Introduction
Understanding the spatial and temporal characteristics of floods across
Europe is important for improving our understanding of the flood generation
mechanisms and hence for enabling better flood estimation and forecasts at
European scale. River floods in Europe are caused by several processes. The
most common naturally occurring river floods are driven by rainfall
(including rain on snow) and snowmelt (sometimes combined with ice jams) and
are modulated by soil moisture (e.g. Hall et al., 2014). Hence, depending on
the time of the year (i.e. season) in which a flood peak occurs, one can
infer the hydrological processes that are likely involved in the generation
of the flood. For example, flood peaks occurring in late winter or early
spring, together with rising air temperatures, can be inferred to be
snowmelt-induced.
A better knowledge of the flood seasonality and hence the most probable
flood generation processes can therefore assist in the identification of
homogeneous regions with a dominant flood season, which is important for
example for regional flood frequency analysis, the analysis of mixed flood
frequency distributions, and in the identification and attribution observed
changes in flood discharges. Additionally, such homogeneous regions can
serve as a benchmark for the assessment of Europe-wide hydrological model
output.
Previous research on flood seasonality in Europe has been limited by two
main constraints. First, most scientific studies have focused on national
scale or on smaller regions, which restricts the results to a relatively
small and local set of flood-generating processes. For example, Beurton and
Thieken (2009) determined three homogeneous flood regions in Germany when
analysing the annual maximum floods (AMFs) of 481 gauging stations.
Similarly, Cunderlik et al. (2004) found three main flood seasonality types
in Great Britain by examining 268 sites. A few studies analysed flood
seasonality at larger scales, for example Mediero et al. (2015) using 102
streamflow records within Europe, but with limited spatial coverage, and
Blöschl et al. (2017) focusing on changes in flood seasonality. Second, most
of the previous studies on flood seasonality focused on the mean date of the
AMF occurrence and/or the temporal concentration of the floods around their
mean date (e.g. Parajka et al., 2009 or Jeneiová et al., 2016 for both
Austria and Slovakia), while the detailed characteristics of monthly flood
seasonality distributions has rarely been studied in Europe. However, if
unimodal, bimodal, or skewed seasonality distributions exist, the mean date
of the AMFs can be misleading and can mask important insights into the flood-generating mechanisms (Ye et al., 2017). It is therefore important to report
not only the mean date to characterise flood seasonality, but also to describe
in detail the temporal flood seasonality characteristics.
To overcome the main research constraints, this paper examines the spatial
and temporal patterns of flood seasonality at continental scale, using an
extensive database that covers all climatic regions in Europe. The focus of
this paper is on the identification of regions with similar seasonal flood
characteristics and on the description of the full temporal distribution of
the flood events within the year.
First, the study area and the European discharge dataset used in this study
are presented, followed by the description of the analysis methods. In the
results section, the spatial characteristics of the mean flood seasonality
are presented together with an analysis of the seasonal flood
characteristics across Europe. Spatial patterns and clusters are identified
based on the monthly distribution of AMFs. The clusters are then examined in
detail, focusing on their monthly flood seasonality characteristics and
their spatial distribution. The paper concludes with a discussion of the
results and the conclusion.
Study area and data
The hydrological data analysed here is based on the dataset presented by
Hall et al. (2015) with subsequent updates. The initial database used in
this study includes data from 5565 hydrometric stations from 38 data sources
(see Supplement for details) located within 6.5∘ W–60∘ E and 29.25–69.25∘ N (Fig. 1).
Floods events are identified following the common definition as the highest
(peak) event in a year (e.g. Garner et al., 2015; Hall et al., 2014). This
does not necessary imply that the river overtops its banks and flows onto
the floodplain during such a flood event. Following this definition, the
dataset consists of the dates of annual maximum discharge or annual maximum
water level (daily mean or instantaneous values). The maximum of each year
is based on the calendar year (January to December) with a few exceptions,
which are based on the respective countries' hydrological year (which can
start in September, October, or November). Only the annual maxima are
analysed here, as the long-term mean of the flood timing is more meaningful
if a single flood peak per year is considered and, additionally, as in some
areas and/or countries the restrictions in data access and licensing limits
the availability of the data to the annual maxima only.
Map of the study area, showing the topography and the location
of the 4105 stations used in this study.
![]()
Maps of station elevation at the catchment outlet (m) (a)
and catchment area (km2) (b). In both panels,
n= 4105 stations.
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Catchments for which it was evident that the flood timing is strongly
affected by known human modifications (e.g. dams or reservoirs) are excluded
from the analysis. All catchments with more than 10 years of data within the
period 1960–2010 were included in the first part of the analysis. In areas
with high station density, such as Austria, Germany, and Switzerland, only
stations with at least 49 years of data in the study period were included, to
balance station density across Europe and to improve the visual
representation on a European map. This selection resulted in 4105
hydrometric stations (Fig. 1) with station elevation ranging from -5.17
to 1961 m (Fig. 2a), catchment areas ranging from 10 to
100 000 km2 (Fig. 2b), and record lengths ranging from 11
to 51 years (Fig. 3). A total of 115 stations in the database have no catchment
area assigned, either due to the existence of karst or missing metadata
information. These stations are not shown in subsequent figures that display
catchment area, which is indicated by a reduced number of stations in the
respective figure caption.
Record length in number of years per station for the
period 1960–2010, n= 4105 stations.
