A spatiotemporal optimization engine for prescribed burning in the Southeast US

Many ecosystems in the Southeast US are dependent upon frequent low-intensity surface fires to sustain native biodiversity, ecosystem services, and endangered species populations. Today, landscape-scale prescribed fire is required to manage these systems for conservation objectives and to mitigate wildland fire risk. Successful application of prescribed fire in this region requires careful planning and assessment of the risks and tradeoffs involved when deciding whether or not to conduct a burn. Many of these risks are closely tied to ambient environmental conditions and are reflected in sets of 'prescription' parameters that define safe and effective operating conditions to meet objectives or regulatory requirements. To facilitate effective decision making and acknowledge growing uncertainties related to climate change effects on wildland fire operations, we developed a spatiotemporal optimization engine to identify near-term optimal burning opportunities for prescribed fire implementation. By mining historical 3-day numerical weather forecasts and observation-based weather data for 2015-2021, we have developed a Bayesian hierarchical model for forecast verification that provides calibrated daily weather forecasts and joint uncertainty estimates on meteorological variables of interest, with the latter serving as a measure of risk associated with prescribed fire activities. Burn allocation decisions are then optimized by considering this risk jointly with the utility of burning a particular habitat parcel. The initial iteration of the optimization engine is demonstrated through a case study of short-term meteorological conditions for the Eglin Air Force Base, located in Florida, USA. Results indicate agreement between the optimization engine and the observed past decision-making, with the largest divergences likely arising primarily from differences between utility functions presumed important and used to develop the optimization engine versus the true utility functions driving management behavior in practice.