Chapter 8 Assessment of Forest Fire Impacts and Emissions in the European Union Based on the European Forest Fire Information System

Barbosa, Paulo; Camia, Andrea; Kučera, Jan; Giorgio, Liberta'; Palumbo, I.; San-Miguel-Ayanz, Jesús; Schmuck, G.

doi:10.1016/s1474-8177(08)00008-9

Cited by 30 publications

(27 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A similar share of fire emissions to total emissions of CO 2 was observed in Portugal during heavy fire campaigns in 2003 and 2005 (Barbosa et al 2009). For August 2003, the contribution of forest fire emissions in Southern Europe to observed particulate levels of PM 2.5 appeared to be comparable to anthropogenic emissions, and they seemed to result in significant impacts on radiative properties of large areas of Europe (Hodzic et al 2007).…”

Section: Forest Fire Emissionssupporting

confidence: 71%

Comprehensive Monitoring of Wildfires in Europe: The European Forest Fire Information System (EFFIS)

San-Miguel-Ayanz¹,

Schulte²,

Schmuck³

et al. 2012

Approaches to Managing Disaster - Assessing Hazards, Emergencies and Disaster Impacts

Self Cite

218

135

View full text Add to dashboard Cite

Section: Forest Fire Emissionssupporting

confidence: 71%

Comprehensive Monitoring of Wildfires in Europe: The European Forest Fire Information System (EFFIS)

San-Miguel-Ayanz¹,

Schulte²,

Schmuck³

et al. 2012

Approaches to Managing Disaster - Assessing Hazards, Emergencies and Disaster Impacts

Self Cite

218

135

View full text Add to dashboard Cite

“…Wildfire carbon emission in a given spatial cell c depends on the overall amount of carbon (total fuel load) and on the local characteristics of fuel (e.g. dead and live fuel, woody and herbaceous one, typical diameters of these fuels) [84]. Also, each component of the a given fuel class is associated with a specific burning efficiency and emission factor for estimating CO 2 releases in atmosphere which may vary for flaming or smoldering fire [84].…”

Section: Assessing Wildfire Impactsmentioning

confidence: 99%

“…dead and live fuel, woody and herbaceous one, typical diameters of these fuels) [84]. Also, each component of the a given fuel class is associated with a specific burning efficiency and emission factor for estimating CO 2 releases in atmosphere which may vary for flaming or smoldering fire [84]. The availability of spatially distributed estimations of carbon stock is essential so as to avoid basing CE assessments on rough approximations with fixed conversion factors for each fuel class.…”

Section: Assessing Wildfire Impactsmentioning

confidence: 99%

“…The availability of spatially distributed estimations of carbon stock is essential so as to avoid basing CE assessments on rough approximations with fixed conversion factors for each fuel class. The recent pan-European map of living forest carbon stock at IPCC Tier 1 level [20] has been used for integrating the method of [84] with a better spatialisation of CE rapid assessment.…”

Section: Assessing Wildfire Impactsmentioning

confidence: 99%

See 1 more Smart Citation

An Architecture for Adaptive Robust Modelling of Wildfire Behaviour under Deep Uncertainty

Rigo

Rodriguez-Aseretto

Bosco

et al. 2013

IFIP Advances in Information and Communication Technology

View full text Add to dashboard Cite

Abstract. Wildfires in Europe -especially in the Mediterranean region -are one of the major treats at landscape scale. While their immediate impact ranges from endangering human life to the destruction of economic assets, other damages exceed the spatio-temporal scale of a fire event. Wildfires involving forest resources are associated with intense carbon emissions and alteration of surrounding ecosystems. The induced land cover degradation has also a potential role in exacerbating soil erosion and shallow landslides. A component of the complexity in assessing fire impacts resides in the difference between uncontrolled wildfires and those for which a control strategy is applied. Robust modelling of wildfire behaviour requires dynamic simulations under an array of multiple fuel models, meteorological disturbances and control strategies for mitigating fire damages. Uncertainty is associated to meteorological forecast and fuel model estimation. Software uncertainty also derives from the datatransformation models needed for predicting the wildfire behaviour and its consequences. The complex and dynamic interactions of these factors define a context of deep uncertainty. Here an architecture for adaptive and robust modelling of wildfire behaviour is proposed, following the semantic array programming paradigm. The mathematical conceptualisation focuses on the dynamic exploitation of updated meteorological information and the design flexibility in adapting to the heterogeneous European conditions. Also, the modelling architecture proposes a multicriteria approach for assessing the potential impact with qualitative rapid assessment methods and more accurate a-posteriori assessment.

show abstract

“…Combustion in wildfires and associated carbon emissions vary depending on the local fire intensity and the characteristics of fuel (fuel moisture, share of dead and live fuel, share of woody and herbaceous fuel, ...) [61]. Specific burning efficiency and emission factors can be associated to classes of fuel and typology of fire (flaming or smoldering) so as for fuel models to be associated with static factors.…”

Section: Rapid Assessment Of Wildfire Impactsmentioning

confidence: 99%

Dynamic Data Driven Ensemble for Wildfire Behaviour Assessment: A Case Study

Leo

Rigo

Rodriguez-Aseretto

et al. 2013

IFIP Advances in Information and Communication Technology

Self Cite

View full text Add to dashboard Cite

Abstract. Wildfire information has long been collected in Europe, with particular focus on forest fires. The European Forest Fire Information System (EFFIS) of the European Commission complements and harmonises the information collected by member countries and covers the forest fire management cycle. This latter ranges from forest fire preparedness to post-fire impact analysis. However, predicting and simulating fire event dynamics requires the integrated modelling of several sources of uncertainty. Here we present a case study of a novel conceptualization based on a Semantic Array Programming (SemAP) application of the Dynamic Data Driven Application Systems (DDDAS) concept. The case study is based on a new architecture for adaptive and robust modelling of wildfire behaviour. It focuses on the module for simulating wildfire dynamics under fire control scenarios. Rapid assessment of the involved impact due to carbon emission and potential soil erosion is also shown. Uncertainty is assessed by ensembling an array of simulations which consider the uncertainty in meteorology, fuel, software modules. The event under investigation is a major wildfire occurred in 2012, widely reported as one of the worst in the Valencia region, Spain. The inherent data, modelling and software uncertainty are discussed and preliminary results of the robust data-driven ensemble application are presented. The case study suitably illustrates a typical modelling context in many European areas -for which timely collecting accurate local information on vegetation, fuel, humidity, wind fields is not feasible -where robust and flexible approaches may prove as a viable modelling strategy.

show abstract

Chapter 8 Assessment of Forest Fire Impacts and Emissions in the European Union Based on the European Forest Fire Information System

Cited by 30 publications

References 5 publications

Comprehensive Monitoring of Wildfires in Europe: The European Forest Fire Information System (EFFIS)

Comprehensive Monitoring of Wildfires in Europe: The European Forest Fire Information System (EFFIS)

An Architecture for Adaptive Robust Modelling of Wildfire Behaviour under Deep Uncertainty

Dynamic Data Driven Ensemble for Wildfire Behaviour Assessment: A Case Study

Contact Info

Product

Resources

About