The Swedish Coastal zone Model (SCM) was used at a test site, the Stockholm archipelago, located in the northern part of the central Baltic Sea, to study the retention capacity of the coastal filter on nitrogen (N) and phosphorus (P) loads from land and atmosphere. The efficiency of the coastal filter to permanently retain nutrients determines how much of the local nutrient loads actually reach the open sea. The SCM system is a nutrient–phytoplankton–zooplankton–detritus-type model coupled to a horizontally integrated, physical model in particular suitable for estuaries. In this study the Stockholm Archipelago, consisting of 86 sub-basins, was divided into three sub-areas: the inner, the intermediate and the outer archipelago. An evaluation of model results showed that the modelled freshwater supply agrees well with observations. The nutrient, salinity and temperature dynamics simulated by the SCM are also found to be in good or acceptable agreement with observations. The analysis showed that the Stockholm Archipelago works as a filter for nutrients that enter the coastal zone from land, but the filter efficiency is not effective enough to retain all the supplied nutrients. However, at least 65 and 72 % of the P and N, respectively, are retained during the studied period (1990–2012). A major part of the retention is permanent, which for P means burial. For N, almost 92 % of the permanent retention is represented by benthic denitrification, less than 8 % by burial, while pelagic denitrification is below 1 %. Highest total amounts of P and N are retained in the outer archipelago, where the surface area is largest. The area-specific retention of P and N, however, is highest in the smaller inner archipelago and decreases towards the open sea. A reduction scenario of the land loads of N and P showed that the filter efficiencies of N and P increase and the export of N from the archipelago decreases. About 15 years after the reduction, the export of P changes into an import of P from the open sea to the archipelago.
The worldwide increase in coastal eutrophication and anoxia has spread exponentially since the 1960s. Coastal oxygen depletion is associated with dense population areas and large river loads of nutrients (Diaz and Rosenberg, 2008). The use of industrially produced fertilizer started in the late 1940s and has since then been contributing to the anthropogenic fertilization of the global marine system (Galloway et al., 2008). The river load of nutrients originating from agriculture activities has been shown to be controlled by the size of the river flow; for example, the flow from the Mississippi River has a large impact on the oxygen conditions in the northern Gulf of Mexico, which suffers from severe hypoxia with “dead zones” as a result (Rabalais et al., 2002).
With the goal of diminishing eutrophication there have been numerous efforts around the world to reduce the land load of nutrients to sea, but the expected results of a healthier environment have not been accomplished in all places (Kemp et al., 2009). The responses of eutrophication and the extent of hypoxic area for changes in nutrient loads are different in different types of systems. Also, changes in climatic and hydrodynamic conditions might lead to a non-linear recovery (Kemp et al., 2009). Nutrients transported from land to sea first enter the coastal zones and are then further transported towards the open sea. However, not all of the supplied nutrients reach the open sea as they are retained in the coastal zone (Fig. 1), which acts as a filter (McGlathery et al., 2007). The retention capacity depends on different chemical, physical and/or biological processes that involve nutrients, e.g. denitrification, permanent burial, algae and plant assimilation (Duarte and Cebrián, 1996; Voss et al., 2005). The filter efficiency of the coastal zone might be of large importance for the water quality in open waters.
Simplified scheme of the retention calculations in the study area. Permanent retention is considered a permanent removal of nutrients from the ecological system and includes burial and, for nitrogen, also denitrification. Temporary retention is defined as the changes in nutrient inventory in the active sediment layer and water column. The temporary retention may change sign depending on whether the nutrient inventory increases or declines.
Retention capacity is, however, not well defined. Johnston (1991) discussed that retention processes are of different magnitudes and irreversibility; for example, plant uptake and litter decomposition provide short- to long-term retention of nutrients. Billen et al. (2011) and Nixon et al. (1996) defined retention as the net effect of temporary and permanent removal from the water phase through different biogeochemical processes. Burial and denitrification lead to a permanent removal of nutrients from the ecological system (Voss et al., 2005). Plant assimilation of nutrients and sedimentation of organic material might influence the temporary retention, defined as a build-up of active nutrient pools in the water and in the sediment. Some of the organic material is more refractory than others, e.g. parts of root systems, which also can influence biogeochemical processes by enhanced sediment oxygen, nutrient and dissolved organic material concentrations (McGlathery et al., 2007). Thus, temporary retention depends on the release rates, translocations, and the longevity of plants, which causes variations in retention capacity depending on the timescale of the study. The net effect of nutrient retention in an area can be studied using the simple method of subtracting the output of nutrients from the input (Johnston, 1991). This simple method of calculating the retention capacity of nitrogen (N) and phosphorus (P) has been used in a number of studies (e.g. Eilola et al., 2014; Hayn et al., 2014; Karlsson et al., 2010; Nixon et al., 1996; Sanders et al., 1997) for different areas of the world. The retention capacity has been discussed to be related to the residence time and depth in different water systems (Balls, 1994; Hayn et al., 2014; Nixon et al., 1996). Hence, the longer a water parcel and its nutrient content stays within a system, the more the containing nutrients are affected by the internal transformation and retention processes.
