ORIGINAL
RESEARCH
ARTICLE
Response
patterns
of
phytoplankton
growth
to
variations
in
resuspension
in
the
German
Bight
revealed
by
daily
MERIS
data
in
2003
and
2004
§
Jian
Su
a,b,*
,
Tian
Tian
c,
Hajo
Krasemann
a,
Markus
Schartau
a,d,
Kai
Wirtz
a aInstituteofCoastalResearch,Helmholtz-ZentrumGeesthacht,Geesthacht,Germanyb
InstituteofOceanography,CentreforMarineandClimateResearch,UniversityofHamburg,Hamburg,Germany cDanishMeteorologicalInstitute,Copenhagen,Denmark
dGEOMARHelmholtzCentreforOceanResearchKiel,Kiel,Germany
Received20June2014;accepted17June2015 Availableonline14July2015
—341 KEYWORDS Resuspension; Chlorophylla; Phytoplankton production; Coastalsea; MERIS; GermanBight
Summary Chlorophyll (chl a) concentration in coastal seasexhibits variability on various spatialandtemporalscales.Resuspensionofparticulatemattercansomewhatlimitalgalgrowth, but canalsoenhanceproductivity because oftheintrusion of nutrient-richporewaterfrom sedimentsorbottomwaterlayersintothewholewatercolumn.Thisstudyinvestigateswhether characteristicchangesinnetphytoplanktongrowthcanbedirectlylinkedtoresuspensionevents withintheGermanBight.Satellite-derivedchlawereusedtoderivespatialpatternsofnetrates ofchlaincrease/decrease(NR)in2003and2004.SpatialcorrelationsbetweenNRandmean watercolumnirradiancewereanalysed.Highcorrelationsinspaceandtimewerefoundinmost areasoftheGermanBight(R2>0.4),suggestingatightcouplingbetweenlightavailabilityand algal growth during spring. These correlations were reduced within a distinct zone in the transitionbetweenshallowcoastalareasanddeeperoffshorewaters.Insummerandautumn, a mismatch was found between phytoplankton blooms (chl a>6mgm3) and spring-tidal induced resuspension events as indicated by bottom velocity, suggesting that there is no phytoplanktonresuspensionduringspringtides.Itisinsteadproposedherethatfrequentand recurrent spring-tidal resuspension events enhance algal growth by supplying remineralized
PeerreviewundertheresponsibilityofInstituteofOceanologyofthePolishAcademyofSciences.
§ThisworkwasinpartsupportedbytheDeutscheForschungsgeGemeinschaft(DFGpriorityprogram1162AQUASHIFT).
* Correspondingauthorat: InstituteofOceanography,Centrefor Marineand ClimateResearch,UniversityofHamburg,Bundesstr.53, 20146Hamburg,Germany.Tel.:+4940428387489;fax:+4940428387488.
E-mailaddress:[email protected](J.Su).
Availableonlineatwww.sciencedirect.com
ScienceDirect
j o ur nal h o m ep a ge: w ww.e ls e vi e r.c o m /l o c at e/ o c ea no
http://dx.doi.org/10.1016/j.oceano.2015.06.001
0078-3234/#2015InstituteofOceanologyofthePolishAcademyofSciences.ProductionandhostingbyElsevierSp.zo.o.Thisisanopen accessarticleundertheCCBY-NC-NDlicense(http://creativecommons.org/licenses/by-nc-nd/4.0/).
1.
Introduction
Coastalareasexhibitgreatvariabilityinphysicaland
biolo-gical processes,makingit difficultto pinpoint
spatio-tem-poralalgalgrowthdistribution patterns.Inlargepart,this
variability results from a complex interplay of sediment
resuspension,phytoplankton growth, grazing, aggregation,
andsinkingofparticulatematter.Primaryfactorscontrolling
coastal phytoplanktondistributionandgrowthinclude
sur-facetemperature,turbidity,rivernutrientloads,andbenthic
andpelagicconsumers,aswellastidalmixing(Loebletal.,
2009; Malone et al., 1983; Soetaert et al., 1994). These
factors interfere with strong horizontal advection (Lucas
etal.,1999).
Resuspensionisaphysicalprocessthatoccurswhen
bot-tom shear stress is high enough to lift sediment particles
(Wainright, 1990). Physical causes of resuspension include
strongwindsandtidalcurrents.Inwinterandspring,strong
windsgenerateturbulentmixing.Inshallow waters,
turbu-lence not only retains suspended particles in the water
column,butalsodetachesbenthicmaterial.Bothprocesses
increasetheconcentrationofsuspendedparticulatematter
(SPM). SPM in turn negatively affects light availability for
phytoplanktongrowth(Fig.1,May etal.,2003;Wild-Allen
etal.,2002).Wind-inducedmixinghasindeedbeenshownto
determineeffectivelythespreadingofalgalspringbloomsin
coastalseas(Meietal.,2010;Tianetal.,2009).Inshallow
coastalseas,strongtidalmixingalsoinfluences
phytoplank-tongrowth(Sharplesetal.,2006).Forinstance,weakened
mixing during neap tides favours stratification (Simpson
et al., 1990). Results from harmonic analysis (von Storch
andZwiers, 2001) ofsatellite SPM images in the southern
North Sea suggest pronounced spring-neap variations,
thereby revealing how changes in tidal mixing govern the
distribution ofSPM (Pietrzak etal.,2011). Insummer and
autumn,tidalcurrentscauseresuspensionofbenthic
mate-rial, which can supply nutrients from sediment layers or
bottom water layers to the water column (Fig. 1). These
remineralizednutrientsoriginatefromthedecompositionof
organic matter that sank out of the water column and
accumulatedon theseabedshortly afterthespringbloom
(Ehrenhaussetal.,2004).Therefore,explainingtheoriginof
bloom events in autumn is difficult because it involves
reconcilingtwoconflictingresuspensioneffects(high
turbid-ity versus nutrient recycling) on phytoplankton growth
(Fichezetal.,1992).
