Vol.:(0123456789)1 3

Climate Dynamics https://doi.org/10.1007/s00382-018-4588-2

A comparative assessment of climate change impacts on drought over Korea based on multiple climate projections and multiple drought indices

Moon‑Hwan Lee1 · Eun‑Soon Im1,2  · Deg‑Hyo Bae3

Received: 6 July 2018 / Accepted: 17 December 2018 © Springer-Verlag GmbH Germany, part of Springer Nature 2019

AbstractThis study assesses future changes in drought characteristics in response to different emission scenarios over Korea based on multiple climate projections and multiple drought indices. To better resolve regional climate details and enhance confi-dence in future changes, multi-model projections are dynamically downscaled, and their systematic biases are statistically removed. Bias-corrected climate data are directly used to calculate the standardized precipitation index (SPI) and standard-ized precipitation evapotranspiration index (SPEI), and they are fed into a hydrological model to generate runoff used for the calculation of the standardized runoff index (SRI). The analysis is focused on changes in the frequencies and severities of severe or extreme droughts measured by the SPI, SPEI, and SRI for the Han River and Nakdong River basins. Fine-scale ensemble projections reveal robust changes in temperatures that monotonically respond to emission forcings, whereas pre-cipitation changes show rather inconsistent patterns across models and scenarios. Temperature and precipitation shifts lead to changes in evapotranspiration (ET) and runoff, which modulate the drought characteristics. In general, the SPEI shows the most robust pattern with significant increases in both drought frequency and severity. This result is mainly due to the excessive potential ET that is hypothetically estimated without considering water availability. While the SPI based on only precipitation exhibits behavior different from that of the SPEI, the SRI that considers actual ET produces an intermediate level of changes between the SPI and SPEI. Compared to the large uncertainty of the frequency changes that overwhelm the change signal due to inconsistency across models and indices, the severity of future drought is likely to be exacerbated with enhanced confidence.

Keywords Drought projection · Multi-model ensemble · Korean river basin · Dynamical downscaling · Standardized drought index

1 Introduction

Previous studies have supported the scientific consensus that the hydrological cycle will be intensified with greater vari-ability due to global warming (Held and Soden 2006; Allan and Soden 2008; Giorgi et al. 2011, 2014; Im et al. 2017a). Accordingly, it is expected to increase nonstationarities and extremes in terms of the statistical properties of hydrocli-matic variables, which may be represented by changes in the occurrence and severity of droughts (Vrochidou et al. 2013; Leng et al. 2015). Although many efforts have been made to project where and how severe droughts might occur in response to enhanced greenhouse gas (GHG) emissions, the projected intensity and the regional extent of future droughts remain quite uncertain. This uncertainty is caused by droughts being a result of complex processes combined

* Eun-Soon Im ceim@ust.hk

* Deg-Hyo Bae dhbae@sejong.ac.kr

1 Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong, China

2 Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Kowloon, Hong Kong, China

3 Department of Civil and Environmental Engineering, Sejong University, Seoul, South Korea

M.-H. Lee et al.

1 3

with meteorological conditions that affect precipitation and evaporation along with geomorphological and pedological characteristics over the target regions. These key factors are described in very different manners between climate models, which serve the main sources of uncertainty leading to the lack of inter-model consistency. Furthermore, quantitative assessments of future droughts are highly dependent on the selection of drought index (Rhee and Cho 2016; Ahma-dalipour et al. 2017a; Huang et al. 2018). Different types of drought indices tend to produce dissimilar characteristics of future drought changes even if they are calculated using inputs from hydroclimatic variables produced by the same modeling system. For example, a drought index based on both precipitation and temperature (e.g., palmer drought severity index, PDSI; standardized precipitation evapo-transpiration index, SPEI) is likely to measure the warming effect more explicitly through enhanced evapotranspiration compared to other drought indices based on only precipita-tion (e.g., standardized precipitation index, SPI).

In particular, the Korean peninsula is a representative region where it is difficult to assess the changes in drought characteristics with climate change due to the large variabil-ity at both the regional and temporal scales. The sensitivity of the Korean peninsula to global warming seems to be very model-dependent, which means that different models pro-duce different behaviors in response to enhanced GHG emis-sions. Kim et al. (2014) demonstrated that drought-prone regions are highly varying according to the models used and the target periods under the A2 scenario from four sta-tistically downscaled global climate model (GCM) projec-tions. These results also differ from the drought projections derived from the dynamically downscaled ECHAM4/HOPE-G A2 scenario (Boo et al. 2004). In addition, the recently updated drought projections based on the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) participating models are markedly different from those rooted in the IPCC AR4 framework. For example, Rhee and Cho (2016) demonstrated using 17 bias-corrected

GCM projections in the Coupled Model Intercomparison Project Phase 5 (CMIP5) that the frequencies of severe or extreme droughts derived from SPI are expected to mostly decrease, which is opposite the results using CMIP3 models presented in Kim et al. (2014). The discrepancy attribut-able to drought indices is also considerable. Although the projected precipitation under the business-as-usual scenario generated from the same modeling system was used to calcu-late the drought index, Yoo et al. (2016) projected the maxi-mum drought risk to be in the northwest part of South Korea from the SPI-based joint distribution of drought duration and severity, while Park et al. (2015) projected the most severe drought to be in the northeast part of South Korea using the Effective Drought Index. Needless to say, drought indices calculated from different hydroclimatic variables may even produce contrasting results for future changes in drought characteristics (Rhee and Cho 2016). In summary, there is little consistency in the drought projections over Korea with respect to climate projections and drought indices, indicating significant room for a systematic investigation and revised assessment.

In this study, we assess future changes in drought char-acteristics for the Han River and Nakdong River basins in Korea based on multiple climate projections and multiple drought indices. Figure 1 presents the modeling sequence from a GCM to a hydrological model and indicates how different GCMs and regional climate models (RCMs) are combined. To better resolve the region-specific climate response to different GHG forcings, two GCM projec-tions (HadGEM2-AO and ACCESS1.0) forced by different Representative Concentration Pathway (RCP) scenarios are dynamically downscaled using two RCMs (WRF and RegCM4) with 12.5 km horizontal resolution. Therefore, a total of nine 25-year regional projections with respect to three scenarios (HIST: 1981–2005, RCP4.5: 2076–2100, and RCP8.5: 2076–2100) are analyzed. Because RCM out-puts should contain non-negligible biases inherited from the GCM driving forcing and arising from imperfect model

Fig. 1 Modeling framework designed in this study. This study compares differences in drought projections according to different climate change sce-narios and drought indices

A comparative assessment of climate change impacts on drought over Korea based on multiple climate…

