A multimodel assessment of the climate change effect on the drought severity duration frequency relationship

Transcription

1 HYDROLOGICAL PROCESSES Hydrol. Process. 27, (2013) Published online 15 June 2012 in Wiley Online Library (wileyonlinelibrary.com) DOI: /hyp.9390 A multimodel assessment of the climate change effect on the drought severity duration frequency relationship Joo Heon Lee* and Chang Joo Kim Department of Civil Engineering, Joongbu University, Geumsan, Chungnam-Do, Korea Abstract: In this study, the patterns of past and future drought occurrences in the Seoul region were analysed using observed historical data from the Seoul weather station located in the Korean Peninsula and four different types of general circulation models (GCMs), namely, GFDL:CM2_1, CONS:ECHO-G, MRI:CGCM2_3_2 and UKMO:HADGEM1. To analyse statistical properties such as drought frequency duration and return period, the Standardized Precipitation Index was used to derive the severity duration frequency (SDF) curve from the drought frequency analysis. In addition, a drought spell analysis was conducted to estimate the frequency and change of drought duration for each drought classification. The results of the analysis suggested a decrease in the frequency of mild droughts and an increase in the frequency of severe and extreme droughts in the future. Furthermore, the average duration of droughts is expected to increase. A comparison of the SDF relationship derived from the observed data with that derived via the GCMs indicated that the drought severity for each return period was reduced as drought duration increased and that the drought severity derived from the GCMs was severer than the severity obtained using the observed data for the same duration and return period. Furthermore, among the four types of GCMs used in this study, the MRI model predicted the most severe future drought for the Seoul region, and the SDF curve derived using the MRI model also resulted in the highest degree of drought severity compared with the other GCMs. Copyright 2012 John Wiley & Sons, Ltd. KEY WORDS SPI; drought frequency analysis; drought spell; SDF curve; climate change Received 16 November 2011; Accepted 20 April 2012 INTRODUCTION In recent years, the frequency of floods and droughts that are due to global warming-related climate change has increased and is accompanied by a rise in the severity of these phenomena [Intergovernmental Panel on Climate Change (IPCC), 2007]. It is difficult to recognize the progression of droughts because, unlike floods, drought progression is slow; however, the magnitude of droughtrelated damage as well as the extent of affected areas and other detrimental effects is more severe than those of floods. Drought characteristics make drought monitoring and forecast difficult, especially from a hydrologic or disaster prevention perspective, and defining the beginning and the end of a drought can be challenging. Thus, many statistical analyses and approaches have been used in an attempt to predict and monitor droughts using sources such as hydrologic data, weather data and drought indices. In addition, various countries are currently conducting studies to monitor droughts in real time. Lee et al. (2006a, b) verified the applicability of certain drought indices, including the Palmer Drought Severity Index, the Standardized Precipitation Index (SPI) and the Surface Water Supply Index, for establishing a drought monitoring system and monitoring drought phenomena. However, as climate changes have had an important impact on meteorological and hydrologic environment in *Correspondence to: Joo Heon Lee, Department of Civil Engineering, Joongbu University, Geumsan, Chungnam-Do, Korea. leejh@joongbu.ac.kr recent years, real-time drought monitoring and short-term drought predictions alone cannot prevent the extent of damage caused by unexpected severe droughts. Therefore, studies that attempt to forecast droughts on a long-term scale by analyzing the effects of climate change on drought phenomena are necessary. Hydrological and weather data are often used to study droughts after they are converted into drought indices, and statistical analyses performed using these drought indices include analyzing drought trends and periodicities or estimating the drought return periods using a drought frequency analysis Kim et al. (2011) analysed drought trends and periodicities using the 6-month SPI timescale, which was calculated using the general circulation model (GCM) data, the Mann Kendall test and a wavelet transform to estimate changes in drought frequency using a drought spell analysis. Khadr (2009) conducted a trend analysis on droughts in the Ruhr River basin, and Torrence and Compo (1998) introduced the wavelet transform analysis and identified a correlation with droughts using the El Nino-Southern Oscillation time series. Grinsted et al. (2004) demonstrated the applicability of the cross-wavelet transform and wavelet transform coherence analysis in investigating drought trends and periodicities. Climate data produced by the GCM and regional climate model, which apply climate change scenarios, are being used to calculate drought indices in an attempt to forecast droughts. Ghosh and Mujumdar (2007) calculated the 12-month SPI timescale from 19 separate GCMs to forecast future droughts for every 10-year Copyright 2012 John Wiley & Sons, Ltd.

