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11th International Conference on Urban Drainage, Edinburgh, Scotland, UK, 2008

Arnbjerg-Nielsen 1

Quantification of climate change impacts on extreme precipitation used for design of sewer systems

K. Arnbjerg-Nielsen

Department of Environmental Engineering, Danish Technical University, DK-2800 Lyngby, Denmark

e-mail [email protected]

ABSTRACT It is anticipated that anthropogenic activities will lead to climate change. In Northern Europe this means more frequent and more severe storms. If traditional design guidelines are used without incorporating climate changes the hydraulic performance of the sewer systems will decrease substantially below the level accepted by the public within the technical lifetime of the system. Three approaches to assess the impact of climate changes on extreme rainfall are studied, all based on output from historical rainseries of the present climate and output from Regional Climate Models. Two models are applied, one being based on an extreme value model, the Partial Duration Series Approach, and the other based on a stochastic rainfall generator model. Finally an approach is based on identification of areas, where the present climate resemble the anticipated future climate for the region in question. Based on all three approaches it is concluded that the design intensities will increase by 10 50% within the next 100 years. The increase in design intensities depend on the duration and the return period of the event.

KEYWORDS Urban drainage; climate change; precipitation, IDF-curves

INTRODUCTION It is anticipated that anthropogenic activities will lead to climate changes that will affect the water cycle substantially. In Northern Europe one of the changes will be more frequent and more severe storms. In order to maintain a given performance of a sewer system the change of design intensities over the expected life time of the sewer system need to be assessed.

The primary objective of the study is therefore to find one or more simple correction factors that can be applied on present rainfall in order to simulate future behaviour of extreme rainfall over the expected technical lifetime of new sewer systems, i.e. typically 100 years. The changes can be quantified by introducing a climate change factor, being the ratio between the expected future and the present design rainfall intensities. The methods are applied on data from Denmark.

DATA The regional climate change simulations used in the present study is based on a HIRHAM4 model with a spatial resolution of 12 by 12 km and a temporal resolution of 1 hour. The


2 Quantification of climate change impacts on extreme precipitation

global climate change model is the HadAM3H AGCM simulation based on a scenario corresponding to an IPCC A2-scenario. The model is described further in Christensen et al (1998). The historical rainfall series in high temporal resolution is based on the Danish Water Pollution Control Committee network of rain gauges (Madsen et al, 2008). The examples shown throughout the paper is based on regionalized information from the Greater Copenhagen Area.

METHODOLOGY Kundezewicz et al (2006) suggests the use of output from regional climate change models directly when assessing climate change impacts on the river basin management scale. Design of urban drainage systems however needs design intensities at a temporal scale of minutes, which the regional climate change models unfortunately are not able to reproduce with sufficient accuracy. Figure 1 illustrates the shortcomings of using the regional climate change models directly for design of urban sewer systems. The parameterization of the regional climate change models does not allow an accurate description of the convective storms on short temporal scales. Therefore scaling methods must be applied that describe the anticipated changes in extreme rainfall at short time scales based on the modelled changes at longer temporal scales.

Most scaling approaches apply the following procedure: 1. Characterize precipitation in point measurements based on historical rainfall series. 2. Characterize a similar property in a regional climate change model covering the region

in question and quantify the difference between "status" and "future". 3. Establish an unambiguous relationship between the historic precipitation series and the

"status" simulation of the climate model. The unambiguous relationship is used to describe the properties of point precipitation in a future scenario.

The method is illustrated in Figure 1.

5 year return period

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Figure 1. The climate change signal from regional climate change models are small compared to the differences between the estimated intensities and the actual observed present rainfall. Further there is a systematic difference between the climate change models and the observations depending on the temporal scale. Therefore the methodology presented in the figure to the right must be applied when assessing future design rainfall. Data for the left part of the figure is from Jrgensen et al (2008) and Madsen et al (2008).