Todate,theroleofresuspensionincoastalphytoplankton
growthhasrarelybeenaddressedonasystemscale.Previous
studiesoftheeffectsofresuspensionwerebasedon
labora-toryworkoronlocalin-situmeasurements(Koschinskyetal.,
2001; Sloth et al., 1996; Tengberg et al., 2003). Spatial
extrapolations of local resuspension effects are limited
because resuspension and phytoplankton growth have a
strong mesoscale component (Gerritsen et al., 2001; Lou
etal.,2000;Stanevetal.,2007).Satelliteoceancolourdata
provideauniquetoolformonitoringtheseeffects.However,
givenspatio-temporalvariationsinbathymetry,atmospheric
forcing, and hydrography, resolving how mixing, nutrient
availability, and light availability promote phytoplankton
bloomsinshallowcoastalregionsremainsachallengingtask,
especiallywhencomparedtosimpleropenoceanconditions
(Lucasetal.,1998).
The German Bight (GB), located in the south-eastern
portion of the North Sea, is a shallow area with average
waterdepthsofabout22m(Fig.2).Insuchshallowwater,
wind andtidal waves have an impact on the bottom, and
resuspensionhasanimpactonthewatercolumn.Our
synop-tic view of biophysical processes in the GB is gradually
improving thanks to continuing in-situ measurements and
nutrients.Thishypothesisiscorroboratedbyalagcorrelationanalysisbetweenresuspension eventsandin-situmeasurednutrientconcentrations.Thisstudyoutlinesseasonallydifferent patternsinphytoplanktonproductivityinresponsetovariationsinresuspension,whichcanserve asareferenceformodellingcoastalecosystemdynamics.
#2015InstituteofOceanologyofthePolishAcademyofSciences.Productionandhostingby Elsevier Sp.z o.o. This is an openaccess article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).
Figure1 Schematicdiagram ofhow resuspensioninfluences phytoplanktongrowth.Theuppergraphgeneralizestheseasonal variationsofthetwomainlimitingfactorsintheGermanBight: light and nutrients. The +/ signs with arrows indicate the positive/negativeeffects.Inspring,phytoplanktonreacts nega-tivelytoincreasedresuspensioninducedbywind.Incontrast,in latesummerandautumn,recurrentresuspensioninducedbythe spring tide refuels phytoplankton growth with remineralized nutrients.
remotely sensed data (Grunwald et al., 2007; Onken and Riethmüller, 2010; Petersen et al., 2008; Staneva et al.,
2009). Factorsthat limit phytoplankton growthin the GB
are typicalof shallow seaareas (Cloern, 1999; Colijn and
Cadée,2003).The primarylimitingfactor, asderivedfrom
theHelgolandRoads(HR)time-seriesdata,islightlimitation
duringspringbloomsandnutrientlimitationduringsummer
andautumnblooms(Fig.A.1;detailedcalculationsaregiven
inAppendixA).
Thisstudyhasanalysedremote-sensingdatafromtheGB
in2003and2004tounraveltheinterdependenciesbetween
algalgrowthandwind/tidalinducedresuspensionoverspace
andtime. The centralquestion addressedin this paper is
whethertypicalresponsepatternsofspringorautumnalgal
bloomscanbeinferredfromvariationsinresuspension.This
studycontributestoanimprovedquantitativeand
mechan-isticdescription ofdirectcoastal oceanographiceffectson
biogeochemistryandplanktonecology.Hence,itservesasa
basisformulti-yearstudiesandcoupledphysical—biological
models.
2.
Methods
2.1. MERIS derivedproducts
Thehigh-resolutionsatelliteoceancolourimagesusedinthis
study were obtained from the Medium Resolution Imaging
Spectrometer(MERIS).MERISprovideshigh-resolutionspectral
dataofwater-leavingradianceforninevisiblechannels.These
spectral datayielddetailedinformation whichisespecially
neededforcase-2waters(Bricaudetal.,1999).TheNorthSea
wasreferredtoascase-2watersbyMorelandPrieur(1977).
Maps ofchla concentrations are astandardMERIS
pro-duct.Here,theversionforcase-2coastalwaterswasused.
TheCase-2-Regional(C2R)processorinversion1.1includes
an atmospheric correction using bands inthe infraredand
blue spectral regions and a neural network to determine
atmosphericcontributionstothesignal(Doerfferand
Brock-mann,2006).AcomparisonofC2Rproductswithbuoydata
shows better agreement with water-leaving reflectances
than that obtained from standard processors (Doerffer
etal.,2010). Thedifference betweenin-situand
remote-sensing measurements, however, is highly dependent on
patchiness, absolute concentrations, and the conversion
from absorption lengths to concentrations. For case-1
waters, deviations are on the order of 10—30%;for chl a
concentrations are above 0.5mgm3. In-situ validations
withcase-2 watersarealsoverydependenton theregion.
ForNorthSeawaters,pixel-wisedifferencesarecomparable
tocase-1watersforhighchlaandlowsuspendedmatter,but
theycanexceedthe100%level(Doerffer,2007).Theoriginal
1kmresolutionwasnotpartofaregulargrid,whichposes
difficulties fortheanalysis, andthereforeforthisstudy, a
2km resolution was used. The reprocessed data included
notonlythestandard output,suchas chla,chromophoric
dissolved organic matter (CDOM) and total suspended
matter(TSM)concentrations,butalsotheapparentoptical
30ʼ 7o E 30ʼ 8oE 30ʼ 9oE 53oN 30ʼ 54oN 30ʼ 55oN 30ʼ 10 10 20 30 30 30 30 Ems Weser Elbe
Helgoland
East Frisian Wadden Sea
North Frisian Wadden Sea
North Sea
Figure2 Topography(with10,20,and30misobath)oftheGermanBight(leftpanel),whichislocatedinthesouth-easternNorthSea (rightpanel).TheblackboxintheleftpanelindicatestheHelgolandarea,forwhichatimeseriesofMERISdatahasbeenextracted. TheareaiscentredontheHelgolandRoadslong-termtime-seriesstation.
parameter for the attenuation coefficient Kd. In the
NorthSea,theobservedSecchidepth(SD)showsahigher
correlation with MERIS Kd in the transitional waters of
theGB(R.Doerffer,pers.comm.).Kdfieldswilltherefore
beusedinthisstudytoapproximatethespatialdistribution
of light attenuation coefficients, also motivated by Tian
et al. (2009) who assimilated satellite-derived Kd to
improve theirmodel predictions for spring bloom events
inthe GB.
