Download Future development in extreme one-hour precipitation over Europe

11th International Conference on Urban Drainage, Edinburgh, Scotland, UK, 2008
Future development in extreme one-hour precipitation over
Europe due to climate change
A.N. Larsen1*, I.B. Gregersen1, O.B. Christensen3, J.J. Linde2, P.S. Mikkelsen2
1
2
Student, Technical University of Denmark, Anker Engelundsvej 1, DK-2800 Kgs. Lyngby, Denmark
Department of Environmental Engineering, Technical University of Denmark, Bygningstorvet, Bldg 115, DK2800 Kgs. Lyngby, Denmark
3
Danish Meteorological Institute, Lyngbyvej 100, DK-2100 København Ø, Denmark
*Corresponding author ([email protected])
ABSTRACT
In this study the potential increase of extreme precipitation in a future warmer European
climate has been examined. Output from the regional climate model HIRHAM4 covering
Europe has been analysed for two periods, a control period 1961–1990 and a scenario 2071–
2100, the latter following the IPCC scenario A2. The model has a resolution of about 12 km,
which makes the results unique in relation to extreme precipitation events critical to urban
drainage systems. Extreme events with one- and 24-hour duration were extracted using the
Partial Duration Series approach, a Generalized Pareto Distribution was fitted to the data and
T-year events for return periods from 2 to 100 years were calculated for the control and
scenario period in model cells across Europe. The analysis shows that there will be an
increase of the intensity of extreme events generally in Europe; Scandinavia will experience
the highest increase and southern Europe the lowest. A 20 year 1-hour precipitation event will
e.g. become a 4 year event in Sweden and a 10 year event in Spain. Intensities for small
durations and high return periods will increase the most, which implies that European urban
drainage systems will be challenged in the future.
KEYWORDS
Climate factor; Europe, Extreme precipitation, Generalized Pareto Distribution, IPCC SRES
A2
INTRODUCTION
European countries have experienced very extreme rain events during the past decade, which
have often been accompanied by an exceedance of the drainage capacity in urban areas
causing flooding damages, traffic-related problems and even death. A relevant question is
whether these events are yet another indication of a human caused climate change and if
extreme precipitation events can therefore be expected to become even more frequent and
extreme in the future.
Many authors have evaluated the potential future change of extreme precipitation over Europe
on the basis of the different climate models assembled under the European PRUDENCE
project (Christensen & Christensen, 2007). Frei et al. (2006) e.g. compared six regional
climate models (RCMs) in relation to their predictions of the changes in 1- and 5-day
precipitation intensity with return periods from 5 to 50 years. Beniston et al. (2007) looked at
summer maximum 1 day and winter maximum 5-day mean precipitation, and May (2007)
analysed daily extreme precipitation. Common for all the mentioned studies is that they work
with regional models with a spatial resolution of around 50 km, use a parametrical general
extreme value (GEV) model to compute the extreme events and show significant increases of
extremes in a future warmer climate for time scales of 24 hours or more. Jørgensen et al.
Larsen et al. 1
11th International Conference on Urban Drainage, Edinburgh, Scotland, UK, 2008
(2006) analysed RCM output with a 12 km spatial resolution, however using a nonparametrical statistical method and constraining the analysed area to Denmark.
The present article considers the future development in one- and 24-hour precipitation over
Europe based on RCM output with a 12 km spatial resolution. 1-hour intensity is considered
relevant to pluvial flooding occurring in urban areas and 24-hours intensity is included to
bridge to fluvial flooding and other studies focusing on higher time scales relevant in nonurban hydrology.
METHODS
Climate Model and Scenarios
Data was abstracted from the regional climate model (RCM) HIRHAM4 from DMI which is
based on the global atmosphere model HadAM3. The boundary conditions for the control
period are based on observed sea surface temperatures and atmospheric conditions
(Christensen et al., 1996). The model area covering the whole of Europe has 131,760 cells in
a 12 km spatial resolution. Two 30-year simulation periods are compared; a control period
(1961-1990) and a scenario period (2071-2100) (Christensen & Christensen, 2007). The
scenario follows IPCC SRES scenario A2, which is a high-middle emission scenario
describing a heterogeneous world with little economical integration, a continuously increasing
population and a slow technological development (IPCC, 2000). The adopted computational
grid is a rotated regular latitude/longitude grid with the rotated South Pole at (27 oE, 37 oS)
and a resolution of 0.11 degrees.