In the present study, filter efficiency is explained as the capacity of the studied area to retain the local nutrient loads from land and atmosphere (see Sect. 2.4). A distinction is made between the permanent removal and temporal retention, of which the latter is caused by changes in the N and P inventory (Fig. 1). There are studies of nutrient retention in different coastal zones around the world, but there are not enough estimates to evaluate and understand its effect on the environmental status of coastal seas. Quantification of the filter efficiencies in different coastal ecosystems as estuaries, archipelagos, lagoons and embayments would increase the understanding and the knowledge necessary for managing the coastal zone. Numerical models have been used to a larger extent for studies in lakes and freshwater catchment areas (e.g. Ahlgren et al., 1988; Hejzlar et al., 2009) than for retention and filter efficiency studies in coastal areas, where only a few studies seem to exist in the literature (e.g. Fennel et al., 2006; Seitzinger and Giblin, 1996; Xue et al., 2013).
The Swedish Coastal zone Model can be used in different areas along the Swedish coast, stretching from the Norwegian border in the west to the Finnish border in the north (different colours, left). In the present study the SCM covers the northern Baltic proper (marked with a red square) and has been used to estimate the coastal filter efficiency of nutrients in the Stockholm inner (red), intermediate (orange) and outer (blue) archipelagos (right). The outlet of the river Norrström is marked by a black arrow and the different basins are shown by the black contours.
The Baltic Sea (Fig. 2), located in northern Europe, is an example of where the
enhanced land load of nutrients to the sea (Gustafsson et al., 2012) has led
to eutrophication and consequently increased frequency and intensity of
cyanobacterial blooms, expanding bottom hypoxia and dead bottom zones (e.g.
Bergström et al., 2001; Conley et al., 2009; Diaz and Rosenberg, 2008;
Vahtera et al., 2007). In fact, the largest anthropogenically induced
hypoxic area in the world is found in the Baltic Sea (Carstensen et al.,
2014), where it varied between 70 000 and 80 000 km
The aim of this study is to quantify the filter efficiency in the eutrophic Stockholm Archipelago (see Sect. 2.1) of N and P and to discuss the relative importance of different physical and/or biological processes using the Swedish Coastal zone Model (SCM). In addition, changes in the filter efficiency along the land–sea continuum, from the inner archipelago to the intermediate and outer archipelago and then to the open Baltic Sea, will be studied in order to evaluate the effect of the size of the archipelago on the filter efficiency.
After a description of the model system (Sect. 2) and an evaluation of the results of SCM (Sect. 3.1), the filter efficiency of the coastal zone is calculated and the effects of a reduced land load of N and P are analysed (Sect. 3.2). Conclusions finalize the study (Sect. 4).
The brackish archipelago of Stockholm (Fig. 2), located at the east coast of
Sweden, is the largest archipelago in Sweden and the second largest in the
Baltic Sea. The archipelago is a continuation of the river Norrström
with an average discharge of about 160 m
The largest point sources of nutrients to the inner archipelago originate
from waste-water treatment facilities of Stockholm, which is situated at the
outlet of the Lake Mälaren. Signs of eutrophication in the Stockholm
Archipelago have been observed as an increased ratio of laminated sediments
since the 1930s (Jonsson et al., 2003) and the eutrophication status in the
inner Stockholm Archipelago was classified as highly
eutrophic in the early 1970s (Lännergren et al., 2009). In the 1970s the sewage treatment
facilities in Stockholm started to chemically precipitate P, which reduced
their P load from about 600 to about 100 t yr
Schematic figure of the Swedish COastal and BIogeochemical model, SCOBI. Oxygen and hydrogen sulfide are simplified for clarity.
