Keller, Luise; Zischg, Andreas Paul; Mosimann, Markus; Rössler, Ole; Weingartner, Rolf; Martius, Olivia (2019). Large ensemble flood loss modelling and uncertainty assessment for future climate conditions for a Swiss pre-alpine catchment. Science of the total environment, 693, p. 133400. Elsevier 10.1016/j.scitotenv.2019.07.206
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Information on possible changes in future flood risk is essential for successful adaptation planning and risk management. However, various sources of uncertainty arise along the model chains used for the assessment of flood risk under climate change.Knowledge on the importance of these different sources of uncertainty can help to design future assessments of flood risk, and to identify areas of focus for further research that aims to reduce existing uncertainties. Herewe investigate the role of four sources of epistemic uncertainty affecting the estimation of flood loss for changed climate conditions for ameso-scale, pre-alpine catchment. These are: the choice of a scenario-neutralmethod, climate projection uncertainty, hydrological model parameter sets, and the choice of the vulnerability function. To efficiently simulate a large number of loss estimates, a surrogate inundationmodelwas used. 46,500 loss estimateswere selected according to the change in annualmean precipitation and temperature of an ensemble of regional climatemodels, and considered for the attribution of uncertainty. Large uncertainty was found in the estimated loss for a 100-year flood event with losses ranging from a decrease of loss compared to estimations for present day climate, to more than a 7-fold increase. The choice of the vulnerability function was identified as the most important source of uncertainty explaining almost half of the variance in the estimates. However, uncertainty related to estimating floods for changed climate conditions contributed nearly as much. Hydrologicalmodel parametrisation was found to be negligible in the present setup. For our study area, these results highlight the importance of improving vulnerability function formulation even in a climate change context where additional major sources of uncertainty arise.
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