2 CLIMATE CHANGE EFFECT ON DROUGHT SEVERITY DURATION FREQUENCY RELATION 2801 period, whereas Lehner et al. (2008) forecasted 2020s (mid-term future) and 2070s (long-term future) droughts for the continent of Europe. Vidal and Wade (2007) used six GCMs, which applied A2 and B2 scenarios, to calculate the 3-, 6-, 12- and 24-month timescale SPIs and then studied the changes in future drought severity by comparing the SPI with the standard normal distribution curve of the standardized SPI. In addition, recent drought forecast studies using a drought frequency analysis have been conducted through the development of analytic methods such as the severity area duration (SAD) and the severity area frequency (SAF) methods. The SAD method analyses drought by replacing the rainfall depth (depth) of the depth area duration that is applied to characterize extreme rainfall events and replaces the depth with an appropriate index to represent the drought severity (Kim et al., 2006). Kim et al. (2010) suggested that it is important to assess climate change vulnerability for the current water supply system through a comparison of current and historical SAD curves. The SAF method is a tool that can be used to effectively analyse droughts by considering the spatial area of impact, where the recurrent characteristics of the drought and the area of impact can be calculated through derivation of the SAF curve (Henriques and Santos, 1999; Chang et al., 2006). Loukas et al. (2008) calculated the 1-, 3-, 6-, 9- and 12-month SPI timescales using the CGCM2, created the SAF curve and assessed the changes in drought severity as a function of climate change, whereas Mishra and Singh (2009) used 12 GCMs to calculate the SPI and predicted the results for each scenario through a comparison with the SAF curve. As a result, Mishra and Singh s (2009) forecast from the 3- and 12-month SPI timescales predicted that extreme droughts were likely to occur in several areas. Akhtari et al. (2008) attempted to analyse drought characteristics in the Razavi and South Khorasan regions of Iran using the 12-month SPI timescale to derive the SAF curve. Alternatively, it is possible to derive the severity duration frequency (SDF) curve using a frequency analysis of past drought events and to use the derived SDF curve to estimate the return period of those events (Yoo and Kim, 2006). Kim and Yoo (2006) applied an SDF curve derived from the SPI and a rectangular pulse model to conduct a drought frequency analysis of the Korean Peninsula and used the results of that analysis to characterize the spatial distribution of drought severity in South Korea. In this study, SPI for multiple timescales (3, 6 and 12 months) were calculated for Seoul weather station in Korea based on the four different GCMs and previously observed precipitation data. The SDF curve was also derived through a drought frequency analysis. The SDF curve was compared with the past SDF curve to analyse the effect of climate changes on the SDF curve. Various drought-spell analyses were used to estimate current and future changes in frequency and duration for each drought classification. Study area MATERIALS AND METHODS The Seoul weather station at the Han River basin was selected from 57 weather stations under the Korea Meteorological Administration (KMA) within the Korean Peninsula to conduct the drought frequency analysis in this study. The Seoul weather station represents the water system at the Han River basin, and observed historical weather data were collected from 1970 to 2010 (Figure 1). GCMs for multimodel assessment TheIPCCDataDistributionCenter( provides 23 GCMs that are produced based on the Special Report on Emissions Scenarios. The National Institute of Meteorological Research of Korea collects the 23 GCM results into a grid. In this study, the four GCMs were used for the analysis as shown in Table I. The four GCMs that provided a common result for the A2 scenario (CO2 830 ppm at 2100) applied in IPCC AR4 were selected: GFDL:CM2_1, CONS:ECHO-G, MRI:CGCM2_3_2 and UKMO:HADGEM1. The downscaling of coarse scale GCM simulations (>200 km) to regional scale (<50 km) is required for reasonable climate impact studies and for providing biascorrected information on local scale climate change (e.g. Giorgi and Mearns, 1991; Wood et al., 2004; Christensen Figure 1. Weather stations in Korea and information of the Seoul Automated Weather Station

3 2802 J. H. LEE AND C. J. KIM Table I. Detailed information on the four GCMs used in this study No. Model (agency: version) Country Atmosphere Resolution Ocean 1 GFDL: CM2_1 USA 144X90 360X200 2 CONS: ECHO-G Germany/Korea 96X48 128X117 3 MRI: CGCM2_3_2 Japan 128X64 144X111 4 UKMO: HADGEM1 UK 192X X216 et al., 2007; Im et al., 2010). This study used the statistical downscaling approach based on a combination of the CSEOF (CycloStationary Empirical Orthogonal Fuction) analysis (Kim and North, 1997) and the WXGEN (Weather Generator) (Sharpley and Williams, 1990), which was used in the study of Jung et al. (2011). The CSEOF generates localized climate scenarios with a resolution of approximately 20 km from the coarse scale GCM simulations, which have a resolution of approximately km, and the WXGEN disaggregates monthly data to daily simulations. A similar approach is used by Bae et al. (2011a, b). CSEOF analysis is one of many eigenvalue techniques applied to decompose the climate data into a set of independent modes and their principal component (PC) time series (Lim et al., 2007). The Korean National Institute of Meteorological Research in KMA used the CSEOF to downscale the 13 GCM simulations to a fine spatial resolution on the basis of 57 climate stations. They first computed CSEOF modes and their PC time series from both observations and GCM simulations for the reference period. The PC time series represents the long-term amplitude fluctuation of climate signals, such as seasonal cycles, El Nino-Southern Oscillation and low frequency variations (Kim, 2002). The multiple regression method is then used to determine the statistical relation between the climate signals of the climate station data and the GCM simulations for the reference period. On the basis of this statistical relationship, the PC time series of GCMs are generated for the future period of Finally, future downscaled data are constructed using the generated PC time series of GCMs and the CSEOF modes of the observation of 57 climate stations (Jung et al., 2011). WXGEN (Sharpley and Williams, 1990), which is based on the weather generator model developed by Richardson (1981), generates daily precipitation, maximum and minimum temperature, solar radiation, relative humidity and wind speed based on the statistics of observed climate variables (Jung et al., 2011). Validation of GCMs To review the uncertainty of precipitation, which is used in this research, observed and projected monthly precipitation (gray shading in the Figure 2) through the four GCMs were depicted. The annual maximum of monthly precipitation (red dots), the annual average of monthly precipitation (white dots) and the annual minimum of monthly precipitations (blue dots) were also presented in Figure 2. In the four GCMs, the annual maximum of monthly precipitation was approximately 300 mm during the years and approximately 400 mm during the years , whereas higher annual maximum monthly precipitation was observed as compared with those by the four GCMs. This result might be the consequence of not simulating the extreme events like typhoons in case of the four GCMs. The GCMs are useful tools that can provide changes patterns in atmosphere ocean ground surface according to greenhouse gas emission scenario in an objective way based on the dynamical and physical process, and generally GCMs simulate climate changes under the assumption of future greenhouse gas emission. However, because of different dynamical systems, grid size, parameterization processes and physical processes, GCMs generate large differences in different models (Im et al., 2010). Although some differences were demonstrated with each model, these models showed approximately 100 mm of the annual average of monthly precipitation in both historically observed data as well as projected data by the four GCMs. It showed that average values well captured the observed data as compared with that of annual maximum precipitation. Figure 2. Observed and projected monthly precipitation by the four GCMs (GFDL, CONS, MRI and UKMO) for the baseline period ( )

