E&EG Journal Volume XXIX, Number 2

Environmental & Engineering Geoscience

M AY 2023

VOLUME XXIX, NUMBER 2

THE JOINT PUBLICATION OF THE ASSOCIATION OF ENVIRONMENTAL AND ENGINEERING GEOLOGISTS AND THE GEOLOGICAL SOCIETY OF AMERICA

SERVING PROFESSIONALS IN ENGINEERING GEOLOGY, ENVIRONMENTAL GEOLOGY, AND HYDROGEOLOGY

Environmental& EngineeringGeoscience

Volume29,Number2,May2023

TableofContents

73UsinganInventoryofUnstableSlopestoPrioritizeProbabilisticRockfallModelingandAcid-BaseAccounting inGreatSmokyMountainsNationalPark

ThomasO’Shea,SamanthaFarmer,ArpitaNandi,EricBilderback,IngridLuffman,andAndrewJoyner

93NewInsightsfromLegacySeismicDataregardingBasaltElevationsandVariabilityontheHanfordSite

JamesT.St.Clair,AdamR.Mangel,andTimC.Johnson

105PossibleFaultCommunicationbetweentheMemphisSandAquiferandtheMississippiRiver

RichardV.Martin,RoyB.VanArsdale,andValarieJ.Harrison

115ShallowLandslideErosionRatesonIndustriallyManagedTimberlands:KeyFactorsAffectingHistoricaland ContemporaryRates

JasonS.Woodward

133ResearchonSide-SlopeMonitoringbyIntegratingTerrestrialLaserScanningandUAV-Based Photogrammetry

YunchuanWang,PingDuan,JiaLi,andZhikeZhang

TechnicalNote

147TermsRelatedtoAnnualFrequencyforProbabilisticAssessments

JeffreyR.Keaton

151Comment&Reply

IraD.SasowskyandAramMohammedRaheem

UsinganInventoryofUnstableSlopestoPrioritize ProbabilisticRockfallModelingandAcid-Base AccountinginGreatSmokyMountainsNationalPark

THOMASO’SHEA

BunnellLammonsEngineering,6004PondersCourt,Greenville,SC29615

SAMANTHAFARMER

VirginiaDepartmentofTransportation,870BonhamRoad,Bristol,VA24201

ARPITANANDI*

DepartmentofGeosciences,EastTennesseeStateUniversity,JohnsonCity,TN37614

ERICBILDERBACK

NationalParkService,GeologicResourcesDivision,P.O.Box25287,Denver,CO 80225

INGRIDLUFFMAN ANDREWJOYNER

DepartmentofGeosciences,EastTennesseeStateUniversity,JohnsonCity,TN37614

KeyTerms: UnstableSlopeManagementProgram,KernelDensityEstimation,ProbabilisticRockfallSimulations,Acid-BaseAccounting,GreatSmokyMountains NationalPark

ABSTRACT

AnimportantfirststepinthegeotechnicalassetmanagementofGreatSmokyMountainsNationalPark (GRSM)isthecreationofanunstableslopeinventory alongmajortransportationcorridors.Slope-stability problemsarefrequentinGRSM,ofteninitiatedin highlyweatheredandfracturedmetasedimentaryrocks. Inthisstudy,anunstableslopeinventorywascreatedusingtheUnstableSlopeManagementProgramforFederalLandManagementAgenciesprotocols.Hazards andriskswereevaluatedfor285unstableslopesalong 243.67kmofroadway.Kerneldensityestimationwas usedtoidentifyunstableslopehotspotsandestablish 14sitesforsite-specificinvestigationstoevaluatepotentialimpactsofdiscreteunstableslopesalongmajor roadways.Two-dimensionalprobabilisticrockfallsimulationsandacid-baseaccountingtestswereusedtopredictrockfallpathwaysandevaluatetheacid-producing

*Correspondingauthoremail:nandi@etsu.edu

potentialofrocks.Simulationsindicatedthatrockmaterialwouldlikelyentertheroadwayatall14sites.Acidbaseaccountingtestresultsindicatedthatslatyrocks oftheAnakeestaFormationandgraphiticschistofthe WehuttyFormationareprimaryacid-producingrocks inrockfall-proneareas.Thisresearchillustratesanapproachforprioritizingareasforsite-specificinvestigationstowardsthegoalofimprovingsafetyinGRSM, includingdevelopingmitigationstrategiesforrockfallby wideningditches,installingbarriers,andencapsulating acidicrockfallmaterial.

INTRODUCTION

Thetermsslopefailureandlandslideareoftenused interchangeablytodescribeawidevarietyofnatural geomorphicprocessesthatresultindownwardmovementofearthmaterials,includingrock,soil,artificial fill,oracombinationofthese(Varnes,1978;Turner andSchuster,1996).Thedifferenttypesofslopefailurescanbedistinguishedbasedonthenatureofmaterialsinvolvedandtheirmovement.Failuresoccur frequentlyinthemountainousterrainsoftheUnited States(e.g.,ColoradoPlateau,AppalachianMountains,CoastalRangesofCalifornia,SouthernRocky Mountains,PacificNorthwestCoastRangeofOregon andWashington,OlympicMountains,andCascade

Range)andrangefromsmallrockfallsorslopecreep tocomplexlandslides,rockavalanches,anddebris flows.Thebehaviorsandoutcomesofslope-failure eventswillvarybasedontheirlocationandmanyunderlyingfactors.Forexample,inthePacificNorthwestregion,failureeventsaremostlytriggeredby rainfall,earthquakes,orvolcanicactivities(Wieczorek andLeahy,2008).IntheAppalachianMountains,includingtheGreatSmokyMountainsNationalPark (GRSM),slopefailuresresultfromcomplexinteractionsamongvariousrockandsoiltypes,jointgeometries,precipitationdurationandintensity,topographic profiles,andhydrologicalconditions(Wieczoreketal., 2000;Moore,2004;andNandiandShakoor,2017).

UnstableSlopeFailuresinGRSM

GreatSmokyMountainsNationalParkstraddles theborderofNorthCarolina(NC)andTennessee (TN),coveringanareaofmorethan500,000acres

(Figure1)(NPS,2019).Theparkisthemostvisited parkofthe62nationalparksintheUnitedStates,accommodatingmorethan14millionvisitorsin2021 (NPS,2021).Theparkgeneratesmorethan$1.05billioninvisitorspendingandprovidesemploymentfor morethan15,000peopleinlocalcommunities(CullinaneandKoontz,2020).Eachyear,unanticipated roadclosuresduetoslope-failureeventsoccurwithin thepark.Theseeventsinterferewithparkobjectives andhaveasignificantnegativeeconomicimpactonthe regionaleconomy(AndersonandCuelho,2017).For example,in2013,heavyrainfallintheGRSMresulted inalandslideneedingabout$4millionforrepair. Smaller-scaleslopefailuresrelatedtomaintenance costsarefrequentandrangefrom$25,000to$200,000, excludingaddedvehicleandemissionscosts,travel time,andmaintenanceofdetourroutes(accordingto correspondencewithGRSMmaintenancepersonnel).

Large-scalelandslidesarenotcommonalongthe GRSMtransportationcorridors,butwhentheydooc-

Figure1.MajortransportationcorridorsinGreatSmokyMountainsNationalPark,TNandNC.

InventoryofUnstableSlopesforHazardAnalysis,GreatSmokyMountains

cur,theycancompletelyorpartiallyclosetheroadnetwork,causingeconomiclossaswellassocialcosts. Whenthegroundconditionsarefavorable,rainfall fromcloudbursts,hurricanes,andstormscantrigger fast-movingflows(Wieczoreketal.,2000).Bogucki (1976)identifiednumerousrockslidesanddebrisflows inGRSMduringaSeptember1951rainstorm.About 50percentofthedebrisflowsfromthoseslidesoccurredintheMountLeConte–SugarlandMountain areaandAlumCaveCreekwatershed,significantly damagingtheroadsandhikingtrails.Morethan60 percentofthedebrisflowshappenedonslateand phylliteoftheAnakeestaFormation,andtherestoccurredonmetasandstoneoftheThunderheadFormation(Bogucki,1976).In2010,threerockfallevents occurredonroadsthatserveGRSMparkvisitors. Thelargestandmostdisruptivefailureeventoccurred onJanuary25,2010,alongasouthboundsection ofRoute0011S(GatlinburgSpur),anarterialaccess routewithinthepark.Asaresult,bothsouthbound lanesofthespurwereclosedformorethan30days (TDOT,2010a).ThoughtheTennesseeDepartmentof Transportation(TDOT)wasresponsiblefor$700,000 inemergencyexpenditureandcleanupfortheJanuary 25,2010,rockslide,eacheventalsoposedariskto GRSMparkvisitorswhofrequentlytravelalongthis route(TDOT,2010b).Arecentslide,closetotheTrout BranchtributaryofLittlePigeonRiver,transformed intoadebrisflowinAugust2012anddamagedthe AlumCavetrail(NandiandShakoor,2017).Aheavy rainfalleventinJanuary2013triggeredalargecutslopeembankmentslopefailureandcreatedalarge landslidethatdestroyedabout200m(∼600ft)of Route0010S(NewfoundGapRoadorU.S.Route441) intheGRSMtowardsNC,amajoreconomiccommercecorridorforcommunitiesoneithersideofthe park(USGS,2013).Regrettably,slope-failureevents intheparkhaveledtofatalities.OnAugust1,2019,a manwaskilledbyafallentreeontheGatlinburgSpur wheremultiplerockslidesoccurredfollowingheavy rainfall.Accordingtoalocalnewsstation,morethan 10cm(4fourin.)ofrainfellinjustover1hour,which triggeredtheevent(CherokeeOneFeather,2019).

In2008,theNationalParkService(NPS)published itsmostrecent GreatSmokyMountainsNationalPark GeologicResourceEvaluationReport.Thereportcompiledinformationrelatedtogeologicissues(e.g.,erosionandslopeprocesses,abandonedmines,airand waterquality)aswellasgeologicfeaturesandprocesses(e.g.,majorfaults,views,tectonicwindows).The reportwasdesignedtobeusedbyparkofficials,scientificresearchers,conservationandenvironmentalconstituencies,andthepublic.Asectionrelatedtogeohazardscanbefoundinthereport.However,itdoesnot provideausabledatabasefortrackingpotentialgeo-

FightingCreekGapRoadand Spur 0013ZZ8.07(5.02)

LittleRiverGorgeRoad001420.31(12.62)

LaurelCreekRoad001512.54(7.79)

ClingmansDomeAccessRoad001711.15(6.93)

ElkmontRoad00182.46(1.53)

LakeviewDriveEast00199.48(5.89)

CadesCoveLoopRoad002616.24(10.09)

CherokeeOrchardRoad00275.83(3.62)

GreenbrierRoad01027.83(4.87)

HeintoogaRidgeRoad01078.59(5.34)

RoaringForkMotorNature Trail 01508.59(5.34)

TotalMileageinStudyArea:243.67(151.41)

hazardsitesalongparkroutes(Thornberry-Ehrlich, 2008).

TheNPSisresponsibleforoperatingandmaintaining510km(315mi)ofroadwaywithinGRSMboundaries,243.67km(151.41miles)ofwhicharepaved (NPS-GRSM,2014,Figure1andTable1.Significantroadswithintheparkinclude:Route0008A,E, F,G,andH(FoothillsParkway),Route0010Nand S(NewfoundGapRoadorU.S.Route441),Route 0011NandS(GatlinburgSpur),Route0014(Little RiverGorgeRoad),Route0015(LaurelCreekRoad), Route0017(ClingmansDomeAccessRoad),Route 0019(LakeviewDriveEast),Route0026(CadesCove LoopRoad),Route0105(CherokeeOrchardRoad), andRoute0107(HeintoogaRidgeRoad).Figure2 showsthecurrentconditionsofsomerepresentative slopesinthepark.Severalpavedroadsthattraverse mountainousterrainservenotonlyparkvisitors,but alsolocalandregionaltraffic.Afternearly80yearsof useonsomeroads,GRSM’stransportationcorridors requireeffectivelong-termmanagement(Anderson, 2016).NPShasrecognizedtheneedtoimplementa proactive,risk-basedstrategicunstableslopemanagementapproachforGRSMtransportationroutesinthe faceoffluctuatingannualbudgetsandaginggeotechnicalassetsthatbecomemoreunstableastheyarecontinuallyexposedtotheenvironment.

TransportationCorridorRiskAssessment

Roadwayandtrailslopesaretransportationor geotechnicalassets,andtheirreliableperformance

Table1. PavedroadsatGRSM,withinthestudyarea,whererouteID correspondstotheroaddesignations.

helpsthetransportationsystemtooperatesafely. Theseassetshavealifecycle;iftheslopesfail,the costofrepaircanbemuchgreaterthanperiodically interveningwithrisk-reductionimprovements.Unfortunately,theslopeassetsareoftenoverlookeduntil theydirectlydamageandimpactthetransportation system.Risk-basedgeotechnicalassetmanagement (GAM)isfundamentalforslopemaintenance;it reducesrisk,improvessystemperformance,and,if activelymanaged,canreduceslopelife-cyclecosts andimprovesafety(Stanley,2011;Anderson,2016).

TohelpfosterGAM,federalandvariousstatedepartmentsoftransportationhavedevelopedroadway unstableslope,landslidehazard,and/orrockfallratingsystemstoratehigh-andlow-hazardareas.This informationallowsdepartmentstoprioritizeareasof concernforslopefailure.Atthefederallevel,aplatformintroducedin2019knownastheUnstableSlope ManagementProgramforFederalLandManagement

Agencies(USMPforFLMA)hasgainedmuchrecognition(Beckstrandetal.,2019).USMPforFLMA isdesignedtoguideeffortsbyfederallandmanagementagencies(FLMAs)andlower-traffic-volume transportationdepartmentstoassessslopehazards andrisksalongtransportationcorridorsinorderto achievetheirowntransportationmaintenancegoals andobjectives(AndersonandCuelho,2017;Stanley andAnderson,2017;andBeckstrandetal.,2019). USMPincludesmanagementtoolsthatareimportant componentsofanyGAMprogram,suchas:condition assessments,examplesofperformancemeasures,and quantitativeriskassessment(QRA)prioritization techniques(Beckstrandetal.,2019).Theprogramwas formulatedbyadoptingandadaptingmethodsfrom acceptedtransportationassetmanagementpractices usedforbridges,pavement,etc.,aswellasexisting GAMprogramssuchasOregon’srockfallhazardratingsystem(RHRS)andAlaska’sUSMP(Thompson,

Figure2.Currentslopeconditionphotosin(a)NewfoundGap(0010N),(b)LittleRiverGorge(0014),(c)GatlinburgSpur(0011S),and(d) ClingmansDome(0017).

InventoryofUnstableSlopesforHazardAnalysis,GreatSmokyMountains

2017).Alaska’sUSMP,whichbuiltuponprogress madebyprogramslikeOregon’sRHRS,wascompletedin2009andprovidedamodelforstakeholders (NPS,U.S.ForestService[USFS],BureauofLand Management[BLM],BureauofIndianAffairs[BIA], andWesternFederalLandsHighwayDivision[WFL]) todeveloptheUSMPforFLMA(Beckstrandetal., 2019).

Nationalparks,includingAcadia,CraterLake,Denali,GRSM,HawaiiVolcanoes,Olympic,Yellowstone,Yosemite,andZion,havebegunutilizingUSMP forFMLAbyperformingslopeconditionhazardand riskassessments.AdditionalNPSunitssuchasVicksburgNationalMilitaryPark,DelawareWaterGap NationalRecreationArea,andtheHeritagePartnershipsProgramoftheNPSIntermountainRegionhave alsobegunusingtheUSMPforFMLA.Positiveoutcomesfromproactivelymanaginggeotechnicalassets arebecomingclearerasgrowingnumbersoforganizationsutilizetheprogram.Recently,Baueretal.(2021) andBanksetal.(2021)utilizedtheUSMPtoratethe unstableslopesalongtheBlueRidgeParkwayandsuggestedbestpracticesbasedontheexperiencesgained fromtheextensivemapping.ResearchersinZionNationalParkconcludedthatreactivemanagementcan befourtofivetimesmoreexpensiveforrockfallevents thanproactivemanagement(FHWA,2020).Additionally,Cappsetal.(2017)concludedthatQRAsarecriticaltounderstandingwherefundsshouldbeallocated toavoidthecommonmistakeoffixingthe“worstfirst” reconstruction-onlypolicy,whichoccurswhenfundingisspentwithoutcarefulconsiderationoftheexposuretoassociatedrisks.ThisconclusionwassupportedbythefindingsofBeckstrandetal.(2017), whichestimatedavalueof$19.7billionforthestateof Alaska’sgeotechnicalassets,i.e.,morethanthreetimes greaterthanthevalueoftheirbridgeinventorybased oncurrentreconstructioncosts.Thetechnicalreport estimatedthatmanagingtheseassetsusingapreservationmodelwouldreduceoveralllife-cyclecostsby 5percent(Beckstrandetal.,2017).

ObjectiveofStudy

Withthegoalofmanaginggeotechnicalassets alongroadways,thisresearchevaluatedslope-failure riskalongtheprimaryGRSMtransportationcorridorstodeterminehowtoprioritizelimitedfinancialresourcesforrisk-reductionmaintenanceorfullmitigation-levelwork.Assuch,thespecificresearch objectiveswereto(1)createaninventoryofunstableslopesandassociatedtransportation-related hazardsandriskratingsusingUSMPorganizedin ageospatialdatabase,(2)delineateunstableslope hotspotareasthathavehighlikelihoodofroadway

interruptionusinggeospatialanalysis,(3)perform site-specificinvestigationsthatpredictroadwayssusceptibletounstableslopeimpactusingprobabilisticsimulations,and(4)performsite-specificacidbaseaccounting(ABA)teststoevaluatetheacidproducingpotential(APP)ofwasterockfromslope failures.

Theinventoryofunstableslopesalongwiththehazardsandriskratingdigitalgeodatabaseandmapswill enableGRSMofficialstotakestepstowardsprioritizingmaintenanceandmitigationeffortsusingcostbenefitanalysesbasedonshort-andlong-termbudgets.Theresearchprovidesanexampleofhigh-risk unstableslopeprioritizationusingdata-drivenhotspot analysis,andapplicationofUSMPtoprovideageologicandenvironmentalframeworkforsite-specific sloperemediationtomaintaintheintegrityofroadwaysinGRSM.

StudyArea

MostofGRSMisintheWesternBlueRidgePhysiographicProvince,withalimitedareaintheTennesseeValleyandRidgePhysiographicProvinceto thenorthwest(Southworthetal.,2012).Boundedto thesouthbyseriesofenechelonzonescollectively calledtheSwannanoaLineament,theparkishometo someofthehighestpeaksintheeasternUnitedStates, someofwhichreachmorethan2,025m(6,644ft) aboveadjacentvalleyfloors.Insomeareasofthe park,mountainslopesmaybeasgreatas44° (Southworthetal.,2012;Hill,2018).MuchofGRSMis withinthehighlandsoftheWesternBlueRidgePhysiographicProvince,whichiscomposedprimarilyof NeoproterozoicmetasedimentaryrocksoftheSnowbirdGroupandGreatSmokyGroup(Southworth etal.,2012)(Figure3).Thenorthwesternportionof theparkiswithinthefoothillsoftheWesternBlue RidgePhysiographicProvince,whichischaracterized byrollinghills.Thefoothillsareprimarilylow-grade greenschistfaciesorhavenotbeenmetamorphosed andrangefromNeoproterozoictoEarlyOrdovician inage(Southworthetal.,2012).Quaternarydeposits ofalluviumandcolluviumoccurinlow-lyingareasof thepark,alongdrainagefeatures,oralongthebaseof cliffsandslopes.

AlongRoute0010,theprimaryrockformationsencounteredincludedmetasandstoneoftheThunderheadFormationandslateandmetasiltstoneofthe AnakeestaFormation.MetasandstoneoftheThunderheadFormation,MetcalfPhyllite,andPigeonSiltstoneandmetasandstoneoftheElkmontFormation wereencounteredalongRoutes0013,0014,and0015. Route0011ismainlycomposedofRoaringFork FormationmetasandstoneandthePigeonSiltstone.

Route0008(H,G,F,andE)crossesHesseQuartzite, WilhiteFormationphyllite,andconglomerate,sandstone,andslateoftheShieldsFormation.Route0019 traversesmostlythroughWehuttyFormation,consistingofslategraphiticandsulfidicschist.TheAnakeesta,Wehutty,andpartoftheCopperhillformations areprimeexamplesofacid-producingrockbecause theycontainsulfidemineralssuchaspyriteandlittleornocarbonateminerals.GRSMisdominatedby fourmajorstructuralsystems:(1)TheGreenbrierand DunnCreekfaultsinthehighlandsandfoothills,(2) theMillerCoveandGreatSmokythrustfaultsinthe foothills,(3)theGatlinburgandPigeonForgefaultsin thefoothills,and(4)thethrustsheetsoftheTennessee Valley,whichareboundedbythePineMountain ThrustFaultandtheGreatSmokyFault(ThornberryEhrlich,2008;Southworthetal.,2012).Mostofthe majorfaultsarepartofaconnectedfaultsystemand

canbeasourceofrockslides(Southworthetal.,2012) (Figure3).

Annualrainfallthroughouttheparkrangesfrom 1.14m(45in.)to2.41m(95in.).Mostoftheprimary roadsareinthe1.50m(59in.)to2.06m(81in.) range,andinhighersectionsofthepark,over2.16m (85in.)ofprecipitationfallsannually(NPS-GRSM, 2017).Moreslopemovementsareexpectedtooccur duringearlyspringandlatefall,whenfrostwedgingconditionsandlargestormeventscreateideal slideconditions(Matsuoka,2001;Sass,2005;and NandiandShakoor,2017).Over3,379km(2,100mi) ofstreamsandriversarecontainedwithinGRSM, ofwhich1,175km(730mi)arefish-bearingand 2,092km(1,300mi)aretributaries(NPS-GRSM, 2017).Tributaries,springs,andprecipitationreplenish waterfallsandsurfacestreams(McKenna,2007). GRSMstreamsarevulnerabletoacidrainbecauseof

Figure3.GeologyandmajorfaultsinGreatSmokyMountainsNationalPark.

InventoryofUnstableSlopesforHazardAnalysis,GreatSmokyMountains

nearbypowerplants,factories,andvolumeoftraffic (McKenna,2007).WaterinGRSMcanbeacidicfrom pollutantsinrain,andfromrockformationsthathave acid-producingpotential(e.g.,AnakeestaFormation, CopperhillFormation,WehuttyFormation).Schaeffer andClawson(1996)conductedgeologicmapping,petrographicanalysis,andABAtestsaspartofaroadand transmissionlineconstructionprojectinsouthwestern NC,wheretheacid-producingrocksofinterestincludedAnakeestaFormationgraphiticschistandthin layersofsulfidicrockwithintheAmmonsFormation, bothofwhicharepresentinGRSM.Theconstruction projectrequiredtheuseofanencapsulatingembankmentdesignsimilartoseverallargehighwayprojects intheBlueRidgePhysiographicProvinceinTNand NCtopreventaciddrainage(Byerly,1996;Schaeffer andClawson,1996).Theirstudyexemplifiesthespecialhandlingrequiredforacid-producingmaterialto minimizeacidrockdrainage(ARD)andavoidcostly mitigationofadverseenvironmentalimpacts(Byerly, 1996).Thepotentialnegativeimpactsonphysical infrastructureandsurfacewatersillustratehowevaluationoftheacid-producingpotentialatrockfallprone cutslopescanhelptoinformwasterockmanagement strategiesandwhyARDrepresentsanimportant considerationfortheGAMstrategyinGRSM.A studybyMathewsandMorgan(1982)showedthe adverseeffectofARDonaquaticlife:Thesalamander (Leurognathusmarmoratus)populationwasalmost destroyeddownstreamfromthehighwaycut-and-fill areasduetothepresenceofsulfidemineralsinthe AnakeestaFormation.Theserocktypesaremore pronetorockfallsandlandslides,andtheyalsohave thepotentialtonegativelyimpactfloraandfaunavia acidificationofwaters(SchaefferandClawson,1996; Lathametal.,2009).

METHODS

ThisstudyutilizedtheUSMPforFLMAprotocols todevelopadigitaldatabaseofunstableslopesand theircurrentconditionsalong243.67km(151.41mi) ofroadinGRSM.Siteinvestigationfielddatawere addedtoageodatabaseinArcGISPro2.7andanalyzedtobetterunderstandthespatialdistributionof unstableslopes.Kerneldensityestimation(KDE)was usedtoidentifyclustersofunstableslopeswithhigh likelihoodofroadwaydisruptionandestablishstudy areasforsiteselection.Two-dimensionalprobabilistic slopestabilitysimulationsandABAtestswereused topredictunstableslopepathwaysandevaluatethe acid-producingpotentialofrockfragments.Thestudy methodsaredisplayedinaflowchart(Figure4)and describedinthefollowingsections.

DataCollectionandPreparationoftheGeodatabase

PrimarydatawerecollectedusingtheUSMPfor FLMAstandardizedfieldformthatorganizeshazardandriskdataintodiscreteattributesandquantifiestheobservations(Cappsetal.,2017;Beckstrand etal.,2019).Theprotocolscanbeusedtoassessseveraltypesofunstableslopes,suchassoilandrock landslides,rockfalls,debrisflows,andthaw-unstable slopes(Cappsetal.,2017).Siteassessmentsranged fromJuly2019toJuly2020.AfieldratingwasconductedforeachunstableslopeusingtheUSMPfor FLMAratingform,whichincludedparameterslisted inTable2.Photographsofeachslopeandglobalpositioningsystem(GPS)coordinateswerealsocollected, andsitedatawereuploadedtotheUSMP.infoweb portal.PreliminaryandtotalUSMPratingswerecalculatedbasedonthehazardandriskparametersasindicatedbyFHWA(2020)observedinthefieldorreportedbyparkofficials.

Secondarydatawereacquiredasspatialdatalayersfromstateandfederaldatadownloadwebsites. TheNPSIntegratedResourceManagementApplications(IRMA)webportal(IRMA.NPS.gov)was usedforroadcenterlines,theparkboundaryshapefile,andthe2016geologicmapofGRSM.Sub-meterresolutionlightdetectionandranging(LiDAR)digital elevationmodels(DEMs)weredownloadedfromthe TennesseeGISClearinghouse(TNGIS.org/LiDAR) andNorthCarolina’sSpatialDataDownloadwebsite(SDD.NC.gov).Primaryandsecondarydatawere compiledandorganizedtocreateageodatabaseofunstableslopesalongprimarytransportationcorridorsin GRSM.

EstablishPriorityAreas:KDE

TheKDEmethodisaninterpolationroutineusedto identifyhotspotsorhigh-riskareasbasedonasetof pointorlinedata.Forthisstudy,theKernelDensity toolfromArcGISPro2.7wasusedtoidentifyclustersofpoorlyratedunstableslopes.Linedatawere usedthatrepresentthelengthoftheaffectedroadwayassociatedwithknownunstableslopes.Eachline wasassociatedwithasymmetricalsurfacecenteredon thelinecalledakernel.Aquartickernelwithafixedintervalbandwidth(searcharea)wasusedinthisstudy (Silverman,1986;ESRI,2021).Thefollowingformula wasusedtocalculatethedensityvalueateachoutput rastercellor(x, y)location(ESRI,2021).

Thisequationwasusedfor disti < radius,where i = 1,…, n aretheinputlinesegmentswithintheradiusdistanceofa(x, y)location;thepopulationfield popi isthetotalUSMPscore;and disti isthedistance betweenlinesegment i andthe(x, y)location.The defaultsearchradiuswasusedinthestudyandwas determinedusinganalgorithmthat(1)calculatedthe weightedmeancenterofinputunstableslopes;(2)calculatedthedistancefromtheweightedmeancenterfor allsites;(3)determinedtheweightedmedianofthese distances, Dm ;and(4)calculatedtheweightedstandarddistance,SD.Oncethesevalueswereestablished, theywereappliedtothefollowingformula:

TheoutputKDErasterwasusedtoestablishpriority studyareasforsite-specificanalysis.

Afterselectingprioritysitesbasedontheresults fromtheKDE,rockfallsimulationsandABAtests wereconductedateachslopecoincidingwithhotspots todevelopageologicandenvironmentalprioritization frameworkforsloperemediation(Figure4).Rockfallsimulationwasconductedbecausefieldinvestigationrevealedthatcommonunstableslopesalongthe roadwaysweremostlycategorizedasrockfalls.Field assessmentswereconductedateachrockslopeto recordbedrocklithology,blockdimension,slopematerialproperties,seederorstartinglocation,andpotentialrockfallpathwaydata.Thetopographicprofileswereextractedfromthe1mDEMandrevised inthefieldusingalaserrangefinder.RockfallsimulationswerecompletedusingRocFallsoftwareutilizing therigidbodyanalysismethodwithtangentialColoradoRockfallSimulationProgram(CRSP)damping

PreliminaryRatingHazardRatingRiskRating DitcheffectivenessSlopedrainageRoutewidth RockfallhistoryAnnualrainfallHumanexposurefactor Blocksize/volumepereventSlopeheight%ofdecisionsightdistance ImpactonuseMaintenancefrequencyRightofwayimpacts AnnualAverageDailyTraffic/usage/economicorrecreationalimportanceStructuralconditionEnvironmental/culturalimpacts RockfrictionMaintenancecomplexity Eventcost

Figure4.Methodologyflowchart,wherethegraycolorcodecorrespondstothestudyobjectives.

Table2. ParametersusedtocalculateUSMPratings.

