Analysis of climate change impacts on the spatial and frequency patterns of drought using a potential drought hazard mapping approach

Transcription

1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 34: (2014) Published online 26 February 2013 in Wiley Online Library (wileyonlinelibrary.com) DOI: /joc.3666 Analysis of climate change impacts on the spatial and frequency patterns of drought using a potential drought hazard mapping approach Chang Joo Kim, Min Jae Park and Joo Heon Lee* Department of Civil Engineering, Joongbu University, Geumsan, Chungnam-Do, Korea ABSTRACT: In this study, the spatial distribution of the potential drought hazard areas in Korea was estimated by conducting frequency analysis with the Standardized Precipitation Index (SPI), and the changes in potential drought hazard areas that appeared because of climate changes were predicted. In an attempt to estimate the changes in the spatial distribution of potential drought hazard areas, past observed data ( ) from 54 automated weather stations under the Korea Meteorological Administration (KMA) and projected precipitation data ( ) by four global climate models (GCMs). CNRM:CM3, CSIRO:MK3, CONS:ECHOG and UKMO:HADCM, were used. The duration frequency (SDF) curves were derived for the 54 weather stations in order to estimate the potential drought hazard areas by employing the potential drought hazard mapping approach. The spatial distribution analysis for the potential drought hazard areas showed that the drought in the Nakdong River basin, which was a frequent drought hazard area in the past, was more aggravated, and more severe droughts were predicted for the future in the Han River basin (Han River, Anseong Stream, West of Han River), which is located in the middle part of the country. From the analysis results of the four GCMs, more severe drought is expected throughout the Korean Peninsula in the future, by using the CONS:ECHOG model compared with the projected data from the four GCMs. Additionally, potential drought areas would shift from the south toward the east and central parts of the country, as projected by the UKMO:HADCM model. KEY WORDS SPI; drought; frequency analysis; SDF curve; potential drought hazard map Received 27 July 2012; Revised 2 December 2012; Accepted 16 January Introduction Damage caused by drought can be severe enough to find a place in the top five worst natural disasters that occurred during the 20th century, of which four were droughts (IPCC, 2007). Korea, which has excellent water supply facilities, experiences drought every year, with the only variations being in drought and regional distribution. This was also confirmed by the weather records showing that large-scale drought has occurred at specified time intervals, spreading from the middle to southern parts of the country or at the national level. The goal of this study is to counteract severe drought caused by climate changes by analysing the spatial distribution characteristics and occurrence pattern of drought, to forecast regional vulnerability toward drought and to propose drought countermeasures against climate changes. Various studies have been conducted to monitor drought in real time with various drought indices (Lee et al., 2006a, 2006b). However, with the hydrological effect on climate change becoming important, the * Correspondence to: J. H. Lee, Department of Civil Engineering, Joongbu University, Geumsan, Chungnam-Do, Korea. leejh@joongbu.ac.kr focus of drought research has shifted from drought forecasting to find countermeasures to cope with the extreme drought conditions that may occur in the future. The most general method to forecast drought is to analyse projected hydrologic data through the global climate model (GCM), in which the climate scenario is applied. These hydrologic data are converted into drought indices, such as the Standardized Precipitation Index (SPI) and the Palmer Drought Severity Index (PDSI), to predict drought. In the drought forecast studies using SPI, Ghosh and Mujumdar (2007) predicted future drought with an SPI (12 months) that was estimated by different GCMs and Vidal and Wade (2007) studied future drought changes by estimating an SPI (3, 6, 12, 24 months), in which the climate change scenario was applied. In the case of the PDSI, Kwon et al. (2009) evaluated drought in 2008 by using meteorological data from 60 weather stations in Korea, and they reviewed drought conditions by using both PDSI and SPI. Although the effects of drought occurrence by precipitation shortages in different regions vary, analysis has shown that drought frequency is increasing, and the of drought differs according to the precipitation status during the winter and spring seasons. Lee et al. (2012) analysed 2013 Royal Meteorological Society

