River-System Digital Twin for Flood Hazard Mapping

Flooding is one of the most destructive natural disasters, leading to widespread damage to infrastructure, loss of life, and major disruptions to urban economies. Rapid urbanization, inadequate drainage systems, and the rising frequency of extreme rainfall events are among the main drivers of flooding. The vulnerabilities of urban systems to flooding necessitate an integrated, simulation-based approach to analyse and compare various flood conditions, fostering proactive flood risk management.

This study aims at developing a River-System Digital Twin (DT) framework for flood hazard mapping and risk reduction. The proposed DT combines advanced hydrodynamic modelling, geospatial analysis, and 3D city modelling to accurately simulate flood scenarios. High-resolution Digital Elevation Models (DEMs), river cross-section data, historical and projected land use and land cover (LULC) maps, and rainfall data for various return periods (e.g., 25, 50, and 100 years) are among the datasets that are used. These datasets are incorporated into a geospatial framework that facilitates both scenario-based analysis and simulation.

The methodology involves building a 3D City Information Model (CIM) by extracting building and vegetation parcels from satellite imagery and using procedural modelling techniques in CityEngine software. This CIM is linked dynamically to flood inundation results obtained from 2D hydrodynamic simulation models. The results obtained from the DT, which include spatially dispersed water depths, flow velocities, and hazard intensity zones, can elucidate the possible effects of flooding on urban infrastructure, such as buildings in flood-prone areas and transit networks.

Preliminary results of the study, conducted in the Adyar River Basin in Chennai, India, indicate that the Digital Twin (DT) effectively captures the geographical variation in inundation patterns across different rainfall scenarios Thus, by integrating 3D City Information Models (CIM) with hydrodynamic simulations for an urban system, the study aims to create a powerful tool for predicting, visualizing, and planning to mitigate the potential impacts of flooding on urban infrastructure and communities.