Hartmann, AJ, Kobler, J., Kralik, M., Dirnböck, T., Humer, F., & Weiler, M.

17 Pages·2017·5.73 MB·German

Hartmann, A. J., Kobler, J., Kralik, M., Dirnböck, T., Humer, F., & Weiler,

M. (2016). Model-aided quantification of dissolved carbon and nitrogen
release after windthrow disturbance in an Austrian karst system.
Biogeosciences, 13(1), 159-174. DOI: 10.5194/bg-13-159-2016
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Biogeosciences,13,159–174,2016
www.biogeosciences.net/13/159/2016/
doi:10.5194/bg-13-159-2016
©Author(s)2016.CCAttribution3.0License.
Model-aided quantification of dissolved carbon and nitrogen release
after windthrow disturbance in an Austrian karst system
A.Hartmann1,2,J.Kobler3,M.Kralik3,T.Dirnböck3,F.Humer3,andM.Weiler1
1FacultyofEnvironmentandNaturalResources,FreiburgUniversity,FreiburgimBreisgau,Germany
2DepartmentofCivilEngineering,UniversityofBristol,Bristol,UK
3EnvironmentAgencyAustria,Vienna,Austria
Correspondenceto:A.Hartmann([email protected])
Received:8June2015–PublishedinBiogeosciencesDiscuss.:31July2015
Revised:4December2015–Accepted:13December2015–Published:15January2016
Abstract. Karst systems are important for drinking water 1 Introduction
supply. Future climate projections indicate increasing tem-
perature and a higher frequency of strong weather events.
Both will influence the availability and quality of water
provided from karst regions. Forest disturbances such as Karst systems contribute around 50% to Austria’s drinking
windthrow can disrupt ecosystem cycles and cause pro- water supply (COST, 1995). Karst develops due to the dis-
nounced nutrient losses from the ecosystems. In this study, solvabilityofcarbonaterock(FordandWilliams,2007)and
we consider the time period before and after the wind dis- itresultsinstrongheterogeneityofsubsurfaceflowandstor-
turbance period (2007/08) to identify impacts on DIN (dis- age characteristics (Bakalowicz, 2005). The resulting com-
solvedinorganicnitrogen)andDOC(dissolvedorganiccar- plex hydrological behaviour requires adapted field investi-
bon) with a process-based flow and solute transport simu- gation techniques (Goldscheider and Drew, 2007). Future
lation model. When calibrated and validated before the dis- climate trajectories indicate increasing temperature (Chris-
turbance,themodeldisregardstheforestdisturbanceandits tensen et al., 2007) and a higher frequency of hydrological
consequencesonDINandDOCproductionandleaching.It extremes(Dai,2012;Hirabayashietal.,2013).Bothwillin-
canthereforebeusedasabaselinefortheundisturbedsystem fluence the availability and quality of water provided from
andasatoolforthequantificationofadditionalnutrientpro- karstregionsbecausetemperaturetriggersnumerousbiogeo-
duction. Our results indicate that the forest disturbance by chemicalprocessesandfastthroughflowwaterhasadispro-
windthrow results in a significant increase of DIN produc- portional effect upon water quality. Furthermore, forest dis-
tion lasting ∼3.7 years and exceeding the pre-disturbance turbances(windthrows,insectinfestations,droughts)posea
average by 2.7kgha−1a−1 corresponding to an increase of threat to water quality through the mobilization of potential
53%.TherewerenosignificantchangesinDOCconcentra- pollutants,andthesedisturbancesarelikelytoincreaseinthe
tions.Withsimulatedtransittimedistributionsweshowthat future(Johnsonetal.,2010;Seidletal.,2014).
the impact on DIN travels through the hydrological system One way to quantify the impact of changes in climatic
within some months. However, a small fraction of the sys- boundaryconditionsonthehydrologicalcycleisthroughuse
temoutflow(<5%)exceedsmeantransittimesof>1year. ofsimulationmodels.Specialmodelstructureshavetobeap-
plied for karst regions to account for their particular hydro-
logicalbehaviour(Hartmannetal.,2014a).Arangeofmod-
els of varying complexity are available from the literature
that deal with the karstic heterogeneity, such as groundwa-
terflowintherockfracturematrixanddissolutionconduits
(Jourdeetal.,2015;Kordillaetal.,2012),varyingrecharge
areas(Hartmannetal.,2013a;LeMoineetal.,2008)orpref-
PublishedbyCopernicusPublicationsonbehalfoftheEuropeanGeosciencesUnion.
