TennesseeRiverBasinReportCard

Methodsreportondatasources,calculation,andadditionaldiscussion

December4,2017

TableofContentsGoalsandobjectivesoftheTennesseeRiverBasinReportCard..............................................3

Processfordevelopingthereportcard...................................................................................3

PotentialImprovementstothereportcardprocess................................................................4

Howarethegradescalculated?..............................................................................................4

ScoringandLetterGrades......................................................................................................5

StressorIndicators..................................................................................................................9Indicator:Development...................................................................................................................9Indicator:Drought..........................................................................................................................11Indicator:Wildfire..........................................................................................................................12Indicator:ForestInsectsandDisease..............................................................................................13Indicator:SedimentSources...........................................................................................................14

ConditionIndicators..............................................................................................................15Indicator:ForestConnectivity.........................................................................................................15Indicator:Aquaticconnectivity.......................................................................................................16Indicator:Aquaticbiodiversity.......................................................................................................17Indicator:BenthicMacroinvertebrateCondition............................................................................18

ResponseIndicators..............................................................................................................19Indicator:Agriculturalbestmanagementpracticesforrunoff.........................................................19Indicator:Agriculturalbestmanagementpracticesforleaching.....................................................21Indicator:ProtectedConnectedForest...........................................................................................22Indicator:Protectedwetlands........................................................................................................23

Indicatorsandcategoriesconsideredbutnotabletobeincluded..........................................24

GoalsandobjectivesoftheTennesseeRiverBasinReportCardTheTennesseeRiverBasinReportCardwasdevelopedasatoolforprioritizationandrestorationdecisionsmadeintheTennesseeRiverBasin.Thereportcarddocumentisalsomeanttoserveasanoutreachtoolforusebymanagerstohighlightparticularissuesofimportancewhencommunicatingconservationandrestorationwiththepublic.ToachieveareportcardthatisrelevanttothegoalsandobjectivesofthewiderTennesseeRiverBasinmanagement,conservation,andrestorationcommunity,theprojectteamsolicitedfeedbackfromparticipantsattheTennesseeRiverBasinPlanningNetworkannualmeetinginChattanooga,TennesseeinAugust2017.TheseparticipantsidentifiedBiodiversity,Recreation,SenseofPlace,WaterQuality,andHabitatasthemostimportantvaluestoconsiderforassessingenvironmentalcondition,andUrbanizationandPopulationGrowth,HabitatFragmentation,PollutionandContaminants,andClimateChangeasthemostimportantstressors.Additionally,thereareparticularmanagementactivitiestrackedbylocalandregionalgroupssuchastheAppalachianLandscapeConservationCooperative(AppLCC)thatrelatetoprotectingthesevaluesandreducingstressorimpacts.ThereportcardwasdesignedtoreflectoneachofthesecomponentsoftheTennesseeRiverBasintoprovideaholisticassessmentofenvironmentalstressors,conditionandmanagement.Theresultsofthereportcardreflectthesethreegroupsofindicators.Overallregionandbasinconditionultimatelyisthegoalofstressorreductionandmanagement,andsoisportrayedasamorecentraltothereportcardresultsandistheelementthatprovidesthegradesforeachregion.TheTennesseeRiverBasinReportCardismeanttoserveasaninitialassessmentofenvironmentalstressors,condition,andmanagementresponseintheBasin.ThereportcardteamattheUniversityofMarylandCenterforEnvironmentalSciencerecognizesthattherearemanyimprovementsthatcanbemadetothereportcard,indicators,datasources,andmethods.

ProcessfordevelopingthereportcardFollowingtheTennesseeRiverBasinPlanningMeetinginAugust2017,theUMCESreportcardteamworkedcloselywithAppLCCandTennesseeRiverBasinPlanningNetworkstafftoidentifydataprovidersandregionalexpertsforeachpotentialvalue,stressor,andmanagementindicator.UMCESprovideddataanalysisforeachoftheindicatorsoncedatawasidentifiedandobtainedfromproviders.InOctober2017,theUMCESreportcardteampresentedthedraftreportcardresultstomembersoftheAppLCCSteeringCommitteeinavirtualmeetingandreceivedextensiveandconstructivefeedbacktoimprovetheutilityandvalueofthereportcardandtheaccompanying

methodsreport.Thereportcardandthismethodsdocumentareinlargepartareflectionofthefeedbackanddirectionreceivedatthatmeeting.

