This study investigated the relationship analysis between wheat cultivated area and the climate data in Sindh province of Pakistan. The extraction of wheat cultivation area is detected using the remote sensing technique. The analysis of the study area reveals the annual mean maximum and the mean minimum temperature tends to risen with a large range of changes. The trend of average annual precipitation showed a large change, thus it was confirmed that the increase and decrease yield of wheat were depend on the various growth periods of wheat crop. The most influential factors are the annual mean precipitation and the annual mean minimum temperature at the seedling stage of wheat crop. The annual precipitation, annual mean maximum, and the annual mean minimum temperature are significant at the growth period. The annual mean maximum and the annual mean minimum temperatures are significant during the ripening stage of wheat crop in the study area. The results of the study showed that wheat production varies with climate change in the Sindh province. In addition, this study will be used as an important basis for solving crop cultivation areas and production problems caused by climate change in the region.

Purpose: Agriculture, which is heavily influenced by climate conditions, is one of the industries most affected by climate change. In this respect, various studies on the impact of climate change on the agricultural market have been conducted. Since climate change is a long-term phenomenon for more than a decade, long-term projections of agricultural prices as well as climate variables are needed to properly analyze the impact of climate change on the agricultural market. However, these long-term price projections are often major constraints on studies of climate changes. The purpose of this study is to analyze the impacts of climate changes on the Korean onion market using ex-post analysis approach in order to avoid the difficulties of long-term price projections. Research design, data and methodology: This study develops an annual dynamic partial equilibrium model of Korean onion market. The behavioral equations of the model were estimated by OLS based on the annual data from 1988 to 2018. The modelling system is first simulated to have actual onion market conditions from 2014 to 2018 as a baseline and then compared it to the scenario assuming the climatic conditions under RCP8.5 over the same period. Scenario analyses were simulated by both comparative static and dynamic approach to evaluate the differences between the two approaches. Results: According to the empirical results, if the climate conditions under RCP8.5 were applied from 2014 to 2018, the yield of onion would increase by about 4%, and the price of onion would decrease from 3.7% to 17.4%. In addition, the average price fluctuation rate over the five years under RCP8.5 climate conditions is 56%, which is more volatile than 46% under actual climate conditions. Empirical results also show that the price decreases have been alleviated in dynamic model compared with comparative static model. Conclusions: Empirical results show that climate change is expected to increase onion yields and reduce onion prices. Therefore, the appropriate countermeasures against climate change in Korean onion market should be found in the stabilization of supply and demand for price stabilization rather than technical aspects such as the development of new varieties to increase productivity.

This study had two main objectives. We first investigated which weather phenomena people were most concerned about in the context of climate change or global warming. Then, we conducted content analysis to find which words were more commonly used with climate change or global warming. For this, we collected web data from Twitter, Naver, and Daum from April to October 2019 in the Republic of Korea. The results suggested that people were more concerned about air quality, followed by typhoons and heat waves. Because this study only considered one warm period in the year of 2019, winter-related weather phenomena such as cold wave and snowfall were not well captured. From Twitter, we were able to find wider range of terminologies and thoughts/opinions than Naver and Daum. Also, more life-relevant weather events such as typhoons and heat waves in Twitter were commonly mentioned compared to Naver and Daum. On the other hand, the comments from Naver and Daum showed relatively narrower and limited terms and thoughts/ opinions. Especially, most of the comments were influenced by headlines of articles. We found many comments about air quality and energy/economic policy. We hope this paper could provide background information about how to promote the climate change education and public awareness and how to efficiently interact with general audiences.

