We propose a novel factor model in the graph frequency domain for multivariate data residing on the vertices of a graph, referred to as a multivariate graph signal. By utilizing graph filters, our model extends the frequency-domain approach of the dynamic factor model from time series to graphs, enabling a graph-aware and multiscale interpretation of factors across graph frequencies. This latent modeling approach reduces the dimensionality of graph signals, thereby improving the understanding of their structure. It also allows the use of the extracted factors for subsequent analyses, such as clustering. We describe the estimation of factors and their loadings and investigate the consistency of the factor estimator. In addition, we propose two consistent estimators for determining the number of factors. The finite sample performance of the proposed method is demonstrated through simulation studies across various graph structures. We also compare it with classical factor analysis and examine how the choice of graph structure affects the results. The findings show that our model achieves lower reconstruction errors and successfully incorporates the graph structure. Furthermore, we illustrate the effectiveness of the proposed method by applying it to G20 economic data, water quality data from the Geum River, and passenger data from the Seoul Metropolitan subway.

This study proposes a machine learning-based decision support model leveraging flow rate-velocity prediction for estimating applicable search ranges in the case of riverine drowning accidents in the primary stream of the Geum River in Korea. This constitutes a new approach that combines a mathematical model, data-driven prediction, and probabilistic factors to overcome the limitations of rescue personnel experience and opinion. As this proposed method yields a more quantified and reasonable search sector than a method dependent on experience, efficient allocation of equipment, manpower, and rescue personnel safety can be expected. Overall, these findings contribute to the decision-making model based on data obtained when searching for missing individuals in the river. Future work should expand model validation to other major rivers in Korea, such as the Han-gang and Nark-dong-gang Rivers. This will enable positive applications of such methods at river rescue sites.

Vertical distribution of heavy metals and conservative elements in tidal-flat sediments of Yubudo Island, Geum River Estuary

Temporal changes in bivalve production on Yubudo Island, Geum River Estuary, based on long-term field records by local resident

River discharge can be categorized into low-, drought-, normal-, and high-flow conditions depending on the flow regime and is an essential parameter in water resource management. Traditionally, the quantitative estimation of daily streamflow has primarily relied on hydrological models; however, such models require extensive effort and cost for data preparation and involve complex implementation procedures. To address these limitations, this study developed a predictive framework using hydrometeorological variables to estimate the daily discharge at streamflow monitoring stations. To improve prediction accuracy, particularly in drought-related low-flow conditions, the interquartile range, representing the statistical variability and cumulative distribution characteristics of streamflow data, was applied. In addition, flow regimes were classified into categorical variables to minimize the influence of extreme high-flow values occurring during flood seasons. The proposed methodology was applied to the Weolsan Bridge station in the Geum River Basin and the Seomjingang Dam station in the Seomjingang River Basin. Artificial Neural Network, Random Forest, and Model Tree were employed, and a multiple regression model was applied for comparison. Model performance was evaluated using k-fold cross-validation and assessed based on correlation coefficients, Kendall’s tau, Spearman’s rho, MAE, RMSE, RAE, and RRSE metrics by comparing model predictions with observed values. The results indicated that the models demonstrated higher reproducibility than the regression-based approach. Among them, the Random Forest model achieved the best predictive performance. Overall, the findings suggest that machine-learning models utilizing hydrometeorological variables provide a viable approach for forecasting low-flow conditions and may serve as a useful tool for drought preparedness and water resource management. The results of this study can be used as foundational research data to establish drought response strategies for sustainable water resource management.

The endangered freshwater fish Gobiobotia brevibarba is endemic to Korea and threatened by habitat disturbance in major river systems. We investigated four wild populations from the Han River basin (IJR, BHR, NHR) and the Geum River basin (GR) using eleven microsatellite loci to assess genetic diversity, population structure, and contemporary gene flow. All populations showed relatively high genetic diversity (HO = 0.709–0.800, HE = 0.707–0.803) and no evidence of inbreeding, although bottleneck signals under the infinite allele mutation model were detected in IJR and BHR. Contemporary effective population size was large in IJR (Ne = 2463) and moderate in NHR (Ne = 467), whereas estimates for BHR and GR were imprecise. Genetic differentiation was very low within the Han River basin (FST = 0.009–0.027) but weak and significant between Han and Geum (FST = 0.085–0.096), and clustering analyses (STRUCTURE, DAPC, find.cluster) consistently supported K = 2, separating Han from Geum River. Gene flow analyses indicated extremely limited interbasin gene flow (<4%) but asymmetric contemporary migration from BHR into both IJR and NHR; all other migration rates were similarly low. These results show that G. brevibarba currently maintains high genetic diversity and two basin-level genetic clusters, underscoring the need to manage Han and Geum River populations as separate units and conserve riffle habitats and longitudinal connectivity.

