김민진 Kim Min-jin , 남원호 Nam Won-ho , 양미혜 Yang Mi-hye , 이지완 Lee Ji-wan , 김태곤 Kim Taegon , 도종원 Do Jong-won , 유규석 You Kyusuk , 김성준 Kim Seong-joon

DOI:10.5389/KSAE.2025.67.2.001 JKWST Vol.67(No.2) 1-12, 2025

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Identifying early warning signs of natural disasters is critical for mitigating vulnerability and enhancing disaster preparedness. While traditional disaster management often relies on numeric hydrometeorological data, a comprehensive approach should incorporate information on the impacts of disasters on human and environmental systems. This study explores the use of news big data to detect precursors of natural disasters by analyzing disaster-related keywords collected through web crawling from January to December 2022. Focusing on South Korea's 2022 natural disasters―including droughts, heat waves, and severe flooding―the study examines the timing and frequency of disaster-related news mentions across regions. A comparative analysis of news data and actual disaster occurrence timelines highlights the feasibility of using media coverage as a near real-time monitoring tool. Findings suggest that integrating news archives with scientific indices can enhance disaster response systems by improving real-time monitoring and situational awareness. This approach demonstrates the potential for leveraging big data to develop a robust disaster early warning and monitoring system.

위성승 Wee Seong Seung , 정남수 Jung Nam-su

DOI:10.5389/KSAE.2025.67.2.013 JKWST Vol.67(No.2) 13-23, 2025

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Farmmap is constructed as a single-layer, so it has limitations in that farmland, infrastructure, and production facilities are mixed in same spatial information. As a way to expand spatial information in agriculture and rural areas, we introduce multiple layers and suggest application and construction methods. In this study, multiple layers were proposed considering accurate calculation of farmland area and policy utilization using the MPC (Multi-layered Primitive Composite) model based on Farmmap. Jeju Special Self-Governing Province was selected as a proposed model (multi-layer) pilot construction area. As a result of the construction, based on the Farmmap, the number of paddy fields increased from 111 single-layer to 499 multi-layer to 388 rice fields, and the area increased by about 45.8 ha. The number of fields (upland) increased by 11,342, and the area increased by about 104 ha. Analysis of the difference based on the National Statistical Office's agricultural area survey shows that the combined area of paddy fields and fields differed by 7,252 ha in the case of a single-layer, but the difference in area accuracy was found to be greatly improved by about 588 ha in the multi-layer.

신용철 Yongchul Shin

DOI:10.5389/KSAE.2025.67.2.025 JKWST Vol.67(No.2) 25-32, 2025

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This study analyzed the impacts of weather (rainfall) and soil environmental (soil temperature and moisture) conditions on soil carbon dioxide (soil CO₂) dynamics according to different land uses. The study sites of Daegu research forest, Chuncheon upland farm and Buan research forest were selected to measure the soil environmental (soil characteristics, soil temperature and soil moisture) variables. The strong correlation was observed between the soil temperature and soil CO₂ values at Daegu, Chuncheon and Buan. However, the correlation between the soil CO₂ and soil moisture was significantly higher at Chuncheon compared to those of Daegu and Buan. The organic matter was artificially supplied for cultivating crops with tillage at Chuncheon. When the soil temperature exceeded over 20℃, the rapid decomposition of organic matter might cause a sharp increase in the soil CO₂ concentration. Usually, the soil CO₂ is released into the atmosphere through soil pores, but the high soil moisture that blocks the soil pores can restrict the movement of soil CO₂ and lead to a sharp rise in the soil CO₂ concentrations. Consequently, the soil CO₂ is influenced by increases in the soil temperature, while the soil moisture appears to affect the sequestration of the soil CO₂ within the soil matrix. Thus, the findings of this study can serve as the basic data for reducing soil carbon emissions in response to the climate crisis.

이예준 Lee Ye-jun , 남원호 Nam Won-ho , 윤동현 Yoon Dong-hyun , 장민원 Jang Min-won , 김진택 Kim Jin-taek , 김한중 Kim Han-joong , 홍성구 Hong Sung-gu , 김대식 Kim Dae-sik

DOI:10.5389/KSAE.2025.67.2.033 JKWST Vol.67(No.2) 33-43, 2025

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Recent climate change has increased the frequency and severity of flooding events, including super typhoons, localized heavy rainstorms, and torrential downpours. According to the Disaster Yearbook published by the Ministry of the Interior and Safety (MOIS), flood-related disasters, including heavy rain and typhoons, have accounted for 90% of total natural disaster damages over the past decade. The continuous and systematic collection of meteorological data (e.g., rainfall, water level, and flow rate) is crucial for hydrological analysis and forecasting to predict and prepare for floods. Flood control centers have established measurement systems to facilitate forecasting. These centers monitor hydrological data every 10 minutes at rainfall and water level observation stations and calculate runoff from rainfall to predict flood potential. Additionally, flood forecasts are issued when river water levels are anticipated to exceed flood warning or alert thresholds. However, compared to the flood forecasting systems currently in operation, predictive capabilities for runoff and flood volume in agricultural reservoirs are lacking. Improved flood forecasting for agricultural reservoirs and catchments is needed, utilizing meteorological forecasts and low water level measurement data. This study aims to apply a grid-based, distributed short-term hydrological model using weather forecast data, field-measured water level data, land cover data, Digital Elevation Model (DEM) data, and soil data. We will calibrate and validate a spatiotemporal rainfall-runoff analysis model for agricultural reservoirs.

