• Journal of Korea Water Resources Association
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• v.50 no.1
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• pp.1-15
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• 2017

Information on radar rainfall with high spatio-temporal resolution over large areas has been used to mitigate climate-related disasters such as flash floods. On the other hand, a well-known problem associated with the radar rainfall using the Marshall-Palmer relationship is the underestimation. In this study, we develop a new bias correction scheme based on the quantile regression method. This study employed a bivariate copula function method for the joint simulation between radar and ground gauge rainfall data to better characterize the error distribution. The proposed quantile regression based bias corrected rainfall showed a good agreement with that of observed. Moreover, the results of our case studies suggest that the copula function approach was useful to functionalize the error distribution of radar rainfall in an effective way.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.23 no.1
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• pp.1-14
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• 2020

In this study, we 1) identified the damaged areas along the south limit line (SLL) of the demilitarized zone (DMZ) by the military's 'DMZ barren land campaign', and 2) categorized the identified damaged areas into a few ecological types. Using high-resolution satellite images, we delineated the total damaged areas to be 1,183.2 ha, which accounted for 50.1% of the 100-m northern buffer regions from the SLL. Of the total damaged areas, 16% were severely damaged, i.e., they had been damaged until recently and so remained barren without vegetation cover. In other areas, the levels of damage were either moderate (59.9%) or slight (24.1%), due to natural succession that turned those areas to grassland or forest. Using satellite image-derived land cover maps and climatic and topographic data, we categorized the damaged areas into seven types: lowland grassland (19.8%), western lowland forest (21.4%), low-altitude forest (25.5%), mid-altitude forest (18.4%), high-altitude forest (6.8%), vicinity in east coast (7.9%), and waterbody (0.2%). These types can be used to identify proper measures to restore ecosystems in the DMZ for now and after Korean reunification.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.224-228
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• 2008

2007년 세계경제포럼(WEF)은 우리가 직면한 최우선 해결과제로 기후변화를 언급하였다. 최저 기온 상승과 가뭄 영향 지역 확대, 폭염일수와 지역적 홍수 위험 증가 등 각종 이상기상이 야기하는 피해 확대에 대한 예상과 우려 때문이다(IPCC, 2007). 세계적으로 고온극한과 호우빈도 증가, 태풍 세기가 강화될 것으로 전망되고 있으며(IPCC, 2007), 국내의 경우 겨울철 한파 감소와 대설 피해 증가, 여름철 집중호우의 강도 심화, 가을철 초대형 태풍 발생으로 인한 피해 가능성이 예측 되고 있다(기상연구소, 2007). 현재, 이러한 현상들을 가시화하고 대처방안을 마련하기 위한 일환으로 기후변화 시나리오(GCM)가 작성되어 연구에 이용되고 있다. 그러나 GCM의 경우, 공간적 해상도가 낮아 지형학적 특성 등을 충분히 반영하지 못하는 단점이 있어 최근에는 공간 해상도가 GCM보다 높은 RCM(Regional Climate Model, 지역기후모델)자료를 적용한 연구도 진행되고 있다. 본 논문에서는 SRES A2 온난화가스시나리오 기반의 기상청 RegCM3 RCM($27km{\times}27km$)로 부터 일(daily)단위 자료를 각각 모의하여 비교하고, BLRPM을 이용하여 일(daily)단위 자료를 시(hourly)단위로 분해(disaggregation)하였다. 그리고 이들을 이용하여 지속기간별 확률강우량을 산정하여 미래 기후변화가 극한 강우에 미치는 영향을 평가하였다.