The SCM is a multi-basin 1-D model based on the equation solver PROgram for Boundary layers in the Environment (PROBE; Svensson, 1998), coupled to the Swedish Coastal and Ocean Biogeochemical model (SCOBI; Eilola et al., 2009; Marmefelt et al., 1999). The model system was developed to calculate physical and biogeochemical states in Swedish coastal waters. The inner, intermediate and outer Stockholm archipelagoes (Fig. 2) are represented by 16, 44 and 26 sub-basins, respectively (see Fig. S1 in the Supplement).
The physical model PROBE calculates horizontal velocities, temperature and
salinity profiles (Svensson, 1998; Omstedt, 2015). The surface mixing is
calculated by a
The water exchange between the sub-basins is controlled by the baroclinic pressure gradients. The net flow through the sounds will be the same as the river discharge from land in order to preserve volume. Inflowing water to a sub-basin is interleaved into its density level without any entrainment, and heavy surface water in one sub-basin may thus reach the bottom level in an adjacent basin. The sea level variations outside the boundary are of minor importance for the SCM results and are therefore not included in the forcing. The water exchange across the boundary between the coastal zone and the open sea is assumed to be in geostrophic balance, since this boundary is open with a width greater than the internal Rossby radius. A time step of 600 s was used in the present simulations.
The SCOBI model describes the biogeochemistry of marine waters in the Baltic
Sea and Kattegat (Eilola et al., 2009). Nine pelagic and two benthic
variables (Fig. 3) are described in the SCM-SCOBI model. In the pelagic zone
three different phytoplankton groups (diatoms, flagellates and others, and
cyanobacteria), one zooplankton group, one pool for detritus and three
inorganic nutrients pools (nitrate, ammonium and phosphate) are represented.
The model also calculates oxygen and hydrogen sulfide concentrations, of
which the latter are represented by “negative oxygen” equivalents (1 mL
H
In the model the following processes are described: phytoplankton assimilation; phytoplankton mortality; nitrogen fixation; zooplankton
grazing; zooplankton excretion of detritus, dissolved inorganic
nitrogen (DIN) and phosphorus (DIP); oxygen- and
temperature-dependent mineralization of detritus, benthic N
and benthic P; and oxygen- and temperature-dependent nitrification
and denitrification. Phytoplankton assimilates carbon (C), N
and P according to the Redfield molar ratio (C : N : P
The SCM-SCOBI model system is forced by weather, the conditions in the sea outside the archipelago, point sources, discharge of freshwater and nutrients from land, and atmospheric deposition of nutrients. The initial values for both the pelagic zone and the sediment are derived from spin-up simulations.
There are two types of land-derived forcing; discharge of water and nutrients from both rivers and surface runoff from the drainage area given by the S-HYPE model (Lindström et al., 2010) and point sources representing sewage plants and industries. The runoff is added to the surface water of each basin and no reduction in river nutrients due to precipitation at river mouths is assumed in this model setup. The point sources of nutrient loads are assigned to the depth levels mostly resembling the actual depth of the discharge. The inorganic riverine nutrient loads are added as DIN and DIP to the SCM. The organic nutrients in the land loads are calculated from the difference between total nitrogen (TN) and DIN and between total phosphorus (TP) and DIP. The bioavailability and the composition (dissolved or particulate) of the organic nitrogen and phosphorus loading from land are generally not known. In the present model configuration the fraction of organic nutrient loads that follows the Redfield ratio is assumed to be bioavailable and will be added to the detritus pool in the model, while the remaining fractions of nutrient loads are treated as conservative tracers in the model.
The weather forcing consists of solar insolation, air temperature, wind,
relative humidity and cloudiness. The insolation and all the radiation and
heat fluxes across the water–air interface are calculated by the PROBE
model. The weather variables are taken from a gridded database developed at
the Swedish Meteorological and Hydrological Institute (SMHI), using 3-hourly
meteorological synoptic monitoring station data, and the depositions of
nitrogen species (NHX and NOX) are calculated by the MATCH model (Robertson
et al., 1999). For the deposition of phosphate, a literature value of
0.5 kg m
The boundary conditions to the open Baltic Sea is set by vertical mean profiles calculated by a one-dimensional PROBE setup for each Baltic open-water area and assimilation of monitoring data. The monitoring data used in the assimilation are extracted from the stations MS4, US5B, SR5, BY31 and BY29 (Fig. 2) depending on depth and time, to get the best representation of the open sea's influence on the SCM model domain.
