Quantifying the Impact of the Prescribed Burning on Mitigating Wildland Fire Risk
- Institution
- SUNY at Buffalo, NY
- Principal investigator
- Jun Zhuang
- Funding
- $300K
- Duration
- 2023–2026
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Abstract
Wildland fires have become a common threat to many communities, resulting in a significant number of deaths and injuries, and enormous economic costs associated with property damages and suppression. The risk is further amplified by a changing climate. Among methods and efforts employed to reduce fire frequency and intensity, prescribed burning has been utilized more widely in recent years. However, prescribed burning can be impeded by a lack of funding, preparation, and technical guidance. Furthermore, management of prescribed burning operations involves interdependent decision-makers and stakeholders (e.g., governments, fire departments, environmental agencies, insurance companies, and private land/property owners) often with conflicting values and objectives. Motivated by the gaps in current knowledge in this domain, and the need for insights that can improve and support field operations, this project will quantify the impact of the prescribed burning usage in mitigating wildland fires. It aims at generating new datasets and models for better planning prescribed burns, which would in turn save lives and reduce the economic losses in hazard-prone regions. The objective of this project is to create novel analytical models to quantify the impact of prescribed burning programs for mitigating the losses and damages caused by wildland fires. To achieve it, the team will: (a) collect, process, and analyze the spatial and temporal data on wildland fire occurrences and costs, prescribed burning usages, mitigation efforts and costs, wildland-urban interface, and associated fuel, weather and topography features; (b) characterize the impact of prescribed burning programs by considering the reduction of the number and intensity of large events, and the associated environmental and biological implications; and (c) collect data on and model the multiple stakeholders’ objective hierarchies, including attributes and metrics to represent their values. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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