Effect of Climate Change on Annual Precipitation in Korea Using Data

atmosphere

Article Eﬀect of Climate Change on Annual Precipitation in Korea Using Data Screening Techniques and Climate Change Scenarios

Ga-Kyun Lim 1 , Byung-Sik Kim 1, Byung-Hyun Lee 1 and Se-Jin Jeung 2,* 1 Department of Urban and Environmental Disaster Prevention Engineering, Kangwon National University, 346, Joongang-Ro, Samcheok-Si, Gangwon-Do 25913, Korea; [email protected] (G.-K.L.); [email protected] (B.-S.K.); [email protected] (B.-H.L.) 2 Kangwon Institute of Inclusive Technology, 1, Kangwondaehak-gil, Chuncheon-si, Gangwon-do 24341, Korea * Correspondence: [email protected]; Tel.: +82-033-570-6819

Received: 8 June 2020; Accepted: 16 September 2020; Published: 24 September 2020

Abstract: Precipitation is essential for understanding hydrological processes and identifying the characteristics that must be considered to protect human lives and property from natural disasters. Hydrological analyses assume that precipitation shows stationarity. However, because of the recent changes in climate, the stationarity of climate data has been widely debated, and a need has arisen to analyze its nonstationary nature. In this study, we reviewed a method to analyze the stationarity of annual precipitation data from 37 meteorological stations that have recorded data for more than 45 years. Six stations that showed abnormal precipitation during the previous year were selected to evaluate the normality of future precipitation. The results showed that a significant trend was present in four out of 37 stations with unstable precipitation in 22 stations and persistent precipitation in 4 stations. The stationarity analysis of future annual precipitation using climate change scenarios suggested that no trend would be present in 11 stations and that unstable precipitation would be present in six stations. Persistent precipitation was identified in four stations. A comparison between the historical and predicted precipitation data conducted with the climate change scenarios showed that an increasing number of stations presented nonstationarity. Therefore, both stationarity and nonstationarity should be considered when performing hydrological analyses using annual precipitation data in Korea. Accordingly, prior to conducting any such analyses, the effect of climate change on annual precipitation should also be considered.

Keywords: hydrologic times series; hydrological data; trend; stable; persistence; climate change

1. Introduction Climate change is altering precipitation patterns in South Korea during summer by increasing the intensity and frequency of precipitation events. In particular, an increase in precipitation has been observed in August, following the monsoon season. Ref./ Oh et al., suggested that the occurrence of precipitation events with 50 mm of rainfall per hour has increased 2.37-fold on average each year ≥ since 1998, [1]. Thus, it can be expected that the present and future climate would show nonstationarity, making it important to infer various statistics from current hydrological data. However, conventional hydrological data analyses assume stationarity and use data from extreme precipitation events to estimate a design scale of hydro-infrastructure for each of the frequencies. As climate change has altered the characteristics of extreme precipitation events, it is important to employ hydrological statistical analyses that do not assume stationarity to identify changes in extreme precipitation events. In particular, statistical probability estimations of extreme events from the analysis of the hydrological

Atmosphere 2020, 11, 1027; doi:10.3390/atmos11101027 www.mdpi.com/journal/atmosphere Atmosphere 2020, 11, 1027 2 of 27 frequency of extreme precipitation events are essential for disaster risk management, land use planning, hydro-infrastructure design, and long-term planning for flood prevention measures. The determination of the persistence, trends, and stability in hydrological data is required to assess the statistical characteristics of nonstationarity. Persistence indicates the duration for which the statistical characteristics of hydrological data remain constant. Trends reflect the patterns of such characteristics, while stability is an indicator of the degree to which the average, variance, and other statistics of a time series remain unchanged. Several studies have been conducted that evaluate persistence in time series. Ref./Vaskar, D et al., analyzed the trends, persistence, and stationarity of the annual precipitation data from 61 stations to evaluate time series characteristics and provide a review of analytical methods, [2]. In contrast, ref./Park et al., selected the Geumgang River basin as their study area and conducted a quantitative analysis of climate change trends in the area by using a range of statistical analyses, including those for evaluating persistence, and their results are likely to support future water resource management efforts and prove useful for identifying hydrological systems, [3]. Several other studies have also evaluated trends. Ref./Lee et al., analyzed the basic characteristics of the hydrological factors of 63 stations and used Hotelling–Pabst and Mann–Kendall Trend tests to evaluate the trends present, [4]. Ref./Park et al., used the precipitation data of Illinois, a state in the midwestern US that employs rain-fed agricultural practices and analyzed the same with a focus on climate dynamics during the cropping period, [3]. Ref./Rawshan, A et al., evaluated the presence of possible trends in the annual, seasonal, maximum, and minimum flow rates of the Yangtze River at the Cuntan and Zhutuo stations in China for the period between 1980 and 2015, [5]. The assessment was conducted using the Mann–Kendall trend test and innovative trend analysis, whereas the Sen slope was used to estimate the magnitude of the observed changes. This study was particularly notable as it provided basic statistics and the results of a trend analysis for nine hydro-meteorological factors in Korea. Ref./Ryu et al., identified the differences between trend analyses conducted using different statistical tests and used Mann–Kendall, Hotelling–Pabst, T, and Sen tests to identify the accuracy of each test for comparison and evaluation, [6]. Ref./Kim et al.,considered the characteristics of a Generalized Pareto distribution (GPA) and climate change and used a Monte Carlo simulation to generate data for a trend analysis to evaluate extreme hydrological event trends in the context of a changing climate, [7]. In addition, they conducted simulations based on the trend analyses using the probability distribution and suggested that an increasing trend was present with more data and a higher level of extortion after conducting the trend analysis for each of the conditions. With regard to stationarity studies, Ref./Kang et al., used the augmented Dickey–Fuller test and analysis of variance to analyze 30 years of precipitation data and provided a more objective trend analysis by evaluating stationarity and homogeneity to identify the variability of the hydrological factors of the Korean Peninsula, [8]. In addition, Ref./So et al., took into account annual maximum precipitation data with regard to duration and used the Mann–Kendall and augmented Dickey–Fuller tests to analyze trends and stationarity, [9]. However, they only considered the augmented Dickey–Fuller test and identified if a trend was present when determining the stationarity and did not include an analysis of other factors that constitute stationarity. The current tendency of related studies in Korea shows that the majority of frequency interpretations and extreme event analyses consider climate change and nonstationarity. The most exemplary case is that of Ref./Kim et al., who used climate change scenarios for the Korean Peninsula provided by the Korea Meteorological Administration. They projected change in extreme precipitation events in Korea through a nonstationarity frequency interpretation considering trends, [10]. Ref./Sung et al., used climate models of five regions to predict the standard drought indices of the Korean Peninsula and compared the results between the models. Hydrological data were used for the time series analysis, and diverse hydrological data were selected for the analysis to identify characteristics, [11]. Ref./Ahn et al., built a detailed climate forecast data production system using WRFV3.4 model based on RCP8.5 scenario data. As a result of forecasting the future climate of Korea Atmosphere 2020, 11, 1027 3 of 27

Atmosphere 2020, 11, x FOR PEER REVIEW 3 of 27 using the RCP 8.5 scenario, the temperature is expected to rise by about 4.6 ◦C and for precipitation, using the RCP 8.5 scenario, the temperature is 1expected to rise by about 4.6 °C and for precipitation, it is expected to increase by about 0.5 mm/day− ,[12]. it is expected to increase by about 0.5 mm/day, [12]. Moreover, a wide range of analytical methods and modeling techniques were applied, Moreover, a wide range of analytical methods and modeling techniques were applied, depending on the purpose of the study, to produce accurate and meaningful results. depending on the purpose of the study, to produce accurate and meaningful results. Furthermore, a range of analytical methods and modeling techniques were applied depending Furthermore, a range of analytical methods and modeling techniques were applied depending on the objectives of the study for producing more accurate and meaningful results. However, on the objectives of the study for producing more accurate and meaningful results. However, the the aforementioned studies either analyzed trends and stationarity separately or used a trend analysis aforementioned studies either analyzed trends and stationarity separately or used a trend analysis to to determine stationarity. This indicates that there were limitations with regard to the way in which these determine stationarity. This indicates that there were limitations with regard to the way in which authors determined stationarity without considering persistence and stability, which also influences these authors determined stationarity without considering persistence and stability, which also stationarity. Therefore, when analyzing the stationarity of hydrological time series data, persistence and influences stationarity. Therefore, when analyzing the stationarity of hydrological time series data, stability must also be considered in the trend analysis. persistence and stability must also be considered in the trend analysis. 2. Methods and Data 2. Methods and Data 2.1. Methods 2.1. Methods 2.1.1. Overview 2.1.1. Overview In this study, we addressed the necessity of determining stationarity based on a time series analysis of theIn precipitation this study, datawe addressed of South Korea the necessity by considering of determining persistence, stationarity trends, and based stability. on a Thetime analysis series wasanalysis conducted of the precipitation using annual data precipitation of South Korea data by from considering 1971 to 2018 persistence, from 37 trends, weather and stations stability. across The theanalysis country. was Stationarity conducted wasusing defined annual as precipitation the state in whichdata from persistence 1971 to 2018 and trendsfrom 37 were weather not observedstations yetacross stability the country. existed. Stationarity In addition, was to evaluate defined climateas the state variability in which with persistence respect to and annual trends precipitation, were not theobserved coefficients yet stability of variation existed. (CV) forIn addition, all the stations to ev werealuate used. climate The variability results of awith previous respect study to [annual13] that usedprecipitation, data from the 1971 coefficients to 1995 were of variation compared (CV) with for those all the of stations the present were studyused. toThe evaluate results of change a previous in the timestudy series [13] ofthat each used hydrological data from 1971 dataset. to 1995 were compared with those of the present study to evaluate changeThe in spatial the time disaggregation series of each with hydrological quantile delta dataset. mapping (SDQDM) method interpolates with the The spatial disaggregation with quantile delta mapping (SDQDM) method interpolates with the inverse distance weighted using the values of the surrounding global climate model (GCM) grids inverse distance weighted using the values of the surrounding global climate model (GCM) grids before performing a bias correction, and then the rate of change for the future period is estimated before performing a bias correction, and then the rate of change for the future period is estimated from the original GCM for each quantile of the data. This statistical method maintains convenience. from the original GCM for each quantile of the data. This statistical method maintains convenience. In addition, the study used climate change scenarios with SDQDM, a model for statistical downscaling, In addition, the study used climate change scenarios with SDQDM, a model for statistical targeting the stations for which nonstationarity was observed. With this, the study aimed to identify downscaling, targeting the stations for which nonstationarity was observed. With this, the study changes in the characteristics of future annual precipitation caused by climate change by analyzing the aimed to identify changes in the characteristics of future annual precipitation caused by climate stationarity of such data. Figure1 shows the process followed in this study. change by analyzing the stationarity of such data. Figure 1 shows the process followed in this study.

