Journal of Hydrology 409 (2011) 1–19

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Journal of Hydrology

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Climate change impact on the hydrology of Spencer Creek watershedin Southern Ontario, Canada

M.G. Grillakis a, A.G. Koutroulis a, I.K. Tsanis b,a,⇑a Department of Environmental Engineering, Technical University of Crete, Chania GR73100, Greeceb Department of Civil Engineering, McMaster University, Hamilton, ON, Canada L8S 4L7

a r t i c l e i n f o s u m m a r y

Article history:Received 28 October 2010Received in revised form 27 June 2011Accepted 30 June 2011Available online 12 July 2011This manuscript was handled byKonstantine P. Georgakakos, Editor-in-Chief

Keywords:Climate-change impactsSpencer CreekOntarioFuture extremesBias correctionCanada

0022-1694/$ - see front matter � 2011 Elsevier B.V. Adoi:10.1016/j.jhydrol.2011.06.018

⇑ Corresponding author at: Department of Civil Ensity, Hamilton, ON L8S 4L7, Canada.

E-mail addresses: tsanis@mcmaster.ca, tsanis@hyd

This study is for the assessment of climate change impact on the future hydrology of Spencer Creekwatershed located in Southern Ontario, Canada under the A2 scenario of the Special Report on EmissionsScenarios (SRES). The study is particularly concerned with changes in the climate variables and the sea-sonal and interannual flow regimes of the study area. The analysis also addresses the annual exceedanceprobability of extreme precipitation, temperature and flow events. Potential hydrologic effects of climatechange were assessed for the Spencer Creek by imposing changes in precipitation and temperaturederived from the North American Regional Climate Change Assessment Program (NARCCAP) climate sim-ulations between 2040 and 2069. The climate models results were used as input to three hydrologicalmodels to produce projections of Spencer Creek watershed discharges. The results were compared tothe observed discharges between 1989 and 2008. Notwithstanding the variability between the differentregional climate model and hydrological model projections that envelop the future climate scenarios andthe hydrological modeling uncertainties, all future simulations show an increase in the average interan-nual discharge, but also a noteworthy change in the seasonal distribution of the discharges. While theformer is mainly attributed to the average annual precipitation, which tends to increase, the change inseasonal distribution of discharges is in line with the temperature increase of the winter and spring sea-sons that results in earlier snowmelt. Important changes were found in the annual exceedance probabil-ity (recurrence interval) of the extreme precipitation, temperature and runoff events.

� 2011 Elsevier B.V. All rights reserved.

1. Introduction

The prediction of the forthcoming climate change at regionalscales is vital for climate change adaptation issues. The climate im-pact studies are based on scenarios that reflect different social bal-ances between the world and local growth, the financial andenvironmental values. Several climate scenarios established bythe Special Report on Emissions Scenarios – SRES (IPCC SRES,2000) are the research basis of international climate-change pro-jects. In this study, the NARCCAP future climate projections wereused, in order to assess the climate change impact on SpencerCreek watershed future hydrology. The gas emission scenario usedin NARCCAP future climate simulations was the A2 scenario, as de-scribed in the Special Report on Emissions Scenarios (Nakicenvoicet al., 2000). It is characterized by a heterogeneous world, withself-reliance and preservation of local identities to be emphasizedand continuous population increase, reaching total population over10 billion people worldwide by 2050. This scenario describes

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gineering, McMaster Univer-

romech.gr (I.K. Tsanis).

regional orientation of the economic development while the tech-nological development is relatively slow, compared to the otherscenarios. This study uses the Global Climate Model (GCM) drivenRegional Climate Models (RCMs) used in the NARCCAP experi-ments. The projections of climate models extend from 2040 to2069. The RCM climate data were bias corrected using daily precip-itation and mean daily temperature data for 1989–2008, collectedby Environment Canada. In order to get better insight into the cor-relation between climate change and water resources, climaticdrivers data of past and future are used as an input to models totranslate the assumed climate changes into hydrological responses(Middelkoop et al., 2001). In the past two decades, many such stud-ies have been conducted, such as those described in Leavesley(1994), Ozkul (2009) and Forbes et al. (2010).

Precipitation and temperature are the most dominant climatedrivers for river hydrology. The importance of temperature be-comes even greater in snow-dominated basins where it controlsthe snowmelt process during the late-winter and spring months.A warmer winter leads to a greater amount of days experiencingtemperatures above zero Celsius degrees resulting in more fre-quent rain events. Thus, the runoff increases while the snow accu-mulation is reduced (Whitfield et al., 2003), which affects not only

Table 1Thresholds of SPI for drought characterization (McKee et al.,1993).

SPI value Category

2 or more Extremely wet1.5–1.99 Severely wet1–1.49 Moderately wet0–0.99 Mildly wet0 to �0.99 Mildly dry�1 to �1.49 Moderately dry�1.5 to �1.99 Severely dry�2 or less Extremely dry

Table 2Distribution types used for extreme-value analysis and corresponding parameters.

Distribution Parameters

1 EV1-Max (Gumbel) lambda, psi2 EV1-Max (Gumbel, L-Moments) lambda, psi3 EV1-Min (Gumbel) lambda, psi4 EV1-Min (Gumbel, L-Moments) lambda, psi5 EV2-Max kappa, lambda6 EV2-Max (L-Moments) kappa, lambda7 EV3-Min (Weibull) kappa, lambda8 EV3-Min (Weibull, L-Moments) kappa, lambda9 Exponential lambda, psi

10 Exponential (L-Moments) lambda, psi11 Galton my, sy, psi12 Gamma kappa, lambda13 GEV-Max kappa, lambda, psi14 GEV-Max (kappa specified) kappa, lambda, psi15 GEV-Max (kappa specified, L-Moments) kappa, lambda, psi16 GEV-Max (L-Moments) kappa, lambda, psi17 GEV-Min kappa, lambda, psi18 GEV-Min (kappa specified) kappa, lambda, psi19 GEV-Min (kappa specified, L-Moments) kappa, lambda, psi20 GEV-Min (L-Moments) kappa, lambda, psi21 Log Pearson III kappa, lambda, psi22 Log Normal my, sy

23 Normal my, sy

24 Normal (L-Moments) my, sy

25 Pareto kappa, lambda, psi26 Pareto (L-Moments) kappa, lambda, psi27 Pearson III kappa, lambda, psi

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the runoff, but also the snowpack parameters such as total snowdepth and snow-cover duration. The snow depth and snow coveraffect the heat exchange between the ground and the atmosphereand thus, the temperature of the ground (Zhang, 2005). The poten-tial impact of a warmer winter on the length of snow season andthe snowmelt distribution is more complex, with earlier springsnowmelt to lengthen the soil heating season and thus, leadingto a later snow accumulation season start (Lawrence and Slater,2010). A shallower winter snowpack provides less insulation fromcold winter air temperatures, thereby freezing the soil to greaterdepths, whereas a deeper snowpack provides more insulationresulting in shallower freezing depth of the soils.

