• Journal of Wellbeing Management and Applied Psychology
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• v.7 no.4
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• pp.15-20
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• 2024

Purpose: Climate change is greatly affecting the frequency and intensity of fires around the world. The main effects of climate change on fires are rising temperatures, dry seasons and extreme droughts, changes in precipitation, increased strong winds, extended fire danger periods, and changes in natural ecosystems. Several factors due to climate change are increasing the risk of large-scale fires, such as wildfires. Research design, data and methodology: Rising temperatures caused by climate change will make forests and grasslands drier, make it easier for wildfires to occur in drier environments and spread quickly to wider areas, and the generated wildfires will release large amounts of greenhouse gases into the atmosphere, such as carbon dioxide (CO₂), and the released greenhouse gases will strengthen the global greenhouse effect, further raising the temperature. As temperatures rise, the risk of wildfires increases in drier environments, and this process is repeated, leading to a vicious cycle of intensifying climate change as more fires occur and more greenhouse gases are released. Results: In conclusion, climate change is increasing the risk of fire occurrence and this phenomenon is expected to become more frequent and severe in the future. Conclusions: In order to cope with the increasing fire risk caused by climate change, fire prevention and management. Fire detection and response systems need to be strengthened, supportive policies and international cooperation are needed to restore ecosystems, and these measures, along with fire prevention, management and countermeasures, should take into account long-term climate change and adaptation as well as short-term responses.

• Asian Journal of Atmospheric Environment
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• v.8 no.3
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• pp.140-153
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• 2014

The extreme heat watch and warnings (EHWW) which is constructed as a part of the climate change adaptation took effect in the summer of 2008, but active response actions failed to be taken because of low perception among citizens. Therefore, a survey investigation targeting citizens residing in Busan and the Gyeongnam province was conducted in order to know the perception regarding EHWW issued by the Korea Meteorological Administration, to identify the main media through which information is acquired, and to propose an improvement measures which may enhance the usefulness and the degree of satisfaction of weather information. The results are as follows; The perception regarding EHWW was not very high as it remained at 59.8% in terms of percentile. Although the statistical significance was not fulfilled in the categories of gender or occupation, significant differences did exist among age groups. The main medium through which citizens acquired information regarding EHWW was the television, which was followed in order by the internet, acquaintances, short message service (SMS), radio, newspapers, the 131 weather hotline, and other media. The usefulness of EHWW was somewhat high (67.2%), and female students were found to utilize the information to a higher degree than male students. The statistics on the level of satisfaction regarding the weather information (65.4%) revealed that most respondents were satisfied. Housewives, professional, and the elder age groups exhibited great satisfaction, leading to the conclusions that the level of perception and interest regarding to the special weather reports (SWR) have an impact on satisfaction of SWR.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.5
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• pp.655-669
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• 2018

In this study, we developed a hybrid forecasting model based on a four-parameter distribution which allows a simultaneous season-ahead forecasting for both seasonal rainfall and sub-daily rainfall in Han-River and Geum-River basins. The proposed model is mainly utilized a set of time-varying predictors and the associated model parameters were estimated within a Bayesian nonstationary rainfall frequency framework. The hybrid forecasting model was validated through an cross-validatory experiment using the recent rainfall events during 2014~2017 in both basins. The seasonal precipitation results showed a good agreement with the observations, which is about 86.3% and 98.9% in Han-River basin and Geum-River basin, respectively. Similarly, for the extreme rainfalls at sub-daily scale, the results showed a good correspondence between the observed and simulated rainfalls with a range of 65.9~99.7%. Therefore, it can be concluded that the proposed model could be used to better consider climate variability at multiple time scales.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.158-158
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• 2023

