NHESSNatural Hazards and Earth System SciencesNHESSNat. Hazards Earth Syst. Sci.1684-9981Copernicus PublicationsGöttingen, Germany10.5194/nhess-17-1177-2017Increasing frequencies and changing characteristics of
heavy precipitation events threatening infrastructure in Europe under climate changeNissenKatrin M.katrin.nissen@met.fu-berlin.deUlbrichUwehttps://orcid.org/0000-0001-7558-6622Freie Universität Berlin, Institute of Meteorology, Carl-Heinrich-Becker-Weg 6–10, 12165 Berlin, GermanyKatrin M. Nissen (katrin.nissen@met.fu-berlin.de)14July20171771177119020October201631October20161June20172June2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://nhess.copernicus.org/articles/17/1177/2017/nhess-17-1177-2017.htmlThe full text article is available as a PDF file from https://nhess.copernicus.org/articles/17/1177/2017/nhess-17-1177-2017.pdf
The effect of climate change on potentially infrastructure-damaging
heavy precipitation events in Europe is investigated in an ensemble of
regional climate simulations conducted at a horizontal resolution of
12 km. Based on legislation and stakeholder interviews the
10-year return period is used as a threshold for the detection
of relevant events.
A novel technique for the identification of heavy precipitation events
is introduced. It records not only event frequency but also event
size, duration and severity (a measure taking duration, size and rain
amount into account) as these parameters determine the potential
consequences of the event. Over most of Europe the frequency of
relevant heavy precipitation events is predicted to increase with
increasing greenhouse gas concentrations. The number of daily and
multi-day events increases at a lower rate than the number of
sub-daily events. The event size is predicted to increase in the
future over many European regions, especially for sub-daily
events. Moreover, the most severe events were detected in the
projection period. The predicted changes in frequency, size and
intensity of events may increase the risk for infrastructure damages.
The climate change simulations do not show changes in event duration.
Introduction
Parts of our infrastructure system are vulnerable to extreme weather.
One of the risks threatening the European infrastructure network is
heavy precipitation. A series of interviews conducted with
infrastructure providers has revealed that land-based transportation
infrastructure (i.e. streets and railway lines) is especially
vulnerable to heavy precipitation but the electricity
network and the telecommunication network can also be affected if
critical components (e.g. electrical substations) are flooded. In
addition to the direct local effects such as short circuits and
submergence, heavy precipitation can cause secondary hazards which in
turn can also result in damages. The most frequent secondary hazards
caused by precipitation are river floods, snow avalanches and
landslides .
In this paper we analyse the effect of climate change on potentially
infrastructure-damaging heavy precipitation events in Europe.
Estimating the risk that hazardous events pose to infrastructure is
a complex task. In addition to hazard strength, it depends on exposure
(the existence of an infrastructure element in the region affected by
the hazard) and vulnerability (susceptibility of the infrastructure
element to the hazard) e.g.. These factors
will however not be the subject of this study. Here we concentrate
only on the hazard frequency and its characteristics, taking into
account the size, duration and severity of the events.
Previous studies have already established that extreme precipitation
is likely to increase with increasing greenhouse gas emissions. In
their 2012 report on changes in climate extremes
the Intergovernmental Panel on Climate
Change (IPCC) states that it is likely that there have already been
statistically significant increases in the number of heavy
precipitation events in more regions of the world than there have been
statistically significant decreases. The trends show, however, strong
regional and sub-regional variations. In addition the report states
that it is likely that the frequency of heavy precipitation or the
proportion of total rainfall from heavy rainfalls will increase in the
21st century over many areas of the globe. A pronounced change is
expected for example in winter in the northern mid-latitudes.
Focusing on Europe, a review of trend analyses and climate change
projections for extreme precipitation and floods was compiled by
. The majority of studies cited in the review
show an increase in extreme precipitation, with the definition of
“extreme” varying between studies. A multi-model ensemble analysis
of heavy precipitation under climate change conditions was conducted
by ,
who analysed a 20-member ensemble of
coupled global model simulations which took part in phase 5 of the
Coupled Model Intercomparison Project (CMIP5). The model ensemble
predicts an increase in the intensity of heavy precipitation events
over Europe for the Representative Concentration Pathway (RCP) 8.5
emission scenario in winter. During summer this increase is restricted
to Northern Europe. analysed a 10-member
ensemble of regional EURO-CORDEX simulations and found that the 95th
percentile of daily precipitation is predicted to increase by up to
35 % in the RCP8.5 scenario until the end of the 21st
century.
With respect to the secondary hazards that can be triggered by heavy
precipitation, a review compiled by
reports an increased risk for landslide fatalities in regions where
climate change increases the frequency and intensity of severe
precipitation. In contrast, the response of river floods to climate
change is less homogeneous. According to a multi-model ensemble of
climate change simulations the frequency of river floods will probably
increase over Western Europe and decrease over most Eastern European
regions . Especially catchments in which
snowmelt dominates the peak flow are expected to see a decrease in
flood magnitude .
This study is the first that analyses projected changes in the
characteristics of heavy precipitation events over Europe. We have
developed a novel technique for the identification of such events that
records event size, duration and severity in addition to event
frequency. Size and severity are especially crucial parameters for
stakeholders as they influence, for example, repair times and
determine whether it is possible to compensate the failure (e.g. by using
alternative routes). In addition, size, duration and severity
determine whether an event is likely to trigger a secondary hazard. The
probability for river floods, for instance, increases with increasing
rain duration and affected catchment fraction, while event size is
less important for landslides, which are often triggered by severe
rain falling on already saturated soil
e.g..
The study was conducted using a high-resolution (0.11∘)
multi-model ensemble of regional simulations considering both
multi-day and sub-daily events. A high spatial model resolution is
important for reproducing the climatology of extreme daily and
sub-daily precipitation over regions with substantial orography
. Daily (or multi-day) and sub-daily
precipitation should be both considered because they cause different
kinds of problems for infrastructure providers and can trigger
different secondary hazards. Moreover, there is indication that they
respond differently to increasing greenhouse gas emissions
e.g..
