Shifts in wind energy potential following land-use driven vegetation dynamics in complex terrain

2018·4.5 MB·English

by  Jiannong Fang

ScienceoftheTotalEnvironment639(2018)374–384
ContentslistsavailableatScienceDirect
Science of the Total Environment
journal homepage: www.elsevier.com/locate/scitotenv
Shifts in wind energy potential following land-use driven vegetation
dynamics in complex terrain
JiannongFanga,AlexanderPeringerb,Mihai-SorinStupariuc,d,IleanaPaˇtru-Stupariud,e,
AlexandreButtlerf,g,h ,FrancoisGolayi,FernandoPorté-Agela,*
aWindEngineeringandRenewableEnergyLaboratory(WiRE),SchoolofArchitecture,CivilandEnvironmentalEngineering(ENAC),ÉcolePolytechniqueFédéraledeLausanne(EPFL),
Lausanne1015,Switzerland
bLandscapeEcologyandResourcesManagement,FacultyFLUS-HKT,Nuertingen-GeislingenUniversity,Germany
cFacultyofMathematicsandComputerScience,UniversityofBucharest,Romania
dTransdisciplinaryResearchCentreLandscape-Territory-InformationSystems(CeLTIS),InstituteofResearchofUniversityofBucharest(ICUB),Romania
eDepartmentofRegionalGeographyandEnvironment,FacultyofGeography,UniversityofBucharest,Romania
fEcologicalSystemsLaboratory(ECOS),SchoolofArchitecture,CivilandEnvironmentalEngineering(ENAC),ÉcolePolytechniqueFédéraledeLausanne(EPFL),Lausanne1015,Switzerland
gSwissFederalInstituteforForest,SnowandLandscapeResearch(WSL),SiteLausanne,Station2,Lausanne1015,Switzerland
hLaboratoiredeChrono-Environnement,UMRCNRS6249,UFRdesSciencesetTechniques,UniversitédeFranche-Comté,16routedeGray,F-25030Besançon,France
iGeographicInformationSystemsLaboratory(LASIG),SchoolofArchitecture,CivilandEnvironmentalEngineering(ENAC),ÉcolePolytechniqueFédéraledeLausanne(EPFL),Lausanne
1015,Switzerland
H I G H L I G H T S G R A P H I C A L A B S T R A C T
• Modelingframeworkcouplinglarge-
eddy simulation of boundary layer,
vegetationdynamicsanddigitalele-
vationmodel.
• Wind energy potential in complex
mountainterrainstronglydependson
land cover changes driven by land-
usechange.
• Trade-offbetweenrenewableenergy
production and biodiversity under
agro-andforestpoliciesinlandscape
development.
• The framework could support the
long-term planning of wind energy
projects under land-use and climate
changescenarios.
A R T I C L E I N F O A B S T R A C T
Articlehistory: Manymountainousregionswithhighwindenergypotentialarecharacterizedbymulti-scalevariabilities
Received30November2017 ofvegetationinbothspatialandtimedimensions,whichstronglyaffectthespatialdistributionofwind
Receivedinrevisedform30April2018 resourceanditstimeevolution.Tothisend,wedevelopedacoupledinterdisciplinarymodelingframework
Accepted6May2018 capableofassessingtheshiftsinwindenergypotentialfollowingland-usedrivenvegetationdynamicsin
Availableonline26May2018
complexmountainterrain.ItwasappliedtoacasestudyareaintheRomanianCarpathians.Theresults
showthattheoverallshiftsinwindenergypotentialfollowingthechangesofvegetationpatterndueto
Editor:R.Ludwig
differentland-usepoliciescanbedramatic.Thissuggeststhattheplanningofwindenergyprojectshouldbe
*Correspondingauthor.
E-mail address:fernando.porte-agel@epfl.ch(F.Porté-Agel).
https://doi.org/10.1016/j.scitotenv.2018.05.083
0048-9697/
J.Fangetal. /ScienceoftheTotalEnvironment639(2018)374–384 375
Keywords: integratedwiththeland-useplanningataspecificsitetoensurethattheexpectedenergyproductionof
Atmosphericboundarylayer theplannedwindfarmcanbereachedoveritsentirelifetime.Moreover,thechangesinthespatialdistri-
Modelcoupling butionofwindandturbulenceunderdifferentscenariosofland-usearecomplex,andtheymustbetaken
Large-eddysimulation intoaccountinthemicro-sitingofwindturbinestomaximizewindenergyproductionandminimizefatigue
Digitalelevationmodel loads(andassociatedmaintenancecosts).Theproposednewmodelingframeworkoffers,forthefirsttime,a
Wood-pasturemodel
powerfultoolforassessinglong-termvariabilityinlocalwindenergypotentialthatemergesfromland-use
Landcover
changedrivenvegetationdynamicsovercomplexterrain.Followingapreviouslyunexploredpathwayof
cause-effectrelationships,itdemonstratesanewlinkageofagro-andforestpoliciesinlandscapedevelop-
mentwithanultimatetrade-offbetweenrenewableenergyproductionandbiodiversitytargets.Moreover,
itcanbeextendedtostudythepotentialeffectsofmicro-climaticchangesassociatedwithwindfarmson
vegetationdevelopment(growthandpatterning),whichcouldinturnhavealong-termfeedbackeffecton
windresourcedistributioninmountainousregions.
©2018ElsevierB.V.Allrightsreserved.
1. Introduction conditions(Moengetal.,2007;Carvalhoetal.,2013;Gopalanetal.,
2014;Castroetal.,2015;Bilaletal.,2016;SanzRodrigoetal.,2017).
