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In recent decades, extreme storm events due to climate change have frequently occurred worldwide, a few of which have even occurred consecutively; we class such rainfall events as mega events. That is to say, if the inter-arrival time between rainfall events with a 100-year frequency is less than the IETD (Inter-Event Time Definition), the event can be considered a mega event. Therefore, the aim of this study was to implement flood inundation analysis using the hypothetical mega event from two consecutively occurring events of 100-year frequency, and select the optimal shelters using a developed method for minimizing casualties from floods. The Gyeongan stream basin, which is a tributary of the Namhan River in Korea, was selected as the study area. This study calculates mega flood discharge using the SSARR (Stream Synthesis and Reservoir Regulation) model, and conducts a flood inundation analysis of mega floods via the level pool method and the HEC-GeoRAS model. An inundation map was constructed, and the inundated area was classified into three zones and five administrative districts. Sixteen shelters were selected as candidates based on the criteria of the local government safety management plans and the Guidelines for Establishing the Disaster Relief Plan of 2013. To evaluate the candidates for evacuation in each district, we selected seven evaluation indicators from the shelter criteria of several countries, and calculated the weights of the indicators using the Analytic Hierarchy Process (AHP) method. As a result, four optimal shelters were selected in the study area. The results of the study can be used as the basic information for analyzing mega natural disaster events and inundation, and for establishing evacuation shelters, which are one of the non-structural flood protection measures. [ABSTRACT FROM AUTHOR]

Wetlands play a vital role in hydrologic and ecologic communities. Since there are few studies conducted for wetland water level prediction due to the unavailability of data, this study developed a water level prediction model using various machine learning models such as artificial neural network (ANN), decision tree (DT), random forest (RF), and support vector machine (SVM). The Upo wetland, which is the largest inland wetland in South Korea, was selected as the study area. The daily water level gauge data from 2009 to 2015 were used as dependent variables, while the meteorological data and upstream water level gauge data were used as independent variables. Predictive performance evaluation using RF as the final model revealed 0.96 value for correlation coefficient (CC), 0.92 for Nash–Sutcliffe efficiency (NSE), 0.09 for root mean square error (RMSE), and 0.19 for persistence index (PI). The results indicate that the water level of the Upo wetland was well predicted, showing superior results compared to that of the ANN, which was used in a previous study. The results intend to provide basic data for development of a wetland management method, using water levels of previously ungauged areas. [ABSTRACT FROM AUTHOR]

Climate change and fast urbanization increased rainfall intensity and runoff discharge. These changes lead to the growing possibility of flood damages. In South Korea, a government established the "Comprehensive Flood Prevention Plan (CFPP)" for each of river basin against flood. The plan is based on the combination of structural measures selected by experienced hydrologists. However, it lacks clear criteria. To solving this problem, this study classifies the structural measures and suggests a combination method, which use relationships between measures, to ensure the objectivity of the process in identifying proper measures. For Anyangcheon river basin, two plans are provided; the first is developed by the study, and the other is by the CFPP. Comparing the two plans, the results show that the combination, selected by proposed method in this study, is economically more feasible compared to CFPP. Therefore, this study expected be useful when selecting and combining structural measures for formulating river basin flood prevention plans. [ABSTRACT FROM AUTHOR]

Rainfall data is frequently used as input and analysis data in the field of hydrology. To obtain adequate rainfall data, there should be a rain gauge network that can cover the relevant region. Therefore, it is necessary to analyze and evaluate the adequacy of rain gauge networks. Currently, a complex network analysis is frequently used in network analysis and in the hydrology field, Pearson correlation is used as strength of link in constructing networks. However, Pearson correlation is used for analyzing the linear relationship of data. Therefore, it is now suitable for nonlinear hydrological data (such as rainfall and runoff). Thus, a possible solution to this problem is to apply mutual information that can consider nonlinearity of data. The present study used a method of statistical analysis known as the Brock–Dechert–Scheinkman (BDS) statistics to test the nonlinearity of rainfall data from 55 Automated Synoptic Observing System (ASOS) rain gauge stations in South Korea. Analysis results indicated that all rain gauge stations showed nonlinearity in the data. Complex networks of these rain gauge stations were constructed by applying Pearson correlation and mutual information. Then, they were compared by computing their centrality values. Comparing the centrality rankings according to different thresholds for correlation showed that the network based on mutual information yielded consistent results in the rankings, whereas the network, which based on Pearson correlation exhibited much variability in the results. Thus, it was found that using mutual information is appropriate when constructing a complex network utilizing rainfall data with nonlinear characteristics. [ABSTRACT FROM AUTHOR]