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Many studies have used data from climate models, such as global climate models (GCMs) and regional climate models (RCMs), to predict the future impact of climate change. However, a bias exists between simulated climate model data and observed data. Therefore, various bias correction methods have been developed to reduce this imbalance. The quantile mapping (QM) method is one of the most widely used approaches worldwide. However, the QM method does not account for the relative change in the raw data since bias correction is only performed on observed data. To solve this problem, the detrended quantile mapping (DQM) and quantile delta mapping (QDM) methods were developed to consider the relative changes in the raw data. Generally, the QM method is performed assuming that the statistical characteristics are the same when using cumulative density functions (CDFs) obtained from climate models and observations. However, the general QM (or QDM) method is performed using daily data, and thus the outcome may be slightly different from the quantiles of observed data in the statistical analysis of extreme data. This difference can lead to distortions when estimating relative changes in rainfall quantiles. Herein, the regional quantile delta mapping (RQDM) method for bias correction, which can solve the problems of the QDM method, was proposed. Additionally, evaluations were performed for the RQDM and existing QDM methods using several statistical approaches for a historical period (S0; 1979–2005). The results revealed that the RQDM method was similarly corrected to the observed results than the QDM method and produced more appropriate outcomes based on statistical evaluations. Moreover, the RQDM method showed a well-preserved relative changes in rainfall quantiles of the raw data, unlike the QDM method. Thus, it was found that the proposed RQDM method showed more robust results than the conventional QDM method.
Sunghun Kim; Kyungwon Joo; Hanbeen Kim; Ju-Young Shin; Jun-Haeng Heo. Regional quantile delta mapping method using regional frequency analysis for regional climate model precipitation. Journal of Hydrology 2020, 596, 125685 .
AMA StyleSunghun Kim, Kyungwon Joo, Hanbeen Kim, Ju-Young Shin, Jun-Haeng Heo. Regional quantile delta mapping method using regional frequency analysis for regional climate model precipitation. Journal of Hydrology. 2020; 596 ():125685.
Chicago/Turabian StyleSunghun Kim; Kyungwon Joo; Hanbeen Kim; Ju-Young Shin; Jun-Haeng Heo. 2020. "Regional quantile delta mapping method using regional frequency analysis for regional climate model precipitation." Journal of Hydrology 596, no. : 125685.
Anthropogenic climate change has led to nonstationarity in hydrological data and their statistical characteristics. To consider nonstationarity in regional frequency analysis, several nonstationary index flood (NS-IF) methods comprising a time-dependent site-specific scaling factor or nonstationary regional growth curves have been suggested. However, these methods have limitations related to underestimation from using sample statistics as a site-specific scaling factor or considering nonstationarity only in regional parameters. To overcome these drawbacks, this study developed a nonstationary population index flood (NS-PIF) method that considers nonstationarity in the statistical characteristics at each site in a region based on nonstationary generalized extreme value distributions. Monte Carlo simulations were conducted for synthetic regions under various nonstationary conditions to compare the performance of the NS-PIF method with those of existing NS-IF methods. Then the applicability of the NS-PIF method to real-world data was assessed via Monte Carlo simulations of regions with annual maximum rainfall data in South Korea. The results indicated that the NS-PIF method can solve the underestimation problem inherent in existing NS-IF methods. Moreover, the NS-PIF method yielded the best performance and provided more reliable and reasonable quantile estimates considering site-specific trends. In addition, the heterogeneity measure based on L-skewness and L-kurtosis was identified as a suitable test of homogeneity for application of the proposed method.
Hanbeen Kim; Ju-Young Shin; Taereem Kim; Sunghun Kim; Jun-Haeng Heo. Regional frequency analysis of extreme precipitation based on a nonstationary population index flood method. Advances in Water Resources 2020, 146, 103757 .
AMA StyleHanbeen Kim, Ju-Young Shin, Taereem Kim, Sunghun Kim, Jun-Haeng Heo. Regional frequency analysis of extreme precipitation based on a nonstationary population index flood method. Advances in Water Resources. 2020; 146 ():103757.
Chicago/Turabian StyleHanbeen Kim; Ju-Young Shin; Taereem Kim; Sunghun Kim; Jun-Haeng Heo. 2020. "Regional frequency analysis of extreme precipitation based on a nonstationary population index flood method." Advances in Water Resources 146, no. : 103757.
