• Korean Journal of Ecology and Environment
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• v.57 no.1
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• pp.28-38
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• 2024

In this study, in order to analyze the water purification efficiency according to the influent water conditions of artificial wetlands, the purification efficiency was compared at two points where sewage treatment water flows in and one point where good effluent flows in. As a result of reviewing the results of the analysis of influent and effluent and the removal efficiency, the T-N and T-P removal efficiency was calculated at 54.7% and 77.4%, respectively, for the two points where sewage treatment water was treated, the treatment efficiency of SS 90.8%, BOD 51.1%, TOC 30.6%, T-N 38.8%, T-P 55.3% was shown. As a result, the efficiency of removing pollutants in the artificial wetland was found to be proportional to the concentration of influent water, and in order to create an efficient artificial wetland, it is judged that thorough review and management at the design stage are necessary considering that the removal efficiency of high-concentration contaminated water was high.

• Journal of Ginseng Culture
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• v.6
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• pp.51-79
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• 2024

In the 1970s and 1980s, during the nascent phase of ginseng disease research, efforts concentrated on isolating and identifying pathogens. Subsequently, their physiological ecology and pathogenesis characteristics were scrutinized. This led to the establishment of a comprehensive control approach for safeguarding major aerial part diseases like Alternaria blight, anthracnose, and Phytophthora blight, along with underground part diseases such as Rhizoctonia seedling damping-off, Pythium seedling damping-off, and Sclerotinia white rot. In the 1980s, the sunshade was changed from traditional rice straw to polyethylene (PE) net. From 1987 to 1989, focused research aimed at enhancing disease control methods. Notably, the introduction of a four-layer woven P.E. light-shading net minimized rainwater leakage, curbing Alternaria blight occurrence. Since 1990, identification of the bacterial soft stem rot pathogen facilitated the establishment of a flower stem removal method to mitigate outbreaks. Concurrently, efforts were directed towards identifying root rot pathogens causing continuous crop failure, employing soil fumigation and filling methods for sustainable crop land use. In 2000, adapting to rapid climate changes became imperative, prompting modifications and supplements to control methods. New approaches were devised, including a crop protection agent method for Alternaria stem blight triggered by excessive rainfall during sprouting and a control method for gray mold disease. A comprehensive plan to enhance control methods for Rhizoctonia seedling damping-off and Rhizoctonia damping-off was also devised. Over the past 50 years, the initial emphasis was on understanding the causes and control of ginseng diseases, followed by refining established control methods. Drawing on these findings, future ginseng cultivation and disease control methods should be innovatively developed to proactively address evolving factors such as climate fluctuations, diminishing cultivation areas, escalating labor costs, and heightened consumer safety awareness.

• Journal of Korea Water Resources Association
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• v.57 no.2
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• pp.111-125
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• 2024

The study has examined alterations in groundwater quality by investigating the influence of rainfall on electrical conductivity (EC) and nitrate concentration in the groundwater of the Geum River Basin in South Korea. Mann Kendall and Sen's Slope estimator were employed to analyze the trends and estimate the trend's magnitude. The administrative map of the study area was utilized to assess the trends of these parameters within each administrative region. Seventeen years (from 2005 to 2021) of data on EC, groundwater levels (GWL), precipitation, and six years (from 2015 to 2020) of nitrate concentration data were utilized for this analysis. The results indicate that, in most administrative regions, there has been an increase in nitrate concentration, and EC, whereas precipitation has seen a slight decrease in a downstream and an increasing trend in upstream. The correlation coefficients calculated between these parameters reveal that there is no direct impact of precipitation on nitrate and EC, but a negative correlation was observed between GWL and EC. The most significant increasing trend in nitrate concentration was observed in two districts (Iksan and Gunsan ), which correspond to regions with significant agricultural activity; about 50% of these districts area are used for agricultural activities.