The work described in this paper was conducted within the EU-funded
research project RAIN (Risk Analysis of
Infrastructure Networks in response to extreme
weather). The RAIN vision is to provide an operational analysis
framework that identifies critical infrastructure components affected
by extreme weather and that can be used to determine the most
effective mitigation strategies. The implications of climate change on
the hazards are one of the core components of the risk analysis
framework.
The remainder of the paper is structured as follows. First the data
sets analysed in this study are introduced. Section 3 describes how
the thresholds for the detection of relevant events were
determined. Next the newly developed method for the detection of
extreme precipitation events is explained. Results for the present-day climate and the climate change signal are presented in Sects. 5
and 6 respectively. Conclusions are given in Sect. 7.
Data
EURO-CORDEX simulations analysed for this study and available
temporal resolution of the data sets.
A multi-model ensemble conducted within the EURO-CORDEX framework
is analysed. The EURO-CORDEX ensemble
includes simulations by various regional climate models (RCMs)
covering the European domain. The RCMs are driven by CMIP5 global
climate model integrations. The data analysed for this study have
a spatial resolution of 0.11∘ (12 km). We analysed 13
simulations for which data at a daily temporal resolution were
available. Two RCM modelling groups have additionally provided data
at 3-hourly temporal resolution for a total of seven simulations. The
climate change signal is studied by comparing results of three different
periods of simulated data for two different greenhouse gas
scenarios. The reference is the “historical” period. It is produced
using observed (1971–2000) greenhouse gas forcing and compared to the
model simulations for the periods 2021–2050 and 2071–2100 forced
with RCP8.5 and RCP4.5 representative greenhouse gas pathways
. At the time of writing the number of
available simulations for the more moderate RCP2.6 scenario was too
low for robust results. This scenario is therefore not considered in
the study. For the simulations forced by the Hadley Centre Global
Environmental Model HadGEM,, data for
year 2100 were not available. For these simulations we had to shift the
analysed period 1 year back. The combinations of global and
regional models used for this study are shown in Table .
The ability of the EURO-CORDEX models to reproduce mean and extreme
precipitation has been analysed by in
a recent study. They compared the output of an ensemble of EURO-CORDEX
simulations forced with ERA-Interim reanalysis data to several
regional observational data sets and were able to show that the
simulations at 0.11∘ capture the observation more closely than
the 0.44∘ simulations with the largest improvement found for
sub-daily rainfall over the Alps.
In our study simulated daily and multi-day rainfall accumulations are
compared with observations using the gridded observational E-OBS data
set . The E-OBS data set provides
accumulated daily precipitation over land in Europe at a resolution of
0.25∘ (28 km). As no sub-daily pan-European
precipitation data set exists, modelled 3-hourly rainfall was
compared to downscaled ERA-Interim reanalysis data (i.e. the
EURO-CORDEX “evaluation” simulations). A comparison to station data
is shown for the example of the World Meteorological Organization
(WMO) station Berlin using precipitation measurements at 1-hourly
temporal resolution.
Thresholds
Dependence of (a) intensity in mm h-1 and
(b) accumulated rain
amount (depth) in mm on rainfall duration. Blue:
Berlin station data with hourly resolution; green: Berlin station
data with daily resolution; red: closest grid point to Berlin from
E-OBS data set at daily resolution. Dots indicate result of POT
analysis, and solid lines are the fitted IDF and DDF curves (see text for details).
Within the RAIN project two surveys have been performed aiming to
determine the thresholds at which failures of infrastructure elements
may occur. Twenty-eight infrastructure providers from the fields of energy,
telecommunication, land transport (streets and rail) and emergency
rescue services were interviewed as well as 18 national and private
weather services . Not unexpectedly, there is no
universal critical value for all types of infrastructure and all areas
within Europe. For some providers the amount of precipitation falling
within a day is more important for others it is the intensity per
hour. The thresholds named by the stakeholders range between
5 mmh-1 (danger of aquaplane) and
30 mmh-1 for high-intensity events and between
50 mmday-1 and 100 mmh-1 for events with
high water accumulation.
It should be noted that the area mean values from gridded data sets,
such as the ones analysed in this study, differ from the point values
that affect an infrastructure element. for
example show that the maximum precipitation value for daily rainfall
at a point within a 40km×40km grid box can
be more than twice as high as the grid-box mean value. Deviations
increase with grid-box size and event severity. Thus, using the point
value thresholds from the stakeholder interviews to detect heavy
precipitation events in the gridded data sets would result in an
underestimation of the number of identified events. We therefore
decided not to use a fixed value threshold for the present
study. Instead, this study will use local return values for a given
return period as thresholds for extreme events (i.e. the amount of
rain per time unit exceeded on average only every n years). This
approach is consistent with engineering practice and legislation
e.g.. Engineers who
design drainage systems to protect infrastructure elements from
(heavy) precipitation usually also determine the required capacity of
the system from the local return levels at a given return period. In
engineering terms these values are referred to as “design rainfall”.
The return periods that should be used are often specified by national
laws and international recommendations. The International Union for
Railways, for example, recommends using 10-year return
periods and German legislation prescribes return
periods between 1 and 50 years for streets depending on the
importance of the street . Using these values
seems reasonable as one can assume that only precipitation, which
exceeds the design rainfall, can be harmful for the infrastructure
component. The resulting thresholds are relative values which depend
on the spatial resolution and on the climatology of the data set and
will thus be calculated separately for each data set.
The return levels are estimated using a peak over threshold (POT)
method, fitting a generalised Pareto distribution to events exceeding
the local 95th percentile (only wet days are taken into account). This
was done following using the “extRemes” package of
the statistical software package R. The return levels differ for
events with different durations and different return periods. As
illustrated in Fig.
for the example of the WMO station
Berlin, the intensity of extreme precipitation events at a given
return level typically increases with decreasing duration
(Fig. a), while the accumulated amount increases with duration
(Fig. b). The relationship between the intensity, the
duration and the frequency of precipitation at a given place can be
described with intensity–duration–frequency (IDF) curves and the
relationship between accumulated rain amount (depth), duration and
frequency with depth–duration–frequency (DDF) curves. The
mathematical expression which best describes the relationship between
intensity (or depth) and duration varies between stations and depends
on the precipitation type (convective, stratiform or mixed) dominating
the duration classes e.g..
