Details zur Publikation |
| Kategorie | Textpublikation |
| Referenztyp | Zeitschriften |
| DOI | 10.1175/AIES-D-22-0095.1 |
Lizenz  |
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| Titel (primär) | Insights into the drivers and spatiotemporal trends of extreme Mediterranean wildfires with statistical deep learning |
| Autor | Richards, J.; Huser, R.; Bevacqua, E.
; Zscheischler, J.
|
| Quelle | Artificial Intelligence for the Earth Systems (AIES) |
| Erscheinungsjahr | 2023 |
| Department | CHS |
| Band/Volume | 2 |
| Heft | 4 |
| Seite von | e220095 |
| Sprache | englisch |
| Topic | T5 Future Landscapes |
| Keywords | Extreme events; Wildfires; Climate change; Statistics; Deep learning |
| 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. To facilitate appropriate risk mitigation, it is imperative to identify the main drivers of extreme wildfires and assess their spatiotemporal trends, with a view to understanding the impacts of the changing climate on fire activity. To this end, we analyze 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 vapor pressure deficit (VPD), air temperature, and drought on wildfire activity. Our results highlight that while 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: +0.04 K yr−1) 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: +4.82 Pa yr−1) give respective increases of 1.2% and 3.6%. Our analysis finds evidence suggesting that global warming can lead to spatially nonuniform changes in wildfire activity. |
| dauerhafte UFZ-Verlinkung | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=28355 |
| Richards, J., Huser, R., Bevacqua, E., Zscheischler, J. (2023): Insights into the drivers and spatiotemporal trends of extreme Mediterranean wildfires with statistical deep learning Artificial Intelligence for the Earth Systems (AIES) 2 (4), e220095 10.1175/AIES-D-22-0095.1 |
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