(RocScience,2002).Theslopematerialpropertiesof thetopographyarelistedinTable3.Onethousand (1,000)rocks,distributedevenlybetweenseeders,i.e., startinglocations,werethrownforeachsimulation, andarecommendeddefaultinitialhorizontalvelocityof1.5m/s(4.9ft/s)wasusedforeveryseeder, whileinitialverticalandrotationalvelocitieswereset to0m/s(0ft/s),asrecommendedbythe RocFallUser Guide (RocScience,2002).Pointseederswereaddedto eachslopebasedonfieldobservations,andlineseederswereaddedalongslopeswherepointsourceswere notobvious,forexample,whererockdebrisandfragmentswereobservedalongthelengthofaslopeand withintheditch.Eachseederrequiredblockshape,dimensions(0.3to1.2m[1to4ft]intheelongateddirection),andadensityvaluethatwasspecifiedusingthe rocktypelibrary.Anappropriaterocktypeforthesite wasselectedtodeterminedensity,whileblockshape(s) anddimensionsweretakenfromfieldnotes.Theblock sizerangedfrom0.3to1.2m(1ftto4ft)inthelong direction,andtheblockshapeswerevariousshapes ofpolygonsselectedfromthelibrary,ascloselyrepresentedinthefield.Additionalinputdatasuchasroadwaywidth,ditchproperties,andpresenceofmitigation measureswerecollectedduringthefieldvisits.Validationofmodelresultswasperformedbycomparingthe rockpathwaysandendpointstophotographstaken duringfieldvisitsandnotesrecordedinthefield.Photographsandfieldnotesprovidedanaccountofrock blocklocationsalongtheslope,containedwithinthe ditch,andoccasionallywithintheroadway.Forseveral sites,tracesofscarsassociatedwithimpactsofblocks ontheroadwayswerealsoobservedandrecorded.

Rocksampleswerecollectedduringfieldassessmentsandweresenttoacommerciallaboratoryfor ABAtests.Rocksampleswerecollectedasloosematerialalongthetoeofslopes,incompliancewiththe scientificresearchandcollectingpermitgrantedbythe NPStominimizeimpacttoparkresources(Figure5). Threesampleswerecollectedatroughlyequaldistance alongthebaseofeachslopeandplacedinlabeledplasticbagsforstorage.Acompositesamplewasprepared foreachsiteusingapproximately333gofmaterial fromeachsamplepointforatotalweightof1kg.An ABAtestusingthemodifiedSobekmethoddescribed bySobeketal.(1978)andLawrenceandMarchant

(1991)wasusedinthisstudy.ABAtestresultsarereportedinunitsofkgCaCO3 pertonneofmaterial. Sampleswithnetneutralizationpotential(NNP)values < 5kgCaCO3 /tareconsideredtohaveasignificantacid-producingpotential.Inpracticalterms, anNNPvalueof 5meansthat5kgofCaCO3 are requiredtoneutralize1t(1metricton)ofsample material.

RESULTS USMPInventory

Intotal,285discreteunstableslopesassessedalong 243.67km(151.41mi)ofroadwayinGRSMwere addedtotheUSMPdatabase.Ofthese,280slopes weredesignatedaslocalizedrockfall,dominatedby wedgeandplanarfailuremechanisms.Thefive(5) remainingsitesweredesignatedassmall-scalelandslidesinsoil-fillembankmentsalongstreambanks. TheUSMPforFLMAclassificationsystemdefines slopeconditionsas“good”whenthetotalUSMP scoreis <200,“fair”whenitis 200and 399,and “poor”whenitis 400.Thisclassificationsystem isbasedonexperienceandwasdesignedforfederal landmanagementagencieswithlowtoverylowtraffic volumes(Beckstrandetal.,2019).Intheassessment, 133slopesrankedas“poor”(45percent),147ranked as“fair”(53percent),andfiverankedas“good” (<2percent)basedontheUSMPforFLMAclassificationsystem.Figure6showsthedistributionof285 slopesclassifiedbyquartilerangetobettercomparelocalsites.Because280outof285slopeswererockfalls, thefivelandslidesitesonsoilslopeswerediscarded fromfurthersite-specificanalysis.

Themajority(72percent)ofunstableslopeswere identifiedalongthreemainroadsinthepark:Routes 0014(LittleRiverGorgeRoad),0010N(Newfound GapRoad),and0011N,S(GatlinburgSpurRoad). Ofthese,32percentwerelocatedalongRoute0014 intheMetcalfPhyllite,CadesSandstone,andThunderheadSandstonegeologicunits,includingfourof the10highest-ratedslopes;18percentwerelocated alongRoute0010N,whichcrossestheNC-TNstate borderintheAnakeestaFormation,CopperhillFormation,andThunderheadFormation;and22percent

Table3. Earthmaterialsparameterpropertiesusedforsimulatedrockfallpathwaysforthe14investigatedsites.

wereidentifiedalongRoute0011NandS,primarilyin thePigeonSiltstoneandtoalesserextentintheRich ButtSandstone.

Theremaining28percentofunstableslopeswere distributedalongtheotherprimarytransportation corridors.Notably,12percentofslopeswereidentifiedalongRoute0008E,F,G,andHfromChilhoweeatthesouthwesttoWearsValleyatthenorth nearSevierville,TN.Additionally,5percentofunstableslopeswereassessedalongRoute0019nearBryson City,NC,withintheWehuttyandCopperhillformations.Mostoftheprimaryroadsandunstableslopes inGRSMwerelocatedontheTNsideoftheparkin thefoothillsoftheWesternBlueRidgePhysiographic Province.

KernelDensityEstimation

TheoutputdensitysurfacecreatedusingKDE hadaspatialresolutionof10mandwaspresentedusingequalintervalclassification.DarkpurplepatchedareasinFigure7havethegreatestdensityofpoorlyratedunstableslopes,aslabeledin Figure6andsubsetinFigure7.ThegeologicformationsatthegreatestdensityofpoorlyratedareasincludedtheAnakeestaFormation,Thunderhead Sandstone,CadesSandstone,MetcalfPhyllite,WehuttyFormation,ShieldsFormation,andPigeonSiltstone.Sixnoticeableclustersofunstableslopeswith ahighlikelihoodofroadwaydisruptionwereidentifiedalongtheGatlinburgSpur(0011),Newfound

Figure5.Compositesampleswerepreparedforeachunstableslope.Thisrepresentativeoutcrop(GRSM-155)(35.4574186°N,83.4956021°W) wasinWehuttyFormationcomposedofdarkmetagraywackeandmetasiltstone,withblackgraphiteschist;outcropiscoveredwithFeoxides, secondarysulfurminerals,andgypsum.Thehandsampleshowninthefigureisagraphiteschist.Netneutralizationpotential(NNP)forthe compositesamplewas 26.4(kgCaCO3 /t).

GapRoad(0010)neartheTN-NCborder,LittleRiver GorgeRoad(0014),andLaurelCreekRoad(0015) (Figure7).LakeviewDriveEastRoad(0019)showed amedium-tolow-densitycluster.FoothillsParkway West(0008)didnotshowanyleadingclusters;however,theroutewasincludedasanadditionalareaof interestforfurthersite-specificstudiesbasedonits documentedhistoryofsporadicrockfallandenvironmentalhazards,suchasacidrockdrainage.Usingthe KDEoutput,14siteswereselectedwithintheclustersandalongRoute0008forsite-specificanalysis, includingprobabilisticrockfallsimulationandABA (Figure7).

ProbabilisticRockfallSimulations

TheRocFalloutputincludesend-pointanalysis, kineticenergy(total,translational,androtational), velocity(translationalandrotational),andbounce height.End-pointanalysisisasignificantfactorcon-

cerningsafetyontheroadway.Therefore,thisstudy primarilyfocusedonthedistributionofrockfallend pointsasthepercentageofrocksrunningoutofthe ditchandpassingtheedgeoftheroadwayclosestto theslope,passingthecenterline,andexitingtheroadwayawayfromtheslope.Validationofrockfallsimulationswasperformedbycomparingmodelresultsto GoogleMapsstreetview,sitephotographs,andfield notes.

Resultsfromthesimulationsshowedrockmaterial enteringtheroadwayatall14sites(Figure8).The distributionsofend-pointlocationsforeachunstable slopearepresentedinTable4.Acrossallsites,most rocks(63.4percent)werecontainedbyditchesanddid notentertheroadway.Endpointsforrocksthatdid entertheroadwayweregenerallyconfinedtoonelane oftrafficclosesttotheslope.Only3.4percentofrocks reachedthecenterline,andonly0.2percentofrocks crossedbothlanesoftraffic.Thepredictedpercentage ofrockscontainedwithinditchesvariedwidelyamong

Figure6.InventorymapofunstableslopesclassifiedbyUSMPtotalscorequartilerange.

O’Shea,Farmer,Nandi,Bilderback,Luffman,andJoyner

slopemodels.Forexample,GRSM-168onFoothills ParkwaySection8Ehadthemosteffectivecontainmentofmaterial,withonlyonerockoutof1,000(0.1 percent)enteringtheroadway.Incontrast,GRSM-088

onLittleRiverGorgeRoadhadtheleasteffectivecontainment,with99.5percentofrock-pathendpoints withinroadway,3.3percentofwhichreachedthecenterline.Aninverserelationshipbetweenditchwidth

Figure7.High-densityclustersofpoorlyratedslopeswereidentifiedusingKDE.Fourteensiteswereselectedwithintheclustersforsite investigation.Thesubsetmapincludesthelocationsofunstableslopescolor-codedbyriskrating.

andthepercentageofrocksenteringtheroadway (Table4)wasnoted;however,thestatisticalrelationshipwasnotanalyzedduetothesmallsamplesize.

EnvironmentalImpact(ABA)

Totalsulfurconcentrationwasreportedasweight percentandrangedfrombelowthedetectionlimit (0.02weightpercent)to1.5weightpercent.Afullac-

countofABAtestresultsisincludedinTable5.Samplesfromfivesitescontainedsignificantconcentrationsoftotalsulfur(>0.5weightpercent).Thesevaluesdirectlycorrelatedwiththesulfideconcentration andthereforetheacid-generationpotentialofthesamples.TestresultsindicatedawiderangeofNNPvalues,from 31.1to +69.2kgCaCO3 /t(Figure9).Notably,samplesfromGRSM-013andGRSM-168had significantsulfideconcentrationsandacid-generation

Figure8.Simulatedrockfallpathwaysforthe14investigatedsites.

O’Shea,Farmer,Nandi,Bilderback,Luffman,andJoyner

Table4. Distributionofend-pointlocationsforeachunstableslope.

GRSMRoute/GRSMDitchWidthinm(ft)%Contained%EndPoints%Reached%ExitedValidation RoadNameID(FieldMeasured)inDitchwithinRoadwayCenterlineRoadwayMethod 0010NNewfoundGapRoadNorth102.1(7)93.96.100GSV,PH

0010NNewfoundGapRoadNorth131.4(4.5)85.514.52.10GSV,PH,USMP 0010NNewfoundGapRoadNorth201.4(4.5)10.889.21.20GSV,PH

0014LittleRiverGorgeRoad70020.179.9210GSV,PH,USMP 0014LittleRiverGorgeRoad870.6(2)77.722.31.70GSV,PH,USMP 0014LittleRiverGorgeRoad880.4(1.25)0.599.53.30GSV,PH

0014LittleRiverGorgeRoad1051.1(3.5)98.021.20GSV,PH,USMP 0008EFoothillsParkwaySection8E1361.8(6)87.412.440.2GSV,PH,USMP 0008EFoothillsParkwaySection8E1683.7(12)99.90.100GSV,PH,USMP

0019LakeviewDriveEast1532.3(7.5)70.927.45.51.7GSV,PH,USMP

0019LakeviewDriveEast1551.5(5)46.253.57.70.3GSV,PH,USMP

0011SGatlinburgSpurRoad(South)2151.8(6)67.832.200GSV,PH,USMP

0011SGatlinburgSpurRoad(South)2161.2(4)41.558.500GSV,PH,USMP 0011NGatlinburgSpurRoad(North)2253.0(10)88.411.600GSV,PH,USMP AVG:63.536.43.40.2

Validationmethods:GSV = GoogleMapsStreetView,PH = photographsandnotesfromfieldvisits,USMP = commentsfromUSMP geodatabase.

potentialsthatdidnotresultinNNPvalues < 5 kgCaCO3 /tduetorelativelyhighneutralizationpotentials.FourrocksamplescollectedfromthreediscreteslopeshadNNPvalues < 5kgCaCO3 /t.The mostnegativevalues, 31.1and 27.6kgCaCO3 /t, werefromtheAnakeestaFormationandwereduplicatesamplescollectedatGRSM-010alongNewfound GapRoadNorth.TwosamplescollectedfromtwodiscreteslopesintheWehuttyFormationalongLakeview

DriveEastalsoindicatedsignificantacid-producing potentialwithNNPvaluesof 26.4and 20.7kg CaCO3 /t.

DISCUSSION

Thegeodatabaseandinventorymapscreatedin thisstudyrepresentanimportantsteptowardsimplementinglong-termGAMprotocolsinGRSM.The

HClSulfideAcidMod.ABANet PasteTotalExtractableSulfurGenerationNeutralizationFizzNeutralization GRSMID/pHSSulfur(bydiff.)PotentialPotentialRatingPotential

Zag = AnakeestaFormation,metagraywackeandmetasiltstone;Za = AnakeestaFormation;Zt = Thunderheadsandstone;Zts = Thunderheadsandstone,darkmetasiltstone,andslate;Zc = Cadessandstone;Zm = MetcalfPhyllite;Zw = WilhiteFormation;Zwe = Wehutty Formation;Zsc = ShieldsFormation,conglomerate;Zp = PigeonSiltstone;Zr = RichButtsandstone.

Table5. CompleteABAtestresultsforthe14investigateddiscreteslopes.

clustermapcreatedusingKDEhighlightssections ofroadwhereslopeswithahighlikelihoodofroadwaydisruptionaremostconcentratedandcanbe usedtocommunicaterisktoparkvisitorsandcommuters.Furthersite-specifickinematicinvestigation ofthestructuralanalysisofbedrockdiscontinuities alongwiththerockfrictionandcohesionwithinthese clusterswillprovideinsightsintowhethersomegeologicorgeometricconditioninfluencesslopestability.Oncestudyareaswereestablishedbasedonresults fromKDEandinputfromparkofficials,site-specific rockfallsimulationsandABAtestswereconducted at14selectedashigh-risksites.Theseinvestigations providedabetterunderstandingofthepotentialimpactsofrockfallsonroadwayinfrastructureandthe environment.

USMPInventoryandKDEs

MostunstableslopesidentifiedinthisstudyarelocatedonthenorthsideoftheparkinTN(88percent) withonlyoneKDEclusteridentifiedinNC.Many oftheseslopesarewithinthefoothillsoftheWestern BlueRidgePhysiographicProvince.Thisprovinceis boundedtothenorthbytheGreatSmokyFaultand tothesouthbytheGatlinburgFaultandischaracter-

izedbyrollinghillswithpredominatelysedimentary bedrock(Neoproterozoic,Cambrian,LowerOrdovician),whichiseitherlow-gradegreenschistfaciesor hasnotbeenmetamorphosed.Aboutaquarterofall slopeswereinthehigher-grademetamorphicrocks ofthehighlandsoftheBlueRidge,andlessthan 7percentofsiteswereintheTennesseeValleyand RidgePhysiographicProvince.Geologicunitswith thegreatestnumberofunstableslopesalongmajor transportationcorridorsareNeoproterozoicinage andincludethePigeonSiltstone(n = 45)andMetcalf Phyllite(n = 45)oftheSnowbirdGroupandthe ThunderheadSandstone(n = 30),CadesSandstone (n = 27),AnakeestaFormation(n = 28),andtheCopperhillFormation(n = 25)oftheGreatSmokyGroup. Theremaining85slopesweredistributedamong11 otherrockformations.The GreatSmokyMountains NationalParkGeologicResourceEvaluationReport by Thornberry-Ehrlich(2008)andpreviousslopesstabilitystudiesatGRSMhinteduponthesamesusceptible rockunits(Moore,2004;Wieczoreketal.,2000;and NandiandShakoor,2017).

TheclusteranalysiscreatedusingtheUSMPtotalscorehelpedtohighlightareaswhereunstable slopesposesignificantrisktoparkvisitorsandcommutersalongGRSMprimaryroutes.Theclustermap

Figure9.Acid-baseaccountingtestdataforGRSM.

O’Shea,Farmer,Nandi,Bilderback,Luffman,andJoyner wasalsohelpfulinestablishingpriorityareaswithin theparkwheresite-specificstudieswereconcentrated. Moreclustersandmoreunstableslopesingeneraloccuronthenorthsideoftheparkbecausethisiswhere themajorityofroadwayswithinthestudyareaexist.Thisrepresentsalimitationofthestudybecause thepresenceofclustersiscontrolledbytheroadways anddatacollectionsites.However,itmayalsobetrue thatrockunitswithinthefoothillsoftheWestern BlueRidgePhysiographicProvincearemoresusceptibletorockfallsandrockslideswhereroadcutsexistthanrockunitswithinthehighlands.Futurestudiescouldevaluatewhetherarelationshipexistsbetweenthemetamorphicgradeofgeologicunitsand instability.

Ultimately,theaimofthisstudywastoassessunstableslopesalongmajortransportationcorridorsin GRSM,sodatacollectionwasconstrainedtoaccomplishthatgoal.TheresearchprovidedexamplesofsitespecificinvestigationslikeprobabilisticrockfallsimulationandABAforselectedsitesthatcouldbeprioritizedfromclusteranalysisusingtheUSMPinventory database.Thistypeofapplicationcouldbeadapted byastatedepartmentoftransportation,FLMA,or futureresearchertosuittheirspecificneeds.Inadditiontoongoingconditionassessmentsandperformancemonitoring,futureeffortshouldbedirected todevelopforecastingmodels,suchastopographical changedetectionusingGPScombinedwithreal-time kinematic(RTK)capabilities,unmannedaircraftsystem(UAS)structurefrommotion(SfM)analysisto generatethree-dimensionalslopemodelsthatcandetectthetemporalchangeofasurface,andterrestrial laserscanner(TLS)andaeriallaserscanner(ALS) datafromUAStodetectslopechangeanddisplacement.Theseforecastingmodelscanprovideestimates offuturechangesintheperformanceofdiscreteslopes, whichcanhelpGRSMparkofficialstoanticipate changestomanagementcostsandevaluateprogram alternatives.

ProbabilisticRockfallSimulations

Accuratelypredictingrockfallsisdifficultdueto variabilityinslopegeometry,uncertainmaterialproperties,andthesensitivityofanalysismethods(Stevens, 1998).However,resultsfromprobabilisticsimulations provideaneffectiveandacceptablemethodforevaluatingthepotentialimpactofrockfallontransportationcorridors.Resultsfromthisstudyshowedrock materialenteringtheroadwayatall14sites,which confirmsthepremisethatGRSM’smajortransportationcorridorsarevulnerabletolocalizedslopefailures.Modelresultsalsoindicatedthatsomesections ofroadwayaremorevulnerablethanothers,mainly

whereditcheffectivenessislimited.Thesepredictions werevalidatedusingacombinationofGoogleStreet View,fieldnotesandphotographs,andcommentsin theUSMPforFLMAgeodatabase.GRSM-136on FoothillsParkwaySection8Estandsoutassomewhat uniquefromtheothersitesduetoitslongandconsistentslope,wideditch,andvegetationneartheslope’s toe.Also,afeatureofinterestisthatvegetationhasa significantdampingeffectonsimulatedrockfalls;however,thisrelationshipiscomplicatedbythefactthat vegetationcancontributetobiologicalweathering,especiallyinfracturedrocks.SiteslikeGRSM-087and GRSM-105alongLittleRiverGorgeRoadstandout becausetheyfeatureblocksthatslightlyoverhangthe roadway.

EnvironmentalImpact(ABA)

InsightsfromABAtestscanbeusedbyGRSMpark officialstohelpdevelopsolidwastemanagementprotocolsatcutslopes.Additionalcostsassociatedwith encapsulatingortransportingacid-producingrockdebrisareimportanttoconsiderforbudgetallocation, whichisanessentialpartoftheGAMprocess.Aspart ofongoingconditionassessmentandperformance monitoring,parkofficialsshouldtakenoteoftheacidproducingpotentialofrockunits.WeatheringofPrecambrianmetasedimentaryrocksintheSouthernAppalachianMountainsiswellrecognized,andtheFederalHighwayAdministrationdevelopedguidelineson evaluationandhandlingofacid-producingmaterials (Byerly,1996).ABAtestresultsindicatedsignificant acid-producingpotentialatthreediscreterockslopes ofthe14sitessampled.Thestudyconfirmedthatthe sulfidemineralscontributetotheacid-generatingpotential,andtheAnakeestaFormationandtheWehuttyFormationpresentthegreatesthazardregardingARD.Atthesesites,itisreasonabletotakespecial precautionswhenhandlingrockfallmaterials.Fieldinvestigationrevealedthatslatymetasiltstonemembers oftheCopperhillFormationmayalsorequirespecialhandlingduetoARD;however,nosampleswere analyzedinthisstudy.SignificantARDseemstobe limitedtoashortlengthofroadway,about21.2km (13.2mi)outof243.7km(151.41mi),almostexclusivelybetweenmilemarkers10and20ofNewfound GapRoad(GRSM-0010N,S),thefirst1.6km(1mi) ofClingman’sDomeAccessRoad(GRSM-0017),and thefirst8km(5mi)ofLakeviewDriveEast(GRSM0019),whereunitsoftheAnakeestaFormation,slaty metasiltstonememberoftheCopperhillFormation,or WehuttyFormationareexposed.

SchaefferandClawson(1996)concludedthatthe AnakeestaFormationisapotentialacid-producing graphiteschistunit,withNNPforthegraphiteschist

unitsrangingfrom 19.27to1.81CaCO3 /t.Hammarstrometal.(2003)conductedathoroughinvestigationofmetalcyclinginGRSMandidentifiedsoils attheHazelCreekMinewithanNNPvalueof 61kg CaCO3 /t.Thatstudypresentedimportantconsiderationsforsulfidemineralsathistoricminesiteswithin thepark;however,thestudydidnotdiscusshowsulfidemineralsandARDcouldimpacttransportation infrastructureorhowsolidwastemanagementpracticesshouldbeincorporatedintoGAMprotocols.

Lathametal.(2009)foundanassociationofsulfide mineralswithunstableslopesinmetagraywackesand graphiticmuscoviteschistsalongtheBlueRidgeParkway.Further,sulfide-inducedheavewasnotobserved duringfieldobservations;however,Bryant(2003)documentedthesameintheSevierShalenearthestudy areaandpresentedchemicaltestsproceduresandvariousARDmitigationoptions.

CONCLUSION

Implementinglong-term,risk-basedstrategicGAM isimperativeforpubliclands,likeGRSM,wheremaintenanceofficialsareresponsibleforachievingperformanceobjectiveswithafluctuatingannualbudget. Thegoaloftheworkdescribedherewastoprovide datatoguideGAMeffortsbyprioritizingsitesandinformingtheselectionofsite-specificinterventions.The studysucceededincreatingthefirstexhaustiveinventoryofunstableslopesalongmajortransportationcorridorsinGRSMandprovidesanexampleofhigh-risk rockslopeprioritizationusingclusteranalysis.Additionally,14site-specificinvestigationswerecompleted thatpredictedrockfallpathwaysusingprobabilistic simulations,andacid-baseaccountingtestswereperformedtoevaluatetheacid-producingpotentialofunstablerocks.ThestudyprovidesageologicandenvironmentalframeworkforsloperemediationtomaintaintheintegrityofroadwaysinGRSM.Thestudywill assistparkofficialsintheireffortsandfosterabetter understandingoflifecyclesofdiscreteunstableslopes.

ThisstudyutilizedtheUSMPforFLMAprotocolto(1)createadetailedinventoryof285unstableslopes,ofwhichfiveslopeswererankedasbeingingoodcondition,147slopeswererankedasfair, and133slopeswererankedaspooraccordingtothe USMPforFLMAclassificationsystem.(2)Fivenoticeableclustersofunstableslopeswithhighlikelihood ofroadwaydisruptionwereidentifiedalongthreemajortransportationcorridorsusingKDE.AsstatedepartmentsoftransportationandFLMAsacrossthe countryadoptandimplementGAMprograms,cluster analysiscanbeusedtotargetremediationandmitigationefforts.Thisissignificantbecause,onceaninven-

toryhasbeencreated,thedecisionofwheretotarget proactivemanagementormitigationcanbedaunting.

Thesite-specificanalysisofthe14high-riskslopes indicatedthat(3)rockfragmentsenteredtheroadway atall14sites,(4)sectionsofroadwaywhereditcheffectivenessislimitedaremorevulnerabletorockfall, suchasalongLittleRiverGorgeRoad(0014),and(5) significantAPPislimitedtoashortlengthofroadwayoverall,becauseonlyabout21.2km(13mi)of roadwayexistwhereAnakeestaFormation,theslaty metasiltstonememberoftheCopperhillFormation,or WehuttyFormationareexposed.Probabilisticrockfall simulationscanprovidevaluableinformationforpark officialswhoareresponsibleforGAMprotocols.Becauserockfalleventsinterferewithtransportationcorridorsduringmostyears,whichcanhaveanegative impactonthelocaleconomy,rockfallmodelinghasa roleinfuturemanagementandmitigationefforts.To alesserextent,thesameistrueforanalysisofacidproducingwasterocksatGRSM.Futurestudiescan evaluatethecorrelationbetweenacid-producingrocks andslopeinstabilityinthepark.

Finally,resultsfromthisstudyaffirmthatGRSM’s majortransportationcorridorsarevulnerabletolocalizedslopefailures.Insightsfromthestudycanbeused byGRSMparkofficialstohelpdevelopshort-and long-rangemanagementandmitigationplans,suchas wideningditches,installingbarriers,andencapsulatingacidicrockfallmaterial.Thesestrategiescaninformparkofficials’effortstomonitortheperformance ofgeotechnicalassetsandmakeperiodicupdatesto theGAMinGRSM.Inadditiontoongoingconditionassessmentsandperformancemonitoring,future effortshouldbedirectedtodevelopforecastingmodels thatestimatefuturechangesinperformanceofdiscrete slopes.Theseforecastingmodelscanfacilitateefforts byGRSMparkofficialstoanticipatechangestomanagementcostsandevaluateprogramalternatives.

ACKNOWLEDGMENT

ThestudywasfinanciallysupportedbyTheUnited StatesDepartmentoftheInterior–TheNationalPark Service/GreatSmokyMountainNationalPark.The AppalachianHighlandsScienceLearningCenterof GreatSmokyMountainsNationalParkprovidedthe researchpermitatthepark.ThereviewersandtheeditoroftheEnvironmental&EngineeringGeoscience (E&EG)journalprovideddetailedandhelpfulcomments,forwhichwearegrateful.

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NewInsightsfromLegacySeismicDataregardingBasalt ElevationsandVariabilityontheHanfordSite

JAMEST.ST.CLAIR*

ADAMR.MANGEL TIMC.JOHNSON

KeyTerms: ColumbiaRiverBasalt,Landstreamer,Hydrostratigraphy,MASW,Tomography

ABSTRACT

MigrationofgroundwatercontaminantsintheGable GapareaoftheHanfordSiteinsoutheasternWashingtonStateisstronglyinfluencedbythedistributionand permeabilityofbasaltsthatliebeneathanunconfined aquifer.Locally,foldingandfaultingoftheColumbia RiverBasaltassociatedwiththeYakimafoldandthrust beltfollowedbyerosionduetotheLakeMissoulafloods resultedinacomplexbasaltsurfacethatrepresentseitheranimpermeablelowerboundarytotheunconfined aquifersystemorlocalizedregionsofincreasedpermeabilitythatpotentiallypromotecommunicationbetweentheunconfinedaquifersystemanddeeper,confinedaquifersystems.Paleo-channelscarvedintothe basaltbyfloodwatersarethoughttoprovidepreferentialflowpathsforgroundwatercontaminants.In2011, aseismiclandstreamercampaignwascarriedouttoimagethebasaltsurfaceandproducedpre-stackdepthmigratedp-wavereflectionimages.Thereflectionimages identifiedtwolargetroughsthatmayrepresentpaleochannelsandseveralareasofpossiblefaulting.Here,the streamerdataarere-analyzedusingrefractiontraveltimeandRayleighwavedispersionanalysestoobtain imagesofcompressionalandshearwavevelocitieswithin thesuprabasaltsedimentsectionsandtheupperbasalt surface.Thecombinedinterpretationofreflectionand seismicvelocityimagesshowscomplexityinthebasalt velocityandelevation,whichvariesby50mormore withinthestudyarea.Theseresults,alongwithotherongoinggeophysicalinvestigations,willbeusedtoinform thesitegeologicmodelandpotentiallyguideplacement offutureboreholesneededtoquantifyverticalflowbetweentheconfinedandunconfinedaquifers.

*Correspondingauthoremail:james.stclair@pnnl.gov

INTRODUCTION

TheHanfordSuperfundsiteinsoutheasternWashingtonStateiswhereplutoniumwasproducedforuse inU.S.nuclearweaponsduringWorldWarIIand throughouttheColdWar.Liquidwastedisposalassociatedwiththeseactivitieshasresultedingroundwatercontaminantplumeswithintheunconfined aquiferemanatingfromthe200EastArea(Figure1) (Hartmanetal.,2009).Therelativelyimpermeablenatureoftheunderlyingbasaltcomparedtotheoverlying sedimentssupportstheconceptualmodelofthebasalt actingasalowerboundaryofthelocalaquifersystem (DOE/RL,2012,AppendixE).Insomeregions,basalt elevationsarehigherthanthewatertable,andflowis confinedlargelytotheregionswherePleistocenefloodingassociatedwithLakeMissoulascouredchannels throughthebasalt(Bjornstadetal.,2010).Thesechannelsandotherlocalgeologicstructuresarethought toprovidepreferentialflowpathsforcontaminant plumestotravelnorthtowardtheColumbiaRiver (Bjornstadetal.,2010).Inaddition,regionalerosion ofunderlyingbasaltlayersduringPleistocenefloodingeventsexposedseveralofthedeeperbasaltunits andinterbedstotheunconfinedaquiferabove,which mayallowverticalcommunicationbetweentheunconfinedaquiferanddeeper,confinedaquiferswithin theColumbiaRiverBasalt(CRB)group(Graham etal.,1984).Giventheseheterogeneitiesandpotential impactsofcontaminantmigrationtotheColumbia River,adetailedexaminationofpaleo-channelgeometryiswarrantedaswellasidentificationofregions thatpotentiallysupportcommunicationbetweendeep, confinedaquifersystemsandtheoverlyingunconfined system.

