Storm surges represent a major threat to many low-lying coastal
areas in the world. In the aftermath of an extreme event, the extent to
which the event was unusual and the potential contribution of climate change
in shaping the event are often debated. Commonly analyzes that allow for
such assessments are not available right away but are only provided with
often considerable time delay. To address this gap, a new tool was developed
and applied to storm surges along the German North Sea and Baltic Sea coasts.
The tool integrates real-time measurements with long-term statistics to put
ongoing extremes or the course of a storm surge season into a climatological
perspective in near real time. The approach and the concept of the tool are
described and discussed. To illustrate the capabilities, several exemplary
cases from the storm surge seasons 2018/2019 and 2019/2020 are discussed. It
is concluded that the tool provides support in the near-real-time assessment
and evaluation of storm surge extremes. It is further argued that the
concept is transferable to other regions and/or coastal hazards.
Introduction
For many low-lying coastal areas, storm surges represent a substantial
threat. While many of the affected places can typically cope with or are
more or less well-adapted to present-day risks, future risks may increase
from, for example, mean sea level rise, subsidence, or changes in storm
activity (e.g., von Storch et al., 2015; Wahl et al., 2017). This may, in the
future, require additional protection or alternative adaptation strategies.
Storm surges and the high water levels at the coast associated with them are
typically caused by the interplay of different factors. These include, for
example, high astronomical tides; the effects of strong winds pushing the
water towards the coast (wind or storm surge); or seasonal, interannual, and
long-term mean sea level changes. Depending on the region, nonlinear
interaction among the different factors occurs and may substantially
contribute to the extremes and enhance the risks (Arns et al., 2017). For
example, in shallow water, the efficiency of the wind in producing the surge
may vary substantially with tidal water levels (phase of the tide), and the
propagation of the tidal wave may, in turn, depend on surge levels
(Horsburgh and Wilson, 2007).
The extreme sea levels that result from such processes pose a major risk to
many of the low-lying coastal areas worldwide that are at least seasonally
affected by storms (von Storch et al., 2015). So far, the most deadly and
devastating storm surges were caused by tropical cyclones. Examples comprise
the storm surges generated by the 1970 Bhola cyclone in Bangladesh that
caused approximately 300 000 casualties or by Hurricane Katrina in 2005
which represents one of the most expensive natural disasters in US history
(Needham et al., 2015).
Extratropical storm surges, although less severe, still bear a substantial
threat (e.g., Weisse and von Storch, 2010; Weisse et al., 2012). In the
mid-latitudes, the German North Sea and Baltic Sea coasts are examples of such
regions and are highly susceptible to the impacts of extreme sea levels. For
instance, in 1953 and 1962 two major disasters occurred at the North Sea
coast. Both flooded several thousand hectares of land and caused several
hundreds or thousands of casualties (Gönnert and Buß, 2009; Hall,
2013). In 1872, the Danish and the German Baltic Sea coasts were devastated
by an extreme storm surge, which still represents the highest on record in
many areas (Feuchter et al., 2013).
Since then, coastal defenses at the German coasts have been improved
significantly. Particularly at the North Sea coast, higher storm surges than
those reported in 1953 and 1962 were observed in more recent years. For
example, a storm surge in January 1976 caused higher water levels than in
1962 at many gauges along the German North Sea coast, while in December 2013
the extratropical storm Xaver caused exceptionally high water levels along
the coast and within the estuaries (Deutschländer et al., 2013;
Rucińska, 2019). However, contrary to the devastating events in 1953 and
1962, no severe damages or casualties were reported due to the reinforced
coastal protection. Due to the latter, public perception of vulnerability
and risk has decreased in recent years (Ratter and Kruse, 2010).
Nevertheless, the risk still exists, and it may further increase in the
expected course of anthropogenic climate change (e.g., Gaslikova et al., 2013;
Wahl, 2017; Weisse et al., 2014).
For the German North Sea coast, there is a considerable number of studies
analyzing either variability and/or long-term changes in extreme sea levels
(e.g., Dangendorf et al., 2014). Such studies focus on either the description
of past and present (e.g., Weisse and Plüß, 2006) or possible future
(e.g., Gaslikova et al., 2013) variability and change in general or link them
to some driving mechanisms (e.g., Woodworth et al., 2007). Studies based on
observations (Dangendorf et al., 2014), modeling approaches (e.g., Vousdoukas
et al., 2016; Woth et al., 2006), or statistical approaches (e.g., Butler et
al., 2007) do exist. The main conclusion from the majority of such studies is
that extreme sea levels along the German North Sea coast have increased over about
the past 100 years. Primarily, this is suggested to be the consequence
of the rising mean sea level. Changes in the wind climate (e.g., Krieger et
al., 2020) produced some interannual and decadal variability but no
noticeable trend (e.g., Weisse et al., 2012). Changes in the tidal regime may
also have contributed to some extent (e.g., Hollebrandse, 2005).
For the Baltic Sea, numerous studies carried out similar analyses. Based on
gauge records, most authors concluded that no significant increases in
extreme Baltic Sea levels have been observed so far (e.g., Richter et al.,
2012; Meinke, 1999; Mudersbach and Jensen, 2008; Weisse et al., 2021). Some of
the northernmost gauges, however, deviate from that picture due to
decreasing relative mean sea levels caused by glacial isostatic adjustment
(GIA) of Earth's crust and changes in atmospheric wind patterns (Barbosa,
2008; Ribeiro et al., 2014; Weisse et al., 2021). Strong correlations of
Baltic Sea level variability with large-scale atmospheric circulation
are reported as well (Hünicke and Zorita, 2006; Karabil, 2017; Karabil et
al., 2017). For the future, however, present studies indicate a further
increase in extreme sea levels mainly in response to a further rising mean
sea level, while storm-related contributions exhibit considerable
uncertainty with the upper bound suggesting an increase of up to a few
decimeters (e.g., Vousdoukas et al., 2017; Weisse et al., 2012, 2021). At some places, the figure may be modified substantially by vertical
land motions such as subsidence or uplift (e.g., Ribeiro et al., 2014; Richter
et al., 2012).
Because of the existing risk and the expected future developments,
information on long-term changes in storm surge activity is of the utmost
importance for decision making (e.g., Kodeih, 2018; Kodeih et al., 2019; Weisse
et al., 2015). Mostly, detailed local information is required, and evaluation
and assessment of ongoing storm surge activity in the context of long-term
variability and climate change are increasingly requested (e.g., Meinke, 2017;
Weisse et al., 2019). This includes, for example, questions on physically
plausible upper limits (Weisse et al., 2019); on probabilities for
co-occurrences of storm surges and other hazards (e.g., river floods in
estuaries); or, often in the immediate aftermath of an event, on the extent
to which this event was “normal” or can be attributed to anthropogenic
influences such as climate change. The latter requires evaluating and
assessing events in near real time within a detection and attribution
framework. This comprises both the assessment of how usual or unusual an
event has been (detection) and the attribution of causes to unusual cases
(attribution) (e.g., Hegerl, 2010). Typically, such information becomes
available with some delay after a severe event or is provided in regular
reports published in intervals on the order of years. From our experiences
in collaborating with decision makers and other stakeholders, we suggest
that near-real-time availability of such localized information and its
contextualization may provide substantial added value to regional
stakeholders and the public discussion in general. Moreover, ongoing
monitoring can detect changes in long-term statistics at an early stage.
In the following, we present a novel tool to map and monitor coastal hazards
in a detection and attribution framework and describe how such information
may be contextualized and can be made publicly available in near real time.
To start with, the tool is developed for storm surges along the German North Sea
and Baltic Sea coasts, and it initially focuses on detection only. We propose
that the approach can be easily transferred to other regions and extended to
other variables and/or attribution. In Sect. 2, the tool and its concept
are described which in the following will be shortly referred to as the storm surge monitor. In
this section also the data and methods used together with the presently
implemented features are described. In Sect. 3, the long-term statistics
are discussed that provide the background against which ongoing events and
seasons are assessed. Additionally, in Sect. 4 cases from two recent
seasons are discussed to illustrate the use of the tool for the evaluation
and contextualization of storm surge events. The results are summarized and
discussed in Sect. 5.
