A new approach to modelling flooding impacts on emergency service accessibility: A case study of Calgary, Alberta

Abstract

Floods are becoming more frequent and the magnitude of direct consequences, relating to destruction of critical infrastructure and loss of life, has highlighted the importance of flood management. This thesis proposes a new methodology to quantify the impact of predicted and historic flooding events on emergency services. The approach moves beyond simple flood inundation mapping by accounting for the relationship between flood depth and vehicular speed. A case study was presented for Calgary Alberta, where the depths of a predicted 100-year flood and an historic 2013 flood event were modelled. The methodology applied geographic information systems (GIS) to flood depth mapping, utilizing digital elevation models (DEMs), flood extents, and hydrological data. Flood depths were then assigned to links comprising the road network, where the maximum vehicle speed was calculated as a function of the standing depth of water on a link. The flooded network was used to derive service areas for several types of emergency services (emergency medical services (EMS), fire, and police), following targeted response times. The results quantified and located the residential and work populations that no longer meet the targeted response times. During both flood scenarios, EMS were found to have the greatest reduction in accessibility, with 23% to 47% of residents and workers, respectively, not served. Fire services were seen to be more resilient with only 3% to 9% of residents and workers, respectively, not served. The results for police services were similar to fire services. However, the former have a greater range of response times, meaning these areas represent those that are completely isolated during both flood events. Overall, the proposed methodology quantified vulnerable populations on a partially degraded network, which can be used to develop evacuation plans and emergency response strategies, minimizing disturbances in the network and the number of people affected.