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)

Journal of Hydrology (2006) 328, 212– 226

available at www.sciencedirect.com

journal homepage: www.elsevier.com/locate/jhydrol

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

KEYWORDS

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 significant 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 rainfall-runoff model TOPMODEL, is utilised. Behavioural parameter sets for TOPMODEL are identified prior to the climate change runs using the Generalised Likelihood Uncertainty Estimation (GLUE) methodology. The ‘‘Low Emissions’’, ‘‘Medium-Low Emissions’’, ‘‘Medium-High Emissions’’ 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 uncertainty 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.

Climate change; Flood; TOPMODEL; Generalised Likelihood Uncertainty Estimation; Stochastic model



* Fax: +44 1349 863 987. E-mail address: [email protected].



0022-1694/$ - see front matter c 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.jhydrol.2005.12.024

An application of the UKCIP02 climate change scenarios to flood estimation

Introduction One of the key assumptions of flood frequency analysis is that the return period of a flood peak of given magnitude is stationary with time (NERC, 1975). This assumption is only valid if a catchment’s long-term climatic, physical and hydrological characteristics are also relatively constant with time. Recent studies (e.g., Hulme et al., 2002; Prudhomme et al., 2003; Fowler et al., 2005; Ekstro ¨m et al., 2005), however, have demonstrated the variability of climate characteristics and the potential for future change. The UK Climate Impacts Programme (e.g., UKCIP02; Hulme et al., 2002), for example, derived several possible climate change scenarios for the UK in the 21st Century using output from the Hadley Centre global climate model (or GCM), HadCM3 and regional climate model (RCM), HadRM3. The regional modelling output includes estimated changes to precipitation and temperature for the UK at a grid box scale of about 50 km by 50 km. The UKCIP02 scenarios therefore supersede the earlier UKCIP98 scenarios (Hulme and Jenkins, 1998) which were based upon an older GCM (HadCM2) with a grid box scale of about 250 km by 250 km. The possible effect of climate change upon flooding is beginning to be recognised in governmental planning policy (e.g., for housing schemes and flood protection schemes). For example, Scottish Planning Policy, SPP, 7 (Scottish Executive, 2004) requires that many types of new development are protected from a flood with a 1 in 200 year return period. The adoption of the 1 in 200 year return period flood is largely based on the simplifying (and precautionary) assumption that the present day 1 in 200 year flood might become the 1 in 100 year flood in the future. However, the possible effects of climate change upon flood frequency are likely to be much more complex than this simple assumption suggests. Many choices must be made in order to estimate the effects of climate change at the catchment scale. These choices include: the choice of GCM (Hulme et al., 2002), climate change scenario (it is not currently possible to assign specific probabilities to climate change scenarios, therefore alternative climate change scenarios must be viewed as having an equal likelihood of occurrence; Hulme et al., 2002), the spatial and temporal implementation of that scenario at the catchment scale (Lettenmaier and Gan, 1990; Panagoulia and Dimou, 1997; Gellens and Roulin, 1998), the method used for flood estimation (usually some form of rainfall-runoff modelling; e.g., Wolock and Hornberger, 1991; Booij, 2005), and the treatment 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 might influence the estimated effect of climate change upon flooding. In the study of Cameron et al. (2000b) the ‘‘MediumHigh’’ UKCIP98 climate change scenario was used as a starting point for a variety of different climate change scenarios for the gauged, upland Wye catchment at Plynlimon, Wales, UK. The scenarios were applied to one thousand year continuous hourly simulations produced using TOPMODEL (as driven by the stochastic rainfall model of Cameron et al., 1999), with uncertainties being explored using the Generalised Likelihood Uncertainty Estimation (GLUE) approach of

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Beven and Binley (1992). It was demonstrated that, while the scenarios had only a small impact upon the likelihood weighted uncertainty bounds in comparison with the current condition scenario, the risk of a given discharge as an element in the distribution of T (return period) year floods was changed. In what follows, the work of Cameron et al. (2000b) is extended in light of the newly available UKCIP02 data. A variant of the continuous simulation methodology (which includes a new objective function for evaluating flood peak simulation) is applied to a gauged, ‘‘real world’’ nonresearch catchment which has significant flooding problems but only limited instrumentation (particularly as regards rainfall measurement). The possible effect of climate change upon flooding is investigated under all four of the UKCIP02 scenarios (rather than the single UKCIP98 ‘‘Medium-High’’ scenario which was used as the starting point in the earlier study). Two further scenarios, based upon the model uncertainty margins available for the UKCIP02 ‘‘High Emissions’’ scenario (Hulme et al., 2002), are also developed in order to explore the possible range of changes to daily rainfall and temperature estimated from GCMs other than HadCM3. Flood events with return periods of up to 1 in 200 years are considered, with particular attention being drawn to the 10, 25, 50, 75, 100 and 200 year events (as it is these types of event which are most commonly of interest to the operational hydrologist consulting on development plans and flood protection schemes). The uncertainties involved in the estimations are discussed and the practical implications for flood management are highlighted.

The study site The study site is the 216 km2 Lossie catchment in the northeast of Scotland, UK (Fig. 1). The catchment is a largely rural catchment with moorland overlying schists, gneisses and valley gravels with some old red sandstone. There is substantial afforestation in the catchment’s headwaters, with arable land in the valley bottoms (note that for the purposes of this study it is assumed that there is no change in land use between the current climatic conditions and the future climate scenarios considered in this paper). Although average annual rainfall (1961–1990) is 830 mm, there is a steep rainfall gradient between the hills in the catchment’s headwaters and the coastal plain where Elgin, the main settlement, is located. At the location of the Scottish Environment Protection Agency’s (SEPA) primary gauging station at Sheriffmills, just upstream of Elgin, the mean annual flow is 2.63 m3 s1, the mean annual flood circa 51 m3 s1, the Q95 is 0.71 m3 s1 and the base flow index (BFI) is 0.52. Average hourly annual maximum (AMAX) flood data are available for this station for the water years 1958–2003. However, continuous average hourly hydrograph information is only available in electronic format for a much smaller period within that record (including the 14 water year period 1990–2003). Notable flooding on the River Lossie is associated with frontal storms of long duration. The largest known event occurred during the period 3rd to 4th August 1829 (Lauder, 1830) and other flooding is known to have occurred prior

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Figure 1 The River Lossie catchment (and part of the neighbouring Findhorn catchment). The location gauging station at Sheriffmills (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.