The uncertainty of future climate predictions is very high. Theclimate models consist the larger source of uncertainty in climatechange impact studies. However, forcing climate model data intohydrological models is the only method that could be applied topredict future changes in hydrology due to climate change. Min-ville et al. (2008) studied the uncertainties of climate change im-pact on the hydrology of the Chute-du-Diable watershed inCanada and concluded that of all sources of uncertainty considered,the largest comes from the choice of a GCM, indicating that impactstudies based on results from only one GCM should be interpretedwith caution. Many studies have been conducted on how to reducethe uncertainty in future climate predictions. Dibike and Coulibaly(2007) stated that hydrological simulations based on precipitationand temperature data downscaled from GCM outputs of the base-line climate should approximate the observed river flows reason-ably well, otherwise it would be very difficult to rely on thisapproach to estimate the possible hydrological impact of any cli-mate-change scenario for the future. Coulibaly (2009) emphasizedthe advantage of a multi-model approach, to both downscaling andhydrologic modeling, and highlighted uncertainty in estimatinghydrological impact of climate change for the Serpent watershedin northeastern Canada. A Bayesian Neural Network (BNN) ap-proach was presented by Khan and Coulibaly (2010) for the esti-mation of uncertainties from global climate models, emissionscenarios, downscaling methods, and hydrologic models for theassessment of the hydrologic effect of climate change for two wa-tershed in northeastern Canada. They suggest that the BNN modelcould be a good alternative method where resources are not avail-able to implement the general multi-model ensembles approach.Kerkhoven and Gan (2011) examined the unconditional sampleof uncertainty of observed streamflows and simulated historicaland future streamflows from a hydrologic model, driven by GCMdata, for two watersheds in Western Canada, using multifractalanalysis. They concluded that the uncertainties associated withmultifractal variation were in the order of ±50%. Studying the po-tential impacts of climate change in southern British Columbiaon the causes of flood flows using the Canadian Centre for ClimateModelling Analysis General Circulation Model (CGCMA1) simula-tion results, Loukas et al. (2002) found that the future climatewould be wetter and warmer than the present climate. They alsofound that the overall flood magnitude and frequency would in-crease. Dibike and Coulibaly (2005) compared two downscalingmethods and two hydrologic models to study the hydrologic im-pact of climate change in the Saguenay watershed in northern Que-bec, Canada. Downscaled results indicated a general increasingtrend in both the mean daily temperature and variability of dailyprecipitation values. Their hydrologic impact analysis suggestedan overall increasing trend in mean annual river flow and reservoirinflow as well as earlier spring peak flows in the basin.

The assessment of climate change impacts in hydrology of anarea is of great importance because it could affect the seasonal orlong-term water availability, or the increased frequency that ex-treme events with disastrous socio-economic consequences mayoccur. Thus, long-term development plans should consider the po-

tential future climate change. The recent study of Sultana and Cou-libaly (2010) focuses on future changes in the hydrologicalprocesses of Spencer Creek watershed in southern Ontario, whichis a similar study site of the present research. For the SRES A2 fu-ture climate-change scenario, the downscaled GCM results indi-cated an increase of approximately 15% in the annual meanprecipitation and 2–3 �C increase in annual mean maximum andminimum temperatures for the 2046–2065 period. The coupledMIKE SHE/MIKE 11 hydrologic model resulted in a 1–5% annual de-crease in snow storage, 1–10% increase in annual ET, a 0.5–6%decrease in the annual groundwater recharge, and an approxi-mately 10–25% increase in annual streamflows, for the same peri-od in Spencer Creek watershed.

Several scenarios of future climate indicate a likelihood of in-creased intense ‘dry and hot’ extremes for many regions aroundthe world (Beniston et al., 2007; Christensen and Christensen,2003; Kundzewicz et al., 2005; Semmler and Jacob, 2004; Kundze-wicz et al., 2006; Easterling et al., 2000; Tsanis et al., 2011). The po-tential for intense precipitation is likely to increase in the warmerclimate of the future, contributing to the growth of flood hazard inareas where inundations are typically triggered by heavy rain(Kundzewicz et al., 2006). However, the question remains as towhether or not the frequency and/or magnitude of extremes is alsoincreasing and, if so, whether it is in response to climate variabilityand change (Kundzewicz et al., 2005). Floods and droughts are

Fig. 1. Study area–Spencer Creek, Ontario, Canada.

M.G. Grillakis et al. / Journal of Hydrology 409 (2011) 1–19 3

complex processes triggered by different mechanisms and are af-fected by different ways by climate change, and thus blanketstatements on the direction of change in extremes are mostprobably inaccurate. The discrimination of any tendency with re-gard to the extreme climatic events that are by definition rareshould be handled with great care and formal uncertaintyanalysis.

This study examined the potential interannual and seasonal cli-mate change on Spencer Creek located in Southern Ontario, Canada,and the future impact on its hydrology through a multi-model ap-proach. The study also elaborates on the statistical significanceand uncertainty of multi-model climate and rainfall–runoff modelresults and the potentially induced severe hydrologic phenomena,such as abnormal precipitation intensity for a specific annualexceedance probability (AEP), flow or extended wet or dry periodscompared to current climate.

2. Methodology

2.1. Climate variables modeling

The precipitation and temperature projections used in this re-search were derived from NARCCAP. NARCCAP is an internationalprogram that serves the high resolution climate scenario needs ofthe United States, Canada, and Northern Mexico that uses regionalclimate models, coupled global climate models that performed cli-mate simulations in past and future periods. NARCCAP researchprogram focuses on the uncertainty across different GCMs andRCMs. Future climate variables used in this study were predictedby three regional climate models driven by three global model

datasets under A2 gas emission scenario described in the SpecialReport on Emissions Scenarios (SRES) (Nakicenvoic et al., 2000).The A2 is at the higher end of the gas emission scenarios. The A2scenario was selected to reflect the impact of the largest potentialclimate change. Four couples of GCM driven RCMs provided dailyprecipitation and daily mean temperature data for the current per-iod 1989–2000 and future period 2040–2069. The RCMs used wereCanadian Regional Climate Model (CRCM) driven by Canadian Glo-bal Climate Model version 3-CGCM3, T47 spatial resolution, (Flato,2005), Hadley Centre Regional Climate Model (HRM3) driven byUnited Kingdom (UK) Hadley Centre Climate Model version 3-Had-CM3) (Gordon et al., 2000; Pope et al., 2000), RegCM model devel-oped at the National Center for Atmospheric Research (NCAR),version 3 (RCM3), driven by Canadian Global Climate Model ver-sion 3 and RCM3 driven by Geophysical Fluid Dynamics Laboratory(GFDL) Climate Model version 2.1-CM2.1, (GFDL, 2004).

2.2. Bias correction

2.2.1. Precipitation bias correction methodA problem with the use of RCMs for hydrological purposes is

that the simulated precipitation differs systematically from the ob-served precipitation (Leander and Buishand, 2007). The biases areoften related to the mean value, standard deviation and the inabil-ity of the variables to reproduce extreme events. Thus, bias correc-tion of RCM model results is sought in order to get more realisticresults from the forced hydrological impact models that makeuse of RCM model data. Sharma et al. (2011) examined the neces-sity of bias correction of raw RCM data by using statisticaldownscaling techniques on raw CRCM4.2 data. They found that

Fig. 2. Cumulative distribution functions (CDFs) for raw (left) and bias corrected (right) precipitation (upper) and temperature (lower) mean monthly values for HamiltonAirport station data between 1989 and 2000. The observed precipitation and temperature data CDFs are included.

