Brief Communication: A case study of risk assessment for facilities associated with earthquake-induced liquefaction potential in Kimhae City, South Korea

Liquefaction causes secondary damage after earthquakes; however, liquefaction related phenomena were rarely reported until after the Mw = 5.4 November 15, 2017 Pohang earthquake in Korea. Both the Mw = 5.8 September 12, 2016 Gyeongju earthquake and Mw = 5.4 November 15, 2017 Pohang earthquake occurred in the fault zone of Yangsan City (located in the south-eastern part of Korea), and both of these earthquakes induced liquefaction. Moreover, they demonstrated that Korea is not safe against the liquefaction induced by earthquakes. In this study, estimations and calculations were performed based on the distances between the centroids of administrative districts and an epicenter located at the Yangsan Fault, the peak ground accelerations (PGAs) induced by Mw = 5.0 and 6.5 earthquakes, and a liquefaction potential index (LPI) calculated based on groundwater level and standard penetration test results from 274 locations in Kimhae City (adjacent to the Nakdong river and across the Yangsan Fault). Then, a kriging method using geographical information systems was used to evaluate the liquefaction effects on the risk levels of facilities. The results indicate that a Mw = 5.0 earthquake induces a small and low level of liquefaction, resulting in slight risk for facilities, but a Mw = 6.5 earthquake induces a large and high level of liquefaction, resulting in a severe risk for facilities.

Assessment of Water Quality Target Attainment and Influencing Factors Using the Multivariate Log-Linear Model in the Nakdong River Basin, Republic of Korea

Because identifying the factors affecting water quality is challenging, water quality assessment of an individual component based on the arithmetic mean method cannot adequately support management policies. Therefore, in this study, we assessed the water quality target attainment at 24 sites in the Nakdong River Basin by applying multivariate log-linear models to identify factors influencing water quality, including flow and season. The temporal and seasonal water quality trend and flow were also analyzed using the calculated model coefficients. Specifically, weekly data on biological oxygen demand (BOD), total phosphorous (TP), and flow during 2013–2018 were used to investigate the 2018 water quality target attainment level for this river. The significance and suitability of the models were analyzed using the F-test, root mean squared error (RMSE), mean absolute percent error (MAPE), and adjusted R2 values. All 24 models applied in this study showed statistical significance and suitability for the prediction of BOD and TP concentrations. Moreover, flow was identified as the main factor affecting water quality and had a predominant effect on BOD and TP concentrations in tributaries and the main stream, respectively. Furthermore, among the 24 sites, BOD and TP targets were evidently attained at 18 and 17 sites, respectively.

Quantification of Phycocyanin in Inland Waters through Remote Measurement of Ratios and Shifts in Reflection Spectral Peaks

This study introduces a semi-empirical algorithm to estimate the extent of the phycocyanin (PC) concentration in eutrophic freshwater bodies; this is achieved by studying the reflectance characteristics of the red and near-red spectral regions, especially the shifting of the peak near 700 nm towards longer wavelengths. Spectral measurements in a darkroom environment over the pure-cultured cyanobacteria Microcystis showed that the shift is proportional to the algal biomass. A similar proportional trend was found from extensive field measurement data. The data also showed that the correlation of the magnitude of the shift with the PC concentration was greater than that with chlorophyll-a. This indicates that the characteristic can be a useful index to quantify cyanobacterial biomass. Based on these observations, a new PC algorithm was proposed that uses the remote sensing reflectance of the peak band around 700 nm and the trough band around 620 nm, and the magnitude of the peak shift near 700 nm. The efficacy of the algorithm was tested with 300 sets of field data, and the results were compared to select algorithms for the PC concentration prediction. The new algorithm performed better than the other algorithms with respect to most error indices, especially the mean relative error, indicating that the algorithm can reduce errors when PC concentrations are low. The algorithm was also applied to a hyperspectral dataset obtained through aerial imaging, in order to predict the spatial distribution of the PC concentration in an approximately 86 km long reach of the Nakdong River.