The characteristics of an aboveground cosmic‐ray neutron sensor (CRNS) are evaluated for monitoring a mountain snowpack in the Austrian Alps from March 2014 to June 2016. Neutron counts were compared to continuous point‐scale snow depth (SD) and snow‐water‐equivalent (SWE) measurements from an automatic weather station with a maximum SWE of 600 mm (April 2014). Several spatially distributed Terrestrial Laser Scanning (TLS)‐based SD and SWE maps were additionally used. A strong nonlinear correlation is found for both SD and SWE. The representative footprint of the CRNS is in the range of 230–270 m. In contrast to previous studies suggesting signal saturation at around 100 mm of SWE, no complete signal saturation was observed. These results imply that CRNS could be transferred into an unprecedented method for continuous detection of spatially averaged SD and SWE for alpine snowpacks, though with sensitivity decreasing with increasing SWE. While initially different functions were found for accumulation and melting season conditions, this could be resolved by accounting for a limited measurement depth. This depth limit is in the range of 200 mm of SWE for dense snowpacks with high liquid water contents and associated snow density values around 450 kg m−3 and above. In contrast to prior studies with shallow snowpacks, interannual transferability of the results is very high regardless of presnowfall soil moisture conditions. This underlines the unexpectedly high potential of CRNS to close the gap between point‐scale measurements, hydrological models, and remote sensing of the cryosphere in alpine terrain.

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 multi-site 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. Besides 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 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.

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Abstract. Within the last decades serious flooding events occurred in many parts of Europe and especially in 2005 the Austrian Federal Province of Tyrol was serious affected. These events in general and particularly the 2005 event have sensitised decision makers and the public. Beside discussions pertaining to protection goals and lessons learnt, the issue concerning potential consequences of extreme and severe flooding events has been raised. Additionally to the general interest of the public, decision makers of the insurance industry, public authorities, and responsible politicians are especially confronted with the question of possible consequences of extreme events. Answers thereof are necessary for the implementation of preventive appropriate risk management strategies. Thereby, property and liability losses reflect a large proportion of the direct tangible losses. These are of great interest for the insurance sector and can be understood as main indicators to interpret the severity of potential events. The natural scientific-technical risk analysis concept provides a predefined and structured framework to analyse the quantities of affected elements at risk, their corresponding damage potentials, and the potential losses. Generally, this risk concept framework follows the process steps hazard analysis, exposition analysis, and consequence analysis. Additionally to the conventional hazard analysis, the potential amount of endangered elements and their corresponding damage potentials were analysed and, thereupon, concrete losses were estimated. These took the specific vulnerability of the various individual elements at risk into consideration. The present flood risk analysis estimates firstly the general exposures of the risk indicators in the study area and secondly analyses the specific exposures and consequences of five extreme event scenarios. In order to precisely identify, localize, and characterize the relevant risk indicators of Correspondence to: M. Huttenlau (huttenlau@alps-gmbh.com) buildings, dwellings and inventory, vehicles, and individuals, a detailed geodatabase of the existing stock of elements and values was established on a single object level. Therefore, the localized and functional differentiated stock of elements was assessed monetarily on the basis of derived representative mean insurance values. Thus, well known difference factors between the analysis of the stock of elements and values on local and on regional scale could be reduced considerably. The spatial join of the results of the hazard analysis with the stock of elements and values enables the identification and quantification of the elements at risk and their corresponding damage potential. Thereupon, Extreme Scenario Losses (ESL) were analysed under consideration of different vulnerability approaches which describe the individual element's specific susceptibility. This results in scenario-specific ranges of ESL rather than in single values. The exposure analysis of the general endangerment in Tyrol identifies (i) 105 330 indiv...

Abstract. Im Rahmen von Risikoanalysen besitzt die Quantifizierung und Monetarisierung der potenziellen Risikoelemente zur Ermittlung des Werteinventars eine wesentliche Rolle, speziell bei regionalen Maßstabsebenen bestehen jedoch große Unsicherheiten. Durch eine differenzierte Bearbeitung und die Anwendung von repräsentativen, aus einzelnen Versicherungspolicen analysierten Werten können diese Unsicherheiten wesentlich minimiert werden. Eingebunden in eine moderne (Geo-)Datenbankstruktur entsteht ein effizientes Instrument für Expositions- und Folgenanalysen naturgefahreninduzierter Risiken.

Abstract. The timing and the volume of snow and ice melt in Alpine catchments are crucial for management operations of reservoirs and hydropower generation. Moreover, a sustainable reservoir operation through reservoir storage and flow control as part of flood risk management is important for downstream communities. Forecast systems typically provide predictions for a few days in advance. Reservoir operators would benefit if lead times could be extended in order to optimise the reservoir management. Current seasonal prediction products such as the NCEP (National Centers for Environmental Prediction) Climate Forecast System version 2 (CFSv2) enable seasonal forecasts up to nine months in advance, with of course decreasing accuracy as lead-time increases. We present a coupled seasonal prediction modelling system that runs at monthly time steps for a small catchment in the Austrian Alps (Gepatschalm). Meteorological forecasts are obtained from the CFSv2 model. Subsequently, these data are downscaled to the Alpine Water balance And Runoff Estimation model AWARE running at monthly time step. Initial conditions are obtained using the physically based, hydro-climatological snow model AMUNDSEN that predicts hourly fields of snow water equivalent and snowmelt at a regular grid with 50 m spacing. Reservoir inflow is calculated taking into account various runs of the CFSv2 model. These simulations are compared with observed inflow volumes for the melting and accumulation period 2015.