Combining statistical and expert evidence using belief functions: Application to centennial sea level estimation taking into account climate change

• A methodology based on belief functions is used to combine statistical judgments with expert evidence. • The objective is to predict the future centennial sea level at a particular location, taking into account climate change. • Likelihood-based belief functions derived from statistical observations are combined with random intervals encoding expert assessments. • Monte Carlo simulations allow us to compute belief and plausibility degrees for various hypotheses about the design parameter. • Uncertainty on sea level rise accounts for most of the uncertainty on the centennial sea level by the end if this century. Estimation of extreme sea levels for high return periods is of prime importance in hydrological design and flood risk assessment. Common practice consists of inferring design levels from historical observations and assuming the distribution of extreme values to be stationary. However, in recent years, there has been a growing awareness of the necessity to integrate the effects of climate change in environmental analysis. In this paper, we present a methodology based on belief functions to combine statistical judgements with expert evidence in order to predict the future centennial sea level at a particular location, taking into account climate change. Likelihood-based belief functions derived from statistical observations are combined with random intervals encoding expert assessments of the 21st century sea level rise. Monte Carlo simulations allow us to compute belief and plausibility degrees for various hypotheses about the design parameter.