An application of the UKCIP02 climate change scenarios to flood estimation by continuous simulation for a gauged catchment in the northeast of Scotland, UK (with uncertainty) David Cameron * Scottish Environment Protection Agency (SEPA), Graesser House, Fodderty Way, Dingwall, IV15 9XB, UK Received 1 April 2005; received in revised form 11 October 2005; accepted 17 December 2005 Summary This paper explores the potential impacts of climate change upon flood frequency for the gauged, Lossie catchment in the northeast of Scotland, UK. This catchment has signif- icant flooding problems, but only limited data availability (particularly with respect to rainfall). A continuous simulation methodology, which uses a stochastic rainfall model to drive the rain- fall-runoff model TOPMODEL, is utilised. Behavioural parameter sets for TOPMODEL are identi- fied prior to the climate change runs using the Generalised Likelihood Uncertainty Estimation (GLUE) methodology. The ‘‘Low Emissions’’, ‘‘Medium-Low Emissions’’, ‘‘Medium-High Emis- sions’’ and ‘‘High Emissions’’ UKCIP02 climate change scenarios, obtained from the HadCM3 global climate model (GCM) and HadRM3 regional climate model (RCM) simulations, are used at the catchment scale. Two further scenarios (‘‘H-Dry’’ and ‘‘H-Wet’’), based upon the model uncertainty margins available for the UKCIP02 ‘‘High Emissions’’ scenario, are also developed in order to explore the possible range of changes to daily rainfall and temperature estimated from GCMs other than HadCM3. It is demonstrated that, while flood magnitude changes under all six of the climate change scenarios considered, the magnitude and direction of that change is dependent upon the choice of scenario. An overlap between the likelihood weighted uncer- tainty bounds estimated under the conditions of the current climate and those estimated under the four UKCIP02 scenarios and the ‘‘H-Dry’’ scenario is also observed. These findings highlight the need to consider multiple climate change scenarios and account for model uncertainties when estimating the possible effects of climate change upon flood frequency. c 2006 Elsevier B.V. All rights reserved. KEYWORDS Climate change; Flood; TOPMODEL; Generalised Likelihood Uncertainty Estimation; Stochastic model 0022-1694/$ - see front matter c 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.jhydrol.2005.12.024 * Fax: +44 1349 863 987. E-mail address: [email protected]. Journal of Hydrology (2006) 328, 212– 226 available at www.sciencedirect.com journal homepage: www.elsevier.com/locate/jhydrol
Transcript
Journal of Hydrology (2006) 328, 212–226
ava i lab le at www.sc iencedi rec t . com
journal homepage: www.elsevier .com/ locate / jhydro l
An application of the UKCIP02 climatechange scenarios to flood estimation bycontinuous simulation for a gauged catchment inthe northeast of Scotland, UK (with uncertainty)
Received 1 April 2005; received in revised form 11 October 2005; accepted 17 December 2005
Summary This paper explores the potential impacts of climate change upon flood frequencyfor the gauged, Lossie catchment in the northeast of Scotland, UK. This catchment has signif-icant flooding problems, but only limited data availability (particularly with respect to rainfall).A continuous simulation methodology, which uses a stochastic rainfall model to drive the rain-fall-runoff model TOPMODEL, is utilised. Behavioural parameter sets for TOPMODEL are identi-fied prior to the climate change runs using the Generalised Likelihood Uncertainty Estimation(GLUE) methodology. The ‘‘Low Emissions’’, ‘‘Medium-Low Emissions’’, ‘‘Medium-High Emis-sions’’ and ‘‘High Emissions’’ UKCIP02 climate change scenarios, obtained from the HadCM3global climate model (GCM) and HadRM3 regional climate model (RCM) simulations, are usedat the catchment scale. Two further scenarios (‘‘H-Dry’’ and ‘‘H-Wet’’), based upon the modeluncertainty margins available for the UKCIP02 ‘‘High Emissions’’ scenario, are also developed inorder to explore the possible range of changes to daily rainfall and temperature estimated fromGCMs other than HadCM3. It is demonstrated that, while flood magnitude changes under all sixof the climate change scenarios considered, the magnitude and direction of that change isdependent upon the choice of scenario. An overlap between the likelihood weighted uncer-tainty bounds estimated under the conditions of the current climate and those estimated underthe four UKCIP02 scenarios and the ‘‘H-Dry’’ scenario is also observed. These findings highlightthe need to consider multiple climate change scenarios and account for model uncertaintieswhen estimating the possible effects of climate change upon flood frequency.
�c 2006 Elsevier B.V. All rights reserved.
KEYWORDSClimate change;Flood;TOPMODEL;Generalised LikelihoodUncertainty Estimation;Stochastic model
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022-1694/$ - see front matter �c 2006 Elsevier B.V. All rights reserved.oi:10.1016/j.jhydrol.2005.12.024
An application of the UKCIP02 climate change scenarios to flood estimation 213
Introduction
One of the key assumptions of flood frequency analysis isthat the return period of a flood peak of given magnitudeis stationary with time (NERC, 1975). This assumption is onlyvalid if a catchment’s long-term climatic, physical andhydrological characteristics are also relatively constant withtime. Recent studies (e.g., Hulme et al., 2002; Prudhommeet al., 2003; Fowler et al., 2005; Ekstrom et al., 2005), how-ever, have demonstrated the variability of climate charac-teristics and the potential for future change. The UKClimate Impacts Programme (e.g., UKCIP02; Hulme et al.,2002), for example, derived several possible climate changescenarios for the UK in the 21st Century using output fromthe Hadley Centre global climate model (or GCM), HadCM3and regional climate model (RCM), HadRM3. The regionalmodelling output includes estimated changes to precipita-tion and temperature for the UK at a grid box scale of about50 km by 50 km. The UKCIP02 scenarios therefore supersedethe earlier UKCIP98 scenarios (Hulme and Jenkins, 1998)which were based upon an older GCM (HadCM2) with a gridbox scale of about 250 km by 250 km.
The possible effect of climate change upon flooding isbeginning to be recognised in governmental planning pol-icy (e.g., for housing schemes and flood protectionschemes). For example, Scottish Planning Policy, SPP, 7(Scottish Executive, 2004) requires that many types ofnew development are protected from a flood with a 1 in200 year return period. The adoption of the 1 in 200 yearreturn period flood is largely based on the simplifying (andprecautionary) assumption that the present day 1 in 200year flood might become the 1 in 100 year flood in thefuture.
However, the possible effects of climate change uponflood frequency are likely to be much more complex thanthis simple assumption suggests. Many choices must bemade in order to estimate the effects of climate changeat the catchment scale. These choices include: the choiceof GCM (Hulme et al., 2002), climate change scenario (itis not currently possible to assign specific probabilities toclimate change scenarios, therefore alternative climatechange scenarios must be viewed as having an equal likeli-hood of occurrence; Hulme et al., 2002), the spatial andtemporal implementation of that scenario at the catchmentscale (Lettenmaier and Gan, 1990; Panagoulia and Dimou,1997; Gellens and Roulin, 1998), the method used for floodestimation (usually some form of rainfall-runoff modelling;e.g., Wolock and Hornberger, 1991; Booij, 2005), and thetreatment of model uncertainties (e.g., Cameron et al.,2000b; Prudhomme et al., 2003; Reichert and Borsuk,2005). It is important to recognise that these choices mightinfluence the estimated effect of climate change uponflooding.
