In order to compute risk, there are three primary inputs: the hazard, the inventory (population and built environment), and the vulnerability. With respect to flooding, the most commonly used measure of vulnerability for estimating damage to the built environment is through depth-damage curves.
For flooding, a primary input is the digital terrain model (DTM). The recent Canadian initiative for a new, high resolution digital terrain model, 1-2m in the south and 5m in the Arctic, provides better vertical information than ever before.
Using this high-resolution data, new flood models are being tested to rapidly simulate flood inundation extents and validate the results against historic events and complex models. Specifically, I have been testing simple complexity models (SCM) such as height above nearest drainage (HAND) or rapid flood spreading model (RFSM) which rely solely on the DTM and its derivative products.
Futhermore, providing a linkage or relationship between these SCM models and a national weather model, which provides river discharge, from a spatially distributed numerical weather prediction model for the country, is being investigated.
Two Versions: REST API or MS Excel format.
This calculator allows a user to input structure details for a single building type or multiple buildings and estimates losses. Download/View the user guide for ER2 Flood. Want to receive notification of updates to ER2 (*Updates are (roughly) quarterly)? Send us an email
MS Excel Worksheet:Basic Version: Version 2.05 of the RAPID RISK EVALUATION (ER2) calculator is available for download.
Advanced Version: Added features in the Advanced Version (version 2.05) include:
Depth-damage curves are an internationally accepted methods used to assess building damage from flooding. However, there are limitations to their use as they are often built solely on the water depth relationship to damage and do not consider other factors - such as submersion time, flow velocity, etc. Depth-damage curves represent an average response of a building class to a given water depth, but often do not communicate variability within the class or uncertainty in the curve, thus, we are exploring other methods to communicate potential building damages based on probabilistic cost model.
Geometry of census areas is available at multiple levels of detail, from province, county, census tract, down to the smallest geometric unit of dissemination area. The current spatial datasets follow topological rules of non-overlap and not having gaps. However, the population and infrastructure is often not equally distributed within these bounds, it is of interest to apply dasymetric mapping techniques to these spatial datasets, using land use/land cover datasets and others, such that estimated damages can more truly reflect the infrastructure and population at risk. Additionally, the smallest geographic unit of downloadable census data from Statistics Canada is available at the dissemination block level, though the smallest geographic unit of spatial data available is the dissemination area. Thus, each dissemination block contains a number of dissemination areas. Once the dasymetric polygons have been created, a method must be developed which is able to appropriately split the census data among these dissemination areas.
In order to support multi-hazard risk application Rapid Risk Evaluator (ER2), currently under development at the Geological Society of Canada, building inventory must be known in order to assess the exposure and vulnerability. There is presently no national dataset of building footprints and many communities do not have their own datasets to support this risk assessment tool.
The objective of this project is to create an application able to automatically generate attributed polygons of building footprints from classified LiDAR data (LAZ files). The intention is to generate a program which allows the user to select/upload a LAZ file and output a geospatial file containing geometry and attribute data of the buildings. The output file can be of any open format compatible file, for example: shapefile, GeoJSON, or KML.