To quantify the fit between modelled values and observations a correlation
coefficient,
The outflow from Lake Mälaren is 3 orders of magnitude larger than the sum of all other S-HYPE freshwater components to the inner Stockholm Archipelago. The output from S-HYPE of freshwater and nutrient loads from Mälaren to the Stockholm Archipelago was therefore used in the evaluation of the freshwater forcing to SCM. Observations of freshwater discharge were retrieved from the Baltic Environmental Database (BED, 2015) at the Baltic Nest Institute, Stockholm University. The correlation between the monthly mean of observed and simulated discharge for the period (1990–2012) was then calculated.
Number of sampling occasions (Occ) during the number of years, number of months during each year, and number of depth levels that were frequently sampled at the different stations used for validation of model results. The position of the stations can be seen in Fig. 4.
Available locations with observations (circles and dots) in the Stockholm Archipelago. Model evaluation of temperature, salinity, DIN, DIP and bottom water oxygen concentration was performed at selected stations (circles marked with letters), which are described in Table 1.
In the evaluation of the results of the SCM in different basins, the long-term averages (1990–2012) of the vertical distribution of salinity, DIN, DIP
and oxygen during winter (November–February) and summer months (May–August)
were compared to corresponding observations for the whole modelled period.
Further, the correlation
Observations from the Stockholm Archipelago (Fig. 4) were provided by Stockholm City and Stockholm University. For the quantitative validation described above, the quality of observations from each site (Table 1) had to fulfil three requirements to be used in the validation process: (1) period coverage – 80 % of the years sampled; (2) annual coverage – at least 7 of the 12 months sampled; and (3) vertical data coverage – at least five depth levels frequently measured over the full depth of the basin. In addition at least 3 months with observations were required for the evaluation of winter and summer conditions. Average values were then calculated for periods and depth levels with dense data distribution. The model output was used in the same way as observations, and the modelled averages were calculated for the same time intervals and depth ranges.
The retention of P and N in a region can be calculated as the difference
between the load and the outflow (Almroth-Rosell et al., 2015; Hayn et al.,
2014; Johnston, 1991; Meier et al., 2012). The input of nutrients is the sum
of inflows from outer areas, rivers, land runoff, point sources and
atmospheric load, while the outflow of nutrients is the export from the area
to outer seas (Fig. 1). N
The different processes that affect retention have been calculated
separately, as they are included in the biogeochemical model SCOBI. Total
retention (
The total retention efficiency was calculated for the entire Stockholm Archipelago, and also separately for the inner, intermediate and outer archipelagos in order to investigate the spatial gradient of retention capacity from the inner coastal zone towards the open Baltic Sea.
The residence time is defined as the average time water, or a dissolved
substance, spends within a particular basin (Bolin and Rodhe, 1973). In the
present study the residence time of the freshwater is calculated to relate
the filter efficiency to physical characteristics of the archipelago as
described by Nixon et al. (1996). A freshwater tracer in the model is used
to determine the freshwater volume (
In the model, denitrification is an O
The maximum concentrations of P and N (mg L
The SCM is also used to investigate the effect of a reduction in the
nutrient load from land to the Stockholm Archipelago. The reductions are
applied to the forcing from 2010 with a river load of 4027 t N yr
Observed (asterisks) and modelled (line) monthly outflow (
The variability of the modelled discharge of water and nutrients by the
S-HYPE model agrees well with observations (Fig. 5 and Table 3) for the
simulated period (1990–2012). A good description of river runoff is needed
because the nutrient loads are strongly related to the magnitude of river
outflow (
The correlation coefficients (
Datasets from eight stations (Table 1) fulfilled the requirements of good data availability and were used in the evaluation of the SCM results. There are aspects that are important to have in mind when comparing model results and observations. In the model the state variables are horizontally averaged in each basin, while observations are measured at one station at a certain location. The Stockholm Archipelago has relatively large spatial salinity gradients and the representativeness of a station when compared to model results can be somewhat limited if, for example, the position of the station is close to an outlet or inlet of the basin. Observations may in general also be influenced by local conditions, e.g. sewage effluents, high sediment fluxes or stagnant conditions, which are smeared out in the average results of the model. Still, we assume for the present study that the station data are good enough for the quantitative model validation and give a background for discussions about model strengths and weaknesses. As an example, validation results are shown for one of the basins where the number of observations is large enough during both summer and winter periods to be included in the validation process. The example is from station Blockhusudden (position G in Fig. 3), where the largest dataset of observations was found. The station is situated at the boundary between the innermost basin Strömmen and the next adjacent sub-basin.
