Rapid urbanization and industrialization have caused water quality issues in urban rivers. Appropriate measures based on water quality monitoring systems and prediction methods are needed for water quality management. While South Korea has operated a water quality monitoring system that measures various environmental factors and has accumulated water quality data, a water quality prediction system is not in place. This study suggests a water quality prediction method based on a long short-term model using water quality and meteorological monitoring data. Additionally, we present a derived input set of the prediction model that can improve the prediction model performance. The prediction model’s performance was evaluated by the coefficient of determination under various conditions, such as the hyperparameters, temporal resolution of input data, and application of upstream and downstream data. As a result, using the temporal resolution of the input data as hourly data improved predictions by an average of 25.6% over three days of the prediction period compared to daily data. Meanwhile, it was analyzed that the hyperparameters and using upstream and downstream data have a minor effect on the model performance. The results of this study underscore the crucial role of the number, duration, and temporal resolution of available monitoring data in water quality management.

Effective flood management in agricultural fields has become increasingly important due to the rising frequency and intensity of rainfall events driven by climate change. This study investigates the applicability of urban flood analysis models—SWMM (1D) and K-Flood (2D)—to irrigated agricultural fields with artificial drainage systems. A case study was conducted in a rural area near the Sindae drainage station in Cheongju, South Korea, using rainfall data from an extreme weather event in 2017. The models simulated inland flooding and were validated against flood trace maps provided by the Ministry of the Interior and Safety (MOIS). Receiver Operating Characteristic (ROC) analysis showed a true positive rate of 0.565, a false positive rate of 0.21, and an overall accuracy of 0.731, indicating reasonable agreement with observed inundation. Scenario analyses were also conducted to assess the effectiveness of three improvement strategies: reducing the Manning coefficient, increasing pump station capacity, and widening drainage channels. Among them, increasing pump capacity most effectively reduced flood volume, while channel widening had the greatest impact on reducing flood extent. These findings demonstrate the potential of urban flood models for application in agricultural contexts and support data-driven planning for rural flood mitigation.

As the frequency and intensity of heavy rains increase, the vulnerability of agriculture to disasters also increases. Consequently, there is a need to improve flood and inundation predictions. To enhance the accuracy of inundation predictions, it is essential to monitor water level and discharge data within agricultural areas. This study was conducted to monitor water levels and rainfall in the Cheongju Sindae area from 2022 to 2023, and the data was utilized as input and validation data for agricultural inundation modeling. Four irrigation drainage canals were installed to a square-shaped concrete structure where the water level gauge is. It was then confirmed that the water level rises with rainfall. The flow velocities were monitored during periods of heavy rainfall. The rating curve, which estimates water level and flow velocity based on observations, was estimated using the software K-HQ. The resulting curve was presented with the Coefficient of Determination (R 2). K-HQ was also used to calculate the equation for the rating curve, taking outliers into account at each data point. Outliers were extracted and the rating curve was recalculated. As the coefficient of determination of three out of four stations exceeded 0.95, the estimated rating curve may be considered reliable for discharge estimation. This study provides critical data for enhancing agricultural inundation modeling accuracy and drainage improvement projects.

This study proposes a hardware-based approach to address agricultural water shortages by directly improving water supply operations, rather than estimating agricultural water demand or supply. Unlike previous studies that focus on evaluating water supply capacity or predicting reservoir inflows through modeling or data-driven methods, this work proposes an operational strategy involving the physical interconnection of reservoirs. Specifically, the study investigates the coordinated use of surplus storage capacity from reservoirs with high watershed ratios to support those with low watershed ratios, thereby enhancing overall water supply reliability. Reservoir inflows were estimated using the Hydrological Operation Model for Water Resources Systems (HOMWRS). The analysis was conducted on reservoirs managed by the Korea Rural Community Corporation (KRC), selected based on data accessibility and availability.

Urban underground spaces are highly vulnerable to flooding due to their complex, multi-level configurations and limited drainage capacity. Although numerous numerical models have been proposed to simulate inundation processes in underground environments, their reliable development and application remain constrained by the lack of reference data for validating model structure and reproducibility. In particular, few studies have systematically investigated inundation behavior within the same underground space under varying inflow locations, numbers of inlets, and discharge conditions. This study presents the development of a two-dimensional surface–underground integrated flood model based on the shallow water equations, with the aim of reproducing inundation processes in complex underground spaces. The model was formulated to represent inflow, internal propagation, and drainage processes within underground spaces in a unified computational framework. Unlike conventional approaches that treat underground spaces as lumped storage elements or simplified links, the proposed model resolves underground spaces as two-dimensional hydraulic domains, allowing lateral flow propagation and spatial water-depth distributions to be explicitly simulated. Physical hydraulic experiments were employed to support model verification and to provide controlled reference conditions for quantitative evaluation. Model validation was conducted using laboratory-scale hydraulic experiments performed with the Kyoto Oike underground space facility (1/30 scale) at the Disaster Prevention Research Institute, Kyoto University. Steady-state inflow conditions were considered at 16 surface–underground connection points, including both single-inlet and sequential multi-inlet configurations. A total of 93 experimental cases were designed by applying constant inflow rates of 100, 200, and 400 mL/s while progressively increasing the number of active inlets. Final water depths, inundation extents, and dominant outflow pathways were measured after steady conditions were reached and were directly compared with numerical results under identical geometrical and boundary conditions. The comparison results demonstrate that the developed model can reasonably reproduce inundation propagation and drainage behavior within complex underground spaces under varying inflow locations, inlet numbers, and discharge levels. Through this study, a numerical model for analyzing inundation processes in complex underground spaces was developed, and the proposed model is expected to support future underground flood risk assessment and evacuation planning in urban environments.