• Journal of the Korean earth science society
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• v.44 no.4
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• pp.255-263
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• 2023

Western coastal area of Chungnam, including Cheonsu Bay and Garorim Bay, has suffered from hot and cold extremes. In this study, the extreme sea surface temperature on the western coast of Chungnam was analyzed using the quantile regression method, which extracts the linear regression values in all quantiles. The regional MOHID (MOdelo HIDrodinâmico) model, with a high resolution on a 1/60° grid, was constructed to reproduce the extreme sea surface temperature. For future prediction, the SSP5-8.5 scenario data of the CMIP6 model were used to simulate sea surface temperature variability. Results showed that the extreme sea surface temperature of Cheonsu Bay in August 2017 was successfully simulated, and this extreme sea surface temperature had a significant negative correlation with the Pacific decadal variability index. As a result of future climate prediction, it was found that an average of 2.9℃ increased during the simulation period of 86 years in the Chungnam west coast and there was a seasonal difference (3.2℃ in summer, 2.4℃ in winter). These seasonal differences indicate an increase in the annual temperature range, suggesting that extreme events may occur more frequently in the future.

• Journal of Korea Water Resources Association
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• v.50 no.1
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• pp.17-28
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• 2017

A recent increase in extreme weather events and flash floods associated with the enhanced climate variability results in an increase in climate-related disasters. For these reasons, various studies based on a high resolution weather radar system have been carried out. The weather radar can provide estimates of precipitation in real-time over a wide area, while ground-based rain gauges only provides a point estimate in space. Weather radar is thus capable of identifying changes in rainfall structure as it moves through an ungauged basin. However, the advantage of the weather radar rainfall estimates has been limited by a variety of sources of uncertainty in the radar reflectivity process, including systematic and random errors. In this study, we developed an ensemble radar rainfall estimation scheme using the multivariate copula method. The results presented in this study confirmed that the proposed ensemble technique can effectively reproduce the rainfall statistics such as mean, variance and skewness (more importantly the extremes) as well as the spatio-temporal structure of rainfall fields.

• Atmosphere
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• v.20 no.3
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• pp.261-271
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• 2010

Carbon isotopic compositions of the YC-2 stalagmite in Yongcheon Cave were analyzed to delineate paleoclimatic variations near Korean peninsula for the past historical period. The YC-2 stalagmite is about 68 mm long and annual growth laminae are distinctively identified. Because the number of growth laminae is at least 242, the stalagmite can be estimated to be at least 241 years old. At about 15 mm from the bottom, one thick brown growth lamina is observed, and this lamina was likely to have been formed when the stalagmite ceased to grow, making the hiatus. High resolution, carbon isotope data indicate past fluctuations of East Asia monsoonal intensity (intimately related to the amount of precipitation). Based on the carbon isotope trend, the stalagmite can be divided into three stages (Stages I, II and III). The highest carbon isotopic compositions of Stage I (${\delta}^{13}C$=-3.3~0.4‰, PDB) indicate that the stalagmite grew during the Little Ice Age when cold and dry climate prevailed with less vegetation. Stage II is characterized by a transitional period from cold and dry to warm and wet climate with a increasing trend of carbon isotopic compositions (${\delta}^{13}C$=-9.6~-0.6‰) and this period indicates the weakening of the Little Ice Age climate. This decreasing trend also suggests that Little Ice Age was terminated near middle 1870's around Korean peninsula. Relatively low carbon isotopic compositions during Stage III (${\delta}^{13}C$=-11.0~-8.0‰) indicates that the climate was changed to warm and wet conditions which are similar to the present.

• Atmosphere
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• v.25 no.4
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• pp.693-706
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• 2015