• Journal of Korean Society of Forest Science
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• v.113 no.1
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• pp.14-30
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• 2024

This study characterized areas at risk of land creep by focusing on a site that has undergone this phenomenon in Ulju-gun, South Korea. Land creep in the area of interest was catalyzed by road expansion work conducted in 2022. The site was examined on the basis of its geological features, topography, effective soil depth, soil hardness, electrical resistivity, and subsurface profile. It consists of a slope covered with sparse vegetation and a concave top that retains rainwater during rainfall. Compositionally, land creep affected the shale, sandstone, and conglomerate formations on the site, which had little silt and more sand and clay compared with areas that were unaffected by land creep. An electrical resistivity survey enabled us to detect a groundwater zone at the site, which explains the softness of the soil. Finally, the effective soil depth at the land creep-affected area was 30.4 cm on average, indicating deep colluvial deposits. In contrast, unaffected sites had an effective soil depth of 24.7 cm on average. These results should facilitate the creation of systems for monitoring and preemptively responding to land creep, significantly mitigating the socioeconomic losses associated with this phenomenon.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.195-207
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• 2024

This study selected major drought events that occurred in the Jeonnam region from 1991 to 2023, examining both meteorological and hydrological drought occurrence mechanisms. The daily drought index was calculated using rainfall and dam storage as input data, and the drought propagation characteristics from meteorological drought to hydrological drought were analyzed. The characteristics of the 2022-23 drought, which recently occurred in the Jeonnam region and caused serious damage, were evaluated. Compared to historical droughts, the duration of the hydrological drought for 2022-2023 lasted 334 days, the second longest after 2017-2018, the drought severity was evaluated as the most severe at -1.76. As a result of a linked analysis of SPI (StandQardized Precipitation Index), and SRSI (Standardized Reservoir Storage Index), it is possible to suggest a proactive utilization for SPI(6) to respond to hydrological drought. Furthermore, by confirming the similarity between SRSI and SPI(12) in long-term drought monitoring, the applicability of SPI(12) to hydrological drought monitoring in ungauged basins was also confirmed. Through this study, it was confirmed that the long-term dryness that occurs during the summer rainy season can transition into a serious level of hydrological drought. Therefore, for preemptive drought response, it is necessary to use real-time monitoring results of various drought indices and understand the propagation phenomenon from meteorological-agricultural-hydrological drought to secure a sufficient drought response period.

• Journal of Korea Water Resources Association
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• v.57 no.7
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• pp.471-479
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• 2024

Flood damage is becoming more serious due to the heavy rainfall caused by climate change. Physically based hydrological models have been utilized to predict stream water level variability and provide flood forecasting. Recently, hydrological simulations using machine learning and deep learning algorithms based on nonlinear relationships between hydrological data have been getting attention. In this study, the Long Short-Term Memory (LSTM) algorithm is used to predict the water level of the Seomjin River watershed. In addition, Climate Prediction Center morphing method (CMORPH)-based gridded precipitation data is applied as input data for the algorithm to overcome for the limitations of ground data. The water level prediction results of the LSTM algorithm coupling with the CMORPH data showed that the mean CC was 0.98, RMSE was 0.07 m, and NSE was 0.97. It is expected that deep learning and remote data can be used together to overcome for the shortcomings of ground observation data and to obtain reliable prediction results.

• Atmosphere
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• v.34 no.3
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• pp.305-317
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• 2024

