A 3D virtual geographic environment for flood representation towards risk communication

TL;DR

The paper tackles the challenge of communicating flood risk to non-experts by marrying a CA-based spatiotemporal flood model with parallel computation and WebGL-based 3D visualization in a virtual geographic environment. It presents a pipeline that accelerates flood simulation and renders intuitive 3D scenes, demonstrated on a Rhine River segment in Bonn with a reported parallel speedup of 6.45. The integrated framework aims to lower the barrier for stakeholders to understand flood dynamics, supporting risk communication and decision-making within cloud ecosystems like GeoIME. The Bonn case validates the approach, showing fast modelling and engaging 3D representations that could be embedded in cloud-based intelligent flood systems to improve resilience and public awareness.

Abstract

Risk communication seeks to develop a shared understanding of disaster among stakeholders, thereby amplifying public awareness and empowering them to respond more effectively to emergencies. However, existing studies have overemphasized specialized numerical modelling, making the professional output challenging to understand and use by non-research stakeholders. In this context, this article proposes a 3D virtual geographic environment for flood representation towards risk communication, which integrates flood modelling, parallel computation, and 3D representation in a pipeline. Finally, a section of the Rhine River in Bonn, Germany, is selected for experiment analysis. The experimental results show that the proposed approach is capable of flood modelling and 3D representation within a few hours, the parallel speedup ratio reached 6.45. The intuitive flood scene with 3D city models is beneficial for promoting flood risk communication and is particularly helpful for participants without direct experience of floods to understand its spatiotemporal process. It also can be embedded in the Geospatial Infrastructure Management Ecosystem (GeoIME) cloud application for intelligent flood systems.

Figures (15)

• Figure 1: The proposed study framework of this article.
• Figure 2: The structure of the CA-based numerical model of floods. The type of neighborhood is the Von Neumann neighborhood, which is composed of a central cell and four adjacent cells.
• Figure 3: The computing workflow for the spatiotemporal modelling of the flood.
• Figure 4: An illustration of the OpenMP parallel principles and execution process.
• Figure 5: The parallel scheme for the computation of flood modelling.