Empirical equations exist to model this relationship and to fit the
curves e.g.. This approach leads to a smoother and
more consistent relationship between the return values and the
rainfall duration. In this study we have fitted the return values to
i=atc+b,
where i is intensity and t is duration, while a, b and c are
the fitting coefficients as in Eq. (5.34). Depth can
be obtained by multiplying i with duration. The dots in
Fig. represent the output of the POT analysis and the solid
lines show the IDF and DDF fits respectively. For Berlin (blue line)
the 10-year return value is 22.3 mm for hourly
durations and 57.6 mm for daily durations. These values lie on
the lower end of the range the infrastructure operators consider as
relevant. We have therefore decided to use the present-day
10-year return value as a threshold for the detection of
potentially infrastructure threatening events.
How much the return values are affected by the temporal and spatial
resolution of the data set is demonstrated by the green and red lines
in Fig. . The effect of the temporal resolution can be seen by
comparing the blue line (calculation from station data at hourly
resolution) with the green line (calculated from station data at daily
resolution). The difference is on the order of 10 % and
caused by the fact that the rainfall amount associated with a strong
24 h event will probably not all fall within the same data
aggregation period but will contribute to the rain amount of two
consecutive periods. Comparing the green and the red lines illustrates
the effect of the spatial resolution. The green curve shows the IDF
(DDF) curve for 10-year return values calculated from daily
station data while the red line is the result of the same calculation
from the grid box in the observational gridded E-OBS data set
, which includes the observational site. The
gridding process again leads to a reduction of the return value by
approx. 10 %. The exact differences depend on the orography
and the grid resolution.
Identification of extreme events
Schematic illustration of the detection and tracking
scheme. (a) Grid boxes exceeding the detection threshold are grouped
into events. (b) Events are tracked in time and space. See text for further explanation.
Precipitation for the period 9 to 13 August 2002
based on the E-OBS data set. Contour lines show daily precipitation
amounts. Colour denotes grid boxes in which the local 10-year return
levels are exceeded.
10-year return level of daily precipitation in the E-OBS data
set and the EURO-CORDEX simulations for the period 1971–2000. (a) E-OBS
data set (b) ensemble mean of EURO CORDEX simulations (c) ensemble
SD.
10-year return level of 3-hourly precipitation. (a) ERA-Interim
downscaled with RCA4 and RACMO22E for the period 1981–2010 (b)
ensemble mean of EURO CORDEX simulations (c) ensemble SD.
A detection algorithm for precipitation extremes in a Lagrangian
perspective was developed, which identifies events of various
durations and spatial extents in gridded data sets. In a first step
the algorithm identifies all grid boxes in which the rainfall exceeds
a local threshold (here the 10-year return value).
Exceedances caused by the same synoptic weather situation
(e.g. passage of a front) are considered as belonging to the same
event. In the algorithm this is realised by assigning all affected
grid boxes embedded in the same area with considerable precipitation
(>95th percentile) to the same event. This is schematically
illustrated in Fig. . Areas with precipitation exceeding the
threshold are shown in red in Fig. a. As all red areas are
located within the same area of substantial rain, outlined by the
green ellipse, they are considered as belonging to the same event and
form a group. For each identified group an envelope surrounding the
group is defined which contains the high-risk area. It is determined
from the area where the smallest possible circle including all
exceedance grid boxes (black outline in Fig. a) and the area
where the 95th percentile is exceeded (green outline) overlap (hatched
area). The events are then tracked in time. The group at time step
t+1 is considered as the next track element which exhibits the largest
overlap to the high-risk area from the previous time step
(Fig. b).
To distinguish between long-duration events with high precipitation
amounts and short-duration events with high rain intensities the
algorithm is applied twice. First, daily to multi-day events are
detected by searching for grid boxes in which the 24, 48 and/or
72 h 10-year return value is exceeded. This is done
for all 13 simulations. Secondly, sub-daily events are detected by
searching for grid boxes in which the 3-hourly 10-year return
level is exceeded. This is done for the seven simulations for which data
at such a temporal resolution were available.
Each detected event can consist of several grid boxes and can last for
several time steps. A precipitation severity index (PSI) is
defined which can be used to compare the strength of the identified
events. It is calculated only from grid boxes and time steps where
the 10-year return level was exceeded and is determined as
follows:
PSI=∑tT∑kKprecipk,tannualprecipk×AK,
where T is the event duration, K is the number of affected grid
boxes and Ak is the size of grid box k. Thus, the severity index
takes the affected area and the amount of precipitation accumulated
over the duration of the event into account. It is normalised by the
long-term mean annual precipitation sum expected for the grid box.
An example for a detected event is shown for a historical case. In
August 2002 record-breaking rainfall amounts and intensities occurred
in Central Europe. They resulted in a large-scale flooding event
e.g.. Applied to the E-OBS data set,
the detection algorithm identifies the event as depicted in
Fig. . Displayed is the 5-day sequence between 9
and 13 August 2002. Shading denotes areas where the 10-year
return levels were exceeded. These areas and time steps are attributed
to the event, and thus the event duration determined by the algorithm is
185 000 km2 and its duration 5 days. The severity
index PSI for this event is 50, which corresponds to the 99th
percentile of the PSI for all detected events within the E-OBS
domain.