to the start of the gauging station record (e.g., in 1915; Meterological Office, 1915). During the period of the gauging 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, August 1970 and April 2000. As regards rainfall data, the only tipping bucket raingauge in the catchment with a long length of hourly rainfall record is located at Torwinny, in the upland area of the catchment’s headwaters. Hourly rainfall data are available for the same 14-year period as the continuous average hourly hydrograph data (1990–2003). However, there are several (limited) periods of missing data within this dataset, most notably with respect to the July 1997 flood event. As other data were unavailable for the Lossie catchment for the periods of missing data, recourse was made to infilling those periods using hourly raingauge data from the neighbouring Findhorn catchment (primarily from the Lochindorb gauge, but also from the Freeburn gauge for the periods where data from Lochindorb were unavailable; Fig. 1), scaled using data obtained from the daily raingauge at Relugas in the lower Findhorn catchment. This part of the Findhorn catchment is subject to similar storm events to those of the Lossie and data for the Relugas gauge were available for the missing periods of the Torwinny record (including the 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 is outlined here. TOPMODEL is a simple semi-distributed model of catchment hydrology that estimates storm runoff from a combi-

nation of variable saturated surface contributing area and subsurface runoff (e.g., Beven, 1986, 1987; Quinn and Beven, 1993). The dynamics of the contributing area for rapid runoff as the catchment wets and dries are based on a quasi-steady state analysis. As with many other TOPMODEL applications (see Beven, 1997), the topographic index ln(a/tan b) was used as an index of hydrological similarity, where a is the area draining through a point, and tan b is the local surface slope. The use of this form of topographic index implies an effective transmissivity profile that declines exponentially with increasing storage deficits. In this study, the topographic index was derived from a digital terrain model using Tarboton’s (1997) D1 algorithm. This algorithm was chosen largely on the basis of its good performance in comparison to other algorithms (Tarboton, 1997), and also because of the format of the digital terrain model and the GIS tools available to the author. Evapotranspiration losses in TOPMODEL are controlled by potential evapotranspiration and storage in the root zone with the parameter SRMAX (effective available water capacity at the root zone; see ‘‘The study site’’). The potential evapotranspiration estimation routine uses the same seasonal sine curve as Beven (1986, 1987) and Blazkova and Beven (1997) with a single mean hourly potential evapotranspiration (PET) parameter. In the absence of any readily available potential evapotranspiration data, recourse was made to estimating PET from the observed rainfall and runoff data available for the catchment (yielding a PET parameter value of 0.0509 mm h1). TOPMODEL (often driven by a stochastic rainfall model) has successfully been used for flood estimation in many continuous 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 1999, 2000a,b) and ungauged catchments (Blazkova and Beven, 2002).

The stochastic rainfall model A stochastic rainfall model, similar to the one detailed in Cameron et al. (1999; see also Cameron et al., 2000a–c), was developed for the Lossie catchment. The stochastic rainfall model is based upon the available observed hourly rainfall data and generates random rainstorms via a Monte Carlo sampling procedure (see Cameron et al., 1999, for a full description of this type of model and its operation). The model characterises a storm in terms of a mean storm intensity, duration, inter-event arrival time, and storm profile. A rainstorm is defined as any event with a minimum intensity of 0.1 mm at an hour, with a minimum duration of 1 h and a minimum inter-event arrival time of 1 h. This definition accounts for all of the rainfall data in the observed series. It is assumed that mean storm intensity is dependent upon storm duration. This is modelled by subdividing the available observed sample of storm events (derived from the 14 year observed hourly rainfall record) into four duration classes of similar mean storm intensity: 1 h, 2–4 h, 5– 34 h and P35 h. Table 1 summarises the number of storms associated with each duration class. Major flooding in the Lossie catchment is associated with long duration (e.g., 2 days), frontal, storm events (see ‘‘The study site’’). The P35 h duration class is therefore the most important duration class in terms of the generation of large flood events. (Please note that the four duration classes are used instead of the seven duration classes adopted by Cameron et al., 1999, for the Wye catchment. For the Lossie catchment, the four duration classes are a better representation of the relationship between mean storm intensity and duration than the seven duration classes. Indeed, the four duration classes were found to produce simulations of extreme rainfall, which more closely fitted the observed data than simulations, which were derived from the seven duration class models. In addition, with respect to seasonality, Cameron et al., 2000c, introduced seasonality to this type of model by splitting the observed rainfall dataset into winter and summer half-years. However, in the current study, the sample of long duration storms with high mean storm intensities was too small to introduce seasonality to the model adequately.) For each duration class, mean storm intensity is modelled using the empirical cumulative density function (cdf) derived from the storm events located within that class. In this particular application, the observed samples were not extrapolated. The reasoning for this is as follows.

Table 1 Number of storms in each duration class of the stochastic rainfall model Duration class (h)

Number of storm

1 2–4 5–34 35–64

3312 2008 894 16

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It should be recalled that the observed rainfall record contains several significant rainfall events, including the event of November 2002 which had a total storm depth of 171.6 mm (corresponding to a mean storm intensity of 3.3 mm h1 and a duration of 52 h). The return period of this event is estimated as being at least 1 in 236 years (as derived using the Flood Estimation Handbook, FEH, statistical methodology; this is the recommended approach for estimating rainfall return periods in the UK; Institute of Hydrology, 1999). The maximum storm depth which can be generated by the model is 211.2 mm (using a mean storm intensity of 3.3 mm h1 and a duration of 64 h). This event is estimated (again using the FEH) as having a return period in excess of 1 in 500 years. Since the highest flood flow return period under consideration in this study is 1 in 200 years, and since this type of event will be generated by a storm from the P35 h duration class, the model is capable of meeting the requirements of the study. It is recognised, however, that for use in other catchments, or for the simulation of flood flows with return periods beyond the scope of the present study, some form of extrapolation (and thus the introduction of parameterisation to the model) would be required (e.g., as per Cameron et al., 1999, 2000a–c). The storm duration and inter-event arrival time characteristics derived from the observed event series are also modelled using their empirical cdfs (with a maximum storm duration of 64 h and a maximum inter-event arrival time of 2447 h). In both cases, it is assumed that the observed samples require no further extrapolation. As regards storm duration, Lauder’s (1830) description of the flood of 3rd and 4th August 1829 (the largest flood known to have affected 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. The observed 14-year rainstorm event series is utilised to provide an extensive database of storm profiles for each duration class. These are normalised by cumulative volume and total duration. During a model run, the normalised profiles are randomly selected in order to provide storm profiles for the simulated rainfall events.

The Generalised Likelihood Uncertainty Estimation (GLUE) framework Every flood frequency estimate is subject to some degree of uncertainty. The major sources of this uncertainty in the continuous simulation approach include the limitations of the observed data series and the choice of rainfall and hydrological models (especially with respect to the model structures, and their calibration/validation). In this study, the Generalised Likelihood Uncertainty Estimation (GLUE) framework of Beven and Binley (1992) was used to assess this 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 existence of multiple acceptable (or behavioural) parameter sets (Beven, 1993). In this study, a variant of Cameron et al.’s (1999, 2000a) procedure for estimating flood frequency within the GLUE framework was used. This procedure is summarised below.