160 A.Hartmannetal.:Model-aidedquantificationofdissolvedcarbonandnitrogenrelease
erentialrechargebycracksinthesoilorfracturedrockout- weusedvirtualtracerexperimentstocreatetransittimedis-
crops(RimmerandSalingar,2006;Tritzetal.,2011). tributionsthatexpressedhowtheimpactofthestormsprop-
Nitrate and dissolved organic carbon (DOC) have both agated through the variable dynamic flow paths of the karst
beenconsideredindrinkingwaterdirectivesandwaterprepa- system. This allowed us to assess the vulnerability of the
rationprocesses(Goughetal.,2014;Mikkelsonetal.,2013; karstcatchmenttosuchimpacts.
Tissier et al., 2013; Weishaar et al., 2003). Though nitrate
pollution of drinking water is usually attributed to fertil-
ization of crops and grassland, an excess input of atmo- 2 Studysite
spheric nitrogen (N) from industry, traffic and agriculture
into forests has caused reasonable nitrate losses from for- Thestudysite,LTERZöbelboden,islocatedinthenorthern
est areas (Butterbach-Bahl et al., 2011; Erisman and Vries, part of the national park “Kalkalpen” (Fig. 1). Its altitude
2000;Gundersenetal.,2006;Kieseetal.,2011).TheNorth- ranges from 550 to 956ma.s.l. and its area is ∼5.7km2.
ern Limestone Alps area is exposed to particularly high ni- Mean monthly temperature varies from −1◦C in January
trogen deposition (Rogora et al., 2006) and nitrate leaching to 15.5◦C in August. The average temperature is 7.2◦C
occursatincreasedrates(Jostetal.,2010).Inadditiontothis, (at 900ma.s.l.). Annual precipitation ranges from 1500 to
forest disturbances such as windthrow and insect outbreaks 1800mm and snow accumulates commonly between Octo-
disrupttheNcycleandcausepronouncednitratelossesfrom berandMaywithanaveragedurationofabout4months.The
the soils, at least in N-saturated systems that received ele- mean N deposition in bulk precipitation between 1993 and
vated N deposition due to elevated NOx in the atmosphere 2006was18.7kgNha−1yr−1,outofwhich15.3kgN(82%)
−
(Bernal et al., 2012; Griffin et al., 2011; Huber, 2005). In was inorganic (approximately half as NO -N and half as
3
contrast to N deposition, atmospheric deposition of DOC is NH+-N;Jostetal.,2010).Duetothedominanceofdolomite,
4
low (Lindroos et al., 2008) and thus has not been identified the catchment is not as heavily karstified as limestone karst
as major driver of DOC leaching from subsoil (Fröberg et systems, but shows typical karst features such as conduits
al.,2007;KaiserandKalbitz,2012;Verstraetenetal.,2014). and sink holes (Jost et al., 2010). The site can be split into
Moreover, studies show contrasting results but point to in- steep slopes (30–70◦, 550–850ma.s.l.) and a plateau (850–
creasedDOC(TOC)leachingfromsoilandcatchmentsafter 950ma.s.l.), with the plateau covering ∼0.6km2. Chromic
forestdisturbances(Huberetal.,2004;Löfgrenetal.,2014; Cambisols and Hydromorphic Stagnosols with an average
Meyeretal.,1983;Mikkelsonetal.,2013;Wuetal.,2014). thickness of 50cm and Lithic and Rendzic Leptosols with
WhilemanystudiesidentifyNandDOCassourceofcon- an average thickness of 12cm can be found at the plateau
taminationinkarstsystems(Einsiedletal.,2005;Jostetal., and the slopes, respectively (WRB, 2006). Both the plateau
2010;Katzetal.,2001,2004;Tissieretal.,2013)orprovide andtheslopesaremainlycoveredbyforest.Norwayspruce
static vulnerability maps (Andreo et al., 2008; Doerfliger et (Piceaabies(L.)Karst.)interspersedwithbeech(Fagussyl-
al., 1999), only very few studies use models to quantify the vaticaL.)wasplantedafteraclearcutaroundtheyear1910.
temporal behaviour of a contamination through the systems The vegetation at the slopes is dominated by semi-natural
(ButscherandHuggenberger,2008).SomestudiesuseNand mixed mountain forest with beech (Fagus sylvatica) as the
DOC to better understand karst processes (Charlier et al., dominant species, Norway spruce (P. abies), maple (Acer
2012; Mahler and Garner, 2009; Pinault et al., 2001) or for pseudoplatanus),andash(Fraxinusexcelsior).Attheslopes
advanced karst model calibration (Hartmann et al., 2013b, no forest management has been conducted since the estab-
2014b), but to our knowledge there are no applications of lishmentofthenationalpark.
suchapproachestoquantifythedrainageprocessesofNand
DOC,andparticularlysoafterstrongimpactsonecosystems 2.1 Availabledata
(e.g. windthrow) that release reasonable amount of nitrate
fromtheforestsoils. A10-yearrecordofinputandoutputobservationswasavail-
In this study, we consider the time period before and af- able. Starting from the hydrological year 2002/03, it en-
ter storm Kyrill (early 2007) and several other storm events velops well the stormy period that began in January 2007.