1 3

dynamics and physical parameterizations (Bae et al. 2011; Lee et al. 2018), statistical bias correction using the quan-tile mapping (QM) method is applied to remove system-atic biases at the daily time scale. The bias-corrected tem-perature and precipitation are directly used to calculate the meteorological drought indices, such as the SPI and SPEI. In addition, the bias-corrected RCM outputs are fed into a distributed hydrological model to generate runoff data for the calculation of a hydrological drought index, such as the standardized runoff index (SRI). Although the variability of precipitation is regarded as the most important factor that determines drought conditions, the atmospheric moisture demand for enhanced evapotranspiration plays a critical role in increasing the actual water stress (Boo et al. 2004; Im et al. 2012; Maule et al. 2013; Abatan et al. 2017). There-fore, a comparison of the SPI, SPEI, and SRI will provide valuable insight into the response of evapotranspiration to global warming as well as the different behaviors of poten-tial evapotranspiration (PET) and actual evapotranspiration (AET). Several studies have addressed the role of increas-ing temperature in future drought conditions under global warming by comparing drought indices with and without the PET term (Jeong et al. 2014; Yoo et al. 2016; Ahmadalipour et al. 2017a; Rhee and Cho 2016). These studies consistently argue that drought indices that include the PET term (e.g., PDSI or SPEI) are more appropriate for characterizing future drought conditions under global warming. However, because the PET term is mostly calculated as a function of only tem-perature under the assumption of unlimited and unrestricted water supply (Kumar et al. 1987), it is difficult for the PET term to properly reflect a realistic hydrologic cycle. On the other hand, the AET calculated in hydrological models seems to be physically more consistent and reasonable in the context of the water balance components. Subsequently, the SRI based on runoff, which is an output of hydrological models that integrate AET, precipitation, and soil moisture, is likely to more properly reflect a possible change in the hydroclimatic regime.

Because the emission scenarios, GCMs, and RCMs can contribute to major sources of uncertainty in climate projec-tions, multiple realizations that employ as many scenarios, GCMs, and RCMs as possible will help enhance the confi-dence level and provide a plausible range of future climate conditions. In this regard, we acknowledge that the number of ensemble members used in this study is not sufficient to cover the full range of projected changes. However, to the best of our knowledge, this study presents the most com-prehensive assessment of future drought conditions over Korea based on a state-of-the-art modeling framework that is composed of GCMs, RCMs, and a distributed hydrologi-cal model. Although several previous studies have addressed similar topics on the potential impact of climate change on future drought, this study could have advantages in terms

of the multiple climate projections combined with different scenarios, GCMs, and RCMs and multiple drought indices including both meteorological and hydrological aspects. The updated assessment presented in this study can provide an opportunity to build on previous findings and contribute to a better understanding of the impact of the climate response on drought conditions under global warming with physi-cally-based modeling results.

2 Data and methodology

2.1 Study area

The target region of this study is the Han River and Nakdong River basins in South Korea. These are the two largest river basins in South Korea, and they cover more than two-thirds of the country (Fig. 2). Because these two river basins cover the most densely populated area of South Korea, they are an important region to assess future drought conditions in response to climate change. The climate of South Korea is characterized by large seasonal variability in precipitation and temperature. In particular, approximately 60% of the annual precipitation is concentrated during the summer sea-son due to the East-Asian monsoon (Bae et al. 2008; Jung et al. 2013). Such a precipitation pattern with strong season-ality may result in conditions vulnerable to drought occur-rence during the dry season.

2.2 Climate change projections

As indicated by the topography derived from the 30 m digi-tal elevation model (DEM) in Fig. 2, the geographical setting of South Korea is characterized by a complicated mountain-ous terrain within the small peninsula. Therefore, GCMs have limited accuracy in simulating the detailed climate features over the Korean peninsula because the coarse grid system of GCMs fails to precisely describe the topographi-cal complexity and even often represents the southern part of Korea as an ocean grid point (Im et al. 2012). Indeed, it is found that the GCM ensemble presented in the IPCC AR5 could not determine the signals of the changes in soil moisture and runoff over South Korea, which is located at the eastern edge of the large Asian continent (see Fig. 12.23 for soil moisture and Fig. 12.24 for runoff in Collins et al. 2013). To overcome this problem and generate reliable cli-mate information that is appropriate for regional to local scales, statistical and/or dynamical downscaling is neces-sary. The pros and cons of each downscaling method are still a subject of debate with no clear conclusions to support the superiority of one method. In this study, we analyze dynami-cally downscaled projections generated by different combi-nations of GCMs and RCMs (Fig. 1). The HadGEM2-AO

M.-H. Lee et al.

1 3

(Hadley Centre Global Environmental Model Version 2-Atmosphere–Ocean, hereafter referred to as HadGEM) projections with a horizontal resolution of 1.75° (longitude) × 1.25° (latitude) are dynamically downscaled using two RCMs, namely, WRF3.4 (Weather Research and Forecast-ing, hereafter referred to as WRF) and RegCM4 (Regional Climate Model Version 4, hereafter referred to as RegCM). The domain of the RCMs covers the eastern regions of Asia centered over South Korea (37.5°N and 127.5°E) with a 12.5 km horizontal resolution (see Fig. 1 in Im et al. 2017b). WRF and RegCM downscaling results driven by HadGEM are generated within the framework of the national downscaling project in South Korea (KMA 2015; Im et al. 2015; Suh et al. 2016; Oh et al. 2016; Oh and Suh 2018). Using RegCM with the same configuration, we perform the dynamical downscaling of ACCESS1.0 (Australian Com-munity Climate and Earth System Simulator Version 1.0, hereafter referred to as ACCESS) with a resolution of 1.875° (longitude) × 1.25° (latitude), which makes it possible to examine the uncertainty from different GCMs. All global

projections generated using both HadGEM and ACCESS are in accordance with the CMIP5 experimental design. Future projections (2076–2100: 25-year) forced by the RCP4.5 and RCP8.5 scenarios are compared against the simulation for a historical period (1981–2005: 25-year), which enables us to understand the impacts of climate change on future drought conditions in response to the different emission forc-ing levels (RCP4.5 vs. RCP8.5). For simplicity, WRF and RegCM driven by HadGEM are denoted HAD_WRF and HAD_REG, respectively, while RegCM driven by ACCESS is denoted ACC_REG. Historical, RCP4.5, and RCP8.5 sce-narios are denoted HIST, R45, and R85, respectively.