FigureFigure 1.1. Flow chart of Stationary test.

Atmosphere 2020, 11, 1027 4 of 27

2.1.2. Structure of the Hydrological Time Series The data were obtained from observations of the hydrological time series, and the trend and periodicity characteristics are components. In addition, the time series can be roughly classiﬁed into two groups: one for deterministic components and the other for stochastic components. The composition of the time series is shown in Equation (1):

Xt = Dt + Tt + Pt + t, (1) where Xt denotes time series data, Dt is the deﬁnite component, Tt is the trend component, Pt is the periodic component, and t is the stochastic component (randomness). In general, a hydrological time series shows either stationarity or nonstationarity. When its average and variance do not change with time, the time series is considered to show stationarity. The opposite holds true for a time series that shows nonstationarity, wherein a trend or periodicity is present or a lack of homogeneity is observed in the hydrological data [13].

2.1.3. Estimation of Persistence

(1) Cumulative Deviation Cumulative deviation is a parametric test to assess whether the average value is diﬀerent before and after a random time during an observation period. As it assumes that the data are normally distributed, this test aims to evaluate whether the average changes after the selected observation, as shown in Equations (2) and (3): E(xi) = µ (2) E xj = µ + ∆; j = m + 1, m + 2, ...... n, (3) where µ is the average before the change, ∆ is the change in the average, and E(xi) is the expected average value before and after the time of change. The Q statistic, which is based on the average, is shown in Equation (4):

Q = max Sk∗∗ . (4)

Given that the change at the time of Sk∗∗ has the maximum test value, a negative value of Sk∗ indicates that the average of the former part is higher than that of the later part [1]. Sk∗ and Sk∗∗ are defined in Equations (5) and (6): Xk S∗ = (x x) (5) k i − i=1 n 2 S∗ X (xi x) S = k ; s2 = − , (6) k∗∗ s n i=1 where n is the number of data points, xi is the time series data value, x is the average of the sample 2 group, s is the standard deviation of the sample group, s is the variance of the sample group, Sk∗ is cumulative deviation, and Sk∗∗ is the standardized cumulative value of annual precipitation. (2) Serial Correlation Coefficient Hydrological persistence indicates a condition in which the hydrological quantities that constitute a hydrological time series are not in a random and independent pattern but continuously maintain similar size. This feature of the time series should be tested prior to a frequency analysis or stochastic simulation. If time series data show persistence, they cannot be used in a frequency analysis, and thus a serial correlation coefficient should be used to determine persistence. Atmosphere 2020, 11, 1027 5 of 27

The serial correlation coefficient is a statistic that evaluates the persistence of a time series. When a serial correlation of variables measures zero, no relationship is present between the observed values, which are independent of one another. However, when the serial correlation is skewed towards one variable, the observed values are correlated within the series, and the current value has an influence on future values. In this case, the consecutive correlation coefficient should be used and is defined in Equation (7) [14]: Pn 1 i=−1 (xi x) (xi+1 x) rl = P− × − (7) n (x x) i=1 i − where xi is an observe value, xi+1 is the next value, x is the average of a time series, and n is the number of data points. The equation for the 5% significance interval is shown in Equation (8):

1 1 1 1 1.96 < rl < + 1.96 (8) − n − √n2 −n √n2

2.1.4. Spearman Rank Correlation Trends in hydrological data are influenced by a combination of natural and artificial factors. The data will either have an increasing or decreasing trend with regard to the average and variance of the time series. Therefore, when statistics, such as the average and variance, remain constant, a time series can be considered to present stationarity. A trend can be identified with listed time series data; however, a statistical analysis is necessary if a clear trend is not observed. When a trend is present in annual and seasonal data, strong auto-correlation is generally present among data points. The statistical methods used to analyze trends include the T-test, Hotelling–Pabst test, nonlinear trend test, Mann–Kendall test, and Sen test. This study used a Spearman rank correlation analysis as it is widely used to evaluate correlations between two variables using a rank-based nonparametric scale [15]. The Pearson correlation analysis is most effective when the data are normally distributed, whereas the Spearman rank correlation has a statistical efficiency of 70% or higher for all possible variables. The Spearman rank correlation equation is shown in Equation (9):

Pn 6 i=1 Di Dj Rsp = 1 × × , (9) − n (n n 1) × × − where n is the number of data points, D for Kx Ky for the time sequence, Kx for the data sequence i i − i with the size, Ky is the time sequence of the raw data of Kxi, and x is the data time sequence [13].

" #0.5 n 2 tt = Rsp − (10) 1 Rsp Rsp − × 2.1.5. Simple T-Test and Simple F-Test The stability of hydrological time series data can be tested by assessing if statistics, such as the average and variance of a time series, change over time. If the average, variance, or other statistics vary with time, the data set is said to present nonstationarity. In general, the methods to analyze the stability of time series data include the Mann–Whitney test, sign test, Abbe test, simple T-test, and simple F-test. This study used a simple T-test and simple F-test to analyze stability, owing to their ease and simplicity of use. The T-test is a parametric statistical analysis that is used to evaluate the diﬀerence between the averages of two groups, whereas the F-test is used to determine whether a diﬀerence is present between variances of two groups and is shown in Equation (11) [13]:

δ2 s2 F = 1 = 1 , (11) t 2 2 δ2 s2 Atmosphere 2020, 11, x FOR PEER REVIEW 6 of 27

where 𝑥 and 𝑥 are the average values, 𝑠 and 𝑠 are the variances, and 𝑛 and 𝑛 are the Atmospherenumber 2020of data, 11, 1027points for each subset. 6 of 27

2.2. Data where s2 is the variance. A time series is considered to show stationarity once the following condition is2.2.1. satisﬁed: Stationv Used, v , 2.5%for Analysis< Ft< v , v , 97.5%. In this condition, v = (n 1) and v = (n 1) are the 1 2 1 2 1 1− 2 2− degrees of freedom for the numerator and denominator, respectively. The hydrological time series data used for the analysis required testing. However, in the case of The T-test for average stationarity is shown in Equation (12): the data from South Korea, the test results showed a low confidence interval because of the short observation period. Therefore, in this study, data were collected for each of the 37 stations located x1 x2 tt = − , (12) across the country that began recording observations2 2 in 1971. These0.5 stations recorded data for at least (n1 1)s +(n2 1)s 45 years, and the characteristics of annual− precipitation1 − 2 time1 series1 data were assessed. Figure 2 shows n1+n2 2 × n1 × n2 the locations of the 37 stations selected for this− study. To identify the trends with regard2 to change2 s in annual precipitation for South Korea, we where x1 and x2 are the average values, s1 and s2 are the variances, and n1 and n2 are the number of dataanalyzed points the for characteristics each subset. of the time series for the 37 weather stations taking into account the spatial distribution of the annual precipitation averages and standard deviations of the data from 2.2.these Data stations. The annual precipitation average was highest for the Jeju area (1905 mm/year for Seogwipo and 1850 mm/year for Seongsan) and lowest for the Daegu station (1052 mm/year). In 2.2.1. Station Used for Analysis addition, the study calculated the CVs to evaluate the variability in annual precipitation. The CVs wereThe obtained hydrological by dividing time seriesthe standard data used deviation for the by analysis the numerical required mean. testing. A However,low CV indicated in the case that ofthe the annual data from data Southhad low Korea, variability the test and results high showed stability. a The low conﬁdenceannual precipitation interval because CV was of found the short to be observationlowest for period.the Sokcho Therefore, station inand this highest study, datafor the were Seongsan collected station, for each indica of theting 37 stationsthat the located annual acrossprecipitation the country data that of the began Sokcho recording station observations was stable in and 1971. least These variable stations while recorded that of data the for Seongsan at least 45station years, was and theunstable characteristics and highly of annual variable. precipitation Figure 3 time shows series the data spatial were distribution assessed. Figure of 2averages, shows thestandard locations deviations, of the 37 and stations CVs selected of the annual for this precipitation study. data in this study.

