Modelling conditional burn probability patterns for large wildland fires

We present a technique for modelling conditional burn probability patterns in two dimensions for large wildland fires. The intended use for the model is strategic program planning when information about future fire weather and event durations is unavailable and estimates of the average probabilistic shape and extent of large fires on a landscape are needed. To model average conditional burn probability patterns, we organised historical fire data from Yellowstone National Park, USA, into a set of grids; one grid per fire. We captured various spatial relationships inherent in the gridded data through use of geometric variables in the main model and by incorporating an autoregressive covariance structure. The final model had ‘good’ predictive ability with an AUC of 0.81 (1.0 is perfect prediction) and the estimated coefficients are consistent with theory and reflect how fires usually behave on the study site landscape. This technique produces a predictive model with finer detail than most landscape-wide models of burn probability and it has advantages over simulation methods for strategic planning because it does not require multiple runs of spread simulation models or information on fire duration.