| NSF Org: |
CMMI Division of Civil, Mechanical, and Manufacturing Innovation |
| Recipient: |
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| Initial Amendment Date: | July 7, 2020 |
| Latest Amendment Date: | June 16, 2021 |
| Award Number: | 2002688 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Georgia-Ann Klutke
CMMI Division of Civil, Mechanical, and Manufacturing Innovation ENG Directorate for Engineering |
| Start Date: | September 1, 2020 |
| End Date: | August 31, 2024 (Estimated) |
| Total Intended Award Amount: | $280,000.00 |
| Total Awarded Amount to Date: | $287,200.00 |
| Funds Obligated to Date: |
FY 2021 = $7,200.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
3124 TAMU COLLEGE STATION TX US 77843-3124 (979)862-6777 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
3131 TAMU College Station TX US 77843-3131 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | OE Operations Engineering |
| Primary Program Source: |
01002122DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.041 |
ABSTRACT
This award contributes to the welfare of the nation by addressing important challenges in reducing economic losses due to wildfires. Increased development and urbanization in fire-prone areas, coupled with evolving climate changes, have significantly increased the vulnerability of human communities and ecosystems to wildfires. Even with large expenditures for fire suppression efforts, annual economic losses, as well as loss of human life, due to wildfires remain high. This award supports research efforts to reduce wildfire activity by developing cost-effective methods to reduce risk through fuel treatment. Fuel treatment involves removing vegetation (i.e., fuel) from a landscape to reduce the potential and severity of large-scale fires. Fuel treatment, which forms a first line of wildfire defense, may include any combination of controlled burning, grazing, and various types of mechanical thinning. This award will contribute to better understanding of what types of fuel treatment options and associated decision-making strategies are more appropriate for particular fire-prone regions. This project will involve both graduate and undergraduate students as well as development of courses that expose students at all levels to quantitative methods to address large-scale societal problems.
This award will support research into new sequential mixed-integer optimization methods to determine the appropriate location, timing and type of fuel treatments over multiple seasons in order to minimize the expected losses from wildfires in a region. The optimization framework will involve formulating and solving non-linear mathematical programming models for fuel accumulation and reduction under resource constraints. To take into account the inherent uncertainties with respect to fire ignition, the approach employs robust optimization techniques. The project will investigate analytical results that describe important structural properties of the models and will develop specialized numerical algorithms to solve realistically-sized instances of the problem. The algorithms will leverage and extend modern techniques at the intersection of robust and combinatorial optimization. The models will be calibrated and validated using historical data from the Texas A&M Forest Service, a state agency charged with overseeing forest management in the state of Texas.
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.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Wildfires have become an increasing threat to natural ecosystems and human livelihood in many parts of the world. Fuel treatments (vegetation reduction methods) are considered a viable option for mitigating wildfire risk and damage; yet existing studies have yielded mixed or inconclusive results on fuel treatment effectiveness especially at the landscape level. This collaborative project involved efforts between two disciplines, engineering (operations research) and forest science and has led to the development of two new optimization methodologies for fuel treatment planning namely, bilevel optimization and mean-risk stochastic integer programming (MR-SIP) with endogenous uncertainty. Both approaches address the need for new decision-making tools for fuel treatment planning under uncertainty in fire occurrence and behavior and wildfire risk.
The project has also resulted in the development of two unique fuel treatment planning case studies in forest science, one for forest dominant ecosystems (East Texas, see figure) and the other for grass dominant ecosystems (West Texas, see figure). The case studies have been used to test and calibrate the new methodologies using actual landscape and fuels data, and historical wildfire occurrence and weather data. The studies evaluate changes in area burned and economic consequences in response to prescribed burning (PB) and thinning from below (TFB) treatments. Using a combination of fire behavior simulation and economic assessment, the results reveal complex relationships between treatment effectiveness and several environmental and operational factors.
In forest dominated ecosystems, the analysis of area burned shows that both PB and TFB effectively reduce fire extent, with effectiveness varying by treatment type and conditions. Fuel treatments indicate reduced effectiveness at the landscape level compared to the site level, primarily due to fires crossing treatment boundaries. TFB is more effective than PB with higher biomass volumes and is less sensitive to fire ignition location, making it particularly advantageous when future fire locations are uncertain. In grassland dominated ecosystems, the results reveal that when considering fuel treatment (different levels of PB) alone, treatment coverage is spread across the study area to high-risk subareas. However, when fuel treatment is integrated with firefighting resource deployment, treatment coverage near operation bases is reduced, prioritizing high-risk subareas far from operations bases.
By clarifying the conditions under which a given type of fuel treatment is more effective than another, this study advances our knowledge of fuel treatment effectiveness especially at the landscape level. Such knowledge can aid in developing and deploying treatment strategies to minimize fire extent and adverse economic consequences in the study region and beyond. Overall, applying the new methodologies (tools) from operations research to forest science will help improve forest and fire management decision-making to minimize wildfire risk. This project will also have impact in terms teaching and education by introducing complex forest and fire management case studies to operations research.
The key outcomes of this project include three journal papers and a PhD dissertation. The first journal paper is on "bilevel optimization for fuel treatment planning”, the second on "landscape-level effectiveness of fuel treatments in a forest-dominated ecosystem in the southern united states", and the third on "mean-risk stochastic integer programming approach for integrated fuel treatment and wildfire response planning with endogenous uncertainty". The PhD dissertation is on "effectiveness of fuel treatments in mitigating wildfire risk and damage in the South-Central United States".
Last Modified: 12/18/2024
Modified by: Lewis Ntaimo
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