Tae Sung Cheong

Small streams exhibit rapid and nonlinear flood responses due to steep slopes, short flow paths, and limited storage capacity, making real-time flood prediction difficult under both computational and data constraints. This study presents a measurement-based flood prediction framework for real-time estimation of flood discharge and depth in small-stream basins. Conventional approaches, such as physically based hydrodynamic models, require detailed boundary conditions and high computational cost, while data-driven models often lack physical interpretability. The proposed framework integrates high-frequency monitoring data from the Small-Stream Smart Monitoring System, short-term rainfall nowcasting from the MAPLE system, and nonlinear regression-based hydraulic relationships within a unified operational structure. Rainfall–discharge nomographs and depth–discharge rating curves were developed using a four-parameter logistic regression model based on long-term observations from 12 small streams in Korea. Additional comparisons with alternative regression forms confirmed the suitability of the 4PL model for representing nonlinear hydrological responses. Forecast rainfall was used to estimate discharge, which was subsequently converted to flood depth through calibrated rating curves. For ungauged reaches, depth–discharge relationships were derived using HEC–RAS-based scenario simulations and the Manning equation to enable spatially continuous prediction along stream networks. Model performance was evaluated using independent validation events, showing mean prediction accuracies of approximately 89% for discharge and 90% for flood depth. The framework reduces computational demand by relying on pre-established relationships while maintaining physically interpretable structures. The results indicate that the proposed approach can support real-time flood prediction in small streams under conditions like those examined in this study, although its applicability to other regions requires site-specific calibration and further validation.

Owing to climate change, annual rainfall and the likelihood of extreme precipitation events are projected to increase in the future (2021-2100), raising concerns about the intensification of flood damage. This study assessed flood risk at the administrative district level using three key components—Hazard, Exposure, and Vulnerability—and 13 detailed indicators that reflect meteorological factors, historical flood records, population data, and regional characteristics. In particular, future changes in flood risk under climate change were quantitatively evaluated using the SSP scenarios. The results showed that regions with the top 10% of flood risk scores exhibited high consistency with flood-prone areas identified on existing flood inundation maps. Under current conditions, approximately 25% of the administrative regions are classified as high- or extremely-high flood-risk areas; however, under future conditions, this proportion increases to as much as 42%, depending on the SSP scenario. In addition, the maximum flood risk value increased by approximately 1.7 times compared to current conditions, with scenario-dependent increases ranging from 1.12 to 2.19 times. The flood risk assessment results and high-risk area classifications presented in this study can serve as foundational data for more detailed flood risk analyses and are expected to provide practical evidence for establishing flood risk management policies and prioritizing investments in flood control infrastructure at both the national and local government levels.

Small streams exhibit rapid and nonlinear flood responses due to steep slopes, short flow paths, and limited storage capacity, making real-time flood prediction challenging under both computational and data constraints. This study proposes an integrated, measurement-based flood prediction framework that enables real-time estimation of flood dis-charge and flood depth in small-stream basins. Unlike conventional approaches that rely on either computationally intensive hydrodynamic models or standalone data-driven methods, the proposed framework combines high-frequency monitoring data, rainfall nowcasting, and nonlinear regression-based hydraulic relationships into a unified system. Rainfall–discharge nomographs and depth–discharge rating curves were developed using a nonlinear four-parameter logistic (4PL) regression model based on long-term observations from twelve representative basins in Korea. Forecast rainfall from the Korea Meteorological Administration MAPLE nowcasting system was used to estimate discharge, which was subsequently transformed into flood depth through calibrated rating curves. To extend prediction capability beyond monitoring locations, additional depth–discharge relationships were derived for ungauged reaches using hybrid approaches based on HEC-RAS scenario simulations and the Manning equation. Validation against major flood events showed strong agreement between observed and predicted values, with mean prediction accuracies of approximately 89% for discharge and 90% for flood depth. The proposed framework effectively captures nonlinear rainfall–runoff behavior while significantly reducing computational complexity compared with conventional hydrodynamic models. These results demonstrate that the framework provides a practical and scalable solution for real-time flood prediction and early warning in small-stream environments, particularly by enabling spatially continuous flood-depth estimation across both gauged and ungauged reaches.