Surface-basedseismicmethodscanprovideusefulinformationaboutsubsurfacestructureasitrelatestokeyhydrologicparameters(e.g.,Hubbardand Linde,2010).Seismicreflectionmethodsutilizewaves thatreflectoffsubsurfaceboundariestoproduceimagesofsubsurfacestratigraphyandstructure.Seismicrefractionmethodsutilizewavesthataretransmittedthroughthesubsurface,turningbacktoward

thesurfaceastheyencounterfaster-velocitymaterialsatdepth(Steeples,2005).Traveltimesofrefracted wavescanbeusedtoimagetheseismicvelocitystructurewithinthesubsurface.Itismostcommontomeasurecompressionalwavevelocity(Vp)withrefraction methods.Shearwavevelocity(Vs)canbemeasured withdispersiveRayleighwaves(Parketal.,1999). Seismicvelocitiesvarysystematicallywithlithology, porosity,saturation,andpressure(e.g.,Mavkoetal., 2009);thus,theyareusefulforidentifyingbedrock depths,thewatertable,andzonesthatarepotentially morepermeable.

SeveralseismicreflectionsurveyshavebeenconductedintheGableGapareaoftheHanfordsite. During1979and1980,approximately80kmofseismicreflectiondatawereacquiredaspartoftheBasalt WasteIsolationProjecttoidentifydeepstoragetargets forspentfuel(SSC,1979,1980).Morerecently,several shallowseismicinvestigationshavebeencarriedout toidentifybasaltelevationsandstructureforhydrologicinvestigations(Cummins,2009;Hydeetal.,2011) Hydeetal.(2011)showedtheutilityoftheseismic landstreamerfortherapidcollectionofseismicdata ontheHanfordsite.Theirresultsshowedarugged andfaultedbasaltsurfacealong12kmofseismicprofiles.Asubsequentlandstreamerstudy(Sunwalletal., 2011)added12kmofseismicprofileswithintheGable Gapareaandfurthersupportedaruggedandfaulted

basaltsurface.A2012studythatintegratedallofthe existingseismicreflectiondataintheareaandcomparedknownbasaltdepthstotheseismicinterpretationsconcludedthatthereflectiondataoftenoverestimatedbasaltdepths,possiblyduetoaninaccurate seismicvelocitymodelusedtoconverttraveltimesto depth(Williamsetal.,2012).

Inthisstudy,12kmoflegacylandstreamerdata (Sunwalletal.,2011)areanalyzedtomeasureVp andVsfromtherefractedp-wavesandthedispersive Rayleighwavesignals.Theresultingvelocitymodels provideanestimateofbasaltdepththatisindependent fromthepre-stackdepthmigrated(PSDM)–derived estimates.Comparingthesenewresultstoborehole observationsandintegratingthemwiththereflection imagesprovidesanupdatedinterpretationofdepthto basaltwithintheGableGapand200Eareasthatwill beusedtorefinethesitegeologicmodel.Zonesofinterestarealsoidentifiedwhereadditionalcharacterizationisneededtoevaluatethepossibilityofvertical communicationbetweentheunconfinedaquiferand deeperaquifersystems.

HydrogeologicandGeophysicalSetting

ThehydrostratigraphyintheGableGapareaconsistsprimarilyofthesand-andgravel-dominatedHanfordunitsoverlyingthefaultedandfoldedCRBgroup

Figure1.(a)LocationoftheHanfordSiteinsoutheasternWashington(afterSunwalletal.,2011).TheGableGapareaisshowningreen.(b) SatelliteviewoftheGableGapareashowingtheseismicprofilelocations(blacklines)andthelocationsofcheckshots(redstars)andwells (whitecircleswithblackoutlines)usedtovalidateseismicmodels.GableMountainAnticlineisindicatedbysymbols.

withsedimentinterbeds(Figure2).Oldersuprabasalt sedimentaryunitsintheareaaretheColdCreekunit andtheRingoldformation,whicharesparselydistributed,havingbeenerodedduringPleistocenefloodingassociatedwithLakeMissoula.Thetopofthe basaltisconsideredtomarkthebottomofanunconfined,suprabasaltaquifer.Aconfinedaquiferalsoexistswithinthebasaltsandinterbeddedsediments.

TheGableGap(Figure1)lieswithintheYakima foldandthrustbelt.Tothenorthofthestudyarea, theasymmetriceast-to-westGableMountainanticline (GMA)foldstheCRBsandtheinterbeddedEllensburgformation.Thenortherndippinglimbofthis anticlineissteep,whereasthesouthernlimbhasa relativelygentledip.Second-ordersynclineandanticlinepairshavebeeninferredtoexistalongthesouthernlimbandthroughoutthestudyarea(e.g.,Ault, 1981).Previousstudieshavehypothesizedthatsecondarynormalandthrustfaultsmayalsoexistalong thesouthernlimboftheGMA(e.g.,Hydeetal.,2011).

TheprincipalCRBunitofhydrologicinterestin theGableGapareaistheElephantMountainmember,whichisconsideredthebaseoftheunconfined, suprabasaltaquifer(Bjornstadetal.,2010).However, erosionrelatedtoPleistocenefloodingofLakeMissoulahaslocallyerodedtheElephantMountainmember,exposingolderbasaltunitsandsedimentinterbeds totheunconfinedaquifer,andmayallowvertical communicationbetweentheunconfinedandconfined aquifersintheseareas(Grahametal.,1984).

AbovetheCRB,theunconfinedsuprabasaltaquifer ishostedintheflood-depositedHanfordformation. TheHanfordformationcontainsbothgravel-(H1and

Figure3.Summaryofdownholeseismicvelocitymeasurements madeinboreholesneartheGableGaparea(RohayandBrouns, 2007).UnsaturatedVpfortheHanford(H-2andH-3)andCold Creeksedimentsareshowninblack,andsaturatedVpintheCold Creek,Ringold(Rg),andbasaltunitsareshowninblue.Vsforall unitsisshowninred.

H3)andsand-(H2)dominatedunits,andtheirdistributionreflectsflooddynamics.TheolderRingoldand ColdCreekunitsareriverdepositscontaininggravels, sands,andsilts.However,withintheGableGaparea, theywerelargelyerodedduringtheLakeMissoula floodsandareunlikelytargetsforrefractionimaging.

RohayandBrouns(2007)usedcheckshotstomeasureVpandVsinthreeboreholesinthe200East Areasouthofthelandstreamerprofiles(Figure1a). Vpdatawerecollectedusingasledgehammersource, andVsdatawerecollectedusingahorizontalacceleratedweightdrop.TheboreholespenetratedtheHanfordunits,theColdCreekunit,theRingoldformation, andtheCRB.Theirmeasurementsreflectunsaturated conditionsfortheHanfordunitsandtheColdCreek unitandsaturated(belowthewatertable)conditions fortheColdCreekunit,Ringoldformation,andCRB (Figure3).Theinfluenceofthewatertablecanbeseen fortheColdCreekformation;VpinunsaturatedCold Creekis1,200–2,000ms 1 ,whilesaturatedColdCreek rangesbetween3,400and3,900ms 1 .Sincefluidsdo notsupportshearstresses,Vsislargelyunaffectedby fluidsaturation.

TheRohayandBrounsmeasurementsindicatethat Vpshouldbeareliablemeansofdistinguishingbasalt fromtheHanfordformation.UnsaturatedColdCreek isalsoeasilydistinguishedfrombasalt.Vpinsaturated ColdCreekandtheRingoldformationoverlapwith thebasaltmeasurements,indicatingthatitwillnotbe possibletodifferentiatethemusingVpmeasurements. Additionally,RingoldVsoverlapswithbasaltVs, suggestingthatevenunsaturatedRingoldcannotbe

Figure2.GeneralizedhydrostratigraphyintheGableGaparea (afterBjornstadetal.,2010).

confidentlydistinguishedfrombasaltonthebasisof seismicvelocityalone.

LegacySeismicData

Thelandstreamerisarapidseismicacquisitionsystemthatconsistsofastringofgeophonesandanacceleratedweightdrop(AWD)towedbehindavehicle. Thegeophonespacingisfixedsothataftereachshot, theentiresystemismovedforward,andthesourcereceiveroffsetsremainconstant.Thestreamersurvey geometryissuitableforreflectionimaging,compressionalwaverefractionimaging,andRayleighwavedispersionanalysis.

Approximately12kmoflandstreamerdatawerecollectedinAprilandMay2011bytheConfederated TribesoftheUmatillaIndianReservationandMontanaTechnologicalUniversity.Thestreamercomprised96gimballed30-Hzgeophonesspaced2m apart.Thestreamerwastowedbyapickuptruck, whichalsotowedanAWD6minfrontofthestreamer. TheAWDwasa227-kgsteelramliftedbyahydraulicpumpandacceleratedbyanelasticband.The AWDwasusedtoverticallystrikea60 × 60-cmsteel plateat2-mintervalsalongeachprofile.Thissource produceshigh-amplitudeseismicenergyinthe5-to 150-Hzbandandfacilitatesrapiddataacquisition. Thedatasetincludes5,564shotgathers,andeach shotrecordcontains2secondsofdatasampledevery 0.5ms.Theminimumoffsetwas6m,andthemaximumoffsetwas196m.

Sunwalletal.(2011)processedthereflectiondata andproducedPSDMimages.However,therawdata (Figure3)showcleardirectandrefractedarrivals withapparentVpthatisconsistentwithsuprabasalt sediments(∼1,200ms 1 )andtheunderlyingbasalt (∼4,500ms 1 ).DispersivesurfacewavesarealsoevidentandsuggestsuprabasaltVsbetween200and 600ms 1 .Inthisarticle,thefirst-arrivaltraveltimes andRayleighwavedispersioncurvesareusedtoimageVpdowntobasaltandVsintheupper15–30m, respectively.

METHODS SeismicTomography

Seismicrefractionmethodsutilizethetraveltimeof thefirstarrivingbodywaveforeachsource–receiver pair.Seismicvelocitiesgenerallyincreasewithdepth, causingseismicraystoturn,orrefract,backtoward thesurfaceastheypropagate.Atshortsource–receiver distances,thefirstarrivaloftenrepresentsthewave travelingdirectlyfromthesourcetothereceiver,and theslopeofthedistance–timecurveistheinverseof

seismicvelocity.Atlongerdistances,itisoftenobservedthatthetravel-timecurvebecomesflatter,indicatingthatthewaveshavetraveledthroughahighervelocitymedium.Seismicvelocitiescanbemeasured directlyfromtheslopesofthetravel-timecurves.However,whenthereislateralvariabilityinthesubsurface, amorerobustapproachisrequired.Tomographyisan iterativemethodwherethesubsurfaceisrepresented bymanysmallelementsofconstantvelocity.Aninitialvelocitystructureischosen(usuallyanincreasein velocitywithdepth),andtraveltimesfortheinitial modelarepredicted.Thedifferencebetweenthepredictedandtheobservedarrivaltimesisusedtofinda modelupdatethatreducesthemisfit.Theprocessof predictingtraveltimesandupdatingthemodelisiterateduntilthedatamisfitbecomesacceptable.

Findinganappropriateupdatetothemodelrequires solvinganinverseproblemthatisgenerallypoorly constrained.Thedatacontainerrors,someregionsof themodelmaynotcontributetothetravel-timeprediction,andsolutionsarenon-unique(e.g.,deWitetal., 2012).Theproblemcanbemadestablebyincluding smoothnessconstraintsonthemodel,knownasregularization.Regularizationconstraintsplaceapenalty onmodelparametersthatareverydifferentfromtheir neighbors.Thereisatrade-offbetweendatamisfitand modelsmoothness,anditispossibletofindmanyvelocitymodelsthatexplainthedata.

TheconventionalapproachtoregularizingtheseismictomographyproblemistominimizetheL2norm (squarerootofthesummedvalues)ofthevelocitygradient.Thisapproachwillnotallowsharpboundaries, andbecausetheL2normseekstonormallydistribute velocitygradientsthroughoutthemodel,ittendsto producesmoothgradientsinareaswherethedatado notconstrainthemodel.Analternativeapproachisto minimizetheL1normofthevelocitygradient(sum oftheabsolutevalues),whichallowssharpboundaries todevelopandsuppressesgradientsinareaswhere thedatadonotconstrainthemodel.IntheGable Gaparea,thetransitionfromsuprabasaltsediments tobasaltislikelyasharpvelocitycontrastforseveral reasons.First,boreholegeophysicalmeasurementsindicatethatthetransitionfromsuprabasaltsediments tobasaltissharp(RohayandBrouns,2007;Hyde etal.,2011;Figure2).Therawseismicshotsalsocontainseveralindicatorsofasharpseismicboundary; theapparentbasaltVpmeasuredontherawdatais

∼4,500ms 1 ,andtheapparentsedimentvelocityis ∼1,200ms 1 (Figure3).Muchofthedataalsocontainaconvertedshearwavephasewithanapparentvelocityof ∼2,200ms 1 (Figure3).Thepresenceofthis phaseindicatesthatVsinthebasaltisgreaterthanthe Vpofthematerialimmediatelyaboveit(St.Clairand Liberty,2019).

Atwo-dimensionaltomographycode(St.Clair, 2015)writteninMATLABwasusedtoinvertmanuallyinterpretedfirst-arrivalobservations.Thecode predictstraveltimesusingtheshortestpathraytracingmethod(Moser,1991),andtheinverseproblemis regularizedwithfirst-orderderivativeoperatorsinthe verticalandhorizontaldirections.TheL1constraint onmodelgradientisimplementedwithaniterative leastsquaresalgorithm(Ajo-Franklinetal.,2007). Thehighspatialdensityofthedataresultsinmanyredundantraypaths;thus,traveltimeswereinterpreted oneverysecondtothirdshotgather.

Travel-timetomography,likemanygeophysicalmethods,suffersfromuncertaintyandnonuniqueness.Uncertaintyisduetonoiseinthedata, thepossiblecorrelationofdifferentmodelparameters, andnon-uniformsensitivityofmodelparameters tothedata.Thesamefactorscontributetononuniquenessofthesolution.Becausetheseissuesare inherentinthemethod,theapproachusedtomodel thedataneedstobeconsistentwiththegeologic setting,usinginformationnotcontainedinthetravel times(e.g.,thepresenceofconvertedphases)asa constraintformodelselectionandvalidation.

Non-uniquenessinthetomographysolutionoccurs bothbecauseoferrorsanduncertaintyintheinput dataandbecausemanydifferentvelocitystructures canhaveidenticaltravel-timecurves(e.g.,Shearer, 1999).ThechoiceofanL1constraintonmodel smoothnessfavorsmodelswithasharpboundary.The rangeofacceptablemodelsisfurtherlimitedbypreferringsolutionswiththeminimumamountofstructure requiredtofitthedatawithinestimateduncertainty. Thepickingerrorisestimatedtobeontheorderof 1–3ms;thus,therootmeansquareerrorsforthemodelsshouldbe ∼3ms.Finally,regionsofthemodelsare maskediftheydonotcontributetothedatamisfit(i.e., wherenoraysarepresent)usingthederivativeweight sum.Thederivativeweightsumrepresentsthetotal lengthofraypathsthatpassthroughagivenmodelcell.

Thedatasetcontainsseveralpointswhereprofiles intersect(Figure1b).Comparingthevelocityestimates andinterpreteddepthstobasaltatthesepointscan givesomeinsightintotheprecisionoftheapproach. Sinceeachprofilewasinvertedindependently,thecoincidentmeasurementscanbeconsideredasindependentobservations.Finally,modelsarevalidatedby comparingthepredicteddepthtobasalttonearbywell observationsand,whereavailable,tovelocityprofiles derivedfromcheckshotdatainnearbywells.

RayleighWaveDispersionAnalysis

Rayleighwavesaresurfacewavesthathaveboth verticalandhorizontalcomponentsofmotion.They

travelatphasevelocities,whichareslightlyslower thanshearwaves,andthelower-frequency(longerwavelength)componentsaresensitivetogreater depths.AtypicalRayleighwavedispersioncurvewill havehigherphasevelocitiesatlowfrequenciesand lowerphasevelocitiesathighfrequencies.

Themultichannelanalysisofsurfacewaves (MASW)approach(Parketal.,1999)wasusedin thisanalysis.Alinearradontransformapproach (Mikeselletal.,2017)wasusedtomaptherawshot gathersintothefrequency-phasevelocitydomain (Figure4b–d),andthedispersioncurvesweremanuallyinterpreted.Thedispersioncurveswerethen invertedforone-dimensionalVsprofilesthatrepresenttheaverageVsstructureacrossthewidthofthe streameraperture(196m).Theresultsaredisplayed aspseudo–two-dimensionalVsimageswitheachonedimensionalmodelmappedtothemidpointofthe streamerforthecorrespondingshotgather.Dueto thehighspatialdensityofthedataset,thedispersion curveswereinterpretedforeverysecondorthirdshot gatherwherenoiselevelallowedaconfidentinterpretationtobemade.Someportionsofthedatawere toonoisytointerpret,andfinalimagesaremaskedto reflecttheabsenceofdata.

Liketravel-timetomography,MASWrequiresaninversesolutiontofindtheoptimalVs-depthprofilethat agreeswiththemeasureddata.Theinversionisiterativeandrequiresamethodforpredictingthedispersioncurveforanygivenmodel.ThepropagatormatrixapproachdescribedinAkiandRichards(2002) wasusedtopredictdispersioncurves,andfirst-order differenceoperatorswereusedtoconstraintheinversionstobesmooth.TheRayleighwavephasevelocity issensitivetoVs,Vp,anddensity.Sincetheinfluence ofVpanddensityissmallcomparedtoVs(Xiaetal., 1999),theVp/Vsratiowasfixedat2,withdensityincreasinglinearlyfrom1,900to2,100mkg 3 fromthe surfacetoadepthof35m.Modelsareparameterized aslayersthatincreaseinthicknessfrom1to3mwith increasingdepth.

Whilethe30-Hzgeophonesusedinthissurvey limittheamountoflow-frequencycontentrequired fordeeperMASWsensitivity,Ivanov(2008)demonstratedthatdispersioncurvescanbeinterpreteddown toaboutanoctavelowerthanthenaturalfrequency ofthegeophones.Frequency-phasevelocityimagesfor thedatapresentedhereshowinterpretabledispersion downto ∼15–20Hz(Figure3).Toindicatewherethe modelsaresensitivetothedata,thepartialderivativesofdatamisfitwithrespecttomodelparameters aresummedforeachmodellayer,andlayersthathave littleinfluenceondatafitaremasked.Thisprocedure suggeststhatthedispersiondataaremostsensitiveto theupper10–20m.Basaltdepthsare50–100m,so

thedatadonotconstrainbasaltproperties;however, theydoprovideinformationaboutshallowsediment properties.

TheMASWapproachproducesone-dimensional modelsthatsmeargeologicstructureoverthewidth ofthegeophoneapertureof196m.Thus,theresulting imagesareunlikelytocapturestronglateralchangesin Vsbutwillhighlightlong-wavelengthstructure.

RESULTS

Therefraction-generatedVpandMASW-generated VsmodelsforLineANorthandLineBarepresented (Figures5and6)andcomparedtothePSDMimages ofSunwalletal.(2011)aswellasnearbyborehole observationsofbasaltelevationandvelocityprofiles obtainedfromdownholecheckshots.Figure1displaysthelocationsofallwellsandcheckshotsthatare comparedtotheseismicresults.Resultsfortheother profilesindicatedinFigure1aredisplayedinthesupplementaryinformation.https://www.aegweb.org/eeg-supplements

LineANorth

LineANorth(Figure5)isanapproximately4.1-km, north-to-southprofile(seeFigure1).Itisthelongest profileinthedatasetandcrossestwotroughsinterpretedaspaleo-channels(distancesof400–800mand 2,000–3,000minFigure5a).Vpinthesuprabasalt

sedimentsectionrangesfrom400and1,500ms 1 ,and basaltVprangesbetween3,000and4,500ms 1 .The 3,000-ms 1 Vpcontourisintermediatebetweensedimentandbasaltvelocitiesandtypicallyliesatthecenterofthesteepestverticalvelocitygradient;thus,this velocitycontourwasselectedtointerpretthetransitionfromsedimenttobasalt.

LowerbasaltVpisapparentalongtheedgesofregionswherethedepthtobasaltisnotwellconstrained (distancesof400–800mand2,000–3,000minFigure 5a).Here,thedatalackthecoveragenecessarytoadequatelyconstrainbasaltproperties.Incontrast,the lowerbasaltvelocitynear3,400–3,500miswellconstrainedandnicelycorrelateswithadiscontinuityin thePSDMreflectionimage(yellowcolorsrepresentingVp ∼2,500m/s;Figure5a).Thisareamayrepresentalocallypermeablebasaltfeature.Therearetwo regionsalongANorthwheretherefractiondatado notconstraindepthtobasaltbetweenprofiledistances of300and1,000mand2,000and3,000m.Here,either thebasaltmaybetoodeeptoimagewiththestreamer offsetorthebasaltvelocitymaybelowercompared tootherregions.Itisalsonoteworthythatintheseareas,thePSDMimageshowsalesscontinuousreflector, suggestingthatthebasaltmaybefracturedorotherwisedamagedthrougherosionorfaulting,supporting theinterpretationthatbasaltVpislower.

Therearethreelocationsalongthisprofilethat areintersectedbyotherlines,andthedepthtothe

Figure4.(a)Every100thshotgathersalongprolifeB,showingclearfirstarrivalswithapparentVpconsistentwithsuprabasaltsediments (1,200ms 1 )andbasalt(4,500ms 1 ).Only0.5secondsofthe2-secondrecordisshown.Manyoftheshotgathersinthisdatasetalsocontain aconvertedphasewithanapparentvelocityaroundhalfthatofthefirstarrival.ThisP-SV-Pphasesuggestsasharptransitionfromsediment tobasalt.ThedataalsocontaindispersiveRayleighwavessuitableforimagingshallowVsstructure(b–d).

NewInsightsfromLegacySeismicDataregardingBasaltElevationsandVariabilityontheHanfordSite

Figure5.(a)LineANorthVpresultoverlaidonPSDMimage.Whitelinesindicatebasaltelevationsobservedinnearbywells.Thickblackline highlightsthe3,000ms 1 Vpcontourusedtointerpretbasaltelevation.Reddotsindicateinterpreteddepthstobasaltoncrossingprofiles. CheckshotVpresultscomparedtorefraction-derivedVpareshownininsets.(b)TheVsimagewithVpcontoursoverlaid(thindashedlines). DashedblacklineisinterpretedtransitionbetweenthesandyH2andthegravel-dominatedH3.

Figure6.(a)LineBVpresultoverlaidonPSDMimage.Whitelinesindicatebasaltelevationsobservedinnearbywells.Thickblackline highlightsthe3,000ms 1 Vpcontourusedtointerpretbasaltelevation.Reddotsindicateinterpreteddepthstobasaltoncrossingprofiles. CheckshotVpresultscomparedtorefractionderivedVpareshownininsets.(b)TheVsimagewithVpcontours(thindashedlines)overlaid. DashedblacklineisinterpretedtransitionbetweenthesandyH2andthegravel-dominatedH3.

3,000-ms 1 contouralongthoseprofilesisshownby reddots.WherelinesBandDcross,interpreteddepth tobasaltonallthreeprofilesisveryclose.The3,000-m s 1 contoursarewithin2mattheLineDcrossingand 4mattheLineBcrossing.LineCWesthasthedepth to3,000-ms 1 atapproximatelythesamedepthasthe reflectionsinthePSDMimage.

Threewellobservationsofbasaltelevationare within500mofLineANorthtocomparetothe refractionresultinFigure5a.Intwocases,thewell observationscorrespondtolocationswheredepthto basaltiswellconstrainedbytherefractions.Well69955-60Adidnotreachbasalt;thus,thiswellrepresents aminimumbasaltdepth.Thedepthtobasaltinwell 699-61-62andthe3,000ms 1 arewithin5m,or ∼10percent,ofthetotalobserveddepth.Thedifferencemaybeattributedtouncertaintyintheseismicresultortorealvariationinbasaltelevationbetweenthe seismicprofileandthewell.Athirdnearbywellcannot becomparedtotheVpresult,astherefractiondatado notimagebasaltatthatlocation.

Twocheckshotvelocityprofilesarealsoavailable alongLineANorth(Figure5a).Well699-55-60Ais ∼475mtotheeastoftheprofileanddidnotreach basalt.Itshowstwothin,high-velocity(Vp = 2,000 ms 1 )layersthatarenotevidentintherefractionresult.Well699-61-62,whichdidreachbasaltatadepth of54m(Figure5a,inset),indicatestwoVplayers withinthesedimentsection(Vp < 1,200andVp = 2,000ms 1 ).Therefractionresultisintermediatebetweenthesetwovelocities.Thebasaltdepthsandvelocityestimatesinwell699-61-62closelymatchthe refractionresult.

Vsalongthisprofilerangesbetween150and 650ms 1 (Figure5b).The1,000-ms 1 Vpcontour closelyresemblesthetransitionbetweenVslessthan 500ms 1 andVsgreaterthan550ms 1 ,suggesting thatthistransitiondifferentiatestheH2fromtheH3 formation.

LineB

LineBisanapproximately2.7-km-longnorthwestto-southeasttrendingprofileinthenorthernpartof thestudyarea(Figure1).Here,asimilardistributionofvelocitiesisobservedinthesuprabasaltsedimentsectionasLineANorth.Thetopofthebasalt showsaprominentdipatprofiledistance ∼1,350m, mirroringthestructuredepictedinthePSDMimage (Figure6a).Recoveredvelocitiesinthatareaarelower comparedtoadjacentsections.CombinedwiththediscontinuousstructureindicatedbythePSDMimage, itsuggeststhatthisisanareawherethebasaltsurfaceisirregularandpossiblyfracturedandpermeable. Ontheeasternedgeoftheprofile,eitherthebasalt

istoodeepforimagingorthevelocityoftheupperbasaltislowercomparedtoelsewherealongthe line.

Fourwellobservationsofbasaltelevationarewithin 500mofLineB(Figure6a).Thewellprojected ontoprofiledistance2,400mis446mawayandsuggeststhattherefractionresultunderpredictsthebasalt depthandagreesmorecloselywiththePSDMimage. Thewellisfarawayandmaynotrepresentthebasalt depthattheseismicprofile.Elsewherealongtheline, thenearbywellsareinbetteragreementwiththerefractionresult.

Well699-61-62is149mtothesouthofLineB. Itisevidentthattherefractionresultrepresentsa smoothedversionofthevelocityprofileobtainedfrom thecheckshot.Therefractionresulthereshowsa smoothtransitionintobasaltvelocities,whichmay explainwhyitoverpredictsbasaltdepthanddemonstratestheuncertaintyinherentintherefraction method.ItisalsopossiblethatbasaltelevationisdifferentatthewellsitethanitisalongLineB.

Vsalongthisprofileshowsasimilarcorrelationto Vpstructure,indicatingthattheH1andH2formationsarenotuniformlythick.Nearthecenterofthe profile,ahigh-velocityanomalyinbothVpandVs suggestsathinningoftheH2layer.

DISCUSSION ComparisontoPSDM

Williamsetal.(2011)compiledalltheavailableseismicreflectiondataintheGableGapareaandcomparedthemtowellobservationsandfoundthatthereflectiondataconsistentlyoverpredictedtheestimated depthtobasalt.MuchofthedatathatWilliamsetal. (2011)reviewedweretime-stackedimagesconverted todepthusingasmooth-velocitymodelderivedfrom checkshots.Williamsetal.(2011)suggestedthatthe depthpredictionerrorswerelikelyduetoapoortimeto-depth-conversionvelocitymodel.Thatargument doesnotapplytothePSDMimages,asthePSDMapproachsimultaneouslyanditerativelyproducesahighresolutionvelocitymodeldirectlyfromthedata(e.g., SheriffandGeldart,1995).Thefinaloutputisareflectionimageindepththatcanbedirectlycomparedto therefraction-derivedVpimages.

ComparedtotheinterpretedrefractionVpimages, basaltdepthspredictedusingthePSDMimagesdo tendtobegreater.However,thisisnotuniversally true.Alongthenorthern-mostendofLineANorth (Figure5a)andalongLineCEast(FigureS3),the refractionVpresultsareremarkablysimilartothe PSDMimages.Ignoringthedepthdiscrepancies,a closesimilaritybetweentopographyalongthebasalt

approach(bluelines)fortheextractedvelocitymodelshowninpanelc.Thehyperbolicreflectiongeneratedbythesteepvelocitygradientat ∼52-mdepthagreeswiththereflectionobservedintherawdata.Blackcurveinpanelcindicatestheaverageverticalvelocitygradientacross thewidthofshot1731,andgraylinesshowverticalvelocityprofilesextractedat2-mintervalsacrossthewidthofthestreameratshot1731.

surfaceinterpretedfromthePSDMimagesandfrom therefractionVpimageswasobserved.Thissuggests thatthestructuralcomplexityindicatedbybothapproachescanbereliablyinterpretedeveniftheabsoluteelevationsareuncertain.