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Five thousand TOPMODEL parameter sets, containing a fairly broad range of parameter values, are initially generated from independent uniform distributions. Five parameters are varied: the exponential scaling parameter (m), effective drained porosity (DTH1), effective available water capacity of the root zone (SRMAX), mean log transmissivity of the soil at saturation of the surface (ln(T0)), and standard deviation of log transmissivity (STDT). Other parameters, such as those derived directly from the observed data (e.g., the mean hourly potential evapotranspiration parameter) are kept constant. A single continuous simulation of the 14 year hourly observed series, utilising observed hourly rainfall inputs, is derived from each TOPMODEL parameter set. A two-step approach is then used to identify behavioural parameter sets. Flood estimation is assessed in the first step. In this study, AMAX data are extracted from each 14 year TOPMODEL simulation, ranked, and assessed against the corresponding observed, ranked, AMAX data using a weighted sum of absolute errors (WSAE) objective function: WSAE ¼

n X

w i  jQ i  qi j;

ð1Þ

i¼1

where Qi is an observed AMAX flood flow, qi is a simulated AMAX flood flow, and vi . ð2Þ w i ¼ Pn i¼1 v i Given G, the highest gauged flow as measured by current meter (83 m3 s1) G where Q i > G; Qi v i ¼ 1 where Q i 6 G.

vi ¼

ð3aÞ ð3bÞ

This objective function was adopted in recognition that the flood flows in an observed data series are, in themselves, often estimates obtained from the extrapolation of a stagedischarge curve, which might have been developed using a limited number of gaugings and/or might not adequately represent out of bank flow conditions (e.g., Kuczera, 1996). It therefore allows all of the observed data to be used in the evaluation process, with lesser weight being given to floods with flow estimates higher than those which have actually been gauged in the field. The use of this objective function also avoids the assumption that the AMAX flood data belong to one particular type of statistical distribution (e.g., Cameron et al., 1999, assumed that the Generalised Extreme Value, GEV, distribution was an adequate representation of the observed Wye catchment AMAX series up

Table 2

to T less than or equal to half the length of the observed data series). Following a period of sensitivity testing, parameter sets which yielded simulations with WSAE values of less than or equal to 12 m3 s1 were retained as behavioural. This threshold was chosen because it allowed large floods to be simulated over a range, which was consistent with SEPA staffs’ experience of the catchment’s flood response. In the second step of the evaluation process, the parameter sets which are retained as behavioural under the flood estimation criterion are also tested via a v2 statistic calculated between the observed and simulated flow duration curves (as per Cameron et al., 2000a). Thirteen points on the flow duration curve are used (Q1, Q5, Q10–Q90, Q95 and Q99), as " # 13 X ðOi  Si Þ2 ; ð4Þ v2d;p ¼ Si i¼1 where d is 12 degrees of freedom, p = 0.9, Oi is the observed percentage time spent beneath a given flow value, and Si is the simulated percentage time spent beneath a given flow value. This yields a rejection threshold of 18.5. Parameter sets 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 because it allowed the characteristics of the observed flow duration curve to be simulated adequately. The behavioural area of the parameter space is then resampled until 1000 behavioural parameter sets are obtained (Table 2 contains the parameter ranges associated with these behavioural parameter sets, together with the initial sampling range used for each parameter. From Table 2, it can be seen that only the m and SRMAX parameters have behavioural parameter ranges which are much smaller than their initial sampling ranges). Likelihood weighted uncertainty bounds for flood frequency are then calculated using the likelihood weight 1/WSAE. The likelihood weights are rescaled over all of the behavioural simulations in order to produce a cumulative sum of 1.0. A cdf of discharge estimates is constructed for each AMAX peak using the rescaled weights. Linear interpolation is used to extract the discharge estimate appropriate to cumulative likelihoods of 0.025, 0.5 and 0.975. This allows 95% 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 TOPMODEL to produce a two thousand year hourly flow simulation for each of the one thousand behavioural TOPMODEL parameter sets (retaining their original 1/WSAE likelihood

Initial sampling range and behavioural parameter space of the 1000 behavioural TOPMODEL parameter sets

Parameter

Initial sampling range

Behavioural range

m (recession) [m] DTH1 (effective drained porosity) SRMAX (maximum root zone storage) [m] T0 (transmissivity) [log] STDT (standard deviation from transmissivity) [log]

0.0010–0.0450 0.0010–1.0000 0.0010–0.2000 0.0010–8.0000 0.0010–10.0000

0.0304–0.0450 0.0011–1.0000 0.0130–0.0348 0.6701–7.9565 0.8054–9.9877

An application of the UKCIP02 climate change scenarios to flood estimation weightings). (The two thousand year simulation length is adopted in order to minimise the effects of the random sampling process used in the rainfall model, in the estimation of floods of return periods of up to 1 in 200 years.) This is done for current climate conditions and for several different climate change scenarios (see next section). Hourly AMAX flood frequency likelihood weighted uncertainty bounds are then calculated for each scenario. This procedure therefore assumes that the parameter sets which are identified as being behavioural under current climate conditions will also be behavioural under climate change. It is recognised that this assumption may not be wholly valid if conditions in the catchment change as a result of climate change. However, as there is no information to allow the future behavioural region of the parameter space to be readily identified, then it is necessary to make this assumption in order to proceed (see also Hall and Anderson, 2002). A discussion of the performance of the continuous simulation approach against the observed data, under current climatic conditions, is provided in the next section.

Performance of the continuous simulation approach against the observed data, under current climatic conditions This section considers the results obtained from running TOPMODEL with both the 14 years of observed hourly rainfall data, and the stochastic rainfall model, under current climatic conditions. The performance of the models against the observed AMAX data (and a traditional statistical analysis of those data) are discussed. The results from driving TOPMODEL with the 14 years of observed rainfall data indicated that the observed flood flows for the major flood events of 2002, 1997 and 2000

217

all lay within the range of the 95% uncertainty bounds, and therefore that TOPMODEL was capable of simulating those events. However, there was some overestimation of much smaller floods, those with flow values of circa 14 m3 s1 (a much lower flow than the mean annual flood of circa 51 m3 s1; see ‘‘The study site’’). One explanation for this behaviour is that the Torwinny raingauge might be less representative of the catchment average rainfalls associated with smaller magnitude flood events than with the catchment-wide storms associated with the flood events of larger magnitudes. An alternative explanation might be that, because the catchment was very saturated during the 2002, 1997 and 2000 flood events, the adequate simulation of those events required parameter values to be sampled from a region of the parameter space different from that which might be sampled for the simulation of much smaller flood flows. Since the focus of this study was very much on flood events greater than the mean annual flood, the behavioural TOPMODEL parameter sets were assumed to be adequate for the purposes of the study. As regards the runs of TOPMODEL with the stochastic rainfall model, Fig. 2 illustrates the 95% likelihood weighted uncertainty bounds (obtained from the two thousand year simulations), 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 these data were obtained from the period 1958–2003, and that this is a longer period than the 14 years of continuous hourly data which were available for identifying the behavioural TOPMODEL parameter sets). In addition, the results from two forms of FEH statistical analysis (pooling group and single site analyses of the 46 years of observed AMAX data; Institute of Hydrology, 1999) are also shown for return periods of greater than or equal to 1 in 2 years (the FEH does not readily provide estimates for smaller return periods). These statistical analyses will now be considered in more detail.