Although RCMs are able to improve physically-based regional details over topographically diverse regions such as Korea, RCM simulations are not accurate enough to be used directly for hydrological implications (Lee et al. 2018). RCM outputs include the cascaded uncertainties stemming from the emission scenarios, GCMs, and RCMs, which in turn can be conveyed in the analysis of drought characteri-zation. Therefore, we apply the QM method to remove the

Fig. 2 Study area and topogra-phy derived from a 30-m DEM. The blue lines indicate the tributaries of the large Korean rivers. The red and green regions indicate the Han River and Nakdong River basins. The yellow dots indicate the 575 grid points of the VIC simula-tion

A comparative assessment of climate change impacts on drought over Korea based on multiple climate…

1 3

systematic biases of the raw RCM output. The QM method fits the cumulative distribution function (CDF) of the raw output to the observed CDF. Because of this concept, this method can adjust comprehensive characteristics of climate variables such as the mean, variance, and extreme values of precipitation and temperature. Furthermore, QM method can efficiently adjust wet-day frequency by setting the simulated precipitation below a certain threshold to a value of zero (dry day). This threshold is calculated based on the cumula-tive probability that the observed precipitation is zero. It helps alleviate a well-known problem of climate models that excessively simulate the frequency of light precipita-tion (Herrera et al. 2010). The QM method is widely used for bias correction of climate model simulations because of its simplicity and effectiveness (Teutschbein and Seibert 2012; Lafon et al. 2013; Teng et al. 2015). The gamma dis-tribution for precipitation and the Gaussian distribution for temperature are used to estimate the probability distribution functions (Piani et al. 2010). To apply the bias correction method, observed meteorological data are necessary. The daily maximum and minimum temperature data are collected from 59 Automated Synoptic Observing System (ASOS) sta-tions of the Korea Meteorological Administration (KMA). The daily precipitation data are collected at 59 ASOS sta-tions, 461 automatic weather system (AWS) stations, and 350 stations of the Ministry of Land, Infrastructure, and Transportation (MOLIT). The collected meteorological data and all model simulations are converted to 575 grid points with a spatial resolution of 1/8° (approximately 12.5 km), which are used for the variable infiltration capacity (VIC) hydrological model shown in Fig. 2, and bias correction is then performed at 575 grid points and the daily time scale.

Note that individual model simulations (e.g., HAD_WRF, HAD_REG, and ACC_REG) have the different magnitude of the bias correction factors that are proportional to their performance in simulating the present-day climate against the observed pattern when applying QM method (Lee et al. 2018). As the systematic biases of raw outputs are cor-rected, bias-corrected precipitation and temperature are dif-ferent with those from raw simulations, being closer to the observed values. However, the long-term trends appeared in the temporal evolution of temperature and precipitation are preserved after bias correction (not shown). The bias-corrected precipitation and temperature are directly used to calculate the SPI and SPEI. In addition, the bias-corrected daily precipitation and daily maximum and minimum tem-peratures are fed into VIC, which is a distributed hydro-logical model. To estimate the parameters embedded in VIC for South Korea, databases that include a 30 m DEM, soil, vegetation type, river network, and land cover, which are provided by the water management information system of Korea, are collected to represent the basin characteris-tics. These data are then converted to 575 grid points with

a spatial resolution of 1/8° (approximately 12.5 km) for VIC simulation (Fig. 2). VIC resolves the water and energy fluxes based on the hydrological processes of mutual inter-actions among the atmosphere, vegetation, and soil (Liang et al. 1994). Many parameters included in VIC have been optimized and verified using observed discharge data from dam inflow sites. Detailed information about the VIC model used in this study can be found in Bae et al. (2017). Daily runoff outputs of VIC from the input dataset derived from nine different climate projections are used to calculate the SRI (see Sect. 2.3).

2.3 Drought indices

Several drought indices have been developed and applied to characterize different types of drought. To comprehensively assess future changes in long-term drought under different degrees of warming, three standardized drought indices with a timescale of 12-month are selected (Fig. 1). The 12-month accumulated hydrologic variables can possibly smooth out the short-term drought. As indicated by Fig. 2 in Shukla and Wood (2008), the difference between the SPI and SRI increases as the accumulation period decreases, and there-fore the 12-month SPI and SRI have a similar pattern for historical period. However, the effect of climate change on drought indices will be clearly identified if the 12-month drought indices show the different characteristics for the future period. Since one of the aims of this study is to emphasize different behaviors among different drought indi-ces under global warming, we take relatively long time scale, 12-month, which is capable of excluding a large fluctuation of drought index imposed by the precipitation variability at short timescales. While the SPI and SPEI are proper for measuring meteorological drought (Duan and Mei 2014; Leng et al. 2015), the SRI is suited for hydrological drought (Shukla and Wood 2008; Jung and Chang 2012). The SPI introduced by Mckee et al. (1993) is calculated using only precipitation. The criterion to discern drought conditions is whether an excess or shortage of accumulated precipita-tion during a specific period (e.g., 12 months for this study) occurs compared to the long-term climatological condition. On the other hand, the SPEI introduced by Vicente-Serrano et al. (2010) considers both precipitation and temperature. Temperature is necessary to calculate PET using the Thorn-thwaite equation (Thornthwaite 1948). Although some stud-ies have reported that drought index incorporating the PET calculation module based on Thornthwaite method, which is strongly dependent on temperature, may overestimate the future drought severity under global warming (Begueria et al. 2013), Thornthwaite method is still widely employed for estimating PET due to its simplicity (Dai 2012; Touma et al. 2015; Ahmadalipour et al. 2017b). The difference between precipitation and PET (hereafter referred to as

M.-H. Lee et al.

1 3

DIF) acts as an effective factor to determine the drought condition. Therefore, the SPEI is able to explicitly reflect the warming effect through enhanced PET, unlike the SPI. In addition to the SPI and SPEI, which are commonly used to examine meteorological drought, the SRI is calculated using runoff simulated by VIC to measure the hydrologi-cal drought. Time series of precipitation, DIF, and runoff are used to calculate the SPI, SPEI, and SRI, respectively, therefore these time series are fitted to appropriate probabil-ity distributions based on their statistical properties. While the 2-parameter gamma distribution is used to estimate the distribution of accumulated precipitation (Lee and Kim 2013), the log-logistic and log-Pearson type-3 probability distributions are used to estimate the distributions of accu-mulated DIF and runoff, respectively (Son and Bae 2015; Rhee and Cho 2016). The Kolmogorov–Smirnov (KS) test is applied to evaluate the goodness of fit that shows how well the theoretical probability distributions fit the estimated distributions of accumulated 12-month precipitation, DIF, and runoff. Their distributions derived from HAD_REG, HAD_WRF, and ACC_REG are statistically significant at the 90% confidence level for all months and grids. The sets of distribution parameters for each drought index, grid, and month that are estimated for the historical period should be the same with those for the future period, under the assump-tion of stationarity such that the distributions do not change with time. These distributions are then converted to normal distributions for standardization. Because the SPI, SPEI, and SRI are classified with the standardized value (Z) of the nor-mal cumulative distribution, they use the same classification to discern drought severity: moderate drought (− 1.00 > Z > − 1.49), severe drought (− 1.50 > Z > − 1.99), and extreme drought (− 2.00 > Z). The analysis is focused on changes in the frequency and severity of severe or extreme droughts, which are defined as Z values less than − 1.5 under the R45 and R85 scenarios.