4 CLIMATE CHANGE EFFECT ON DROUGHT SEVERITY DURATION FREQUENCY RELATION 2803 Range Table II. Moisture conditions and drought classifications using SPI Dry condition More than Extremely Wet to Very wet to Moderately wet to 0.99 Near normal 1.00 to 1.49 Moderately dry 1.50 to 1.99 Severely dry Less than 2.00 Extremely dry The annual minimum of monthly precipitation also showed similar trends of around 10-mm level in observed data and approximately mm in the four GCMs, which is considered that projected precipitations by the four GCMs in this research can be used in drought analysis studies. Choice of drought index The SPI was developed by McKee et al. (1993) for defining and monitoring local droughts. It was intended to identify drought periods as well as the severity of droughts, at multiple timescales, such as at 1, 3, 6, 12 or 24 months. A regional SPI was developed and tested for the Alentejo region (Paulo et al., 2005) using several timescales. Different timescales reflect lags in the response of various water resources to precipitation anomalies. For example, soil moisture conditions respond to precipitation anomalies on a relatively short scale, whereas ground water, streamflow and reservoir storage reflect longer-term precipitation anomalies. SPI values range from -2.0 (extremely dry) to +2.0 (extremely wet). A drought event occurs when the SPI is continuously reaches an severity of 1.0 or less (Table II). The SPI is based on the probability distribution of the long-term precipitation record for a desired period. The timescale, embedded in SPI computing, is the length in months of the moving window for which precipitation totals are obtained. The long-term record of precipitation totals for each ending month is fitted to a probability distribution, which is then transformed into a normal distribution so that the mean SPI for the location and desired period is zero (Edwards and Mckee, 1997). The SPI is computed by fitting a probability density function to the frequency distribution of precipitation summed over the timescale of interest. This computation is performed separately for each month (or for any temporal basis of the raw precipitation time series) and for each location in space. Each probability density function is then transformed into the standardized normal distribution. The gamma distribution is defined by its frequency or probability density function and is calculated as follows: gx ðþ¼ 1 b a ΓðÞ a xa 1 e x=b ; for x > 0 (1) where a > 0isashapefactor,b > 0 is a scale factor and x > 0 is the amount of precipitation. Γ(a) is the gamma function, which is defined as ΓðÞ¼ a Z 1 0 y a 1 e y dy (2) Fitting the distribution to the data requires estimating a and b. Edwards and McKee (1997) suggested estimating these parameters using the approximation of Thom (1958) for maximum likelihood as follows: ^a ¼ 1 4A where for n observations, r ffiffiffiffiffiffiffiffiffiffiffiffiffiffi! 1 þ 1 þ 4A 3 ^b ¼ x^a A ¼ lnðþ x ; P lnðþ x n The estimated parameters are then used to find the cumulative probability of an observed precipitation event for the given month and timescale: Gx ðþ¼ Z x 0 gx ðþdx ¼ (3) (4) Z x Γ ^a 1^b^a ð Þ ¼ x^a 1 e x=^bdx (5) Substituting t for x=^b reduces the equation to an incomplete gamma function. McKee et al. (1995) used an analytical method along with recommended software from Press et al. (1986). Because the gamma function is undefined for x = 0 and a precipitation distribution may contain zero, the cumulative probability becomes Hx ðþ¼qþð1 qþgx (6) where q is the probability of zero precipitation. The cumulative probability, H(x), is then transformed to the standard normal random variable Z with mean zero and variance one, which is the value of the SPI. Following Edwards and McKee (1997) and Hughes and Saunders (2002), we used the approximate conversion provided by Abramowitz and Stegun (1965) as an alternative: Z ¼ SPI ¼ t c 0 þ c 1 t þ c 2 t 2 1 þ d 1 t þ d 2 t 2 þ d 3 t 3 0 ; for 0 < Hx ðþ 0:5 (7a) Z ¼ SPI ¼þ t c 0 þ c 1 t þ c 2 t 2 1 þ d 1 t þ d 2 t 2 þ d 3 t 3 ; for 0:5 < Hx ðþ< 1 where vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi " # u 1 t ¼ t ln ðhx ðþþ 2 ; for 0 < Hx ðþ 0:5 vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi " # u 1 t ¼ t ln ð1 Hx ðþþ 2 ; for 0:5 < Hx ðþ< 1 (7b) (8a) (8b)

