Seung Taek Chae

This study quantified uncertainties involved in assessing the future flood vulnerability in 33 urban areas with population exceeding designated thresholds in South Korea. The driver-pressure-state-impact-response (DPSIR) framework was utilized as the study procedure, integrating social, economic, and environmental factors. In addition, a total of 220 cases of combinations were examined, encompassing twenty general circulation models combined with shared socioeconomic pathway scenarios, five weight determination methods, and three multi-criteria decision-making (MCDM) techniques, as sources of inherent uncertainties in the process. The rankings of urban flood vulnerability (UFV) for the selected cities were comprehensively assessed considering all combinations, followed by an analysis of variance test to investigate contributing sources of uncertainties. As a result, Incheon and Busan were found to be vulnerable to flooding, while Yeongcheon and Andong appeared to be safe cities. Some cities exhibited wide ranges in their rankings, such as Daegu, Yangpyeon, and Jeongeup. The identified contributing sources were weighting (58%), MCDM (27%), and the combination of weighting and MCDM methods together (15%). This study revealed that weight determination methods and MCDM techniques are the primary sources of uncertainties in the assessment of future UFV instead of multiple GCMs and SSPs. This finding underscores the importance for decision-makers and stakeholders to carefully consider these uncertainties for sustainable flood risk management and prevention.

This study proposed a systematic approach to quantifying city flood vulnerability (CFV) related to climate change using several multi-criteria decision-making methods in medium-sized cities and investigated the sources of uncertainty in this assessment. In addition, this study was intended to explore ways for quantifying flood vulnerability and mitigating the impact of data uncertainty on flood vulnerability through multi-criteria decision-making (MCDM) methods. The MCDM method was applied as a representative method to quantify flood vulnerability, which considers regional priorities. This study used the weighted summation method, TOPSIS, and VIKOR to calculate all CFVs for medium-sized cities. Furthermore, fuzzy- and grey-TOPSIS were included to account for the uncertainty inherent in the MCDM methods, such as the usage of average values and varying weighting values for all CFV indicators across stakeholders. This study incorporated expert surveys and the entropy approach to derive subjective and objective weights for all conceivable indicators. As a result, we looked at the proposed grey-TOPSIS technique, which can minimize the uncertainty. Finally, grey-TOPSIS can notably provide robust and sustainable prioritizing since it actively reflects the views of multiple stakeholders and takes uncertainty in the data into account.

Evapotranspiration (ET) is the amount of water lost from the global surface, and it represents water and Earth's energy cycle. The intensity and frequency of climate variables have been changed because of the ongoing climate crisis, leading to increased climate disasters, such as heat waves and droughts. The abrupt climate crisis affects the variation of ET because climate variables highly influence ET. However, the future potential ET (PET) estimates include various uncertainty resulted from the variations in the projection of climate variables. In this context, the uncertainty in the projected future PETs should be quantified for the high reliability. Therefore, this study projected future global PET using Penman-Monteith (PM) for the near (2031-2065) and far (2066-2100) futures and quantified the corresponding uncertainty. The six climate variables of 14 CMIP6 GCMs were used for estimating historical PET which were compared to those from the NCEP/NCAR reanalysis data using the five evaluation metrics. The changes in PETs for four Shared Socio-economic Pathways (SSPs) scenarios were calculated for the near and far futures compared to the historical period (1980-2014). Subsequently, the uncertainties of PETs were quantified using the reliability ensemble average method. As a result, the future PET in high latitudes showed the most significant variability compared to the other latitudes. The future PET in the southern hemisphere was higher than the historical PET. Especially the PET in the mid-latitudes of southern hemisphere was the highest among the other latitudes. In addition, the uncertainty of PET was the highest in the high latitudes of the northern hemisphere while the mid-latitude in the northern was the lowest. This study provides insight into evaluating the global water cycle based on PET and helps establish appropriate policies for climate impact assessment.