Uncertainty with friction parameters and impact on risk analysis

Abstract

Flood modelling is an essential component of risk analysis, with a greater demand for accurate and robust modelling to be undertaken at large spatial scales. Understanding of uncertainty in the modelling becomes increasingly critical not only in ensuring model results are reliable, but also in a wider context of (re)insurance regulations such as Solvency II. This research investigates how the uncertainty in the friction parameter impacts on model outputs and how this parameter influences uncertainty associated with evaluations of exposure (the estimation of damage caused by flood waters), and with evaluation of hydraulic outputs, including water depths and extents. Two basic approaches are adopted to representing friction values in a model; a uniform manning’s n value, and spatially distributed values based on underlying land type surfaces, and the use of appropriate friction values in these surfaces. The range of friction values in both approaches is based on literature values and are designed to represent the values typically used in modelling exercises, assuming a uniform distribution for this range. This uncertainty is also analysed in a wider Monte Carlo method, comparing other sources of uncertainty in flood modelling, including hydrological input uncertainty, DTM uncertainty and the uncertainty associated with the computational model used. 3 test cases, with different hydraulic properties are used to provide generic conclusions to the test cases. Two urban test cases with transcritical flow conditions and a river overtopping event in a rural/urban domain. The results from the model results are analysed with typical modelling evaluation techniques, such as binary flood extent comparison and depths comparison measures, as well as measures of exposure, here defined as the cost of damage associated with modelled water depths. The results demonstrate that modelling uncertainty is reduced by increasing the number of frictional surfaces in the modelling, indicating that through marginal pre-processing effort better representation of microscale hydraulics can be achieved, particularly in urban areas. Model results are also far more sensitive to uniform values, which also demonstrate an increased level of uncertainty, even in large scale modelling. The uncertainty associated with friction values though is shown to be relatively small compared to the uncertainty of the numerical scheme, and also displays significant parameter interaction.