• Journal of Climate Change Research
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• v.9 no.1
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• pp.31-45
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• 2018

This study implemented the prediction of drought properties (number of drought events, intensity, duration) using the user-oriented systematical procedures of downscaling climate change scenarios based the multiple global climate models (GCMs), AIMS (APCC Integrated Modeling Solution) program. The drought properties were defined and estimated with Effective Drought Index (EDI). The optimal 10 models among 29 GCMs were selected, by the estimation of the spatial and temporal reproducibility about the five climate change indices related with precipitation. In addition, Simple Quantile Mapping (SQM) as the downscaling technique is much better in describing the observed precipitation events than Spatial Disaggregation Quantile Delta Mapping (SDQDM). Even though the procedure was systematically applied, there are still limitations in describing the observed spatial precipitation properties well due to the offset of spatial variability in multi-model ensemble (MME) analysis. As a result, the farther into the future, the duration and the number of drought generation will be decreased, while the intensity of drought will be increased. Regionally, the drought at the central regions of the Korean Peninsula is expected to be mitigated, while that at the southern regions are expected to be severe.

• Journal of Wetlands Research
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• v.17 no.1
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• pp.80-90
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• 2015

Variability of precipitation pattern and intensity are increasing due to the urbanization and industrialization which induce increasing impervious area and the climate change. Therefore, more severe urban inundation and flood damage will be occurred by localized heavy precipitation event in the future. In this study, we analyze the future frequency based precipitation under climate change based on the regional frequency analysis. The observed precipitation data from 58 stations provided by Korea Meteorological Administration(KMA) are collected and the data period is more than 30 years. Then the frequency based precipitation for the observed data by regional frequency analysis are estimated. In order to remove the bias from the simulated precipitation by RCP scenarios, the quantile mapping method and outlier test are used. The regional frequency analysis using L-moment method(Hosking and Wallis, 1997) is performed and the future frequency based precipitation for 80, 100, and 200 years of return period are estimated. As a result, future frequency based precipitation in South Korea will be increased by 25 to 27 percent. Especially the result for Jeju Island shows that the increasing rate will be higher than other areas. Severe heavy precipitation could be more and more frequently occurred in the future due to the climate change and the runoff characteristics will be also changed by urbanization, industrialization, and climate change. Therefore, we need prepare flood prevention measures for our flood safety in the future.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.399-405
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• 2010

최근 들어 수자원 계획, 관리 및 평가 등의 정책수립시 의사결정 지원을 위하여 수문, 사회, 경제 및 환경 분야 등 다양한 수자원관련 정보를 지표를 이용하여 제시하려는 노력들이 이루어지고 있다. 따라서 본 연구에서는 국가 및 지역별 수자원여건을 평가하기 위하여 WMO의 국가별(도시별 평균) 월별 강수량 자료를 이용하여 년평균강수량에 따른 월별 강수량의 변동정도와 국가의 발전정도 등을 고려하여 국가별 수자원 관리여건을 평가하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.149-153
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• 2006

Precipitation is the most important component and critical to the study of water and energy cycle. This study investigates the propagation of precipitation retrieval uncertainty in the simulation of hydrologic variables for varying spatial resolution on two different vegetation cover. We explore two remotely sensed rain retrievals (space-borne IR-only and radar rainfall) and three spatial grid resolutions. An offline Community Land Model (CLM) was forced with in situ meteorological data In turn, radar rainfall is replaced by the satellite rain estimates at coarser resolution $(0.25^{\circ},\;0.5^{\circ}\;and\;1^{\circ})$ to determine their probable impact on model predictions. Results show how uncertainty of precipitation measurement affects the spatial variability of model output in various modelling scales. The study provides some intuition on the uncertainty of hydrologic prediction via interaction between the land surface and near atmosphere fluxes in the modelling approach.

• Journal of Korea Water Resources Association
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• v.49 no.3
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• pp.197-205
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• 2016

Recently, natural hazards have occurred frequently due to climate change. The research need for predicting variability and tendency of precipitation and temperature has been increased. However, it is difficult to determine the characteristics of precipitation and temperature within a confidence range since they change due to complex factors with choppy and too many components. If their characteristics having more than one component are decomposed, then it can be useful for determining the variation of such characteristics more accurately. In this study, Korean precipitation and temperature were decomposed and their Intrinsic Mode Function (IMF) were extracted from Empirical Mode Decomposition (EMD). Finally, the characteristics of Korean precipitation and temperature data were analyzed in terms of periodicity and tendency.

• Journal of Climate Change Research
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• v.6 no.3
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• pp.203-208
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• 2015

This study was conducted to provide the agricultural climatological basic data for the reset of sowing period of the winter crop on the double cropping system with rice. During the past 30 years from 1981 to 2010, mean air temperature has risen by $0.45^{\circ}C$ per 10 years (with statistical significance), while precipitation has decreased by 6.74 mm per 10 years and the numbers of days for precipitation has reduced by 0.23 days per 10 years (with no statistical significance) in the sowing period ($1^{st}$ Oct. to $5^{th}$ Nov.) of winter crop. It was analyzed that double cropping system of rice and winter crops need to be reset in the way of delaying the sowing time of winter crops, because rising trend of temperature was clear while variability of precipitation was great and the trend was not clear in the sowing period of winter crops. We have also analyzed the meteorological features of the sowing period of winter crops in 2014, and found that mean air temperature in 2014 was higher than that in normal years (similar to recent temperature change feature) while precipitation in 2014 was much more frequent than that in normal years (unlike recent precipitation features). Such tendency in 2014 made the sowing of winter crops difficult because mechanical sowing could not be worked in flooded paddy fields. Heavy rain in October 2014 was also analyzed as a rare phenomenon.