The storm surge monitorGeneral concept
Information on sea level extremes is typically available and used in several
different ways. For example, real-time data are used to monitor extremes to
support the preparedness and protection of inhabitants, assets, and
infrastructures in coastal regions. Potential long-term changes, which need
to be assessed to adopt risk management procedures or coastal protection
measures, are typically not assessed in real time but become available only
at fixed time intervals (typically several years) or in the aftermath of an
exceptionally extreme case when specific analyses have been carried out. The assessment and evaluation of extremes aiding public and scientific
debate regarding the extent to which such events were unusual are therefore
possible only with considerable delay. To address this gap, we propose a
concept in which real-time measurements are put into context with observed
long-term conditions, that is, their statistics such as mean conditions,
variability, expected extremes, or long-term changes in near real time. By
minimizing delays between the occurrence of extremes and their
climatological assessment, we propose to add value to the ongoing public and
scientific debates.
The concept was implemented and tested in a prototype web tool, which in the
following is referred to as the storm surge monitor (or abbreviated as the monitor) and which is available in
both a German (https://sturmflut-monitor.de, last access: 19 January 2022) and an English
(https://stormsurge-monitor.eu, last access: 19 January 2022) version. The tool is based on
and monitors several frequently considered parameters describing the storm
surge climate such as the height, frequency, duration, or intensity of
extreme events. Both single events and entire storm surge seasons are
evaluated against the climatological averages and their long-term changes.
Tide gauge data
We used historic and real-time water level measurements from 10 tide gauges
along the German North Sea and Baltic Sea coasts and their estuaries (Fig. 1)
to set up the monitor. The data-processing workflow is illustrated in Fig. 2. The historic data were used to derive long-term statistics, while the
real-time data were used to describe and measure the ongoing events. For the
historic data, the available record length, temporal resolution, and source
of the data vary (Table 1). Depending on that, we either used the twice-daily
high water level or hourly data. When hourly data were available,
information on the duration and intensity of extreme events could be
derived. On the other hand, the maxima of extremes may be underestimated due
to the sampling frequency. Only tide gauges with available records starting
no later than the 1950s were selected.
Schematic diagram of the data-processing workflow.
Summary of the tide gauge records used to calculate the long-term
statistics.
Tide gaugePeriodTemporal resolutionData sourceHusum1936–2018High waterGerman Federal Waterways and Shipping Administration (WSV), provided by the German Federal Institute of Hydrology (BfG)Heligoland Binnenhafen1953–2018High waterWSV, provided by BfGCuxhaven1901–2018High waterWSV, provided by BfG1919–2018HourlyUniversity of Hawaii Sea Level Center (UHSLC) (Caldwell et al., 2015)Hamburg St. Pauli1951–2019High waterWSV, provided by BfGBremen Weserwehr UW1954–2019High waterWSV, provided by BfGNorderney1901–2018High waterWSV, provided by BfG and the Coastal Research Station (FSK) of the Lower Saxony Water Management, Coastal Defence and Nature Conservation Agency (NLWKN)Flensburg1955–2019HourlyWSV, provided by BfGKiel Holtenau1955–2019HourlyWSV, provided by BfGTravemünde1950–2018HourlyWSV, provided by BfGWarnemünde1954–2018HourlyWSV, provided by BfG
Real-time data for all tide gauges are available every minute and are
automatically fetched four times daily from PEGELONLINE (https://pegelonline.wsv.de, last access: 14 January 2022). They are subsequently resampled to either
high water levels or hourly values depending on and matching the temporal
resolution of available historical data at each tide gauge. All tide gauges
measure relative to a land-based reference frame (relative sea level), and
the data contain contributions from atmospheric and oceanic dynamics as well
as from solid-Earth processes (Stammer et al., 2013). As it is relative sea
level that is important for coastal management and protection (Rovere et al.,
2016; Stammer et al., 2013), in our analyses the data are therefore
intentionally not decomposed or detrended to retain contributions from sea
level rise or subsidence.
Near-real-time data processing and information provision
To ensure near-real-time information provision, the monitor is automatically
updated four times daily at 01:30, 07:30, 13:30, and 19:30 CET. When the water
level within the last fetched period exceeds a gauge-specific threshold, a
new storm surge event at this particular gauge is detected. Subsequently,
new plots are automatically generated to provide the latest information and
a near-real-time assessment of the event and its long-term and seasonal
contextualization. While the availability of such an assessment represents
the major purpose of the monitor, it has some limitations caused by
potentially undetected errors in the real-time data. Since real-time data
are normally raw data from measurements, it is possible and unavoidable
that, for example, due to instrument failures during a storm no or only
erroneous data are accessible, which could then affect the detection and
classification of an extreme. Also, other technical problems can occur that
lead to unusable values over extended periods. For example, the tide gauge
at Norderney experienced a technical problem from May to September 2018. The
measurements during that period were initially invalid with randomly high,
low, or missing values, and a corresponding notification was posted on the
PEGELONLINE website. To deal with such erroneous data in near real time, a
quick quality control algorithm was implemented that marks and removes
measurements whose absolute values of minute increments exceeded 0.3 m
(spikes) or had values that were not within a reasonable range of 1–12 m.
When updated quality-controlled data become available, new plots are
produced, and figures and assessments may change compared to the initial
near-real-time assessment.
Detection of storm surge events
An important step in detecting and assessing storm surges is the definition
of corresponding thresholds for the extremes. For the German North Sea and
Baltic Sea coast, there are two different methods used in practice. The DIN 4049-3 standard defines a storm surge as an event in which regionally defined
thresholds are exceeded. These thresholds are defined such that an event
with a specific severity can be expected on average once for a given period,
e.g., the once-in-20-years event (DIN 4049-3, 1994). For their forecasts and
to issue public storm surge warnings, on the other hand, the German Federal
Maritime and Hydrographic Agency (Bundesamt für Seeschifffahrt und
Hydrographie, BSH) uses uniform exceedance thresholds for larger regions
(Gönnert, 2003). While each method has advantages and disadvantages, the
thresholds from the BSH were used in this study, as they are used in public
warnings. Accordingly, for the German North Sea coast, a storm surge event
is detected when the water level exceeds the local mean tidal high water
level (MThw, Table 2) by at least 1.5 m. The event is subsequently further
classified as a severe or very severe event when its maximum water level
exceeds MThw by at least 2.5 or 3.5 m. Because of the different
atmospheric and oceanographic conditions, storm surges at the German Baltic
Sea coast are divided into four classes. Events in which the maximum water
level exceeds the mean water level (MW) by 1.00 m are referred to as storm
surge events. Events with water levels of 1.25–1.50 m above MW or
1.50–2.00 m above MW are referred to as medium or severe events. Cases in
which the maximum water level is higher than 2.00 m above MW are referred to
as very severe events. While for routine practices, mean tidal high water
levels and mean water levels are regularly updated, we used fixed values
calculated over the common reference period 1961–1990 (Table 2) so that
events at all gauges can be compared, changes over a longer period can be
monitored, and results are presented relative to a fixed reference. In the
following, the period 1961–1990 is referred to as the reference period in
the analyses.
Mean tidal high water levels (MThw) and mean water levels (MW) in meters relative to the German reference level NHN (Normalhöhennull) of the
reference period 1961–1990 for the tide gauges along the North Sea and
Baltic Sea coasts.
North Sea Baltic Sea Tide gaugeMThwGaugeMWHusum1.58 mFlensburg-0.02 mHeligoland Binnenhafen1.07 mKiel Holtenau0 mCuxhaven1.46 mTravemünde0.02 mHamburg St. Pauli1.93 mWarnemünde0 mBremen Weserwehr UW2.47 mNorderney1.14 mDefinition of storm surge seasons
At the German North Sea and Baltic Sea coasts, storm surge activity is most
pronounced in the winter season from October to March (Jensen and
Müller-Navarra, 2008). In the monitor, the period from July to June of
the following year was therefore used to define and characterize a storm
surge season. Each storm surge season is denoted by the year in which the
season ends. For instance, the period from July 2018 to June 2019 is
referred to as season 2018/2019 and marked as 2019 in the plots. The course of
a storm surge season and the monthly distributions are also derived and
shown from July to June of the following year. When the ongoing season ends,
new long-term statistics are automatically computed, in which the statistics
of the concluded season will be included.
Main features presently implemented
The monitor was implemented for 10 tide gauges so far. Four of them are
located at the German North Sea coast; two of them are in the Elbe and Weser
estuaries; and four of them are along the German Baltic Sea coast (Fig. 1).
