https://doi.org/10.12652/Ksce.2025.45.5.0523

최지안(Choi, Gian);윤설민(Yun, Sul-Min);김민규(Kim, Min-Gyu);장선우(Chang, Sun Woo)

Underground dams are an effective method for securing water resources by installing subsurface barriers in regions with limited surface water availability or in preparation for drought. This study evaluated the effects of underground dam installation on groundwater levels and storage through laboratory-scale physical experiments. The porous medium of the experimental model was vertically layered with two types of glass beads to reproduce low-permeability and high-permeability aquifers. Steady-state water levels and flow rates were then measured under a constant inflow condition. Subsequently, a subsurface barrier was installed to block the main aquifer flow, allowing observation of upstream groundwater level changes and storage increase. The experimental results were compared with numerical simulations using MODFLOW, and sensitivity analyses were conducted under varying upstream inflow conditions. The results showed that the installation of underground dams increased upstream groundwater levels and storage. The increase in stored water was quantified using the Additional Water Storage Ratio (AWSR), which rose from 11 % to 27 % as the upstream inflow increased from 0.6 cm3/s to 1.6 cm3/s. This demonstrated that underground dams can ensure a stable level of groundwater storage even under conditions of reduced inflow. This study quantitatively verified the effectiveness of underground dams and demonstrated their potential as a practical measure for securing water resources under drought conditions. In addition, it provides essential baseline data for the development of future strategies for sustainable groundwater management.

https://doi.org/10.12652/Ksce.2025.45.5.0533

서일원(Seo, Il Won);권시윤(Kwon, Siyoon)

In this study, we collected and analyzed hydraulic and dispersion data measured in natural rivers. Then, using these data, we proposed new prediction equations for the longitudinal and transverse mixing coefficients of a two-dimensional advection-dispersion model. The result of the longitudinal mixing coefficient showed that the dimensionless values of the longitudinal mixing coefficient obtained from small and medium-sized rivers in Korea and the St. Clair River in the U.S. were found to be 10~400, which was much larger than the theoretical value of 5.93 proposed by Elder (1959). The result of analysis of the correlation between the longitudinal mixing coefficient and river hydraulic and topographic factors revealed that ??_??/???? has a positive correlation with ??/?? and  ??/?? , but no significant correlation with ??/???? . The longitudinal mixing coefficient prediction equation derived in this study was found to have an excellent mean absolute percentage error of 13.8 %. In the case of the transverse mixing coefficient, ??_??/???? showed a linear relationship with ??/?? and ??/???? , expressed in logarithmic form, but no significant correlation was found with ??/?? . The results of comparing the transverse mixing coefficient prediction equation proposed in this study with existing empirical equations showed that the Bansal (1971) equation and the Deng et al. (2001) equation overpredict the measured values, and the Baek and Seo (2013) equation, Baek and Lee (2023) equation, and genetic programming based equation by Aghababaei et al. (2017) underpredict some measured values, while the proposed equation predicts values with high consistency with the measured values.

https://doi.org/10.12652/Ksce.2025.45.5.0549

강나래(Kang, Narae);김치영(Kim, Chiyoung);윤정수(Yoon, Jungsoo);황석환(Hwang, Seokhwan)

Precipitation data collected from rain gauges are used as input data for rainfall-runoff analysis models and various disaster forecasting and warning systems. For this reason, rain gauge data play a crucial role in disaster analysis and prediction. This study aimed to propose an appropriate rain gauge resolution by analyzing the characteristics of different rain gauge resolutions. We analyzed precipitation by station layout (location and density) and conducted accuracy experiments under varying rainfall intensities using precipitation gauges with resolutions of 0.1 mm, 0.5 mm, and 1.0 mm installed at the calibration station. The results revealed considerable variability in precipitation observations depending on the layout and spatial density of AWS and TM stations, with AWS showing lower spatial reproducibility than TM. Furthermore, performance tests of precipitation gauges indicated that the 1.0 mm resolution gauge produced an error of about 1.45 %, which did not increase significantly with higher rainfall intensity, showing a relatively stable pattern. In contrast, errors for 0.5 mm and 0.1 mm gauges increased sharply with rainfall intensity, reaching 7.32 % and 16.3 %, respectively. Given the recent rise in the frequency of heavy rainfall events, the importance of selecting a rain gauge resolution suitable for accurately capturing extreme precipitation is expected to grow. In this regard, the 1.0 mm gauge, which demonstrates consistent accuracy under strong rainfall conditions, may serve as a reliable option for mitigating flood risks during heavy rainfall events.

https://doi.org/10.12652/Ksce.2025.45.5.0557

김주훈(Kim, Joo Hun);김지성(Kim, Ji Sung)