Leiocassis longirostris, a large benthic catfish, has been classified as Regionally Extinct (RE) in South Korea since the 1970s. Assessing the success of ongoing restocking efforts in the Geum River is challenging due to limitations in traditional capture-based monitoring methods. We developed a highly sensitive, species-specific quantitative real-time PCR (qPCR) assay targeting the mitochondrial cytochrome b gene (mt-cyb) for this restocked fish. Assay validation confirmed high specificity against a panel of 25 co-occurring fish species, including 8 related siluriforms, and an excellent limit of detection of 10 copies per reaction. We applied this assay to environmental DNA (eDNA) extracted from paired water and sediment samples collected from 13 stations across the Geum River Basin. eDNA was detected at three water and five sediment stations. Notably, detection rates and average concentrations were consistently higher in a sediment sample (peaking at 1609.93 copies L-1), an observation consistent with the species' benthic ecology. These findings provide the first molecular evidence that restocked L. longirostris may disperse and persist in the mid to lower reaches of the Geum River. This validated eDNA qPCR tool is essential for guiding the integrated monitoring and adaptive management strategies required to support the long-term recovery of this ecologically important species. This study is the first species-specific qPCR of the bagrid catfish in South Korea.

Abandonment and population fluctuations of prehistoric villages: Focusing on the Geum River Basin during Korea's Bronze Age

Biogeochemical responses to water management: Insights from the regulated Geum River, Korea

ABSTRACT Harmful Algal Blooms (HABs) threaten aquatic ecosystems, necessitating effective monitoring strategies in water resource management. Satellite-based remote sensing has emerged as a popular method to address the limitations of in-situ monitoring. However, cloud covers can obstruct optical imagery, causing data loss. Synthesis Aperture Radar (SAR) imagery, with its capabilities, can penetrate through any weather conditions. We applied SAR imagery with the Faster Regional Convolutional Neural Networks (Faster R-CNN) model to detect the algal bloom. The dataset of the Geum River Basin was obtained from 2020 to 2022. The sigma naught values (dB) were analyzed from the SAR imagery to clarify the reflectance properties of algae in VH and VV polarizations. The values ranged between −12 dB and −33 dB and −5 dB and −27 dB for VH and VV polarization, respectively. The model was developed with hyperparameter optimization to detect the algal bloom by splitting the training from 2020 to 2022, and the testing dataset 2022. Evaluation metrics including precision, recall, and F1 scores yielded values of 0.600, 0.692, and 0.643, respectively. The developed model was simulated to identify the seasonal outbreak. The result illustrated that the algal blooms were detected only in the summer of 2021 and 2022. Furthermore, the model was validated in supporting an existing algal alert report, demonstrating the potential for real-time monitoring. Finally, this study highlights the effectiveness of employing SAR imagery with the Faster R-CNN model to develop an algorithm for detecting algal blooms, offering advancements in water management practices.

This study analyzed the relationship between zooplankton communities and water quality characteristics, with a focus on total organic carbon (TOC), in 22 reservoirs within the Geum River basin that share similar climatic conditions but exhibit varying levels of pollution. Across all reservoirs, zooplankton community structures showed the highest correlations with TOC, suspended solids (SS), chlorophyll-a (Chl-a), and Secchi depth (SD), with stronger associations observed for rotifers and cladocerans compared to copepods. The classification of zooplankton community composition patterns, followed by an analysis of their associations with TOC concentrations, revealed relatively distinct differences between high-TOC and low-TOC reservoirs, indicating that TOC functions as a key determinant of community composition. Meanwhile, network analysis based on overall water quality characteristics indicated that patterns of water quality similarity among zooplankton-based communities differed somewhat from those based solely on TOC concentrations, suggesting that TOC may exert an independent influence on zooplankton community structure. In high-TOC reservoirs, typical eutrophic characteristics—such as elevated chlorophyll-a, total phosphorus, and suspended solids, along with reduced water transparency—were observed, accompanied by higher zooplankton abundance and a greater proportion of rotifers within the community. In contrast, low-TOC reservoirs, despite exhibiting no marked differences in other water quality variables, showed higher diversity of cladocerans alongside rotifers, further supporting the independent role of TOC in shaping zooplankton community structures. These findings highlight TOC not only as a general indicator of pollution but also as an ecologically significant factor influencing zooplankton community composition and carbon dynamics in reservoir ecosystems. They suggest that TOC should be considered a key variable in future assessments and management of lentic ecosystems.