Because for 2003 and2004 arelatively highnumber of
cloud-free scenes could be processed, the present study
concentratedontheseyears.Consequently,allsubsequently
integrateddatawereconfinedto these 2years. MERIS chl
atime-seriesatHelgolandwereextractedbyaveragingchla
withintheHelgolandarea(indicatedinFig.2).
2.2. Supportingmodeland time-seriesdatasets
Timeseriesofcurrentvelocityinthebottomlayerandmixed
layerdepthswereobtainedfromthemodelrundescribedby
Stanevaetal.(2009),whousedthethree-dimensional
Gen-eralEstuarineTransportModel(GETM).GETMisa
free-sur-face, baroclinic, hydrostatic model especially adapted to
tidal flats and shallow waters (Burchard and Bolding,
2002).The modelsystem isforced by 6-hourly ECMWF
re-analysisdata,hourlyriverrun-off,andtime-varyinglateral
boundaryconditionsofseasurfaceelevations(Stanevaetal.,
2009).Hence, thetwo major factors (tide andwind)
con-sideredinthispaperarewelldescribed.
HR(5481101800N,78540E)timeserieswereusedtovalidate
the MERIS chl a data and to investigate the correlation
between nutrients and chla in summer andautumn. The
samplesforchlameasurementatHRwereextractedfrom
fluorescencedatausinganalgalgroupanalyser(Knefelkamp
etal.,2007;Tianetal.,2011).
Sea-leveldatawerecollected attheHZGLangeoogpile
(78280E,538430N)station.Sea-leveldatawereusedto
repre-sentthespringandneaptidalcycleindicesofthecoastalGB.
2.3. Parameterstoidentifythephotosynthesis— irradiance relationship
ThedailyirradianceaveragedovertheMLD(Im,Einstm2d1)
wascalculatedas:
Im¼
I0
kz½1expðkzÞ; (1)
whereI0isthedailysurfaceirradiance[Einstm2d1],kis
theattenuationcoefficient[m1],andzisthesurfacemixed
layer depth [m]. I0 was estimated from measurements of
photosynthetically active radiation (PAR) at Helgoland
(http://coast.hzg.de/data/helgo_rad.html), k from the
MERISderivedKd,andmixed layerdepthszfrom3DGETM
results.
Net rates of chl a increase/decrease (NR, mgm3d1)
werecalculatedaccordingto:
NRchl;n¼chlnchl0
n ; (2)
wherechl0isthechlaconcentration[mgm3]attheonsetof
thebloomandchlnrepresentsthechlaconcentrationatdayn.
ToestimatethespatialdistributionoftheNR—Im
correla-tion,theentireGBregionwasdividedintosmallersubregions
ofarea 0.058 by0.058. Bygradually increasing thesizeof
each subregion, Im was modified until the range of Im in
adjacent subregions reached a critical value of
5Einstm2d1.Hence,allsubregionscontainedaspectrum
of light regimes. Furthermore, the correlation coefficient
betweenImandNRwascalculatedinsubregionsofincreased
sizetorepresentthecorrelationsineachsmallersubregion.
3.
Results
3.1. Resuspensionversus photosynthesisin winter—spring
MERISandthein-situHRtimeseriesdepictedsimilar
tem-poral changes in chl a concentrations during spring 2003
and 2004 (Figs. 3 and 4, upper panel). They showed a
pronounced phytoplankton bloom at the end of March in
2003andendofAprilin2004,asindicatedbythesignificant
increaseofchla.Thelocalagreementbetweenthesetwo
independent time series generates enough confidence in
thecredibilityofthesatellite-deriveddatatoreflectactual
patternsinchlaconcentrationinspace(onefailed
valida-tionofMERISdatawithHRtime-seriesin2005wasshownin
AppendixB).
Threeconsecutivepatternsofchla(22March,3April,and
13 April 2003 in Fig. 3a—c) illustrate the spatio-temporal
developmentofthespringbloom.Thebloomwasinitiated
near the coast (mean water depth <10m), from where
filaments with elevated chl a concentrations spread into
offshoreregions (Fig. 3aand b).The development of
fila-ments was morepronounced offshoreof the North Frisian
IslandsthanintheoffshoreregionoftheEastFrisianIslands
(Fig. 2). The latterregion, however,was partly hidden by
clouds.After10days,theinitialcoastalbloomprogressedto
the central parts of the GB (Fig. 3b and c). Eventually,
elevatedchlaconcentrations becamewidespreadoverthe
entirecentralGB(Fig.3c).Atthesametime,anextended
patchwith reduced chl a concentrations appeared in the
deeperpartoftheGermanBight(greencolourinFig.3c).In
2004,MERIS andthe in-situHR time-series both showed a
pronouncedspringbloomfromtheendofApriltotheendof
May(Fig.4,upperpanel).Threeconsecutivepatternsofchla
(21April,26Apriland16May,Fig.4a—c)wereselectedto
illustratethedevelopmentofthespringbloom.Thegeneral
featuresofthedevelopment ofthebloomweresimilar to
2003.
The timingof the springbloom is mainlycontrolled by
temporal differences in the amount of light penetrating
throughthe watercolumn. Turbiditymeasurementsreveal
sporadicchangesinspaceandtimeintheGB.These
varia-tionsareassociatedwithresuspensionofsedimentparticles.
Coastalareas are highly turbid duringthe winter months.
Likewise,on 22March2003,highturbiditywasinducedby
strong resuspension, as seen in MERIS-derived TSM data
(Fig. 5a). In a qualitative manner, the distribution of the
average irradiance (Im, Fig. 5b) was comparable to the
distribution of TSM,because Im is a function ofthe
light-attenuationcoefficientthatishighlycorrelatedwithTSM.NR
between22Marchand3April2003andqualitativelymatched
the distribution of Im (Fig. 5c). Substantial correlations
between these three variables indicate a high sensitivity
of phytoplankton growth to turbidity at the scale of the
wholeGB. Toavoidrepetitivedescriptions,themapsofIm
andNRin2004werenotshownhere.ImandNRin2004were
calculated using two scenes from the initial phase of the
springbloom(21and26April).