4 M.G. Grillakis et al. / Journal of Hydrology 409 (2011) 1–19

the bias corrected CRCM4.2 data, improved significantly the HBVhydrologic model ability to accurately simulate streamflow ascompared to the use of the raw CRCM4.2 data. The bias-correc-tion method used in this study to correct the frequency and theintensity of daily precipitation of climate models is the one pre-sented by Ines and Hansen (2006). The method was applied toeach of the 12 calendar months, in a two-step procedure, withthe first step to correct the precipitation frequency and the sec-ond to correct the precipitation intensity. The procedure cali-brates both the frequency and the intensity distribution of dailymodeled precipitation relative to a target station. If the modeledprecipitation frequency is greater than observed frequency for agiven month, averaged across years, it is corrected by discardingrainfall events below a calibrated threshold. To correct the inten-sity distribution, each modeled precipitation amount above thecalibrated threshold is mapped from the modeled precipitationintensity distribution to the observed distribution. A gamma dis-tribution (Wilks, 1995) was used for both observed and modeledprecipitation intensities.

The correction procedure of precipitation frequency truncatesthe empirical distribution of the raw daily climate model precipita-tion above the ~xRCM threshold value, such that the mean frequencyof precipitation above the threshold matches the observed meanrainfall frequency. The threshold value ~xRCM is estimated for eachof the 12 calendar months by using the following equation:

~xRCM ¼ F�1RCMðFobsð~xÞÞ ð1Þ

where F(�) and F�1(�) are the cumulative distribution function (CDF)and its inverse, and subscripts indicate RCM precipitation predic-

tion or observed daily precipitation. The threshold observed precip-itation amount (~x) of model daily precipitation was set to 0.1 mm.

For the precipitation intensity correction, the two-parametergamma distribution of Eq. (2) is applied to fit the truncated dailyclimate-modeled and observed precipitation data for each of the12 calendar months (Ines and Hansen, 2006). The CDF of the trun-cated daily climate-model precipitation was then mapped to theCDF of the observed data as shown in Eq. (3).

FGðx;a; bÞ ¼ 1baCðaÞ x

a�1 exp � xb

� �; x P ~x ð2Þ

FGðx;a; bÞ ¼Z x

~xf ðtÞdt ð3Þ

where a is the shape parameter and b is the scale parameter of thegamma distribution as determined by Maximum Likelihood Estima-tion. Finally, the corrected RCM precipitation amount x0P on day i iscalculated by substituting the fitted gamma CDFs into the followingequation:

x0P i ¼F�1

I;obsðFI;RCMðxiÞÞ xi P ~x

0 xi < ~x

(ð4Þ

2.2.2. Temperature bias correction methodA similar method was used to correct bias in the RCM temper-

ature series, proposed by Rao and Hamed (2000). The RCM dailytemperature distribution was mapped to the observed distributionfor each of the 12 calendar months without any correction of thefrequency distribution and with no truncation to the model

Fig. 3. Observed (black lines) and future simulated (grey lines) climate trends, for precipitation (left figures) and temperature (right figures) climatic variables, for the tworeference stations Hamilton RBG (upper figures) and Hamilton Airport (lower figures). A linear regression line was fitted to each data set.

M.G. Grillakis et al. / Journal of Hydrology 409 (2011) 1–19 5

temperatures. Instead of the gamma distribution used by Rao andHamed (2000), a normal distribution was used to map the temper-ature distribution.

2.3. Standardized precipitation index analysis

Drought indices such as the Standardized Precipitation Index(SPI) (McKee et al., 1993) are indispensible tools to detect, monitorand evaluate drought events in both time and space (Koutrouliset al., 2010). The SPI is a probability index that considers precipita-tion to determine the rarity of a drought or an anomalously wetevent at a particular time scale. The SPI index can be computedfor different time scales. Historical monthly precipitation is fittedto a gamma probability distribution. The cumulative probabilityis then transformed to a standard normal distribution with meanzero and variance of one, which is the SPI index. The gamma distri-bution probability of 48-month average precipitation correspondsto the 48-month SPI index on the standard normal distribution.The SPI index value is then the standardized deviation of the trans-formed precipitation total from the mean precipitation. Accord-ingly, three-month SPI is calculated using the three-monthaverage precipitation.

The gamma distribution is defined by its frequency or probabil-ity density function as given in the following equation:

gðxÞ ¼ 1baCðaÞ x

a�1e�x=b for x > 0 ð5Þ

where a is a shape parameter (a > 0), b is a scale parameter (b > 0), xis the precipitation amount (x > 0) and C(a) is the gamma function(Wilks, 1995). The index has a negative value during droughts, andpositive value for wet conditions. As the dry or wet conditions be-come more severe, the index becomes more negative or positive(Heim, 2000). The positive ‘‘wet’’ part of the SPI range is arbitrarydivided in four categories; mildly wet (0.99 > SPI > 0), moderatelywet (1.49 > SPI > 1), severely wet (1.99 > SPI > 1.5), and extremely-wet conditions (SPI > 2.0). A wet event is considered to start whenthe SPI value reaches 1.0 and ends when the SPI becomes negativeagain (McKee et al., 1993). Thresholds of the SPI for drought charac-terization are presented in Table 1. There is general agreement thatthe SPI index computed for short time scales such as 3 or 6 months,describes wet or dry events that affect agricultural practices, whileon longer scales such as 24 or 48 months, it describes the effects of aprecipitation deficit or excess on different water-resources compo-nents (Koutroulis et al., 2010). It seems reasonable to deduce thatonly long time scale SPI analysis would be meaningful in the pres-ent work where the precipitation and temperature effects on futurestreamflow of Spencer Creek are studied. The index was estimatedfor both observed and four future precipitation projections.

2.4. Spencer Creek hydrology modeling

Hydrological simulations were performed using three widelyused semi-distributed rainfall–runoff models, HBV model (IHMS5.10.1, HBV 7.1), Hydrologic Engineering Center Hydrologic

Table 3Observed average (1989–2008) and future (2040–2069) climate models projected average precipitation (mm/day) and temperature (�C) for both Hamilton Airport and HamiltonRBG stations. The change between past and future averages is also presented as a percent change for precipitation and as change in �C.

Past Future AVG RCM

CRCM–CGCM3 HRM3–HadCM3 RCM3–CGCM3 RCM3–GFDL

Hamilton RBG P (mm/day) 2.29 2.67 (+16.5%) 2.57 (+12.4%) 2.73 (+19.5%) 2.68 (+17.0%) 2.66 (+16.3%)T (�C) 8.84 11.57 (+2.7 �C) 11.17 (+2.3 �C) 11.06 (+2.2 �C) 10.78 (+1.9 �C) 11.15 (+2.3 �C)

Hamilton Airport P (mm/day) 2.44 2.75 (+12.7%) 2.68 (+9.9%) 2.84 (+16.7%) 2.64 (+8.4%) 2.73 (+11.9%)T (�C) 8.01 10.71 (+2.7 �C) 10.29 (+2.3 �C) 10.0 (+2.0 �C) 9.90 (+1.9 �C) 10.22 (+2.2 �C)

Fig. 4. Seasonal variability between the observed and future simulated climate variables. The precipitation (left figures) and temperature (right figures) variability isexpressed as difference between each climate projection and past average monthly value for Hamilton RBG (upper figures) and Hamilton Airport (lower figures).

Table 4Nash–Sutcliffe efficiency (NS) for the calibration (1990–1999) and validation (1999–2008) of the three hydrological models for the observed flow data. The efficiency ofthe whole period of observed data (1990–2008) is also provided.

