1 Spatial Modelling of the Terrestrial Environment - Georeferencial

Flood Inundation Modelling Using LiDAR and SAR Data 103
very recently lack of data has been the central problem rather than the reverse. Nor can
standard sensitivity, calibration and uncertainty analysis techniques be smoothly transferred
to distributed problems. In many cases these are designed to operate with limited amounts of
point data rather than distributed fields. It is likely that as we increasingly move to distributed
calibration and validation, then new methodologies will be required to achieve, for example,
distributed calibration against distributed data, use of distributed classification and shape
data in uncertainty analysis and to assess spatially distributed, and often highly non-linear
(Bates et al., 1998b), sensitivities in distributed models. None of these methodologies has
yet been adequately researched.
In moving to an increasingly complex analysis of model spatial behaviour we should
also not lose sight of the fact that end-users will require better guidance in order to evaluate
correctly the information being imparted to them. Scientific uncertainty and risk have
proved difficult concepts to communicate, yet to continue with a view of models as single
deterministic predictors seems flawed and, one suspects, susceptible to legal challenge.
The value of spatial data is that it forces modellers to uncover and analyse uncertainties
previously suppressed in the calibration process and our challenge is to both incorporate it
in our models and provide a more realistic view of the science that we are able to achieve.
Acknowledgements
The research reported in this chapter has been made possible by a number of research grants
but in particular UK Natural Environment Research Council grant number GR3 CO 030 and
the European Union Framework 5 grant ‘Development of an European Flood Forecasting
System’.
References
Alsdorf, D., Melack, J.M., Dunne, T., Mertes, L.A.K., Hess, L.L. and Smith, L.C., 2000, Interferometric
radar measurements of water level changes on the Amazon flood plain, Nature, 404,
174–177.
Aronica, G., Bates, P.D. and Horritt, M.S., 2002, Assessing the uncertainty in distributed model
predictions using observed binary pattern information within GLUE, Hydrological Processes, 16,
2001–2016.
Aronica, G., Hankin, B. and Beven, K., 1998, Uncertainty and equifinality in calibrating distributed
roughness coefficients in a flood propagation model with limited data, Advances in Water
Resources, 22, 349–365.
Bates, P.D., 2000, Development and testing of a sub-grid scale model for moving boundary hydrodynamic
problems in shallow water, Hydrological Processes, 14, 2073–2088.
Bates, P.D. and De Roo, A.P.J., 2000, A simple raster-based model for floodplain inundation, Journal
of Hydrology, 236, 54–77.
Bates, P.D. and Hervouet, J.-M., 1999, A new method for moving boundary hydrodynamic problems
in shallow water, Proceedings of the Royal Society of London, Series A, 455, 3107–3128.
Bates, P.D., Horritt, M. and Hervouet, J.-M., 1998b, Investigating two dimensional finite element
predictions of floodplain inundation using fractal generated topography, Hydrological Processes,
12, 1257–1277.
Bates, P.D., Horritt, M., Smith, C. and Mason, D., 1997, Integrating remote sensing observations of
flood hydrology and hydraulic modelling, Hydrological Processes, 11, 1777–1795.