3.2. Thesensitivity mapofalgalgrowthto limiting light
FromFigs.3and5,aqualitativepictureofthespatial
depen-dencybetweenalgalgrowthandlightpenetrationcouldbe
obtained.Toconsolidatetheinterpretation,thiscorrelation
wasquantifiedbycalculatingthespatialdistributionofthe
NR-irradiancerelationshipin2003and2004(Fig.6aandb).
Figure3 Upperpanel:overlayofMERISchlatimeseriesatHelgoland(blackline)andHRin-situmeasuredchlatimeseries(light purpleline,cf.Tianetal.,2011)in2003.Thegreylinesrepresentthedatesofthethreescenesshowninthelowerpanels,which displaythedevelopmentphasesofaspringbloom.Lowerpanel:selectedscenesofMERIS-derivedchlaintheGBon(a)22March,(b) 3April,and(c)13April2003.ThebathymetryoftheGBisgivenbyisobathlines.(Forinterpretationofthereferencestocolourinthis figurelegend,thereaderisreferredtothewebversionofthearticle.)
Figure4 Upperpanel:overlayofMERISchlatime-seriesatHelgoland(blackline)andHRin-situmeasuredchlatimeseries(light purpleline)in2004.Thegreylinesdenotethedatesofthethreescenesshowninthelowerpanels,whichdisplaythedevelopment phaseofaspringbloomin2004.Lowerpanel:selectedscenesofMERIS-derivedchlaintheGBon(a)21April,(b)26April,and(c) 16May2004.ThebathymetryoftheGBisgivenbyisobathlines.(Forinterpretationofthereferencestocolourinthisfigurelegend,the readerisreferredtothewebversionofthearticle.)
Figure5 (a)MERIS-derivedtotalsuspendedmatter(TSM[gm3])asaproxyforturbidityduringtheonsetofthespringbloom,(b) mean water column irradiance within the mixed layer (Im [Einstm2d1]) and (c) net rates of chl a increase/decrease (NR [mgchlam3d1]).ImisafunctionofKd(day 81),meanPAR(days 81—93)andmeanMLD(days81—93).NRiscalculatedduring thephytoplanktongrowthphase(days81—93;seeFig.3aandb).
Figure6 Upperpanel:2DmapofthecoefficientofdeterminationR2betweenNRandIm(Fig.5bandc)in2003(a)and2004(b).Pixel resolutionis0.058.Theblackboxesin(a)indicatetwoareaswithhighcorrelationandlowcorrelationrespectively.Forthesetwo boxes,scatterplots of NRand Imare shownin bottompanels (cand d).In thehigh-correlationarea(R=0.91, p<0.001),the illustrationshedslightonthethresholdofImtopositiveNRis4Einstm2d1(dashedlines).TheslopeoftheNR-Imrelationshipis denotedbya[mgchlaEinst1m1].
The correlationdensitydistributionmapshowedthatIm
andNRwerehighlycorrelated(R2>0.5)inmostregionsin
thecentralGBandwithin thecoastalzone (depth<10m,
Fig. 6a). Regionswith low correlation between Im and NR
formed a transition zone between coastal areas and the
centralGB,withdepthsbetween10mand15m.Overlarge
areas,thistransitionzonecoincidedwiththeextendedpatch
ofdecreasedchlaconcentrationsalongthecoastalmargin.
The correlation map in 2004 illustrated a similar spatial
pattern (Fig. 6b). To show examples of the relationship
betweenImandNRinhigh-andlow-correlationareas,scatter
plotsofImandNRareshownin0.58by0.58geographicboxes
(Fig.6candd).BecauseNRishighlydependentonIminareas
ofhighcorrelation(Fig.6c),apositiveNRwasachievedwhen
Im exceeded 4Einstm2d1. Scatters in low-correlation
regions (Fig. 6d) suggested that factors other than light
availabilityaffectedphytoplanktongrowth.
Toquantifyinmoredepththerelationshipbetweenalgal
growthandlightinregionsofhighcorrelation,theslopeof
subregional NR—Im regressions was estimated (a), which
describes the sensitivity of algal growth to light (Fig. 7).
TheareaslocatedontheoffshoresideoftheFrisianIslands
(blackdots)andtheElbeEstuary(greydots)showedalow
slope(a<12mgchlaEinst1m1)andalsoalimited
varia-bilityinawithrespecttotherelativelyhighrangeoflight
availability(Fig.7).Inbothregions,algalgrowthappearedto
be insensitive to light because of highly turbid coastal
waters.Moreover,apositivecorrelationexistedbetweena
andImontheoffshoresideoftheFrisianIslands(blackdots,
R=0.29,p<0.001),indicatingthat algalgrowthcould be
sensitiveto light iflight conditionsimproved. Incontrast,
therangeofawaslargeinthedeeperGB(Fig.7,bluedots).
Thelargevariabilityinamaybeduetostrongvariationsin
hydrographic conditions along with other possiblefactors,
whichwillbediscussedinSection4.1.
3.3. Mismatchbetweenstrongresuspension eventsandthe peaksofchlainsummerand autumn
BetweenAprilandOctober2003and2004,theMERISchla
timeseriesatHelgolandrevealedaseriesofrecurrentbloom
eventsafterthespringblooms(Figs.8dand4).Abloomevent
(chl a>6mgm3) with cloud-free scenes on 13, 16, and
22September2003wasselected tostudythechlaspatial
pattern(Fig. 8a—c). Thisautumn bloomrevealed spatially
similar gradientsfrom nearshore tooffshore in large-scale
pigmentdistribution,aswasalsoobservedduringthespring
bloom.Unfortunately,threecloud-freescenescouldnotbe
found in 2004 to show the development of the autumn
blooms. Therefore, this analysis focusses on summer—
autumnbloomdynamicsin2003.
Insummerandautumn2003,significantwindstormswere
absent inthe GB, and repeated resuspensionevents were
identifiedasinducedbytides.Low-passfiltered,daily
bot-tomcurrentvelocity(Vb)calculatedbyGETMexceededthe
criticalVbforresuspensionof0.006ms1inalmostevery
bi-weeklyspring phase (Fig. 9, Ziervogel andBohling,2003).