3 Results

3.1 Changes in hydroclimatic variables

Figure 3 presents the spatial distributions of the changes in annual mean temperature and annual precipitation over South Korea derived from HAD_WRF, HAD_REG, and ACC_REG under the R85 and R45 scenarios. For tem-perature change, pronounced warming is evident across the whole region of South Korea. The temperature response is roughly proportional to the GHG concentrations, indicating that the degree of warming in R85 is much larger than that in R45. Quantitatively, the temperature averaged over South Korea is projected to increase by 4.8–5.4 °C under R85 and 2.9–3.2 °C under R45. These changes are all statistically

significant at the 95% confidence interval based on the two-tailed t-test. Therefore, it is very likely that the temperature increases in response to enhanced GHG emissions are clear and unequivocal. The responses of the model under global warming for precipitation changes are rather different from those seen in temperature changes. Precipitation changes seem to be less consistent than temperature changes, and their statistical significance is also limited to certain regions. For example, the regions where HAD_WRF and HAD_REG project significant increases in precipitation do not coincide with each other. Likewise, the spatial patterns of precipi-tation changes projected by HAD_REG and ACC_REG are also quite different. The magnitude of the precipita-tion change also varies individually according to the mod-els used. This finding supports that precipitation changes exhibit large uncertainties that are induced by the RCMs and GCMs, which is in line with the results of many other stud-ies (e.g., Dosio et al. 2015; Dosio and Panitz 2016; Tabari and Willems 2018). Furthermore, precipitation changes are unlikely to show the strong sensitivity to emission forcing as shown by temperature. Although the precipitation changes become stronger in R85 than those in R45, the difference between R85 and R45 seems to be less significant, as in the case of ACC_REG. Simply, precipitation changes are more sensitive to the models that are used than to the emission forcing, which is in contrast to the much greater sensitiv-ity of the temperature response to emission forcing. Based on these different behaviors that appear in the temperature and precipitation changes, it is reasonable to expect that the SPI calculated from the input of precipitation and the SPEI calculated from the inputs of both precipitation and tem-perature may provide inconsistent results in terms of drought characteristics in response to global warming (see Sect. 3.2).

To understand how changes in temperature and pre-cipitation are related to changes in runoff and evapotran-spiration, we examine the relationship between changes in temperature and changes in PET and AET as well as the relationship between changes in precipitation and changes in runoff (Fig. 4). While AET and runoff are calculated in the VIC simulation, PET is obtained based on the Thornth-waite equation. As expected, a temperature increase leads to the enhancement of evapotranspiration, forming a posi-tive correlation between temperature and evapotranspira-tion. Because PET is a hypothetical measure of evaporative demand, AET cannot exceed PET unless AET is equal to PET under saturated wet conditions (Kim and Rhee 2016). Indeed, the PET increase consistently surpasses the AET increase regardless of the projection. An important point to note is that the discrepancy between PET and AET does not remain steady but rather is amplified with an increase in temperature. It is evident that the increases in AET and PET are distinctly different when the temperature increase becomes larger. While PET is continuously increased in

A comparative assessment of climate change impacts on drought over Korea based on multiple climate…

1 3

accordance with temperature increases, the increase in AET appears to stabilize. As a result, the increase in PET exceeds the increase in AET by more than twofold when it comes to R85. More specifically, ACC_REG projects the PET to increase up to 38%, but the AET increases by approximately 15%. Therefore, the drought indices based on PET and AET may be significantly different under accelerated warming conditions, although they show similar behaviors for the his-torical period. Next, the change in runoff is also positively correlated with the precipitation increase, showing large dispersion across models. Interestingly, the proportionality of runoff to precipitation seems to be rather dependent on the magnitude of the precipitation increases. For example, HAD_REG, which projects a relatively small increase in

precipitation, shows an increase in runoff that is less than the increase in precipitation. However, HAD_WRF and ACC_REG project increases in runoff that are larger than the increases in precipitation. This result might be explained by the coupled behavior among AET, runoff and precipitation. If the increase in precipitation is large enough to meet the water availability required for AET, the increase in runoff tends to surpass the increase in precipitation because the AET enhancement is limited to a certain level.

3.2 Changes in future drought characteristics

The different behaviors of precipitation, PET, AET, and run-off in response to warmer climate conditions are transmitted

Fig. 3 Spatial patterns of the changes in annual mean temperature (°C) and annual precipitation (%) for a future period under R85 and R45 relative to the historical period. The black dots indicate that the

changes in temperature or precipitation are statistically significant at the 95% confidence level

M.-H. Lee et al.

1 3

to the drought indices. Figure 5 presents the spatial distribu-tions of the frequency changes in severe or extreme drought measured by the SPI, SPEI and SRI under the R45 and R85 scenarios. The drought frequency is counted as the number of months with an index less than − 1.5, corresponding to the criteria of severe drought based on the typical classi-fication used for standardized indices. This calculation is first performed based on individual projections (e.g., HAD_WRF, HAD_REG and ACC_REG), and the ensemble mean of three projections is then obtained. Because consistency among different projections can support confidence in the future change signal, we check whether the three projections all agree with the sign of the changes. First, the changes in the SPI-based drought frequency are in stark contrast to those of the SPEI. Based on the inputs used for their cal-culation, this controversy can be explained by the effect of increased temperature via the PET term. In general, the SPI, which is fully dependent on precipitation variability, projects the alleviation of the occurrence of future drought. Consistent with the changes in precipitation seen in Fig. 3, the regions with reduced drought frequency are projected to become extended in R85 compared to those in R45. However, incorporating the PET term makes it possible to drive the changes in drought frequency toward the opposite direction. In particular, the SPEI under R85 shows a strong increase in drought with 100% inter-model consistency, except for some regions along the southern coast where the three projections all show precipitation increases. The oppo-site behavior of the SPI and SPEI reveals the critical role of temperature in shaping drought characteristics under a warmer climate that should not be ignored, which is in line with the results highlighted by Rhee and Cho (2016) and Marcos-Garcia et al. (2017). However, an equally important fact is that the SPEI seems to be overly sensitive to increased

temperature without considering moisture availability (Dai 2013; Joetzjer et al. 2013). The spatial patterns of the SRI based on runoff that synthetically considers hydrological processes, including AET, show a general tendency to follow the SPI, but the quantitative change is somewhat intermedi-ate between that of SPI and SPEI.