In the study of Cameron et al. (2000b) the ‘‘Medium-High’’ UKCIP98 climate change scenario was used as a start-ing point for a variety of different climate change scenariosfor the gauged, upland Wye catchment at Plynlimon, Wales,UK. The scenarios were applied to one thousand year con-tinuous hourly simulations produced using TOPMODEL (asdriven by the stochastic rainfall model of Cameron et al.,1999), with uncertainties being explored using the General-ised Likelihood Uncertainty Estimation (GLUE) approach of
Beven and Binley (1992). It was demonstrated that, whilethe scenarios had only a small impact upon the likelihoodweighted uncertainty bounds in comparison with the currentcondition scenario, the risk of a given discharge as an ele-ment in the distribution of T (return period) year floodswas changed.
In what follows, the work of Cameron et al. (2000b) is ex-tended in light of the newly available UKCIP02 data. A var-iant of the continuous simulation methodology (whichincludes a new objective function for evaluating flood peaksimulation) is applied to a gauged, ‘‘real world’’ non-research catchment which has significant flooding problemsbut only limited instrumentation (particularly as regardsrainfall measurement). The possible effect of climatechange upon flooding is investigated under all four of theUKCIP02 scenarios (rather than the single UKCIP98 ‘‘Med-ium-High’’ scenario which was used as the starting pointin the earlier study). Two further scenarios, based uponthe model uncertainty margins available for the UKCIP02‘‘High Emissions’’ scenario (Hulme et al., 2002), are alsodeveloped in order to explore the possible range of changesto daily rainfall and temperature estimated from GCMsother than HadCM3. Flood events with return periods ofup to 1 in 200 years are considered, with particular atten-tion being drawn to the 10, 25, 50, 75, 100 and 200 yearevents (as it is these types of event which are most com-monly of interest to the operational hydrologist consultingon development plans and flood protection schemes). Theuncertainties involved in the estimations are discussed andthe practical implications for flood management arehighlighted.
The study site
The study site is the 216 km2 Lossie catchment in the north-east of Scotland, UK (Fig. 1). The catchment is a largely rur-al catchment with moorland overlying schists, gneisses andvalley gravels with some old red sandstone. There is sub-stantial afforestation in the catchment’s headwaters, witharable land in the valley bottoms (note that for the purposesof this study it is assumed that there is no change in land usebetween the current climatic conditions and the future cli-mate scenarios considered in this paper). Although averageannual rainfall (1961–1990) is 830 mm, there is a steep rain-fall gradient between the hills in the catchment’s headwa-ters and the coastal plain where Elgin, the mainsettlement, is located.
At the location of the Scottish Environment ProtectionAgency’s (SEPA) primary gauging station at Sheriffmills, justupstream of Elgin, the mean annual flow is 2.63 m3 s�1, themean annual flood circa 51 m3 s�1, the Q95 is 0.71 m3 s�1
and the base flow index (BFI) is 0.52. Average hourly annualmaximum (AMAX) flood data are available for this station forthe water years 1958–2003. However, continuous averagehourly hydrograph information is only available in electronicformat for a much smaller period within that record (includ-ing the 14 water year period 1990–2003).
Notable flooding on the River Lossie is associated withfrontal storms of long duration. The largest known event oc-curred during the period 3rd to 4th August 1829 (Lauder,1830) and other flooding is known to have occurred prior
Figure 1 The River Lossie catchment (and part of the neighbouring Findhorn catchment). The location gauging station atSheriffmills (circle) is shown together with the location of the raingauge at Torwinny (triangle) and the locations of the raingauges(triangles) used to infill the missing periods of the Torwinny rainfall record.
214 D. Cameron
to the start of the gauging station record (e.g., in 1915;Meterological Office, 1915). During the period of the gaug-ing station’s operation, significant flooding in Elgin occurred(in descending order of flood damage) in November 2002(reputed to be the largest event since 1829), July 1997, Au-gust 1970 and April 2000.
As regards rainfall data, the only tipping bucket rainga-uge in the catchment with a long length of hourly rainfall re-cord is located at Torwinny, in the upland area of thecatchment’s headwaters. Hourly rainfall data are availablefor the same 14-year period as the continuous averagehourly hydrograph data (1990–2003). However, there areseveral (limited) periods of missing data within this dataset,most notably with respect to the July 1997 flood event. Asother data were unavailable for the Lossie catchment forthe periods of missing data, recourse was made to infillingthose periods using hourly raingauge data from the neigh-bouring Findhorn catchment (primarily from the Lochindorbgauge, but also from the Freeburn gauge for the periodswhere data from Lochindorb were unavailable; Fig. 1),scaled using data obtained from the daily raingauge at Relu-gas in the lower Findhorn catchment. This part of the Find-horn catchment is subject to similar storm events to thoseof the Lossie and data for the Relugas gauge were availablefor the missing periods of the Torwinny record (includingthe July 1997 storm event).
The hydrological model
Full details of TOPMODEL may be found in Beven et al.(1995) and Beven (1997, 2001), so only a brief summary isoutlined here.
TOPMODEL is a simple semi-distributed model of catch-ment hydrology that estimates storm runoff from a combi-
nation of variable saturated surface contributing area andsubsurface runoff (e.g., Beven, 1986, 1987; Quinn andBeven, 1993). The dynamics of the contributing area forrapid runoff as the catchment wets and dries are basedon a quasi-steady state analysis. As with many other TOP-MODEL applications (see Beven, 1997), the topographic in-dex ln(a/tanb) was used as an index of hydrologicalsimilarity, where a is the area draining through a point,and tanb is the local surface slope. The use of this formof topographic index implies an effective transmissivityprofile that declines exponentially with increasing storagedeficits.
In this study, the topographic index was derived from adigital terrain model using Tarboton’s (1997) D1 algorithm.This algorithm was chosen largely on the basis of its goodperformance in comparison to other algorithms (Tarboton,1997), and also because of the format of the digital terrainmodel and the GIS tools available to the author.
Evapotranspiration losses in TOPMODEL are controlled bypotential evapotranspiration and storage in the root zonewith the parameter SRMAX (effective available watercapacity at the root zone; see ‘‘The study site’’). The po-tential evapotranspiration estimation routine uses the sameseasonal sine curve as Beven (1986, 1987) and Blazkova andBeven (1997) with a single mean hourly potential evapo-transpiration (PET) parameter. In the absence of any readilyavailable potential evapotranspiration data, recourse wasmade to estimating PET from the observed rainfall and run-off data available for the catchment (yielding a PET param-eter value of 0.0509 mm h�1).
TOPMODEL (often driven by a stochastic rainfall model)has successfully been used for flood estimation in many con-tinuous simulation studies on both gauged (Beven, 1986,1987; Blazkova and Beven, 1997, 2004; Cameron et al.,
An application of the UKCIP02 climate change scenarios to flood estimation 215
1999, 2000a,b) and ungauged catchments (Blazkova andBeven, 2002).
The stochastic rainfall model
A stochastic rainfall model, similar to the one detailed inCameron et al. (1999; see also Cameron et al., 2000a–c),was developed for the Lossie catchment.
The stochastic rainfall model is based upon the availableobserved hourly rainfall data and generates random rain-storms via a Monte Carlo sampling procedure (see Cameronet al., 1999, for a full description of this type of model andits operation). The model characterises a storm in terms ofa mean storm intensity, duration, inter-event arrival time,and storm profile. A rainstorm is defined as any event witha minimum intensity of 0.1 mm at an hour, with a minimumduration of 1 h and a minimum inter-event arrival time of1 h. This definition accounts for all of the rainfall data inthe observed series.