Themagnitudeofthechlapeakstendedtoincreaseinlate
autumn.Furthermore,allchlapeaksshowedatiming
mis-matchwiththeresuspensionevents,indicatingthatchlawas
notdirectlyresuspendedduringthespringtide(e.g.,from
benthicdiatoms). 2 4 6 8 10 12 14 16 18 20 0 5 10 15 20 25 30
Mean water column irradiance [Einst m−2 d−1]
Slope of NR− Im , α [10 −3 mg chl a Einst −1 m −1 ]
Figure7 Relationshipbetweenmeanwatercolumnirradiance(Im)andtheslopeoftheNR-Imrelationship(a).Theanalysisoftidal flatareasintheWaddenSeawasexcludedbecauseerrorsignalsweregeneratedwhenthetidalflatsrandry.Thedotsrepresent MERIS-basedobservationsofhighIm(black)andhighavalues(lightpurple),whereastheremainingpart(wherebothaandImvaluesarelow)is plottedingrey.TheblackdottedareasexhibitarelativelylowchangerangeofawhenImincreasesandarelocatedontheoffshoreside oftheFrisianIslandsandtheElbeestuary(inthesub-figure).ThebluepixelsarelocatedinoffshorewatersintheGB,whichrepresent theareawithalargechangerangeinaandasmalloneinIm.(Forinterpretationofthereferencestocolourinthisfigurelegend,the readerisreferredtothewebversionofthearticle.)
Inconsiderationofallthesefeatures,itwashypothesized
thatspring-tideinducedresuspensionimportednutrient-rich
pore waterfrom sediments or bottom waterlayers during
spring tides during the nutrient limitation period, which
gradually accumulated nutrients within the water column,
eventuallyleadingtophytoplanktonblooms.Becausenutrient
measurementswerespatiallyandtemporallysparse,HRdata
wereusedtoprovidefurtherverificationofthishypothesis.
Figure8 SelectedscenesofMERIS-derivedchlaintheGBon(a)13September,(b)16September,and(c)22September2003, embracingatypicalsummer-autumnbloomevent.(d)Thetemporalchangeofchlainthelowerpaneliscomparedtotidalelevation andbottomcurrentvelocityintheupperpanel.ThetidalelevationisderivedfromLangeoogpilestationtimeseries,andthebottom currentvelocityisobtainedfromthemodelresultsatHelgoland.
150 160 170 180 190 200 210 220 230 240 250 260 270 280 290 300 0 2 4 6 8 10 Chl a [mg m −3 ] Time [day] 0 2 4 6 8 10 12 Vb [10 −3 m s −1 ]
Figure9 MERIS-derivedchla(blacksolidline)and bottomcurrentvelocity(green line,low-passfiltered) atHelgolandduring summerandautumnin2003.Resuspensioneventswiththedailybottomvelocityexceeding0.006ms1(overgreendashedline)are indicatedbygreenarrows.Anautumnbloomisdefinedasaneventwhenthechlaconcentrationexceeds6mgm3.Basedonthis bloomdefinition,wherethechlaconcentrationshouldexceed6mgm3,fourautumnbloomeventsweredetectedduringlateautumn (blackarrows).(Forinterpretationofthereferencestocolourinthisfigurelegend,thereaderisreferredtothewebversionofthe article.)
3.4. Timelagbetweenresuspensionandpeaksof nutrientsand chla
Ifautumnbloomsweretriggeredbyaresuspendednutrient
supply,thereshouldbeadelaybetweenresuspensionandthe
chlapeakduetothetimeneededtorefillnutrientreservesin
depletedphytoplanktoncellsandtoachieveamassive
build-upofbiomass.Becausetherefillingprocessesofphosphate
andnitratearedifferent(aswillbediscussedinSection4.2),
alagcorrelationanalysiswasusedforboth phosphateand
nitrate. Analysis of in-situmeasured phosphate at HR and
resuspension intensity (Vb>0.006ms1) revealed a
maxi-mumcorrelationatnotimelagin2003(R=0.51,Fig.10a)
andata1-daylagin2004(R=0.46,Fig.10b),indicatingthat
phosphatereplenishmentwasdirectlyaccompaniedby
resus-pension events. The lag correlation analysis for nitrate
−5 0 5 0.3 0.35 0.4 0.45 0.5 0.55 0.6
Lag time [days]
R [correaltion coefficient]
delayed PO4 vs. bottom velocity
delayed PO3 vs. bottom velocity delayed PO3 vs. bottom velocity
delayed PO4 vs. bottom velocity
(a) 2003 −5 0 5 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5
Lag time [days]
R [correaltion coefficient] (b) 2004 −5 0 5 0.1 0.15 0.2 0.25 0.3 0.35 0.4
Lag time [days]
R [correaltion coefficient] (c) −5 0 5 0 0.1 0.2 0.3 0.4 0.5
Lag time [days]
R [correaltion coefficient] (d) 0 5 10 15 20 25 30 −0.4 −0.2 0 0.2 0.4 0.6 0.8
Lag time [days]
R [correlation coefficient]
delayed Chl a vs. bottom velocity
(e) 0 5 10 15 20 −0.4 −0.2 0 0.2 0.4 0.6 0.8
Lag time [days]
R [correlation coefficient]
delayed Chl a vs. bottom velocity
(f)
Figure10 LagcorrelationsbetweenHRin-situmeasurementsandbottomvelocitywhicharegreaterthanthecriticalthresholdof 0.006ms1(resuspensionevents)duringsummerandautumn(June—October)2003(leftpanel)and2004(rightpanel).(aandb) Correlationsbetweenlaggedphosphateconcentration[mmolm3]andsimulatedbottomvelocity[ms1].Thegreylinedenotesthe maximumofthecorrelationfunctionatzerotimelagin2003anda1-daylagin2004,indicatingnutrientreplenishmentaccompanied by tidal-induced resuspension (R=0.51 in 2003, R=0.46 in 2004, p<0.001). (c and d) Correlation between lagged nitrate concentration[mmolm3]andsimulatedbottomvelocity[ms1].Thegreylinedenotesthemaximumofthecorrelationfunction atalagof3daysin2003and1dayin2004.(eandf)Correlationbetweenlaggedin-situmeasuredchla[mgm3]andbottomvelocity. Thegreylinedenotesthemaximumofthecorrelationfunctionatalagof24days(R=0.64,p<0.001)in2003and17days(R=0.8, p<0.001)in2004,indicatingthatresuspensioneventsprecededthechlapeakbythisamountoftime.
showed a maximum correlation at a 3-day time lag in
2003 and at a 1-day lag in 2004, with relatively lower
correlations(R<0.4,Fig.10candd).
