• Journal of Wetlands Research
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• v.20 no.2
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• pp.105-115
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• 2018

The frequency analysis of hydrometeorological data is one of the most important factors in response to natural disaster damage, and design standards for a disaster prevention facilities. In case of frequency analysis of hydrometeorological data, it assumes that observation data have statistical stationarity, and a parametric method considering the parameter of probability distribution is applied. For a parametric method, it is necessary to sufficiently collect reliable data; however, snowfall observations are needed to compensate for insufficient data in Korea, because of reducing the number of days for snowfall observations and mean maximum daily snowfall depth due to climate change. In this study, we conducted the frequency analysis for snowfall using the Bootstrap method and SIR algorithm which are the resampling methods that can overcome the problems of insufficient data. For the 58 meteorological stations distributed evenly in Korea, the probability of snowfall depth was estimated by non-parametric frequency analysis using the maximum daily snowfall depth data. The results of frequency based snowfall depth show that most stations representing the rate of change were found to be consistent in both parametric and non-parametric frequency analysis. According to the results, observed data and Bootstrap method showed a difference of -19.2% to 3.9%, and the Bootstrap method and SIR(Sampling Importance Resampling) algorithm showed a difference of -7.7 to 137.8%. This study shows that the resampling methods can do the frequency analysis of the snowfall depth that has insufficient observed samples, which can be applied to interpretation of other natural disasters such as summer typhoons with seasonal characteristics.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.698-701
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• 2007

지하수 개발가능량 산정을 위한 함양량의 평가는 수문계의 물리적인 형태나 함수층의 수리성 분석 및 수직인 지질분포를 파악하여 어떤 조건하에서 물이 유입 유출되는가를 파악한 후에만 가능하다. 또한 지하수계의 물리적인 형태를 이해함으로써 조사지역의 지표수계나 지하수계의 양계를 통해서 흐르는 물의 양을 결정짓는 물수지 분석이 수행되어야 한다. 이에 따라 강수량, 증발산량, 지하수 유출량, 지표유출량 그리고 하천유출량 등을 수문학적으로 고려해야만 한다. 본 연구는 지표수-지하수 결합모형을 도입하여 분포형 지하수 함양량의 시공간적인 변동성을 파악하는 데 그 목적이 있다. 이를 위해 지표수-지하수 결합모형인 SWAT-K모형을 미호천 유역에 적용하였으며, 지표수의 총유출량과 지하수위의 공간분포자료를 이용하여 검정과 검증을 수행하였다. 전체유역에 대한 연평균 함양량은 수문총량의 약 19%인 것으로 나타났다. 1999년${\sim}$2004년까지의 소유역별 연간 함양량 결과를 월별로 나타냈으며, HRU(Hydrologic Response Unit)별 함양량의 공간분포를 통해 월별, 계절별 특성을 살펴볼 수 있었다. 소유역 모두 강수가 집중하는 7-9월에 걸쳐 많은 함양이 이루어지며 $1{\sim}3$월에는 상대적으로 함양이 적은 것을 볼 수 있다. 월함양량의 경우 최대 약200mm범위내에서 유역의 토지이용 및 토양특성, 경사등에 따라 매우 비균질하게 분포하는 것을 확인할 수 있었다. 이와같은 함양량의 시공간적 불균일성으로 인해 지하수 관리방안은 소유역별 함양특성을 반영해야 할 것으로 판단된다.의 종분산지수가 일반적인 자연대수층에 비해 9.1배 정도 높다는 것을 의미한다. 이는 시험대수층의 투수성이 매우 높아 염소이온의 용질이송이 매우 빠르게 발생되었기 때문이다. 본 연구에서 추정된 종분산지수를 Gelhar et al.(1992)의 연구 결과와 비교 분석한 결과에서도 시험규모에 비해 매우 높은 수리분산이 발생된 것으로 나타났다. 그리고 염소이온의 확산면적을 추정하기 위해, 수렴흐름 추적자시험에 의한 종분산지수와 시험대수층의 평균선형유속을 이용하여 종분산계수를 구하였다. 현장에서 수행된 양수시험에 의한 평균선형유속 22.44 m/day와 평균 종분산지수 0.4155 m를 적용하여 산정된 종분산계수는 $9.32\;m^2/day$이었다. 따라서, 시험부지 내 충적층에서 일정한 양수율$(2,500\;m^3/day)$로 지하수를 개발할 시에 양수정 주변지역으로 유입되는 염소이온의 확산면적은 1일 $9.32\;m^2$ 정도일 것으로 나타났다.적인 $OH{\cdot}$ 의 생성은 ascorbate가 조직손상에 관여할 가능성을 시사하였다.었다. 정확한 예측치를 얻기 위하여 불균질 조직이 조사야에 포함되는 경우 보정이 요구되며, 골반의 경우 골 조직의 보정이 중요한 요인임을 알 수 있었다. 이를 위하여 불균질 조직에 대한 정확한 정보가 요구되며, 이는 CT 영상을 이용하는 것이 크게 도움이 되리라 생각된다.전시 슬러지층과 상등액의 온도차를 측정하여 대사열량의 발생량을 측정하고 슬러지의 활성을 측정할 수 있는 방법을 개발하였다.enin과 Rhaponticin