(2008)thathitcentralEurope.Thestorms,fromnowonre- Itincludeddailyrainfallmeasurementsandstreamdischarge
ferredtoasthewinddisturbanceperiod,causedstrongdam- measurements from stream sections 1 and 2 (Fig. 1). We
agetotheforestsinourstudyarea,adolomitekarstsystem. obtained the discharge of the entire system with a simple
Weapplyanewtypeofsemi-distributedmodelthatconsiders topography-based up-scaling procedure that is described in
the spatial heterogeneity of the karst system by distribution more detail in Hartmann et al. (2012a). Irregular (weekly
functions. We aimed at comparing the hydrological and hy- to monthly) observations of DOC, DIN and SO2− concen-
4
drochemicalbehaviour(DOC,DIN)ofthesystembeforeand trations are available for precipitation and at weir 1. DOC,
duringthewinddisturbingperiod.Inparticular,wewantedto NO−, SO2− and NH+ (since January 2010) samples were
3 4 4
+
understandifandhowDOCandDINinputtothehydrologi- filtered (0.4µm) before the analysis. NH concentrations
4
calsystemchangedbytheimpactofthestorms.Furthermore, were measured by spectrophotometry (Milton Roy Spec-
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A.Hartmannetal.:Model-aidedquantificationofdissolvedcarbonandnitrogenrelease 161
Figure 1. Study site and location of measurement devices (Hart-
mannetal.,2012a;modified).
tronic).WeeklyDOC,SO2− andNO− sampleswerepooled
4 3
to provide volume weighted biweekly (until March 2009) Figure2.Intra-annualandinterannualvariationsin(a)DOCcon-
centrations,(c)DINconcentrationsand(e)discharge,andrelation
andmonthly(thereafter)samples.DOCsampleswereacidi-
betweendischargeand(b)DOCand(d)DINbeforeandduringthe
fied with 0.5mL HCl 25% and were measured with a Mai-
winddisturbanceperiod.
hakTOCOR100andaCPNTOC/DOCanalyser(Shimadzu
Corp., Japan). NO− and SO2− concentrations were deter-
3 4
mined by ion chromatography with conductivity detection.
−
DIN input was then calculated as the sum of NO3-N and 6mgL−1 during high discharge (similar to Hagedorn et al.,
+ + −
NH -N. Since NH is either transformed into NO or ab- 2000). However, there was no obvious difference between
4 4 3
+
sorbed in the soil, NH concentrations in runoff are very thepre-disturbanceperiod(Fig.2b).
4
small or not detectable. Therefore we calculated DIN out-
−
putsasNO -N.Additionally,irregularobservationsofsnow
3
waterequivalentattheplateauallowedforindependentsetup
ofthesnowroutines.
3 Methods
2.2 Recentdisturbances
3.1 Themodel
Kyrillintheyear2007andsomesimilarlystrongstormssub-
sequent to 2008 caused some major windthrows as well as
singletreedamages.Awindthrowdisturbanceof∼5haoc- 3.1.1 Modelhydrodynamics
curred upstream of weir 1. Though no direct measurements
existastothetotalextentofthewindthrowareaweestimate The semi-distributed simulation model considers the vari-
that5–10%ofthestudysitehasbeensubjecttowindthrow ability in karst system properties by statistical distribution
(Kobleretal.,2015).Wedidnotobserveasignificantchange functionsspreadoverZ=15modelcompartments(Fig.3).