5 2804 J. H. LEE AND C. J. KIM Monthly SPI were calculated for multiple timescales 3, 6 and 12 months. Figures 3 and 4 show the time series of the 3-month SPI timescale, which was calculated using the historical and GCM data, in which the dotted line represents the severely dry stage and the solid line represents the extremely dry stage. On the basis of the Historical Drought Event Survey Report (MLTM, 1995; MLTM, 2002), extreme drought events were confirmed from Seoul area during the years and , and severe drought events also Figure 3. The 3-month SPI time series calculated using the historically observed data ( ) occurred during and year 2007 (Lee and Kim, 2011). Estimated SPI during this period appropriately monitored those drought events as extremely dry stage (with 2.0 SPI) and severely dry stage (with 1.5 SPI 2.0), indicating that SPI is a suitable indicator for drought monitoring in this study. In the SPI time series by the four GCMs, extremely dry stage would be occurred more frequently during the years as compared with those during the years Methods for assessing the climate change effect on drought patterns To predict the trends of future projected droughts, drought spell analyses were performed for each drought classification and drought duration using the 3-, 6- and 12-month SPI timescales based on the historical and GCM data. In addition, a drought frequency analysis was conducted for the SDF curve derivation. Drought spell A drought spell analysis was performed to assess the characteristics of both historical and projected droughts at the Seoul region. The monthly SPI that had been calculated using the observed and GCM data were divided into each of the drought classification stages (mild, moderate, severe and extreme), and the frequency at each stage was estimated by the drought spell analysis. The theoretical value of the a) GFDL:CM2_1 b) CONS:ECHO-G c) MRI:CGCM2_3_2 d) UKMO:HADGEM1 Figure 4. The 3-month SPI time series calculated using the GCMs ( )

6 CLIMATE CHANGE EFFECT ON DROUGHT SEVERITY DURATION FREQUENCY RELATION 2805 probability density function for each drought classification was as follows: mild drought, 34.1%; moderate drought, 9.2%; severe drought; 4.4%; and extreme drought, 2.3% (as shown in Table III). The period for historical and projected droughts consisted of four different time frames divided into 30-year periods, including the period of the observed data (S0), years (S1), years (S2) and years (S3), as shown in Table IV. The SPIs that were calculated using the historical and GCM data were classified according to the different drought durations, and the changes in occurrence patterns were analysed. To analyze the monthly 3-, 6- and 12-month SPI timescales for each drought duration, the drought events with values that were classified as severer than a moderate Table III. Drought classification by the SPI and corresponding class probabilities Drought classification Range Probability (%) Mild 0 x > Moderate 1.0 x > Severe 1.5 x > Extreme 2.0 x 2.3 Table IV. Four different time frames divided into 30-year periods including historically observed data used in this study ID Period (years) Data type S Observed data S GCMs S GCMs S GCMs drought (with 1.0 SPI) were selected, and the drought spell analysis was then performed according to the different drought durations. The results were used to calculate the number of drought occurrences, accumulated magnitude, average magnitude and maximum magnitude according to the duration to analyse the differences between historical and projected future drought durations. Drought frequency analysis The second method that was used to analyse drought characteristics involved determining the statistical characteristics of the droughts that occurred at Seoul by deriving the SDF curve through a drought frequency analysis. The procedure used for the drought frequency analysis used in the derivation of the SDF curve is shown in Figure 5. The analysis periods in this study were between 1970 and 2010 for the past and 2011 and 2099 for the future. First, the monthly 3-, 6- and 12-month SPI timescales that were calculated from the historical and the GCM data were arranged by maximum drought severity for each of the continuous durations from 1 to 12 months. The observed data were arranged into a series containing the entire period, whereas the GCM data were arranged into three, 30-year time slice from 2011 to 2040, from 2041 to 2070 and from 2071 to To select the best fit probability distribution for the calculated SPI, goodness-of-fit tests were conducted at a significance level of 5% for 15 different probability distribution, including the gamma, general extreme value (GEV), Gumbel, log-gumbel, log-normal, log-pearson type III, normal, Pearson type III, Weibull and Wakeby distributions, which are commonly used in frequency analysis of hydrologic data. The Kolmogorov Smirnov test was used in the goodnessof-fit test, and as a result, the log-gumbel, gamma, GEV, Gumbel, log-normal and Weibull distribution types were Figure 5. Flow chart of the drought frequency analysis used to derive the SDF curve using the SPI

7 2806 J. H. LEE AND C. J. KIM determined to be the appropriate probability distributions for the frequency analysis of the drought index. As a result of the Kolmogorov Smirnov test, GEV distribution, which showed the best fit probability distribution among the candidate probability, was selected as the optimal probability distribution for the frequency analyses. After conducting the drought frequency analysis for each drought duration for the observed and GCM data, the SDF curve was derived. RESULTS AND ANALYSIS Changes in drought severity and frequency Table V and Figure 6 show the changes in frequency for each drought classification using the drought spell, where the x-axis represents the analysis period and the y-axis represents the frequency (%) of each drought classification. In the case of mild and moderate droughts, SPI for the observed historical period revealed a frequency of 33.54% Table V. Frequency of the 3-month SPI timescale for each drought classification as calculated from the observed data (S0) and GCMs (S1, S2 and S3) Period Frequency of mild drought (%) Frequency of moderate drought (%) Observed KMA KMA S GCMs GFDL CONS MRI UKMO Average GFDL CONS MRI UKMO Average S S S Period Frequency of severe drought (%) Frequency of extreme drought (%) Observed KMA KMA S GCMs GFDL CONS MRI UKMO Average GFDL CONS MRI UKMO Average S S S a) Mild drought b) Moderate drought c) Severe drought d) Extreme drought Figure 6. Frequency of the 3-month SPI timescale by drought spell for each drought classification as calculated from the historical data (S0) and GCMs (S1, S2 and S3)

