1

FLOODRISKMAPPINGFROMORBITALREMOTESENSING1

2

Manuscriptfor“GlobalFloodHazard:applicationsinmodeling,mappingand3

forecasting”,G.J-PSchumann,P.D.Bates,G.T.Aronica,andH.Apel,editors.4

(Inreview)5

6

G.RobertBrakenridge7

DartmouthFloodObservatory8

CSDMS/INSTAAR,UniversityofColorado,Boulder,COUSA9

10

Introduction11

StandardizedmethodsforfloodriskevaluationintheUnitedStateswere12

developedbytheUnitedStatesGeologicalSurveyovermorethanacentury13

(Klingeman,2005;Wahletal.,1995).Theyuseanextensivenetworkofriver14

gaugingstationsandassociatedtimeseriesofannualfloodpeakdischarge;manyof15

theseextendfor50-100y.Tomeetregulatoryandinsurancerequirements,flood16

riskassessmentsmustbenotonlyobjectiveandscientificallydefensible,butalso17

uniformlyapplicableacrosshighlyvariablehydrologicalregimes.Theresultsare18

commonlysubjecttolegalchallengesaspropertyownerscontestthelevelofrisk19

assigned;consistencyofmethodisthuscritical.Throughthesestandardmethods,20

riskismodeled:aflooddischargeofparticularcalculatedrecurrenceintervalis21

2

routedthroughthechannelandacrossthelandscapeviaregulatoryagency-22

approvedhydrodynamicmodelssuchasHEC-RAS(FEMA,2002).23

24

ManydevelopednationsoutsidetheUnitedStateshavesimilarriskevaluation25

methodologies.Quitecommonly,a“100y”dischargeandassociatedfloodplainare26

defined:thisfloodplainisthelandareaalongariverwhere,atitsmargins,a1%27

annualexceedanceprobabilityiscalculatedforinundationbyfloodwater(interior28

portionsmayexperiencemuchhigherinundationfrequencies).Thus,the29

probabilityPethatoneormorefloodsoccurringduringanyperiodwillexceeda30

givenfloodthresholdcanbeexpressed,usingthebinomialdistribution,as31

Pe=1–[1–(1/T)]n (Equation1)32

whereTisthethresholdreturnperiod(e.g.100y)andnisthenumberofyearsin33

theperiod.Forfloods,theeventmaybemeasuredinpeakm³/sorheight;most34

commonlythecalculationusesatimeseriesofannualfloodpeakdischarges(Flynn35

etal.,2006;Klingeman,2005).36

37

Inregardtothisapproach,manydevelopingnationshavealessdeveloped38

hydrologicalmeasurementinfrastructure,andrelativelyfewreliableinsiturecords39

ofpastfloodpeakdischarges.Theneedforreliablefloodhazardinformationmaybe40

evenmorecritical,however,asagriculturalandmanufacturingeconomiesexpand,41

andpopulationgrowthandmigrationincreasesettlementoffloodplainlands42

(Brakenridgeetal.,2016b).Intheselocations,adifferentkindofhydrological43

modelingoffloodhazard,generallyonarelativelycoarsespatialscaleandwithout44

3

abundantstreamflowrecords,isoneapproachtowardsaddressingtheneed(as45

discussedelsewhereinthisbook).Suchapproachesdonotrequireinsituflood46

measurements,butinsteadreconstructthefloodhistoryfromclimatologicaldata47

inputandtopography-assistedmodeling.48

49

Thepresentchapteroffersathirdapproach:combinedanalysisof:50

1) A1998-presenttimeseriesofsatellitepassivemicrowavedatathatrecords51

floodhydrographsatselectedmeasurementsites(Brakenridgeetal.,52

2012a;DeGroeveetal.,2015a;VanDijketal.,2016),and53

2) Opticalsensorimagingandmappingoffloodevents,alsosustainedovera54

similartimespan.55

Thesetogetherproduceaninundationrecordwhichiscoupledtothe56

microwaveinformation:inordertoassignexceedanceprobabilitiestothemapped57

inundationlimits.58

59

Usingthisapproach,standardfloodprobabilitydistributionscanbeappliedto60

agloballyconsistentobservationalperiodofrecordofnearly20y(1998-present).61

Notethatastandard“ruleofthumb”forextrapolationoffloodprobability62

distributionsis2x:iftheperiodofannualpeakflowrecordis20y,the“40y”event63

canbeestimated.Thus,theserecordsshouldallowestimationat-a-siteofthis64

discharge,andsomemappedfloods,iftheyarethelargestofrecord,willbeassigned65

recurrenceintervalsinexcessof20y.Evenareliable25yfloodplain(annual66

exceedanceprobability=4%)isveryusefulriskinformationinanyregionwhere67

4