A further detailed analysis of drought frequency is per-formed in two target basins to examine the statistics of how the mean and extremes of drought frequency will change in terms of individual ensemble members. Figure 6 pre-sents the box-whisker plot of the frequency of severe or extreme droughts (e.g., SPI, SPEI, and SRI < − 1.5) for the Han River and Nakdong River basins. Each box in a given model and scenario is generated using 185 and 195 values corresponding to VIC grids included in the Han River and Nakdong River basins, respectively. For the historical period, the drought indices calculated using observational data are compared with those from the three individual pro-jections to validate their HIST simulations. Compared with the observed pattern, the meteorological drought indices, such as SPI and SPEI, from the HIST simulations tend to underestimate the spatial variability. Therefore, the degree of dispersion bounded by the upper and lower ends of the whiskers is generally smaller in the simulated indices than that in the observed indices. However, the median values equivalent to the central tendency agree reasonably well with the observed values. The indices for future periods exhibit uneven changes with respect to those from the HIST simu-lations across scenarios and individual models. Although a well-defined pattern does not clearly stand out, several important changes can be found from this complexity. The relevant pattern appearing in most cases is the intensification of spatial variability, particularly for the SPEI. Compared to the other two indices, both the median and maximum (e.g.,

Fig. 4 Changes in PET and AET against the change in annual mean temperature (a), and change in runoff against the change in annual precipita-tion (b). Blue and orange colors indicate R45 and R85

A comparative assessment of climate change impacts on drought over Korea based on multiple climate…

1 3

the upper end of the whisker) from the SPEI are systemati-cally shifted toward a higher value regardless of the scenario and model. On the other hand, the SPI under R85 projects a decrease in drought frequency in terms of the median, however, there is no consistency at the end of the upper whisker. From a statistical viewpoint, the SRI shows mini-mum robustness in future change patterns against the HIST due to the large inter-model variability with both increasing and decreasing changes.

The drought severity measured by the averaged value of the indices corresponding to severe or extreme drought cases (e.g., SPI, SPEI, and SRI < − 1.5) indicates that the consistency across indices is much improved compared to the changes in drought frequency. Note that the number of drought events used for the average is not the same across indices, and the SPEI pattern is obtained using a larger num-ber of drought events than used for the SPI or SRI as the frequency of SPEI-based droughts increases. Shifted pat-terns in temperature and precipitation under global warming

tend to intensify the severity of both meteorological and hydrological droughts across South Korea, except for some randomly scattered spots. For R85, which is characterized by strong increases in temperature and precipitation, the SPI systematically produces less severity than produced by the SPEI because the increases in temperature and precipitation act in opposite ways to exacerbate the drought; however, they at least agree with the same sign of increase. Therefore, the drought characteristics measured by the SPI suggest that while drought frequency is likely to decrease with increas-ing precipitation under global warming, drought severity is likely to increase once drought occurs. Because the SPI explicitly excludes the effect of temperature, these contrast-ing behaviors of decreasing frequency and increasing sever-ity can be interpreted in the changes in precipitation charac-teristics. A redistribution of precipitation against intensity is speculated to be responsible for modulating drought charac-teristics. For example, a reduction in weak intensity precipi-tation despite an increase in mean precipitation (see Fig. 8

Fig. 5 Spatial patterns of the average changes in drought frequency (months/year) derived from the three projections (HAD_WRF, HAD_REG, and ACC_REG) under R85 and R45 based on the SPI

(a, d), SPEI (b, e), and SRI (c, f). The black dots indicate that the three climate projections consistently projects an increase or decrease in drought frequency

M.-H. Lee et al.

1 3

in Im et al. 2017) may contribute to aggravating the drought condition. Interestingly, the SRI shows the drought severity to be at an intermediate level between that of the SPI and SPEI. The SRI may not allow unrealistically high moisture departure from the surface to the atmosphere by considering the surface water balance, which is not taken into account in the context of the PET formulation. At the same time, the

SRI is able to overcome the problematic aspects of the SPI that is less sensitive to warming by completely ignoring the effects of increased temperature. We stress that we do not intend to emphasize the strengths of the SRI or provide a quantitative assessment of the best index, which is beyond the scope of this study, however, we attempt to understand the characteristics of future drought depending on different

Fig. 6 Box-whisker plots of drought frequency (months/year) for the historical period and future period under R85 and R45 for the Han River basin (a, c, e) and Nakdong River basin (b, d, f). The OBS and

numbers 1, 2, and 3 on the X-axis indicate the results estimated by observations, HAD_WRF, HAD_REG, and ACC_REG, respectively

A comparative assessment of climate change impacts on drought over Korea based on multiple climate…

1 3

indices. For R45, which is characterized by increases in tem-perature and precipitation that are relatively lower than those in R85, the locations with greater intensification of drought severity coincide largely across the indices. In addition to spatial coherence, the magnitudes of the severity change also show similarity even though the SPI is still slightly lower than the SRI or SPEI. The robustness of this change is also supported by enhanced inter-model consistency across most regions.

Figure 8 presents the same quantities as in Fig. 6 except for the drought severity averaged by severe or extreme drought cases. Compared to the box-whisker statis-tics derived from the observational data for the histori-cal period, the drought severity simulated by individual ensemble members tends to underestimate the spatial variability, particularly in the SPI, which is similar to the discrepancy noted in the analysis of drought frequency. However, the HIST simulations are able to capture median values that are close to the observed values, indicating skill in simulating the central tendency. Compared to

the changes in drought frequency, which exhibit large uncertainty that overwhelms the change signal due to the inconsistency across models and indices, the severity of future drought is likely to be exacerbated with enhanced confidence. While the median of the drought indices is dominant in the range greater than − 2.0 for the historical period, future projections mostly push all indices toward the category of extreme drought less than − 2.0, except for the projection by the SPI under R85. The patterns of the severity changes vary depending on the index, but all indi-ces commonly project lower median and minimum values against the HIST simulations. The degree of aggravation derived from the SRI is roughly equivalent to that from the SPEI. Consistent with the spatial patterns of severity changes seen in Fig. 7, the difference between the SPEI and SRI is not significant. As the difference between PET and AET is amplified with an increase in temperature, the changes in drought frequency measured by the SPEI and SRI show quite different behaviors, but these differences are not as obvious as those in drought severity.

Fig. 7 The same as Fig. 5, but for drought severity

M.-H. Lee et al.

