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A spatiotemporal optimization engine for prescribed burning in the Southeast US.

Reetam Majumder1, Adam J Terando2, J Kevin Hiers3

  • 1Southeast Climate Adaptation Science Center, NC State University, 127 David Clark Labs, Raleigh, 27695, NC, USA.

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A new optimization engine identifies optimal prescribed fire opportunities by analyzing weather forecasts and risks. This tool aids land managers in balancing ecological goals with safe, effective prescribed burning for Southeast US ecosystems.

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Area of Science:

  • Ecology
  • Environmental Management
  • Climate Science

Background:

  • Southeast US ecosystems rely on frequent, low-intensity surface fires for biodiversity and ecosystem services.
  • Prescribed fire is crucial for managing these ecosystems and mitigating wildland fire risks.
  • Effective prescribed burning requires careful planning, risk assessment, and consideration of environmental conditions.

Purpose of the Study:

  • To develop a spatiotemporal optimization engine for identifying optimal near-term prescribed fire opportunities.
  • To integrate climate change uncertainties into wildland fire management decisions.
  • To provide a tool for improved decision-making in prescribed fire implementation.

Main Methods:

  • Developed a Bayesian hierarchical model for forecast verification using historical weather data (2015-2021).
  • Created a spatiotemporal optimization engine incorporating calibrated weather forecasts and uncertainty estimates.
  • Optimized burn allocation by considering fire risk and habitat parcel utility.

Main Results:

  • The optimization engine demonstrated agreement with historical prescribed burn decisions in a Florida case study.
  • Forecast verification provided calibrated daily weather forecasts and joint uncertainty estimates.
  • Discrepancies between the model and past decisions highlight potential differences in utility functions.

Conclusions:

  • The developed optimization engine can aid in identifying optimal prescribed fire windows.
  • Integrating weather forecast uncertainty is key for risk assessment in prescribed fire management.
  • Further refinement of utility functions is needed to align model predictions with management practices.