Rawshotgatherscontainvisiblebasaltreflections andcanbeusedtovalidatetheVp-derivedbasalt depthestimates.Traveltimesforaone-dimensional velocityprofileextractedfromthetomographyresult werepredictedfromLineANorthinanareathat showsrelativelylittlelateralstructure.TheVpwasextractedalongtheapertureofashotgather(196m) centeredatthemidpointbetweenthesourceandthe farthestoffsetreceiverforshotgather1731.Thismidpointliesatprofiledistanceof ∼1,362minFigure5a. Predictedtraveltimesforallpossiblerefractedandreflectedphasesweregeneratedfortheaveraged,onedimensionalmodelusingatau-papproach(Shearer, 1999)andwereoverlainontherawdata.Figure 7ashowsthetrace-normalizedshotgatherwitha 200-msAGCapplied.Thebasaltreflectionisvisibleat ∼0.15secondsand100-moffset.Figure7b showsthepredictedtravel-timecurvesfortheonedimensionalmodel(bluelines);refractedphaseshave

straightslopes,andreflectedphasesarehyperbolic. Thereflectiontimepredictedbytau-pcloselymatches thereflectionobservedintheshotgather.Figure7b alsoshowstheobservedfirst-arrivaltimescompared totheraytracingpredictions,whichcloselymatchthe tau-ppredictions.Thissuggeststhatthediscrepancy betweenthetwoapproachesisrelatedtotheinherentnon-uniquenessofboththeVptomographyand thePSDMapproachandreinforcestheneedtovalidategeophysicalresultswithmultipleindependent observations.

CoincidentmeasurementsofbasaltelevationinterpretedfromtheVpresultsalongLinesANorth,B, andDsuggestthattheprecisionoftherefraction methodisontheorderof ±2–4m.Thislikelyappliestoregionswherethebasaltsurfaceislaterallyuniform.Inareaswherethebasalthasroughtopography, therefractiondatawilltendtosmoothoutthesmallscalevariationsassociatedwithsedimentvelocitytransitions.Agoodexampleofthisisthecomparisonof theVpimageandthecheckshotfromwell699-61-62 (Figure6a).TheVptomogramshowsasmoothgradientfromsedimenttobasaltvelocities,whereas150m away,thecheckshotshowsahardboundary.There-

Figure7.Shotgather1731alongLineANorthwitha200-msagc.Thisshotgathercorrespondstoaprofiledistanceof ∼1,362min Figure5a.(a)Therawdatawhereareflectioncanbeseenat ∼0.15secondand100-moffset.(b)Thesameshotgatherwithpickedtravel times(reddots),predictedtraveltimesforthetomographymodel(greenline),andpredictedarrivaltimesofallphasesgeneratedbythetau-p

flectionimageshowsroughtopographyonthebasalt atthislocation.Sincetherefractiondatacanresolve onlyasmoothedversionofthestructure,thedepthto basaltispoorlyconstrained.

VariationsinBasaltVp

TheVptomogramsshowvariationsinbasaltvelocitythatmayindicateareaswherethebasaltisfracturedandlikelytobepermeable.Thehighestbasalt Vp(∼5,000ms 1 )onLineANorth(Figure5a) occurswherethePSDMimageshowsastrongcoherentreflection,suggestingasmoothbasaltsurface (betweenx = 100–300mand1,200–2,000m).On theedgesofthesesections,Vpisnotaswellconstrainedduetoalackofdatacoverage.However, basaltVpislowerattransitionsinreflectioncoherence.Theseareasmayrepresentarubblybasaltsurfaceoraregionofincreasedfracturedensitywhere communicationbetweentheunconfinedandconfinedaquiferscouldpotentiallyoccur.However,well observationsareneededtoconfirmthis.

Atprofiledistancesgreaterthan3,000mforLineA North,Vpinthebasaltis ∼3,500–4,500ms 1 ,and thereflectionimageindicatesseveraldiscontinuities. Here,thelowVpalongthetopofthebasaltiswell constrainedandmayindicatefracturedandpermeable basalt.ThecombinationoflowbasaltVpanddiscontinuousstructureinthePSDMimagesupportsaninterpretationoffoldedand/orfracturedbasalt.

BasaltElevationMap

The3,000-ms 1 Vpcontourmarksthetransition fromsuprabasaltsedimentstobasaltandshowsgood agreementwithwellobservations(Figures5and6). ThereareafewsectionsofLineANorthwherethe topofthebasaltistoodeeptobeimagedwiththe 196-mstreameroffset,andtheseareareaswherepaleochannelscarvedthroughthebasalthavebeenpreviouslyinterpreted(Sunwalletal.,2011).Here,the PSDMimageswereusedtointerpretbasaltelevation withtheunderstandingthattheymaybebiasedtoward overlydeepestimates.

CombiningthedepthtoVp = 3,000ms 1 where itiswellconstrainedwithPSDMinterpretationsand wellobservationsyieldsatwo-dimensionalvisualizationofthebasaltsurfaceintheGableGaparea.Figure 8showsaminimumcurvaturesurfacefittothedata maskingeverywhereinthestudyareathatismorethan 250mawayfromanobservation.Itshowsacomplex basaltsurfaceconsistentwithpreviousinterpretations ofpaleo-channelsandsecond-ordersynclineanticline pairssuperimposedonthesouthernlimboftheGMA.

Mostofthevariationinbasaltelevationissupportedbytheseismicresults;however,wellobserva-

Figure8.Minimumcurvaturesurfacefittowelldata(reddots), interpreteddepthstoVp = 3,000ms 1 (blacklines),andbasalt elevationsinterpretedfromreflectionsonLineANorth,whererefractionsdonotconstrainbasaltelevation(whitedots).

tionsalsoshowlargevariationsovershortdistances. Forexample,twowellsnorthofLineCEastshowelevationdifferencesof24moveradistanceof190m.

VariationsinShallowVsandVp

BoththeVp-andtheRayleighwave–derivedVsimagesindicatelateralvariationsinshallow(<20mdeep) sedimentproperties.Thesevariationslikelyrepresent changesingrainsizedistributionorsandyversusgravellydepositsoftheHanfordformation.Forexample, thedashedlinesinFigures5band6bindicateaninterpretedboundarybetweenthesandyH2andthe gravel-dominatedH3unit.Giventhedifferentgrain sizedistributionineachunit,adifferenceinporosityandhydraulicconductivityisalsoexpected,and variationsinthedistributionandthicknessofthese twounitsmayimpactverticalfluidinfiltrationandunsaturatedflowwithinthevadosezone.Futurestudies withlower-frequencygeophonescouldpotentiallyimagedeeperVsstructureandprovidethepossibilityof usingVp/Vsratiostodiscriminatebetweendryand saturatedconditions.

CONCLUSIONS

Theseismiclandstreamerisaneffectivetoolfor characterizingnear-surfacehydrostratigraphyatthe HanfordSite.Whilepreviouswork(Hydeetal.,2011; Sunwalletal.,2011)focusedonthereflectedwave field,thisanalysisdemonstratesthattherefractionand Rayleighwavedatacanprovideadditionalinformationonbasaltelevationsandproperties.

Theintegrationofreflection,refraction,Rayleigh wavedata,andboreholeinformationhasrevealedpreviouslyundetectedfeatureswithinthestudyarea.The refractiondataprovidedanupdatedestimateofbasalt elevationandallowedtheidentificationoflow-velocity zonesthatcoincidewithdiscontinuitiesinthereflectionimages.Theselow-velocityzonesmaybeindicativeoffracturessupportingverticalcommunication betweentheconfinedandunconfinedaquifers.Integratingwellobservationswiththeseismicinterpretationssuggeststhattherefractionapproachprovided basaltelevationestimatesthatagreemorecloselywith wellobservationscomparedtoPSDM.Bothmethodssupporttheinterpretationofahighlyvariable basaltsurface.VsstructurederivedfromRayleigh waveanalysiswasbroadlyconsistentwithVpstructureintheupper10–20m,indicatingvariationsin thedistributionofHanfordformationunits.This information,alongwithcontinuinggeophysicalinvestigations,willbeusedtorefinethesitegeologic model.

ACKNOWLEDGMENTS

ThisdocumentwaspreparedbytheDeepVadoseZone–AppliedFieldResearchInitiativeatPacificNorthwestNationalLaboratory.Fundingforthis workwasprovidedbytheU.S.DepartmentofEnergy (DOE)RichlandOperationsOffice.ThePacificNorthwestNationalLaboratoryisoperatedbytheBattelle MemorialInstitutefortheDOEundercontractDEAC05-76RL01830.

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PossibleFaultCommunicationbetweentheMemphis SandAquiferandtheMississippiRiver

RICHARDV.MARTIN*

St.SimonsIsland,GA31522

ROYB.VANARSDALE VALARIEJ.HARRISON

DepartmentofEarthSciences,UniversityofMemphis,Memphis,TN38152

KeyTerms: MemphisSand,MemphisAquifer,Faulting, Aquifer,ShelbyCounty,Tennessee

ABSTRACT

TheEoceneMemphisSandaquiferisthemajor sourceofdrinkingwaterformunicipalitiesintheupperMississippiEmbayment,withthecityofMemphis, TN,beingthelargestconsumer.ConcernsaboutcontaminationoftheMemphisaquiferfromsurfacewaters haveprimarilyfocusedonlocalgroundwatertransmissionthroughtheUpperClaiborneaquitardanderosional windowsthroughtheaquitard.Thisstudyusedrecent faultmappingtoshowthatfaultsextendupwardfrom theMemphisaquifertoverynearthesurfaceinand adjacenttoMemphis.TheMeeman-ShelbyandCuba faultsextendundertheMississippiRiverandupwardto thebaseoftheMississippiRiveralluvium.Groundwaterlevels(potentiometricsurface)inMemphisaquifer monitorwellsH002andLdF004andtheMississippi Riverwaterlevelsduringtheyearsof2007through2011 showstrongcorrelation(0.744829and0.779691,respectively).Webelievethiscorrelationmaybeduetodirect connectionthroughfaultzones.

INTRODUCTION

TheMemphisaquiferserveswesternTennesseeand particularlythecityofMemphisandShelbyCounty astheprimarysourceofpotablewater.Production fromtheMemphisaquiferisapproximately794ML/d inMemphis(Maupinetal.,2014).Waterquality oftheMemphisaquiferwasthoughttobevery goodthroughouttheaquifer(ParksandCarmichael, 1990c),butLarsenetal.(2003)reportedthatwater qualityisbetterinthelowerportionoftheaquifer thanintheupperportion.Theyattributedthisquality differencetoseepageofsurfacewaterintotheupper portionoftheMemphisaquifer.ThelowerMemphis

*Correspondingauthoremail:docrvmartin@gmail.com

aquiferbeneathShelbyCountycarriesoldwaterfrom outcroprechargeinFayetteCountyandisunaffected bymodernsurfacewaters(Figure1).

ThemostseriouspotentialproblemwithgroundwaterqualityintheMemphisaquiferiscontamination fromsurfaceleakagedownintotheaquifer.Garbage dumpsandvariouscontaminatespillsareamajor concernwheretheoverlyingaquitardisabsent,thin, orfaulted.Theaquiferisthickandcontinuouswith goodqualitywaterandrecharge.Iftheaquifercanbe protectedfromcontamination,theMemphisaquifer shouldbeabletocontinuetomeettheneedsofits users.

Possibleleakageandrechargefromsurfacewaters intotheMemphisaquiferhavebeendiscussedby severalauthors(Parks,1990;ParksandCarmichael, 1990a;KingsburyandParks,1993;Larsenetal.,2003; Carmichaeletal.,2018;andTowell,2021).Thispaper focusesonpossiblecommunicationbetweensurface waterandsubsurfaceMemphisaquiferwateralongrecentlymappedfaultsintheMemphisarea(Haoetal., 2013;MartinandVanArsdale,2017)(Figure1).

StratigraphyofShelbyCounty

TheMemphisSand(Memphisaquifer)iscomposedprimarilyoffinetocoarsesand.Italsoincludes clay,silt,andligniteunits(ParksandCarmichael, 1990c)andisupto275m(902ft)thickbeneath ShelbyCounty(Martin,2008).Inascendingorder, theEoceneMemphisSandisoverlainbytheEocene CookMountainFormation,EoceneCockfieldFormation,PlioceneUplandComplex,Pleistoceneloess,and Pleistocene–Holocenealluvium(Figure2).TheCook MountainFormationisalow-permeability,predominatelyclayunit,andtheCockfieldFormationisaninterbeddedsandandclayunit.Whencombined,these twoformationsserveastheoverlyingUpperClaiborne aquitardoftheMemphisSandaquifer(Grahamand Parks,1986).UnderlyingtheMemphisSand,thereis theFlourIslandFormation.Thisgenerallysiltyclay

unitistheaquitardbeneaththeMemphisaquifer, whichinturnoverliestheFortPillowSandaquifer. TheEoceneFortPillowaquiferisthedeepestaquifer inwestTennessee,anditprovidesapproximately5% ofmunicipalandindustrialwater(GrahamandParks, 1986).

MemphisSandAquifer

RechargeoftheMemphisaquiferisfromitsoutcropbelt,beneathMississippiRivertributaries,and throughsandfacies,erosionalwindows,andfaults

throughtheUpperClaiborneaquitard(Figures1 and3)(ParksandCarmichael,1990a,b).Precipitationandstream-bottominfiltrationintotheMemphisSandoutcropbelt(unconfinedMemphisaquifer) aretheprincipalrechargeroutestorechargethe Memphisaquifer,afterwhichthewaterflowswestwardandnorthwestwarddown-gradientalongthe ∼1° structuraldip(Larsenetal.,2022).ThepotentiometricsurfaceoftheMemphisaquiferslopes gentlywestwardandnorthwestwardatapproximately0.5m/kmwheretheaquiferisconfined (Figure1).

Figure1.LocationmapshowingwesternTennesseefaults,LdF004(LdF4)well,andH002well.FaultingisafterMartinandVanArsdale (2017).

AsignificantfeatureintheMemphisaquiferpotentiometricsurfaceisaconeofdepressionunderMemphis,whichwasreportedbyParksandCarmichael (1990a)tobetheresultofover100yearsofwaterremoval(Figure3).Theconeofdepressionisnowstable,indicatingthatrechargeoftheaquiferhasreached equilibriumwithwithdrawal.

Allthegroundwatermonitorwellsinthisstudy (Figure3)showaseasonalriseandfallofwaterlevels,withthehighlevelsoccurringinthelatewinter andspringandthelowlevelsoccurringinthesummerandfall.Thisseasonalriseandfallpatternis alsopresentinMississippiRiverwaterlevels.Parks andCarmichael(1990a)describedthewaterlevelin theLdF004groundwatermonitorwell(Figures1and 3)asfluctuatingwiththeMississippiRiverlevel.The LdF004monitorwellisinLauderdaleCounty,north ofMemphis,anditis5kmfromtheMississippiRiver. WellLdF004sitsatoptheloess-coveredbluffsata surfaceelevationof133.2m(437ft).TheMemphis aquiferwaterlevelsriseandfallinwellLdF004in parallelwithrisingandfallingstagesintheMississippi River.CorrelationofMemphisaquiferwaterlevelsin theLdF004monitorwellwithwaterlevelsintheMississippiRiverwasexplainedbyParksandCarmichael (1990a)asduetopressurevariationintheMemphis aquiferbecauseofMississippiRiverwaterloading.A similarcorrelation(Anderson,2005)occursbetween theMemphisaquiferwaterlevelandMississippiRiver stageintheH002groundwatermonitoringwellinCrittendenCounty,AR,15kmwestofMemphis(Figures 1and3).

Wherethegroundwaterpotentiometricsurfaceis depressed,suchasbeneathMemphis,theMemphis aquifermayrechargefromtheoverlyingCockfield Formationormodernriveralluvium.Thisisparticularlytrueifthepotentiometricdepressionisat,ornear, areaswheretheoverlyingUpperClaiborneaquitard

isthin,sandy,absent,and/orfaulted(Carmichael etal.,2018).TheeasternlimitoftheUpperClaiborneaquitardisasinuouserosionalcontact,and thereareseveralwindowsthroughtheaquitardin ShelbyCounty(Figures1and3).Faultinglocallycuts throughtheMemphisSandandtheoverlyingaquitard (Figures3and4).Surfacedrainagealongaportion ofNonconnahCreekinsouthMemphisleaksintothe Memphisaquiferwherefaultingapparentlyputsthe aquiferintocommunicationwiththesurfacedrainage (KingsburyandParks,1993;Larsenetal.,2003).Dependinguponthepotentiometricsurfacesattheselocations,theMemphisaquifermayberechargedor mayexpelwateralongfaults(ParksandCarmichael, 1990a).

PossibleFaultCommunicationbetweenMemphis AquiferandSurfaceWater

Verticalfaultdisplacementreachingtoornear thegroundsurfaceinandadjacenttoMemphishas beendescribedontheMeeman-ShelbyFault(Williams etal.,2001;Haoetal.,2013;andVanArsdaleetal., 2017),EllendaleFault(Velascoetal.,2005;Deen, 2006;andVanArsdaleetal.,2012),andMemphis Fault(Velascoetal.,2005;VanArsdaleetal.,2012) (Figures1and3).High-resolutionseismicreflection profilesgatheredprimarilyalongtheMississippiRiver showthedown-to-the-eastMeeman-ShelbyFaultextendingthroughtheMemphisaquiferupwardinto theMississippiRivervalleyalluviumandlocallyundertheMississippiRiver(Figures3and4)(Hao etal.,2013;VanArsdaleetal.,2017).MartinandVan Arsdale(2017)mappedthedown-to-the-westCuba Faultthatissub-paralleltotheMeeman-ShelbyFault (Figure3).TheCubaFaultextendsupwardtothebase oftheMississippiRiveralluvium.MartinandVanArsdale(2017)alsomappedtheWolfandHatchiegraben faults,whicharewestorientedandextendunderthe MississippiRiveranditsalluvium(Figure3).

Carmichaeletal.(2018)describedcommunicationbetweentheMemphisaquiferandthesurface MississippiRiveralluviumattheTennesseeValleyAuthority(TVA)AllenplantinShelbyCountyabout1.6 km(1mile)eastoftheMississippiRiver(Figure3). Theyattributedthecommunicationtolocalpoorqualityoftheaquitard,apossibleerosionalwindowin theaquitard,and/orpossiblefaultcommunication. Carmichaeletal.(2018)mappedtwofaultsatandnear theTVAplantsite.TheirSW/NE-orientedfaultlies acrosstheplantsiteandwithintheirwelltestpattern. Theirnorth-orientedfaultequatestotheCubaFault ofMartinandVanArsdale(2017).TheSW/NEfault indicatescross-faultingwithinthegrabenbetweenthe Meeman-ShelbyandCubafaults,whichaddstothe

Figure2.CenozoicstratigraphicsectionofwesternTennesseeand easternArkansas.

X-X location.(B)CrosssectionX-X .

Martin,VanArsdale,andHarrison

Figure3.(A)Mapofthestudyareafaults,ShelbyCounty,Memphisaquiferpotentiometricsurface(dashedcontoursinft),withconeof depressionfromKingsbury(2018),MemphisRiverGaugingStation734,TVAAllenplant,groundwatermonitoringwellsusedinthisstudy, waterfields(yellow),windowsinUpperClaiborneaquitard(blue)fromParks(1990),locationofseismiclineinFigure4,andcrosssection

FaultCommunicationbetweenAquiferandMississippiRiver?

numberofpotentiallyhydrologicallycommunicative faults.Towell(2021)conductedagroundwatertracer surveyanddocumenteddownwardverticalmovement ofsurfacewaterthroughatleasttwodifferentfaults,

puttingtheMemphisaquiferincommunicationwith near-surfacewatersatDaviswellfield.

IndiscussingthestratigraphiccontrolofthewaterlevelsintheMississippiRiveralluviumandin

Figure4.FaultswithintheMeeman-ShelbyfaultzonedisplaceUpperClaiborneandHoloceneMississippiRiveralluvium.Seismicline“A” isshowninFigure3.RedlineinAistopofMemphisSandaquifer.BaseofMississippiRiveralluviumis36.5m(120ft)inboreholeMS-2 (fromVanArsdaleetal.,2017).LineCshowsfaultsextendingtoneartothetopoftheMississippiRiverfloodplain.

theCockfieldFormation,Parksetal.(1985)described probablecommunicationthroughleakageduetolocalthinningandineffectivequalityofconfiningbeds separatingthetwounits.TheythendescribedthepresenceoffaultingthatmayputtheMemphisaquiferand CockfieldFormationindirecthydrologicconnection.

TheMemphisaquiferandtheMississippiRiver appeartobeincommunication,asshownbywaterleveldatafromtheLdF004andtheH002monitor wells(Figures1and3–5).Thequestioniswhether thecorrelationbetweenthegroundwaterlevelsand MississippiRiverstagesiscausedbyMississippi Riverwaterloading,assuggestedbyParksetal. (1985),hydrologiccommunicationthroughalocally transmissiveaquitard,orcommunicationalongfaults.

Parksetal.(1985)didnotmapfaultinginany detailintheirpaper.However,theydidmapa northwest-trendingfaultneartheLdF004well roughlyequivalenttotheHatchiegrabennorthfault andanortheast-trendingfaultthatweinterprettobe oneoftheLauderdalefaults(Figures1and3)(Martin andVanArsdale,2017).Morerecentmappingin westernTennesseehasdocumentedmoreextensive surfaceandnear-surfacefaultinginandadjacentto ShelbyCounty(Figures1,3,and4)(Coxetal.,2001, 2006;VanArsdaleetal.,2012,2017;Haoetal.,2013; andMartinandVanArsdale,2017).

Webelievesurfacewatermaybepassingdownto theMemphisaquiferthroughfaultleakage(Figure3). BenseandPerson(2006)discussedthemechanicsof faultleakageinpoorlylithifiedsiliciclasticsediments. Anisotropicpermeabilityalongfaultsisafunctionof depthofburial,throwalongthefault,andclaycontentofthefaultedunits.Lateralpermeabilitymaybe greatlylimitedbyhigherclaycontentandsmearingon eithersideofthefault,sanddrag,andgrainreorientation.Verticalsegmentationofthefaultplane,which maybeafunctionoflithologyoneithersideofthe faultplaneandthrow,canalsoleadtoincreasedverticalpermeabilityanddecreasedlateralpermeability.

Inpoorlylithifiedsediment,faultmovementcan causeclaytosmearparalleltothefaultplane,but italsodragssandgrainsintothefaultdamagezone.

Morefaultdisplacementandmoreclaycanreduce lateralpermeability.However,moreverticalfaultdisplacementmayleadtomoresandbeingdraggedinto thefaultdamagezone,therebyincreasingverticalpermeability.BenseandPerson(2006)appliedthisidea tosedimentabove500mdepth.Ourinvestigationwas within200mofthegroundsurfaceandthuswithinthe depthrangeoftheBenseandPerson(2006)study.

Afurtherconcernisthatfaultsmayextendthrough thefullthicknessoftheMemphisSand(Figure3). BenseandPerson(2006)showedthatcommunication alongfaultscanextendwellintotheaquifer,whichin ourstudyisthethickMemphisSand,andpotentially allowcontaminationdeepintotheMemphisaquifer.

RESULTS

TheMississippiRiverwaterlevelvariesinanannualpattern,withhighwaterinthelatewinterand springduetospringsnowmeltandincreasedrain. Annuallowwaterleveloccursinthesummerand fall(U.S.ArmyCorpofEngineersdata,https:// rivergages.mvr.usace.army.mil/).MississippiRiver waterlevelswereaccessedfromMississippiRiver GaugeStation734alongtheTennessee/Arkansasborder(Figure3)forthetimeperiodfromJanuary1,2007, toDecember31,2011.WechosetomakeallgroundwaterlevelcorrelationswithGauge734becauseitis approximatelyinthemiddleofthesixgroundwater monitorwellsevaluatedinthisstudy(Figure3).These sixMemphisaquifergroundwatermonitoringwells hadcompletedailywaterlevelsforJanuary1,2007, throughDecember31,2011(U.S.GeologicalSurvey data,https://waterdata.usgs.gov/tn/nwis/gwand https://waterdata.usgs.gov/ar/nwis/gw).Thegeographicdistributionofthesixwellscoveredthestudy areaofsouthwestTennesseeandadjacentArkansas: LdF004wellinLauderdaleCounty,TN;theShM 040,ShL089,andShP076wellsinShelbyCounty, TN;andtheBBB1andH002wellsinCrittenden County,AR(Figure3).Allsixwellswerecomparedto theMississippiRiverGauge734usingtheCorrelation toolinExcel(Table1).

Table1. Correlation(R2 values)betweenelevationofgroundwaterinmonitoringwellsintheMemphisSandaquiferandMississippiRiverstage elevationsattheMemphisMississippiRiverGaugingStation734fortheyears2007through2011.

FaultCommunicationbetweenAquiferandMississippiRiver?

Figure5.GraphsshowingMississippiRiverwaterlevels(orange)andH002groundwaterlevels(blue)fromJanuary1,2007,through September1,2011.

DuringthetimeintervalofJanuary1,2007,through December31,2011,correlationcoefficientsforwaterelevationinwellsBBB1,ShP076,ShM040,and ShL089andMississippiRiverstagesarelow(Table1). However,duringthissametime,thecorrelationfor wellH002was0.809076,andthatforwellLdF004was 0.779691.

DISCUSSION

SixmonitoringwellswithdailygroundwaterelevationdatawerecomparedtoMississippiRiverwater

surfaceelevationsatGauge734(Table1).TheMississippiRiverhasaseasonalpatternofhigherwater levelsinthelatewinterandinthespring,withlow waterlevelsinthesummerandfall.Fourofthewells (LdF004,ShP076,ShM040,andShL089)arelocated ontheloess-coveredbluff(Figure3)eastoftheMississippiRiver.WellsShP016,ShM040,andShL089 showedlowcorrelationtotheMississippiRiverstages (Table1).TheBBB1monitoringwellisontheMississippiRiverfloodplain,approximately12kmfromthe MississippiRiver,andittoohadalowcorrelationwith theMississippiRiverstage.

Thereareseveralreasonswhycorrelationbetween theriverandthesewellscouldbelow.Oneobviousreasonisdistancefromtheriver.However,theBBB1is12 kmwestoftheriverandhadacorrelationof0.547279. TheShL089is12kmeastoftheriverandhadacorrelationof0.282481.TheShM040is14kmeastofthe riverandhadacorrelationof0.090687.Anotherfactorforpoorcorrelationcouldbelocalwaterproduction,particularlyhigh-volumeproductioninthecity ofMemphis.Thetwoeasternwells,ShL089andShM 040,arenearthreewellfields.TheBBB1isnotclose toawellfieldbutisinanareaofintermittentlyhigh irrigationwaterusage.

WaterlevelsinwellsLdF004andH002andMississippiRiverstageshowedhighcorrelationcoefficients (0.779691and0.809076,respectively).WellLdF004 is5kmeastoftheMississippiRiverandlocated60 m(200ft)abovetheMississippiRiveralluvialsurface onanarrowloess-covered,east/west-orientedridge withMississippiRiveralluviumonboththenorthand southflanksoftheridge.WellH002sitsontheMississippiRiverfloodplain2.5kmwestoftheMississippi River(Figure3).WellsH002andLdF004(Figure3) areunderlainby53mand54m,respectively,ofUpperClaiborneaquitard(Figure6),andthereisnoevidencethattheaquitardisofpoorqualitynearthe wells.Studieslookingforwindowsintheaquitardin ShelbyCounty(Parks,1990;Larsenetal.,2022)have notfoundanywindowsclosetowellH002.

TheH002wellisveryclosetotheMeeman-Shelby faults(Figures1,3,and4),whichtrendundertheMississippiRiverandextendupwardintotheMississippi Riverfloodplainalluvium(VanArsdaleetal.,2017). Similarly,wellLdF004appearstohavebeendrilled intotheNE-trendingLauderdalefaultzone(Figures1 and3).

CONCLUSIONS

ThecorrelationcoefficientsbetweentheMississippi RiverGaugeStation734andgroundwaterlevelsin monitoringwellsBBB1,ShP076,ShM040,andShL 089werelessthan0.63.If,asproposedbyParksetal.

Figure6.GeophysicallogoftheH002groundwatermonitoringwell withtops.Depthsareshowninftbecausetheoriginallogwasmeasuredinfeet.TheUpperClaiborneaquitardis175ft(53m)thick.

(1985),waterlevelsintheMemphisSandarecontrolledbypressureintheMemphisaquifercausedby MississippiRiverstages,thenwewouldexpecttosee highercorrelationcoefficientsamongallofthemonitoringwellsandMississippiRiverstage(Table1). However,thecorrelationcoefficient(0.809076)betweenthewaterlevelintheH002groundwatermonitoringwellandtheMississippiRiverGaugeStation 734mayindicatedirectcommunicationbetweenMississippiRiversurfacewaterandtheMemphisSand aquiferalongafault.Thefaultsmostlikelyresponsible forthiscommunicationaretheMeeman-Shelbyfaults (Figures3and5)(Haoetal.,2013;VanArsdaleetal., 2017).Similarly,wepostulatethatthehighcorrelation (0.779691)betweenMississippiRiverwaterstageand thewaterlevelintheLdF004MemphisaquifermonitoringwellisduetowatermovingthroughtheUpper ClaiborneaquitardalongaLauderdalefault.

Thispostulatedfaultcommunicationraisesthe concernofpotentialcontaminationoftheMemphis aquiferbytheMississippiRiverorothersurfacecontamination.PossiblecontaminationoftheMemphis aquifershouldalsobeaconcernwhereverfaultingextendsto,ornearto,thegroundsurfacethroughoutthe ShelbyCountyarea.Theprincipalfaultsofconcern aretheHowe,Meeman-Shelby,Millington,Memphis, HurricaneCreek,Ellendale,andWolfgrabenfaults (Figure3).Werecommendthateast-westseismicreflectionlinesshouldbeacquiredacrosstheH002and LdF004monitoringwellsitestodeterminewhether anear-surfacefaultisindeedclosetothesewells.If thisrelationshipisconfirmed,thentheotherMemphisareafaultsinFigure3shouldbeinvestigatedfor potentialavenuesofcontaminationoftheMemphis aquifer.