2 62 C. J. KIM et al. the statistical characteristics of drought in the Korean Peninsula with the PDSI and SPI. With the PDSI, a high frequency of drought was found from the southern part of Korea, near the Nakdong, Yeongsan and Sumjin river basins, and large-scale droughts during the past with the 4- to 6-years interval were captured by wavelet transform analysis. Recently, studies on drought characteristics have started to analyse the spatial distribution of future drought by applying statistical analysis methods, such as SAD ( area duration), SAF ( area frequency) and SDF ( duration frequency). SAD method is used to analyse drought events by substituting the rainfall depth in DAD (depth area duration) with an adequate drought index. Kim et al. (2006) proposed a SAD curve by considering affected areas with drought duration based on different levels of drought. Kim et al. (2010) compared previous and current SAD curves and proposed that a vulnerability assessment for the current water resource supply system is urgently needed. SAF method is used to estimate the recurrent characteristics and affected areas of drought (Henriques and Santos, 1999; Chang et al., 2006). Loukas et al. (2008) derived an SAF curve by using SPI (1, 3, 6, 9 and 12 months), which was estimated using CGCM2, and studied the changes based on climate changes. Akhtari et al. (2008) analysed the drought characteristics in the areas of Razavi and South Khorasan in Iran by deriving an SAF curve using SPI (12 months). SDF curve is a relationship that estimates the return period, duration and of possible drought events (Yoo and Kim, 2006). Lee and Kim (2011) quantitatively evaluated the return period and duration of previous drought events by deriving the SDF curves for major weather stations by conducting drought frequency analysis. Kim and Yoo (2006) performed drought analysis for the Korean Peninsula by applying SDF curves that were derived by using a rectangular pulse model, and they characterized the spatial distribution of the drought of the entire South Korean region. Jung and Chang (2011) estimated the relative Standardized Precipitation Index (rspi) and relative Standardized Runoff Index (rsri) for the Willamette River basin in the state of Oregon in the United States. They prepared drought hazard maps for drought durations of 3, 6, 12 and 24 months and analysed drought risk under A1B and B1 climate change scenarios. Many preceding studies of drought analysis have focused on drought frequency analysis, drought monitoring and short-term forecasting of droughts using various types of hydrologic variables and drought indices. Relatively little research has been conducted on quantifying changes in the possible future occurrence of droughts as a result of climate change. Research on this subject would help to make it possible to predict potential drought hazard areas and quantify the spatial distribution of drought. Although it is important to establish a real-time drought monitoring system at the national level in order to prepare for drought-related disasters, it is also very important to conduct research on the prediction of the effect of climate change on long-term drought vulnerability in order to prepare systemically for drought hazards. The objective of this study is to assess the possible future effects of climate change on meteorologically drought-vulnerable areas of Korea. Future potential drought hazard areas were examined by assessing meteorological drought conditions for each basin. The scope of this research is limited in that it does not address hydrological drought, such as water supply capability and water resources supply facilities in the basins, which could be the important parameters in drought disaster risk assessment. The fundamental strength of SPI is that it can be calculated for a variety of time scales (1 month to 24 months). This versatility allows SPI to be used to monitor short-term water supplies, such as soil moisture, which is important for agricultural production, and longterm water supplies, such as ground water, streamflow and reservoir levels (Mishra and Singh, 2010). Therefore, if potential drought hazard areas are assessed using SPI for different time scales, it is not only possible to directly predict meteorological drought but also to indirectly predict agricultural and hydrological drought. In this study, changes in the spatial distribution of the potential drought hazard area of Korea are estimated, and potential drought hazard areas that will be shifted by climate changes are also predicted. The SPI (6 months) was estimated for 54 automated weather stations, and SDF curves were derived for each automated weather station through drought frequency analysis. Using the derived SDF curves, a Potential Drought Hazard Map (PDHM) was prepared, and the changes of drought and frequency were analysed. 2. Materials and methods 2.1. Study area and precipitation data To calculate the drought, after confirming the reliability and consistency of their precipitation data, 54 automated weather stations under the Korea Meteorological Administration (KMA) were selected to estimate the spatial distribution of potential drought hazard areas, as shown in Figure 1. South Korea contains 20 large river basins, also shown in Figure 1, and these basins are divided into 109 watersheds (sub-basins). The Han, Nakdong, Geum, Sumjin and Yeongsan river basins are representative of the five largest rivers in Korea. These five basins occupy approximately 68% of South Korea. The names, code number and area of 20 basins are listed in Table 1. Among the basins used in this study, the sub-basins that fall under North Korea (1020, Gomitan Stream; 1021, Upstream of Imjin River; 1022, Hantan River; 1023, Downstream of Imjin River) were excluded from the study. Observed precipitation data from 1976 to 2010 were used for the analysis of past drought events, and

3 POTENTIAL DROUGHT HAZARD MAPPING USING DROUGHT FREQUENCY ANALYSIS 63 (a) (b) Figure 1. Locations of automated weather stations and the basin hierarchy of Korea. (a) Location of 54 automated weather stations and 109 watersheds. (b) Location of 20 basins of Korea. Table 1. Information about the 20 river basins of Korea. Basin name Code number Area (km 2 ) Basin name Code number Area (km 2 ) Han River Geum river Anseong Stream Sapgyo stream West of Han River West of Geum river East of Han River Mangyeong, Dongjin Nakdong river Sumjin river Hyeongsan river South of Sumjin river Taehwa river Yeongsan river Hoeya, Sooyeong Tamjin river East of Nakdong river South of Yeongsan river South of Nakdong river West of Yeongsan river precipitation data projected by the four GCMs (CNRM:CM3, CSIRO:MK3, CONS:ECHOG, UKMO: HADCM) were used for future events GCMs and downscaling The Intergovernmental Panel on Climate Change (IPCC) Data Distribution Centre ( provides GCM results based on the Special Report on Emissions Scenario (SRES). The National Institute of Meteorological Research of Korea collects 23 GCM results into a grid. In this study, the GCMs that provide a common result for the A2 scenario applied in the IPCC AR4 were selected: CNRM:CM3, CSRIRO:MK3, CONS:ECHOG and UKMO:HADCM (Table 2). The A2 scenario is where the regulation for Table 2. Detailed information on the four GCMs used in this study. No. Model (agency:version) Resolution Country Atm. Ocn. 1 CNRM:CM3 France CSIRO:MK3 Australia CONS:ECHOG Germany/Korea UKMO:HADCM UK the greenhouse gas emissions is not implemented (CO 2 emission at 830 ppm in the year 2100), so it represents a high emission scenario.