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.4
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• pp.348-356
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• 2016

Carbon dioxide ($CO_2$) gases concentration in atmosphere has been growing since preindustrial times. By sequestering a large amount of atmospheric carbon (C), terrestrial ecosystems are thought to offer a mitigation strategy for reducing global warming. Woody agro-ecosystems such as fruit tree are among the least quantified and most uncertain elements in the terrestrial carbon cycle. $CO_2$ and energy fluxes were measured by the eddy covariance method on a 15-year old apple orchard of South Korea in 2006. Environmental parameters (net radiation, precipitation, etc.) were measured along with fluxes. The results showed that during late June, the ability to sequestrate C was significant at an apple orchard ecosystem and it reached on the peak of $-6.5g\;C\;m^{-2}\;d^{-1}$. We found that in the apple orchard, the daily average of net ecosystem exchange of $CO_2$ (NEE) and cumulative NEE on a yearly basis were $-1.1g\;C\;m^{-2}$ and $-396.9g\;C\;m^{-2}$, respectively. These results reveal that there is high carbon sequestration in the apple orchard of South Korea, which is the same magnitude with repect to that of a natural forested ecosystem of the same biome rank (temperate-humid deciduous forest).

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.316-316
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• 2023

2022년 8월 수도권 이상폭우로 인해 서울 도심지역의 지하시설, 도로, 주택 등에 침수가 발생하면서 인명 및 재산피해가 발생하였으며, 특히 동서로 가로지르는 정체전선으로 좁고 긴 비구름이 집중되면서 국지적으로 피해가 집중되었다. 서울시의 경우 도시화에 따른 불투수지역 증가 및 내수배제 불량에 따른 빗물 역류로 인한 피해가 지속적으로 발생하고 있으며, 최근에는 기후변화에 따른 방재성능목표 강우량을 초과하는 빈도의 이상폭우로 인해 하천범람과 내수배제 불량에 따른 복합적인 원인으로 침수피해 가중되고 있는 실정이다. 또한 서울시의 경우 전체 자연적, 사회적, 경제적, 환경적 요인 등의 지역적 편차가 매우 큰 도시로 지형적인 특성뿐만 아니라 취약시설(병원, 학교 등), 수방시설물(하천, 배수시설, 빗물펌프장 등) 및 방재시설(대피소, 구호소 등) 밀도 등에 따른 침수 취약성 및 위험성 등의 편차가 매우 크기 때문에 지역특성에 대한 피해사례가 다원화 되고 있는 실정이다. 본 연구에서는 30년 이상의 종관기상관측(ASOS)과 서울시 자치구별 20년 이상의 방재기상관측(AWS)자료를 기반으로 CMIP6 SSP(Shared Socioeconomic Pathways, 공통사회 경제경로)시나리오에 따른 극한기후 지수(강수강도, 호우일수, 지속기간, 1일 최대강수량, 95퍼센타일 강수일수 등)에 대한 재현성을 평가하고 공간자기상관분석 등 시공간적인 강우특성에 대한 변화를 전망하였다. 특히 여름철 강우의 경우 자치구별 편차가 크게 나타났고 이를 통해 대도시의 도심지역의 경우 세분화하여 지역의 정확한 강우특성을 파악하는 것이 필요하다는 것을 확인할 수 있었다. 본 연구의 결과는 도심지의 방재성능 초과강우 정의와 기준을 수립하고, 장기적인 수자원 및 도시계획 차원의 대책을 마련하는데 활용될 수 있을 것으로 판단된다. 기후위기에 따른 기록적인 호우(지역별 방재성능을 초과하는 강우)에 따른 재해는 구조적인 대책을 통해 모두 저감할 수 없는 한계가 있다. 하지만 인명피해를 최소화하는 것을 목표로 기후위기에 대한 적응단계로 인식하고 수리·수문학적, 사회경제학적 등 지역특성에 따른 방재성능목표 강우량에 대한 재검토와 더불어 법제도(풍수해보험, 저류조설치 의무화 등), 개인별 재해예방, 취약계층 안전망 확보, 반지하주택 침수안전대책, 재해지도 개선 등 구조적/비구조적인 대책을 통합 수립 및 보완하는 것이 필요한 시점이다.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.42 no.5
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• pp.579-596
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• 1997