8 CLIMATE CHANGE EFFECT ON DROUGHT SEVERITY DURATION FREQUENCY RELATION 2807 and 11.38%, respectively, whereas the GCM showed a decreased drought frequency over time. At the S3 period, the average frequency of mild and moderate drought decreased to 27.68% and 9.29%, respectively. For severe droughts, the S1 period showed a lower drought frequency relative to the historical data, but the projected frequency gradually increased over time. For extreme droughts, the SPI of historical data indicated a frequency of 0.81%, which was lower than the theoretical probability density function value, but the frequency increased significantly with all GCMsbased on the projected future SPI. At the S3 period, the average frequency of extreme drought increased to 7.09%. In particular, the MRI showed a frequency of 1.94% in the S2 period, which is similar to that given by the other GCMs, but it showed a higher frequency (9.77%) in the S3 period relative to the other GCMs. Overall, the MRI predicted the highest drought frequency at all stages, and the CONS showed the lowest frequency. When frequency were separated for each drought classification, the frequency of mild droughts decreased over time, the frequency of moderate and severe droughts increased slightly over time and the frequency of extreme droughts had the greatest and most pronounced increase. Changes in drought duration Drought events that had consistent values below ( 1) in the monthly SPI (3-month SPI time series was selected and classified by each duration of 1 12 months. Table VI shows the changes in drought duration of past and future drought events. The analysis showed that, historically, droughts with a short duration of 1 3 month occurred frequently in the past, whereas drought events with longer durations (i.e. more than 6 months) showed an increase in the future. Drought events with duration of 8 months were found in the case of the GFDL, whereas drought events with 8- and 10-month durations were also found in the MRI. Even in terms of the accumulated magnitude, according to the duration, the GCMs yielded lower (severer) values than the historical data; the same results were found for the average and maximum severity. Changes in the drought SDF relationship A drought frequency analysis was performed using the 3-, 6- and 12-month SPI timescales that were derived from the historical and GCM output for the Seoul weather station. Figure 7. SDF curve derived from the observed historical data ( ) and the 3-month SPI timescale Table VI. Changes in drought occurrence for each drought duration using the 3-month SPI timescale and a drought spell analysis (with 1.0 SPI) Duration (months) No. drought events (%) Magnitude of drought event (negative sum of spi values during the event) Observed GFDL CONS MRI UKMO Observed GFDL CONS MRI UKMO 1 26 (55.3) 45 (47.9) 14 (24.1) 38 (51.4) 27 (37.0) (21.3) 31 (33.0) 15 (25.9) 13 (17.6) 23 (31.5) (19.1) 11 (11.7) 16 (27.6) 13 (17.6) 9 (12.3) (3.2) 6 (10.3) 1 (1.4) 5 (6.8) (4.3) 3 (5.2) 2 (2.7) 4 (5.5) (3.2) 4 (6.9) 2 (2.7) 2 (2.7) (1.4) 3 (4.1) (1.1) 3 (4.1) (1.4) Duration (months) Average severity (magnitude/duration) Maximum severity Observed GFDL CONS MRI UKMO Observed GFDL CONS MRI UKMO

9 2808 J. H. LEE AND C. J. KIM The SDF curve was derived for each return period (30, 50, 100, 200, 300 and 500 years) and drought duration (1 12 months). In Figure 7 10, the x-axis represents the duration of drought, and the y-axis represents the drought severity, which refers to the value obtained by dividing the drought magnitude by the drought duration. Table VII shows the results of the drought frequency analysis for the 3-month SPI timescale using the observed data ( ) for each duration. Figure 7 shows the SDF curve that was derived using the observed historical data, and Figures 8 10 show the projected SDF curves that were derived from the GCMs. The SDF curve derivation showed that the projected drought severity for each duration in the GCMs was lower (severer) than the drought severity for each duration in the observed data. Furthermore, as the duration (1 12 months) increased, the severity of the drought for each return period increased as well. The same pattern of a) GFDL:CM2_1 b) CONS:ECHO-G c) MRI:CGCM2_3_2 d) UKMO:HADGEM1 Figure 8. Projected SDF curves derived from the GCMs ( ) and the 3-month SPI timescale a) GFDL:CM2_1 b) CONS:ECHO-G c) MRI:CGCM2_3_2 d) UKMO:HADGEM1 Figure 9. Projected SDF curves derived from the GCMs ( ) and the 3-month SPI timescale

10 CLIMATE CHANGE EFFECT ON DROUGHT SEVERITY DURATION FREQUENCY RELATION 2809 a) GFDL:CM2_1 b) CONS:ECHO-G c) MRI:CGCM2_3_2 d) UKMO:HADGEM1 Figure 10. Projected SDF curves derived from the GCMs ( ) and the 3-month SPI timescale Table VII. Result of drought frequency analysis for the 3-month SPI timescale using the observed data ( ) for each duration Observed data Duration (months) Frequency 30 years 50 years 100 years 200 years 300 years 500 years results was found in the frequency analysis using the 6- and 12-month SPI timescales. Using the SDF curve derived from the drought frequency analysis, we compared the severity and the frequency of the drought for multiple timescales SPI as a function of drought duration (1 12 months). Table VIII and Figure 11 show the comparison results of the drought severity corresponds to the 200-year return period for each drought duration (1, 6 and 12 months) and each 3-, 6- and 12-month SPI timescales using the SDF curve derived from observed data (S0) and GCM output (S1, S2 and S3). Figure 11 shows these results in graphical form, where the x-axis represents the analysis period (S0 to S3) and the y-axis represents drought severity; the analysis period is the same as that shown in Table III. An analysis of the differences in the drought severity according to the different SPI timescales revealed a higher degree of decreasing trend in drought severity over time (S0 S3) in the 3-month SPI timescale than that in the 6- and 12-month SPI timescales for the identical duration and return period (200 year). This result may be because the increased summer precipitation was included in the calculations process for the 6- and 12-month SPI timescales. An examination of the differences in the drought severity according to duration (1 month to 12 months) revealed a higher drought severity (milder drought) for the 12- and 6-month durations relative to the drought severity for the 1-month duration. An examination of the differences in the drought severity according to the GCMs showed that in all the four GCMs, drought severity increased over time, although the difference in drought severity between the GCMs was larger in the 3-month SPI timescale than that in the 12-month SPI timescale. In particular, the MRI showed the largest decrease in drought severity over time for the 3- and 6-month SPI timescales compared with the other GCMs.