4 64 C. J. KIM et al. Figure 2. Comparisons of observed versus projected monthly precipitation by four GCMs (CNRM:CM3, CSIRO:MK3, CONS:ECHOG, UKMO:HADCM) for the baseline period ( ) for the Seoul automated weather stations. Downscaling of these coarse-scale GCM simulations (above 200 km) to the regional scale (below 50 km) is required to conduct reasonable climate impact studies and provide bias-corrected information on the local scale of climate change (e.g. Giorgi and Mearns, 1991; Wood et al., 2004; Christensen et al., 2007; Im et al., 2010). Statistical downscaling is a method used to calibrate the climate model result with a statistical relationship between the GCM result and the observation result; this method is generally classified into two types: the weather generator and the transfer function. In this study, a hybrid method was applied with the transfer function for the spatial downscaling and with the weather generator for the temporal downscaling. The transfer function used in this study spatially downscaled the GCM to the km scale for the 54 automated weather stations using the Cyclo-Stationary Empirical Orthogonal Function (CSEOF) (Kim and North, 1997) and a multiple linear regression method. Additionally, the temporal downscaling of the monthly weather variables to the daily scale was performed using the WXGEN Weather Generator (Sharpley and Williams, 1990; Bae et al., 2011a, 2011b). The CSEOF analysis is one of the many eigentechniques used to decompose the data into a set of independent modes and their PC time series. The major difference between the CSEOF analysis and other eigentechniques is that the spatial patterns of each mode extracted from the CSEOF represent the complete spatiotemporal evolution of the prominent climate signals (e.g. seasonal cycle, prominent intraseasonal oscillation and ENSO-related evolution) over a cyclic period (Lim et al., 2007). The WXGEN, which is based on the weather generator model developed by Richardson (1981), generates daily precipitation, maximum temperature, minimum temperature, relative humidity and wind velocity, among other factors, based on the statistics of the observed climate variables (Jung et al., 2011) Validation of GCMs For the changes in the climatic and hydrologic variables due to climate change, a greenhouse gas emission scenario is selected, where the weather is predicted with a weather model. In the course of prediction with the greenhouse gas emission scenario, various uncertainties are generated (Maurer, 2007). The first issue is the uncertainty of the future greenhouse gas emission scenario; the second issue is the limitation of the GCM that simulates weather based on this emission scenario; the third issue is errors generated as a result of the calibration method of the spatio-temporal bias of the coarse-scale GCM result. Some degree of error is associated with the prediction of future climatic and hydrological events (floods and droughts) using GCMs that have inherent uncertainties. In this study, four different GCMs were used to predict future droughts with less error than would result from use of a single GCM. The differences among the four different GCMs are illustrated by the differences in the results predicted with them. To validate the reliability of the projected precipitation data by the GCMs, statistical characteristics were compared with the observed and simulated data from the Seoul weather stations during the baseline period ( ). Figure 2 depicts annual precipitation (grey shading) results of the data from the observed values and the four GCMs for the baseline period ( ). The figure represents the annual maximum monthly precipitation (triangle), the annual average monthly precipitation (quadrilateral) and the annual minimum monthly precipitation (rhombus). In the case of the annual maximum monthly precipitation, approximately mm of precipitation fell according to the observed historical data, whereas approximately mm of precipitation occurred during Exceptionally high precipitations

5 POTENTIAL DROUGHT HAZARD MAPPING USING DROUGHT FREQUENCY ANALYSIS 65 of mm in 1998 and mm in 2006 were observed. In the case of the four GCMs, approximately 400 mm of annual maximum monthly precipitation was recorded during , and mm was recorded during When examining the number of high precipitation event (above 600 mm) over the 35 years, nine instances occurred in the observed data, whereas no event was found with the CNRM:CM3 and CSIRO:MK3 models, four times with the CONS:ECHOG model and two times with the UKMO:HADCM model. These results may be due to the exclusion of extreme events, such as typhoons, in the simulation process of the four GCM data. However, unlike the annual maximum precipitation, the annual average monthly precipitation and the annual minimum monthly precipitation yielded similar results as the observed data, which suggests that the projected precipitation by the four GCMs could be used for drought analysis. Additionally, the differences in the precipitation found by the various models may be due to different mechanical systems, resolutions, parameterization processes and physical processes (Im et al., 2010) Choice of drought index For the quantitative analysis of drought, drought indices are generally used. The most frequently used three indices in the drought study are the SPI, PDSI and SWSI. The Crop Moisture Index (CMI) and Soil Moisture Index (SMI) are also used. Each drought index is used as a means to monitor the meteorological, agricultural or hydrological drought according to their development purposes. In this study, the SPI was chosen, by which meteorological drought can be assessed to analyse the spatiotemporal distribution of the potential drought hazard areas in Korea. The intention was to evaluate the potential hazard of drought by estimating the meteorological drought conditions instead of considering the water supply capabilities and facilities. The SPI is a representative meteorological drought index that can estimate the of drought using only precipitation. This index was intended to identify drought periods and the of droughts at multiple timescales, such as at 1, 3, 6, 12 or 24 months. Thus, the SPI has the merit of simultaneously evaluating short- and long-term droughts by selecting different time scales. The SPI, developed by McKee et al. (1993, 1995), was based on the concept that the drought is initiated by a precipitation shortage, which induces water shortage as opposed to water demand. In the SPI, drought is classified according to the moisture condition per range of the index, as shown in Table 3. In this study, major historical drought events in Korea were analysed to assess the applicability and accuracy of the SPI (6 months) for drought monitoring. Historical drought investigation based on the Historical Drought Event Survey Report (MLTM, 1995, 2002) confirmed that extreme droughts occurred, mainly in the central Table 3. Moisture conditions and drought classifications using the SPI. SPI range Moisture 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 and southern regions of Korea, in , 1988, , and (Lee and Kim, 2011). First, whether the weather stations in Seoul (representing the central part of the Korean Peninsula) and Gwangju (representing the southern part of Korean Peninsula) accurately monitored past drought events in the central and southern parts of Korea was analysed (Figure 3). Extreme droughts occurred in the central region of the country in 1981, 1988 and 2001, and in the southern region in 1994, 1995 and The SPI (6 months) for the year of each drought event was estimated to be less than 2.0, which indicates that the SPI reproduced the past drought events well. Second, a drought map was prepared for past drought events to assess how well the SPI (6 months) reproduces the spatial distribution of drought. The results were compared with past drought records (Figure 4). The estimated SPI (6 months) adequately reproduced the past drought events as extremely dry stages ( 2.0 SPI) and severely dry stages ( 1.5 SPI > 2.0) for the hazard areas in the drought years. These results suggest that SPI is a suitable drought index for use in this study. Figure 5 shows the SPI (6 months) time series during the years ( : observed data, : four GCMs data) for the Seoul weather station. It was found that the future SPI projected by the four GCMs expressed a severely dry stage (with 1.5 SPI > 2.0) and an extremely dry stage (with 2.0 SPI), as projected by the SPI of the observed period. A box plot of SPI (6 months) time series was prepared using precipitation projections estimated from the observed data and GCM for the past (baseline, ) and future ( ). Figure 6 shows a box plot of SPI time series for the Seoul weather station for the past (baseline, ) and future ( ). The maximum, minimum and average precipitation and the 75th and 25th percentiles of precipitation are also shown. Figure 6 shows that the average and quartile values for the past and the future are similar, but there are differences in the maximum and minimum SPI for the past and the future. It should be noted that the minimum value is the main evaluation subject ( ) value. For the baseline period ( ), the minimum SPI of the observed data was However, four GCMs had values lower than the observed

