简介：Determinationofsedimentstabilityinthefieldischallengingbecausebedshearstress(BSS),adeterminingfactorofsedimenterosion,can’teasilybedirectlymeasured.Totacklethischallengeandreliablyassesssedimenterodibilityinafastflowingriver,astandaloneunderwatercamerasystemandanewinsituflume(ISF)weredevelopedandappliedinthisstudy.ThecamerasystemwasusedtorecordsedimentmovementandthenewISFwasusedformeasuringcriticalbottomshearstress(CBSS).Thecameracanbedeployedaloneinwatertorecordvideosortakepictureswithlightemittingdiode(LED)lightingandflexibleschedulesettings.TheISFisbasedontheconceptthattheamountofforceneededtoerodethesameparticleunderdifferentflowconditionsshouldbesimilar.TwohighresolutionAcousticDopplerCurrentProfilers(ADCP)alsoweredeployedinthefieldtocollectvelocity-depthprofileswhichareusedbyconventionalmethodstocalculateBSSwiththelawofthewall.ThesedimenterodibilitywasthenassessedbasedonthecomparisonbetweentheobtainedCBSSandBSSandthenfurtherverifiedwiththerecordedobservationsfromthedeployedcamera.Theresultsrevealthatthewidelyusedconventionalmethodcanproducelargeuncertaintiesandisnotadequatetoprovidemeaningfulconclusionundertheseconditions.

简介：Inmarineengineering,thestrengthofasubmarinesedimentisanindispensableparameterforassessmentofconstruction.Inthisstudy,afree-fallconepenetratornamedIPenwasdevelopedtorealizearapidandefficientmeasurementofsedimentstrength.Theequipmentischaracterizedbymodulardesignandself-containeddataacquisition.Itisequippedwithanaccelerationsensor,awaterpressuresensor,andapiezoconepenetrationtest(CPTu)probe.Itisdesignedtobereleasedfromnearseabedsurfacewithareleaserandthenfallfreelytoprovideahigherpenetrationvelocity.Itsmaximumworkingdepthisapproximately2500mandmaximumpenetrationdepthisapproximately3m.Toderivethecorrelationbetweenpenetrationresistanceandsedimentstrength,acalibratorwasdevisedtodeterminethepenetration-ratefactor.Inaddition,thefactorapplicabletoinsitutestpointswasdeterminedinlaboratoryexperiments.InJune2016,theIPenwastestedinsituintheSouthYellowSea,China,duringasharedvoyagefundedbytheNationalScienceFoundation.Meanwhile,undisturbedcolumnsampleswerecollectedforlaboratorytests.Basedontheinsitutestresults,itwasdemonstratedthattheIPencouldaccuratelyrecordtheworkingstatesofvarioussensorsduringthefreelyfallingcourse.IPentestresultsreliablyreflectedthesedimentstrengthatallthetestingpointswhencomparedwithlaboratorycalibrationtests,insituvanetestsandpenetrationtests,laboratorypenetrationtests,andunconsolidatedandundrainedtriaxialcompressiontests.

简介：Arobustmethodforcharacterizingthemineralogyofsuspendedsedimentincontinentalriversisintroduced.Itencompasses3steps:thefiltrationofafewmillilitersofwater,measurementsofX-rayenergydispersivespectrausingScanningElectronMicroscopy(SEM),androbustmachinelearningtoolsofclassification.ThemethodisappliedtosuspendedparticlescollectedfromvariousAmazonianrivers.Atotalofmorethan204,000particleswereanalyzedbySEM-EDXS(EnergyDispersiveX-raySpectroscopy),i.e.about15,700particlespersamplingstation,whichleadtotheidentificationof15distinctgroupsofmineralogicalphases.ThesizedistributionofparticlescollectedonthefilterswasderivedfromtheSEMmicrographstakeninthebackscatteredelectronimagingmodeandanalyzedwithImageJfreeware.Thedeterminationofthemainmineralogicalgroupscomposingthebulksedimentassociatedwithphysicalparameterssuchasparticlesizedistributionoraspectratioallowsaprecisecharacterizationoftheloadoftheterrigenousparticlesinriversorlakes.InthecaseoftheAmazonianriversinvestigated,theresultsshowthattheidentifiedmineralogiesareconsistentwithpreviousstudiesaswellasbetweenthedifferentsamplescollected.Themethodenabledtheevolutionofgrainsizedistributionfromfinetocoarsematerialtobedescribedinthewatercolumn.Implicationsabouthydrodynamicsortingofmineralparticlesinthewatercolumnarealsobrieflydiscussed.Theproposedmethodappearswellsuitedforintensiveroutinemonitoringofsuspendedsedimentinriversystems.

简介：Therelationbetweenthewaterdischarge(Q)andsuspendedsedimentconcentration(SSC)oftheRiverRamgangaatBareilly,UttarPradesh,intheHimalayas,hasbeenmodeledusingArtificialNeuralNetworks(ANNs).Thecurrentstudyvalidatesthepracticalcapabilityandusefulnessofthistoolforsimulatingcomplexnonlinear,realworld,riversystemprocessesintheHimalayanscenario.ThemodelingapproachisbasedonthetimeseriesdatacollectedfromJanuarytoDecember(2008-2010)forQandSSC.ThreeANNs(T1-T3)withdifferentnetworkconfigurationshavebeendevelopedandtrainedusingtheLevenbergMarquardtBackPropagationAlgorithmintheMatlabroutines.Networkswereoptimizedusingtheenumerationtechnique,and,finally,thebestnetworkisusedtopredicttheSSCvaluesfortheyear2011.ThevaluesthusobtainedthroughtheANNmodelarecomparedwiththeobservedvaluesofSSC.Thecoefficientofdetermination(R2),fortheoptimalnetworkwasfoundtobe0.99.ThestudynotonlyprovidesinsightintoANNmodelingintheHimalayanriverscenario,butitalsofocusesontheimportanceofunderstandingariverbasinandthefactorsthataffecttheSSC,beforeattemptingtomodelit.Despitethetemporalvariationsinthestudyarea,itispossibletomodelandsuccessfullypredicttheSSCvalueswithverysimplisticANNmodels.