IHMS–HBV HEC-HMS SAC–SMA

Calibration period 1990–1999 0.74 0.60 0.56Validation Period 2000–2008 0.75 0.48 0.66Total Period 1990–2008 0.75 0.54 0.61

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Modeling System HEC-HMS model (v3.4) and Sacramento SoilMoisture Accounting (SAC-SMA) model.

The HBV (Bergström, 1995; Lindström et al., 1997; Bergströmet al., 1997) model consists of four subroutines, a subroutine forsnow accumulation and snowmelt based on the degree-day ap-proach, a soil moisture accounting procedure to update the soilwater, the runoff generation routine and a flow-routing procedureconsisting of a simple filter with triangular distribution of weights(SMHI, 2006).

The Hydrologic Engineering Center Hydrologic Modeling Sys-tem (HEC-HMS) is designed to simulate the precipitation-runoffprocesses of dendritic-shaped watershed systems. Its applicabilityextends to a wide range of geographic areas and has been used forsolving a wide range of problems. The HEC-HMS model consists of

four sub-models, the meteorological model that includes evapo-transpiration and snow model components, the loss model that ac-counts for the losses in precipitated water before and during runoff

Fig. 5. Observed and simulated flows for the three hydrological models at Dundas in the validation hydrological year 2004–2005.

Fig. 6. Dundas average flow by month for observed data and each hydrologic model 1989–2008 (left). Dundas average flow by month for simulated flows against observeddaily data 1989–2008 and least square lines (right). Coefficients of determination R2 values are 0.98 for IHMS–HBV, 0.95 for HEC-HMS and 0.97 for SAC–SMA.

M.G. Grillakis et al. / Journal of Hydrology 409 (2011) 1–19 7

occur, the transformation model which routes the runoff and final-ly the baseflow model (HEC-HMS User’s Manual, 2009).

The SAC-SMA model (Burnash et al., 1973; Burnash,1995) con-ceptualizes the soil profile as a series of reservoirs with capacitiesand release coefficients. It computes the surface runoff that occurswhen the storage capacity of the upper zone free water is ex-ceeded, the runoff from impermeable surfaces, the direct runofffrom additional impervious surfaces and the interflow and base-flow contributions. The SAC-SMA model was combined with a sim-ple degree-day factor snow model.

The efficiency of the model simulations as expressed by thecloseness between observed and simulated flows was evaluatedusing the Nash–Sutcliffe (NS) coefficient (Nash and Sutcliffe,1970) equation, as given in equation

NS ¼ 1�PðQC � QRÞ2PðQR� QRmeanÞ2

ð6Þ

where QR is the observed flow and QC the computed flow, QRmean isthe average observed flow over the calibration period between 1990and 1999, and the validation period between 2000 and 2008. Therange of NS lies between 1.0 (perfect fit) and �1. A result lower

than zero indicates that the mean value of the observed time serieswould have been a better predictor than the model.

2.5. Extreme event analysis

The future extreme event analysis was performed by using 27distributions (Table 2) that can be used to fit the past and futuredatasets. Pearson’s chi-square (v2) test was used to examine thegoodness of fit of each distribution. Each distribution was testedfor its goodness of fit at a 95% confidence level following the at-tained significance percentage a, defined in the following equation:

Aattained ¼ 1� x2ðm ¼ k� r � 1; qÞ ð7Þ

where m are the degrees of freedom of chi square test, k is the num-ber of bins used in chi square test, r is numbers of parameters of thedistribution and q is the Pearson parameter. The theoretical back-ground of all the tested distributions is described in Kozanis et al.(2010).

Fig. 7. Observed, simulated and three model average simulated flow at Dundas over the past period 1990–2008 (left). Simulated and three model average simulated flow atDundas over the future period 2040–2069 (right). The linear regression lines indicate the trend of the average simulated flows.

a b

Fig. 8. Difference between the past and future simulated monthly flows for Dundas as delivered by the three-hydrological model ensemble (left) and difference between thepast and future IHMS–HBV simulated monthly flows for Dundas (right).

8 M.G. Grillakis et al. / Journal of Hydrology 409 (2011) 1–19

3. Case study area

The studied area in this work is the Spencer Creek watershed lo-cated in the Southern Ontario, Canada as shown in Fig. 1 (the gridsused by the climate models in NARCCAP are also shown). It extendsover an area of 160.4 km2 as estimated from a 10 m � 10 m resolu-tion digital-terrain model. The watershed area was divided intothree subbasins. The most upstream subbasin includes the upperSpencer Creek tributaries above the Westover flow gauge. The next

downstream or middle watershed extends from Westover to theHighway 5 flow gauge. The most downstream subbasin extendsfrom the Highway 5 flow gauge to the flow gauge at Dundas, whichis the outlet of the case study area. The meteorological data for thecase study area were obtained at two meteorological stations,Hamilton Airport and Hamilton Royal Botanical Garden (RBG).The Hamilton RBG station was discontinued in 1997, and the timeseries was therefore completed with data collected from the newstation Hamilton RBG CS about 3 km northwest of the Hamilton

Fig. 9. Past to future precipitation and flow differences (upper) and temperature to flow (lower) scatter plots (blue circles). Histograms (red columns) and normal distributiondensities (red line) for each variable.

Table 5Past simulated (1990–2008) and future (2040–2069) average flow (m3 s�1) for the Dundas station. The change between past and future averages is also presented as a percentchange.

Past simulatedaverage flow(m3 s�1)

Future

CRCM–CGCM3 HRM3–HadCM3 RCM3–CGCM3 RCM3–GFDL All RCMs average

Future flow(m3 s�1)

Percentchange (%)

Future flow(m3 s�1)

Percentchange (%)

Future flow(m3 s�1)

Percentchange (%)

Future flow(m3 s�1)

Percentchange (%)

Future averageflow (m3 s�1)

Percentchange (%)

1.55 1.78 +15.1 1.58 +2.0 1.91 +23.6 1.71 +10.5 1.74 12.8

M.G. Grillakis et al. / Journal of Hydrology 409 (2011) 1–19 9

RBG station (Fig. 1). The data from these two stations were used forthe bias correction of the RCM projections. Fig. 2 shows the ob-served, raw RCM and bias corrected RCM precipitation and temper-ature data for Hamilton Airport station between 1989 and 2000.

The Spencer Creek watershed is complex, due to its heteroge-neous soil properties, extensive river network and different typesof land use (HRCA, 1990). The land use of the case study areas con-sists of urban and paved areas (21.7%), agricultural land (46.8%),

Table 6Selected distributions fitted to each dataset, attained fitting performance and corresponding characteristics.