11 2810 J. H. LEE AND C. J. KIM Table VIII. Comparison of the drought severity during a 200-year return period for each drought duration (1, 6 and 12 months) and each 3-, 6- and 12-month SPI timescales using the SDF curve derived from observed data (S0) and GCM data (S1, S2 and S3) 3-month SPI, 1-month duration 3-month SPI, 6-month duration 3-month SPI, 12-month duration Observed KMA KMA KMA S GCMs GFDL CONS MRI UKMO GFDL CONS MRI UKMO GFDL CONS MRI UKMO S S S month SPI, 1-month duration 6-month SPI, 6-month duration 6-month SPI, 12-month duration Observed KMA KMA KMA S GCMs GFDL CONS MRI UKMO GFDL CONS MRI UKMO GFDL CONS MRI UKMO S S S month SPI, 1-month duration 12-month SPI, 6-month duration 12-month SPI, 12-month duration Observed KMA KMA KMA S GCMs GFDL CONS MRI UKMO GFDL CONS MRI UKMO GFDL CONS MRI UKMO S S S Return periods for the drought events in historical drought years The return periods for major drought events that occurred previously in the Seoul region were examined using the SDF curve that was derived from the drought frequency analysis on the 6-month SPI timescale. To select historical drought events, the past major drought years were surveyed based on reports of historical drought records of Korea. The surveyed reports include the Historical Drought Event Survey Reports by the Ministry of Land, Transport and Maritime Affairs of Korea (MLTM, 1995; MLTM, 2002), and these reports confirmed that a severe drought occurred in Seoul during the years between 1988 and 1989 and between 2000 and The 6-month SPI timescale that was derived from the observed data ( ) accurately replicated the droughts in 1988 to 1989 and 2000; the duration, the drought magnitude and the drought severity for the previous drought events are shown in Table IX. The drought that occurred throughout the central region of the Korean Peninsula in 1988, as investigated by the 6-month SPI timescale from the observed data, was found to have duration of 7 months and a return period of 289 years. In 2000, a severe drought occurred throughout the southern region of Korea, and a drought with a duration of 4 months was observed from the 6-month SPI timescale of Seoul weather station, with a return period of 24 years. In addition, the return periods that were estimated by the GCM-derived SDF curves for the historical major drought events are shown in Table X. To compare the return period of the historical drought events, we applied the four different SDF curves derived from observed data ( ) as well as GCMs ( , and ) through the drought frequency analysis using the 6-month SPI timescale. In case of SDF curve for the years 2011 to 2040, the historical drought events for the years 1988 to 1989 showed significant differences in return periods, with a 189-year return period for the CONS and a 46-year return period for the UKMO. In contrast, the GFDL and the MRI showed return period of 500 and 308 years, respectively, indicating the longer return periods based on the GCM data compared with those based on the observed data. This result may be due to the milder drought severity in the S1 period compared with the S0 period, as can be seen in Figure 11. For the drought event that occurred in the year 2000, the MRI model showed a 25-year return period similar to the observed data, whereas the GFDL, CONS and UKMO models showed approximately a 10-year return period or approximately half of the return period derived from the MRI model and observed data. In case of the SDF curve for the years 2041 to 2070, the drought events for the years 1988 to 1989 each showed shorter return periods for the GFDL, MRI and UKMO models of 47, 168 and 95 years, respectively, whereas the CONS model showed a longer return period with 500 years. For the drought event in the year 2000, the four GCMs showed shorter return periods than the corresponding results of the SDF curves for the years 2011 to In case of the SDF curve for the years 2071 to 2099, the drought events for the years 1988 to 1989 and 2000 all showed significantly shorter return periods than the observed data.

12 CLIMATE CHANGE EFFECT ON DROUGHT SEVERITY DURATION FREQUENCY RELATION 2811 a) SPI (3) - Duration(1) b) SPI (3) - Duration(6) c) SPI (3) - Duration(12) d) SPI (6) - Duration(1) e) SPI (6) - Duration(6) f) SPI (6) - Duration(12) g) SPI (12) - Duration(1) h) SPI (12) - Duration(6) i) SPI (12) - Duration(12) Figure 11. Comparison of the drought severity during a 200-year return period for each duration (1, 6 and 12 months) and each 3-, 6- and 12-month SPI timescales using the SDF curve derived from observed data (S0) and GCM data (S1, S2 and S3) Table IX. Drought magnitude and severity of major historical drought events at the Seoul based on the 6-month SPI timescale Year Drought month Duration (months) Magnitude Severity August to February April to July The results of the analyses indicated that the four GCMs showed shorter return periods for historical droughts over time. CONCLUSION Projected future drought occurrence patterns were analysed using historically observed data from the Seoul weather station on the Korean Peninsula and the four GCMs that reflect the climatic change scenarios. 1. Estimating the changes in the frequency for each drought classification using the drought spell analyses showed that the projected SPIs that were calculated using the GCMs were found to decrease in frequency for mild and moderate drought cases over time. However, in cases of severe droughts, the frequency was found to increase slightly over time, and the frequency was found to increase sharply in the case of extreme droughts. In particular, the MRI model generally showed a longer return period of droughts relative to the frequency projected by the other GCMs, and the extreme drought frequency was found to be higher in the S3 period ( ) compared with the S1 and S2 periods. In other words, we predicted that severe and extreme droughts, rather than mild droughts, would occur more frequently in the future.