Artificial neural networks (ANNs) have been extensively used to forecast monthly precipitation for water resources management over the past few decades. Efforts to produce more accurate and stable forecasts face ongoing challenges as the so‐called single ANN (S‐ANN) approach has several limitations, particularly regarding uncertainty. Many attempts have been made to deal with different types of uncertainties by applying ensemble approaches. Here, we propose a new ANN ensemble model (ANN‐ENS) dealing with uncertainty in model structure and input variable selection to provide a more accurate and stable forecasting performance. This model is structured by generating various input layers, considering all the candidate input variables (i.e., large‐scale climate indices and lagged precipitation). We developed a modified backward elimination method to select the preliminary input variables from all the candidate input variables. Then, we tested and validated the proposed ANN‐ENS using observed monthly precipitation from 10 meteorological stations in the Han River basin, South Korea. Our results demonstrated that the ANN‐ENS enhanced the forecasting performance in terms of both accuracy and stability. Although a significant uncertainty was introduced by using all the candidate input variables, the forecasting result outperformed S‐ANNs for all employed stations. Additionally, the ANN‐ENS provided a more stable forecasting performance in comparison with S‐ANNs, which are highly sensitive. Moreover, the generated ensemble members were slightly biased at some stations, but were generally reliable.
Taereem Kim; Ju‐Young Shin; Hanbeen Kim; Jun‐Haeng Heo. Ensemble‐Based Neural Network Modeling for Hydrologic Forecasts: Addressing Uncertainty in the Model Structure and Input Variable Selection. Water Resources Research 2020, 56, 1 .
AMA StyleTaereem Kim, Ju‐Young Shin, Hanbeen Kim, Jun‐Haeng Heo. Ensemble‐Based Neural Network Modeling for Hydrologic Forecasts: Addressing Uncertainty in the Model Structure and Input Variable Selection. Water Resources Research. 2020; 56 (6):1.
Chicago/Turabian StyleTaereem Kim; Ju‐Young Shin; Hanbeen Kim; Jun‐Haeng Heo. 2020. "Ensemble‐Based Neural Network Modeling for Hydrologic Forecasts: Addressing Uncertainty in the Model Structure and Input Variable Selection." Water Resources Research 56, no. 6: 1.
For multivariate frequency analysis of hydrometeorological data, the copula model is commonly used to construct joint probability distribution due to its flexibility and simplicity. The Maximum Pseudo-Likelihood (MPL) method is one of the most widely used methods for fitting a copula model. The MPL method was derived from the Weibull plotting position formula assuming a uniform distribution. Because extreme hydrometeorological data are often positively skewed, capacity of the MPL method may not be fully utilized. This study proposes the modified MPL (MMPL) method to improve the MPL method by taking into consideration the skewness of the data. In the MMPL method, the Weibull plotting position formula in the original MPL method is replaced with the formulas which can consider the skewness of the data. The Monte-Carlo simulation has been performed under various conditions in order to assess the performance of the proposed method with the Gumbel copula model. The proposed MMPL method provides more precise parameter estimates than does the MPL method for positively skewed hydrometeorological data based on the simulation results. The MMPL method would be a better alternative for fitting the copula model to the skewed data sets. Additionally, applications of the MMPL methods were performed on the two weather stations (Seosan and Yeongwol) in South Korea.
Kyungwon Joo; Ju-Young Shin; Jun-Haeng Heo. Modified Maximum Pseudo Likelihood Method of Copula Parameter Estimation for Skewed Hydrometeorological Data. Water 2020, 12, 1182 .
AMA StyleKyungwon Joo, Ju-Young Shin, Jun-Haeng Heo. Modified Maximum Pseudo Likelihood Method of Copula Parameter Estimation for Skewed Hydrometeorological Data. Water. 2020; 12 (4):1182.
Chicago/Turabian StyleKyungwon Joo; Ju-Young Shin; Jun-Haeng Heo. 2020. "Modified Maximum Pseudo Likelihood Method of Copula Parameter Estimation for Skewed Hydrometeorological Data." Water 12, no. 4: 1182.
Regional frequency analysis (RFA) is used to improve the accuracy of quantiles at sites where the observed data is insufficient. Due to the development of technologies, complex computation of huge data set is possible with a prevalent personal computer. Therefore, machine learning methods have been widely applied in many disciplines, including hydrology. There are also many previous studies that apply the machine learning methods to RFA. The main purpose of this study is to apply the artificial neural network (ANN) model for RFA. For this purpose, performance of RFA based on the ANN model is measured. For the homogeneous region in Han River basin, rainfall gauging sites are divided into training and testing groups. The training group consists of sites where the record length of data is more than 30 years. The testing group contains sites where the record length of data is spanned from 10 to 30 years. Various hydro-meteorological variables are used as an input layer and parameters of generalized extreme value (GEV) distribution for annual maximum rainfall data are used as an output layer of the ANN model. Then, the root mean square error (RMSE) values between the predicted and observed quantiles are calculated. To evaluate the model performance, the RMSEs of quantile estimated by the ANN model are compared to those of the index flood model.
