• Journal of the Korean Geographical Society
• /
• v.51 no.1
• /
• pp.23-40
• /
• 2016

In this study, long-term changes in means and extreme events of precipitation during summer rainy period called Changma (late June~early September) are examined based on rainfall data observed by Chukwooki during Joseon Dynasty (1777~1907) and by modern rain-gauge onward (1908~2015) in Seoul, Korea. Also, characterizations of the relevant changes in synoptic climate fields in East Asia are made by the examination of the NCEP-NCAR reanalysis I data. Analyses of 239-year time series of precipitation data demonstrate that the total precipitation as well as their inter-annual variability during the entire Changma period (late June~early September) has increased in the late 20th century and onward. Notably, since the early 1990s the means and extreme events during the summer Changma period (late June~mid-July) and Changma break period (late July~early August) has significantly increased, resulting in less clear demarcations of sub-Changma periods. In this regard, comparisons of synoptic climate fields before and after the early 1990s reveal that in recent decades the subtropical high pressure has expanded in the warmer Pacific as the advection of high-latitude air masses toward East Asia was enhanced due to more active northerly wind vector around the high pressure departure core over Mongolia. Consequently, it is suggested that the enhancement of rising motions due to more active confluence of the two different air masses along the northwestern borders of the Pacific might lead to the increases of the means and extreme events of Changma precipitation in Seoul in recent decades.

• Journal of the Korean Data and Information Science Society
• /
• v.27 no.5
• /
• pp.1155-1168
• /
• 2016

This paper considers a probability density estimation problem of climate values. In particular, we focus on estimating probability densities of summer extreme temperature over South Korea. It is known that the probability density of climate values at one location is similar to those at near by locations and one doesn't follow well known parametric distributions. To accommodate these properties, we use a mixture of conditional autoregressive species sampling model, which is a nonparametric Bayesian model with a spatial dependency. We apply the model to a dataset consisting of summer maximum temperature and minimum temperature over South Korea. The dataset is obtained from University of East Anglia.

• Journal of Climate Change Research
• /
• v.9 no.2
• /
• pp.157-169
• /
• 2018

This study explores the long?term trends of extreme temperatures of 270 observation stations in East Asia (China, Japan, and Korea) for 1961?2013. The 5th percentile of daily minimum temperatures (TN05%) and 95th percentile of daily maximum temperatures (TX95%), derived from the quantile regression, are particularly examined in term of their linear and nonlinear trends. The warming trends of TN05% are typically stronger than those of TX95% with more significant trends in winter than in summer for most stations. In both seasons, warming trends of TN05% tend to amplify with latitudes. The nonlinear trends, quantified by the $2^{nd}$?order polynomial fitting, exhibit different structures with seasons. While summer TN05% and TX95% were accelerated in time, winter TN05% underwent weakening of warming since the 2000s. These results suggest that extreme temperature trends in East Asia are not homogeneous in time and space.

• Korean Journal of Agricultural Science
• /
• v.48 no.2
• /
• pp.311-331
• /
• 2021

Climate change with extreme hydrological events has become a significant concern for agricultural water systems. Climate change affects not only irrigation availability but also agricultural water requirement. In response, adaptation strategies with soft and hard options have been considered to mitigate the impacts from climate change. However, their implementation has become progressively challenging and complex due to the interconnected impacts of climate change with socio-economic change in agricultural circumstances, and this can generate more uncertainty and complexity in the adaptive management of the agricultural water systems. This study was carried out for the agricultural water supply system in Seongju dam watershed in Seonju-gun, Gyeongbuk in South Korea. The first step is to identify system disturbances. Climate variation and socio-economic components with historical and forecast data were investigated Then, as the second step, problematic trends of the critical performance were identified for the historical and future climate scenarios. As the third step, a system structure was built with a dynamic hypothesis (causal loop diagram) to understand Seongju water system features and interactions with multiple feedbacks across system components in water, agriculture, and socio-economic sectors related to the case study water system. Then, as the fourth step, a mathematical SD (system dynamics) model was developed based on the dynamic hypothesis, including sub-models related to dam reservoir, irrigation channel, irrigation demand, farming income, and labor force, and the fidelity of the SD model to the Seongju water system was checked.

• Nuclear Engineering and Technology
• /
• v.47 no.1
• /
• pp.59-65
• /
• 2015

Background: After the Fukushima accident, the importance of hazard analysis for extreme external events was raised. Methods: To analyze typhoon-induced hazards, which are one of the significant disasters of East Asian countries, a statistical analysis using the extreme value theory, which is a method for estimating the annual exceedance frequency of a rare event, was conducted for an estimation of the occurrence intervals or hazard levels. For the four meteorological variables, maximum wind speed, instantaneous wind speed, hourly precipitation, and daily precipitation, the parameters of the predictive extreme value theory models were estimated. Results: The 100-year return levels for each variable were predicted using the developed models and compared with previously reported values. It was also found that there exist significant long-term climate changes of wind speed and precipitation. Conclusion: A fragility analysis should be conducted to ensure the safety levels of a nuclear power plant for high levels of wind speed and precipitation, which exceed the results of a previous analysis.