PotentialImprovementstothereportcardprocess

TheUMCESreportcardteamrecognizesthatthecurrentTennesseeRiverBasinreportcardprocesswasimperfect.TheinitialprojectplanincludedascopingprocesstodevelopareportcardforthewholeoftheAppLCCgeography.TheprojectwasredirectedtoachieveapreliminaryreportcardfortheTennesseeRiverBasin,essentiallybeginningwiththeevaluationofvaluesandstressorsattheTennesseeRiverBasinPlanningNetworkmeetinginAugust2017.Theseprojectdirectionchangesarementionedsolelytosuggestthatafutureprocesscanbeimprovedtoproduceareportcardthatincludessomeelementsthatwereseentobeimportant,butwhichwerenotabletobeincludedinthisreportcardwithoutadditionalanalysis,time,anddeliberation.

TheUMCESteamenvisionsaprocessthatmorecloselyadherestothereportcardprocessthathassuccessfullycreatedreportcardsinnumerouslocationsworldwide.Thisprocessincludesco-designandco-developmentofthereportcardproductwithstakeholdersandend-usersoftheproductfromtheinitialdiscussionsabouttheprocessgoalsandobjectives.Thesestepswerenotachievablewiththelimitedtimeandresourcesavailableforthecurrentproject,butwouldgreatlyenhanceengagementwithstakeholdersandend-userstocreateareportcardthatiswidelyacceptedbytheTennesseeRiverBasinenvironmentalconservationandprotectioncommunity,andisseenasavaluabletoolforenvironmentaldecisionmakingbyregionalandlocalmanagersandoutreachtotheircommunities.Thecurrentreportcardpresentsafirststepincreatingthisoutcome.

Howarethegradescalculated?

Thisreportdocumentsthedatasources,calculationsforeachindicator,interpretation,calculationandassignmentofscoresforindicatorsintheTennesseeRiverBasin.Italsoenumeratesdataidentifiedforindicatorsnotincludedinthereportcard.

Figure 1: Preliminary results for the Tennessee River Basin Report Card. See Table 1 for indicator definitions.

ScoringandLetterGrades

Allmeasurementswerestandardizedtoa0-100scaletoenableaggregationofindividualindicatorresultstothegoalscore.Scoresweredistributedinevenincrementstoenableeaseofaggregation.Itisimportanttonotethatthescoringschemeisnotareflectionofa“curve”oralenientgradingsystem;thegoalteamsandexpertadvisorsdeterminedthroughdataanalysiswhatdatavaluesrepresentedgoodandbadgrades,andthoseweretranslatedtothefinalscoringschemedistributedintothe0-100scalein20-pointincrements.Finalscoresweregivenagradebasedonthesimplegradingschemeasbelow:

Figure 2: Scoring scheme for the Tennessee River Basin Report Card.

Therewereseveralpotentialscoringmethodsthatwereappliedforreportcardindicators,including:

1. Pre-determinedscoring.Forsomeindicators,thedataproviderhadalreadyprovidedaratingofobservationsorresults.Thesemayhavebeenmeasuredagainstaregionallyspecificdesiredcondition,orsomeothermethod.Weusethismethodwhentheassessmentmethodswerefromanacceptedsource,usinggenerallyacceptedpractices.

2. Comparisontogeographicrangeofdata.Forseveralindicators,datawerecomparedtotheregionalrangeofdata.Themostdesirable(forexample,lowestpercentofforestthreatenedbywildfire)wasthetopscore,andtheleastdesirablevaluebecamethelowestpossiblescore.

Figure3.Examplecomparisonofanindicatoracrosstwodifferentgeographicranges.Thedatapresentedarethepercentofeachsub-watershedexpectedtoexperienceanincreaseinhousingdensityinforestedareasbetween2000and2030.

3. Comparisontonationalaverage.Whereestablishedgoalsandthresholdshadnotbeenpreviouslydefined,datawerenormalizedbytheaverageandstandarddeviationofeachindicatorcalculatedoveralargerrepresentativegeography(e.g.,nationally,orovertheTRBitself).TheresultingZ-Score,wasdividedinto5levelsboundedbythestandarddeviationofthedatafromthemeanoverthelargerrepresentativegeography.IfthebasinaveragewaswithinonestandarddeviationoftheUSaverage,theresultingscorewasa“C”,forexample.Thistechniqueassumesthatthemostdesiredconditionexistssomewhereinthereferencegeography.

Figure4.Exampleschemeforcomparisontothenationalaverage.

Table 1: Indicator data sources and scoring schemes.