As a safe and renewable non-polluting resource, wind energy Amicroscalemodeltypicallyadoptsadomainsizerangingfrom
has been undergoing a large-scale development worldwide in the 1to20kmandagridresolutionrangingfrom1to100m.Themajor-
pastdecade.Withthecontinuousadvanceofwindpowertechnol- ityofmicroscalemodelsapplyComputationalFluidDynamics(CFD)
ogyanddecreaseofitseffectivecosts,itisforeseenthatwindpower techniquestosolvetheNavier-Stokes(N-S)equationsgoverningtur-
penetrationlevelswillcontinuetoincreaseinmanycountries.Accu- bulentflowsintheatmosphericboundarylayer(ABL)withstationary
ratewindenergypotentialassessmentsarecrucialtothesuccessful forcing(i.e.mesoscaletendenciesareneglected).Amongthevarious
developmentofwindpowerplants.Knowingthespatialdistribution CFD methods, Large-Eddy Simulation (LES) based on the spatially-
ofwindandturbulenceataspecificsiteguidesthewindpowerplan- filteredN-Sequationshasbecomeanimportanttoolforsimulating
nerstoselectthewindfarmsite,evaluatetheproductionandcost, turbulentflows.LEShasbeensuccessfullyvalidatedandappliedfor
and optimize the micro-siting of wind turbines to maximize wind thestudyofABLflowsoverhomogeneousterrain.Thedevelopment
energyproductionandminimizefatigueloads. andapplicationofLES-basednumericalmodelsforwindfieldsim-
There are two main methods for assessing wind energy poten- ulationsovercomplexterrainisontherise(Wanetal.,2007;Silva
tial: one is based on observational data and the other relies on Lopesetal.,2007;Wood,2000;WanandPorté-Agel,2011;Diebold
numericalwindfieldsimulations.Inpractice,thetwomethodsare etal.,2013;BechmannandSørensen,2011;ChengandPorté-Agel,
often combined to produce wind resource maps which, in some 2013; Mirocha et al., 2014; Liu et al., 2016; Fang and Porté-Agel,
countries/regions such as Canada, USA, and Europe, are collected 2016;ShamsoddinandPorté-Agel,2017).ToapplyLEStocomplex
togetherasnationalwindatlases.Variouswindflowmodelingsys- heterogeneousterrain,however,thereisaneedforcouplingitwith
temstargetedatdifferentscaleshavebeendevelopedandappliedto high-resolutiondataoftherelevantsurfaceproperties,namelysur-
windresourceassessment.Examplesofmesoscalesystemsinclude faceelevation(topography)andlandcover.Conventionally,Monin
the widely used Weather Research and Forecasting (WRF) model ObukhovSimilarityTheory(MOST)isusedassurface-layerscheme
(Skamarocketal.,2008)developedforbothresearchandoperational torelatevegetationwiththeturbulentfluxesinthenear-wallgird
purposesandtheoperationalConsortiumforSmall-scaleModeling cellsbyassigningcorrespondingaerodynamicsurfaceroughnesses,
(COSMO)model(Nolanetal.,2014).Mesoscalemodelingtypically while obstacles and tall canopies are explicitly modeled in CFD
coversadomainsizeof107mto105mwithagridresolutionof105m when they are directly influencing the sites of interest. So far, in
to103m.Generally,severallevelsofphysicaldownscalingbydomain numericalwindfieldsimulations,vegetationwastreatedasastatic
nesting are required within these ranges to overcome the compu- surfacepropertyliketopographyandlittleresearchhasbeendone
tational complexity. Initial and boundary conditions are obtained tostudythelong-termimpactsofvegetationdynamicsonlocalwind
from large scale weather models like the Global Forecast System conditionsandtheirimplicationsonwindenergyprojectplanning.
(GFS)producedbytheNationalCentersforEnvironmentalPrediction Theneedtoaddresslandscapestructuraldynamicscanbederived
(NCEP).ManystudieshavevalidatedmesoscalemodelssuchasWRF from the 2009 European Environment Agency Report concerning
andapplythemforwindresourceassessmentandwindpowerfore- Europe’sonshoreandoffshorewindpotential.Amongthestepsto
cast.Sofarsatisfactoryresultshavebeenreportedmainlyforcases beaddressedinthefuture,thereportexplicitlymentionstheneces-
wheretheterrainfeaturesaresimple.Forexample,Hahmannetal. sityofamoredetailedanalysisofareaswheremodelpredictionand
(2015)showthatWRFisabletopredicttheannualmeanwindspeed observed wind velocities differed most, notably mountainous and
within the measurement uncertainty in the North and Baltic Seas. forestedareas.Furthermore,theanalysisofspecificvulnerabilitiesof
Itisworthmentioningthatglobalwinddata,basedonWRFisnow biodiversity related to specific bird and other species and of land-
available(https://www.globalwindatlas.info/).However,incomplex scapeswasrequested,aswellastheexplicitconsiderationofsuch
terrain,mesoscalemodelstendtoyieldconsiderableerrorsforboth vulnerabilitiesinmappingwindenergypotentialinEurope.
wind speed and wind direction predictions (Jiménez and Dudhia, Inmountainousregions,ahighpotentialforwindturbinesexists,
2012,2013).Thisismainlyduetothefactthateffectsoflocaltopog- especiallyathigheraltitudesandonthecrestsofthesemiddle-range
raphyandlandcoverareinsufficientlycapturedatrelativelycoarse mountains. For example, the Juvent wind farm in the Jura moun-
grid resolutions in mesoscale models. To tackle this problem, fur- tains of Switzerland has 16 wind turbines installed on two hills,
ther downscaling to microscale is required. Recently, the coupling MontSoleil(alt.1291m)andMontCrosin(alt.1268m),wherethe
of mesoscale and microscale models has become a strong area of land cover varies from grasslands to forests. Unfortunately, these
researchforthenextgenerationofwindresourceassessment.Thisis regionsarealsooftenverysensitivetoturbineinstallations,because
becauseitcanbetterhandlethemulti-scalenatureoftheproblemby of their high value in terms of biodiversity and landscape scenery
simulatingthevariousphysicalprocessesdirectlyandcaptureboth (Bergmeieretal.,2010;Etienne,1996).Duringthepastdecades,at
largescaleweatherandmicroscaletopographyeffectsonlocalwind higheraltitudestherewasadrasticland-useandlandcoverchange,
376 J.Fangetal. /ScienceoftheTotalEnvironment639(2018)374–384
drivenbychangeinagro-policy.Moreemphasiswasputonproduc- terrain.Theterrainmodelingblockconsistsofveryhighresolution
tivelowlandsandsubsidiesforlow-intensityland-useformsinthe DigitalElevationModels(DEMs)linkedwithsuitable,wavelet-based
mountains decreased (Perrenoud et al., 2003). This resulted in the generalizationandfilteringtechniquestoprovidedescriptionofthe
reductionofland-useintensity,ifnotabandonment,andledtoforest landsurfacepropertiesattherelevantscales.Thevegetationmod-
encroachmentonformermountainpastures.Historicalphotographs eling block adopts the dynamic landscape and vegetation model
showthatthicketsestablishedonlargepastureareaswithin30years (WoodPaM) to develop a set of alternative tree cover mosaics for
(BarbezatandBoquet,2008;Dovcˇiaketal.,2008). arangeofland-useintensitiesintheSouthernCarpathianpasture-
We address the Southern Carpathian mountains in Romania, woodlands.Throughtheuseofaterrain-followingcoordinatetrans-
wherethisconflictisdramaticbecauseofmajorshiftsinagro-policy formationandsimilarity-theory-basedwallmodeling,LESiscoupled
and land-use practices in the post-communist period (Maruska et withhigh-resolutioninformationoftherelevantlandsurfaceprop-
al.,2012).AsinmanyEuropeanmountainousregions,woodedpas- ertiesobtainedfromtheterrainandvegetationmodelingblocks.The
tures are a traditional land use form and an important part of proposed modeling framework is applied to a case study area in
the cultural heritage, as in the Carpathian mountains. Moreover, theRomanianCarpathiansforwhichhighresolutionterraindataare
pasture-woodlandsareasemi-naturallandscape(Buttleretal.,2009) available.Thedrivingquestionsareasfollows:(1)Towhatextent
thatisespeciallyrichinbiodiversitywhencomparedtograsslands doesthewindenergypotentialinmountainlandscapesdependon
and forests (Gillet et al., 1999; Dufour et al., 2006). Unfortunately, complex topography and vegetation structure? (2) Do the vegeta-
wooded pastures are very sensitive to land-use changes (Peringer tiondynamicsandshiftsinvegetationstructurecausedbyland-use
etal.,2013).Woodedpasturescoverlargeareas,predominantlyat changeimpactthewindenergypotential?(3)Whatarethesubse-
higherelevations,whereherdingoflivestockclearedthelandscape quentimplicationsonwindpowerplanning?
