Probability based models for estimation of wildfire risk

Abstract

We present a probability-based model for estimating fire risk. Risk is defined using three probabilities: the probability of fire occurrence; the conditional probability of a large fire given ignition; and the unconditional probability of a large fire. The model is based on grouped data at the 1 km2-day cell level. We fit a spatially and temporally explicit non-parametric logistic regression to the grouped data. The probability framework is particularly useful for assessing the utility of explanatory variables, such as fire weather and danger indices for predicting fire risk. The model may also be used to produce maps of predicted probabilities and to estimate the total number of expected fires, or large fires, in a given region and time period. As an example we use historic data from the State of Oregon to study the significance and the forms of relationships between some of the commonly used weather and danger variables on the probabilities of fire. We also produce maps of predicted probabilities for the State of Oregon. Graphs of monthly total numbers of fires are also produced for a small region in Oregon, as an example, and expected numbers are compared to actual numbers of fires for the period 1989–1996. The fits appear to be reasonable; however, the standard errors are large indicating the need for additional weather or topographic variables.

Human-caused fire occurrence modelling in perspective: a review

“…Usually, lower elevation (Kalabokidis et al 2007;Sebastián-López et al 2008;Kwak et al 2012;Narayanaraj and Wimberly 2012;Liu and Wimberly 2015) and smaller slope gradient (Preisler et al 2004;Syphard et al 2008;Dondo Bühler et al 2013;Oliveira et al 2014;Argañaraz et al 2015) increase HCF occurrence. Since surface temperature and humidity are affected by terrain, these may be reflecting climatic conditions.…”

“…Accordingly, the temporal dimensions for fire observations may be daily (Crosby 1954;Haines et al 1983;Alonso-Betanzos et al 2003;Lozano et al 2007;Albertson et al 2009;Wotton et al 2010;Padilla and Vega-Garcia 2011;Sakr et al 2011), monthly (Preisler et al 2004;Boulanger et al 2014) or yearly (Todd and Kourtz 1991;Prestemon and Butry 2005;Hu and Zhou 2014;Karouni et al 2014). However, longer time-spans of several years are the most frequent (Pew and Larsen 2001;Chuvieco et al 2008;Avila-Flores et al 2010;GonzalezOlabarria et al 2011;West et al 2016).…”

“…Temporal variables drive short-term models. High mean and maximum temperatures (Alonso-Betanzos et al 2003;Preisler et al 2004;Carvalho et al 2008Carvalho et al , 2010Vilar et al 2010;Magnussen and Taylor 2012;Bedia et al 2014;Karouni et al 2014;Najafabadi et al 2015), low precipitation (Albertson et al 2009;Vasilakos et al 2009;Zhang et al 2010;Plucinski et al 2014;Guo et al 2016a) and low relative humidity (AlonsoBetanzos et al 2003;Padilla and Vega-Garcia 2011;Chang et al 2013;Karouni et al 2014) favour fires and are often used in models. However, fire science has developed methods to estimate the decrease in moisture content caused by weather on litter and fine fuels, medium compact organic layers and deep organic soil layers or heavy fuels for fire danger rating (Dimitrakopoulos et al 2011).…”

Modelling spatial patterns and temporal trends of wildfires in Galicia (NW Spain)

“…For example, Prestemon et al (2002) developed a model with fixed effects as to assess whether there is a spatial behavior in the occurrence of fires between administrative units from North Florida. Preisler et al (2004) used temporal and spatial effects through a logistic regression to study the probability of fires in Oregon (USA) since 1970. Meanwhile, Brillinger et al (2006) Chen et al (2014) also analyze the risks and causes of fires using spatial econometrics.…”

Developing the model of ecosystem in natural disasters conditions

“…In [1][2][3][4] this task is solved using methods of statisti cal analysis for retrospective databases. But insufficient observability of events related to natural disasters oc currence and poor accuracy of environment parameters prevent obtaining reliable predictions, which deteriorates the value of independent applying the statistical methods.…”