300

250

Q [m3/s]

200

150

100

50

0 1

10

100

1000

T [yrs]

Figure 2 95% likelihood weighted uncertainty bounds derived from annual maximum peaks of 1000 behavioural TOPMODEL parameter sets, driven using the stochastic rainfall model, with 2000 year hourly simulation length. Circles – observed data; dark solid line – observed series with fitted GL distribution (L-moments; single site analysis); dash-dot line – FEH pooling group analysis (GEV distribution); dashed lines – 95% uncertainty bounds obtained under current climatic conditions; dotted line – median simulation obtained under current climatic conditions.

218 The FEH pooling group methodology is the recommended statistical approach for estimating long return period floods in the UK (Institute of Hydrology, 1999) and utilises pooled flood data from catchments with similar hydrological characteristics to those of the catchment of interest. In this case, the pooling group comprised of data from gauging stations in the northeast of Scotland, which are subject to the same type of storm and flood events as the Lossie catchment. Fig. 2 illustrates the results obtained from fitting a Generalised Extreme Value, GEV, distribution (using Lmoments) to the pooled data (this distribution was selected because it was found to yield the most suitable fit to the pooled data). The single site analysis results shown are those of obtained from fitting a Generalised Logistic, GL, distribution (again using L-moments) directly to the observed AMAX data. This fit was selected for the basis of illustration in Fig. 2, because, in addition to providing an acceptable fit to the observed data, it also estimates flood flows for a given return period which are higher, for this site, than those estimated using other distributions. From Fig. 2, it can be seen that the continuous simulation uncertainty bounds largely ‘‘bracket’’ the observed AMAX data (obtained from the period 1958–2003) at return periods of greater than about 1 in 2 to 1 in 3 years, and also the statistical fits at return periods of greater than about 1 in 3 years. However, some overestimation of floods of lesser return periods is also apparent. This also occurred when TOPMODEL was driven with the 14 years of observed rainfall data (see explanation above). In addition, the largest AMAX event (the November 2002 event of 155 m3 s1) lies just above the 97.5% uncertainty bound. This is an interesting result as this event was ‘‘bracketed’’ by the 95% uncertainty bounds obtained when TOPMODEL was run using the 14 years of observed rainfall record (see above). This result might have occurred because the observed data sample might not be representative of the underlying population of flood events. Alternatively, it may be because the rainfall model, being derived from a particular 14 year observed dataset might not be able to fully simulate other possible samples (or ‘‘realisations’’) of the observed dataset. These two possible explanations are not mutually exclusive, but of them, the former might be more likely. The reasoning for this is as follows. Of the 1958–2003 observed AMAX dataset, the two largest events, those of 1997 and 2002, occur within the 14 years of observed hourly data which were available for the development of the stochastic rainfall model. These events (and in particular that of 2002) are reputed to be the largest flood events on the River Lossie since 1829 (see ‘‘The study site’’). It is therefore not unreasonable to assume that the data used in the development of the stochastic rainfall model contained an adequate representation of major floodinducing storms for the River Lossie catchment. In addition, since it is known that major flooding occurred on the River Lossie prior to the start of the gauging station record (e.g., in 1829 and 1915; see ‘‘The study site’’), it is quite possible that the observed AMAX data alone might not be a fully representative dataset for estimating flood return periods. Indeed, it is interesting to note that the continuous simulation flood frequency curves are largely consistent with those obtained from the FEH statis-

D. Cameron tical analyses (e.g., the flood frequency curve derived from the pooling group approach is very similar to that of the median model simulation at return periods of greater than about 1 in 30 years). Both approaches suggest that the 2002 flood event might have a higher return period than that estimated from the observed data’s (Gringorten) plotting positions alone. Furthermore, the range of the 95% uncertainty bounds is generally consistent with SEPA staff’s local hydrological knowledge of the catchment and its response to flood events. The current climate simulations, and the modelling approach used to derive them, were therefore assumed to be adequate for the aims of this study.

Climate change scenarios The UKCIP02 climate change scenarios (available from the coupled ocean–atmosphere UK Hadley Centre experiments, HadCM3) were used in this study (Hulme et al., 2002). There are four UKCIP02 scenarios: ‘‘Low Emissions’’, ‘‘MediumLow Emissions’’, ‘‘Medium-High Emissions’’ and ‘‘High Emissions’’. These scenarios were derived from four of the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (IPCC SRES). Table 3 summarises the increases in global carbon dioxide and temperature associated with each scenario for the 2080s time period (2071– 2100 average). The modelling process used to generate the four UKCIP02 scenarios also accounted for sulphate aerosols. Since it is not currently possible to assign probabilities to climate change scenarios, each of the four UKCIP02 scenarios are assumed to have equal likelihoods. The global scenarios span nearly the full range of the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (IPCC SRES). However, it should be noted that the changes estimated to climate variables (such as temperature and rainfall, see below) under the UKCIP02 scenarios are in part dependent upon the structure of HadCM3. The use of a different GCM, with a different model structure, but using the same SRES input scenario, may result in estimates to changes in climate variables which are qualitatively similar to those estimated using HadCM3, but quantitatively different (Hulme et al., 2002). In an attempt to make a provision for this uncertainty, Hulme et al. (2002) suggest uncertainty margins for average temperature and

Table 3 Changes in global temperature (°C) and atmospheric carbon dioxide concentration (parts per million) for the 2080s period for the four UKCIP02 scenarios (adapted from Hulme et al., 2002) SRES emissions scenario

UKCIP02 climate change scenario

Increase in global temperature (°C)

Atmospheric CO2 concentration (ppm)

B1 B2

Low Emissions Medium-Low Emissions Medium-High Emissions High Emissions

2.0 2.3

525 562

3.3

715

3.9

810

A2 A1F1

An application of the UKCIP02 climate change scenarios to flood estimation average rainfall for the winter and summer half-years for each of the four UKCIP02 scenarios (see below). HadCM3 produces estimates of climate change at the global scale (e.g., each HadCM3 grid box for the UK is of the order of 250–300 km; Hulme et al., 2002). In order to provide estimates at a (Europe) regional scale, the output from HadCM3 has been used to drive a high resolution model of the global atmosphere (HadAM3H), and the output from this model has been used to drive a regional model of the European atmosphere (HadRM3; Hulme et al., 2002). HadRM3 utilises grid boxes of the order of 50 km. The ‘‘Medium-High Emissions’’ UKCIP02 scenario for the 2080s (2071–2100) was produced from an ensemble of three HadRM3 simulations. Pattern-scaling of this scenario (using estimated changes to global average temperature) was used to produce a ‘‘Medium-High Emissions’’ UKCIP02 scenario for two additional time-slices (the 2020s, 2011–2040, and the 2050s, 2041–2070), and the other three UKCIP02 scenarios for each 30 year time-slice (Hulme et al., 2002). The regional modelling output contains many climate variables, including temperature and rainfall (but not potential evapotranspiration, see below). Data are available for a baseline 1961–1990 simulation and for the three future time slices. In this study, the regional modelling output for all four of the UKCIP02 scenarios for the 2080s was used. The 2080s were selected because this time slice is the most relevant to long term development plans (e.g., in terms of housing and flood protection).