For each tide gauge, a web page is generated providing figures, texts, and
interpretations illustrating (a) the average conditions during the reference
period and (b) their long-term trend. Recent storm surges are contextualized
within the long-term development, and the ongoing storm surge season is
compared to the reference period. Depending on the availability of
historical data, different measures and statistics are available for each
tide gauge. Information on storm surge height and frequency is presented for
all gauges, while information on their duration and intensity is provided
only for the gauges where historic hourly data exist (Cuxhaven, Flensburg,
Kiel, Travemünde, and Warnemünde). To illustrate the main features
of the monitor, the figures of the season 2019/2020 at the tide gauge Cuxhaven
are discussed exemplarily.
Storm surge height
Information on two height indicators is provided, the development of the
annual maximum water level over time and the return period of extremes
(Fig. 3). Specifically, the maximum water level of the current season (red
dot) is compared with the variations of the annual maxima of the previous
seasons (black curve) (Fig. 3a). The plot allows for a quick visualization
of the observed long-term changes and the extent to which the highest water
level in the ongoing season is unusual. For an easy visualization, the current
season is highlighted in all the annual plots by a vertical red-dotted line.
The gray line provides an estimate of the linear trend with the gray-shaded
area representing the 95 % confidence interval. The trend line is shown in
solid when the estimated trend is significantly different from zero at the
level of 95 % and dashed otherwise. The scale on the left axis is labeled
as the water level relative to the German reference level (Normalhöhennull,
NHN), while the right axis is labeled about the severity classification of
storm surges (water level relative to MThw for the North Sea coast or
relative to MW for the Baltic Sea coast).
(a) Maximum water level per storm surge season (past seasons –
black; ongoing season – red) in meters (left ordinate: relative to NHN; right
ordinate: relative to MThw of the reference period) and corresponding
linear trend at Cuxhaven (gray line) together with the 95 % confidence
interval (light-gray band). (b) Return periods of storm surge events at
Cuxhaven from the past (gray symbols) and the ongoing season (colored
symbols; green – minor; blue – severe; red – very severe events) together
with the estimated distribution (dark-gray curve) and the corresponding 95 % confidence band (area between the light-gray curves) derived from past
annual maxima. The events of the ongoing season and the top five severe
historical events in the available period are listed on the right. Please note that the date formats in this figure are month-day and year-month-day.
The second plot provides estimates of the return periods of the storm surges
that have occurred in the current ongoing season (Fig. 3b). Return periods
are widely used to estimate the likelihood and severity of extreme events
(e.g., Haigh et al., 2015; Wahl et al., 2017). To estimate the return period
of storm surges, we fitted a generalized extreme value (GEV) distribution to
the annual maximum values (Hennemuth et al., 2013). Here annual maxima refer
to the block maxima within the historic storm surge seasons. The parameters
of the distribution (location, scale, and shape) were derived by using the
maximum-likelihood estimation (MLE).
Figure 3b displays the extreme value distribution (dark-gray curve)
estimated from the historical data for Cuxhaven together with its 95 %
confidence interval (two light-gray lines). This fit is subsequently used to
evaluate the return period of the latest (ongoing) storm surges in
near real time. When an event occurs, a colored symbol whose color denotes
its severity (green – minor; blue – severe; red – very severe) is added.
Additionally, an entry on the top of the list of current events is generated
(right). In the example season (Fig. 3b), several smaller events with
return periods of less than 5 years and a larger event with a return
period between about 5–10 years can be identified. To further put the recent
events into perspective, a list of the five severest historical events
during the available period (seasons 1901–2019 here) is given below the
list of events of the current season. These are represented by gray open
circles with different sizes indicating their magnitudes.
Storm surge frequency
Similarly, two plots assessing the frequency of storm surges are generated
(Fig. 4). To visualize the long-term development, variability, and change,
the number of storm surges per season over time is shown together with its
trend (Fig. 4a). Generally, in all plots, a trend that is/is not different
from zero at the 95 % confidence level is shown by a solid/dashed line.
The severity of the events is marked with different colors to illustrate the
number of events in the different severity classes in each season. Again the
current season is highlighted in red. While still ongoing, it can already
and preliminarily be put into context with previous seasons, long-term
variability, and change.
(a) Number of storm surges per season (colored bars; green –
minor; blue – severe; red – very severe) and the corresponding linear trend
(gray line) together with the 95 % confidence band (gray shaded) at
Cuxhaven. (b) Development of the ongoing storm surge season illustrated by
the number of storm surges per month (blue) and their sum since the onset of
the season (blue line) at Cuxhaven. For contextualization, also the
historical monthly maximum number of events (gray bars) and the 50th
percentile (orange curve) and the range between the 3rd and 97th percentiles
(yellow-shaded area) from the cumulative number of events in the reference
period 1961–1990 are shown.
The second plot (Fig. 4b) was designed to illustrate the course of the
ongoing season. It can be used to assess, for example, if the onset of a
storm surge season was very early or late, if there was an unusual number of
events within a particular month, or if the whole season is unusually active
or inactive compared to the average annual cycle. For this, the number of
events in each month of the current season (blue bars) can be compared with
the maximum number of events in the corresponding month over the reference
period (1961–1990, gray bars). Further, the blue curve shows the cumulative
number of events from the beginning of the current season until the day of
the website visit. It can be compared to the reference given by the 50th
percentile (orange curve) and the range between the 3rd and the 97th
percentiles (yellow-shaded area) of the reference period. For example, when
the blue curve remains below the orange one, this indicates that fewer
events than usual were observed so far. If the blue curve is above the
orange line but still within the yellow-shaded area, this suggests that the
frequency of such a season is still within a normal range but already
belongs to the more active ones. If the blue curve exceeds the yellow-shaded
area, indications for an exceptionally active season do exist.
Storm surge duration and intensity
In addition to storm surge height and frequency, duration and intensity are
widely used measures to describe the characteristics of storm surges (Cid et
al., 2016; Zhang et al., 2000) that are also important from the perspective of
coastal protection and risk management (e.g., Kodeih et al., 2019).
Therefore, information on both measures was included in the monitor,
although it can only be provided for those tide gauges where long-term
hourly data were available. In the following analyses, duration denotes the
number of hours for which the water level exceeds the given storm surge
threshold, while intensity refers to the area between the water level
measurements and the storm surge threshold and has units of meter × hour (m h). Regarding storm surge duration, the seasonal sum is a key indicator
for coastal erosion. Regarding intensity, the maximum intensity of storm
surges in a season is also critical for potential damages to coastal
infrastructure.
In the monitor, the total duration and the maximum intensity of storm surges
in each season are both shown within a long-term context (Fig. 5). In both
cases, the current season is highlighted with an orange bar to be easily
separable from the previous seasons (gray bars). Assessment of single events
within a season can be derived from Fig. 5c and d, in which the events of the
current seasons (red dots) are shown relative to the monthly distributions
derived from historical data. The historical reference (blue) is illustrated
in the form of a box plot bounded by the 3rd and 97th percentiles. The
median is given by the solid horizontal blue line, and the historical
maximum in each month is denoted by the blue dot. Assessment can be obtained
from the relative positions of the red dots, which indicate whether an event
lasted longer or was more intense in comparison to the monthly statistics of
the reference period.
(a, b) Total duration and maximum intensity of storm surges per
season at Cuxhaven for past (gray bars) and the ongoing (orange bar) seasons
together with the linear trend (black line) and the 95 % confidence
interval. (c, d) Box plots for the monthly duration and intensity of storm
surges in the reference period (blue box – 3rd to 97th percentile; blue line –
median; blue point – maximum) together with the events of the ongoing season
(orange).
Long-term changes
In this section, the capabilities of the monitor in supporting assessments
of long-term changes are briefly illustrated.
North Sea coastHeight
The annual maximum water level increased at all tide gauges over the
available periods (e.g., Figs. 3a, 6). Except for Heligoland, the trends are
significantly different from zero at the 95 % confidence level. The mean
increase of the annual maximum water level since 1950 is about 20–40 cm at
the coastal gauges and about 60–100 cm at the estuarine gauges Bremen and
Hamburg. The reasons for the increases are still discussed in the literature
but are likely to be the result of the interplay between several factors,
such as mean sea level rise, variability in the wind climate, astronomical
tide cycles, and the implementation of hydro-engineering measures with
different contributions at the coast and in the estuaries (e.g., von Storch
and Woth, 2008; von Storch et al., 2008; Hein et al., 2021; Jensen et al.,
2021).