StressorIndicators

Indicator:Development

Datasource:Weidner,E.&Todd,A.(2011)FromtheForesttotheFaucet:DrinkingWaterandForestsintheUS.USDAForestService.Availableontheweb:http://www.fs.fed.us/ecosystemservices/FS_Efforts/forests2faucets.shtml

Calculationmethod:TheForesttotheFaucetdatasetprovidesaprojectionofthethreatofdevelopmenttoforests,summarizedbyHUC12watershed.ThisprojectionisderivedfromDavidTheobald’sSpatiallyExplicitRegionalGrowthModel(SERGoMv3),whichtakesintoaccountlandprotectionstatus,censusblockpopulationandchangeovertime,roaddensity,andtraveltimealongmajorroadstopopulationcenterstomaphousingdensityin2000andprojectitto2030.Themodelresultswerevalidatedbyhindcastinghousingdensityfor2000usingdatafrom1980and1990andcomparingwithobservedhousingdensitypatternsfor2000.Theobald’sworkclassifiedhousingdensityinto12classesspanningUrbantoUndevelopedprivateland.Later,Steinetal.2009collapsedtheseintothreecategories:Rural1(>40acresperhousingunit),Rural2(10-40acresperhousingunit),andExurbanRural(lessthan10acresperhousingunit).Forthisreportcard,thedifferenceinhousingdensitybetween2000and2030wasusedtoidentifyareasprojectedtochangecategories(e.g.,fromRural2toRural1,orfromExurbanRuraltoRural2).AswasimplementedfortheForesttoFaucetsanalysis,allforestsprojectedtochangecategorieswereclassifiedashighlythreatenedbydevelopment.WhenexpressedasapercentofallforestsineachHUC12watershednationally,thenationalmeanwascalculatedas14.6%andthenationalstandarddeviationas22.6%.

ForeachHUC12watershedintheTRB,wecalculatedaZ-scoreforforeststhreatenedbydevelopmentusingthenationalaverageandstandarddeviation:

DevelopmentZscore=(Development%-14.6)/22.6

TheDevelopmentZ-scorewasthenscaledfrom0to100representingaZ-scoreof2.5(highlythreatened)to-2.5(notthreatened).

Citations:Stein,S.R.McRoberts,L.Mahal,M.Carr,R.Alig,S.Comas,D.Theobald,andA.Cundiff.2009.PrivateForests,PublicBenefits:IncreasedHousingDensityandOtherPressuresonPrivateForestContributions.USDAForestService,PacificNorthwestResearchStation,PNW-GTR-795.December2009.

Theobald,D.2005.LandscapepatternsofexurbangrowthintheUSAfrom1980to2020.EcologyandSociety.10(1):32.

Figure1:DevelopmentthreatinforeststhroughouttheTRB.

Indicator:Drought

Data source: https://www.ncdc.noaa.gov/temp-and-precip/drought/historical-palmers/

Calculation method: The Palmer Drought Severity Index (PDSI) is based on a physical water-balance model, uses both precipitation and surface air temperature as input, and takes the precedent condition into account. It is generally considered superior to other statistically based drought indices, that are often based purely on past statistics of limited climate variables. While the PDSI is not without criticisms, it is reliably used in the context needed for this report card. The PDSI is a continuous value but is often classified monthly to describe drought conditions as moderate, severe, or extreme.

We downloaded monthly PDSI values by climate division for the lower 48 US states. We then calculated the number of months of extreme drought (i.e., months with PDSI values < -4) over the past 10 years for each climate division. For the lower 48 states, the mean number of months of extreme drought was 9.2 with a standard deviation of 9.4. We then identified the 19 climate divisions that overlapped the TRB. We intersected the TRB with the climate divisions to assign data to each HUC12 watershed. The number of months of extreme drought ranged from 1 to 20 across HUC12 watersheds. We used the national statistics to calculate an extreme drought Z-score, which was scaled from 0 to 100 using -2.5 and 2.5 as endpoints.

Citations:Dai, A., 2011b: Drought under global warming: A review. Wiley Interdisciplinary Reviews: Climate Change, 2, 45-65

Figure2:Monthsofextremedrought,evaluatedviathePalmerDroughtSeverityIndex,overthepast10years.

Indicator:Wildfire

Datasource:Weidner,E.&Todd,A.(2011)FromtheForesttotheFaucet:DrinkingWaterandForestsintheUS.USDAForestService.Assessableviatheweb:http://www.fs.fed.us/ecosystemservices/FS_Efforts/forests2faucets.shtml