FigureFigure 2. 2.Stations Stations for for the the study. study.

To identify the trends with regard to changes in annual precipitation for South Korea, we analyzed the characteristics of the time series for the 37 weather stations taking into account the spatial distribution of the annual precipitation averages and standard deviations of the data from these stations. The annual precipitation average was highest for the Jeju area (1905 mm/year for Seogwipo and 1850 mm/year for Seongsan) and lowest for the Daegu station (1052 mm/year). In addition, Atmosphere 2020, 11, 1027 7 of 27 the study calculated the CVs to evaluate the variability in annual precipitation. The CVs were obtained by dividing the standard deviation by the numerical mean. A low CV indicated that the annual data had low variability and high stability. The annual precipitation CV was found to be lowest for the Sokcho station and highest for the Seongsan station, indicating that the annual precipitation data of the Sokcho station was stable and least variable while that of the Seongsan station was unstable and highly variable. Figure3 shows the spatial distribution of averages, standard deviations, and CVs of the annual precipitation data in this study. Atmosphere 2020, 11, x FOR PEER REVIEW 7 of 27 Atmosphere 2020, 11, 1027 8 of 27

(a) Annual precipitation (b) Annual precipitation

(Average) (Standard deviation)

(CV)

FigureFigure 3. Average, 3. Average, coe ﬃcoefficientcient of of variation variation (CV), (CV), andand standardstandard deviation deviation of ofthe the annual annual precipitation precipitation (mm).(mm)(a). is(a) the is the average average of of the the annual annual precipitation, precipitation, ( (bb)) is is the the standard standard deviation deviation of the of theannual annual precipitation,precipitation, and and (c) is (c the) is the CV CV value value of of the the annual annual precipitation. precipitation.

2.2.2. Climate Change Scenarios The GCM, a numerical climate model, has several types and produces different simulation results depending on the type of input data used. Meteorological data predicted by the GCM are statistically downscaled for practical use. The KMA operates a certification system to encourage the Atmosphere 2020, 11, 1027 9 of 27 use of diverse scenarios. The system recommends 13 scenarios at a regional level that are produced with the combinationAtmosphere 2020, 11,of x FOR GCMs PEER REVIEW scenarios and statistical down-scaling (Table1). For such8 of statistical27 downscaling, SDQDM has been recommended as it was designed to preserve the long-term trends of 2.2.2. Climate Change Scenarios climate models and conducts a bias correction [16]. In this study, 13 regional scale scenarios produced The GCM, a numerical climate model, has several types and produces different simulation by SDQDM, a statistically detailed technique, were used with the GCM scenarios provided by the results depending on the type of input data used. Meteorological data predicted by the GCM are Standardstatistically Scenario Certificationdownscaled for Systempractical ofuse. the The Korea KMA Meteorologicaloperates a certification Administration. system to encourage Figure the4 shows the climateuse change of diverse scenarios. scenarios. FigureThe system5 shows recommends the spatial 13 scenarios distribution at a regional of averages, level that standard are produced deviations, and CVswith of the the futurecombination annual of GCMs precipitation scenarios and data statistical in this down-scaling study. The (Table results 1). showedFor such statistical that the annual precipitationdownscaling, average SDQDM will be has highest been recommended for the Seogwipo as it was stationdesigned (2445to preserve mm /theyear) long-term and lowest trends for the of climate models and conducts a bias correction [16]. In this study, 13 regional scale scenarios Daegu station (1023 mm/year). The standard deviation was the highest in Seogwipo (733), the lowest produced by SDQDM, a statistically detailed technique, were used with the GCM scenarios provided in Ulleungby (201),the Standard the highest Scenario in Certification CV (Gunsan System (0.32)), of the and Korea the Meteorological lowest in Pohang Administration. (0.21). Figure 4 shows the climate change scenarios. Figure 5 shows the spatial distribution of averages, standard deviations, andTable CVs 1. ofThirteen the future global annual climate precipitatio modelsn data (GCMs) in this usedstudy. for The the results study. showed that the annual precipitation average will be highest for the Seogwipo station (2445 mm/year) and lowest for Resolution No.the GCMsDaegu station (1023 mm/year). The standard deviation was theInstitution highest in Seogwipo (733), the lowest in Ulleung (201),(Degree) the highest in CV (Gunsan (0.32)), and the lowest in Pohang (0.21). 1 CanESM2 2.813 2.791 Canadian Centre for Climate Modelling and Analysis (CCCMA) Table 1.× Thirteen global climate models (GCMs) used for the study. 2 CESM1-BGC 1.250 0.942 National Center for Atmospheric Research (NCAR) Resolution× No. GCMs Institution 3 CMCC-CM 0.750 (Degree)0.748 × Centro Euro-Mediterraneo per I Cambiamenti Climatici 4 CMCC-CMS1 CanESM2 1.8752.8131.865 × 2.791 Canadian Centre for Climate Modelling and Analysis (CCCMA) × 5 CNRM-CM52 CESM1-BGC 1.406 1.2501.401 × 0.942 NationalCentre NationalCenter for deAtmospheric Recherches Research Meteorologiques (NCAR) 3 CMCC-CM 0.750× × 0.748 6 GFDL-ESM2G 2.500 2.023Centro GeophysicalEuro-Mediterraneo Fluid per Dynamics I Cambiamenti Laboratory Climatici 4 CMCC-CMS 1.875× × 1.865 7 HadGEM2-AO5 CNRM-CM5 1.875 1.4061.250 × 1.401 Centre National de Recherches Meteorologiques × Met Office Hadley Centre for Climate Science and Services 8 HadGEM2-ES6 GFDL-ESM2G 1.875 2.5001.250 × 2.023 Geophysical Fluid Dynamics Laboratory × 7 HadGEM2-AO 1.875 × 1.250 9 INM-CM4 2.000 1.500Met Office Hadley Institute Centre of Numerical for Climate Science Mathematics and Services 8 HadGEM2-ES 1.875× × 1.250 10 IPSL-CM5A-LR 3.750 1.895 9 INM-CM4 2.000× × 1.500 InstituteInstitut of Numerical Pierre-Simon Mathematics Laplace 11 IPSL-CM5A-MR10 IPSL-CM5A-LR 2.500 3.7501.268 × 1.895 × Institut Pierre-Simon Laplace 12 MRI-CGCM311 IPSL-CM5A-MR 1.125 2.5001.122 × 1.268 Meteorological Research Institute × 13 NorESM1-M12 MRI-CGCM3 2.500 1.1251.895 × 1.122 Meteorological Norwegian Research Climate Institute Centre 13 NorESM1-M 2.500× × 1.895 Norwegian Climate Centre

Figure 4. Climate Change Scenarios Used in This Study. Figure 4. Climate Change Scenarios Used in This Study.

Atmosphere 2020, 11, x FOR PEER REVIEW 9 of 27 Atmosphere 2020, 11, 1027 10 of 27

(a) Annual precipitation (b) Annual precipitation

(Average) (Standard deviation)

(CV)

Figure 5. Average, coecoefficientﬃcient of variation (CV), and standardstandard deviation of annual precipitation (mm) using the climate change scenarios. ( a) is the average of the annual precipitation, (b) is thethe standardstandard deviation of the annual precipitation, andand ((cc)) isis thethe CVCV valuevalue ofof thethe annualannual precipitation.precipitation.

Atmosphere 2020, 11, x FOR PEER REVIEW 10 of 27

2.2.3. Performance of Climate Change Scenarios Atmosphere 2020, 11, 1027 11 of 27 To verify the performance of MME from the 13 climate change scenarios of GCMs, the study draws a box plot, using spatially calculated averages for monthly precipitation and mean temperature2.2.3. Performancedata provided of Climate by 80 Changeweather Scenarios stations for the reference period for each of the models. In addition, theTo coefficient verify the performanceof variation of (CV) MME between from the 13models climate was change calculated scenarios by of station GCMs, thepoint study for annual precipitationdraws and a box average plot, using temperatures spatially calculated of the averages late 21st for monthlycentury precipitation (2071–2100). and mean temperature data provided by 80 weather stations for the reference period for each of the models. In addition, The study statistically down-scaled the 13 national standard scenarios, using the SDQDM of the the coefficient of variation (CV) between models was calculated by station point for annual precipitation AIMS providedand average by temperatures the Asia Pacific of the lateEconomic 21st century Coop (2071–2100).eration Climate Center (APCC). In this chapter, the study comparesThe study probability statistically down-scaled density functions the 13 national (PDFs) standard and spatial scenarios, distribution using the SDQDMbetween MME, (precipitationof the AIMSand providedtemperature), by the Asiawhich Pacific is down-s Economiccaled Cooperation for the Climate period Center of reference (APCC). In climate, this and observationchapter, data. the This study aims compares to verify probability the applicability density functions of (PDFs)statistical and spatialdown-scaling distribution results, between for North Korea’s MME,climate (precipitation observation and. temperature), which is down-scaled for the period of reference climate, and observation data. This aims to verify the applicability of statistical down-scaling results, for North ForKorea’s annual climate precipitation, observation. the PDFs were compared between the observation data and before application Foror after annual application precipitation, of the the PDFs SDQDM. were compared This is between well matched the observation to the data bell-shaped, and before normal distributionapplication for the or average after application value, ofwhile the SDQDM.variables This were is well gathered matched around to the bell-shaped, the median. normal Comparing average distributionvalues (mode for the and average median) value, and while variances variables between were gathered before around down-scaling the median. (blue Comparing line in Figure 6), and afteraverage down-scaling values (mode (red and median) line in andFigure variances 6), th betweene study before found down-scaling that they were (blue linewell in matched Figure6), to those and after down-scaling (red line in Figure6), the study found that they were well matched to those of of observationobservation data data after after down-scaling. down-scaling. For For quantitative results, results, the averagethe average value value of OBS of is 1116OBS mm,is 1116 mm, the averagethe average of the ofraw the scenario raw scenario is 1196, is 1196, the the average average of of the downscaleddownscaled scenario scenario is 1162 is 1162 mm, andmm, it and it is confirmedis confirmed that the observation that the observation and the and after the down-scaling after down-scaling are are similar. similar. The The result result of of comparing spatial distributionspatial between distribution before/after between before down-scaling/after down-scaling suggested suggested that that thethe latter latter simulated simulated the spatial the spatial distributiondistribution very close very close(Figure (Figure 7a,c)7a,c) to to that that of of observationobservation data data (Figure (Figure7a). 7a).