in intra- and interannual variability in DOC concentrations Thatwayitsimulatesarangeofvariablydynamicpathways
anddischargebeforeandduringthewinddisturbanceperiod throughthekarstsystem.Thedetailedequationsofthemodel
(Fig. 2a,e). Runoffconcentrations ofDIN showedclear re- hydrodynamicsaresimilartoitspreviousapplications(Hart-
sponsestothedisturbances.Withthefirstwindthroweventit mannetal.,2013a,c,2014b).TheyaredescribedintheAp-
startedtoincreaseuntil2008/09andslowlydecreasedagain pendix. Since in our case the model is used to simulate the
in 2010/11 (Fig. 2c). Comparison of DOC concentrations dischargeoftheentiresystemandaweirwithinthesystem,
with discharge before and during the wind disturbance pe- some small modifications had to be performed. Preceding
riodrevealedasimilarpattern.Asshownbyotherstudieson studiesshowedthatweir1(Fig.1)receivesitsdischargepar-
DOCmobilization(e.g.,RaymondandSaiers,2010),apos- tially from the epikarst and partially from the groundwater,
itive correlation between concentrations and discharge (on reaching it partially as concentrated and partially as diffuse
log10 scale) occurred for DOC with concentrations up to flow (Hartmann et al., 2012a). Consequently we derive its
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162 A.Hartmannetal.:Model-aidedquantificationofdissolvedcarbonandnitrogenrelease
in the rock matrix, additional processes were included in
themodelstructure.Similartoprecedingstudies(Hartmann
etal.,2013a,2014b)SO2− dissolutionG [mgL−1]for
4 SO4,i
compartmenti iscalculatedby
(cid:18)Z−i+1(cid:19)aGeo
G =G · , (2)
SO4,i max,SO4 Z
wherea [–]isanothervariabilityparameterandG
Geo max,SO4
[mgL−1]istheequilibriumconcentrationofSO2−inthema-
4
trix.DOCismostlymobilizedattheforestfloor(Borkenet
al., 2011). Stored in the soil or diffusively and slowly pass-
ingdownwards,largepartsoftheDOCareabsorbedorcon-
sumed by microorganisms, but when lateral flow and con-
centratedinfiltrationincrease,netleachingofDOCincreases
aswell.ForthatreasonourDOCtransportroutineonlypro-
videswatertotheepikarstwhenitissaturated(Eq.A4)with
Figure3.Diagramofmodelstructure;itisassumedthatdischarge
increasing DOC net production toward the more dynamic
andhydrochemistryatthetwoweirsiscomposedbydifferentmix-
modelcompartments(Fig.3).ItsDOCconcentrationP
turesofdiffuserecharge(green),concentratedrecharge(red),dif- DOC,i
fuse groundwater flow (blue) and concentrated groundwater flow [mgL−1]foreachmodelcompartmentisfoundby
(purple).
P =P ·(cid:18)Z−i+1(cid:19)−aD1OC, (3)
DOC,i DOC
Z
dischargeQ [Ls−1]by
weir
where a [–] is the DOC variability constant and P
DOC DOC
Q (t)= f [mgL−1] is the DOC net production at soil compartment
weir Epi
1. Similar to other studies that assessed N input to a karst
h XZ XZ i
· fEpi,conc· Rconc,i(t)+(1−fEpi,conc)· Rdiff,i(t) system (Pinault et al., 2001), we used a trigonometric se-
i i
+(1−f )·(cid:2)f ·Q (t)+(1−f ) riestoassessthetimevariantnetproductionofDIN,PDIN,i
Epi GW,conc GW,Z GW,conc [mgL−1],tothesoil:
XZ−1 i
· Q (t) ,
i GW,i (1) PDIN,i =PDIN+ADIN·sin(cid:18)365.25·(cid:0)JD+SPH,DIN(cid:1)(cid:19). (4)
2π
where fEpi is the fraction from the epikarst and (1−fEpi) Here, PDIN is the mean amount of dissolved inorganic N in
the fraction from the groundwater. fEpi,conc and fGW,conc thesoilsolution,whileAN [mgL−1]andSPH,DIN[d]arethe
represent the concentrated flow fractions of the epikarst amplitude of the seasonal signal and the phase shift of sea-
andgroundwatercontributions,respectively.Table1listsall sonalDINuptake(immobilizationbyplantsandsoilorgan-
modelparameters,withashortdescriptionforeach. isms) and release (net DIN in the soil water) cycle, respec-
tively.J istheJuliandayofeachcalendaryear.Duetoits
D
3.1.2 Modelsolutetransport
seasonalvariation,P canalsobenegative,meaningthat
DIN,i
uptakeofDINtakesplace.
To model the non-conservative transport of DOC and, DIN
andSO2−,weequippedthemodelwithsolutetransportrou-
4 3.2 Modelcalibrationandevaluation
tines. SO2− was included as an additional calibration vari-
4
able because it proved to be important to reduce model With 14 model parameter that controlled the hydrodynam-
equifinality(Beven,2006)byaddingadditionalinformation ics and 7 parameters that allow for the non-conservative
about groundwater dynamics (Hartmann et al., 2013a, b). solute transport, the calibration of the model was a high-
Theinclusionofthesethreesolutesallowedforamorereli- dimensional problem. For that reason we have chosen the
ableestimationofmodelparameters(Hartmannetal.,2012b, Shuffled Complex Evolution Metropolis (SCEM) algorithm
2013a)and,lateron,theevaluationofpossiblechangesinthe (Vrugt et al., 2003), which proved to be capable of explor-
dynamic of solute concentrations during the stormy period. ing high-dimensional optimization problems (Fenicia et al.,
For most of the model compartments solute transport sim- 2014;Feyenetal.,2007;Vrugtetal.,2006).Asperformance
ulations simply followed the assumption of complete mix- measureweusedtheKling–Guptaefficiency(KGE;Guptaet
ing. However, to represent net production and leaching of al.,2009).Forcalibration,KGEwasweightedequallyamong
DOC and DIN in the soil, as well as dissolution of SO2− all solutes, one-third for the discharge of the entire system
4
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A.Hartmannetal.:Model-aidedquantificationofdissolvedcarbonandnitrogenrelease 163
Table1.Modelparameters,description,rangesandcalibratedvalueswithKGEperformancesforthecalibrationandvalidationsamples.