Calculationmethod:WeusedwildfireriskassessmentssummarizedintheForeststotheFaucetdataset,whichwerebasedonthewildfirehazardpotential(WHP)mapproducedbytheUSDAForestService,FireModelingInstitute.Thesedataareusedtohelpinformevaluationsofwildfireriskorprioritizationoffuelsmanagementneedsacrossverylargelandscapes(millionsofacres).Areasofhighwildfirepotentialaredescribedashavingfuelsandrecurringweatherconditionsconducivetofireconditions,particularlythosedifficultforsuppressionresourcestocontain.Themapisintendedtobepairedwithspatialdatadepictinghighlyvaluedresourcesandassetssuchascommunities,structures,andpowerlines.AsintheForeststotheFaucetanalysis,areascategorizedasthreatenedbywildfirepotentialwererankedashavinghighorveryhighwildlandfirepotentialintheWHPmap.Theseareasweresummarizedbysub-watershed(HUC12-level)acrosstheTRBtoarriveatthepercentofforeststhreatenedbywildfire.AcrosstheentireUS,themeanwildfirepotentialwas25.0%withastandarddeviationof38.8%attheHUC12level.WeusedthesestatisticstocalculateaZ-scoreandscaledtheZ-scorefrom100to0using-2.5(lowwildfirepotential)to2.5(highwildfirepotential)endpoints.

Citations:Dillon,GregoryK.;Menakis,James;Fay,Frank.2015.Wildlandfirepotential:Atoolforassessingwildfireriskandfuelsmanagementneeds.In:Keane,RobertE.;Jolly,Matt;Parsons,Russell;Riley,Karin.Proceedingsofthelargewildlandfiresconference;May19-23,2014;Missoula,MT.Proc.RMRS-P-73.FortCollins,CO:U.S.DepartmentofAgriculture,ForestService,RockyMountainResearchStation.p.60-76.

Figure3:Wildfirepotential

Indicator:ForestInsectsandDisease

Datasource:Weidner,E.&Todd,A.(2011)FromtheForesttotheFaucet:DrinkingWaterandForestsintheUS.USDAForestService.http://www.fs.fed.us/ecosystemservices/FS_Efforts/forests2faucets.shtml

Calculationmethod:TheForesttotheFaucetdatasetprovidesasummaryoftheNationalInsectandDiseaseRiskMap(NIDRM)createdbytheForestHealthTechnologyEnterpriseTeam(FHTET).WeusedthesedatatosummarizetheriskofforestinsectanddiseaseacrosstheTRB.TheNIDRMdefinesinsectanddiseaseriskasforeststhat"withoutremediation,25percentormoreofthestandinglivebasalarea(BA)oftreesgreaterthan1inchindiameterwilldieoverthenext15years(startingin2005)duetoinsectsanddiseases”(Krist,etal,2006).TheForesttotheFaucetdatasetsummarizedthesedataasthepercentofforestsineachHUC12watershedhighlythreatenedbyinsectanddisease.Wecalculatedanationalaveragescoreof4.0%andstandarddeviationof11.3%,whichwereusedtocalculateaZ-score.WescaledtheZ-scorefrom100to0using-2.5(lowthreatlevel)to2.5(highlythreatened).

Citations:Krist,F.,F.Sapio,B.Tkacz.2006.MappingRiskfromForestInsectsandDiseases.ForestHealthTechnologyEnterpriseTeam,USDAForestService.FHTET2007-06.

Figure4:Threatofforestinsectsanddisease.

Indicator:SedimentSources

Datasource:TheSoilVulnerabilityIndex(SVI)isnotapublisheddataset.ThedatarepresentgeospatiallayersrepresentingsoilvulnerabilityandtreatmentneedsoncultivatecroplandcurrentlyunderdevelopmentbytheRADGISLabandCEAPmodelingTeam.TheresultsarebasedonfindingsfromtheCEAPCroplandReports.

Formoreinformation,pleasecontact:KevinIngram,RADGISLabCoordinatorResourceAssessmentDivision-SSRAUSDA-NaturalResourcesConservationService5601SunnysideAvenueBeltsville,MD20705-5410

Calculationmethod:TheUSDAprovidedtheSoilVulnerabilityIndexforcultivatedcropland(SVI-cc)attheHUC12levelfortheentireTRB.Thesedatausesoilandtopographiccharacteristicsforcultivatedcroplandtoestimatesoilvulnerabilitytosedimentgenerationduringrunoff.Vulnerabilitytorunoffisprovidedin4classes,withclasses3and4consideredpriority1lands(highlyvulnerable).Priority1landsaretargetedfortreatmentandmanagementprogressisassessedagainstthefractionofprioritylandsthathavebeentreatedforrunoff(e.g.,viaagriculturalbestpracticessuchaswintercovercropimplementationandriparianbufferplacement.)Therefore,theSVI-ccwasevaluatedbycalculatingthepercentofeachHUC12watershedthatwasreportedaspriority1croplandforrunoff.Thisvaluerangedfrom0to46.2%acrosstheTRBwithameanof4.3%andastandarddeviationof6.2%.WeusedthesestatisticstocalculateaZ-scorethatrescaledthepercentpriority1databetween0and100basedontheZ-scorerangeof2.5to-2.5.