It is assumed that mean storm intensity is dependentupon storm duration. This is modelled by subdividing theavailable observed sample of storm events (derived fromthe 14 year observed hourly rainfall record) into four dura-tion classes of similar mean storm intensity: 1 h, 2–4 h, 5–34 h and P35 h. Table 1 summarises the number of stormsassociated with each duration class. Major flooding in theLossie catchment is associated with long duration (e.g., 2days), frontal, storm events (see ‘‘The study site’’). TheP35 h duration class is therefore the most important dura-tion class in terms of the generation of large flood events.
(Please note that the four duration classes are used in-stead of the seven duration classes adopted by Cameronet al., 1999, for the Wye catchment. For the Lossie catch-ment, the four duration classes are a better representationof the relationship between mean storm intensity and dura-tion than the seven duration classes. Indeed, the four dura-tion classes were found to produce simulations of extremerainfall, which more closely fitted the observed data thansimulations, which were derived from the seven durationclass models. In addition, with respect to seasonality, Cam-eron et al., 2000c, introduced seasonality to this type ofmodel by splitting the observed rainfall dataset into winterand summer half-years. However, in the current study, thesample of long duration storms with high mean storm inten-sities was too small to introduce seasonality to the modeladequately.)
For each duration class, mean storm intensity is mod-elled using the empirical cumulative density function (cdf)derived from the storm events located within that class.In this particular application, the observed samples werenot extrapolated. The reasoning for this is as follows.
Table 1 Number of storms in each duration class of thestochastic rainfall model
Duration class (h) Number of storm
1 33122–4 20085–34 89435–64 16
It should be recalled that the observed rainfall recordcontains several significant rainfall events, including theevent of November 2002 which had a total storm depth of171.6 mm (corresponding to a mean storm intensity of3.3 mm h�1 and a duration of 52 h). The return period of thisevent is estimated as being at least 1 in 236 years (as de-rived using the Flood Estimation Handbook, FEH, statisticalmethodology; this is the recommended approach for esti-mating rainfall return periods in the UK; Institute of Hydrol-ogy, 1999). The maximum storm depth which can begenerated by the model is 211.2 mm (using a mean stormintensity of 3.3 mm h�1 and a duration of 64 h). This eventis estimated (again using the FEH) as having a return periodin excess of 1 in 500 years. Since the highest flood flow re-turn period under consideration in this study is 1 in 200years, and since this type of event will be generated by astorm from the P35 h duration class, the model is capableof meeting the requirements of the study. It is recognised,however, that for use in other catchments, or for the simu-lation of flood flows with return periods beyond the scope ofthe present study, some form of extrapolation (and thus theintroduction of parameterisation to the model) would be re-quired (e.g., as per Cameron et al., 1999, 2000a–c).
The storm duration and inter-event arrival time charac-teristics derived from the observed event series are alsomodelled using their empirical cdfs (with a maximum stormduration of 64 h and a maximum inter-event arrival time of2447 h). In both cases, it is assumed that the observed sam-ples require no further extrapolation. As regards stormduration, Lauder’s (1830) description of the flood of 3rdand 4th August 1829 (the largest flood known to have af-fected the Lossie catchment to date; see ‘‘The study site’’)suggests a storm duration of less than 64 h.
The final component of the model is a storm profile. Theobserved 14-year rainstorm event series is utilised to pro-vide an extensive database of storm profiles for each dura-tion class. These are normalised by cumulative volume andtotal duration. During a model run, the normalised profilesare randomly selected in order to provide storm profilesfor the simulated rainfall events.
The Generalised Likelihood UncertaintyEstimation (GLUE) framework
Every flood frequency estimate is subject to some degree ofuncertainty. The major sources of this uncertainty in thecontinuous simulation approach include the limitations ofthe observed data series and the choice of rainfall andhydrological models (especially with respect to the modelstructures, and their calibration/validation). In this study,the Generalised Likelihood Uncertainty Estimation (GLUE)framework of Beven and Binley (1992) was used to assessthis uncertainty (see also Beven, 1993; Beven and Freer,2001; Cameron et al., 1999, 2000a,b).
The GLUE methodology rejects the concept of a single,global optimum parameter set and instead accepts the exis-tence of multiple acceptable (or behavioural) parametersets (Beven, 1993). In this study, a variant of Cameronet al.’s (1999, 2000a) procedure for estimating flood fre-quency within the GLUE framework was used. This proce-dure is summarised below.
216 D. Cameron
Five thousand TOPMODEL parameter sets, containing afairly broad range of parameter values, are initially gener-ated from independent uniform distributions. Five parame-ters are varied: the exponential scaling parameter (m),effective drained porosity (DTH1), effective available watercapacity of the root zone (SRMAX), mean log transmissivityof the soil at saturation of the surface (ln(T0)), and standarddeviation of log transmissivity (STDT). Other parameters,such as those derived directly from the observed data(e.g., the mean hourly potential evapotranspiration param-eter) are kept constant.
A single continuous simulation of the 14 year hourly ob-served series, utilising observed hourly rainfall inputs, is de-rived from each TOPMODEL parameter set. A two-stepapproach is then used to identify behavioural parametersets.
Flood estimation is assessed in the first step. In thisstudy, AMAX data are extracted from each 14 year TOP-MODEL simulation, ranked, and assessed against the corre-sponding observed, ranked, AMAX data using a weightedsum of absolute errors (WSAE) objective function:
WSAE ¼Xni¼1
wi � jQi � qij; ð1Þ
where Qi is an observed AMAX flood flow, qi is a simulatedAMAX flood flow, and
wi ¼viPni¼1vi
. ð2Þ
Given G, the highest gauged flow as measured by currentmeter (83 m3 s�1)
vi ¼G
Qi
where Qi > G; ð3aÞ
vi ¼ 1 where Qi 6 G. ð3bÞ
This objective function was adopted in recognition that theflood flows in an observed data series are, in themselves, of-ten estimates obtained from the extrapolation of a stage-discharge curve, which might have been developed using alimited number of gaugings and/or might not adequatelyrepresent out of bank flow conditions (e.g., Kuczera,1996). It therefore allows all of the observed data to be usedin the evaluation process, with lesser weight being given tofloods with flow estimates higher than those which haveactually been gauged in the field. The use of this objectivefunction also avoids the assumption that the AMAX flooddata belong to one particular type of statistical distribution(e.g., Cameron et al., 1999, assumed that the GeneralisedExtreme Value, GEV, distribution was an adequate repre-sentation of the observed Wye catchment AMAX series up
Table 2 Initial sampling range and behavioural parameter space
Parameter
m (recession) [m]DTH1 (effective drained porosity)SRMAX (maximum root zone storage) [m]T0 (transmissivity) [log]STDT (standard deviation from transmissivity) [log]
to T less than or equal to half the length of the observeddata series).
Following a period of sensitivity testing, parameter setswhich yielded simulations with WSAE values of less than orequal to 12 m3 s�1 were retained as behavioural. Thisthreshold was chosen because it allowed large floods to besimulated over a range, which was consistent with SEPAstaffs’ experience of the catchment’s flood response.