inthepastandcontributedtothetoday’shighwindpotential.Land
abandonmentfollowingagro-politicalchangesisfollowedbypatch- 2. Modelingframework
wise forest development on former grasslands. In a meta-analysis
conducted at the level of the whole Carpathian region (Munteanu 2.1. Studysite
etal.,2014)itwasmentionedthat,duringcommunismandinthe
post-communist period, in Romania forest loss and land abandon- Thestudyarea(Fig. 1)isatFundata intheSouthernRomanian
mentwereparallelprocessesinducedbydifferentdrivers:economic, Carpathians,forwhichhighresolutiongeographicalsurveydataare
politicalandsocio-demographic.Aregionalstudy(Pa˘tru-Stupariuet available, hence it allows a synergistic combination of the three
al.,2016)conductedinsurroundingsoftheFundatasiteshowedthat above-mentionedmodelingblocks.Thestudysitehasatotalsurface
indeed, between 1912 and 1980, the forest cover reduced its sur- 27.46km2 andthealtituderangesbetween930mand1400m.The
facealmostbyhalfwhilethepasturesalmostdoubledtheirsurface. vegetation cover is a mixture of ungrazed forest and wooded pas-
Conversely,thetimeperiod1980–2009ischaracterizedbyaslight tures (Pa˘tru-Stupariu et al., 2017). It is worth mentioning that the
forestregrowth,ofalmost10%,tothedetrimentofpastures.Thispro- characteristics and patterns of Fundata are similar to those of the
cessishighlylikelytocontinueandevenbecomemorewidespread sitesintheSwissJuramountainswherewindenergyisunderfast
inthefuture,andcanbeexpectedtoinfluencewindenergypoten- development.
tial by an increase of surface roughness and is known to threaten
landscape-scalebiodiversity. 2.2. Windmodeling
The modeling of vegetation dynamics in wooded pastures as a
responsetovariousscenariosofland-usesrequiresaprocess-based We used a new-generation tuning-free LES framework devel-
approach that incorporates the major ecological processes of tree oped at the Wind Engineering and Renewable Energy Laboratory
recruitmentandgrowth,andtheinfluenceoflivestockwhilegrazing (WiRE) of EPFL (hereafter called WiRE-LES). The details of WiRE-
andbrowsing(Gillet,2008).Ahighspatialresolutionisrequiredto LESareprovidedinAppendixA.TheWiRE-LEShasbeenextensively
mimictheestablishmentofyoungtreesclosetotheirseedparents validated in a variety of canonical flow conditions over both flat
andtherebytherealisticformationofawoodland-grasslandmosaic terrainandsimpletopography(Porté-Ageletal.,2000;Wanetal.,
thatimpliesahighsurfaceroughnessandmajoreffectsonwindflow 2007;StollandPorté-Agel,2008;WanandPorté-Agel,2010;Wuand
(PeringerandRosenthal,2011).Moreover,abalancedrepresentation Porté-Agel, 2011; Lu and Porté-Agel, 2011; Cheng and Porté-Agel,
oftheecologyofwoodyplants,grasslandcommunitiesandlivestock 2013;FangandPorté-Agel,2016;ShamsoddinandPorté-Agel,2017).
isrequiredtoensureareliablemodelresponsetoscenarioparam- Yet,windfieldinmountainousregionishighlyturbulentanditis
etersandacrosslargeareaswithcomplextopographyandvariable stronglymodulatedbylocal,non-linearinteractionswiththeunder-
growthconditionsfortreesandforageforlivestock(Weisbergetal., lying multi-scale topography and heterogeneous vegetation cover.
2006).Ingeneral,appropriatemodelsofpasture-woodlanddynam- Furthermore,geomorphologyandextensivelanduseinmountain-
ics are few, but the wood-pasture model WoodPaM (Gillet, 2008) ous regions lead to the evolution of vegetation cover resulting in
wasappliedintheSouthernCarpathiansrecentlyanddemonstrated complexheterogeneouspatterns,suchasforest-grasslandmosaicsof
reasonablelandscapestructuralchangesaccordingtodistinctland- pasture-woodlands.ToapplytheWiRE-LESundersuchcomplexand
useintensitiesandagro-politicalrecommendations(Peringeretal., dynamicsurfaceconditions,wecoupleitwithhigh-resolutiondata
2016). oftherelevantsurfacepropertiesobtainedfromtheterrainandveg-
Based on these recent advances in wind resource assessment, etationmodelingblocksdescribedinthenexttwosubsections.The
vegetation dynamics and terrain modeling, we aim to develop a high-resolutiondataofthetopographyisdirectlyusedasinputtothe
modelingframeworkcapableofassessingtheshiftsinwindenergy WiRE-LEStodeterminethesurfaceelevationzsforthegenerationof
potentialfollowingvegetationdynamicsinmountainlandscapesand thecomputationalgrid.Thehigh-resolutiondataofthevegetation
applyit toacase study toobtainrelevantguidelinesforthelong- coverisfirstusedtocalculatethecanopyheight(h)anddensity(k).
term planning of wind energy projects over complex terrain. To Then,thosepropertiesareusedtodeterminetheroughnesslength
achievethis,wedevelopedaninterdisciplinaryapproachthatcon- (z )anddisplacementheight(d)inthewallmodelexpressedbyEq.