Characterizing the performance of precipitation (hereafter PRE) products in estimating the uncertainties in daily PRE in the era of global warming is of great value to the ecosystem's sustainability and human survival. This study intercompares the performance of different PRE products (gauge-based, satellite and reanalysis) sourced from the Frequent Rainfall Observations on GridS (FROGS) database over diverse climate zones in Africa and identifies regions where they depict minimal uncertainties in order to build optimal maps as a guide for different climate users. This is achieved by utilizing various techniques, including the triple collection (TC) approach, to assess the capabilities and limitations of different PRE products over nine climatic zones over the continent. For daily scale analysis, the uncertainties in light PRE (0.1 5mm/day) are prevalent over most regions in Africa during the study duration (2001-2016). Estimating the occurrence of extreme PRE events based on daily PRE 90th percentile suggests that extreme PRE is mainly detected over central Africa (CAF) region and some coastal regions of west Africa (WAF) where the majority of uncorrected satellite products show good agreement. The detection of PRE days and non-PRE days based on categorical statistics suggests that a perfect POD/FAR score is unattainable irrespective of the product type. Daily PRE uncertainties determined based on quantitative metrics show that consistent, satisfactory performance is demonstrated by the IMERG products (uncorrected), ARCv2, CHIRPSv2, 3B42v7.0 and PERSIANN_CDRv1r1 (corrected), and GPCC, CPC_v1.0, and REGEN_ALL (gauge) during the study period. The optimal maps that show the classification of products in regions where they depict reliable performance can be recommended for various usage for different stakeholders.

• Journal of Agricultural Extension & Community Development
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• v.30 no.4
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• pp.213-227
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• 2023

This paper aims to examine the farmers' perceptions of the importance and feasibility of climate change awareness and adaptive measures in agriculture, utilizing paired sample t-tests and Importance-Performance Analysis (IPA). Significant differences were found in farmers' views on the importance and urgency of climate change issues, with specific factors standing out. The IPA analysis identified key issues requiring sustained attention, including climate change magnitude, extreme weather events, livestock damage scale, pest fluctuations, and variability in flowering periods. Additionally, the study revealed significant disparities in farmers' perceptions of the importance and feasibility of adaptive measures, except for specific items related to heat indices.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.1
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• pp.165-174
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• 2019

As a nonstationarity is observed in hydrological data, various studies on nonstationary frequency analysis for hydraulic structure design have been actively conducted. Although the inherent diversity in the atmosphere-ocean system is known to be related to the nonstationary phenomena, a nonstationary frequency analysis is generally performed based on the linear trend. In this study, a nonstationary frequency analysis was performed using climate indices as covariates to consider the climate variability and the long-term trend of the extreme rainfall. For 11 weather stations where the trend was detected, the long-term trend within the annual maximum rainfall data was extracted using the ensemble empirical mode decomposition. Then the correlation between the extracted data and various climate indices was analyzed. As a result, autumn-averaged AMM, autumn-averaged AMO, and summer-averaged NINO4 in the previous year significantly influenced the long-term trend of the annual maximum rainfall data at almost all stations. The selected seasonal climate indices were applied to the generalized extreme value (GEV) model and the best model was selected using the AIC. Using the model diagnosis for the selected model and the nonstationary GEV model with the linear trend, we identified that the selected model could compensate the underestimation of the rainfall quantiles.

• 한국균학회소식:학술대회논문집
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• 2018.05a
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• pp.41-41
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• 2018

