What are the Data requirements for flood inundation modelling?

Flood modelling is important for predicting flood events, particularly those with high magnitudes. The results derived from these forecasts constitute an important part of information for authorities, planners and the general public, for awareness, and to manage flooding and the risks associated with it. The main outputs from flood modelling are flood inundation and hazard maps that are used for visualizing the extents, depth and velocity of flood water, which are all vital for determining and analyzing areas that are at potential risk during a flood event. These maps form the basis for flood risk maps, which are utilized in assessing costs and impacts of floods.

The data requirements of flood inundation models have been reviewed by Smith et al. (2006). They fall into four distinct categories, (a) topographic data of the channel and floodplain to act as model bathymetry, (b) time series of bulk flow rates and stage data to provide model input and output boundary conditions, (c) roughness coefficients for channel and floodplain, which may be spatially distributed, and (d) data for model calibration, validation and assimilation.

Mason et al. (2010) have been stated the response of this question as: Flood inundation models also need discharge and stage data to create model boundary conditions. The data are usually gained from gauging stations spaced 10-60 km apart on the river network, which provide input to flood warning systems. Modelers and engineers ideally require gauged flow rates to be precise to 5% for all flow rates, with all significant tributaries in a catchment gauged. However, issues with the rating curve extrapolation to high flows and gauge bypassing may mean discharge measurement errors may be much higher than this allowable and acceptable value during floods. At a few sites where the gauge installation is significantly bypassed at high flow errors may even be as large as 50%. The data requirements of an alternative scenario in which input flow rates are forecasted by a hydrological model using rainfall data as an input, rather than being measured by a gauge, are not considered here.

Estimates of bottom roughness coefficients in the channel and floodplain are also necessitated. The role of these coefficients is to parameterise those energy losses not represented explicitly in the model equations. In practice, they are usually appraised by calibration, which often results in them compensating for model structural and input flow errors. As a conclusion, it can be difficult to disentangle the contribution due to friction from that attributable to amends. The simplest method of calibration is to calibrate utilizing two separate global coefficients, one for the channel and the other for the floodplain.

A final requirement is for appropriate data for model calibration, validation and assimilation. If a model can be successfully validated using independent data, this gives confidence in its predictions for future events of similar magnitude under similar conditions (Mason et al., 2010).