Joohyung Lee; Hanbeen Kim; Taereem Kim; Jun-Haeng Heo. Application of artificial neural network model for regional frequency analysis at Han River basin, South Korea. 2020, 1 .
AMA StyleJoohyung Lee, Hanbeen Kim, Taereem Kim, Jun-Haeng Heo. Application of artificial neural network model for regional frequency analysis at Han River basin, South Korea. . 2020; ():1.
Chicago/Turabian StyleJoohyung Lee; Hanbeen Kim; Taereem Kim; Jun-Haeng Heo. 2020. "Application of artificial neural network model for regional frequency analysis at Han River basin, South Korea." , no. : 1.
Climate change has emerged as one of the defining issues of the early 21st century. Recent research confirms that the imprint of human induced climate change can be recognized in current accident events. There is a high probability of observed trends, such as increases in heat waves and heavy extreme rainfall events, intensifying over the 21st century. Extreme weather and climate events are anticipated to generate significant risks to societies and ecosystem. This paper focuses on estimation rainfall quantile using sclaling model for short duration IDF curve in North Korea. It is very important to manage the flood control facilities because of increasing the frequency and magnitude of severe rain storms. For managing flood control facilities in possibly hazardous regions, data sets such as elevation, gradient, channel, land use and soil data should be filed up. Using this information, the disaster situations can be simulated to secure evacuation routes for various rainfall scenarios. The aim of this study is to investigate and determine extreme rainfall quantile estimates in North Korea Cities using index flood method with L-moments parameter estimation. Regional frequency analysis trades space for time by using annual maximum rainfall data from nearby or similar sites to determine estimates for any given site in a homogeneous region. Regional frequency analysis based on pooled data is recommended for estimation of rainfall quantiles at sites with record lengths less than 5T, where T is return period of interest. Many variables relevant to precipitation can be used for grouping a region in regional frequency analysis. For regionalization of Han River basin, the k-means method is applied for grouping regions using variables of meteorology and geomorphology. The results from the k-means method are compared for each region using various probability distributions. In the final step of the regionalization analysis, goodness-of-fit measure is used to evaluate the accuracy of a set of candidate distributions. And rainfall quantiles by index flood method are obtained based on the appropriate distribution(GEV and GLO). Therefore, it could be possible to estimate rainfall quantiles using scale invariance and frequency analysis for Wonsan, Jangjeon, and Pyeonggang rainfall stations in North Korea. And then, rainfall quantiles based on various scenarios are used as input data for disaster simulations.
Acknowledgements
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2019R1A2C2010854).
Younghun Jung; Kyungwon Joo; Joonhak Lee; Jun-Haeng Heo. Extreme events Analysis of Short-Duration Rainfall and Intensity-Duration-Frequency curve using Scaling Model in North Korea. 2020, 1 .
AMA StyleYounghun Jung, Kyungwon Joo, Joonhak Lee, Jun-Haeng Heo. Extreme events Analysis of Short-Duration Rainfall and Intensity-Duration-Frequency curve using Scaling Model in North Korea. . 2020; ():1.
Chicago/Turabian StyleYounghun Jung; Kyungwon Joo; Joonhak Lee; Jun-Haeng Heo. 2020. "Extreme events Analysis of Short-Duration Rainfall and Intensity-Duration-Frequency curve using Scaling Model in North Korea." , no. : 1.
The quantile mapping method is a bias correction method that leads to a good performance in terms of precipitation. Selecting an appropriate probability distribution model is essential for the successful implementation of quantile mapping. Probability distribution models with two shape parameters have proved that they are fit for precipitation modeling because of their flexibility. Hence, the application of a two-shape parameter distribution will improve the performance of the quantile mapping method in the bias correction of precipitation data. In this study, the applicability and appropriateness of two-shape parameter distribution models are examined in quantile mapping, for a bias correction of simulated precipitation data from a climate model under a climate change scenario. Additionally, the impacts of distribution selection on the frequency analysis of future extreme precipitation from climate are investigated. Generalized Lindley, Burr XII, and Kappa distributions are used, and their fits and appropriateness are compared to those of conventional distributions in a case study. Applications of two-shape parameter distributions do lead to better performances in reproducing the statistical characteristics of observed precipitation, compared to those of conventional distributions. The Kappa distribution is considered the best distribution model, as it can reproduce reliable spatial dependences of the quantile corresponding to a 100-year return period, unlike the gamma distribution.
