North Korea is a food-deficit nation in which climate change could have a significant impact on drought. We analyzed drought characteristics in the Hwanghae Plain, North Korea using both the multiple timescales of the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI) from 1981 to 2100. The probability of non-exceedance for a one-month SPEI below −1.0 was only 1.1% in the spring season of 1995 but increased to 24.4% in 2085. The SPEI for a ten-year return period varied from −0.6 to −0.9 in 1995 and decreased to −1.18 in 2025. The results indicate that severe drought is more likely to occur in future as a result of climate change. The seasonal drought conditions were also significantly influenced by climate change. The largest decrease in the SPEI occurred in late spring and early summer, both of which are important for rice growth. Drought characteristics include severity, duration, and intensity. Therefore, we applied the time series of SPIs and SPEIs to the runs theory and found that the drought intensity identified by one-month SPEIs in 1995 was at a level of 1.21, which reached 1.39 in 2085, implying that climate change will intensify drought in the future.

A linear regression machine learning model to estimate the reference evapotranspiration based on temperature data for South Korea is developed in this study. FAO56 Penman–Monteith (FAO56 P–M) reference evapotranspiration calculated with meteorological data (1981–2021) obtained from sixty-two meteorological stations nationwide is used as the label. All study datasets provide daily, monthly, or annual values based on the average temperature, daily temperature difference, and extraterrestrial radiation. Multiple linear regression (MLR) and polynomial regression (PR) are applied as machine learning algorithms, and twelve models are tested using the training data. The results of the performance evaluation of the period from 2017 to 2021 show that the polynomial regression algorithm that learns the amount of extraterrestrial radiation achieves the best performance (the minimum root-mean-square errors of 0.72 mm/day, 11.3 mm/month, and 40.5 mm/year for daily, monthly, and annual scale, respectively). Compared to temperature-based empirical equations, such as Hargreaves, Blaney–Criddle, and Thornthwaite, the model trained using the polynomial regression algorithm achieves the highest coefficient of determination and lowest error with the reference evapotranspiration of the FAO56 Penman–Monteith equation when using all meteorological data. Thus, the proposed method is more effective than the empirical equations under the condition of insufficient meteorological data when estimating reference evapotranspiration.

The aim of this study is to analyze the characteristics of drought, such as intensity and trends, based on SPEI (Standardized Precipitation Evapotranspiration Index) at 8 stations in South Korea from 1981 to 2010. The traditional SPEI is based on the Thornthwaite equation for estimating evapotranspiration; SPEI_th. However, a standard of agricultural water management in Korea suggests the FAO Penman-Monteith equation; SPEI_pm. Therefore, we analyzed the intensity, variability, and trends of drought using SPEI_th and SPEI_pm, respectively, and compared the results. SPEI_pm showed slightly more intensive drought rather than SPEI_th except for Chuncheon and Gwangju. In 5 stations—excluding Cheoncheon, Gwangju and Jinju—the cumulative probability that SPEI_pm was below −1.5 was significantly increased from 1981–1995 to 1996–2010. In addition, the northwest and southwest regions had higher intensity of 1-month droughts, and the central and southwest regions had a higher intensity of 3-month droughts. According to the Mann–Kendall test, there was a decreasing trend of 1-month SPEI during the fall season and 3-month SPEI during winter season.

The change in the impervious-pervious balance has significantly altered the stream water quality, and thus the threshold of the impervious surface area in the watershed has been an active research topic for many years. The objective of this study is to verify the correlation between impervious surfaces and water quality and to determine the threshold of the percentage of the impervious surface area (PISA) for diagnosing the severity of future stream water quality problems in the watershed as well as regulating the PISA in Korea. Statistical results indicated that the PISA is a suitable indicator of water quality at the watershed scale and can illustrate the water quality problems caused by the impervious surface. In addition, the results from this study suggest that controlling the PISA within about 10% in watersheds is a fundamental strategy to mitigate the degradation of water quality.

Since the salinity of irrigation water is a critical constraint to the production of certain vegetable crops, salinity has been considered as one of the most important factors of irrigation water. The objective of this study was to investigate the response of lettuce and Chinese cabbage to various salinity levels of irrigation water in greenhouse cultivation. A pot experiment was conducted with different salinities under a glasshouse condition in Korea. A completely randomized experimental design was used with three replications. The analysis results of crop growth and yield of lettuce and Chinese cabbage indicated that the factors that were more significantly affected by saline irrigation water were crop yields rather than crop components such as number of leaves, leaf length, and leaf width. In this study, the point of salt concentration during an increase in salinity levels of irrigation water (EC_w) at which yield starts to decline was determined to be 0.9 and 1.5 dS/m for lettuce and Chinese cabbage, respectively. Furthermore, the present study demonstrated that the continuous irrigation of saline water under greenhouse conditions could lead to a significant increase in electric conductivity (EC_e) level and Na⁺ concentration in soil, as well as Na⁺ concentration in leaves of crops.

The purpose of this study is to analyze the possible relationship between industrial structure and economic development in rural areas in South Korea. Accordingly, this study uses the Herfindahl–Hirschman Index and a two-step cluster analysis method to conduct an empirical analysis of the rural areas of Chungcheongbuk-do as the research object. The results show that among the 11 regions with concentrated industrial structures, the cluster results of 2 regions changed from the decentralized low employment cluster in 2010 to a concentrated high employment cluster in 2015, while the cluster results of other regions remained unchanged. Among the 18 regions with decentralized industrial structure, the cluster results of 5 regions changed from the concentrated high employment cluster in 2010 to the decentralized low employment cluster. Meanwhile, the cluster results of three regions changed from the decentralized low employment cluster in 2010 to the concentrated high employment cluster in 2015, while the cluster results of other regions remained unchanged. Based on this, it can be concluded that, for general rural areas, a low level of industrial diversification, that is, a concentrated industrial structure, is more conducive to promoting the economic development of rural areas. However, there is a special case, namely that rural areas with certain specific advantages, a high level of industrial diversification, or a decentralized industrial structure are more conducive to the development of the regional economy.