Parameter Dataset Selecteddistribution

Attaineda (%)

Datacount

Meanvalue

Standarddeviation

Skewness Kurtosis kappa lambda Psi my sy

Precipitation(mm/day)

Observed P EV2-Max 47.2 20 39.9 14.29 2.18 5.32 0.22 7.42CRCM–CGCM3 EV1-Max (Gumbel) 71.6 30 67.54 18.82 0.95 1.22 14.7 4.02HRM3–HadCM3 Log Normal 31.1 30 46.65 15.68 1.54 3.74 3.79 0.33RCM3–CGCM3 EV2-Max (L-

Moments)84.7 30 72.03 26.74 1.61 2.23 0.25 14.8

RCM3–GFDL EV2-Max (L-Moments)

60.7 30 68.64 22.58 1.13 1.29 0.24 13.5

Max Temp(�C)

Observed Tmax Exponential 77.9 20 27.94 1.52 �0.43 0.59 0.66 26.42CRCM–CGCM3 Normal 60.7 30 30.46 1.91 0.24 0.46 30.46 1.91HRM3–HadCM3 EV1-Max (Gumbel) 31.1 30 31.15 2.77 0.65 0.5 2.16 13.85RCM3–CGCM3 EV3-Min (Weibull) 71.7 30 30.61 1.59 �0.38 0.29 0.04 1.11RCM3–GFDL LogNormal 71.7 30 31.69 2.56 0.74 0.35 3.45 0.08

Min Temp(�C)

Observed Tmin Normal (L-Moments)

77.9 20 15.57 3.04 0.07 0.18 15.57 3.08

CRCM–CGCM3 Normal (L-Moments)

84.7 30 12.13 3.22 �0.26 �0.1 12.1 3.27

HRM3–HadCM3 Gamma 84.7 30 10.34 2.8 �0.17 0.11 13.63 1.32RCM3–CGCM3 Exponential (L-

Moments)43.5 30 13.01 2.89 0.21 0.19 0.35 10.12

RCM3–GFDL Gamma 60.7 30 12.86 2.44 �0.2 �1.12 27.82 2.16

Flow(m3 s�1)

Observed Q GEV-Min (kappaspecified, L-Moments)

23.3 19 15.18 4.68 �0.53 �0.13 0.15

HBV–Past EV1-Max (Gumbel) 44.7 19 13.22 3.91 0.29 �0.29 3.05 3.76CRCM–CGCM3 Exponential (L-

Moments)71.7 30 14.32 5.6 0.89 �0.15 0.16 8.07

HRM3–HadCM3 EV2-Max (L-Moments)

84.7 30 13.8 7.09 2.63 9.61 0.32 3.3

RCM3–CGCM3 GEV-Max (L-Moments)

100 30 18.85 7.6 1.07 0.99 0.07 5.61 2.7

RCM3–GFDL GEV-Max (kappaspecified)

60.7 30 17.6 10.07 1.4 1.34 0.15 6.14 2.12

Table 7Best-fitting distributions estimates for 2% annual exceedance probability for each dataset and corresponding 95% upper and lowerconfidence limits.

Parameter Dataset Estimated value on 0.02 (2%)annual exceedance probability

Upper 95%confidencelimit value

Lower 95%confidencelimit value

Precipitation (mm/day) Observed P 79.5 117.5 53.5CRCM–CGCM3 116.3 140.2 86.2HRM3–HadCM3 86.5 107.5 69.4RCM3–CGCM3 156.9 246.0 111.5RCM3–GFDL 143.6 218.0 104.1

Max Temp (�C) Observed Tmax 32.4 35.7 29.6CRCM–CGCM3 34.4 35.6 33.2HRM3–HadCM3 38.3 35.3 41.8RCM3–CGCM3 32.8 33.1 31.8RCM3–GFDL 37.3 39.2 35.4

Min Temp [�C] Observed Tmin �21.8 �19.4 �24.2CRCM–CGCM3 �19.9 �16.8 �21.0HRM3–HadCM3 �16.9 �14.5 �19.5RCM3–CGCM3 �21.5 �17.1 �26.6RCM3–GFDL �18.4 �16.4 �20.4

Flow (m3 s�1) Observed Q 23.8 26.9 20.8HBV–Past 23.4 29.6 18.2CRCM–CGCM3 32.6 43.0 24.0HRM3–HadCM3 35.9 61.9 22.9RCM3–CGCM3 40.6 58.3 28.8RCM3–GFDL 45.6 67.7 31.1

10 M.G. Grillakis et al. / Journal of Hydrology 409 (2011) 1–19

forested areas (15%), wetland (14.9%), bare field (0.7%) and watersurface (0.9%). The watershed is characterized by relatively flattopography, except for local glacial features, with elevations rang-ing between 100 m and 340 m. The length of the main river net-work of all tributaries is approximately 100 km, while thelongest flow path is about 35 km. Precipitation is fairly uniformly

distributed throughout the year, and snow cover is characteristicbetween December and March typically causing spring snow-melthigh flows. Rainfall and temperature data from Hamilton RBG andHamilton Airport and three streamflow gauging station data fromWestover, Highway 5, and Dundas between 1989 and 2008 wereused as input for the three hydrological models. The evapotranspi-

M.G. Grillakis et al. / Journal of Hydrology 409 (2011) 1–19 11

ration was estimated using the Blaney–Criddle method (Allen andPruitt, 1986). Calibration and validation were carried out in dailytime steps for all models using hydrographs at the outlet of eachof the three subbasins. The optimization of model parameterswas carried out manually for IHMS–HBV and HEC-HMS models,while for SAC-SMA model’s parameters a genetic algorithm optimi-zation was used. Genetic algorithms are globally oriented insearching and thus, potentially useful in solving optimization prob-lems in which the objective function responses contain multipleoptima and other irregularities (Wang, 1998). The available datawere split into two periods, 1/1/1989–31/12/1999 for calibration,and 1/1/2000–31/12/2008 for validation. The first year of calibra-tion 1/1/1989–31/12/1989, was used to initialize all models. Whilethe hydrological modeling of Spencer Creek watershed was per-formed in a three subbasin setup for all hydrological models, theanalysis of the past and future flows was carried out only at theDundas gauge.

A two-ensemble prediction system was used to evaluate the fu-ture climate change impact on Spencer Creek hydrology. The fourNARCCAP regional climate models (RCMs), CRCM–CGCM3,HRM3–HadCM3, RCM3–CGCM3 and RCM3–GFDL, provided the cli-mate variables for the contemporary period between 1989–2000and future period 2040–2069. The future climate experiments usedcover a wide range of future anomalies in the climate variables.The climate variables were studied both individually and in anequally weighted ensemble of all RCMs. The future change in theinterannual and seasonal runoff of Spencer Creek was studiedusing the four RCM data between 2040 and 2069 in three widelyused, semi distributed hydrological models. The IHMS–HBV, HEC-HMS and SAC-SMA models were calibrated separately for the ob-served climate variables using the observed runoff. An equallyweighted ensemble of the future flow results was used then forthe analysis of the seasonal and interannual changes in the futureflow regime. In the extreme event frequency analysis, only IHMS–HBV which is the best fitting hydrological model was used.

4. Results

4.1. Climate variables interannual and average monthly trends

Observations over a twenty year period of data from the tworeference stations of Hamilton RBG and Hamilton Airport stationswere analyzed to show the interannual trend of precipitation andtemperature. The analysis was also extended to the future projec-tions of the climate model variables. The analysis was performedapplying linear regression to the average annual precipitationand temperature data, respectively. The aim of the trend analysiswas to detect any existing persistent interannual change in the cli-mate variables. There was a clear indication that both precipitationand temperature exhibited a slight to moderate increase in the pasttwo decades data, but also in the future simulations of the climatemodels (Fig. 3). Amongst the analyzed data, only Hamilton RBGstation precipitation records exhibited a slight negative trend. Thisprobably is attributed to the change in the location of the Hamiltongauge made in 1997, where Hamilton RBG station was discontin-ued and the time series continued at a new station HamiltonRBG CS, about 3 km northwest. The results of the trend analysisshow that the change rate in average precipitation was estimatedat �0.73 mm/year and 3.54 mm/year in observed precipitationdata, while for the future RCM ensemble, it was estimated to be2.81 mm/year and 3.80 mm/year for Hamilton RBG and HamiltonAirport, respectively. The respective estimated temperature in-crease rate was 0.014 �C/year and 0.041 �C/year for the observeddata, while for the future RCM ensemble the rate was estimatedat 0.052 �C/year and 0.061 �C/year for Hamilton RBG and Hamilton

Airport, respectively. The trends estimated for the observed datafrom the Hamilton Airport station can be considered more reliable,due to its data consistency, compared to Hamilton RBG data thatwere collected at two locations. The change rates are more pro-nounced in future projections than these in the past two decades.