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It is likely that complicated and non-linear processes occur in the scaling between step 2 and 3 in Figure 1, but there are at present no clues as to how to describe these processes. Therefore a simple method is adopted here, in line with the studies carried out in the UK (Bogner et al, 2002). The assumption is simply, that the ratio of point to spatial statistics will remain constant in a changed climate. The formulae for calculation of climate factors for future rainfall the property in question then reads:

fRCMpresent

RCMfuture

urbanpresent

urbanfuturec

a

a

a

a==

,

,

,

,

(Eq. 1)

The most simple way to apply Eq 1 is by using the intensities in the RCM simulations directly. An example of this approach is shown in Jrgensen et al (2008). However, this approach may lead to biased results based on the systematic differences based on the temporal scales as shown in Figure 1. Therefore a total of three other methods are applied, two of them based on the scaling methodology outlined in Eq. 1, and the last method being a completely different method. The methods are outlined below in separate sections.

APPROACH A: PARTIAL DURATION SERIES The theoretical outline of Approach A is only discussed briefly below. A more thorough description can be found in Madsen et al (2002) and Larsen et al (2008).

The partial duration series model entails four parameters, of which one is fixed prior to estimation of the three other parameters. The model reads as follows (Madsen et al, 2002):

),,(,;11 20210 gzTFzzT +=

+= (Eq. 2)

Where zT is the design intensity at return period T z0 is the threshold value defining the extreme value population of the rainfall

statistic in question is the average annual number of threshold exceedances is the mean of the exceedances 2 is the coefficient of variation of the exceedances

The partial duration series model is a widely used to model properties of extreme events. It is the assumption of Approach A that each of the parameters in the model represents a pseudo-physical property of extreme rainfall and that the climate change of extreme rainfall for this pseudo-physical property can be described by means of the climate change factor as formulated in Eq. 1. This means that the partial duration series model is applied on both the present and future climate simuliations in the regional climate models, and that the climate factor is calculated for each of the parameters in the model and for each duration in question. Further, the same model is applied on the historical rain series. Estimation is carried out by means of L-moments as discussed by Landwehr et al (1979) and Hoskins and Wallis (1995). z0 is fixed at a predefined level for both present and future climates. The estimated climate change factors for each of the parameters are presented in Table 1. The calculated net changes on design rainfall is illustrated in Figure 2.



Table 1. Parameter estimates of the climate change factors for each of the parameters of the partial duration series model for each of the durations. By definition the climate change factor has no units. Duration (hours)

2

1 1.252 1.365 1.030 3 1.218 1.269 1.040 6 1.205 1.175 1.047 12 1.326 1.103 1.020 24 1.451 1.132 1.020

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Figure 2. The left figure shows the calculated changes in design rainfall intensities based on approach A. The changes are compared to 95% confidence intervals of the present climate. The right figure shows the resulting climate factors for the various durations. The relatively small climate factor for high return periods and durations are due to the low estimates of 2 shown in Table 1.

APPROACH B: RAINFALL STOCHASTIC GENERATOR As the name indicates, the input and output of this approach is rainfall series and not just design rainfall. As such, the use of this approach, if successful, is more generally applicable than the other approaches. The principle of the approach is to define a pseudo-physical description of rainfall with as few parameters as possible. Having defined estimation techniques on actual rainfall it is then possible to generate artificial rainfall with the same statistical properties as the original series.

The rainfall generator used for the study is the Random Parameter Bartlett-Lewis Rectangular Pulse model. Estimation procedures and validation of the method is discussed in Onof et al (2000). The model uses a total of 8 parameters to describe a rainfall series with a resolution in time of one hour. It should be stressed that even though the parameters are denoted rate of storm arriveal, mean cell intensity, mean cell duration, etc. the parameters are only pseudo-physical and that the assumption of Eq. 1 is as critical as in Approach A.

The method can be applied on rain series with a resolution of about one hour or lower. In order to achieve a higher resolution a scaling method must be applied, typically in the form of a random cascade disaggregator (eg. Onof et al, 2002). Several studies have shown that this method is a simple and efficient way to obtain rainfall with higher resolution while preserving the statistics of rainfall.