6 66 C. J. KIM et al. Seoul weather station Gwangju weather station Figure 3. SPI (6 months) time series calculated using the historical observed data ( ). May 1988 Jun 1988 Jul 1994 Aug 1994 Sep 2009 Aug 2001 Sep 2001 Oct 2001 Dec 2008 Jan 2009 SPI Figure 4. Drought map of major historical drought event monitored by SPI (6 months). [Correction added 3 July 2013 after original online publication: in Figure 4 the dates for the maps have been corrected.] data (CNRM:CM3, 3.62; CSIRO:MK3, 4.52; CONS:ECHOG, 3.36 and UKMO:HADCM, 4.16). These four GCMs also had values for the future period ( ) that were notably lower than their values for the baseline period. The four GCM SPI time series and the box plot analysis indicated that severely dry stages ( 1.5 SPI > 2.0) and extremely dry stages ( 2.0 SPI)willoccurmore frequently in the future than they have in the past Drought frequency analysis and potential drought hazard mapping A drought hazard is an event in which a reduction of water occurs that is significant enough to bring severe economic, social and environmental hardships to the population. Therefore, the PDHM developed in this study is not a map of the region where water shortage occurred or crops were damaged, but a representation of the potential damage areas that can induce water shortage owing to the precipitation shortage for a certain period. The potential drought hazard area in this study does not indicate the vulnerable areas under hydrological drought or agricultural drought hazard areas, but the potential hazard areas where potential damage is large because of frequent meteorological droughts. The PDHM developed in this study was prepared by the drought frequency analysis via a series of processes, as shown in Figure 7. Drought frequency analysis using monthly SPI (6 months) is mainly used to develop the PDHM. The analysis period in this study was between 1976 and 2010 (S0) for the past and between 2011 and 2099 for the future. The GCM data were arranged into three 30-year intervals as (S1), (S2) and (S3). A goodness-of-fit test (Kolmogorov Smirnov test) was performed at a significance level of 5% to select the optimum probability distribution type subjected to the probability distributions of Gamma, General Extreme Value (GEV), Gumbel, Log-Gumbel, Lognormal, Log-Pearson type III, Normal, Pearson type III, Weibull and Wakeby probability distributions.

7 POTENTIAL DROUGHT HAZARD MAPPING USING DROUGHT FREQUENCY ANALYSIS 67 CNRM:CM3 CSIRO:MK3 CONS:ECHOG UKMO:HADCM Figure 5. SPI (6 months) time series calculated using the observed ( ) and four GCM data ( ) for the Seoul automated weather stations. (a) (b) Figure 6. Box Plot of observed and projected SPI time series by four GCMs for the period of (a) baseline ( ) and (b) future ( ) periods for the Seoul automated weather stations. Goodness-of-fit tests using various probability distributions indicated that the log-gumbel, gamma, GEV, Gumbel, log-normal and Weibull distributions were adequate for use in the frequency analysis using SPI. Table 4 shows the goodness-of-fit test results for the weather stations at Daejeon and Busan and the best fit was obtained with the GEV distribution. Therefore, the GEV distribution was selected for use in the frequency analysis. The frequency analysis was executed with an estimated SPI (6 months) through the observed and the four GCM data. The SDF curves for the 54 weather stations were derived via frequency analysis. The spatial distribution of the 54 weather stations drought was determined to obtain the average drought per 109 watersheds using the derived SDF curves. The Inverse Distance Weight (IDW) method was used as an interpolation method to perform the spatial distribution in this study. Finally, the PDHM was prepared to estimate the changes in potential drought hazard areas by using the four GCMs for three different time intervals (S1, S2 and S3). 3. Result and discussions 3.1. Analysis of changes in frequency and of severe drought using the drought spell A drought spell, which is an extended period of drought months, was analysed before preparing the PDHM through drought frequency analysis, subjected to the

8 68 C. J. KIM et al. Figure 7. Process of developing the potential drought hazard map (PDHM) using drought frequency analysis. Table 4. Results of goodness-of-fit test (Kolmogorov Smirnov statistics) for the Daejeon and Busan weather stations. Duration (month) Log-Gumbel (3P) Gamma (3P) GEV Gumbel (3P) Lognormal (3P) Weibull (3P) Daejeon Busan Shaded Cell: Best fit probability distribution by K-S test.