Figure 6. Observation and probability density functions of before/after application of the spatial Figure 6.disaggregation Observation with and quantile probability delta mapping density (SDQDM) functions (annual of precipitation). before/after application of the spatial disaggregation with quantile delta mapping (SDQDM) (annual precipitation).

Atmosphere 2020, 11, x FOR PEER REVIEW 11 of 27 Atmosphere 2020, 11, 1027 12 of 27

(a) OBS (b) Raw Scenario (c) Down-scaled Scenario

Figure 7. Spatial distribution for observation and before/after down-scaling (annual precipitation). Figure 7. Spatial distribution for observation and before/after down-scaling (annual precipitation).(a), (a), (b), and (c) represent OBS, Raw-Scenario, and Down-scaled Scenario respectively. (b), and (c) represent OBS, Raw-Scenario, and Down-scaled Scenario respectively. 3. Evaluation of Time Series Characteristics Using Observational Data of Precipitation 3. Evaluation of Time Series Characteristics Using Observational Data of Precipitation 3.1. Hydrological Persistence Test 3.1. HydrologicalA persistence Persistence test was Test conducted using the cumulative deviation to evaluate annual precipitation data.A Whenpersistence persistence test was exists, conducted data averages using showthe cumulative inconsistency deviation and randomness. to evaluate Therefore, annual precipitationpersistence in data. data isWhen indicative persistence of nonstationarity. exists, data averages After evaluating show inconsistency persistence using and randomness. a cumulative Therefore,deviation analysis,persistence one in stationdata is showedindicative increasing of nonstationarity. annual precipitation After evaluating since 1978.persistence Out of using the 37a cumulativeselected stations, deviation 19 and analysis, 17 stations one showedstation showed increasing increasing trends in annual annual precipitation precipitation since from 1978. 1982 toOut 1989 of theand 37 from selected 1994 tostations, 1997, respectively. 19 and 17 stations The study showed also usedincreasing the serial trends correlation in annual coe precipitationfficient to evaluate from 1982persistence. to 1989 and In a from previous 1994 study,to 1997, persistence respectively. was The only study explained also used for the serial Daegwallyeong correlation stationcoefficient [13]. toHowever, evaluate the persistence. results ofthis In a study previous suggested study, that persistence four stations was(i.e., only Daegwallyeong, explained for the Ulleung, Daegwallyeong Seogwipo, stationand Ganhwa) [13]. However, showed persistence,the results of as this summarized study suggested in Table that2. In four addition, stations the (i.e., graphs Daegwallyeong, of Figures8 Ulleung,and9 show Seogwipo, the results and of Ganhwa) the persistence showed analysis persistence, using theas summarized serial correlation in Table coe ffi 2.cient. In addition, Each graph the graphsshows anof inflectionFigures 8 point, and 9 annual show precipitation,the results of thethe averages persistence before analysis and after using the the inflection serial point,correlation and a coefficient.standardized Each cumulative graph shows value an of inflection annual precipitation. point, annual The precipitation, graphs in Figurethe averages8 show before the persistence and after theanalysis inflection results point, for theand Daegwallyeong,a standardized cumulative Ulleung, Seogwipo, value of annual and Ganhwa precipitation. stations. The In graphs contrast, in Figurethe graphs 8 show in Figure the persistence9 show the resultsanalysis for result the Busan,s for the Gwanju, Daegwallyeong, Haenam, and Ulleung, Jeju stations, Seogwipo, which and did Ganhwanot show stations. persistence. In contrast, The map the shown graphs in in Figure Figure 10 9 indicates show the the results stations for the with Busan, and without Gwanju, persistence. Haenam, and Jeju stations, which did not show persistence. The map shown in Figure 10 indicates the stations with and without persistence.

Atmosphere 2020, 11, x FOR PEER REVIEW 12 of 27

Atmosphere 2020, 11, 1027Table 2. Serial correlation coefficient analysis for annual precipitation data. 13 of 27

No. Station 𝒓𝒍 Decision No. Station 𝒓𝒍 Decision 90 SokchoTable 2. Serial correlation 0.03 coeNPﬃcient analysis156 for annualGwangju precipitation 0.00 data. NP 100 Daegwanryeong 0.54 P 159 Busan −0.12 NP 101 No.Chuncheon Station 0.30r l NPDecision 162 No. Tongyeong Station −0.07rl DecisionNP 105 90Gangneung Sokcho 0.24 0.03 NP NP 165 156 GwangjuMokpo − 0.000.03 NP NP 108 100 SeoulDaegwanryeong 0.200.54 NP P168 159 Yeosu Busan −0.060.12 NP NP − 101 Chuncheon 0.30 NP 162 Tongyeong 0.07 NP 112 Incheon 0.20 NP 170 Wando −0.21 NP 105 Gangneung 0.24 NP 165 Mokpo 0.03 NP 114 Wonju 0.14 NP 184 Jeju −0.15 NP 108 Seoul 0.20 NP 168 Yeosu 0.06 NP 115 Ulleung 0.35 P 188 Sungsan −0.18 NP 112 Incheon 0.20 NP 170 Wando 0.21 NP 119 114Suwon Wonju 0.21 0.14 NP NP 189 184 Seogwipo Jeju 0.280.15 NP P − 129 115Seosan Ulleung 0.18 0.35 NP P192 188 SungsanJinju 0.00 0.18 NP NP 130 119Uljin Suwon 0.19 0.21 NP NP 201 189 Ganghwa Seogwipo 0.27 0.28 P P 129 Seosan 0.18 NP 192 Jinju 0.00 NP 131 Cheongju 0.10 NP 202 Yangpyeong 0.17 NP 130 Uljin 0.19 NP 201 Ganghwa 0.27 P 133 131Daejeon Cheongju 0.04 0.10 NP NP 203 202 YangpyeongIcheon 0.090.17 NP NP 135 133Chupungnyeong Daejeon 0.15 0.04 NP NP 211 203 Icheon Inje − 0.090.03 NP NP 138 135 PohangChupungnyeong −0.030.15 NP NP 212 211 Hongcheon Inje 0.190.03 NP NP − 138 Pohang 0.03 NP 212 Hongcheon 0.19 NP 140 Gunsan −0.04− NP 244 Imshil 0.16 NP 140 Gunsan 0.04 NP 244 Imshil 0.16 NP 143 Daegu 0.12− NP 245 Jeongeup 0.26 NP 143 Daegu 0.12 NP 245 Jeongeup 0.26 NP 146 Jeonju 0.05 NP 261 Haenam 0.26 NP 146 Jeonju 0.05 NP 261 Haenam 0.26 NP 152 152Ulsan Ulsan −0.160.16 NP NP − 𝑈U.% == −0.3,0.3, U𝑈.%==0.262; 0.262; P: P: Persistence; Persistence; NP: NP: No Persistence.No Persistence. 2.5% − 97.5%

(a) Uljin (b) Inje

FigureFigure 8. 8.Serial Serial correlation correlation coe coefficientﬃcient graphs graphs showing showing persistence. persistence. ( a(),a), ( b(b),), (c (),c), and and (d (d) each) each represent represent graphsgraphs of of Uljin, Uljin, Inje, Inje, Cheongju, Cheongju, and and Busan. Busan.

Atmosphere 2020, 11, x FOR PEER REVIEW 13 of 27 Atmosphere 2020, 11, 1027 14 of 27 Atmosphere 2020, 11, x FOR PEER REVIEW 13 of 27

(a) Jeongeup (b) Daejeon (a) Jeongeup (b) Daejeon

(c) Seogwipo (d) Jeonju (c) Seogwipo (d) Jeonju Figure 9. Serial correlation coefficient graphs showing no persistence. (a), (b), (c), and (d) each FigureFigurerepresent 9.9.Serial graphsSerial correlation correlation of Jeongeup, coe coefficientﬃ Daejeon,cient graphs grSeogwipo,aphs showing showing and no persistence.Jeonju. no persistence. (a), (b), ( (ac),), and(b), ((dc)), each and represent (d) each graphsrepresent of Jeongeup,graphs of Daejeon,Jeongeup, Seogwipo, Daejeon, Seogwipo, and Jeonju. and Jeonju.

