The approach taken by waterRIDE™ in flood forecasting focuses on leveraging readily available data (hydraulic modelling results, digital elevation models, property GIS datasets, critical infrastructure GIS datasets etc) to provide an indication of likely flood behaviour and, often more importantly, the consequences (or affectation) of such flooding on the community and emergency management efforts.
Flood model results are usually only available on the same spatial framework on which they were modelled (ie the model network).
For example, a 2D gridded model with a 10m cell size would provide an indication of flooding down to a 10m by 10m square resolution. Whilst this may be suitable for the description of flood behaviour on a regional level, when you are looking to determine the maximum depth on an urban land parcel, the 10m resolution is unlikely to be adequate. Perhaps, a 2m cell size would be more suitable?
Along similar lines, in 1D models, outputs are only reported at discrete locations (usually cross sections or nodes) in the floodplain, which exacerbates the problem encountered with our 2D model example.
By mapping model results to a new framework (usually, a finer scale terrain framework (DEM)), you are able to enhance the interpretation of the model results, especially with any parameters associated with depth (eg flood extents, velocity x depth, hazard etc).
The presumption behind this approach is that the framework on which the model was built adequately describes the hydraulics of the floodplain, and that by mapping we are only enhancing the interpretation of flood depth (and associated parameters). If this is the case, then by mapping, we are effectively enhancing the quality of the outputs without the need to endlessly increase the resolution of the modelling.
waterRIDE™ provides a wealth of specialised tools to ensure you get quality outcomes when mapping your model results, allowing you to overcome “issues” such as:
“glass walls” (in both 1D and 2D models)
Flat water surfaces across discrete elements in a model (eg gridded finite difference models or finite volume models)
Significant changes in framework resolutions
Surface smoothing prior to and during mapping
Mapping areas of shallow flow
Merging areas of overlapping model results (eg different catchments or ocean and river flooding)
Obtaining the “peak of peaks” for multiple model runs (eg 100yr storm with 1hr, 2hr, 6hr duration storms etc)