS-10A Länsi-Tonttu 53 1996-2013 268 5.7

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- 39A 43 1996-2013 81 4.0

FIN coast In - Virolahti 291 6 1996-2013 60 14.9 - Kyvy-9 16 1996-2013 86 11.4 Ahvenkoskenlahti 28 1996-2013 175 6.3 - Kyvy-1 Ängsön 42 1996-2013 93 8.2 - UUS-13 Porvoo 48 4 1996-2013 124 32.7 - Vanhankaupunginselkä 37 1996-2013 121 7.9 4 - UUS-16 Pohjanpitäjänlahti Offshore West - LL12 82 1996-2012 627 3.6 - LL9 69 1997-2012 30 4.5 - H1 3.4 Offshore Middle - LL3A 68 1996-2013 76 5.1 - LL7/LL7S 77-100 1996-2013 74 4.6 - F3 80 1996-2013 42 4.3 - F1 - LL5 - XIV3 3.5 Offshore East - 4 61 1999-2014 14 2.4 - 2Rus 37 1999-2014 15 5.8 - 3Rus 48 1999-2014 13 4.4

a)

62 b)

Хлорофилл a:

1.4 10.6 19.7

Fig. 11.1.Distribution of Chl (µg l-1) in the GOF between (a) between June and September in 2010-2014, and (b) Chl in the eastern GOF in July 2013.

Fig. 11.2. Geometric means of chlorophyll a in the Gulf of Finland in July to August 2003-2011using ENVISAT MERIS satellite instrument.

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Fig. 11.3. Surface accumulations of blue-green algae in the Baltic Sea between the end of June and August in 2008-2014 produced using ENVISAT MERIS satellite instrument.

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Fig. 11.4.Trends of standardized values of chlorophyll a in different sub-regions of the GOF between June and September in 1996- 2014. The standardized values of each sub-region are presented on the same scale.

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Fig. 11.5. Long term trends of Chl (µg l-1) in the shallow regios of the inner and outer Neva estuary (II and IIIa) and (b) deep sub-region (IIIb) of the eastern GOF between July and August in 2003-2014. Based on HELCOM requirements for open GOF, the target value in the region IIIb is 2 µg l-1.

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Fig. 11.6. The time series of algal bloom index, defined for the offshore waters between the central and western GOFusing Alg@line measures in 1992-2014 and remote sensing data in 2004-2014.

Fig. 11.7. Combined information from the seasonal intensity, length and severity of cyanobacteria surface accumulations (Cyanobacteria Surface Accumulations a.k. CSA–index; blue bars) together with estimated target value for the GOF (dashed red line). The CSA value responds negatively to increasing eutrophication, i.e. low values indicate increased eutrophication.

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Fig. 11.8. Summertime Chl in June to September 1996-2013 with their target values (GES) and average concentrations for six year periods in the western and middle open GOF as well as in Estonian and Finnish coastal waters. Target values are based on definition by HELCOM (2013a), Aroviita et al. (2012) and Lips (2004). Excluding the Finnish Coast In (FCI), the y-axes in each sub-region are on the same scale. The high values in FCI originated mainly from the data in Vantaa estuary

11.6 References

Anttila, S., Fleming-Lehtinen, V., Attila, J., Junttila, S, Hällfors, H. New remote sensing based cyanobacterial surface accumulation indicator for the Baltic Sea. Manuscript.Kahru, M., &Elmgren, R. (2014).Satellite detection of multi-decadal time series of cyanobacteria accumulations in the Baltic Sea.Biogeosciences Discussions, 11, 3319-3364.

