The Boknis Eck (BE) time series station, initiated in 1957, is one
of the longest-operated time series stations worldwide. We present
the first statistical evaluation of a data set of nine physical,
chemical and biological parameters in the period of 1957–2013. In
the past three to five decades, all of the measured parameters underwent
significant long-term changes. Most striking is an ongoing decline
in bottom water oxygen concentration, despite a significant decrease
of nutrient and chlorophyll
Long-term observations in oceanography are crucial when
it comes to improving the understanding of the state of ecosystems and monitoring their long-term developments. They have been a core
strategy in the last 50 decades and are still considered to have high
priority today
Long-term monitoring is especially important for a dynamic system such
as the Baltic Sea, where high variations, which are effective on different timescales, are triggered by natural and anthropogenic causes. The
natural hydrographic setting of the Baltic Sea is defined by the small
connection to the North Sea, where water exchange takes place through
the Danish Straits. The Baltic Sea displays a strong stratification
throughout the year, resulting from differences in the salinity of
different water masses
Over decadal timescales, anthropogenic influences like eutrophication
have been impacting the ecosystem of the Baltic Sea. Marine
eutrophication caused by enhanced nutrient input is a widespread
phenomenon in coastal areas worldwide, and in the Baltic Sea in
particular
Although the Baltic Sea is one of the best-studied coastal areas
Location of the Boknis Eck time series station at the
entrance of Eckernförde Bay in the southwestern Baltic Sea,
indicated by the black square.
The Boknis Eck (BE) time series station is located at the entrance of the
Eckernförde Bay (54
Starting with measurements of temperature, salinity and oxygen on
30 April 1957, the number of parameters has increased almost continuously.
Chlorophyll
The routine of measurements and analysis has changed little during the
observation period. Monthly samples have been taken from research vessels
during half-day trips, the sampling usually starting around 09:00 to 10:00 in
the morning. Seawater has been sampled at six standard depths (0.5 or 1; 5,
10, 15, 20; 25 or 26
Synopsis of physical, chemical and biological time series at Boknis Eck. Solid lines indicate regular monthly measurements with only up to two following months missing (rare), dotted lines indicate only very irregular measurements with large gaps. Colours represent financial support by different funders. DWK: Deutsche Wissenschaftliche Kommission; HELCOM: Helsinki Commission; BMBF: Bundesministerium für Bildung und Forschung; IfM: Institut für Meereswissenschaften.
Synopsis of methods used for determining oceanographic parameters at the Boknis Eck time series station since 1957. n.r.: not reported; CTD: conductivity, temperature, depth; SCFA: segmented continuous flow analyser; Meth.: methanol.
The time series of BE provides a highly valuable data set for three main
reasons. Firstly, the time span of observation covers 56 years and hence
provides continuous information on changes in the time span of decades.
Secondly, there have only been minor changes in the methods used for
determining the parameters, and careful calibration avoided shifts or
inaccuracies in the data. This consistency strongly enhances the quality of
the data, as shifts in the data signals through different methods of analysis
can be excluded. Thirdly, the location of Boknis Eck was initially chosen
because it reflects the hydrographic setting of the Kiel Bight
The complete time series of nine parameters covering the period from 1957 to
2013 is described and statistically evaluated for the first time. Statistical
tests covering the long-term development of median (Mann–Kendall test
(MKT), Sen's slope) and extreme values (quantile regression of the 10th and 90th
percentile) were applied (for details, see below). Prior to the analysis, data
were averaged as sampling was conducted at slightly different standard depths
(differences 1
The measurements were averaged in cases of several dates per month
(< 5 %) and assumed to be representative of the whole month.
