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.

The purpose of this research is to provide an objective and accurate regional vulnerability index at a finer resolution with the research period from 1983 to 2012 in Korea. To find the spatial patterns and characteristics of regional vulnerability, this research conducted four different types of analyses. First, the most vulnerable regions in terms of demographic, climatological-geographic, socioeconomic, and technological factors were respectively investigated. Second, total vulnerability index combining all the four factors was examined. Next, the most influential factors deciding vulnerability and common spatial patterns of vulnerability were extracted using empirical orthogonal function (EOF) analysis. Lastly, the degree of clustering for each factor was checked using Moran’s I and local indicators of spatial association (LISA). The result found the most vulnerable provinces were Jeolla and Gyeongsang Province, regarding to demographic and climatological- geographic factors, respectively. In the case of socioeconomic factors, the difference between urban and rural areas was larger than the difference between provinces. In addition, the EOF analysis showed that demographic factors would be the most influential factors which explained 32.2 percent of the total variance of data. Lastly, climatological and geographic factors represented the highest degree of clustering (global Moran’s I: 0.51).

본 연구에서는 미계측 유역에서 오염부하량 모의를 위해 LOADEST (LOAD Estimator) 기반 회귀모형의 최적 매개변수를 추정하고, 다중회귀분석 기법을 이용하여 유역특성에 따른 회귀 모의 모형의 매개변수 추정 방법을 개발하였으며, 개발된 모형의 적용성을 평가하였다. 오염부하량 모의 모형으로, T-N (Total-Nitrogen)은 LOADEST의 5번 회귀모형을, T-P (Total-Phosphorous)는 3번 회귀모형을 선택하였다. 모의결과, T-N, T-P 모두 선택된 회귀모형이 실측치를 잘 반영하였으나, 두 물질 모두 오염부하량이 과소 모의되어 실측치와 편의가 발생하는 것으로 나타나, 분위사상법을 이용하여 모의치의 편의보정을 실시하였다. 보정결과, 모형의 정확도는 크게 변하지 않았으나, 오염부하량이 과소 모의 되는 경향이 감소하는 것으로 나타났다. 다중회귀분석을 이용하여 회귀모형 매개변수와 유역특성간의 회귀식을 개발하였으며, 개발된 식을 평가한 결과, 실측치를 잘 반영하여 모의할 수 있는 것으로 나타났으며, 기존 매개변수에 의한 모의치와 유사한 모의능력을 갖는 것으로 나타났다. 본 연구에서 개발된 매개변수 추정방법은 실측자료가 확보되지 않은 소유역에 대한 오염부하량 모의와 정책결정을 위한 스크린 모델로서 활용할 수 있을 것으로 사료된다.