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Aroviita, J. Hellsten, S., Jyväsjärvi, J., Järvenpää, L., Järvinen, M., Karjalainen, S.M., Kauppila, P., Keto, A., Kuoppala, M., Manni, K., Mannio, J., Mitikka, S., Olin, M., Perus, J., Pilke, A., Rask, M., Riihimäki, J., Ruuskanen, A., Siimes, K., Sutela, T., Vehanen, T. & Vuori, K.-M. 2012. Guidelines for the ecological and chemical status classification of surface waters for 2012–2013 – updated assessment criteria and their application).Environmental Administration Guidelines 7/2012: 1–144. (in Finnish) http://hdl.handle.net/10138/41788

EU, 2010. Commission Decision of 1 September 2010 on criteria and methodological standards on good environmental status of marine waters. 2010/477/EU. Union, E.

Fleming-Lehtinen, V. & Kaitala, S. 2006. Phytoplankton spring bloom intensity idex for the Baltic Sea estimated for the years 1992 to 2004. Hydrobiologia 554: 57-65.

GOST 17.1.4.02-90, 1999. Water. Spectrophotometric determination of chlorophyll a, Moskow (in Russian)

HELCOM, 2013a. Approaches and methods for eutrophication target setting in the Baltic Sea region. Balt. Sea Environ. Proc. No. 133.

HELCOM, 2013b. HELCOM core indicators: Final report of the HELCOM CORESET project. Balt. Sea Env. Proc. Commission, B. S. E. P.

HELCOM, 2014a. Eutrophication status of the Baltic Sea 2007-2011 - A concise thematic assessment. Balt. Sea Env. Proc., 143.

HELCOM, 2014b. Manual for Marine Monitoring in the COMBINE Programme of HELCOM. Baltic Marine Environment Protection Commission (Helsinki Commission).

Lips, U. 2004. Assessment of the ecological status of coastal waters in Viru-Peipsi catchment area. Estonian Marine Academy, Tallinn.

Pitkänen, H. 1991. Nutrient dynamics and trophic conditions in the eastern Gulf of Finland: The regulatory role of the Neva estuary. Aqua Fennica 21, 2: 105-115.

Pitkänen, H. & Välipakka, P. 1997. Extensive deep water oxygen deficit and benthic phosphorus release in the Eastern Gulf of Finland in late summer 1996. In: J. Sarkkula (ed.). Proceedings of the Final Seminar of the Gulf of Finland Year 1996, March 17-18, 1997 Helsinki. Suomen Ympäristökeskuksen moniste 105: 51-59.

Rantajärvi, E. (ed.) 1998. Phytoplankton blooms in the Finnish sea areas and in the Baltic Proper during 1997. Meri – Report Series of the Finnish Institute of Marine Research 36: 5-8.

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12 Oxygen indicator assessment using results from an autonomous profiling buoy TUT MSI 12.1 Introduction According to Marine Strategy Framework Directive’s (MSFD) Article 11 all member states have to establish and implement monitoring programmes to assess the status of their marine environment. Until now, data used for the eutrophication status assessments in the Baltic Sea originates mostly from the conventional monitoring programmes [1]. Among others, indicators based on dissolved oxygen measurements are used in some countries but not yet in Estonia. To collect oxygen data, the programme in Estonia includes research vessel based measurements at monitoring stations in the coastal and open sea areas. For the improved assessment of marine environmental status data with better temporal and spatial resolution is needed, especially in the off-shore areas. Regarding oxygen measurements the idea is to use data from an autonomous profiling buoy with a high temporal resolution situated in the Gulf of Finland (GOF).

An indicator on oxygen debt has been proposed in TARGREV [2] based on temporally sparse data and it has been concluded that this indicator is restricted to deep basins [3]. In EUTRO-OPER [3] process an indicator is being developed for assessing oxygen consumption in the stagnant layer below the productive surface layer during summer. Assessing global distribution of marine hypoxia, oxygen minimum zones (OMZ) have been defined [4]. The method in the previously mentioned article was to sort data to uniquely isolate each hydrocast. From this reduced dataset dissolved oxygen measurements where oxygen concentration was below the specified maximum were selected. From the selected measurements the shallowest and deepest values of dissolved oxygen in each hydrocast were extracted and plotted with a connecting line to reveal the depth-resolved OMZ as a function (more details in the article [4]).