Present-day climateDaily and multi-day events
Looking at the historical period (1971–2000), daily 10-year
return values for the multi-model ensemble are highest over Iceland,
western Norway, the Alps, north-western Spain and the Mediterranean
coast (Fig. b). As expected the return values in the
multi-model ensemble mean are generally higher than in the E-OBS data
set (Fig. a) because of the higher horizontal resolution of
the simulations. In addition, the models show a very pronounced
north–south gradient with higher return values at more southerly
latitudes, which is not present in the E-OBS observations. In the
Mediterranean the inter-model standard deviation (SD) is also high,
indicating a large spread between the model simulations in this region
(Fig. c). It is not possible to decide whether the differences
between the model simulations and the observations can be regarded as
model deficits as have shown that the
magnitude of the uncertainty associated with the E-OBS data set is of
the same order as the uncertainty associated with regional climate
model simulations even on a seasonal scale.
The 48 and 72 h return levels exhibit a very similar spatial
distribution to the 24 h return values with somewhat higher
accumulated amounts and the maximum values in some grid boxes
exceeding 300 mm in 48 h and 400 mm in
72 h respectively (not shown, but available from ).
Sub-daily events
As there is no gridded pan-European observational data set at
3-hourly resolution available, Fig. a shows 10-year
return levels calculated from precipitation data obtained by
downscaling the ERA-Interim reanalysis with the
two regional EURO-CORDEX models for which 3-hourly data were stored
(ensemble mean of RCA4 and RACMO22E). The period shown on the upper
panel is 1981–2010 instead of the reference period 1971–2000 that is
shown on the lower panels as ERA-Interim only starts in 1979. The high
similarity in the return levels in panels Fig. a and b
suggests that the precipitation distribution might be strongly
influenced by the regional models and should be regarded with caution.
The 3-hourly 10-year return values for the multi-model
ensemble reach values up to 50 mm in 3 h. The highest
values can be found over the Alps, at the Mediterranean coast,
southern Iceland and north-western Spain (Fig. b). The
inter-model SD is highest over the southern
Mediterranean, the north-west of Spain and north-eastern Europe
(Fig. c). Results in these regions may not be robust enough
for interpretation.
Climate change signal
Relative change of probability of events with 10-year return
period between the historical simulations (1971–2000) and the scenario
simulations (a 2021–2050 for RCP4.5 scenario, b 2071–2100 for
RCP4.5 scenario, c 2021–2050 for RCP8.5 scenario, d 2071–2100 for
RCP8.5 scenario). Ensemble mean for daily and multi-day events. Dots
denote statistical significance and inter-model
consistency. Areas
marked in panel (d) are selected for further analysis.
Seasonal cycle of detected daily and multi-day heavy
precipitation events in four different European regions
(British Isles, Iberian Peninsula, Scandinavia and Central Europe). The ensemble mean for
the historical simulation (grey), the RCP4.5 (light colour) and the
RCP8.5 (bright colour) scenario simulation
for the period 2071–2100 are shown.
Size and strength distribution of detected daily and
multi-day heavy precipitation events in four different European regions
(British Isles, Iberian Peninsula, Scandinavia and Central Europe). The
ensemble mean for the historical simulation (grey), the RCP4.5 (light colour) and the
RCP8.5 (bright colour)
scenario simulation for the period 2071–2100 are
shown. The box plots show the median, 25th and 75th percentile and
the whiskers span the range from the smallest to the largest event.
The climate change signal in the multi-model ensemble is studied by
comparing frequency, size, severity and duration of heavy
precipitation events between the simulated historical period
(1971–2000) and the two scenario periods (2021–2050 and
2071–2100). First, the differences in the number of detected events
were calculated at each grid point. The result was tested both for
statistical significance and for consistency between the model
simulations. This was done in two steps. In the first step, the
significance in each model was tested independently using the
following bootstrap technique: the detected events were randomly
redistributed between the two periods (historical and scenario) and
the difference between the two periods was determined. This process
was repeated 1000 times. If the observed difference between the two
periods exceeded the randomly obtained difference in 90 % of
the cases, the change is significant at the
90 % level. According to IPCC recommendations
a hypothesis can be considered as
“very likely” at a statistical significance level of
90 %. The test was applied to neighbouring groups of nine grid
boxes and the result was assigned to the central box. Including
neighbouring boxes increases the sample size, rewards regions which
show a consistent signal and punishes inhomogeneous areas. The second
step of the testing process is meant to ensure consistency between the
model simulations. Only those grid boxes at which 90 % of the
simulations show a statistically significant signal of the same sign
pass the test. Grid boxes passing both tests are marked by black dots
in Figs. and . The test is much stricter than the one
applied by , where only 66 % of the
models had to agree on the direction of the change.
Changes in daily and multi-day events
The climate change simulations suggest that the frequency at which an
individual grid box is hit by a long-duration heavy precipitation
event increases with increasing greenhouse gas concentrations over
most European regions (Fig. ). The highest increases can be
found on the westward-facing sides of the European coasts, for example
over western Scandinavia, western Ireland, western Scotland and the
western Balkans. By the end of the century and under RCP8.5 conditions
infrastructure elements in these regions may be affected by
potentially damaging long-lasting precipitation events more than twice
as often as under present-day climate conditions (i.e. every
4–5 years instead of once every 10 years). In Central
Europe the frequency may increase to once every 5–7 years (up
to 100 %) at some grid boxes. The lowest increase, and for
some grid boxes even a decrease, is simulated for the western
Mediterranean region. For the first half of the century the
simulations predict a modest increase for both emission scenarios over
most of Europe, mostly staying below 1 event in 20 years
(probability change of 50 %). For this period only few
grid boxes pass the strict two-step test for statistical significance
and consistency. The climate signal increases steadily over time and
with increasing emissions, suggesting a robust relationship between
greenhouse gas concentrations and the frequency of heavy precipitation
events. This is reflected in the size of the area where the signal is
statistically significant, which also increases with time and emission
levels.
When counting the detected extreme precipitation events there is
a clear dependence of the number of events and of the detected changes
on the season. The annual cycle of the events for the sub-regions
marked in Fig. is depicted in Fig. , both for the
historical period (1971–2000) and for the scenario period
2071–2100. It illustrates that in the Mediterranean area most extreme
precipitation events occur during autumn. In Western, Central and
Northern Europe, however, it is more likely to experience
such an event during summer.
