A Bayesian approach to exploring the influence of climate variability modes on fire weather conditions and lightning-ignited wildfires

Abstract

Understanding the relationships between large-scale, low-frequency climate variability modes, fire weather conditions and lighting-ignited wildfires has implications for fire-weather prediction, fire management and conservation. This article proposes a Bayesian network framework for quantifying the influence of climate modes on fire weather conditions and occurrence of lightning-ignited wildfires. The main objectives are to describe and demonstrate a probabilistic framework for identifying and quantifying the joint and individual relationships that comprise the climate-wildfire system; gain insight into potential causal mechanisms and pathways; gauge the influence of climate modes on fire weather and lightning-ignition relative to that of local-scale conditions alone; assess the predictive skill of the network; and motivate the use of techniques that are intuitive, flexible and for which user‐friendly software is freely available. A case study illustrates the application of the framework to a forested region in southwest Australia. Indices for six climate variability modes are considered along with two hazard variables (observed fire weather conditions and prescribed burn area), and a 41-year record of lightning-ignited wildfire counts. Using the case study data set, we demonstrate that the proposed framework: (1) is based on reasonable assumptions provided the joint density of the variables is converted to multivariate normal; (2) generates a parsimonious and interpretable network architecture; (3) identifies known or partially known relationships between the variables; (4) has potential to be used in a predictive setting for fire weather conditions; and (5) climate modes are more directly related to fire weather conditions than to lightning-ignition counts.

Discerning the influence of climate variability modes, regional weather features and time series persistence on streamflow using Bayesian networks and multiple linear regression

“…The class of Bayesian networks used in this study is explained in an earlier paper (Bates et al, 2021) and so is only briefly described here. The approach, called sparse dynamic Bayesian networks (SDBNs), incorporates the temporal dimension by including lagged and non-lagged variables.…”

“…The indices chosen to characterize these modes were Niño3.4 index (N34) for ENSO; dipole mode index (DMI) for IOD, tripole index (TPI) for IPO, an observation‐based index for SAM, and for QBO the mean zonal winds at 30 hPa (U30) and 50 hPa (U50) over Singapore. Further details on these indices are given in Bates et al (2021) and Table 2.…”

Lightning-Induced Wildfires: An Overview

“…The study observed seasonal cycles and long-term patterns in lightning ignitions. Bates et al [138] used the Bayesian network as a framework to assess climate variability in lightning-induced wildfires, and unlike the lightning ignitions, the climate modes were allied to the fire weather conditions. Dorph et al's [139] study in Victoria, Australia, also indicated that lightning-caused wildfires are steered by weather conditions.…”

A Spatial Assessment of Wildfire Risk for Transmission-Line Corridor Based on a Weighted Naïve Bayes Model

“…The BNW can solve the uncertainty of evaluating wildfires by combining the prior conditional probability and the relationship between factors (Jiang et al, 2016;Albuquerque et al, 2017;Bates et al, 2021). However, the network complexity may be timeconsuming and storage-consuming of the model.…”