2021
DOI: 10.3390/rs13122247
|View full text |Cite
|
Sign up to set email alerts
|

Climate Variability May Delay Post-Fire Recovery of Boreal Forest in Southern Siberia, Russia

Abstract: Prolonged dry periods and increased temperatures that result from anthropogenic climate change have been shown to increase the frequency and severity of wildfires in the boreal region. There is growing evidence that such changes in fire regime can reduce forest resilience and drive shifts in post-fire plant successional trajectories. The response of post-fire vegetation communities to climate variability is under-studied, despite being a critical phase determining the ultimate successional conclusion. This stu… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
7
0
1

Year Published

2022
2022
2024
2024

Publication Types

Select...
6

Relationship

1
5

Authors

Journals

citations
Cited by 11 publications
(8 citation statements)
references
References 75 publications
0
7
0
1
Order By: Relevance
“…In Canada, NECB source areas included Nunavvut, the Northwest Territories, northern regions of Saskatchewan and Manitoba, and northwest Quebec which have experienced substantial drought and fire disturbance (Whitman et al, 2019; Zhao et al, 2021). In Siberia, NECB source occurred primarily across the tundra, driven by R eco outpacing GPP, and within the southern boreal zone which has been impacted by drought and fire (Sun et al, 2021; Veraverbeke et al, 2021).…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…In Canada, NECB source areas included Nunavvut, the Northwest Territories, northern regions of Saskatchewan and Manitoba, and northwest Quebec which have experienced substantial drought and fire disturbance (Whitman et al, 2019; Zhao et al, 2021). In Siberia, NECB source occurred primarily across the tundra, driven by R eco outpacing GPP, and within the southern boreal zone which has been impacted by drought and fire (Sun et al, 2021; Veraverbeke et al, 2021).…”
Section: Discussionmentioning
confidence: 99%
“…Zhao et al, 2021). In Siberia, NECB source occurred primarily across the tundra, driven by R eco outpacing GPP, and within the southern boreal zone which has been impacted by drought and fire (Sun et al, 2021;Veraverbeke et al, 2021).…”
Section: Regional Necb Emission Statusmentioning
confidence: 99%
“…It is important to have a holistic view and consider jointly different disturbance types because they can accumulate over time and space. For instance, drought affects more strongly post-fire young forests than mature forests in Siberia, delaying their recovery [70]. As a result, very large areas of the boreal forests may experience at least one type of natural disturbance in the future (e.g., in Canada [30]).…”
Section: How Extreme Events May Put Boreal Forests At Risk?mentioning
confidence: 99%
“…Climate change has driven mixed and negative impacts on boreal forest AGB though shifts in moisture availability (D'Orangeville et al, 2018;Hember et al, 2017a;Luo et al, 2019) and more frequent and intense droughts (Itter et al, 2019;Rogers et al, 2018), as well as increasing frequency of disturbance events like fires (Abatzoglou et al, 2018;Hanes et al, 2018;Jia et al, 2019;Walker et al, 2019) and disease or pest outbreaks (Jia et al, 2019;Rogers et al, 2018). These processes interact with each other in complex ways (Burrell et al, 2021;Peng et al, 2011), and can have different impacts on mature and younger forests Sun et al, 2021). The complexity of these interacting process and forest responses has generated considerable uncertainty in the current extent and drivers of change in boreal AGB (D'Orangeville et al, 2018;Girardin et al, 2016;Wang et al, 2021), and are likely to continue changing with further climate warming (Zhang et al, 2022).…”
Section: Introductionmentioning
confidence: 99%
“…As a result of the growing need for near‐term predictions of AGB change, some studies have attempted to inform management decisions by conducting site‐level modeling using statistical and machine learning (ML) methods (Lidberg et al, 2020; Liu et al, 2018; Sun et al, 2021). Existing ML studies have two primary potential hurdles that have limited their widespread use for biomass change prediction.…”
Section: Introductionmentioning
confidence: 99%