Abstract
Extreme wildfires continue to be a significant cause of human death and biodiversity destruction within countries that encompass the Mediterranean Basin. Recent worrying trends in wildfire activity (i.e., occurrence and spread) suggest that wildfires are likely to be highly impacted by climate change. In order to facilitate appropriate risk mitigation, it is imperative to identify the main drivers of extreme wildfires and assess their spatio-temporal trends, with a view to understanding the impacts of the changing climate on fire activity. To this end, we analyse the monthly burnt area due to wildfires over a region encompassing most of Europe and the Mediterranean Basin from 2001 to 2020, and identify high fire activity during this period in eastern Europe, Algeria, Italy and Portugal. We build an extreme quantile regression model with a high-dimensional predictor set describing meteorological conditions, land cover usage, and orography, for the domain. To model the complex relationships between the predictor variables and wildfires, we make use of a hybrid statistical deep-learning framework that allows us to disentangle the effects of vapour-pressure deficit (VPD), air temperature, and drought on wildfire activity. Our results highlight that whilst VPD, air temperature, and drought significantly affect wildfire occurrence, only VPD affects wildfire spread. Furthermore, to gain insights into the effect of climate trends on wildfires in the near future, we focus on the extreme wildfires in August 2001 and perturb VPD and temperature according to their observed trends. We find that, on average over Europe, trends in temperature (median over Europe: +0.04K per year) lead to a relative increase of 17.1% and 1.6% in the expected frequency and severity, respectively, of wildfires in August 2001; similar analyses using VPD (median over Europe: +4.82Pa per year) give respective increases of 1.2% and 3.6%. Our analysis finds evidence suggesting that global warming can lead to spatially non-uniform changes in wildfire activity.
| Original language | English (US) |
|---|---|
| Journal | Artificial Intelligence for the Earth Systems |
| DOIs | |
| State | Published - Sep 13 2023 |
Bibliographical note
KAUST Repository Item: Exported on 2023-09-18Acknowledged KAUST grant number(s): OSR-CRG2020-4394
Acknowledgements: The research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. OSR-CRG2020-4394. Support from the KAUST Supercomputing Laboratory is gratefully acknowledged. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 101003469. J.Z. acknowledges funding from the Helmholtz Initiative and Networking Fund (Young Investigator Group COMPOUNDX, Grant Agreement VH-NG-1537). The authors would like to thank the three reviewers of this paper for their helpful feedback.