0
sistsofcouplingthreebuildingblocks:windmodeling,terrainmod- (A.4). In this study, the analytic expression proposed by Raupach
eling,and vegetationmodeling.The windmodeling blockis based (1994)isusedtocalculatez0anddasfunctionsofhandk.
onanew-generationtuning-freeLEStechniqueforhigh-resolution The computational domain for the LES and WoodPaM simula-
eddy-resolving simulations of wind and turbulence over complex tions, along with the surface elevation distribution, are shown in
J.Fangetal. /ScienceoftheTotalEnvironment639(2018)374–384 377
Fig.1. a)LocationofthestudysiteintheRomanianCarpathians(source:GoogleMaps).b)Orthophotomapofthestudysite.Thecoordinatesofthelowerleftcornerare
45◦24(cid:3)54(cid:3)(cid:3)Nand25◦14(cid:3)23(cid:3)(cid:3)E.Therectangledelineatedingreenrepresentsthecomputationaldomainofthewindandvegetationsimulations.
Fig. 2. A neutrallystratified atmosphericboundary-layer flow over higherthanthemeanelevation,whichareusuallyfavorableforthe
thismountainousregionissimulatedbytheWiRE-LESfordifferent installationofwindturbines.
surfacevegetationpatterns.Themaximumslopehereisbelow45◦,
sotheTFCTmethod(seeAppendixAfordetails)isusedtorepresent
2.3. Terrainmodeling
thetopography.Theboundary-layer heightissetto1000mabove
themeansurfaceelevation.Consideringthefactthattheareahasa
TheterrainmodelingreliedonprocessingahighresolutionLiDAR
characteristicstrongwest-wind(Dragomiretal.,2016),theflowis
data set. The data was collected in October 2013 by the company
drivenbyaconstantmeanpressuregradientalongthexdirection.
Primul Meridian through customized airborne laser scanning. The
Thedomainisdiscretizedinto192 × 96 × 96points,resultingina
companyprocessedandclassifiedthepointsbyusingtheTerraScan
gridresolutionofdx = dy = 25manddz≈10m.Periodicbound-
softwarepackage,asfollows.Inafirststage,theclassificationwas
ary conditions are applied in the horizontal directions. The upper
made automatically by the internal algorithms of TerraScan. Then,
boundaryconditionisspecifiedasstress-free.Thebottomboundary
in a second stage, in the cases where the algorithms did not yield
conditionisnopenetrationtogetherwiththewallstressdetermined
theexpectedresults,e.g.duetogroundmorphology,theyusedthe
according to Eq. (A.4). For each landcover scenario, the large-eddy
TerraScan tools for performing a manual classification. Finally, the
simulationisfirstrunforalongenoughtimetoguaranteethatthe
companyprovidedtheclassifiedpointcloudintheLAS-dataformat.
quasi-steadyconditionsarereached.TheLESresultsarethenaver-
The average point density was 22.5 points per square meter,
agedintimetoyieldstatisticsforthecomparisonbetweendifferent
suchthatfinevegetationfeaturesbecamevisibleinthepointcloud
landcoverscenarios.
(Fig. 3). The points were classified by the company as ground
To have a quantitative measure on the shift of wind energy
(152,378,749 points), vegetation (461,054,334 points) and other
potentialanditsrelationshipwiththelandscapeevolution,herewe
classes-includingbuildings,haystacks,otherconstructionssuchas
introduce an indicator of wind energy potential, called WEP. It is
fences(5,706,879points).Theaveragepointspacingwas0.48mfor
definedas
thegroundand1.65mforthevegetation.
(cid:2) (cid:3) WeusedtheArcGIS10.1softwareforproducing1mrastergrids
WEP= U3 (1) representing(i)theDigitalTerrainModel(DTM)-derivedonlyfrom
the ground points, by actually taking into account the last returns
oftheLiDARpulses,(ii)theDigitalSurfaceModel(DSM)-weused
whereUisthehub-heighttime-averagedwindspeedand(cid:5)(cid:6)denotes
the points classified as vegetation for deriving the model; (iii) the
the spatial averaging performed over the places with elevations
Canopy Height Model (CHM) - it was derived as raster difference
between the DSM and the DTM; (iv) the Canopy Density Model
(CDM)-producedfromthevegetationpoints.Forderivingthemod-
elsfromthepointcloud,weusednaturalneighborasinterpolation
method,withgroundbelongingtotheDSM,becausetheyperformed
betterthanlinearinterpolationandgroundnotincludedintheDSM.
The comparison relied on visual checks of the output and on the
computation of some basic statistics of the CHM grids: maximum,
minimum,mean,standarddeviation,percentageofnegativecanopy
andpercentageofcanopyabove30m,respectively(Table1).
WeperformedadditionalcorrectionstotheDTM,CHMandCDM
beforeusingtheminthewindandvegetationmodels.Thisincluded
removing‘nodata’cellsandchangingcellswithnegativecanopyval-
uestozero.Byadjustingthenumberofrowsandcolumnstobedivis-
ibleby25,wefinallygenerated,throughstandard averagingtech-
niques,DTM,CHMandCDMgridswithacellsizeof25m.Thegrids
had147rowsand245columns,thuscoveringasurfaceof22.51km2
Fig.2. Thecomputationaldomainforthelarge-eddysimulation(LES)andWoodPaM
simulations.Herezistheelevationfromsealevelinmandzsisthesurfaceelevation. andtheywerefurtherusedforthewindandvegetationmodeling.
378 J.Fangetal. /ScienceoftheTotalEnvironment639(2018)374–384
Fig.3. Left:3DviewofthegroundandvegetationpointsforasampleoftheLiDARdata.Thecolorramprepresentstheheightaboveground.Right:aerialphotooftheactual
terrain.(Forinterpretationofthereferencestocolorinthisfigurelegend,thereaderisreferredtothewebversionofthisarticle.)
