2017

Modeling the impacts of climate change on forest fire danger in Europe

Sectorial results of the PESETA II Project

Camia, Andrea Libertá, Giorgio San-Miguel-Ayanz, Jesús

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This publication is a Technical report by the Joint Research Centre (JRC), the European Commission’s science and knowledge service. It aims to provide evidence-based scientific support to the European policy-making process. The scientific output expressed does not imply a policy position of the European Commission. Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use which might be made of this publication.

Contact information: Name: J.San-Miguel-Ayanz E-mail: jesus.san-miguel@ec.europa.eu

JRC Science Hub https://ec.europa.eu/jrc

JRC105684

PDF ISBN 978-92-79-66259-1 doi:10.2760/768481

Luxembourg: Publications Office of the European Union, 2017

© European Union, 2017

The reuse of the document is authorised, provided the source is acknowledged and the original meaning or message of the texts are not distorted. The European Commission shall not be held liable for any consequences stemming from the reuse.

How to cite: Camia, A., Liberta, G., San-Miguel-Ayanz, J., Modeling the impacts of climate change on forest fire danger in Europe, doi:10.2760/768481

All images © European Union 2017

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TABLEOFCONTENTS

1. Introductionandobjectives ........................................................................... 42. Methods ................................................................................................ 53. Results ................................................................................................ 114. Discussionandconclusion ............................................................................. 15. References .............................................................................................. 2

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1. Introductionandobjectives

This constitutes a sectorial analysis of the PESETA II project of the European CommissionJointResearchCenterintheareaofwildfires.

WildfiresareaseriousthreattoEuropeanforests,andclimateisthemostimportantdrivingfactor affecting wildfire potential over time (Flannigan et al., 2000). Wildfires are anenvironmental, economic and social problem particularly in the southern Europeancountries,wherewildfiresregularlyburnthousandsofhectaresof forestsandother lands.Changesinwildfireregimesmayhavestrongimpactsonnaturalresourcesandecosystemsstability, with consequent direct and indirect economic losses. On the other hand, activeforestmanagementandwildfiremanagementpracticeshavesomepotential tocounteracttheimpactsofachangingclimate.

The FOREST Action hosts the European Forest Fire Information System (EFFIS)1. EFFISsupports wildfire protection efforts in the EU countries and provides the EuropeanCommission services and the European Parliament with information on European forestfires.Thisprojectbuildsoffoftools,modelsanddatasetsavailableinEFFIS.

Fire danger is “a general term used to express an assessment of both fixed and variablefactorsofthefireenvironmentthatdeterminetheeaseofignition,rateofspread,difficultyofcontroland fire impact(s)” (MerrillandAlexander,1987).Firedangerdependsonmanyfactors that canchangeover time (e.g.,weather, fuel load, fuel typeandcondition, forestmanagementpractices,socio-economiccontext…).

Todaymostwildfires in Europe are caused by human activity (i.e., anthropogenic ignitionsources).However,ithasbeenshownthatthetotalburnedareainMediterraneanEurope,and thus the overall impact of forest fires, changes significantly from year to year largelybecauseofweatherconditions(CamiaandAmatulli,2009).ExtremefiredangerconditionsinSouth-easternEurope leadingtomajorwildfireeventshave, inmanycases,beendrivenbyanexplosivemixofstrongwindsandextremelyhightemperatures,followingprolongeddroughtperiods(San-Miguel-Ayanzetal.,2012).

Meteorologically-based fire danger indices evaluate and summarize the fire dangerconsideringcurrentandpastweather.Theseindices,normallyappliedonadailybasis,canalsoprovide seasonal summaries to compare theoverallwildfirepotential of a given yearduetometeorologicalconditions.

Based on these indices,maps of projected change of fire danger in Europe under climatechangearebeingdeveloped.Inaddition,statisticalmodelslinkingmeteorologicalfiredangerand area burned are being developed, to support assessments of the expected impact ofchangedfiredangerconditions.

1 http://effis.jrc.ec.europa.eu/

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2. Methods

1.1 Fire danger assessment

This study uses the Canadian Fire Weather Index (FWI) system (Stocks et al., 1989; VanWagner,1987)as itsmeteorological fire indexbecauseof itswidespreaduse (SanMiguel-Ayanzetal.2003)includinginEuropewhereitisthefiredangerratingsystemusedinEFFIS.