There has been an impetus in the development of biocontrol agents (BCAs) with the removal of a number of chemical compounds in the market, especially in the European Union. This has been a major driver in the development of Integrated Pest Management systems (IPM) for both pest and disease control. For control of mycotoxigenic fungi, there is interest in both control of colonization and more importantly toxin contamination of staple food commodities. Thus the relative inoculum potential of biocontrol agent vs the toxigenic specie sis important. The major bottlenecks in the production and development of formulations of biocontrol agents are the resilience of the strains, inoculum quality and formulation with effective field efficacy. It was recently been shown for mycotoxigenic fungi such as Aspergillus flavus, under extreme climate change conditions, growth is not affected although there may be a stimulation of aflatoxin production. Thus, the development of resilient biocontrol strains which can may have conserved control efficacy but have the necessary resilience becomes critical form a food security point of view. Indeed, under predicted climate change scenarios the diversity of pests and fungal diseases are expected to have profound impacts on food security. Thus, when examining the identification of potential biocontrol strains, production and formulation it is critical that the resilience to CC environmental factors are included and quantified. The problems in relation to the physiological competence and the relative humidity range over which efficacy can occur, especially pre-harvest may be increase under climate change conditions. We have examined the efficacy of atoxigenic strains of A. flavus and Clanostachys rosea and other candidates for control of A. flavus and aflatoxin contamination of maize, and for Fusarium verticillioides and fumonisin toxin control. We have also examined the potential use of fluidized-bed drying, nanoparticles/nanospheres and encapsulation approaches to enhance the potential for the production of resilient biocontrol formulations. The objective being the delivery of biocontrol efficacy under extreme interacting climatic conditions. The potential impact of climate change factors on the efficacy of biocontrol of fungal diseases and mycotoxins are discussed.

• Journal of Korea Water Resources Association
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• v.46 no.6
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• pp.585-596
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• 2013

The objective of this study is to evaluate the hybrid downscaling method combined Step-Wise Scaling (SWS) method with Regional Climate Model (RCM) simulation data for climate change impact study on hydrology area. The SWS method is divided by 3 categories (extreme event, dry event and the others). The extreme events, wet-dry days and the others are corrected by using regression method, quantile mapping method, mean & variance scaling method. The application and evaluation of SWS method with 3 existing and popular statistical techniques (linear scaling method, quantile mapping method and weather generator method) were performed at the 61 weather stations. At the results, the accuracy of corrected simulation data by using SWS are higher than existing 3 statistical techniques. It is expected that the usability of SWS method will grow up on climate change study when the use of RCM simulation data are increasing.

• Atmosphere
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• v.33 no.1
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• pp.1-20
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• 2023

Holding the longest observation data from April 1904, Busan is one of the essential points to understand the climate variability of the Korean Peninsula without missing data since implementing the modern weather observation of the South Korea. Busan is featured by coastal areas and affected by various climate factors and fluctuations. This study aims to investigate climate variability and changes in climatic variables, extremes, and several weather indexes. The statistically significant change points in daily mean rainfall intensity and temperature were found in 1964 and 1965. Based on the change point detection, 117 years were divided into two periods for daily mean rainfall intensity and temperature, respectively. In the long-term temperature analysis of Busan, the increasing trend of the daily maximum temperature during the period of 1965~2021 was larger than the daily mean temperature and the daily minimum temperature. Applying Ensemble Empirical Mode Decomposition, daily maximum temperature is largely affected by the decadal variability compared to the daily mean and minimum temperature. In addition, the trend of daily precipitation intensity from 1964~2021 shows a value of about 0.50 mm day^-1, suggesting that the rainfall intensity has increased compared to the preceding period. The results in extremes analysis demonstrate that return values of both extreme temperatures and precipitation show higher values in the latter than in the former period, indicating that the intensity of the current extreme phenomenon increases. For Wet-Bulb Globe Temperature (effective humidity), increasing (decreasing) trend is significant in Busan with the second (third)-largest change among four stations.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.6B
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• pp.531-539
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• 2011

It has been widely acknowledged that climate system associated with extreme rainfall events was difficult to understand and extreme rainfall simulation in climate model was more difficult. This study developed a new model for extracting rainfall filed associated with extreme events as a way to characterize large scale climate system. Main interests are to derive location, size and direction of the rainfall field and this study developed an algorithm to extract the above characteristics from global climate data set. This study mainly utilized specific humidity and wind vectors driven by NCEP reanalysis data to define the rainfall field. Geometric first and second moments have been extensively employed in defining the rainfall field in selected zone, and an ellipsoid based model were finally introduced. The proposed geometric moments based ellipsoid model works equally well with regularly and irregularly distributed synthetic grid data. Finally, the proposed model was applied to space-time real rainfall filed. It was found that location, size and direction of the rainfall field was successfully extracted.