Event-Based Copula Modeling of Compound Fire-Weather Extremes for Wildfire Risk Assessment

Wildfire impacts are increasingly driven by the joint occurrence and persistence of multiple meteorological drivers, such as atmospheric dryness and strong winds, rather than by isolated univariate extremes. Growing evidence shows that such compound conditions strongly influence wildfire characteristics, including event duration, spatial extent, and intensity, motivating the use of multivariate probabilistic frameworks for wildfire risk analysis [1,2]. Traditional approaches based on marginal extremes or linear dependence are often inadequate for representing tail dependence and joint exceedance behavior, potentially leading to biased estimates of rare but high-impact wildfire events [3,4]. 

This study develops a spatially explicit, event-based probabilistic framework for modeling wildfire-relevant meteorological drivers and derived event characteristics using copula-based dependence structures. The methodology follows a two-stage workflow. In the first stage, hourly gridded fields of a humidity-related variable and wind are transformed into per-cell daily time series, extracting daily extrema and duration metrics based on physically motivated thresholds. A combined condition identifies hours when both drivers are simultaneously active, enabling the construction of compound duration indicators. This spatially explicit, per-cell representation is consistent with established wildfire risk and susceptibility frameworks that rely on pixel-level meteorological and environmental descriptors and supports the consistent aggregation of local information into larger spatial units relevant for regional risk assessment and comparison [5]. 

In the second stage, extreme events are detected and modeled to build an event-based probabilistic dataset and generate long synthetic event catalogs. Event identification relies on return-period exceedance of annual maxima, combined with moving-window logic and minimum inter-event time constraints. Event-level descriptors, including maximum driver intensity and persistence, are used to quantify spatially aggregated impacts, consistent with prior work on joint modeling of wildfire duration and size [6,7]. Marginal distributions are fitted to event-level variables and transformed into the probability domain prior to dependence modeling, following established copula theory [3]. Multivariate dependence is then modeled using copulas, allowing synthetic events to be generated while preserving observed dependence structures among drivers and event characteristics [4,8]. 

The framework builds on recent advances in compound and multihazard analysis [1,2], copula-based frequency analysis [3], and comparative evaluations of multivariate extreme modeling strategies [9]. By exporting spatially aggregated event-impact matrices and event frequencies, the approach is designed for integration into downstream wildfire hazard and risk assessment engines. Preliminary results of a pilot implementations at regional level in Italy (Liguria, Tuscany, Marche), adopting a 40-years weather dataset (1981–2023), are shown. 

 

References 

 [1] Zscheischler & Fischer (2020), Weather and Climate Extremes. 
[2] Sadegh et al. (2018), Geophysical Research Letters. 
[3] Salvadori & De Michele (2004), Water Resources Research. 
[4] Bhatti & Do (2019), International Journal of Hydrogen Energy. 
[5] Trucchia et al. (2022), Fire. 
[6] Ghizzoni et al. (2010), Advances in Water Resources. 
[7] Xi et al. (2020), Stochastic Environmental Research and Risk Assessment. 
[8] Najib et al. (2022), Natural Hazards. 
[9] Tilloy et al. (2020), Natural Hazards and Earth System Sciences.