9 POTENTIAL DROUGHT HAZARD MAPPING USING DROUGHT FREQUENCY ANALYSIS 69 (a) (b) Figure 8. Comparison of (a) frequency and (b) average of drought months between the past (S0) and future periods (S1, S2, S3) using the SPI (with 1.5 SPI). Table 5. Comparison of frequency and average of drought months between the past (S0) and projected periods (S1, S2, S3) using the SPI (with 1.5 SPI). Station Drought months of historical period (S0) Frequency Average GCMs Drought months of future period (S1, S2, S3) Frequency Average Difference between past and future period Frequency Average Seoul CNRM:CM CSIRO:MK CONS:ECHOG UKMO:HADCM Average Daejeon CNRM:CM CSIRO:MK CONS:ECHOG UKMO:HADCM Average Daegu CNRM:CM CSIRO:MK CONS:ECHOG UKMO:HADCM Average Gwangju CNRM:CM CSIRO:MK CONS:ECHOG UKMO:HADCM Average Seoul, Daegu, Daejeon and Gwangju weather stations, which represent the Han, Nakdong, Geum and Yeongsan river basins, respectively. Changes in the drought frequency and the due to climate change were analysed through drought spell analysis along with the seasonal fluctuations during the past and future. The SPI (6 months) of the less than severely dry stage (with 1.5 SPI) was estimated via the observed data and four GCM data. The analysis results of the monthly drought frequency and for the Seoul, Daegu, Daejeon and Gwangju weather stations are shown in Figure 8 and Table 5, respectively. It was determined from the Seoul weather station data that the drought frequency increased by 8.66%, the drought frequency of the past period (S0) was 4.52% and the future drought frequency (S1, S2, S3 and four GCMs averaged) was 4.92%. The average drought during the drought period was intensified by approximately 11.96% comparing the past ( 1.76) and future ( 1.97) drought (four GCMs averaged). For the Daejeon weather station, the frequency of drought months was found to be 10.71% during the past (S0) and 5.17% in the future (four GCMs averaged),

10 70 C. J. KIM et al. showing a decrease of 51.72%, whereas the average drought during the drought period was intensified by approximately 8.26% when comparing the past ( 1.83) and future ( 1.98) (four GCMs averaged). For the Daegu weather station, the frequency of drought months was found to be 8.33% during the past (S0) and 5.24% in the future (four GCMs averaged), showing a decrease of 37.08%, whereas the average drought during the drought period was intensified by approximately 3.43% when comparing the past ( 1.93) and future ( 1.99) (four GCMs averaged). For the Gwangju weather station, the frequency of drought months was found to be 7.14% during the past (S0) and 3.96% in the future (four GCMs averaged), showing a decrease of 44.62%, whereas the average drought during the drought period was intensified by approximately 5.82% when comparing the past ( 1.82) and future ( 1.93) (four GCMs averaged). The seasonal drought frequency and drought analysis results are shown in Figure 9 and Table 6, respectively. Based on the Seoul weather station data, the frequency of drought months increased during spring and summer and decreased during autumn and winter. The average drought for the drought months was intensified during the spring, summer and winter seasons. Based on the Gwangju weather station data, the drought frequency was projected to decrease compared with the historical data, and the average drought was intensified during the winter, summer and autumn seasons. The drought spell analysis results reveal that in the future, although the drought frequency is predicted to decrease compared to that of the past, the average drought during drought months (with 1.5 SPI) will be intensified, which also shows a large difference by seasons. This result indicates that although the drought occurrence frequency decreased, the and damage could be significant in the future Deriving the SDF curves and climate change effect on the SDF relationship Figure 10 shows samples of the derived SDF curves from the Seoul and Gwangju weather stations using drought frequency analysis and the SPI (6 months) from the observed (S0) and projected data (S1, S2, S3) obtained with the CNRM:CM3 model. The x-axis represents the drought duration (1 12 months), while the y-axis represents drought ; the return period was set as 10, 20, 30, 50, 100, 200, 300 and 500 years. The derived SDF curves confirmed that with an increase in drought duration, the drought becomes smaller for the same return period. Based on the SDF curve for the S0 (observed) period, the drought for the same return period at the Gwangju weather station, located in the southern part of Korea, appeared lower than that of the Seoul weather station, which indicates that the southern part of Korea was vulnerable to drought in past years. The likelihood of future drought was assessed by the CNRM:CM3 model among the four GCMs. The Seoul weather station results show drought becoming more severe in the future and the short-term drought over 1 3 months will intensify during the S2 period. For the Gwangju weather station, the drought will become more severe until the S2 period, but it will become less severe during the S3 period. The changes in drought by climate change were analysed using the SDF curves prepared by the drought frequency analysis from the Seoul, Daegu, Daejeon and Gwangju weather stations. Figure 11 shows a comparison of the drought for 6-months duration, as obtained from the SDF curve for the 200-year return period. The x-axis represents three different 30- year intervals, S1 ( ), S2 ( ) and S3 ( ), while the y-axis represents drought based on a 6-month duration for the 200-year return period. The black dashed line indicates the drought estimated by the drought frequency analysis from the observed data ( ); this line enables a comparison of the drought with the results from the four GCMs (Tables 7 and 8). The drought comparison for the four major weather stations through the derived SDF curves shows that the drought is more severe at the Seoul station for all GCMs compared with the observed data and the highest change in drought, which was approximately 40%, was observed with the CONS:ECHOG model. However, more severe droughts were observed from the Daegu, Daejeon and Gwangju weather stations with the CONS:ECHOG model than from the Seoul station, and the drought was less severe with the CNRM:CM3, CSIRO:MK3 and UKMO:HADCM models. Of the four models, CONS:ECHOG showed the lowest drought for all weather stations Potential drought hazard mapping and changes in drought hazard area by climate change A drought can be defined as a prolonged period of unusually dry weather in an area where rain might normally be expected. The concept of the potential drought hazard area in this study can be defined as a frequent drought occurrence area that is vulnerable to meteorological drought. The assessment of the potential hazard area by drought was performed by estimating the meteorological drought history instead of considering the water supply capabilities and facilities. In this study, the drought frequency analysis was performed using the SPI (6 months), and the SDF curves were derived for each of the 54 automated weather stations to map the potential drought hazard area of Korea. Figures show the Potential Drought Hazard Maps (PDHMs), which were produced and expressed based on 109 watersheds. Figures show the PDHMs corresponding to the 200-year return period with durations of 3, 6 and 12 months and were prepared from the observed and projected GCM (S1 S3) data.