![]()
Results
European flood seasonality characteristics
Figure 4 shows the mean flood seasonality and the temporal concentration of
flood occurrence within the year. A distinct spatial pattern of the mean
timing of floods within the year can be observed (Fig. 4a). Below
60∘ latitude, the mean seasonality transitions from winter floods
in the west to spring floods in the east due to increasing continentality.
Stations located in mountainous areas (e.g. the Alps, the Carpathians, and
the Pyrenees) exhibit predominately summer floods and disrupt this west-to-east transition of mean flood timings. Above 60∘ latitude, spring
floods dominate the spatial pattern, except for coastal areas in which
autumn and winter floods are observed. The temporal concentration of floods
around the mean date of flood occurrence (R value) (Fig. 4b) is highest in
north-eastern Europe. High temporal concentration is also apparent at the
western coast of Europe, except northern Europe, where floods are spread more
evenly throughout the year. Catchments on the foothills of mountainous areas
(e.g. around the Alps and the Carpathians) also tend to have smaller
R values and sometimes exhibit a uniform occurrence of floods throughout
the year. The orange crosses in Fig. 4b indicate the stations for which
circular uniformity could not be rejected at a significance level of
α= 0.1. The characteristics of stations with uniform flood
occurrence are later examined in detail (see Fig. 6).
A complimentary procedure for examining the flood seasonality is the
assessment of the frequency of floods occurring in the winter and summer
half-years (Fig. 5). This assessment assists in the identification of
regions in Europe with prolonged yet concentrated flood seasonality. For
Europe, the winter and the summer half-years are defined for this procedure
as October to March and April to September, respectively.
There is a clear dominance of summer floods in mountain ranges (e.g.
Pyrenees, Alps, and Carpathians) and in the northern and north-eastern parts
of Europe, which can be characterised by a continental climate. In the rest
of Europe, floods predominately occur in the winter half-year. Transitional
areas, for which no clear seasonal distinction can be made (< 60 % of either winter of summer half-year floods), can be found in and
around Poland, Lithuania, Belarus, and parts of the Ukraine. In these
transitional areas, no half-year flood season dominates, as the AMFs of these
stations tend to occur in March and April around the cut off date separating
the winter versus summer half-years. Additionally, a less well-defined flood
seasonality can be found on the foothills of mountains, where both winter
and summer floods occur (mixed distribution), depending on whether floods
are snowmelt-induced, summer-rainfall-induced, or the floods are uniformly
distributed throughout the year.
Percentage of winter half-year (October to March) and
summer half-year (April to September) floods. Dark purple or orange colours
indicate dominance of the winter or summer half-year respectively; light
colours indicate an almost equal occurrence in the two half-years;
n= 4105 stations.
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In order to examine further the relationship between uniform flood
occurrence and week seasonality (low R values), the spatial location of the
stations for which circular uniformity could not be rejected (at a
significance level of α= 0.1) is shown in Fig. 4b. The stations
with a uniform flood seasonality distribution are found predominately at low
to medium-high altitudes (< 1000 m) (Fig. 6a) and in small
catchments (Fig. 6b). However, for some of the stations with a small flood
concentration index R, uniformity could not be rejected at a significance
level of α= 0.1, which reveals that small R values do not
necessarily indicate uniformity. These stations possess a skewed or a
bimodal distribution of flood occurrence throughout the year.
For the European continent, stations with high elevation tend to have a high
flood concentration index R and mainly occur in early summer (mean
seasonality in May and June) (Fig. 6a). Uniformity in the seasonal flood
occurrence was rejected for stations across all elevation levels. Only at
lower elevations (< 1000 m), uniform distributions are observed.
From Fig. 6b it is apparent that uniformity cannot be rejected for
catchments of all sizes (note: 115 catchments with missing catchment area
are not shown). Larger catchments tend to have a few stations with a flood
concentration index < 0.2, most of which can be considered as having
a uniform distribution, whereas catchments with less than 1000 km2
exhibit more often smaller R values and these tend to have uniform
distributions (Fig. 6b). Generally, hydrological stations that have a small
catchment area and a low catchment elevation are predominately found near
the foothills of mountains. Therefore, uniformity of flood occurrence
throughout the year seems to be predominately conditioned by geographical
location, which corroborates the initial hypothesis obtained from mapped
location shown in Fig. 2b.
Flood concentration index R of floods in Europe
(1960–2010) dependent on station elevation (m a.s.l.),
n= 4105 (a), and catchment area (km2),
n= 3900. (b) Point colour indicates
the mean timing of floods (D‾) derived for that station location. Grey points indicate the 186
stations for which circular uniformity could not be rejected (α= 0.1).
![]()
Mean frequency of seasonal floods by ranges of outlet
elevation (m a.s.l.), n= 3919 (a), and catchment
area (km2), n= 3804 (b). In both panels, the
ranges on the x axis were selected so that an approximately equal number of
stations is allocated to each range. In both panels, stations with a uniform
distribution are excluded.
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The mean frequency of floods in each season (based on individual flood
events) is shown in Fig. 7 (for all 3919 stations for which the null
hypothesis of circular uniformity was rejected at α= 0.1).
Floods occurring between January and March are classified here as winter
floods, spring floods occur between April and June, summer floods occur between
July and September, and autumn floods occur between October and December. Figure 7a displays an increase in the mean frequency of summer floods with
increasing elevation and conversely a tendency towards decreases in the
frequency of autumn and winter floods due to the increasing dominance of
summer floods (see also Fig. 6a for the similarity with the mean flood
seasonality). Autumn floods have the highest frequency in most of the
elevation ranges analysed. In two elevation ranges (91 to 125 m and
> 440 m), spring floods have the highest occurrence frequency.