riskinformationisotherwiselacking,andthesegeospatialdatacanbemadequickly68

availableviathemethodologyanddatadescribedhere.69

70

Orbitalremotesensinginthelate20thandearly21stcenturieshasprovideda71

richarchiveofactualfloodinundationextents.Forsuchdata,seeforexample72

(Brakenridgeetal.,2016a).Somenationsalreadyusesatellite-basedmapsofany73

extremefloodeventforfloodplainregulation,onthesimpleprinciplethatwhathas74

occurred,mayoccuragain(deMoeletal.,2009).Exploredhereisaglobally75

applicablestrategy,however,towardstransformingsuch“mappedlargeflood”76

informationintoquantitativefloodrisk.Suchmapscan,inturn,alsobeusedto77

validateandcalibratefloodriskmapscreatedusingmodelingapproaches.78

MicrowaveRadiometryforMeasuringRiverDischarge79

Asnoted,oncealargefloodhasbeenmappedfromspace,theneedisto80

constrain“howlarge/howrare”isthemappedevent.Inthisregard,satellite81

microwavesensorsprovideglobalcoverageoftheEarth’slandsurfaceonadaily82

basisand,atcertainwavelengths,withoutmajorinterferencefromcloudcover.83

Griddeddataproducts,updatedinnearrealtime,areavailable(DeGroeveetal.,84

2015a).Theproductsarelowinspatialresolution(bestavailableresolutionfor85

theseglobalcoveragesensorsis8-10km).However,usingastrategyfirstdeveloped86

forwide-areaopticalsensors(Brakenridgeetal.,2005;Brakenridgeetal.,2003b),87

sensorssuchasAMSR-E,AMSR-2,TRMM,andGPM(figure1)canmeasureriver88

5

dischargechangesatcertainlocations,bymonitoringthesurfacewaterareasignal89

fromindividualimagepixelsovertime.90

91

Themethodissimpleinconcept:asriversriseanddischargeincreases,water92

areawithinthesingle-pixelsatellitegaugingsites(~10kmx10km),asselected93

fromagriddedglobalimageproduct(figure2),alsoincreases(Brakenridgeetal.,94

2012a;Brakenridgeetal.,2007;DeGroeveetal.,2015b;DeGroeveetal.,2006;De95

GroeveandRiva,2009).Thiswaterareachangetracksriverwidthanddischarge96

variationinamanneranalogoustohowstage(riverlevel)tracksdischargeatinsitu97

gaugingstations.Therelationshipofflowareatodischargeisviathecontinuity98

equation99

Q=wdv (Equation2)100

101

whereQiswaterdischargeinm3/s,wisflowwidth,diswaterdepth,andviswater102

flowvelocity(m/sec),asintegratedacrosstheflowcrosssection.Asdischarge103

increases,andprovidedthechannelisnotrectangularinshape,flowwidth104

increasesatacrosssection,andflowareaincreasesoverallwithinariverreach:in105

thiscase,a10km2measurementsite.106

107

6

108

Figure1.Temporalcoverage,1998topresent,ofpassivemicrowavesensorsbuilt109

andoperatedbyNASAandbyJAXA(theJapaneseSpaceAgency).Eachsatellite110

providesdailyornear-dailyimagingoftheglobe.111

112

Notethata~10km37GHzimagepixelinthesegriddedproducts,centered113

overariver,iscommonly"mixed";itincludesbothwater(lowemission)andland114

(highemission).Astheproportionofwaterarearises,thenetemittedradiation115

declines.Themicrowavesignalisthusverysensitivetoflowwidthchanges.The116

physicalmechanismsareexploredelsewhereforthisfrequencyradiation(e.g.,117

reasonsforlowemissionfromwaterandmuchhigheremissionfromland,118

Brakenridge,etal.,2007).However,thesamemethodologycanalsousethenearIR119

bandsofopticalsensors:again,watersurfacesprovidemuchlowerradiancethan120

adjoininglandsurfaces,butcloudcoverwillintermittentlyinterfere(Brakenridgeet121

al.,2005;VanDijketal.,2016).122

123

7

124

125

126

Figure2.LocationofSatelliteGaugingSiteDFO#30overAyeyarwadyRiverandits127

floodplaininMyanmar.ThesiteisasinglepixelselectedfromtheJRCgrid;pixelis128

10kminsizeandproducesthedailyMvalue.The95thpercentileofthehighest129

(driestandwarmest)valuesfroma9x9pixelarrayinthesurroundingarea130

producesthebackgroundcalibrationCvalue.131

132

Asdischargealongariverincreases,theflowarea,asseenfromabove,should133

generallyincreasemonotonically(hysteresiseffectscanlocallyoccur,however).134

Althoughinsitugaugingstationsinsteadcommonlyusestage,thereisnoapriori135