Recent extreme rainfall events have revealed the limitations of current disaster prevention design standards, which do not adequately reflect the nonstationarity and frequency of extreme events under climate change. This study applied the SSP5-8.5 climate change scenario to quantitatively assess medium- to long-term rainfall variations and conducted an empirical analysis of the Bubhwa Stream watershed. It included the estimation and validation of design rainfall, intensity-duration-frequency (IDF) curves, and temporal rainfall distributions. The results show that future rainfall will be characterized by a significant increase in both the frequency and intensity of short-duration extreme storms. The design rainfall values based on the climate scenario are approximately 15% higher than those derived from historical observations. These findings highlight the necessity of incorporating nonstationary frequency analyses and temporal disaggregation techniques into flood design practices. By systematizing the design rainfall estimation procedure and providing empirical verification based on various climate scenarios, this study establishes a technical foundation for improving disaster prevention guidelines. The results underscore the academic and practical need to transition from static design standards to dynamic and adaptive design criteria in an era of climate change.

As climate change intensifies, heatwaves are increasingly recognized as climate disasters that pose serious threats to public safety and health. The current national heatwave crisis alert system in the Republic of Korea, which is based solely on perceived temperature, fails to reflect regional climatic variations. To address this issue, the Republic of Korea has developed a new heatwave crisis alert system based on the Wet Bulb Globe Temperature (WBGT); however, its uniform criteria cause confusion in practical applications. This study introduces a region-specific heatwave crisis alert system tailored to the environmental conditions in the Republic of Korea using meteorological data from 65 nationwide observational stations spanning 30 years, from 1994 to 2023. Analysis of the daily mean, maximum, and minimum temperatures reveals distinct regional heatwave patterns. Based on these results, a three-tier alert system—Caution, Warning, and Severe—is introduced, incorporating both the WBGT and the number of days exceeding the 30-year average to improve the accuracy. Heat wave events that happened in 2019 and 2022 were used for validation and confirmed the effectiveness of the new system in capturing regional differences and enhancing alert reliability. This approach provides a scientific and policy-relevant foundation for public health protection and climate change adaptation, while supporting efforts to reduce the socioeconomic impacts of extreme heat.

Climate changes have increased heavy rainfall, intensifying flood damage, especially along small streams with steep slopes, fast flows, and narrow widths. In Korea, nearly half of flood-related casualties occur in these regions, underscoring the need for effective flood early warning systems. However, predicting flood depths is challenging due to the complex channels and rapid flood wave propagation in small streams. This study developed a flood early warning framework (FEWF) tailored for small streams in Korea, optimizing rainfall–discharge nomographs using hydro-informatic data from four streams. The FEWF integrates a four-parameter logistic model with real-time updates with a nomograph using a robust constrained nonlinear optimization algorithm. A simplified two-level early warning system (attention and severe) is based on field-verified thresholds. Discharge predictions estimate the water depth in unmeasured cross-sections using the Manning formula, with real-time data updates allowing for the dynamic identification of the flood depth. The framework was validated during the 2022 flood event, where no inundation or bank failures were observed. By improving flood prediction and adaptive management, this framework can significantly enhance disaster response and reduce casualties in vulnerable small stream areas.