Combining these results with the ones obtained from Figs. 4 to 6, one
can conclude that the high mean frequency of spring floods occurs either in
catchments with intermediate elevation in north-eastern Europe or in, or
around, mountainous areas (flood timing is often towards the end of June,
close to July which is the first month used for the classification of summer
floods).
Smaller catchments in Europe are more similar regarding their mean frequency
of seasonal floods (Fig. 7b, note: 115 catchments with unknown catchment
area are not shown). With increasing catchment area, the percentage of
spring floods increases. This observed tendency is related to the uneven
spatial distribution of larger catchments in the database (Fig. 2b).
Stations with large catchment areas can be found predominately in central
and eastern to north-eastern parts of Europe, which are dominated by spring
floods.
Therefore, at European scale, catchment attributes such as catchment outlet
elevation or catchment area alone cannot be considered as good indicators
for flood seasonality. At large scale the geographical location is the most
important part in determining the seasonal flood characteristics.
Spatial distribution of the six clusters of monthly flood
frequencies (a), n= 3919 stations. The
vertical axes of the panel on the right shows the catchment outlet
elevation (m a.s.l.) (b), n= 3804 stations.
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Flood seasonality clusters
In the previous section, the strong influence of the geographical location
on the timing of flood occurrence at a given station is apparent. Therefore,
it is of interest to identify larger scale regions in Europe with relatively
similar seasonal flood occurrence. These regions are identified based on the
monthly frequencies of the AMF with the help of cluster analysis, after the
best possible initial centroid locations are determined (see Sect. 3.2.1
for methodological details).
Table 1 summarises the sensitivity of the location of the initial centroids
and shows the percentage of how often a specific number of clusters (5 ≤k≤ 7) obtained the highest mean silhouette value
s‾i from the 10 000 random initial cluster centroids and the highest
overall s‾i value. The results in Table 1 indicate
that, with the same initial centroid placement for five, six, or seven clusters (same
as five clusters plus one or two additional initial centroids for six and
seven clusters respectively), 46 % of the random samples generated the highest
s‾i values for k= 6 clusters. Additionally, the six
initial cluster centroids result in clusters that obtain the maximum
s‾i of 0.443 for all 10 000 random initialisations. In
the initialisations for which five or seven clusters obtain the highest
s‾i, the maximum s‾i are always
lower than the maximum that is obtained with six clusters. Therefore, the sets
of initial centroid locations that obtain the highest s‾i of all random initialisations (0.443) for k= 6 are chosen as
candidates for further selection of the initial centroid position. From
these only the sets of initial locations are retained, for which the elbow
method (based on the reduction in the total within cluster sum of squares, TSSwithin) also results in k= 6 optimal clusters. As several sets
with different initial centroid locations fulfil this criterion, the initial
set of centroids that yields the lowest TSSwithin for k= 6 is selected
and is used in the remainder of the study.
Number of clusters and average silhouette value
s‾(i) for 10 000 random initial
cluster centroids.
Number of
Samples
Maximum average
clusters (k)
with highest s‾i
value s‾i
5
39 %
0.438
6
46 %
0.443
7
15 %
0.396
Mean flood seasonality (D‾) as a function of catchment outlet elevation (m a.s.l.) and
catchment area (km2) grouped by the six clusters.
Colour of the points in all panels indicates the mean timing of floods at
that hydrometric station. Total number of stations in the analysis is 3919;
in each panel n denotes the number of stations assigned to a specific
cluster (in panels a–f there are 115 stations (69, 4, 19, 14, 3, and 6
stations respectively) not being displayed due to unknown catchment area).
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Spatial distribution and characteristics of flood seasonality
clusters
The six clusters of monthly flood frequencies in Europe and
their characteristics.
Cluster
Location
Number of
Average
Average
Average
Average
R value
number
stations
silhouette
station
catchment
of all
over all
width
elevation
area
Di
Di
s‾i
(m a.s.l.)
(km2)
(DOY)
1
Western, central and
1427
0.51
220.9
2193.0
25 January
0.60
southern Europe
(25)
2
Mountainous regions
595
0.40
538.8
2010.1
30 June
0.53
(181)
3
Central and eastern Europe
934
0.37
207.6
3950.8
22 March
0.51
(81)
4
Western British–Irish Isles,
405
0.26
263.8
1757.9
5 December
0.36
Western coast of Norway
(339)
and northern Mediterranean
5
Northern Europe
307
0.56
204.9
3940.4
19 May
0.85
(139)
6
North-eastern Europe
251
0.62
126.2
6607.4
15 April
0.84
(105)
Figure 8 depicts the spatial distribution of the six clusters of monthly
flood occurrences obtained using the methodology described above. Table 2
and Fig. 9 assist in interpreting of the clusters shown in Fig. 8.
Cluster 1 is located in western, central, and southern Europe and contains
most of the stations (∼ 36 %). The mountainous regions in
Europe (highest average outlet elevation), the Alps, and the Carpathian and
Scandinavian mountains in Cluster 2 account for ∼ 15 % of
the stations. Most stations, located in central and eastern Europe up to
55∘ N (∼ 24 %), are assigned to Cluster 3.
