1개월과 3개월 장기 예보를 지원하기 위해 기상청에서 현업운용 중인 GloSea6 기후예측시스템에는 대기 중 대 기화학-에어로졸 물리과정(UKCA)이 연동되어 있지 않다. 본 연구에서는 저해상도의 GloSea6와 여기에 대기화학-에어로 졸 과정을 연동시킨 GloSea6-UKCA를 CentOS 기반 리눅스 클러스터에 설치하여 2000년 봄철에 대한 예비적인 예측 결과를 살펴보았다. 현업 고해상도 GloSea6 모델이 방대한 전산자원을 필요로 한다는 점을 고려할 때, 저해상도 GloSea6와 GloSea6-UKCA 모델은 대기화학-에어로졸 과정의 연동에 따른 효과를 살펴보기에 적합하다. 저해상도 GloSea6와 GloSea6-UKCA는 2000년 3월 1일 00Z부터 75일 간 구동되었으며, 두 모델이 예측한 2000년 4월 지상 기온과 일평균 강수량의 공간 분포를 ERA5 재분석자료와 비교하였다. GloSea6-UKCA가 예측한 기온과 강수 분포는 기존 GloSea6에 비해 ERA5 재분석자료에 보다 더 유사해졌다. 특히 우리나라를 포함한 동아시아 지역에 대해 과대 모의 경 향이 있던 봄철 지상 기온과 일평균 강수량의 예측 결과의 개선이 주목할 만하다. 또한 적분 시간에 따른 예측된 기온 과 강수량의 시계열에서도 GloSea6-UKCA가 GloSea6보다 재분석자료에 더 가까워진 시간 변화 경향을 살펴볼 수 있었 다. 이는 대기화학-에어로졸 과정이 GloSea6에 연동되었을 때 동아시아지역 봄철 예측 성능이 개선될 수 있음을 보여준다.

PURPOSES : With the recent enactment of the 「Framework Act on Sustainable Infrastructure Management」 in Korea, the establishment of mid- to long-term management plans for social infrastructure and the feasibility evaluation of maintenance projects have become mandatory. To this end, the life cycle cost analysis is essential. However, owing to the absence of a deterioration model, trials and errors are in progress. METHODS : In this study, a deterioration model was established for bridges, which are the representative social infrastructures of roads, particularly for expansion joints that can cause enormous damage to not only the superstructure but also the substructure. The deterioration model was classified into rubber and steel, based on the material of the expansion joint. The analysis used the inspection and climate data conducted in Korea over the last 12 years. The Bayesian Markov Hazard model was applied as the analysis technique. RESULTS : The average life expectancy by type of expansion joint was analyzed to be 8.9 and 6.6 years for rubber and steel, respectively. For probabilistic life cycle cost analysis, the probability distribution of the life expectancy, validity range by confidence level, and Markov transition probability matrix were presented. CONCLUSIONS : In this study, the basis for deterministic and probabilistic life cycle cost analysis of expansion joints was laid. In future studies, it will be necessary to establish a standardized deterioration model for all types of infrastructure, including all bridge elements.

기후변화는 동·식물의 서식지와 개체군을 감소, 소멸시키며, 생물다양성 보존에 위협이 되고 있다. 특히, 도롱뇽과 (Hynobiidae)에 속한 종들은 다른 분류군들에 비해 행동권이 작고, 분산 능력이 극히 제한되기 때문에 기후변화에 매우 취약한 분류군이다. 본 연구에서는 한국꼬리치레도롱뇽(Onychodactylus koreanus)의 관찰지점과 종 분포 모델링 기법을 바탕으로 국내 서식하고 있는 한국꼬리치레도롱뇽의 주요 분포지역과 서식특성을 파악하고 기후변화에 따른 분포변화를 예측하였다. 그 결과 고도가 그들의 분포에 가장 주요한 영향을 끼친 환경변수로 확인되었으며, 강원도와 경상북도와 같은 고도가 높은 산림 지역에 밀집된 분포 형태를 보였다. 이처럼 종 분포 모델에서 예측된 공간적 분포 범위와 서식특성은 선행 조사 결과를 충분히 포함하고 있었다. 기후변화에 따른 분포변화를 확인한 결과, 한국꼬리치레 도롱뇽은 현재 분포 범위에 비해 RCP4.5 시나리오에서 62.96% 가 감소할 것으로, RCP8.5 시나리오에서는 98.52% 감소할 것으로 예측되어 기후변화로 인해 서식 적합 공간들이 급격하게 감소하는 것으로 확인되었다. 모델의 AUC 값은 현재에서 0.837, RCP4.5에서 0.832, RCP8.5에서 0.807로 높게 측정되었다. 이러한 결과들은 기후변화로 인해 영향을 받는 양서류의 보전 대책 수립에 중요한 기초자료가 될 수 있을 것이다. 추후, 한국꼬리치레도롱뇽의 생활사에 따른 서식지 특성과 미세한 서식 요인들이 반영된 다양한 분석기법을 통한 추가적인 연구가 수행된다면 종 감소에 영향을 끼치는 주요환경 요인들을 밝혀낼수 있을 것으로 판단된다.