Jun-Haeng Heo; Hyunjun Ahn; Ju-Young Shin; Thomas Rodding Kjeldsen; Changsam Jeong. Probability Distributions for a Quantile Mapping Technique for a Bias Correction of Precipitation Data: A Case Study to Precipitation Data Under Climate Change. Water 2019, 11, 1475 .
AMA StyleJun-Haeng Heo, Hyunjun Ahn, Ju-Young Shin, Thomas Rodding Kjeldsen, Changsam Jeong. Probability Distributions for a Quantile Mapping Technique for a Bias Correction of Precipitation Data: A Case Study to Precipitation Data Under Climate Change. Water. 2019; 11 (7):1475.
Chicago/Turabian StyleJun-Haeng Heo; Hyunjun Ahn; Ju-Young Shin; Thomas Rodding Kjeldsen; Changsam Jeong. 2019. "Probability Distributions for a Quantile Mapping Technique for a Bias Correction of Precipitation Data: A Case Study to Precipitation Data Under Climate Change." Water 11, no. 7: 1475.
Jungho Seo; Sunghun Kim; Hyunjun Ahn; Ju-Young Shin; Jun-Haeng Heo. Applicability of Burr XII Distribution for Rainfall Frequency Analysis using Monte Carlo Simulation. Journal of Korean Society of Hazard Mitigation 2019, 19, 11 -21.
AMA StyleJungho Seo, Sunghun Kim, Hyunjun Ahn, Ju-Young Shin, Jun-Haeng Heo. Applicability of Burr XII Distribution for Rainfall Frequency Analysis using Monte Carlo Simulation. Journal of Korean Society of Hazard Mitigation. 2019; 19 (3):11-21.
Chicago/Turabian StyleJungho Seo; Sunghun Kim; Hyunjun Ahn; Ju-Young Shin; Jun-Haeng Heo. 2019. "Applicability of Burr XII Distribution for Rainfall Frequency Analysis using Monte Carlo Simulation." Journal of Korean Society of Hazard Mitigation 19, no. 3: 11-21.
Sunghun Kim; Ju-Young Shin; Hyunjun Ahn; Jun-Haeng Heo. Selecting Climate Models to Determine Future Extreme Rainfall Quantiles. Journal of Korean Society of Hazard Mitigation 2019, 19, 55 -69.
AMA StyleSunghun Kim, Ju-Young Shin, Hyunjun Ahn, Jun-Haeng Heo. Selecting Climate Models to Determine Future Extreme Rainfall Quantiles. Journal of Korean Society of Hazard Mitigation. 2019; 19 (1):55-69.
Chicago/Turabian StyleSunghun Kim; Ju-Young Shin; Hyunjun Ahn; Jun-Haeng Heo. 2019. "Selecting Climate Models to Determine Future Extreme Rainfall Quantiles." Journal of Korean Society of Hazard Mitigation 19, no. 1: 55-69.
Climate variability is strongly influencing hydrological processes under complex weather conditions, and it should be considered to forecast reservoir inflow for efficient dam operation strategies. Large-scale climate indices can provide potential information about climate variability, as they usually have a direct or indirect correlation with hydrologic variables. This study aims to use large-scale climate indices in monthly reservoir inflow forecasting for considering climate variability. For this purpose, time series and artificial intelligence models, such as Seasonal AutoRegressive Integrated Moving Average (SARIMA), SARIMA with eXogenous variables (SARIMAX), Artificial Neural Network (ANN), Adaptive Neural-based Fuzzy Inference System (ANFIS), and Random Forest (RF) models were employed with two types of input variables, autoregressive variables (AR-) and a combination of autoregressive and exogenous variables (ARX-). Several statistical methods, including ensemble empirical mode decomposition (EEMD), were used to select the lagged climate indices. Finally, monthly reservoir inflow was forecasted by SARIMA, SARIMAX, AR-ANN, ARX-ANN, AR-ANFIS, ARX-ANFIS, AR-RF, and ARX-RF models. As a result, the use of climate indices in artificial intelligence models showed a potential to improve the model performance, and the ARX-ANN and AR-RF models generally showed the best performance among the employed models.