Therefore, the corresponding date was chosen to be the middle of the month
(the 15th). Gaps were filled by linear interpolation in the case of one or two
missing months in a row; larger gaps were filled by replacement with the
median of the corresponding month. In the case of missing values, the temperature
at the surface (1
Besides the measured parameters, oxygen saturation and density gradient were
derived from measurements. Density was calculated using the UNESCO algorithm
for density in seawater
Mean seasonal cycle of temperature (1957–2013), salinity
(1957–2013), oxygen (1957–2013), chlorophyll
The Mann–Kendall test (MKT) is a nonparametric, statistical test to
decipher significant monotonic long-term trends in time series. The
MKT tests the null hypothesis that all variables are randomly
distributed against the alternative hypothesis that a monotonic trend
exists in the time series on a given significance level
The MKT can be modified to decipher trends in seasonal data,
e.g. monthly data like the BE time series, when a homogeneous trend is
present
If a trend is present in the time series according to the MKT,
a median slope was computed according to
The decrease or increase in extreme values within the time series was
evaluated by quantile regression, which is a least-squares
optimisation technique to find the conditional quantile in a time
series
To assess the significance of the trend, the method proposed by
Following this approach, 500 surrogate time series were generated. As the generation of the surrogate data required complete time series, both the quantile regression and the generation of the data sets were performed with the interpolated and gap-filled time series.
Time series of temperature (1
The numerical model used in this study is a three-dimensional coupled
sea-ice ocean model of the Baltic Sea
The oxygen consumption sub-model
Test statistics of monthly Mann–Kendall tests part I. Only
significant results are shown. Tau
For comparison, mean values of the water column in both measured and modelled parameters were compared. Averaging was necessary as the model had a higher spatial resolution than the sampling. Model output for the Boknis Eck station on the day of sampling was compared to the measurements by means of linear regression and the deviation from the bisectrix in the regression plot.
Furthermore, linear regression was performed with daily as well as monthly (day of BE observations) model output to assess the trends in modelled temperature, salinity and oxygen content. These were compared to the linear regression of the monthly observation of these three parameters at Boknis Eck.
To test the hypothesis of altered stratification, the development of the thermocline was further investigated with the model output of the BSIOM. Different criteria in temperature difference across the thermocline were applied and the trend in the length of the stratification period was assessed via linear regression.
Surface temperature was dominated by a clear seasonal cycle
(Fig.
Temperature significantly increased in January, April and May, with rising
trends between 0.03 and 0.06
The temperature distributions for August, usually the warmest month in
a year, revealed that warmer temperature anomalies increased in frequency during the second half of the series from 1985
onwards (Fig.
Average winter concentration (December-January-February,
DJF, red dots) of nutrients at the Boknis Eck time series station, as
well as linear decreasing trends (solid line represents the linear trend, broken lines the 95 %
confidence interval). Note the different
The temperature at 25
The bottom water salinity at 25
No significant trend was detectable with the seasonal MKT for salinity in the
period from 1957 to 2013 (Table
Oxygen concentration at 25
Similar trends were found in oxygen saturation calculated from
measured oxygen concentrations, temperature and salinity
(Eq.
Normalised temperature distribution for observations at BE in
August for the first half (blue) and the second half (red) of the
time series at the surface water (1
The density gradient varied seasonally with minima during winter and
maxima during summer months. Significant trends could be found for
three months: the highest significant increase was found in April,
when the gradient rose by
0.002
The phosphate concentration in the middle of the water column (10
Phosphate was the only series where the standard deviation fluctuated
intensively after the gap filling from 1967 to 1979 and
1983 to 1986. Hence, for the MKT, only the time series starting on
7 January 1986 is used. Testing for monthly trends revealed
significant negative trends for the winter months December to March, as
well as September (Table
Increasing number of months with oxygen concentrations at
25
The phosphate concentration at 25
Test statistics of monthly Mann–Kendall tests part II. Only
significant results are shown. Tau
The seasonal dynamic of the nitrate concentration at 10
The nitrite concentration at 10
Nitrite measurements have only been available since 1988, and the trends only
refer to this period (1988–2013). No homogeneous trends could be detected
for all seasons; hence the months were tested individually for a long-term
trend. A significant downward trend was evident in January, March and April
(Table
Descriptive and test statistics for Mann–Kendall test (MKT)
and quantile regression. SD: standard deviation; filled: time
series after gap filling described in Sect.