1 3

3.3 Roles of temperature and evapotranspiration in drought projection

The different characteristics of meteorological and hydro-logical droughts in response to global warming are identified based on multiple climate projections and multiple drought indices. Interestingly, the uncertainty arising from different indices is large enough to overwhelm the spread of the inter-model variability in terms of the changes in the frequency of severe or extreme droughts. This result could have important implications for drought assessments, suggesting that any single index may suffer from limitations in comprehensively considering the different aspects of droughts. The SPI and SPEI are arguably the most popular indices used to meas-ure meteorological drought, but several studies comparing

the SPI and SPEI (e.g., Rhee and Cho 2006; Marcos-Garcia et al. 2017) demonstrated that both indices are able to pro-duce contradictory results under global warming due to the significant role of increased temperature in future drought conditions. Given that temperature increases in response to emission forcing show much higher confidence than shown by precipitation changes, a drought index that accounts for only precipitation and ignores temperature entirely might be unsuitable for quantifying the impacts of climate change on drought. However, a meteorological drought index that accounts for both precipitation and temperature may still be insufficient for a reliable assessment of future drought. This is because these indices, such as SPEI and PDSI, incorporate the concept of PET, and the drawbacks embedded in the PET formulation have been persistently addressed by previous

Fig. 8 The same as Fig. 6, but for drought severity

A comparative assessment of climate change impacts on drought over Korea based on multiple climate…

1 3

studies (e.g., Dai 2013; Joetzjer et al. 2013). In this regard, the hydrological drought index that utilizes the output from a hydrological model may be an alternative way to imple-ment AET while considering water balance instead of PET.

To investigate how AET and PET behave differently in response to the degree of warming, we present a scatter plot of the annual AET against annual PET averaged over the Han River and Nakdong River basins based on individual projections and scenarios (Fig. 9). First, the relationship between AET and PET is systematically shifted accord-ing to the scenario (e.g., HIST, R45, and R85). The PET, depending primarily on temperature, continues to increase as warming is accelerated from HIST to R85. However, the temperature dependency of AET is much less than that of PET, which induces a large difference between them that is proportional to warming. Therefore, the empirical relation-ship between PET and AET, which is established under the present climate, may not be guaranteed in warmer climate conditions. In particular, the potential problems embedded in PET will be maximized under meteorological conditions of higher temperatures combined with lower precipitation.

As an illustrative example, we compare the runoff and DIF defined as the difference between precipitation and PET in ascending order of annual precipitation (e.g., 25 values each for HIS, R45, and R85). To avoid the smooth-ing resulting from the ensemble mean, we present the results derived from HAD_REG (Fig. 10), but the same conclu-sion is derived from the other two projections (not shown). DIF and runoff are variables that are directly fitted to the probability distribution for the calculation of the SPEI and SRI. For the historical period, DIF is consistently lower than

runoff. The gap between DIF and runoff becomes wider in a warmer climate because the increase in PET greatly exceeds the changes in precipitation and runoff. More importantly, DIF with a large negative value emerges in very dry years ranked by lower precipitation. A negative DIF is obtained if evapotranspiration exceeds precipitation. This result may reinforce that the utilization of PET may not be physically adequate to represent meteorological drought, particularly under a warmer climate.

4 Summary and conclusions

While there are some regions, such as the Mediterranean, Central America, Brazil, and South Africa, where substan-tial increases in meteorological drought are projected based on CMIP5 models (Collins et al. 2013), the reliability of future projections in terms of drought characteristics in response to climate warming is somewhat restricted over Korea. It is acknowledged that the state-of-the-art climate models still suffer from a lack of accuracy in simulating the detailed climate features over Korea due to its geographi-cal complexity and monsoon-dominated climate. Although some studies have recently been published with the purpose of investigating the climate change impact on drought over river basins in the Korean territory, there is little consist-ency among their results, and there is a room to improve the methodology to become more comprehensive.

In this study, the impacts of climate change on meteoro-logical and hydrological droughts are characterized based on multiple climate projections and multiple drought indices.

Fig. 9 Relationship between annual PET and AET derived from the HIST (black) simulation and R45 (blue) and R85 (orange) projections for the Han River (a) and Nakdong River (b) basins

M.-H. Lee et al.

1 3

Two global climate projections (HadGEM and ACCESS) forced by the R45 and R85 scenarios are dynamically down-scaled using two RCMs (WRF and RegCM). After the sys-tematic biases embedded in downscaled climate projections are statically removed using the QM method, bias-corrected climate data are directly used to calculate meteorological drought indices, such as the SPI and SPEI. Alternatively, bias-corrected climate data are fed into a distributed hydro-logical model to generate runoff data required for the calcu-lation of a hydrological drought index, the SRI. Compari-sons among the SPI, SPEI and SRI are helpful to explore the critical role of temperature in shaping the drought character-istics under a warmer climate.

The temperature increase under enhanced GHG emissions is clear and unequivocal, whereas the magnitudes of pre-cipitation changes show rather inconsistent patterns across models and scenarios. The first-order effects induced by the increases in temperature and precipitation are expected to enhance evapotranspiration and runoff, respectively. Subse-quently, these changes are able to modify the drought char-acteristics in terms of frequency and severity. The changes

in the frequency of severe or extreme droughts exhibit large uncertainties. In particular, the inconsistency across drought indices seemingly overwhelms the inter-model variability, which indicates the limitation arising from the use of a sin-gle specific index. On the other hand, the severity of future drought is expected to deteriorate in a warmer climate in terms of both meteorological and hydrological aspects. The discrepancy across projections and indices is much lower for drought severity than for drought frequency. The PET that is calculated based on a crude formulation with a single input of temperature reveals a rather physically unrealistic fea-ture as warming accelerates. While the comparison between the SPI and SPEI emphasizes that the effect of temperature should not be ignored in drought assessments, the compari-son between the SPEI and SRI highlights that water balance should be considered during the calculation of evapotranspi-ration. This finding could be essential for assessing reliable drought projections and improving the different behaviors of individual indices in response to a warmer climate. While this study focuses on drought severity and frequency with a 12-month timescale, we acknowledge that the spatial extent

Fig. 10 Scatter plot of the annual DIF and runoff in ascending order of annual precipitation for the Han River (a, b, c) and Nakdong River (d, e, f) basins derived from HAD_REG

A comparative assessment of climate change impacts on drought over Korea based on multiple climate…

1 3

and duration of drought at seasonal timescales are critical to the comprehensive assessment of drought impacts (Ahma-dalipour et al. 2017b; Rhee and Cho 2016). We will extend our study to the spatial extent and duration of drought with different timescales (e.g., 3 months, 6 months), which will be able to consider the large intra-seasonal and spatial vari-ability of precipitation over South Korea in a more proper way.

Acknowledgements This research is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure, and Transport (Grant 18AWMP-B083066-05). We thank Prof. Joong-Bae Ahn and Mr. Yeon-Woo Choi at Pusan National University for providing WRF projections driven by HadGEM2-AO. We also extend our thanks to Prof. Myoung-Seok Suh and Dr. Seok-Geun Oh at Kongju National University for providing the RegCM4 projections driven by HadGEM2-AO.