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Figure 3. Parameter estimates of the rainfall stochastic generator at one of the locations, Holbaek. Adapted from Onof and Arnbjerg-Nielsen (2008).



The parameters of the rainfall generator was estimated on a monthly basis at seven locations in Denmark. The variation of the parameter estimates between locations was then studied in light of the uncertainty of the estimates and the following was concluded:

There was a significant variation of the parameter estimates (and thus the rainfall) between months, i.e. there was an important annual variation in the rainfall generation process

The parameter variation between locations were insignificant, i.e. the rainfall generation process could be assumed to be identical throughout the country

Therefore only one model was set up to represent the present and future climate of Denmark. The model was estimated for each month separately. Figure 3 shows the parameter estimates for present and future climates based on estimation of parameters in the RCM simulations. Figure 4 compares the artificial rainfall series generated by means of the model to the observations of present climate. As could be expected, there is a significant variation in the extreme statistics almost as large as the climate change signal. The climate change factor is calculated based on an average of 20 simulations of present and future climate, each 100 years long. The climate factors are shown in Figure 5.

Intensity, 60 minutes

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Figure 4. Comparison of properties of artificially generated rainfall series with historical observations (red lines) as well as a nationwide model of extreme statistics (solid red line with 95% confidence intervals).

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Figure 5. Calculated climate factors by means of Approach B. The factor is calculated as the ratios of the median values of 20 simulations of present and future climate for each return period.

APPROACH C: CLIMATE ANALOGUES The last approach to be studied is an engineering approach rather than a scientific approach. In engineering applications it is often necessary to use rainfall data from another area than the


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design catchment in question because local data are not available. This situation is often dealt with simply by choosing the nearest rainfall data of suitable resolution or by choosing data from a region with similar characteristics.

This method can also be applied in order to assess climate change impacts on extreme rainfall. The method is changed so that the objective is to find an area with suitable rainfall information that resembles the anticipated climate in Denmark around year 2100. The design intensities of these areas can then be applied directly for design of sewer systems in Denmark. In order to compare the approach with approaches A and B a climate change factor is calculated based on the ratio between the present rainfall at the suitable locations divided by the present design intensites in Denmark.

The regions where suitable information may be found is assessed by calculating the one year return period for the duration one hour in the future climate simulation for all land pixels in Denmark. The range of intensities thus found is used to mark all land pixels with similar intensities throughout Northern Europe in the present climate simulation, see Figure 6. The areas that are highlighted are primarily Eastern Britain, Western France, Benelux, Denmark, parts of Sweden and areas west of the Alps. This information is then combined with primarily the following auxillary information:

Denmark lies near the coast and is a low-lying area The anticipated temperature changes correspond to moving 600 - 800 km southward

Based on this information data from France (Coste an Loudet, 1987) and Germany (Malitz, 2005) are collected. The actual sites are shown in Figure 6. At the French sites the available information is restricted to parameters defining intensity-duration-frequency relationships for return periods 2 and 10 years. In Germany the Kostra project has provided nationwide estimates of exceedance series from which data has been extracted for selected locations and durations.

The German study uses a different formulation of the partial duration series, using a two-parameter model rather than the three-parameter model used in Approach A. Applying the two-parameter model on the Danish historical data and the regional climate model simulations leads to significantly lower estimates of design intensities at high return periods and also lower impact of the climate changes. Therefore the German model is only used to assess low and moderate return periods, i.e. less than or equal to 10 years. The model provides estimates of durations from 5 minutes to 48 hours.

The results obtained by using this approach are illustrated in Figure 7. The results are close to the other two approaches; the calculated climate factors are between 1,18 and 1,40 depending on duration and return period.

DISCUSSION The calculated climate factors are shown in Table 2. The following can be observed when comparing the results:

There are systematic differences between the methods. Generally Approach A gives the highest climate change factors and Approach B gives the smallest climate factors.

There is very good agreement between Approach A and Approach C. The climate factor tends to be higher for small durations and higher return periods.