Figure5:Theareaofcroplandsinpriority1riskforrunoff,asassessedbytheSoilVulnerabilityIndex.

ConditionIndicators

Indicator:ForestConnectivity

Datasource:AppLCCNatureserveakaLandscapeConservationDesign(LCD2)

Calculationmethod:TheLandscapeConservationDesign(LCD2)projectusedconservationplanningsoftwaretoidentifyareasofthelandscapeimportantforlandscapeconnectivity.Thesewerelabeledlocalandregionalcoresandlinkages,covering22770km2oftheTRB,orabout21.5%ofthebasinarea.Thelandcovertype(e.g.,agriculture,urban,forest)intheseareasisausefulmeasureoftheconnectivityofforestsintheregion.HUC12watershedswithnoconnectedforestscontributetoregionalforestfragmentation,impedingthedispersalofplantsandanimalstonewhabitatandmovementacrosslandscapesinresponsetochangesinclimate.Areasofhighforestcoverinwatershedsdesignatedasimportantforregionalandlocalforestconnectivityhavethehighestdesiredcondition.Therefore,wecalculatedtheareaofforestinregionsdesignatedaslocalorregionalcoresandlinkagesintheLandscapeConservationDesignresults.Thisconnectedforestlayerincludedallforests,privateandpublic,regardlessofprotectionlevel,andwasbasedonthe2011NationalLandCoverDataset.AcrossHUC12watershedsintheTRB,meanlocalandregionalcoreandlinkageforestareawas21.2km2andthestandarddeviationwas32.3km2.WeusedthesestatisticstocalculateaZ-scorethatscaledtheconnectedforestlayerbetween0(noconnectedforest)to100(ahighareaofconnectedforest)using-2.5and2.5astheZ-scoreendpoints.

Figure6:Connectedforestarea.

Indicator:Aquaticconnectivity

Datasource:NationalHydrographyDataandTigerroads

Calculationmethod:Aquaticconnectivityisdegradedbydams,primarily,butalsobyroadcrossings.Bothtypesofman-madefeaturesimpedethedispersalofaquaticorganismsalongstreamsandrivers.Weassumedaquaticconnectivitywashighforallstreamswithoutthesefeaturesandlowforstreamswiththem.TheNationalHydrographyDatasetincludesthepointlocationsofalldamsinthebasin,includingbothlargehydroelectricgenerationfacilitiesandsmall“millpond”dams.ForeachHUC12watershed,wecalculatedthenumberofdamsperkmofstreamlength.AcrosstheTRBtherewereanaverageof8.7dams/1000kmofstreamlengthwithastandarddeviationof18.8dams/1000kmofstreamlength.WeusedthesestatisticstocalculateaZ-scorethatscaledthedams/kmstreamlengthbetween0and100using2.5(ahighnumberofdams)and-2.5alownumberofdamsastheZ-scoreendpoints.

Roadcrossingsalsodegradeaquaticconnectivity.UsingdataonroadsprovidedbytheTigerroadsdatabase,wecalculatedthenumberofroad-streamintersectionsforeachHUC12watershedanddividedbythetotalstreamlengthineachHUC12.Therewerebetween0and5.2roadcrossingsperkmofstream,withameanof0.946andastandarddeviationof0.589crossingsperkm.WeusedthesestatisticstocalculateaZ-scorethatscaledthedatabetween0and100usingZ-scoresof2.5and-2.5asendpoints.

Afterscalingeachofthesesub-indicatorsbetween0and100wecalculatedanaverageaquaticconnectivityscoreforeachHUC12watershed.Therefore,inthefinalaquaticconnectivityindicator,theweightedscoresfordamdensityandroadcrossingdensityareweightedequally.

Figure7:Aquaticconnectivityscoresthatreflectdataonbothdamandroadcrossingdensity.

Indicator:Aquaticbiodiversity

Datasource:TennesseeValleyAuthority

Calculationmethod:TheTVAsampled499streams(spanning386differentHUC12watersheds)between2010and2014.FishsamplingfollowedTVA’sIndexofBioticIntegrityprotocol(modifiedfromKarr(1981)),usingmultiplegearsandtechniquestoobtainrepresentativesamplesofthefishcommunityfromalldiscerniblehabitattypeswithinriffle,run,andpoolareas.Fishcapturedwereidentified,counted,checkedforapparentdiseaseandreleased.Somevoucherspecimenswerekepttodocumentnewoccurrencesortoconfirmidentification.SampleswereanalyzedwithTVA’sStreamSurveycomputerprogramtoproduceIBIscoresforeachstation.TheIBIanalysisratedsamplingresultsagainstreferenceconditionsbasedonfishcommunitiesoccurringunderpristineconditionswithinthesameecoregionandsimilardrainagearea.Thisanalysisuses12metricsormeasuresofcommunityattributessuchasspeciesrichnessandcomposition,trophicstructure,fishabundance,fishcondition,andhybridization.IBIscores,rangingfrom12to60,andclassificationswereusedtoindicatethelevelofecologicalconditionreflectedbyfishcommunities.

