Characterizing Uncertainty in City-Wide Disaster Recovery through Geospatial Multi-Lifeline Restoration Modeling of Earthquake Impact in the District of North Vancouver

Restoring lifeline services to an urban neighborhood impacted by a large disaster is critical to the recovery of the city as a whole.Since cities are comprised of many dependent lifeline systems,the pattern of the restoration of each lifeline system can have an impact on one or more others.Due to the often uncertain and complex interactions between dense lifeline systems and their individual operations at the urban scale,it is typically unclear how different patterns of restoration will impact the overall recovery of lifeline system functioning.A difficulty in addressing this problem is the siloed nature of the knowledge and operations of different types of lifelines.Here,a city-wide,multi-lifeline restoration model and simulation are provided to address this issue.The approach uses the Graph Model for Operational Resilience,a data-driven discrete event simulator that can model the spatial and functional cascade of hazard effects and the pattern of restoration over time.A novel case study model of the District of North Vancouver is constructed and simulated for a reference magnitude 7.3 earthquake.The model comprises municipal water and wastewater,power distribution,and transport systems.The model includes 1725 entities from within these sectors,connected through 6456 dependency relationships.Simulation of the model shows that water distribution and wastewater treatment systems recover more quickly and with less uncertainty than electric power and road networks.Understanding this uncertainty will provide the opportunity to improve data collection,modeling,and collaboration with stakeholders in the future.

Modeling the Adverse Impact of Rainstorms on a Regional Transport Network

Cities are centers of socioeconomic activities,and transport networks carry cargoes and passengers from one city to another. However, transport networks are influenced by meteorological hazards, such as rainstorms,hurricanes, and fog. Adverse weather impacts can easily spread over a network. Existing models evaluating such impacts usually neglect the transdisciplinary nature of approaches for dealing with this problem. In this article, a mesoscopic mathematical model is proposed to quantitatively assess the adverse impact of rainstorms on a regional transport network in northern China by measuring the reduction in traffic volume. The model considers four factors: direct and secondary impacts of rainstorms, interdependency between network components, and recovery abilities of cities. We selected the Beijing-Tianjin-Hebei region as the case study area to verify our model.Socioeconomic, precipitation, and traffic volume data in this area were used for model calibration and validation.The case study highlights the potential of the proposed model for rapid disaster loss assessment and risk reduction planning.