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ShallowLandslideErosionRatesonIndustrially ManagedTimberlands:KeyFactorsAffectingHistorical andContemporaryRates

JASONS.WOODWARD*

ConservationPlanningDepartment,GreenDiamondResourceCo.,P.O.Box68, Korbel,CA95550

KeyTerms: ShallowLandslide,LandslideInventory, ShallowLandslideErosionRates,MassWastingAssessment,TimberHarvest,TimberlandManagement Practices,ForestPracticeRules,HabitatConservation Plan

ABSTRACT

Timberharvestingandrelatedmanagementpractices associatedwithindustrialtimberlandshavechangeddramaticallyinthelasttwodecades.Industrialtimberlandsarenowmorecarefullyassessedandmitigated. RecentstudiesofmasswastinginnorthernCalifornia includedareviewofhistoricalaerialphotographsfrom theearly1940sthrough2016andfieldmeasurements ofnearly3,000shallowlandslidesonindustriallymanagedtimberlands.Significantimprovementshavebeen seeninmanagementpracticesovertimethatinclude butarenotlimitedtoreducedharvestunitsizes,increasedstreamsidetreeretention,reducedroaddensity, andimprovedroad-buildingpractices.Theseimprovementsarearesultofavarietyofsourcessuchasevolvingstateregulations,voluntaryconservationplans,and increasedprofessionaloversight.Subsequently,significantdecreasesinmanagement-relatederosionarebeing observedacrosstheareaincludedinthisstudy.Observationsshowthatimprovementsinmanagementpracticeshavepositivelyaffectedregionalmasswasting.In thisinvestigation,significantchangeshavebeennotedin bothcausalmechanismsandlandslideerosionrates.The studydatashowsthatbeforetheyear2000,nearly85 percentoflandslide-relatederosionwasdeterminedto betheresultofhistoricallogging,eitherbyharvesting orfromroads(generallypoordesignand/orlocation).

Shallowlandslideerosionrateshavevariedoverthedurationoftimereviewedforthisstudy,peakinginthe 1970s.Since2000,erosionratesacrossthestudyarea havedecreasedto20m3 /km2 /yr,whichisa92percent reductioncomparedwiththehistoricalrate.

*Correspondingauthoremail:jwoodward@greendiamond.com

INTRODUCTION

Timberlandmanagementcannegativelyimpactthe landscapeandcanleadtoincreasedshallowlandslide incidencesanderosionrates.Thecorrelationbetween increasedlandslidingandtimberharvestingiswell documentedintheliterature(CroftandAdams,1950; BishopandStevens,1964;Swanston,1974;Sidle,1992; andCafferataandSpittler,1998).Thesestudieswere basedonreviewsofhistoricalmanagementpractices andmethods,manyofwhichweregenerallyunregulated.Althoughforestryboardsandregulationshave beeninexistenceinCaliforniasince1885,ingeneral, alackofenforcementpreventedanymeaningfulenvironmentalprotections(Lundmark,1975).Forestmanagementhaschangedsubstantiallyovertime,soitis appropriatetolookathistoricalandlong-termtrends comparedwithmodern-daypracticesandcorrespondingmodern-eraerosionrates.

ParticularlyinCalifornia,managementpractices havechangeddramaticallyoverthelastfourdecades andprobablymostsignificantlyoverthelasttwo.Regulationshavechanged,harvestmethodshavechanged, andgeologichazardawarenessandoversitehavebecomecommonplace.Managingindustrialtimberlands isnolongerassimpleascuttingtreesformoney;ithas becomemoreaboutresponsiblymanagingaforestfor multipleresourcevalues.

Thisstudywasbasedonlong-termmonitoring projectsassociatedwithanaquatichabitatconservationplan(AHCP).ThedevelopmentoftheAHCP (GreenDiamondResourceCo.,2006)thatisspecific tothisstudywasacollaborationbetweentheprivate landowner,GreenDiamondResourceCo.,andfederal regulatoryagencies(NationalMarineFisheriesServiceandU.S.FishandWildlifeService)andincludeda consistencydeterminationwiththeCaliforniaDepartmentofFishandWildlife.Thedatawascollectedas partofamasswastingassessmentembeddedwithin theAHCP.Shallow-seatedlandslideswerethefocusof datacollectionbetween2008and2016,coveringover 121,500hectares(300,000acres)innortherncoastal

Figure1.StudyarealocatedinHumboldtandDelNorteCounties, northerncoastalCalifornia.Surveylocationsshowncorrespond withrandomlyselectedsegmentsoffish-bearingClassIandnon–fish-bearingClassIIwatercourses.Note:Atthisscale,surveylocationsoverlapinmanyinstances.

California.Deep-seatedlandslideswerenotevaluated forthisstudy.Randomlyselectedsurveylocationsincludedhillsideareasadjacenttomorethan6.5percent oftheperennialflowingstreamswithinthestudyarea.

STUDYAREA

ThestudyareaisonthewestcoastoftheUnited StatesofAmericainnortherncoastalCalifornia.The ownership,showninFigure1,coversportionsof HumboldtandDelNorteCountiesandspansthe California–OregonborderonthenorthernendtotimberlandsnearthecityofRioDellonthesouthernend andasfarinlandastheheadwatersofRedwoodCreek.

Theareaincludesbothwholewatershedsandportions ofwatersheds.Theprimarywatershedsinclude,from northtosouth,RowdyCreek,SmithRiver,Wilson

Figure2.Geologicmap,modifiedfromJenningsetal.,2010.HumboldtandDelNortecounties,California,USA.IncludesNational WeatherService(NWS)CooperativeObserverProgram(COOP) Sites.

Creek,KlamathRiver,RedwoodCreek,MapleCreek, LittleRiver,MadRiver,andtheEelRiver.Elevations rangefromnearsealeveltoover1,000m(3,300ft). Averageannualprecipitationacrosstheproperty variesbyasmuchas100cm(40in.)peryear.AnnualprecipitationdatawasgatheredfromtheWesternRegionalClimateCenter(WRCC)thatoriginated fromsevenweatherobservationsitesthatarepart oftheNationalWeatherService’sCooperativeObserverProgram(COOP).ThesesitesarelocatedbetweenScotiaandCrescentCity.Althoughsomeofthe datafromthosesitesareincomplete,long-term(e.g., 15–130years)trendsaredemonstrated,asshownin Table1.ThegeographicdistributionoftheCOOPsites isshowninFigure2.EurekaandScotiaaretheonly twositesthatarestillonlineandpresentlymonitoring andreportingprecipitationdata.WiththeEurekasite beingmorecentrallylocatedandcontainingthemost robustdataset,itwastheprimarysourceofdataused inthestudy’sdetailedanalysis.

RegionalandGeologicSetting

Thestudyareaisinatectonicallyactiveareajust northoftheMendocinoTripleJunction(MTJ),where theNorthAmerican,Gorda,andPacificPlatescollide.Seismogenicfaultsystemsintheareaarepartof theMTJandincludethenorthendoftheSanAndreasFaultzonetothesouthwest,theMendocino FractureZonetothesouthwest,andthesouthernend oftheCascadiasubductionzonetothewest,justoff thecoastline.Asaresultofthecompressionalforces exertedontheregionduetotheconvergingNorth American,Pacific,andGordaPlates,therearenumerouson-landupper-platethrustfaultsthroughoutthe regionthatarealsoconsideredpotentialsourcesfor seismicshaking(Caoetal.,2003;Kelsey,2001).They includebutarenotlimitedtoLittleSalmonfault,Mad Riverfaultzone,BaldMountain-BigLagoonfaults, andGroganandSurpurCreekfaults.Thestructural orientationoftheseupper-platethrustfaultsistypicallynorthwest-trending.

Earthmaterialsvarythroughoutthestudyareabecauseofthehighlyactivetectonicregimedescribed previously.Atthesouthernextentofthestudyarea, thebedrockisdominatedbyMiocenetoLatePleistocenedepositsoftheWildcatFormation(Ogle,1953).

TheWildcatFormationisthoughttobeacoarseningupwardregressionalsedimentsequencedepositedin theancestralEelRiverbasin.Tothenorth,theremainderofthepropertyisdominatedbydepositsof theCoastal,Central,andEasternBeltsoftheFranciscanComplex,whichrangeinagefromPliocene toEarlyJurassic(McLaughlinetal.,2000).Bedrock withintheFranciscanComplexincludessedimentary,igneous,andmetamorphicrocktypes;themost commonearthmaterialsencountered(fromnorthto south)aresandstoneandmetasandstone,greenstone, mélange,andschist.Asimplifiedillustrationofthe

distributionofthesematerials,modifiedfromJennings etal.,2010,isshowninFigure2.Theseunitsaresomewhatspecifictowatershedswithinthestudyareaand aretypicallycharacterizedby:(a)brokentosheared moderatelyinduratedsandstoneandmetasandstone (largelyinthecentralportionofthestudyarea),(b) highlyshearedsiltstonesandmudstonesinanargillaceousmatrix(mostlyfoundinthecentralportion ofthestudyarea),(c)quartz-micaschist(primarily foundintheeasternportionofthestudyarea),and (d)moderate-towell-induratedfracturedgraywacke (mainlyfoundinthenorthernportionofthestudy area).Throughoutthestudyarea,bedrockisfound tobecappedbyPleistocenetoHolocenealluvialsedimentsormarineterracedeposits(Irwin,1997).

Geomorphologyvariesacrossthestudyareaandis characterizedasmoresubduedinthesouth,becoming moreruggedandincisedtothenorthandinland. Landslidingisprevalentthroughout,andtypesof landslidesaretypicallyassociatedwithorattributed totheunderlyingbedrock.Debrisslidesanddebris flowsarethemostdominanttypesoflandslidesseen acrossthestudyarea.However,inthesouthwhere therearemorelow-gradientslopesandyounger less-consolidateddepositsoftheWildcatGroup, anincreaseisseeninearthslidesandtranslational landslidescomparedwithotherareas.Innergorges (Kelsey,1988)areprevalentinthecentralportionof thestudyareawhereexamplesarefoundofthesteepestterrainsthatarecommonplaceintheKlamath Riverwatershed.

METHODS

Thestudymethodsdiscussedinthissectionare basedonormodifiedfrompreviousworkand literature(Wieczorek,1984;KeatonandDeGraff,

Table1. SummaryofregionalclimatedatafromtheWesternRegionalClimateCenter.Geographicaldistributionofthestationsisshownin Figure2.*

* Dataaccessedfrom:https://wrcc.dri.edu/Climate/west_coop_summaries.php.

1996;WashingtonForestPracticeBoard,1997;and Brardinonietal.,2003).

AerialPhotographInterpretation

Historicalaerialphotographswereassessedbyusing SOKKISHAMS-27(SokkiaCo.,Ltd.,Atsugi,Japan) andAbramsCB-1stereoscopes(AbramsInstrument Co.,Lansing,MI).Attributeswererecordedofactivelandslideswhilereviewingaerialimageryandwere mappedintoEsriArcMapbasedGeographicInformationSystem(GIS)usinglightdetectionandranging (LiDAR)bareearth1-meterdigitalelevationmodel (DEM)asabase.PostprocessingoftherawLiDAR dataanddevelopmentoftheDEMwasdoneatGreen DiamondResourceCoin2008&2009.AgeclassificationswerebasedonKeatonandDeGraff(1996). Mostoftheaerialphotographsinthestudy’scollectionwereatascaleof1:12,000,withsomeassmallas 1:38,000.Stereo-pairedaerialimageryyearsincluded 1942,1948,1954,1958,1962,1966,1969,1975,1978, 1984,1988,1997,and2001.Orthographicallyrectifiedaerialimagerywasalsoreviewedandincludedthe years2005,2006,2009,20102012,2014,and2016.In total,thestudycovereda74-yearperiod.Theavailabilityoforthographicallyrectifiedimageryischangingrapidly.Inthepast,aerialphotoflightshadtobe contractedandpurchased.Flightsweretypicallyflown everythreeyearsorso.Morerecently,however,orthographicallyrectifiedimageryhasbeenavailableevery onetotwoyears,eitherthroughpublicaccesssources orthroughcontractedflights.Althoughthephotoresolutionisnotasgoodinsomecases,landslidedetectionusingorthographicallyrectifiedphotosisstilladequate,inpartduetothegreaterfrequencyofphoto setsavailable.

Theearliestaerialphotographsreviewedwereflown in1942and,asavailable,atleastonesetfromeach decadethereafterwasreviewed.Aerialphotocoverage acrossthesampleareawasgood.However,becauseof theaerialextentoftheprojectandchangesinownershipovertheyears,notallphotosetscoveredtheentirestudyarea.Inmostcases,therewasatleastone photosetthatcoveredeachwatershedorgroupofwatershedsforeachdecade.Inseveralcases,aerialphotographswereusedfromtwodifferentphotoyearsto fullyreviewanareaforaparticulardecade;hence,the extensivelistofaerialphotographsused.

Fieldwork

Thedataforthestudywascomposedofinformationgatheredaspartofamasswastingassessment. Thisprocessincludedsurveyinghillsidesforshallowseatedlandslidesbetween2008and2016.Surveysfor

landslideswerecompletedonhillslopesadjacentto half-milelongperennialflowingClassI(fishbearing)andClassII(non-fishbearing)streamsegments (Woodwardetal.,2017),aswellasSteepStreamside Slope(SSS)buffersthatwereretainedforlandslide prevention.Surveylocationswererandomlyselected asdescribedinWoodwardetal.(2012)andtheyare showninFigure1.Followingareviewofaerialphotographs,two-tothree-personcrewsreviewedeachof thesurveylocations.Thegeneralpurposeofeachof thesefieldsurveyswastoconfirmanylandslidesidentifiedinaerialimageryandreviewthesampleareain thefieldforanyadditionallandslidesthatmayhave beenmissedthroughremotesensing.Inall,hillslopes weresurveyedadjacentto298km(185mi)ofstream segments(oversixpercentoftheperennialstream network),aswellas37hectares(92acres)ofSSS buffers(15percentofthetotalSSSbuffers).Primary datacollectedforeachlandslideincludeddimensions (length,width,anddepth)forboththesourcearea andthedisplacedlandslidedebrisremainingonthe slope,andtopographicprofiles,crosssections,activitylevels,deliveryestimates,averageslopegradients, anddistancetonearestwatercourse.Landslidedepths wereestimatedusinginformationfromscarpheights andfield-developedtopographicprofilesofeachlandslide.Landslidesweremappedinthefieldontobase mapsgeneratedfromLiDARwith1meterorbetter resolutionandlatertransferredintoGIS.GlobalPositioningSystem(GPS)coordinateswerealsocollected fortheheadandtoeofeachlandslide.Alllandslides greaterthan19m2 (200ft2 )inaerialextentwerefield reviewedaspartofthiswork.

ANALYSIS

AlllandslideswereenteredintoaGISdatabasefor analysis.Landslidevolumes(Volls )werecalculatedusingtheequationofhalfanellipsefromCrudenand Varnes(1996);seeEq.1.Volumeswerecalculatedfor boththesourceareaanddisplacedlandslidedebris thatremainedonthehillside.Cumulativeortotalvolumesarereferredtointhisstudyintermsofthesource areas.Landslidedeliveryisdefinedastheamountof materialthatevacuatedthehillsideandenteredawatercourse.Deliveryvolumesarecalculatedbysubtractingthevolumeoflandslidedebrisremainingonthe hillsidefromthevolumeofthesourcearea:

Volls = 1 6 πL × W × D, (1)

where D = depthoflandslidesourcearea, L = length oflandslidesourcearea,and W = widthoflandslide sourcearea.

Erosionrateswerecalculatedusingthesumofthe totalvolumeofmobilizedlandslidesediment(asmeasuredinthefieldfromtheidentifiedsourcearea)of alllandslidesoverthetimespanofaerialphotographs reviewed(Eq.2).Duetotheextensivespanoftimeof theaerialphotorecord,erosionrateshavebeenfurther dividedbydecadeandotherkeyperiodsoftime.For decadalrates,theratiowasextrapolatedofthelandslidevolumeofsediment(Volls )observedfromthereviewofaerialphotographsforeachdecadetothetotal volumeofsedimentofalllandslidesinthedata:

Erosionrate = Volls t , (2) where t = time.

Analysisofthedatafocusedonseveralkeyaspectsofmanagement-relatedmasswasting.Landslide visibilityanddetectionwereassessedbycomparing theabilitytoidentifylandslidesusingbothstereopairedaerialphotographsandorthographicallyrectifiedaerialphotography.Erosionrateswerecompared withevolvingstateregulationsandindustrialtimberlandmanagementpractices,bydecade.Additionally, causalmechanismswerereviewedforbothcontemporaryandhistoricalmanagementpractices.Eachof theseaspectsisdiscussedinthenextsection.

FINDINGS

LandslideVisibilityandDetection

Comparingthereviewofaerialimagerywithintensivefieldreconnaissance,anaerialphoto-detection rateof12percentwasestablished,whichwaswithin therangeofsimilarstudies(Robisonetal.,1999; Brardinonietal.,2003).Higherlandslidedetection ratesmayhavebeenlocallyunattainableduetothe lushenvironmentoftheredwoodregionwithitsmore robustvegetationcoverthatobscuresthelandscape. Thedistributionofthelandslidesobservedbyclass sizecomparedtocountandvolumeisshowninFigure3a.Thesizeoflandslidesthataredetectablevaries dependingonthescaleandtypeofimagery.Using stereo-pairedaerialphotographs,typicallyatascaleof 1:12,000,itwasfoundthatlandslidesweredetectable downto20m2 (215ft2 )inplanarview,whichwas smallerthansimilarworkbyImaizumietal.(2008), whofoundslidesdetectableto50m2 (538ft2 ).However,ourfindingshererepresentthesmallestlandslides detected,anditwasfoundthattheirlegitimacywas typicallyquestionableandrequiredidealphotoconditionstobeidentifiedwithanyconsistency.Although somelandslidesaredetectableinaerialimageryatthat size,manyfactorscancomeintoplay,suchassunangle,vegetation,andshade,allofwhichcanobscureor hidesmallerlandslides,makingitdifficulttoconsider

Figure3.Landslidedata.(a)Landslidearea(m2 )classesversus numberofslidesversusvolume(m3 )(includesonlylandslidesobservedinaerialphotos).(b)Landslidesthatwereobservedinthe fieldbutnotobservedinaerialphotographs.(c)Alllandslides;landslideslessthan150m2 ( 1,600ft2 )inaerialextentaccountforonly 11%ofthetotalvolumeoflandslidesinthisstudyyetconsistof overhalf(65%)oftheslidesinthedataset.

thisareliablesizefordetection.Consideringthis,an attemptwasmadetobetterdefinethesmallestlandslidesthatweremorereadilydetectable.Todoso,landslidesthatwerefoundduringthefieldreconnaissance butwereunabletodetectinaerialphotographswere assessed.

Figure3bhighlightslandslidesthatwereobserved onthegroundbutwerenotabletobedetected withaerialphotography.Aslandslidesbecomelarger and/ormorerecent,theyareeasiertoseeandthen wouldbedetectableonaerialimagery.Therefore,itis logicalthatmostofthelandslidesseenontheground

wereconcentratedinthesmallersizeclasses.Ifthe smallerclasssizesarecomparedinFigure3aandb, only4percentinthe0–150-m2 classofthelandslides couldbeidentified,whichisonlyone-thirdofthe detectionrateseenoverall.However,inthenextclass sizeup(150–300m2 ),11percentofthelandslideswere detectableinthatclass,whichisconsistentwiththe overallrateof12percent.Frequentlywhenreviewing watersheds,aerialphotosetswerenotavailableatmore frequenttimeintervalsthan10years.Historicallandslides,roughly10yearsoldormore,werelikelytohave significantvegetationgrowthandweremoredifficult todiscern.Althoughlandslideswereidentifieddown to20m2 ,landslides150–300m2 insizeappearedto bethesmallestmappableunitthatwasstillreliablydetectableforthestudyarea.

Thesmallestlandslides,inthe0–150-m2 class,dominatedthepopulationoflandslidesinthesampleset asshowninFigure3c,butonlyaccountedforasmall portionofthetotalvolume.Thisareaclassrepresents nearlytwo-thirds(65percent)ofthelandslidesobservedbutaccountedforonly11percentofthetotal volume.Conversely,thelargerlandslidesdominated thetotalvolumeofsediment,especiallythoseinthe greaterthan1,650-m2 sizeclass,whichaccountedfor 27percentofthetotalvolume(Figure3c)andaccountedforonly2%ofthelandslidesobserved.These dataallowedareevaluationoftheminimummapunit oflandslidesnecessarytobereviewedinfuturestudies.Omittingthesesmallerlandslideshadanegligibleimpactontheoveralldata,includingerosionrates. Indoingsoonecouldconductasimilarstudywith lessthanhalftheeffortandwithoutcompromisingthe results.

Detectinglandslidesusingaerialphotographyisan essentialelementoftheworkbecausetheyareusedto establishdecadalerosionrates.Figure3ashowsthat eliminatingthereviewoflandslideslessthan150m2 wouldreducethenumberoflandslidesobservedinthe photorecordby21percent(77ofthe371landslides) yethaveanegligibleimpactonoverallcumulativevolumeasthoselandslidesaccountfor1percentofthe totalvolume.Thatportionofthesamplesetiswell distributedovertimeandthereforewouldnotlikely haveasignificantimpactwhenestimatingaverageannualrates.Ultimately,settingaminimummapunitsize of150m2 willallowbetteruseoftimeandmakework moreefficientbysignificantlyreducingtheamountof fieldworkandcostsinvolvedwhilestillproducingarobustsamplesettoworkwith.

SedimentVolumeandErosionRates

Atotalof2,995landslideswerereviewedandmeasuredinthefieldandofthose,371werealsoidentified

duringthereviewofhistoricalaerialimagery.Active todormanthistoriclandslidesweredetectedineach ofthedecadesreviewedwithaerialimagery.Although thelandslidesobservedinaerialimageryaccountedfor only12percentofthenumberoflandslidesthatwere reviewed,theyaccountedfor49percentofthetotal volumeofsediment.Asaresult,thoseidentifiedwith historicalaerialimageryprovidedanopportunityto lookatbothlong-termanddecadalerosionratesin coastalnorthernCalifornia.

Insomecases,alandslideinventoryandanalysis mayonlybefeasiblethroughremotesensing.Thismay beduetoavarietyofreasonssuchastimeconstraints, accesslimitations,orbudgetissues.Acomparisonwas madeofthecumulativevolumeoflandslide-related sedimentbetweenfieldandphoto-identifiedlandslides tothephoto-onlyidentifiedlandslidesinFigure4.The variationbetweenthetwowasapparentthroughout mostvolumeclasses.Thegapwaslargestwithsmaller landslideswhicharetypicallyhardertodetectinaerial photography.Brardinonietal.(2003)sawsimilarvariationsintheirsurveyincoastalBritishColumbia. Thesedatahighlighttheimportanceoffield-based datatoaccuratelyevaluatelandslidevolumes.Without afield-basedcomponent,volumeestimatescanvaryby asmuchas25percent,dependingonsizeclass,because ofthemultitudeoflandslidesthatcannotbedetected withaerialphotographs.Whenconductinglandslide inventorieswithouttheabilitytoconductfieldreconnaissance,datashouldbeconsideredtocompensateforthismissingcomponent.Largerlandslidesare morereadilydetectibleinaerialphotographs;thereforeitisimportanttohaveawidetemporalrangeof aerialphotographstocapturethemajorityofthose landslideswhichwouldhelpcompensateforthedata

Figure4.Acomparisonofcumulativevolumeofsedimentofboth field-andphoto-identifiedlandslidesversusphoto-onlyidentified landslides.

ShallowLandslideErosionRates thatwouldbemissed(thesmallersizeclass)fromfield reconnaissance.

Erosionratesaretypicallyevaluatedfromavailable aerialimagery,whichisoftenduringabriefperiod forthespecifiedstudyarea.Theimageryinthisstudy spansamuchlongerperiod(74years)thanmanypreviousstudies(e.g.,CafferataandSpittler,1998[38 years];Brardinonietal.,2003[30years];andImaizumi etal.,2008[38years]),andcoverskeyperiodsencompassingthebroadevolutionoftimberlandmanagementpracticesandregulations.Theonsetofaerial photographybeginsatatime(1942and1948)whenthe studyareaislargelycharacterizedbyold-andsecondgrowthtimberwithvirtuallynoforestregulations,allowingauniqueopportunitytoevaluateerosionrates overbothhistoricalandmoderntimesofindustrial timberlandmanagement.

Drivenbyevolvingtechnology,regulations,and environmentalawareness,timberlandmanagement practiceshavebeenchangingformorethanacenturyresultinginsignificantimpacts,bothpositiveand negative.Theevolutionofeachofthesefactorshassignificantlyinfluencedslopestabilityanderosionrates associatedwithshallowlandsliding.Theperiodofthis studyisuniqueasitcapturesold-growthandmature second-growthforestsofthe1940sandearly1950s. Asaresult,astrongcorrelationcanbeseenbetween increasingerosionratesandthelargelyunregulated harvestingofthelate1950sthroughthemid-1970s thatwasdrivenbyadvancementsinthetechnology ofground-based/tractoryarding.Thisisfollowed bydecreasingerosionratesaftertheestablishment offorestpracticeregulationsandadvancementsin technology(mid-1970stopresent).Someexamples ofthistechnologyaretheuseofcableyardingwhich replacedtractorsonsteepslopeswhilethepassingof theZ’berg-NejedleyForestPracticeActof1973ledto improvedroadbuildingpracticesandtotheestablishmentofstream-protectionzones.Additionally,timberlandmanagementpracticeshaveseensignificant changesinareassuchasself-imposedHabitatConservationPlansandyardingmethods.Thelatestchanges, overthelasttwotothreedecades,mayverywellbe drivingthecontinueddecreasingtrendinerosionrates seeninrecentyears.Forthesereasons,erosionrates wereevaluatedoverdecadaltimeperiods,aswellas determiningthreesignificantperiodsintheindustry. Thesearedefinedaslongterm(1942–2016),thehistoricalloggingera(1954–1997),andthemodernlogging era(2000–2016).Thedaterangeschosenwerebased onacombinationofobservedtrendscombinedwith thedatesofaerialphotographsreviewed(Figure5).

Long-termerosionratesforthestudyareaare 145m3 /km2 /yrandcovertheentireperiodofaerial photosetsreviewed.Onaverageitwasfoundthat

Figure5.Erosionratesforshallowlandsliding.Temporallimitsfor historicalandmodernloggingerasandlong-termerosionratesare basedonaerialphotographdatesusedinthestudy.

deliveryrateswere52percentoftheerosionrates(48 percentoflandslidedebrisremainedonthehillside). Historicalloggingeraerosionrateswere60percent greaterat243m3 /km2 /yrandweredefinedasthe periodfromthemid-1950sthroughthelate1990s. Thisperiodischaracterizedbythelargelyunregulated eraofthe1950sand1960s,combinedwithatransitioningperiodofthemid-1970sthroughthe1990s thatincludedsignificantregulatorychangesinthe industry.Asnotedearlier,therewasasignificantrise inforestpracticeregulationsinthemid-1970sand theregulationscontinuedtoevolvethroughoutthe followingdecades.Geologicconsiderationsquickly becamepartoftheprocessbeginninginthelate 1970swhentheCaliforniaDepartmentofForestry (CDF),nowtheDepartmentofForestryandFire Protection(CALFIRE),contractedtheCalifornia DivisionofMinesandGeology(CDMG),nowthe CaliforniaGeologicSurvey(CGS),tomapthegeologyandlandslidesinseveralsensitivewatersheds alongthenorthcoastofCalifornia(Bedrossian,2015). The1940swereexcludedfromthisperiodasmostof thestudyareawascharacterizedasold-growthor maturesecond-growthforestsatthattimeandclosely representedtheconditionsofamatureorvirtually unharvestedforest.Themoderneraischaracterized bykeyinfluencesfromtheregulatoryaspectaswell asadvancesintechnologythatbeganaroundtheyear 2000.Erosionratesinthemodernloggingera(post2000)havedeclinedsignificantlyto20m3 /km2 /yr andaredownmorethan90percentcomparedwith peakratesinthe1970s.Tobetterunderstandthese trends,itisessentialtolookatexternalfactorsthat haveaffectederosionrates,examinedinthe Discussion section.

CausalMechanisms

Determiningcausalmechanismsforhistorical landslidescanbedifficult.Therearerarelyfirsthand

Table2. Causalmechanismsattributedtolandslidesobservedinaerial photographs.Resultsareexpressedasapercentageoferosionvolumes.Notethattherewasnodeterminationbetweenlegacyroadrelatedinfluencesandmodernroad-relatedinfluences.Asaresult, road-relatedcausesidentifiedintheModernLoggingEramaybea resultofeitherorpotentiallyboth.

