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The climate change has clearly affected most parts and systems in the globe. Particularly, the recent global warming is presumed as the main reason of the increasing severe flood frequency, resulting a significant damages on the human life and property. This increasing trend on the occurrence of severe flood is very remarkable in the Korean Peninsula as well. Therefore, the appropriate flood prevention and mitigation methods are required. In this study, to assess the climate change effects on flooding risk areas in the Nakdong river basin, the flood and river acts from Korea and other countries were investigated and the current situation and problems were analyzed. The flood risk considering climate change were also assessed for the flood risk areas among the natural disaster risk areas proposed in 2010 to suggest the future management strategy against the future severe flood damage. The proposed flood risk assessment results for the flood risk areas might be used as a basic information to develop climate change adaptation strategy.

Various drought indices developed from previous studies can not consider the inherent uncertainty of drought because they assess droughts using a pre-defined threshold. In this study, to consider inherent uncertainty embedded in monthly streamflow data, Hidden Markov Model (HMM) based drought index (HMDI) was proposed and then probabilistic assessment of hydrologic drought was performed using HMDI instead of using pre-defined threshold. Using monthly streamflow data (1966～2009) of Pyeongchang river and Upper Namhan river provided by Water Management Information System (WAMIS), applying the HMM after moving-averaging the data with 3, 6, 12 month windows, this study calculated the posterior probability of hidden state that becomes the HMDI. For verifying the method, this study compared the HMDI and Standardized Streamflow Index (SSI) which is one of drought indices using a pre-defined threshold. When using the SSI, only one value can be used as a criterion to determine the drought severity. However, the HMDI can classify the drought condition considering inherent uncertainty in observations and show the probability of each drought condition at a particular point in time. In addition, the comparison results based on actual drought events occurred near the basin indicated that the HMDI outperformed the SSI to represent the drought events.

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