By the end of the century the annual mean change in the number of
events in the pessimistic RCP8.5 scenario amounts to 49 % for
the British Isles, 99 % for Scandinavia and 73 % for
Central Europe. Over the Iberian Peninsula the overall change is
negative (-6 %). Here the climate change simulations
suggest a slight increase in the number of heavy precipitation events
for some winter months, while the number of events during the other
seasons decreases. Over Scandinavia, the British Isles and Central
Europe the number of detected events increases under climate change
conditions during all seasons. Over Britain and Ireland the summer
maximum may start to extend well into autumn. For this region, the
highest increase in terms of absolute numbers is found for autumn and
the lowest for spring. The annual cycle of the climate change signal
for the British Isles suggests that an increase of the sea surface
temperatures in the North Atlantic may be one aspect leading to the
increase in the number of heavy precipitation events in this
region. The ocean remains comparably warm well into the winter season
leading to enhanced evaporation. This in turn increases the water
supply available for precipitation. The effect decreases when the
ocean is at its coldest at the end of the winter season. Central
Europe shows an increase for the number of events for all seasons,
which in terms of percentage change is highest during winter. This may
be caused by an increase in winter temperatures under climate change
conditions. The highest and temporally most homogeneous percentage
increase can be found for Scandinavia. For the RCP8.5 scenario the
number of events almost doubles until the end of the century in all
seasons. For the more moderate RCP4.5 scenario the changes expected
until the end of the century amount to approximately 60 % of
the changes predicted for the RCP8.5 scenario. An exception is the
Iberian Peninsula, which shows a non-homogeneous behaviour between the
scenarios for some months consistent with the lack of statistical
significance and inter-model consensus evident in Fig. .
Relative change of probability of events with 10-year return
period between the historical simulations (1971–2000) and the scenario
simulations (a 2021–2050 for RCP4.5 scenario, b 2071–2100 for
RCP4.5 scenario, c 2021–2050 for RCP8.5 scenario, d 2071–2100 for
RCP8.5 scenario). Ensemble mean for sub-daily events. Dots
denote statistical significance and inter-model consistency.
The increases in event counts due to increasing greenhouse gas levels
shown in Fig. appear in some regions more moderate than the
frequency increases found for some individual grid boxes
(Fig. ). This is due to the fact that the frequency at which
a grid box is hit depends on the number of events in the area and on
their size as a larger event affects more grid boxes. Thus, for
Fig. each event is only counted once regardless of its size,
while it is counted for each grid box it affects in Fig. . For
an individual infrastructure element the analysis on grid-box basis
shown in Fig. would be more relevant as larger events
increase the risk for the element to be affected. The size of the
larger 50 % of events is predicted to slightly increase in
all analysed regions except for Scandinavia (Fig. ). In terms
of severity, the strongest events occur in the climate change
simulations, even though the median of the PSI remains
unchanged or even decreases. No pronounced changes in event duration
could be detected anywhere within Europe (not shown).
Changes for sub-daily events
The increase in occurrence probability under climate change conditions
at single grid boxes reaches values that are approximately 2 times
larger for sub-daily events (Fig. ) than for long-lasting
events (Fig. ), making it necessary to use different colour
scales for the figures. The increases become stronger and more
significant with increasing greenhouse gas concentrations. The
regions showing the highest increase in high-intensity events at the
end of the 21st century are similar to those detected for long-lasting
events: Scandinavia and the western coasts of the British Isles as
well as Iceland. Here, the occurrence probability for sub-daily
precipitation event in the RCP8.5 scenario increases in some grid
boxes from once every 10 years by more than 300 % to
once every 2–3 years. Even the western Mediterranean region
shows a pronounced increase for sub-daily events which is similar in
magnitude to the increase simulated for France and Germany (up to
100 % in some grid boxes).
Again, the increases in event counts per region (Fig. ) are
more moderate than the increases in the frequency at which individual
grid boxes are affected (Fig. ). The annual increase of the
number of sub-daily events averaged over the region of the British
Isles is 51 %, for Scandinavia 107 %, for Central
Europe 56 % and for the Iberian Peninsula 16 % for
the RCP8.5 scenario. The difference in the strength of the increases
between event numbers and frequency at the grid-box scale can be
attributed to the fact that the events are predicted to increase in
size (Fig. ).
The seasonal cycle of sub-daily event occurrence is similar to the one
found for daily and multi-day events (Fig. ): over the
British Isles, Central Europe and Scandinavia heavy precipitation
events mostly occur during summer, while the most active season in the
western Mediterranean region is autumn. In terms of percentage the
highest increases in the number of sub-daily heavy precipitation
events can be found during autumn and winter (Iberian Peninsula,
British Isles, Central Europe) or spring (Scandinavia). The
simulations suggest that Central Europe will start to see
high-intensity precipitation events during winter, which were almost
non-existent during the historical period of the climate simulations
(Fig. ) and in the downscaled ERA-Interim simulations (not
shown). This result is probably caused by higher winter temperatures
in the scenario simulations. During summer the increase in the number
of sub-daily heavy precipitation events is less pronounced in most
regions. The western Mediterranean even sees a decrease in the number
of events during this season. With a more moderate greenhouse gas
increase under the RCP4.5 scenario the simulated changes until the end
of the century are almost as strong (75 %) as the changes
predicted for the pessimistic RCP8.5 scenario.
Seasonal cycle of detected sub-daily heavy
precipitation events in four different European regions
(British Isles, Iberian Peninsula, Scandinavia and Central Europe). The ensemble mean for
the historical simulation (grey), the RCP4.5 (light colour) and the
RCP8.5 (bright colour) scenario simulation
for the period 2071–2100 are shown.
Sub-daily events are in general smaller and weaker (in terms of
PSI) than daily and multi-day events (Figs. and
). Sub-daily events tend to be stronger over the Iberian
Peninsula and Central Europe than over Scandinavia and the British
Isles. The strongest events of the time series for all regions except
Scandinavia are detected for the scenario periods, although the median
of the PSI remains stable. Again, no change was found for the
event duration (not shown).