Forthisreportcard,werescaledtheIBIscoresbetween0and100insuchawaythattheclassificationsdefinedbytheTVA(Excellent,Good,Fair,Poor,VeryPoor,andNoFish)werepreservedasgradesofA,B,C,D,andFinthereportcard.

Citations:Karr,J.R.,K.D.Fausch,P.L.Angermier,P.R.Yant,andI.J.Schlosser.1986.Assessingbiologicalintegrityinrunningwaters,amethodanditsrationale.IllinoisNaturalHistorySurvey.SpecialPublication5.28pp.

Karr,J.R.1981.Assessmentofbioticintegrityusingfishcommunities.Fisheries6:21-27.

Figure8:TheIndexofBioticIntegrity(IBI)basedonTVAsamplingcampaignsbetween2010and2014.

Indicator:BenthicMacroinvertebrateCondition

Datasource:TennesseeValleyAuthority

Calculationmethod:TheTVAsampled499streams(spanning386differentHUC12watersheds)between2010and2014.ThiseffortincludedaBenthicMacroinvertebrateCommunitySamplingprogramthatusedaqualitativeapproachandprovidedanecologicalclassificationbasedondiversityamongthreepollutionintoleranttaxonomicorders(mayflies(Ephemeroptera),stoneflies(Plecoptera)andcaddisflies(Trichoptera),akaEPT)andthedensityoftolerantorganisms.Theprimaryoutputisa“good”,“good/fair”,“fair”,“fair/poor”,or“poor”ratingbaseduponthenumberofEPTfamiliessampledandecoregionclassification.Generalinterpretationofobservedrelativeabundanceoftolerantorganismsandothertaxacanbeusedtohelpdeterminethenatureofenvironmentalproblemsaffectingthecommunity.WerescaledtheEPTscoresfrom0to100inawaythatpreservedtheclassificationsprovidedbytheTVA.

Citations:

Kerans,B.L.andJ.R.Karr.1994.Abenthicindexofbioticintegrity(B-IBI)forriversoftheTennesseeValley.EcologicalApplications4(4):768-785.

Figure9:Ephemeroptera,PlecopteraandTrichopterascores

ResponseIndicators

Indicator:Agriculturalbestmanagementpracticesforrunoff

Datasource:TheConservationEffectsAssessmentProject(CEAP)ConservationBenefitsIdentifier(CCBI)arenotpublisheddata,butrepresentgeospatiallayersreflectingtreatmentneedsoncultivatecroplandcurrentlyunderdevelopmentbytheRADGISLabandCEAPmodelingTeam.TheresultsarebasedonfindingsfromtheCEAPCroplandReports:https://www.nrcs.usda.gov/wps/portal/nrcs/detail/national/technical/nra/ceap/na/?cid=nrcs143_014144.

Formoreinformation,pleasecontact:KevinIngram,RADGISLabCoordinatorResourceAssessmentDivision-SSRAUSDA-NaturalResourcesConservationService5601SunnysideAvenueBeltsville,MD20705-5410

Calculationmethod:TheCEAPConservationBenefitsIdentifier(CCBI)geospatialdatalayerisanattempttotranslatecoreCEAPCroplandstudyfindingsabout“conservationtreatmentneeds”intoactionableinformationsuitableforsupportingagencylandscapeplanningandprogramdeliveryatthefieldlevel.ItisintendedtoaddressthegeneraldesirewithinNRCSandoutsidetheagencythatscientificfindingsfromCEAPbeeffectivelyincorporatedintoagencyconservationimplementationefforts.ThesedatarevealtheextenttowhichhighprioritycroplandsidentifiedbytheSVIhavebeentreatedthroughresourceconservationstrategiesthatavoidmanagementthatleadstoexcessiveerosionornutrientapplications,controllossesofsedimentandnutrientsfromfarmfields,andtrapsedimentandnutrientsthatdoleavethefieldsbeforetheyreachsurfacewaters.