13 2812 J. H. LEE AND C. J. KIM Table X. Comparison of the return periods for major historical drought events that occurred at the Seoul using the SDF curve derived from the observed and GCM data Return period of major historical droughts based on the SDF ( ) (years) Return period of major historical droughts based on the projected SDF ( ) (years) Year KMA GFDL CONS MRI UKMO Return period of major historical droughts based on the projected SDF ( ) (years) Return period of major historical droughts based on the projected SDF ( ) (years) Year GFDL CONS MRI UKMO GFDL CONS MRI UKMO Changes in the drought duration for the drought event (with 1.0 SPI) indicated that the number of occurrences of droughts with longer durations increased according to the projected data obtained from the GCMs relative to the observed data. Specifically, long-term droughts of more than 6 or 10 months in duration, which have never occurred in the historical data, were found in the projected data from the GCMs, including the GFDL and the MRI. 3. In the SDF curve derived from the drought frequency analysis for the Seoul weather station, the drought severity for each return period decreased as drought duration increased, whereas the droughts identified from the GCMs were found to be more severe than those found in the historically observed data. The drought severity was compared for each SPI and GCM by using the SDF curve derived from drought frequency analysis using the 3-, 6- and 12-month SPI timescales. The differences in the drought severity for each SPI showed that the drought severity for the 3-month SPI timescale was most severe relative to the 6- and 12-month SPI timescales during the same duration and the same return period and that the drought severity tends to be more pronounced over time. Examining the drought severity for each GCM indicated that all the four GCMs predicted more intensified droughts over time, especially based on the SDF curve using the MRI model, which was found to show the most extreme drought severity compared with the other GCMs. 4. The return periods for historical major drought events were examined by using the SDF curve derived from the drought frequency analysis of the 6-month SPI timescale. Severe droughts were determined to have occurred in the years 1988 and Results of the drought investigation with the 6-month SPI timescale confirmed that severe droughts occurred in the years 1988 to 1989 (7-month duration) and 2000 (4-month duration). The results obtained by estimating the return period using the SDF curve revealed a 289-year return period for the drought event between 1988 and 1989 and a 24-year return period for the drought of Furthermore, when estimating the return periods of historical droughts using the SDF curve derived from the GCMs, some GCMs showed longer return periods because of differences in the drought severity according to the analysis period. However, in the four GCMs, the return periods were shorter than the SDF curve derived from the observed data, and the return periods of historical droughts were estimated to decrease more over time. Although uncertainty regarding the ability of GCMs to accurately reflect climatic change scenarios cannot be ruled out, we conclude that the multimodal GCMs predict intensified drought severity and drought duration in the Seoul region of Korea, relative to previous drought characteristics, and that the magnitude of these factors will increase over time. ACKNOWLEDGEMENTS This research was supported by a grant from the CCAPH-K Research Center funded by the Ministry of Land, Transport and Maritime Affairs of the Korean government. REFERENCES Abramowitz M, Stegun A Handbook of Mathematical Formulas, Graphs, and Mathematical Tables. Dover Publications Inc: New York, USA. Akhtari R, Bandarabadi SR, Saghafian B Spatio-temporal pattern of drought in Northeast of Iran. International Conference on Drought management: Scientific and technological innovations, 1, Zaragoza, Spain. Bae DH, Jung IW, Lee BJ, Lee MH. 2011a. Future Korean water resources projection considering uncertainty of GCMs and hydrological models. Journal of Korea Water Resources Association 44(5): (in Korean). Bae DH, Jung IW, Lettenmaier DP. 2011b. Hydrologic uncertainties in climate change from IPCC AR4 GCM simulations of the Chungju basin, Korea. Journal of Hydrology 401: Chang YG, Kim SD, Choi GW A Study of Drought Spatio- Temporal Characteristics Using SPI-EOF Analysis. Journal of Korea Water Resources Association 39(8): (in Korean). Christensen JH, Hewitson B, Busuioc A, Chen A, Gao X, Held I, Jones R, Kolli K, Kwon WT, Laprise R, Magana Rueda V, Mearns L, Men endez CG, Raisanen J, Rinke A, Sarr A, Whetton P Regional Climate Projections. In Climate Change The Physical Science Basis. Contribution of WGI to the IPCC AR4.