Statistical methods
Data was defined and extracted according to the Partial Duration Series (PDS) approach using
Type 2 censoring (e.g.Mikkelsen et al., 1995), and the extreme events were fitted to a
Generalized Pareto Distribution (GPD) (Madsen et al., 2002; Madsen, 2005). The intensity of
a T-year event was therefore estimated by equation 1:
κˆ
αˆ ⎢ ⎛ 1 ⎞ ⎥
zˆT = βˆ + ⎢1 − ⎜
⎟ ⎥
κˆ ⎢ ⎝ T ⋅ λ ⎠ ⎥
⎣
⎦
(1)
where β is the location parameter, α is the scale parameter, κ is the shape parameter and λ,
the expected number of extreme events per year, is pre-fixed. Further details about the
employed statistical methods and the results may be found in Gregersen et al. (in preparation).
From the equation above it can be derived that the location parameter is most important for
low return periods and that the shape parameter is important for high return periods. The
statistical analysis was accomplished by the use of the statistical software EVA (DHI, 2005).
Definition of the ‘climate factor’
A climate factor (CF) is a simple but very useful tool to describe the potential development in
extreme precipitation due to climate change. It was defined in this study as the ratio between
an extreme rain intensity in the scenario period and in the control period, following the
equation given below:
zˆT , scenario ( x, y )
(2)
CFT ( x, y ) =
zˆT ,control ( x, y )
If the climate factor is above one, the scenario will have higher intensity than the control
period, and vice versa.
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Development in extreme precipitation over Europe
11th International Conference on Urban Drainage, Edinburgh, Scotland, UK, 2008
RESULTS
The number of extreme events per year was set to 2.5 in this study, based on earlier
knowledge and experience (Mikkelsen et al., 1999; Madsen 1998; Gregersen & Larsen,
2007). By utilizing a goodness-of-fit test it was also shown that the Generalized Pareto
Distribution gives a statistically reasonable description of the extreme events across Europe.
Figure 1. Return periods when utilizing both a ranking and a PDS method for a single cell in
Denmark for control and scenario, respectively. The statistical uncertainty of the PDS
estimates due to sampling variability is shown as the 95% confidence limits (dotted lines).
Figure 1 shows two methods of evaluating the extreme events; the non-parametric ranking
method commonly used in urban drainage and the parametric PDS-GPD method. Here it can
be seen that the parametric method and the ranking method give similar results for return
periods below one third of the length of the time series as also concluded by Arnbjerg-Nielsen
(1997). For higher return periods the parametric approach is most usable to describe data, as
extrapolation is possible and robust estimates insensitive to outliers can be calculated. It is
however worth noticing that the uncertainty will become larger for higher return periods for
both methods.
Figure 2. The intensity of a 5-year event for control (left) and future scenario (right). The
increased precipitation can be seen as a general shift in colours.
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11th International Conference on Urban Drainage, Edinburgh, Scotland, UK, 2008
Figure 3. Climate factors for 2, 5, 10, 20, 50 and 100 year events for one hour intensity. Maps for
24-hour intensity are available in Gregersen & Larsen (2007).
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11th International Conference on Urban Drainage, Edinburgh, Scotland, UK, 2008
T-year events in Europe
The 5-year event was calculated on the basis of equation 1 and mapped for control and
scenario period, see Figure 2. The shift in colours indicates that there is a general change
towards higher intensities in the scenario period, and a huge change in the
alpine/Mediterranean and Baltic areas is seen. One thing which also is worth noting is that the
storm tracks shift from a North-west direction to a more western direction.
Figure 3 shows the climate factor for the duration of one hour for return periods between 2
and 100 years. The climate factor is above one in most cases. When the return period
increases, the maps become more scattered. This suggests that the climate factor becomes
more specific for a certain area but is probably more likely due to variability caused by
inherent chaotic features of the RCM. The highest climate factor is seen around the Baltic Sea
with climate factors as high as 2. However the model has some problems simulating the
temperature in the Baltic Sea (Christensen & Christensen, 2007). The intensity of the 5-year
event is also seen to increase above the Alps.
Climate factors for specific countries
Data from a network of rain gauges has been recorded in Denmark since 1979 and analysis of
these data show regional differences (Madsen et al., 1998). Jørgensen et al. (2006) however
showed that for Denmark the spatial variation of T-year events do not exhibit the same
tendencies if the resolution of the RCM is changed or if the RCM is run with varying
boundary conditions. This observation is expected to be valid for Europe too and therefore, to
illustrate the potential consequences of climate change for urban drainage practice in different
countries average intensities for a given T-year event were calculated for France, United
Kingdom, Sweden, Romania, Spain, Austria and Denmark. Figure 4 shows the average Tyear event for these countries for control and scenario period, respectively.