Figure 10. Persistence analysis result for each station. Figure 10. Persistence analysis result for each station. Figure 10. Persistence analysis result for each station.

Atmosphere 2020, 11, 1027 15 of 27

3.2. Hydrological Trend Test To evaluate the annual precipitation data, we used the Spearman rank correlation test to analyze possible trends. This study also conducted a two-tailed statistical test with signiﬁcance at the 5% level for each of the dataset. An increasing or decreasing trend with regard to the average and variance may be present in a time series. Therefore, when the statistical characteristics such as average and variance are constant with time, the data cannot be considered stationary. Therefore, when statistics, such as the average and variance, are constant over time, the data are considered to show stationarity. Table3 shows the results of the Spearman rank correlation analysis. Out of the 37 stations, the annual precipitation data from the Uleungdo, Seongsan, Seogwipo, and Inje stations showed increasing trends with variations between stations. Among the four stations, Seongsan station showed the greatest trend. In a past study [13], data from 1972 to 1995 were used to analyze the trends of Daegwallyeong station. AtmosphereHowever, 2020, 11, x in FOR this PEER study, REVIEW no trend was observed for this station as the decrease in annual precipitation 15 of 27 since 1994 was oﬀset by the current increasing trend. The map in Figure 11 shows the results of the trend analysis.

FigureFigure 11. 11. TrendTrend analysis analysis resultresult for for each each station. station.

3.3. Hydrological Stable Test A simple T-test and F-test were used to test the stability of annual precipitation. Two-tailed tests with significance at the 5% level were conducted for each dataset. The data in which statistics, such as the average and variance, were not consistent were considered nonstationary. Table 4 summarizes the simple T-test and F-test results. For the simple T-test, 23 of the 37 stations showed unstable annual precipitation, including the Uljin, Cheongju, Daejeon, Chupungnyeong, Pohang, Gunsan, Daegu, Jeonju, Busan, and Ulsan stations. The simple F-test results show that the annual precipitation data from two stations, Seongsan and Haenam, were unstable. According to [13], out of the stations that showed unstable data, the Uljin, Cheongju, Daejeon, and Chupungnyeong stations showed the greatest changes in trends, and data from the Daegwallyeong, Ganghwa, and Imsil stations were found to be stable. However, in this study, data from all stations, except Imsil, were unstable, whereas the data from 23 stations, including Uljin, Cheongju, and Imsil, were analyzed for instability. Figure 12 shows the simple T-test and F-test results.

Atmosphere 2020, 11, 1027 16 of 27

Table 3. Analysis of the Spearman rank correlation of annual precipitation data.

No. Station tt Decision No. Station tt Decision 90 Sokcho 0.94 NT 156 Gwangju 0.03 NT 100 Daegwanryeong 0.42 NT 159 Busan 0.80 NT − 101 Chuncheon 1.83 NT 162 Tongyeong 1.61 NT 105 Gangneung 0.20 NT 165 Mokpo 0.71 NT − 108 Seoul 1.39 NT 168 Yeosu 1.01 NT 112 Incheon 1.21 NT 170 Wando 1.74 NT 114 Wonju 0.29 NT 184 Jeju 0.59 NT 115 Ulleung 3.53 T 188 Sungsan 3.16 T 119 Suwon 1.43 NT 189 Seogwipo 2.12 T 129 Seosan 0.17 NT 192 Jinju 0.46 NT 130 Uljin 0.33 NT 201 Ganghwa 0.75 NT − 131 Cheongju 0.74 NT 202 Yangpyeong 1.69 NT 133 Daejeon 0.24 NT 203 Icheon 0.42 NT 135 Chupungnyeong 0.19 NT 211 Inje 2.04 T 138 Pohang 0.50 NT 212 Hongcheon 1.09 NT 140 Gunsan 0.56 NT 244 Imshil 0.81 NT 143 Daegu 1.04 NT 245 Jeongeup 0.83 NT 146 Jeonju 0.11 NT 261 Haenam 0.83 NT − 152 Ulsan 0.29 NT t = 2.02, t = 2.02; T: Trend; NT: No Trend. 2.5% − 97.5%

3.3. Hydrological Stable Test A simple T-test and F-test were used to test the stability of annual precipitation. Two-tailed tests with signiﬁcance at the 5% level were conducted for each dataset. The data in which statistics, such as the average and variance, were not consistent were considered nonstationary. Table4 summarizes the simple T-test and F-test results. For the simple T-test, 23 of the 37 stations showed unstable annual precipitation, including the Uljin, Cheongju, Daejeon, Chupungnyeong, Pohang, Gunsan, Daegu, Jeonju, Busan, and Ulsan stations. The simple F-test results show that the annual precipitation data from two stations, Seongsan and Haenam, were unstable. According to [13], out of the stations that showed unstable data, the Uljin, Cheongju, Daejeon, and Chupungnyeong stations showed the greatest changes in trends, and data from the Daegwallyeong, Ganghwa, and Imsil stations were found to be stable. However, in this study, data from all stations, except Imsil, were unstable, whereas the data from 23 stations, including Uljin, Cheongju, and Imsil, were analyzed for instability. Figure 12 shows the simple T-test and F-test results.

Table 4. Simple T-test and F-test analysis of annual precipitation data.

T-Test F-Test T-Test F-Test No. Station Decision No. Station Decision tt Ft tt Ft 90 Sokcho 0.34 1.11 S 156 Gwangju 4.35 1.45 US − − 100 Daegwanryeong 0.03 0.52 S 159 Busan 8.28 0.98 US − 101 Chuncheon 0.53 0.71 S 162 Tongyeong 12.87 0.77 US − − 105 Gangneung 0.06 0.48 S 165 Mokpo 6.26 1.29 US − − 108 Seoul 0.47 0.72 S 168 Yeosu 4.37 1.12 US − − 112 Incheon 0.42 0.88 S 170 Wando 12.98 1.15 US − − 114 Wonju 0.06 0.55 S 184 Jeju 1.24 0.58 S − − 115 Ulleung 0.11 0.47 S 188 Sungsan 13.73 3.35 US − − 119 Suwon 0.02 0.90 S 189 Seogwipo 6.61 0.84 US − − Atmosphere 2020, 11, x FOR PEER REVIEW 16 of 27

Table 4. Simple T-test and F-test analysis of annual precipitation data.

T-Test F-Test T-Test F-Test No. Station Decision No. Station Decision 𝑡 𝐹 𝑡 𝐹 90 Sokcho −0.34 1.11 S 156 Gwangju −4.35 1.45 US 100 Daegwanryeong 0.03 0.52 S 159 Busan −8.28 0.98 US 101 Chuncheon −0.53 0.71 S 162 Tongyeong −12.87 0.77 US Atmosphere105 2020Gangneung, 11, 1027 −0.06 0.48 S 165 Mokpo −6.26 1.29 US17 of 27 108 Seoul −0.47 0.72 S 168 Yeosu −4.37 1.12 US 112 Incheon −0.42 0.88 S 170 Wando −12.98 1.15 US 114 Wonju −0.06 0.55 Table S 4. Cont. 184 Jeju −1.24 0.58 S 115 Ulleung −0.11 0.47 S 188 Sungsan −13.73 3.35 US T-Test F-Test T-Test F-Test 119No. SuwonStation −0.02 0.90 Decision S 189No. SeogwipoStation −6.61 0.84 Decision US 129 Seosan 0.00t t 0.47Ft S 192 Jinju −tt1.67 Ft1.06 S 130129 Uljin Seosan −19.46 0.00 0.56 0.47 US S 201 192 Ganghwa Jinju 1.67 0.37 1.06 0.64 S S − 131130 Cheongju Uljin −19.7619.46 0.580.56 US 202 201 Yangpyeong Ganghwa 0.37−5.21 0.640.85 S US − 133131 Daejeon Cheongju −22.5919.76 0.590.58 US 203 202 Yangpyeong Icheon 5.21−1.34 0.851.42 US S − − 133 Daejeon − 22.59 0.59 US 203 Icheon 1.34− 1.42 S 135 Chupungnyeong 19.38− 0.64 US 211 Inje − 5.90 1.19 US 135 Chupungnyeong 19.38 0.64 US 211 Inje 5.90 1.19 US 138 Pohang −17.98− 0.49 US 212 Hongcheon − −2.53 0.81 US 138 Pohang 17.98 0.49 US 212 Hongcheon 2.53 0.81 US 140 Gunsan −15.41− 0.79 US 244 Imshil − −2.47 1.84 US 140 Gunsan 15.41 0.79 US 244 Imshil 2.47 1.84 US 143 Daegu −14.37− 0.76 US 245 Jeongeup − −2.88 1.78 US 143 Daegu 14.37 0.76 US 245 Jeongeup 2.88 1.78 US 146 Jeonju −4.24− 1.04 US 261 Haenam − −1.02 2.18 US 146 Jeonju 4.24 1.04 US 261 Haenam 1.02 2.18 US − − 152152 Ulsan Ulsan 3.41 3.41 0.90 0.90 US 𝑡 = ±2.02, 𝐹 = ±1.98; S: Stable; US: Unstable. .%,.%t = 2.02, F.%,.%= 1.98; S: Stable; US: Unstable. 2.5%, 97.5% ± 2.5%, 97.5% ±

FigureFigure 12. 12.Stability Stability testtest results.results.

