Valuating fire suppression risk data
| Authors: | Alex T. Masarie, Yu Wei, Erin J. Belval, Matthew P. Thompson, Iuliana Oprea, Maryam Tabatabaei, Dave E. Calkin |
| Year: | 2019 |
| Type: | Scientific Journal |
| Station: | Rocky Mountain Research Station |
| DOI: | https://doi.org/10.1016/j.apm.2018.11.049 |
| Source: | Applied Mathematical Modelling |
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.