The aforementioned article was the source for this work in estimating hypoxic areas in the GOF based on buoy data. In the article OMZ was defined by maximum and minimum depth values where oxygen concentration was below a specified limit. In this study we look at only the minimum depth values of each profile where oxygen concentration is below a specified limit which gives us (together with hypsographic curve values of the GOF) the change in hypoxic area or the average hypoxic area in GOF during the study period. To evaluate our results (to see if the buoy alone is fit to describe the whole GOF) the buoy data results are compared to results from CTD measurements (Fig.1) from the same period.

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12.2 Autonomous profiling buoy The buoy system is developed in Estonia by Flydog Solutions. The buoy acquires vertical profiles with a predefined time step and depth range (time step of 3 hours and depth interval of 2-80 m). The buoy is situated in the Baltic Sea, GOF (59°41.2 N, 24°37.3 E; Fig.1) and records vertical profiles of temperature, salinity, chlorophyll-a fluorescence, turbidity, dissolved oxygen, and phycocyanin. Data is transmitted in real time via GSM connection.

Figure 12.1. CTD measurement stations and buoy location.

12.3 Method Buoy data from April to July 2014 was processed by averaging oxygen concentrations to the nearest pressure integer for each recorded vertical profile. From buoy profiles, deeper sections of the profiles were selected, where oxygen concentration was <=1.4 mg l-1 (<=2.0 ml l-1). From every selected profile the minimum depth value was found. From these minimum depth values an average was used to estimate the hypoxic area of GOF based on a hypsographic curve from bathymetric data [5]. From the minimum depth values also the change in hypoxic area can be calculated. When looking at the change in minimum depth values in time and comparing them to the change of minimum salinity values from the previously selected parts of profiles it can be concluded whether the hypoxic water mass is locally generated or originates from inflows.

The average depth, where hypoxia was present in spring-summer 2014 was 67 m. The corresponding GOF bottom area with hypoxia would be 3748 km2 or ~14% of total bottom area. It can be seen that the border between the oxic and hypoxic layers was moving upwards in the water column in summer 2014 and, thus, the hypoxic area was also growing (Fig. 2). Minimum salinity values were decreasing in time in the water column part where hypoxia was present, which tells that the low oxygen concentrations were locally generated and not due to the inflow of hypoxic water masses.

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Figure 12.2. The change in hypoxic area based on buoy data from April to July in 2014.

From CTD measurements the average minimum depth in the water column where hypoxia was present is 65 metres for the time period 13.-15.05.2014. 65 metres corresponds to the bottom area of roughly 15 % or 4155 km2 (Fig 3.).

Figure 12.3. Hypoxic area of GOF from CTD measurements from 13.-15.05.2014. When comparing the result from buoy data and CTD data then it is seen that the extent of average hypoxic area is quite similar which supports the idea to use buoy measurements for the whole GOF.

12.4 References

[1] HELCOM, “Eutrophication status of the Baltic Sea 2007-2011. A concise thematic assessment,” Balt. Sea Environ. Proc. No. 143, 2014.

[2] HELCOM, “Approaches and methods for eutrophication target setting in the Baltic Sea region,” Balt. Sea Environ. Proc. No. 133, 2013.

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[3] HELCOM, “EUTRO-OPER 4-2015. Development of oxygen consumption indicator,” 2015.

[4] J. J. Helly and L. A. Levin, “Global distribution of naturally occurring marine hypoxia on continental margins,” Deep Sea Res. Part I Oceanogr. Res. Pap., vol. 51, no. 9, pp. 1159– 1168, 2004.

[5] O. Andrejev, A. Sokolov, T. Soomere, R. Värv, and B. Viikmäe, “The use of high-resolution bathymetry for circulation modelling in the Gulf of Finland,” Est. J. Eng., vol. 16, no. 3, pp. 187–210, 2010.

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