219

In addition, in order to explore the uncertainty associated with the model structure of HadCM3, the uncertainty margins suggested by Hulme et al. (2002) were considered. Of the four UKCIP02 scenarios, the ‘‘High Emissions’’ scenario has the largest uncertainty margins. Indeed, the uncertainty 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 also lists the temperatures estimated for the 1961–1990 baseline scenario) over all four of the UKCIP02 scenarios. In this study, the ‘‘High Emissions’’ uncertainty margins were used in order to generate two additional climate change scenarios: the ‘‘H-Dry’’ scenario (the largest increases to temperature change and the largest decreases to rainfall available from the ‘‘High Emissions’’ scenario uncertainty margins; Table 4) and the ‘‘H-Wet’’ scenario (the largest decreases to temperature change and the largest increases to rainfall available from the ‘‘High Emissions’’ scenario uncertainty margins; Table 4). Within the limitations of the uncertainty margins suggested by Hulme et al. (2002), these two scenarios encapsulate the upper and lower limits of the (climate change induced) changes to daily rainfall and daily mean temperature estimated by GCMs other than HadCM3. (Please note that other possible uncertainties associated with GCM/RCM simulation, such as the possible ‘‘dampening’’ of extreme rainfalls associated with convective storms as a result of the rainfall averaging procedure used in the climate models, are not

Table 4 Hulme et al.’s (2002) suggested uncertainty bounds to be applied to the UKCIP02 High Emissions scenario of changes in average winter and summer temperature and precipitation, together with an implementation of those uncertainty bounds as the H-Dry and H-Wet scenarios presented in this paper

High H-Dry H-Wet

Winter average temperature (°C)

Summer average temperature (°C)

Winter average precipitation (%)

Summer average precipitation (%)

±2 +2 2

±2 +2 2

±20 20 +20

+40 0 +40

Table 5 Estimated percentage changes to daily rainfall on a monthly basis for HadRM3 grid box 145 for the climate change scenarios for the 2080s Month

Low percentage change

Med-Low percentage change

Med-High percentage change

High percentage change

H-Dry percentage change

H-Wet percentage change

January February March April May June July August September October November December

12.36 10.93 5.96 0.45 5.28 11.40 15.87 15.48 9.46 1.62 4.49 9.34

14.46 12.79 6.98 0.53 6.17 13.34 18.57 18.11 11.06 1.89 5.26 10.92

20.34 17.98 9.81 0.75 8.68 18.76 26.10 25.46 15.56 2.66 7.39 15.36

23.98 21.20 11.57 0.88 10.24 22.12 30.79 30.03 18.34 3.14 8.72 18.11

3.98 1.20 8.43 0.88 10.24 22.12 30.79 30.03 18.34 23.14 11.28 1.89

43.98 41.20 31.57 40.88 29.76 17.88 9.21 9.97 21.66 16.86 28.72 38.11

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D. Cameron

Table 6 Current conditions (1961–1990) daily mean temperature, together with estimated increases to daily mean temperature under each climate change scenario, as estimated for HadRM3 grid box 145 for the 2080s

January February March April May June July August September October November December

Current temperature (°C)

Low temperature increase (°C)

Med-Low temperature increase (°C)

Med-High temperature increase (°C)

High temperature increase (°C)

H-Dry temperature increase (°C)

H-Wet temperature increase (°C)

2.12 2.3 3.21 5.3 8.2 10.79 12.17 11.9 9.94 7.05 4.42 2.73

1.29 1.3 1.44 1.56 1.61 1.67 1.82 2.02 2.11 1.99 1.73 1.46

1.51 1.52 1.68 1.82 1.89 1.96 2.13 2.37 2.46 2.32 2.02 1.71

2.13 2.14 2.36 2.56 2.65 2.75 3 3.33 3.46 3.27 2.84 2.4

2.51 2.52 2.78 3.02 3.13 3.24 3.54 3.92 4.09 3.85 3.35 2.83

4.51 4.52 4.78 5.02 5.13 5.24 5.54 5.92 6.09 5.85 5.35 4.83

0.51 0.52 0.78 1.02 1.13 1.24 1.54 1.92 2.09 1.85 1.35 0.83

considered in this paper beyond the extent of Hulme et al.’s, 2002, uncertainty margins.) This study therefore considers a total of six climate change scenarios: the four UKCIP02 scenarios with no modification, and the ‘‘H-Dry’’ and ‘‘H-Wet’’ scenarios. Tables 5 and 6 list the estimated changes to daily rainfall and daily mean temperature (on a monthly basis) for each scenario for the HadRM3 grid box which includes the Lossie catchment (box number 145). From Table 5, it can be seen that, for the 2080s, the four UKCIP02 scenarios estimate increases to rainfall between November and April, with decreases during the other months (the smallest changes are associated with the ‘‘Low Emissions’’ scenario and the largest changes are associated with the ‘‘High Emissions’’ scenario). The ‘‘H-Dry’’ scenario estimates increased rainfall in January, February and April with decreases during the other months. The ‘‘H-Wet’’ scenario estimates increased rainfall in every month (note that, of all of the scenarios considered in this study, this scenario estimates the largest increases to rainfall). Temperature increases are estimated for all six of the scenarios (Table 6), with the largest increases being associated with the ‘‘H-Dry’’ scenario. In order to perturb the TOPMODEL/stochastic rainfall model simulations, estimated percentage changes to both rainfall (Table 5) and potential evapotranspiration were required from each climate change scenario. However, changes to potential evapotranspiration were not available and had to be estimated from certain of the other variables simulated from the regional output. Originally, it was intended to use a Penman–Monteith type approach to estimate potential evapotranspiration. However, Ekstro ¨m et al. (submitted for publication) suggest that using such an approach with HadRM3 output might produce estimates which are physically unrealistic for future climates. They suggest that simpler, temperature based approaches might be more appropriate. Recourse was therefore made to using the Thornthwaite (1948) method in order to estimate daily potential evapotranspiration for each calendar month from the regional temperature data simulated for the 1961–1990 baseline,