The change between the past and the future datasets was alsoestimated for the two climate variables. The interannual mean pre-cipitation and temperature for the past and future datasets are pre-sented in Table 3. It is worth mentioning that all RCM futureprojections exhibit an increase in both precipitation and tempera-ture average compared to past climate data. The RCM ensemblechange was +16.3% and +11.9% for precipitation and +2.3 �C and+2.2 �C for Hamilton RBG and Hamilton Airport, respectively. Forannual precipitation the changes are translated in 836 mm to971 mm and 891 mm to 996 mm for the above stations. RCM3–GFDL model provided the most conservative projection in termsof future temperature increase (+1.9 �C). This model also projectedthe most conservative Hamilton Airport precipitation increase(+8.4%), while HRM3–HadCM3 model projected the most conser-vative precipitation increase for Hamilton RBG station (+12.4%).The greatest changes in precipitation were projected by RCM3–CGCM3 at +19.5% and +16.7% for Hamilton RBG and Hamilton Air-port, respectively, while for temperature, the most significantchanges were projected by CRCM–CGCM3 at +2.7 �C and +2.7 �C,respectively.

Taking into account the future projected increases in both pre-cipitation and temperature, an average monthly analysis was per-formed to obtain insight into how the changes are distributedseasonally. It was found that temperature variability in future pro-jections is greater in January (+3.42 �C, +3.72 �C), February(+2.64 �C, +2.74 �C), May (+2.75 �C, +2.62 �C), June(+2.69 �C, +2.51 �C), July (+2.48 �C, +2.02 �C) and December(+2.35 �C, +2.25 �C) for the average of all RCM projections (seeFig. 4). The values in parentheses indicate the temperature changefor Hamilton RBG and Hamilton Airport stations, respectively. Onlythe CRCM–CGCM3 spring temperature projection diverges signifi-cantly from the other RCMs (Fig. 4). Conversely, precipitation pro-jections between all RCMs do not produce a clear signal of monthlydistribution change in the precipitation regime. The ensemble fu-ture RCM projection shows the higher increase of precipitation inJanuary with 0.99 mm and 0.88 mm increase on Hamilton RBGand Hamilton Airport stations, respectively, and the lower increasein February, July, November and December months.

4.2. Hydrological models calibration results

The three hydrological models were successfully calibratedusing a 10-year period of observed data. The calibrated modelswere then tested for their efficiency over a separate 10-year period.The Nash–Sutcliffe estimator results are presented for the calibra-tion, validation period, and for the whole past period 1/1/1990–31/12/2008 in Table 4. The IHMS–HBV model performed very wellaccording to Nash–Sutcliffe (NS) criterion over the calibration(NS = 0.74) and validation (NS = 0.75) period, while for HEC-HMS(calibration NS = 0.63, validation NS = 0.48) and SAC-SMA (calibra-tion NS = 0.56, validation NS = 0.66) models the results were mod-erate. Fig. 5 demonstrates the observed flow and the flow from thethree hydrological models simulations from September 2004 toAugust 2005 in daily-time steps. Analyzing the results of thehydrological models on an average monthly basis, it was foundthat HBV model represents the monthly flow dynamics moderatelygood in terms of coefficient of determination R2 = 0.98. However,the HEC-HMS and SAC-SMA hydrological models hinge on theirperformance in the high flow months (Fig. 6). Notwithstandingthe inherent uncertainty due to moderate HEC-HMS and SAC-SMA models performance, the comparison between the past and

Fig. 10. Frequency curves for the observed and the projected precipitation of each climate model, for the maximum daily precipitation, including 95% confidence limits.

12 M.G. Grillakis et al. / Journal of Hydrology 409 (2011) 1–19

the future simulated flows of these models provides valuable infor-mation about the climate change effect on future hydrology ofSpencer Creek.

4.3. Interannual and seasonal flow trends

As shown in Section 4.1 and Fig. 3, precipitation exhibits a po-sitive trend both in observed and future projected data for thetwo analyzed stations. As expected, the positive trend in the pre-cipitation is also depicted in the past and future simulated flows.The positive trend on the past flow was estimated over the equallyweighted ensemble of past simulated flows using linear regression.The positive increase in Dundas average annual surface flow wasestimated at 0.0046 m3 s�1 per year for the period 1990–2008.The corresponding annual increase estimated from the equallyweighted ensemble of all future simulations was 0.0121 m3 s�1

per year (Fig. 7). Following the respective precipitation trend re-sults, the increase in flow is more pronounced in the future. Theaverage past simulated flow was 1.55 m3 s�1. The projected changein the future was estimated from 2% (HRM3–HadCM3) to 23.6%

(RCM3–CGCM3) between the RCM projections with an average of12.8% (Table 5).

The increase in future precipitation and temperature is not uni-form through the year. The winter and the later spring–early sum-mer months have the greater future temperature change. Thefuture precipitation also increases in a non-uniform patternthrough the year. Thus, the seasonal effect of precipitation andtemperature increase for the resultant future flow is complex. Acomparison between equally weighted ensembles of simulatedflows in the past and future periods, reveal an increase in autumnand winter flows, particularly the August to February monthly flowincreases, with a maximum in January and February, where thechange is 0.71 m3 s�1 and 0.63 m3 s�1 (or 32.5% and 33.5%), respec-tively (Fig. 8a).

Fig. 8a presents the difference between the past and future sim-ulated monthly flows as delivered by the three-hydrological modelensemble. The January flow increase could be attributed to thehigh precipitation increase in that month, as shown in Section 4.1.In contrast, the similar flow increase in February cannot beattributed to the same reason as January, because there is no

Fig. 11. Frequency curves for the observed and projected maximum daily temperature of each climate model, including 95% confidence limits.

M.G. Grillakis et al. / Journal of Hydrology 409 (2011) 1–19 13

change in future February precipitation. However, it can be attrib-uted to change in the future January and February months’ temper-ature. The warmer February temperature from 2.5 �C to 3 �C in thefuture decreases the snow fraction and increases the winter snow-melt. As a result, the snowpack decreases for the annual basis andsnowmelt terminates earlier in the spring.

In their study, Boyer et al. (2010) presented similar changes infuture winter and spring flow regime of St. Lawrence River tribu-taries, about 650 km northeast of Spencer Creek. The earlier start-ing snowmelt explains the decrease by 14% in April flow, becauseof the thinner snowpack left to be melted in April (Fig. 8a), despitethe slight increase in the precipitation of April. The results from thebest-fitting model, IHMS–HBV indicate a larger decrease (20.5%) inApril flow which is shown in Fig. 8b. Also it is shown that there isno significant change in the future-flow simulations in monthsMay and August.