The objective of this study was to access the effect of climate and soil factors on alfalfa dry matter yield (DMY) by the contribution through constructing the yield prediction model in a general linear model considering climate and soil physical variables. The processes of constructing the yield prediction model for alfalfa was performed in sequence of data collection of alfalfa yield, meteorological and soil, preparation, statistical analysis, and model construction. The alfalfa yield prediction model used a multiple regression analysis to select the climate variables which are quantitative data and a general linear model considering the selected climate variables and soil physical variables which are qualitative data. As a result, the growth degree days(GDD) and growing days(GD), and the clay content(CC) were selected as the climate and soil physical variables that affect alfalfa DMY, respectively. The contributions of climate and soil factors affecting alfalfa DMY were 32% (GDD, 21%, GD 11%) and 63%, respectively. Therefore, this study indicates that the soil factor more contributes to alfalfa DMY than climate factor. However, for examming the correct contribution, the factors such as other climate and soil factors, and the cultivation technology factors which were not treated in this study should be considered as a factor in the model for future study.

기후변화는 곤충의 성장, 발육, 생존, 생식력, 분포범위 등 생활사의 변수들에 영향을 준다. 특히 외래곤충의 경우 생태계 정착 및 확산이 빨라 지고 있으며, 생태계 교란, 토착종 감소 등 생물다양성을 감소시키는 직접적인 원인 중 하나이다. 알팔파바구미는 1990년대 제주도에서 처음 발견 후 남부지방에 대량 발생하여 농업해충으로 인식되었다. 최근 하면처로 이동하는 개체에 의한 밭작물의 피해와 여러 시군에서 서식이 확인되며 확산의 우려되고 있다. 본 연구에서는 기후변화가 알팔파바구미에 미치는 영향에 대해 파악하였다. 미래의 기후 시나리오 RCP 4.5와 RCP 8.5에서 알팔파바구미의 잠재적 분포를 추정하기 위해 MaxEnt 모델을 적용하였다. 모형의 변수는 2015~2017년까지 알팔파바구미의 서식이 확인된 66개 지점과 종의 생태특성 및 예측변수간 상관성을 고려한 6개(bio3, bio6, bio10, bio12, bio14, bio16)의 생물기후를 사용하였다. 예측된 모형의 적합 도는 평균 0.765로 잠재력이 의미 있는 값이며, 최고 따뜻한 분기의 평균기온(bio10)이 60~70%로 높은 기여도를 나타냈다. 2050년과 2070년의 시나리오(RCP 4.5, RCP 8.5)에 대한 모형의 결과는 한반도 전역에서 알팔파바구미의 분포 변화를 보여 주었으며, 기온상승에 따른 전국적 확산이 예측되었다.

Prediction of the seasonal occurrence and potential distribution of agricultural pests has accomplished by software toolsimplementing species distribution models (SDMs). In this aspect, we used CLIMEX software to evaluate the seasonaloccurrence and potential distribution of Indian meal moth, Plodia interpunctella (Hübner), which is one of household mothsdamaging dried fruits in pantries. Based on the simulation, the beginning of period for suitable climate was predictedto be from mid-March to end-March, while it might be end in late October to early November. The peak time for P.interpunctella was ranged from early or mid-July to mid-August, but depended on local geography. When applying RCP8.5 climate change scenario, it was predicted that P. interpunctella would not occur due to intensive rainfall in July andAugust in 2060.