This study examined the efficiency of satellite images in terms of detecting wheat cultivation areas, and then analyzed the possibility of climate change through an correlation analysis of time series climate data from the western regions of Gyeongnam province, Korea. Furthermore, we analyzed the effect of climate change on wheat production through a multiple regression analysis with the time series wheat production and climate data. A relatively accurate distribution was achieved on the wheat cultivation area extracted through satellite image classification with an error rate of less than 10% in comparison to the statistical data. Upon correlation analysis with time series climate data, significant results were displayed in the following changes: the monthly mean temperature of the seedling stage, the monthly mean duration of sunshine, the monthly mean temperature of the growing period, the monthly mean humidity, the monthly mean temperature of the ripening stage, and the monthly mean ground temperature. Accordingly, in the study area, the monthly mean temperature, precipitation, and ground temperature generally increased whereas the monthly mean duration of sunshine and humidity decreased. The monthly mean wind speed did not display a particular change. In the multiple regression analysis results, the greatest effect on the production and productivity of wheat as climate factors included the annual mean humidity of the seedling stage, the annual mean temperature of the wintering period, and the annual mean ground temperature of the ripening stage. These results demonstrate that there is a change in wheat production depending on the climate change in the study area. in addition, it is determined that this study will be used as important basic data in the resolution of food security problems based on climate change.

본 연구에서는 기후변화에 따른 메콩강 유출변화 분석을 목적으로 하고 있다. HadGEM3-RA로 부터 생산된 동아시아 지역 RCP 4.5 및 8.5 시나 리오의 일 자료를 기반으로 편의보정을 통해 미래 기후변화 시나리오를 구축한 후, SWAT 모형을 이용하여 메콩강 주요지점인 Kratie(유역면적: 646,000 km2, 메콩강의 연평균 유량의 88%)에서의 유출변화 모의하고 유황분석을 수행하였다. 기후변화 분석 결과 Kratie 유역의 미래 강수량은 기준 년 연평균 강수량 대비 미래 년 기간의 연평균 강수량은 두 시나리오 모두 증가하는 것으로 분석되었으며 월별 강수량 변화 분석을 통해 6 월∼11월에 강수량의 증가가 비교적 크게 나타나며 특히 RCP 8.5 시나리오에서 강수량의 변동 폭 및 증가량이 크게 나타남을 확인하였다. 시나리 오별 월평균 최대 및 최소기온의 변화는 두 시나리오 모두 미래 기온의 상승을 전망하고 있으며 특히 RCP 8.5 시나리오의 온도증가 폭이 크게 나타 나는 것을 확인 하였다. 또한 하천유황변화 분석결과 유역의 유량변동성이 더욱 커질 것으로 분석되었으며 저수계수 값이 52∼57% 감소하고 갈 수계수 값이 67∼74% 감소하는 것으로 나타나 하천의 갈수상황이 지속되어 미래에 가뭄이 보다 심화될 것으로 분석되었다.

본 연구에서는 하천 제방에 대한 홍수취약성을 평가하는 새로운 기법을 제시하고 기후변화에 따라 변화하는 수위에 대하여 제방에 미치는 영향을 분석하였다. 이를 위해 먼저 2차원 지하수침투 모형인 SEEP/W를 이용하여 제방의 침투거동을 분석하여 침투안전성을 평가하였다. 침투거동뿐만 아니라 기후변화에 따른 하천환경여건을 고려하는 제방의 취약성 분석 기술이 필요함으로써 본 연구에서는 추가적으로 제방의 취약성 분석에 필요한 인자를 도출하여 제방의 홍수취약성지수(levee flood vulnerability index； LFVI)에 의한 취약성 평가기법을 새로이 개발 하였다. 대상지역을 한강 본류 서울 구간으로 선정하여 하도별 제방의 크기를 조사하였고 조사한 제방을 상류부, 중류부, 하류부로 구분하여 3개의 대표 제방을 선정하였다. 이들 대표 제방지점에서 현재의 계획홍수위와 기후변화 시나리오 RCP8.5를 고려한 계획홍수위를 적용하여 제방의 활동 안전율과 제방 홍수취약성지수를 분석하였다. 그리고 제방홍수취약성지수를 구성하는 각각 인자들에 대하여 기후변화에 따른 변화 정도를 파악하였다. 이들 인자들을 종합적으로 활용한 제방홍수취약성지수 값을 이용하여 최종적으로 기후변화에 따른 제방의 취약성을 추정할 수 있도록 하였다.