Cluster 5 and 6, located predominately in northern and north-eastern Europe,
are the two smallest clusters, containing ∼ 8 and
∼ 6 % of all stations respectively. Most of stations
assigned to Cluster 6 are located above 60∘ N and are low-lying.
Clusters 1, 5, and 6 are well defined (i.e. high within-cluster similarity or
average silhouette width s‾i). Cluster 4 is the least
well-defined cluster in terms of s‾iand
also in terms of spatial coherence. The stations in Cluster 4 are found in
several regions of Europe (western British–Irish Isles, western coast of
Norway, and northern Mediterranean). Cluster 4 has the smallest average
catchment area and the highest spread of flood occurrence around the mean
date of flood occurrence (early December) (see also “mean of all” in Fig. 10). The largest catchment areas are found in northern and north-eastern
Europe (Cluster 3, 5, and 6). The average dates of the flood timing (Di)
in Cluster 5 and Cluster 6 are mid-May and mid-April, respectively, with
all floods being highly concentrated around the average date. Cluster 1 is
also strongly seasonal with a mean flood occurrence in late January, whereas
the mountainous areas (Cluster 2) have their mean flood occurrence in
summer. Here Cluster 2 is considered to be spatially coherent, although the
different mountainous areas are not necessary spatially connected to each
other.
Overall, although the geographic location is not included as a variable for
clustering, the location of a hydrometric station seems to be an important
factor influencing flood seasonality and hence for determining the
membership of a cluster. There are few stations that do not fit the large-scale,
coherent cluster pattern (i.e. spatial outliers).
Mean seasonality and temporal concentration of floods for
each station (small points), the mean over all floods within specific
clusters (large points), and the mean of all mean flood seasonalities (large
points with crosses) within specific clusters. Colours correspond to
clusters. Distance to centre is a measure of the temporal flood concentration index R, with the centre corresponding to R= 0, the
black dashed circle to R= 0.5, and the outer full circle to
R= 1. The grey dashed circles correspond to intervals of 0.1 R.
Total number of stations: n= 3919.
![]()
Figure 10 shows the mean flood seasonality (D‾) for each station, the overall mean seasonality of all floods
belonging to the same cluster, and the mean of all mean flood seasonalities,
together with the respective temporal concentration around these means. The
stations within their respective clusters display similar concentrations, as
indicated by R values > 0.9 of the mean of the cluster mean flood
seasonalities (large points with crosses). The exception is Cluster 4, which
has the lowest temporal concentration of the mean floods, with R= 0.71. In
this cluster, the temporal concentrations of the floods of the individual
stations are lower than those of the stations in the other clusters. The
R values of the mean of all AMFs (large points) in both Cluster 5 and
Cluster 6 (R= 0.85 and R= 0.84, respectively) are close to the mean of all
mean seasonalities. This indicates that, in these clusters, not only the
mean seasonalities (D‾) but also the individual floods
(Di) are temporally concentrated. The mean seasonalities of most of the stations assigned to these
two clusters have a strong temporal concentration around their regional mean
(high R value), and only a few stations have a larger spread around the mean
date of flood occurrence. The R values of the regional mean seasonality of
all AMF in the other clusters are much smaller than the R values of the mean
of all mean seasonalities. This indicates that the clustering algorithm
performs well with regard to clustering stations that have a similar mean
seasonality, but individual flood events may exhibit higher temporal
variability.
Temporal characteristics of individual flood seasonality
clusters
Frequency of occurrence of maximum annual floods by month
for the cluster centroids, CCs (i.e. cluster means). Total number of stations:
n= 3919; in each panel n denotes the
number of stations assigned to a specific cluster.
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The mean flood seasonality for both stations and clusters has limited
information content, as it only reflects the first moment of the seasonality
distribution. Therefore, it is of interest to examine the full monthly flood
distribution of each cluster. Theoretically, if all floods were equally
spread over the year, each month would contain 8.3‾ % of all the
AMFs. Figure 11 shows the relative monthly frequency of AMFs of the cluster
centroids (CCs) (i.e. the respective cluster means). All CCs have at least 1 month in which more than 18 % of the annual maximum floods occur. This
indicates that in each of the CCs there is a dominant month in which floods
tend to occur and therefore a distinct flood seasonality. The CCs of Cluster
5 and Cluster 6 in northern and north-eastern Europe have the most
pronounced flood seasonality, where a single month (May and April,
respectively) contains > 65 % of the AMFs. For both CCs the
months before and after this peak account for ∼ 10 and
∼ 15 %, respectively, with the remaining months containing
less than 3 % of the AMFs. CC 1 (western and southern Europe) and CC 2
(mountainous regions) exhibit an almost bell-shaped distribution, with the
AMFs peaking in the winter and summer halves of the year, respectively. CC 3
(central and eastern Europe) peaks at the beginning of the year (strongest
peak in March, 28 %), with less than 10 % if AMFs in the rest of the
year. CC 4 is the cluster with the least pronounced peak in the monthly
flood frequencies. There is a small peak of flood occurrences in the winter
months (October to September, each month < 20 %) and a low flood
season in the summer months.
Box plot of the percentage of floods per month for each
station, grouped by cluster (a–f). The top and bottom of the boxes show the
75th and 25th percentiles (i.e. the upper and lower quartiles) respectively,
whiskers extend to 1.5× interquartile range beyond the box, the
black band indicates the median, and outliers are shown as points. Total
number of stations: n= 3919; in each
panel n denotes the number of stations assigned to a specific cluster.