11 POTENTIAL DROUGHT HAZARD MAPPING USING DROUGHT FREQUENCY ANALYSIS 71 (a) Seoul (Han River basin) Daejeon (Geum River basin) Daegu (Nakdong River basin) Gwangju (Yeongsan River basin) (b) Seoul (Han River basin) Daejeon (Geum River basin) Daegu (Nakdong River basin) Gwangju (Yeongsan River basin) Figure 9. Seasonal characteristics and changes of (a) drought frequency and (b) average of drought months (with 1.5 SPI) for the past (S0) and future periods (S1, S2, S3). Figure 13 shows the PDHM over 6-months duration. The PDHM prepared from the observed data shows that severe droughts occurred frequently at the Yeongsan and the Nakdong river basins, which are located in the southern area of Korea. Droughts having a less than an SPI of 2.0 were found in the middle part of Korea, the Han River, east and west of the Han River and the Sapgyo Stream. Severe drought lower than 2.40 was found in the Nakdong River, west of the Nakdong River and the Yeongsan River basin. Additionally, extreme droughts were observed in the Sumjin, Mangyeong and Dongjin river basins.

12 72 C. J. KIM et al. Table 6. Seasonal characteristics and changes of drought frequency and average of drought months (with 1.5 SPI) for the past (S0) and future periods (S1, S2, S3). Station GCMs Frequency Seasonal distribution of drought months Winter Spring Summer Autumn Average Frequency Average Frequency Average Frequency Average Seoul CNRM CSIRO ECHOG HADCM Average Observed Daejeon CNRM CSIRO ECHOG HADCM Average Observed Daegu CNRM CSIRO ECHOG HADCM Average Observed Gwangju CNRM CSIRO ECHOG HADCM Average Observed The PDHM using the CNRM:CM3 model indicated drought relief during the S1 period in all the river basins except the Nakdong River basin (Taehwa 4.47%, Hoeya River and Sooyeong River 15.68%). However, droughts were more severe in the Han River (Han River 36.55%, Anseong Stream 43.06%, West of Han River 56.61%, East of Han River 36.59%), the Nakdong River (Taehwa River 24.32%, Hoeya River and Sooyeong River 32.90%) and the Geum River (Sapgyo Stream 28.43%) basins during the S2 period. However, the drought was less severe in the other basins during the S3 period, except for the Nakdong River basin (Taehwa 11.37%, Hoeya River and Sooyeong River 23.90%). The average increasing and decreasing trends during the S1 S3 period were compared against the observed data, which indicated that the drought became more severe in the Han and Nakdong rivers and less severe in the rest of the basins. The PDHM prepared by the CSIRO:MK3 model showed that drought was more severe in all of the basins during the S1 period compared with the observed data with large-scale drought in the Han River (Han River 43.75%, Anseong Stream 43.80% and West of Han River 58.79%) and Nakdong River (Taehwa River 41.20%, Hoeya River and Sooyeong River 55.72%) basins. Also, the droughts became more severe in the Han River (Han River 12.18%, East of Han River 19.08%) and Nakdong River (Taehwa River 22.63%, Hoeya River and Sooyeong River 38.70%, south of Nakdong River Namhae 28.42%) basins during the S2 period. However, the drought was less severe in all the basins during the S3 period, except for the Nakdong River (Hoeya River and Sooyeong River 7.18%) basin. The average increase and decrease of values during the S1 S3 periods were compared against the past observed data, which indicated that the drought became more severe in the Han and Nakdong Rivers, whereas the drought was less severe in the other basins. The PDHM prepared by the CONS:ECHOG model showed that the drought was more severe in the Nakdong River (Taehwa River 13.19%, Hoeya River and Sooyeong River 22.48%) and Geum River (West of Geum River 5.58%) basins, with further severe tendencies (Han River 28.77%, Anseong Stream 28.05%, West of Han River 40.90% and East of Han River 11.73%) during the S1 period. More severe drought was found in all the basins during the S2 period, with 40% more than that of the S1 period from the Han River (Anseong Stream 51.66%, West of Han River 63.27%), Nakdong River (Taehwa River 57.96%, Hoeya River and Sooyeong River 81.93%, Nakdong River Namhae 63.12%), Geum River (Sapgyo Stream 51.81%, West of Geum River 41.08%), Sumjin River (South of Sumjin River 44.87%) and Yeongsan River (South of Yeongsan River 45.35%) basins. Meanwhile, more severe drought was found in the Han River basin (Han River 43.86%, West of Han River 47.73%, East Han River 39.90%) during the S3