Ammonium concentration at 10
The ammonium concentration at 25
The mean yearly distribution of the chlorophyll
The two parts of the time series were separately tested for trends with the
MKT. Chlorophyll
Secchi depth displayed fluctuations throughout the whole period from 1986 to
2013 (not shown). The mean Secchi depth was
Linear regression of model output (BSIOM at location of BE)
vs. observations (BE time series) for salinity, temperature and
oxygen concentration. The top row shows the surface, and the bottom one the
bottom layer comparison. Note that in the model the bottom layer is
21–24
The agreement between model output and observations at the location of Boknis
Eck was overall good with respect to the
Model output was used to investigate the thermal stratification and
the oxygen consumption rate with a daily instead of monthly
resolution. The time during which the water column at BE was stable
stratified was difficult to determine, as the duration of
stratification highly depended on the temperature gradient criterion
assumed. Temperature gradients between 0.6 and
1.4
The effect of the rising temperature trend on oxygen consumption rates
was relatively small. Only 13 %
(0.2
Statistical analysis of the time series at BE revealed that significant trends were present in all of the nine analysed parameters. The trends comprise physical, chemical and biological parameters and indicate that the whole system undergoes significant changes, resulting, for example, in altered living conditions for biota.
Temperature trends at BE were in good agreement with trends in other regions
of the Baltic Sea. The positive tendency of 0.2
Descriptive and test statistics for seasonal (MKT) and linear regression of winter (DJF mean) concentrations. Percentage of missing values in brackets refers to the part of the time series used for statistic analysis. SD: standard deviation; DJF: December-January-February.
Comparison of linear trends in observed and modelled parameters
temperature (surface and bottom layer), salinity and oxygen concentration
(bottom layer). To ensure comparability, trends only relate to the period
1970–2010, limited by the model output. Note that the bottom layer is at
a depth of 25
No significant trend could be detected for salinity at BE in the median,
which is in agreement with findings of the BACC author team
The nutrient and phytoplankton cycle, the latter indicated by the
chlorophyll
Trends (1970–2010) and length of stratification period in the BSIOM output depending on different temperature criteria used to detect stable stratification. Temp.: temperature; strat.: stratification; doy: day of year; d: days; dec.: decade.
The peak in bottom water ammonium appeared consistently with a lag of 1
month after increased chlorophyll
Although the seasonal cycle varied only little with respect to the yearly
course, the magnitude of the nutrient and chlorophyll
Accompanied by this significant decrease in nutrient concentrations is
a decline in chlorophyll
The annual cycle of Secchi depth with a minimum in March and a maximum
during winter matched the findings of the chlorophyll
The oxygen concentration declined significantly with a simultaneous increase
in hypoxic and anoxic events in the bottom water during the period 1957–2013
at BE. The spreading of hypoxic and even anoxic zones in marine coastal
ecosystems is known to occur worldwide and is often related to eutrophication
Schematic overview of possible causes for ongoing oxygen
decline.
The BSIOM reproduced observed temperature, salinity and oxygen at the location of BE with an acceptable range of uncertainty. For temperature, it became obvious that the timing of the monthly observation is important for the magnitude of trend, and trends could differ by around 30 % compared to the observed trend. The timing is especially important when parameter variation is high, e.g. for the surface temperature. Observed temperature at the bottom showed only small variations, and trends did not depend strongly on the temporal resolution. In general, the model reproduced the measurements accurately and can therefore be applied in order to look in detail at the development of temperature stratification discussed with oxygen depletion below.
Salinity was reproduced less precisely than temperature (lower correlation, magnitude of trend), but as changes in the ventilation system seem to be driven by temperature changes (see below), salinity was not further considered for finding reasons for the ongoing oxygen decline by model output analysis.
Oxygen trends were similar, despite the simplified oxygen parametrisation in the model. Although concentrations were overestimated, the trend in oxygen depletion is captured well by the model. However, it needs to be noted that only a small fraction of the decreasing oxygen concentration trend can be attributed to a temperature-enhanced oxygen consumption. Most of the oxygen depletion in the model is based on an increase in primary production. Although an increase in primary production is questionable at BE due to significantly decreasing nutrient concentrations, it cannot be excluded, as no direct measurements are available. In general, the oxygen consumption rate is captured well by the model and reflects the trend observed at BE well, but the reasons for that cannot completely be confirmed.