기존 기후변화 영향평가에서 발생하는 불확실성에 대한 연구들은 전체과정에서 총 불확실성과 그 전파에 대한 것보다 각 단계별 불확실성에 초점을 맞추어 연구가 진행되었다. 따라서 본 연구에서는 first-order Taylor series expansion에 기반하여 전망의 분산을 이용하는 Uncertainty Delta Method (UDM)를 제안하였으며, 이 방법은 각 단계별 불확실성 정량화와 증감정도, 단계별 불확실성 비율, 총 불확실성의 전파 과정 제시가 가능 하다. 본 연구에서는 기후변화 영향평가 과정의 단계별 불확실성 정량화와 전파과정 분석을 위해 미래 2030년부터 2059년까지를 대상으로 2개 배출 시나리오, 3개 GCM, 2개 상세화기법, 2개 수문모형을 사용하였다. 결과를 분석하면, UDM을 이용한 총 불확실성은 5.45(배출시나리오: 4.45, 상세화기법: 0.45, 상세화기법: 0.27, 수문모형: 0.28)이며, 배출 시나리오의 불확실성(4.45)이 가장 크게 나타났다. 불확실성은 각 단계를 거칠수록 증가하는 것으로 나타났다. 이러한 결과는 어떠한 배출시나리오를 선정하느냐에 따라 미래 수자원전망이 매우 달라질 수 있음을 의미한다. 다음으로 Hawkins and Sutton (2009)가 제안한 Fractional Uncertainty Method (FUM)을 이용한 기후변화 영향평가 불확실성 분석에서 가장 불확 실성이 큰 요인은 배출 시나리오(FUM 불확실성: 0.52)이며, 이 결과는 UDM 결과와 동일하게 나타났다. 따라서 이 연구에서 제안한 UDM은 기후 변화 영향평가에서의 불확실성 이해와 적합한 분석 및 미래 기후변화 대비 보다 나은 수자원 전망이 가능하도록 기여할 것으로 판단된다.

본 연구의 목적은 금강유역(9,645.5 km2)을 대상으로 극한 기후변화 사상에 따른 수문 및 유황의 변동을 평가하는 것이다. 본 연구에서는 객관적인 극한 기후변화 사상을 평가하기 위해 강우관련 극한지수(STARDEX)를 적용하고, GCM 10개의 RCP 8.5 기후변화 시나리오에 대해 4개의 평 가기간별(Historical: 1975~2005, 2020s: 2011~2040, 2050s: 2041~2070, 2080s: 2071~2100)로 분석하였다. 분석 결과 5개의 습윤 (CESM1-BGC, HadGEM2-ES), 중간(MPI-ESM-MR) 건조(INM-CM4, FGOALS-s2) 극한 기후변화 사상 시나리오를 선정하여 SWAT 모형에 적용하였다. 2080s 기간에서 중간시나리오 대비 2080s의 증발산은 -3.2~+3.1 mm로 변화하였고, 2080s의 총 유출량은 +5.5~+128.4 m3/s 변화하였다. 건조한 시나리오의 경우 2020s 중간시나리오대비 큰 변화를 보였다. 건조한 시나리오에서의 2020s의 증발산량은 -16.8~-13.3 mm 의 변화를 보였고, 총 유출량은 -264.0~132.3 m3/s의 변화를 보였다. 유황 변동의 경우, 2080s 기간의 습윤한 시나리오에서 CFR은 +4.2~+10.5, 2020s 기간의 건조한 시나리오에서는 +1.7~2.6으로 변화 하였다. 극한 기후변화 시나리오를 적용한 금강유역의 수문인자의 변화에 따라 유황분 석을 실시한 결과, INM-CM4는 극한 건조상태를 나타내기에 적절한 시나리오로 나타났고 FGOALS-s2는 유황변동이 큰 가뭄 상태 분석에 적절한 시나리오로 나타났다. HadGEM2-ES는 유황변동이 작게 나타났기 때문에 최대유량 분석 시 활용 가능한 시나리오로 평가되었고, CESM1-BGC 의 경우 유황변동이 큰 것으로 나타나 극한 홍수 분석 시 적용할 수 있는 시나리오로 평가되었다.

