What controls uncertainty in flood risk estimates? An analysis across the Rhine River basin.

Floods are among the costliest and deadliest natural hazards. Flood risk assessments are required to better manage risk associated with floods. Nowadays, numerous flood risk models are available at various scales, from catchment to regional or even global scale. These models estimate risk (usually expressed in terms of the probability of flood loss) as the product of the hazard, exposure and vulnerability. Flood risk models are affected by numerous uncertainties that propagate through the model and contribute to the final uncertainty in risk estimates. Knowing which uncertainty sources mostly control risk estimates is essential to guide efforts for model improvement, as well as to help risk managers make better decisions. Past efforts to quantify and attribute the output uncertainty of risk models have reached conflicting conclusions. This may be because these studies used different risk models and different uncertainty and sensitivity analysis approaches; or, that they were conducted at relatively small (catchment and/or city) scale, in places with different climatic, hydrological, and socio-economic characteristics.

In this project, we investigate dominant uncertainties of a flood risk model across a much larger scale, namely the entire Rhine River basin, and explore whether dominant uncertainties at specific places can be linked to their physical or socio-economic characteristics. In particular, we analyse two model outputs: the Average Annual Losses (AAL) and Loss Exceedance Curves (LECs). For each output, we first identify the dominant input uncertainties (among uncertainty in the flood depth estimates, vulnerability curves and exposure dataset) in each spatial unit of the modelled domain; and second, we link those dominant input uncertainties to the characteristics of the spatial units.

We find that uncertainties in the vulnerability component dominate the AAL. The dominant uncertainties for the LECs change with the return period of loss, with vulnerability becoming increasingly important with increasing return period. Topography (flat versus steep terrains), degree of urbanization and economic value of the buildings are key characteristics for determining how dominant uncertainties change spatially within our study domain.