Conference Agenda

In urban areas, flood risks have become more severe due to climate change and rapid urbanization, resulting in escalating damage. To address this challenge, advanced models that incorporate complex factors such as topography, structures, and drainage systems are crucial. This study proposes a new two-dimensional urban flood model that effectively incorporates detailed topographic variations and urban structures while directly integrating rainfall-runoff processes. By employing high-definition topographic data and linking a SWMM-based model at the source code level, it precisely simulates the irregular runoff patterns characteristic of urban settings. When applied to the Sillim drainage area, the proposed model significantly improved predictions of inundation extent and depth compared to existing approaches and revealed the influence of urban structures on flooding more clearly. This model can be broadly utilized in urban planning, disaster prevention, and drainage system design. Moreover, through continued data integration and refinement, it is expected to provide practical solutions for urban flood management.

Extreme weather events resulting from climate change increasingly threaten sewage treatment facilities, critical to public health and safety. Current disaster response systems in Korea lack systematic methodologies for assessing flood risks in environmental facilities. This study introduces a novel approach to flood risk assessment, integrating advanced modelling and expert analysis techniques.

A two-dimensional flood analysis model was used to simulate flood scenarios, accounting for external factors like river overflow and internal vulnerabilities such as facility inflows. The modified HAZOP technique and AHP method were employed to qualitatively and quantitatively evaluate the risk and vulnerability of unit processes. This methodology combines inundation analysis with expert-driven risk indicators, offering precise insights into flood-prone processes.

The study emphasizes the practical application of these findings, proposing protective measures and emergency plans for high-risk processes. By leveraging advanced modelling and analytical tools, this research contributes to enhancing disaster resilience in sewage treatment facilities, demonstrating the necessity of transitioning from reactive to preventive disaster management systems.

Urban flooding represents an increasing risk due to climate change and urban expansion. This project proposes a real-time flood prediction system based on a 2D hydrodynamic model optimized for rapid computations, utilizing rainfall data from Canal de Isabel II radars and forecast sources. The system provides estimations of flood depth and flow velocity through a web-accessible platform, enabling real-time decision-making to mitigate impacts.

The methodology integrates high-resolution rainfall data, a simplified simulation model, and risk evaluation based on hydrodynamic parameters. The flood forecasting model is designed to deliver near-real-time results with an extended prediction range. Validation was conducted by comparing the system’s outputs with previous detailed 1D models overflows, demonstrating acceptable accuracy while significantly reducing computational time.

This study presents the design, implementation, and evaluation of the proposed system, highlighting its applicability for urban drainage management and disaster prevention. The approach shows potential scalability to other urban areas, enhancing resilience to extreme weather events and optimizing resource allocation in flood risk management.

Storage and discharge are two fundamental solutions to strengthening pluvial flood resilience. However, the “imbalanced” combination of the two measures in practice raises concerns in hydrologic, economic, spatial and social aspects. This research aims to quantify the balanced relationship between storage capacity and discharge capacity (SDB) for pluvial flood protection and provide planning strategies for different urban topographies. We suggest and apply a quantification method using different modelling tools -hydrological and hydrodynamic model- in two cases with distinct physical and socio-economic characteristics: one in flat polder area in Nanjing, China, and the other in a sloping hilly area in Feldbach, Austria. By means of comparative analysis of these cases, insights are gained into the modelling methods, results and proposed strategies for each context. The novel approach and findings of the case study provide valuable guidance for other cities and urban areas around the world to perform similar analysis before setting targets for long-term flood resilience planning.

Pluvial flooding in urban areas is an increasing concern, particularly in coastal regions like Costa del Sol Occidental in southern Spain, which is experiencing rapid urbanization and seasonal population increase due to tourism. Despite the severity of recent pluvial flood events, such as the October 2024 floods in Valencia and Málaga, there are no comprehensive models or maps addressing pluvial floods in urban areas and the related compound floods in the region. This research aims to fill this gap by developing flood risk and impact maps that incorporate pluvial flooding, coastal hydrodynamics, and future climate projections. By integrating 1D/2D sewer models and coastal simulations, the study will estimate the joint probability of compound flooding events. The results will enable local authorities to adopt resilient flood management strategies and improve flood risk management in the context of climate change and increasing flood risks.

Hydrological models often neglect the interplay of flooding mechanisms such as storm surge, rainfall, high tide, and groundwater emergence that may occur in coastal urban areas. Therefore, they could provide inadequate predictions.

In this paper, we report on a study aimed at developing an algorithm to tightly couple groundwater and sewer models that could be applied to effectively evaluate grey and green infrastructure performance during compound flooding in coastal urban areas. The models developed in open-source programs such as MODFLOW and SWMM were coupled with a custom-built Python algorithm. The coupled models were calibrated and validated for a test site located in Long Island (NY, USA).

Overall, tightly coupled MODFLOW and SWMM models can represent a substantial advancement in urban hydrological modelling, offering both immediate practical benefits for flood management and long-term strategic value for urban water infrastructure planning.