This study tries to reveal abnormal trends in climate change from 60 stations in Korea during 1981-2010 by comparisons to the standard station, Chupungnyeong station. Trends in climate change from station with the abnormalities, and their implication and causes are also discussed. Although Wando, Wonju, Mungyeong and Mokpo stations show the most abnormalities, normal trends in climate change from some climate data are also found from Mokpo station. On the other hand, some climate data from Suwon, Jeonju, Jinju, Icheon and Geumsan stations indicate the most normalities. It should be noted that variabilities of climate data are largely different, indicating that clear trends in climate change may not be extracted. The fact that some stations with the abnormalities from some climate data also show the normalities should be also noted. This study suggests that most stations with the most abnormalities may be relevant to relocation of station.

The safety of the future mankind becomes a world issue due to the climate change driven by global warming. It is inevitable to observe everywhere in daily life the impact of climate change. The level of emergency differs between long-term, mid-term, and short-term, which depends on situation. The impact of climate change in daily life is fairly diverse, and therefore multiple research units of different backgrounds often work together on the measuring and forecasting of the impacts. This paper aims to study the potentially useful methods to analyze and forecast the impact on the changes in transport activities of Canadian Inuit. This is part of the research on the climate change’s impact on the Northern Sea Route area. Either natural or man-made change of external environment forces human to adjust their daily life in order to maintain their utility level of life. Individual or individuals’ group react to the external impact, the way of which differ from others. This fact calls a simulation research on the whole set of input-intermediate process-output. Agents representing individuals or certain subpopulation are given a set of rules to react to stimulus and interact with other agents behave in the realm of simulation. This paper provides methodological discussions on how analyzing and forecasting future change in transport activities in response to climate change. The discussion goes around activity-based approaches that are recently popular in urban planning and transportation planning among available simulation approaches. This paper then discusses the implication and future research agenda.

The purpose of this study is to identify the effectiveness of satellite images in detecting the areas of rice production in the Barisal of Bangladesh. We also investigated the effect of climate change on the crop production through comparative analysis of rice production area and production statistics with climate data at multi-temporal time scale. This analysis found that the classification of rice fields extracted through satellite image and made as the number of rice cultivation areas did not exceed 10 percent of the statistical data. Climate data analysis showed that the average temperature during the ripening stage has the greatest impact on Boro’s production. It would be more make sense if you can describe the results of how precipitation is also important for rice production in addition to temperature. Therefore, it is believed that this research could serve as a key basis for solving food security issues due to climate change.

This study analyzes relationship between land cover change and local climate data of 46 weather stations in South Korea from the 1980s to 2000s. The used area shows proportional relationships to mean daily min. temperature and mean temperature, and a reverse relationship is found between the used area and relative humidity. However, the forest indicates reverse relationships to mean daily min. temperature and mean temperature. The agricultural land causes to increase in relative humidity and decrease in mean wind speed, while the water increases mean daily min. temperature, daily min. temperature and mean temperature as well as mean wind speed. The urbanization type (used area + barren) shows high correlations with temperature and humidity. The suburbanization type (agricultural land + forest + grass + wetland) has high correlations with temperature and wind speed. High correlations are also found between the waterfront type (wetland + water), and temperature and wind speed. It can be concluded that change in land cover around weather station obviously influences on climate data of the weather station and it is also expected that reliability and homogeneity of climate data from a weather station can be enhanced by this study.

Recently IPCC (International Panel on Climate Change, 2007) pointed out that global warming is a certain ongoing process on the earth, due to which water resources management is becoming one of the most difficult tasks with the frequent occurrences of extreme floods and droughts. In this study we made runoff predictions for several control points in the Geum River by using the watershed runoff model, SSARR (Streamflow Synthesis and Reservoir Regulation Model), with daily RCP 4.5 and RCP 8.5 scenarios for 100 year from 1st Jan 2006 to 31st Dec 2100 at the resolution of 1 km given by Climate Change Information Center. As a result of, the Geum River Basin is predicted to be a constant flow increases, and it showed a variation in the water circulation system. Thus, it was found that the different seasonality occurred.

