A globally-consistent modelling approach to assess socio-economic wildfire risks

Abstract

Wildfires cause extensive damage to physical assets exposed to them. So far, assessing the risk of these events remains an understudied area of global disaster risk assessment. Probabilistic risk estimates covering the range and likelihood of devastating events are crucial for various applications such as prioritising adaptation measures and determining insurance pricing. Quantifying tail risks such as a one-in-a-hundred-year impact has important implications for disaster risk management, including the pricing of insurance. However, short observational time series render modelling efforts indispensable for risk assessments on a global scale. In parallel, increasing data availability allows for the use of machine learning techniques to predict wildfire behaviour. In this context, an open-source wildfire risk model based on globally available data would facilitate the accessibility of such analysis to stakeholders from both the public and private sector. Here, we present such a machine learning model that estimates wildfire probabilities and we integrate these within a global socio-economic risk framework.  We determine burning probabilities based on MODIS burnt area, a set of predictors and a country-and-biome specific machine learning model. The chosen predictors include weather variables, land use covariates and population density. We enhance the model with spatial and temporal feature-engineered covariates, such as the count of neighbouring burnt cells and time since the last fire in each cell. The model employs XGBoost, a tree boosting system, tailored for each country and biome. The model generates stochastic, counterfactual historic wildfire seasons by leveraging the inherent randomness in its predictions, further influenced by temporal and spatial covariates. Secondly, we compute socio-economic impacts as the combination of the newly developed wildfire hazard, an exposure representing physical assets; and a vulnerability that was calibrated on historic fire damage data. We compute wildfire risks by combining the resulting impacts with their respective probabilities. This renders a globally consistent modelling approach of wildfire risk to physical assets. Our model's stochastic representation of wildfire hazards enables the analysis of extreme events with return periods extending beyond available observational data, enhancing our understanding of potential high-impact scenarios.