This is less apparent for Approach B than for the other two factors.



Figure 6. The left figure shows where the simulations of the regional climate model suggest that the present climate may resemble future extreme rainfall in Denmark. The right figure shows the locations that have been selected to represent future Danish rainfall.

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Figure 7. The left figure shows the variation of the actual data from Denmark, Germany, and France, the latter two representing anticipated future climate in Denmark. The right figure shows the calculated climate factors based on this approach for return periods 2 and 10 years.

The three approaches are independent of each other, except that the data used in Approach A and Approach B are identical. The first two approaches have the virtue of being more objective than the last approach; on the other hand the last approach has proved to be a simple and effective tool in many engineering applications.

Approach A and C have the advantage, that they specifically address the extreme part of rainfall. Approach B needs to address more general properties of rainfall and will therefore automatically put less emphasis on the extreme statistics of the rain series than the other two methods. This may indicate, that calculation of climate factors for extreme rainfall should be assessed mainly on Approach A and C. However, the use of the rain series obtained by means of Approach B will be very important in situations where environmental impacts are to be addressed. This is particularly so in situations where the impacts are known to be subject to annual variations. As such climate change impacts on river basin management could improve if urban discharges to streams and rivers are calculated by using such types of rain series.

When designing and analysing hydraulic performance of sewer systems often both design storms and long historical rain series are used as input. In order to ensure that the results are


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the same for both types of input it is suggested to ignore the correlation between the climate factor and the duration of the event. Therefore the recommended climate factors depend only on the return period in question. The recommended climate factors are then suggested to be a simple mean of the climate factors calculated by means of Approach A and Approach C for the typical durations in sewer systems. For Denmark this leads to the climate factors suggested in Table 3.

Table 2. Calculated climate factors calculated by means of the three methods for different return periods and durations. 2 year return period 10 year return period 100 year return period A B C A B C A B 5 min - 1.12 1.23 - 1.14 1.39 - 1.17 10 min - 1.13 1.22 - 1.15 1.30 - 1.27 30 min - 1.12 1.31 - 1.14 1.30 - 1.15 1 hour 1.27 1.11 1.32 1.37 1.14 1.37 1.53 1.11 3 hours 1.18 1.10 1.19 1.28 1.13 1.25 1.45 1.14 6 hours 1.12 1.09 1.15 1.21 1.07 1.19 1.38 1.09 12 hours 1.12 1.08 - 1.15 1.07 - 1.21 1.07 24 hours 1.18 1.07 - 1.21 1.06 - 1.27 1.08

CONCLUSIONS In order to maintain a given performance of a sewer system, the change of design intensities over the expected life time of the sewer system needs to be assessed. In Denmark, the change is quantified by introducing a climate factor, being the ratio between the expected future and the present design rainfall. The objective of this report is to assess the climate factor for Denmark based on available rain data and information about climate change.

The main findings of the study can be summarized as follows: 1. The inherent uncertainties when using these methods makes it irrelevant to make

regionalized results within Denmark. Therefore a national assess-ment of climate factors is made.

2. The main cause of uncertainty is believed to be due to the climate change model. When more climate change model results become available the findings of this report should be verified.

3. The climate factor increases with increasing return period and decreasing duration of the duration. The influence of changes in the return period is larger than the influence of changes in the duration.

4. The study shows that the climate factor is between 1.1 and 1.5 for Denmark for return periods between 2 years and 100 years and durations between 10 minutes and 24 hours.

5. If the expected technical lifetime is different from 100 years the climate change factors should be adjusted accordingly.

The suggested climate factors are shown in Table 3. The climate factors correspond to anticipated changes in design intensities within the next 100 years.



Table 3. Suggested climate change factors when designing sewer systems with an anticipated technical lifetime of 100 years. 2 year return period 10 year return period 100 year return period All durations 1.2 1.3 1.4

ACKNOWLEDGEMENT The study was funded in part by the Danish Water and Waste Water Association under contract F&U 2006/4. Approach A and B were studied in collaboration with Dr. W. May, Danish Meteorological Institute, and Dr. C. Onof, Imperial College, respectively. The author wishes to thank these persons for valuable discussions throughout the work.