As Fig. 3a but for the tide gauges Husum (a) and Hamburg St. Pauli
(b).
Although the trends are positive, the annual maximum water level strongly
varies between seasons and from gauge to gauge. Among the gauges, either the
storm surge of February 1962 (Heligoland, Bremen, and Norderney) or the storm
surge of January 1976 (Husum, Cuxhaven, and Hamburg) is the highest since
the beginning of data availability. Among the analyzed North Sea
tide gauges, the highest water level since the 1950s occurred on 3 January
1976 at Hamburg St. Pauli with about 4.5 m above MThw. In the last decade, a
storm surge in December 2013 represents the highest event. At some gauges
(Cuxhaven, Hamburg, Bremen, and Norderney), it is among the five highest
events over the available periods.
Frequency
Annual storm surge frequency increased at all gauges (e.g., Figs. 4a, 7).
Except for Heligoland, the trends are significantly different from zero at
the 95 % confidence level. In the 1950s, about one to three storm surges usually
occurred in a season. Over the past few decades, the annual number of storm
surges has increased by about one at Heligoland and Norderney, and it has
nearly doubled at Husum and Cuxhaven. At the estuarine gauges, storm surge
frequency has increased even more strongly. On average, there are about 5 times as many storm surges per season nowadays as there were in the 1950s.
In addition to the positive trends, the number of events also varies from
season to season and from gauge to gauge. In general, there are fewer events
at Heligoland, Cuxhaven, and Norderney, while events occur more frequently at
Husum, Hamburg, and Bremen. This may be not only due to differences in the specific
configuration of the coastline and bathymetry relative to the prevailing
wind direction during storm surges that make a location more or less
susceptible to storm surges (e.g., Gönnert, 2003) but also partly an
effect of the common threshold used to detect surges in the monitor (see
Sect. 2).
As Fig. 4a but for the tide gauges Husum (a) and Hamburg St. Pauli
(b).
Duration and intensity
Due to the limited availability of high-temporal-resolution data, the
statistics for storm surge duration and intensity can only be evaluated for
Cuxhaven. As introduced in Sect. 2.6.3, the total duration of all events
in a season and the intensity of the most intense event in a season are
evaluated (Fig. 5a, b). For both measures, upward trends significantly
different from zero could be inferred. Specifically, both measures have
about doubled since the 1920s. The annual total duration increased from
about 5 to 12 h, and the annual maximum intensity increased from
about 1 m h to about 3 m h. This is
likely caused by an increase in mean sea level that raised the baseline upon
which wind-induced fluctuations act rather than a change in storm climate
(e.g., Weisse et al., 2012; Weisse and Meinke, 2016). Averaged over the
reference period, the mean values of the total duration and the maximum
intensity were about 10 h and 2 m h, respectively.
Baltic Sea coastHeight
At the German Baltic Sea coast, the annual maximum water levels show strong
variability over the available period. Linear trends within this period vary
in their signs from gauge to gauge and because of the strong interannual
variability; none of the trends is significantly different from zero at the
95 % confidence level (Fig. 8). This is consistent with the results
based on annual data, which cover a longer period (e.g., Meinke, 1999).
Although no significant changes in the extremes could be detected, the mean
sea level has increased along the German Baltic Sea coast (e.g., Weisse and
Meinke, 2016; Weisse et al., 2021) and is expected to rise further in the
future (e.g., Grinsted, 2015; Hieronymus and Kalén, 2020). Therefore, it
is likely that the annual maximum water levels may increase in the future as
well. Thus, ongoing monitoring is important, although no significant trends
in the annual maxima water level could be detected so far.
As Fig. 3a but for the gauges Flensburg (a) and Warnemünde
(b).
Over the past 7 decades, the annual maximum water level has varied between
about 0.7 and 2.0 m above MW at the analyzed gauges. The average maximum
water level is around 1.2 m above MW. The data in the monitor do not include
the highest storm surge at the German Baltic Sea coast that occurred on 13
November 1872 (Jensen and Müller-Navarra, 2008; Rosenhagen and Bork, 2009;
von Storch et al., 2015; Weisse and Meinke, 2016) and for which only limited
reliable measurements are available. According to these, the maximum water
level was about 3.3 m above MW (e.g., Jensen and Müller-Navarra, 2008)
along the southwestern coast of the Baltic Sea. This is much higher than any
extreme event that occurred later in this region. Since the 1950s, none of
the water levels at the analyzed Baltic Sea gauges exceeded 2 m above MW
(Fig. 8). However, this value was nearly hit in Travemünde on 4
January 1954 when the water level reached a value of 1.97 m above MW and in
Kiel on 4 November 1995 when the water level reached a maximum of 1.96 m above MW (not shown).
Frequency
The storm surge frequency at the analyzed Baltic Sea gauges shows pronounced
interannual and decadal variability. Over the available periods since the
1950s, the frequency has varied between zero and nine events. Except for
Warnemünde, all gauges show a maximum of events observed in the season
1989/1990 (e.g., Flensburg, Fig. 9a). In Warnemünde the highest number of
events in a season was five and was observed in 2001/2002 (Fig. 9b). Trends
in storm surge frequency are not significant at all gauges (e.g., Fig. 9).
As mean sea level increased over the period (e.g., Weisse et al., 2021),
insignificant trends in the extremes may be due to the large interannual
variability in the extremes that hamper detection in relatively short
records. Overall, the storm climate over the area does not show
significant trends (e.g., Weisse et al., 2021). This result is consistent with
other studies analyzing different periods (e.g., 1883–1997 in Meinke, 1999,
and 1948–2011 in Weidemann, 2014) and in which the effects of mean sea level
rise have been excluded. Moreover, the wind climate over the Baltic Sea
shows large interdecadal variability: for the last 6 decades, an increase
in the number of days with westerly components has been detected during
winter (Gräwe et al., 2019; Lehmann et al., 2011), while days with
easterly winds have decreased (Gräwe et al., 2019). Since storm surges in
the southwestern Baltic Sea are mainly connected with strong easterly winds,
this variability in wind climate could further contribute to the
insignificant trends in storm surge activity since 1950, against the
background of rising mean sea level.
As Fig. 4a but for the gauges Flensburg (a) and Warnemünde
(b).
Duration and intensity
Information on duration and intensity can be derived at all selected Baltic
Sea gauges, since hourly data are available. The total duration and the
maximum intensity of storm surges per season show strong variabilities.
Trends at all gauges vary around zero and are not statistically different
from zero at the 95 % confidence level (Fig. 10). This is consistent
with the findings from other studies that analyzed data from model hindcasts
over different periods (e.g., Weidemann, 2014).
(a, b) As Fig. 5a but for the gauges Flensburg (a) and
Warnemünde (b). (c, d) As Fig. 5b but for the gauges Flensburg (c) and
Warnemünde (d).
Evaluation of recent storm surge seasons
In this section, cases from two recent storm surge seasons (2018/2019 and
2019/2020) are exemplarily discussed to illustrate the capabilities of the
monitor in aiding the identification of unusual events and/or seasons
together with regional differences.
North Sea coastHeight
In the season 2018/2019, the annual maximum water levels at all gauges fell into
the lowest category of severity (1.5–2.5 m above MThw) (e.g., Figs. 3a,
6). This immediately implies that all events observed in the season were
minor (green marks in Fig. 11a, b). Except for Husum, the highest event in
the season occurred on 8 January 2019, and its return period varies between
about 1 and 3 years depending on the tide gauge. For Heligoland and
Norderney, this was also the only event detected in this season using the
thresholds defined in Sect. 2. It should be noted that the use of
different thresholds (e.g., DIN 4049-3, 1994) can lead to different results.
At other gauges, more events were registered, but they only slightly
exceeded the lowest threshold (e.g., Husum and Hamburg in Fig. 11a, b). On
average, such events may occur several times a year, and the season 2018/2019
was not unusual in terms of storm surge height.
As Fig. 3b but for the tide gauges Husum (a, c) and Hamburg St.