The FWI System has six components rating fuel moisture content and potential firebehaviour in a common fuel type (i.e., mature pine stand) and in no slope conditions. AdiagramshowingtheFWIsystemstructureispresentedinFigure1.

Calculations are based on daily, noontime measurements of air temperature, relativehumidity,wind speed and previous 24-h precipitation. The first three components of FWIconsist of numerical rating values of the moisture content of forest floor layers withdifferentdryingratesandatvariousdepths.Specifically,theFineFuelMoistureCode(FFMC)ratesthemoistureoflitterandotherdeadfinefuelsatthetopofthesurfacefuellayer;theDuff Moisture Code (DMC) rates the moisture of the loosely compacted organic layer ofmoderatedepth;theDroughtCode(DC)representsthemoisturecontentofthedeeplayerofcompactorganicmatter. Thesethreemoisturecodescarrydifferentuseful informationbeingindicatorsofeaseofignitionandflammabilityoffinefuels(FFMC),fuelconsumptioninmedium-sizewoodymaterialandmoderatedufflayers(DMC),fuelconsumptioninlargelogsandamountofsmoulderingindeepdufflayers(DC)(Alexander2008).

The last three FWI codes are fire behaviour indices that score the expected rate of firespread (Initial Spread Index - ISI), the fuelavailable for combustion (BuildUp Index -BUI),andthefirelineintensity(FWI).FWIisthefinalindexthatcombinesISIandBUI(seeFigure1)andrates,theenergyoutputrateperunitlengthofthefirefront,i.e.thefirelineintensitycalculatedaccordingtoByram’sformulation(Byram1959).

An important aspect of the FWI system is that the output only depends on weatherobservations and does not consider differences in fuel types or topography, providing auniform, relative way of rating fire danger through fuel moisture and fire behaviourpotential(VanWagner1987).

Figure1.DiagramoftheFWIsystem(explanationsinthetext)

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Severaluncertaintiesarepresent inprojecting long-termclimatechange impactson forestfires. The current assessment does not take into account changes in fuel conditions(vegetation), ignitions, andhumanactivity e.g. adaptationor causality, thatmay influenceburnt area and thus wildfire impact. Communicating these aspects is of paramountimportancefordefiningtheboundaryconditionsoftheresultsofthisstudy.

1.2 Fire danger and climate change

HighresolutionsclimatechangesimulationsproducedintheframeworkoftheENSEMBLESproject (Van der Linden andMitchell, 2009) using state-of-the-art Global ClimateModels(GCM)andRegionalClimateModels(RCM)werethemaininputtocomputedailyvaluesofFWIcomponents.Dataextracted fromtheoriginalGCM-RCMsimulationswherecorrectedfor themodel biases in the context of PESETA II Project (Dosio, 2011) using themethodsdescribedbyDosioandParuolo (2011).Correctionswhereapplied toprecipitationandairtemperature,whilerelativehumidityandwindspeedwerenotcorrected;aEuropeanwide,high resolution and robust enough dataset of weather observations to perform suchcorrections is currently not available. We have thus considered two options for these 2variables:1)maintainingthevariablesasgivenbythemodeloutputor2)replicatingthe30-yearseries1981-2010ofECMWFERA-Interim(Berrisfordetal.2009)infuturescenarios.

Wecompared intermediate resultsof the twooptionsandconcludedthat thesecondonewas preferred. In fact, Relative humidity andWind speed as given by themodels did notexhibit a clear climate change signalwhile having a strong inter-model offset, introducingunnecessarybiasintheprojections(seeFigure2).

Figure2.MultiannualEuropeanaveragesofWindspeed(left)andRelativehumidity(right)accordingtothreeRCMsimulationsunderA1Bscenario.Theinter-modeldifferencesforthetwovariablesaremuchbiggerthananydetectabletrendovertime.

We considered the SRES emission scenarioA1B, using the three simulations suggested byDosio(2011)asrepresentingaverageandextremerealizationsofclimatechangeunderthegiven scenario. Inaddition,weconsidered three simulation runs for theemission scenarioE1.Table1presentsasummaryofthescenariosandsimulationsultimatelyconsideredare.

Table1.Emissionscenariosandsimulationsusedforforestfiredangermodelling

Scenario Simulations(Institute–RCM–GCM) TemporaldomainA1B KNMI-RACMO2-ECHAM5 1961-2099 METO-HC-HadRM3Q0-HadCM3Q0 1961-2100 DMI-HIRHAM5-ECHAM5 1961-2099E1 MPI-REMO-E1 1961-2099 MPI-REMO-E2 1961-2099 MPI-REMO-E4 1961-2099

.