3.4. Estimation of Hydrological Stationarity

As shown in Table5, data from the 14 stations that showed stability without trends or persistence were deemed to show stationarity. The data from 23 stations that showed abnormality in two out of the three features (i.e., persistence, trends, and stability) were deemed nonstationary. According to [13], data from the Daegwallyeong, Uleungdo, Yangpyeong, and Jecheon stations showed nonstationarity. However, in this study, we found that the data from 23 stations, including Uleungdo station, showed nonstationarity. Moreover, the data from Daegwallyeong station were considered to show stationarity. Figure 13 shows the results of the stationarity analysis for the annual precipitation data of the 37 stations. Atmosphere 2020, 11, 1027 18 of 27

Table 5. Estimation of the stationarity of annual precipitation data.

No. Station T1 T2 T3 Stationarity No. Station T1 T2 T3 Stationarity 90 Sokcho NP S NT O 156 Gwangju NP US NT X 100 Daegwanryeong PS NT X 159 Busan NP US NT X 101 Chuncheon NP S NT O 162 Tongyeong NP US NT X 105 Gangneung NP S NT O 165 Mokpo NP US NT X 108 Seoul NP S NT O 168 Yeosu NP US NT X 112 Incheon NP S NT O 170 Wando NP US NT X 114 Wonju NP S NT O 184 Jeju NP S NT O 115 Ulleung P S T X 188 Sungsan NP US TX 119 Suwon NP S NT O 189 Seogwipo P US TX 129 Seosan NP S NT O 192 Jinju NP S NT O 130 Uljin NP US NT X 201 Ganghwa PS NT X 131 Cheongju NP US NT X 202 Yangpyeong NP US NT X 133 Daejeon NP US NT X 203 Icheon NP S NT O 135 Chupungnyeong NP US NT X 211 Inje NP US TX 138 Pohang NP US NT X 212 Hongcheon NP US NT X 140 Gunsan NP US NT X 244 Imshil NP US NT X 143 Daegu NP US NT X 245 Jeongeup NP US NT X 146 Jeonju NP US NT X 261 Haenam NP S NT O 152 Ulsan NP US NT X AtmosphereT: Trend; 2020 S:, Stable;11, x FOR P: Persistence; PEER REVIEW O: Stationary; NT: No Trend; US: Unstable; NP: No persistence; X: Nonstationary.18 of 27

FigureFigure 13.13.Stationarity Stationarity analysisanalysis result.result.

4. Prediction of Changes in Time Series Characteristics with Climate Change Scenarios

4.1. Selected Stations We selected 25 stations that showed nonstationarity based on the results of the stationarity analysis using current annual precipitation data. With the exception of Chupungnyeong and Seongsan stations, for which climate change scenarios were not available, all stations were used for the stationary analysis using future annual precipitation scenarios for South Korea with SDQDM, a statistical downscaling model. Figure 14 shows the spatial distribution of the observations for this study. The blue dot is the entire station, and the red dot is the station analyzed using climate change scenario data.

Atmosphere 2020, 11, 1027 19 of 27

4. Prediction of Changes in Time Series Characteristics with Climate Change Scenarios

FigureFigure 14. 14.Target Target stations. stations.

4.2.4.2. HydrologicalHydrological PersistencePersistence TestTest Using Using Climate Climate Change Change Scenarios Scenarios ToTo determinedetermine thethe hydrologicalhydrological persistencepersistence ofof futurefuture annualannual precipitationprecipitation data,data, wewe usedused aa persistencepersistence analysis analysis with with the the cumulative cumulative deviations deviations and serial-correlationand serial-correlation coeﬃcients coefficients of future of annual future precipitationannual precipitation data of the data 23 aforementionedof the 23 aforementioned stations. [13 stations.] suggested [13] thatsuggested persistence that persistence was only present was only for Daegwallyeongpresent for Daegwallyeong station. However, station. in this However, study, the analysisin this study, of the current the analysis annual precipitationof the current indicated annual theprecipitation presence of indicated persistence the in thepresence Jeongeup, of persistence Uleungdo, Seogwipo,in the Jeongeup, and Seongsan Uleungdo, stations, Seogwipo, whereas theand analysisSeongsan using stations, climate whereas change the scenarios analysis suggestedusing climate that change this feature scenarios did notsuggested appear that in any this station.feature Figuredid not 15 appear shows in the any cumulative station. Figure graphs 15 of shows the stations the cumulative for which graphs the data of didthe notstations show for persistence, which the namelydata did the not Jeongeup, show persistence, Daejeong, namely Seogwipo, the Jeonge and Jeonjuup, Daejeong, stations. Table Seogwipo,6 summarizes and Jeonju the stations. results of Table the serial6 summarizes correlation the coe resultsﬃcient of analysis,the serial and correlation Figure 16 coefficient shows the analysis, results ofand the Figure persistence 16 shows analysis. the results of the persistence analysis.

Table 6. Analysis of the coefficient using climate change scenarios.

No. Station 𝑟 Decision No. Station 𝑟 Decision 100 Daegwanryeong −0.009 NP 156 Busan 0.194 NP 115 Ulleung 0.108 NP 159 Tongyeong 0.104 NP 130 Uljin 0.019 NP 162 Mokpo 0.092 NP 131 Cheongju −0.067 NP 165 Yeosu 0.149 NP 133 Daejeon −0.045 NP 168 Wando 0.118 NP 135 Pohang 0.012 NP 170 Seogwipo 0.095 NP 138 Gunsan 0.058 NP 188 Yangpyeong −0.064 NP 140 Daegu −0.015 NP 189 Inje 0.069 NP 143 Jeonju −0.012 NP 202 Hongcheon −0.001 NP 146 Ulsan 0.093 NP 211 Imshil 0.052 NP 152 Gwangju 0.04 NP 212 Jeongeup −0.003 NP

𝑈.% = −0.218, 𝑈.% = 0.197; P: Persistence; NP: No Persistence.

Atmosphere 2020, 11, x FOR PEER REVIEW 20 of 27 Atmosphere 2020, 11, 1027 20 of 27 Atmosphere 2020, 11, x FOR PEER REVIEW 20 of 27

(a()a Jeongeup) Jeongeup (b) Daejeon(b) Daejeon

(c) Seogwipo (d) Jeonju (c) Seogwipo (d) Jeonju FigureFigure 15.15. Cumulative deviation graphsgraphs usingusing climateclimate changechange scenarios scenarios (no (no persistence). persistence). ( a(),a), (b (),b), (c (),c), and (d) eachandFigure ( representd) each15. Cumulative represent graphs ofgraphs Jeongeup,deviation of Jeonge Daejeon,graphsup, Daejeon,using Seogwipo, climate Seogwipo, and change Jeonju. and scenarios Jeonju. (no persistence). (a), (b), (c), and (d) each represent graphs of Jeongeup, Daejeon, Seogwipo, and Jeonju.

Figure 16. Persistence analysis results using the climate change scenarios.

FigureFigure 16. 16.Persistence Persistence analysis analysis results results using using the the climate climate change change scenarios. scenarios.

Atmosphere 2020, 11, 1027 21 of 27

Table 6. Analysis of the coeﬃcient using climate change scenarios.

No. Station rl Decision No. Station rl Decision 100 Daegwanryeong 0.009 NP 156 Busan 0.194 NP − 115 Ulleung 0.108 NP 159 Tongyeong 0.104 NP 130 Uljin 0.019 NP 162 Mokpo 0.092 NP 131 Cheongju 0.067 NP 165 Yeosu 0.149 NP − 133 Daejeon 0.045 NP 168 Wando 0.118 NP − 135 Pohang 0.012 NP 170 Seogwipo 0.095 NP 138 Gunsan 0.058 NP 188 Yangpyeong 0.064 NP − 140 Daegu 0.015 NP 189 Inje 0.069 NP − 143 Jeonju 0.012 NP 202 Hongcheon 0.001 NP − − 146 Ulsan 0.093 NP 211 Imshil 0.052 NP 152 Gwangju 0.04 NP 212 Jeongeup 0.003 NP − U = 0.218, U = 0.197; P: Persistence; NP: No Persistence. 2.5% − 97.5%

4.3. Hydrological Trend Test Using Climate Change Scenarios This study used a Spearman rank correlation analysis to analyze trends in annual precipitation using climate change scenarios. A two-tailed statistical test with significance at the 5% level was used for each dataset. Table6 shows the trends in annual precipitation data for 11 out of the 23 stations, including the Busan, Seogwipo, Yangpyeong, and Jeongeup stations. In a previous study [13], data from 1972 to 1995 was used to analyze the trends of Daegwallyeong station. However, in this study, the analysis of this data indicated no significant trend for this station as the decrease in the annual precipitation since 1995 was offset by the current increasing trend. A comparison between the current data and that of the Future 3 scenario (2071 to 2100), the latter of the climate change scenarios, indicated that Seogwipo station showed the highest rate of increase (28.7%). The lowest rate of decrease was present at Daegu station (10.1%). In addition, Busan and Yangpyeong stations showed increasing trends of 8.6% and 3.8%, respectively. In addition, Uljin and Inje stations showed decreasing trends of 1.3% and 2.3%, respectively. These results suggest that variations in the trends of annual precipitation of the target stations. The results of the trend analysis are summarized in Table7, while Figure 17 provides the graphs of six stations and depicts changes in current and future annual precipitation. Figure 18 shows the results of the trend analysis.