In order to quantify the effect of precipitation and temperaturevariability on future flow change, two periods of equal length, onein the past and one in the future were considered. For the past per-iod, the annual observed precipitation and temperature for the en-tire basin, and the simulated ensemble flow was used between

1990 and 2008. For the same length period of 2050–2068, the pro-jected precipitation and temperature of each climate model andthe ensemble flow of all hydrologic models for the individual cli-mate models were considered. The difference between the futureand the past precipitation, temperature and flow data for the fourRCMs were then fitted to normal distributions to calculate themean of each variable difference (Fig. 9). From the produced plots,the average effect of each climate variable on flow was estimated.The results show that the mean increase (DP%) of 18% in precipita-tion and the mean increase in temperature (DT �C) by 2.5 �C willlead to a proportional mean 20% increase in the flow (DQ) atDundas.

4.4. Climate variables and flow extremes

Extreme event analysis for future climate variables and futureassessed flows is a rather daunting task due to a number of uncer-tainty sources that should be considered. The largest source ofuncertainty in this study is the limited available data in past andfuture periods. Thus, the projections of extreme events were lim-ited to a 0.02 (2%) annual exceedance probability (AEP). The cli-

14 M.G. Grillakis et al. / Journal of Hydrology 409 (2011) 1–19

mate models predictions induce another main source of uncer-tainty that should be considered. Even though climate models sim-ulate the integrated accumulations of precipitation well, theyunderestimate the intensity of the precipitation events and overes-timate the number of rainy days, comparing to the observed pre-cipitation (Stephens et al., 2010). Moreover, the uncertaintyinduced of hydrological models estimations and possible futureland-use changes are also sources of uncertainty for the futuremaximum flow estimation. Uncertainty in extreme event analysisis also affected by the fitting distribution selected to project the ex-treme events to various AEPs. To address the climate models in-duced uncertainty of future extremes, multiple climate-modelprojections were used to produce a range of possible futureextremes.

Twenty-seven distributions (Table 2) were tested on each pastand future dataset. Pearson’s chi-square (v2) test was used toexamine the goodness of fit of each distribution. Amongst the dis-tributions that are accepted for a significance level of 95% for each

Fig. 12. Frequency curves for the observed and projected minimum daily

dataset, the best-fitting distribution was selected following the at-tained significance percentage a (Eq. (7)). The distribution used ineach dataset and the estimated parameters are presented inTable 6.

An analysis of the AEP and the recurrence interval of daily pre-cipitation, minimum and maximum temperature, and flow wascarried out for the observed and the future projected datasets byeach RCM. The N-year recurrence interval is defined as the dailyprecipitation amount, which is equaled or exceeded once everyN years on average (Semmler and Jacob, 2004). The AEP is then de-fined as the 1/N-year recurrence interval and describes the proba-bility of the specific event to occur in a year. The annual maximumdaily precipitation, minimum and maximum temperature and flowfor the entire basin were used. The AEP of 2% (or 50-year recur-rence interval) values and the 95% upper and lower confidence lim-its are summarized in Table 7 for all the analyzed parameters.Regarding precipitation, all future projections show an increasein the AEP for a given daily precipitation. The degree of the increase

temperature of each climate model, including 95% confidence limits.

Fig. 13. Frequency curves for observed, past and future HBV simulated flow of each climate model, for the maximum daily values, including 95% confidence limits.

M.G. Grillakis et al. / Journal of Hydrology 409 (2011) 1–19 15

16 M.G. Grillakis et al. / Journal of Hydrology 409 (2011) 1–19

differs between the future projections (Fig. 10). The comparisonbetween the different projections and the past data was conductedfor the AEP = 0.02 (50-year recurrence interval).

The observed maximum daily precipitation for an AEP = 0.02was estimated to be 79.5 mm with 95% upper and lower confi-dence limits of 117.5 mm and 53.5 mm, respectively (Fig. 10).The HRM3–HadCM3 model projection provides the most conserva-tive estimates, with 86.5 mm for the AEP = 0.02 precipitation and107.5 mm and 69.4 mm 95% upper and lower confidence limits,respectively. The CRCM–CGCM3 model resulted moderate changein precipitation, with an AEP = 0.02 precipitation of 116.3 mm,and 140.2 mm and 86.2 mm upper and lower confidence limits,respectively (Fig. 10). Higher changes were obtained by RCM3–CGCM3 and RCM3-GFDL models with 156.9 mm (95% confidencelimits 246 mm and 111.5 mm) and 143.6 mm (95% confidence lim-its 218 mm and 104.1 mm) precipitation of AEP = 0.02, respectively(Fig. 10). It can be noticed that higher changes in precipitation ex-

Fig. 14. Estimated values at 0.02 (2%) annual exceedance probability (50 yr recurrentemperature and max flow with corresponding upper and lower 95% confidence limits.

tremes were associated with the RCM3–CGCM3 and RCM3-GFDLmodel predictions that use the same RCM (RCM3).

For the past maximum daily average temperature withAEP = 0.02, the maximum temperature is 32.4 �C with 35.7 �C and29.6 �C the 95% upper and lower confidence limits, respectively(Fig. 11). The past minimum temperature of 0.02 AEP was�21.8 �C with �19.4 �C and �24.2 �C 95% upper and lower confi-dence limits, respectively (Fig. 12). The HRM3–HadCM3 deliveredthe higher increase in both future minimum and maximum tem-peratures of �16.9 �C (95% confidence limits �14.5 �C and�19.5 �C) and 38.3 �C (95% confidence limits 35.3 �C and 41.8 �C),respectively. The RCM3–GFDL model predicted moderate changes,with minimum �18.4 �C (95% confidence limits �16.4 �C and�20.4 �C) and maximum temperature 37.3 �C (95% confidence lim-its of 39.2 �C and 35.4 �C). The most conservative predictions wereassociated with the CRCM–CGCM3 and RCM3–CGCM3 models. Theformer predicts minimum and maximum temperatures of �19.9 �C

ce interval) of observed and model projected max precipitation, max and min

Fig. 15. Standardized Precipitation Index of 48 months for observed and future monthly precipitation.

Table 848 month SPI for past (Past) and projected (Proj) precipitation for each model, models average (AvgCM), and the % difference (% diff) of projections.

SPI category Past CRCM–CGCM3 HRM3–HadCM3 RCM3–CGCM3 RCM3–GFDL AvgCM

Proj %diff Proj %diff Proj %diff Proj %diff Proj %diff

2 or more 0.0 0.3 0.3 0.0 0.0 1.6 1.6 2.6 2.6 1.1 1.11.5–1.99 5.7 5.1 �0.6 2.6 �3.1 8.6 2.9 2.9 �2.8 4.8 �0.91–1.49 14.0 11.8 �2.2 14.1 0.1 8.3 �5.7 6.7 �7.3 10.2 �3.80–0.99 34.7 34.2 �0.5 40.9 6.2 31.6 �3.1 41.9 7.1 37.1 2.40 to �0.99 20.7 32.3 11.5 19.8 �0.9 28.8 8.0 31.9 11.2 28.2 7.5�1 to �1.49 18.1 7.0 �11.1 12.1 �6.0 17.6 �0.6 3.2 �14.9 10.0 �8.2�1.5 to �1.99 6.7 7.3 0.6 8.6 1.9 3.5 �3.2 5.8 �1.0 6.3 �0.4�2 or less 0.0 1.9 1.9 1.9 1.9 0.0 0.0 5.1 5.1 2.2 2.2

M.G. Grillakis et al. / Journal of Hydrology 409 (2011) 1–19 17

(95% confidence limits�16.8 �C and�21.0 �C) and 34.4 �C (95% con-fidence limits 35.6 �C and 33.2 �C), respectively. The latter modelpredicted minimum and maximum temperatures of �21.5 �C (95%confidence limits of �17.1 �C and �26.6 �C) and 32.8 �C (95% confi-dence limits of 33.1 �C and 31.8 �C), respectively. The CRCM–CGCM3and RCM3–CGCM3 models, which resulted in the most conservativepredictions, used the same GCM (CGCM3). Figs. 11 and 12 demon-strate the maximum and minimum temperature frequency curves,respectively, for the four model projections.