2.4. Vegetationmodeling Although not shown directly, the consequences of future land-use
changewillbediscussed.Anyprospectivesimulationsforthefuture
The spatially explicit dynamic model WoodPaM (Gillet, 2008) consequences of land-use change were hindered by the lack of
wasdesignedtosimulatevegetationdynamicsinpasture-woodlands appropriateclimatechangescenariosinthearea,whereashistoric
dependentonthemultipletraditionalandextensivelanduseforms, climatedatawasathand.
includingcattlestockingandlogging.Themodelincorporatesmost The land-use scenarios were distinguished by the density of
of the processes necessary to predict the long-term impact of sil- livestock,whichdeterminesgrazingpressureonvegetationandeco-
vopastoral management and climate change on herbaceous and nomic yield for farmers. Livestock densities were set to 0.3, 0.5,
forest plant communities, as well as cattle habitat use in hetero- 0.7and0.9AdultBovineUnits(ABU)perhectarefor124days/year.
geneous pasture-woodlands (Peringer et al., 2013). WoodPaM is a These values reflect the minimum, the optimal and the maxi-
grid-basedsimulationmodel,witheachcellbeing25mwide.Itfea- mum livestock densities according to the Romanian management
turesanindividual-basedsubmodeloftreeregenerationandspread, guidelines for sustainable use of pasture-woodlands (Maruska et
whichpredictsthestructureofforeststandslocatedinagridcelland al.,2012).Theintensivegrazingwith0.9ABU/haaimedtoindicate
inneighboringcells.Incontrasttoforestlandscapemodels,Wood- overusethatinthelongrunleadstopastureswithalmostnotrees.
PaMisabletorealisticallysimulategradientsfromopengrassland Thelow-intensityscenariowith0.3ABU/haaimedtoindicatealand-
to closed forest continuously in space and time. The routines for scape dominated by forest with only small grassland area. Wood
widespreadtreeestablishmentingrasslandsallowmodelingofthe exploitationwassimulatedassingle-treecuttingandsettoareturn
characteristicpatch-mosaicofgrasslandandforestprevalentinthe intervalofthecutdownoftreesinagridcellof200yearsbecause
studyarea. ofslowtreegrowthinthemountainclimate.Itwasthesameforall
WoodPaM was applied to simulate a set of alternative forest- grazingscenariosandthusreflectsthelow-intensityexploitationof
grasslandmosaicsinthestudyareabasedonscenariosofdifferent forestforfirewoodbythelocalpeople.
land-useintensities.Thesemosaicsweresimulatedtodevelopsince Themodeloutputattheendofsimulations(year2000CE)was
1650CE(beginningofintensiveland-useinthearea)overaperiod the number of tree individuals per grid cell in 4 height classes. In
of350yearsuntil2000CEundertheassumptionofconstantgrazing order to estimate the nature conservation value of the simulated
pressure(andtreecutting).Theentirelandscapewasinitiallypas- landscapes,thetreecoverpergridcellwasestimatedfromthetree
turedexceptforlargeanddenseforestsshowninFigs.4and5as individuals and was classified into habitat types that harbor spe-
denseandtallstandsoftrees(lightblueforcanopyheightandyellow cificbiodiversity,namelyunwooded,sparselyanddenselywooded
forcanopydensity).Thesearegenerallydominatedbybeech,which pasture and forest (Gallandat et al., 1995). Transferred to the LES
indicatesanexclusionfromgrazingbyforestmanagement,because
browsingintolerantbeechcannotregenerateunderherbivorepres-
sure. The differences among simulated landscapes therefore show
fundamentalrelationshipsamongland-useintensityandlandscape
structureexpressedbytreecoverdistributionbutdonotshowsuc-
cessional dynamics for land-use intensification or extensification.
Table1
Basicstatisticsofthecanopyheightmodel(CHM)forthefourpossibilitiestogener-
atethemodel:interpolationmethods-L(inear)andN(atural)N(eighbor)andground
belongstothedigitalsurfacemodel(DSM)(yes/no).
Interp. Groundin Max Min Mean Std. Negative Canopy>
method DSM dev. CHM(%) 30m(%)
L Yes 65.18 −2.03 4.48 7.22 0.44 0.15
L No 141.59 −9.31 4.85 7.49 0.81 0.17
NN Yes 61.10 −0.48 4.48 7.15 0.18 0.12
NN No 98.86 −7.68 4.85 7.45 0.56 0.15
Fig.4. ThepresentcanopyheightatFundatasiteintheRomanianCarpathians.
J.Fangetal. /ScienceoftheTotalEnvironment639(2018)374–384 379
Fig.5. ThepresentcanopydensityatFundatasiteintheRomanianCarpathians. Fig.7. Theturbulenceintensityattheturbinehubheightof100msimulatedby
theWiRE-LESmodellingframeworkforthepresentlandscapeatFundatasiteinthe
wind modeling framework are the canopy height and density val- RomanianCarpathians.Herethesolidcontourlinesshowtheelevation.
uespercellestimatedbasedonthephysicalclassofvegetationcover
percell.Thesevalueswerethentranslatedintothesurfacerough-
inthemostbalancedmanner(Fig.10),whichiscausalforthehighest
nessanddisplacementheightvaluesasexplainedintheSection2.2.
(c-)biodiversity at landscape scale and corresponds to the under-
Altogether, scenario simulations demonstrated the long-term con-
lyingrecommendationof“optimumgrazing”.Grasslandandforest
sequences of different land-use intensities in mountain pasture-
habitatsarealmostequallypresentandsatisfytherequirementsfor
woodlandsandprovidedinformationfortheestimateofWEP,nature
verydistinctgroupsoftargetspecies.Forest-grasslandecotoneswith
conservationvalueandfarmer’sincomebasedonlivestockdensities.
varyingconditions(sparselyanddenselywoodedpastures)arethe
keyhabitatforpasture-woodlandsandreachtheirhighestcover.The
3. Resultsanddiscussions otherscenarioseitherfavorgrasslandorforestonthecostofeach
other.Intheminimumgrazingscenario(Fig.8d),thevalleyswere
Forthecurrentlandcover,thehigh-resolutionmapsofcanopy filledwithforest,thesizeofopenpasturelandwassmallandevenon
heightanddensityobtainedbytheterrainmodelingareshownin thehilltopsthescatteredtreesweremoredense.