Table 7. Spearman rank correlation analysis of annual precipitation using climate change scenarios.

No. Station tt Decision No. Station tt Decision 100 Daegwanryeong 1.91 NT 159 Tongyeong 2.91 T 115 Ulleung 0.98 NT 162 Mokpo 3.04 T − 130 Uljin 0.59 NT 165 Yeosu 3.36 T 131 Cheongju 0.70 NT 168 Wando 3.17 T 133 Daejeon 1.36 NT 170 Seogwipo 4.14 T 135 Pohang 1.94 NT 188 Ganghwa 2.32 T 138 Gunsan 2.49 T 189 Yangpyeong 2.16 T 140 Daegu 1.18 NT 201 Inje 0.00 NT 143 Jeonju 1.69 NT 202 Hongcheon 0.59 NT 146 Ulsan 2.37 T 211 Imshil 1.93 NT 152 Gwangju 2.51 T 212 Jeongeup 2.58 T 156 Busan 2.33 T t = 1.98, t = 1.98; T: Trend; NT: No Trend. 2.5% − 97.5% Atmosphere 2020, 11, x FOR PEER REVIEW 21 of 27

4.3. Hydrological Trend Test Using Climate Change Scenarios This study used a Spearman rank correlation analysis to analyze trends in annual precipitation using climate change scenarios. A two-tailed statistical test with significance at the 5% level was used for each dataset. Table 6 shows the trends in annual precipitation data for 11 out of the 23 stations, including the Busan, Seogwipo, Yangpyeong, and Jeongeup stations. In a previous study [13], data from 1972 to 1995 was used to analyze the trends of Daegwallyeong station. However, in this study, the analysis of this data indicated no significant trend for this station as the decrease in the annual precipitation since 1995 was offset by the current increasing trend. A comparison between the current data and that of the Future 3 scenario (2071 to 2100), the latter of the climate change scenarios, indicated that Seogwipo station showed the highest rate of increase (28.7%). The lowest rate of decrease was present at Daegu station (10.1%). In addition, Busan and Yangpyeong stations showed increasing trends of 8.6% and 3.8%, respectively. In addition, Uljin and Inje stations showed decreasing trends of 1.3% and 2.3%, respectively. These results suggest that variations in the trends of annual precipitation of the target stations. The results of the trend analysis are summarized in Table 7, while Figure 17 provides the graphs of six stations and depicts changes in current and future annual precipitation. Figure 18 shows the results of the trend analysis.

Table 7. Spearman rank correlation analysis of annual precipitation using climate change scenarios.

No. Station 𝒕𝒕 Decision No. Station 𝒕𝒕 Decision 100 Daegwanryeong 1.91 NT 159 Tongyeong 2.91 T 115 Ulleung −0.98 NT 162 Mokpo 3.04 T 130 Uljin 0.59 NT 165 Yeosu 3.36 T 131 Cheongju 0.70 NT 168 Wando 3.17 T 133 Daejeon 1.36 NT 170 Seogwipo 4.14 T 135 Pohang 1.94 NT 188 Ganghwa 2.32 T 138 Gunsan 2.49 T 189 Yangpyeong 2.16 T 140 Daegu 1.18 NT 201 Inje 0.00 NT 143 Jeonju 1.69 NT 202 Hongcheon 0.59 NT 146 Ulsan 2.37 T 211 Imshil 1.93 NT 152 Gwangju 2.51 T 212 Jeongeup 2.58 T Atmosphere 2020, 11156, 1027 Busan 2.33 T 22 of 27

𝑡.% = −1.98, 𝑡.% = 1.98; T: Trend; NT: No Trend.

Atmosphere 2020, 11, x FOR PEER REVIEW 22 of 27 (a) Uljin

(b) Seogwipo

(d) Inje

(e) Yangpyeong

Figure 17. Cont.

Atmosphere 2020, 11, 1027 23 of 27

AtmosphereAtmosphere 2020 2020, 11, 11, x, FORx FOR PEER PEER REVIEW REVIEW 2323 of of27 27

(f)( fJeongeup) Jeongeup

FigureFigureFigure 17. 17. 17. TrendTrend Trend analysis analysis analysis of of of observed observed observed and and and future future future annual annual annual precipitation. precipitation. precipitation. ( (aa ),(),a (), (bb (),),b (), (cc ),(),c (), (dd (),),d (), (ee ),(),e and), and and ( (ff )() feach each) each representrepresentrepresent graphs graphs graphs of of of Uljin, Uljin, Uljin, Seogwipo, Seogwipo, Seogwipo, Busan, Busan, Busan, Inje, Inje, Inje, Yangpyong, Yangpyong, and and Jeongeup. Jeongeup.

FigureFigure 18. 18. Trend Trend analysis analysis using using climate climate change change scenarios. scenarios. Figure 18. Trend analysis using climate change scenarios.

4.4.4.4.4.4. Hydrological Hydrological Hydrological Stability Stability Stability Analysis Analysis Analysis Using Using Using the the the Climate Climate Climate Change Change Change Scenarios Scenarios Scenarios ForForFor the the annualannual annual precipitationprecipitation precipitation stability stability stability analysis analysis analysis using using using climate climate climate change change change scenarios scenarios scenarios (2011–2099), (2011–2099), (2011–2099), we used we we usedsimpleused simple simple T-test T-test and T-test F-test and and F-test analyses. F-test analyses. analyses. With the With With results, the the re we results,sults, used we awe two-tailedused used a atwo-tailed two-tailed test with test signiﬁcancetest with with significance significance at the 5% atlevelat the the for5% 5% eachlevel level data. for for each Tableeach data. 8data. shows Table Table the 8 8 Simpleshows shows the T-test the Simp Simp andle le F-testT-test T-test results.and and F-test F-test The results. Uljin,results. Busan,The The Uljin, Uljin, Seogwipo, Busan, Busan, Seogwipo,Yangpyeong,Seogwipo, Yangpyeong, Yangpyeong, Inje, and JeongeupInje, Inje, and and Jeongeup stations Jeongeup showed stations stations instability.showed showed instability. instability. [13] indicated [13] [13] indicated thatindicated the datathat that the showed the data data showedinstabilityshowed instability instability for the Daegwallyeong, for for the the Daegwallyeong, Daegwallyeong, Ganghwa, Ganghwa, Ganghwa, and Imsil and stations.and Imsil Imsil Instations. stations. this study, In In this thethis study, results study, the suggestedthe results results suggestedthatsuggested the data that that were the the unstabledata data were were for unstable allunstable 21 stations for for all all (Table21 21 stations stations7), which (Table (Table was 7), also7), which which supported was was also also by supported the supported simple by T-test by the the simpleandsimple F-test T-test T-test results. and and F-test Figure F-test results. results.19 shows Figure Figure the 19 results 19 shows shows of the the the results stability results of test.of the the stability stability test. test.

Atmosphere 2020, 11, x FOR PEER REVIEW 24 of 27

Table 8. Simple T-test and F-test analysis of annual precipitation using climate change scenarios.

T-Test F-Test T-Test F-Test No. Station Decision No. Station Decision Atmosphere 2020, 11, 1027 𝐭𝐭 𝐅𝐭 𝐭𝐭 𝐅𝐭 24 of 27

100 Daegwanryeong −11.44 1.005 US 159 Tongyeong −3.41 1.45 US Table 8. Simple T-test and F-test analysis of annual precipitation using climate change scenarios. 115 Ulleung −4.33 0.71 US 162 Mokpo −5.75 1.11 US 130 Uljin T-Test−4.47 F-Test0.79 US 165 Yeosu T-Test−7.86 F-Test1.08 US No.131 StationCheongju −2.03 1.40 Decision US No. 168 Station Wando −10.19 0.93 Decision US tt Ft tt Ft 133 Daejeon −4.41 1.03 US 170 Seogwipo −4.83 1.19 US 100 Daegwanryeong 11.44 1.005 US 159 Tongyeong 3.41 1.45 US − − 115 Ulleung 4.33 0.71 US 162 Mokpo 5.75 1.11 US − − 130135 UljinPohang 4.47−6.46 0.791.38 US US 165 201 YeosuGanghwa 7.86−4.4 1.081.27 US US − − 131 Cheongju 2.03 1.40 US 168 Wando 10.19 0.93 US − − 133138 DaejeonGunsan 4.41−10.19 1.030.93 US US 170 188 SeogwipoYangpyeong 4.83−6.03 1.191.31 US US − − 135 Pohang 6.46 1.38 US 201 Ganghwa 4.4 1.27 US 140 Daegu − −4.38 1.23 US 189 Inje − −10.20 0.70 US 138 Gunsan 10.19 0.93 US 188 Yangpyeong 6.03 1.31 US − − 140143 DaeguJeonju 4.38 2.39 1.230.97 USUS 189202 Hongcheon Inje 10.20−9.65 0.701.24 US US − − 143 Jeonju 2.39 0.97 US 202 Hongcheon 9.65 1.24 US − 146 UlsanUlsan 6.66−6.66 1.091.09 US US 211 211 Imshil Imshil 9.82−9.82 0.860.86 US US − − 152 Gwangju 9.63 0.67 US 212 Jeongeup 9.49 1.10 US 152 Gwangju − −9.63 0.67 US 212 Jeongeup − −9.49 1.10 US 156 Busan 4.28 0.90 US 156 Busan − −4.28 0.90 US t = 1.67, F = 1.53; S: Stable; US: Unstable. 2.5%, 97.5% ± 2.5%, 97.5% ± 𝑡.%,.% = ±1.67, 𝐹.%,.% = ±1.53; S: Stable; US: Unstable.