![]()
Figure 12 depicts the full range of relative monthly flood frequencies of
all stations assigned to each cluster, to allow an in-depth interpretation
of the results beyond the cluster mean (i.e. CC shown in Fig. 11). The shape
of the monthly flood seasonality distribution of the medians of each cluster
(i.e. monthly percentages) resembles the shape of the cluster centroids in
Fig. 11 for most clusters. Differences in the shape of the seasonality
distributions are mainly caused by individual stations that contain months
with a frequency of AMFs deviating from the majority of the stations.
In Cluster 1, the shape of the distribution remains similar; however, for
individual stations (outliers) the winter months have a much higher
percentage of flood occurrences (up to 55 %), in the summer months the flood occurrences stay below
15 % of the AMF (Fig. 12a). In Cluster 2, June and July remain the months
with the highest percentages. For some stations, August, and to a lesser
extent May, are the most important months (Fig. 12b). This example shows the
importance of not solely relying on the CC, as the aforementioned
characteristic could not have been detected when examining the CC in Fig. 11b alone. October to February remain months with low flood occurrences
even when a detailed station-based analysis for Cluster 2 is performed. Within
Cluster 3, March and April stand out as the most important months of
flooding, as it already seen in Fig. 11c. However, it becomes apparent now
that these months have the highest spread between stations, while the other
months have a much smaller spread (Fig. 12c). In Cluster 4, the CCs show
frequent floods in October to January (Fig. 11d). However, when taking into
account the full range of all the stations, the months April, May, and June
also contain a high percentage of AMFs (up to 55 %) for individual
stations (Fig. 12d). This characteristic is detectable in neither the CC
nor the median of the cluster and indicates that, for some stations (i.e.
locally), these months are very important in terms of flooding. The
appearance of months with an additional secondary peak in flood occurrence
indicates the possible existence of a bimodal distribution for several
stations in Cluster 4. The medians and all individual stations of Cluster 5
and Cluster 6 exhibit a high occurrence of flooding in May and April,
respectively, as was already indicated in Fig. 11. Most other months of
the year show very low frequencies for the median with a very low spread
between stations. The exceptions are the months immediately before and after
the main flood month, which can have high percentages of flooding for
individual stations as well (Fig. 12e and f). This high concentration of
floods around a single month is the reason for the stations in Cluster 5 and
6 showing very high R values in Fig. 4b.
In summary, when taking all stations within a cluster into account, one can
see that the main characteristics that are present in the monthly
distributions of the CC are retained. However, some additional
characteristics such as the emergence of a bimodal distribution in Cluster
4, which was smoothed-out in the CC or additional months with higher
relative monthly flood frequencies and outliers, can be identified.
Flood-dominant and flood-scarce months
To further investigate the possible existence of a bimodal seasonality
distribution, the classification into flood-dominant and flood-scarce months
is performed, on records with more than 30 years of data (nsub) (see
Sect. 3.3.1. for methodological details). Each cluster has more than
80 % of their stations with series longer than 30 years (see Table 3 for
the exact numbers), and this ensures each cluster is still well represented in
the following analysis.
Six clusters and their characteristic flood seasonality
distributions, based on the subset of station with records > 30 years.
Cluster
Location
No. of stations
Bimodal
Unimodal
Undefined
Primary
Secondary
number
> 30 years
No.
(%)
No.
(%)
No.
(%)
Flood season
Flood Season*
1
Western, central, and southern Europe
1171
1
0.09
861
73.53
309
26.39
Dec to Mar
–
2
Mountainous regions
548
20
3.65
393
71.72
135
24.64
May to Aug
Mar
3
Central and eastern Europe
850
24
2.82
604
71.06
222
26.12
Feb to Apr
Jun & July
4
Western British–Irish Isles,
Western coast of Norway,
331
29
8.76
184
55.59
118
35.65
Oct to Jan
May
and northern Mediterranean
5
Northern Europe
254
4
1.57
249
98.03
1
0.39
May & Jun
–
6
North-eastern Europe
202
1
0.50
199
98.51
2
0.99
Apr
–
All
Europe
3356
79
2.35
2490
74.2
787
23.45
–
–
* Only for bimodal flood seasonality distributions in clusters
with at least 20 stations.
Percentage of stations with months that can be considered
significantly (α= 0.05) flood-dominant (upward
bars) or flood-scarce (downward shaded bars) grouped by cluster (a–f).
Months for which no station showed significance in the respective category
are marked with a star. Total number of stations with records > 30 years: nsub= 3356. In
each panel, nsub indicates the number of stations
assigned to a specific cluster.
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Figure 13 shows the percentages of stations in each cluster for which a
specific month can be considered, as being flood-dominant or scarce
(statistically significant at α= 0.05). In Cluster 5 and Cluster 6,
the months May and April respectively are classified as flood-dominant for
100 % of the stations. There are four clusters with at least 1 month
that can be considered not to be flood-dominated for 100 % of the station
(marked by stars above the x axis in Fig. 13). In Cluster 1, this is the
month June and September, in Cluster 3, September, in Cluster 5, February
and March, and August to October and in Cluster 6, August to November. In
all clusters, there is not a single month, for which all stations would
exhibit flood scarcity (i.e. 100 %). Five out of six clusters (apart from
Cluster 2) have at least 1 month that can be considered not to be flood-scarce for 100 % of the station (marked by stars below the x axis in Fig. 13). This is February for Cluster 1, March for Cluster 3, October and
November for Cluster 4, and March and April for Cluster 5 and Cluster 6
respectively. Based on the percentage of stations that have flood dominance,
there is again an indication in Cluster 4 that some of the stations might
have a bimodal distribution with a primary peak in winter, and a secondary
peak in April and May, with 12.01 and 10.21 % of the stations having a
significant flood-dominated month respectively.