DatafromtheCCBIwereprovidedattheHUC12scale,andrepresentedasummaryoftheareaofhighprioritycroplandandtheareaofthesecroplandsthathavebeentreatedforrunoff.Weusedtheseresultstocalculatethepercentageofhighpriorityareathathasbeentreatedforrunoff.AttheHUC12level,thepercentageoftreatedhighprioritycroplandrangedfrom0to100%,exhibitedameanvalueof62.6%andastandarddeviationof37.6%.WeusedthesestatisticstocalculateaZ-scorewhichwasscaledfrom0to100using-2.5and2.5asendpoints.

Figure10:Thepercentageoftreatedhighprioritycropland.

Indicator:Agriculturalbestmanagementpracticesforleaching

Datasource:TheConservationEffectsAssessmentProject(CEAP)ConservationBenefitsIdentifier(CCBI)arenotpublisheddata,butrepresentgeospatiallayersreflectingtreatmentneedsoncultivatecroplandcurrentlyunderdevelopmentbytheRADGISLabandCEAPmodelingTeam.TheresultsarebasedonfindingsfromtheCEAPCroplandReports:https://www.nrcs.usda.gov/wps/portal/nrcs/detail/national/technical/nra/ceap/na/?cid=nrcs143_014144.

Formoreinformation,pleasecontact:KevinIngram,RADGISLabCoordinatorResourceAssessmentDivision-SSRAUSDA-NaturalResourcesConservationService5601SunnysideAvenueBeltsville,MD20705-5410

Calculationmethod:DatafromtheCCBIwereprovidedattheHUC12scale,andrepresentedasummaryofthenumberofhighpriorityacresandtheareaofthesecroplandsthathavebeentreatedforleaching.Weusedtheseresultstocalculatethepercentageofhighpriorityareathathasbeentreatedforleaching.AttheHUC12level,thepercentageoftreatedhighprioritycroplandrangedfrom0to100%,exhibitedameanvalueof56.7%andastandarddeviationof41.3%.WeusedthesestatisticstocalculateaZ-score,whichwasscaledfrom0to100using-2.5and2.5asendpoints.

Figure11:Thepercentageofhighpriorityareathathasbeentreatedforleaching.

Indicator:ProtectedConnectedForest

Datasource:(1) USProtectedAreasDatabase(https://gapanalysis.usgs.gov/padus/)(2) AppLCCLandscapeConservationDesign(LCD)layersoflocalandregionalforestcores

andlinkages(3) NationalLandCoverDataset(NLCD)(https://www.mrlc.gov/)

Calculationmethod:ManagementprogresstowardscreatingandmaintainingforestconnectivityacrosstheTRBwasevaluatedbycalculatingtheareaofconnectedprotectedforestineachHUC12.ConnectedforestwasdeterminedusingtheAppLCC(LCD)datalayersasdescribedearlier.ThepercentoftheseconnectedforeststhatareprotectedfromdevelopmentwascalculatedusingtheUSProtectedAreasDatabase(PAD)version1.4.ThePADusesGAPStatusCodestodescribethedegreetowhichlandismanagedforconservation.LandinCodes1and2havethehighestdegreeofmanagementforconservation,whilestatus3landssupportmultipleuses,includingresourceextraction(forestry,mining,etc.),butareprotectedfromdevelopment.Status4landshavemoreambiguousprotection,butintheTRBthisdesignationgenerallyreferstoDepartmentofDefenselandandStatelandtrusts.Therefore,weclassifiedalllandsincodes1-4as“protected”forthepurposeofthisreportcard.Werecognizethatmanyactivitiesthathavethepotentialtoinfluencebiodiversityarepermittedintheseprotectedlands.

WecalculatedthepercentageofconnectedforestthatwasprotectedfromdevelopmentforeachHUC12.Percentconnectedforestrangedfrom0to100%withameanvalueof34.7%andastandarddeviationof37.3%.WeusedthesestatisticstocalculateapercentconnectedforestZ-scorewhichwasrescaledbetween0and100using-2.5and2.5asendpoints.

Figure12:EvaluationofprotectedlandstatuswithinconnectedforestsintheTRB.

Indicator:Protectedwetlands

Datasource:USProtectedAreasDatabasehttps://gapanalysis.usgs.gov/padus/

USNationalWetlandsInventoryhttps://www.fws.gov/wetlands/

Calculationmethod:Wetlandsprovideuniqueecosystemservicesrelatedtohabitat,carbonsequestration,andwaterquality.Managementactionsthatprotectwetlandsfromdevelopmentandotherformsofimpairmentarenecessary.Forthisindicator,weusedtheNationalWetlandsInventorydatatodefinethespatialdistributionofwetlands.WethenintersectedthislayerwiththeprotectedareasdatabasedescribedearliertocalculatethepercentageofwetlandareaineachHUC12thatwasprotected.Theresultingdataonpercentofwetlandsprotectedrangedfrom0to100andexhibitedameanof15.6%andastandarddeviationof24.7%.WeusedthesestatisticstogenerateaZ-scorethatwasrescaledfrom0to100using-2.5and2.5asendpoints.

