• Journal of Korean Society of Forest Science
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• v.110 no.1
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• pp.64-71
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• 2021

Recently, climate change has gradually accelerated the occurrence of landslides. Among the various effects caused by landslides,debris flow is recognized as particularly threatening because of its high speed and propagating distance. In this study, the impacts of various factors were analyzed using quantification theory(I) for the prediction of debris flow hazard soil volume in Seoraksan National Park, Korea. According to the range using the stepwise regression analysis, the order of impact factors was as follows: vertical slope (0.9676), cross slope (0.6876), altitude (0.2356), slope gradient (0.1590), and aspect (0.1364). The extent of the normalized score using the five-factor categories was 0 to 2.1864, with the median score being 1.0932. The prediction criteria for debris flow occurrence based on the normalized score were divided into four grades: class I, >1.6399; class II, 1.0932-1.6398; class III, 0.5466-1.0931; and class IV, <0.5465. Predictions of debris flow occurrence appeared to be relatively accurate (86.3%) for classes I and II. Therefore, the prediction criteria for debris flow will be useful for judging the dangerousness of slopes.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.2
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• pp.49-70
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• 2022

Sea Surface Temperature (SST), one of the ocean features, has a significant impact on climate, marine ecosystem and human activities. Therefore, SST prediction has been always an important issue. Recently, deep learning has drawn much attentions, since it can predict SST by training past SST patterns. Compared to the numerical simulations, deep learning model is highly efficient, since it can estimate nonlinear relationships between input data. With the recent development of Graphics Processing Unit (GPU) in computer, large amounts of data can be calculated repeatedly and rapidly. In this study, Short-term SST will be predicted through Convolutional Neural Network (CNN)-based U-Net that can handle spatiotemporal data concurrently and overcome the drawbacks of previously existing deep learning-based models. The SST prediction performance depends on the seasonal and interannual SST variabilities around the southern coast of Korea. The predicted SST has a wide range of variance during spring and summer, while it has small range of variance during fall and winter. A wide range of variance also has a significant correlation with the change of the Pacific Decadal Oscillation (PDO) index. These results are found to be affected by the intensity of the seasonal and PDO-related interannual SST fronts and their intensity variations along the southern Korean seas. This study implies that the SST prediction performance using the developed deep learning model can be significantly varied by seasonal and interannual variabilities in SST.

• Journal of Korean Society of Forest Science
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• v.112 no.3
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• pp.340-351
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• 2023

Landslides are major natural geological hazards that cause enormous property damage and human casualties annually. The vulnerability of mountainous areas to landslides is further exacerbated by the impacts of climate change. Soil depth is a crucial parameter in landslide and debris flow analysis, and plays an important role in the evaluation of watershed hydrological processes that affect slope stability. An accurate method of estimating soil depth is to directly investigate the soil strata in the field. However, this requires significant amounts of time and money; thus, numerous models for predicting soil depth have been proposed. However, they still have limitations in terms of practicality and accuracy. In this study, 71 seismic survey results were collected from domestic mountainous areas to estimate soil depth on hill slopes. Soil depth was estimated on the basis of a shear wave velocity of 700 m/s, and a database was established for slope angle, elevation, and soil depth. Consequently, the statistical characteristics of soil depth were analyzed, and the correlations between slope angle and soil depth, and between elevation and soil depth were investigated. Moreover, various soil depth prediction models based on slope angle were investigated, and corrected linear and exponential soil depth prediction models were proposed.

• Korean Chemical Engineering Research
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• v.61 no.1
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• pp.142-154
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• 2023

As the importance of the environment has been recognized worldwide, the need to calculate and reduce carbon emissions has been drawing an increasing attention throughout various industrial sections. Thereby the discipline of LCA (Life Cycle Assessment) involving raw material preparation, production processes, transportation and installation has been established. There is a clear research gap between the need and the practice for Korean Case of renewable energy industry, particularly wind power. To bridge the gap, this study conducted LCA research on wind power generation in the Korean area of Yeongdeok, an example of a domestic onshor wind power complex using SimaPro, which is the most widely used LCA system. As a result of the study, the energy recovery period (EPT) of one wind turbine is about 10 months, and the GHG emitted to generate power of 1 kwh is 15 g CO₂/kWh, which is competitive compared to other energy sources. In the environmental impact assessment by component, the results showed that the tower of wind turbines had the greatest impact on various environmental impact sectors. The experience gained in this study can be further used in strengthening the introduction of renewable energy and reducing the carbon emission in line with reducing climate change.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.543-551
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• 2023

