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The floodplains of rivers are relevant living spaces for population globally and provide favorable locations for economic development. However, these areas are commonly exposed to floods, and the increasing population together with the changes in storminess as a result of global warming mean that the risks from flooding are expected to rise. Most studies investigating the impact that climatic change has on flood risk are based on a cascade of global climate model simulations coupled with regional climate models, hydrologic models, inundation models, and flood impact models. However, this approach is subject to uncertainties. Model results are found to be sensitive to climate forcing, the structure of the underlying models, the choice of methods used for downscaling and bias correction, and the use of extreme value analysis for both current and future climate conditions. Moreover, uncertainties are expected to propagate through the model cascade. To overcome these problems, we propose a method for analyzing and mapping the sensitivity of population exposure in floodplains to changes in flood magnitude. The method is based on downward counterfactuals, namely perturbations of a selected flood scenario by increasing its magnitude, interpreted in this case as the worsening of a today's design flood event as a result of climatic changes. The increase in the impact of a current design flood compared to its counterfactual illustrates the sensitivity to changes in hazard. We calculate the normalized gradients of the flood exposure curves, that is, the increase in the exposure and magnitude of the perturbed event relative to the exposure and magnitude of the current scenario. We test the applicability of the method on local, national, and global scale by using existing data sets, including flood hazard maps, flood protection standards, floodplain delineation, river network definition, and spatial population distribution. The gradients were found to vary remarkably across the globe and are overall smaller in the upper range of flood magnitudes that in the lower range. Based on these results, we compare the drivers of the sensitivity in different parts of the world and identify river reaches with the highest relative gradients. These river reaches might be the most affected by climate change and thus deserve an indepth investigation of the underlying characteristics of the floodplains and the need for climate change adaptation. ; Mobiliar Lab for Natural Risks, Oeschger Centre for Climate Change Research, University of Bern ; European Union (EU) 754446 ; UGR Research and Knowledge Transfer Fund-Athenea ; University of Granada

In flood risk management, the choice of vulnerability functions has a remarkable impact on the overall uncertainty of modelling flood damage. The spatial transferability of empirical vulnerability functions is limited, leading to the need for computation and validation of region-specific vulnerability functions. In data-scarce regions however, this option is not feasible. In contrast, the physical processes of flood impact model chains can be developed in these regions because of the availability of global datasets. Here we evaluated the implementation of a synthetic vulnerability function into a flood impact model. The function bases on expert heuristics on a targeted sample of representative buildings (targeted heuristics). We applied the vulnerability function in a meso-scale river basin and evaluated the new function by comparing the resulting flood damage with the damage computed by other approaches, (1) an ensemble of vulnerability functions available from the literature, (2) an individual vulnerability function calibrated with region-specific data, and (3) the vulnerability function used in flood risk management by the Swiss government. The results show that targeted heuristics can be a valuable alternative for developing flood impact models in regions without any data or only few data on flood damage.

In flood risk management, the choice of vulnerability functions has a remarkable impact on the overall uncertainty of modelling flood damage. The spatial transferability of empirical vulnerability functions is limited, leading to the need for computation and validation of region-specific vulnerability functions. In data-scarce regions however, this option is not feasible. In contrast, the physical processes of flood impact model chains can be developed in these regions because of the availability of global datasets. Here we evaluated the implementation of a synthetic vulnerability function into a flood impact model. The function bases on expert heuristics on a targeted sample of representative buildings (targeted heuristics). We applied the vulnerability function in a meso-scale river basin and evaluated the new function by comparing the resulting flood damage with the damage computed by other approaches, (1) an ensemble of vulnerability functions available from the literature, (2) an individual vulnerability function calibrated with region-specific data, and (3) the vulnerability function used in flood risk management by the Swiss government. The results show that targeted heuristics can be a valuable alternative for developing flood impact models in regions without any data or only few data on flood damage.

Abstract. Effective flood risk management requires a realistic estimation of flood losses. However, available flood damage estimates are still characterized
by significant levels of uncertainty, questioning the capacity of flood damage models to depict real damages. With a joint effort of eight
international research groups, the objective of this study was to compare, in a blind-validation test, the performances of different models for the
assessment of the direct flood damage to the residential sector at the building level (i.e. microscale). The test consisted of a common flood case
study characterized by high availability of hazard and building data but with undisclosed information on observed losses in the implementation
stage of the models. The nine selected models were chosen in order to guarantee a good mastery of the models by the research teams, variety of the
modelling approaches, and heterogeneity of the original calibration context in relation to both hazard and vulnerability features. By avoiding
possible biases in model implementation, this blind comparison provided more objective insights on the transferability of the models and on the
reliability of their estimations, especially regarding the potentials of local and multivariable models. From another perspective, the exercise
allowed us to increase awareness of strengths and limits of flood damage modelling, which are summarized in the paper in the form of take-home messages
from a modeller's perspective.