• Journal of Korea Water Resources Association
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• v.53 no.9
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• pp.701-715
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• 2020

This study compared several reference evapotranspiration estimated using eight methods such as FAO-56 Penman-Monteith (FAO PM), Hamon, Hansen, Hargreaves-Samani, Jensen-Haise, Makkink, Priestley-Taylor, and Thornthwaite. In addition, by analyzing the monthly deviations of the results by the FAO PM and the remaining seven methods, monthly optimized correction coefficients were derived and the improvement effect was evaluated. These methods were applied to 73 automated synoptic observation system (ASOS) stations of the Korea Meteorological Administration, where the climatological data are available at least 20 years. As a result of evaluating the reference evapotranspiration by applying the default coefficients of each method, a large fluctuation happened depending on the method, and the Hansen method was relatively similar to FAO PM. However, the Hamon and Jensen-Haise methods showed more large values than other methods in summer, and the deviation from FAO PM method was also large significantly. When comparing based on the region, the comparison with FAO PM method provided that the reference evapotranspiration estimated by other methods was overestimated in most regions except for eastern coastal areas. Based on the deviation from the FAO PM method, the monthly correction coefficients were derived for each station. The monthly deviation average that ranged from -46 mm to +88 mm before correction was improved to -11 mm to +1 mm after correction, and the annual average deviation was also significantly reduced by correction from -393 mm to +354 mm (before correction) to -33 mm to +9 mm (after correction). In particular, Hamon, Hargreaves-Samani, and Thornthwaite methods using only temperature data also produced results that were not significantly different from FAO PM after correction. It can be also useful for forecasting long-term reference evapotranspiration using temperature data in climate change scenarios or predicting evapotranspiration using monthly or seasonal temperature forecasted values.

• Journal of Navigation and Port Research
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• v.46 no.4
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• pp.375-384
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• 2022

The shipping industry is cyclically impacted by complex variables such as various economic indicators, social events, and supply and demand. The purpose of this study was to analyze the operating profit of 13 Korean liner companies over 30 years, including the financial crisis of the late 1990s, the global financial crisis of the late 2000s, and the COVID-19 global pandemic. This study was conducted to also identify factors that impacted the profit ratio of Korea's liner shipping companies according to economic conditions. It was divided into ocean-going and short-sea shipping, reflecting the characteristics of liner shipping companies, and was analyzed by hierarchical multiple regression analysis. The time series data are based on the Korean International Financial Reporting Standards (K-IFRS) and comprise seaborne trade volume, fleet evolution, and macroeconomic indicators. The outliers representing the economic downturn due to social events were separately analyzed. As a result of the analysis, the China Container Freight Index (CCFI) positively impacted ocean-going as well as short-sea liner shipping companies. However, the Korean container shipping volume only impacted ocean-going liners positively. Additionally, world and Korea's GDP, world seaborne trade volume, and fuel price are factored in the operating profit of short sea liner shipping. Also, the GDP growth rate of China, exchange rate, and interest rate did not significantly impact both groups. Notably, the operating profitability of Korea's liner shipping shows an exceptionally high rate during the recessions of 1998 and 2020. It is paradoxical, and not correlated with the classical economic indicators. Unlike other studies, this paper focused on the operating profit before financial expenses, considering the complexity as well as difficulty in forecasting the shipping cycle, and rendered conclusions using relatively long-term empirical analysis, including three economic shocks.