The same lag correlation analysis (with the lag time
extendedto 30days)betweenchlapeakandVbshowed
that resuspension preceded blooms by 24 days in 2003
(R=0.64, Fig. 10e) and by 17 days in 2004 (R=0.8,
Fig.10f).Thistimelagcouldbethetimeneededfor
phyto-planktontobuildupbiomassoraphysiologicalresponseto
nutrients.Nevertheless,theexistenceofatimelagalready
partiallyexplainsthemismatchbetweenresuspensionevents
andchlapeaks,asapparentintheMERISdatainFig.9,and
thereforethenumberofdaysofdelayshouldbeinterpreted
withcaution.
4.
Discussion
Thecombinationofin-situmeasurements,satellitedata,and
modelresultsprovidesaframeworkforiterativeintegration
andassessmentofspatialandtemporalvariabilityof
phyto-planktonproductionintheGB.Previousstudieshavealready
addressedbloomdynamicsintheGB,butmostofthesehave
been limited by coarse spatial and temporal resolution
(Colijn et al., 1990; Joint and Pomroy, 1993; Stoeck and Kroncke, 2001; Sündermann et al., 1999). Although the
implementable MERIS data for the GB were restricted to
dayswithlittletonocloudcover,thesedataweresufficient
toresolveextensivefilamentformationandtorevealdetails
on patches that emerged in the course of phytoplankton
bloom.Specificationofthesedetailssupportedmodel
devel-opment, pinpointing spatial variations that need to be
resolved by studies aimed at quantifying coastal primary
production on a larger scale. The results obtained here
supporttheestablishmentofcausallinksbetweenturbulent
mixing,resuspension,andphytoplanktongrowthonshorter
timescales.
4.1. Resuspensionasanegativefactor: light attenuationand photosynthesis
ThehydrodynamicsoftheGBareextensivelyforcedbywind
(Becker etal., 1999) andstrong tidal currents (>1ms1,
Stanevaetal.,2009).Thisforcingcangeneratehigh
kinetic-energydissipation,inducingsmall-tolarge-scaleerosionof
bottomsediments(Gayeretal.,2006).Localdifferencesin
sedimentcomposition,with varyingsiltandsandcontents,
contributetothevariabilityapparentinstrongtemporaland
lateralgradientsinsuspended-mattercontent(Beckeretal.,
1992,1999).ReducedSPMhasalreadybeenshowntoactas
a primary trigger for the phytoplankton spring bloom
throughouttheGB(Tianetal.,2009).Inamulti-yearspring
bloomstudyintheGB,Tianetal.(2011)foundthatthespring
bloomwas preceded by aperiod of high wind speeds and
developedonlyasthewindslackened.
Inthepresentstudy,majorpartsoftheGBinspringhave
revealedahighcorrelationbetweenturbidity-relatedmean
lightlevelsandnetratesofchlaincrease/decrease(Fig.6a).
Insuchregions,chlaincrementscanthereforeserveasan
unequivocalproxyforphytoplanktongrowth.Becausemean
lightlevelsdependmostlyonlocalresuspension,high
corre-lationsindicatetheimportantroleofvariationsinturbulent
shear stress in bloom onset. Variations in turbulent shear
stressdependonthetidalphase,butaredominatedbystorm
events,whicharemostfrequentinwinterandspring.Bottom
shearstressisparticularlyenhancedwhenstrongwindsact
againstthemeancurrentdirection(Stanevaetal.,2009).
Incontrasttoareaswithacleargrowth—lightrelationship
(inanalogytoinvitromeasurementsofphotosynthesisrates
atvaryingPAR),centralregionswithintheGBatintermediate
waterdepthsbetween20mand30mandlocatedofftheEast
FrisianIslandsdonotsustainalineargrowthresponsetolight
enhancement(Fig.6a).Whethertheseareasaresubjectto
extensivegrazingcannotbeassessedduetothescarcityof
measurementsavailable for 2003. Massivegrazing by
zoo-plankton would compromise estimates of algal growth
obtainedfrom temporalchanges inchlaconcentration.In
2004, however, no horizontal gradients in abundance of
mesozooplanktonconsumerswereobservedbeforeorduring
the bloomphase (Renz et al., 2008). Therefore, in 2004,
grazing pressure could not explain the observed pattern
separation(highvaluesinzonesoflowgrowth—light
correla-tion).Nevertheless,althoughthe2003evidenceis
inconclu-sive,grazingpressureisstillapossiblecandidatetoexplain
thepatternseparationobservedin2003.
Otherpossiblereasonsformoderatetolowgrowth—light
correlationcanbeassociatedwithmediocredataqualityand
withtemporallystronglossfactorslikesedimentation,wind
mixing(Koseffetal.,1993),orriverrunoff(Radachetal.,
1990).Theseerrors, however,should contributeto spatial
heterogeneity,producingmorescatteredpatternsin
corre-lationintensitythanwereobservedinthisstudy.
4.2. Resuspensionas apositivefactor:nutrient enrichmentandlagged growthresponse
Analysisofsatelliteimagesshowsthatthespring-neaptidal
mixing (fortnightly tidal cycle) controls near-surface SPM
concentration in the southern North Sea (Pietrzak et al.,
2011). Spring-neap modulation of tidal mixing can have
significant effects on thetiming and magnitude of
phyto-planktongrowth (Cloern, 1991;Sharples, 2008).In spring,
the spring-neap tidal cycle regulates stratification, which
mayaffectthetimingofspringblooms.However,itwasnot
possible to gather enough cloud-free scenes during this
periodtoprovethispoint.Consequently,thefocusshifted
to the summer—autumn period, when nutrient limitation
prevailsinthesystem(Fig.A.1).Ofcourse,lightcouldalso
bea limitingfactor duringcertain months in summer and
autumn(e.g.,September2003,Fig.A.1).However,itcould
be a candidate only to explain the missing correlation
betweenresuspension andnutrientreplenishment (hereit
isreferredtoasasecondaryfactor).