References

Abatan AA, Gutowski WJ Jr, Ammann CM, Kaatz L, Brown BG, Buja L, Bullock R, Fowler T, Gilleland E, Gotway JH (2017) Multiyear droughts and pluvials over the upper Colorado River basin and associated circulations. J Hydrometeor 18:799–818. https ://doi.org/10.1175/JHM-D-16-0125.1

Ahmadalipour A, Moradkhani H, Demirel MC (2017a) A compara-tive assessment of projected meteorological and hydrological droughts: Elucidating the role of temperature. J Hydrol 553:785–797. https ://doi.org/10.1016/j.jhydr ol.2017.08.047

Ahmadalipour A, Moradkhani H, Svoboda M (2017b) Centennial drought outlook over the CONUS using NASA-NEX down-scaled climate ensemble. Int J Climatol 37:2477–2491. https ://doi.org/10.1002/joc.4859

Allan RP, Soden BJ (2008) Atmospheric warming and the amplifica-tion of precipitation extremes. Science 321:1481–1484. https ://doi.org/10.1126/scien ce.11607 87

Bae DH, Jung IW, Lettenmaier DP (2011) Hydrologic uncertainties in climate change from IPCC AR4 GCM simulations of the Chungju basin, Korea. J Hydrol 401:90–105. https ://doi.org/10.1016/j.jhydr ol.2011.02.012

Bae DH, Sohn KH, So JM (2017) Utilization of the Bayesian method to improve hydrological drought prediction accuracy. Water Resour Manag 31:3527–3541. https ://doi.org/10.1007/s1126 9-017-1682-x

Begueria S, Vicente-Serrano SM, Reig F, Latorre B (2013) Stand-ardized precipitation evapotranspiration index (SPEI) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. Int J Climatol 34(10):3001–3023. https ://doi.org/10.1002/joc.3887

Boo KO, Kwon WT, Oh JH, Baek HJ (2004) Response of global warming on regional climate change over Korea: an experiment with the MM5 model. Geophys Res Lett 31:L21206. https ://doi.org/10.1029/2004G L0211 71

Collins M, Knutti R, Arblaster J, Dufresne JL, Fichefet T, Friedling-stein P, Gao X, Gutowski WJ, Johns T, Krinner G, Shongwe M, Tebaldi C, Weaver AJ, Wehner M (2013) Long-term climate change: projections, commitments and irreversibility. In: Qin D, Plattner GK, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds.) Climate change 2013: the physical sci-ence basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change [Stocker TF. Cambridge University Press, Cambridge

Dai A (2013) Increasing drought under global warming in observations and models. Nat Clim Change 3:52–58. https ://doi.org/10.1038/nclim ate16 33

Dosio A, Panitz HJ (2016) Climate change projections for CORDEX-Africa with COSMO-CLM regional climate model and differences with the driving global climate models. Clim Dyn 46:1599–1625. https ://doi.org/10.1007/s0038 2-015-2664-4

Dosio A, Panitz HJ, Schubert-Frisius M, Luthi D (2015) Dynamical downscaling of CMIP5 global circulation models over CORDEX-Africa with COSMO-CLM: evaluation over the present climate and analysis of the added value. Clim Dyn 44:2637–2661. https ://doi.org/10.1007/s0038 2-014-2262-x

Duan K, Mei Y (2014) Comparison of meteorological, hydrological and agricultural drought responses to climate change and uncer-tainty assessment. Water Resour Manag 28:5039–5054. https ://doi.org/10.1007/s1126 9-014-0789-6

Giorgi F, Im ES, Coppola E, Diffenbaugh NS, Gao XJ, Mariotti L, Shi Y (2011) Higher hydroclimatic intensity with global warming. J Clim 24:5309–5324. https ://doi.org/10.1175/2011J CLI39 79.1

Giorgi F, Coppola E, Raffaele F (2014) A consistent picture of the hydroclimatic response to global warming from multiple indices: models and observations. J Geophys Res 119:11695–11708. https ://doi.org/10.1002/2014J D0222 38

Held IM, Soden BJ (2006) Robust responses of the hydrological cycle to global warming. J Clim 19:5686–5699. https ://doi.org/10.1175/JCLI3 990.1

Herrera SL, Fernandez FJ, Gutierrez JM (2010) Evaluation of the mean and extreme precipitation regimes from the ENSEMBLES regional climate multimodel simulations over Spain. J Geophys Res 115:D21117. https ://doi.org/10.1029/2010J D0139 36

Im ES, Ahn JB, Kim DW (2012) An assessment of future dryness over korea based on the ECHAM5-RegCM3 model chain under A1B emission scenario. Asia-Pac J Atmos Sci 48:325–337. https ://doi.org/10.1007/s1314 3-012-0031-5

Im ES, Ahn JB, Jo SR (2015) Regional climate projection over South Korea simulated by the HadGEM2-AO and WRF model chain under RCP emission scenarios. Clim Res 63:249–266. https ://doi.org/10.3354/cr012 92

Im ES, Choi YW, Ahn JB (2017a) Robust intensification of hydro-climatic intensity over East Asia from multi-model ensemble regional projections. Theor Appl Climatol 129:1241–1254. https ://doi.org/10.1007/s0070 4-016-1846-2

Im ES, Choi YW, Ahn JB (2017b) Worsening of heat stress due to global warming in South Korea based on multi-RCM ensemble projections. J Geophys Res: Atmos 122:11,444–11,461. https ://doi.org/10.1002/2017J D0267 31

Jeong DI, Sushama L, Naveed Khaliq M (2014) The role of tempera-ture in drought projections over North America. Clim Change 127:289–303. https ://doi.org/10.1007/s1058 4-014-1248-3

Joetzjer E, Douville H, Delire C, Ciais P, Decharme B, Tyteca S (2013) Evaluation of drought indices at interannual to climate change timescales: a case study over the Amazon and Mississippi river basins. Hydrol Earth Syst Sci 9:13231–13249. https ://doi.org/10.5194/hess-17-4885-2013

Jung IW, Chang H (2012) Climate change impacts on spatial patterns in drought risk in the Willamette River Basin, Oregon, USA. Theor Appl Climatol 108:355–371. https ://doi.org/10.1007/s0070 4-011-0531-8

Jung IW, Bae DH, Lee BJ (2013) Possible change in Korean streamflow seasonality based on multi-model climate projections. Hydrol Pro-cess 27(7):1033–1045. https ://doi.org/10.1002/hyp.9215

Kim D, Rhee J (2016) A drought index based on actual evapotranspira-tion from the Bouchet hypothesis. Geophys Res Lett 43:10,277–10,285. https ://doi.org/10.1002/2016G L0703 02

Kim CJ, Park MJ, Lee JH (2014) Analysis of climate change impacts on the spatial and frequency patterns of drought using a potential

M.-H. Lee et al.