Invasive pests have posed an ecological threat as climate change has been accelerated, suggesting early prediction ofinvasive pests is required to minimize damages by them. As one of predictive tools, CLIMEX has been effectively usedin a few regions, including US, Australia, and Europe. It allows us to predict a species distribution on a local area inresponse to climatic conditions: and thus, potential distribution of invasive species, risk assessment of agricultural pests,and suitability of biological control agents have been tested by CLIMEX. In this study, we introduced how to use CLIMEXfor predicting a species distribution differed by climate change in terms of its functions, required data, and examplesof its application.

This study was aimed to find yield prediction model of Italian ryegrass using climate big data and geographic information. After that, mapping the predicted yield results using Geographic Information System (GIS) as follows; First, forage data were collected; second, the climate information, which was matched with forage data according to year and location, was gathered from the Korean Metrology Administration (KMA) as big data; third, the climate layers used for GIS were constructed; fourth, the yield prediction equation was estimated for the climate layers. Finally, the prediction model was evaluated in aspect of fitness and accuracy. As a result, the fitness of the model (R2) was between 27% to 95% in relation to cultivated locations. In Suwon (n=321), the model was; DMY = 158.63AGD –8.82AAT +169.09SGD - 8.03SAT +184.59SRD -13,352.24 (DMY: Dry Matter Yield, AGD: Autumnal Growing Days, SGD: Spring Growing Days, SAT: Spring Accumulated Temperature, SRD: Spring Rainfall Days). Furthermore, DMY was predicted as 9,790±120 (kg/ha) for the mean DMY(9,790 kg/ha). During mapping, the yield of inland areas were relatively greater than that of coastal areas except of Jeju Island, furthermore, northeastern areas, which was mountainous, had lain no cultivations due to weak cold tolerance. In this study, even though the yield prediction modeling and mapping were only performed in several particular locations limited to the data situation as a startup research in the Republic of Korea.

In this study, rainfall characteristics with stationary and non-stationary perspectives were analyzed using generalized extreme value (GEV) distribution and Gumbel distribution models with rainfall data collected in major cities of Korea to reevaluate the return period of sewer flooding in those cities. As a result, the probable rainfall for GEV and Gumbel distribution in non-stationary state both increased with time(t), compared to the stationary probable rainfall. Considering the reliability of ξ1, a variable reflecting the increase of storm events due to climate change, the reliability of the rainfall duration for Seoul, Daegu, and Gwangju in the GEV distribution was over 90%, indicating that the probability of rainfall increase was high. As for the Gumbel distribution, Wonju, Daegu, and Gwangju showed the higher reliability while Daejeon showed the lower reliability than the other cities. In addition, application of the maximum annual rainfall change rate (ξ1·t) to the location parameter made possible the prediction of return period by time, therefore leading to the evaluation of design recurrence interval.

This study aims to offer basic data to effectively preserve and manage pine forests using more precise pine forests’ distribution status. In this regard, this study predicts the geographical distribution change of pine forests growing in South Korea, due to climate change, and evaluates the spatial distribution characteristics of pine forests by age. To this end, this study predicts the potential distribution change of pine forests by applying the MaxEnt model useful for species distribution change to the present and future climate change scenarios, and analyzes the effects of bioclimatic variables on the distribution area and change by age. Concerning the potential distribution regions of pine forests, the pine forests, aged 10 to 30 years in South Korea, relatively decreased more. As the area of the region suitable for pine forest by age was bigger, the decreased regions tend to become bigger, and the expanded regions tend to become smaller. Such phenomena is conjectured to be derived from changing of the interaction of pine forests by age from mutual promotional relations to competitive relations in the similar climate environment, while the regions suitable for pine forests’ growth are mostly overlap regions. This study has found that precipitation affects more on the distribution of pine forests, compared to temperature change, and that pine trees’ geographical distribution change is more affected by climate’s extremities including precipitation of driest season and temperature of the coldest season than average climate characteristics. Especially, the effects of precipitation during the driest season on the distribution change of pine forests are irrelevant of pine forest’s age class. Such results are expected to result in a reduction of the pine forest as the regions with the increase of moisture deficiency, where climate environment influencing growth and physiological responses related with drought is shaped, gradually increase according to future temperature rise. The findings in this study can be applied as a useful method for the prediction of geographical change according to climate change by using various biological resources information already accumulated. In addition, those findings are expected to be utilized as basic data for the establishment of climate change adaptation policies related to forest vegetation preservation in the natural ecosystem field.

