Assessment of regional drought vulnerability and risk using principal component analysis and a Gaussian mixture model

Due to the complex characteristics of drought, drought risk needs to be quantified by combining drought vulnerability and drought hazard. Recently, the major focus in drought vulnerability has been on how to calculate the weights of indicators to comprehensively quantify drought risk. In this study, principal component analysis (PCA), a Gaussian mixture model (GMM), and the equal-weighting method (EWM) were applied to objectively determine the weights for drought vulnerability assessment in Chungcheong Province, located in the west-central part of South Korea. The PCA provided larger weights for agricultural and industrial factors, whereas the GMM computed larger weights for agricultural factors than did the EWM. The drought risk was assessed by combining the drought vulnerability index (DVI) and the drought hazard index (DHI). Based on the DVI, the most vulnerable region was CCN9 in the northwestern part of the province, whereas the most drought-prone region based on the DHI was CCN12 in the southwest. Considering both DVI and DHI, the regions with the highest risk were CCN12 and CCN10 in the southern part of the province. Using the proposed PCA and GMM, we validated drought vulnerability using objective weighting methods and assessed comprehensive drought risk considering both meteorological hazard and socioeconomic vulnerability.

     

Comparison of primary productivity algorithms for Korean waters

This study compares five primary productivity algorithms for Korean waters. Five algorithms are in the form of vertical generalized production models: One algorithm is for gross primary production and four are for net primary production. The five algorithms were evaluated using 117 in situ primary production datasets observed by 20 cruises from 1994 to 2011 in Korean waters (East Sea, Yellow Sea, East China Sea, and Yeosu Bay). The results show that the regionally-tuned variants give better results than the original formulation. We recommend, among the tested algorithms, YSVGPM (Yellow Sea Vertically Generalized Productivity Model) for gross primary productivity algorithm and Kameda-Ishizaka algorithm for net primary productivity algorithm for estimating primary production in Korean waters.     

Variability of SeaWiFs chlorophyll-a in the southwest Atlantic sector of the Southern Ocean: Strong topographic effects and weak seasonality

This study examined 11-year (1997–2008) weekly and monthly time series of satellite-observed ocean color to understand the dominant temporal and spatial patterns of chlorophyll-a in the southwest Atlantic sector of the Southern Ocean. Using empirical orthogonal function analysis and k-means classification, we classified the study area into eight regions, which were in good accordance with the oceanographic and topographic features. Examination of the chlorophyll-a time series in each region revealed that contrary to our expectation, regular seasonal phytoplankton blooms were observed only in a limited area. Of the eight regions, only two showed typical seasonal blooms, and one showed weak seasonality, whereas other regions exhibited irregular seasonal blooms of variable duration. We attribute the absence of regularity in seasonal blooms to relatively shallow winter mixing, which would prevent entrainment of limiting micronutrients such as iron and silicate. In the southwest Atlantic sector of the Southern Ocean, topographic effects and sea ice may be the most important factors controlling primary productivity. In the South Georgia region, chlorophyll-a showed a significant correlation with geostrophic current velocity, indicating that topographic effects depend on the interaction of current strength and topographic structure. Interannual variability of the surface chlorophyll in some regions also revealed longer periodicity (～6 years). The periodicity seemed to be related to El Niño–Southern Oscillation and to sea-ice dynamics influenced by the Antarctic Circumpolar Current.     

Assessment of regional drought risk under climate change using bivariate frequency analysis

Since water supply failure is one of the primary impacts of drought, drought risk should be quantified in the context of a lack of available water. To assess the drought risk, water supply system performance indices such as reliability, resiliency, and vulnerability are usually introduced as they correspond to primary drought characteristics, i.e., frequency, duration, and magnitude. In this study, we developed a drought risk index (DRI) through weighted averaging the performance indices derived using bivariate drought frequency analysis. We suggested two types of DRI: observed DRI (DRI_O) and designed DRI (DRI_D). DRI_O was calculated using an observed (or synthesized) time series of water shortages. DRI_D was estimated from the bivariate drought frequency curves, which are the probabilistic magnitudes of water shortages corresponding to a particular duration. The historical maximum drought event that represents the maximum DRI_O has generally been used as the target security level. However, we could establish a practically applicable target security level considering that the future water supply failure risk is represented by DRI_D. We defined regional drought safety criteria in this study by comparing DRI_O and DRI_D. Application of the criteria to the Nakdong river basin in South Korea showed that W1 (Byeongseongcheon) and W4 (Hyeongsangang) had the lowest and highest drought risk, respectively, and the drought safety criteria showed an average range of 5–20 years.