The Seokjang-ri site in Gongju, first discovered in 1964 along the Geum River, represents the earliest identified Paleolithic archaeological site in southern Korea, symbolizing the foundational point of Paleolithic archaeology in the region. However, limitations in structural reinterpretation have persisted due to prolonged excavation history, urbanization-induced landscape changes, and insufficient digitization of early survey records. This study aims to reconstruct the spatial structure and sedimentary environment of the site using an integrated Archaeogeological approach, including paleo-topographic analysis, geophysical surveys (Ground-Penetrating Radar and electrical resistivity tomography), and core sampling. Paleo-topographic analysis revealed that the site occupies a flat area on the southern slopes originating from northern mountainous terrain, highlighting its advantageous geographical suitability for human settlement. Geophysical surveys identified discrepancies between previously documented excavation positions and actual subsurface structures, attributed to limitations in early surveying techniques and coordinate system discrepancies. Furthermore, core sampling indicated that the site's surroundings consist of coarse-grained sediment layers formed in high-energy alluvial environments. However, direct subsurface core data from within the archaeological site was not obtained, limiting precise sedimentary environment interpretation. This research quantitatively elucidates the geological and topographic characteristics of the Seokjang-ri site, providing potential avenues for structural reinterpretation. The findings contribute essential foundational data for future detailed excavations and the establishment of effective preservation management strategies.

Enhancing harmful algal bloom predictions through integrated modeling of turbidity and nutrient dynamics in monsoon climate reservoirs.

The present study conducted a preliminary investigation of perfluorinated compounds (PFCs) within the Geum River Basin, focusing on potential contamination sources such as downstream areas, wastewater treatment plants, and major rivers in the basin. The targeted substances for this study were PFHxS (Perfluorohexane Sulfonate), PFOA (Perfluorooctanoic Acid), and PFOS (Perfluorooctane Sulfonate)—three PFCs that have been subjects of extensive research due to their environmental persistence and potential hazards. These substances are listed under the Stockholm Convention and are managed as water pollutants in South Korea. The findings of this study are intended to serve as foundational data for the management of PFCs in the region. A total of 15 sampling points were selected, focusing on key sites expected to influence the concentration of PFCs in the Geum River Basin, including 12 points from major downstream areas and wastewater treatment plants, as well as two tributaries (located in the middle and lower reaches of the Geum River) and one point along the main river. The three PFCs were analyzed using LC-MS/MS. Sample preparation was carried out following the water quality pollution testing standards (ES 04506.1) and the residual pollutants testing standards (ES 10390.1, ES 10363.1a). Solid-phase extraction (SPE) was performed using HLB cartridges to extract the compounds. The investigated downstream areas and wastewater treatment plants exhibited relatively high PFCs detection rates of over 94%. In some measurement points, PFOS and PFOA tended to show higher concentrations in the effluent compared to the influent. Additionally, while the concentrations in the rivers were low, below 10 ng/L, the presence of PFCs was confirmed across the entire Geum River Basin. At sites where effluent from wastewater treatment plants joined the river system, the total concentration of the three PFCs and the average concentration of PFOA were found to be the highest, indicating that wastewater treatment plants could be significant sources of PFC contamination. The concentrations of PFCs detected in the treatment plant processes were highest in the effluent from the primary sedimentation tank, followed by the effluent from the secondary sedimentation tank, influent to the secondary sedimentation tank, and influent to the primary sedimentation tank. For most substances, except for PFHxS, the concentrations in the effluent were either higher or similar to those in the influent, suggesting that PFCs were not efficiently reduced in the treatment plants, with some showing an increasing trend. This study confirmed the widespread presence of PFCs throughout the Geum River Basin. It is necessary to identify and address the major sources of these contaminants while conducting continuous monitoring to study their behavior in the environment. Furthermore, to better understand the impacts of these persistent pollutants, additional research is needed, including the analysis of various other PFCs and their precursors, beyond the commonly studied PFOS, PFOA, and PFHxS.

Long-term streamflow data at a hyper-resolution (less than 1 km) is essential for hydroclimatic extreme and ecological assessment, which is not available over a river basin where rapid socioeconomic growth have been experienced. Here, we use the Variable Infiltration Capacity-River Routing Model (VIC-RRM) to reconstruct naturalized daily streamflow at 90–meter resolution for the Geum River, one of South Korea’s major rivers, over 1951–2020. VIC-RRM demonstrates high temporal consistency with a correlation coefficient exceeding 0.6 for observed streamflow seasonality at over 60% of the 90 gauge stations along the Geum River. However, 36% of the stations show low modified Kling-Gupta Efficiency (0.2–0.4), primarily due to uncertainties in runoff data and human disturbance impacts like irrigation and reservoir storage. Our simulated naturalized data reveal decadal variability in the 1990s and an increase in day-to-day variability of the Geum River in the 2010s compared to those in the 1970s. This dataset provides physically consistent naturalized streamflow data for reference data to evaluate climate change-driven changes in streamflow for the Geum River.

