Discussions for water resources vulnerability and index development with sustainable concept are actively being made in recent years. Based on such index, water resources vulnerability of present and future is determined and diagnosed. This study calculated the water resources vulnerability rankings by 152 nations, using indicator related to water resources assessment that can be obtained from World Bank, VRI (Vulnerability Resilience Indicator), ESI (Environmental Sustainability Index). In order to quantitatively assess of water resources vulnerability based on this indicator, TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) technique was applied to index water vulnerability and to determine the rankings by nations. As a results, South Korea was ranked as the 88th among the 152 nations including Korea. Among the continents, Oceania was the least vulnerable and Afirica was the most vulnerable in continents. WUnited State, Japan, Korea and China were vulnerable in order among the major countries. Therefore, water resources vulnerability rankings by nations in this study helps us to better understand the situation of South Korea and provide the data for water resources planning and measure.

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This study presents the feasibility of fuzzy multi-criteria decision making (MCDM) techniques for the robust prioritization of projects. It is applied to water resources planning problem. Results from weighted sum method (WSM), analytic hierarchy process (AHP), revised analytic hierarchy process (R-AHP), and TOPSIS are compared with those from Fuzzy WSM, Fuzzy, AHP, Fuzzy R-AHP, and Fuzzy TOPSIS. For the calculation, all weights on criteria and the normalized data were obtained from the same investigation. As a result, the rankings from four MCDM techniques are slightly different while those from fuzzy MCDM show the comparatively consistent ranking. Therefore, it is desirable to use fuzzy MCDM technique when MCDM is used for the prioritization problem, since fuzzy MCDM can include the uncertain variability of input data and weighting values on criteria.

Urban flood vulnerability monitoring needs a large amount of socioeconomic and environmental data collected at regular time intervals. Collecting such data volume is a significant constraint in assessing changes in flood vulnerability. This study proposed a novel method to monitor spatiotemporal changes in urban flood vulnerability from satellite nighttime light (NTL) data. Peninsular Malaysia was chosen as the research region. A flood vulnerability index (FVI), estimated from socioeconomic and environmental data for a year, was linked to NTL data using a machine learning algorithm called Adaptive neuro-fuzzy inference system (ANFIS). The model was calibrated and validated with administrative unit scale data and subsequently used to predict FVI at a spatial resolution of 10 km for 2000‒2018 using NTL data. Finally, changes in estimated FVI at different grid points were evaluated using the Mann-Kendall trend method to determine changes in flood vulnerability over time and space. Results showed a nonlinear relationship between NTL and flood vulnerability factors such as population density, Gini coefficient and percentage of foreign nationals. The ANFIS technique showed a good performance in estimating FVI from NTL data with a normalized root-mean-square error of 0.68 and Kling-Gupta Efficiency of 0.73. The FVI revealed a high vulnerability in the urbanized western coastal region (FVI ~ 0.5 to 0.54), which matches well with major contributing regions to flood losses in Peninsular Malaysia. Trend assessment showed a significant increase in flood vulnerability in the study area from 2000 to 2018. The spatial distribution of the trend indicated an increase in FVI in the urbanized coastal plains, particularly in rapidly developing western and southern urban regions. The results indicate the potential of the technique in urban flood vulnerability assessment using freely available satellite NTL data.

This study aimed to assess the changes in aridity in East Asia (EA) over the next 80 years for the restriction of global warming based on Paris agreement goals. Eight General Circulation Models (GCMs) that provide simulations for 1.5 and 2.0°C global warming scenarios were used for this purpose. The Penman-Monteith Equation was utilized to calculate potential evapotranspiration (PET). The land-use projections data was used to identify the agricultural lands that aridity could impact. The results showed a likely increase in rainfall and PET in EA over the next 80 years. However, the spatial variability of the relative increase in rainfall and PET would cause an aridity shift in 1.2−9.7% of the total land area. Though most of the area would experience a transition to a wetter climate, nearly 2% of the land would experience a transition to a drier climate. It would cause nearly 4.4 and 6.2 thousand km2 of agricultural land to be converted from semi-arid to arid and 31.1 and 42.2 thousand km2 of land from sub-humid to semi-arid in the early period for 1.5 and 2.0°C temperature rise scenarios, respectively. This indicates nearly one and a half times more expansion of aridity on agricultural land in the early period for only a 0.5°C increase in temperature. A decrease in aridity in the far future for both scenarios would cause a reduction of total arid lands and, thus, its impacts on agriculture. Overall, the study revealed a possible reduction of aridity in EA in the long run if the Paris agreement is enforced and global warming is limited.