Discussion
The overall increase for projected extreme precipitation found in this
study is supported by other scientific publications. There is
a consensus that an increase in air temperatures, which is associated
with increasing greenhouse gas concentrations, will probably lead to
an increase in extreme precipitation. The IPCC report states that
proportionately more precipitation is expected per precipitation event
over most regions for future climate periods
. This can be explained by the fact that
with increasing surface temperatures, the moisture-holding capacity of
the atmosphere increases. When sufficient moisture is available,
precipitation extremes are expected to increase by 6–7 % per
degree K according to the Clausius–Clapeyron (CC) relation.
For daily and multi-day event frequencies at the grid-box scale
the spatial distribution of the changes is in good agreement with
, who analysed a similar multi-model ensemble
but using a lower threshold (95th percentile). Our results also agree
with many studies in the literature which compare temperature
dependent changes of daily and sub-daily extremes e.g.and
references therein. With respect to Europe,
, for example, show for a regional
model simulation and a Dutch station that already under present-day
conditions extreme hourly precipitation increases with temperature at
a much higher rate than extreme daily precipitation, even exceeding
the CC relation. A similar analysis by
investigating 10 min intensities for Swiss stations also shows
super CC scaling for convective events. A different conclusion is
reached by , who analysed changes in the
90th percentile of wet days for sub-daily and daily precipitation in
winter using a coupled atmosphere–ocean RCM. In their study the model
predicts the highest percentage changes over most of Europe for daily
events. The result may, however, be influenced by the choice of the
threshold used. According to , comparing
percentiles of wet days between two periods may not correctly
represent changes in extremes if the number of dry time steps differs
between the two periods. Another factor of uncertainty is the choice
of the convection scheme used in the RCM as this can have a strong
influence on simulated heavy precipitation
.
Size and strength distribution of detected sub-daily heavy
precipitation events in four different European regions
(British Isles, Iberian Peninsula, Scandinavia and Central Europe). The
ensemble mean for the historical simulation (grey), the RCP4.5 (light colour) and the
RCP8.5 (bright colour) scenario simulation for the period 2071–2100 are
shown. The box plots show the median, 25th and 75th percentile and
the whiskers span the range from the smallest to the largest event.
To eliminate the uncertainties associated with convection
parameterisation some pioneering studies have been performed in which
convection-permitting regional climate change simulations are
analysed. For southern Britain convection-resolving simulations
studied by predicted stronger increases for
high sub-daily precipitation percentiles (wet days only) than for high
daily percentiles with scaling at a super CC
rate. , in contrast, found that the
intensities of both hourly and daily extreme precipitation scale with
temperature at the CC rate in a 2.2 km resolution RCM
simulation over the Alps. Comparing a 7 km
convection-parameterising model with a convection-permitting
2.8 km resolution RCM in southern Germany,
found similar climate change signals for both
models. If only wet days are considered the scaling reaches super CC
rates for hourly precipitation in the convection-permitting model. If
all days are taken into account the CC relationship holds.
This discussion shows that it is important to analyse sub-daily and
daily events as it is not possible to extrapolate sub-daily changes
from daily data. As studying the evolution of extreme events has been
put increasingly into the focus of climate change studies, it would be
desirable to routinely store sub-daily data when conducting CMIP
simulations.
Conclusions
In this study we analysed projected changes in the characteristics of
heavy precipitation events over Europe. A novel technique was
developed and applied, which identifies and tracks heavy precipitation
events. It allows us to evaluate not only event frequency but also event
size, duration and severity.
According to an ensemble of regional climate model simulations from
the EURO-CORDEX initiative forced with increasing greenhouse gas
concentrations, the probability of infrastructure failures due to
heavy precipitation will increase in the future. When planning new
infrastructure, drainage systems should be installed that allow for
higher discharges than currently observed. This study shows that all
over Europe the highest increases can be expected for sub-daily events
with high intensities. At some grid boxes the increases are predicted
to increase by 300 % until the end of the 21st century under
the pessimistic RCP8.5 scenario. However, also under the more moderate
RCP4.5 scenario the increases are substantial and can reach up to
75 % of the changes simulated for RCP8.5.
The number of daily and multi-day events with high rain amounts is
also predicted to increase in the future for most European regions,
however, at a slightly lesser rate (up to 150 %). An
exception is the western Mediterranean region where the number of
daily and multi-day events is predicted to decrease.
The climate change simulations also suggest that the areas affected by
heavy precipitation events may become larger in many European regions
especially for sub-daily events. This can have consequences for
infrastructure networks. Larger-scale events may damage more
infrastructure elements at the same time. It becomes more difficult to
compensate for damages by using neighbouring elements and more
personnel may be needed for repairs and emergency services. In
addition, the probability that the event can lead to a river flood
increases. The median in event strength remains stable or even
decreases in all analysed areas. Nevertheless, the strongest events
within the time series are always detected in the scenario
periods. This suggests that infrastructure providers may have to cope
with unprecedented events in the future.
The simulations analysed for this study are available from
the Earth System Grid Federation (ESGF) data server. The ensemble-mean
distribution of present-day 10-year return levels as well as event frequency
for different scenario periods and event durations is stored in a public
repository ().
The authors declare that they have no conflict of
interest.
Acknowledgements
The work described in this paper was conducted within the EU-funded
research project RAIN (Risk Analysis of
Infrastructure Networks in response to extreme
weather). The RAIN research project is a Seventh Framework Program
under contract no. 608166. Uwe Ulbrich's research has been partially
funded by Deutsche Forschungsgemeinschaft (DFG) through grant CRC1114
“Scaling Cascades in Complex Systems”, project A01.