The frequency analysis was also extended to the past and futureflows. The future flows were taken only for IHMS–HBV model whichcaptured the high-flow season very well. For January to April, theaverage difference between the observed flow in Dundas and thesimulated by IHMS–HBV flow was 9.2% (Fig. 6). The respective dif-ference for HEC-HMS and SAC-SMA was 35.4% and 35.8%. The resultsof the frequency analysis demonstrate that future flow maxima alsoincrease when compared to both observed and past simulated flows.For the 0.02 AEP, the future projections of HRM3–HadCM3 andCRCM–CGCM3 show an increase in maximum flow to 35.9 m3 s�1

(95% confidence limits of 61.9 m3 s�1 and 22.9 m3 s�1) and32.6 m3 s�1 (95% confidence limits of 43.0 m3 s�1 and 24.0 m3 s�1),respectively, compared to the observed 23.8 m3 s�1 (95% confidencelimits of 26.9 m3 s�1 and 20.8 m3 s�1) and simulated 23.4 m3 s�1

(95% confidence limits of 29.6 m3 s�1 and 18.2 m3 s�1) maximumflows (Fig. 13). RCM3–CGCM3 and RCM3–GFDL predict more signif-icant changes to 40.6 m3 s�1 (95% confidence limits of 58.3 m3 s�1

and 28.8 m3 s�1) and 45.6 m3 s�1 (95% confidence limits of67.7 m3 s�1 and 31.1 m3 s�1), respectively (Fig. 13). The RCM3–CGCM3 and RCM3-GFDL models that projected the more significantchanges in flow of AEP = 0.02 use the same RCM (RCM3). In Fig. 14,the 2% AEP projection of each variable and the 95% confidence limitsare provided.

In order to examine the potential future extremes from a differ-ent viewpoint than the frequency analysis, the SPI index was esti-mated for the observed and the future projected precipitation. TheSPI index allows the comparison of dry and wet periods betweenthe past and the future at different temporal scales. In order to fo-cus on the long-term extremes, the SPI of 48 months was selectedfor analysis. The SPI of 48 months was produced from each projec-tion and compared to the SPI time series produced from the ob-served precipitation (Fig. 15). The results of the analysis aresummarized in Table 8 according to the index categories of Table 1.In the past precipitation SPI of 48 months, there are no periodsranked as extremely dry or extremely wet. This is rather expectedgiven the short record length. In future projections, moderatelyand severely wet and dry conditions were reduced by 13.2% ofthe time on average (Table 8). This reduction caused an increasein mildly wet or dry conditions, but most importantly, it increasedthe extremely dry and extremely wet conditions to 3.3% of thetime.

5. Conclusions

The results obtained from the present study under the A2 SRESgas-emission scenario (Nakicenvoic et al., 2000) indicate that mod-est changes are expected in Spencer Creek hydrology. The mostimportant hydrology drivers, precipitation and temperature, wereanalyzed and found to incorporate important interannual trends,both in past data and future projections. The CRCM–CGCM3,RCM3–CGCM3, RCM3–GFDL and HRM3–HadCM3 climate-modelprojections agree on increase of precipitation by 16.3% and 11.9%and temperature by +2.3 �C and +2.2 �C for the Hamilton RBGand Airport stations, respectively. In addition to the average annual

18 M.G. Grillakis et al. / Journal of Hydrology 409 (2011) 1–19

increases, changes were also predicted for the seasonal distributionof the variables.

All climate models had similar increases of future temperaturefor the months of December, January, May, June and July, by morethan 2 �C. The future precipitation projections do not exhibit clearseasonal trend, but the RCM average shows January to be themonth with the higher increase (0.99 mm and 0.88 mm for Hamil-ton RBG and Hamilton Airport stations, respectively), while Febru-ary, July, November and December months show no change.Additionally, important changes were found in the 2% AEP (50-yearrecurrence interval) of annual maximum daily precipitation, wherethey were found to range from 8.8% to 97.4% higher for futureprojections.

The 48-month SPI analysis also shows that there is an increasein extreme wet and dry conditions according to the SPI ranking.Important changes are also expected in minimum and maximumtemperature AEPs. Common feature in both minimum and maxi-mum temperature change is the positive change in the 2% AEPcomparing to the past curves. The increase in 2% AEP for the max-imum temperature was 0.4–4.9 �C, while the increase for the min-imum temperature was 0.3 �C to 4.9 �C. The above valuescorrespond to the 2% AEP of the fitted distributions and do not in-clude the uncertainty range, based on 95% confidence intervals.

Three hydrological models were calibrated and validated usinga twenty-year period of observed data. The IHMS–HBV outper-forms in comparison to the HEC-HMS and SAC-SMA models interms of Nash–Sutcliffe performance. The latter two models donot perform well in simulating the high-flow seasons. Thus, allthree models were used for the average annual flow change anal-ysis of Dundas, but only IHMS–HBV for the flow recurrence intervalanalysis. An 18% increase in future precipitation and 2.5 �C increasein future temperature, results in a 20% increase on the average an-nual future flow at Dundas. The frequency analysis of maximumdaily streamflow (Q), show an increase of 37–91.6% in the 2%AEP flow comparing to the respective flow derived from the ob-served data (Table 7).

As shown in this study, the quantitative impact of these changesin basic hydrometeorologic characteristics can be substantial atsmall watershed scale. The national policies on resources manage-ment, such as for floods and droughts, provide a specific frameworkof objectives, principals, definitions and measures to adopt forassessing the potential impacts of climate change on water re-sources. This framework enables decision-makers to develop andconstantly review flood risk and drought management plans. Theensemble results from multi-hydrological models forced under mul-ti-climate models results give a collective picture of probable hydro-logical trends and embedded uncertainties in interpretations.

Generally, the study presents a wide range of predicted changesin the hydrologic processes, basic meteorological characteristicsand embedded uncertainty, clearly highlighting the advantage ofmulti-models approach in assessing climate-change impacts atcatchment scale. Quantitative results of hydrological change pro-vide the data required to improve knowledge and adaptation pol-icy to water-resources management. Thus, despite the limitationsand uncertainty in projections, long-term strategic planningshould consider the potential climate-change impacts in such acomplex watershed.

Acknowledgements

The Environment Canada and Hamilton Conservation Authority(HCA) are gratefully acknowledged for making available the pre-cipitation, temperature and discharge data in the study area. MillsLibrary at McMaster University provided the geographic and land-use data. The authors are grateful to Dr. P. Coulibaly, Associate Pro-fessor at McMaster University and the members of his research

group, Mrs. Z. Sultana and Dr. J. Samuel, for their assistance in biascorrecting the NARCCAP daily precipitation and temperature data.The National Center of Atmospheric Research (NCAR, http://ncar.-ucar.edu/) is thankfully acknowledged for providing online theNARCCAP regional climate change data. The authors also gratefullyacknowledge the reviewers Dr. Robert Jarrett and Emeritus Profes-sor Jetse Kalma for their comments that helped to improve themanuscript.

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