Spatiotemporal prediction of wildfire size extremes with Bayesian finite sample maxima.

ABSTRACT: Wildfires are becoming more frequent in parts of the globe, but predicting where and when wildfires occur remains difficult. To predict wildfire extremes across the contiguous United States, we integrate a 30-yr wildfire record with meteorological and housing data in spatiotemporal Bayesian statistical models with spatially varying nonlinear effects. We compared different distributions for the number and sizes of large fires to generate a posterior predictive distribution based on finite sample maxima for extreme events (the largest fires over bounded spatiotemporal domains). A zero-inflated negative binomial model for fire counts and a lognormal model for burned areas provided the best performance. This model attains 99% interval coverage for the number of fires and 93% coverage for fire

SUBMITTER: Joseph MB 

PROVIDER: S-EPMC6851762 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature