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Multiparameter Causal Models for the Estimation and Explainability of Wildfire Burned Area
Author(s) -
Amir Mustofa Irawan,
Merce Vall-llossera,
Carlos Lopez-Martinez,
Adriano Camps,
Gerard Portal,
Alberto Alonso-Gonzalez,
Miriam Pablos
Publication year - 2025
Publication title -
ieee journal of selected topics in applied earth observations and remote sensing
Language(s) - English
Resource type - Magazines
SCImago Journal Rank - 1.246
H-Index - 88
eISSN - 2151-1535
pISSN - 1939-1404
DOI - 10.1109/jstars.2025.3573263
Subject(s) - geoscience , signal processing and analysis , power, energy and industry applications
Understanding the causal relationships between local, continental, and global variables is crucial for improving the accuracy and reliability of wildfire estimation. However, uncovering these complex interactions remains a challenge in wildfire modelling. This study employs a data-driven causal discovery approach using the Peter-Clark algorithm with momentary conditional independence (PCMCI) to analyse time-series data and construct causal graphs. The aim is to enhance understanding of land–atmosphere interactions with wildfires in South Asia, including India, Pakistan, Myanmar, and surrounding regions. Additionally, a do-calculus approach is applied to perturb multiscale input variables, estimating the burned area under worst-case conditions. Results highlight the dominant role of 500-hPa geopotential height anomalies (Δ Z 500 ) in driving surface dryness and wildfire occurrence. By simulating the physical relationships between local- and global-scale variables, including oceanic and climatic indices with varying time lags, the model identifies Δ Z 500 as the primary causal factor leading to increased burned area magnitude and frequency. When Δ Z 500 is bootstrapped to the 100th percentile, indicating the development of a strong upper tropospheric ridge, the estimated burned area rises significantly. Under this extreme scenario, the mean burned area reaches ∼4.2 log ha (∼15,000 ha), while extreme cases exceed 5 log ha (∼100,000 ha), with a pairwise p-value ≤ 0.0001. This burned area surpasses that of all other input variables under equivalent worst-case conditions. By integrating PCMCI and do-calculus, this approach advances causal inference in wildfire modelling, providing valuable insights into the interactions governing wildfire dynamics and their potential future risks.

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