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ThespatialdomainoftheGCM-RCMsimulationsin

Table1 cover theentire European continent at about25x25Km2horizontal resolution forA1Band50x50Km2forE1.Thetemporaldomaincoversthetimeperiod1961to2100.

Daily FWI components have been computed for the entire spatial and temporal domainsavailable.30-yearaveragesfortheperiods1961-1990and2071-2100areusedtodefinethereference and future periods for impact assessment with the difference between yearlyaverages for theperiods calculated toestimate thepotential impactof climate changeonfiredanger.

MapsofpresentandprojectedfiredangerconditionscoveringEurope,attheresolutionofclimatedataavailabilityandusingtheFWIsystemasbaselineindicatorhavebeenproduced.

1.3 Impact assessment

To assess the impact of a changing fire danger, the statistical relationship of themeteorological fire danger indices with the burned areas was explored using past dataseries.

Burnedareawascomputed fromtheEuropeanFireDatabaseofEFFIS (Camiaetal.2010),the main repository of individual wildfire event records in Europe. The database storesabout2millionwildfireeventsrecordsfrom21countriesinEuropecoveringavariabletimeperiod depending on the country. In southern Europe, available data start from 1985.SummarisedinformationfromthedatabaseisavailablethroughEFFIS2.

Daily FWI components have been computed from the surface fields of the ECMWF ERA-Interimarchive (Berrisfordetal.2009),dailydata1981to2010withapproximately79kmspatialresolution.

ThemodellingeffortsarebeingconcentratedintheMediterraneanEurope,byfarthemostaffectedregion intermsofburnedareaand impactof forest fires. Inaddition, inthisareatheavailablewildfirehistoricalseriesismuchlongerthanelsewhere.

GriddedFWIcomponentshavebeenspatiallyaveragedover5southernEuropeancountrieswheremost of theburned area is located (over 85%of the total burned area in Europe):Portugal (PT), Spain (ES), Southern France (FRMed), Italy (IT) and Greece (GR). This studyareaispresentedinFigure3.

2 http://effis.jrc.ec.europa.eu/fire-history

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Figure3.Countriesandsub-countryregionofFranceforspatialaggregationofFWIandburnedarea

historicalseriestosupportimpactassessmentwithstatisticalmodelling.

ThemodellingapproachtocalculateburnedareabasedontheFWIprojectionswasinitiallybasedon simpleandmultiple linear regression techniques.After the firstattempts, itwasdecidedtobasethestatisticalmodellingonMultivariateAdaptiveRegressionSpline(MARS)techniques (Friedman1991). This techniquewasconsideredmoreappropriate than linearregressionbecause thebuiltpiecewise functionsallowamore robustextrapolationof theresults outside the original data range. This is desirable in view of the predictions inprojected climate scenarios. This technique was already successfully applied in similarstudiesperformedelsewhere(Balshietal.2009).

Following the modelling results and combining them with the projected fire danger, weestimated changes in burned area under changing climate conditions for the selectedEuropeanMediterraneancountries.

Theeconomicimpactoftheprojectedburnedareaunderfuturefiredangerconditionshasbeen carried out following themethodology inMavsar et al. (2011) based on restorationcost. The method moves from the idea that the cost of replacing goods and servicesprovided by a natural resource can offer an estimate of the value of the resource. Theunderlyingassumptionisthat,ifpeopleincuracosttorestoretheservicesofanecosystem,then the service must worth at least what people has paid to replace them. A similarapproach is applied for the analysis of financial damages to infrastructures or to theeconomic evaluation of environmental damages following natural disasters (e.g. storms,floods).

ThedamagevalueDVforagivenlocationisestimatedusing:

DV=DL*RC*(1+r)t

Where:

DL=DamageLevel

RC=RestorationCost

r=Discountrate

t=Restorationperiod

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Thedamagelevelisafunctionofthefireseveritylevelinthelandcoveraffected,withthreeclassesforeseen(low,medium,high)

The restoration cost of forest and otherwooded landswas estimated using as proxy theafforestationpremiumfromtheEURuralDevelopmentProgramsasdefinedbynationalandregionalauthorities.Theafforestationcostsincludefullplantingandmaintenancecostsforthefirst5yearsdiscountedattheinitialyear,withadiscountrateof3%.Baseyearforthepriceswas2009anddatawereadjustedtotheaverageEUPurchasingPowerParities(PPP).