• Journal of the Korean earth science society
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• v.32 no.7
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• pp.732-749
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• 2011

In this study, 27-year (1979-2005) regional climate over the CORDEX East Asia domain was reproduced using a regional climate model, RegCM4, driven by HadGEM2-AO output, and the model's simulation skill was evaluated in terms of surface air temperature and precipitation. The RegCM4 reasonably simulated the spatial distribution and interannual variability and seasonal variability of surface air temperature, while it had systematic biases in the simulation of precipitation. In particular, simulated rainband of East Asian summer monsoon was southward shifted below $30^{\circ}N$ as compared with the observation, thereby, summer mean precipitation over South Korea was significantly underestimated. Simulated temperature from the RegCM4 driven by the HadGEM2-AO output was comparable to that driven by the reanalysis. However, the RegCM4 driven by the HadGEM2-AO had prominently poor skill in the simulation of precipitation. This can be associated with the distorted monsoon circulations in the driving data (i.e., HadGEM2-AO) such as southward shifted low-level southwesterly, which resulted in the erroneous evolution of East Asian summer monsoon simulated by RegCM4.

• Journal of Korea Water Resources Association
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• v.50 no.2
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• pp.75-87
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• 2017

In recent decades, extreme events have been significantly increased over the Korean Peninsula due to climate variability and climate change. The potential changes in hydrologic cycle associated with the extreme events increase uncertainty in water resources planning and designing. For these reasons, a reliable changing point analysis is generally required to better understand regime changes in hydrologic time series at watershed scale. In this study, a hierarchical changing point analysis approach that can apply in a watershed scale is developed by combining the existing changing point analysis method and hierarchical Bayesian method. The proposed model was applied to the selected stations that have annual rainfall data longer than 40 years. The results showed that the proposed model can quantitatively detect the shift in precipitation in the middle of 1990s and identify the increase in annual precipitation compared to the several decades prior to the 1990s. Finally, we explored the changes in precipitation and sea level pressure in the context of large-scale climate anomalies using reanalysis data, for a given change point. It was concluded that the identified large-scale patterns were substantially different from each other.

• Journal of the korean society of oceanography
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• v.39 no.1
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• pp.72-95
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• 2004

The Yellow Sea is characterized by relatively shallow water depth, varying range of tidal action and very complex coastal geometry such as islands, bays, peninsulas, tidal flats, shoals etc. The dynamic system is controlled by tides, regional winds, river discharge, and interaction with the Kuroshio. The circulation, water mass properties and their variability in the Yellow Sea are very complicated and still far from clear understanding. In this study, an effort to improve our understanding the dynamic feature of the Yellow Sea system was conducted using numerical simulation with the ROMS model, applying climatologic forcing such as winds, heat flux and fresh water precipitation. The inter-annual variability of general circulation and thermohaline structure throughout the year has been obtained, which has been compared with observational data sets. The simulated horizontal distribution and vertical cross-sectional structures of temperature and salinity show a good agreement with the observational data indicating significantly the water masses such as Yellow Sea Warm Water, Yellow Sea Bottom Cold Water, Changjiang River Diluted Water and other sporadically observed coastal waters around the Yellow Sea. The tidal effects on circulation and dynamic features such as coastal tidal fronts and coastal mixing are predominant in the Yellow Sea. Hence the tidal effects on those dynamic features are dealt in the accompanying paper (Kim et at., 2004). The ROMS model adopts curvilinear grid with horizontal resolution of 35 km and 20 vertical grid spacing confirming to relatively realistic bottom topography. The model was initialized with the LEVITUS climatologic data and forced by the monthly mean air-sea fluxes of momentum, heat and fresh water derived from COADS. On the open boundaries, climatological temperature and salinity are nudged every 20 days for data assimilation to stabilize the modeling implementation. This study demonstrates a Yellow Sea version of Atlantic Basin experiment conducted by Haidvogel et al. (2000) experiment that the ROMS simulates the dynamic variability of temperature, salinity, and velocity fields in the ocean. However the present study has been improved to deal with the large river system, open boundary nudging process and further with combination of the tidal forcing that is a significant feature in the Yellow Sea.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.154-154
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• 2018

To interpret the climate projections for the future as well as present, recognition of the consequences of the climate internal variability and quantification its uncertainty play a vital role. The Korean Peninsula belongs to the Far East Asian Monsoon region and its rainfall characteristics are very complex from time and space perspective. Its internal variability is expected to be large, but this variability has not been completely investigated to date especially using models of high temporal resolutions. Due to coarse spatial and temporal resolutions of General Circulation Models (GCM) projections, several studies adopted dynamic and statistical downscaling approaches to infer meterological forcing from climate change projections at local spatial scales and fine temporal resolutions. In this study, stochastic downscaling methodology was adopted to downscale daily GCM resolutions to hourly time scale using an hourly weather generator, the Advanced WEather GENerator (AWE-GEN). After extracting factors of change from the GCM realizations, these were applied to the climatic statistics inferred from historical observations to re-evaluate parameters of the weather generator. The re-parameterized generator yields hourly time series which can be considered to be representative of future climate conditions. Further, 30 ensemble members of hourly precipitation were generated for each selected station to quantify uncertainty. Spatial map was generated to visualize as separated zones formed through K-means cluster algorithm which region is more inconsistent as compared to the climatological norm or in which region the probability of occurrence of the extremes event is high. The results showed that the stations located near the coastal regions are more uncertain as compared to inland regions. Such information will be ultimately helpful for planning future adaptation and mitigation measures against extreme events.