Wasthesummer—autumnchlaperiodicityduetodirectly
resuspendedsubsurfacechlorophyll?Balch(1981)foundthat
in summer, diatom blooms off Monhegan Island always
occurredatspringtides,possiblybecauseofincreased
nutri-entsor the upward movementof a subsurfacechlorophyll
maximumlayer.Instudiesoftime-series chladataoffthe
Connemaracoast,Ireland,Roden(1994)reportedchlorophyll
peaksoccurringatneaptidesinlatesummer.Hearguedthat
the driving mechanism was a localized accumulation of
found no increase in chl a immediately following the
spring tide and no clear match between phytoplankton
bloomsandneap tides(Fig.9).Instead,aclearmismatch
betweenthespringtideandthepeakofchlawasobserved,
indicatingthatresuspendedchlorophyllcannotbea
candi-datefor explaining thesummer—autumnchlaperiodicity
intheGB.
Sourniaetal.(1987)recordedsomevariabilityinnutrient
concentrationsassociatedwithspringtides,butvariationsin
chladidnotappeartobeassociatedwiththespring-neap
cycle in the western English Channel, which is strongly
influenced by tides. This finding supports the hypothesis
thatresuspensioneventsassociatedwithspringtidesrefuel
remineralizednutrientsfromsedimentsorthebottomwater
layer (Fig. 10a). These benthic nitrate fluxes depend on
denitrificationinthesediments(Halletal.,1996;Wainright,
1990),andthereforespringtidesrefuelnitratefrom
sedi-ments.Thiscouldexplainwhynitratereplenishmentshowed
arelativelowcorrelationwithresuspensionevents(Fig.10c
andd).ThephosphatedatafromtheICESdatasetintheGB
showedagraduallyincreasingtrendinsummerandautumn
(Fig. A.1). Resuspension events can stimulate phosphate
transportfromthebottomwaterlayertothewholewater
column.However,thetimelagbetweenresuspensionevents
and phytoplankton growth needs to be further discussed
(Fig. 10b). This delay is the result of net phytoplankton
growth, which is determined by the plankton community
structureorbythephysiologicalstatesofthealgae.Related
modelstudies,likethatof WirtzandPahlow(2010),have
shown that in addition to the time needed to build up
biomasswithinanexponentialgrowthphase,phytoplankton
populationsoftenneedtoacclimatetheirphysiology(and
theirinternalstoichiometry)tonovelnutrientconditions,
whichcantakeuptoseveraldays.Infact,adistinctresponse
pattern in phytoplankton growth wasobserved after the
spring tide,at times when thebottom velocity exceeded
criticalvaluesof0.006ms1.Theresponsesignal,however,
wasdelayedbyafewweeks.Chlaconcentrationsgradually
increased withthe onset of the neap tidal period,and a
short-term maximumwas reachedapproximately 10days
afterthespring-tideresuspensionevent(Fig.9).The
repe-titionofthistemporalpatterninautumnthereforereflects
themodulationoftwotimescales,thespringtidecycleand
thenetalgalgrowthrateofphytoplankton.Anomaliesseen
inthis temporal patternmust beattributedtosecondary
factorsthatcouldnotbefurtherspecifiedhere.
4.3. Limitations ofremote-sensingdata
Tocharacterizeintermediate-scalechlavariability,itwasa
methodologicalprerequisite to rely on data accuracy and
data with sufficient temporal resolution. In a previous
remote-sensingstudyoftheNorthSea,HendersonandSteele
(1993)encounteredproblemsininterpretingsub-mesoscale
(1—10km) plankton dynamics using satellite chl a data
stemming from the Coastal Zone Colour Scanner (CZCS,
1978—1986). With more modern instruments like MERIS,
thesensitivityandthesignal-to-noiseratioaresignificantly
increased. Recent reports (Doerffer et al., 2010) showed
systematic differences of less than 10% between in-situ
and satellite-measured water-leaving reflectance. The
frequency of scenes,2 out of3 days, is limitedand must
befurther reducedto ausablevalue ofonceevery3days
(Müller et al., 2015). This remains a critical factor when
resolving temporal changes that coincide with time-series
data(e.g.,Figs.6and9).Inaddition,thesystematicerrorsof
satellite-deriveddatashowperiodicfeatures,whicharenot
yetfullyunderstood,butmostlikelyresultfromilluminating
and observing geometries and are frequencies related to
revisitsundersimilargeometricalconditions(Müller,2010).
This variability should also beinterpreted carefully for
other reasons. Satellite-derived chl a estimates in highly
turbidwaterarestillsubjecttosignificantuncertainties.A
mismatchwasalsofoundbetweenMERISdataandin-situHR
time seriesin 2005(Fig. B.1).Moreover, inapplications of
SeaWiFS, chl a, concentration was considerably
overesti-matedfortheBayofBiscaywhenSPM opticallydominated
thebackscatter(Gohinetal.,2005).Althoughaccuracyhas
beenmuchimprovedbyincreasingMERISspectralresolution
andusingmoreelaborate(neuralnetwork)algorithms,the
presenceofaremainingbiasinthesimultaneous
determina-tionofTSM,CDOM,andchlacannotbefullyexcluded.In
fact, TSM,CDOM, and chla can be simultaneously
deter-minedwithoptimalaccuracyonlyaslongastheyareequally
prominent.Ifoneofthecomponentsbecomesthedominant
erroron theretrieval fortheothercomponents,thiserror
increases significantly and can reach several hundred per
cent in extreme cases such as estuarine turbidity zones.
Because thepresentanalysis wasbased mainlyonasingle
measurement source, the true independence of the data
comparedcannotbeassumed.
5.
Conclusions
Inspring2003and2004,spatialpatterns ofphytoplankton
productionwerewelldescribedusingsatellite-derived
light-attenuationdatawithin mostcoastalareasandthedeeper
openwatersoftheGB.Thisanalysiswassupportedbygood
correlationsbetweenphytoplanktonnetalgalgrowthrates
andlightavailability.Weakcorrelationsprevailedonlywithin
adistinctivezone(thecoastalmargin),whichrepresentsthe
transition between shallow well-mixed coastal waters and
the off-shore area. Phytoplankton within this transitional
zone was found to be particularly sensitive to SPM
resus-pendedfromtheseabedorfromnear-bottomwaterlayers.