Parameter Description Unit Ranges Optimizedvalues
Lower Upper Sample1 Sample2
Vmean,S meansoilstoragecapacity mm 0 1500 450.18 599.13
fvar,S fractionofthespoilthathasavariabledepth – 0 1 0.06 0.02
Vmean,E meanepikarststoragecapacity mm 0 1500 1495.49 1233.98
aSE soil/epikarstdepthvariabilityconstant – 0 2 1.69 1.91
Kmean,E epikarstmeanstorageconstant d 1 50 2.65 8.27
afsep rechargeseparationvariabilityconstant – 0 2 0.88 1.44
KC conduitstorageconstant d 1 10 1.37 1.03
aGW groundwatervariabilityconstant – 0 2 2.00 1.88
fEW fractionofweirdischargeoriginating – 0 1 0.56 0.72
fromtheepikarst
fWE,conc fractionofweirdischargeoriginating – 0 1 0.57 0.47
fromtheepikarstasconcentratedflow
fWGW,conc fractionofweirdischargeoriginating – 0 1 0.01 0.06
fromthegroundwaterasconcentratedflow
PDOC DOCproductionparameter mgL−1 0 15 1.79 1.57
aDOC DOCvariabilityconstant – 0 2 0.92 1.05
PDIN DINproductionparameter mgL−1 −5 10 −1.35 0.11
SPH,DIN phaseofannualDINproduction d 0 365 0 2
ADIN amplitudeofannualDINproduction mgL−1 0 10 3.36 1.84
Gmax,SO4 equilibriumconcentrationofSO4inmatrix mgL−1 0 50 2.74 3.07
aGeo equilibriumconcentrationvariabilityconstant – 0 2 0.11 0.04
∗ ∗
KGEweighted weightedmulti-objectivemodelperformance – 0 1 0.56/0.49 0.52/0.53
∗ ∗
KGEQ,tot modelperformancefordischargeofentiresystem – 0 1 0.41/0.33 0.35/0.42
∗ ∗
KGEQ,W modelperformancefordischargeofweir – 0 1 0.67/0.62 0.61/0.66
∗ ∗
KGEDOC modelperformanceforDOCconcentrations – 0 1 0.38/0.35 0.43/0.32
∗ ∗
KGEDIN modelperformanceforNO3concentrations – 0 1 0.48/0.40 0.48/0.45
∗ ∗
KGESO4 modelperformanceforSO4concentrations – 0 1 0.74/0.62 0.64/0.65
∗Calibration/validationwithothersample.
andtwo-thirdsforthedischargeofweir1,whoseobservation viceversa.Aparametersetisregardedstablewhenthecal-
precisionwasregardedtobemorereliablethantheup-scaled ibrationwithbothsamplesyieldssimilarparametersetsand
discharge.KGEisdefinedas theirKGEconcerningdischargeandthesolutesdoesnotre-
ducesignificantlywhenapplyingthemtotheothersample.
q
KGE=1− (r−1)2+(α−1)2+(β−1)2, (5)
σ µ 3.3 Changeinhydrochemicalbehaviourwiththe
withα= S and β= S, (6) stormyperiod
σ µ
O O
wherer isthelinearcorrelationcoefficientbetweensimula- Afterthemodelevaluation,weusethedifferentcomponents
tionsandobservations,andµ /µ andσ /σ arethemeans of the KGE in Eqs. (5) and (6) to explore the impacts of
S O S O
andstandarddeviationsofsimulationsandobservations,re- the storm disturbance period on the hydrochemical com-
spectively.αexpressesthevariabilityandβ thebias. ponents. By assuming that the model is able to predict to
Tocheckforthestabilityofthecalibratedparameters,we hydrochemical behaviour that prevailed without the impact
perform a split-sample test (Klemeš, 1986). Since the pre- of the storms adapting the hydrochemical parameters of the
disturbance time series was too short to be split into two model in Eqs. (3)–(4) and analysing the difference between
equally long periods, we perform a both-sided split-sample theadaptedhydrochemicalsimulationsandthenon-adapted
test by bootstrapping two independent 4-year time series of simulations,weareabletoquantifythechangeinsolutemass
observations (first sample: discrete sampling of 50% of the balanceduetothestormimpact.Wedefinethetimespanfor
valuesofeachobservedtimeseries;secondsample:remain- our adaption as the time when the different components of
ing 50% of the observations). We calibrate our model with KGE exceed the range of their pre-disturbance variability.
