As the effects of climate change are becoming severe, countries need to substantially reduce carbon emissions. Small hydropower (SHP) can be a useful renewable energy source with a high energy density for the reduction of carbon emission. Therefore, it is necessary to revitalize the development of SHP to expand the use of renewable energy. To efficiently plan and utilize this energy source, there is a need to assess the future SHP potential based on an accurate runoff prediction. In this study, the future SHP potential was predicted using a climate change scenario and an artificial neural network model. The runoff was simulated accurately, and the applicability of an artificial neural network to the runoff prediction was confirmed. The results showed that the total amount of SHP potential in the future will generally a decrease compared to the past. This result is applicable as base data for planning future energy supplies and carbon emission reductions.

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.

Airlines provide one of the most popular and important transportation services for passengers. While the importance of the airline industry is rising, flight cancellations are also increasing due to abnormal weather factors, such as rainfall and wind speed. Although previous studies on cancellations due to weather factors considered both aircraft and weather factors concurrently, the complex network studies only treated the aircraft factor with a single-layer network. Therefore, the aim of this study was to apply a multilayer complex network (MCN) method that incorporated three different factors, namely, aircraft, rainfall, and wind speed, to investigate aircraft cancellations at 14 airports in the Republic of Korea. The results showed that rainfall had a greater impact on aircraft cancellations compared with wind speed. To find out the most important node in the cancellation, we applied centrality analysis based on information entropy. According to the centrality analysis, Jeju Airport was identified as the most influential node since it has a high demand for aircraft. Also, we showed that characteristics and factors of aircraft cancellation should be appropriately defined by links in the MCN. Furthermore, we verified the applicability of the MCN method in the fields of aviation and meteorology. It is expected that the suggested methodology in this study can help to understand aircraft cancellation due to weather factors.

In this study, fuzzy mathematics and VIKOR were employed to develop a flood vulnerability assessment system for Ulsan Metropolitan City, South Korea in 2018. HEC-HMS model was used to simulate the major rivers’ runoff in Ulsan Metropolitan City, and HEC-RAS model was used to convert the 1-D runoff simulation results into 2-D results of inundation map, while the simulation results were exhibited through ArcMap. Hazards, sensitivity and adaptivity were selected as the three evaluation indicators for fuzzy comprehensive evaluation, and pressure resilience, state resilience and response resilience were utilized as the indicators for ranking and comparison of the VIKOR method for local governments (administrative districts) of Ulsan Metropolitan City. As the result, Dong-gu district, Ulju-jun County and Nam-gu district showed the best results by both the fuzzy mathematical and the VIKOR method in hazard and pressure, sensitivity and state, adaptivity and response indicator. Fuzzy mathematics and the VIKOR method provide two different ways to study the flood protection capacity of Ulsan Metropolitan City, comparing the historical statistics, VIKOR method’s evaluation results match well with the flooding statistics in Ulsan Metropolitan City.

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.

Previous study on natural disaster events has been conducted under the assumption that each event is independent of others, which underestimates the risk of natural disasters by ignoring interactions between events. To solve this problem, the concept of compound natural disaster (CND) which is the combination of events has been proposed; however, there is no quantitative method for defining CND. The aims of this study are to examine the estimation methods for IETD (Inter-Event Time Definition), which separates continuous rainfalls into independent rainfall events, and to define NIETD (Natural disaster Inter-Event Time Definition), which is a criterion for determining the independence of natural disasters. This study used the method of average annual number of events for estimating NIETD. Two natural disasters can be defined as CND if the duration between them is less than the NIETD. We estimated the NIETD as 8 days using natural disasters that occurred in South Korea and identified a total of 89 CNDs of 14 different types such as consecutive rainfall events. The largest number of CNDs was caused by the combination of rainfall and typhoon, which also resulted in the most damage. To examine the randomness of event occurrences, we applied a bootstrapping approach and found that there is no evidence of randomness. The frequency analysis showed that CNDs consisting of rainfall and typhoon (7.6years), and consecutive rainfalls (9.4years) had overwhelmingly more frequent occurrences than other types. The CND definition and concept proposed in this study could be useful in the research on CND.