Figs. 4 and 5, respectively. The mean wind speed field and turbu- Theintensivegrazingscenarioledtoalandscapestructurethat
lenceintensity(TI)attheturbinehubheightof100mpredictedby corresponded to current conditions. This can be understood as a
theWiRE-LESforthecurrentlandcoverarepresentedinFigs.6and legacyeffectfromthecommunistperiod,whenland-usewasinten-
7,respectively.Later,thissimulationcaseisusedasareferencefor siveeveninremoteregions.Thewidespreadcoveroflowcanopyin
theevaluationofshiftsinwindenergypotentialfollowingvegetation Figs.4and5,however,showsthatearlysuccessionalshrubestablish-
dynamicswithdifferentland-usescenarios. mentispresentfollowingpost-communistland-useextensification.
Vegetation patterns in the computational domain (Fig. 2) for a In a nutshell, the scenarios demonstrate a strong and combined
series of land-use scenarios that compare agro-economic recom- influence of land-use intensity and topography on the tree cover
mendations for the management of Romanian pasture-woodland distribution.
landscapes(Maruskaetal.,2012;Peringeretal.,2016)areshownin TheWiRE-LESwasappliedtosimulatewindandturbulenceover
Fig.8.Underintensivegrazingaveryopenlandscapewithonlyfew the study area for the four land-use scenarios. Fig. 9 shows the
andscatteredisolatedtreesemerged(Fig.8a).Theoveruseofpasture mean wind speed shifts at the surface of the turbine hub height
woodlandsledtostructuralsimplicity.Undermaximumgrazing,the for the scenario landscapes with respect to the current one. The
density of scattered trees was higher throughout the landscape. A results demonstrate that the wind field in complex mountain ter-
realisticwood-pasturemosaiclandscapeemergedasitwaspredicted rain strongly depends on land cover and corresponding land use.
by the management guideline for pasture-woodland conservation. Differentstructureofforest-grasslandmosaicpatternscouldaffect
Underoptimumgrazing,forestpatchesdevelopedpredominantlyon the wind speed dramatically, thus having a significant impact on
thevalleyslopesthatareunattractiveforlivestockbutlargepastures thepoweroutputfromwindturbines/farmstobeinstalledlocally.
withonlyscatteredtreeswerefoundonthehilltops(compareFigs.2 Whereasthewindspeedpatternfortheintensivegrazingscenario
and8c).Thislandscapepatternprovidesthedifferenthabitattypes only slightly differs from the current state (Fig. 9a), strong devia-
namelyunwooded,sparselyanddenselywoodedpastureandforest tions towards lower wind speed are shown for the scenarios with
lowergrazingintensity.Slowingdownthewindflowalreadysetsin
withslightdensificationofthescatteredtreesonthehilltoppastures
(Fig. 9b for maximum grazing). The development of a structurally
richmosaiclandscapeunderoptimumgrazingbroadensthiseffect
(Fig. 9c). With minimum grazing the entire landscape is affected
(Fig.9d).
Despitetheoveralltrendofdecreasingwindspeedwithlower-
inggrazingpressure,locally,themeanwindspeedattheturbinehub
heightcaneitherincreaseordecreasecomparedtothecurrentsitu-
ation.Forexample,Fig.9ashowsamaximumincreaseof3.6m/sand
amaximumdecreaseof1.8m/s,respectively.
Fig.10showsthedecreaseofWEPrelativetothecurrentsitua-
tionandinrelationtothecoverofhabitattypesinthefourscenario
landscapes.Theareacoveredbyeachhabitatwasestimatedfromthe
mapsinFig.8a–dandprovidesinformationonthespatialdistribu-
Fig.6. Themeanwindspeedfieldattheturbinehubheightof100msimulatedby
tionoftreesmoredetailedthantreecoverdoes.Theresultsshow
theWiRE-LESmodellingframeworkforthepresentlandscapeatFundatasiteinthe
RomanianCarpathians.Herethesolidcontourlinesshowtheelevation. that, for high grazing pressures (high ABUs), the WEP decrease is
380 J.Fangetal. /ScienceoftheTotalEnvironment639(2018)374–384
Fig.8. Simulatedtreecoverdistributionaccordingtoland-usescenarios(grazingpressureandsingle-treecutting)inthecomputationaldomainofFig.2.Theentirelandscape
waspasturedexceptforseveralpatchesofforest,whichareshownindarkgreeninpanela)andasdenseandtallstandsoftreesinFigs.4and5.Darkergreentonesindicate
highertreecover.a)Intensivegrazing(0.9ABU/hafor124daysperyear);b)maximumgrazing(0.7ABU/hafor124daysperyear);c)optimumgrazing(0.5ABU/hafor124days
peryear);d)minimumgrazing(0.3ABU/hafor124daysperyear).Grazingintensitiesareexplainedinthetext.
minimized.Whilewiththelowestgrazingpressure(i.e.0.3ABU/ha) increase of tree cover, WEP decreases rapidly when shifting from
andconsequentlyoverallhightreecover,theWEPdecreaseishigh- intensive (0.9ABU/ha) to maximum grazing (0.7ABU/ha). The rea-
est.Thisisconsistentwiththephysicsthattheoverallhighsurface sonistheemergenceofscatteredtreeseverywhereonthehilltops.
roughnessduetodensetreecoverslowsdowntheflow.Therela- Theysumuptoarelativelylowtreecoverbutleadtohighsurface
tivedecreaseofWEPcanreachupto40%inthecaseofpoorpasture roughness.Asaresult,certainsmallchangesinthelandscapethat
management and the development of a landscape mosaic where maynotbesoobvious,canproducesubstantialdecreasesinWEP.
treeless grasslands decline and wooded habitats dominate (mini- These landscape changes may be significant already after 25years
mumgrazingscenario).ThelossofWEPissignificantalreadywith ofpoorpasture-managementorabandonment(Dovcˇiaketal.,2008)
thedominanceofsparsely-woodedhabitatsinthecaseoftheopti- anddensethicketsmayevendevelopafter36years(historicalpho-
mumgrazingscenario(0.7ABU/ha).Forestdevelopmentcontributes tographs of the area). Future climate warming will speed up tree
lessbecauseinourcaseitisdominantinthevalleysforwhichWEPis growthinthemountainsbyprolongedgrowingseasons.Theresults
notevaluated.TheimportanceoftreelessgrasslandforWEPisshown generally support the argument that turbine installations should
by the optimum grazing scenario, where the prevail of sparsely- considerlandmanagementplanstoensuretheexpectedenergypro-
woodedhabitatsonthecoastofgrasslandleadstoadramaticWEP duction for their lifetime (typically around 20–25years). This also
decreaseof30%. impliesthatasinglesetofturbinescanbeinplacefor25years,then
It is important to note that Fig. 10 demonstrates a non-linear decommissioned,withnewturbinesinstalledindifferentlocations.
relationshipbetweenWEPandoveralltreecover.Withonlyaslight However,thishasramifications,likemoreroadsanddisturbance.
