DEUCALION – Determining and Visualising Impacts of Greenhouse Climate Rainfall in Alpine Watersheds on Torrential Disasters

  • Roland Kaitna (BOKU)
  • Andreas Gobiet (Wegener Center)
  • Franz Sinabell (WIFO)
  • Markus Stoffel (Dendrolab)

Torrential processes like flash floods, debris flows, and debris floods represent a serious hazard in Alpine environments. Apart from the basic disposition of a watershed (e.g., relief energy, sediment availability) and variable disposition (e.g., seasonal changes), the triggers of these events are mostly intense convective storms or advective rainfalls. Therefore changes of climatic conditions are likely to have direct and potentially drastic impacts on the frequency and ensuing magnitude of torrential disasters. The aim of the DEUCALION project was to assess changes of potential future disasters for different alpine watersheds based on retrospective analyses and predictions of changes of triggering conditions. The main contribution of the project is a better assessment of past, contemporary and potential future torrential hazards and subsequent assessment of associated risks for human assets on vulnerable fans and cones. Within the DEUCALION project we analysed three characteristic study regions, representing different geomorphic settings and characteristic climatic influences (north-west, south-west, and north-east). Connecting climate change modelling with the earlier identified thresholds we found that for the "best" climate change scenario there is almost no change of debris flow probability in all study regions from May to June, and a decrease in July and August. For the "worst" climate change scenario we found an increased probability for some seasons in all regions, especially in summer and in the north-east. Scenario-based hazard modelling, based on variation of event volumes, showed only minor influence on the hazard zones for our three test sites. Scenario-based risk and life cycle modelling reveals the importance of mitigation measures, which have a strong effect on the predicted costs.