Average cost function (
The objective correlation coefficients (Eq. 2) and the cost function value (Eq. 3) for the different state variables correspondingly implied that the model manages to simulate the average vertical winter and summer profiles with good or acceptable skills in the basin Strömmen (Fig. 6g), except for the average seasonal value of DIN that was described as not good. The differences between model results and observations of DIN may be a result of the location of the monitoring station.
The long-term average summer depth profiles of modelled salinity and oxygen in the basin Strömmen correlate well with observations, while the winter values of salinity were too low, especially in the surface layers (Fig. 7a, b). This difference is partly due to the fact that the salinity of a station at the entrance to the basin more reflects the boundary conditions of the downstream basin than the mean conditions in Strömmen. The surface winter concentrations of oxygen were too high but decreased with depth and became too low in the lower layers (Fig. 7b). It might be expected that winter surface oxygen concentrations in observations should be higher than in summer because of the temperature effect on oxygen saturation concentrations as seen from the model results. However, the number of observations during winter are limited and occurred mostly in November and February, which may influence the average values of the observations.
The SCM modelled (lines) and observed (circle and diamond) vertical
average profiles (1990–2012) of salinity
The results indicate that there is an impact from local conditions at the monitoring station that is not captured by the model setup. The modelled DIN depth profiles show higher values at about 15 m depth during both winter and summer (Fig. 7c), while the DIP profiles values seems to be satisfactory at all depth and periods (Fig. 7d). Also, the individual observations show higher concentrations of both DIN and DIP around 15 m depth, which is where the halocline has its largest vertical gradient. This depth level corresponds to the depth where two sewage water treatment plants relieve their sewage water in the model. The winter stratification was stronger in the model because of the lower surface salinity. This hampers the vertical transports of oxygen and has an influence on the winter oxygen conditions in the deep water that were lower in the model compared to the observations from the more well ventilated entrance area.
The average seasonal variation in the surface temperature and the bottom water oxygen concentrations was captured by the model, but not the increase in surface nutrients, especially DIN, during autumn (Fig. 8). The surface salinity was overall somewhat low, which is probably a result of the location of the monitoring station, as described above.
In the other basins used in the evaluation (vertical and seasonal profiles
are not shown) of the SCM state variables during winter, summer and season
were simulated with good or acceptable skills, except for the average
vertical summer profiles of DIN in the basin Solöfjärden (Fig. 6c)
and oxygen concentration in the basin Sandöfjärden (Fig. 6a). The
combined model skills, which were calculated as the average of the
individual
Simulated (lines) and observed averages (squares) of the seasonal variation and the standard deviation (vertical lines) of the observations in the basin Strömmen (1990–2012) of surface temperature (Temp), salinity, DIN and DIP and of the bottom water oxygen concentrations. Time periods with dense number of observations (grey asterisks) determined the time intervals (grey shaded area) used in the calculations.
The load and the inventories of N and P may change and vary between the
beginning and the end of a studied period, and thus the determination of total
nutrient retention depends on the timescales of consideration as discussed
in Sect. 3.2.3. During the period 1990–2012, on average
174 t P yr
Transport scheme of N and P (t yr
Largest amounts of P and N in the model were retained in the outer
archipelago compared to the intermediate and inner archipelagos (Fig. 9).