Pauli (b, d): (a, b) season 2018/2019 and (c, d) season 2019/2020. Please note that the date formats in this figure are month-day and year-month-day.
Compared to the season 2018/2019, higher annual maximum water levels were observed
in the following season, 2019/2020 (Figs. 3a, 6). During 10–12 February
2020, a storm named Sabine by the German Weather Service (Deutscher Wetterdienst) (Haeseler et al., 2020) and Ciara and Elsa by the UK Met Office and Norwegian Meteorological Institute, respectively, induced a series of
consecutive storm surges. The highest water levels of the season were
observed during these days. The estimated return period of the highest event
varies from 3 to 8 years at different gauges (Fig. 11c, d). In addition
to this series of events, more minor events were detected at Husum,
Cuxhaven, Hamburg, and Bremen. They are categorized as minor events with
estimated return periods shorter than 1 year, indicating that such minor
events are common for these gauges. While somewhat more active than the
previous season, again the season 2019/2020 was not unusual in terms of storm
surge height.
Frequency
In terms of storm surge frequency, the two seasons 2018/2019 and 2019/2020
differ significantly. In the season 2018/2019, storm surge frequency is around the
average frequency of the reference period. A total of one or two events was
observed at Cuxhaven (Fig. 4a), Heligoland, and Norderney (not shown),
whereas six events were observed at Husum (Fig. 12a), and eight events were observed at
Hamburg (Fig. 12b) and Bremen (not shown).
In the season 2019/2020, the number of events was at least twice as high at five
of the six tide gauges. Especially, the number of 13 events at Husum is
remarkable and exceeds the 3rd–97th percentile of the reference period (Fig. 12c). The season ranks among the top three in terms of storm surge
frequencies at this gauge (Fig. 7a).
A look at the course of the season and the long-term average reveal that
the first storm surges usually occur in November or December and that the
majority occur between November and February (Fig. 12). The course of the
season 2018/2019 broadly followed this development along the long-term median
(Fig. 12a, b). For some gauges, frequencies eventually exceeded the
long-term median, but the values remained well below the 97th percentiles.
Moreover, the numbers of events in the individual months were exceptional nowhere.
As Fig. 4b but for the tide gauges Husum (a, c) and Hamburg St.
Pauli (b, d): (a, b) season 2018/2019 and (c, d) season 2019/2020.
In contrast, the season 2019/2020 was substantially different. The season
initially started late, and the storm surge frequencies were moderate and
mostly below the long-term median. This character substantially changed in
February when the storm Sabine caused a large number of events within a
relatively short period (Fig. 12c, d). For example at Husum, nine events
(five of which were caused within only about 2 d) were registered in
February 2020, which exceeds the maximum of seven events detected so far in
February (Fig. 9c). Consequently, the cumulated number of events in the
season exceeds the 3rd–97th percentile range, and the season eventually represented a
rather unusual season in terms of storm surge frequency, although their
height was mostly moderate.
As Fig. 3b but for the gauges Flensburg (a, c) and Warnemünde
(b, d): (a, b) season 2018/2019 and (c, d) season 2019/2020. Please note that the date formats in this figure are month-day and year-month-day.
As Fig. 4b but for the gauges Flensburg (a, c) and Warnemünde
(b, d): (a, b) season 2018/2019 and (c, d) season 2019/2020.
Duration and intensity
The two storm surge seasons also differ in terms of their total duration and
their maximum intensity. This is demonstrated exemplarily for Cuxhaven
(Fig. 5). Here, both measures were significantly higher in the season
2019/2020, especially the total duration. In this case, this can be attributed
to the unusually large number of shorter events in this season (see
discussion above), while the individual events were neither exceptionally
intense nor long-lasting (Fig. 5c, d).
In summary, for the German North Sea coast, the storm season 2018/2019
represents a rather typical storm surge season in all aspects. All detected
events were relatively low, and their return periods were mostly shorter than
1 year. Although the number of events at some gauges was slightly above
the average level, it is still far from the upper bound defined by the
long-term 97th percentile. In contrast, the season 2019/2020 was more
active and unusual in some aspects. It was characterized by a slow onset,
which was more than compensated by an unusual series of events in February.
In consequence, the total number of events at the end of the season was only
slightly below or even above the upper bound of the long-term distribution.
While the surges were mostly moderate in height, the total duration of the
storm surges was also doubled compared to the reference period.
Baltic Sea coastHeight
Contrary to the North Sea where the two storm surge seasons 2018/2019 and
2019/2020 were rather similar in height but differed in frequencies, both
seasons differed in height in the Baltic Sea with the first season being the
stronger one. This illustrates that in both areas different meteorological
conditions are required to generate storm surges. The differences between
both seasons are shown exemplarily for Flensburg and Warnemünde (Fig. 13). At both gauges, the highest storm surge in the season 2018/2019 occurred
on 2 January 2019 and was caused by the storm Zeetje (Perlet, 2019). The
maximum water levels reached 1.67 and 1.65 m, respectively, at Flensburg and
Warnemünde. In both cases, the event represented severe storm surges. In
Warnemünde its height was very close to the highest value of 1.71 m in
the record, and the estimated return period of the event in January 2019 was
between about 50–60 years. At the other gauges, the frequency of such
events is somewhat larger, and the return periods of the January 2019 event
vary between about 10–20 years. Regarding height, the season 2019/2020 was
less active with events having return periods well below 5 years (Fig. 13).
(a, b) As Fig. 5c but for the gauges Flensburg (a) and
Warnemünde (b). (c, d) As Fig. 5d but for the gauges Flensburg (c) and
Warnemünde (d).
Frequency
In contrast to the North Sea, both seasons in the Baltic Sea were relatively
typical in terms of storm surge frequency. This is again shown exemplarily
for Travemünde and Warnemünde (Fig. 14). While in 2018/2019 the
storm surge frequency at both gauges was slightly above the average of the
reference period, in 2019/2020 it was below average in Flensburg and above
average in Warnemünde. In all cases, values fell within usual ranges,
except for the maximum number of surges observed in October 2018 in
Flensburg. Note, however, that total numbers are small, which makes
comparisons less robust.
Duration and intensity
Contrary to the North Sea, the season 2018/2019 was more active than 2019/2020
in terms of total duration and intensity (Fig. 10). While for the North
Sea, the season 2019/2020 was outstanding in terms of duration due to a series
of moderate events, for the Baltic Sea the season 2018/2019 stands out in
terms of intensity caused by a major event in January 2019 (Fig. 15). In
Flensburg also the duration and intensity of the event on 29 October 2018
were exceptionally high (Fig. 15a, c). Again this illustrates that
different atmospheric conditions are required to trigger storm surges along
the German North Sea and Baltic Sea coasts.
Discussion and summary
A new tool and approach for monitoring the storm surge hazard in the context
of long-term variability and climate change were proposed. They aim at
providing near-real-time information for the evaluation and contextualization of
ongoing extremes and at bridging the gap between the availability of
real-time data and the considerable time delay before assessments are
published. The approach was implemented into a prototype web tool (the
storm surge monitor) and implemented exemplarily for the German North Sea and Baltic Sea
coasts. With the help of the monitor, storm surges are detected in real time
and are set into a climatological context in near real time. This way, not
only an assessment of the current storm surge season or ongoing events is
achieved, but also the development over the past seasons is documented. The
tool aims at providing easily accessible information to the public, scientists,
and stakeholders that aid in the evaluation of extremes, for example, to
assess in near real time if and to what extent an event or a season is
unusual compared to the statistics of the extreme events in the past
decades. Measures to assess long-term changes can also be inferred from the
monitor. Because of the existing risk and the expected future developments,
such information can be highly relevant for decision making (e.g., Kodeih,
2018; Kodeih et al., 2019; Weisse et al., 2015) or within public debate.
The monitor also documents long-term changes in storm surge climate. Such
changes can in principle originate from various factors, such as changes in
storm activity, astronomical tide cycles, or sea level rise. Locally,
waterworks may also play a role. To date, there is no clear evidence
suggesting a significant long-term change of storm activity in the German
coastal regions (Feser et al., 2015; Krieger et al., 2020; Krueger et al.,
2019; Stendel et al., 2016; Weisse et al., 2012). For the North Sea gauges,
likely, the observed changes in storm surge climate are largely related to
the local mean sea level rise (e.g., Weisse et al., 2012; Woodworth et al.,
2011). Rising relative sea levels in the area, for the most part, not only are
related to global mean sea level rise and climate change but also contain
contributions from non-climatic factors such as land subsidence. The latter
may result from natural phenomena (e.g., GIA) or local anthropogenic
activities (e.g., groundwater extraction, dredging, or waterworks) (e.g.,
Rovere et al., 2016; Stammer et al., 2013; Tamisiea and Mitrovica, 2011).