thefirstsampleandevaluateitwiththesecondsample,and Duringthistimeperiodwecompensatefortheapparentde-
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164 A.Hartmannetal.:Model-aidedquantificationofdissolvedcarbonandnitrogenrelease
viations by adapting the hydrochemical parameters. This is in the soil and sulfides in the dolomite. Its variability con-
donetwice–oncebymanualadaptionandanothertimeus- stant is quite low (<0.1). Weighted KGEs, as well as their
ing an automatic calibration scheme. Their new values will values for the individual simulation variables, are relatively
indicatechangesintheseasonality,productionorinterannual stable.Overall,calibrationonbothsamplesprovidedsimilar
variations. parameter values. Due to its higher stability concerning the
evaluation period, we chose the second sample for further
3.4 Transittimedistributions analysis.
Thedischargesimulationsfollowadequatelythevariations
The signal of the storm impact will travel at various veloc-
intheobservations(Fig.4),althoughsomesmalleventsare
ities and via pathways through the karst system. While fast
not reproduced by the model and although the simulations
flowpathsandsmallstorageswilltransportthesignalrapidly
oftheweir’sdischargetendtounderestimatepeakflows.No
to the system outlet, slow pathways and large storages will
obviousdifferencescanbeseenbetweenthepre-disturbance
delay and dilute the signal. Transit time distributions indi-
andwinddisturbanceperiod.Thehydrochemicalsimulations
catehowfastsurfaceimpactstravelthroughthehydrological
tend to follow the observations as well (Fig. 5). However,
system. We derive transit time distributions from the model
thereissometimessomeunderestimationoftheDOCpeaks
by performing a virtual tracer experiment with continuous
for the pre-disturbance period. The DIN simulations appear
injection over the entire catchment at the beginning of the
to be more precise during the pre-disturbance period, but
impact of the stormy period. When a model compartment
there is a systematic underestimation when the disturbance
reaches 50% of the tracer concentration is considered me-
takesplace.
diantransittime.Thethus-derivedtransittimeswillelaborate
how the hydrological system propagates the signal through
4.2 Modelperformanceduringthewinddisturbance
the system including all slow and fast pathways as defined
period
by Eqs. (12) and (18). As for DIN and DOC we assume
completeandinstantaneousmixingwitheachmodelstorage
Thereisadeviationbetweenpre-disturbanceanddisturbance
(soil, epikarst, and groundwater) at each compartment; the
period simulated and observed variability and bias for DIN
timethatwerefertoas“meantransittime”ofamodelcom-
(Fig.6).AsimilartendencycanbefoundforDOC.However,
partmentisthetimethevirtualtracerneedstopassthrough
onlyforDINarethedeviationsdifferenttothevariationsal-
theparticularmodelstorage.Incombinationwiththefluxes
readyfoundduringthepre-disturbanceperiod(whichisalso
that are provided from each of the model compartments, it
thecalibration/validationperiod).ThevariationsinDOCap-
ispossibletoquantifythefractionalcontributionoffastand
pear to be systematic, too, but they fall within its ranges of
slowflowpaths,respectively.Wewillapplythevirtualtracer
variabilityduringthepre-disturbanceperiod.
from the previously assessed beginning of the impact un-
til the end of the time series to assess the transit time dis-
4.3 AdaptionofNparametersforthewinddisturbance
tribution. In addition, we apply a second virtual tracer that
period
also lasts only for the disturbance period (as estimated in
Sect. 3.3) to evaluate the filter and retardation potential of
The very first signs of the impact were found on 1 May
thekarstsystem.
2007, and they lasted to the end of the hydrological year
2010/11. In a first trial (Table 2), the model parameters for
the DIN production were adapted manually to compensate
4 Results
for the changes in observed DIN concentrations with a fo-
4.1 Modelperformance cus on reducing the difference indicated by the bias, β, and
variability,α,componentsofKGE .Inasecondtrial,we
DIN
Table1showsthecalibratedparametersforthetwosamples. useanautomaticcalibrationschemetoachievetheoptimum
Theyindicateathicksoilandarelativelythinepikarst.The KGE .AsindicatedbythehighestKGE(Table2),theau-
DIN
dynamics expressed by the storage constants indicate days tomaticcalibrationprovidedthehighestKGE ,butthisis
DIN
andweeksfortheconduits(modelcompartmenti=Z)and achieved by improving variability α and correlation r. Al-
the epikarst, respectively. The distribution coefficient of the mostnoimprovementisreachedforthebiasβ.Eventhough
groundwaterislargerthanthesoil/epikarststorageconstant. resulting in a slightly lower improvement of KGE the
DIN
ForDOCandDINthereareanaturalproductionratesof1.6– manualcalibrationresultsinamuchmoreacceptablereduc-
1.8and−1.35−0.1mgL−1,respectively.TheDOCdistribu- tion in the bias (Fig. 6). Its parameter values showed a pro-
tion coefficient is between 0.9 and 1.1. The phase shift and duction rate, P , of DIN almost 2mgL−1 larger than the
DIN
amplitudeforDINshowedthatthereisaseasonalvariation pre-disturbancevalue;anamplitude,A ,around1mgL−1
DIN
in DIN net production, with its maximum release at April smaller; and a phase shift, S , towards a week earlier
PH,DIN
eachyearforbothofthesamples.SO2−isdominatedbythe intheyear,resultinginamoreacceptablesimulationofDIN
4
concentration in the precipitation input with some leaching dynamicsduringthedisturbanceperiod(Fig.7).
