Flood risk models capture a variety of processes and are associated with large uncertainties. In this paper, the uncertainties due to alternative model assumptions are analysed for various components of a probabilistic flood risk model in the study area of Vorarlberg (Austria). The effect of different model assumptions for five aspects is compared to a reference simulation. This includes: (I, II) the selection of two model thresholds controlling the generation of large sets of possible flood events; (III) the selection of a distribution function for the flood frequency analysis; (IV) the building representation and water level derivation for the exposure analysis and (V) the selection of an appropriate damage function. The analysis shows that each of the tested aspects has the potential to alter the modelling results considerably. The results range from a factor of 1.2 to 3, from the lowest to highest value, whereby the selection of the damage function has the largest effect on the overall modelling results.

Design flood estimation is an essential part of flood risk assessment. Commonly applied are flood frequency analyses and design storm approaches, while the derived flood frequency using continuous simulation has been getting more attention recently. In this study, a continuous hydrological modelling approach on an hourly time scale, driven by a multisite weather generator in combination with a $k$-nearest neighbour resampling procedure, based on the method of fragments, is applied. The derived 100-year flood estimates in 16 catchments in Vorarlberg (Austria) are compared to (a) the flood frequency analysis based on observed discharges, and (b) a design storm approach. Beside the peak flows, the corresponding runoff volumes are analysed. The spatial dependence structure of the synthetically generated flood peaks is validated against observations. It can be demonstrated that the continuous modelling approach can achieve plausible results and shows a large variability in runoff volume across the flood events.

This paper investigates the hydrological response of glacierized headwater catchments to future climate change in the Ötztal Alps, Austria. To this end, two conceptual hydrological models, HBV (Hydrologiska Byråns Vattenbalansavdelning) and HQsim, are applied for the simulation of future daily discharge in three (nested) catchments with varying degrees of glaciation. The models are forced with downscaled climate change projections, and outputs from an empirical glacier model, which is able to simulate future glacial evolution. Under the future conditions, the outcomes show initially that runoff increases for all catchments without changes in the runoff regimes. In the long term, summer runoff is expected to decrease and winter/spring runoff is expected to increase in all catchments. These runoff changes are accompanied with regime shifts from glacial/glacio-nival runoff regimes to runoff regimes with a higher nival component. Changing runoff conditions might also lead to changes in the seasonality of annual flood peaks with an earlier appearance of flood peaks, and an increasing appearance of low flow conditions during summer months. The outcomes of the two hydrological models show minor differences. The results of this study provide improved understanding of the future impact of climate change on the water cycle of glacierized Alpine catchments.

Flood risk assessment is an important prerequisite for risk management decisions. To estimate the risk, i.e. the probability of damage, flood damage needs to be either systematically recorded over a long period or modelled for a series of synthetically generated flood events. Since damage records are typically rare, time series of plausible, spatially coherent event precipitation or peak discharges need to be generated to drive the chain of process models. In the present study, synthetic flood events are generated by two different approaches to modelling flood risk in a meso-scale alpine study area (Vorarlberg, Austria). The first approach is based on the semi-conditional multi-variate dependence model applied to discharge series. The second approach relies on the continuous hydrological modelling of synthetic meteorological fields generated by a multi-site weather generator and using an hourly disaggregation scheme. The results of the two approaches are compared in terms of simulated spatial patterns of peak discharges and overall flood risk estimates. It could be demonstrated that both methods are valid approaches for risk assessment with specific advantages and disadvantages. Both methods are superior to the traditional assumption of a uniform return period, where risk is computed by assuming a homogeneous return period (e.g. 100-year flood) across the entire study area.

The aim of this study is to investigate potential impacts of climate change on the seasonality of runoff in a mountainous watershed, located in the Austrian Alps. In order to consider the full range of possible climate variation, hypothetical climate change scenarios were used to force a hydrological model to simulate runoff time series for potential future climate conditions. The variation of runoff seasonality is illustrated with a three-dimensional representation of daily discharge data, directional statistics of annual flood peaks and the analysis of seasonal occurrence of runoff peaks. The results show that changes in temperature and precipitation patterns could have considerable effects on seasonal runoff variability in the investigated watershed. Generally, a possible increase in temperature may cause an increase in seasonal variability of runoff. Further, annual flood peaks are projected to occur throughout the entire year in the investigated Alpine watershed, whereas moderate high flows may increase in winter (December–February).

This article presents a flood risk analysis model that considers the spatially heterogeneous nature of flood events. The basic concept of this approach is to generate a large sample of flood events that can be regarded as temporal extrapolation of flood events. These are combined with cumulative flood impact indicators, such as building damages, to finally derive time series of damages for risk estimation. Therefore, a multivariate modeling procedure that is able to take into account the spatial characteristics of flooding, the regionalization method top-kriging, and three different impact indicators are combined in a model chain. Eventually, the expected annual flood impact (e.g., expected annual damages) and the flood impact associated with a low probability of occurrence are determined for a study area. The risk model has the potential to augment the understanding of flood risk in a region and thereby contribute to enhanced risk management of, for example, risk analysts and policymakers or insurance companies. The modeling framework was successfully applied in a proof-of-concept exercise in Vorarlberg (Austria). The results of the case study show that risk analysis has to be based on spatially heterogeneous flood events in order to estimate flood risk adequately.