and for each climate change scenario. It is emphasised that this method was used only in order to provide estimates of relative percentage changes to potential evapotranspiration (which could be used to perturb TOPMODEL’s PET parameter, see below). The estimated values of potential evapotranspiration themselves were not used in the modelling process. Table 7 lists the estimated percentage changes. It is interesting to note that, for the four UKCIP02 scenarios, and the ‘‘H-Dry’’ scenario, the percentage changes are estimated as being higher in the winter than in the summer. This is simply because the estimated changes to daily temperature are proportionally higher in the winter months than in the summer months (e.g., for the Medium-High scenario, the temperature for January is estimated as being 2.12 °C for 1961–1990, but rising by 2.13 °C in the 2080s; the temperature for July is estimated as being 12.17 °C for 1961–1990, and rising by 3 °C in the 2080s, Table 6). With respect to the H-Wet scenario, certain of the winter months (e.g., October) are estimated to have higher percentage changes to potential evapotranspiration than are estimated for the summer months. For other winter months (e.g., March), the opposite is observed (indeed, reductions to potential evapotranspiration are estimated for the months of January and February). This can be explained as follows. Although the ‘‘H-Wet’’ scenario employs a uniform decrease (2 °C) to the changes to daily mean temperature estimated under the ‘‘High Emissions’’ scenario (Tables 4 and 6), this decrease affects the size of the change in daily mean temperature by a different proportion for each month. For example, the temperature change for January is 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 August is estimated as being 3.92 °C under the ‘‘High Emissions’’ scenario, but 1.92 °C under the ‘‘H-Wet’’ scenario (i.e., about 32% of the temperature change estimated under the ‘‘High Emissions’’ scenario). In addition, the Thornthwaite (1948) method uses a heat index in the calculation of potential evapotranspiration.

An application of the UKCIP02 climate change scenarios to flood estimation

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Table 7 Estimated percentage changes to potential evapotranspiration (estimated using the Thornthwaite, 1948, method) for HadRM3 grid box 145 for the climate change scenarios for the 2080s

January February March April May June July August September October November December

Low percentage change

Med-Low percentage change

Med-High percentage change

High percentage change

H-Dry percentage change

H-Wet percentage change

15.22 13.80 10.68 6.11 3.72 3.49 4.39 5.72 6.98 8.29 10.51 13.87

16.86 15.30 11.92 6.97 4.46 4.22 5.24 6.81 8.12 9.52 11.89 15.52

20.75 18.90 15.12 9.43 6.36 6.20 7.71 9.79 11.47 13.10 15.55 19.32

22.62 20.65 16.82 10.88 7.64 7.48 9.30 11.65 13.61 15.16 17.64 21.35

34.46 32.23 26.22 17.97 13.62 13.28 15.11 17.31 19.03 20.74 24.63 31.05

2.25 2.63 1.58 2.14 1.65 2.17 4.12 6.59 8.65 9.45 8.53 4.49

This heat index is derived from the daily mean temperatures (for each month) for the year as a whole. The estimated change in potential evapotranspiration for a given month is therefore dependent upon the estimated daily mean temperature for that month and the heat index for the year (together with other fixed parameters used in the Thornthwaite, 1948, method). In the case of the reductions to potential evapotranspiration estimated for January and February, the increases to the temperatures estimated for those months are proportionally much smaller (relative to the increases estimated for the other months) than the increase in the heat index. As a result, potential evapotranspiration is estimated to decrease for January and February but increase from March to December (and the year as a whole). With respect to the actual process of perturbing the TOPMODEL/stochastic rainfall model simulations, it was assumed that the percentage changes estimated for rainfall and potential evapotranspiration for HadRM3 grid box 145 could be applied directly to the scale of the Lossie catchment. This assumption was made on the basis that there were insufficient observed data available for the catchment in order to allow adequate downscaling of the percentage changes (obtained from the regional climate modelling) to the catchment scale (as described in ‘‘The study site’’, there is only one raingauge in the catchment with a reasonable length of record; furthermore, SEPA does not hold any temperature or other climatological data for this catchment which could be used on a catchment-wide basis for downscaling). The following procedure was adopted in order to estimate the influence of climate change upon flood frequency for each UKCIP02 scenario. The stochastic rainfall model was used to drive a two thousand year continuous simulation (with hourly timestep) for each of the one thousand behavioural TOPMODEL parameter sets. There are many alternative approaches to perturbing simulated rainfall data (including changing storm depths, Cameron et al., 2000b, and/or storm durations and inter-arrival times) and each approach may affect the magnitude and direction of any

change in flood frequency to a different extent. The UKCIP02 scenarios do not provide quantitative information on possible changes to storm characteristics (such as inter-arrival times or storm durations). The scenarios only include changes to daily rainfall amounts. As the purpose of this study was to utilise information directly from the UKCIP02 scenarios and apply it to the catchment scale, only changes to rainfall amounts were considered. However, it is recognised that changing other storm characteristics may result in a different magnitude of change to flood frequency to that reported here. The perturbation to rainfall (for a given month within the simulated sequence) was achieved by uniformly applying the percentage change to rainfall (as estimated from the climate change scenario for that month) to all rainfall amounts within that month. All of the scenarios featured the perturbation of an identical hourly rainfall series to that generated under current climatic conditions. This was done in order to prevent the possible impacts of climate change being confounded with random rainfall realisation (where rainfall realisation refers to the random sampling process used by the stochastic rainfall model to generate rainstorms; see Cameron et al., 1999). Changes to potential evapotranspiration were achieved through the perturbation of the PET variable used in TOPMODEL. For a given month within the simulation, this perturbation was calculated as PET r ¼ PET i þ

PET i  CHG ; 100

ð5Þ

where PETr is the value of PET used in a model run for that month, PETi is the value of PET calculated from rainfall and runoff for the catchment (0.0509 mm h1), and CHG is the percentage change estimated for that month (Table 7). Upon completion of the two thousand year simulations, the likelihood weighted uncertainty bounds for the hourly AMAX flood peaks were calculated using the procedures outlined in ‘‘The GLUE framework’’. These were compared with those already available for the current climatic conditions.

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D. Cameron

Results and discussion

300

300

250

250

200

200

Q [m3/s]

Q [m3/s]

Fig. 3a–d illustrates the 95% likelihood weighted uncertainty bounds (and median simulation) associated with current climatic conditions and for the ‘‘Low Emissions’’ and ‘‘High Emissions’’ UKCIP02 scenarios (these two scenarios are chosen for illustration as they encompass the range of flood frequency curves generated under all four of the UKCIP02 scenarios), and the ‘‘H-Dry’’ and ‘‘H-Wet’’ climate change scenarios, respectively. Observed AMAX data (obtained from the period 1958–2003) are also shown. Fig. 4 illustrates the cdfs calculated using the scaled likelihood weights and discharge estimates (see ‘‘The GLUE framework’’) for the 1 in 200 year return period flood simulated under current conditions, and under all six climate change scenarios. From these results, it can be seen that flood magnitude changes under each climate change scenario. As might be expected, the magnitude of the change is dependent upon the choice of scenario (and therefore the choice of estimated changes to rainfall and potential evapotranspiration associated with that scenario). The direction of the change is also dependent upon this choice. For example, under the ‘‘H-Dry’’ scenario (Figs. 3c and 4), flood magnitude decreases. However, under all of the other scenarios flood magnitude increases (with the smallest increases being