Becauseofopposingresponsesinalgalgrowthto
resuspen-sion, phytoplankton growth becomes difficult to estimate
during post-bloom periodsin summer and autumn. A
mis-match between distinct phytoplankton blooms and tidally
induced resuspension events indicates that phytoplankton
resuspensionplaysalimitedroleduringspringtides.
Resus-pension-imported nutrient-rich water from sediments or
bottom water layers, however, did stimulate time-lagged
phytoplanktongrowthresponses.
In summary, typical spring and summer—autumn bloom
patternscan beinferred on regionalscales. Phytoplankton
growthco-varieswithresuspensioninbothpositiveand
nega-tivedirections:positivelyinsummer—autumn,andnegatively
inwinter—spring,assummarizedinFig.1.Nowthatregions
where phytoplankton responds differently to variations in
physical forcing have been identified, it will be possible
ecologicalmodelstogeneratebetterexplanations(or
predic-tions)ofevent-scalephytoplanktonblooms.
Acknowledgements
WewishtothankourcolleagueR.Riethmüllerforproviding
usthemeasurementsfromtheLangeoogpilestationandK.
Wiltshire for providing HR time series in 2003 and 2004
(Wiltshireetal.,2008).Wealsowish tothankK.O'Driscoll
forrevisingthepaperandJ.Cloernforprovidinghismodel
code(Cloern,1999).
Appendix
A.
Limiting
factors
Thelimitingfactorsonphytoplanktongrowthwere
recon-structedaccordingtoCloern(1999).Hedevelopedasimple
index to determine the relative importance of light and
nutrient limitation for phytoplankton growth. Colijn and
Cadée (2003) used this index to study the eutrophication
problemintheWaddenSeaandfoundthatthelight
limita-tionfarexceeded theeffectsofnutrient limitation.Loebl
etal.(2008)followedthisapproachandshowedincreasing
nitrogenlimitation duringsummer inthenorthernWadden
Sea.Theseapplicationsofthisapproachrevealedthatthis
index issuitableforturbid coastalwatersliketheGerman
Bight.
Toinvestigateseasonalchangesinthelimitingfactorson
phytoplanktongrowth,thisindexwasappliedbasedonthe
HelgolandRoadstimeseriesof2003(Wiltshireetal.,2008).
Tocalculateirradiance(I0),photosynthesisavailable
irradi-ance(PAR)andSecchidepthdatawereused.Thecalculation
ofnutrient-limiting resources(N0)was based onphosphate
data, and KPO4 was set to 0.5 in accordance with Moll
(1998).Theindiceswereillustratedwithmonthlymeandata.
ThedetailedequationscanbefoundinCloern(1999).UsingI0
andN0,acontourplotoftheratioofgrowthratesensitivityto
lightandnutrients(R,Fig.A.1)wasgenerated.Thecriteria
usedin Cloern(1999)wereused to interpret theresource
limitationmap,whereR>10wasdefinedasastronglight
limitation, R<0.1 as a strong nutrient limitation, and
0.1<R<10asajointlimitationoflightandnutrients.
InFig.A.1,thelightlimitationwasdominantinJanuary
andFebruary(R>10)untilthespringbloomstartedinMarch
(R1)in2003,becausehighSPMconcentrationconstrained
phytoplanktongrowthbeforethespringbloom(Tianetal.,
2009).Lightwasthefactortriggeringthespringbloom.From
May to August, the GB was under a nutrient limitation
(R<0.1).Therefore,remineralizationcombinedwith tidal
mixing could be important for triggering the
summer—au-tumnbloomsintermsofplacingRbetween0.1and10.The
resourcelimitationmapsuppliedthebackgroundinformation
for thehypothesis of this paper andfinally supportedthe
authorsconcept.
To describe the re-mineralization processes in summer
and autumn, nutrient data (especially for phosphate) are
needed.However,nutrientmeasurementswerespatiallyand
temporallysparse. Phosphatedata wereobtainedfrom an
ICES nutrient dataset (http://ecosystemdata.ices.dk/).
Phosphate data were also measured using surface bottle
samples taken during cruises close to the Elbe Estuary
(Fig.A.1map).Thesenutrientdatawereusedtoconstruct
theresourcelimitationmap(Fig.A.1diamonds).Theindices
showed thatthe nutrientsstarted to refuelby the endof
June.Bymid-August,nutrientshadalreadyceasedto bea
limiting factor, while the wind was still not very strong
(unpublishedwinddatafromHelgolandRoads).Agradually
increasingtrendinthesummerandautumnperiodsuggested
thattidalmixingcouldbeacandidateotherthanwindmixing
forrefuellingnutrients.
1 2 3 4 5 6 1 2 3 4 5 6 K =0.5 Scaled nutrient N , Scaled light I, Jan Feb Mar Apr May Jun Jul Aug Sep Oct 09−Mar 16−Mar 04−May 25−May 01−Jun 09−Jun 22−Jun 30−Jun 07−Jul 23−Jul 04−Aug 10−Aug 17−Aug 31−Aug R=0.1 R=1 R=10
FigureA.1 ResourcelimitationmapfollowingtheapproachdescribedinCloern(1999)(foradetailedcalculation,seeAppendixA). HelgolandRoadsPhotosynthesisAvailableRadiance(PAR)and nutrientdatawereusedtocalculatethemonthlyindex(KPO4¼0:5 accordingtoMoll(1998)),whichisrepresentedbytheblackdots.ThediamondsrepresenttheindicescalculatedfromtheICESnutrient dataset.Themapintheupperleftpanelshowsthelocations(blackdots)ofthein-situmeasurements.I0isthelightlimitationfactor, andN0isthenutrientlimitationfactor.TheareaofR>10indicatesstronglightlimitation,theareaofR<0.1indicatesstrongnutrient limitation,andtheareaof0.1<R<10indicatesajointlimitationoflightandnutrients.
Appendix
B.
Comparison
of
MERIS
data
with
HR
time
series
data
in
2005
In2005MERISandthein-situHRtime-seriesdidnotmatch
verywell,anditwasnotpossibletofindclearscenestoshow
the development of the spring bloom (Fig. B.1). Because
atmospheric conditions vary from year to year, obtaining
reliablelong-termsatellitedatais stillchallenging(Müller
et al., 2015). Therefore, validation of MERIS data in-situ
measurementsis necessarywhenusing multi-yearsatellite
data.
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