Forecasting and decisions in the birth-death-suppression Markov model for wildfires

TL;DR

This paper develops a Markov model to analyze wildfire dynamics and suppression strategies, providing insights into optimal resource allocation under uncertainty and changing conditions.

Contribution

It introduces a birth-death-suppression Markov model for wildfires, offering a new analytical framework for decision-making in wildfire management.

Findings

Model captures wildfire evolution including ignition and suppression effects

Analytical results inform optimal resource allocation strategies

Results align with modern wildfire suppression practices

Abstract

As changing climates transform the landscape of wildfire management and suppression, agencies are faced with difficult resource allocation decisions. We analyze trade-offs in temporal resource allocation using a simple but robust Markov model of a wildfire under suppression: the birth-death-suppression process. Though the model is not spatial, its stochastic nature and rich temporal structure make it broadly applicable in describing the dynamic evolution of a fire including ignition, the effect of adverse conditions, and the effect of external suppression. With strong analytical and numerical control of the probabilities of outcomes, we construct classes of processes which analogize common wildfire suppression scenarios and determine aspects of optimal suppression allocations. We model problems which include resource management in changing conditions, the effect of resource mobilization delay, and allocation under uncertainty about future events. Our results are consistent with modern resource management and suppression practices in wildland fire.