Finding New High Ground in Emergency Response Planning Classification
Recently, we've been undertaking some interesting research on applying Emergency Response Planning (ERP) classifications at much finer scales than previously possible. This has been achieved by the use of automated analysis using an adaptive road network algorithm that senses when a road becomes impassible in a flood and searches for an alternative route. CSS Principal, David Tetley presented this work at the 2014 Floodplain Management Authorities conference in Deniliquin and received a Highly Commended Paper award.
The software we have developed to automate the ERP classification takes results from a flood hydraulic model (such as TUFLOW) and overlays that with a detailed DEM, a carefully prepared road network and a GIS file of the Precincts for which you wish to report Emergency Response Planning (ERP) classifications. We have successfully used this approach to classify ERP classifications down to individual household lots. The software exports ERP classifications for each precinct as well as a range of other interesting information including road inundation times and durations, evacuation distances and routes for vehicle, walking and rising road options, mapping of low flood island and other high risk areas.
As an example from the adaptive road network algorithm, the first 3 figures to the right present 3 different potential evacuation routes for the same property calculated at different times during the flood.
This information is automatically combined with the Office of Environment and Heritage's guideline (Flood Emergency Response Planning Classification Of Communities) to produce ERP classifications for each precinct. The fourth figure presents a sample of results from the software showing the ERP classification as well as complimentary information concerning expected road cutoffs and depths.
During our research we also developed a potential alternative ERP classification schema that presents ERP risk on a gradient of 13 categories of increasing risk. We believe this method has significant potential, particularly when applied at small scales. Specifically, it's easier to understand and less tied to a specific hypothetical flood. Please refer to our paper for more information. The fifth figure to the right illustrates a sample of the ERP gradient results.