Grant: Discovery Grants Program - Individual

OBJECTIVES: 1) Develop statistical tools, methodology and models that impact wildland fire science and management; 2) Train HQP  in modern statistical methods emphasizing collaborative, interdisciplinary, team-based data science; 3) Disseminate advancements in  statistics, environmetrics, wildland fire science and management to the scientific community; 4) Transfer advancements to fire  management by providing conceptual frameworks, software and decision support systems tools.  APPROACH: My research is motivated by the scientific study of large, complex marked spatio-temporal point-process and fire-weather  data sets. Topics include: Data Visualization: Visualization tools for spatio-temporal point patterns and their marks will be developed to explore key questions.  (Eg: How does a burn reduce ignition risk and how does this dissipate? How do fire lifetimes vary spatially and temporally? Is the  clustering of ignitions due to hot spots or stochastic clustering? How can one best visualize model-based information in decision support  tools?) Fire Occurrence: Fine-scale spatio-temporal fire occurrence prediction models for the presence/absence, for counts, and for large escaped fires  will be developed. New design schemes will create more relevant measures of exposure and lead to gains in gain  statistical efficiency. Methodology will be created to develop models over large spatial extents. Changes to detection effectiveness will  be quantified and used to reduce confounding effects when monitoring impacts of climate change. Fire Duration: Models for individual fire lifetimes and for spatial patterns in fire survival times over large landscapes will be developed.  These will characterize the key epochs of a fire's lifetime (eg, time from detection to report, initial attack getaway time, suppression  time). A large data set with time-varying covariates for historical fires will be compiled and novel models that forecast future lifetimes over space-time will be created for fire management. Stochastic Models: Spatio-temporal point process cluster models will be developed to model fire arrivals. Marks will be coupled to these and/or the other ignition models to model fire regime characteristics, such as area burned or the fire-load (the number of fires active on the landscape) over space-time.  Decision Support Systems: Components from the above set of topics will be incorporated into model-based decision support tools which will better inform fire management. IMPACTS: The proposed components will lead to significant advances which further the scientific understanding of wildland fires. Students will be trained in modern statistics in an interdisciplinary, collaborative team setting. This research will impact fire science and fire management, statistics and environmetrics, operations research, and produce technology that impacts insurance and natural resources sectors.