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A.Hartmannetal.:Model-aidedquantificationofdissolvedcarbonandnitrogenrelease 165
Figure4.Observedvs.simulateddischargesfortheentirekarstsystemandweir1.
Figure5.Observedvs.simulated(a)DOCand(b)DINatweir1.
4.4 Transittimedistributions 5 Discussion
Thetransittimedistributionsshowthatthesoilandepikarst 5.1 Reliabilityofcalibratedparametersandmodel
systemreactsquiterapidlytothevirtualinjection.Fiftyper- simulations
cent of the injection concentration is reached within ∼60
days (Fig. 8a), while most of groundwater system requires Most of the calibrated model parameters are in ranges that
∼100daystoreach50%oftheinjectionconcentration,with are in accordance with other modelling studies or field ev-
afewflowpathsrequiringupto300days(Fig.8c).Asimilar idence. General differences between the calibrated parame-
behaviour is found when the impact ends (Fig. 8b, d). This tervaluesoftheboth-sidedsplit-sampletestmaymostlybe
behaviour also shows that some of the slowest flow paths due to the comparatively low resolution of the hydrochemi-
justreachtheinputconcentrationbeforetheystarttodecline calvariables(SO ,DOCandDIN),whichactuallyincreased
4
again. asaresultofthebootstrappingprocedure.However,thegood
multi-objectivesimulationperformanceofthemodel,aswell
asitsevaluationbythesplit-sampletest,indicateanoverall
acceptable performance of the model. At almost 3–8 days
theepikarststorageconstantisinaccordancewithfieldstud-
ies on the epikarst storage behaviour that found retention
times of some days to a few weeks (Aquilina et al., 2006;
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166 A.Hartmannetal.:Model-aidedquantificationofdissolvedcarbonandnitrogenrelease
Table 2. Calibrated pre-storm parameters for DIN dynamics and
twoscenariosforadaptingthemodelatthestormyperiod.
Parameter Unit Calibrationtype
pre-storm manual automatic
PDIN mgL−1 0.11 2.10 0.00
SPH,DIN d 2.00 9.00 23
ADIN mgL−1 1.80 0.70 2.63
∗
KGE – 0.29 0.41 0.46
DIN
∗
variabilityα – 0.75 1.04 1.05
DIN
∗
biasβ – 0.70 1.01 0.83
DIN
∗
correlation – 0.40 0.41 0.49
DIN
∗For2006/07–2011/12.
byplantsorsoilorganismsandthattheproductionperiodis
Figure6.IndividualcomponentsoftheKGE:(a)ratioofsimulated shorterbutmorepronouncedduetoitslargervalueofampli-
andobservedvariabilities,(b)ratioofsimulatedandobservedaver-
tude.However,weexpectthesedifferencestobeminorsince
agevalues,and(c)theircorrelationforthewinddisturbanceperiod;
thephaseshiftS ofbothcalibrationsamplesisalmost
forcomparisontheKGEcomponentsandtheirinterannualvariabil- PH,DIN
the same, as well as their annual maximum (P +A )
ityarealsoshownforthepre-stormperiodandafterthecorrection DIN DIN
of 2.01 and 1.95mgL−1. This behaviour indicates a maxi-
oftheDINproductionmodelparametersduringthewindperiod.
mumofDINproductionandleachingatthetimeoftheyear
when snowmelt reaches its maximum (March to April) and
whenDINuptakebyplantsisstilllow(Jostetal.,2010).The
Perrinetal.,2003).Thesoilandtheepikarststoragecapac- dissolutionequilibriumconcentrationsof2.7–3.1mgL−1for
ity are quite large. These high values may be explained by SO2− indicate the abundance of the precipitation input, ox-
4
structural errors of the model that result in unrealistic cali- idationofsulfides(e.g.pyrite)inthedolomiteandtracesof
bratedparametervalues,inparticularpossibleparameterin- evaporatesinthesmallPlattenkalkoccurrences(Kraliketal.,
teractionsbetweentheirstoragecapacitiesandstoragecoef- 2006).
