The Northeast USA, particularly New York State has experienced an increase in extreme 24-hour precipitation during the past 50 years. Recent events such as Hurricane Irene and Superstorm Sandy have revealed vulnerability to intense precipitation within the transportation sector. For regional resiliency, one has to understand the exposure of regional network/systems to correlated risks or simultaneous extremes. Stronger knowledge of correlated extreme events and the resultant simultaneous regional network vulnerabilities can support emergency management division in creating more effective disaster relief and response systems. Current disaster relief studies mostly focus on simulating traffic flow on the network or evaluating different dispatching and vehicle routing scenarios in response to disaster; it is not prognostic with underlying climate information. There is a necessity to understand the underlying reasons which generates the spatial-temporal demand. There is also a necessity to and forecast, based on climate, individual level behavior and their nodal functions during a simultaneous extreme rainfall event. This proposal brings together cutting edge hydroclimatology science, space-time statistical modeling expertise and state of the art transportation sector’s modeling frameworks and decision support tools to address the following questions:

(a) How best can space-time distribution of rainfall intensity for extreme rainfall events be estimated using multiple sources of rainfall data?

(b) How best can spatio-temporal rainfall fields for extreme rainfall events be simulated to assess risks (return periods of failure) across a regional transportation network?

(c) How individuals, as intelligent agents with different demographics, react to different scenarios and shift their travel behavior? and finally (d) How the spatial-temporal demand for mobility in the network will be impacted under extreme events.