associated with the ‘‘Low Emissions’’ UKCIP02 scenario and the largest increases with the ‘‘H-Wet’’ scenario; the ‘‘H-Wet’’ scenario results are perhaps unsurprising given the large percentage increases adopted for rainfall in each month, Table 5). The range of these results therefore supports the consideration of output from multiple climate change scenarios and GCMs (as has been approximated in this study). It can also be seen that, for the ‘‘H-Dry’’ scenario and the four UKCIP02 scenarios, there is an overlap in the uncertainty bounds estimated for each climate change scenario and those estimated for the current climate. This overlap was also observed by Cameron et al. (2000b) in applying the UKCIP98 ‘‘Medium High’’ scenario for flood estimation for the Wye catchment, Plynlimon, Wales. Given the uncertainty estimation procedure used in this study, this finding indicates that there are uncertainties associated with the observed data series and hydrological model structure and that those uncertainties should be considered explicitly (as has been done in this study). (The lack of overlap in the ‘‘H-Wet’’ scenario can be explained by the very high percentage increases in daily rainfall and smaller increases in daily mean temperature adopted for this scenario, Tables 4–6. These changes cause a much larger change to flood magnitude than is exhibited under the other climate change scenarios.)

150

150

100

100

50

50 0

0 1

10

1

1000

300

250

250

200

200

150

100

1000

100

1000

T [yrs]

300

150

100

100

50

50

0

0

1

c

10

b

T [yrs]

Q [m3/s]

Q [m3/s]

a

100

10

100

T [yrs]

1000

1

d

10

T [yrs]

Figure 3 95% likelihood weighted uncertainty bounds derived from annual maximum peaks of 1000 behavioural TOPMODEL parameter sets, driven using the stochastic rainfall model, with 2000 year hourly simulation length. Circles – observed data; dashed lines – 95% uncertainty bounds obtained under climate change scenario for the 2080s; dotted line – median simulation obtained under climate change scenario for the 2080s; solid lines – 95% uncertainty bounds and median simulation obtained under current climatic conditions. a: ‘‘Low Emissions’’ UKCIP02 scenario; b: ‘‘High Emissions’’ UKCIP02 scenario; c: ‘‘H-Dry’’ scenario; d: ‘‘HWet’’ scenario.

An application of the UKCIP02 climate change scenarios to flood estimation Table 8 contains the hourly discharge estimates for the 10, 25, 50, 75, 100 and 200 year return period AMAX flood events obtained from the 0.025, 0.5 and 0.975 model simulations. Table 8 also expresses the climate change flood estimates in terms of percentage difference from the current climate estimates. These results raise several interesting issues. Firstly, it can be seen that, for the four UKCIP02 scenarios and the ‘‘H-Wet’’ scenario, the percentage differences generally tend to increase with return period. However, for the ‘‘H-Dry’’ scenario, the opposite occurs. This finding can be explained by the locations within the simulated rainfall sequence at which the different percentage changes in rainfall have been implemented. For example, the long return period floods are simulated during months which have the smallest percentage decreases in rainfall under the ‘‘H-Dry’’ scenario, but have fairly large percentage increases in rainfall under the other scenarios (e.g., December and March; Table 5). Secondly, with the exception of the ‘‘H-Wet’’ scenario, the percentage changes to flood flows estimated for each scenario are smaller than the highest estimated percentage changes to rainfall for that scenario. In the case of the ‘‘HWet’’ scenario, the percentage changes estimated for the flood flows are higher than the highest estimated percentage changes to rainfall estimated for that scenario (e.g., the largest change estimated for rainfall is 43.98% in January, Table 5, but the lowest increase to flood flows is 49% for the median simulation of the 1 in 10 year return period event). These results can be explained by the nonlinearity of 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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catchment is simulated as having enhanced antecedent soil moisture conditions, leading to an increase in flood magnitude when storms occur. Under the other scenarios, rainfall is estimated to decrease for some months and increase for others (Table 5). Potential evapotranspiration is also generally estimated to increase by a greater amount in the other scenarios than in the ‘‘H-Wet’’ scenario (Table 7). Taken together, these factors influence the catchment’s antecedent soil moisture conditions to a different extent to the ‘‘HWet’’ scenario and result in a different magnitude of storm response. Interestingly, these findings also suggest that the flood characteristics of the Lossie catchment are more sensitive to the changes estimated to rainfall than those estimated for potential evapotranspiration (given the modelling approach used in this study). Similar findings were also obtained by Cameron et al. (2000b) for the Wye catchment, Wales, UK. Overall, these results therefore support the use of rainfall-runoff modelling for examining the possible effects of climate change upon flood frequency. Thirdly, while differences in catchment, climate change modelling approach, and slight differences in the GLUE procedure preclude a strict quantitative comparison with the earlier study of Cameron et al. (2000b), it is interesting to note that, in general terms, the percentage changes associated with the 1 in 100 year return period flood event estimated under the ‘‘Medium-High Emissions’’ UKCIP02 scenario (Table 8) for the Lossie catchment, are similar, if slightly higher, to those estimated for the Wye catchment under the UKCIP98 ‘‘Medium-High’’ scenario. For example, the median simulation for the 2080s for the Wye is estimated as being about 8% higher by the 2080s than under current climatic conditions (where rainfall and potential

1

0.9

0.8

0.7

cdf

0.6

0.5

0.4

Current conditions Low Emissions Medium-Low Emissions

0.3

0.2

Medium-High Emssions High Emissions H-Dry H-Wet

0.1

0 100

150

200

250

300

350

Q (m3/s)

Figure 4 Cumulative density functions calculated using scaled likelihood weights and discharge estimates for the 200 year return period flood event for current climatic conditions, and for the ‘‘Low Emissions’’, ‘‘Medium-Low Emissions’’, ‘‘Medium-High Emissions’’, ‘‘High Emissions’’, ‘‘H-Dry’’ and ‘‘H-Wet’’ climate change scenarios for the 2080s.

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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 0.975 model 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.

