Using Precursor Information to Update Probabilistic Hazard Maps

PI: E Bruce Pitman

Co-PIs: Abani Patra (Tufts University) and Greg Valentine (University at Buffalo), and colleagues at Duke University and Marquette University.

probabilistic hazard map at belham valley, montserrat.

Short term hazard maps at the Belham Valley, Montserrat.


The prediction and management of "extreme events", from volcanic eruptions to floods to stock market crashes, requires a careful analysis of the hazard event, its inputs, and its consequences. Data of different kinds, and of differing fidelity, must be incorporated into a detailed analysis of the impending hazard. Strategies for minimizing the hazard impact must be decided, and a plan of action set in motion. In many natural hazard scenarios, precursory information becomes available before the disaster strikes. Responding to an impending hazard means that time is limited, so analysis and decision-making must proceed on an accelerated timetable. Modeling, numerical simulation, leading to predictive capacity, play an important role during this pre-event timeframe.

The project will seek to combine different data sources, to make quantifiable statistical prediction that can be used by civil protection authorities to inform decision-making.

funding source