Cause

HistoricalLogging Era,1950–1999, N = 366(%)

ModernLogging Era,2000–2016, N = 12(%)

Harvesting440

Road4023

Natural1577

accountsofthelandslidefailureandestablishingthe timingofandcorrelationwithcontributingfactors isdifficult.Relativetimingcanbeestablishedusing differencesinvegetationtypeandage.However,it isoftendifficultforanestimatetobemoreaccurate thanacoupleofdecades.Aerialphotographsarea keycomponentinthisanalysisastheycanallowthe captureofanthropogenicinfluencesbeforetheevent andcanbeconstrainedbetweenphotosets.Table 2showsacomparisonofcausalmechanismsofthe historicloggingeratothemodernloggingerausing thestudy’sdatasetoflandslidesthathavebeenverifiedinaerialphotographs.Thetablegroupslandslide causalmechanismsintothreecategories;harvesting, road,andnaturallyoccurring(natural).Landslides characterizedasrelatedtoharvestingarethosehavingoccurredinaharvestedareawithin20yearsof operations.Road-relatedlandslidesarecharacterized asthosethatoffsetortruncatealloraportionofa haulroadorskidtrailprismorweredeterminedto havebeendirectlyinfluencedbyroaddrainage.While therearesignificantdifferencesintheimpactsonslope stabilityinlegacyandcontemporaryroads,these havebeenlumpedintoonecategoryforsimplicityas differentiatingthetwowasnotpartofthescopeofthis work.Naturallyoccurringlandslidesarecharacterized asthosethathavenoobservableconnectionwith anthropogenicinfluencessuchasroadsorharvesting asdefinedabove.InTable2,areversalisseenin causalmechanismsofshallowlandslidingbetweenthe historicalandmodernloggingeras.Withinthestudy area,itwasobservedthatanthropogenicinfluencesof landslidesandrelatederosionrateswerereducedto 23percentinthemodernera,whereastheyaccounted for88percentofhistoricalerosion.Todate,there hasbeennolandslidesedimentvolumeattributable toharvesting,asdefinedabove,inthemodernloggingera.Whilerealizingthattheperiodsarenot equal,landslidesarenotoccurringasfrequentlyas theyusedtoandmanagement-relatedlandslidinghas declined.

DISCUSSION

KeyInfluencesAffectingErosionRates

Thisstudyevaluatedthedecadalerosionratesin comparisonwiththeevolutionofforestpracticerules andprivatemanagementpractices,aswellasregional climaticandseismicinfluences.Indoingso,astrong correlationwasfoundbetweenerosionratesandevolvingforestmanagementpracticesandregulations.Beit intentionallyorinadvertently,bothmanagementpracticesandregulationshavebeenaffectingthemostsensitiveareasonthelandscaperegardingslopestability andaredoingsoinpositiveways.Seismicandclimatic influencesalsoappeartohavebeenfactorsinfluencingratesaswell.Duringtheperiodofthisstudy,there wassignificantseismicactivityandelevatedprecipitationeventswithintheregion.Additionally,theroleof geologicoversightandgeneralknowledgeonharvest activitieshaschangedovertimeandmayalsobeinfluencinglandsliderates.

ChangesinCaliforniaStateForestRegulations

Before1973,thetimberindustrywasvirtuallyunregulatedwithnolimitstothesizeofharvestareas, andtherewerenoprotectionmeasuresforstreamsor wildlifeorforunstableorpotentiallyunstableslopes. Changesinforestrywereobservedaftertheapproval oftheZ’berg-NejedleyForestPracticeActof1973. TheAct,administeredbytheStateBoardofForestry, camewithadeclarationthat“theforestresources andtimberlandsofthestatefurnishhigh-qualitytimber,recreationalopportunities,andaestheticenjoymentwhileprovidingwatershedprotectionandmaintainingfisheriesandwildlife”(California,1974,Chapter8,Article1,Section4512(b)).Inresponsetothe Act,theCaliforniaForestPracticeRuleswererevised andwereregionallyspecifictothree ForestDistricts Thesemorestringentrulesincludedlimitstoharvest unitsizes,riparianprotectionthatincludedtreeretentionalongstreamsideslopes,andnewroadbuilding standards,allofwhichhavecontinuedtoevolveand havehadsignificantimpactsonmanagement-related masswasting(California,2022).

KeyperiodsoftimeinchangingthestateofCalifornia’sforestregulationsthathaveimpactedmasswastingarelistedbelow:

1970s–ThepassingoftheZ’bergNejedleyForest PracticeActof1973(California,1974)drivessignificantchangestotheCaliforniaForestPractice Rules(CAFPR).ThroughaTimberHarvestPlan (THP)process,fish-bearingstreamswereprotected by30-meter-wide(100ft)tree-retentionbuffersand

15-meter-wide(50ft)buffersonsomenon–fishbearingstreams.PriortotheActtherewerenoprotectionmeasuresforstreams.Additionally,harvest blockswerelimitedto32hectares(80acres)insize inthecoastdistrict,althoughtherewereexemptions thatallowedmanyharvestblockstobeupto48 hectares(120acres).

1980s–ThefirstsignificantrevisionstostreamprotectionareaswithintheCAFPRsestablishedthe WatercourseandLakeProtectionZone(WLPZ) rulesin1983(Martin,1989).Thisdefinedspecific criteriaforidentifyingtypesofwatercoursesand associatedWLPZsinthefieldandexpandedthe widthsofthezones,upto61meters(200ft)onClass I,fish-bearing,streamsandupto46meters(150 feet)onClassII,perennial-flowingnonfish-bearing, streams(CDF,1985).

1990s–RevisionstotheCaliforniaForestPractice RulesWLPZandroadsandlandingsrulesin1991 resultedinrestrictionsontheplacementoffillmaterialonsteepslopes(CDF,1992).Additionalchanges totheWLPZrulesatthatsametime,whichincluded elevatedcanopyretention,increasedtheoveralllevel ofprotectionofstreamsideslopes.In1994,changes toCAFPRsilviculturerules(silvicultureisthetheoryandpracticeofcontrollingtheestablishment, composition,andgrowthofforests)andsustainedyieldplans(theyieldofcommercialwoodthatan areaofcommercialtimberlandcanproducecontinuouslyatagivenintensityofmanagementconsistentwithrequiredenvironmentalprotectionand whichisprofessionallyplannedtoachieveovertime abalancebetweengrowthandremoval)resultedin smallerharvestblocksandreducedharvestrates (CDF,1994).Even-agedmanagement(thegoalof attainingormaintainingoneageclassofastand oftimberasopposedtomanyageclassesunder uneven-agedmanagement)wasnowlimitedtoa maximumof16hectares(40acres).Thesilvicultureusedwasmandatedtomaximizesustainedproduction,whichforindustrialtimberlandownerswas basedonasustainedyieldplan.

2000s–Increasedprotectionofstreamsideslopes wasmandatedthroughmodificationstoWLPZ rules.IntegrationoftheThreatenedandImpaired (T&I)WatershedrulesintotheCAFPRsin2001 increasedthewidthofClassIwatercoursezones (CDF,2001).In2010,theCAFPRsaddedthe AnadromousSalmonidProtection(ASP)rulepackage,resultingingreaterprotectionofstreamside slopesintermsofareaandelevatedlevelsofcanopy retention(CALFIRE,2010.Notethatduetostate rebrandingoftheDepartmentofForestryandFire Protection,CDFbecameknownasCALFIREin 2008.)

Manyofthesechangescanbeseenovertimein aerialphotographs.Figure6illustratesavisualevolutionofportionsofthestudyareaovera68-year period.InFigure6a,maturesecond-growthandoldgrowthforestsofthe1940sareseen.Thistransitions tolargelyunregulatedindustrialtimberlandsinthe 1950sto1970s,asseeninFigure6bandc,atatime whentherewasessentiallynoharvestacreagelimitand nowatercourseprotection.Figure6dandehighlight anevolvingregulatedstateofindustrialtimberlands ofthe1980sand1990s.Theevolutionfinisheswitha contemporaryviewofindustriallymanagedtimberlandsshownona2016orthophotographwithsmaller harvestunitblocksofvaryingagesandadendritic networkofwiderriparianbuffersalongwatercourses withscatteredgeologicprotectionsappliedtounstable slopesandotherwildliferetentionareas(Figure6f). Thedatashowsthaterosionratesdropoffsharply inthe1980s,whichcoincideswiththeaftereffectsof themostsignificantregulatorychangesinthetimber industryofthatera(Figure5).Thesechangesbegan withtheZ’bergNejedleyForestPracticeActof1973 (California,1974)andappearedtobeshowingresults bytheendofthe1980s.Limitingthesizeofharvest blockswouldhavehadadramaticimpactonslope stabilityalone.However,thenewforestpracticerules alsoprovidedanadjacencyrestrictionforclear-cut blocks(therulestipulatedthathistoricalclear-cut blocksmustbeatleast3yearsoldormorethan91 meters[300ft]awayfromproposedclear-cutblocks), whichspreadouttheseharvestareasspatiallyandtemporallyratherthanallowingthebasin-wideclearings ofthepast(Figure6bandc).Inaddition,fish-bearing streamswerenowprotectedwithatreeretention bufferthatretainedrootstrengthandallowedevapotranspirationtocontinueinsomeoftheareasmost sensitivetoslopestability—theslopesimmediately adjacenttostreams.Theseretentionareasvariedover theyearsandbyforestdistrictbutsomeoftheearly zones(mid-tolate-1970s)were15–30meterswide (50–100ft).

Inthe1980s,newstandardsforplanning,building, andmaintainingroadswereimplemented,whichrequiredlandownerstosizeculvertsforspecificallysized stormevents,requirednewroaddrainageanddesign methods,andrequiredmaintenanceofroadsafter completionofloggingoperations(Martin,1989).In addition,erosioncontrolruleswereimplementedthat addressedwatercoursecrossingsbytractors,brought extraprecautionsforwinterperiodlogging,and providedspecificrequirementsonwater-breakconstruction.Eachstandardwassignificantasthechanges simplydidnotexistbeforethe1973ForestPracticeAct andtheforestpracticerulesthatwerederivedfromit. DriveninpartbytheForestPracticeAct,section208

Figure6.Anevolutionoftimberharvestingthroughaerialphotographs.(a)1948photoshowingbothrecentlyharvestedtimberlandsinthe lowerleftcornerandunharvestedold-growthtimberlandsinthecentralandupperrightportionofthephoto.(bandc)1958and1975photos showingunregulatedground-basedtractor-harvesting(whitesquigglylines).Atthattime,therewerenolimitsonthesizeofaharvestarea andnoriparianretentionorslopestabilityretentionstandards.(d)1984photoshowingground-basedandcable-yardingharvestthatwas thenlimitedtolessthan48hectares(120acres)(harvestblocksinthisphotoarelessthan20hectares[50acres]).Roadbuildingissignificantly reducedcomparedtopreviousdecades/photos.Someriparianprotectionareascanbeseenalongthemainriverandinasmallerstreamin theupperrightcornerofthephotos.(e)1997photowithmultipleharvestunitsshowninthearea;atthistimeriparianprotectionareasare muchmoreprevalent.(f)2016imageryofbasinshowingharvestinginthemoderneraoflogging.Noticetheextensivedendriticpatternof riparianprotectionzones(outlinedingreen).Note:Photosnottoscale.Photo(a)originalscalewas1:24,000.Photos(b),(c),(d),(e),and(f) originalscalewas1:12,000.Allphotoswerereducedbythesamepercentageforthisfigure.

ofthefederalCleanWaterActalsoplayedakeyrolein changesthatcameaboutinthelatterhalfofthe1980s. In1985,thechairmenoftheStateWaterBoardand BoardofForestry(BOF),thedirectorsoftheCaliforniaDepartmentofForestry(CDF)andDepartment ofFishandGame(DFG),andtheexecutivedirector oftheCaliforniaForestProtectiveAssociationsigned anagreementtoassessforestpractices.Thisagreement establishedamultidisciplinaryteamthatconducted aone-yearqualitativefieldassessmentoftheimpacts onwaterqualityresultingfromcontemporarytimber operations(Martin,1989).Theteamwascomprised ofresourcespecialistsfromDFG,CDF,theState WaterQualityControlBoard,andtheforestproducts industry,andwasknownasthe208AssessmentTeam. Theteamexamined100completedstate-issuedTimberHarvestPlansthroughoutthestateandthefinal reportwascompletedin1987(Martin,1989).Known asthe208Report,thisreportspawnedmanychanges toregulationsthataffectedslopestability.

Asadirectresultofthe208Report,newroadsand landingsrulesandWLPZruleswereimplementedin 1991(CDF,1992).Newrulesforroadsandlandings

coveredallaspectsofconstructionwithanemphasis placedonconstructiontechniquesandactivitiesthat wouldaidinthereductionofexcessivesoildisplacement,theavoidanceofunstableareas,anoverall reductionoferosion,andthepotentialforsediment depositioninwatercourses.Thatsameyear,theWLPZ ruleswereamendedforthefirsttimesince1983,alsoas adirectresultofthe208Report.Amongthosechanges wastherecognitionoftorrentsalamanderhabitat, whichincreasedtherecognitionofClassIIstreams andassociatedprotectionzones.Inthemid-1990s, thesenewrulesbroadenedthereviewteamagency’s regulatoryrolebyaddingspecificprotectionmeasures andoperationallimitationstoprotectorenhancewatertemperature,filterstripproperties,upslopestability,fishandwildlifevalues,andsustained-yieldrules.

Thesecontinuedchangeshavecontributedtoa furtherreductioninerosionratesovertime.However, despitethesechangestoregulations,anincreasein erosionrateswasobservedinthe1990scompared withthe1980s(Figure5).Thismaybeexplainedby twofactorsworkinginconjunctionwitheachother: strongtomajorearthquakesfollowedbyseveralyears

ShallowLandslideErosionRates

withsubstantialprecipitation,allofwhichoccurred inthe1990s;and,moresignificantly,nearlyallofthis happenedbeforethe1997photoset.Thisisdiscussed laterinthesection SeismicandClimaticInfluences. Regulationshavecontinuedtoprogressinrecent timesandaremoreprotectivethanpreviously.In2001, theimplementationoftheT&IWatershedrulesrequiredmappingofhabitatforanadromoussalmonids andtherebyincreasedtheamountofClassIwatercoursesthatwereidentifiedandthenprotected,resultinginadditionalprotectionofstreamsideslopesin thoseareas(CDF,2001).Morerecently,in2010,the ASPrulepackagewasimplementedaspartoftheupdatedCAFPRsatthattime(CALFIRE,2010).This broughtforththelargestandmostcomplexchanges toWLPZstodate,especiallyonlower-ordernon–fishbearingstreams.Atthattime,theWLPZssawincreasesinoverallwidthaswellaselevatedlevelsof canopyretention.Thegoaloftheseregulatorychanges wastoaddresswildlifehabitats.However,these WLPZswerealsosomeofthemostsensitiveareas potentiallyimpactingslopestability.Additionally,significantimprovementsinroadmanagementwereseen thatledtoareductioninroad-relatedlandslides.The mostrecentCAFPRroadrulepackage(CALFIRE, 2015.Developedin2013andimplementedinthe2015 CAFPRs)highlightedroadsurfacedrainageimprovementsthathelpedpreventroad-relatedlandslides.In themodernera,culvertsaresizedfor100-yearstorms, includingsedimentanddebris,andditch-reliefculvert spacing,sizing,andplacementareimprovedtoavoid triggeringshallowlandslideandroad-edgefailures.AlthoughthesespecificationsareenforcedviatheCA FPRs,thespecificdesignrequirementsareattributed totheworkofCafferataetal.,2004.Improvedroad managementandincreasedprotectionoftheWLPZs havecertainlyplayedakeyroleinthereductionofobservederosionrates.Othershavenoticedthiscorrelationaswell.Forexample,KleinandAnderson(2012) notedsimilareffectstotheseregulatorychangeselsewhereintheregionbyassessingtotalsedimentload.

ManagementPractices

AlongwiththecontinuingchangestotheCalifornia ForestPracticeRules,timberlandmanagementpracticeshavealsoevolvedandimprovedovertime.Such changeshavebeennotedthroughouttheredwood region(ValachovicandStandiford,2017).Among thosechangesaremodifiedriparianbuffers,preventativemasswastingzones,road-managementplans,and low-impactharvestmethods.Factorsimpactingerosionratesthathavebeenassociatedwithmanagement practicesincludevoluntaryhabitatconservationplans (HCPs),developmentandimplementationofpreven-

tativelandslidebuffers,innovativeriparianmanagementzones(RMZs)thatprotectaquatichabitat,lowimpactground-basedyardingmethods,andimproved roadmanagementplanning.(Note:riparianmanagementzonesorRMZsarestreamsidehabitatretention areaslocatedalongriversandstreams.Theseareasare analogoustotheWLPZthatwasestablishedaspart oftheCAFPRs.)Theadvancementofthesemanagementpracticesovertimehasaidedinthedeclineof erosionratesandmayhavehadtheirmostdramatic effectinthemodernloggingerawhenmanyofthese factorsweredevelopedandimplemented(Figure5).

Habitatconservationplanshavebeenunderdevelopmentinthestudyregionsincetheearly1990s.The SimpsonTimberCompanyestablishedthefirstHCP intheindustryfornorthernspottedowlsin1992, whichincreasedtreeretentionlevelsinClassIand ClassIIstreams(SimpsonTimber,1992).In1999,the PacificLumberCompany,nowknownasHumboldt RedwoodCompany,establishedanHCPfortheirownershipthatelevatedretentioninRMZswhencomparedtotheCAFPR(HumboldtRedwoodCompany, 2019).TheirHCPalsoaddressedslopestabilityissues byestablishingpreventativeprotectionmeasuresfor areasdefinedasMassWastingAreasofConcern.

In2007,anAquaticHabitatConservationPlan (AHCP)wasimplementedacrossthestudyareawhich includednumerousmeasuresthathaveinfluencedthe observeddeclineinerosionrates(GreenDiamondResourceCo.,2006).AmongthemosteffectivewererevisionstotheRMZsmentionedearlier,seeninFigure 6f.TheRMZsvariedinwidthandwerecharacterized bytwozonesofcanopyretention—aninnerzoneof 85percentandanouterzoneof70percentoverstory canopyclosure—thatwereappliedtoslopesadjacent toperennialflowingstreams.AtthetimeofimplementationofthisAHCPin2007,theRMZsresulted inanincreaseintreeretentioninthesestreamsideareasrelativetotheCAFPRWLPZ.Thewidthsofthe areasweregenerallythesame;however,thecanopy retentionoftheWLPZwasless.Bycomparison,the WLPZrequiredtheretentionofonly50percentofthe overstoryandunderstorycanopycoveronperennial streamsatthattime.Althoughgenerallythesame,in somecircumstances,dependingonstreamclassificationandyardingmethods,theseRMZsalsoprovided awiderbufferedareaincomparisontotheCAFPR WLPZ.

Preventativestreamsidelandslideprotectionzones werealsodevelopedaspartoftheGreenDiamond AHCP.Theseareastargetsteepstreamsideslopesand enhancetreeretentioninareasthataretypicallyprone toproducingshallowlandslides.Implementedin2007, thesebufferswererevisedin2011(Woodwardetal., 2012)and2015(Woodwardetal.,2017).Asampleset

Figure7.PreventativelandslidebuffersknownasSteepStreamside Slopes(SSS).Thesebuffersaresimilartoariparianmanagement zone(RMZ)buthaveelevatedtreeretentionstandards.EachSSSis furtherbrokenintoariparianstabilitymanagementzone(RSMZ) and,dependingonslopesteepness,astabilitymanagementzone (SMZ).TheapplicationoftheSSSisdeterminedbythesteepness ofslopethresholdthatisspecifictodifferentgroupsofwatersheds.

oftheseareasisreviewedperiodicallyforeffectiveness andtodate,nopost-harvestmanagement-relatedlandslideshavebeendetectedinthoseareas.Aschematic diagramofanSSSbufferisshowninFigure7along withacomparisonofastandardRMZ.

Privatelandownersalsoaddressroadbuildingand management.Poorroadbuildingandmanagement havebeenknowntobesignificantcontributorsto landslideinitiationandsedimentinputassociatedwith timberharvesting(SwansonandDyrness,1975;Amaranthusetal.,1985).AspartoftheGreenDiamond AHCP(GreenDiamondResourceCo.,2006),acomprehensiveroadmanagementplanwasimplemented,a three-partplanintendedtoaddressallroadsacrossthe propertybytheendoftheplandesign.Thefirstpart isatimberharvestplanassessmentthataddressesall appurtenantroadswithintheplanareabyupgrading roadsthataregoingtobeusedanddecommissioning unnecessaryroads.Thesecondisaroad-maintenance programthatreviewsalltruck-andATV-accessible roadseverysixyearsformaintenanceandupkeep.The thirdpartisawatershed-by-watershedcompleteassessmentofallroadswithaninventoryofsediment sourcesanddeterminationofimminentriskoffailure thatistobecompletedbytheendoftheplandesign.

TechnologyChanges

Technologicaladvancementisanotherareawhere notablechangesinthetimberindustryhavehad benefitsfortheenvironment.Cableyardingbegan toreplaceground-basedtractoryardinginthelate

1970sandearly1980s,whichsignificantlyreduced roadbuildingandassociatederosion.Yardingmethodsutilizing“shovels”wereregionallyintroduced around2004andhavehelpedreducesurfaceerosion associatedwithtimberoperations.Shovelyarding isaground-basedyardingmethod,analternative totractoryarding.Unliketractoryarding,shovel yardingdoesnotrequiretheconstructionoruseof skidtrailstooperatewithinaharvestblock.Shovels aretrack-mountedmachinesthatoperateontopof slashwithinaharvestblockandrarelyexposebare mineralsoil(i.e.,baredirtwithoutthecoveroforganic debris).Thesemachines“leapfrog”logsacrossthe slopeasthemachinepivotsandmovesfromonespot tothenext,workingtowardanearbyroadorlanding (Figure8c).Logsaretypicallyfullysuspendedasthey aremovedfromonelocationtothenext.Thismethod isutilizedonslopeswithinclinationsupto45percent (approximately24degrees).Figure8illustratesa visualcomparisonbetweenhistoricaltractor-yarding methodsandmodernshovel-yardingmethods.

SeismicandClimaticInfluences

Historicalrecordsindicatethattheregionhas shownelevatedlevelsofseismicactivity(Youdand Hoose,1978;McPhersonandDengler,1992;and Dengleretal.,1995)thathaveresultedinincreased landsliding(YoudandHoose,1978;McPhersonand Dengler,1992).Researchregardingseismicallyinducedlandslidinghasshownthatearthquakescan generatelong-termlandslidingandsubsequentslide debris(Keefer,1994).Keefer(1994)alsonotesthat thesmallestearthquakelikelytogeneratelandsliding isaroundamagnitude(M)of4andthattheseearthquakesgenerallyproduceonlyafewlandslides.The effectsoflargerearthquakesoccurringintheregion, M6andgreater,havebeenevaluatedduringthestudy period.

AccordingtotheUnitedStatesGeologicalSurvey (USGS)earthquakedatabase,therehavebeen32 strongtomajor(M6toM7.9)earthquakesinthe regionbetween1940and2016.Agraphispresentedof thoseearthquakesandtheirtemporaldistributionby decadeinFigure9.Comparatively,theaveragemagnitudeandnumberofearthquakesthatoccurredwere greatestinthe1990s.Compoundingthis,inAprilof 1992,threeearthquakes,M7.2,M6.6,andanotherM 6.6,struckwithin24hoursofeachothernearPetrolia, California,deliveringmodifiedMercallishakingintensitiesofmoderateandgreateracrossthestudyarea. Thisclusterofstrongtomajorearthquakeswasprecededandfollowedbymajorearthquakes(eachM7.0) in1991and1994.Theseearthquakesresultedinnumerouslandslidesthroughouttheregion(McPherson

andDengler,1992).Bycomparison,accordingtothe USGSearthquakedatabase,the1940sand1950sproducedasimilarnumberofearthquakes;however,the averagemagnitudewaslessthaninthe1990s.Based onthesedata,ahigherlandslideincidencewouldbe

expectedinthe1990scomparedwithotherdecadesin thisstudyandmay,inpart,explaintheriseinerosion ratesduringthe1990swhichareseeninFigure5.

agemagnitude,bydecade.DataaccessedfromUSGSEarthquake Hazardswebsite:https://www.usgs.gov/programs/earthquakehazards/earthquakes.

Researchalsoshowsthathighrainfallintensityand durationcantriggeranincreaseinlandslideevents (Campbell,1975;CannonandEllen,1985;andWieczorek,1987).Tofurtherevaluatetheriseinerosion seeninthe1990s(Figure5),averagedecadalrainfall andstormeventswerereviewed.Studieshaveshown thatitmaybenecessarytoreviewpeakhourlyprecipitationtoidentifylandslidetriggeringstormevents (CannonandEllen,1985,Wieczorek,1987).Unfortunately,hourlyprecipitationisnotavailableandthere isnohistoricalhourlyprecipitationdataavailablefor thisregiontoaccuratelyassessrainfallintensity.UsingthelimitedavailabledatafromtheWRCCEureka WeatherForecastOffice(WFO)COOPsite,landslidetriggeringstormeventswereassessedbysummarizingelevatedmonthlyprecipitationevents.Thesestorm events,characterizedaselevatedmonthlyprecipitation eventsthatrecorded25cm(10in.)ormoreofprecipitation,havearecurrenceintervalofoneandahalf yearsthroughoutthisstudyperiod.Includedaresix noteworthyeventswhererainfallexceeded35cm(14 in.):Nov.1973,Dec.1983,Nov.1984,Dec.1996,Feb.

Figure8.Tractor-andshovel-basedlogging.(a)Aerialimageryoftractor-basedloggingintheearly1980s.Adenseskidroadnetworkoccupies theentireharvestblockexposingbaresoilthroughoutthe54-hectare(134-acre)area.(b)Tractor-basedlogginginKlamathRiverwatershed. (c)Track-mountedshovelisoperatingontopofslash;noskidroadsarenecessaryforthistypeofmachinery.(d)Harvestblockutilizingshovel yardingharvestmethodsinan8.9-hectare(22-acre)clear-cut.Noticethereisnobaresoilexposedwithintheblockexceptfortheloggingroad crossingthroughtheblock.Thereddish/browncolorintheharvestedareaisdriedfirandredwoodslash.

Figure9.Alookatseismicityoverthestudyperiod.Acomparisonofthenumberofregionalearthquakes M6.0versusaver-

Figure10.Acomparisonofaveragedecadalprecipitation,monthly stormevents,anderosion.PrecipitationdatafromEurekaweather forecastofficeatWoodleyIsland,CA.Dataareshownbydecade andcalculatedusingwateryears(OctobertoSeptember),notcalendaryears.Eachofthestormeventsforthe1990sfollowsthethree strongtomajorearthquakesof1992.Note:asshowninTable1, Eurekarepresentstheleastamountofrainfallacrossthestudyarea andthereforethesedatarepresentthelowendofextremeclimate conditionsfortheperiodsacrossthestudyarea.Dataaccessedfrom https://wrcc.dri.edu/Climate/west_coop_summaries.php.

1998,andDec.2002;both1996and2002exceeded 53cm(21in.).Itisassumedthatpeakhourlyevents capableoftriggeringlandslidesaremostlikelytooccurduringmonthlystormeventslikethesewhereprecipitationishigherthannormal.ThesedataaresummarizedinFigure10,whichshowsthathigh-intensity stormeventsandannualprecipitationwereelevated inthe1990scomparedtomostdecadesinthisstudy. Onlythe1950sand2000ssawsimilaraverageannualprecipitationandelevatedmonthlyprecipitation events.Theaverageannualprecipitationinthe1990s wasthreetofiveinchesgreaterthanthatofthepreviousthreedecades.Theassessmentofstormevents inthisstudyshowsthattherewereninemonthswith greaterthan25cm(10in.)ofrainfallinthe1990swith only17monthsinthepreviousthreedecades.Mostof theselargestormeventsoccurredafterthe1992earthquakes,discussedearlier.

Climaterecordsdemonstratedthatbothannualprecipitationandstormevents(monthswithgreaterthan 25cmofrainfall)weregreaterinthe1990sthanin mostdecadeswithinthisstudy;thebulkoccurredduringfouryearsfrom1995to1998.Seismicrecords alsoshowedthatthe1990ssawbothmorefrequent andhighermagnitudeearthquakesthaninanyother decadeinthestudy.Withtheincreasesinannualprecipitation,stormevents,andseismicity,anincrease wouldbeexpectedinerosionrates,whichisseen inthe1990scomparedwiththe1980sand2000s. Figures9and10bothillustratethiscorrelation.Afterthe1990s,anothersharpdropinerosionratesis notedinthemodernloggingera.Whiletherewaselevatedprecipitationinthe2000s(Figure10),seismicity

wassignificantlylesswhencomparedwiththe1990s (Figure9)and,whencoupledwithimprovingmanagementpracticesasdiscussedearlier,itmaybepartofthe reasonadropinerosionrateswasseenoverthisperiod (Figure10).

IncreasedGeologicKnowledgeandOversight Geologicinputassociatedwithtimberharvesting beganinthemid-1970swiththepassingoftheZ’bergNejedleyForestPracticeActof1973.In1978,under provisionsofSection208oftheFederalWaterPollutionControlActandwithfundingfromtheEnvironmentalProtectionAgency(EPA),theCaliforniaDepartmentofForestryhiredseveralgeologistsunder TitleIIGeologicDataCompilationProject tomapthegeologyandlandslidesinseveralsensitivewatershedsin northernCalifornia(Bedrossian,2015).Thegoalwas tobetterunderstandnon-pointsourcesofsediment pollutionfromlandslideswithinprospectiveTHPs.It alsomadegeologicandgeomorphicmappingavailable toforestersforTHPlayoutaswellasforreviewing agencies.However,areviewoflocalplansbyCaliforniaDivisionofMinesandGeology,waslimiteduntil the1990s.WiththeadditionoftheT&Irulesintothe 2001CAFPRs(CDF,2001),theCaliforniaGeologic Survey’sinvolvementwithTHPsgrew.Atthattime, CGSstaffinginHumboldtandDelNorteCounties wentfromoneemployeetofiveemployees.Licensed geologistsfromCGSreviewedallsubmittedTHPsand planswithcomplexgeologicissuesandtypicallyreceivedon-sitefieldevaluationsknownasPre-Harvest Inspections.Asaresultoftheincreaseinstatereview, moreforestersbegantoseekprivateconsultinggeologiststoreviewTHPsduringthelayoutphase.THPs withcomplexgeologicissuestypicallyincludedageologicevaluationfromalicensedgeologist.Someindustrialtimbercompanieshavegeologistsonstaffto reviewharvestplansincludingWeyerhaeuser,Green DiamondResourceCo.,andHumboldtandMendocinoRedwoodCo.,tonameafew.Geologiststypicallyreviewin-houseLiDARandgeologicmapping, aswellaspublishedgeologicmapping.AtGreenDiamondResourceCo.,mostplansreceivesomelevelof fieldreviewand20percent,onaverage,receiveinput intheformofamodifiedgeologicandgeomorphic maporageologicreportthatissubmittedwiththe THP.Additionally,thelevelofknowledgeofgeology, andmorespecificallyslopestability,foraforesteris likelyatanall-timehigh.Variousassociationsprovide geologicseminarsforforestersandsomeindustrial companiesprovideongoinggeologictrainingfortheir forestrystaff.TheCaliforniaLicensedForestersAssociation(CLFA)hasaguidelinethathelpsforestersdeterminetheneedforinputfromageologist(CLFA,

1999).Thisguideline,coupledwithtraining,canhelp forestersduringharvestplanlayouttoidentifypotentialhazardsandseekappropriateprofessionalinput whenneeded.Thelevelofawarenessregardingslope stabilityhasincreasedovertimeandhaslikelycontributedtoareductioninerosionrates.

