Abstract
Coastal communities are vulnerable to floods from storm events which are further exacerbated by storm surges. Additionally, coastal towns provide specific challenges during flood events as many coastal communities are peninsular and vulnerable to inundation of road access points. Publicly available lidar data has been used to model areas of inundation and resulting flood impacts on road networks. However, these models may overestimate areas that are inaccessible as they rely on publicly available Digital Terrain Models. Through incorporation of Digital Surface Models to estimate bridge height, a more accurate model of flood impacts on rural coastal residents can be estimated.
Highlights
1.1 General InstructionsCoastal communities will face an array of challenges with an increasing frequency of storm events and rising sea levels
This study examines the implications of modifying transportation analyses through the inclusion of Digital Surface Models (DSMs) to identify bridge locations and elevations in order to improve overall transportation network models associated with storm events
A network analysis was conducted that incorporated inundated roads as barriers to identify coastal areas that would be inaccessible during a 100 year flood event
Summary
Coastal communities will face an array of challenges with an increasing frequency of storm events and rising sea levels. Lidar data has been used to develop Digital Surface Models (DSMs) to analyse impacts of bridge locations and heights on water flow during flood events, and can be used to more effectively align simulated impacts with measured impacts of flooding (Meesuk 2015, Abdullah 2012). The incorporation of DSMs has been shown to provide a better simulation model for urban flooding, few studies have been shown to incorporate DSMs for identifying bridge locations and heights to enhance transportation models in rural areas (Meesuk 2015, Abdullah 2012, Camill 2012, Johnston 2014). This study examines the implications of modifying transportation analyses through the inclusion of DSMs to identify bridge locations and elevations in order to improve overall transportation network models associated with storm events
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