Taereem Kim; Ju-Young Shin; Hanbeen Kim; Sunghun Kim; Jun-Haeng Heo. The Use of Large-Scale Climate Indices in Monthly Reservoir Inflow Forecasting and Its Application on Time Series and Artificial Intelligence Models. Water 2019, 11, 374 .
AMA StyleTaereem Kim, Ju-Young Shin, Hanbeen Kim, Sunghun Kim, Jun-Haeng Heo. The Use of Large-Scale Climate Indices in Monthly Reservoir Inflow Forecasting and Its Application on Time Series and Artificial Intelligence Models. Water. 2019; 11 (2):374.
Chicago/Turabian StyleTaereem Kim; Ju-Young Shin; Hanbeen Kim; Sunghun Kim; Jun-Haeng Heo. 2019. "The Use of Large-Scale Climate Indices in Monthly Reservoir Inflow Forecasting and Its Application on Time Series and Artificial Intelligence Models." Water 11, no. 2: 374.
Rainfall erosivity is one of the key parameters in Universal Soil Loss Equation (USLE), which has been used to predict the amount of soil loss by water for 50 years. Investigating spatial and temporal trends in rainfall erosivity is important for soil and water conservation planning. Rainfall erosivity (the R factors) in many regions is expected to be altered due to changes in rainfall patterns related to rainfall intensity and the frequency and spatial distribution of storm events that may occur with climate change. In South Korea, some researchers have studied temporal variation in meteorological and hydrologic phenomena with climate change, particularly temperature and precipitation trends. The purpose of this study is to investigate spatial and temporal variations in rainfall erosivity and erosivity density and to improve our understanding of the evolution of rainfall erosivity in South Korea. First, we calculated rainfall erosivity at 46 stations for 1961–2015 using 5-min precipitation data. Second, we examined spatial and temporal variability in the rainfall erosivity; trends and change points of R factor time series and analyzed the relationships between rainfall erosivity and climate indices (such as the precipitation amount, number of effective events, and duration). Four trend tests such as the t-test, MK test, modified version of the MK test, and BBS-MK test, were used to detect trends in the annual R factor, total duration, number of effective events, total depth, mean maximum 30-min intensity, and total kinetic energy time series for all employed stations. The results provide insights into the evolution of rainfall erosivity and the effects of large-scale climatic circulation on rainfall erosivity.
Ju-Young Shin; Taereem Kim; Jun-Haeng Heo; Joon-Hak Lee. Spatial and temporal variations in rainfall erosivity and erosivity density in South Korea. CATENA 2019, 176, 125 -144.
AMA StyleJu-Young Shin, Taereem Kim, Jun-Haeng Heo, Joon-Hak Lee. Spatial and temporal variations in rainfall erosivity and erosivity density in South Korea. CATENA. 2019; 176 ():125-144.
Chicago/Turabian StyleJu-Young Shin; Taereem Kim; Jun-Haeng Heo; Joon-Hak Lee. 2019. "Spatial and temporal variations in rainfall erosivity and erosivity density in South Korea." CATENA 176, no. : 125-144.
Jinseok Jung; Hyunjun Ahn; Changsam Jung; Jun-Haeng Heo. A Study on the Estimation of the Extreme Quantile of Probability Distribution According to Skewness Coefficient and Sample Size. Journal of Korean Society of Hazard Mitigation 2018, 18, 485 -496.
AMA StyleJinseok Jung, Hyunjun Ahn, Changsam Jung, Jun-Haeng Heo. A Study on the Estimation of the Extreme Quantile of Probability Distribution According to Skewness Coefficient and Sample Size. Journal of Korean Society of Hazard Mitigation. 2018; 18 (7):485-496.
Chicago/Turabian StyleJinseok Jung; Hyunjun Ahn; Changsam Jung; Jun-Haeng Heo. 2018. "A Study on the Estimation of the Extreme Quantile of Probability Distribution According to Skewness Coefficient and Sample Size." Journal of Korean Society of Hazard Mitigation 18, no. 7: 485-496.
To improve our capacity to use available wind speed data, it is necessary to develop a new statistical temporal downscaling method that uses one or a few input variables of any temporal scale for mean wind speed data to obtain wind statistics at finer temporal resolution. In the present study, a novel statistical temporal downscaling method for wind speed statistics and probability distribution is proposed. The proposed method uses the temporal structure to downscale the wind speed statistics to a fine temporal scale without the use of additional variables. The Weibull distribution of the hourly and 10-min mean wind speed data is obtained by the downscaled wind speed statistics. The proposed method provides the downscaled Weibull distribution of fine temporal wind speed data using coarse temporal wind statistics. Particularly, the use of sub-daily mean wind speed data in the downscaling of the wind speed Weibull distribution leads to good estimation precision. The Weibull distribution downscaled by the proposed method successfully reproduces the wind power density based on the wind potential energy estimation.