reasonwhythisisamoresensitiveflowmonitor.Ifriverchannelsarenot136

rectangularinshape,dischargevariationisexpressedbybothstageandwidth137

variations;and,alongmanyrivers,widthvariationwithflowisquiterobust.Also,138

sinceareachinsteadofasinglecrosssectionismonitored,thesensitivityoftheflow139

8

areameasurementdependsonthecompletesuiteofriver/floodplainmorphologic140

featureswithinthereach,andincludingin-channelbarsandlowfloodplainsurfaces,141

slip-offslopesalongmeanderbends,braidedchannelsandislands,andfloodplain142

oxbowlakeswhichareconnectedtothemainchannel.Asforinsitustations,the143

bestsatellitegaugingsitesthusmustbecarefullyselected;inthiscase,forreaches144

wheresurfaceareachangessignificantlyoverthefullrangeofin-channelandflood145

discharges.146

147

Oneimplementationofsatellitemicrowave-basedflowareainformationfor148

operationalhydrologicalmeasurementsistheRiverWatchprocessoratthe149

DartmouthFloodObservatory(DFO),UniversityofColorado,150

http://floodobservatory.colorado.edu/.RiverWatchusestheNASA/JapaneseSpace151

Agency(JAXA)AdvancedScanningMicrowaveRadiometer(AMSR-E)bandat36.5152

GHz,theNASA/JapaneseSpaceAgencyTRMM37GHzchannel,and37GHzdata153

fromthenewAMSR-2andGPMsensors.Thedischargeestimator(theremote154

sensingsignal)istheratioofthedaily"M",microwaveemissivityfroma155

measurementpixelcenteredovertheriveranditsfloodplain,andacalibratingvalue156

("C"),the95thpercentileoftheday'sdriest(brightest)emissivitywithina9pixelx157

9pixelarraysurroundingthemeasurementpixel.(figure2).The95thpercentile158

excludesoutliersduetosensornoisewhilestillprovidingasuitablenon-hydrologic159

backgroundmeasurement.At~37GHz,C/Misprimarilysensitivetochanging160

surfacewaterareawithintheMpixel;usingtheratioremovesotheremission161

9

variability(e.g.fromsurfacetemperature)thataffectsallpixelsinthearea(De162

Groeveetal.,2015b;DeGroeveetal.,2006;DeGroeveandRiva,2009).163

164

ThetimeseriesatsiteswithinreachofTRMM(<50degreeslatitude)beginin165

January1998(figure1).ThenAMSR-Edata(mergedwiththeTRMMinformation)is166

addedwhensuchbecomesavailableinmid-2002.TheseriescontinuesusingTRMM,167

only,duringtheAMSRhiatusbetweenAMSR-EterminationandinitiationofAMSR-168

2;figure1)andthenitaddsAMSR-2andGPM(nowmergingthetwodatastreams,169

into2016).Themicrowaverecordathigherlatitudesitesbeginsinmid-2002170

(followinglaunchofAMSR-E),andthereisdatagapin2012-2013betweenthe171

terminationofAMSR-EandinitiationofAMSR-2.Thegriddingalgorithmthat172

producestheglobaldailyimagesisperformedattheEuropeanCommission’sJoint173

ResearchCentre(JRC);theoriginaldataarenearrealtimeswathinformationfrom174

eachsensorprovidedbyNASAand/orJAXA.AJRCtechnicaldocumentprovides175

furtherinformationincludingdatasources(DeGroeveetal.,2015b).176

177

JRCproducesadailyglobalgridat10km(neartheequator)pixelresolution,178

andpublishesdailyratiodataforfixedpixelswithinthat4000x2000pixelgrid.At179

lowerlatitudes,thecoverageislessthandailyfromAMSR-EandAMSR-2:thelatest180

RiverWatchversionusesaforwardrunning,4-daymeanofthedailyresultsto181

avoidsuchdatagaps.Becauseriverdischargeexhibitsstrongtemporal182

autocorrelation,suchaveragingalsoprovidesusefulsmoothingandnoisereduction.183

10

Also,whenmultiplesamplesforonepixelareavailableinoneday,thelatestsample184

valueisusedatJRCinthegriddedproduct.185

186

AtDFO,thelatestratiodatafromtheJRCareingestedtwiceeachday,andthe187

web-hosteddisplaysandcalculateddischargedataforeachsatellitegaugingsiteare188

thenupdated.Eachsitedisplayincludestwo(html)onlinewebpages:oneprovides189

plotsoftheresultsbutalsosometabulardata:190

(E.g.,http://floodobservatory.colorado.edu/SiteDisplays/30.htm).191

Thesecondpresentsthesignal/dischargeratingcurve(seebelow)andaccesstothe192

completerecordofsatellite-measureddischarge:193

(http://floodobservatory.colorado.edu/SiteDisplays/30data.htm).194