Current

0.025 0.5 0.975

80 88 95

n/a n/a n/a

98 107 119

n/a n/a n/a

110 121 138

n/a n/a n/a

117 129 150

n/a n/a n/a

122 136 158

n/a n/a n/a

132 151 181

n/a n/a n/a

Low

0.025 0.5 0.975

81 89 96

1 1 1

100 109 121

2 2 2

112 125 142

2 3 3

120 134 155

3 4 3

125 141 165

2 4 4

137 156 187

4 3 3

Med-Low

0.025 0.5 0.975

81 89 97

1 1 2

101 110 123

3 3 3

114 126 144

4 4 4

122 136 158

4 5 5

127 143 167

4 5 6

139 160 189

5 6 4

Med-High

0.025 0.5 0.975

84 92 99

5 5 4

104 114 128

6 7 8

119 131 150

8 8 9

126 142 165

8 10 10

131 150 174

7 10 10

145 168 200

10 11 10

High

0.025 0.5 0.975

85 93 101

6 6 6

106 117 131

8 9 10

121 135 155

10 12 12

130 146 169

11 13 13

135 154 180

11 13 14

149 172 208

13 14 15

H-Dry

0.025 0.5 0.975

73 79 86

9 10 9

89 98 108

9 8 9

102 111 126

7 8 9

109 120 137

7 7 9

113 127 146

7 7 8

125 141 168

5 7 7

H-Wet

0.025 0.5 0.975

120 131 143

50 49 51

147 161 181

50 50 52

166 184 210

51 52 52

178 198 230

52 53 53

185 208 246

52 53 56

205 233 278

55 54 54

Percentage changes (%chg.) between the flood flows estimated under the current climate and under the climate change scenarios are also shown (to the nearest whole percentage).

evapotranspiration were perturbed in a similar manner to the present study); the corresponding simulation for the Lossie is about 10% higher than under current climatic conditions. In addition, Cameron et al. (2000b) estimated the current 1 in 100 year return period event for the Wye (as derived from the median simulation) as having a recurrence of between 1 in 48 and 1 in 60 years by the 2080s. Interestingly, the current 1 in 100 year return period for the Lossie is estimated (under the median model simulation) as having a 1 in 60 year return period by the 2080s under the ‘‘Medium-High Emissions’’ scenario of UKCIP02 (Table 9; for each climate change scenario Table 9 also lists the future return periods estimated for the flood flows which are estimated as having return periods of 10, 25, 50, 75 and 200 years under

current climatic conditions). While this (qualitative) consistency with the earlier study is encouraging, it is worth remembering that that study considered output from only one UKCIP98 scenario. When all six of the scenarios in the present study are examined (Table 9), it is apparent that the magnitude and direction of the change estimated to flood return period (Table 9) depends upon the choice of climate change scenario (as per the flood magnitude results, above). For example, with respect to the median simulation, the present day 1 in 200 year event is estimated to have a return period of 1 in 159 years under the ‘‘H-Dry’’ scenario but only 1 in 18 years, under the ‘‘H-Wet’’ scenario. The findings of the present study therefore highlight the importance of considering a range of climate change scenarios (as per Prudhomme

Table 9 Estimated return periods (T) for the 2080s for flood flows which are estimated as having return periods of 10, 25, 50, 75, 100 and 200 years under current climatic conditions (as derived from the median model simulation, Table 8) Current T (yr)

Low T (yr)

Med-Low T (yr)

Med-High T (yr)

High T (yr)

H-Dry T (yr)

H-Wet T (yr)

10 25 50 75 100 200

9 23 42 61 81 159

9 22 40 56 75 148

8 19 33 46 60 108

8 17 29 40 52 93

15 39 78 114 159 332

3 5 7 9 12 18

The results are shown for each climate change scenario.

An application of the UKCIP02 climate change scenarios to flood estimation et al., 2003) and of using some form of uncertainty estimation (such as GLUE) when estimating the possible effects of climate change upon flood frequency. It is suggested that these factors need to be taken into account in order to make well informed flood management decisions. For instance, where practicable, new flood prevention schemes could be designed to be adaptive (e.g., flood banks with the capacity to be raised at a later date). Development control (e.g., new housing schemes) is a more difficult issue (as once a development is in it generally cannot be easily moved or modified!). The author suggests that enhanced dialogue between the scientific and political communities could lead to improved (and preferably consistent) guidance to the practicing hydrologist dealing with development control (e.g., in Scotland, UK, it is often the responsibility of the individual hydrologist to strike an appropriate balance between a precautionary approach to flood risk, such as design flood levels, and what can be realistically achieved on site).

225

(2000b), was also observed. However, this earlier study used the output from only one scenario from a single GCM as the starting point for exploring the possible effects of climate change upon flood frequency. By considering multiple climate change scenarios, this new study has demonstrated that while flood magnitude (and return period) is estimated to change under each climate change scenario, the magnitude and direction of that change is dependent upon the choice of scenario. For example, flood magnitude decreases under the ‘‘H-Dry’’ scenario, but increases under the other scenarios considered in this paper (the smallest increase was associated with the ‘‘Low Emissions’’ scenario and the highest increase with the ‘‘H-Wet’’ scenario). 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.

Acknowledgements Conclusions This paper has used the UKCIP02 climate change scenarios as a basis for extending the work of Cameron et al. (2000b) in order to explore the possible effects of climate change upon flooding for a gauged catchment with limited data availability (the Lossie catchment, Scotland, UK). A variant of the continuous simulation methodology developed by Cameron et al. (1999) was used. The methodology utilises a stochastic rainfall model to drive the rainfall-runoff model TOPMODEL for a series of continuous two thousand year simulations with hourly timestep. The uncertainty in the resulting hourly annual maximum flood peaks is handled within the GLUE framework of Beven and Binley (1992). The ‘‘Low Emissions’’, ‘‘Medium-Low Emissions’’, ‘‘Medium-High Emissions’’ and ‘‘High Emissions’’ UKCIP02 climate change scenarios, derived from the HadCM3 GCM and HadRM3 RCM (Hulme et al., 2002) were used at the catchment scale. The ‘‘High Emissions’’ scenario uncertainty margins suggested by Hulme et al. (2002) for comparing HadCM3 and HadRM3 output with other GCM climate change scenarios were used to generate two additional climate change scenarios (‘‘H-Dry’’ and ‘‘H-Wet’’). The implementation of the climate change scenarios at the catchment scale featured the uniform application of the changes to the daily rainfall totals (for each calendar month) estimated by from the regional modelling output to the hourly level. Changes to potential evapotranspiration were also estimated from the regional modelling output and these were used to perturb the parameter of the potential evapotranspiration model used by TOPMODEL. The results for the ‘‘Medium-High Emissions’’ UKCIP02 scenario for the 1 in 100 year return period flood event were found to be qualitatively similar to the results obtained in the earlier study of Cameron et al. (2000b) (this study featured the application of the ‘‘Medium-High’’ UKCIP98 to flood estimation on the Wye catchment, Wales). The overlap in the likelihood weighted uncertainty bounds (as estimated for the conditions of the current climate and those estimated under climate change), noted by Cameron et al.

The author gratefully acknowledges Keith Beven’s comments on the first draft of this paper and the use of David Tarboton’s TauDEM software in deriving TOPMODEL’s topographic index. The UKCIP02 scenarios were made available by DEFRA. ( c Crown Copyright 2002. The UKCIP02 Climate Scenario data have been made available by the Department for Environment, Food and Rural Affairs (DEFRA). DEFRA accepts no responsibility for any inaccuracies or omissions in the data nor for any loss or damage directly or indirectly caused to any person or body by reason of, or arising out of any use of, this data.) The comments of two anonymous referees assisted in the revision of this paper. The opinions expressed in this paper are those of the author and do not necessarily reflect the view of the Scottish Environment Protection Agency.



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