The study examines the effects of parameters that define the characteristics of raindrops on the simulated precipitation during the summer season over Korea using the Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6) cloud microphysics scheme. Prescribed parameters, defining the characteristics of hydrometeors in the WDM6 scheme such as a_R, b_R, and f_R in the fall velocity (V_R) - diameter (D_R) relationship and shape parameter (𝜇_R) in the number concentration (N_R) - D_R relationship, presents different values compared to the observed data from Two-Dimensional Video Disdrometer (2DVD) at Boseong standard meteorological observatory during 2018~2019. Three experiments were designed for the heavy rainfall event on August 8, 2022 using WRF version 4.3. These include the control (CNTL) experiment with original parameters in the WDM6 scheme; the MUR experiment, adopting the 50th percentile observation value for 𝜇_R; and the MEDI experiment, which uses the same 𝜇_R as MUR, but also includes fitted values for a_R, b_R, and f_R from the 50th percentile of the observed V_R - D_R relationship. Both sensitivity experiments show improved precipitation simulation compared to the CNTL by reducing the bias and increasing the probability of detection and equitable threat scores. In these experiments, the raindrop mixing ratio increases and its number concentration decreases in the lower atmosphere. The microphysics budget analysis shows that the increase in the rain mixing ratio is due to enhanced source processes such as graupel melting, vapor condensation, and accretion between cloud water and rain. Our study also emphasizes that applying the solely observed 𝜇_R produces more positive impact in the precipitation simulation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.3
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• pp.315-328
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• 2024

Precipitation is a crucial meteorological variable widely used as essential input data in most hydrological models. However, due to climate change, there is an escalating precipitation variability. Trend analysis plays an important role in planning and operating water resources systems. As recently developed, Innovative Polygon Trend Analysis (IPTA) is useful in identifying and and analyzing the trends of hydrologic variables. In this study, the IPTA was applied to monthly precipitation data obtained from 13 meteorological observatories in Jeolla province, along with synthesized precipitation data according to Shared Socioeconomic Pathways (SSP) scenarios. The trend results were compared those obtained from the Mann-Kendall test and the Sen's slope estimation which are generally used in practice. The results revealed monthly precipitations from February to July and November had increasing trends, and monthly precipitation in October had a decreasing trend. IPTA, Mann-Kendall test, and Sen's slope estimation detected trends in 75.00 %, 5.13 %, and 5.13 % of 156(13 stations × 12 months) time series of monthly precipitation, respectively, which indicates that the IPTA is more sensitive in trend detection compared to the Mann-Kendall test and Sen's slope estimation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.2
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• pp.149-160
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• 2024

Severe casualties and property damage are occurring due to urban floods caused by extreme rainfall. However, there is a lack of research on preparedness, appropriate estimation of flood damages, assessment of losses, and compensation. Particularly, the flood damage estimation methods used in the USA and Japan show significant differences from the domestic situation, highlighting the need for methods tailored to the Korean context. This study addresses these issues by developing an optimized flood damage estimation technique based on the building characteristics. Utilizing the flood prediction solution developed by the Korea Institute of Science and Technology Information (KISTI), we have established an optimal flood damage estimation technology. We introduced a methodology for flood damage estimation by incorporating vulnerability curves based on the inventory of structures and apply this technique to real-life cases. The results show that our approach yields more realistic outcomes compared to the flood damage estimation methods employed in the USA and Japan. This research can be practically applied to procedures for flood damage in urban basement residences, and it is expected to contribute to establishing appropriate response procedures in cases of public grievances.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.2
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• pp.173-182
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• 2024

In recent, there has been a trend toward primarily utilizing data-driven models employing artificial intelligence technologies, such as machine learning, for flood prediction. These data-driven models offer the advantage of utilizing pre-training results, significantly reducing the required simulation time. However, it remains that a considerable amount of flood data is necessary for the pre-training in data-driven models, while the available observed data for application is often insufficient. As an alternative, validated simulation results from physically-based models are being employed as pre-training data alongside observed data. In this context, we developed a flood mapping accelerator to generate flood maps for pre-training. The proposed accelerator automates the entire process of flood mapping, i.e., estimating flood discharge using HEC-1, calculating water surface levels using HEC-RAS, simulating channel overflow and generating flood maps using RAS Mapper. With the accelerator, users can easily prepare a database for pre-training of data-driven models from hundreds to tens of thousands of rainfall scenarios. It includes various convenient menus containing a Graphic User Interface(GUI), and its practical applicability has been validated across 26 test-beds.