Author(s): Pablo Valles; Mario Morales-Hernandez; Volker Roeber; Daniel Caviedes-Voullieme; Pilar Garcia-Navarro
Linked Author(s): Pilar García-Navarro
Keywords: Debris transport; Microplastics; Lagrangian particle-tracking; Flood risk; Shallow water
Abstract: Floods caused by river overflows or extreme precipitation are among the most frequent and damaging natural disasters. Governments and institutions must therefore develop predictive tools to forecast flow evolution during such events. Numerical simulation is a valuable technology for analyzing, understanding and preventing natural hazards, enabling risk assessment under various scenarios. Beyond flow dynamics, understanding debris transport is crucial, as it is a major factor in natural and environmental catastrophes. Geophysical flows often require complex multiphysics models. High-resolution digital terrain models and 2D modeling improve estimates of water depth and velocity in channels and floodplains. To improve flood risk estimation, hydraulic risk models should incorporate additional flood-related aspects, such as transport of sediment, pollutants, and large debris. In particular, the transport of objects can be critical to the evolution of a natural disaster such as floods as they can obstruct infrastructure like bridges, reducing capacity and causing floods comparable to those of higher return periods. As for pollutants, studying the transport of plastics or other toxic substances can be of great interest in relation to ecology. This work presents a Lagrangian model to study debris transport, with transport equations differing by material type: drag forces dominate for macroplastics or seeds, while advection and dispersion govern microplastics. The hydraulic model is governed by an Eulerian model based on the 2D shallow water equations. These models are part of the SERGHEI framework (Caviedes-Voullieme et al., 2023). The Lagrangian model has been validated with analytical and experimental cases, to be subsequently used to analyze realistic cases.
Year: 2025