Abstract

Geophysical hazards – landslides, tsunamis, volcanic avalanches, etc. – which lead to catastrophic inundation are rare yet devastating events for surrounding communities. The rarity of these events poses two significant challenges. First, there are limited data to inform aleatoric scenario models, how frequent, how big, where. Second, such hazards often follow heavy-tailed distributions resulting in a significant probability that a larger-than-recorded catastrophe might occur. To overcome this second challenge, we must rely on physical models of these hazards to “probe” the tail for these catastrophic events. We will present an emulator-based strategy that allows great speed-up in Monte Carlo simulations for creating probabilistic hazard forecast maps. This approach offers the flexibility to explore both the impacts of epistemic uncertainties on hazard forecasts and of non-stationary scenario modeling on short term forecasts. Collaborators: Jim Berger (Duke), Eliza Calder (Edinburgh), Abani Patra (Buffalo), Bruce Pitman (Buffalo), Regis Rutarindwa (Marquette), Robert Wolpert (Duke)