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We investigated the potential impact of climate change on people–caused forest fire occurrence in South Korea. Logistic regression analysis methods were used to develop daily fire occurrence predictio...n models for each of nine study areas. These models were then coupled with climate scenario data produced by two General Circulation Models (CCCma and CCSR/NIES) to predict future people–caused fire occurrence in those nine areas. Our results suggest the number of fire days will increase by roughly 7 to 58% depending upon the district. However, as the prediction of fire occurrence was varied by the land use, the vegetation, human activity, forest management policy and etc., more factors related this part should be need to research more with this study.続きを見る
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