A shift of first fowering date (FFD) of spring blossoms (cherry, peach and pear) over the northest Asia under global warming is investiaged using dynamically downscaled daily temperature data with 12.5 km resolution. For the study, we obatained gridded daily data with Historical (1981~2010), and Representative Concentration Pathway (RCP) (2021~2100) 4.5 and 8.5 scenarios which were produced by WRFv3.4 in conjunction with HadGEM2-AO. A change on FFDs in 21st century is estimated by applying daily outputs of WRFv3.4 to DTS phonological model. Prior to projection on future climate, the performances of both WRFv3.4 and DTS models are evaluated using spatial distribution of climatology and SCR diagram (Normalized standard deviation-Pattern correlation coefficient-Root mean square difference). According to the result, WRFv3.4 and DTS models well simulated a feature of the terrain following characteristics and a general pattern of observation with a marigin of $1.4^{\circ}C$ and 5~6 days. The analysis reveals a projected advance in FFDs of cherry, peach and pear over the northeast Asia by 2100 of 15.4 days (9.4 days). 16.9 days (10.4 days) and 15.2 days (9.5 days), respectively, compared to the Historical simulation due to a increasing early spring (Februrary to April) temperature of about $4.9^{\circ}C$ ($2.9^{\circ}C$) under the RCP 8.5 (RCP 4.5) scenarios. This indicates that the current flowering of the cherry, peach and pear over analysis area in middle or end of April is expected to start blooming in early or middle of April, at the end of this century. The present study shows the dynamically downscaled daily data with high-resolution is helpeful in offering various useful information to end-users as well as in understanding regional climate change.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.20 no.3
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• pp.1-18
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• 2017

This study constructed a high-resolution bioclimatic classification map of South Korea which classifies land into homogeneous zones by similar environment properties using advanced statistical techniques compared to existing ecological area classification studies. The climate data provided by WorldClim(1960-1990) were used to generate 27 bioclimatic variables affecting biological habitats, and key environmental variables were derived from Correlation Analysis and Principal Component Analysis. Clustering Analysis was performed using the ISODATA method to construct a 30'(~1km) resolution bioclimatic classification map. South Korea was divided into 21 regions and the results of classification were verified by correlation analysis with the Gross Primary Production(GPP), Actual Vegetation map made by the Ministry of Environment. Each zones' were described and named by its environmental characteristics and major vegetation distribution. This study could provide useful spatial frameworks to support ecosystem research, monitoring and policy decisions.

• Journal of Korea Water Resources Association
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• v.51 no.2
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• pp.89-98
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• 2018

Satellite data have attracted attention on research such as natural disaster and climate changes because satellite data is very advantageous for observing a wide range of variability. However, there are still limited spatial and temporal resolutions in satellite data. To overcome these limitations, fusion of various sensors and combination of primary products are used. In this study, surface temperature data of 500 m spatial resolution was produced by fusion of GOCI and MI data of COMS. Also these LST are used with NDVI for estimating TVDI. Soil moisture condition of the Korean peninsula was evaluated by these TVDI and it was compared with SSMI derived from ASCAT surface soil moisture data. As a result, COMS TVDI and ASCAT SSMI showed similar spatial distribution and suggested the possibility of observing the soil moisture using COMS. Therefore, the TVDI estimations can be used as a basis for estimating the high resolution soil moisture, and the application of the COMS can be expanded for various studies.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.661-676
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• 2017

This study suggests a new method not only to produce the 1 km-resolution seasonal prediction but also to improve the seasonal prediction skill of temperature over South Korea. This method consists of four stages of experiments. The first stage, EXP1, is a low-resolution seasonal prediction of temperature obtained from Pusan National University Coupled General Circulation Model, and EXP2 is to produce 1 km-resolution seasonal prediction of temperature over South Korea by applying statistical downscaling to the results of EXP1. EXP3 is a seasonal prediction which considers the effect of temperature changes according to the altitude on the result of EXP2. Here, we use altitude information from ASTER GDEM, satellite observation. EXP4 is a bias corrected seasonal prediction using genetic algorithm in EXP3. EXP1 and EXP2 show poorer prediction skill than other experiments because the topographical characteristic of South Korea is not considered at all. Especially, the prediction skills of two experiments are lower at the high altitude observation site. On the other hand, EXP3 and EXP4 applying the high resolution elevation data based on remote sensing have higher prediction skill than other experiments by effectively reflecting the topographical characteristics such as temperature decrease as altitude increases. In addition, EXP4 reduced the systematic bias of seasonal prediction using genetic algorithm shows the superior performance for temporal variability such as temporal correlation, normalized standard deviation, hit rate and false alarm rate. It means that the method proposed in this study can produces high-resolution and high-quality seasonal prediction effectively.