본 연구는 강건성 지수와 불확실성 분석기법을 활용하여 기후변화 취약성 평가과정에서 발생하는 불확실성을 정량화하였다. 본 연구는 우리나 라의 6개 광역시(부산, 대구, 인천, 광주, 대전, 울산)를 대상으로 다기준 의사결정기법 중 하나인 TOPSIS 기법을 이용하여 용수공급 취약성 순위를 산정하였다. 강건성 지수는 두 대상 도시의 순위가 가중치의 변화로 인해 순위역전현상이 발생할 수 있는 가능성을 정량화하고 불확실성 분석 기법 은 두 도시 사이에 순위역전이 발생할 수 있는 가중치의 최소 변화량을 산정한다. 그 결과 인천과 대구는 용수공급 측면에서 취약한 것으로 나타났 으며, 대구와 부산은 용수공급 취약성에 민감한 것으로 나타났다. 따라서 대구는 다른 대안에 비해 상대적으로 용수공급이 취약한 지역으로 나타났 으나, 취약성에 민감하기 때문에 기후변화 적응대책 수립 및 시행을 통해 취약성이 크게 향상될 수 있을 것으로 판단된다. 본 연구는 기후변화와 용 수공급 측면에서의 적응전략을 계획하고 수립하는데 있어서 우선적으로 고려해야하는 방향을 제안하는 데 사용될 수 있다.

IPCC 제5차 평가보고서에 따르면 극한강우의 빈도 및 강도가 증가할 가능성이 매우 높을 것으로 예측되고 있다. 실제로 극한강우에 따른 침수피 해가 증가하고 있으며, 이에 따라 기후변화의 영향을 반영한 미래 확률강우량 추정이 필요하다. 본 연구에서는 기후변화 RCP 8.5 시나리오로부터 도출된 미래 연 최대 일강수량 자료의 추세분석과 scale-invariance 기법을 이용하여 미래 확률강우량을 추정하였다. 먼저, 기상청 관할 60개 기상 관측소의 관측 강우자료를 이용하여 관측소별로 스케일 특성을 검토한 후, 현재기후 모의자료를 이용하여 scale-invariance 기법의 적용가능성을 검증하였다. 그 후, 미래 일 강수량 시계열을 scale-invariance 특성에 따라 유도된 IDF 곡선식에 적용하여 기후변화의 영향을 반영한 지속시간별 확률강우량을 추정하였다. 대부분의 지점에서 확률강우량이 증가할 것으로 예측되었으나, 일부 지역의 경우에는 감소할 가능성도 있음을 살펴볼 수 있다.

본 연구에서는 우리나라 강수 및 기온과 다양한 기상인자와의 교차상관관계 분석을 통해 대규모 기후변동이 우리나라에 어떠한 영향을 주는지를 분석하였다. 강수 및 기온자료의 경우 앞선 연구인 “경험적 모드분해법을 이용한 기상인자와 우리나라 강수 및 기온의 상관관계 분석 : I. 자료의 분 해 및 특성분석”의 연구결과를 통해 주기성, 경향성에 따라 분해한 강수 및 기온자료의 내재모드함수를 사용하여, 자료의 변동이 심하고 잡음이 포 함된 원 자료를 통한 상관관계 분석보다 좀 더 명확한 상관계수를 계산하였다. 이렇게 분해된 기상자료와 기상인자간의 교차상관관계 분석을 통해 그 시간차와 상관계수를 계산하여, 주기성과 경향성 측면에서 어떠한 상관관계가 있는지를 분석하였다. 그 결과 주기성 측면에서 엘니뇨현상에 의 한 기후변동이 우리나라 기상현상과 밀접한 관련이 있으며, 경향성 측면에서 기후변화로 인한 해수면 온도 증가추세가 우리나라 기상현상과 밀접 한 연관이 있음을 알 수 있었다.