ficients.Sincethesoilandthevegetationcontrolsthefraction
ofrainthatislosttoevapotranspiration,thishighcalibrated 5.2 Impactofstorms
valuemightbeduetotreerootsrangingthroughthesoilinto
theepikarst(Heilmanetal.,2012)orrockdebris(Hartmann The deviation between simulated and observed time series
etal.,2012a). (Fig. 5) already indicates that DIN is the only solute that
Similar to the epikarst storage constant, the conduit stor- shows a clear impact of the storms. This is further corrob-
ageconstant,K ,is,withitsvalueof1.1days,intherange oratedbyconsideringtheindividualcomponentsofKGEin
C
ofpreviousmodellingstudies(Fleuryetal.,2007;Hartmann Fig. 6. It is well known that nitrate leaching to the ground-
etal.,2013a).Thehighvaluesoftheepikarstvariabilitycon- waterincreasessharplyaftertreedamage(dieback)inforests
stant and the groundwater constant indicate a low develop- whereNisnotstronglylimited(Bernaletal.,2012;Griffinet
ment of preferential flow pathsin the rock, which is typical al.,2011;Huber,2005).SuchdisturbancesdisrupttheNcy-
fordolomiteaquifers(FordandWilliams,2007).Alowde- cle.ThelossoftreeNuptakefavoursnitrificationofsurplus
+
gree of karstification was already known for our study site NH bymicroorganisms.Moreover,above-(i.e.foliage)and
4
(Jostetal.,2010)andthecalibratedrechargeareasfallwell belowground(i.e.fineroots)litterfromdeadtreesenhances
withintherangesfoundinpreviousmodellingstudies(Hart- themineralizationoforganicmatter,ammonificationandni-
mannetal.,2012a,2013c). trification. Both processes are accelerated by increased soil
The hydrochemical parameters mostly show realistic moisture and soil temperature due to the loss of the forest
values. A DOC production parameter, P , of ∼1.6– canopy.Subsequently,leachingofNincreaseswithincreased
DOC
1.8mgL−1 resulted in realistic simulated concentrations at seepage fluxes due to decreased interception and water up-
the weir. For DIN production the two calibration samples takebytrees.SincethesimpleDINroutineofthemodelcan-
result in values of −1.4 and 0.1mgL−1, going along with nottakeintoaccountsuchchanges,theunderestimatedDIN
amplitudesof3.4and1.8,respectively.Hence,thereappears concentrationsandtheiramplitudeshowtheeffectofforest
to be some correlation between the production and ampli- disturbance on the leaching of DIN from the studied catch-
tude parameters, P and A . Negative values indicate ment.Thereisalsoanapparentlysystematicdeviationofthe
DIN DIN
that, during some periods of the year, all DIN is consumed DOCvariabilityα.Butitsvariationsduringthepre-stormpe-
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A.Hartmannetal.:Model-aidedquantificationofdissolvedcarbonandnitrogenrelease 167
Figure 7. Observed and simulated DIN dynamics using the pre-storm parameters (red line), the scenario 1 parameters derived from the
deviationsassessedbytheKGEcomponents(orangeline),andthescenario2parametersderivedbysystematicvariation(dark-redline).
Figure8.Meantransittimesfor(a)thesoilandepikarstand(c)thegroundwaterstoragederivedbyaninfinitevirtualtracerinjectionstarting
withthebeginningofthewinddisturbanceperiod,and(b)thereactionofthesoilandepikarstand(d)thegroundwaterstorageastheimpact
ends.
riodaresimilarlylarge,thuspointingtoanegligibleeffectof pathsarelikelyduetothesteepnessofthecatchmentslopes
forestdisturbanceonDOCleaching.Numerousstudieshave (Boyer et al., 1997; Sakamoto et al., 1999; Terajima and
identified the forest floor as a DOC source (Borken et al., Moriizumi, 2013). The missing signal of forest disturbance
2011; Michalzik et al., 2001). Windthrow generally causes on DOC concentrations at weir 1 even shortly after the dis-
a (short-term) pulse of above- and belowground litter (Har- turbancemaybeduetotheminorextensionofthedisturbed
monetal.,2011).Thereby,mineralizationofthesurpluslit- area, the minor increase of surface and shallow subsurface
ter input concurrent with improved soil climatic conditions flow due to the relative low slope of the disturbed area, the
likely increased the leaching of DOC from the forest floor buffering of increased topsoil DOC leaching due to absorp-
(Fröberg et al., 2007; Kalbitz et al., 2007). Concurrent, in- tion of DOC within the subsoil (Borken et al., 2011; Huber
creasedsoilwater,surfaceandshallowsubsurfaceflowmay et al., 2004), missing DOC-rich riparian source areas (i.e.
favour increased soil DOC leaching to downslope surface wetlands, floodplains) and the reduction in pre-disturbance
waters(Monteithetal.,2006;NeffandAsner,2001;Sander- organic matter input to soil (i.e. litter, root exudates; Hög-
manetal.,2009).Inmountainouscatchmentsthelatterflow berg and Högberg, 2002). Theoretically, hydrological pro-
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