The retentions of all supplied P and N, including the net import from
upstream areas, within the inner, intermediate and outer Stockholm
archipelagos amounts to 18, 23 and 48 % for P, respectively,
and 14, 26 and 60 % for N, respectively. The area of the three
zones increases from inner (109 km
Karlsson et al. (2010) found in their empirical study for 1982–2007 that about 15 % of the total input of N and 10 to 13 % of the total input of P were retained in the inner Stockholm Archipelago. However, their numbers are based on the total input and thus both the land load and an estimated input from outer areas, i.e. the intermediate Stockholm Archipelago. A recalculation from the given numbers in their study resulted in a filter efficiency of about 25 and 24 % for N and about 21 and 30 % for P of the nutrient load from land and atmosphere for the periods 1982–1995 and 1996–2007, respectively. These numbers of the filter efficiency are higher than the numbers in the present model study. To be able to compare the numbers, a recalculation of the filter efficiency in the SCM for the latter period (1996–2007) in the inner archipelago was performed, but this did not change the SCM results considerably. The largest difference between the two studies is caused by the calculation of net exchange of nutrients through the sounds. The transport through the sounds was in Karlsson et al. (2010) calculated from average volume flows estimated from mass balance calculations for salt together with budget calculations using observations of average nutrient concentrations. In the present study the exchange of nutrients between the inner and the intermediate archipelago was part of the dynamic model calculations in the SCM. The SCM net outflow from the inner archipelago for N and P was about 11 and 8 %, respectively, larger compared to the net outflow of the nutrients in the Karlsson et al. (2010) study. Another difference between the two studies was the land load of P, which was about 8 % lower in the SCM. The difference in land load of N was only about 1 %. Thus, calculations from an empirical model based on Knudsen's relations (Knudsen, 1900) and calculations using long-term average values resulted in about 10 % higher retention efficiency values compared to the calculations from SCM, a coupled numeric physical–biogeochemical model with high vertical resolution and a small time step. In spite of the difference in models, the result are surprisingly close.
The external annual load and retention (t yr
The average temporary retention in SCM for the entire simulated period is negative in all three parts of the archipelago for both P and N (Figs. 9 and 10). The reason for negative temporary retention is mainly a decrease in the benthic nutrient pools during the period (Fig. 12). The largest decrease (29 %) is found in the pelagic pool of N in the inner archipelago, which coincides with the decrease in N load from point sources (Fig. 10). In the intermediate and outer Stockholm archipelagos the pelagic pool of N remains at about the same level through the whole simulation period. The large decreases in the benthic pools of N and P (14–18 %) occur in the intermediate and outer archipelagos, while there are only small changes in the pelagic and benthic pools of P in the inner archipelago. Because of the nutrient retention there is a reduced net transport of N and P from the inner archipelago towards the intermediate and outer archipelagos and further to the open sea during the simulated period (Fig. 9). The annual temporary retention of P in the entire Stockholm Archipelago increases with time during the simulated period (Fig. 10). There is a change to positive values at the end of the period, when there again is a build-up of the benthic pools of P (Fig. 12). The build-up is most likely a result of better oxygen conditions in the modelled deep water (not shown) during the end of the simulation period, which lead to a lower release of P from the sediment to the water column (Eilola et al., 2009). For the temporary retention of N there is no visible trend in the variation with time. In addition to the nutrient load from land and the net export of nutrients to outer areas, there is also an extensive circulation of nutrients between the coast and the open sea. The importance of imported nutrients into the coastal zones from sea have been discussed in earlier studies (e.g. Humborg et al., 2003) in which it was concluded that many estuaries has a net import of DIN and DIP from sea, e.g. Chesapeake Bay (Boynton et al., 1995). This is also shown for, for instance, the Mid-Atlantic Bight, where almost 3 times the riverine input of N is denitrified (Fennel et al., 2006). In different parts of the shelf in the Gulf of Mexico the denitrified proportion of the land input of N is in total 86 %, where locally on the different part of the shelves the denitrification fraction of the supply from land varied between 68 and 341 % (Xue et al., 2013). Thus, in many cases the import is larger than the export and the coastal zones works as a filter not only for the nutrients from land but also for the nutrients from the open sea, as also discussed in Sect. 3.2.3.