In the second step of the evaluation process, the param-eter sets which are retained as behavioural under the floodestimation criterion are also tested via a v2 statistic calcu-lated between the observed and simulated flow durationcurves (as per Cameron et al., 2000a). Thirteen points onthe flow duration curve are used (Q1, Q5, Q10–Q90, Q95
and Q99), as
v2d;p ¼
X13i¼1
ðOi � SiÞ2
Si
" #; ð4Þ
where d is 12 degrees of freedom, p = 0.9, Oi is the observedpercentage time spent beneath a given flow value, and Si isthe simulated percentage time spent beneath a given flowvalue. This yields a rejection threshold of 18.5. Parametersets which provide simulations which meet, or fall below,this threshold are retained as behavioural. This threshold(also used in Cameron et al., 2000a) was chosen becauseit allowed the characteristics of the observed flow durationcurve to be simulated adequately.
The behavioural area of the parameter space is thenresampled until 1000 behavioural parameter sets are ob-tained (Table 2 contains the parameter ranges associatedwith these behavioural parameter sets, together with theinitial sampling range used for each parameter. From Table2, it can be seen that only the m and SRMAX parametershave behavioural parameter ranges which are much smallerthan their initial sampling ranges). Likelihood weighteduncertainty bounds for flood frequency are then calculatedusing the likelihood weight 1/WSAE.
The likelihood weights are rescaled over all of the behav-ioural simulations in order to produce a cumulative sum of1.0. A cdf of discharge estimates is constructed for eachAMAX peak using the rescaled weights. Linear interpolationis used to extract the discharge estimate appropriate tocumulative likelihoods of 0.025, 0.5 and 0.975. This allows95% uncertainty bounds, in addition to a median simulation,to be derived (see also Blazkova and Beven, 2002, 2004;Cameron et al., 1999, 2000a,b).
The stochastic rainfall model is then used to drive TOP-MODEL to produce a two thousand year hourly flow simula-tion for each of the one thousand behavioural TOPMODELparameter sets (retaining their original 1/WSAE likelihood
An application of the UKCIP02 climate change scenarios to flood estimation 217
weightings). (The two thousand year simulation length isadopted in order to minimise the effects of the random sam-pling process used in the rainfall model, in the estimation offloods of return periods of up to 1 in 200 years.) This is donefor current climate conditions and for several different cli-mate change scenarios (see next section). Hourly AMAXflood frequency likelihood weighted uncertainty boundsare then calculated for each scenario.
This procedure therefore assumes that the parametersets which are identified as being behavioural under currentclimate conditions will also be behavioural under climatechange. It is recognised that this assumption may not bewholly valid if conditions in the catchment change as a re-sult of climate change. However, as there is no informationto allow the future behavioural region of the parameterspace to be readily identified, then it is necessary to makethis assumption in order to proceed (see also Hall andAnderson, 2002).
A discussion of the performance of the continuous simu-lation approach against the observed data, under currentclimatic conditions, is provided in the next section.
Performance of the continuous simulationapproach against the observed data, undercurrent climatic conditions
This section considers the results obtained from runningTOPMODEL with both the 14 years of observed hourly rain-fall data, and the stochastic rainfall model, under currentclimatic conditions. The performance of the models againstthe observed AMAX data (and a traditional statistical analy-sis of those data) are discussed.
The results from driving TOPMODEL with the 14 years ofobserved rainfall data indicated that the observed floodflows for the major flood events of 2002, 1997 and 2000
0
50
100
150
200
250
300
1 10
T
Q [
m3/
s]
Figure 2 95% likelihood weighted uncertainty bounds derived fparameter sets, driven using the stochastic rainfall model, with 200solid line – observed series with fitted GL distribution (L-moments;(GEV distribution); dashed lines – 95% uncertainty bounds obtainsimulation obtained under current climatic conditions.
all lay within the range of the 95% uncertainty bounds,and therefore that TOPMODEL was capable of simulatingthose events. However, there was some overestimation ofmuch smaller floods, those with flow values of circa14 m3 s�1 (a much lower flow than the mean annual floodof circa 51 m3 s�1; see ‘‘The study site’’). One explanationfor this behaviour is that the Torwinny raingauge might beless representative of the catchment average rainfalls asso-ciated with smaller magnitude flood events than with thecatchment-wide storms associated with the flood eventsof larger magnitudes. An alternative explanation might bethat, because the catchment was very saturated duringthe 2002, 1997 and 2000 flood events, the adequate simula-tion of those events required parameter values to be sam-pled from a region of the parameter space different fromthat which might be sampled for the simulation of muchsmaller flood flows. Since the focus of this study was verymuch on flood events greater than the mean annual flood,the behavioural TOPMODEL parameter sets were assumedto be adequate for the purposes of the study.
As regards the runs of TOPMODEL with the stochastic rain-fall model, Fig. 2 illustrates the 95% likelihood weighteduncertainty bounds (obtained from the two thousand yearsimulations), together with the median model simulation,for flood flows with return periods of up to 1 in 200 years.The observed AMAX data are also illustrated (note that thesedata were obtained from the period 1958–2003, and thatthis is a longer period than the 14 years of continuous hourlydata which were available for identifying the behaviouralTOPMODEL parameter sets). In addition, the results fromtwo forms of FEH statistical analysis (pooling group and sin-gle site analyses of the 46 years of observed AMAX data; Insti-tute of Hydrology, 1999) are also shown for return periods ofgreater than or equal to 1 in 2 years (the FEH does not readilyprovide estimates for smaller return periods). These statisti-cal analyses will now be considered in more detail.
100 1000
[yrs]
rom annual maximum peaks of 1000 behavioural TOPMODEL0 year hourly simulation length. Circles – observed data; darksingle site analysis); dash-dot line – FEH pooling group analysised under current climatic conditions; dotted line – median
Table 3 Changes in global temperature (�C) and atmo-spheric carbon dioxide concentration (parts per million) forthe 2080s period for the four UKCIP02 scenarios (adaptedfrom Hulme et al., 2002)
SRESemissionsscenario
UKCIP02climatechangescenario
Increasein globaltemperature(�C)
Atmospheric CO2
concentration(ppm)
B1 Low Emissions 2.0 525B2 Medium-Low
Emissions2.3 562
A2 Medium-HighEmissions
3.3 715
A1F1 High Emissions 3.9 810
218 D. Cameron
The FEH pooling group methodology is the recommendedstatistical approach for estimating long return period floodsin the UK (Institute of Hydrology, 1999) and utilises pooledflood data from catchments with similar hydrological char-acteristics to those of the catchment of interest. In thiscase, the pooling group comprised of data from gauging sta-tions in the northeast of Scotland, which are subject to thesame type of storm and flood events as the Lossie catch-ment. Fig. 2 illustrates the results obtained from fitting aGeneralised Extreme Value, GEV, distribution (using L-moments) to the pooled data (this distribution was selectedbecause it was found to yield the most suitable fit to thepooled data).
The single site analysis results shown are those of ob-tained from fitting a Generalised Logistic, GL, distribution(again using L-moments) directly to the observed AMAXdata. This fit was selected for the basis of illustration inFig. 2, because, in addition to providing an acceptable fitto the observed data, it also estimates flood flows for a gi-ven return period which are higher, for this site, than thoseestimated using other distributions.
From Fig. 2, it can be seen that the continuous simulationuncertainty bounds largely ‘‘bracket’’ the observed AMAXdata (obtained from the period 1958–2003) at return peri-ods of greater than about 1 in 2 to 1 in 3 years, and alsothe statistical fits at return periods of greater than about1 in 3 years. However, some overestimation of floods of les-ser return periods is also apparent. This also occurred whenTOPMODEL was driven with the 14 years of observed rainfalldata (see explanation above).