Forcomparisonpurposes,areference20thpercentileofthemeasureddischargefor195

eachdayoftheyearisalsoprovidedandprovidesausefullowflowthreshold.196

ProductionofSignal/DischargeRatingCurves197

Asisthecaseforriverstagemeasuredatinsitugaugingstations,independent198

informationisneededtotranslatethedischarge-sensitiveobservable(inthiscase,199

watersurfacearea)tothecorrespondingdischargevalue.Thetransformationis200

accomplishedbyanempiricalratingequationthatmatchesthesignalto201

independentdischargeinformation.ForRiverWatch,thecalibratingdischarge202

valuesareobtainedbyrunsofaglobalrunoffmodel(WBM)(Cohenetal.,2011).203

Fiveyears(2003-2007)provideabundantdailymodeloutputforcalibration;204

additionalyearscomparingmodelandremotesensingcouldfurtherrefineand205

11

possiblyextendtheresultingratingcurves(iflargermodeledflowsoccurthan206

previously).TheWBMmodel,alsousingaglobalgridresolutionof~10km,inputs207

climateandlandsurfacevariablesandproducesdailyriverdischargevaluesfor208

theseyearsateachmeasurementsite.Earlierworkdeterminedthatadequate209

calibrationinformationforeachsite’sratingcurvecanbeobtainedbycomparing210

justthemonthlydailymaximum,minimum,andmeanvalues,son=180forthe5y211

run(Brakenridgeetal.,2012a;Cohenetal.,inpreparation,2013;Cohenetal.,212

2011).Figure3providessampleresultsatonesiteasascatterplot;figure4213

illustratesthesamedataintimeseriesform.Inthelattercase,thesignaldataare214

firsttranslatedtodischargevaluesusingthescatterplot’sratingcurveinorderto215

showthetwotimeseriesonthesamescale.216

217

218

Figure3.ScatterplotcomparingWBM-modeleddailydischargeovera5y(January-219

Decembermonthlydailymaximum,minimum,andmeandischarges)totheC/M220

ratioforRiverWatchsite30.Therelationshipisempiricalandabettercurvecould221

befittothesedata,butastraightlineisausefulfirst-approximationratingcurve.222

12

223

224

Figure4.Samedataasinfigure3,butarrangedastimeseriesofmaximum(left)225

mean(middle),andminimum(right)dischargevalues.Theredlineshowsthe226

modelresultsandthebluelineistheremotesensingastransformedbytherating227

equationinfigure3.228

229

Withoutotherinformation,itisnotpossibletodeterminewhichdeparturesin230

asmoothmonotonicrelationinfigure3isfromerrorsintheremotesensingsignal231

andwhichfromerrorsinthemodel.Forthepurposeofthefloodriskassessments232

tobedescribedhere,however,thecorrelationofindependentmodeltoremote233

sensingfurtherestablishesthatthemicrowavewaterareasignalisindeed234

respondingtodischargevariation.Also,andwhetherornotWBMisstrongly235

affectedbymodelbiasandisreportingconsistentlytoo-highortoo-lowdischarge236

numbers,suchbiaswillnotaffecttheriskprobabilities.Figure5providestheentire237

remotesensingdailytimeseriesatthissatellitegaugingsite,andusingtherating238

curveinfigure3.Becausetheratiosignalisrespondingtosurfacewater(and239

flooding)extentwithintheMpixel,therelativeheightsofthefloodhydrographs240

shownshouldaccuratelyreflectthetruetimeseriesoffloodingthere:regardlessof241

anymodelbiasinthedischargecalibration.Thisresultthenprovidestheessential242

13

informationneededforfloodhazardmapping:amethodtoconstraintheobserved243

frequency/exceedanceprobabilityofanimagedflood.244

245

Figure4.Daily(4-dayforwardrunningmean)dischargevaluesforsatellitegauging246

site30ontheAyeyarwadyRiver.Thelowflowthreshold(greenline)isthe20%247

percentiledischargeforeachday;thefloodthresholdsuserecurrenceintervals248

computedusingtheLogPearsonIIIdistributionandtheannualmaximumdaily249

values.Majorfloodingin2015approachedthecalculated5yrecurrenceinterval;250

thefloodofrecord,in2004,wasproducedbyaverydamagingtropicalstorm251

(Brakenridgeetal.,2016b);floodinghereexceededthe25ythreshold.252

253

Thereisanimportantfactorthatmaychangetheexactreturnperiodstobe254

assignedtotheannualfloodpeaksshowninfigure4.Thatis,thestraightlinerating255