최근 기후변화로 인한 자연재해가 증가하면서 강수 및 기온자료의 시계열에 대한 변동성과 추세를 분석하여 그 변화를 예측하는 연구의 필요성 이 점점 커지고 있다. 하지만 강수나 기온의 경우 복합적인 요소에 의해 변동이 일어나 자료의 변동성이 매우 심하고 너무 많은 요소를 포함하게 되 어 그 특성을 정확히 판단하기가 쉽지 않다. 따라서 자료의 시계열을 분해하게 되면 각 특성을 가진 요소를 추출할 수 있으므로, 정확한 변동 특성을 파악할 수 있다. 본 연구에서는 우리나라 강수 및 기온자료를 경험적 모드분해법(Empirical Mode Decomposition, EMD)을 통해 주기별로 분해 하여 각각의 내재모드함수(Intrinsic Mode Function, IMF)를 추출하였다. 또한, 추출된 내재모드함수의 에너지 밀도를 이용한 유의성 검정을 통 해 원자료로부터 유의미한 자료를 포함하고 있는 내재모드함수를 선별하고, 이들의 주기성, 경향성을 분석하였다.

Jeju Island is the highest rain-prone area in Korea that possesses affluent water resources, but future climate changes are predicted to further increase vulnerabilities as resultant of increasing of extreme events and creating spatial-temporal imbalance in water resources. Therefore, this study aimed to provide basic information to establish a proper water resources management plan by evaluating the effects of climate change on water resources using climate change scenario. Direct runoff ratio for 15 years (2000~2014) was analyzed to be 11∼32% (average of 23%), and average direct runoff ratio for the next 86 years (2015∼2100) was found as 28%, showing an increase of about 22% compared to the present average direct runoff ratio (23%). To assess the effects of climate change on long-term runoff, monthly runoff variation of future Gangjeong watershed was analyzed by dividing three time periods as follows: Present (2000∼2030), Future 1 (2031∼2070) and Future 2 (2071∼ 2100). The estimated results showed that average monthly runoff increases in the future and the highest runoff is shown by Future 2. Extreme values has been expected to occur more frequently in the future as compared to the present.

The purpose of the research is to analyze damages of TYLCV (Tomato Yellow Leaf Curl Virus) in the context of climate changes and to find the spatial distribution of the damages and characteristics of regions. A TYLCV is generally known for a plant disease related to temperature. Its occurrence rate increases when temperature rises. This disease first occurred in 2008 and rapidly spread nationwide. Due to the spread of a TYLCV, a number of Tomato farms in Korea were damaged severely. To analyze damages of the pest in the context of climate changes, this research estimated production loss under the current situation and RCP scenarios. Additionally, Hot Spot Analysis, LISA, and Cluster analysis were conducted to find spatial distribution and properties of largely damaged regions under RCP scenarios. The results explained that additional production loss was estimated differently by regions with the same temperature rising scenario. Also, largely damaged regions are spatially clustered and factors causing large damages were different across regional cluster groups. It means that certain regions can be damaged more than others by diseases and pests. Furthermore, pest management policy should reflect the properties of each region such as climate conditions, cultivate environment and production technologies. The findings from this research can be utilized for developing rural management plans and pest protection policies.

This study has calculated the change of wind speed according to the features of land surface roughness using the surface wind data provided by the Korean peninsula data of HadGEM3-RA and has analyzed the characteristics of the future upper wind over South Korea driven by several climate change scenarios. The simulation found that the more the time passes, the more the wind speed increases in the previous time period of upper wind and annual average wind speed time series analysis of three kinds of Representative Concentration Pathways (RCP). The wind speed of all three kinds of RCP increased in the summer and winter but decreased in the spring and fall in the analysis of seasonal time series and spatial distribution. The wind speed would be expected to increase in most months except April and November in the analysis of the monthly mean maximum wind speed. The histogram analysis shows the mean wind speed of upper wind over 3m/s. As the time passes, the wind speed increases more than in the past. Certain areas such as the areas under the urbanization development would be anticipated to raise the wind speed throughout all months.