At the analyzed gauges at the German Baltic Sea coast, no significant
long-term change in storm surge activity has been detected so far. For the
annual maximum water levels, this is in agreement with, e.g., the results of
Meinke (1999) and the review in Weisse et al. (2021). Similarly,
non-significant trends have been found for storm surge frequency, also in
agreement with previous studies (e.g., Weidemann, 2014).
It is plausible that storm surge height, frequency, duration, and intensity
may change in the future, as sea level continues to rise along the German
North Sea and Baltic Sea coasts (e.g., Grinsted, 2015; Weisse and Meinke, 2016;
Hieronymus and Kalén, 2020; Weisse et al., 2021). Thus, continuous
monitoring of the storm surge climate in a climatological context may foster
early detection and attribution and support adaptation and public
debate.
Nowadays, web-based applications are frequently used tools to develop links
between the results of scientific research and public demands. For sea level
extremes, various such efforts do exist. These tools provide online access
to the statistics of extreme water levels or document the severity and
consequences of historical flooding. Examples are the Extreme Water Levels
site from NOAA (https://tidesandcurrents.noaa.gov/est/, last access: 14 January 2022) or the SurgeWatch
site for the UK coast (https://www.surgewatch.org/, last access: 14 January 2022, Haigh et al., 2015). We built upon experiences from developing such tools, but
contrary to existing ones our storm surge monitor focuses on the
near-real-time evaluation and contextualization of extremes against the
background of long-term variability and change to provide an up-to-date and
continuously available piece for coastal climate services. Both the monitor
and the statistics are freely available online. They are expected to be
useful and meaningful to the public. In particular, the monitor was found to
be useful to the media, since it fits their needs to focus on actual threats
and to contextualize them within a scientific frame. After the
implementation of the monitor, numerous interview requests were served and
background information was provided based on the monitor.
The monitor is further relevant to multi-sector coastal stakeholders who
demand such information for coastal flood risk management and planning. For
example, presently a discussion is ongoing with users asking for an
extension including also thresholds following the definitions given in the
DIN 4049-3 standard (DIN4049-3, 1994). Further and interestingly, the series of storm
surges that made the season 2019/2020 outstanding along the North Sea coast
occurred shortly after the conclusion of the transdisciplinary project
EXTREMENESS (Weisse et al., 2019) in which physically plausible but yet
unobserved extremes and their potential impacts were discussed and modeled.
One type of such potentially high-impact events identified by stakeholders
was a series of storm surges that, even when only of moderate heights, may
provide challenges for coastal protection (Schaper et al., 2019; Weisse et
al., 2019).
The monitor can further serve for educational purposes, for example,
illustrating changing storm surge activity at German coasts. Finally, it
can also be useful to researchers as auxiliary information supporting their
research. We argue that the tool has the potential to be developed into a
larger suite of tools including, for example, other regions or other coastal
hazards such as sea level rise or storm activity.
Code availability
The code used for data pre- and post-processing is available on request from the authors.
Data availability
The data used in this paper are available from the third party sources listed in Table 1. Real-time data are available from https://www.pegelonline.wsv.de/ (PEGELONLINE, 2022) as described in the text. Near-real-time analyzes are available from the monitor websites in German (https://sturmflut-monitor.de, STURMFLUT-MONITOR, 2022) and in English (https://stormsurge-monitor.eu, STORMSURGE-MONITOR, 2022).
Author contributions
RW and IM initiated the idea of the storm monitor and designed it. XL
processed the data, performed the analyses, and programmed the web tool. All
authors equally contributed to the preparation of the manuscript. The
revised version including the suggestions from the reviewers was prepared by
RW and IM.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This work was financially supported by the European Union through the project “European advances on CLImate Services
for Coasts and SEAs” (ECLISEA) under the ERA4CS (European Research Area for Climate Services) framework (EU grant agreement no. 690462).
The article processing charges for this open-access publication were covered by the Helmholtz-Zentrum Hereon.
Review statement
This paper was edited by Paolo Tarolli and reviewed by Klaus Grosfeld and Andreas Sterl.
ReferencesArns, A., Dangendorf, S., Jensen, J., Talke, S., Bender, J., and
Pattiaratchi, C.: Sea-level rise induced amplification of coastal protection
design heights, Sci Rep., 7, 40171, 10.1038/srep40171, 2017.Barbosa, S. M.: Quantile trends in Baltic sea level, Geophys. Res. Lett., 35, L22704, 10.1029/2008gl035182, 2008.Butler, A., Heffernan, J. E., Tawn, J. A., and Flather, R. A.: Trend
estimation in extremes of synthetic North Sea surges, J. Roy. Stat. Soc. C.-App., 56, 395–414, 10.1111/j.1467-9876.2007.00583.x, 2007.Caldwell, P. C., Merrifield, M. A., and Thompson, P. R.: Sea level measured
by tide gauges from global oceans – the Joint Archive for Sea Level
holdings (NCEI Accession 0019568), Version 5.5, NOAA National Centers for
Environmental Information [data set], 10.7289/V5V40S7W, 2015.Cid, A., Menendez, M., Castanedo, S., Abascal, A. J., Mendez, F. J., and
Medina, R.: Long-term changes in the frequency, intensity and duration of
extreme storm surge events in southern Europe, Clim. Dynam., 46, 1503–1516,
10.1007/s00382-015-2659-1, 2016.Dangendorf, S., Müller-Navarra, S., Jensen, J., Schenk, F., Wahl, T.,
and Weisse, R.: North Sea storminess from a novel storm surge record since
AD 1843, J. Climate, 27, 3582–3595, 10.1175/jcli-d-13-00427.1,
2014.Deutschländer, T., Friedrich, K., Haeseler, S., and Lefebvre, C.: Severe
storm XAVER across northern Europe from 5 to 7 December 2013, Deutscher
Wetterdienst, available at: https://www.dwd.de/EN/ourservices/specialevents/storms/20131230_XAVER_europe_en.pdf?__blob=publicationFile&v=20131234 (last access: 14 January 2022), 2013.DIN 4049-3: Hydrologie. Teil 3: Begriffe zur quantitativen Hydrologie
135DVWK (Deutscher Verband für Wasserwirtschaft und Kulturbau) (1999):
Statistische Analyse von Hochwasserabflüssen, DVWK 215/1999, Verlag
Paul Parey, Hamburg, 10.31030/2644617, 1994.Feser, F., Barcikowska, M., Krueger, O., Schenk, F., Weisse, R., and Xia,
L.: Storminess over the North Atlantic and northwestern Europe – A review,
Q. J. Roy. Meteor. Soc., 141, 350–382, 10.1002/qj.2364, 2015.Feuchter, D., Jörg, C., Rosenhagen, G., Auchmann, R., Martius, O., and
Brönnimann, S.: The 1872 Baltic Sea storm surge, in: Weather extremes
during the past 140 years, edited by: Brönnimann, S. and Martius, O.,
Geographica Bernensia G89, 10.4480/GB2013.G89.10, 2013.Gaslikova, L., Grabemann, I., and Groll, N.: Changes in North Sea storm
surge conditions for four transient future climate realizations, Nat.
Hazards, 66, 1501–1518, 10.1007/s11069-012-0279-1, 2013.
Gönnert, G.: Sturmfluten und Windstau in der Deutschen Bucht. Charakter,
Veränderungen und Maximalwerte im 20. Jahrhundert, Die Küste, 67,
185–365, 2003.