Therestorationcostsof landcovertypesotherthanforestandotherwoodedlandswherederivedandadjustedfromtheliterature.

Thediscountratewassetto3%afterthecriticalanalysisofthesocialdiscountratesappliedto Cost-Benefit Analysis of investment projects in EU, given that there is no official socialdiscountrateforforestryorenvironmentalprojectsattheEUlevel.

Therestorationperioddependsontheageoftheburnedforest.Fortheassessmentofthedamage cost, threeage classeswere consideredwith corresponding reference restorationtimes:0-30years(t=15);30-50years(t=40);>50years(t=60).

The restoration period of other land cover types was set to 1 year (e.g. grasslands,heathlands,annualcrops)or5years(e.g.otherwoodedlands,multi-annualcrops).

Usingthedamagevalueformulaappliedtodatasuchascurrentlandcovermap,forestageandadministrativeboundaries,amapofpotentialeconomic impactof fires inEuropewasderived for different fire severity levels (Oehler at al., 2012). The map provides anassessmentoftheexpecteddamagevalueperhaforeach250x250m2pixelunderdifferentfireseverityscenarios(Figure4).

Figure4.MapofestimatedwildfiredamagevalueinEuropeaccordingtolow,mediumandhighfire

severityscenarios(fromOehleratal.,2012.Modified)

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FortheassessmentoftheeconomicimpactoftheprojectedburnedareaunderfuturefiredangerconditionsweassumedamediumfireseveritylevelthroughoutsouthernEuropeandaveraged the restoration cost for each of the 5 regions (PT, ES, FRMed, IT and GR). Theeconomic impact was than computed as product of the projected burned area and theaveragerestorationcostofeachregion.

1.4 Adaptation options

Forest firemitigationoptionsvaryfromcountrytocountryandfromregiontoregion,andno single comprehensive database of wildfire mitigation measures exists in Europe. Inaddition,fundingofmeasuresrelatedtowildfiremitigationisdifferentatEuropean,countryandregionallevel,andoftenscatteredamongdifferentadministrations.

ThismakesitdifficultsettingaEuropeanpictureofexpenditureonwildfirepreventionandthe observed effects at European level. Therefore, modelling the effect of adaptationmeasures is challengingwithouthavingbaseline information, evenmoredifficult for long-termmulti-decadalscenarios.Roughestimatesof forestdamagesduetofiresexist forthecurrentclimaticconditions.

Future climate scenarios foresee the increase in drought periods in the Mediterraneanregion and the worsening of fire danger conditions, which are expected to affect firefrequencyandburntareasintheregion,severityoffireeventsandtheirpotentialimpact.

Adaptationofwildfiremanagementstrategiestoachangingclimateimpliesevaluatingandimplementinga rangeofoptionsandactivitieswithan integratedapproach.There isnoageneralized approach applicable, since fire environments and socio-economic contextchangesignificantlyacrossEurope,aswellastheexpectedimpactofforestfires.

Although no quantitative data are yet available in this respect for Europe, it is widelyrecognizedthatenforcingandoptimizingfiresuppressioneffortsisnotasolutionsincefirepreventionactivitiesareatleastequallyimportant.Weconsiderthatthisconcepthastobestrengthenedfacetoachangingclimate.Alreadytoday,whenextremeweatherconditionsoccur in areas where little or no fire prevention has been carried out (e.g., fire hazardreduction,fueltreatment,prescribedfires),thereisnosuppressioneffortsufficienttostopacatastrophicspreadingfire.

Inthissense,webelievethatfireexclusionpoliciesareariskyoptionfortheMediterraneanregions, while on the contrary integrated management and prescribed burning areimportantcomponentsoffuturefiremanagement.

Climate change will alter fire regimes bringingmore severe burning conditions andmorefrequentwildfires.Alteredfireregimeswillhaveinturnanecologicalimpactaffectingforestcomposition, structure and biomass storage, with a feed-back effect on the fireenvironment.Therefore, firemanagementstrategiesadaptedtoachangingclimateshouldbe integrated with forest management, because of the ecological impact of future fireregimesonforests,andfortheeffectsofforestmanagementonwildfirehazard.

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3. Results

1.5 Fire Danger

MapsofpresentandprojectedfiredangerconditionsinEurope,attheresolutionofclimatedataavailabilityandusingtheFWIsystemasbaselineindicatoraregiveninFigures5to8.

