Collaborative Research: CDI-Type II: Integrated Weather and Wildfire Simulation and Optimization for Wildfire Management
Prof. Xianlin Hu, Georgia State University (Lead Institution PI)
Profs. Ming Xue (OU PI) and Yang Hong (OU co-PI)
Prof. Lewis Ntaimo (Texas A&M Univerity PI)
Dr. James Nutaro (Orkridge National Lab PI)
Summary Wildfires cause great destruction including the loss of life and damage to property, infrastructure and the environment. The complexity of wildfire management arises from the uncertain dynamic interactions and dependencies among multiple system components. These include highly dynamic and nonlinear wildfire
behaviors, weather conditions, and firefighting resource management. In previous research, these components have been largely treated in isolation in their own fields. To achieve effective wildfire management, decision-making support tools that integrate all these components as a whole are needed. The objective of this project is to develop new models and computation methods that integrate weather prediction, wildfire simulation, data assimilation and stochastic optimization for effective wildfire response management. In doing so, the project makes two key paradigm-shifting advances in wildfire modeling and management: 1) coupled weather and wildfire modeling and data assimilation for two-way
interactive dynamic weather-wildfire prediction, and 2) Integrated wildfire simulation and stochastic optimization for wildfire containment. The project focuses on computational thinking for understanding the complexity in the natural systems of weather and wildfire behavior, and in the man-made system of firefighting resources management. Due to the stochastic and multiscale nature of the problem data associated with these systems, the project also involves data assimilation and parallel/distributed computational methods for robust weather and wildfire behavior predictions.
Intellectual merit. The novelty of this research lies in integrating weather prediction, wildfire simulation and stochastic optimization for wildfire management as never done before. The integrated approach also includes coupled-system data assimilation for robust predictions of wildfire behavior. The project will leverage the PIs’ previous work in numerical weather prediction, remote sensing, advanced data assimilation, wildfire simulation, and stochastic optimization. If successful, the project will advance the state of the science in wildfire modeling and management by enabling faster initial attack response and better utilization of limited firefighting resources. Given sufficient computing resources afforded by large parallel/distributed computing systems, the results of this project can support real-time decision-making for wildfire
Broader impacts. The interdisciplinary nature of the research team and the cooperation experience among the team members provide great potential for producing fruitful outcomes that will benefit communities vulnerable to wildfires. The recent California wildfires in November 2008 forced thousands of people to flee their homes and destroyed at least 400 houses and 500 mobile homes. The results of this project will aid in wildfire management to alleviate such losses caused by wildfires through robust firefighting resource
management decisions. The PIs’ collaboration with the Texas Forest Service will enable validation and transfer of the resulting knowledge, systems and tools to real wildfire management. If successful, the results of the research will also benefit other emergency response applications such as those in homeland security. The PIs will provide interdisciplinary training to both undergraduate and graduate students, develop a web-based education and training simulation environment for wildfire simulation and
management to foster collaboration. The PIs will strengthen and complement existing K-12 outreach programs at Georgia State University (GSU), University of Oklahoma (OU), and Texas A&M University (TAMU) for minority students, and will make a concerted effort to broaden the participation of underrepresented students in research and education. Results of the research will be presented at conferences and published in refereed journals.