Due to the recent appearance of shares socioeconomic pathway (SSP) scenarios, there have been many studies that compare the results between Coupled Model Intercomparison Project (CMIP)5 and CMIP6 general circulation models (GCMs). This study attempted to project future drought characteristics in the Cheongmicheon watershed using SSP2-4.5 of Australian Community Climate and Earth System Simulator-coupled model (ACCESS-CM2) in addition to Representative Concentration Pathway (RCP) 4.5 of ACCESS 1-3 of the same institute. The historical precipitation and temperature data of ACCESS-CM2 were generated better than those of ACCESS 1-3. Two meteorological drought indices, namely, Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were used to project meteorological drought while a hydrological drought index, Standardized Streamflow Index (SDI), was used to project the hydrological drought characteristics. The metrological data of GCMs were bias-corrected using quantile mapping method and the streamflow was obtained using Soil and Water Assessment Tool (SWAT) and bias-corrected meteorological data. As a result, there were large differences of drought occurrences and severities between RCP4.5 and SSP2-4.5 for the values of SPI, SPEI, and SDI. The differences in the minimum values of drought index between near (2021–2060) and far futures (2061–2100) were very small in SSP2-4.5, while those in RCP4.5 were very large. In addition, the longest drought period from SDI was the largest because the variation in precipitation usually affects the streamflow with a lag. Therefore, it was concluded that it is important to consider both CMIP5 and CMIP6 GCMs in establishing the drought countermeasures for the future period.

This study projects precipitation over East Asia comprising of East China, South Korea, and Japan using ten Global climate models (GCM) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) and their Multi-Model Ensemble (MME). The historical GCMs were assessed using statistical metrics and compromise programming for their abilities in replicating the observed precipitation in order to assess their skills in projecting precipitation relative to the MME. Linear scaling method was used in bias correcting the GCMs using Global Precipitation Climatology Center (GPCC) as reference observed data. Probability density function and mean monthly precipitation of GCMs for the different months over the period 2021–2060 and 2061–2100 were compared with those of the observed under SSPs 2–45 and 5–85. The study revealed that NorESM2-MM, MRI-ESM2-0, MME and ACCESS-ESM1-5 have the best abilities to replicate the observed precipitation in the area of study. Under SSP 2–45, spatial projections show that the changes in precipitation for 2021–2060 are expected to range between −32.0–4.0% while those for 2061–2100 are −46.7–4.4%. Under SSP 5–85, the expected changes in precipitation range from −40 to 6.6% during 2021 to 2060 while the range is −87.3 to 6.6% with the highest decreases expected for CanESM5. The MME projects the changes of −12.0–6.6% during this period. The mean monthly precipitations for the different months over 2021–2060 and 2061–2100 show that while some GCMs project increases in mean monthly precipitation, others projected decreases depending on the months. Study show, no model exhibits the exact features of the observed precipitation. A model’s projection skill may be independent of its ability to replicate the properties of the observed climate. Therefore, there is a need for caution in choosing GCMs for the climate projection. This study suggests the use of the MME or the other models that have closest projections to it for climatic and hydrological studies in the region.

In this study, the uncertainty in runoff simulations using hydrological models was quantified based on the selection of five evaluation metrics and calibration data length. The calibration data length was considered to vary from 1 to 11 years, and runoff analysis was performed using a soil and water assessment tool (SWAT). SWAT parameter optimization was then performed using R-SWAT. The results show that the uncertainty was lower when using a calibration data length of five to seven years, with seven years achieving the lowest uncertainty. Runoff simulations using a calibration data length of more than seven years yielded higher uncertainty overall but lower uncertainty for extreme runoff simulations compared to parameters with less than five years of calibration data. Different uncertainty evaluation metrics show different levels of uncertainty, which means it is necessary to consider multiple evaluation metrics rather than relying on any one single metric. Among the evaluation metrics, the Nash–Sutcliffe model efficiency coefficient (NSE) and normalized root-mean-squared error (NRMSE) had large uncertainties at short calibration data lengths, whereas the Kling–Gupta efficiency (KGE) and Percent Bias (Pbias) had large uncertainties at long calibration data lengths.

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.