Ju-Young Shin; Changsam Jeong; Jun-Haeng Heo. A Novel Statistical Method to Temporally Downscale Wind Speed Weibull Distribution Using Scaling Property. Energies 2018, 11, 633 .
AMA StyleJu-Young Shin, Changsam Jeong, Jun-Haeng Heo. A Novel Statistical Method to Temporally Downscale Wind Speed Weibull Distribution Using Scaling Property. Energies. 2018; 11 (3):633.
Chicago/Turabian StyleJu-Young Shin; Changsam Jeong; Jun-Haeng Heo. 2018. "A Novel Statistical Method to Temporally Downscale Wind Speed Weibull Distribution Using Scaling Property." Energies 11, no. 3: 633.
Climate indices characterize climate systems and may identify important indicators for long-term precipitation, which are driven by climate interactions in atmosphere-ocean circulation. In this study, we investigated the climate indices that are effective indicators of long-term precipitation in South Korea, and examined their relationships based on statistical methods. Monthly total precipitation was collected from a total of 60 meteorological stations, and they were decomposed by ensemble empirical mode decomposition (EEMD) to identify the inherent oscillating patterns or cycles. Cross-correlation analysis and stepwise variable selection were employed to select the significant climate indices at each station. The climate indices that affect the monthly precipitation in South Korea were identified based on the selection frequencies of the selected indices at all stations. The NINO12 indices with four- and ten-month lags and AMO index with no lag were identified as indicators of monthly precipitation in South Korea. Moreover, they indicate meaningful physical information (e.g. periodic oscillations and long-term trend) inherent in the monthly precipitation. The NINO12 index with four- and ten- month lags was a strong indicator representing periodic oscillations in monthly precipitation. In addition, the long-term trend of the monthly precipitation could be explained by the AMO index. A multiple linear regression model was constructed to investigate the influences of the identified climate indices on the prediction of monthly precipitation. Three identified climate indices successfully explained the monthly precipitation in the winter dry season. Compared to the monthly precipitation in coastal areas, the monthly precipitation in inland areas showed stronger correlation to the identified climate indices.
Taereem Kim; Ju-Young Shin; Sunghun Kim; Jun-Haeng Heo. Identification of relationships between climate indices and long-term precipitation in South Korea using ensemble empirical mode decomposition. Journal of Hydrology 2018, 557, 726 -739.
AMA StyleTaereem Kim, Ju-Young Shin, Sunghun Kim, Jun-Haeng Heo. Identification of relationships between climate indices and long-term precipitation in South Korea using ensemble empirical mode decomposition. Journal of Hydrology. 2018; 557 ():726-739.
Chicago/Turabian StyleTaereem Kim; Ju-Young Shin; Sunghun Kim; Jun-Haeng Heo. 2018. "Identification of relationships between climate indices and long-term precipitation in South Korea using ensemble empirical mode decomposition." Journal of Hydrology 557, no. : 726-739.
In this study, the performance of four statistical tests was evaluated to assess the following time-series types: stationary in variance and trend in mean (S_T), stationary in variance and no trend in mean (S_NT), nonstationary in variance and trend in mean (NS_T), and nonstationary in variance and no trend in mean (NS_NT). The four statistical tests included two stationarity tests, the Kwiatkowski–Phillips–Schmidt–Shin (KPSS) and Philips and Perron (PP) tests, and two trend tests, the Mann-Kendall (M-K) and regression tests. In each case, the sample size, standard deviation for noise, and several parameters were randomly generated to produce 1000 samples. The four tests were then conducted to determine if the data were stationary or non-stationary with trend or without trend. The results showed that there are several important patterns depending on the conditions of Monte Carlo experiments to investigate the performances of the four statistical tests with the four time-series types. These tests were also conducted to evaluate the time-series types of the observed and projected annual daily maximum precipitation series in eight cities of the United States. Results showed that cases of S_NT, which is the general assumption for the classical statistical frequency analysis, became less represented, while the two trend cases (NS_T and S_T) became more represented as time went on from HIST (1950–1999) to a representative concentration pathway (RCP) 4.5 or RCP 8.5 (2000–2099). NS_T cases in RCP 8.5 occurred more frequently than those in RCP 4.5. These results suggest that because of climate change, the assessment of time-series types should be considered when examining annual maximum precipitation and designing water-related infrastructure.
