Over the past century, significant progress has been made in the modelling of flood inundation and tsunamis, leading to continuous advancements in this critical field. Despite these developments, modelling remains challenging due to the inherent complexity and chaotic nature of these systems. Recent breakthroughs have been driven by enhancements in modelling structures and algorithms, data assimilation techniques, integration of remote sensing data, the combination of diverse modelling approaches, and the adoption of cutting-edge technologies such as artificial intelligence (AI), machine learning (ML), and cloud computing.
An important area of advancement is the ability to generate quantitative estimates of uncertainty associated with model simulations and predictions, enhancing the reliability and applicability of models. These improvements have been instrumental in practical applications such as risk mapping, damage assessment, real-time forecasting, hydraulic structure design, river and coastal restoration, and water resources planning, while also supporting critical decision-making processes in disaster management and climate adaptation strategies.
At the same time, the rapid growth in data need and algorithmic complexity widens the gap between academic researchers and practitioners, which compromises the adoption of modern modelling methods. As a result, practical application will also be a focus of this session with the of development of simplified, yet scientifically sound floodplain models.
This session invites contributions showcasing the latest innovations in flood and tsunami modelling, with a focus on diverse approaches, applications, and technologies and their practical application. Topics of interest include, but are not limited to:
- Innovations in modelling tools, structures, and techniques.
- Advances in data collection, assimilation, and processing.
- Comparative studies of different modelling approaches.
- Hydrodynamic and inundation modelling.
- Integration of remote sensing data with modelling frameworks.
- Applications of machine learning (ML) and artificial intelligence (AI) for real-time inundation simulation.
- Flood frequency analysis and forecasting.
- Risk assessment, hazard mapping, and resilience planning.
- Modelling of flash floods, storm surges, and tsunami impacts.
- Case studies of urban and rural flood and tsunami modelling.
- Quantifying and addressing uncertainties in modelling and predictions.
- Supporting decision-making through improved modelling outputs.
- Cross-disciplinary approaches and innovative methodologies.
The session aims to highlight the breadth of advancements in flood and tsunami modelling, promote collaboration across disciplines, and inspire new approaches to address the challenges posed by these dynamic systems.
Keywords: Floodplain inundation, Risk assessment, Storm surge, Tsunami, Uncertainty, Hydrodynamic modelling, Remote sensing, Flood frequency analysis, Flood forecasting