Figure13:Percentwetlandsprotectedfromdevelopment.

Indicatorsandcategoriesconsideredbutnotincluded

Throughoutthereportcardprocess,manyindicatorsandindicatorpossibilitiesweresuggestedtothereportcardteamandconsidered.Manyoftheseindicatorsweredroppedforthefinaldraftversionofthereportcardthatwehaveprovidedatthistime.Theindicatorsdroppedwerenotincludedforvariousreasons,whichareoutlinedforeachindicatorinthefollowingsection.

Whenscopingforthereportcard,itwasbroughttothereportcardteam’sattentionthatincludingsocial,economicandculturalindicatorswouldbebeneficialforthemanagementpartiesinvolvedinimprovingthehealthoftheTennesseeRiverBasin.Asthereportcardprogressed,discussionsleadtodatathatwaseithernotrelevanttoamanagementlever,orthedatawasnotabletobeassessedthroughoutthebasinorcomparedtoarelevantthreshold.

Somesocial,economicandhumanhealthindicatorswereidentifiedbutnotincludedastheywerenotdirectlyshowntobelinkedwiththeenvironmentalhealthoftheTennesseeRiverBasinandthereforecouldnotbereadilychangedthroughenvironmentalmanagement.Theseindicatorsareasfollows:

Indicator:RecreationalopportunitiesSourcesconsidered:NationalSurveyofFishingandHuntingNationalParkServiceVisitationStatisticsReasonfornotevaluating:Datawasaggregatedatthestatelevel,andthereforecouldnotbeattachedtothespatiallevelthatwasneededinthisreportcard.TheTRBincludessmallportionsofmanystates.

County-leveldataforhuntingparticipationcouldbepursuedforfurtheriterationsofthereportcard.TheReportCardTeamwasonlyabletogainstatedataforhuntinglicensesforasubsetofthestatesintheTRB.

Indicator:ProtectedCulturalAreasorChangeinCulturalPreservationSourcesconsidered:StateHistoricPreservationOfficesTennesseeHistoricalCommissionGISDatabaseBlueRidgeNationalHeritageAreaManagementPlanTennesseeCivilWarNationalHeritageAreaevaluationNPSTrailofTearsTNDepartmentofTouristDevelopment2016EconomicImpactReportTNHistoricalCommissionReasonfornotevaluating:Culturalresourcesanddatathatwerepreliminarilyevaluateddidnothaverelevantthresholdstobeassessedagainst,orwereonlyforveryspecificstatesorpiecesofthebasin.

ItwasalsosuggestedtolookintohowsomeindicatorseffectnativepeopleofWesternNorthCarolina,andtheReportCardTeamthinksthisshouldbepursuedfurtherifthereistobeanotheriterationofthisprocess.Indicator:FoodInsecuritySourcesconsidered:MaptheMealGapReasonfornotevaluating:NotimmediatelyrelevanttotheenvironmentalhealthandmanagementoftheTennesseeRiverBasin.Indicator:AccesstohealthyfoodsSourcesconsidered:USDAFoodEnvironmentAtlasReasonfornotevaluating:NotimmediatelyrelevanttotheenvironmentalhealthandmanagementoftheTennesseeRiverBasin.Indicator:ObesitySourcesconsidered:CDCDiabetesInteractiveAtlasReasonfornotevaluating:NotimmediatelyrelevanttotheenvironmentalhealthandmanagementoftheTennesseeRiverBasin.Indicator:Lifeexpectancy(prematuredeath)Sourcesconsidered:NationalCenterforHealthStatistics–mortalityfilesReasonfornotevaluating:NotimmediatelyrelevanttotheenvironmentalhealthandmanagementoftheTennesseeRiverBasin.Indicator:AccesstoexerciseopportunitiesSourcesconsidered:BusinessAnalyst,Delormemapdata,ESRI,&USCensusTigerlineFilesReasonfornotevaluating:NotimmediatelyrelevanttotheenvironmentalhealthandmanagementoftheTennesseeRiverBasin.Indicator:Educationlevel-somecollegeSourcesconsidered:AmericanCommunitySurveyReasonfornotevaluating:NotimmediatelyrelevanttotheenvironmentalhealthandmanagementoftheTennesseeRiverBasin.