13 POTENTIAL DROUGHT HAZARD MAPPING USING DROUGHT FREQUENCY ANALYSIS 73 (a) S0 ( ) Observed S1 ( ) S2 ( ) S3 ( ) (b) S0 ( ) Observed S1 ( ) S2 ( ) S3 ( ) Figure 10. Derived SDF curves for (a) Seoul and (b) Gwangju weather station using SPI (6 months) from the observed data and the CNRM:CM3 model. period. Averaging the fluctuation against the observed data during the S1 S3 periods showed that the drought became more severe in all the basins, especially with the aggravated drought in the Han, Nakdong and Geum river basins. The PDHM prepared by the CONS:ECHOG model indicated that the drought would be extreme throughout the Korean Peninsula in the future compared with the results of the other GCMs. The PDHM prepared by the UKMO:HADCM model showed more severe drought in all the basins during the S1 period with large-scale drought in the Nakdong River (Taehwa River 51.21%, Hoeya River

14 74 C. J. KIM et al. Seoul (Han River basin) Daejeon (Geum River basin) Daegu (Nakdong River basin) Gwangju (Yeongsan River basin) Figure 11. Comparison of the drought between the observed (S0) and four GCM-derived (S1, S2, S3) SDF (return period: 200 years, duration: 6 months) at four different weather stations. Table 7. Comparison of the drought between the observed (S0) and four GCM-derived (S1, S2, S3) SDF (return period: 200 years, duration: 6 months) at four different weather stations. Gauge Observed GCMs Projected S1 S2 S3 Difference Projected Difference Projected Difference Average difference Seoul 1.72 CNRM CSIRO ECHOG HADCM Average Daejeon 2.42 CNRM CSIRO ECHOG HADCM Average Daegu 2.44 CNRM CSIRO ECHOG HADCM Average Gwangju 2.21 CNRM CSIRO ECHOG HADCM Average Observed ( ), S1 ( ), S2 ( ), S3 ( ). Difference : Percentage change between past and future. +, drought conditions improved;, drought intensified.

15 POTENTIAL DROUGHT HAZARD MAPPING USING DROUGHT FREQUENCY ANALYSIS 75 Table 8. Percentage change of drought for 20 large river basins of Korea by climate change between observed and four GCM averaged PDHM (return period: 200 years, duration: 6 months). Basin name Observed Severity S1 S2 S3 Difference Severity Difference Severity Difference Ave. difference Han River Anseong Stream West of Han River East of Han River Nakdong river Hyeongsan river Taehwa river Hoeya, Sooyeong East of Nakdong river South of Nakdong river Geum River Sapgyo Stream West of Geum River Mangyeong, Dongjin Sumjin river South of Sumjin River Yeongsan River Tamjin River South of Yeongsan river West of Yeongsan river Observed ( ), S1 ( ), S2 ( ), S3 ( ). Difference : Percentage change in drought between past (S0) and future period (S1, S2, S3). +, drought conditions improved;, drought intensified. and Sooyeong River 66.39%, Nakdong River Namhae 41.81%) basin. During the S2 period, less severe drought was observed in all the basins except the Han River (West of Han River 5.89%) and Nakdong River (Hoeya River and Sooyeong River 5.04%) basins. During the S3 period, the drought became more severe farther from the Nakdong River basin (Hyeongsan River 33.26%, Taehwa River 60.76%, Hoeya River and Sooyeong River 69.89%, east of Nakdong River 34.77%, Nakdong River Namhae 25.44%), following the same trend as in the S1 period. The average increase and decrease during the S1 S3 periods were compared against the observed data and indicated that the drought would be more severe in the Han and the Nakdong rivers, with far more severe tendencies in the Nakdong River basin. The above results drawn from the UKMO:HADCM model indicate that future drought will move from the southern to the eastern sea of the Korea Peninsula. The PDHM for drought durations of 3, 6 and 12 months show that for each basin, drought differed depending on the drought duration. However, the spatial distributions of potential drought hazard areas in the basins were similar for the different drought durations considered. The average drought severities in the Han River basin were compared using potential drought hazard maps prepared by using observed data, as shown in Figures The severities were 2.13 (extremely dry) for a drought duration of 3 months, 1.80 (severely dry) for a drought duration of 6 months and 1.33 (moderately dry) for a drought duration of 12 months. As the drought duration increased, dry conditions improved. The same trends were observed for the other basins. Figures 15 and 16 show the drought change of potential drought hazard areas by the four GCMaveraged PDHMs compared with the observed results. From the results, it is observed that the drought became more severe in the Han River (Han River 20.74%, Anseong Stream 18.70%, West of Han River 30.39%) and the Nakdong River (Taehwa River 27.52%, Hoeya River and Sooyeong River 40.74%, Nakdong River Namhae 15.04%) basins during the S1 period. During the S2 period, drought increased in the Han and Nakdong river basins, following the same trend as during the S1 period but with increased. During the S3 period, large-scale aggravated drought was observed in the Han River (West of Han River 17.65%) and Nakdong River (Taehwa River 24.02%, Hoeya River and Sooyeong River 35.23%) basins, with overall drought relief in the other basins. The average drought fluctuations during the S1 S3 periods, compared with the observed data, indicated that the drought became aggravated in the Han and Nakdong river basins. Using the four different GCMs, the future potential drought hazard areas in South Korea for the three different time frames, S1, S2 and S3, in 30-year units could be estimated. It is suggested that appropriate drought management plans should be established for the basins which were predicted to be potential drought hazard areas. In addition, emergency water resources supply facilities, such as development of groundwater and construction of small-scale reservoirs, should be secured in anticipation of extreme drought events.