CONCLUSIONS

Minimummapunitscanhaveasignificantimpactonthelevelofeffortrequiredforamasswastingassessment.Accordingtoobservationsinnorthern California,onecouldincreasetheefficiencyoffuture landslideinventoriesbysettingaminimummapunitof 150m2 (1,615ft2 ).Usingthissizethreshold,89percentofthetotallandslidesedimentvolumewouldbe recordedfromonly35percentofthelandslidessurveyed,a65percentreductioninfieldwork.Acase couldalsobemadetoreducefieldeffortsevenfurtherbyevaluatingaminimummapunitof300m2 (3,230ft2 ),whichwouldreducethefieldevaluationeffortsbymorethan80percentandstillcapture78percentofthetotallandslidesedimentvolume.Ineither case,forefficiencyoreconomics,aminimummapunit shouldbecarefullyconsideredanddesignedtocaptureabalancethatwillaccuratelycharacterizesedimentvolumeswithapracticalnumberofdatapoints.

Thetimespanofthisstudyprovidesarareandinsightfullookattheeffectsoftimberlandmanagement practicesinnorthernCalifornia.Withmostlymature forestsoccupyingthestudyareaduringthe1940s,premanagementandpost-managementlookscanbecapturedatthesewatersheds.Asmanagementactivities increasedinthedecadesfollowingthe1940s,acompoundingrisewasclearlyseeninlandslide-relatederosion.TheZ’berg-NejedleyForestPracticeActpassed whileerosionrateswereattheirpeakandalthoughit tookseveralyearstoimplement,thereisnomistaking thedramaticeffectsithadonreducingerosionrates whichwereseenbytheendofthe1980s.Thecontinuingdownwardtrendindecadalerosionratescorrelates stronglywiththeevolutionofregulationsandmanagementpractices,especiallythoserelatedtoroadsand streams.

Theabilitytodetectandrecordlandslidesisat anall-timehighthankstotheimprovedqualityand theincreasedfrequencyofremotelysenseddataand imagery.Todayitiseasiertotracklandslide-related erosionthanitwaspreviously.ThisstudyofhistoricallandslideerosionshowsthatratesinthemodernloggingerainnorthernCaliforniahavedeclined bymorethan90percentsincetheirpeakinthe 1970s.Technologicaladvanceshavecontributedto thischangeandhavebeenkeyinreducingground disturbanceassociatedwithmodern-dayoperations.

However,evolvinggovernmentregulationshavebeen thecatalystinmakingthesechangesoccurbeginningwiththeestablishmentoftheZ’berg-Nejedley ForestPracticeActof1973.Thisinturnhasledto anevolutionofmanagementpracticesandformore landowners,thatincludesself-imposedregulationlikehabitat-conservationplansandroad-management plans,whichmaybethemostsignificantfactorsassociatedwiththeimprovementsseeninthemodernloggingera.Observationsshowthatconscientious landownerscanandareconductingtimberharvesting withoutsignificantadverseimpactsonwatershedresources.Onceadestructiveprocess,managingindustrialtimberlandshasevolvedtobecometheresponsibilityofmanagingahealthyfunctioningforest.

ACKNOWLEDGMENTS

Thisworkwouldnothavebeenpossiblewithout thededicationoftheSimpsonfamilywhichhasmaintainedownershipoftimberlandsonthewestcoastfor over130years.Theircommitmenttothesafetyof theiremployeesandthestewardshipoftheirlandsis unprecedented.Ialsothankthegeologistsinvolved incollectingandreviewingtheimmenseamountof datapresented:ScottMatheson,ScottKirkman,Evan Saint-Pierre,NickGraehl,DanHadley,EstherStokes, WilliamTroxler,RonnaBowers,LymanPetersen, MichaelTanner,DavidPerry,JasonBrooks,BrianMcMullen,KyleTerry,MattKowalski,NickHawthorne, AnnieFehrenbach,BrianCook,andRossHiatt. Thankyouallforyourhardworkanddedicationto combingtheoftensteepandbrushyterrainofnortherncoastalCalifornia.ThankstomyfriendandcolleagueMr.JohnM.Curless,CEG,whohelpedreview andprovidecommentsforthiswork.Lastly,aspecial thanksforthewisdomandsupportofmyfriendand coworkerMatthewR.House(AquaticBiologist)who providedmanyhoursofdiscussiononallaspectsof thiswork,includingthispaperandeditorialreviews.

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ResearchonSide-SlopeMonitoringbyIntegrating TerrestrialLaserScanningandUAV-Based Photogrammetry

YUNCHUANWANG

FacultyofGeography,YunnanNormalUniversity,Kunming650500,China,andKey LaboratoryofVirtualGeographicEnvironment,MinistryofEducation, NanjingNormalUniversity,Nanjing210023,China

PINGDUAN* JIALI ZHIKEZHANG

FacultyofGeography,YunnanNormalUniversity,Kunming650500,China

KeyTerms: SideSlope,TerrestrialLaserScanning, Photogrammetry,DeformationDetection

ABSTRACT

Side-slopedeformationmonitoringcomparesmonitoringdatafromthesameareaoverdifferentperiodsand measuresthedeformationvariables.Becauseofthegaps andcoarsenessofside-slopemonitoringdata,asideslopemonitoringmethodthatintegratesterrestriallaser scanning(TLS)andunmannedaerialvehicle(UAV)–basedphotogrammetrypointcloudsisproposed,aimingtosolvetheproblemofslopemonitoringincomplexscenes.First,TLSandUAV-basedphotogrammetrypointcloudsareacquired.Then,thetwotypesof pointcloudsareregisteredbyaniterativeclosestpoint algorithm.Next,thedatagapareasintheTLSpoint cloudaredetected,andagap-fillingmethodisusedto integratetheUAV-basedphotogrammetrypointcloud withtheTLSpointcloud.Finally,side-slopedeformationisdetectedbasedonamultiscalemodel-to-model cloudcomparisonalgorithm.AsideslopeinChenggong, Kunming,China,istakenasanexample.Thesurface deformationofthesideslopewasmonitoredduringJanuaryandJune2021.Theexperimentalresultsshowthat theregistrationerrorsofthetwo-phaseintegrationpoint cloudare0.039mand0.035m.Therootmeansquare errorsofthefourgroundcheckpointsare0.033mand 0.038m.Finally,thesideslopeisfoundtohavedeformed andformedamaindeformationarea,whichshowsthat thissideslopewasinanactivestate.

INTRODUCTION

Asideslopeisacriticalsurfacewithacertaininclinationformedbynaturalgravityorhumanfactors inrockandsoil.Sideslopeshavealargeinclination, andnaturaldisasterssuchaslandslidesandrockslides occurwhenthelongitudinaltensileforceexceedsthe shearstrength(Ferreroetal.,2010;Bonneauand Hutchinson,2019).Thestateofasideslopecanbe periodicallyinvestigatedandmonitoredtoobtainits deformationpattern,whichhasanimportantrole instabilityassessmentsandevaluationofdisaster susceptibility(Passalacquaetal.,2015).

*Correspondingauthoremail:dpgiser@163.com

Atpresent,thereareseveralmethodsusedtomonitorsideslopes(Scottetal.,2020).(1)Intraditional side-slopemonitoringmethods,thesideslopeismeasuredwithatotalstation,extensometers,inclinometers,andothertraditionaltools.Then,thestability oftheside-slopeform,area,volume,cracksandtheir lengthsandwidths,side-slopeangle,surfaceroughness,andotherparametersareassessed(Brückletal., 2006;Dewitteetal.,2008).Side-slopeanddeformationinformationcanbeexpressedonlyabstractlyand inefficientlyviatraditionalmethods,whichmakesit difficulttomeetthecurrentdemandforefficientand timelyside-slopemonitoring.(2)Terrestriallaserscanning(TLS)andairbornelaserscanning(ALS)arealso usedforside-slopemonitoring(Abellánetal.,2014; Ranaetal.,2014;Careyetal.,2019;Panetal.,2019; Delaneyetal.,2020;Sonetal.,2020;andAlietal., 2021).Lightdetectionandranging(LiDAR)isused toobtainthree-dimensional(3D)laserpointcloudsof sideslopes,whichareusedtodetectthedeformation area.Thismethodhasahighaccuracy,buttheTLS pointcloudischallengedbyterrainandenvironmentalocclusion,anditisdifficulttoobtaincompletesideslopedata.Thehighcost,largeamountofdata,and lowprocessingefficiencyarealllimitationsofALS.

Furthermore,themonitoringaccuracyissignificantly affectedbyweather,suchascloudsandrain,andit isdifficulttocapturesmalldeformationsintheearly stagesofdisasters(Pitkänenetal.,2019;Kovani ˇ cetal., 2020),whichmakesitlessapplicableinthedetection ofsmallslopesandtheircracks.(3)Inside-slopemonitoringbasedonhigh-resolutionremote-sensingtechnology,high-resolutionremote-sensingimages,suchas satelliteimagesoraerialimagesfromunmannedaerial vehicles(UAVs),canvisually,graphically,andcomprehensivelyrepresentthecharacteristicsofsideslopes. Richgeometricstructureandtextureinformationand multiperiodimagescanbeobtained,andmultiview andmultiscaledynamicmonitoringoftheside-slope developmentprocesscanbeperformed(Fourniadis etal.,2007;LazzariandGioia,2017;Liuetal.,2019; Wangetal.,2019;andRodriguezetal.,2020).However,satelliteimageshavealongrevisitperiod,andthe imagesareeasilyaffectedbycloudsandfoggyweather. Itisalsodifficulttomeettheaccuracyrequirements oververticalornear-verticalslopes.Theshortcomingsofsatelliteimagesintimeliness,spatialresolution,andaccuracyareresolvedbytheflexibleadvantagesofUAVs.Previousresearchhasshownthata densepointcloud(photogrammetrypointcloud)constructedbasedonUAVimagesandphotogrammetry technologyprovidesanefficientandlow-costmethod forside-slopemonitoring(Westobyetal.,2012;Zhang etal.,2018;andJamesetal.,2019).

Asideslopeisoftencharacterizedbysteepslopes anddisorderedvegetationcover.Wheninstrument portability,operationalrequirements,andterrainconditionsoftheslopeareconsidered,UAVandTLStechniquescanprovideaneffectivesolution.However,any oneofthesemeasurementtechniquesmayhavecertain problemswhenusedalone,suchasdatagapscaused byocclusionorinsufficientresolution.Insomescenarios,thegapscanbereducedtoacertainextentby

performingTLSatmultiplelocations,butthisgreatly increasestheworkloadandtimerequired,andthisis inconsistentwithourpurposeofreducingfieldwork. WhileUAV-basedphotogrammetrypointcloudshave becomealow-costalternativetoTLSpointclouds, thecombinationofTLSpointcloudsandphotogrammetrypointcloudscaneffectivelyimprovethequality,accuracy,andacquisitionefficiencyofthedata set,providingsatisfactoryresultsforcapturingthe complexcombineddetailsoftheregionofinterest (Balsa-BarreiroandFritsch,2018;Šašaketal.,2019). Side-slopemonitoringhasmorestringentdatarequirements,whichputsforwardnewchallengestodataintegrationmethods.

Therefore,TLSdevicesandUAVswereusedfor jointair–groundmonitoringinthisstudy.Theability ofTLShigh-precisionpointcloudsinthemonitoring ofsmallside-slopedeformationishighlighted,anda UAVphotogrammetrypointcloudwasusedtocompensatefortheTLSdatagapproblemarisingfrom perspectiveandocclusion.TheintegratedTLSand UAVphotogrammetrypointcloudmethodwasused toachieveside-slopesurfacedeformationmonitoring onatypicalsideslopeinYunnanProvince,China.

STUDYAREAANDDATA StudyArea

Asideslopewaschosenastheresearchobject (Figure1),locatedinChenggongDistrict,Kunming City,YunnanProvince,China.Theaverageelevation oftheareaexceeds1,900m.Thetargetslopewasexcavatedduringhighwayconstructiontoformarelatively highandsteepsideslope,withprominentsourceand accumulationareas,andthedipangleisnearlyvertical.Theupperpartofthesourceareaofthesideslope ismainlyexposedrock,mainlycomposedofshale,

Figure1.Studyareamap.

withthinlaminations,aroughtexture,smallcracks, weakweatheringresistance,andthecontinuousflakingoffinesandymaterial.Thereareprominentblock anddebrisdepositsonthelowerpartofthesideslope, mainlyfromthewallditchattheupperpart,andafew plantsaredistributedattheedge.

Theregionalclimateismainlycontrolledbysubtropicalhighpressure.Theannualaveragetemperatureis14.7°Celsius,andtheaverageprecipitationis 790mm.FromMaytoOctobereachyear,theclimate iscontrolledbythewarmandhumidaircurrentsofthe IndianOceanandthePacificOcean,withabundant watervaporandhighprecipitation.Thesideslopeis inastateofalternatingdryandwetconditionsdue totheuniqueplateauclimate,whichisnotconducive tothestabilityofthesideslopeandcaneasilyinduce landslidedisasters.

TLSDataAcquisition

TheTLSpointclouddataforthesideslopewere obtainedinJanuaryandJune2021.Thesurfacesof thesideslopeweresurveyedwithaLeicaP40TLS (Figure2a),withaneffectivescanningdistanceof270 mandanangularaccuracyof8 .Threeblackand whitecirculartargetswereplacedonstableground,

andreal-timekinematic(RTK)technologywasused toobtainthegeodeticcoordinatesofthethreetarget centers,whichwereusedtoconverttheTLSdatainto thegeodeticcoordinatesystem.Theglobalnavigation satellitesystem(GNSS)equipmentwasaLeicaGS15, andthemeasurementerrorofthetargetwaswithin ±0.03m.

UAVImageAcquisition

UAVimagesofthestudyareawerecollectedin JanuaryandJune2021.ADJIPhantom4Prowas usedforthisresearch(Figure2b).Thetrajectoryof theUAVflightwassetinadvance;theimageoverlap was85percent,thesideoverlapreached75percent, andarelativeflightheightof60mfromtheground wasmaintainedatthebottomofthissideslope. Theacquisitionofimagedatainthestudyareawas achievedbyautonomousUAVflight,thehorizontal routewiththebestversatilitywasused,andtheUAV imagewasacquiredfromtheverticalperspectiveto acquireimagedatainashorttimeandreducedata redundancy.Thesameflightrouteandrelatedparameterswereusedinthetwoaerialflightoperations.In total,153UAVimageswereacquiredthefirsttimeand 154UAVimageswereacquiredthesecondtime,with anaveragegroundresolutionof0.016m/pixel.

Fivegroundcontrolpoints(GCPs)wereevenlydistributedaroundthesideslope.AnL-shapedsignwas drawnwithredpaintonthestableareaasthesignfor theGCPs,withdimensionsofapproximately0.8m × 0.8m.Tenlocationswererandomlyselectedascheckpoints(CPs),andthelocationsoftheCPsincludedthe groundandsomeside-slopepositionsaccessibletothe operators.MeasurementsoftheGCPsandCPswere performedusingaGNSS-RTK,andthemeasurement errorwaswithin ±0.03m.

Side-SlopePointCloudGeneration

ALeicacyclonewasusedforTLSpointcloudprocessing.First,theTLSpointcloudwasgeoreferenced. Threetargetpointsinthepointcloudwereassigned geographiccoordinatesasmeasuredbyGNSS-RTK andprojected.ThefinalTLSpointcloudwasconvertedtotheWorldGeodeticSystem1984(WGS84) UniversalTransverseMercator(UTM)Zone48N (EPSG:32648)projectioncoordinatesystem.TheaverageregistrationerrorofthethreetargetsintheJanuaryTLSpointcloudwas0.012m,andthatinJune was0.011m.Then,thepointcloudwasimportedinto CloudComparesoftware,andthevegetationandnoise wereremovedwiththemovingleastsquaressmoothingandstatisticaloutlierremovalalgorithms.

Figure2.(a)TLSdataacquisitionand(b)UAVimageacquisition.

ThefinalTLSpointcloudsinJanuaryandJune areshowninFigure3aandFigure3b,respectively, withaspatialresolutionof0.015m.TheTLSpoint cloudscontainmanydatagapareas.Themainreason isthatthevegetationonthegroundandsideslopes causedobstruction,andpointcloudinformationin theobscuredareacouldnotbeobtained.Therefore, theTLSpointcloudswithgapslackspatialcontinuityandcannotbeusedtomonitorside-slopesurface deformation.

TheUAVimageswereprocessedusingPix4DMappersoftware.First,thekeypointsinUAVimages weredetectedandmatched,followedbyautomatic bundleadjustment.Inthetwoperiodsofphotogrammetryprocessing,fiveidenticalanduniformlydistributedGCPswereinvolvedinthisstep.Sparse3D pointcloudsofthesideslopewithWGS84UTM48N (EPSG:32648)projectioncoordinatesweregenerated. Theaerialtriangulationresultsshowthattheaveragerootmeansquareerror(RMSE)was0.021m, whichmeetstheaccuracyrequirements.Finally,dense pointcloudswereconstructedbythemultiviewstereo (MVS)technique.

Thetreesonthegroundandtopofthesideslope wereremovedbytheclothsimulationfilter(CSF)algorithminCloudComparesoftware(Zhangetal.,2016), andthefinalphotogrammetrypointcloudsinJanuary andJuneareshowninFigure3candFigure3d,respectively.TheaveragepointclouddensitiesinJanuaryand Junewere589points/m2 and575points/m2 ,respec-

tively.Moreimportantly,thephotogrammetrypoint cloudwasverycompleteanduniform.

SIDE-SLOPEMONITORINGMETHOD INTEGRATINGTLSANDUAV PHOTOGRAMMETRYPOINTCLOUDS

Method

Theside-slopemonitoringmethodintegratingTLS andUAVphotogrammetrypointcloudsisshownin Figure4.Themethodconsistsofthreemainsteps: pointcloudregistration,pointcloudintegration,and side-slopedeformationmonitoring.TheTLSpoint cloudandUAVphotogrammetrypointcloudforthe sameperiodwereregisteredtothesamereferencecoordinatesinthefirststep.Thedatagapareasinthe TLSpointcloudwerefilledbythephotogrammetry pointcloudduringpointcloudintegrationtoproduce acompletedataset.TheTLS–UAVphotogrammetry pointcloudsinthetwoperiodswereanalyzedviatime series,therebymonitoringthedeformationoftheside slopeovertherelevanttimerange.Thefollowingsubsectionsintroducethepointcloudregistration,point cloudintegration,andside-slopedeformationmonitoringmethods.

PointCloudRegistration

TLSpointcloudsandUAVphotogrammetrypoint cloudsmustbegeoreferencedbeforeintegration.The

Figure3.(a)TLSpointcloudinJanuary2021,(b)TLSpointcloudinJune2021,(c)UAV-basedphotogrammetrypointcloudinJanuary 2021,and(d)UAV-basedphotogrammetrypointcloudinJune2021.

Figure4.Schematicworkflow.

iterativeclosestpoint(ICP)algorithm(Besland McKay,1992)wasusedtoaccuratelyregisterthepoint clouds.SincethenumberofUAVphotogrammetry pointcloudpointswassubstantiallylessthanthatof theTLSpointcloud,toavoidthephenomenonofredundantpointsnotfindingcorrespondingpointsduringthesearch-forpairedpoints,theUAVphotogrammetrypointcloudandTLSpointclouddatasetswere usedasthereferencedataandthetargetdata,respectively.The3Ddistancewasminimizedbetweenthe photogrammetryandTLSpointcloudsbyfindingthe besttranslationandrotationparameters,asshownin Eq.1,

Therefore,thesurfacetopographyandslightundulationscanbeonlyapproximatelyrepresentedbythe photogrammetrypointcloud.Here,thepointclouds obtainedbasedonTLSandUAVarecompared,as showninTable1.Amongthem,TLSpointcloudshave advantagesinpointclouddensityandaccuracy,while UAVphotogrammetrypointcloudshaveadvantagesin pointcloudintegrityandacquisitionefficiency.Therefore,theshortcomingsofasinglemethodcanberemediedbypointcloudintegration.

where q and p representtheUAVphotogrammetry pointcloudandTLSpointcloud,respectively; n is thenumberofpointclouds; R and T aretherotationmatrixandtranslationmatrix,respectively;and E isthedistanceerrorunderthecurrentregistration parameters.

PointCloudIntegration

ThefinalTLSpointcloudandUAVphotogrammetrypointclouddatasets(Figure3)showthedifferenceindetailontheside-slopesurface.TheTLSpoint cloudishighlydetailedandhasahighspatialdensity, withadensityofapproximately8000points/m2 ,so thatthesurfacetextureofthesideslopecanbeobserved.UniformandlargespatialseparationischaracteristicofUAVphotogrammetrypointclouds,with apointclouddensityofapproximately600points/m2 .

Ifthetwokindsofpointcloudsareintegrateddirectly,thentheaccuracyoftheTLSpointcloudisreduced,producingafinalpointcloudthatcontainssubstantialambiguity.Therefore,onlythegapsintheTLS pointcloudwerefilledwiththeUAVphotogrammetrypointcloud.MATLABsoftwarewasusedforpoint cloudintegration,andthecomputerprocesserwasan AMDRyzenR5-5600XCPUwith16GBRAM.The algorithmprocessisasfollows.

Step1:Definitionofthescopeofthearea.TheTLS pointcloudandUAVphotogrammetrypointcloud areprojectedontothe x-y axisplane.Theside-slope distributionrangeintheTLSpointcloudisusedas

Table1. ComparisonofTLSandUAV-basedphotogrammetrypoint clouds.

areferencetocalculatetheminimumboundingrectangleofthesideslope.See“step1”inFigure5.

Step2:Detectiongridcreation.Takingtheminimumboundingrectangleastheboundaryrange,the actualsizeofthesideslopeandtheresultsofmultipleexperimentsareconsidered.Finally,theminimumboundingrectangleofthesideslopeisdivided bya2.5cm × 2.5cmregulargrid,whichisusedas anareathresholdtodetectthegapareaintheTLS pointcloud.See“step2”inFigure5.

Step3:Gapdetection.ThegridsaretraversedtoobtainthegapgridbasedonEq.2,whichrepresents thegapareaintheTLSpointcloud.See“step3”in Figure5.

Gap (i ) = 0, ni ≥ 1 1, ni = 0 (2)

where i isthecurrentgrid,and n isthenumberofTLS pointcloudsinthegrid.When Gap is1,thecurrent gridisagapgrid.

Step4:Pointcloudfilling.TheUAVphotogrammetrypointcloudcorrespondingtothegapareasis extractedandusedtofillthegapsbytraversingall thegapgridsandintegratingitwiththeTLSpoint cloud.See“step4”inFigure5.

Step5:Photogrammetrypointcloudcryptographic interpolation.Toensurethatthespatialdensityof theintegratedpointcloudsisuniform,theUAV photogrammetrypointcloudinthefilledareasis interpolatedbythenaturalneighborinterpolation method(Sibson,1981;Watson,1994).Theresulting pointclouddensityisclosetothatoftheTLSpoint cloud.See“step5”inFigure5.

Side-SlopeDeformationMonitoring

Amultiscalemodel-to-modelcloudcomparison (M3C2)wasusedtomonitortheside-slopedeformation(Lagueetal.,2013).Thebasicstepstomonitorthe side-slopesurfacedeformationprocessinthisstudyare asfollows(Figure6):

Pointcloudnormalvectorcalculation.Thetwofinal TLS–UAVphotogrammetrypointcloudsinJanuary andJunearedefinedasClouds1and2(samebelow),andtheentireoriginalpointcloudsarefully involvedinthecomputation.Wedefinetheradius as D/2(Thediameter D isusedtolimitthesearch rangeofthenormalvectorcalculation,whichisset to0.25minthispaper)foreachpointinCloud1. Thepointswithin D/2ofthecurrentpointarefitted

Figure5.Pointcloudintegrationschematic:(step1)definingthescopeofthearea;(step2)creatingadetectiongrid;(step3)gapdetection; (step4)pointcloudfilling;(step5)photogrammetrypointcloudcryptographicinterpolation.

tothelocalsurface,andthenormaliscalculatedas thenormalvectorofthepoint,asshowninFigure 6a.

Pointclouddistancecalculation.Wedefineasearch cylinderwithradius d/2(The d isusedtolimitthe searchrangeofthepointcloudcalculationdistance, whichissetto0.15minthispaper)wheretheaxis passesthrougheachpointandpointsinthedirectionofitsnormalvector.Then,thecylindertruncatessubsetsoftheClouds1and2pointclouds(i.e., thepointcloudslocatedinsidethecylinder).These twosubsetsofClouds1and2areprojectedontothe axisofthecylinder,andtheiraveragepositionsare calculated.Thedistancedifferencebetweentheaveragepositionofthetwosubsetsalongthenormal directionrepresentstheM3C2distanceofthepoint, asshowninFigure6b.

Side-slopedeformationareacharacterization.All pointcloudsareexecutedwiththeabovesteps,and theM3C2changefortheentirepointcloudrepresentsthedeformationresultforthesideslope.Here, positivevaluesrepresentpositivedeformation(deposition),andnegativevaluesrepresentnegativedeformation(erosion).

RESULTS

PointCloudIntegrationResults

Figure7showsthefinalTLS–UAVphotogrammetryintegratedpointclouds.Theproblemofsingledata

defects(Figure3)iswellsolvedbytheintegration oftheTLSandUAVphotogrammetrypointclouds. Figure7showsacomparisonoflocalslopedetails amongtheTLSpointcloud,theUAVphotogrammetrypointcloud,andtheintegratedpointcloud,which highlightstheadvantagesoftheproposedmethod,as wellastheshortcomingsofasinglepointcloud.The TLSpointcloudhasvoidsandunevenpointcloud density,andtheUAVphotogrammetrypointcloud isslightlysparse.Therealsurfacemorphologyofthe slopeisreproducedbytheTLS–UAVphotogrammetry integratedpointcloud.

PointCloudAccuracyAnalysis

PointCloudRegistrationError—TheRMSEwas usedtoevaluatetheregistrationerrorofTLSandUAV photogrammetrypointclouds;itsformulaisasfollows:

where x, y,and z arethecoordinatepointsintheUAV photogrammetrypointcloud; xco , yco ,and zco arethe correspondingpointsofthephotogrammetryinthe TLSpointcloud,respectively;and n isthenumberof pointclouds.

Figure6.M3C2distancecalculationprocess:(a)pointcloudnormalvectorcalculation,whereClouds1and2aretheTLS–UAVphotogrammetrypointcloudsinJanuaryandJune,respectively,and(b)M3C2distancecalculation.

Thefinalpointcloudregistrationerrorisshown inTable2.TheregistrationRMSEbetweentheTLS andUAVphotogrammetrypointcloudswas0.039 minJanuaryand0.036minJune,indicatingalow registrationerror.Intheregistrationprocessofthe ICPalgorithm,theUAVphotogrammetrypointcloud looksfortheclosestpointintheTLSpointcloudas itscorrespondingpointandshortensthespatialdistancebetweenthem.However,therearestillsomedifferencesbetweentheoriginalUAVphotogrammetry pointcloudandtheTLSpointcloudinsensorresolutionandcoverage.Therefore,thebestcorrespondingpointscannotbefoundatsomepoints,affecting theentireregistrationandaccuracyevaluationprocess andresultinginthefinalpointcloudregistrationerror beingmaintainedatapproximately0.03–0.04m.

PointCloudAbsoluteAccuracy—Theabsoluteaccuracyofthefinalpointcloudswasverifiedbyadirectcloud-to-cloudcomparisonwiththeclosestpoint distance(C2C)between10independentCPsandthe TLS–UAVphotogrammetrypointclouds,andtheresultsareshowninTables3and4.Fourofthe10CPs areonstableground,andsixareontheslope.ThefinalRMSEsoftheCPC2Cdistancesforthetwoperiodswere0.051mand0.093m.TheCPdistributionis showninFigure8.

FourgroundCPswerefoundtomaintainnearly thesamerangeofC2Cdistancesinthetwoperiods ofaccuracyvalidation.Themaximumincreasewas 0.013m,whichislessthantheacquisitionerrorof theGNSSmeasurementequipmentandcantherefore beconsideredasystematicerrorintroducedbythe measurement.FourgroundCPswereusedasthe accuracyindex.Becauseoftheinstabilityoftheslope, themeasuredcoordinatesoftheCPsontheslope maybedifferentfromtheiractualpositions,andthe deformationoftheslopeitselfleadstoachangeinthe positionsoftheCPs.TheseparatelycalculatedC2C RMSEsofthefourgroundCPsinthetwoperiods were0.033mand0.038m.Moreover,theRMSE ofallCPsofthefirst-phasepointcloudwas0.051 m(consideringthatnodeformationoccurredatthis time),whichconfirmsthattheabsoluteaccuracyof thefinalpointcloudwasrelativelyhigh.