Figure 4. T-year event for specific European countries for control (left) and scenario (right)
period.
Table 1 quantifies what can be seen on Figure 4 for the 5, 20 and 100-year events in the
control period by listing the climate factor and the corresponding return period of an event
with the same intensity in the scenario, as previously suggested by Grum et al. (2006).
Sweden stands out from the rest with a high climate factor for all return periods. Here the 100
year event in control period will happen every 13 year in the scenario according to Table 1.
Denmark follows Sweden but with a slightly lower climate factor. The reason for this is (see
Figure 3) that the areas around the Baltic Sea will have increasing extreme precipitation in the
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11th International Conference on Urban Drainage, Edinburgh, Scotland, UK, 2008
scenario. It is therefore expected that the Baltic countries has similar tendencies as Sweden.
France is also seen to have a high climate factor, albeit lower than for the area around the
Baltic Sea. The United Kingdom, Romania, Austria and Spain however have lower climate
factors for the different return periods.
Table 1. T-year event, 1-hour intensity, for the control period for selected countries, together
with the corresponding climate factor and return period for the scenario period.
Z5
control
[mm/hour]
10.79
France
8.28
UK
7.61
Sweden
8.71
Romania
9.71
Spain
9.11
Austria
8.57
Denmark
CF
[-]
1.22
1.16
1.47
1.17
1.15
1.17
1.20
CF
T
Z100
CF
T
T
Z20
control
scenario
control
scenario
scenario
[year] [mm/hour] [-]
[year]
[year] [mm/hour] [-]
2.5
15.12
1.27
8
22.05
1.35
33
2.8
11.35
1.20
10
16.21
1.25
41
1.8
10.80
1.72
4
15.86
2.10
13
2.5
11.30
1.23
8
14.98
1.31
28
2.8
13.07
1.19
10
18.02
1.22
39
2.6
12.44
1.19
10
17.70
1.21
43
2.9
12.30
1.36
8
18.48
1.60
26
Impact of duration and return period on the climate factor
Figure 5 shows, for Denmark as an example, that the climate factor is highest when the
duration is low and the return period is high. It is seen that for a 1-year event the duration is
not important and the climate factor will be about 1.1, independent of the duration. For a low
duration the climate factor will however be higher, meaning that Denmark will get more highintensive rain for short durations. The tendency shown for Denmark is assumed to be valid for
whole Europe too.
Figure 5. Duration curve for the average climate factor in Denmark. Climate factors were
only available for 1-hour and 24-hour precipitation, and the connecting lines between pairs of
data do therefore not represent any deterministic relationship.
Annual variation of extreme events
Figure 6 shows the change in the yearly variation of the number of extreme events. The
number of extreme events is seen to increase in the winter-spring period. For Denmark more
than a doubling of the number of extreme event is observed in January. Although the number
will increase more in the winter-spring period than during summer, it should be noted that the
number of extreme events is still highest in the summer period. This conclusion is in
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11th International Conference on Urban Drainage, Edinburgh, Scotland, UK, 2008
agreement with Frei et al. (2006), which (based on 1-day and 5-day precipitation data from
RCMs) shows that both the frequency and intensity of the extreme events will increase in the
winter, whereas only the intensity will increase in the summer.
Figure 6. The factor for the number of monthly extreme precipitation events for different
countries. Calculated as the number of events in scenario in a given month divided by the
number of events in control in the same month.
Uncertainty
The uncertainty of the climate factor for Denmark is shown in Figure 7. The uncertainty
increases for growing return periods, and this tendency is assumed to be valid for the whole of
Europe. The climate factor is above one for return periods below 20 years with a probability
of 68%. This means that in the A2 scenario there will be an increase in extreme precipitation
for 20 years return period with a certainty of 68%.
Figure 7. Confidence intervals for the average climate factor for Denmark. Having the
uncertainty on the PDS T-year event estimates in control and scenario, the confidence limits
for the climate factor are found by use of the law for propagation of uncertainty.
DISCUSSION
The climate factor was calculated for areal rainfall (12x12km) at the hourly time scale,
whereas rainfall measurements for urban drainage applications are mostly point
measurements with 5- or 1-minutes resolution. The intensities of extreme rainfalls at the
minute-time scale are of course higher than those of longer durations, and temporal
downscaling of the RCM precipitation data prior to calculating climate factors has therefore
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11th International Conference on Urban Drainage, Edinburgh, Scotland, UK, 2008
been suggested. However, the existing temporal downscaling methods (e.g. Olsson, 1998;
Onof et al., 2000; Güntner et al. 2001; Arnbjerg-Nielsen, 2008) have not yet proved to
preserve the extremity of rainfall at the minute-scale but only at the hourly-scale. An extreme
event recorded as a point measurement is furthermore higher than an areal measurement (e.g.