As climate change continues to prompt an increasing demand for advancements in disaster and safety management technologies to address abnormal high water temperatures, typhoons, floods, and droughts, sea surface temperature has emerged as a pivotal factor for swiftly assessing the impacts of summer harmful algal blooms in the seas surrounding Korean Peninsula and the formation and dissipation of cold water along the East Coast of Korea. Therefore, this study sought to gauge predictive performance by leveraging statistical methods and deep learning algorithms to harness sea surface temperature data effectively for marine anomaly research. The sea surface temperature data employed in the predictions spans from 2018 to 2022 and originates from the Heuksando Tidal Observatory. Both traditional statistical ARIMA methods and advanced deep learning models, including long short-term memory (LSTM) and gated recurrent unit (GRU), were employed. Furthermore, prediction performance was evaluated using the attention LSTM technique. The technique integrated an attention mechanism into the sequence-to-sequence (s2s), further augmenting the performance of LSTM. The results showed that the attention LSTM model outperformed the other models, signifying its superior predictive performance. Additionally, fine-tuning hyperparameters can improve sea surface temperature performance.

• Journal of Korea Water Resources Association
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• v.54 no.8
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• pp.577-587
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• 2021

Drought is generally considered to be a natural disaster caused by accumulated water shortages over a long period of time, taking months or years and slowly occurring. However, climate change has led to rapid changes in weather and environmental factors that directly affect agriculture, and extreme weather conditions have led to an increase in the frequency of rapidly developing droughts within weeks to months. This phenomenon is defined as 'Flash Drought', which is caused by an increase in surface temperature over a relatively short period of time and abnormally low and rapidly decreasing soil moisture. The detection and analysis of flash drought is essential because it has a significant impact on agriculture and natural ecosystems, and its impacts are associated with agricultural drought impacts. In South Korea, there is no clear definition of flash drought, so the purpose of this study is to identify and analyze its characteristics. In this study, flash drought detection condition was presented based on the satellite-derived drought index Evaporative Stress Index (ESI) from 2014 to 2018. ESI is used as an early warning indicator for rapidly-occurring flash drought a short period of time due to its similar relationship with reduced soil moisture content, lack of precipitation, increased evaporative demand due to low humidity, high temperature, and strong winds. The flash droughts were analyzed using hydrometeorological characteristics by comparing Standardized Precipitation Index (SPI), soil moisture, maximum temperature, relative humidity, wind speed, and precipitation. The correlation was analyzed based on the 8 weeks prior to the occurrence of the flash drought, and in most cases, a high correlation of 0.8(-0.8) or higher(lower) was expressed for ESI and SPI, soil moisture, and maximum temperature.

• Journal of Distribution Science
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• v.10 no.1
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• pp.43-52
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• 2012

With the slowdown in environmental issues and the change of environmental consciousness, ecotourism is being discussed in various social fields. Ecotourism is being popularized for environmental protection, and now it is becoming a mainstream product from one of mass tourism. Ecotourism's emphasis on sustainable development in the tourism destination's society, economy, and environment, through ecotourism study and education, enable people to understand the core value of the ecological environment. 2011 was nominated as "the Year of World Forest" by the UN. In the recent years, forests are becoming increasingly important with their own values and functions in environment, economy, society, and culture. In particular, the global environmental issues caused by climate change are becoming an international agenda. Forests are the only effective solution for the carbon dioxide that causes global warming. Moreover, forests constitute a major part of ecotourism, and are now most used by ecotourists. For example, Korea, wherein 60% of the land is forest, attracts ecotourists. With the increasing interests in environment, the number of tourists visiting the ecosystem forest, which is highly valued for its conservation, is increasing significantly every year and is receiving considerable attention from the government. However, poor facilities in the forest ecotourism sites and improper market strategies are the reasons for the poor running of these sites. Furthermore, tourists' environmental awareness affects ecology environmental pollution or the optimization of forest ecotourism. In order to verify the relationships among tourist attractiveness, environmental consciousness, charm degrees of the attractions, and attitudes after tours, we established some scales based on existing research achievement. Then, using these scales, the researcher completed the questionnaire survey. From December 20, 2010 to February 20, 2011, after conducting surveys for 12 weeks, we finally obtained 582 valid questionnaires, from a total of 700 questionnaires, that could be used in statistical analysis. First, for the method of research and analysis, the researcher initially applied the Cronbach's (Alpha) for verifying the reliability, and subsequently applied the Exploratory factor analysis for verifying the validity. Second, in order to analyze the demographics, the researcher makes use of the Frequency analysis for the AMOS, measurement model, structural equation model computing, and also utilizes construct validity, convergent validity, discriminant validity, and nomological validity. Third, for the analysis of the ecotourists' environmental consciousness, impacts on tourist attractiveness, charm degrees of the attractions, and attitudes after the tour, the researcher uses AMOS 19, with the path analysis and equation of structure. After the research, researchers found that high awareness of natural protection lead to high tourist motivation and satisfaction and more positive attitude after the tour. Moreover, this research shows the psychological and behavioral reactions of the ecotourists to the ecotourist development. Accordingly, environmental consciousness does not affect the tourist attractiveness that has been interpreted as significant. Furthermore, people should focus on the change of natural protection consciousness and psychological reaction of ecotourists while ensuring the sustainable development of ecotourists and developing some ecotourist programs.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.6
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• pp.1018-1023
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• 2010