Fig.9. Themeanwindspeedshift,respectivelythedifferencewithrespecttothemeanwindspeedinthecurrentlandscape,forfourdifferentland-usescenarios:a)Intensive
grazing;b)maximumgrazing;c)optimumgrazing;d)minimumgrazing.Herethesolidcontourlinesshowtheelevation.
J.Fangetal. /ScienceoftheTotalEnvironment639(2018)374–384 381
Moreover, Fig. 10 demonstrates a tradeoff between high WEP
andstructurallydiverselandscapeswithhighhabitatdiversityand
nature conservation value in combination with farmers’ income.
Intensive grazing cleans up the landscape from even scattered
trees, providing low roughness and highest wind energy potential
and highest agro-economic benefit, but densely wooded habitats
lack. The recommended maximum grazing (0.7ABU/ha) combines
habitat provision for nature conservation purposes and high agro-
economicalincomesforlocalpeople.However,windenergypoten-
tialissuggestedtobe15%lower.Optimalpasturemanagementfora
balancedhabitatdiversity(optimumgrazingof0.5ABU/ha)isasso-
ciated with a strong decrease of WEP and also lower income for
farmers.Thedisregardofpasture-woodlandmaintenance(minimum
grazingof0.3ABU/ha)notonlyleadstolossesofbiodiversitybythe
disappearance of treeless grassland habitats and to losses of local
incomebutalsodramaticallydecreasesWEP.Insummary,oursim-
Fig.10. Thecoverofhabitattypesandrelativedecreaseofwindenergypotential ulations suggest that the potential to find a balance among WEP,
(WEP)forfourdifferentland-usescenarios:a)Intensivegrazing(0.9ABU/ha);b)max- agro-economicbenefitstothelocalpeopleandnatureconservation
imumgrazing(0.7ABU/ha);c)optimumgrazing(0.5ABU/ha);d)minimumgrazing interestsliesinanoptimizedandintegratedmanagementschemeof
(0.3ABU/ha).Habitatsaredefinedbytreecoverclasses.unwooded:0–2%;sparsely
thelandscapeandshouldbeguaranteedbyincentivesfrommulti-
wooded:2–20%;denselywooded:20–70%;forest:>70%.
plesources.Whileagronomydepartmentsmayensurehumanand
natureaspects,windenergycompaniesarecalledtoinvestinproper
landmanagementthatensuresconstantlyhighenergyyields.
Fig. 11 shows the locations of the maximum mean wind speed
under the present land cover and two projected land covers due
toland-usechanges.Itturnsoutthatthelocationwiththehighest
windspeedalsochangesfromscenariotoscenario.Therefore,when
optimizingthemicro-sitingofwindturbines,theassociatedchanges
inthespatialdistributionofmeanwindspeedmustbetakeninto
accounttomaximizewindenergyproduction.
Fig.12showstheturbulenceintensities(TI)atthesurfaceofthe
turbinehubheightforthefourland-usescenarios.Interestingly,the
spatialpatternsoftheTIdistributionaresimilarbetweenthevarious
land-usescenarios,indicatingthatwithrespecttoTItheterrainplays
a dominant role as compared to the land cover. Nevertheless, the
simulatedlandscapechangeforlessthanoptimumpasturemanage-
ment(e.g.Fig.12c)leadstolargerareastobeaffectedbymedium
valuesofTI.Thiscanbeunderstoodasaneffectofthespreadofscat-
Fig.11. Locationsofthemaximummeanwindspeedfordifferentland-usescenarios: tered trees and the entire grassland-forest mosaic becoming more
1)Present;2)maximumgrazing;3)minimumgrazing. complexinpattern.Therefore,whenoptimizingthemicro-sitingof
Fig.12. Turbulenceintensitydistributionsforfourdifferentland-usescenarios:a)Intensivegrazing;b)maximumgrazing;c)optimumgrazing;d)minimumgrazing.Herethe
solidcontourlinesshowtheelevation.
382 J.Fangetal. /ScienceoftheTotalEnvironment639(2018)374–384
windturbines,thechangesinthespatialdistributionofturbulence TheWiRE-LESsolvesthethree-dimensionalspatiallyfilteredN-S
intensity associated with future landscape changes must be also equationswrittenintherotationalformasfollows:
takenintoaccounttominimizeturbinefatigueloadsandassociated
maintenancecosts. ∂u˜
i =0, (A.1)
∂x
i
(cid:4) (cid:5)
4. Conclusions ∂∂u˜ti +u˜j ∂∂ux˜i −∂∂ux˜j =−∂∂p˜x∗ +m∂∂x2∂u˜xi −∂∂txij +Fi, (A.2)
Theresultsofthecasestudydemonstratethattheoverallshifts j i i j j j
in wind energy potential following the changes of vegetation pat-
where t denotes time, u˜ is the i-th component of the instanta-
ternduetodifferentland-usepoliciescanbedramatic.Thissuggests i
neous resolved velocity, x is the i-th component of the position
thatthewindpowerplanningshouldbeintegratedwiththeland-use i
planningataspecificsitetoensurethattheexpectedenergyproduc- vector, p˜∗ = p˜/q+ 12u˜iu˜i is the modified pressure with the den-
sityqandresolvedkineticenergyincluded,mdenotesthekinematic
tionofthewindfarmtobeinstalledcanbereachedoveritsentire
lifetime.Followingapreviouslyunexploredpathwayofcause-effect viscosity, and tij is the ij-th component of the SGS stress tensor.