A simulation study was carried out to delineate potential effects of the lake-induced climate change on crop productivity around Lake Imha which was formed after a multi-purpose dam construction in Andong, Korea. Twenty seven cropping zones were identified within the 30 km by 25 km study area. Five automated weather stations were installed within the study area and operated for five years after the lake formation. A geostatistical method was used to calculate the monthly climatological normals of daily maximum and minimum temperature, solar radiation and precipitation for each cropping zone before and after the dam construction. Daily weather data sets for 30 years were generated for each cropping zone from the monthly normals data representing "No lake" and "After lake" climatic scenarios, respectively. They were fed into crop models (ORYZA1 for rice, SOYGRO for soybean, CERES-maize for corn) to simulate the yield potential of each cropping zone. Calculated daily maximum temperature was higher after the dam construction for the period of October through March and lower for the remaining months except June and July. Decrease in daily minimum temperature was predicted for the period of April through August. Monthly total radiation was predicted to decrease after the lake formation in all the months except February, June, and September and the largest drop was found in winter. But there was no consistent pattern in precipitation change. According to the model calculation, the number of cropping zones which showed a decreased yield potential was 2 for soybean and 6 for corn out of 27 zones with a 10 to 17% yield drop. Little change in yield potential was found at most cropping zones in the case of paddy rice, but interannual variation was predicted to increase after the lake formation. the lake formation.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.1
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• pp.55-67
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• 2021

Hornbeams (Carpinus spp.), which are widely distributed in South Korea, are recognized as one of the most abundant species at climax stage in the temperate forests. Although the distribution and vegetation structure of the C. laxiflora community have been reported, little ecological information of C. tschonoskii is available. Little effort was made to examine the distribution shift of these species under the future climate conditions. This study was conducted to predict potential shifts in the distribution of C. laxiflora and C. tschonoskii in 2050s and 2090s under the two sets of climate change scenarios, RCP4.5 and RCP8.5. The MaxEnt model was used to predict the spatial distribution of two species using the occurrence data derived from the 6th National Forest Inventory data as well as climate and topography data. It was found that the main factors for the distribution of C. laxiflora were elevation, temperature seasonality, and mean annual precipitation. The distribution of C. tschonoskii, was influenced by temperature seasonality, mean annual precipitation, and mean diurnal rang. It was projected that the total habitat area of the C. laxiflora could increase by 1.05% and 1.11% under RCP 4.5 and RCP 8.5 scenarios, respectively. It was also predicted that the distributional area of C. tschonoskii could expand under the future climate conditions. These results highlighted that the climate change would have considerable impact on the spatial distribution of C. laxiflora and C. tschonoskii. These also suggested that ecological information derived from climate change impact assessment study can be used to develop proper forest management practices in response to climate change.

• Journal of the Korean earth science society
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• v.27 no.2
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• pp.159-176
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• 2006

Geographic factors and mathmatical location of the Korean Peninsula have great influences on the variation patterns and appearances over a period of ten days of summer precipitation. In order to clarify the influence of several climate factors on precise climate classification in the middle part region of the Korea, weather entropy and the information ratio were calculated on the basis of information theory and of the data of 25 site observations. The data used for this study are the daily precipitation phenomenon over a period of ten days of summer during the recent thirteen years (1991-2003) at the 25 stations in the middle part region of the Korea. It is divided into four classes of no rain, $0.1{\sim}10.0mm/day,\;10.1{\sim}30.0mm/day$, 30.1mm over/day. Their temporal and spatial change were also analyzed. The results are as follows: the maximum and minimum value of calculated weather entropy are 1.870 bits at Chuncheon in the latter ten days of July and 0.960 bits at Ganghwa during mid September, respectively. And weather entropy in each observation sites tends to be larger in the beginning of August and smaller towards the end of September. The largest and smallest values of weather representative ness based on information ratio were observed at Chungju in the beginning of June and at Deagwallyeong towards the end of July. However, the largest values of weather representativeness came out during the middle or later part of September when 15 sites were adopted as the center of weather forecasting. The representative core region of weather forecasting and climate classification in the middle part region of the Korea are inside of the triangle region of the Buyeo, Incheon, and Gangneung.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2014.10a
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• pp.1-24
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• 2014