In addition, the largest AMAX event (the November 2002event of 155 m3 s�1) lies just above the 97.5% uncertaintybound. This is an interesting result as this event was ‘‘brack-eted’’ by the 95% uncertainty bounds obtained when TOP-MODEL was run using the 14 years of observed rainfallrecord (see above). This result might have occurred becausethe observed data sample might not be representative ofthe underlying population of flood events. Alternatively, itmay be because the rainfall model, being derived from aparticular 14 year observed dataset might not be able tofully simulate other possible samples (or ‘‘realisations’’)of the observed dataset. These two possible explanationsare not mutually exclusive, but of them, the former mightbe more likely. The reasoning for this is as follows.
Of the 1958–2003 observed AMAX dataset, the two larg-est events, those of 1997 and 2002, occur within the 14years of observed hourly data which were available for thedevelopment of the stochastic rainfall model. These events(and in particular that of 2002) are reputed to be the largestflood events on the River Lossie since 1829 (see ‘‘The studysite’’). It is therefore not unreasonable to assume that thedata used in the development of the stochastic rainfall mod-el contained an adequate representation of major flood-inducing storms for the River Lossie catchment.
In addition, since it is known that major flooding oc-curred on the River Lossie prior to the start of the gaugingstation record (e.g., in 1829 and 1915; see ‘‘The studysite’’), it is quite possible that the observed AMAX dataalone might not be a fully representative dataset for esti-mating flood return periods. Indeed, it is interesting to notethat the continuous simulation flood frequency curves arelargely consistent with those obtained from the FEH statis-
tical analyses (e.g., the flood frequency curve derived fromthe pooling group approach is very similar to that of themedian model simulation at return periods of greater thanabout 1 in 30 years). Both approaches suggest that the2002 flood event might have a higher return period than thatestimated from the observed data’s (Gringorten) plottingpositions alone. Furthermore, the range of the 95% uncer-tainty bounds is generally consistent with SEPA staff’s localhydrological knowledge of the catchment and its responseto flood events.
The current climate simulations, and the modelling ap-proach used to derive them, were therefore assumed tobe adequate for the aims of this study.
Climate change scenarios
The UKCIP02 climate change scenarios (available from thecoupled ocean–atmosphere UK Hadley Centre experiments,HadCM3) were used in this study (Hulme et al., 2002). Thereare four UKCIP02 scenarios: ‘‘Low Emissions’’, ‘‘Medium-Low Emissions’’, ‘‘Medium-High Emissions’’ and ‘‘HighEmissions’’. These scenarios were derived from four of theIntergovernmental Panel on Climate Change Special Reporton Emissions Scenarios (IPCC SRES). Table 3 summarises theincreases in global carbon dioxide and temperature associ-ated with each scenario for the 2080s time period (2071–2100 average). The modelling process used to generatethe four UKCIP02 scenarios also accounted for sulphateaerosols. Since it is not currently possible to assign probabil-ities to climate change scenarios, each of the four UKCIP02scenarios are assumed to have equal likelihoods.
The global scenarios span nearly the full range of theIntergovernmental Panel on Climate Change Special Reporton Emissions Scenarios (IPCC SRES). However, it should benoted that the changes estimated to climate variables (suchas temperature and rainfall, see below) under the UKCIP02scenarios are in part dependent upon the structure ofHadCM3. The use of a different GCM, with a different modelstructure, but using the same SRES input scenario, may re-sult in estimates to changes in climate variables which arequalitatively similar to those estimated using HadCM3, butquantitatively different (Hulme et al., 2002). In an attemptto make a provision for this uncertainty, Hulme et al. (2002)suggest uncertainty margins for average temperature and
An application of the UKCIP02 climate change scenarios to flood estimation 219
average rainfall for the winter and summer half-years foreach of the four UKCIP02 scenarios (see below).
HadCM3 produces estimates of climate change at the glo-bal scale (e.g., each HadCM3 grid box for the UK is of theorder of 250–300 km; Hulme et al., 2002). In order to pro-vide estimates at a (Europe) regional scale, the output fromHadCM3 has been used to drive a high resolution model ofthe global atmosphere (HadAM3H), and the output from thismodel has been used to drive a regional model of the Euro-pean atmosphere (HadRM3; Hulme et al., 2002). HadRM3utilises grid boxes of the order of 50 km.
The ‘‘Medium-High Emissions’’ UKCIP02 scenario for the2080s (2071–2100) was produced from an ensemble of threeHadRM3 simulations. Pattern-scaling of this scenario (usingestimated changes to global average temperature) was usedto produce a ‘‘Medium-High Emissions’’ UKCIP02 scenariofor two additional time-slices (the 2020s, 2011–2040, andthe 2050s, 2041–2070), and the other three UKCIP02 sce-narios for each 30 year time-slice (Hulme et al., 2002).
The regional modelling output contains many climatevariables, including temperature and rainfall (but not po-tential evapotranspiration, see below). Data are availablefor a baseline 1961–1990 simulation and for the three fu-ture time slices. In this study, the regional modelling outputfor all four of the UKCIP02 scenarios for the 2080s was used.The 2080s were selected because this time slice is the mostrelevant to long term development plans (e.g., in terms ofhousing and flood protection).
Table 4 Hulme et al.’s (2002) suggested uncertainty bounds to baverage winter and summer temperature and precipitation, togethH-Dry and H-Wet scenarios presented in this paper
Winter averagetemperature (�C)
Summer averagetemperature (�C
High ±2 ±2H-Dry +2 +2H-Wet �2 �2
Table 5 Estimated percentage changes to daily rainfall on a mscenarios for the 2080s
In addition, in order to explore the uncertainty associ-ated with the model structure of HadCM3, the uncertaintymargins suggested by Hulme et al. (2002) were considered.Of the four UKCIP02 scenarios, the ‘‘High Emissions’’ sce-nario has the largest uncertainty margins. Indeed, theuncertainty margins associated with this scenario (Table 4)includes the range of the changes estimated to daily rainfall(Table 5) and daily mean temperature (Table 6; Table 6 alsolists the temperatures estimated for the 1961–1990 base-line scenario) over all four of the UKCIP02 scenarios.
In this study, the ‘‘High Emissions’’ uncertainty marginswere used in order to generate two additional climatechange scenarios: the ‘‘H-Dry’’ scenario (the largest in-creases to temperature change and the largest decreasesto rainfall available from the ‘‘High Emissions’’ scenariouncertainty margins; Table 4) and the ‘‘H-Wet’’ scenario(the largest decreases to temperature change and the larg-est increases to rainfall available from the ‘‘High Emis-sions’’ scenario uncertainty margins; Table 4). Within thelimitations of the uncertainty margins suggested by Hulmeet al. (2002), these two scenarios encapsulate the upperand lower limits of the (climate change induced) changesto daily rainfall and daily mean temperature estimated byGCMs other than HadCM3. (Please note that other possibleuncertainties associated with GCM/RCM simulation, suchas the possible ‘‘dampening’’ of extreme rainfalls associ-ated with convective storms as a result of the rainfall aver-aging procedure used in the climate models, are not
e applied to the UKCIP02 High Emissions scenario of changes iner with an implementation of those uncertainty bounds as the
)Winter averageprecipitation (%)
Summer averageprecipitation (%)
±20 +40�20 0+20 +40
onthly basis for HadRM3 grid box 145 for the climate change
Table 6 Current conditions (1961–1990) daily mean temperature, together with estimated increases to daily mean temperatureunder each climate change scenario, as estimated for HadRM3 grid box 145 for the 2080s
considered in this paper beyond the extent of Hulmeet al.’s, 2002, uncertainty margins.)