The maps in Figure 5 show annual averages of FWI over 30 years’ periods, 1961-1990(reference)and2071-2100forbothemissionscenariosA1BandE1.

The fire dangermaps in the reference period 1961-1990 are quite similar confirming theeffectiveness of corrections formodels biases.Maps of end of the century projected firedanger show marked differences among A1B simulations, while are more similar for E1simulations.

Figure 6 presents maps of difference between 2071-2100 and 1961-1990 annual FWIaverages (referred to as climate change signal). Here it is evenmore clear that the threesimulations driven by A1B emission scenario exhibit different spatial patterns for theprojectedchangeinfiredanger.

Specifically,theDMIsimulation,whichistheoverallcolderandwettercaseofthethreeA1Bruns(seeDosio2011fordetails),resultedalmosteverywhereinarelativereductionoffiredanger increase versus the other A1B simulations, with larger differences in the Easternboundaryofthemodelledarea.Furthermore,inalargeareaofcentralandnorthernEuropefiredangerisprojectedtodecrease.

In the METO simulation, the warmer and drier or the three runs, fire danger increasessharply in Eastern Europe relative to the other A1B scenarios. It also increases in South-westernEuropebutnotasmuchasintheKNMIsimulation.

ThesimulationsdrivenbytheE1emissionscenarioshowoveralllessincreaseinfiredangerandlesspronounceddiversificationinthespatialdistributionofchangeinthethreeruns.Allthreesimulationsexhibitastronger increase in firedanger intheSouthernregions,withamorepronouncedchangeintheE1,especiallyintheSouth-west,lesspronouncedinE2andintermediateinE4.Inthelattersimulation,anabsolutereductionoffiredangerispredictedin Western Russia, while a reduction is also predicted in Ireland according to the E2simulation.

ForabetterfocusonSouthernEuropethesamevariable,i.e.FWIdifferencebetween2071-2100and1961-1990, ispresentedatthegreaterscaleinFigures7and8forscenariosA1BandE1respectively.

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Figure5.Firedangermaps(annualFWIaverages)inreference(1961-1990)andfuture(2071-2100)

periods,accordingtoselectedsimulationsunderA1BandE1emissionscenarios.

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Figure6.Climatechangesignal,i.e.,arithmeticdifferencebetweenfutureandreferenceperiodsFWI

averagesaccordingtoselectedsimulationsunderA1BandE1emissionscenarios.

WithreferencetoFigure7(A1Bscenario),accordingtotheDMIsimulationarelativesharperincreaseof firedanger versus theother simulations is visible in the southernBalkanarea,GreeceandsouthernItaly.TheincreaseoffiredangerintheKNMIsimulationismuchmorepronounced in the Western study regions (Portugal, Spain and southern France). METOsimulationexhibitsan increase in firedangerwhichoverall lookshigher than theDMIandDMI,thoughmoredistributedthroughoutthestudyarea.

DifferencesintheE1simulationsaresubtler(Figure8).Arelativeoverallhigher increaseispredictedinE1,especiallyinSpain,PortugalandsouthernFrance.E2simulationresultsinarelative lower increaseof firedangeras compared to theother twoof the sameemissionscenario, with significant changes restricted to the more southern regions. E4 simulationpredicts changes in fire danger conditions somewhere in the middle of the E1 and E2extremes.

CountryaveragesofFWI(referenceandfuture)aregiveninTable3ofnextsection,allowinga quantitative appreciation of the estimated evolution of fire danger in the countries ofSouthernEurope.

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Figure7.ClimatechangesignalinSouthernEuropeaccordingtoselectedsimulationsunderA1B

emissionscenario.

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Figure8.ClimatechangesignalinSouthernEuropeaccordingtoselectedsimulationsunderE1

emissionscenario.

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1.6 Impact assessment

Burned area as a function of fire danger indiceswas exploredwithMARS; resulting basalfunctionsandRsquaredaregiveninTable2.ModelsestimatethelogofthemonthlyburnedareausingasinputmonthlyaveragesofFWIandISIovereachregion.

The historical series used to build themodelswas from1985-2010 for ES, FRMed and IT,1981-2010 forPTand1983-2000 forGR.The timeseries forGreecewasstoppedon2000becausewildfiredataof the lastdecadewasconsiderednotreliableenoughformodellingpurposes.

We focused the models on the main fire season in the Mediterranean, i.e. the summermonthsofJunethroughOctober(86%oftheannualburnedareaintheregionhappensonaveragefromJunethroughOctober).