We acknowledge the E-OBS data set from the EU-FP6 project ENSEMBLES
(http://ensembles-eu.metoffice.com) and the data providers in the
ECA&D project (http://www.ecad.eu). We would like to thank the two
anonymous reviewers for their constructive comments, which helped to improve
the manuscript. Our special thanks go to Erik van Meijgaard and Grigory
Nikulin for providing the high-temporal-resolution precipitation
data. Edited by: Paolo Tarolli
Reviewed by: two anonymous referees
References
Ban, N., Schmidli, J., and Schär, C.: Heavy precipitation in a changing climate: does short-term summer precipitation increase faster?, Geophys. Res. Lett., 42, 1165–1172, 2015.Cavicchia, L., Scoccimarro, E., Gualdi, S., Marson, P., Ahrens, B.,
Berthou, S., Conte, D., Dell'Aquila, A., Drobinski, P., Djurdjevic, V.,
Dubois, C., Gallardo, C., Li, L., Oddo, P., Sanna, A., and Torma, C.:
Mediterranean extreme precipitation: a multi-model assessment, Clim. Dynam.,
10.1007/s00382-016-3245-x, online first, 2016.
Coles, S.: An Introduction to Modeling of Extreme Values, Springer, London, UK, 2001.Collins, W. J., Bellouin, N., Doutriaux-Boucher, M., Gedney, N., Hinton, T.,
Jones, C. D., Liddicoat, S., Martin, G., O'Connor, F., Rae, J., Senior, C.,
Totterdell, I., Woodward, S., Reichler, T., and Kim, J.: Evaluation of the
HadGEM2 model, Met Office Hadley Centre Technical Note HCTN 74, Met Office,
FitzRoy Road, Exeter EX1 3PB, available at:
https://digital.nmla.metoffice.gov.uk/archive/ (last access: 11 July
2017), 2008.Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and Vitart, F.: The ERA-interim reanalysis: configuration and performance of the data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, 10.1002/qj.828, 2011.
FGSV: Teil: Entwässerung (RAS-Ew), Forschungsgesellschaft für Straßen- und Verkehrswesen, Richtlinie für die Anlage von Straßen, Köln, Germany, 2005.Fosser, G., Khodayar, S., and Berg, P.: Climate change in the next
30 years: what can a convection-permitting model tell us that we did
not already know?, Clim. Dynam., 48, 1987–2003,
10.1007/s00382-016-3186-4, 2016.Gariano, S. L. and Guzzetti, F.: Landslides in a changing climate, Earth Sci. Rev., 162, 227–252, 10.1016/j.earscirev.2016.08.011, 2016.Gill, J. C. and Malamud, B. D.: Reviewing and visualizing the interactions of natural hazards, Rev. Geophys., 52, 680–722, 10.1002/2013RG000445, 2014.
Göber, M., Zsótér, E., and Richardson, D. S.: Could a perfect model ever satisfy a naïve forecaster? On grid box mean versus point verification, Meteorol. Appl., 15, 359–365, 2008.Groenemeijer, P., Becker, N., Djidara, M., Gavin, K., Hellenberg, T.,
Holzer, A. M., Juga, I., Jokinen, P., Jylhä, K., Lehtonen, I.,
Mäkelä, H., Morales Napoles, O., Nissen, K. M., Paprotny, D.,
Prak, P., Púčik, T., Tijssen, L., and Vajda, A.: Past cases of
extreme weather impact on critical infrastructure in Europe, project report,
available at:
http://rain-project.eu/wp-content/uploads/2015/11/D2.2-Past-Cases-final.compressed.pdf
(last access: 11 July 2017), 2015.Haylock, M. R., Hofstra, N., Klein Tank, A. M. G., Klok, E. J., Jones, P. D.,
and New, M.: A European daily high-resolution gridded data set of surface
temperature and precipitation for 1950–2006, J. Geophys. Res., 113, D20119,
10.1029/2008JD010201, 2008.Hirabayashi, Y., Mahendran, R., Koirala, S., Konoshima, L., Yamazaki, D., Watanabe, S., Kim, H., and Kanae, S.: Global flood risk under climate change, Nat. Clim. Change, 3, 816–821, 10.1038/nclimate1911, 2013.Holzer, A. M., Becker, N., van Gelder, P. H. A. J. M., Gregow, H.,
Groenemeijer, P., Juga, I., Morales Napoles, O., Nissen, K. M., Nurmi, P.,
Paprotny, D., Púčik, T., Ulbrich, U., Vajda, A., and
Venäläinen, A.: Present state of risk monitoring and warning
systems in Europe, project report, available at:
http://rain-project.eu/wp-content/uploads/2015/11/D2.3-Warning-Systems.pdf
(last access: 11 July 2017), 2015.Jacob, D., Petersen, J., Eggert, B., Alias, A., Christensen, O. B., Bouwer, L., Braun, A., Colette, A., Déqué, M., Georgievski, G., Georgopoulou, E., Gobiet, A., Menut, L., Nikulin, G., Haensler, A., Hempelmann, N., Jones, C., Keuler, K., Kovats, S., Kröner, N., Kotlarski, S., Kriegsmann, A., Martin, E., Meijgaard, E., Moseley, C., Pfeifer, S., Preuschmann, S., Radermacher, C., Radtke, K., Rechid, D., Rounsevell, M., P. Samuelsson, P., Somot, S., Soussana, J. F., Teichmann, C., Valentini, R., Vautard, R., Weber, B., and Yiou, P.: EURO-CORDEX: new high-resolution climate change projections for European impact research, Reg. Environ. Change, 14, 563–578, 10.1007/s10113-013-0499-2, 2014.Kendon, E. J., Roberts, N. M., Fowler, H. J., Roberts, M. J., Chan, S. C., and Senior, C. A.: Heavier summer downpours with climate change revealed by weather
