Valuating fire suppression risk data

Abstract Efficient and effective wildland fire response requires interregional coordination of suppression resources. We developed a mathematical model to examine how scarce resources are shared. Best-fit models describe regional resource allocation according to driving risk factors. By regressing a linear system of ordinary differential equations with GIS-data for demand predictors like suppression resource use, ongoing fire activity, fire weather metrics, accessibility, and population density onto pre-smoothed Resource Ordering Status System (ROSS) wildfire personnel and equipment requests, we fit a national scale model. We report statistical properties of the best-fit parameters and indicate how these findings might be interpreted for personnel and equipment sharing by examining test cases for national, central/southern Rockies, and California interregional sharing. Abrupt switching behavior across medium and high alert levels was found in test cases for national, central/southern Rockies, and California interregional sharing.