Percentage of stations within the same consecutive monthly
flood classification, grouped per cluster. The x axes show the maximum (a, c)
and minimum number (b, d) of consecutive months classified
as flood-dominant (a, b) and flood-scarce (c, d). Total number of
stations with records > 30 years:
nsub= 3356.
![]()
To obtain further insights into the temporal characteristics of the
different flood periods, Fig. 14 shows the maximum and minimum duration
(in months) for flood-dominant (a and b) and flood-scarce (c and d) periods
respectively. In each panel, the percentages are plotted for each cluster
separately.
There is a very small number of stations (< 1 %) in Clusters 1,
2,
and 4 for which no significant flood-dominant season could be identified
(i.e. 3, 2, and 2 stations, respectively) (Fig. 14a). This means that the
floods are not uniformly distributed throughout the year (as stations for
which uniformity could not be rejected were already removed from this
dataset in a previous step), but the number of floods per month does not
cross the Luppern threshold for the months to be classified as flood-dominant.
All clusters contain stations that have a maximum of five consecutive flood-dominant months, with exception of Cluster 6, which has one station that has
six consecutive months (Fig. 14a). Most of the stations have a maximum
length of two consecutive months apart from Cluster 1, which has the highest
number of stations with three consecutive months (Fig. 14a).
All stations in Clusters 5 and 6 have at least one flood-scarce month. In
all other clusters, < 5 % of the stations have no flood-scarce
month (Fig. 14c and d), i.e. most of the stations have a minimum duration of
1 flood-scarce month (Fig. 14d).
The existence of at least one flood-scarce month is of importance, as this
is a necessary condition for the identification of bimodal flood seasonality
distributions in the next part of the analysis. Most of the stations in
Cluster 5 and 6 have the same maximum and minimum duration of 2 months when
considering the flood-dominant months (Fig. 14a and b) and also the same
number of maximum and minimum length of flood-scarce months between 9 and 10
months (Fig. 14c and d). This indicates that the seasonal flood seasonality
distributions in these clusters are likely to be unimodal.
Classification of flood seasonality distributions
Based on the alternating occurrence of flood-dominant and flood-scarce months
(see Sect. 3.3.2 for methodological details), the flood seasonality
distributions of 79 stations are classified as bimodal. A total of 2490 stations have a
unimodal seasonality distribution due to the uninterrupted occurrence of the
flood-dominant months (Table 3).
Cluster 4 has the highest number of stations (29) and the highest percentage
of stations (∼ 9 %) with bimodal distributions of all
clusters. Cluster 4 has also the highest percentage of stations without a
clearly defined flood seasonality distribution (∼ 36 % are
undefined) and the lowest number of unimodal stations (∼ 56 %). This indicates that Cluster 4 is the cluster with the most diverse
flood seasonality distributions, which is consistent with its low average
silhouette values detected before and corroborates the results from the
analysis steps performed before. In Cluster 2 and Cluster 3, 20 and 24
stations are classified as bimodal, and the other clusters contain less than 5
bimodal stations.
Primary and secondary flood seasons are identified, for each cluster
separately, if the median of the monthly flood percentage is > 8.33 % (1/12). Primary flood seasons are based on all stations with at
least 30 years of data, and secondary flood seasons are identified from the bimodal
flood seasonality distributions, from clusters with at least 5 bimodal
stations (excluding the months that are already included in the primary
flood season) (Table 3).
Box plot of the percentage of floods per month for each
station, of all stations with at least 30 years of data, grouped by their
cluster (a–f) and by their annual flood seasonality distribution being
bimodal, undefined, or unimodal (first, second, and third columns, respectively). The top and bottom
of the boxes show the 75th and 25th percentiles (i.e. the upper and lower
quartiles) respectively, whiskers extend to 1.5× interquartile
range beyond the box, the black band indicates the median, and outliers are
shown as points. Panels containing less than 5 stations show points instead
of box plots. Number of stations:
nsub= 3356;
n denotes the number of stations within each
flood distribution classification.
![]()
In Fig. 15, the monthly bimodal, unclassified, and unimodal flood
seasonality distributions are shown for each cluster separately, to depict
the differences between the different flood seasonality distributions in
detail. In Cluster 1, 74 % of the stations have unimodal flood
distributions and one station is classified as bimodal (Fig. 15a.3 and a.1 respectively). The other stations in Cluster 1 have an unclassified
seasonality distribution (Fig. 15a.2), which is mainly due to the absence
of an additional month classified as either flood-dominant or flood-scarce.
Cluster 2, Cluster 3, and Cluster 4 show a monthly seasonality distribution
with a distinct secondary flood season (Fig. 15b.1 to d.1). In the
mountainous regions (Cluster 2), the secondary peak in the bimodal flood
seasonality distribution in March precedes the main flood season in summer.
In central and eastern Europe (Cluster 3) the main flood season in February
to April is followed by secondary flooding in June and July (influence of
summer rainfall events). In Cluster 4, the primary flood season in October
to January is followed by an additional month of flooding in May. A total of 98 % of
the stations in Cluster 5 and Cluster 6 in northern and north-eastern Europe
are classified as unimodal (Fig. 15e.3 to f.3 respectively).