equationshowninfigure3clearlyproducessomewhattoohighdischargesforthe256

highestwaterareasignalvalues(figure3).Adjustingtheratingequationtoflatten257

theslopewouldproducesomewhatsmallerfloodpeaksandalsoalterthe258

correspondingreturnperiodsforthelargerevents.Thisdependencyindicatesthe259

importanceofdevelopingthehighestqualityratingcurves(thesameneedexistsfor260

insitugaugingstations).261

14

AssessingRiverWatchAccuracy262

Asnoted,theaccuracyofthesatellitegaugingsiteresultsdependsinparton263

riverandfloodplainmorphology.Othersite-specificfactorssuchasvegetationare264

alsoimportant(Revilla-Romeroetal.,2014).Usingboththemodelandtheremote265

sensingresults,withoutanyground-basedinformation,itisalsopossibleto266

calculateusefulstatisticscomparingtheoverallaccuracyofthedischargetime267

seriesresults.Forexample,flowareasmaynotchangeverymuchinresponseto268

dischargealongsomereaches,inwhichcasetheexpectedsignalrangeissmall269

comparedtothedailynoisethatcanbeinducedbyotherfactors(seebelow).Two270

descriptivestatisticsforasampleofsitesinMyanmaralongtheAyeyarwadyanda271

majortributary(theChindwinRiver)areprovidedasTable1toillustratetheir272

utilityandapplication.Thesignal/modelagreementisasimplerankingand273

classificationofthesignal/modelleastsquaresregression(coefficientof274

determinationr2)results,asinfigure3(eitherforstraightlinefitsorsecondorder275

polynomialsthatcanbettermatchthedata).Amongdifferentsites,higherr2276

indicatesastrongercorrelation;itismorelikelythattheremotesensingsignalis277

accuratelytrackingriverdischargevariationifitisstronglycorrelatedtomodeled278

dischargeoutput.Thesethresholdswerechosentogroupther2valuesintoclasses:279

>.7,Excellent,.6-.69,VeryGood,5-.59,Good,.4-.49,Fair,<.4,Poor.280

281

Second,thesitesvaryinmaximumsignalrangeovertheperiodofrecord(in282

thetable,fromalowof.08to.20,ormorethan2-fold).Theyalsovaryintheaverage283

dailysignalchange(fromalowof.08to.14,ornearly2-fold).Thus,somesitesmay284

15

exhibitaverysmalltotalrange,butsignificantdailyvariation,muchofwhichmay285

benoise;othersalargetotalrangeandrelativelysmalldailyvariation(stronger286

signal/noise).Theratioprovidesaconsistent“signalstrength”measurethatcanbe287

similarlyclassifiedfromexcellenttopoor.Thefollowingthresholdswerechosento288

groupthesignal/noisevaluesintoclasses:>.8,Excellent,.7-.79,VeryGood,.6-.69,289

Good,.47-.59,Fair,<.47,Poor.290

291

Thetwometricsseparatelyprovideanobjectiveassessmentofhowwellthe292

remotesensingagreeswiththemodeling,andhowstronglythesignalisrecording293

dischargevariationcomparedtotheday-to-dayvariationthatmaybemainlynoise294

alongmanyrivers.Knownsourcesfornoisemayinclude:1)geolocationerror(the295

geographicfootprintoftheswathimagedataincorporatedintothegriddedglobal296

productvariesslightly);2)sensornoise(theradiancemeasurementshavefinite297

precision);and3)non-surfacewaterareaeffectsontheratio(soanydifferential298

environmentalfactorsaffectingtheMpixelovertheriverandthedriestpixelsinthe299

Ccalibrationarray.300

301

Site Signal/Model SignalRange DischargeRange Signal/Noise r2302

Agreement anddaily303

304

108 VeryGood .11,.008 21,091m3/s Good .66305

23 Good .08,.009 25,507m3/s Fair .57306

26 VeryGood .09,.013 17,242m3/s Fair .67 307

29 Good .12,.014 35,891m3/s Fair .57308

16

30 VeryGood .20,.013 35,245m3/s VeryGood .70309

Table1.Summaryofmicrowavedischargemeasurement(RiverWatch)sitecharacteristics310

andaccuracyforsitesalongtheChindwin(108and23)andAyeyarwady(26,29,30).The311

signalrangestatisticrecordsthetotalmeasuredvariabilityofthedischarge-estimator312

signal;largervaluesindicateasitewheretheremotesensingsignalismoresensitiveto313

dischargevariation.Thenoisestatisticreferstotheaveragesignalvariabilityonadaily314

basis;largervaluesindicatemorenon-hydrologicnoise.Ther2valuesarecoefficientsof315