Exceptforpoint10,theC2CdistancesoftheCPson thesideslopeallincreasedsignificantly,exceedingthe scopeofsystemerror.Point10waslocatedinthemiddleedgeofthesideslopeamongthesixside-slopeCPs, andtheotherswerelocatedinthelowerpartoftheside slope.Therefore,itcanbeconsideredthatthesecondphasepointcloudexhibitedprominentsignsofdeformationcomparedwiththefirst-phasepointcloud.

Then,theabsoluteprecisionoftheTLSpointclouds andUAVphotogrammetrypointcloudsinthetwoperiodsbeforetheintegrationwerecalculatedandcomparedwiththeintegratedpointcloud,andtheresults areshowninTable4.Fromtheresults,theaccuracy ofTLSpointcloudsandUAVphotogrammetrypoint

Figure7.FinalTLS–UAVintegratedphotogrammetrypointclouds.

Table2. Registrationaccuracy.

Side-SlopeMonitoringwithIntegratedTechniques

1 D representstheC2CdistanceofCPsandintegratedpointclouds,and

cloudsislowbeforeintegration.MostoftheseCPs havethesamedistanceintheTLSpointcloudasafter integration;however,thereareindividualpoints,such aspoint9,locatedinthemissingareaoftheTLSpoint cloud,thatmaketheabsoluteaccuracyofthiskindof pointpoor.IntheUAVphotogrammetrypointcloud, thissituationdoesnotexistbecausetherearenogaps, butbecauseitsoverallaccuracyisrelativelylow,the accuracyoftheCPsisslightlylowerthanthatofthe integratedpointcloud.Thisresultshowstheadvantageoftheintegrationmethodintermsofprecision.

Whilemaintainingthehighabsoluteprecisionofthe TLSpointcloud,theintegratedUAVphotogrammetry pointcloudmakesupfortheresultingprecisionlossby fillinginthemissingareassothatthefinalintegrated pointcloudhashigherprecisionthanthepointcloud beforeintegration.

DeformationMonitoring

Toquantifythesurfacedeformationprocessduring thisperiod,theM3C2algorithmwasusedtomoni-

Table3. Absoluteaccuracyofintegratedpointcloud.1

Table4. Accuracyofpointclouddatasets.

Figure8.CheckpointdistributionandC2Cdistance:(a)Cloud1and(b)Cloud2.

torthedeformationoftheTLS–UAVphotogrammetrypointcloudsbetweenthetwoperiods.TheM3C2 algorithmcalculatesthedistancebetweenpointclouds alongthedirectionnormaltothesurface,estimatingaspatiallyvariableuncertaintyforeachpoint cloudinaccordancewithterrainroughnessandcoregistrationerror,whichprovidesmoreaccurateresults (DiFrancescoetal.,2020;Houtetal.,2020).Thedifferencemodelsforthemaindeformationareaand significantchangeareaareshowninFigure9aand Figure9b.Thesignificantchangearea(< 0.05mand >0.05m)ismainlythelowerdebrisaccumulationarea ofthesideslopeandupperexposedrockarea,i.e., theyellow,red,andblueareasinFigure9b.Basedon this,theareawasdividedintoanerosionalarearepresentedbytheblueboxandadepositionalarearepresentedbytheredboxinFigure9b.Thedeformation resultsoftheupperpartofthesideslopeshowednegativevalues(bluepartinFigure9b),indicatingthat thesurfacerockweatheredandspalledovernearlyhalf ayear.Furthermore,multiplesmall-scalecrackswere observedtohavedeveloped(e.g.,Figure10).Inthedifferentialmodel,mostoftherockspallingareaexists nearthesurfacecracks,whicharemoreprominentin certainrockblocks.

Thepositivedeformationinthedifferentialmodel (redpartinFigure9b)correspondstothedebrisaccumulationareaatthefootofthesideslope.Theresultsshowedcontinuousdistributioncharacteristics, andtheincreasereachedavalueofapproximately 0.100minsomeareas.Duetothecontinuousspalling ofrockdebris,thebottomsurfaceofthesideslope graduallyincreasedinelevation,forminganincreasinglylargeinclinedsurface,andwhenthecontinuousspallingexceededthebearingrangeofthesideslopesurface,materialgraduallystartedtopileupon theground,asshowninFigure10b.Therefore,althoughthetimeintervalbetweenthetwoperiodsof datacollectionwasonly6months,therockmassof thesideslopeexperiencedunambiguousweathering duringthistime,whichmeansthatthesideslopeis notsafe.Thereisariskofrockfallorcollapseofthe sideslopewithnear-verticalformandlittlevegetation cover.

DISCUSSION

Thedataacquisitionaspectsareasfollows.TLSoffersbetterperformanceindetectingsmallterraindisplacements,butpracticalapplicationsareverylimited

Figure9.Deformationresults:(a)Significantchange.(b)M3C2resultsforClouds1and2.

insomespecificscenarios.Inonestudy(Kovani ˇ cetal., 2020),theauthorsperformedalargenumberofTLS measurementsinasingleday,anditwasdifficultto collectacompletepointcloudwithoutadatagap. Therefore,onlythemostlikelydeformationareaofthe sideslopeisusuallymeasuredbyTLStoimprovethe workefficiency.Atthesametime,toacertainextent, thegroundsurfaceofthesideslopecoveredbyvegetationiscapturedbyTLS,whichprovidesmoreaccurateresultsforsurfacedeformationdetection.Inthese areas,thepointcloudprovidedbyUAV-basedphotogrammetryisinsufficient.UAV-basedphotogrammetryisrelativelyinexpensiveandeasytoimplement, coveringalmosttheentireareaoftheresearchobjectin ashortperiod.Thehigh-resolutiontopographyofthe entireareacanbesurveyed.Inaddition,UAVshave betterapplicabilityinavarietyofenvironments,especiallyforslopesorlandslideobjectswithlargedrops andinclinations.TLSusuallyhasdifficultygenerating effectivepointcloudsduetothelackofasuitablelocation,whileside-slopeinformationcanbeobtained byobliquephotography,whichcanbuildadenserand higherqualityphotogrammetrypointcloud,allowing thefinalTLS–UAVphotogrammetrypointcloudto provideslopedeformationmonitoring.

TheGCPscontributedtotheimplementationof themethodinthisstudy.Ontheonehand,theaccurateregistrationofTLSandUAVphotogrammetry pointcloudsisthebasisofpointcloudintegrationand multitemporaldeformationmonitoring.ThegeoreferencesoftheTLSpointcloudandUAVphotogrammetrypointcloudareprovidedbytheGCPs,thusavoidingthecoarsematchingprocessintheintegratedpoint cloud.Moreover,theTLSandUAVphotogrammetry pointcloudsthataretoberegisteredareacquiredin

thesameperiod.Themaindifferencebetweenthetwo isthespatialdensityofthepointcloud,sotheUAV photogrammetrypointcloudcanbeapproximatedas asubsetoftheTLSpointcloud.Therefore,thepurposeofprecisepointcloudregistrationcanbeachieved bytheICPalgorithm.Ontheotherhand,GCPsare involvedintheprocessofconstructingdensepoint cloudsfromUAVimages,whichgreatlyreducesthe elevationdistortionofphotogrammetrypointclouds. Inthisarticle,thesameGCPswereusedfortheUAV photogrammetrypointcloudsinbothperiods(Peppa etal.,2019).Thepositioninguncertaintyerrorcaused byRTKmeasurementwasavoidedtoensuretheaccuracyofthefinaldeformationdetection.

Inaddition,agap-fillingmethodwasusedtointegrateUAV-basedphotogrammetrypointcloudswith theTLSpointcloudinthisstudy.The3Dterrainisdescribedmorerealisticallyandorderlywiththismethod thandirectlymixingtwotypesofpointclouds,and itreducesdataredundancy,asshowninFigure11. Cloudsaandbshowthedifferencesbetweenthegapfillingandintegrationmethodandthedirectmixing method,withCloudaandCloudbconvergingindifferentwaysbasedonCloud1.Thepurposeofsupplementingdataintegritycanbeachievedwithboth methods,buttheaccuracyoftheTLSpointcloudis reducedbythepointclouddensityoftheUAVphotogrammetrypointcloudinCloudb,whichlosesthe surfacedetailsoftheslope.Adigitalsurfacemodel (DSM)ofCloudsaandbwasbuiltbycreatinga triangulatedirregularnetwork.Theresultsshowthat directlymixingtheUAVphotogrammetryandTLS pointcloudsintroducesmorenoisetothefinalDSM, whilethegap-fillingandintegrationmethodshowsa smootherandmorerealisticresult.

Figure10.Side-slopesurfaceandfootcharacteristics.(a)Cracksontheside-slopesurface.(b)Thedebrisontheside-slopesurfacebeginsto accumulateonthegroundafteritexceedsthetolerancerangeofthesurface.

Inadditiontoside-slopemonitoring,theproposed TLSandUAVphotogrammetrypointcloudintegrationschemecanbeappliedtoavarietyofgeologicalapplications.Forexample,terracesinagricultural landscapesareaspecialtypeoflanduse.Theyare distributedinstepsalongsteepslopes,whichcaneffectivelyimprovetheproductivityoflandinmountainousareas.Therefore,itisofgreatsignificanceto recordandobservetheevolutionofagriculturallandscapessuchasterracestoachievesustainabledevelopmentinmountainousareas(Capolupoetal.,2018; Maurietal.,2021;andPijletal.,2021).However,terracesshowsomespecialmorphologicalcharacteristics, suchastheexistenceofverticalslopesandsurfacesand sidesusuallycoveredwithmessyvegetation,andTLS isverydifficulttoperformonsteepslopes.UAVscan easilyacquireinformationonthetopsurfaceofterraces,butitisdifficulttocapturehigh-precisionelevationinformation.Therefore,theproposedintegration schemecanbeeffectivelyusedfortheconstructionof high-precisiontopographicdataforterracedenvironmentstofacilitatetheirgeomorphologicalandevolutionaryanalysis.Similarly,theproposedmethodcan beappliedtothegeologicalinvestigationofslopesin open-pitmines,where,afteralongservicelife,high andsteepslopesareoftengenerated,andtheassessmentoftheirstabilitystateisrelevanttothesafetyof operators(Tongetal.,2015;Espositoetal.,2017;and Bamfordetal.,2020).Duetothescaleandformof open-pitmines,TLScanusuallyonlybeperformedat thebottom,whichcanobtainmostofthesideinformationoftheminingareaslope,whiletheUAVphotogrammetrypointcloudscansupplementinformationindatashadowareastobuilda3Dmodelofthe open-pitminingareaslopeandfurtherobtaininformationontherockmassstructuralplane.Inaddition, theproposedmethodcanbeappliedtopostdisaster investigationsandrapidroadrescueworkafterearthquakesandlandslides(Stringeretal.,2021).Aftera disaster,theharshgeologicalenvironmentcangenerategreatdifficultiesfortherescueandgeologicalsur-

veystaff,anditisdifficultfortheinvestigatorstoreach theupperareasofslopesandlandslides.Atthistime, aUAVcanmakeuseofitsfastandflexiblecharacteristicstoachievelarge-scalerapidmodelingofdisastersandtheirsurroundingareas,whileTLScanbe appliedtospecificmainareasonslopesandlandslides. Throughtheeffectiveintegrationofthetwo,efficiency andaccuracycanbebalanced,andaftercharacterizingthebasicconditionofthesurroundingarea,zoning,dangerousrockpositioning,andstructuralsurfaceyieldmeasurementscanbeeffectivelycarriedout forthemainareaofthedisaster,providingbasicdata aboutthegeologicaldisasterandprovidingascientific basisforemergencydecision-making.

CONCLUSION

Solutionsfordeformationmonitoringinspecial side-slopeenvironmentswereexploredinthisstudy. Toovercomethelimitationsofasinglemeasurement methodandconsidertheparticularityoftheterrain environment,UAV-basedphotogrammetryandTLS pointclouddatasetswerecombinedtocreateanintegrateddataset.TheintegratedTLS–UAVphotogrammetrymethodwasproventobeaneffectivemethod withhighacquisitionefficiency,highspatialresolution,andcompletereconstructionofsideslopes.In thismethod,precise3Dpointcloudsofthekeyarea ofthesideslopearemeasuredbyTLS,whichremovestheneedtorepositionthestationmultipletimes andthusreducesthefieldworkload.Thedatagapsin theTLSpointcloudandtherestofthetargetslope aremeasuredbyUAV-basedphotogrammetry,which improvesoperationalefficiency.Ultimately,theUAV photogrammetrypointcloudfillsinthegapsinthe TLSpointcloudtogenerateadatasetthatcoversthe targetslopecompletely.Basedonthetwophasesofintegratedpointclouddata,thedeformationvariables anddeformationzonesonthesideslopecanbesuccessfullydetected,whichprovidesanimportantreferenceforthecurrentstateassessmentofsideslopes.

Figure11.Comparisonofpointcloudintegrationmethods.CloudaandDSMabasedonthefillingandintegrationmethod(basedonCloud 1).CloudbandDSMbbasedondirectmixing(basedonCloud1).

ACKNOWLEDGMENTS

TheauthorswishtoacknowledgefinancialsupportfromtheNationalNaturalScienceFoundation ofChina(41961061),theYunnanFundamentalResearchProjects(202001AT070057),theRevitalizing YunnanTalentsSupportProgram,theScientificResearchFundProjectofYunnanProvincialDepartment ofEducation(2021Y502),andYunnanNormalUniversity2021GraduateResearchandInnovationFund (YJSJJ21-B82)forthisstudy.

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TechnicalNote

TermsRelatedtoAnnualFrequencyforProbabilisticAssessments

JEFFREYR.KEATON*

WSPUSA,4600EWashingtonSt,Phoenix,AZ85034

Probabilisticassessmentsareusedtoquantifymany hazardousprocessesintermsoffrequencyofoccurrenceofeventsofdifferentsizesoccurringwithin specifiedareas.Hazardousprocessesquantifiedinthis mannerincludefloodlevelsatlocationsalongriver channels,earthquakesofvaryingmagnitudeswithin theproximityofpopulationcenters,eruptiveevents atactivevolcanoes,tsunamirunupatcoastallocations,heavyprecipitationeventsatspecificlocations, andmanyothers.Thetermsannualfrequency(AF), annualexceedanceprobability(AEP),exceedance probability(EP),andaveragereturnperiod(ARP, sometimescalledthemeanrecurrenceinterval)arerelated.BothAEPandEPrepresenttheprobabilitythat aneventequaltoorlargerthanacertainsizewilloccurduringaspecifiedperiodoftime,butthespecified periodforAEPisrestrictedto1year.Thedifference betweenthetworesultsliesinthewaythatoutcome andtimeareconsideredinthemodel(Crovelli,2000); outcomesconsideredinadiscretetimeperiod(i.e., 1year,orlargestfloodatapointonariverinagiven year)followabinomialprobabilitymodel,whereas outcomesaggregatedoveracontinuoustimeperiod followaPoissonprobabilitymodel(e.g.,earthquakes ofM > 5since1974withepicenterswithin100km ofaspecificlocation).Thedifferencesbetweenthe twomodelresultsarerelativelyminor,especiallyconsideringtheuncertaintyinvolvinginunderstanding thedetailsofthehazardousprocesses.Crovelli(2000) pointsoutthatnatureisnotrandombecausenatural eventsoccurforphysicalreasons(e.g.,afloodreaching elevationXatpointYalongariverchannel),butthe reasonsaretoocomplexorpoorlyunderstoodtobe modeleddeterministically.Randomness,therefore,is anecessaryassumptionofprobabilitymodels.

Forthisdiscussion,EP = AFfora1-yeartimeperiod, t (i.e.,AEP);otherwise,EPisattachedtothetime periodoftheanalysis(e.g.,50-yearEP).AF = (number)/year = year 1 andARP = 1/AFaredirectlyrelatedandareplottedasreciprocalvalues,withtheaxes labeledonboththebottomandtopofthegraphin Figure1.FourpairsoflinesareplottedinFigure1 representingfourtimeperiodsofinterest: t = 1,10,

*Correspondingauthoremail:jeff.keaton@wsp.com

50,and100years.ThebinomialandPoissonmodelresultsareplottedasbrokenblacklinesandsolidred lines,respectively.Thebinomialplotfor t = 1year isastraightlinewithaslopeof 1inlog-logspace (Figure1)thathasidenticalvaluesontheEPandAF axesandreciprocalvaluesontheARPaxis.Theexceedanceprobabilityforatleastonefloodin t years (EP(t))withagivenprobabilityinanyoneyear(i.e., annualfrequencyorAFexpressedasARP)forthebinomialdistributionis

EP (t ) = 1 (1 AF )t ; ARP ≥ 1 (1)

AF = 1 (1 EP (t ))1/t ; AF ≤ 1 (2) where t isthetimeperiodofinterest,sometimescalled exposuretime,andARPistheaveragereturnperiod thatmustbegreaterthanorequalto1year.For t = 1,EP(t) = AF.Byinspection,itcanbeseeninEq.1 thatwhenARP = 1,AF = 1andEP(t) = 1;however, ifARP < 1,thenAF > 1andEP(t) > 1,whichisdisallowedinprobabilitymodelsthatrangefrom0to1. Therefore,thebrokenblacklinerepresentingEP(1)in Figure1terminatesatEP(t) = AF = ARP = 1for thebinomialprobability.Forperiodsofinterest t > 1, EP(t)approaches1.

Usingthesametermsasinthebinomialprobability model,thePoissonprobabilitymodelis

EP (t ) = 1 exp(1 (1/ARP ) × t )

= 1 exp( AF × t ) (3)

AF = ( ln[1 EP (t )])/t (4) whereexp( )representsthebaseofNapierianlogarithms, e, raisedtothepowerofthevalueinsidethe parenthesesandln[·]isthenaturallogarithmofthe valueinsidethesquareparentheses.ThePoissonprobabilitymodelisnotrestrictedbyARPandreturnsvaluesforEP(t)thatapproach1forARP < 1(redlinein Figure1).

Thedifferencebetweenthebinomialresultandthe Poissonresultfor t = 1yearforvaluesofARPbetween about0.2and10yearsisvisibleinlog-logspace,butas ARPexceeds10years,thelinesappeartobesuperimposed(Figure1,brokenblackandsolidredlinesfor t = 1year).Forlongerperiodsofinterest, t = 10,50,

and100years,thebrokenblackandsolidredlinesappeartobesuperimposedastheybecomeasymptotic toEP = 1.Thethreebluecirclesnumbered1,2,and3 servetoillustratetheregionboundedby(EP = 0.02, ARP = 50),(EP = 0.636,ARP = 50),and(EP = 0.02, ARP = 2,500),respectively.Thegreen,darkred,teal, andpurplelinesintersectinthebluecircles.

Atpoint1inFigure1,AF = 0.02000whenEP = 0.02and t = 1inthebinomialmodel(Eq.2);however, EP = 0.0198whenAF = 0.020and t = 1inthePoisson model(Eq.3).Furthermore,AF = 0.0198forEP = 0.02and t = 1valuesinthePoissonmodel(Eq.4), andARP = 49.5whenAF = 0.0200andAF = 0.0198 whenARP = 50.0.

Point2inFigure1isonthecurvefor t = 50years whereitintersectswiththelineconnectingARP = 50years;EP = 0.6358isonthebinomialline,indicatingthattheso-called50-yearflood(ARP = 50)hasan ∼63.6percentchanceofoccurringatleastonceduring anaverage50-yearperiodoftime.EP = 0.632ison thePoissonlineatARP = 50,whichisslightlylower thanthebinomialline.NotethatthelinerepresentingEP = 0.6358intersectsthecurvesfor t = 10,50, and100yearsatAF = 0.1,0.02,and0.01,respectively, whichisthesameasARP = 10,50,and100.

Point3inFigure1isonthe t = 50yearscurveat EP = 0.02.Thisistheexceedanceprobabilitytermthat wasusedforearthquakegroundmotionintheInternationalBuildingCode(IBC,2000).Theintersectionof

EP = 0.02withthebinomialcurvefor t = 50yearscorrespondswithARP = 2,500andAF = 0.0004.However,EP = 0.02intersectswiththePoissoncurvefor t = 50yearsatARP = 2,475.4andatAF = 0.000404 whenARP = 2,500.

Instatisticalanalysesforcodesandinsurance purposes,earthquakesaretreateddifferentlythan floods.Allearthquakesofapproximatelymagnitude 5havesomepotentialtobegintocausesomedamagetoreasonablywell-constructedbuildingsonstable sites;therefore,earthquakesaremodeledwithPoisson statisticsforhazardcharacterizationanddesignprovisionsinbuildingcodes.Additionally,earthquakes canoccurasforeshocks,mainshocks,andaftershocks. Fromaninsuranceperspective,damagetoabuilding fromamainshockwouldbecoveredbyearthquakeinsurancepolicies.Well-constructedbuildingsmightsufferminordamageinamainshockbutbemoreseverely damagedbysmallermagnitudeaftershocksbecauseof theminordamagecausedbythemainshock.Floods areanalyzedforadministrationoffloodinsurancepurchasedwithannualpremiums;therefore,onlythemost severeinundationhazardwithaspecificannuallikelihoodofoccurrenceatparticularlocationsalongriver channelsiswhatneedstobecharacterized.Thelikelihoodoflowerlevelsofinundationisirrelevantfor floodinsurance.

ThefloodingstudiesintheUnitedStatestypicallyrefertoFederalEmergencyManagementAgency floodinsuranceratemaps(FEMA,2020)asthebasisforfloodinsuranceintheUnitedStatesasareas thataresubjecttoinundationbyfloodsthathavea 1percentchanceofbeingequaledorexceededduring anygivenyear.ThisinundationchanceofEP = 0.01 and t = 1correspondstoARP = 100andAF = 0.01 withthebinomialmodelandARP = 99.5andAF = 0.01005withthePoissonmodel.

Crovelli(2000)describesthePoissonprobability modelasafirst-approximationmodelandthebinomialprobabilitymodelasanapproximationofan approximation.Thelevelofuncertaintyintheinputs toextremefloodingincidencessuggeststhatthetwo modelsareconsideredessentiallyequivalent.While thePoissonmodelmaybemoreprecise,thebinomial modelhasacharacteristicthatishelpfulincommunicatingexceedanceprobabilitiesandaveragereturn periodsasreciprocals.Inotherwords,aprobability of2percentin50yearscorrespondstoa2,500-year averagereturnperiodinthebinomialmodel,rather thana2,475.4-yearreturnperiod,asitdoesinthe Poissonmodel.

REFERENCES

Crovelli,R.A.,2000, ProbabilityModelsforEstimation ofNumberandCostsofLandslides:U.S.Geological

Figure1.Comparisonofbinomialprobabilitymodel(blacknumbersandbrokenblacklines)andPoissonprobabilitymodel(red numbersandsolidredlines).

TermsRelatedtoAnnualFrequencyforProbabilisticAssessments

SurveyOpen-fileReport00-240:Electronicdocument, availableathttps://pubs.usgs.gov/of/2000/ofr-00-0249/ ProbModels.html

FederalEmergencyManagementAgency(FEMA),2020, FloodInsuranceRateMaps:Electronicdocument,availableat

https://www.fema.gov/sites/default/files/2020-07/how-toread-flood-insurance-rate-map-tutorial.txt

InternationalBuildingCode(IBC),2000, InternationalBuilding Code2000:Electronicdocument,availableathttps://codes. iccsafe.org/content/IBC2000

Commenton:AssociationbetweenCOVID-19andHeavyMetalPollutionin IraqiCitiesDeterminedfromHierarchicalPrediction

IRAD.SASOWSKY*

DepartmentofGeosciences,UniversityofAkron,Akron,OH44325-4101

Irecentlyreadthepaper“Associationbetween COVID-19andHeavyMetalPollutioninIraqiCities DeterminedfromHierarchicalPrediction”byAram MohammedRaheemintheNovember Environmental &EngineeringGeoscience issue.Itappearstomethat theapproachusedhastwofatalflaws(itemsoneand twobelow)andtwomajorconcerns(itemsthreeand four)andthattheconclusionsarenotsupportedbythe data.Idescribemyconcernsbelow,keepinginmind thestatedconclusion:

Basedontheresultsoftheproposedstatisticalmodels, thereisapositivelinearrelationshipbetweenconfirmed anddeathCOVID-19caseswiththedifferenttypesof heavymetaldistribution.Thisindicatesthatincreasing anytypeofheavymetalconcentrationbeyondtheallowableupperlimitmayresultinincreasingCOVID-19confirmedanddeathcases.

1)ThecruxofthepaperisgiveninFigures6and 7,wheretheauthorplotscumulativecontaminant concentrationversuscumulativeCOVIDdeaths (bothaspercentages).Itisnevervalidtomakethis kindofplot,asitwillalwaysshowa falsecorrelation. Asanexample,Iusedarandomnumber generatortocreatetwostringsofnumbersbetween 1and200,placedtheseinanExcelsheet,andlabeledthem“deaths”and“metals.”Imadeascatter plotofthem,andthenIcreatedrunningsummationsforthevalues,astheauthordidwithhisdata, andplottedthemagainsteachother.Theresultis FigureR1,whichshowsan r correlationof0.98betweenthesetworandomsetsofnumberswhenplottedasthecumulativepercentage(right-handgraph) andhowtheywouldlookasascatterplot(left-hand graph,nocorrelation).

Amoreappropriateplotwouldsimplybeascatterplotoftherawdata.However,whenthisis made,usingdatatheauthorprovidedforcadmium(FigureR2),itcanbeseenthereisno significantrelationship.Theconclusionsarenot supported.

2)Thedataonheavymetalsusedarenotrepresentativeoftheregions,atleastinmanyofthecases. Theyaretakenfromspecificallycontaminatedsites. Itisinappropriatetotakedatafromaspecific contaminationsiteandinterpretitasbeingrepresentativeofawholeregion.Therefore,evenifan appropriatestatisticalmethodhadbeenapplied, andacorrelationfound,theconclusionswouldbe suspect.

3)ThereisnodocumentationthatCOVIDcases could beaffectedbyexposuretometals(eventhough theauthorstates,“Thismodelisbasedonthe factthattheincreaseinheavymetalcontaminationcanincreasetherateofdeathresulting fromreducedhumanbodyresistance,”withoutany citation).

4)Thetitleofthearticlestatesthatahierarchical predictionisused.Iseenoevidenceofthisin themanuscript.Evenifithadbeenused,noutilitywouldbeobtainedduetotheproblemslisted above.

Beyondtheinappropriatenessofusingcontaminationdatafromspecificindustrialsitestodrawconclusionsaboutentirecounties,theuseofthedatasums inthefinalgraphs(whicharethecruxofthepaper) iserroneous;therefore,theconclusionsareinvalid. Thereisnotademonstratedlinkbetweenpollution andCOVIDcases.

*Correspondingauthoremail:ids@uakron.edu

ReplytoCommenton:AssociationbetweenCOVID-19andHeavyMetalPollutioninIraqiCitiesDetermined fromHierarchicalPrediction

ARAMMOHAMMEDRAHEEM* UniversityofKirkuk,Iraq

IthankyouforthisconcernregardingmyCOVID19paper.Toclarifythisconcern,severalpointsshould bestatedasfollows:

*Correspondingauthoremail:engaram@yahoo.com; aram_raheem@uokirkuk.edu.iq

1)Thedatausedinthepublishedpaperarerealdata, notrandomdata,andtheywerecollectedfrom originalsources.

2)Themainprincipleofthispaperisthatwhenthere aretwohumans,oneisweak(exposedtoheavy metalcontamination)andtheotherisstrong(no heavymetalcontamination),andbothofthemare

FigureR1.Demonstrationofhowafalsecorrelationwillresultifinsteadofcross-plottingtwovariables(left),thesumofvaluesisplotted (right).

FigureR2.Scatterplotoftheauthor’scadmiumdata.Nocorrelation;theconclusionsofthepaperareunsupported.

attackedbytheCOVID-19virus,thenthefirsthumanisexpectedtogethurtmorethanthesecond one.Thisislogic,anditwasverifiedintheusedstatisticalmodel.

3)Anystatisticalmodelshouldbebuiltbasedonlogic expectationsanddependonrealdata,andthese conditionsweresatisfiedintheusedmodel.

4)Thepaperhasbeenreviewedbyanonymousexperts inthefieldofthestudy,andtheirquestionshave beenansweredaccordingly.

5)Tojustifyanyconcernabouttheusedstatisticalmodelorabouttheconcept,Iencouragethe

writerofthelettertoperformafieldinvestigation abouttherealcasesofhumanswithandwithout heavymetalcontaminationwhoareexposedtothe COVID-19virus.Then,amanuscriptcanbewrittendiscussingtheconcept,criticizingthisstatistical modeloranyothermodels,andprobablycoming upwithabettermodel.Indeed,Iwillbemorethan happytoreadandgetthebenefitofsuchapossible writtenmanuscript.

Ithanktheeditorialboard,andIdorespectanydecisionthatwillbemaderegardingmypublishedpaper.

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Cover photo

Looking southwest towards Trinidad, CA (Trinidad Head can be seen in the upper left corner), narrow strips of tree retention, known as Watercourse Lake Protection Zones (WLPZ) or Riparian Management Zones (RMZ), are intended to protect and preserve wildlife habitat. Coincidently, these areas are typically located in the most landslide-prone areas on the landscape and, thus, help prevent management-related mass wasting. Riparian buffers were first implemented as part of the Forest Practice Act in 1973 and have evolved over time. Photo by Jason Woodward, June 20, 2019. See article on page 115.

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