A new method for the analysis of fire spread modeling errors

Abstract

Fire spread models have a long history, and their use will continue to grow as they evolve from a research tool to an operational tool. This paper describes a new method to analyse two-dimensional fire spread modeling errors, particularly to quantify the uncertainties of fire spread predictions. Measures of error are defined from the respective spread distances of the actual and simulated fires at specified points around their perimeters. A ratio error provides a correction factor for the spread model bias. The characteristics of the error are defined by a probability model, which is used to construct error bounds on fire spread predictions. The method is applied to the Bee Fire, which burned 3848 ha on the San Bernardino National Forest, California, in summer 1996. The study focused on the early, presuppression stages of the fire. A mesoscale spectral model was used to simulate weather conditions on a grid interval of 2 km. The FARSITE system was used to simulate fire growth during the first 105 min of the fire. The case study showed how difficult fire spread modeling is under the conditions presented by the Bee Fire.