16 76 C. J. KIM et al. Observed ( ) CNRM:CM3 S1 CNRM:CM3 S2 CNRM:CM3 S3 CSIRO:MK3 S1 CSIRO:MK3 S2 CSIRO:MK3 S3 CONS:ECHOG S1 CONS:ECHOG S2 CONS:ECHOG S3 UKMO:HADCM S1 UKMO:HADCM S2 UKMO:HADCM S3 SPI SPI Figure 12. Projected change in potential drought hazard area between observed period (S0) and projected period (S1, S2, S3) by four GCMs (return period: 200 years, duration: 3 months). 4. Conclusion In this study, a drought frequency analysis was performed with an SPI (6 months) estimated using observed data and the data of four GCMs. SDF curves were derived for 54 weather stations located throughout the country. Additionally, PDHMs were prepared to estimate the potential drought hazard areas that will appear due to climate changes. The conclusions drawn from the analysis are as follows: (1) The uncertainty analysis result found by using projected precipitation data via the four GCMs showed similar trends in the annual average and annual minimum of monthly precipitation, which are key subjects in this study, even though extreme events could not be simulated as in the observed data. Additional future precipitation was compared with the observed data using the annual average of monthly precipitation of the observed and four GCM data. These results show that the precipitation in the East of Han River, South of the Sumjin River and East of the Nakdong River is remarkably decreased when compared with past observations. (2) The SPI (6 months) estimation, based on the observed and four GCM data, expressed past drought events per area and defined the scale of drought damage as extremely dry and severely dry stages, which proved to be a suitable tool in this drought study. (3) The SDF curves, prepared by the frequency analysis with the observed and four GCM data, confirmed that the drought became reduced with prolonged drought duration with the same frequency. Additionally, the future drought of each GCM via frequency analysis resulted in different values, perhaps due to the different

17 POTENTIAL DROUGHT HAZARD MAPPING USING DROUGHT FREQUENCY ANALYSIS 77 Observed ( ) CNRM:CM3 S1 CNRM:CM3 S2 CNRM:CM3 S3 CSIRO:MK3 S1 CSIRO:MK3 S2 CSIRO:MK3 S3 CONS:ECHOG S1 CONS:ECHOG S2 CONS:ECHOG S3 UKMO:HADCM S1 UKMO:HADCM S2 UKMO:HADCM S3 SPI SPI Figure 13. Projected change in potential drought hazard area between observed period (S0) and future period (S1, S2, S3) by four GCMs (return period: 200 years, duration: 6 months). mechanical systems, grid sizes, parameterizations and physical processes in the four GCMs. (4) The drought analysis for the return period of 200 years and a duration of 6 months for the representative automated weather stations (Seoul, Daegu, Daejeon and Gwangju) and representative river basins (Han River, Nakdong River, Geum River and Yeoungsan River) in Korea revealed that the lowest drought severities (extremely severe drought) were observed in the CONS:ECHOG model for all the weather stations, with an intense average fluctuation compared with other areas. (5) The changes in the potential drought hazard areas as per the four GCMs, based on the PDHMs prepared by using the observed data, predicted drought relief in the CNRM:CM3 and CSIRO:MK3 models, even though differences existed in the drought run duration. However, severe drought was predicted in the future in the CONS:ECHOG and UKMO:HADCM models. In addition, extreme future drought events throughout the Korean Peninsula were predicted by the CONS:ECHOG model compared with the results of the other GCMs, whereas the UKMO:HADCM model forecasted that potential drought hazard areas will move from the southern to the eastern part of the country. (6) The analysis of the potential drought hazard based on the PDHMs prepared by the observed data indicated that drought became more severe in the Nakdong River basin (Taehwa River, Hoeya River and Sooyeong River and Nakdong River Namhae), which was evaluated as a major potential drought hazard area in the past. It was noted that severe droughts were predicted in the Han River (Han River, Anseong Stream, West of Han River) basins located in the middle part of the country.

18 78 C. J. KIM et al. Observed ( ) CNRM:CM3 S1 CNRM:CM3 S2 CNRM:CM3 S3 CSIRO:MK3 S1 CSIRO:MK3 S2 CSIRO:MK3 S3 CONS:ECHOG S1 CONS:ECHOG S2 CONS:ECHOG S3 UKMO:HADCM S1 UKMO:HADCM S2 UKMO:HADCM S3 SPI SPI Figure 14. Projected change in potential drought hazard area between observed period (S0) and future period (S1, S2, S3) by four GCMs (return period: 200 years, duration: 12 months). Observed ( ) S1 ( ) S2 ( ) S3 ( ) SPI SPI Figure 15. Changes in potential drought hazard area between observed period (S0) and four GCM averaged projected period (S1, S2, S3) (return period: 200 years, duration: 6 months).

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