forecast resolution model, Nature Climate Change, 4, 570–576, 10.1038/nclimate2258, 2014.Kirschbaum, D. B., Adler, R., Hong, Y., and Lerner-Lam, A.: Evaluation of a preliminary satellite-based landslide hazard algorithm using global landslide inventories, Nat. Hazards Earth Syst. Sci., 9, 673–686, 10.5194/nhess-9-673-2009, 2009.Lenderink, G. and van Meijgaard, E.: Increase in hourly precipitation extremes beyond expectations from temperature changes, Nat. Geosci., 1, 511–514, 10.1038/ngeo262, 2008.Madsen, H., Lawrence, D., Lang, M., Martinkova, M., and Kjeldsen, T.: Review of trend analysis and climate change projections of extreme precipitation and floods in Europe, J. Hydrol., 519, 3634–3650, 10.1016/j.jhydrol.2014.11.003, 2014.Malitz, G.: KOSTRA-DWD-2000 Starkniederschlagshöhen für
Deutschland (1951–2000), Grundlagenbericht, available at:
https://www.dwd.de/DE/fachnutzer/wasserwirtschaft/kooperationen/kostra/grundlagenbericht_pdf.pdf?__blob=publicationFile&v=3
(last access: 11 July 2017), 2005.Mastrandrea, M. D., Field, C. B., Stocker, T. F., Edenhofer, O., Ebi, K. L.,
Frame, D. J., Held, H., Kriegler, E., Mach, K. J., Matschoss, P. R.,
Plattner, G.-K., Yohe, G. W., and Zwiers, F. W.: Guidance Note for Lead
Authors of the IPCC Fifth Assessment Report on Consistent Treatment of
Uncertainties, available at: http://www.ipcc.ch (last access: 11 July
2017), 2010.Molnar, P., Fatichi, S., Gaál, L., Szolgay, J., and Burlando, P.: Storm type effects on super Clausius–Clapeyron scaling of intense rainstorm properties with air temperature, Hydrol. Earth Syst. Sci., 19, 1753–1766, 10.5194/hess-19-1753-2015, 2015.Moss, R., Babiker, M., Brinkman, S., Calvo, E., Carter, T., Edmonds, J.,
Elgizouli, I., Emori, S., Erda, L., Hibbard, K., Jones, R., Kainuma, M.,
Kelleher, J., Lamarque, J. F., Manning, M., Matthews, B., Meehl, J.,
Meyer, L., Mitchell, J., Nakicenovic, N., O'Neill, B., Pichs, R., Riahi, K.,
Rose, S., Runci, P., Stouffer, R., van Vuuren, D., Weyant, J., Wilbanks, T.,
van Ypersele, J. P., and Zurek, M.: Towards New Scenarios for Analysis of
Emissions, Climate Change, Impacts, and Response Strategies,
Intergovernmental Panel on Climate Change, p. 132, available at:
http://www.ipcc.ch/pdf/supporting-material/expert-meeting-report-scenarios.pdf
(last access: 11 July 2017), 2008.Nissen, K. M.: Pan-European gridded data sets of heavy precipitation
probability of occurrence under present and future climate, Freie
Universität Berlin, Dataset,
10.4121/uuid:63c786a4-5ea4-471d-9d90-c2e0c71006a9, 2016.Prein, A. F. and Gobiet, A.: Impacts of uncertainties in European gridded
precipitation observations on regional climate analysis, Int. J. Climatol.,
37, 305–327, 10.1002/joc.4706, 2016.
Prein, A. F., Gobiet, A., Truhetz, H., Keuler, K., Goergen, K., Teichmann, C., Fox Maule, C., van Meijgaard, E., Déqué, M., Nikulin, G., Vautard, R., Colette, A., Kjellström, E., and Jacob, D.: Precipitation in the EURO-CORDEX 0.11∘ and 0.44∘ simulations: high resolution, high benefits?, Clim. Dynam., 46, 383–412, 10.1007/s00382-015-2589-y, 2016.Scoccimarro, E., Villarini, G., Vichi, M., Zampieri, M., Fogli, P. G., Bellucci, A., and Gualdi, S.: Projected changes in intense precipitation over Europe at the daily and subdaily time scales, J. Climate, 28, 6193–6203, 10.1175/JCLI-D-14-00779.1, 2015.Scoccimarro, E., Gualdi, S., Bellucci, A., Zampieri, M., and Navarra, A.: Heavy precipitation events over the Euro-Mediterranean region in a warmer climate: results from CMIP5 models, Reg. Environ. Change, 16, 595–602, 10.1007/s10113-014-0712-y, 2016.
Seneviratne, S. I., Nicholls, N., Easterling, D., Goodess, C. M., Kanae, S., Kossin, J., Luo, Y., Marengo, J., McInnes, K., Rahimi, M., Reichstein, M., Sorteberg, A., Vera, C., and Zhan, X.: Changes in climate extremes and their impacts on the natural physical environment, Chap. 3, 109–230, Cambridge University Press, Cambridge, UK, and New York, NY, USA, 2012.
UIC: Earthworks and Track Bed for Railway Lines, Union Internationales des Chemins der Fer, 3
Edn., Paris, France,
2008.Ulbrich, U., Brücher, T., Fink, A. H., Leckebusch, G. C., Krüger, A., and Pinto, J. C.: The Central European flood of August 2002: Part 1 – Rainfall periods and flood development, Weather, 58, 371–377, 10.1256/wea.61.03A, 2003.
van Westen, C. J.: Remote sensing and GIS for natural hazards assessment and disaster risk management, in: Treatise on Geomorphology, 259–298, Academic Press, San Diego, CAP, 2013.Westra, S., Fowler, H. J., Evans, J. P., Alexander, L. V., Berg, P., Johnson, F., Kendon, E. J., Lenderink, G., and Roberts, N. M.: Future changes to the intensity and frequency of short-duration extreme rainfall, Rev. Geophys., 52, 522–555, 10.1002/2014RG000464, 2014.Willems, P.: Revision of urban drainage design rules after assessment of climate change impacts on precipitation extremes at Uccle, Belgium, J. Hydrol., 496, 166–177, 10.1016/j.jhydrol.2013.05.037, 2013.WMO: The Guide to Hydrological Practices. II Management of Water Resources
and Application of Hydrological Practices, available at:
http://www.whycos.org/hwrp/guide/index.php (last access: 11 July 2017),
2009.