14 CLIMATE CHANGE EFFECT ON DROUGHT SEVERITY DURATION FREQUENCY RELATION 2813 Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds). Cambridge University Press: Cambridge and New York. Edwards DC, McKee TB Characteristics of 20th Century Drought in the United States at Multiple Time Scales. Climatology Report, Number 97 2, Colorado State University Fort Collins CO. Ghosh S, Mujumdar PP Nonparametric methods for modeling GCM and scenario uncertainty in drought assessment. Water Resources Research 43(7): W Giorgi F, Mearns LO Approaches to the simualtion of regioanl climate change - A review. Reviews of Geophysics 29: Grinsted A, Moore1 JC, Jevrejeva S Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Processes in Geophysics 11: Henriques AG, Santos MJJ Regional Drought Distribution Model. Physics and Chemistry of the Earth(B) 24(1 2): Hughes BL, Saunders MA A drought climatology for Europe. International Journal of Climatology 22: Im ES, Jung IW, Chang H, Bae DH, Kwon WT Hydroclimatological response to dynamically downscaled climate change simulations for Korean basins. Climatic Change 100: IPCC Climate Change 2007: The Physical Science Basis. Working group I contribution to the fourth assessment report of the intergovernmental panel on climate change, Summary for Policymakers. Cambridge University Press: Cambridge UK. Jung IW, Bae DH, Lee BJ Possible Change in Korean Streamflow Seasonality Based on Multi-Model Climate Projections. Hydrological Processes. DOI: /hyp.9215 Khadr M Analysis of Meteorological Drought in the Ruhr Basin by Using the Standardized Precipitation Index. World Academy of Science Engineering and Technology 57: Kim KY Investigation of ENSO variability using cyclostationary EOFs of observational data. Meteorology and Atmospheric Physics 81(3 4): Kim KY, North GR EOFs of harmonizable cyclostationary processes. Journal of the Atmospheric Sciences 54(19): Kim DH, Yoo CS Analysis of Spatial Distribution of Droughts in Korea through Drought Severity Duration frequency Analysis. Journal of Korea Water Resources Association 39(9): (in Korean). Kim BK, Kim SD, Kyoung MS, Kim HS Spatio-Temporal Drought Quantification using Severity Area Frequency Curve. Proceedings of 2006 Annual Conference of Korea Water Resources Association: (in Korean). Kim HS, Park JH, Yoon JY, Kim SD Application of SAD Curves in Assessing Climate-change Impacts on Spatio-temporal Characteristics of Extreme Drought Events. Journal of Korean Society Civil Engineering 30(6B): (in Korean). Kim CJ, Seo JW, Park MJ, Shin JS, Lee JH Climate Change and Future Drought Occurrence of Korean Peninsula. Proceedings of 2011 Annual Conference of Korea Society of Hazard Mitigation: 205 (in Korean). Lee JH, Kim CJ Derivation of Drought Severity Duration Frequency Curves Using Drought Frequency Analysis. Journal of Korea Water Resources Association 44(11): (in Korean). Lee JH, Jeong SM, Kim SJ, Ko YS. 2006a. Development of Drought Monitoring System : I. Applicability of Drought Indices for Quantitative Drought Monitoring. Journal of Korea Water Resources Association 39(9): (in Korean). Lee JH, Jeong SM, Kim JH, Ko YS. 2006b. Development of Drought Monitoring System : II. Quantitative Drought Monitoring and Drought Outlook Methodology. Journal of Korea Water Resources Association 39(9): (in Korean). Lehner B, Doll P, Alcamo J Estimating the impact of global change on flood and drought risks in Europe: a continental, integrated analysis. Climatic Change 75(3): Lim YK, Shin DW, Cocke S, Larow TE, Schoof JT, O Brien JJ, Chassignet EP Dynamically and statistically downscaled seasonal simulations of maximum surface air temperature over the southeastern United States. Journal of Geophysical Research 112: D DOI: /2007JD Loukas A, Vasiliades L, Tzabiras J Climate change effects on drought severity. Advances in Geosciences 17: McKee TB, Doesken NJ, Kleist J The relationship of drought frequency and duration to time scales. In: Preprints, 8 th conference on applied climatology, January, Anaheim, CA: McKee TB, Doesken NJ, Kleist J Drought monitoring with multiple time scales. In: Preprints, 9 th conference on applied climatology, January, Dallas, TX: (preprints). Ministry of Land, Transport and Maritime Affairs (MLTM) Historical Drought Event Survey Report (in Korean). Ministry of Land, Transport and Maritime Affairs (MLTM) Historical Drought Event Survey Report (in Korean). Mishra AK, Singh VP Analysis of drought severity-area frequency curves using a general circulation and scenario uncertainty. Journal of Geophysical Research 114: D DOI: /2008JD Paulo AA, Ferreira E, Coelho C, Pereira LS Drought class transition analysis through Markov and Loglinear models, an approach to early warning. Agricultural Water Management 77(1 3): Press WH, Flannery BP, Teukolsky SA, Vetterling WT Numerical Recipes. Cambridge University Press: Cambridge, UK. Richardson CW Stochastic simulation of daily precipitation, temperature, and solar-radiation. Water Resources Research 17(1): Sharpley AN, Williams JR Erosion/productivity impact calculator: 1. Model documentation. U.S. Dept. of Agric., Washington, DC, Tech. Bul Thom HCS A note on the gamma distribution. Monthly Weather Review 86: Torrence C, Compo GP A practical guide to wavelets analysis. Bulletin of the American Meteorological Society 79(1): Vidal JP, Wade S A multimodel assessment of future climatological droughts in the United Kingdom. International Journal of Climatology 29(14): Wood AW, Leung LR, Sridhar V, Lettenmaier DP Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Climatic Change 62(1 3): Yoo CS, Kim DH Evaluation of Drought Events Using the Rectangular Poisson Process Model. Journal of Korea Water Resources Association 39(4): (in Korean).