This study uses geo-spatial crop modeling to quantify the biophysical impact of weather extremes. More specifically, the study analyzes the weather extreme which affected maize production in the USA in 2012; it also estimates the effect of a similar weather extreme in 2050, using future climate scenarios. The secondary impact of the weather extreme on food security in the developing world is also assessed using trend analysis. Many studies have reported on the significant reduction in maize production in the USA due to the extreme weather event (combined heat wave and drought) that occurred in 2012. However, most of these studies focused on yield and did not assess the potential effect of weather extremes on food prices and security. The overall goal of this study was to use geo-spatial crop modeling and trend analysis to quantify the impact of weather extremes on both yield and, followed food security in the developing world. We used historical weather data for severe extreme events that have occurred in the USA. The data were obtained from the National Climatic Data Center (NCDC) of the National Oceanic and Atmospheric Administration (NOAA). In addition we used five climate scenarios: the baseline climate which is typical of the late 20th century (2000s) and four future climate scenarios which involve a combination of two emission scenarios (A1B and B1) and two global circulation models (CSIRO-Mk3.0 and MIROC 3.2). DSSAT 4.5 was combined with GRASS GIS for geo-spatial crop modeling. Simulated maize grain yield across all affected regions in the USA indicates that average grain yield across the USA Corn Belt would decrease by 29% when the weather extremes occur using the baseline climate. If the weather extreme were to occur under the A1B emission scenario in the 2050s, average grain yields would decrease by 38% and 57%, under the CSIRO-Mk3.0 and MIROC 3.2 global climate models, respectively. The weather extremes that occurred in the USA in 2012 resulted in a sharp increase in the world maize price. In addition, it likely played a role in the reduction in world maize consumption and trade in 2012/13, compared to 2011/12. The most vulnerable countries to the weather extremes are poor countries with high maize import dependency ratios including those countries in the Caribbean, northern Africa and western Asia. Other vulnerable countries include low-income countries with low import dependency ratios but which cannot afford highly-priced maize. The study also highlighted the pathways through which a weather extreme would affect food security, were it to occur in 2050 under climate change. Some of the policies which could help vulnerable countries counter the negative effects of weather extremes consist of social protection and safety net programs. Medium- to long-term adaptation strategies include increasing world food reserves to a level where they can be used to cover the production losses brought by weather extremes.

• Korean Journal of Environmental Biology
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• v.41 no.4
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• pp.505-518
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• 2023

The key to invasive pest management lies in preemptive action. However, most current research using species distribution models is conducted after an invasion has occurred. This study modeled the potential distribution of the globally notorious sweet potato pest, the sweet potato weevil(Cylas formicarius), that has not yet invaded Korea using MaxEnt. Using global occurrence data, bioclimatic variables, and topsoil characteristics, MaxEnt showed high explanatory power as both the training and test areas under the curve exceeded 0.9. Among the environmental variables used in this study, minimum temperature in the coldest month (BIO06), precipitation in the driest month (BIO14), mean diurnal range (BIO02), and bulk density (BDOD) were identified as key variables. The predicted global distribution showed high values in most countries where the species is currently present, with a significant potential invasion risk in most South American countries where C. formicarius is not yet present. In Korea, Jeju Island and the southwestern coasts of Jeollanam-do showed very high probabilities. The impact of climate change under shared socioeconomic pathway (SSP) scenarios indicated an expansion along coasts as climate change progresses. By applying the 10th percentile minimum training presence rule, the potential area of occurrence was estimated at 1,439 km² under current climate conditions and could expand up to 9,485 km² under the SSP585 scenario. However, the model predicted that an inland invasion would not be serious. The results of this study suggest a need to focus on the risk of invasion in islands and coastal areas.