Gönnert, G. and Buß, T.: Sturmfluten zur Bemessung von
Hochwasserschutzanlagen, Landesbetrieb Straßen, Brücken und
Gewässer 2/2009, ISSN 1867–7959, 2009.Gräwe, U., Klingbeil, K., Kelln, J., and Dangendorf, S.: Decomposing
Mean Sea Level Rise in a Semi-Enclosed Basin, the Baltic Sea, J. Climate, 32, 3089–3108, 10.1175/JCLI-D-18-0174.1, 2019.Grinsted, A.: Projected Change – Sea Level, in: Second Assessment of Climate
Change for the Baltic Sea Basin, edited by: The BACC II Author Team, Springer,
Cham, 10.1007/978-3-319-16006-1_14, 2015.Haeseler, S., Bissolli, P., Dassler, J., Zins, V., and Kreis, A.: Orkantief
Sabine löst am 09./10. Februar 2020 eine schwere Sturmlage über
Europa aus, DWD, available at: https://www.dwd.de/DE/leistungen/besondereereignisse/stuerme/20200213_orkantief_sabine_europa.pdf (last access: 14 January 2022), 2020Haigh, I. D., Wadey, M. P., Gallop, S. L., Loehr, H., Nicholls, R. J.,
Horsburgh, K., Brown, J. M., and Bradshaw, E.: A user-friendly database of
coastal flooding in the United Kingdom from 1915–2014, Sci. Data, 2, 150021 (2015), 10.1038/sdata.2015.21, 2015.Hall, A.: The North Sea Flood of 1953, Environment & Society Portal,
Arcadia, no. 5, Rachel Carson Center for Environment and Society, 10.5282/rcc/5181, 2013.Hegerl, G. C., Hoegh-Guldberg, O., Casassa, G., Hoerling, M. P., Kovats, R. S., Parmesan, C., Pierce, D. W., and Stott, P. A.: Good Practice Guidance Paper on Detection
and Attribution Related to Anthropogenic Climate Change, in: Meeting Report
of the Intergovernmental Panel on Climate Change Expert Meeting on Detection
and Attribution of Anthropogenic Climate Change, edited by: Stocker, T. F., Field, C. B., Qin, D., Barros, V., Plattner, G.-K., Tignor, M., Midgley, P. M., and Ebi, K. L., IPCC Working Group I Technical Support Unit, University of Bern,
Bern, Switzerland, available at: https://archive.ipcc.ch/pdf/supporting-material/ipcc_good_practice_guidance_paper_anthropogenic.pdf (last access: 19 January 2022), 2010.Hein, S. S. V., Sohrt, V., Nehlsen, E., Strotmann, T., and Fröhle, P.: Tidal
Oscillation and Resonance in Semi-Closed Estuaries – Empirical Analyses from
the Elbe Estuary, North Sea, Water, 2021, 848, 10.3390/w13060848,
2021Hennemuth, B., Bender, S., Bülow, K., Dreier, N., Keup-Thiel, E.,
Krüger, O., Mudersbach, C., Radermacher, C., and Schoetter, R.: Statistical
methods for the analysis of simulated and observed climate data, applied in
projects and institutions dealing with climate change impact and adaptation,
CSC Rep. 13, Climate Service Center, Germany, available at: https://www.climate-service-center.de/imperia/md/content/csc/projekte/csc-report13_englisch_final-mit_umschlag.pdf (last access: 14 January 2022), 2013.Hieronymus, M. and Kalén, O.: Sea-level rise projections for Sweden
based on the new IPCC special report: The ocean and cryosphere in a changing
climate, Ambio, 49, 1587–1600, 10.1007/s13280-019-01313-8, 2020.Hollebrandse, F. A. P.: Temporal development of the tidal range in the
southern North Sea, master, MS thesis, Delft Univ. of Technol., Delft, The
Netherlands, available at: http://resolver.tudelft.nl/uuid:d0e5cb29-1c09-4de3-a1e6-0b17a9cb43ec (last access: 14 January 2022), 2005.Horsburgh, K. J. and Wilson, C.: Tide-surge interaction and its role in the
distribution of surge residuals in the North Sea, J. Geophys. Res.-Oceans, 112, C08003, 10.1029/2006jc004033, 2007.Hünicke, B. and Zorita, E.: Influence of temperature and precipitation
on decadal Baltic Sea level variations in the 20th century, Tellus A, 58,
141–153, 10.1111/j.1600-0870.2006.00157.x, 2006.
Jensen, J. and Müller-Navarra, S. H.: Storm surges on the German coast,
Die Küste, 74, 92–124, 2008.Jensen, J., Ebener, A., Jänicke, L., Arns, A., Hubert, K., Wurpts, A.,
Berkenbrink, C., Weisse, R., Yi, X., and Meyer, E.: Untersuchungen zur
Entwicklung der Tidedynamik an der deutschen Nordseeküste (ALADYN), Die
Küste, 89, 10.18171/1.089105, 2021.Karabil, S.: Influence of Atmospheric Circulation on the Baltic Sea Level
Rise under the RCP8.5 Scenario over the 21st Century, Climate, 5, 71, 10.3390/cli5030071, 2017.Karabil, S., Zorita, E., and Hünicke, B.: Mechanisms of variability in decadal sea-level trends in the Baltic Sea over the 20th century, Earth Syst. Dynam., 8, 1031–1046, 10.5194/esd-8-1031-2017, 2017.Kodeih, S.: Catalogue of potential coastal climate indicators for a
pan-European coastal climate service web tool, Deliverable 1.D, Work Package
1, Project ECLISEA, available at: https://www.ecliseaproject.eu/wp-content/uploads/2019/08/d1d_report_final.pdf (last access: 13 December 2021), 2018.Kodeih, S., Cozannet, G. L., Maspataud, A., Meyssignac, B., Maisongrande,
P., Ayerbe, I. A., Emmanouil, G., and Vlachogianni, M.: Climate information
needs from multi-sector stakeholders, Deliverable 1.B, Work Package 1,
Project ECLISEA, available at: https://www.ecliseaproject.eu/wp-content/uploads/2019/08/d1.b_report_stakeholder-needs_final_20180629.pdf (last access: 13 December 2021), 2019.Krieger, D., Krueger, O., Feser, F., Weisse, R., Tinz, B., and von Storch,
H.: German Bight storm activity, 1897–2018, Int. J. Climatol., 41, E2159–E2177, 10.1002/joc.6837, 2020.Krueger, O., Feser, F., and Weisse, R.: Northeast Atlantic storm activity
and its uncertainty from the late nineteenth to the twenty-first century,
J. Climate, 32, 1919–1931, 10.1175/jcli-d-18-0505.1, 2019.Lehmann, A., Getzlaff, K., and Harlaß, J.: Detailed assessment of climate
variability in the Baltic Sea area for the period 1958 to 2009, Clim.
Res., 46, 185–196, 10.3354/cr00876, 2011.
Meinke, I.: Sturmfluten in der südwestlichen Ostsee – dargestellt am
Beispiel des Pegels Warnemünde, Marburger Geographische Schriften, 134,
1–23, 1999.Meinke, I.: Stakeholder-based evaluation categories for regional climate
services – a case study at the German Baltic Sea coast, Adv. Sci. Res., 14,
279–291, 10.5194/asr-14-279-2017, 2017.Mudersbach, C. and Jensen J.: Statistische Extremwertanalyse von
Wasserständen an der Deutschen Ostseeküste, in: Abschlussbericht 1.4
KFKI-VERBUNDPROJEKT Modellgestützte Untersuchungen zu extremen
Sturmflutereignissen an der Deutschen Ostseeküste (MUSTOK), Universität Siegen, 10.2314/GBV:609714708, 2008.Needham, H. F., Keim, B. D., and Sathiaraj, D.: A review of tropical
cyclone-generated storm surges: Global data sources, observations, and
impacts, Rev. Geophys., 53, 545–591, 10.1002/2014rg000477,
2015.PEGELONLINE: Real-time data, available at: https://www.pegelonline.wsv.de/, last access: 14 January 2022.Perlet, I.: Ostsee-Sturmflut am 02.01.2019, Berichte zu Ostsee-Sturmfluten
und -Hochwassern, BSH, available at:
https://www.bsh.de/DE/THEMEN/Wasserstand_und_Gezeiten/Sturmfluten/_Anlagen/Downloads/Ostsee_Sturmflut_20190102.pdf?__blob=publicationFile&v=20190105 (last access: 14 January 2022), 2019.