Region Equation R2 Cross validation

R2

Portugal 7.206315 + 0.2875863 * max(0, FWI - 12.95) - 0.5236354 * max(0, 12.95 - FWI) - 0.5736034 * max(0, ISI - 3.76)

0.80 0.74

Spain 7.669756 + 0.1504978 * max(0, FWI - 14.59) - 0.4332947 * max(0, 14.59 - FWI) + 0.6127046 * max(0, 5.78 - ISI)

0.68 0.61

France Med

6.283384 - 0.4090681 * max(0, 12.91 - FWI) + 0.3973366 * max(0, FWI - 15.43) - 1.10153 * max(0, ISI - 5.62)

0.69 0.62

Italy 6.886724 + 0.2024325 * max(0, FWI - 6.04) - 0.761246 * max(0, 6.04 - FWI)

0.80 0.75

Greece 8.237785 - 0.2898507 * max(0, 18.27 - FWI) + 0.2992717 * max(0, FWI - 24.75) - 0.4916414 * max(0, ISI - 6.02)

0.79 0.72

Table2.MultivariateAdaptiveRegressionSplinemodels.Explainedvariableisthelogarithmofthemonthlyburnedarea;predictorsaremonthlyaveragesovertheregion.

InTable3wepresentaverageobservedandprojectedFWIvaluesaccordingtothedifferentsimulations. In A1B emission scenario the average overall change is in the order of 30%increase,lesspronouncedtowardsEast,withtheexceptionofDMIwherealargeincreaseispredictedinGreece.InE1theoverallexpectedincreaseisaround20%,evenlysharedacrosssoutherncountries.

ResultsoftheimplementationoftheMARSmodelontheprojectedfiredangerscenariosaregivenintables4and5.Notethatwhatisforbrevityreportedinthetablesasannualaverageactuallyreferstothemonthsofthemainfireseason(JunetoNovember).

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With reference to themodelled burned area (Table 4), in the A1B emission scenario theaverage increase goes from 72% and 93% in the Iberian Peninsula, to 184% increase inSouthernFrance,withItalyandGreecehaving121%and112%increasesrespectively.

ThesharperriseintheburnedareaascomparedtotheFWIchangereflectstheexponentialnatureoftherelationship.

The projected increase of burned area in the E1 scenario are accordingly much lessremarkable,goingfrom26%and35%inPortugalandSpainrespectivelyto49%and71%inGreeceandItalyrespectively.SouthernFranceinthiscaseremainsinthemiddlerangewithanestimatedincreaseof35%.

Despite the relative less remarkablepercentage increase inhectaresburned in the South-western countries, the majority of the projected burned area would still remain in theIberianPeninsulainbothemissionscenariosreflectingthehigherbaselinevalues.

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Projected2071-2100FWI(A1B) Projected2071-2100FWI(E1)

Region

ObservedaverageFWI KNMI METO DMI

AverageA1B

A1BAverage

change(%) MPI-E1 MPI-E2 MPI-E4E1

Average

E1Average

change(%)Portugal 21.2 28.9 28.3 26.9 28.0 32% 26.1 25.4 26.0 25.8 22%Spain 21.4 28.4 27.9 26.6 27.6 29% 25.9 25.8 25.7 25.8 21%FranceMed 15.1 21.0 22.0 17.8 20.3 35% 18.0 17.4 18.0 17.8 18%Italy 13.5 17.2 17.6 17.0 17.2 28% 16.8 16.0 16.8 16.5 22%Greece 22.5 26.0 26.8 29.9 27.6 22% 26.4 26.3 26.9 26.5 18%

Table3.ObservedandprojectedfiredangerassessedwithFWIannual(JunetoNovember)averagesovertheregions.

Projected2071-2100annualburnedarea-ha(A1B) Projected2071-2100annualburnedarea-ha(E1)

Region

Observedannualburnedarea(ha) KNMI METO DMI Average

A1BAverage

change(%) MPI-E1 MPI-E2 MPI-E4 AverageE1Averagechange(%)

Portugal 106,874 237,028 211,873 169,894 206,265 93% 144,552 138,786 142,668 142,002 33%Spain 133,323 253,039 225,394 210,702 229,712 72% 165,415 168,046 172,066 168,509 26%FranceMed 16,442 52,406 60,440 27,290 46,712 184% 22,301 19,996 24,085 22,127 35%Italy 67,976 151,532 148,916 150,153 150,200 121% 123,240 102,234 123,693 116,389 71%Greece 53,030 93,290 99,371 145,089 112,583 112% 75,301 74,060 87,575 78,979 49%