leastsquaresregressionoftheindependentWBMmodelingdischargeresultstotheremote316

sensingsignal(over5years,2000-2010,monthlydailymaximum,mean,andminimum317

values,n=180).318

319

Wheregroundgaugingstationsandsatellitegaugingsitesareco-located,the320

remotesensingcanalsobedirectlycalibratedtodischargedirectlyviatheground321

information.SelectedU.S.sites(e.g.,figure5)thereforecomparemodel-basedand322

groundstation-basedratingcurves:providingbothanassessmentofmodelbiasand323

oftheoverallaccuracyoftheRiverWatchinformation.Intheexampleshown,the324

riverchannelismeandering,butonly45mwide;thusalsodemonstratingthatthe325

RiverWatchmethodisnotlimitedtolargeriversbutinsteadrequiresonly326

floodplain/channelreacheswheretotalsurfacewaterextentchangesrobustlyas327

dischargechanges.328

329

17

330

Figure5.Model-andgroundstation-basedratingcurvesforTrinityRiver,331

Texas,RiverWatchsite446intheDartmouthFloodObservatory/Universityof332

Coloradoarray.TheWBMmodelwasusedtoproducetheblacklineratingcurve,333

whichisfittowidelyscattereddata.Aco-locatedUSGSgaugingstationwasusedto334

producetheredlineratingcurve,withmuchbettercorrelationtotheremote335

sensing.ComparisonofthetwocurvesindicatesaWBMmodelpositivedischarge336

biasincreasingwithhigherdischarges.Also,atthislocation,theWBMmodelmay337

performrelativelypoorlybecauseitdoesnotincorporateupstreamrivercontrol338

structures.339

SatelliteGaugingSiteSelection340

Thereareseveralfactorsaffectingtheselectionofgauginglocations.Itis341

importantthattheMpixelbelocatedtoavoidsaturation(completefillingofthe10342

kmmeasurementpixelbywater)duringfloodevents.Itisalsonecessarythatthe343

pixelmonitorsarelativelyuniformstretchofriverwithoutmajortributary344

junctions,ornearbystreams,orothervariablewaterbodiesthatmaychangein345

18

surfaceareawithoutdirectlyindicatingdischargechangesatthesiteintended.The346

measurementsitecan,however,include,suchriver-connectedfeaturesasoxbow347

lakesandotherwater-fillednegativerelieffloodplainfeatures(Lewinand348

Ashworth,2014)thatareconnectedtotheriver:theirexpansionorcontractionis349

responsivetolocalriverdischargechanges.350

351

Notethattheremaybesignificanttimelagsandhysteresisbetweenthefilling352

anddrainingoffloodplainsandthedischargestraversingthetrunkstreamchannel353

(Brakenridgeetal.,2007).Considerinthisregardthatthemicrowavemethodisnot354

usingreachwatersurfaceareaasasimpleproxyforriverflowwidth.Instead,a10355

kmx10kmparceloffloodplainandchannellandwithinterconnectedwater356

featuresisrecordingdischargevariation.Thesitesmustbevisuallyinspectedin357

mapformtoensurethat,ineachcase,theflowareachangesrelatetotheriverbeing358

monitoredandtoevaluatethepotentialinfluenceoftimelagsandalsoflowcontrol359

structuresalongtheriver.Anotherimportantconfoundingfactorisirrigated360

agriculture,especiallyricepaddies.Suchfarming,ineithertheMortheCpixel,can361

produceanentirelyerroneouschangeinthesignalratioasregardsdischarge;362

insteadthesignalrecordsirrigationchanges.363

364

Despitetherequirementforcarefulevaluationofeachpotentialriver365

measurementsite,thereareatleastseveralthousandsofadditionalRiverWatch366

sitesthatcouldbeestablishedandbeyondthe~300nowbeingpublishedonline367

(http://floodobservatory.colorado.edu/DischargeAccess.html).Themicrowave368

19

ratiosignalinformationisalreadyavailableforeachcelloftheglobalgrid.Aswell,it369

ispossibletouseobservedsitenumericalcorrelationstoknowndischargevariation370

enmasse:toselect,viathedegreeofcorrelation,thebestsitestoexaminefurther371

(VanDijketal.,2016).Throughthissatelliteobservationalmethod,insitugauging372

stationsarenotrequiredtoconsistentlyevaluatefloodriskalongsatellite-373

monitoredriverreachesandfloodplains:atleastforpredictedrecurrenceintervals374

ranginguptoapproximately40y.375

FloodMappingFromOpticalSatellites376

OneusefulmethodformappingfloodsandfloodhazardusesthetwoNASA377

MODISsensors(aboardthesatellitesTerraandAqua).Theseprovide36optical378

spectralbands;mostbandsofferspatialresolutionof1000or500m.However,two379

bands,inthevisibleandnear-IRportionsofthespectrum(620–670nm,band1,380

and841–876nm,band2)providespatialresolutionof250m;band2inparticular381

stronglydifferentiatessurfacewaterfromland.Suchinformationhasbeenusedto382

maptheinundationextentsreachedbyfloodsatmanylocationsworldwide383

(Brakenridgeetal.,2003a;Brakenridgeetal.,2012b;Policelli,2016),figure6.Allof384

the(twicedaily,globalcoverage)imagedatasincelate1999areavailableinvarious385

formatsinpublicNASAandotherdataarchives.386

387

Asfloodsevolve,dailymappingoftheinundationextentsprovideanearreal388

timeindicationoffloodseverity.Floodingcanbecomparedtopreviouslymapped389

water,suchaswinterlowflowconditionsandtypicalannualhighwater(figure6).390

20

Thus,accurate“flood”mappingdoesnotonlyinvolvemappingmaximumflood391