Ratter, B. M. W. and Kruse, N.: Klimawandel und Wahrnehmung – Risiko und
Risikobewusstsein in Hamburg, in: Klimawandel und Klimawirkung, edited by: Böhner, J. and Ratter, B. M. W., Hamburg 2010, Hamburger Symposium Geographie, Band 2, Institut für Geographie der Universität Hamburg, ISBN 9783980686594, 2010.Ribeiro, A., Barbosa, S. M., Scotto, M. G., and Donner, R. V.: Changes in
extreme sea-levels in the Baltic Sea, Tellus A, 66, 20921, 10.3402/tellusa.v66.20921, 2014.Richter, A., Groh, A., and Dietrich, R.: Geodetic observation of sea-level
change and crustal deformation in the Baltic Sea region, Phys. Chem. Earth,
53–54, 43–53, 10.1016/j.pce.2011.04.011, 2012.
Rosenhagen, G. and Bork, I.: Rekonstruktion der Sturmwetterlage vom 13.
November 1872, Die Küste, 75, 51–70, 2009.Rovere, A., Stocchi, P., and Vacchi, M.: Eustatic and relative sea level
changes, Current Climate Change Reports, 2, 221–231, 10.1007/s40641-016-0045-7, 2016.Rucińska, D.: Describing Storm Xaver in disaster terms, Int. J. Disast. Risk. Re., 34, 147–153, 10.1016/j.ijdrr.2018.11.012, 2019.Schaper, J., Ulm, M., Arns, A., Jensen, J., Ratter, B., and Weisse, R.:
Transdisziplinäres Risikomanagement im Umgang mit extremen
Nordsee-Sturmfluten – Vom Modell zur Wissenschafts-Praxis-Kooperation, Die
Küste, 87, 75–114, 10.18171/1.087112, 2019.Stammer, D., Cazenave, A., Ponte, R. M., and Tamisiea, M. E.: Causes for
contemporary regional sea level changes, Annu. Rev. Mar. Sci., 5,
21–46, 10.1146/annurev-marine-121211-172406, 2013.Stendel, M., van den Besselaar, E., Hannachi, A., Kent, E. C., Lefebvre, C.,
Schenk, F., van der Schrier, G., and Woollings, T.: Recent change –
Atmosphere, in: North Sea region climate change assessment, edited by: Quante, M. and Colijn, F., 55–84, 10.1007/978-3-319-39745-0_2, 2016.STORMSURGE-MONITOR: Near-real-time analyzes, available at: https://stormsurge-monitor.eu, last access: 19 January 2022.STURMFLUT-MONITOR: Near-real-time analyzes, available at: https://sturmflut-monitor.de, last access: 19 January 2022.Tamisiea, M. E. and Mitrovica, J. X.: The moving boundaries of sea level
change: understanding the origins of geographic variability, Oceanography,
24, 24–39, 10.5670/oceanog.2011.25, 2011.von Storch, H. and Woth, K.: Storm surges: perspectives and options, Sustain.
Sci., 3, 33–43, 10.1007/s11625-008-0044-2, 2008.von Storch, H., Gönnert, G., and Meine, M.: Storm surges – An option for
Hamburg, Germany, to mitigate expected future aggravation of risk,
Environ. Sci. Policy, 11, 735–742, 10.1016/j.envsci.2008.08.003, 2008.von Storch, H., Jiang, W., and Furmanczyk, K. K.: Storm surge case studies.
In: Coastal and Marine Hazards, Risks, and Disasters, edited by: Shroder, J. F., Ellis, J. T., and Sherman, D. J., Elsevier, Boston, 10.1016/B978-0-12-396483-0.00007-8, 2015.Vousdoukas, M. I., Voukouvalas, E., Annunziato, A., Giardino, A., and Feyen,
L.: Projections of extreme storm surge levels along Europe, Clim. Dynam., 47,
3171–3190, 10.1007/s00382-016-3019-5, 2016.Vousdoukas, M. I., Mentaschi, L., Voukouvalas, E., Verlaan, M., and Feyen,
L.: Extreme sea levels on the rise along Europe's coasts, Earths Future, 5,
304–323, 10.1002/2016ef000505, 2017.Wahl, T.: Sea-level rise and storm surges, relationship status:
complicated!, Environ. Res. Lett., 12, 111001, 10.1088/1748-9326/aa8eba, 2017.Wahl, T., Haigh, I. D., Nicholls, R. J., Arns, A., Dangendorf, S., Hinkel,
J., and Slangen, A. B. A.: Understanding extreme sea levels for broad-scale
coastal impact and adaptation analysis, Nat. Commun., 8, 16075,
10.1038/ncomms16075, 2017.Weidemann, H.: Klimatologie der Ostseewasserstände: Eine Rekonstruktion
von 1949–2011, Dissertation Universität Hamburg, available at: https://ediss.sub.uni-hamburg.de/handle/ediss/5561 (last access: 14 January 2022), 2014.
Weisse, R. and Meinke, I.: Meeresspiegelanstieg, Gezeiten, Sturmfluten und
Seegang, in: Klimawandel in Deutschland: Entwicklung, Folgen, Risiken und
Perspektiven, edited by: Brasseur, G. P., Jacob, D., and Schuck-Zöller, S., ISBN 978-3-662-50396-6, 2016.Weisse, R. and Plüß, A.: Storm-related sea level variations along
the North Sea coast as simulated by a high-resolution model 1958–2002,
Ocean Dynam., 56, 16–25, 10.1007/s10236-005-0037-y, 2006.Weisse, R. and von Storch, H.: Marine Climate and Climate Change, Springer Berlin Heidelberg, Berlin,
Heidelberg, 10.1007/978-3-540-68491-6, 2010.Weisse, R., von Storch, H., Niemeyer, H. D., and Knaack, H.: Changing North
Sea storm surge climate: An increasing hazard?, Ocean Coast. Manage., 68,
58–68, 10.1016/j.ocecoaman.2011.09.005, 2012.Weisse, R., Bellafiore, D., Menéndez, M., Méndez, F., Nicholls, R.
J., Umgiesser, G., and Willems, P.: Changing extreme sea levels along
European coasts, Coast. Eng., 87, 4–14, 10.1016/j.coastaleng.2013.10.017, 2014.Weisse, R., Bisling, P., Gaslikova, L., Geyer, B., Groll, N., Hortamani, M.,
Matthias, V., Maneke, M., Meinke, I., Meyer, E. M., Schwichtenberg, F.,
Stempinski, F., Wiese, F., and Wöckner-Kluwe, K.: Climate services for
marine applications in Europe, Earth Perspectives, 2, 3, 10.1186/s40322-015-0029-0, 2015.Weisse, R., Grabemann, I., Gaslikova, L., Meyer, E., Tinz, B., Fery, N.,
Möller, T., Rudolph, E., Brodhagen, T., Arns, A., Jensen, J., Ulm, M.,
Ratter, B., and Schaper, J.: Extreme Nordseesturmfluten und mögliche
Auswirkungen: Das EXTREMENESS Projekt, Die Küste, 87, 39–45, 10.18171/1.087110, 2019.Weisse, R., Dailidienė, I., Hünicke, B., Kahma, K., Madsen, K., Omstedt, A., Parnell, K., Schöne, T., Soomere, T., Zhang, W., and Zorita, E.: Sea level dynamics and coastal erosion in the Baltic Sea region, Earth Syst. Dynam., 12, 871–898, 10.5194/esd-12-871-2021, 2021.Woodworth, P. L., Flather, R. A., Williams, J. A., Wakelin, S. L., and
Jevrejeva, S.: The dependence of UK extreme sea levels and storm surges on
the North Atlantic Oscillation, Cont. Shelf Res., 27, 935–946, 10.1016/j.csr.2006.12.007, 2007.
Woodworth, P. L., Menendez, M., and Gehrels, W. R.: Evidence for
Century-Timescale Acceleration in Mean Sea Levels and for Recent Changes in
Extreme Sea Levels, Surv. Geophys., 32, 603–618, 10.1007/s10712-011-9112-8, 2011.Woth, K., Weisse, R., and von Storch, H.: Climate change and North Sea storm
surge extremes: an ensemble study of storm surge extremes expected in a
changed climate projected by four different regional climate models, Ocean
Dynam., 56, 3–15, 10.1007/s10236-005-0024-3, 2006.Zhang, K. Q., Douglas, B. C., and Leatherman, S. P.: Twentieth-century storm
activity along the US east coast, J. Climate, 13, 1748–1761, 10.1175/1520-0442(2000)013<1748:Tcsaat>2.0.Co;2, 2000.