1 3

drought hazard mapping approach. Int J Climatol 34:61–80. https ://doi.org/10.1002/joc.3666

Korea Meteorological Administration (2015) Production of fine-scale climate change data over the Korean peninsula using RCP sce-narios. Research report (CATER 2012–3080), 822

Kumar KK, Kumar KR, Rakhecha PR (1987) Comparison of Penman and Thornthwaite methods of estimating potential evapotranspira-tion for Indian conditions. Theor Appl Climatol 38:140–146. https ://doi.org/10.1007/BF008 68097

Lafon T, Dadson S, Buys G, Prudhomme C (2013) Bias correction of daily precipitation simulated by a regional climate model: a comparison of methods. Int J Climatol 33:1367–1381. https ://doi.org/10.1002/joc.3518

Lee JH, Kim CJ (2013) A multimodel assessment of the climate change effect on the drought severity–duration–frequency relationship. Hydrol Process 27:2800–2813. https ://doi.org/10.1002/hyp.9390

Lee MH, Lu M, Im ES, Bae DH (2018) Added value of dynamical downscaling for hydrological projections in the Chungju basin, Korea. Int J Climatol Rev

Leng G, Tang Q, Rayburg S (2015) Climate change impacts on mete-orological, agricultural and hydrological droughts in China. Glob Planet Change 126:23–34. https ://doi.org/10.1016/j.glopl acha.2015.01.003

Liang X, Lettenmaier DP, Wood EF, Burges SJ (1994) A simple hydro-logically based model of land surface water and energy fluxes for general circulation models. J Geophys Res 99:14415–14428. https ://doi.org/10.1029/94JD0 0483

Marcos-Garcia P, Lopez-Nicolas A, Pulido-Velazquez M (2017) Com-bined use of relative drought indices to analyze climate change impact on meteorological and hydrological droughts in a Medi-terranean basin. J Hydrol 554:292–305. https ://doi.org/10.1016/j.jhydr ol.2017.09.028

Maule CF, Thejll P, Christensen JH, Svendsen SH, Hannaford J (2013) Improved confidence in regional climate model simulations of precipitation evaluated using drought statistics from the ENSEM-BLES models. Clim Dyn 40:155–173. https ://doi.org/10.1007/s0038 2-012-1355-7

McKee TB, Doesken NJ, Kleist J (1993) The relationship of drought frequency and duration to time scales. Preprints, Eighth Conf. on Applied Climatology. Anaheim, CA, Am Meteorol Soc 179–184

Oh SG, Suh MS (2018) Changes in seasonal and diurnal precipitation types during summer over South Korea in the late twenty-first century (2081–2100) projected by the RegCM4.0 based on four RCP scenarios. Clim Dyn. https ://doi.org/10.1007/s0038 2-017-4063-5 (in press)

Oh SG, Suh MS, Lee YS, Ahn JB, Cha DH, Lee DK, Hong SY, Min SK, Park SC, Kang HS (2016) Projections of high resolution cli-mate changes for South Korea using multiple-regional climate models based on four RCP scenarios. Part 2: precipitation. Asia-Pac J Atmos Sci 52(2):171–189. https ://doi.org/10.1007/s1314 3-016-0018-8

Park CK, Byun HR, Deo R, Lee BR (2015) Drought prediction till 2100 under RCP 8.5 climate change scenarios for Korea. J Hydrol 526:221–230. https ://doi.org/10.1016/j.jhydr ol.2014.10.043

Piani C, Haerter JO, Coppola E (2010) Statistical bias correction for daily precipitation in regional climate models over Europe.

Theor Appl Climatol 99:187.192. https ://doi.org/10.1007/s0070 4-009-0134-9

Rhee J, Cho J (2016) Future changes in drought characteristics: regional analysis for South Korea under CMIP5 projections. J Hydromete-orol 17:437–451. https ://doi.org/10.1175/JHM-D-15-0027.1

Shukla S, Wood AW (2008) Use of a standardized runoff index for characterizing hydrologic drought. Geophys Res Lett 35:L2045. https ://doi.org/10.1029/2007G L0324 87

Son KH, Bae DH (2015) Drought analysis according to shifting of climate zones to arid climate zone over Asian monsoon region. J Hydrol 529:1021–1029. https ://doi.org/10.1016/j.jhydr ol.2015.09.010

Suh MS, Oh SG, Lee YS, Ahn JB, Cha DH, Lee DK, Hong SY, Min SK, Park SC, Kang HS (2016) Projections of high resolution climate changes for South Korea using multiple-regional cli-mate models based on four RCP scenarios. Part 1: surface air temperature. Asia-Pac J Atmos Sci 52(2):151–169. https ://doi.org/10.1007/s1314 3-016-0017-9

Tabari H, Willems P (2018) Seasonally varying footprint of climate change on precipitation in the Middle East. Scientific Report 8:4435. https ://doi.org/10.1038/s4159 8-018-22795 -8

Teng J, Potter NJ, Chiew FHS, Zhang L, Wang B, Vaze J, Evans JP (2015) How does bias correction of regional climate model pre-cipitation affect modelled runoff? Hydrol Earth Syst Sci 19:711–728. https ://doi.org/10.5194/hess-19-711-2015

Teutschbein C, Seibert J (2012) Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods. J Hydrol 456:12–29. https ://doi.org/10.1016/j.jhydr ol.2012.05.052

Thornthwaite CW (1948) An approach toward a rational classification of climate. Geogr Rev 38:55–94. https ://doi.org/10.2307/21073 9

Touma D, Ashfaq M, Nayak MA, Kao SC, Diffenbaugh NS (2015) A multi-model and multi-index evaluation of drought charac-teristics in the 21st century. J Hydrol 526:196–207. https ://doi.org/10.1016/j.jhydr ol.2014.12.011

Vicente-Serrano SM, Beguria S, Lopez-Moreno JI (2010) A multisca-lar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. 23: 1696–1718. https ://doi.org/10.1175/2009J CLI29 09.1

Vrochidou AEK, Tsanis IK, Grillakis MG, Koutroulis AG (2013) The impact of climate change on hydrometeorological droughts at a basin scale. J Hydrol 476:290–301. https ://doi.org/10.1016/j.jhydr ol.2012.10.046

Yoo J, Kwon HH, Lee JH, Kim TW (2016) Influence of evapotranspi-ration on future drought risk using bivariate drought frequency curves. KSCE J Civ Eng 20:2059–2069. https ://doi.org/10.1007/s1220 5-015-0078-9

Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.