extentreachedbyanevent,butalsocomparisontowaterextentthatistypicalfor392

thesametimeofyear.Figure6illustratesthelargeamountofnormalannualwater393

variabilityinasummermonsoon-affectedregion,whichmustfirstbemaskedbefore394

unusualfloodingcanbediscerned.Mappingoversomeyearscantherebyprovide395

bothincreasinglycomprehensivefloodhazardinformation(moreprobabilityof396

imagingandmappingtheunusualhighfloods)andbetterinformationconcerning397

whataretypicalandatypicalhighwaterconditions.398

399

21

400

Figure6.MODISremotesensing-producedmapofmajor2015floodinginMyanmar401

(lightred),andalsothetypicalannualhighwater(2014,lightblue).Recurrence402

intervalsattheRiverWatchmeasurementsitesforthe2014“flood”are403

approximately1.5y.DarkblueisapermanentwatermappedbytheShuttleWater404

BoundaryData(https://lta.cr.usgs.gov/srtm_water_body_dataset)fromFebruary,405

2000,andrepresentstypicalwintersurfacewaterextent.406

407

22

RemoteSensing-basedFloodHazardQuantification408

Floodinundationmappingcanbecoupledwitheitherground-orspace-based409

rivergaugingtoconstrainthefrequencyofthemappedflood.Thus,usingriver410

dischargetimeseries,standardfloodfrequencymethods(Flynnetal.,2006)can411

analyzetheseriesofannualpeakdischargeswithintheperiodofrecordtoevaluate412

floodrecurrenceintervals/annualexceedanceprobabilitiesforanyparticularevent.413

IntheU.S.,theLogPearsonIIIprobabilitydistributionisused.Thereareother414

potentialissuesinvolvedincreatingariskassessmentatagaugingstation,415

particularlyforlarge,rareevents,andincludingthepossibilitytoapplyskewness416

coefficientsusingregionaldataandwithinthestandardizedLogPearsonIIIorother417

distributionfunctions.Thesetechniquesarewelldescribedintheliterature,and418

providevariousmethodsforextendingand/orextrapolatingthedataprovidedby419

anysingleriverpeakdischargetimeseries.420

421

Inanycase,however,theneedforspatialextrapolationoveraflood-prone422

landscaperemains.Asingleimageofalargefloodeventshowstheinundation423

extentoverlongreachesoffloodplain,andthelocalexceedanceprobabilitymay424

varysignificantlywithlocation.Theprobabilityestimatesobtainedfromatime425

seriesatadischargetimeseriesmeasurementsitestrictlyapplyonlytofloodingat426

thatsite.Thisposesaninterestingcontrasttomodel-basedriskmapping(other427

chaptersinthisbook):suchmodelspredictinundationfromaspecificrecurrence428

intervalanddischargevalueinaspatiallycontinuousmanner,andusing429

informationaboutchannelandfloodplainmorphologyandflowroutingequations.A430

23

floodimageinsteaddirectlyprovidestheinundationextent,withoutknowledge431

everywhereoftherecurrenceinterval.432

433

Infloodmodeling,thespatialcontinuityissueisimplicitlyaddressedby434

interpolation.Forexample,the20yfloodatsiteAmaybecalculatedat12,000m3/s435

andthatatdownstreamsiteB,16,000m3/s.Atariverlocationmid-waybetween436

thetwostations,andiftherearenomajortributaryjunctions,a20ydischargeof437

14,000m3/smaybemodeledforinundationpredictionthere(ingrid-based438

modeling,thisinterpolationisatthegridresolution).Borrowingfromthis439

approach,ifthesatelliteimagemapsafloodatbothstationlocations,witha440

calculatedrof12yatAbut18yatB,100kmdownstream,theimagedfloodlimits441

mayhave,inthiscase,arangeofrsomewherewithin12to18y.Theriskvaluesand442

mapinformationthenbecomesevenmorecomplex,asadditionalfloodsofdifferent443

returnperiodsaremapped.Suchcomplexitywouldnotaddresstheneedtoidentify444

inaconsistentwayfloodplainlandareasataparticularriskofflooding.445

446

Amorepracticalapproachistoinsteaddividethefloodeddrainagenetwork447

intoaseriesofsegmentedriverreaches:eachmonitoredneartheirmidpointsby448

eitheraground-basedorspace-baseddischargemeasurementsite(figures7and8).449

Underthisapproach,theassociatedrisassumedtoapplytotheinundationextent450

throughoutthereach,andlargerorsmallerfloodsimagedandmappedforthesame451

reachwilleachalsohavespecificrecurrenceintervals.Inthiscase,noattemptis452

madetointerpolatebetweenriverdischargemeasurementsites,butinsteadrisk453

24

454

455

456

457

458

25

Figure7.RecurrenceintervalandpeakdischargeestimatesforRiverWatchsite30459

alongthelowerAyeyarwadyRiver.Themicrowavemeasurementsiteisdefinedby460

thewhite10kmsquare.Inundationfor,top,anormalwinterflowof6253m3/son461

February11-22,2000;middle,observedviaMODISat250mspatialresolution,462

floodedareaforatypicalsummermonsoonal“flood”,r=1.5y(27,138m3/s,463

observed2013),andbottom,observedviaMODISagain,floodedareaformajor464

floodingin2004,r=24y(50,579m3/s.Notethatduringthisfloodamajor465

distributarytotheeast(theareaisattheheadoftheAyeyarwadydelta)isalso466

flooded.467

468

mapsarepreparedseparately,foreachmonitoredriverreach.Acombined469

mappingandmodelingapproach,whereinthemodelingprovidesthecontinuityand470

abilitytomapaparticularrecurrenceintervalflood,andtheremotesensing-based471

mappingprovidestheobservationalvalidation,reachbyreach,appearstobethe472