Buoyancyeffectsarenotconsideredinthisstudy(i.e.neutralstabil-
relationships, a new linkage of agro- and forest policies to renew-
ityisassumed).TheflowisdrivenbyanexternalforceF.VariousSGS
ableenergyproductionandbiodiversitytargetswasdemonstrated.
modelsareimplementedintheWiRE-LES.Usedinthisstudyisthe
Regarding the sustainable and optimized landscape management,
well-tested Lagrangian averaged scale-dependent dynamic model
thereisacompromisetobetargetedthatbalancesbiodiversity,agro-
(StollandPorté-Agel,2006)whichistuning-freeandhasbeenshown
economic income and wind energy potential (WEP). Our coupled
tobeparticularlywellsuitedtocapturingunresolvedsmall-scaletur-
modelalsosuggestsnon-linearrelationshipsthatmayleadtostrong
bulenceincomplexenvironments(StollandPorté-Agel,2006;Wan
decreasesinWEPfollowingonlyslightincreaseintreecover.More-
etal.,2007;StollandPorté-Agel,2008;ChengandPorté-Agel,2013).
over,thechangesinthespatialdistributionofwindandturbulence
TheincompressibilityofthefluidexpressedbyEq.(A.1)issatis-
underdifferentscenariosofland-usearecomplex.Inparticular,the
fiedbytheprojectionmethod(AlbertsonandParlange,1999;Fang
locationwiththehighestwindspeedchangesfromscenariotosce-
andPorté-Agel,2016).Alltheinvolvedpartialdifferentialequations
nario.Therefore,whenoptimizingthemicro-sitingofwindturbines,
arenumericallysolvedbyusingavertically-staggeredgridarrange-
theassociatedchangesinwindspeedandturbulencemustbetaken
mentwiththespatialderivativesofflowvariablesinthehorizontal
into account to maximize wind energy production and minimize
andverticaldirectionsbeingcalculatedbasedonthepseudo-spectral
fatigueloads(andassociatedmaintenancecosts).Theproposednew
andsecond-ordercentraldifferencemethodsrespectively(Albertson
modeling framework offers, for the first time, a powerful tool for
andParlange,1999;Porté-Ageletal.,2000).Thisarrangementallows
assessing vegetation dynamics induced long-term uncertainties in
the Poisson’s equation for pressure resulting from the projection
windenergypotentialovercomplexterrain.
methodtobesolvedefficientlyineachtimestepwithouttheneedof
iterationwhentheterrainisflat.Allnon-lineartermsarede-aliased
Acknowledgments in Fourier space by the 3/2 rule (Canuto et al., 1988). Time inte-
grationisdonebytheAdams-Bashforthscheme.High-performance
This work was supported by the Swiss National Science Foun- computing techniques are utilized in the WiRE-LES to accelerate
dation in the framework of the Romanian-Swiss Research Pro- thecalculationsandmakethecomputationalcostofhigh-resolution
gramme, project WindLand, project codes: IZERZ0-142168 and 22 simulationaffordable.Thisisdonethroughtheparallelizationusing
RO-CH/RSRP and was partially carried out withintheframe of the horizontal slab decomposition and MPI for inter-processor com-
SwissCentreforCompetenceinEnergyResearchontheFutureSwiss munication and the implementation of OpenMP within each MPI
ElectricalInfrastructure (SCCER-FURIES) withthefinancialsupport task.
oftheSwissInnovationAgency(Innosuisse-SCCERprogram),con- In the simulation of turbulent flows over complex terrain (e.g.,
tractnumber:1155002544.Computingresourceswereprovidedby hills,mountains,urbanareas),thecomplexityoftopographyleadsto
EPFL through theuse ofthefacilitiesof itsScientificITandAppli- challengesindiscretizingthecomputationaldomain.IntheWiRE-
cation Support Center and by the Swiss National Supercomputing LES,twomethodsofrepresentingtopographyhavebeendeveloped
Centre(CSCS).WewishtothankMagdaNa˘pa˘rus¸-Aljancˇicˇfordiscus- andimplemented.Theyaretheimmersedboundarymethod(IBM)
sionsregardingtheGISapproachesandIoanaStoicescuforfruitful andtheterrain-followingcoordinatetransformation(TFCT)method
collaborationinthefieldcampaigns. (Wanetal.,2007;ChengandPorté-Agel,2013;FangandPorté-Agel,
2016).TheadvantageofIBMisthatnochangehastobemadeforthe
AppendixA. WiRE-LES conventionalCartesiangrid.Tomodelthetopographyeffectsonthe
flow,anartificialforceisintroducedtostoptheflowinsidethesolid.
LES is an eddy-resolving numerical model, which is supe- Inaddition,properinterpolationsforvelocitynearthesolidsurface
rior to the conventional Reynolds-Averaged Navier-Stokes (RANS) areneededtosatisfytheboundaryconditions.Meanwhile,toenforce
approachinpredictingunsteadyturbulentflows.Computationally, bothno-slipanddivergence-freeconditions,thePoisson’sequation
LESisfeasibleforveryhighReynoldsnumberflows,suchastheABL, for pressure should be solved together with the equation for the
forwhichdirectnumericalsimulationoftheoriginalN-Sequations artificialforce(Fangetal.,2011).Intheterrain-followingcoordinate
isunrealizableduetoitsrequirementofusingenoughgridresolu- transformationmethod,thephysicaldomainisdiscretizedusinga
tiontoresolveturbulencefromtheenergy-containingintegralscale body-fittedgrid.Withthecoordinatetransformationdefinedas
downtotheKolmogorovscale.InLES,turbulenteddieswithscales
largerthanthegridscaleareresolvedandtheneteffectoftheunre- x¯=x, y¯=y, z¯=Hz−zs, (A.3)
solved smaller scale motions is modeled by a subgrid-scale (SGS) H−zs
model.TheaccuracyofLESreliesontheSGSmodel.Amajoradvance-
ment in LES has been the introduction of the dynamic procedure where H is the maximum height of the domain and zs is the sur-
(Germanoetal.,1991),whicheliminatestheneedoftuningparame- faceelevation,thegridbecomesaregularoneinthenewcoordinate
tersinSGSmodels.Basedonthedynamicprocedure,moreadvanced system, so all calculations can be done using the same numerical
SGSmodelshavebeendeveloped. schemesasforflatterrain.Forthederivativecalculationsinthex,y,
J.Fangetal. /ScienceoftheTotalEnvironment639(2018)374–384 383
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Accordingly,thePoisson’sequationforpressureisrewrittenforthe mationandimmersedboundarymethodsinlarge-eddysimulationofwindfields
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