Climatic change was observed and analyzed in view of impacts on agricultural ecosystem, inter alia on rice cropping. The changed climate gave rise to earlier transplanting of rice seedling and later harvest after 40 years. Also phenological change and prolonged growth duration was observed. The meteorological data was selected from the standardized climatological data of 30 year normals of 1960s and 2000s, which were published by Korea Meteorological Administration. Development stages and growing periods of rice crop were compared by analyzing critical and optimum temperatures of each growth stage during these two periods. The first appearance date of $15^{\circ}C$ was ranged from Apr. 29 to May 23 in the year-normals of 1960s and it varied from Apr. 24 to May 16 in the normals of 2000s. The difference of the first appearance date of $15^{\circ}C$ was 0~10 days earlier in the year-normals of 2000s than the 1960s. The last harvesting date was determined to be the last appearance date of mean air temperature $15^{\circ}C$. The difference in the last appearance date of $15^{\circ}C$ was 1 to 13 days later in the year-normals of 2000s than in 1960s. The plant height of a rice variety, Hwayoung-byeo was 101~109 cm in 4 local areas, Seoul, Kangneung, Kwangju and Daegu. The plant height became 1~4 cm taller under warm condition. Rice grain yields estimated with daily weather data for the year-normals of 1960s and 2000s were 453~580 kg $10a^{-1}$ and 409~484 kg $10a^{-1}$ respectively. Rice grain yield of the former period was 50~100 kg $10a^{-1}$ higher than that hat in the later period.

• Korean Journal of Environment and Ecology
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• v.32 no.5
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• pp.469-477
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• 2018

Hanabusaya asiatica is an endemic species whose distribution is limited in the mid-eastern part of the Korean peninsula. Due to its narrow range and small population, it is necessary to protect its habitats by identifying it as Key Biodiversity Areas (KBAs) adopted by the International Union for Conservation of Nature (IUCN). In this paper, we estimated potential natural habitats for H. asiatica using maximum entropy model (MaxEnt) and identified candidate sites for KBA based on the model results. MaxEnt is a machine learning algorithm that can predict habitats for species of interest unbiasedly with presence-only data. This property is particularly useful for the study area where data collection via a field survey is unavailable. We trained MaxEnt using 38 locations of H. asiatica and 11 environmental variables that measured climate, topography, and vegetation status of the study area which encompassed all locations of the border region between South and North Korea. Results showed that the potential habitats where the occurrence probabilities of H. asiatica exceeded 0.5 were $778km^2$, and the KBA candidate area identified by taking into account existing protected areas was $1,321km^2$. Of 11 environmental variables, elevation, annual average precipitation, average precipitation in growing seasons, and the average temperature in the coldest month had impacts on habitat selection, indicating that H. asiatica prefers cool regions at a relatively high elevation. These results can be used not only for identifying KBAs but also for the reference to a protection plan for H. asiatica in preparation of Korean reunification and climate change.

• The Journal of Engineering Geology
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• v.30 no.4
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• pp.469-483
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• 2020

The results of long-term groundwater level and quality monitoring can be used not only as the basic data for evaluating the impact of various disasters including climate change and establishing responses, but also as key data for predicting and managing geological disasters such as earthquakes. Some countries use groundwater level and quality monitoring for researches to predict earthquakes and to assess the impacts of the earthquake disaster. However, a few cases in Korea report on individual groundwater quality factors (i.e., dissolved ions) observed before and after the earthquakes, being different from other countries. To establish the abnormality criteria for groundwater quality in Pohang, groundwater samples were collected and analyzed five times from 14 agricultural or private wells existing in Shingwang-myeon and Heunghae-eup. As a result of the analysis, it was found that Ca2+ was the dominant cation in Shingwang-myeon, while Na+ was the dominant cation in Heunghae-eup. The elevated NO3- concentration in Shingwang-myeon is contributed to the agricultural activity in the area. A high concentration of Fe was detected in a well on Heunghae-eup; the concentration exceeded the drinking water standard by nearly 100 times. Relatively higher dissolved ions were observed in the groundwater of Heunghae-eup, and it is considered as the result of the flow velocity difference and water-rock reaction accompanying the difference in bedrock and sediment characteristics. The groundwater of Shingwang-myeon appeared to be most affected by the weathering of granite and silicates, while that of Heunghae-eup was mainly affected by the weathering of silicates and carbonate. The background concentrations (baselines) of groundwater Shingwang-myeon and Heunghae-eup was identified through the survey; however, the continuous monitoring is required to monitor the possible changes and the repeatability of seasonal variation.