Integrating mechanistic dynamics to characterize the causes of extremes in environmental processes

Music City Center 

Clusters of extremes in space such as floods and wildfires are ubiquitous in the environment. These spatial clusters of extreme events vary over time as well. Environmental processes are governed by underlying dynamical/mechanistic relationships that concern many variables that can interact both linearly and non-linearly. Indeed, the mechanistic dynamics of such processes can produce extremes through internal sources (e.g., transient growth) and via external forcing. In this research, we build a statistical modeling framework that can include a spatio-temporal dynamics but allows for different types of forcing (extremes and non-extremes). That is, this model should allow for "typical" behavior in the bulk of the marginal distribution but allow for non-trivial extremes (i.e., asymptotic dependence/independence) in either tail.  We use real data to demonstrate that this method can also allow for the modeling of event-level data without having to extract maxima or threshold exceedances.