• Journal of Wetlands Research
• /
• v.15 no.1
• /
• pp.105-114
• /
• 2013

Recently, Due to Climate change, extreme rainfall occurs frequently. In many preceding studies, Because of extreme hydrological events changes, it is expected that peak flood Magnitude and frequency of drainage infrastructures changes. However, at present, probability rainfall in the drainage facilities design is assumed to Stationary which are not effected from climate change and long-term fluctuation. In the future, flood control safety standard should be reconsidered about the valid viewpoint. In this paper, in order to assess impact of climate change on drainage system, Future climate change information has been extracted from RCP 8.5 Climate Change Scenario for IPCC AR5, then estimated the design rainfall for various durations at return periods. Finally, the design flood estimated through the HEC-HMS Model which is being widely used in the practices, estimated the effect of climate change on the Design Flood of Mihochen basin. The results suggested that the Design Flood increase by climate change. Due to this, the Flood risk of Mihochen basin can be identified to increase comparing the present status.

• Journal of Korea Water Resources Association
• /
• v.51 no.10
• /
• pp.905-916
• /
• 2018

The purpose of this study is to evaluate the climate change impact on watershed hydrology and flow duration in Geum River basin ($9,645.5km^2$) especially by extreme scenarios. The rainfall related extreme index, STARDEX (STAtistical and Regional dynamical Downscaling of EXtremes) was adopted to select the future extreme scenario from the 10 GCMs with RCP 8.5 scenarios by four projection periods (Historical: 1975~2005, 2020s: 2011~2040, 2050s: 2041~2070, 2080s: 2071~2100). As a result, the 5 scenarios of wet (CESM1-BGC and HadGEM2-ES), normal (MPI-ESM-MR), and dry (INM-CM4 and FGOALS-s2) were selected and applied to SWAT (Soil and Water Assessment Tool) hydrological model. The wet scenarios showed big differences comparing with the normal scenario in 2080s period. The 2080s evapotranspiration (ET) of wet scenarios varied from -3.2 to +3.1 mm, the 2080s total runoff (TR) varied from +5.5 to +128.4 mm. The dry scenarios showed big differences comparing with the normal scenario in 2020s period. The 2020s ET for dry scenarios varied from -16.8 to -13.3 mm and the TR varied from -264.0 to -132.3 mm respectively. For the flow duration change, the CFR (coefficient of flow regime, Q10/Q355) was altered from +4.2 to +10.5 for 2080s wet scenarios and from +1.7 to +2.6 for 2020s dry scenarios. As a result of the flow duration analysis according to the change of the hydrological factors of the Geum River basin applying the extreme climate change scenario, INM-CM4 showed suitable scenario to show extreme dry condition and FGOALS-s2 showed suitable scenario for the analysis of the drought condition with large flow duration variability. HadGEM2-ES was evaluated as a scenario that can be used for maximum flow analysis because the flow duration variability was small and CESM1-BGC was evaluated as a scenario that can be applied to the case of extreme flood analysis with large flow duration variability.

• Wind and Structures
• /
• v.1 no.2
• /
• pp.145-160
• /
• 1998

In recent years, the effects of a possible climate change have been discussed in regard to wind loading on buildings and structures. Simple scenarios based on the assumption of global warming suggest an increase of storm intensities and storm frequencies and a possible re-distribution of storm tracks. Among recent publications, some papers seem to verify these scenarios while others deny the influence of climatic change. In an introductory step, the paper tries to re-examine these statements. Based on meteorological observations of a weather station in Germany, the existence of long-term trends and their statistical significance is investigated. The analysis itself is based on a refined model for the wind climate introducing a number of new basic variables. Thus, the numerical values of the design wind loads used in modern codes become more justified from the probabilistic point of view.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2018.05a
• /
• pp.157-157
• /
• 2018

A reliable and accurate downscaling model which can provide climate change information, obtained from global climate models (GCMs), at finer resolution has been always of great interest to researchers. In order to achieve this model, linear methods widely have been studied in the past decades. However, nonlinear methods also can be potentially beneficial to solve downscaling problem. Therefore, this study explored the applicability of some nonlinear machine learning techniques such as neural network (NN), extreme learning machine (ELM), and ELM autoencoder (ELM-AE) as well as a linear method, least absolute shrinkage and selection operator (LASSO), to build a reliable temperature downscaling model. ELM is an efficient learning algorithm for generalized single layer feed-forward neural networks (SLFNs). Its excellent training speed and good generalization capability make ELM an efficient solution for SLFNs compared to traditional time-consuming learning methods like back propagation (BP). However, due to its shallow architecture, ELM may not capture all of nonlinear relationships between input features. To address this issue, ELM-AE was tested in the current study for temperature downscaling.

• Journal of Climate Change Research
• /
• v.5 no.4
• /
• pp.323-329
• /
• 2014

Since the terrain of Korea is complex, micro- as well as meso-climate variability is extreme by locations in Korea. In particular, air temperature of agricultural fields is influenced by topographic features of the surroundings making accurate interpolation of regional meteorological data from point-measured data. This study was carried out to compare spatial interpolation methods to estimate air temperature in agricultural fields surrounded by rugged terrains in South Korea. Four spatial interpolation methods including Inverse Distance Weighting (IDW), Spline, Ordinary Kriging (with the temperature lapse rate) and Cokriging were tested to estimate monthly air temperature of unobserved stations. Monthly measured data sets (minimum and maximum air temperature) from 588 automatic weather system(AWS) locations in South Korea were used to generate the gridded air temperature surface. As the result, temperature lapse rate improved accuracy of all of interpolation methods, especially, spline showed the lowest RMSE of spatial interpolation methods in both maximum and minimum air temperature estimation.