A noncontact measurement method is used for measuring surface velocities as small streams have fast flow velocities and complex channel shapes, making it difficult to measure flow discharges in the event of a flood. These measurement methods convert the surface velocity into average velocity using the surface-average flow velocity conversion coefficient. Existing methods for estimating surface-average flow velocity conversion coefficients assume a flow velocity distribution according to water depth or use a simplified approach based on the roughness of the channel bed, thereby suffering from poor accuracy depending on the hydraulic characteristics of small streams or bed conditions. To solve this problem, we developed a surface-average flow velocity conversion coefficient estimation formula that reflects the hydraulic characteristics of small streams. To develop the conversion coefficient estimation formula, the flow velocity distribution was measured by depth for various flow conditions at a steep artificial river experimental facility, and the conversion coefficient was based on these measurements. The new estimation formula aligned with the results of the measurement conversion coefficient; therefore, it can be applied to a small-stream smart measurement management system, which is expected to enable more accurate discharge measurements. In the future, research will be conducted on the applicability of the developed estimation formula under various small-stream flow conditions.

As the frequency of local extreme rainfall increases due to climate change, frequent flood damage is occurring in small stream watersheds. Casualties in these small-streams can only be significantly reduced through non-structural measures, such as the development of flood early warning systems. Howeve owever, the development and evaluation of flood early warning systems have been limited due to a lack of measurement data in small streams. Recently, the National Disaster Management Institute operated five test beds for small streams across the country. A dditionally, the Ministry of Interior and Safety, along with local governments, established a smart measurement and management system for approximately 10% of all small streams, totaling 2,200, by 2027, making it possible to secure measurement data in small streams. Considering onsidering the flow characteristics of small streams, an optimization system is developed using the rainfall-discharge nomograph and rating curve rather than a numerical-based method that requires considerable calculation time. To evaluate the developed system, the discharge and depth data measured at the time of a flood accident on August 16, 2021, are compared on a one-to-one basis with the prediction results. The prediction results show that the measured discharge and depth distribution of the five small streams over time were accurately reproduced. Based on these results, the developed rainfall-discharge nomograph and rating curve are expected to serve as effective tools to support flood early warning systems.

Current flood risk management projects have been criticized for their high carbon emissions, raising the need for carbon emission reduction and carbon absorption efforts to mitigate environmental impacts and achieve carbon neutrality goals. The research develops a comprehensive green disaster risk management toolkit to calculate the carbon emissions and absorption quantitatively based on the unit volume of materials and processes employed in a flood risk management project. As a result of applying the developed toolkit to a about 22,300 small stream restoration projects in Korea, the total carbon emissions were estimated to be 1,158,840.7 tons of CO₂, of which 89.4% of the total carbon emissions originated from concrete-related construction activities, such as cement and ready-mixed concrete pouring. As a result of evaluating the nationwide carbon absorption results of all small stream restoration projects, total absorption by 2030 is expected to be 3.0 to 10.2 times higher than carbon emissions. The comprehensive toolkits are expected to support the selection of customized processes, materials, and methods by providing a systematic approach to calculate and minimize carbon emissions, ultimately contributing to the achievement of carbon neutrality goals in flood risk management projects.

Currently, Korea is undergoing significant local extreme rainfall, which contributes to more than 80% of flood disasters. Additionally, there is an increasing occurrence of such extreme rainfall in small stream basins, accounting for over 60% of flood disasters. Consequently, it becomes imperative to forecast runoff and water levels in advance to effectively mitigate flood disasters in small streams. The Flood Early Warning Framework (FEWF) presents one solution to reduce flood disasters by enabling the forecast of discharge and water levels during flood events. However, the application of FEWF in existing research is challenging due to the short flood travel time characteristic of small streams. This research proposes a methodology for constructing FEWF tailored to small streams using the nomograph and rating curve method. To evaluate the effectiveness of FEWF, a 6-year dataset from the Closed-circuit television-based Automatic Discharge Measurement Technique (CADMT) was utilized. The results indicate that FEWF successfully forecasts discharge and depth during flood events. By leveraging CADMT technology and real-time data, the development of precise and dependable FEWFs becomes possible. This advancement holds the potential to mitigate the consequences of extreme rainfall events and minimize flood-related casualties in small stream basins.