This study therefore considers a total of six climatechange scenarios: the four UKCIP02 scenarios with no mod-ification, and the ‘‘H-Dry’’ and ‘‘H-Wet’’ scenarios. Tables5 and 6 list the estimated changes to daily rainfall and dailymean temperature (on a monthly basis) for each scenariofor the HadRM3 grid box which includes the Lossie catch-ment (box number 145).
From Table 5, it can be seen that, for the 2080s, the fourUKCIP02 scenarios estimate increases to rainfall betweenNovember and April, with decreases during the othermonths (the smallest changes are associated with the‘‘Low Emissions’’ scenario and the largest changes are asso-ciated with the ‘‘High Emissions’’ scenario). The ‘‘H-Dry’’scenario estimates increased rainfall in January, Februaryand April with decreases during the other months. The‘‘H-Wet’’ scenario estimates increased rainfall in everymonth (note that, of all of the scenarios considered in thisstudy, this scenario estimates the largest increases to rain-fall). Temperature increases are estimated for all six of thescenarios (Table 6), with the largest increases being associ-ated with the ‘‘H-Dry’’ scenario.
In order to perturb the TOPMODEL/stochastic rainfallmodel simulations, estimated percentage changes to bothrainfall (Table 5) and potential evapotranspiration were re-quired from each climate change scenario. However,changes to potential evapotranspiration were not availableand had to be estimated from certain of the other variablessimulated from the regional output.
Originally, it was intended to use a Penman–Monteithtype approach to estimate potential evapotranspiration.However, Ekstrom et al. (submitted for publication) suggestthat using such an approach with HadRM3 output might pro-duce estimates which are physically unrealistic for futureclimates. They suggest that simpler, temperature based ap-proaches might be more appropriate.
Recourse was therefore made to using the Thornthwaite(1948) method in order to estimate daily potential evapo-transpiration for each calendar month from the regionaltemperature data simulated for the 1961–1990 baseline,
and for each climate change scenario. It is emphasised thatthis method was used only in order to provide estimates ofrelative percentage changes to potential evapotranspiration(which could be used to perturb TOPMODEL’s PET parame-ter, see below). The estimated values of potential evapo-transpiration themselves were not used in the modellingprocess.
Table 7 lists the estimated percentage changes. It is inter-esting to note that, for the four UKCIP02 scenarios, and the‘‘H-Dry’’ scenario, the percentage changes are estimatedas being higher in the winter than in the summer. This is sim-ply because the estimated changes to daily temperature areproportionally higher in the winter months than in the sum-mer months (e.g., for the Medium-High scenario, the tem-perature for January is estimated as being 2.12 �C for1961–1990, but rising by 2.13 �C in the 2080s; the tempera-ture for July is estimated as being 12.17 �C for 1961–1990,and rising by 3 �C in the 2080s, Table 6). With respect tothe H-Wet scenario, certain of the winter months (e.g.,October) are estimated to have higher percentage changesto potential evapotranspiration than are estimated for thesummer months. For other winter months (e.g., March),the opposite is observed (indeed, reductions to potentialevapotranspiration are estimated for the months of Januaryand February). This can be explained as follows.
Although the ‘‘H-Wet’’ scenario employs a uniform de-crease (�2 �C) to the changes to daily mean temperatureestimated under the ‘‘High Emissions’’ scenario (Tables 4and 6), this decrease affects the size of the change in dailymean temperature by a different proportion for eachmonth. For example, the temperature change for Januaryis estimated as being 2.51 �C under the ‘‘High Emissions’’scenario, but 0.51 �C under the ‘‘H-Wet’’ scenario (i.e.,about 12% of the temperature change estimated under the‘‘High Emissions scenario); the temperature change for Au-gust is estimated as being 3.92 �C under the ‘‘High Emis-sions’’ scenario, but 1.92 �C under the ‘‘H-Wet’’ scenario(i.e., about 32% of the temperature change estimated underthe ‘‘High Emissions’’ scenario).
In addition, the Thornthwaite (1948) method uses a heatindex in the calculation of potential evapotranspiration.
Table 7 Estimated percentage changes to potential evapotranspiration (estimated using the Thornthwaite, 1948, method) forHadRM3 grid box 145 for the climate change scenarios for the 2080s
An application of the UKCIP02 climate change scenarios to flood estimation 221
This heat index is derived from the daily mean temperatures(for each month) for the year as a whole. The estimatedchange in potential evapotranspiration for a given monthis therefore dependent upon the estimated daily mean tem-perature for that month and the heat index for the year (to-gether with other fixed parameters used in theThornthwaite, 1948, method). In the case of the reductionsto potential evapotranspiration estimated for January andFebruary, the increases to the temperatures estimated forthose months are proportionally much smaller (relative tothe increases estimated for the other months) than the in-crease in the heat index. As a result, potential evapotrans-piration is estimated to decrease for January and Februarybut increase from March to December (and the year as awhole).
With respect to the actual process of perturbing the TOP-MODEL/stochastic rainfall model simulations, it was as-sumed that the percentage changes estimated for rainfalland potential evapotranspiration for HadRM3 grid box 145could be applied directly to the scale of the Lossie catch-ment. This assumption was made on the basis that therewere insufficient observed data available for the catchmentin order to allow adequate downscaling of the percentagechanges (obtained from the regional climate modelling) tothe catchment scale (as described in ‘‘The study site’’,there is only one raingauge in the catchment with a reason-able length of record; furthermore, SEPA does not hold anytemperature or other climatological data for this catchmentwhich could be used on a catchment-wide basis fordownscaling).
The following procedure was adopted in order to esti-mate the influence of climate change upon flood frequencyfor each UKCIP02 scenario. The stochastic rainfall modelwas used to drive a two thousand year continuous simulation(with hourly timestep) for each of the one thousand behav-ioural TOPMODEL parameter sets. There are many alterna-tive approaches to perturbing simulated rainfall data(including changing storm depths, Cameron et al., 2000b,and/or storm durations and inter-arrival times) and each ap-proach may affect the magnitude and direction of any
change in flood frequency to a different extent. The UK-CIP02 scenarios do not provide quantitative information onpossible changes to storm characteristics (such as inter-arri-val times or storm durations). The scenarios only includechanges to daily rainfall amounts. As the purpose of thisstudy was to utilise information directly from the UKCIP02scenarios and apply it to the catchment scale, only changesto rainfall amounts were considered. However, it is recogni-sed that changing other storm characteristics may result in adifferent magnitude of change to flood frequency to that re-ported here.
The perturbation to rainfall (for a given month within thesimulated sequence) was achieved by uniformly applyingthe percentage change to rainfall (as estimated from theclimate change scenario for that month) to all rainfallamounts within that month. All of the scenarios featuredthe perturbation of an identical hourly rainfall series to thatgenerated under current climatic conditions. This was donein order to prevent the possible impacts of climate changebeing confounded with random rainfall realisation (whererainfall realisation refers to the random sampling processused by the stochastic rainfall model to generate rain-storms; see Cameron et al., 1999).