REFERENCES Arnbjerg-Nielsen, K. (2006): Significant climate change of extreme rainfall in Denmark. Wat. Sci. Tech., 54(6-

7), 1-8 Bogner, K, Onof, C og Townsend, J (2002): Climate change and the Hydraulic Design of Sewerage Systems

Volume IIA - Time Series and Design Event Update. Report 03/CL/10/4. UK Water Industry Research Limited, London. ISBN 1 84057 329 5

Christensen, O.B., Christensen, J.H., Machenauer, B., & M. Botzet (1998) Very-high resolution regional climate simulations over Scandinavia present climate. J of Climate, 11, 3204-3229.

Coste, C. and Loudet, M. (1987): L'assainissement en milieu urbain ou rural - Tome 1 : Les rseaux et les ouvrages de retenue. Paris (France): Editions du Moniteur, 240 p. ISBN 2.281.11094.X.

Hosking, J. R. M., & Wallis, J.R. (1995). Some statistics useful in regional frequency analysis, Water Resour. Res., 29(2), 271281, 1993. (Correction, Water Resour. Res., 31(1), 251, 1995.)

Jrgensen, A.T.; Linde, J.J.; Christensen, O.B. & Mikkelsen, P.S. (2006): Future extreme precipitation in Denmark: An analysis based on future climate scenario simulation with a 12 x 12 km regional climate model. In: Molnar, P.; Einfalt, T.; Burlando, P.; Sevruk, B. & Schmid, W. (Eds.): Extreme precipitation, multisource data measurement and uncertainty. Proc. 7th Int. Workshop on Precipitation in Urban Areas, Extreme Precipitation, Multisource Data Measurement and Uncertainty , St. Moritz, Switzerland, 7-10 December 2006, pp. 139-143.

Kundzewicz, Z.W., Radziejewski, M., & Pinskwar I (2006): Precipitation extremes in the changing climate of Europe. Clim. Res., 31(1), 51-58

Landwehr, J. M., Matalas, N.C., & Wallis, J.R. (1979). Probability weighted moments compared with some traditional techniques in estimating Gumbel parameters and quantiles, Water Resour. Res., 15(5), 1055 1064

Larsen, A.N., Gregersen, I.B., Christense, O.B., Linde, J.J. & Mikkelsen, P.S. (2008): Quantification of climate change impacts on extreme precipitation used for design of sewer systems. 11th Int. Conf. on Urban Drainage, Aug-Sep 2008 (submitted).

Madsen,H., Arnbjerg-Nielsen,K., & Mikkelsen, P.S. (2008): Evidence of increased storm intensities in Denmark: Regional analysis of extreme rainfall data, 1979-2005. Submitted to Atm. Res.

Madsen, H., Mikkelsen, P.S., Rosbjerg, D. and Harremos, P., (2002) Regional estimation of rainfall-intensity-duration-frequency curves using generalised least squares regression of partial duration series statistics, Water Resources Research, 38(11), 1239, doi: 10.1029/2001WR001125.

Malitz, G. (Ed.) (2005): KOSTRA-DWD-2000. Starkniederschlagshhen fr Deutschland (1951 2000) - Grundlagenbericht. Abteilung Hydrometeorolo-gie, Deutscher Wetterdienst.

Onof. C., & Arnbjerg-Nielsen, K. (2008): Quantification of climate change impacts on high resolution design rainfall for urban areas. Submitted for Atm. Res.

Onof, C., Chandler, R.E., Kakou, A., Northrop, P.J., Wheater, H.S., Isham V. (2000). Rainfall modelling using Poisson-cluster processes, Stochastic Environmental Research and Risk Assessment, 14, 384-411

Onof, C., Townend, J., Kee, R., Kellagher, R.B.B. (2002) Rainfall Disaggregation Tool, Report SR608, HR Wallingford

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