Spatial distribution of the stations with bimodal flood
seasonality distributions (n= 79) with
the point size scaled by concentration R (a), and stations with unimodal
distributions (n= 2490) with the point
size scaled by the length of the flood-dominant period (b).
![]()
Spatial distribution of bimodal and unimodal flood seasonality
distributions
Figure 16 shows the spatial pattern and flood seasonality characteristics of
the stations with a clear bimodal or unimodal flood seasonality distribution
for each cluster separately.
For most of the stations that exhibit a bimodal seasonality distribution,
the R value is low (mean R value of all bimodal stations is 0.35) (Fig. 16a). However, bimodality does not necessarily imply a low concentration
around the mean. If, for instance, the two flood seasons are separated by
only one flood-scarce month, the R value can be high even in the case of
bimodality. The station with the highest R value of all bimodal stations
(R= 0.73) is located in Finland (Cluster 5), for which the secondary flood
season occurs just 2 months before the primary flood season. Even though
bimodality in the flood seasonality distribution is only detected in a small
number of stations, the locations of these stations are not randomly
distributed across Europe, but rather located in close spatial proximity to
each other in spatially distinct regions.
Unimodal flood seasonality can be found in all regions across Europe (Fig. 16b). In northern and eastern Europe (Cluster 5 and Cluster 6), the duration
of the period of flood-dominant months (unimodal seasonality distribution)
is the shortest found among all clusters, and lasts on average 1.73 and 1.65
months, respectively. The stations in Cluster 1 have, on average the longest
duration (∼ 3.2 months). In Cluster 2, Cluster 3, and Cluster 4
the periods of unimodal flood-dominant months last, on average 2.57, 2.16,
and 2.65 months respectively.
Discussion
This study provides a detailed analysis of the seasonality characteristics
of annual maximum floods in Europe. While previous studies analysed the mean
flood seasonality at national or regional scale, this paper aims to
identify large-scale geographical regions with similar temporal flood
characteristics and describe the flood seasonality of these regions in
detail.
From the results obtained one can conclude that in Europe the station
elevation (i.e. the catchment outlet elevation) or the catchment area
explains the timing of the flood occurrence to a lesser degree than
geographical location. Previous studies (e.g. Lecce, 2000, in the US) have
suggested that catchment area has a strong effect on the flood seasonality
(higher flood frequency in summer and autumn due to short-duration summer
storms of limited areal coverage in small catchments, i.e. < 100 km2). In Europe, this does not seem to be the case. Smaller
catchments show little difference in their flood seasonality when compared
to larger catchments. Here a noticeable change with catchment area is only
apparent in spring floods, which have a higher frequency in larger
catchments. This increase in spring floods with larger catchment area is
likely linked to the uneven spatial distribution of larger catchments, which
are predominately found in eastern Europe, where a continental climate is
dominant. Therefore, in Europe the observed changes of seasonal floods
cannot be linked to catchment area alone due to the existence of a spatially
coherent west-to-east transition from winter to spring floods across Europe.
It would have been interesting to investigate if the catchment mean or
maximum elevation would correlate better with the observed flood seasonality
patterns and clusters compared to the catchment outlet elevation used here;
however, this information was only available for few of the stations and
could be analysed in future if such information becomes available.
To obtain homogenous regions with distinct flood seasonalities, the clustering
was performed on the monthly frequency of the annual maximum flood
occurrence. Even without using the geographical location as a variable in
the clustering, spatially coherent larger-scale clusters emerged that
displayed distinct characteristics with regard to their flood seasonality
distributions. The spatial seasonality patterns detected are similar to
those found in smaller scale studies (some of which used different methods).
For example, the clusters detected in this study had similar spatial
boundaries to the three clusters identified by Beurton and Thieken (2009) in
Germany. Cunderlik et al. (2004) detected three regions with different flood
seasonality in Great Britain, whereas this study identified two clusters.
Their region with a high number of floods in November (flood type 1)
corresponds approximately to Cluster 4 identified in this study and their
flood type 3 (floods occurring on average in January) corresponds to Cluster
1. They considered flood type 2 a transitional type between type 1 and type
3, which is included in Cluster 1 of this study due to the similarity of monthly
AMF frequencies. Overall, differences between local scale and continental
scale analyses can be expected, as the differences in the monthly flood
frequencies that appear to be important at a smaller scale may be of lesser
importance at a larger scale. Larger differences in the monthly flood
seasonality distributions are observed across Europe due to the existence of
a larger variety of flood generation processes.
Based on the mean flood seasonality, the temporal concentration of the AMF
around the mean timing, and the geographical location on the map, one can
hypothesise the causes behind the observed patterns. For example, Cluster 5
and Cluster 6 in eastern and north-eastern Europe are likely to be
predominately driven by snowmelt processes (see also Blöschl et al., 2017),
which result in a high temporal concentration within a month due to the
relatively fast melting of the snow once the temperatures rise in the
spring. Compared to Cluster 5 and Cluster 6, Cluster 3 is located further to
the south and further to the west. These locations are marked by earlier
snowmelt and a stronger maritime influence, which cause the floods in
Cluster 3 to occur earlier in the year and exhibit a stronger influence of
winter precipitation. For Cluster 2, which is primarily located in and
around mountainous regions, one can infer that the AMFs are caused by both
snowmelt and glacier melt in the summer, and by heavy precipitation occurring in
the summer months. For the coastal stations located in Cluster 1, with
strong maritime influence, station elevation has little influence on the
temporal occurrence of the annual maximum floods, as snow accumulation and
snowmelt are scarce. Therefore, the floods in this cluster can be considered to
be mainly driven by extreme precipitation in late winter and early spring
(see also Blöschl et al., 2017).
