1. Problem statementwildfire has become an increasing threat to human, natural, and cultural resources

The Texas A&M Agrilife Research is receiving a $506,693 Cooperative Agreement award from the Department of the Interior's Bureau of Land Management through the Joint Fire Science Program (CFDA 15.232) to develop a stochastic optimization modeling system for evaluating the effectiveness of landscape fuel management coupled with fire suppression. The project aims to design and evaluate landscape fuel treatment strategies and inform fire suppression for forest- and grass-dominated ecosystems in the Southeastern U.S. and Southern Great Plains regions. The modeling system and analysis approaches will provide a systematic tool for conducting integrative analysis of landscape fuel treatment and fire suppression strategies across different fuel, weather, and terrain settings. The project outcomes are expected to advance knowledge on the effectiveness of landscape fuel management, guide regional wildfire risk assessments, and inform fuel treatment and fire response operations and policy development.

Effective mitigation of wildfire risk and associated damages to valued resources entails integrative strategies for landscape fuel management and fire suppression. Yet, tools for evaluating and determining such strategies are lacking. Although considerable efforts have been made in understanding vegetation fuel treatment effects on fire behavior particularly using simulation, previous studies in general neither incorporated fuel treatment across landscapes nor coupled fuel treatment with fire suppression, and most of these studies were focused on the western united states. Other regions, especially forest-dominated ecosystems in the southeastern united states and prairie and steppe-dominated ecosystems in the southern great plains have also faced an elevated risk threat of wildfire while supporting the agricultural and forestry sectors and a wide range of ecosystem services of national and international importance.2. Objectivesour goals are a) to develop a stochastic optimization modeling system for evaluating the effectiveness of landscape fuel management coupled with fire suppression and b) to use the modeling system to design and evaluate landscape fuel treatment strategies and to inform fire suppression for typical forest- and grass-dominated ecosystems in the southeastern united states and the southern great plains, respectively. These goals will be achieved by accomplishing the following objectives:1) to develop a landscape-level stochastic optimization model that integrates fuel management with fire suppression by considering their overall costs, fire behavior, and impacts on valued resources 2) to develop and evaluate landscape fuel treatment strategies for both forest- and grass-dominated ecosystems using the landscape stochastic optimization model AND3) to assess tradeoffs synergies between landscape fuel management and fire suppression and develop metrics for informing fire response operations.3. Benefitsthe products of this project will be delivered via a suite of tailored methods to practitioners, field managers, researchers, and policy makers. Our findings will advance the knowledge about the effectiveness of landscape fuel management in terms of fire behavior, overall costs of fuel and fire management, and impacts on valued resources, guiding local and regional wildfire risk assessments and fuel treatment and fire response operations and strategy policy development. Additionally, our models and approaches will provide a systematic tool along with a broad scope of data necessary for conducting more integrative and in-depth analysis of landscape fuel treatment and fire suppression strategies in a variety of fuel, weather, and terrain settings in different regions.4. Task statement relevancythis study focuses on task statement II: fuels treatment effectiveness across landscapes. IT will address all three research questions with an emphasis on questions 1 and 3. We will establish statistical relationships between fire behavior (as well as fire response strategies) and landscape fuel treatment characteristics and develop metrics for informing fire suppression using our integrative modeling output derived from different fuel, weather, and geographic conditions. Collaboration with texas a m forest service will ensure the relevance of selected fuel and fire management alternatives and the real-world applicability of our project outcomes. In addition to the results specific to the study regions, our generic stochastic optimization modeling system and analysis approaches will be applicable to other regions.