현재 최고 수준의 대순환 모형에서 북동아시아 여름몬순 강도의 계절예측 능력은 낮으나 북서태평양 아열대 고기압 강도의 예측률은 상대적으로 높다. 북서태평양 아열대 고기압은 북서태평양 지역 및 동아시아 지역에서 가장 주된 기후 변동성이다. 본 연구에서 NCEP 계절예측시스템에서 예측된 북서태평양 아열대 고기압의 예측성에 대해 논의될 것이다. 한편, 북동아시아 여름몬순의 경년변동성은 북서태평양 아열대 고기압과 높은 상관성을 가지고 있다. 본 연구에서는 이 관계에 근거하여, NCEP 계절예측시스템과 정준상관분석을 이용한 계절예측 모형을 제안하고 그 예측률을 평가하였다. 이 방법은 북동아시아 지역 여름철 강수량 편차에 대한 계절예측에 있어 통계적으로 유의한 예측성능을 제공한다.

최근 WMO는 온실가스 배출량 시나리오(SRES)를 대신하여 대표농도경로(RCP)를 바탕으로 새로운 기후변화 시나리오를 생산하였으며 기상연구소는 RCP 시나리오를 바탕으로 한반도의 새로운 기후변화 시나리오를 생산하였다. 본 연구에서는 과거 관측값을 바탕으로 평년(1981-2010)의 애멸구의 우화시기와 세대수를 추정하였으며, RCP 8.5 시나리오를 바탕으로 2020년대(2015-2024), 2050년대(2045-2054)와 2090년대(2085-2094) 애멸구의 우화시기와 세대수를 예측하였다. 평년 애멸구 월동 1세대수의 우화일인 176.0±0.97일과 비교하여 2050년대에서는 13.2±0.18일(162.8±0.91일), 2090년대에는 32.1±0.61일(143.9±1.08일) 앞당겨질 것을 예측되었다. 그리고 애멸구의 연간 세대수는 2050년대에서는 현재보다 2.0±0.02세대, 2090년대에는 5.2±0.06세대 증가할 것으로 예측되었다.

Temperature is one of important factors to determine insect phenology. Based on the bioclimatic law, the relationship between climate change and ecosystem change was studied from 2008 to 2013 in HECRI by monitoring the spring emergence patterns of three Papilionidae species (Papilio xuthus, P. machaon, and Sericinus montela). The overwintering pupae were set on the wood plate and adult emergence were monitored and recorded in every morning. The first spring emergence of P. xuthus, P. machaon and S. montela in 2013 were Apr 19th, May 1st and Apr 22th, respectively. And peak time of three species were May 7th, May 11th and May 9th, respectively. Study on temperature-dependent development was conducted to investigate the temperature effect on adult emergence of overwintering S. montela pupae at four different constant temperatures (15, 20, 25 and 30°C) with photoperiod 10:14(L:D). The low temperature threshold of female, male and both sexes combined were 12.39, 12.16, and 12.37°C, respectively. Developmental period of overwintering pupae to adults decreased with increasing temperature from 15 to 30°C. Thermal constant of female, male and both sexes combined were 220.26, 192.31, and 200.18DD, respectively. The relationship between thermal constant and cumulative adult emergence was predicted by temperature-dependent development. Estimate through 7 times on the highest temperature was equal and results were distinctively divided into two pattern(2008～2010 and 2011～2013). The relationship between observed and estimated values was presented by linear regression (r2=0.97)

본 연구는 기후변화에 따른 난대상록수종인 황칠나무의 적지예측모델을 개발하기 위하여 수행하였다. 생장 및 입지환경 인자들간의 관계 구명을 통하여 양적·질적 자료 분석이 가능한 수량화 분석 방법에 의하여 황칠나무의 적지예측 평가기준을 도출하였다. 적지예측 프로그램은 ESRI, ArcView GIS 프로그램을 이용하여 개발하였다. 개발된 프로그램은 적지예측의 정확성 검토를 위하여 다양한 난대 상록활엽수가 분포하고 있는 전남 완도 지역에 적용하였다. 황칠나무의 적지예측 분석 결과, 최적지 표고 401~500m, 경사도 15°이하, 국소지형은 산복 계곡부위, 퇴적양식 붕행토, 방위가 남쪽인 요철사면으로 나타났다. 완도지역의 황칠나무 최적지 등급별 맵핑 면적은 Ⅰ등급 1,487.2ha(25.4%), Ⅱ등급 1,020.3ha(17.4%), Ⅲ등급 2,231.8ha(38.2%), Ⅳ등급 1,110.5ha(19.0%)로 나타났다.