Table4.ObservedandprojectedburnedareaassessedwithMARSmodels(JunetoNovember)

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Projected2071-2100annualcost(kEURO) Projected2071-2100annualcost(kEURO)

Region

Averagereconstructioncost(EURO/ha)

Currentestimatedannualreconstruction

cost(kEURO) KNMI METO DMIAverageA1B MPI-E1 MPI-E2 MPI-E4

AverageE1

Portugal 6,117 653,738 1,449,874 1,296,004 1,039,219 1,261,699 884,206 848,936 872,682 868,608Spain 3,422 456,205 865,849 771,252 720,979 786,027 566,016 575,018 588,775 576,603FranceMed 10,306 169,461 540,108 622,916 281,254 481,426 229,836 206,085 248,230 228,050Italy 6,657 452,546 1,008,820 991,403 999,637 999,953 820,470 680,619 823,484 774,858Greece 4,505 238,879 420,231 447,628 653,565 507,141 339,200 333,608 394,492 355,767

Table5.Currentandprojectedreconstructioncostsintheregions(mainfireseason,JunetoNovember)

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InTable5wepresenttheexpectedeconomicimpactestimatedusingthereconstructioncostmodelapplied to the projected burned area, assuming an intermediate average fire severity level. Therelative intra-country projected cost increase is obviously equivalent to the relative burned areaincrease. In termsof inter-countrycomparison,notethatdifferencesarealsodrivenbythevariedaveragereconstructioncostsinthecountries,whicharealsoduetothedifferentagestructuresofforeststands(asthecostalsodependsontimeneededtorecover),whichmaybedifferentattheendofthiscentury.

TheaverageannualcostforallthesouthernEuropeancountriesconsideredisprojectedtoincreasefromthecurrentestimated1,971M€peryearto4,036M€underemissionscenarioA1Band2,804M€underemissionscenarioE1.InbothscenariosPortugalisprojectedtoremainthecountrywiththehighestoverallcostsduetoforestfires,followedbyItaly(whilecurrentlyisfollowedbySpain).

Asmentioned in the introductorysectionseveralother factors relevant forwildfiresarenot takenintoaccountinthisapproachsuchaschangesinfuelconditionsandhumanactivities.Thesefactorsmayalsoinfluencetheburnedareaandthuswildfireimpact.Stressingtheseaspectsisofparamountimportancefordefiningtheboundaryconditionsoftheresultsofthisstudy.

4. DiscussionandconclusionIn thiswork,wepresenta firstattempt toquantify theestimatedeconomic impactofwildfires insouthernEuropeancountries.Theassessmentisdoneforcurrentyearsandforendofthecenturyprojections,accordingtotwodifferentclimatechangescenariosrespectivelydrivenbyA1BandE1atmosphericemissionsscenarios.

TheassessmentsarebasedonmeteorologicallybasedindicesundertheassumptionthatthemaindriveroffireregimesinMediterraneanEuropeisweather.Howeverotherimportantfactorssuchasfuel (biomass) availability and conditions, ignitions sources and human activity (adaptation orcausality) affect fire activity and thuswildfire impact. This aspect has not been considered in thestudy.

The damage value based on the restoration cost approach underestimates the actual economiclosses.Costswhicharenotaccountedforarethoseincurredbecauseofthemissedbenefitsuntiltheforestisrestored(i.e.,temporarylossofecosystemservices),orthedirectcostoffirefighting.

Other associated costs related to indirect effects ofwildfires on aspects such as humanhealthorsecondaryeffectsonothernaturalhazards,suchase.g.increasedpotentialforflooding,arealsonotaccountedfor.

Aconsistent,significantlyhigherimpactofscenarioA1Bisobserved,withanaverage97%increaseof burned area in southern Europe projected at the end of the century, which is on average 2.4timesbiggerthantheburnedareaincreasepredictedwithE1scenario.

ResultsvarygreatlyacrossEurope,withmarkeddifferencesamongmodelrunswithinthescenarios.The use of ensemble models is therefore essential in this respect, as well as site specificdifferentiationofadaptationstrategies.

Fire management strategies adapted to a changing climate should be integrated with forestmanagement and strengthen fire prevention activities such as targeted fuel treatments andprescribedfires.