简介：ThedinoflagellatecystassemblagesontheNanjiIslandintheEastChinaSea,aredocumentedatthefirsttimetoconstructaquantitativeoverviewofthecystbankfrom2014to2015.Thirty-fourmorphotypesfromsixgroupsareidentifiedandquantifiedateightsamplingsitesaroundtheisland,includingahighproportionofpotentiallytoxigenicspecies(14%).Autotrophicdinocystsconstitute74%ofthetotalcystcounts,whichisrelativelylow(twotothirty-threepermillilitresediment)comparedwithpreviousstudiesinadjacentareas.ScrippsiellatrochoideaandProtoperidiniumavellanaarethemostabundantautotrophicandheterotrophicspecies,respectively.Amultivariateanalysisisperformedtoassessassociationsbetweendinocystsandabioticorbioticvariables.Differentiationamongseasonsisevidentinthedetrendedcorrespondenceanalysis(DCA)ordinationplot,whileaspatialpatternisnotclearlyrevealeddespiteheterogeneityofthehydrodynamicconditionsbetweensamplingsites.Solublereactivephosphate,theratioofnitrogentophosphorusconcentrationsandKareniamikimotoibloomarethethreefactorssignificantly(P<0.05)relatedtosurfacesedimentcystassemblagedefinedbythecanonicalcorrespondenceanalysis(CCA),highlightingtheimportanceofnutrientregimetoadinocystdistributioninthisarea.AlthoughattemptstoaddresstheoriginofHABeventsinrecentyearsusingseedbankshavefailed,knowledgecanbevaluableforfurtherinvestigationofdinocystdynamicsandpotentialtoxinthreatsontheNanjiIsland.

简介：Soilsusceptibilitytodetachmentandtransportsub-processesoferosionisgenerallycontroledbytheaggregatebreakdownmechanism.Measuringparticlesizeandaggregationtotheestimateerodibilitypotentialofsoilsisimportantundererosiverainfallconditions.TheAggregateSizeDistribution(ASD)isoneofthemostimportantdeterminantsofsoilstructurealongwithsoilorganicmattercontentfordescribingtheefficiencyofapplied,sustainablemanagementstrategies.Thisstudyaimedtocomparetheperformancesofthreedifferentaggregatesizedistributionmodelstopredictthecharacteristicaggregatesizeparameter(mediandiameter,D50)forerodedsedimentfrominterrillerosionprocessesofRain-SplashTransport(RST)andRaindropImpactedFlowTransport(RIFT).TheASDsof1143collectedsedimentsamplesfromtheRSTandRIFTprocessesweremeasuredandmodeledbytheLog-normal,Fractal,andWeibullapproaches.TheD50value,asacharacteristicparameterforaggregatesizedistributions,derivedfromthecumulativeASDcurvewascomparedforsoilsfromdifferentlandusetypesanddifferentslopeandrainfallintensityconditions.TheperformanceofeachmodelwasevaluatedusingtheMeanSquareError(MSE)andCoefficientofDetermination(R^2).TheWeibullapproachwasthemostaccuratemodelshowingthebestfitwiththelowestMSEvalues(0.0002≤MSE≤0.0048)andhavingthegreatestR2values(0.936≤R^2≤0.998)whencomparedwiththeLog-normalandFractalmodels.Herewith,forsemi-aridlanduseandsoil,specificshapeandscaleparametersfortheWeibulldistribution,therespectiveASDsweresuccessfullyre-generatedformodelingtheerodedsedimentofthesimulatedRSTandRIFTinterillprocesses.

简介：MostofthestudiesonArtificialNeuralNetwork(ANN)modelsremainrestrictedtosmallerriversandcatchments.Inthispaper,anattempthasbeenmadetocorrelatevariabilityofsedimentloadswithrainfallandrunoffthroughtheapplicationoftheBackPropagationNeuralNetwork(BPNN)algorithmforalargetropicalriver.ThealgorithmandsimulationaredonethroughMATLABenvironment.Themethodologycomprisedofacollectionofdataonrainfall,waterdischarge,andsedimentdischargefortheNarmadaRiveratvariouslocations(alongwithtimevariables)andapplicationtodevelopathreelayerBPNNmodelforthepredictionofsedimentdischarges.Fortrainingandvalidationpurposesasetof549datapointsforthemonsoon(16June-15November)periodofthreeconsecutiveyears(1996–1998)wasused.Fortestingpurposes,theBPNNmodelwasfurthertrainedusingasetof732datapointsofmonsoonseasonoffouryears(2006–07to2009–10)atninestations.Themodelwastestedbypredictingdailysedimentloadforthemonsoonseasonoftheyear2010–11.ToevaluatetheperformanceoftheBPNNmodel,errorswerecalculatedbycomparingtheactualandpredictedloads.Thevalidationandtestingresultsobtainedatalltheselocationsaretabulatedanddiscussed.Resultsobtainedfromthemodelapplicationarerobustandencouragingnotonlyforthesub-basinsbutalsofortheentirebasin.Theseresultssuggestthattheproposedmodeliscapableofpredictingthedailysedimentloadevenatdownstreamlocations,whichshownonlinearityinthetransportationprocess.Overall,theproposedmodelwithfurthertrainingmightbeusefulinthepredictionofsedimentdischargesforlargeriverbasins.