하지연 Ha Ji Yeon , 김상민 Kim Sang Min

DOI:10.5389/KSAE.2025.67.2.045 JKWST Vol.67(No.2) 45-54, 2025

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The purpose of this study was to understand the water resource conditions in the Nakdong River basin through a spatio-temporal analysis of rainfall and potential evapotranspiration. The Penman-Monteith method was used to estimate potential evapotranspiration, and the difference between rainfall and potential evapotranspiration (P-PET) was calculated. To compare and analyze the spatial distribution, meteorological data-such as maximum temperature, minimum temperature, wind speed, humidity, and sunshine duration-from 21 weather observation stations (ASOS) in the Nakdong River basin were used, covering the period from 2011 to 2023. This period was standardized to the shortest observation timeframe for comparison purposes. This analysis revealed that the potential evapotranspiration across 21 stations ranged from 885 mm to 1,112 mm, with an average of 936 mm. The P-PET results showed that the southern region, particularly Busan, had abundant water resources, while northern areas like Bonghwa and Andong experienced water deficits. For analyzing the temporal distribution, 13 stations out of the 21, which had longer data periods, were selected, and data from 1973 to 2023 were used. The Mann-Kendall analysis indicated increasing trends in potential evapotranspiration at five stations.

김솔희 Kim Solhee , 전정배 Jeon Jeongbae , 석승원 Seok Seungwon , 정재영 Jung Jaeyoung , 김태곤 Kim Taegon

DOI:10.5389/KSAE.2025.67.2.055 JKWST Vol.67(No.2) 55-67, 2025

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This study developed an agricultural parcel detection model based on the YOLO-Seg algorithm using high-resolution satellite imagery of CAS500-1 (Compact Advanced Satellite 500-1). We analyzed the effects of grid overlap and confidence threshold settings on object detection accuracy. The main results showed that a 60% grid overlap achieved optimal performance with 70% detection accuracy and about 5% false detection rate within one minute of processing time. By analyzing ratios of overlapping from 10% to 90%, the detection accuracy improved, but the processing time significantly increased, requiring a balance between accuracy and efficiency. The proposed method of spatial continuity correction using polygon centroids improved detection accuracy by approximately 5%p while significantly reducing false detection rates. The system demonstrated 80% detection accuracy when the confidence threshold was set between 0.5 and 0.7, which outperformed traditional detection methods. Additionally, our approach addressed the boundary detection issues in split grid analysis through an optimized overlap strategy. This automated detection system can be effectively applied to agricultural monitoring, resource management, and policy development, providing a cost-effective solution for large-scale agricultural parcel monitoring.

양예린 Yang Yerin , 최순군 Choi Soon-kun , 이병모 Lee Byeong-mo , 전상민 Jun Sang-min , 이종문 Lee Jong-mun , 엽소진 Yeob So-jin

DOI:10.5389/KSAE.2025.67.2.069 JKWST Vol.67(No.2) 69-81, 2025

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Best Management Practices (BMPs) are proactive strategies to mitigate agricultural non-point source (NPS) pollution. Among non-structural BMPs, furrow vegetative cover reduces runoff velocity and prevents soil loss by introducing cover crops in furrows. However, field monitoring of NPS pollution reduction is often constrained by environmental factors, time limitations, and high costs. Therefore, this study aimed to evaluate the effectiveness of furrow vegetative cover in reducing phosphorus runoff in sloped upland areas using the APEX model. The study was conducted in a sloped upland field at the National Institute of Agricultural Sciences in Wanju-gun, Jeollabuk-do, South Korea. Model calibration was performed by comparing simulated values with observed monitoring data. The control treatment involved conventional fertilization management and crop cultivation, while the experimental treatment applied furrow vegetative cover in addition to the control conditions. Model validation demonstrated a strong agreement between simulated and observed runoff and phosphorus loads, with R² = 0.96, NSE = 0.95, and PBIAS = 12.5% for runoff, and R² = 0.72, NSE = 0.65, and PBIAS = 23.5% for total phosphorus (T-P). Under historical climate conditions (1991-2020), the analysis revealed that furrow vegetative cover reduced runoff by up to 20.3% and T-P by up to 50.4%. The results of this study indicate that furrow vegetative cover is significantly effective in mitigating agricultural NPS pollution. Future research should focus on scenario-based modeling to assess the long-term impacts of BMPs under climate change conditions and explore the applicability of other BMPs.