The retention per area unit (t km
The total content (g m
From the present results it can be concluded that the Stockholm Archipelago
works like a filter for nutrients that enter the coastal zone from land and
atmosphere. However, a rather large area of the archipelago is needed to
effectively retain the nutrients. About 82 and 86 % of P and N supplies,
respectively, pass the small inner archipelago and are exported to the
intermediate archipelago. In the intermediate and the outer archipelago all
local supplies of nutrients from land and atmosphere are retained together
with a fraction of the nutrients imported from the inner archipelago. The
filter efficiencies increase with increased coastal area from land to the
sea continuum (Fig. 13). However, the filter efficiency of the entire
Stockholm Archipelago is not effective enough to retain of all the nutrients
that enter the system from land and the atmosphere, but still at least
65 and 72 % of the supplied P and N, respectively, are retained. The
total retention numbers (permanent and temporary) correspond to 141 t P yr
The filter efficiency of P (left) and N (right) vs. the
logarithmic ratio between the average depth and the freshwater residence
time of the study areas (month yr
The present study was performed in an area characterized as an eutrophic archipelago in an inland sea with basins having oxic, hypoxic and anoxic bottom waters. Nixon et al. (1996) showed that the retention of P and N correlated to the log scale of the ratio between the average depth and the residence time of the study areas, which is confirmed by the results from the studies by Billen et al. (2011), Hayn et al. (2014) and Nielsen et al. (2001) as well as by the present study (Fig. 13). The freshwater residence time in the Stockholm Archipelago is 48 days in the inner, 108 days in the middle and inner, and 185 days in the entire area. No clear relationship was found between the filter efficiency and the average depth, which vary between 17 and 20 m for the three areas. These results are in agreement with Nixon et al. (1996), who showed that including the depth in the analysis of retention vs. residence time did not much improve their regression. In the present study the change in the filter efficiency with residence time is about 0.5–0.6 % per day. The results of the present retention estimates are in agreement with results from previous studies (Billen et al., 2011; Hayn et al., 2014; Nielsen et al., 2001; Nixon et al., 1996), but with somewhat higher values in the entire archipelago (Fig. 13). Their studies were performed in various types of systems: coastal lagoons, drowned river estuaries, coastal embayments, and inland seas in North America and in Europe. Those systems varied from being relatively pristine to systems with large point sources (eutrophic), and they also varied between oxic and hypoxic and/or anoxic conditions. In shallow areas larger parts of the sinking particulate organic material may reach all the way down to the seafloor, where it can be exposed to retention processes such as burial and denitrification. On the other hand, in a much deeper area a larger part of the organic material may become remineralized within the water column on its way down to the seafloor. The nutrients can then be reused by phytoplankton and/or be further transported out from the system. Long residence times in a system increase the time of exposure for biogeochemical transformation processes and sedimentation within the system and larger parts of the nutrients may be retained.
Denitrification increases the retention in areas with longer residence times
(Nixon et al., 1996; Finlay et al., 2013) as also seen from Fig. 13. In the
Randers Fjord the residence time was short (6 days) and the filter
efficiencies of N and P were lower, 10 and 9 %, respectively
(Nielsen et al., 2001), compared to the Stockholm Archipelago, where the
freshwater residence time is longer. The denitrified proportion of the
permanently retained N was also lower, about 60 % compared to in the
Stockholm Archipelago (92 %). Oxygen is an important factor regulating
the magnitude of denitrification. In waters with longer residence time, the
bottom water might be less ventilated, and thus the bottom water oxygen
concentrations might be lower with higher denitrification as a result. As a result of
the forced reduction in the oxygen concentrations with 134
Benthic primary producers and benthic fauna are also important for the retention of nutrients in shallow coastal ecosystems (McGlathery et al., 2007; Norkko et al., 2012). Assimilation of nutrients during primary production does not directly change the inventory of N and P but rather transfers the nutrients into organic material. Plant uptake at the bottom can, for example, lead to increased burial and also influence on the oxygen-dependent biogeochemical processes in the sediment due the plant metabolism (McGlathery et al., 2007). These processes are not yet implemented in the SCM, which only includes pelagic primary production, and therefore are the influences by bottom living plants included in the present study. Including these processes may have some impact on the model dynamics, for example on bottoms where seagrasses and burrowing macrofauna might influence the decomposition of organic material and the permanent burial of nutrients and organic matter. The evaluation of forcing and model results indicates, however, that the model system is able to reproduce much of the observed physics and nutrient dynamics in the archipelago, which gives confidence to the budget estimations of nutrient retention in the area. A quantitative evaluation of the effect and the implementation of benthic flora and fauna to the model is therefore left for future work.
Pelagic (upper) and benthic (middle) pools of P (left) and N (right) in the inner (red), intermediate (orange), outer (turquoise) and entire (black) Stockholm Archipelago. The filter efficiencies (%) of N (red) and P (blue) load from land and atmosphere are shown for the entire Stockholm Archipelago (lower), where the small peaks derive from leap years.
It is also important to know whether a system is in balance with the nutrient loads or not since it would affect the retention capacity. In this study the temporary retention is negative for both N and P in all three areas of the Stockholm Archipelago, which implies that the system is not in a steady state. This imbalance is, however, expected since there are reductions of the nutrient loads in the first part of the simulation period (Fig. 10a, b). However, the possibility that the results may be influenced by unknown initial conditions of sediment concentrations should not be excluded. There are only few observations available, and the knowledge about the amount of sediment nutrients involved in biogeochemical cycles is poor.