Monitoring dam displacement is crucial for preventing catastrophic losses to infrastructure and human life. The purpose of this study was to analyze the time-series displacement of the Hwanggang Dam in North Korea by applying the PS-InSAR technique using satellite SAR data. A total of 105 Sentinel-1 C-band SLC images acquired from the ascending orbit (2017-2024) and 80 images from the descending orbit (2017?2021) were utilized in this study. Interferometric pairs were generated under a baseline constraint of 100 m, and pixels exhibiting a coherence value equal to or greater than 0.75 were identified as persistent scatterers to estimate dam displacement. The analysis results showed that the average displacement in the ascending orbit was 1.4 mm for the concrete dam and -4.5 mm for the fill dam. In the descending orbit, the average displacement was 0.6 mm for the concrete dam and -5.7 mm for the fill dam. In both satellite orbits, the concrete dam exhibited very small displacements, indicating structural stability. The fill dam section exhibited a gentle subsidence trend of several mm/yr. To validate the displacement analysis, we analyzed data from both ascending and descending orbits over the same area and confirmed the consistency of displacement trends between the two orbits, indirectly confirming the validity of satellite-based displacement analysis. This study demonstrated the effectiveness of a satellite-based indirect monitoring system for infrastructure with limited access. Future research is needed to enhance the reliability of displacement analysis by combining ascending and descending orbits to decompose vertical and horizontal components.

https://doi.org/10.12652/Ksce.2025.45.5.0567

장선우(Chang, Sun Woo);이동섭(Rhee, Dong Sop);정일문(Chung, Il-Moon)

Understanding aquifer responses to hydrological perturbations is essential for sustainable groundwater management. The Mean Action Time (MAT) is a statistical metric derived from the first temporal moment of a system’s response function, representing the characteristic time during which the effects of a disturbance are prominently expressed. MAT effectively captures aquifer response dynamics, even under conditions of gradual transitions or incomplete convergence. When derived directly from experimental or observational data, MAT inherently reflects the heterogeneity of the aquifer medium while simultaneously accounting for nonlinear processes and uncertain boundary conditions. This allows a more comprehensive assessment of the actual dynamic behavior of groundwater systems, which may not be adequately captured by simplified numerical models or analytical approximations. In this study, data from laboratory-scale experiments by Chang and Clement (2012) that reproduced the dynamic response of a coastal aquifer with a freshwater-seawater interface were used. MAT was calculated based on the observed movement of the the interface during both advancing and receding phases. In addition, auxiliary indicators associated with MAT, such as the Variance of Action Time (VAT) and the 99 % response time, were computed to compare aquifer response times and patterns, enabling quantitative evaluation of aquifer performance and stability. The results demonstrate that MAT provides a useful indicator not only for seawater intrusion and freshwater recovery processes but also for contaminant transport assessment, infrastructure management, and broader groundwater management decision-making. This approach integrates experimental and field observations to evaluate aquifer dynamics and offers a novel analytical framework that enhances predictive reliability.

https://doi.org/10.12652/Ksce.2025.45.5.0577

송윤섭(Song, Yunseop);황석환(Hwang, Seokhwan);윤정수(Yoon, Jungsoo);강나래(Kang, Narae);나우영(Na, Wooyoung)

Conventional approaches to evaluating Early Warning Systems (EWS) have primarily relied on real-time hydrological data?such as precipitation, water level, and runoff?to quantitatively assess the accuracy and lead time of alerts. However, the recent proliferation and real-time dissemination of unstructured data from news media and Social Network Services (SNS) have opened new avenues for performance evaluation. This study proposes a novel methodology that utilizes unstructured media data to assess the predictive performance of a flash flood early warning system and examines the validity of this approach. A database was constructed by collecting nationwide media reports on flood and inundation events that occurred in South Korea from 2020 to 2024. Posts from platforms with high user engagement and accessible search functions?among major domestic news outlets and SNS?were treated as proxy observations for actual flood occurrences. Based on this media-derived event record, the study evaluated the spatiotemporal accuracy of the EWS predictions. Although some variations in evaluation outcomes were observed depending on the spatial and temporal granularity of the media data, the results generally indicated that the early warning system effectively captured real flood and inundation events. Furthermore, comparative analyses showed that the predictive performance was not significantly different between evaluations based solely on news articles and those incorporating SNS data. This suggests that media-based validation?regardless of data type?can serve as a viable tool for assessing the reliability of flood early warning systems.

https://doi.org/10.12652/Ksce.2025.45.5.0589

이달별(Lee, Dalbyul)

This study analyzed the impact of natural hazards damage on neighborhood type changes in seven metropolitan cities (Seoul, Busan, Daegu, Incheon, Gwangju, Daejeon, and Ulsan) excluding Sejong. Neighborhood changes from 2010 to 2023 were tracked using logistic regression analysis as an analysis unit for the census output area. First, three neighborhood types(Struggling, New·High, and Old·Single) were derived through k-means cluster analysis, and the change in neighborhood types between 2010 and 2023 was calculated. As a result of logistic regression analysis, the likelihood of neighborhood change decreased by 18.0 % as the average annual damage from natural disasters increased. The possibility of neighborhood changes affected by natural disasters differed according to city and neighborhood type. As the damage increased, the likelihood of change in Struggling and High·New type decreased by 8.5 %, while the likelihood of change in Old·Single type decreased by 35.9 %, further intensifying the vulnerability of neighborhoods due to natural hazards. Neighborhood changes were promoted in Daegu due to increased damage, but changes were relatively suppressed in Incheon and Ulsan. Natural hazards also affected neighborhood change characteristics, and increased damage reduced the likelihood of neighborhood improvement and decline by 17.6 % and 13.2 %, respectively. These results empirically demonstrated that natural hazards can reduce the likelihood of neighborhood change in large cities overall and weaken resilience by suppressing the likelihood of change, especially in vulnerable neighborhood types and neighborhoods in certain cities. It also raises the need to establish disaster response and regeneration strategies suitable for neighborhood type and regional characteristics.