Myoung-Jin Um; Jun-Haeng Heo; Momcilo Markus; Donald J. Wuebbles. Performance Evaluation of four Statistical Tests for Trend and Non-stationarity and Assessment of Observed and Projected Annual Maximum Precipitation Series in Major United States Cities. Water Resources Management 2017, 32, 913 -933.
AMA StyleMyoung-Jin Um, Jun-Haeng Heo, Momcilo Markus, Donald J. Wuebbles. Performance Evaluation of four Statistical Tests for Trend and Non-stationarity and Assessment of Observed and Projected Annual Maximum Precipitation Series in Major United States Cities. Water Resources Management. 2017; 32 (3):913-933.
Chicago/Turabian StyleMyoung-Jin Um; Jun-Haeng Heo; Momcilo Markus; Donald J. Wuebbles. 2017. "Performance Evaluation of four Statistical Tests for Trend and Non-stationarity and Assessment of Observed and Projected Annual Maximum Precipitation Series in Major United States Cities." Water Resources Management 32, no. 3: 913-933.
The spatial and temporal structures of extreme rainfall trends in South Korea are investigated in the current study. The trends in the annual maximum rainfall series are detected and their spatial distribution is analyzed. The scaling exponent is employed as an index representing the temporal structure. The temporal structure of the annual maximum series is calculated and spatially analyzed. Subsequently, the block bootstrap based Mann-Kendall test is employed detect the trend in the scaling exponent series subsampled by the annual maximum rainfalls using a moving window. Significant trends are detected in a small number of stations and there are no significant trends in many stations for the annual maximum rainfall series. There is a large variability in the temporal structures of the extreme rainfall events. Additionally, the variations of the scaling exponent estimates for each month within a rainy season are larger than the variation of the scaling exponent estimates on an annual basis. Significant trends in the temporal structures are observed at many stations unlike the trend test results of annual maximum rainfall series. Decreasing trends are observed at many stations located in the coastal area, while increasing trends are observed in the inland area.
Younghun Jung; Ju-Young Shin; Hyunjun Ahn; Jun-Haeng Heo. The Spatial and Temporal Structure of Extreme Rainfall Trends in South Korea. Water 2017, 9, 809 .
AMA StyleYounghun Jung, Ju-Young Shin, Hyunjun Ahn, Jun-Haeng Heo. The Spatial and Temporal Structure of Extreme Rainfall Trends in South Korea. Water. 2017; 9 (10):809.
Chicago/Turabian StyleYounghun Jung; Ju-Young Shin; Hyunjun Ahn; Jun-Haeng Heo. 2017. "The Spatial and Temporal Structure of Extreme Rainfall Trends in South Korea." Water 9, no. 10: 809.
This study develops hourly water level forecasting models with lead-times of 1 to 3 h using an artificial neural network (ANN) for Anyangcheon stream, one of the major tributaries of the Han River, South Korea. To consider the backwater effect from this river, an enhanced tributary water level forecasting model is proposed by adding multiple water level data on the main river as input variables into the conventional ANN structure which often uses rainfall and upstream water level data. Four types of ANN models per each lead-time are built with increasing complexity of the input vector, and their performances are compared. The results indicate that the inclusion of multiple water level data on the main river to the network provides water level forecasts with greater accuracy at the Ogeumgyo gauging station of interest. The final best ANN models for water level forecasts with lead-times of 1 to 2 h show good performance with root mean square errors (RMSE) below 0.06 m and 0.12 m, respectively. However, the final best ANN model for forecasting 3 h ahead was unsatisfactory, showing underestimation at many rising parts of the hydrograph.
Ji Youn Sung; Jeongwoo Lee; Il-Moon Chung; Jun-Haeng Heo. Hourly Water Level Forecasting at Tributary Affected by Main River Condition. Water 2017, 9, 644 .
AMA StyleJi Youn Sung, Jeongwoo Lee, Il-Moon Chung, Jun-Haeng Heo. Hourly Water Level Forecasting at Tributary Affected by Main River Condition. Water. 2017; 9 (9):644.
Chicago/Turabian StyleJi Youn Sung; Jeongwoo Lee; Il-Moon Chung; Jun-Haeng Heo. 2017. "Hourly Water Level Forecasting at Tributary Affected by Main River Condition." Water 9, no. 9: 644.
