2023
DOI: 10.1186/s42408-023-00227-x
|View full text |Cite
|
Sign up to set email alerts
|

The effectiveness of past wildfire at limiting reburning is short-lived in a Mediterranean humid climate

David A. Davim,
Carlos G. Rossa,
José M. C. Pereira
et al.

Abstract: Background The study of wildfire interactions (i.e., spread limitation and reburns) is gaining traction as a means of describing the self-limiting process of fire spread in the landscape and has important management implications but has scarcely been attempted in Europe. We examined to what extent previously burned areas restricted the development of individual large wildfires (> 500 ha) in mainland Portugal. Results For the 1984–2021 period, we… Show more

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
0
0

Year Published

2024
2024
2024
2024

Publication Types

Select...
1

Relationship

0
1

Authors

Journals

citations
Cited by 1 publication
(1 citation statement)
references
References 84 publications
0
0
0
Order By: Relevance
“…Results show that 0.75 hectares burnt in high intensity are avoided for every hectare of controlled burns invested. Recent work in Portugal (Davim et al 2022;Davim et al 2023) found a lower ratio, i.e., 1:0.2, whereas another work in the same area found an effect of 1:0.9 (Price et al 2015). Our results are within the range found in the literature and denote a modest effect of a prescribed burn in reducing highintensity res predicted under climate change.…”
Section: Modelingsupporting
confidence: 73%
“…Results show that 0.75 hectares burnt in high intensity are avoided for every hectare of controlled burns invested. Recent work in Portugal (Davim et al 2022;Davim et al 2023) found a lower ratio, i.e., 1:0.2, whereas another work in the same area found an effect of 1:0.9 (Price et al 2015). Our results are within the range found in the literature and denote a modest effect of a prescribed burn in reducing highintensity res predicted under climate change.…”
Section: Modelingsupporting
confidence: 73%