Cluster 4 is the most geographically dispersed cluster of all. The stations
of Cluster 4 are located at the western coast of the British–Irish Isles,
the western coast of Norway, and the northern coasts of the Mediterranean.
The temporal distribution of the AMFs within the year shows a bimodal
distribution for almost 9 % of the stations in this cluster. The floods
can be considered to be predominately driven by late autumn and early winter
precipitation (primary flood season), but also contain some floods caused by
spring and early summer precipitation (secondary flood season).
In this study, only the annual maximum floods were available, but if more
than one large flood event per year would be analysed (e.g. using partial
duration series) the number of stations with bimodal distributions would
probably be higher, as secondary flood maxima occurring in a year would also
be included. The differences in the flood seasonality characteristics
between using annual maxima or multiple peak events per year should be
investigated as soon as appropriate data for such an analysis become
available at a European scale.
Nevertheless, the detailed spatial and temporal flood seasonality
information obtained in this study is important for practical applications,
particularly for regions in which only limited information exists so far.
Such applications include but are not limited to farming operations,
fluvial ecosystem management, water management (reservoirs and dams),
hydropower production, flood protection policy, and regional flood frequency
analysis (see for example Barnett et al., 2005; Blöschl et al., 2017; Klaus
et al., 2016; Köplin et al., 2014; Ryberg et al., 2016). Additionally,
knowledge of the existence of these large-scale flood seasonality
characteristics provides an important baseline for future research on floods
in Europe. For example, the flood seasonality patterns can be used as an
additional metric to test large-scale hydrological models for their ability
to reproduce the spatial and temporal flood characteristics.
Moreover, the detailed assessment based on monthly flood occurrence, along
with the identification of the spatial patterns of uniform, unimodal, and
bimodal flood seasons, provides a starting point to assess the existence of
one dominant or multiple flood-generating processes for distinct regions in
Europe. For example, if the monthly flood seasonality distribution consists
of a single peak change that ranges over just a few months (similar to Cluster
5 and Cluster 6), then it is likely that the floods are generated by one
temporally distinct driving process, such as snowmelt. However, if the
temporal flood seasonality distribution is spread over several months it is
likely that several drivers (rain, rain on snow, snowmelt, or soil moisture)
tend to cause floods. Mixed flood drivers seem to be involved in shaping
the flood seasonality in Cluster 4, which contains stations that are located
at the eastern coast of the British–Irish Isles and the northern
Mediterranean coast, which have a different Köppen–Geiger climate
classification, but yet a similar flood seasonality distribution.
Overall, the existence of a large-scale pattern of flood seasonality
characteristics points to climate being the dominant large-scale control,
with local catchment characteristics modulating the climatic inputs. Given
the variety of flood generation processes across Europe and their distinct
local interplay, the detailed attribution of the detected large-scale pattern to specific climate characteristics and weather patterns is
beyond the scope of this study and merits a follow-up study.
The assessment presented here summarises the temporal flood seasonality
distributions and the spatial patterns over the period 1960–2010. The timing
of floods within this period, however, varies and changes in the flood
seasonality distribution have been linked to a changing climate. For
example, shifts in the timing of floods have been observed (e.g.
Arheimer and Lindström, 2015; Blöschl et al., 2017; Wilson et al.,
2010). For example, the timing of snowmelt-generated floods has shifted
towards earlier in the season as a result of earlier snowmelt due to rising
temperatures. This shift is projected to continue for as long as snowmelt is
relevant for flooding (e.g. Teutschbein et al., 2015 for Swedish
catchments).
If snow processes become less important due to higher temperatures in the
distant future, flood seasonality patterns in north-eastern Europe may
change completely if other flood generation processes may become dominant.
In Europe, more research is needed to determine how the spatial and temporal
clusters of flood seasonality might change in future, due to the complexity
of the flood generation processes (precipitation occurrence and temperature,
and soil moisture), their interactions, and also their interactions with the
physical catchment properties (Stewart, 2009). If raising temperatures in
the future result in diminished snow accumulation during winter, the annual
maximum flood caused by spring snowmelt might be replaced by an annual
maximum flood caused by increased precipitation depending on the season in
which the rainfall occurs. The effect of increasing temperatures leading to
earlier floods could also be masked by a concurrent precipitation increase
in the cold season, resulting in higher snow accumulation and delayed melt
(Kay and Crooks, 2014), causing the floods to occur later.
Due to differences in the flood generation processes and how they are
captured by hydrological models, future projections of flooding diverge.
This is particularly the case for floods generated by high-intensity
precipitation, which is difficult to project into the future (Kundzewicz et
al., 2017). Additionally, not only the amount of precipitation determines
flood generation, but also the spatio-temporal distribution and the
antecedent soil moisture conditions, which will also affect the flood
seasonality projects. Given the aforementioned future uncertainties together
with the absence of a detailed study on the flood-generating processes
across Europe, it is currently elusive to formulate a hypothesis of how the
spatial and temporal clusters of flood seasonality observed in the current
study period may evolve in future.