Changes to potential evapotranspiration were achievedthrough the perturbation of the PET variable used in TOP-MODEL. For a given month within the simulation, this per-turbation was calculated as
PETr ¼ PETi þPETi � CHG
100; ð5Þ
where PETr is the value of PET used in a model run for thatmonth, PETi is the value of PET calculated from rainfall andrunoff for the catchment (0.0509 mm h�1), and CHG is thepercentage change estimated for that month (Table 7).
Upon completion of the two thousand year simulations,the likelihood weighted uncertainty bounds for the hourlyAMAX flood peaks were calculated using the procedures out-lined in ‘‘The GLUE framework’’. These were comparedwith those already available for the current climaticconditions.
222 D. Cameron
Results and discussion
Fig. 3a–d illustrates the 95% likelihood weighted uncer-tainty bounds (and median simulation) associated with cur-rent climatic conditions and for the ‘‘Low Emissions’’ and‘‘High Emissions’’ UKCIP02 scenarios (these two scenariosare chosen for illustration as they encompass the range offlood frequency curves generated under all four of the UK-CIP02 scenarios), and the ‘‘H-Dry’’ and ‘‘H-Wet’’ climatechange scenarios, respectively. Observed AMAX data (ob-tained from the period 1958–2003) are also shown. Fig. 4illustrates the cdfs calculated using the scaled likelihoodweights and discharge estimates (see ‘‘The GLUE frame-work’’) for the 1 in 200 year return period flood simulatedunder current conditions, and under all six climate changescenarios.
From these results, it can be seen that flood magnitudechanges under each climate change scenario. As might beexpected, the magnitude of the change is dependent uponthe choice of scenario (and therefore the choice of esti-mated changes to rainfall and potential evapotranspirationassociated with that scenario). The direction of the changeis also dependent upon this choice. For example, under the‘‘H-Dry’’ scenario (Figs. 3c and 4), flood magnitude de-creases. However, under all of the other scenarios floodmagnitude increases (with the smallest increases being
0
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Figure 3 95% likelihood weighted uncertainty bounds derivedparameter sets, driven using the stochastic rainfall model, with 2000lines – 95% uncertainty bounds obtained under climate change sceunder climate change scenario for the 2080s; solid lines – 95% uncclimatic conditions. a: ‘‘Low Emissions’’ UKCIP02 scenario; b: ‘‘HiWet’’ scenario.
associated with the ‘‘Low Emissions’’ UKCIP02 scenarioand the largest increases with the ‘‘H-Wet’’ scenario; the‘‘H-Wet’’ scenario results are perhaps unsurprising giventhe large percentage increases adopted for rainfall in eachmonth, Table 5). The range of these results therefore sup-ports the consideration of output from multiple climatechange scenarios and GCMs (as has been approximated inthis study).
It can also be seen that, for the ‘‘H-Dry’’ scenario andthe four UKCIP02 scenarios, there is an overlap in the uncer-tainty bounds estimated for each climate change scenarioand those estimated for the current climate. This overlapwas also observed by Cameron et al. (2000b) in applyingthe UKCIP98 ‘‘Medium High’’ scenario for flood estimationfor the Wye catchment, Plynlimon, Wales. Given the uncer-tainty estimation procedure used in this study, this findingindicates that there are uncertainties associated with theobserved data series and hydrological model structure andthat those uncertainties should be considered explicitly(as has been done in this study). (The lack of overlap inthe ‘‘H-Wet’’ scenario can be explained by the very highpercentage increases in daily rainfall and smaller increasesin daily mean temperature adopted for this scenario, Tables4–6. These changes cause a much larger change to floodmagnitude than is exhibited under the other climate changescenarios.)
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b
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from annual maximum peaks of 1000 behavioural TOPMODELyear hourly simulation length. Circles – observed data; dashednario for the 2080s; dotted line – median simulation obtainedertainty bounds and median simulation obtained under currentgh Emissions’’ UKCIP02 scenario; c: ‘‘H-Dry’’ scenario; d: ‘‘H-
An application of the UKCIP02 climate change scenarios to flood estimation 223
Table 8 contains the hourly discharge estimates for the10, 25, 50, 75, 100 and 200 year return period AMAX floodevents obtained from the 0.025, 0.5 and 0.975 model simu-lations. Table 8 also expresses the climate change floodestimates in terms of percentage difference from the cur-rent climate estimates. These results raise several interest-ing issues.
Firstly, it can be seen that, for the four UKCIP02 scenar-ios and the ‘‘H-Wet’’ scenario, the percentage differencesgenerally tend to increase with return period. However,for the ‘‘H-Dry’’ scenario, the opposite occurs. This findingcan be explained by the locations within the simulated rain-fall sequence at which the different percentage changes inrainfall have been implemented. For example, the long re-turn period floods are simulated during months which havethe smallest percentage decreases in rainfall under the‘‘H-Dry’’ scenario, but have fairly large percentage in-creases in rainfall under the other scenarios (e.g., Decem-ber and March; Table 5).
Secondly, with the exception of the ‘‘H-Wet’’ scenario,the percentage changes to flood flows estimated for eachscenario are smaller than the highest estimated percentagechanges to rainfall for that scenario. In the case of the ‘‘H-Wet’’ scenario, the percentage changes estimated for theflood flows are higher than the highest estimated percent-age changes to rainfall estimated for that scenario (e.g.,the largest change estimated for rainfall is 43.98% in Janu-ary, Table 5, but the lowest increase to flood flows is 49%for the median simulation of the 1 in 10 year return periodevent). These results can be explained by the nonlinearityof the rainfall-runoff process.
For example, with respect to the ‘‘H-Wet’’ scenario,since high percentage rainfalls occur in every month, the
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Figure 4 Cumulative density functions calculated using scaled likeperiod flood event for current climatic conditions, and for theEmissions’’, ‘‘High Emissions’’, ‘‘H-Dry’’ and ‘‘H-Wet’’ climate cha
catchment is simulated as having enhanced antecedent soilmoisture conditions, leading to an increase in flood magni-tude when storms occur. Under the other scenarios, rainfallis estimated to decrease for some months and increase forothers (Table 5). Potential evapotranspiration is also gener-ally estimated to increase by a greater amount in the otherscenarios than in the ‘‘H-Wet’’ scenario (Table 7). Taken to-gether, these factors influence the catchment’s antecedentsoil moisture conditions to a different extent to the ‘‘H-Wet’’ scenario and result in a different magnitude of stormresponse.
Interestingly, these findings also suggest that the floodcharacteristics of the Lossie catchment are more sensitiveto the changes estimated to rainfall than those estimatedfor potential evapotranspiration (given the modelling ap-proach used in this study). Similar findings were also ob-tained by Cameron et al. (2000b) for the Wye catchment,Wales, UK. Overall, these results therefore support theuse of rainfall-runoff modelling for examining the possibleeffects of climate change upon flood frequency.
Thirdly, while differences in catchment, climate changemodelling approach, and slight differences in the GLUE pro-cedure preclude a strict quantitative comparison with theearlier study of Cameron et al. (2000b), it is interesting tonote that, in general terms, the percentage changes associ-ated with the 1 in 100 year return period flood event esti-mated under the ‘‘Medium-High Emissions’’ UKCIP02scenario (Table 8) for the Lossie catchment, are similar, ifslightly higher, to those estimated for the Wye catchmentunder the UKCIP98 ‘‘Medium-High’’ scenario. For example,the median simulation for the 2080s for the Wye is esti-mated as being about 8% higher by the 2080s than under cur-rent climatic conditions (where rainfall and potential
250 300 3503/s)
Current conditions
Low Emissions
Medium-Low Emissions
Medium-High EmssionsHigh EmissionsH-Dry
H-Wet
lihood weights and discharge estimates for the 200 year return‘‘Low Emissions’’, ‘‘Medium-Low Emissions’’, ‘‘Medium-Highnge scenarios for the 2080s.