mostproductivewayforwardforlarge-regionmappingoffloodrisk.(DeGroeveet473

al.,2015a)474

475

476

477

478

479

480

481

26

482

483

484

485

Figure8.RecurrenceintervalandpeakdischargeestimatesforRiverWatchsite26486

alongtheupperAyeyarwadyRiver.Inundationfor,top,anormalwinterflowof487

27

~200m3/sinFebruary,2000;middle,atypicalmonsoonseasonflood,recurrence488

intervalof1.1y,~9000m3/s,in2002,and,bottom,inundationduringarareflood,489

recurrenceintervalof21y,18,200m3/s,in2013.490

Conclusion491

Theoutlinedapproachcouplesmicrowaveandopticalremotesensingto492

producequantitativefloodriskmaps.Thischapterdescribeshowthetwocanbe493

integrated:theincreasinglycomprehensiveglobalsatellitemaprecordofflood494

eventscanbematchedtoexceedanceprobabilitiesfrommicrowavedischarge495

recordstoproduceriskmapswithoutanyinsituinformation.Forthetwoexamples496

provided,theperiodofsatellite-microwave-observedflooddischargesbeginsin497

1998andisnowapproaching20y.Byaddingtheassociatedmicrowaverecord,the498

reach-levelfloodmapspreparedfromMODISopticalremotesensingshownotonly499

actualmappedfloods,butalsotheassociatedannualexceedanceprobabilities.Asis500

thecaseforfloodmodelingproducts,thesemapsareaquantitativeguidetofuture501

risk.502

503

Thereisalsoanimportantadditionaluseofsuchmaps:fornearrealtime504

floodinundationprediction.Unlesschannelsandfloodplainshavechangedsince505

previouslymappedfloods(e.g.,throughleveeconstructionorremoval),similar506

flooddischargesinthefuturealongthemonitoredreachesshouldcontinueto507

producesimilarinundationextents.Thispresentsanobservationalratherthan508

modelingpathforwardforfloodinundationprediction.Thatis,ifaparticularflood509

28

hydrographwasmeasured,eitherviaaninsitugaugingstationorthesatellite510

microwaveapproach,andtheresultingfloodplaininundationwasalsoobservedand511

mapped,thenthemapcanbeusedtounderstandwhatlandareaswillbe512

submergedwhenthatdischargeisonceagainattained.Alibraryofinundationmaps513

canbeassembled,andreferredtowhenfloodingagainoccurs.Notehowever,that514

floodhydrographvolume,aswellassimplypeakdischargeandstage,mayalso515

affectmaximuminundationextent,andespeciallyoverlargefloodplains.Flooded516

areaisarelativelynewhydrologicalobservablethatismadepossibleviaorbital517

remotesensing.Productiveresearchcannowbeaccomplishedrelatingdifferent518

aspectsoffloodhydrographstothesocietallyandecologicallyrelevantvariablesof519

inundationextentandduration.520

521

Thissatellite-basedfloodriskmethodispresentedinoutlineformhere,but522

therearecomplexitiesinvolvedinitspracticalimplementationandvalidation.For523

example,shorteningthereachlengthsandobtainingdatafromadenserarrayof524

dischargemeasurementsiteswouldincreasetheaccuracyanddetailofrisk525

mapping:bytestingandcomparingindividualmeasurementsiteresults.Asinmany526

otherareasofwork,replicationofthehazardresultsobtainedareimportantforthe527

resultstobetrusted.Also,considerthattheremayberelativelylargestepchanges528

betweencontiguousreachesinthecalculatedreturnperiodsforaparticular529

mappedflood,forexample,duetotheinfluenceoftributarydischarges:thiscould530

poseachallengeinthepreparationofregionalmaps,butalsoreflectstherealityof531

theactualfloodhistoryandfuturerisk.Ifregionalmapsareneeded,anotheruseful532

29

strategymaybetocouplethedescribed,reach-specificobservationalapproachwith533

regionalandevenglobalfloodhazardmodelingmethodssuchasaredescribedin534

otherchaptersinthisbook.Forthis,continuousspatialcoverageviaremotesensing535

isnotneeded(itisprovidedbythemodeling).Instead,thediscretereacheswhere536

riskischaracterizedbyremotesensingcanprovidecriticalmodelvalidation.537

538

Finally,thereisevidentutilityinfocusedattemptstoimageandmapthevery539

largestfloodeventsintheperiodofrecord(e.g.,the2004floodinginfigure4and6).540

Suchworkprovidesanunambiguousmappedhazardareafromthemostinfrequent541

andoftenexceptionallydamaginglargeevents,andnowtheirexpectedfrequency542

canalsobeapproximatelyconstrained.Indeed,thesesatelliteimagesand543

associatedmapsneedtobepreservedas“memorials”forposterityandinthesame544

waythatmanycommunitiesacrosstheglobepreservehighwatermarksfrom545

historicextremefloods(Davies,2014).Givenachangingclimate,andalsochanges546

inwatershedlandcoverandothercharacteristics,thereisalackofsupportfor547

assumingtemporalstationarityintheseriesofannualpeakdischarge(Millyetal.,548

2008).Yetitisonlybyusingthisassumptionthatprobabilitydistribution-based549

exceedanceprobabilitiescanbecalculated.Thiscautionsagainstmappingflood550

hazardasastaticquantity.Instead,quantitativefloodriskmapscanbeusedasa551

startingpoint,andpredictivemodelsthatprovideinformationforhowfloodregime552

maychangeintothefutureshouldbeincorporatedintofloodhazardmappingas553

well.554

30

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