The purpose of this study is to evaluate the impacts of the upstream Soyanggang and Chungju multi-purpose dams on the frequency of downstream floods in the Han River basin, South Korea. A continuous hydrological model, SWAT (Soil and Water Assessment Tool), was used to individually simulate regulated and unregulated daily streamflows entering the Paldang Dam, which is located at the outlet of the basin of interest. The simulation of the regulated flows by the Soyanggang and Chungju dams was calibrated with observed inflow data to the Paldang Dam. The estimated daily flood peaks were used for a frequency analysis, using the extreme Type-I distribution, for which the parameters were estimated via the L-moment method. This novel approach was applied to the study area to assess the effects of the dams on downstream floods. From the results, the two upstream dams were found to be able to reduce downstream floods by approximately 31% compared to naturally occurring floods without dam regulation. Furthermore, an approach to estimate the flood frequency based on the hourly extreme peak flow data, obtained by combining SWAT simulation and Sangal’s method, was proposed and then verified by comparison with the observation-based results. The increased percentage of floods estimated with hourly simulated data for the three scenarios of dam regulation ranged from 16.1% to 44.1%. The reduced percentages were a little higher than those for the daily-based flood frequency estimates. The developed approach allowed for better understanding of flood frequency, as influenced by dam regulation on a relatively large watershed scale.
Jeong Eun Lee; Jun-Haeng Heo; Jeongwoo Lee; Nam Won Kim. Assessment of Flood Frequency Alteration by Dam Construction via SWAT Simulation. Water 2017, 9, 264 .
AMA StyleJeong Eun Lee, Jun-Haeng Heo, Jeongwoo Lee, Nam Won Kim. Assessment of Flood Frequency Alteration by Dam Construction via SWAT Simulation. Water. 2017; 9 (4):264.
Chicago/Turabian StyleJeong Eun Lee; Jun-Haeng Heo; Jeongwoo Lee; Nam Won Kim. 2017. "Assessment of Flood Frequency Alteration by Dam Construction via SWAT Simulation." Water 9, no. 4: 264.
Hanbeen Kim; Sooyoung Kim; Hongjoon Shin; Jun-Haeng Heo. Appropriate model selection methods for nonstationary generalized extreme value models. Journal of Hydrology 2017, 547, 557 -574.
AMA StyleHanbeen Kim, Sooyoung Kim, Hongjoon Shin, Jun-Haeng Heo. Appropriate model selection methods for nonstationary generalized extreme value models. Journal of Hydrology. 2017; 547 ():557-574.
Chicago/Turabian StyleHanbeen Kim; Sooyoung Kim; Hongjoon Shin; Jun-Haeng Heo. 2017. "Appropriate model selection methods for nonstationary generalized extreme value models." Journal of Hydrology 547, no. : 557-574.
In this study, we use factor analysis and spatial analysis to study the spatio-temporal distribution of daily precipitation on Jeju Island, which includes marine and mountainous areas. The precipitation time series from 82 weather stations were used to fill in missing and ungauged data for some periods at 38 stations, and then the daily spatial distribution was analyzed from 1992 to 2013. Factor analysis and multiple regression showed that the statistical characteristics of the extended data fit well to those of the observed time series. The point precipitation characteristics, such as the mean and standard deviation, show small differences between observed and extended data, and the relationships to elevation also have similar behavior for seasonal and annual precipitation. However, the spatial precipitation characteristics for observed and extended data show significant differences with respect to elevation because the data for high-elevation areas are mainly interpolated for the period 1992 to 2013. For annual and five-year moving-average spatial precipitation, the amount of precipitation shows seasonal and spatial variability. Annual spatial precipitation in high-elevation areas usually shows high variation over time. These results suggest the need to consider completing missing and ungauged data series for assessment the spatio-temporal variation of precipitation.
Myoung-Jin Um; Jun-Haeng Heo; Nam-Won Kim. Spatio-temporal variations of precipitation considering the orographic effects on Jeju Island. Atmospheric Research 2016, 181, 236 -249.
AMA StyleMyoung-Jin Um, Jun-Haeng Heo, Nam-Won Kim. Spatio-temporal variations of precipitation considering the orographic effects on Jeju Island. Atmospheric Research. 2016; 181 ():236-249.
Chicago/Turabian StyleMyoung-Jin Um; Jun-Haeng Heo; Nam-Won Kim. 2016. "Spatio-temporal variations of precipitation considering the orographic effects on Jeju Island." Atmospheric Research 181, no. : 236-249.