Wildfire risk analysis

Rego, Francisco; Colaço, Conceição

doi:10.1002/9780470057339.vnn023

Cited by 2 publications

(3 citation statements)

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“…IPCC [53] defines the concept of risk as "the potential for adverse consequences for human or ecological systems, recognising the diversity of values and objectives associated with such systems". According to [54], risk can be defined as a combination of hazard and damage; therefore, the Emission Risk assessment model can be defined as (Equation ( 1)):…”

Section: Basis Of the Modelmentioning

confidence: 99%

“…The cause is not relevant in terms of the risk of emission, and the exposure to fire is included as part of the other variables. Damage (D) is the consequence in terms of GHG emissions from the stored carbon in the analysed ecosystem as a result of combustion produced by a forest fire [54]. The damage degree depends on the value at risk, in this case, total carbon from vegetation and soils stored in the ecosystem, and its vulnerability to emitting GHG emissions.…”

Section: Basis Of the Modelmentioning

confidence: 99%

“…Value at risk (VAR) is defined as the potential loss due to an adverse event [54]. In the REMAS context, the VAR variable includes carbon fixed in both vegetation and soils.…”

Section: Damage = Value At Risk × Emission Vulnerabilitymentioning

confidence: 99%

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Development of a Model to Estimate the Risk of Emission of Greenhouse Gases from Forest Fires

Lerma-Arce

Yagüe-Hurtado

Berg

et al. 2022

Fire

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While the Mediterranean basin is foreseen to be highly affected by climate change (CC) and severe forest fires are expected to be more frequent, international efforts to fight against CC do not consider forest fires’ greenhouse gas (GHG) emissions risk and the possibility of its mitigation. This is partly due to a lack of a methodology for GHG risk spatial assessment and consideration of the high value of carbon stocks in forest ecosystems and their intrinsic risk. To revert this, an innovative GHG emission risk model has been developed and implemented in a pilot forest area. This model considers geospatial variables to build up emission vulnerability based on potential fire severity and resistance of a landscape, value at risk and the hazard of a fire occurrence. The results classify low, moderate and high emission risks in the analysed areas. This identification of hotspots allows the prioritisation of fire prevention measures in a region to maximise the reduction of GHG emissions in the case of a fire event. This constitutes the first step in a holistic and consistent CC mitigation that not only considers anthropic GHG sources but also possible GHG emissions by forest fires that can be actively prevented, managed and reduced.

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Section: Basis Of the Modelmentioning

confidence: 99%

Section: Basis Of the Modelmentioning

confidence: 99%