FigureFigure 19. 19.Stability Stability test test results results using using the the climate climate change change scenarios. scenarios.

4.5.4.5. Estimation Estimation of of Hydrological Hydrological Stationarity Stationarity Using Using Climate Climate Change Change Scenarios Scenarios WeWe analyzed analyzed persistence, persistence, trends, trends, and stability and stabilit to determiney to determine the stationarity the ofstationarity annual precipitation of annual dataprecipitation based on climatedata based change on climate scenarios. change The scenarios. results are The summarized results are insummarized Table9 for in the Table 23 stations 9 for the that23 showedstations nonstationarythat showed nonstationary with current data,with includingcurrent data, the including Daegwallyeong, the Daegwallyeong, Uleungdo, Yangpyeon, Uleungdo, andYangpyeon, Jecheon stations. and Jecheon However, stations. the Ho analysiswever, of the future analysis data of found future that data nonstationary found that nonstationary was present was in 23 stations, including those of the current study. Figure 20 shows the results of the stationarity analysis.

Atmosphere 2020, 11, x FOR PEER REVIEW 25 of 27

present in 23 stations, including those of the current study. Figure 20 shows the results of the

Atmospherestationarity2020 analysis., 11, 1027 25 of 27

Table 9. Estimation of the stationarity of annual precipitation data using climate change scenarios. Table 9. Estimation of the stationarity of annual precipitation data using climate change scenarios. No. Station 𝐓𝟏 𝐓𝟐 𝐓𝟑 Stationary No. Station 𝐓𝟏 𝐓𝟐 𝐓𝟑 Stationary 100 Daegwanryeong NP US NT X 159 Tongyeong NP US T X No. Station T1 T2 T3 Stationary No. Station T1 T2 T3 Stationary 115 Ulleung NP US NT X 162 Mokpo NP US T X 100 130Daegwanryeong Uljin NP NP USUS NTNT X X 165159 Tongyeong Yeosu NPNP US US T TX X 115131 Ulleung Cheongju NP NP USUS NTNT X X 168162 Wando Mokpo NP NP US US T TX X 130133 Uljin Daejeon NP NP USUS NTNT X X 170165 Seogwipo Yeosu NP NP US US T TX X 131135 Cheongju Pohang NP NP USUS NTNT X X 201168 Ganghwa Wando NP NP US US T TX X 133138 Daejeon Gunsan NP NP USUS NT T X X 188170 YangpyeongSeogwipo NPNP US US T TX X 135140 Pohang Daegu NP NP USUS NTNT X X 189201 Ganghwa Inje NPNP US USNT TX X 138143 Gunsan Jeonju NP NP USUS TNT X X 202188 HongcheonYangpyeong NPNP US USNT TX X 140146 Daegu Ulsan NP NP USUS NT T X X 211189 Imshil Inje NP NP US USNT NT X X 143152 JeonjuGwangju NPNP USUS NTT XX 212 202 JeongeupHongcheon NPNP US UST NT X X 146156 Ulsan Busan NP NP USUS TT X X 211 Imshil NP US NT X 152 Gwangju NP US T X 212 Jeongeup NP US TX 156T: Trend; Busan S: Stable; P: NPPersistence;US O:TX Stationary; NT: No Trend; US: Unstable; NP: No persistence; X: T:Nonstationary. Trend; S: Stable; P: Persistence; O: Stationary; NT: No Trend; US: Unstable; NP: No persistence; X: Nonstationary.

FigureFigure 20.20. ResultsResults ofof thethe stationaritystationarity analysisanalysis usingusing climateclimate changechange scenarios.scenarios.

5.5. Conclusions TheThe studystudy collectedcollected annualannual precipitationprecipitation datadata fromfrom 3737 weatherweather stationsstations acrossacross KoreaKorea thatthat hadhad collectedcollected datadata forfor 4545 yearsyears oror longerlonger withwith thethe aimaim toto analyzeanalyze thethe stationaritystationarity ofof thethe data.data. WeWe alsoalso conductedconducted persistence,persistence, trend,trend, andand stabilitystability analysesanalyses andand thenthen screenedscreened outout stationsstations thatthat showedshowed nonstationaritynonstationarity ofof annualannual precipitationprecipitation data.data. For the remainingremaining stations,stations, wewe usedused climateclimate changechange scenariosscenarios andand analyzedanalyzed thethe stationaritystationarity ofof futurefuture annualannual precipitationprecipitation data.data. The resultsresults cancan bebe summarizedsummarized asas follows.follows. (1)(1) AfterAfter analyzing analyzing the the persistence persistence of dataof data from from 37 stations, 37 stations, annual annual precipitation precipitation at Haenam at Haenam station wasstation found was to found have to increased have increased since 1978. since In 1978. addition, In addition, 19 stations, 19 stations, including including Gangneung, Gangneung, Ulsan, Jeonju,Ulsan, Ganghwa,Jeonju, Ganghwa, and Seongsan, and Seongsan, showed increasingshowed increasing precipitation precipitation trends from trends 1982 tofrom 1989, 1982 whereas to 1989, 17 whereas stations, including Chupungnyeong, Daejeon, Gunsan, Chuncheon, Uleungdo, and Gwangju, showed increasing

Atmosphere 2020, 11, 1027 26 of 27 precipitation trends from 1994 to 1997. Stationarity was observed in four stations: Daegwallyeo, Uleungdo, Seogwipo, and Ganghwa. (2) The trend analysis for the annual precipitation of the target stations suggested that no trends were present for most regions. However, the Uleungdo, Seongsan, Seogwipo, and Inje stations showed some evidence of trends. Among these, Seongsan station showed the distinct trend of increasing annual precipitation. (3) The stability analysis of 23 stations, including that of the Uljin, Cheongju, Daejeon, and Chupungnyeong stations, showed that the annual precipitation data was unstable. In particular, the Seogwipo station registered the highest annual precipitation (more than 2000 mm) from 2009 to 2012 and 2014 to 2016. Therefore, it can be concluded that the influence of such periods caused data instability. (4) Based on the results of the persistence, trend, and stability analyses, stationarity was detected. In particular, 14 stations showed stationarity, whereas 25 stations showed nonstationarity. The stations with nonstationarity accounted for 67% of all stations. Most of the stations in the central region, except the Inje, Hongcheon, and Yangpyeong stations, and most of the stations in the southern region, except the Jinju and Jeju stations, showed stationarity. The principle reason for the nonstationarity of these stations in the southern region was the influence of typhoons that led to persistence and instability in the annual precipitation data. (5) The stationarity analysis for future annual precipitation using climate change scenarios showed that out of 23 stations, 12 stations showed trends, 23 stations had unstable data, and no station showed persistence as climate change caused changes in annual precipitation characteristics. Therefore, future hydrological analysis should consider the influence of climate change on the characteristics of hydrological time series. In addition, the results obtained were different from those of a previous study [13], wherein nonstationarity was observed for multiple stations, including Daegwallyeong, Uleungdo, Yangpyeong, and Jecheon. However, in this study, 25 stations, including Uleungdo, showed nonstationarity. Moreover, the results of the stationarity analysis of future annual precipitation using climate change scenarios suggests that climate change will influence the characteristics of hydrological time series data. For example, the stations that showed a trend in the present also showed persistence without the trend. This underlines the necessity of reconsidering present hydrological analyses with the variable characteristics of annual precipitation. Therefore, in hydrological analyses that use annual precipitation in South Korea, nonstationarity needs to be considered beyond the current assumption of stationarity. In addition, current analyses should be reviewed by considering the current characteristics of annual precipitation and the influence of climate change on future annual precipitation. Moreover, it is also necessary to statistically identify climate characteristics in diverse conditions by analyzing the correlations among a range of hydro-meteorological factors with regard to annual precipitation in the future as time series characteristics with respect to trends, stability, and persistence are likely to be affected by climate change. Furthermore, the results of this study are expected to contribute to long-term plans regarding the preventive management measures that must be undertaken with regard to natural phenomena, including those for flooding, as well as additional studies that are conducted on time series that consider the influence of climate change on diverse hydro-meteorological factors.

Author Contributions: Investigation, B.-H.L.; Writing—original draft, G.-K.L.; Writing—review & editing, B.-S.K. and S.-J.J. All authors have read and agreed to the published version of the manuscript. Funding: This work was funded by the Korea Meteorological Administration Research and Development Program under Grant KMI [2018-03010]. This study was supported by a 2017 research grant from Kangwon National University [number 620170154]. Acknowledgments: The authors express their sincere thanks to the Korea Meteorological Administration and Kangwon National University. Atmosphere 2020, 11, 1027 27 of 27

Conﬂicts of Interest: The authors declare no conﬂict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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