Following a request from The Association of British Insurers to the UK Environment Agency to increase the robustness and extent of pluvial flood modelling Ambiental undertook a robust pluvial modelling test to set a best practice standard on modelling inputs.
The test set out to determine which set of inputs gave the highest level of predictive accuracy for urban pluvial flood modelling. Inputs analysed included a variety of topographic surface representations, various topographical resolutions, and different approaches to employing building information within models.
A variety of Digital Terrain Models (DTM) and Digital Surface Models (DSM) were created, these included:
Each of these were then resampled to different horizontal grid resolutions (2, 3, 4 & 5m for LiDAR; 5m for IfSAR) to understand resolution effects. Hydrogical data was based on gauge data from the University of Hull which showed over 110mm of sustained rainfall with rates of over 6mm/hour – an event estimated to be in excess of a 150 year return period based on the CEH flood estimation handbook.
To enable binary classification of a buildings’ flood status building-level information was extracted from modelled flood depth grids using GIS analysis and comparison made with observed flood data to determine:
Predictive ability of each simulation was assessed using a measure of fit which penalises over- and under-prediction of flooded buildings. To identify those model simulations which exhibit a significant degree of ‘skill’ in predicting flooded and non-flooded buildings (as opposed to those correct due to chance) the Kappa statistic was used.
Primarily due to the overprediction of flooding caused by artefacts in DSM’s these performed less well than either DTM’s. Of the DTM approaches, simulations based on DTM + buildings slightly underperformed bald earth DTM due to the effect adding detailed building information has upon floodplain storage. LiDAR was proven significantly better than IfSAR for building-level pluvial modelling due to poorer representation of flow paths and accumulation areas with IfSAR data.
“We found that including building data actually reduces model effectiveness.”
David Martin, Technical Director
To ensure optimum predictions of the likelihood of individual buildings flooding in urban pluvial situations the following inputs should be used: