Testing new sources of topographic data for flood propagation modelling under structural, parameter and observation uncertainty

Abstract

This study assessed the utility of EUDEM, a recently released digital elevation model, to support flood inundation modelling. To this end, a comparison with other topographic data sources was performed (i.e. LIDAR, light detection and ranging; SRTM, Shuttle Radar Topographic Mission) on a 98-km reach of the River Po, between Cremona and Borgoforte (Italy). This comparison was implemented using different model structures while explicitly accounting for uncertainty in model parameters and upstream boundary conditions. This approach facilitated a comprehensive assessment of the uncertainty associated with hydraulic modelling of floods. For this test site, our results showed that the flood inundation models built on coarse resolutions data (EUDEM and SRTM) and simple one-dimensional model structure performed well during model evaluation.

Damage and protection cost curves for coastal floods within the 600 largest European cities

“…This study employs the EU-DEM 23 , which is largely SRTM based and would thus be expected to provide similar accuracy. A first study comparing SRTM and EU-DEM products for flood modeling did not find significant differences 15 . However, since all elevation data were averaged to the same resolution as SRTM, the benefit of the higher resolution of the EU-DEM was not assessed.…”

A review of low‐cost space‐borne data for flood modelling: topography, flood extent and water level

“…Neal et al (2012b) demonstrated that simpler models might achieve similar accuracies to more complex model in some circumstances. In a model conditioning and evaluation context, simpler models with coarse-resolution low-accuracy topographic data might even over perform that based on high-resolution high-accuracy ones (Mukolwe et al, 2015). Those examples illustrate that simply bringing in more detailed data neither cannot solve the issue of equifinality nor improve the model performance.…”

Preface—Special Issue: Facets of Uncertainty

“…Tyralla and Schumann (2016) present a new approach for dealing with model structural uncertainty. Mylopoulos and Sidiropoulos (2016) consider the management of an aquifer under uncertain hydrogeology of the subsurface and propose a stochastic optimization approach, while Mukolwe et al (2016) deal with uncertainties on the surface, studying the influence of various elevation datasets on accuracy of flooding models. Kabeya et al (2016) quantify the effects of forest harvesting versus climate on streamflow variability and trend, while Koga et al (2016) present an urban-scale hydrological application accounting for the uncertainty in land-use delineation.…”