Unraveling the Complexity of Wildland Urban Interface Fires

Abstract

Recent wildland urban interface fires have demonstrated the unrelenting destructive nature of these events and have called for an urgent need to address the problem. The Wildfire paradox reinforces the ideology that forest fires are inevitable and are actually beneficial; therefore focus should to be shifted towards minimizing potential losses to communities. This requires the development of vulnerability-based frameworks that can be used to provide holistic understanding of risk. In this study, we devise a probabilistic approach for quantifying community vulnerability to wildfires by applying concepts of graph theory. A directed graph for community in question is developed to model wildfire inside a community by incorporating different fire propagation modes. The model accounts for relevant community-specific characteristics including wind conditions, community layout, individual structural features, and the surrounding wildland vegetation. We calibrate the framework to study the infamous 1991 Oakland fire in an attempt to unravel the complexity of community fires. We use traditional centrality measures to identify critical behavior patterns and to evaluate the effect of fire mitigation strategies. Unlike current practice, the results are shown to be community-specific with substantial dependency of risk on meteorological conditions, environmental factors, and community characteristics and layout.

High wildfire damage in interface communities in California

“…Wildfire models are used to predict the behaviour of specific wildfire events, as well as gauge the relative wildfire risk in different areas, yet without non-wildland vegetation, buildings, propane tanks, wood piles and vehicles included as potential vectors for wildfire, model performance in the WUI is likely to be poor. Although some WUI fire and fuel models exist (Haas et al 2013;Dietenberger and Boardman 2017), further research into the dynamics of wildfire spread and hazard in the WUI with its diverse natural and manufactured fuels could improve model predictions of wildfire behaviour and effects (Mell et al 2010;Mahmoud and Chulahwat 2018). In addition, continued focus on studying and mapping areas at risk of wildfire, as well as identifying which mitigation strategies are most effective in these areas are key components to reducing future building destruction by wildfire.…”

Modeling wildfire spread in wildland-industrial interfaces using dynamic Bayesian network

“…Some of deterministic fire spread models include the turbulent forest fire model based on the distribution and dissipation of energy [38][39][40], the shortest travel time [41][42][43], the shortest path algorithms [44], fuzzy cellular automata [45], and the integration of cellular automata and genetic algorithm [46]. Some of probabilistic fire spread models include the works based on continuous-time Markov chain [47], stochastic shortest path [48], and the most probable path [49].…”

A Practical Approach to Assess the Wildfire Ignition and Spreading Capacities of Vegetated Areas at Landscape-scale

“…However, most wildfires are reported to have been caused by human activity, either intentionally or accidentally 11 . Human activity patterns have become a determinant during the wildfire ignition phase 12,13 .…”

“…However, most wildfires are reported to have been caused by human activity, either intentionally or accidentally 11 . Human activity patterns have become a determinant during the wildfire ignition phase 12,13 . Mansuy et al 14 contrast the anthropogenic factors to the macro-environmental ones and report that the human footprint affects almost equal wildfire risk both inside and outside the North-American protected landscapes.…”