First, possible processes causing oxygen depletion in the bottom water are discussed here based on the time series of observations at Boknis Eck. Processes discussed include nutrient remobilisation, the physical process of decreasing solubility of gases with increasing temperature, lower oxygen supply by altered ventilation and temperature-enhanced oxygen consumption rate.
Nutrient remobilisation from the sediment due to lower oxygen concentrations
in the bottom water are often cited to contribute to ongoing oxygen decline
Increasing temperature also decreases the solubility of oxygen. However, the oxygen saturations showed a significant decrease as well. In contrast to oxygen concentration, the oxygen saturation already takes into account changes in temperature. A decrease in the oxygen saturation means that the decrease in concentration cannot be attributed completely to the physical effect of solubility.
Rising temperatures further enhance remineralisation of organic matter
that is deposited on the bottom, resulting in increased oxygen
consumption
Moreover, there is evidence for an alteration in the ventilation at
Boknis Eck. At BE, the density gradient across the pycnocline
strengthened significantly in the period 1957–2013, especially in
spring, when the rise in temperature was greater at the surface than at
the bottom. A stronger stratification earlier in the year hampers
ventilation and therefore oxygen supply to the bottom water layer and
might be a possible reason for intensified oxygen decline. A schematic
overview of possible reasons for the ongoing oxygen decline despite
decreasing eutrophication is given in Fig.
Whether advection of oxygen-enriched or depleted water through the Danish
Straits is a possible reason for the ongoing oxygen decline at BE cannot be
estimated accurately on the basis of a one-dimensional time series such as BE.
After the main inflowing events (see Sect.
The daily model output indicated a prolongation of the stratification
period at BE, although the length of the stratification strongly
depends on the temperature difference criteria chosen to detect the
thermocline. All of the chosen criteria between 0.6 and
1.4
The second possible reason was oxygen decline due to enhanced
remineralisation rates. In the BSIOM, oxygen consumption is only based on
temperature-dependent consumption rate, which is related to prescribed
primary production (see Sect.
The detection of significant long-term trends in all of
the studied oceanographic parameters in the period of 1957 to 2013
revealed that Boknis Eck is subjected to extensive changes that
comprise biological, biogeochemical and physical factors, with
implications for the ecosystem. The observed trends for increasing
temperature and decreasing oxygen concentration in the bottom water
are representative of the southwestern Baltic Sea; decreasing
chlorophyll
Oxygen concentration in the bottom water at BE decreased significantly despite the decrease in nutrient concentrations. Based on the observed temporal development of the physical and biological parameters at BE, we hypothesise that enhanced remineralisation due to temperature increase and a longer lasting stratification may enhance oxygen depletion. It could be proved that the remobilisation of phosphate from anoxic sediments might act as a long-term nutrient source. However, the remobilisation is unlikely to significantly enhance oxygen consumption through organic matter mineralisation by triggering phytoplankton growth, as the general trend was decreasing.
The comparison to the temporarily higher resolved model output revealed that the period of stratification had an prolonging effect, although the magnitude of the prolongation depends on the temperature criteria chosen to identify the stratification. Oxygen depletion trends were captured well by the model, but the reasons for increased oxygen consumption were only ca. 13 % attributable to the temperature increase. The remaining part may be attributed to an increase in primary production implemented in the BSIOM. Furthermore, it could be shown that the monthly temporal resolution of BE observations may lead to inaccuracies in the trends, especially when the parameter shows high short-term fluctuations. Continuing the monthly measurements at the Boknis Eck time series station is of major importance for monitoring and understanding future changes in the southwestern Baltic Sea. An extension of the parameters including in situ primary production would be helpful in verifying the hypothesis of increasing primary production despite a decrease in nutrients as a reason for the ongoing oxygen decline.
The authors thank the captain and crew of the RV