Sensor networks enable the collection of high-frequency, large water quality datasets that provide valuable information for managing eutrophication, such as chlorophyll a (Chl-a) concentration. Deep learning models have been successfully applied to derive useful insights from large-scale environmental data. However, sensor data often contain missing values, presenting challenges for applying deep learning models. Therefore, we employed the reverse time attention model with a decay mechanism (RETAIN-D) to simultaneously conduct feature engineering, prediction, and interpretation within a single model structure. Various environmental, hydrological, and meteorological variables were utilized as input features to predict the exceedance of Chl-a criteria. Data were collected from 2018 to 2022 at four monitoring sites along the Geum River, South Korea. RETAIN-D demonstrated strong prediction performance (accuracy = 0.84–0.90, AUC = 0.69–0.91, F-measure = 0.89–0.90 on the test set) across varying Chl-a criteria. Environmental variables were more important than hydrological and meteorological for predicting the exceedance of Chl-a criteria. The contribution of input features to the model prediction was generally higher in more recent time steps when the Chl-a criterion of the target site was applied. These results highlight the effectiveness of RETAIN-D in analyzing high-frequency time series data from sensor networks.

The journal Fishes retracts the article titled “Distribution Pattern and Brood Parasitism Characteristics of an Endangered Fish, Pseudopungtungia nigra, in the Geum River Basin, South Korea” [...]

Currently, algal blooms pose a significant threat to water ecosystems and human health worldwide. Timely and accurate monitoring and forecasting of algal blooms are essential for maintaining ecological balance in water bodies. Airborne remote sensing (ARS) has emerged as an efficient and rapid method for acquiring large-scale environmental information and has gained attention in the field of algal bloom monitoring. In this study, we analyze the applicability and effectiveness of airborne remote sensing in algal bloom monitoring through four representative cases: Lake Erie, Lake Pinto, Geum River, and Lake Taihu. The results demonstrate that airborne remote sensing offers high temporal and spatial resolution, allows for all-weather observation, and enables rapid response in algal bloom monitoring. However, airborne remote sensing also presents challenges such as high costs and susceptibility to weather conditions. Furthermore, this study proposes future directions for the development of airborne remote sensing. It aims to enhance our understanding of the potential applications of airborne remote sensing technology in algal bloom monitoring, providing valuable insights for further improvements in monitoring techniques and water environment protection.

Iksan’s historical and cultural status and source in the history of Northeast Asia occupy a very important position. Iksan had already attracted attention as a center of Bronze Age culture based on the recovery of a large amount of archaeological relics in the 1970s. The ruins of Buyeo, Iksan and Jeonju(Wanju) within a 60km radius of Iksan were set as the ‘Iksan Cultural Area’. It is presumed that technological innovations were achieved in regions including Iksan by residents who came down from the political upheaval in Liaodong and the northwest region. Archaeologically, the founding period of Mahan is believed to be between the 3rd and 1st centuries B.C. in connection with the introduction of iron culture. In this case, the Mahan small states of Iksan is likely to be the Geonma states that appears in China's records of the 『Samkukji』. This small state’s name is connected to Jimomilji in the 「Gwanseumeungheomgi」 and Jimohyeon, Jimamaji, Geummajeo, Geumma in the geography of the 『Samguksagi』, showing regional historicity. Also, considering the spatial scope of Mahan as the southwestern part of the Korean Peninsula in Gyeonggi, Chungcheong, and Jeolla regions, Iksan small states is considered to encompass the Geum River and Mangyeong River areas such as Okgu, Impi, Hamyeol and Geumma. In the late 4th century, the Iksan region was subordinated to Baekje along with the western regions of Jeollabuk-do, including Gunsan, Jeonju(Wanju), Gimje, Buan, Jeongeup and Gochang, and was incorporated into the local system after the transfer of the capital to Ungjin. After the 5th century, the Ipjeom-ri and Ungpo-ri tombs and the Sanwol-ri ruins in Daeya-myeon in the Geum River area related to Iksan show that they came under the control of central political forces. Even in the 6th century, Iksan was an important area through the fact that even Baekje’s Geummajeo, Geumma-gun, and Geumma-san were continuously mentioned in the literature records. Even in the 7th century, the theory of Iksan as the capital and separate capitals means that it had the same weight and importance as Sabi, the capital of Baekje at the time.

Contribution of carbonate-derived dissolved inorganic carbon into autochthonous particulate organic carbon in two small temperate Korean rivers (Geum and Seomjin)