Einfalt et al., 1998; Jørgensen et al., 2006), and space-time downscaling, possibly using
stochastic high-resolution rainfall time-space simulators (e.g. Willems, 2001), should
therefore ideally be performed prior to calculating climate factors. This approach would
furthermore potentially allow some level of calibration (or at least comparison) of the extreme
characteristics of downscaled rainfall output from RCMs with those calculated based on
ground observations of extreme rainfall data, in addition to the comparisons with yearly or
seasonal rainfall, temperature and other atmospheric conditions that are conducted today.
Such approaches have however not yet been tried out. We therefore argue that climate factors
calculated straight-forward as in this study, based on 12 km, 1-hour RCM data representing
the highest resolution so far used in time-slice climate change simulations, represent the most
trustworthy estimate of the effects of anthropogenic climate change on future extreme
precipitation intensities relevant for urban drainage.
Figure 7 shows only the uncertainty in Denmark, but we expect that similar patterns exists for
other regions in Europe. The confidence interval is seen to increase with an increasing return
period, which is natural due to the inherent uncertainty associated with estimating extreme
events corresponding to return periods at or exceeding the 30-years simulated data used to
represent both the present and future climate. Figure 7 essentially means that the probability
of the climate factor being larger than one is 68% for return periods between 2 and 20 years,
and lower for higher return periods, and we therefore cannot argue with great statistical
confidence that the extreme precipitation will increase.
An important precondition is also the climate change scenario chosen as background for the
RCM scenario simulations. The IPCC A2 scenario represents a high-middle scenario in terms
of atmospheric CO2 concentration, and there are several other IPCC scenarios that may, or
may not be taken as a starting point for an analysis. Such scenario uncertainty is probably a
larger contributor to the total uncertainty related with extreme rainfall projections based on
RCM simulations than the lack of “calibration” with extreme rainfall data and the uncertainty
represented by Figure 7.
The point is however not whether the actual climate factors presented in this study are
accurate but the general conclusions that (i) it is the most extreme rainfall properties in terms
of (high) return period and (low) duration that will be affected the most, and (ii) with high
uncertainty comes high potential consequence to society.
Whether urban drainage designers should react on the climate factor suggested in this study is
therefore basically a philosophical question related to the interpretation of the Precautionary
Principle. We cannot (yet) say for sure that the future warmer climate in Europe will bring
more extreme rainfall intensities, but there is a lot of scientific theory as well as practical
evidence (i.e. observations) that point in this direction. This calls for a complete change of
philosophy in terms of future planning, design and rehabilitating urban sewer systems in the
direction of flexible/adaptive approaches that are robust to an uncertain future, as well as
further research both in the underlying phenomena and in methods for risk-based planning
and design.
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11th International Conference on Urban Drainage, Edinburgh, Scotland, UK, 2008
CONCLUSION
In this study the potential increase of extreme precipitation in a future warmer climate has
been examined, based on data from RCM simulations with 12 km resolution covering Europe.
Two periods were analysed, a control period 1961-1090 and a scenario 2071-2100, the latter
following the IPCC scenario A2.
The number of extreme events generally increases in the winter and spring season, however
the total amount of extreme events will still be highest in the summer period. A ‘climate
factor’ expressing the relative difference between future and present extreme 1- and 24-hours
precipitation intensities was furthermore calculated for return periods from 2-100 years.
The climate factor is generally above one across Europe, i.e. increasing extreme precipitation
can be expected anywhere in Europe. The factor is however high especially around the Baltic
Sea (1.5-2 for a 1-hour duration) and in the Middle of Europe, whereas it is more moderate in
southern Europe (around 1.2 in Spain). Generally, the climate factor increases with increasing
return period and decreasing duration, i.e. it is the storms that are most critical to urban
drainage systems that are affected the most.
Another way to express this is by looking at the future return period of storms that today are
considered 100 years storms. A 100-year event in the control period for Sweden will have a
return period of 13 years in scenario. Damages related to urban flooding will therefore occur
more frequently if this prediction is valid, because most urban drainage systems are not made
to handle 100-year events.
ACKNOWLEDGEMENT
We thank the Danish Meteorological Institute for providing the RCM data analysed in this
work. Also thanks to Henrik Madsen, DHI, who helped setting up the EVA software and to
Alex T. Jørgensen, PH-Consult, for providing basic Matlab programs for treatment of the raw
RCM data.
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