Over the last five years LISFLOOD-FP has been used in a range of fluvial and coastal flooding studies and the following summarises what we have learned about flood inundation in general and this class of model in particular. Lessons have been learned in four main areas:

Calibration, validation and benchmarking studies

LISFLOOD-FP has so far been validated for five river reaches: the Meuse in the Netherlands (Bates and De Roo, 2000), the Thames in the UK (Horritt and Bates, 2001a; Aronica et al., 2002), two sections of the the Severn in the UK (Horritt and Bates, 2001b; Horritt and Bates, 2002) and the Imera basin in Sicily (Aronica et al., 2002). In each case the modelled inundation extent has been compared to a flood extent maps derived from either air photography, satellite Synthetic Aperture Radar images or ground survey. The data sets represent the best inundation extent information currently available. The flow routing performance of the model has also been analysed in the case of the Meuse and the Severn. In addition, for the many of these studies the model as benchmarked against other standard hydraulic codes as detailed in Table 1. In each case the ability of the model to predict inundation extent is compared in terms of the measure:

Where Aobs and Amod represent the sets of pixels observed to be inundated and predicted as inundated respectively.

Reach name (and length)Validation dataMaximum LISFLOOD-FP performance (F)Number of calibration runsBenchmarked against …
Meuse (35 km) Air photo inundation extent, SAR inundation image, point hydrometry 82% 1 TELEMAC-2D, planar lid approximation to free surface
Thames (3 km) Air photo inundation extent, SAR inundation image 84% 25 TELEMAC-2D, planar lid approximation to free surface
Upper Severn (60 km) SAR inundation images for two events, point hydrometry 73% 500 TELEMAC-2D, HEC-RAS, planar lid approximation to free surface
Imera (15 km) Ground surveyed flood extent 85% 500 -
Lower Severn (15 km) 4 Airborne SAR scenes througha single event 89-72% 35 -

Table 1 : Summary of LISFLOOD-FP calibration, validation and benchmarking studies for fluvial application.

The model has also been compared to observed extent data for three coastal flooding applications (see Bates et al., in press). Although the data quality is not as good for these tests similar conclusions have been drawn. Results of these studies are outlined in Table 2.

AreaType of floodingDomain sizeGrid resolutionNumber of cellsEventModel accuracy (F)Computational time (on a 2.5 GHz pc)

Towyn, North Wales, UK

Defence overtopping

12.5 x 9 km





~60 minutes

Fleetwood, UK

Defence overtopping

2.3 x 6.3 km





~5 minutes

North Norfolk, UK

Defence breach

40.25 x 42 km




0.91 for the 1938 event.

~5 minutes

Table 2: Summary of LISFLOOD-FP calibration and validation studies for coastal application.

From these studies the following general conclusions can be drawn about the fixed time step version of the LISFLOOD-FP model:

Scaling behaviour

In order to test the behaviour of the model with respect to changing grid scale, Horritt and Bates (2001b) conducted a scaling analysis for the River Severn reach discussed above. For the 60 km reach between the gauges at Montford Bridge and Buildwas models were constructed at 10, 25, 50, 100, 250, 500 and 1000m scales. Topography for each was parameterised using a laser altimetry survey made available by the UK Environment Agency. A ~1 in 50 year event which occurred in October 1998 was simulated and calibration studies were undertaken for each model. The results were analysed in terms of model ability to predict inundation extent and flood wave travel time through the reach. This analysis showed:

Uncertainty analysis

Given uncertainty over friction values, Aronica et al. (2002) have conducted Monte Carlo analysis of parameter uncertainty for the LISFLOOD-FP code using the Generalised Likelihood Uncertainty Estimation (GLUE) technique of Beven and Binley (1992). Dense sampling of the parameter space for the Thames and Imera models showed that there was no single well defined optimum, and instead a broad region the model’s parameter space provided an acceptable fit to the observed data (see Figure 1). Further, they hypothesised that for different events these regions will be overlapping but not identical. They concluded that:

Aronica et al. (2002) proposed a method in which the global likelihoods calculated using the F statistics for each model realisation could be mapped back into real space using the measure Piflood:

Here we take the flood state as predicted by the model for each pixel for each realisation, and weight it according to the measure of fit F to give a generalised relative risk measure for each pixel (see Figure 2), fij takes a value of 1 for a flooded pixel and is zero otherwise and Fj is the global performance measure for simulation j. Piflood will assume a value of 1 for pixels that are predicted as flooded in all simulations and 0 for pixels always predicted as dry.

Figure 1: Plot of the F performance measure over the parameter space for the River Thames model.


Figure 2: Probability map of predicted inundation, Piflood, for the December 1992 event for River Thames. The observed shoreline derived from interpretation of satellite Synthetic Aperture Radar data is shown as a red line.

Adaptive time stepping

Comparison of the fixed and adaptive time step versions of the model against analytical solutions and for real test cases have shown that this version of the model is capable of solving a number of the problems with LISFLOOD-FP identified above (see Hunter et al., in press A and B; Hunter et al., in review). This studies have concluded that:

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