The total land load (rivers, land runoff and atmosphere) of P and
N (t yr
The fastest response in the nutrient load reduction experiment is seen in the pelagic pool of N which rapidly decreases, but reaches a steady state after about 3 years with reduced loads (Fig. 14). The pelagic pool of P decreases in the inner archipelago but increases slightly in the outer areas. The changes in P pools are slower compared to those in N pools. The large and fast decrease in pelagic N in the inner archipelago results in a decreased N : P ratio (Table 4), as well as (not shown) lower chlorophyll concentrations, reduced sedimentation, and increased export of P from the inner archipelago to the outer areas and the Baltic proper. The anoxic areas also decrease by about 30 % as a result of the lower deposition of organic material on the seafloor (not shown). The changes in the benthic pools of N and P occur over a longer time period, and the benthic P pool does not reach a steady state until about 40 years after the reduction.
In the reduction scenario the transport of N to the open sea from the Stockholm Archipelago decreases by 62 % within four years (Table 4). The filter efficiency of N in the entire archipelago increases at the same time from 79 to 90 % as a result of the load reduction. The longer response time of P compared to N is also observed in the filter efficiency (Fig. 14).
The filter efficiency of P at the end of the spin-up run is about 100 %. This implies that, under the 2010 conditions, all of the P land load is retained in the Stockholm Archipelago when the system is in steady state. This is not the case when the original model forcing is used, which implies that the Stockholm Archipelago is still adjusting to the load reductions already implemented. Thus, under present conditions, the coastal region might continue to improve without further actions.
The filter efficiency of P decreases to 74 % during the first years after
the reduction, coinciding with the large decrease in the N pelagic pool and
the decrease in N
These results indicate that local nutrient load abatements can improve the environmental state of a semi-enclosed coastal site (the inner archipelago) that is locally impacted by humans. The results also imply that, for the first 5–15 years, increased nutrient concentrations might be expected locally. However, this effect largely depends on the water residence time and on which nutrient limits the seasonal phytoplankton production initially. However, for the more open coastal zone, represented in the present study by the intermediate and outer archipelago, the response to further nutrient load reductions was minor. This shows that, for open coastal areas, the interactions between the open sea and the coastal zone are probably more important than the land–sea connection.
The present study can conclude that even the eutrophicated Stockholm Archipelago can, after further nutrient load abatements, act as a sink for open-water phosphorus. Similar behaviour was found in Chesapeake Bay (Boynton et al., 1995), which acts as a sink for the total load of P and thus P from land, atmosphere and from the open sea.
Archipelagos are complex areas with many basins and several shallow sounds, which affect the transport of water and the dissolved and particulate nutrients. For the first time, the SCM was used to study the capacity of the coastal filter of nutrients. An evaluation showed that, overall, model results agree with observations.
We focused our study in the northern Baltic proper and investigated retention of N and P in the Stockholm Archipelago. The main findings are described below.
The coastal zone works as an efficient filter for the land loads of nutrients. Under prevailing conditions the total retention are 65 and 72 % of P and N, respectively, supplied from land.
A sensitivity experiment reducing the land load of nutrients showed that the retention capacity of N and P increased. In this case the export of N from the archipelago decreased and P was imported from the open sea.
The average filter efficiency is dependent on the spatial dimensions of the coastal area. Thus, nutrient retention per area is largest in the inner archipelago and decreases towards the open sea.
Average water depth and water residence time regulate the retention of nutrients that occur mostly in the sediment due to processes such as burial and denitrification.
The pools of nutrients in the water and in the sediment change with nutrient loads on different timescales and affect the temporal nutrient retention in the area. N has a rather short response time of about 3 years, while it takes about 40 years for P to reach balance in a system with constant forcing. Changing N : P ratios in the archipelago due to the different response timescales also have an impact on the nutrient retention capacity on decadal timescales.
Coastal management needs to take the aspects of time and balance between nutrient loads and pools into account in the assessment of impacts from nutrient load abatements. On shorter timescales the retention capacity of P might be less effective when the nutrient load from land decreases.
The model data on which the results in the present study are based on are stored and available from the
Swedish Meteorological and Hydrological Institute. Please send your request to ocean.data@smhi.se.
Monitoring data can be extracted from the SHARK database at
The research presented in this study is part of the Baltic Earth programme
(Earth System Science for the Baltic Sea region; see