Table 8 Flood flows (Q) estimated for return periods (T) of up to 1 in 200 years as derived from the 0.025, 0.500 and 0.975model simulations under current climatic conditions and for each of the climate change scenarios for the 2080s
Scenario Bound T = 10 %chg. T = 25 %chg. T = 50 %chg. T = 75 %chg. T = 100 %chg. T = 200 %chg.
Percentage changes (%chg.) between the flood flows estimated under the current climate and under the climate change scenarios are alsoshown (to the nearest whole percentage).
224 D. Cameron
evapotranspiration were perturbed in a similar manner tothe present study); the corresponding simulation for theLossie is about 10% higher than under current climaticconditions.
In addition, Cameron et al. (2000b) estimated the cur-rent 1 in 100 year return period event for the Wye (as de-rived from the median simulation) as having a recurrenceof between 1 in 48 and 1 in 60 years by the 2080s. Interest-ingly, the current 1 in 100 year return period for the Lossieis estimated (under the median model simulation) as havinga 1 in 60 year return period by the 2080s under the ‘‘Med-ium-High Emissions’’ scenario of UKCIP02 (Table 9; for eachclimate change scenario Table 9 also lists the future returnperiods estimated for the flood flows which are estimated ashaving return periods of 10, 25, 50, 75 and 200 years under
Table 9 Estimated return periods (T) for the 2080s for flood flows100 and 200 years under current climatic conditions (as derived f
The results are shown for each climate change scenario.
current climatic conditions). While this (qualitative) consis-tency with the earlier study is encouraging, it is worthremembering that that study considered output from onlyone UKCIP98 scenario.
When all six of the scenarios in the present study areexamined (Table 9), it is apparent that the magnitude anddirection of the change estimated to flood return period(Table 9) depends upon the choice of climate change sce-nario (as per the flood magnitude results, above). For exam-ple, with respect to the median simulation, the present day 1in 200 year event is estimated to have a return period of 1 in159 years under the ‘‘H-Dry’’ scenario but only 1 in 18 years,under the ‘‘H-Wet’’ scenario. The findings of the presentstudy therefore highlight the importance of considering arange of climate change scenarios (as per Prudhomme
which are estimated as having return periods of 10, 25, 50, 75,rom the median model simulation, Table 8)
T (yr) High T (yr) H-Dry T (yr) H-Wet T (yr)
8 15 317 39 529 78 740 114 952 159 1293 332 18
An application of the UKCIP02 climate change scenarios to flood estimation 225
et al., 2003) and of using some form of uncertainty estima-tion (such as GLUE) when estimating the possible effects ofclimate change upon flood frequency. It is suggested thatthese factors need to be taken into account in order to makewell informed flood management decisions.
For instance, where practicable, new flood preventionschemes could be designed to be adaptive (e.g., flood bankswith the capacity to be raised at a later date). Developmentcontrol (e.g., new housing schemes) is a more difficult issue(as once a development is in it generally cannot be easilymoved or modified!). The author suggests that enhanceddialogue between the scientific and political communitiescould lead to improved (and preferably consistent) guidanceto the practicing hydrologist dealing with development con-trol (e.g., in Scotland, UK, it is often the responsibility ofthe individual hydrologist to strike an appropriate balancebetween a precautionary approach to flood risk, such as de-sign flood levels, and what can be realistically achieved onsite).
Conclusions
This paper has used the UKCIP02 climate change scenariosas a basis for extending the work of Cameron et al.(2000b) in order to explore the possible effects of climatechange upon flooding for a gauged catchment with limiteddata availability (the Lossie catchment, Scotland, UK). Avariant of the continuous simulation methodology devel-oped by Cameron et al. (1999) was used. The methodologyutilises a stochastic rainfall model to drive the rainfall-run-off model TOPMODEL for a series of continuous two thou-sand year simulations with hourly timestep. Theuncertainty in the resulting hourly annual maximum floodpeaks is handled within the GLUE framework of Beven andBinley (1992).
The ‘‘Low Emissions’’, ‘‘Medium-Low Emissions’’, ‘‘Med-ium-High Emissions’’ and ‘‘High Emissions’’ UKCIP02 cli-mate change scenarios, derived from the HadCM3 GCMand HadRM3 RCM (Hulme et al., 2002) were used at thecatchment scale. The ‘‘High Emissions’’ scenario uncer-tainty margins suggested by Hulme et al. (2002) for compar-ing HadCM3 and HadRM3 output with other GCM climatechange scenarios were used to generate two additional cli-mate change scenarios (‘‘H-Dry’’ and ‘‘H-Wet’’).
The implementation of the climate change scenarios atthe catchment scale featured the uniform application ofthe changes to the daily rainfall totals (for each calendarmonth) estimated by from the regional modelling outputto the hourly level. Changes to potential evapotranspirationwere also estimated from the regional modelling output andthese were used to perturb the parameter of the potentialevapotranspiration model used by TOPMODEL.
The results for the ‘‘Medium-High Emissions’’ UKCIP02scenario for the 1 in 100 year return period flood event werefound to be qualitatively similar to the results obtained inthe earlier study of Cameron et al. (2000b) (this study fea-tured the application of the ‘‘Medium-High’’ UKCIP98 toflood estimation on the Wye catchment, Wales). The over-lap in the likelihood weighted uncertainty bounds (as esti-mated for the conditions of the current climate and thoseestimated under climate change), noted by Cameron et al.
(2000b), was also observed. However, this earlier study usedthe output from only one scenario from a single GCM as thestarting point for exploring the possible effects of climatechange upon flood frequency.
By considering multiple climate change scenarios, thisnew study has demonstrated that while flood magnitude(and return period) is estimated to change under each cli-mate change scenario, the magnitude and direction of thatchange is dependent upon the choice of scenario. For exam-ple, flood magnitude decreases under the ‘‘H-Dry’’ sce-nario, but increases under the other scenarios consideredin this paper (the smallest increase was associated withthe ‘‘Low Emissions’’ scenario and the highest increase withthe ‘‘H-Wet’’ scenario). These findings highlight the need toconsider multiple climate change scenarios and account formodel uncertainties when estimating the possible effects ofclimate change upon flood frequency.
Acknowledgements
The author gratefully acknowledges Keith Beven’s com-ments on the first draft of this paper and the use of DavidTarboton’s TauDEM software in deriving TOPMODEL’s topo-graphic index. The UKCIP02 scenarios were made availableby DEFRA. (�c Crown Copyright 2002. The UKCIP02 ClimateScenario data have been made available by the Departmentfor Environment, Food and Rural Affairs (DEFRA). DEFRA ac-cepts no responsibility for any inaccuracies or omissions inthe data nor for any loss or damage directly or indirectlycaused to any person or body by reason of, or arising outof any use of, this data.) The comments of two anonymousreferees assisted in the revision of this paper. The opinionsexpressed in this paper are those of the author and do notnecessarily reflect the view of the Scottish EnvironmentProtection Agency.
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