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5. References

Alexander,M.E.,2008.ProposedrevisionoffiredangerclasscriteriaforforestandruralareasinNewZealand. Christchurch: National Rural Fire Authority, Wellington, in association with theScionRuralFireResearchGroup.

BalshiMS,McGuireAD,Duffy P, FlanniganM,Walsh J,Melillo J, 2009.Assessing the responseofarea burned to changing climate in western boreal North America using a MultivariateAdaptiveRegressionSplines(MARS)approach.GlobalChangeBiology,15,578-600.

Berrisford,P.,D.Dee,K.Fielding,M.Fuentes,P.Kallberg,S.KobayashiandS.Uppal,2009.TheERA-InterimArchive.ECMWF,ShinfieldPark,Reading.ERAReportSeries1(1):16

Byram, G.M., 1959. Combustion of forest fuels. In K.P. Davis (Ed.), Forest Fire: Control and Use.McGraw-Hill,NewYork,pp.155-182.

Camia, A., Amatulli, G., 2009. Weather factors and fire danger in the Mediterranean, in: EarthObservation of Wildland Fires in Mediterranean Ecosystems (E. Chuvieco, ed.), Springer-Berlag,BerlinHeilderberg,pp.71-82.

Camia, A., Durrant Houston, T., San-Miguel-Ayanz, J., 2010 The European Fire Database:Development, Structure and Implementation. In: D.X. Viegas (Ed.) Proceedings VIInternationalConferenceonForestFireResearch,Coimbra,Portugal.

ChristensenJ.H.,ChristensenO.B.,2007.AsummaryofthePRUDENCEmodelprojectionsofchangesinEuropeanclimatebytheendofthecentury.Clim.Change81:7–30

DosioA.,2011.AnalysisoftheENSEMBLEShighresolutionA1Bclimatechangesimulations.ReportofPESETAIIproject,p.26.

Dosio, A., Paruolo P., 2011. Bias correction of the ENSEMBLES high-resolution climate changeprojections foruseby impactmodels: Evaluationon thepresent climate. J.Geophys.Res.116(D16):D16106.

Flannigan,M.D.,Stocks,B.J.,Wotton,B.M.,2000.ClimateChangeandForestFires,TheScienceoftheTotalEnvironment262:221-229.

Friedman JH (1991) Multivariate adaptive regression splines. The Annals of Statistics, 19, 1-141.Merrill, D. F., Alexander, M. E., 1987. Glossary of forest fire management terms,National Research Council of Canada, Canadian Committee on Forest Fire Management,Ottawa,Ontario.

Mavsar R., Pettenella D., San-Miguel J., Camia A., 2011. Development of a methodology for theanalysis of socio-economic impact of forest fires in Europe. In: Proc. 5th InternationalWildfireConference,SunCity,SouthAfrica,9-13May2011.

OehlerF,OliveiraS,BarredoJI,CamiaA,SanMiguelAyanzJ,PettenellaD,MavsarR,2012.AssessingEuropean Wildfire Vulnerability. Geophysical Research Abstracts, Vol. 14, EGU2012-9452,EGUGeneralAssembly2012.

San-Miguel-Ayanz,J.,Carlson,J.D.,Alexander,M.,Tolhurst,K.,Morgan,G.,Sneeuwjagt,R.,Dudley,M., 2003. Current methods to assess fire danger potential, in: Wildland fire danger

estimation and mapping - The Role of Remote Sensing Data (E. Chuvieco, ed.), WorldScientificPublishing,Singapore,pp.22-61.

San-Miguel-Ayanz,J.,MorenoJ.M.,CamiaA.,2012AnalysisoflargefiresinEuropeanMediterraneanlandscapes: Lessons learntandperspectives.ForestEcologyandManagement.Manuscriptacceptedforpublication.

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Stocks,B.J.,Lawson,B.D.,Alexander,M.E.,VanWagner,C.E.,McAlpine,R.S.,Lynham,T.J.DubéD.E.,1989.TheCanadianForestFireDangerRatingSystem:Anoverview.TheForestryChronicle65,258-265.

Van der Linden, P. andMitchell, J.F.B. (Eds.), 2009. ENSEMBLES: climate change and its impacts:Summaryof researchand results from theENSEMBLESproject,MetOfficeHadleyCentre,FitzRoyRoad,ExeterEX13PB,UK.160pp.

VanWagner, C. E., 1987. Development and structure of the Canadian Forest FireWeather IndexSystem,CanadianForestryService,Ottawa,Ontario,pp.37.

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doi:10.2760/768481

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