matthew p. miller, diane m. mcknight, steven c. chapra...

A model of degradation and production of three pools of dissolved organic matter in an

alpine lake

Matthew P. Miller,a,1,* Diane M. McKnight,a Steven C. Chapra,b and Mark W. Williamsc

a Department of Civil, Environmental, and Architectural Engineering, Institute of Arctic and Alpine Research, University of Colorado,Boulder, Colorado

bDepartment of Civil and Environmental Engineering, Tufts University, Medford, Massachusettsc Department of Geography, Institute of Arctic and Alpine Research, University of Colorado, Boulder, Colorado

Abstract

We investigated the transport and production of dissolved organic matter (DOM) in an alpine lake in theColorado Front Range during snowmelt and the summer ice-free season by employing a new approach fordistinguishing between three pools of DOM based on fractionation and spectroscopic characterization. Reactivetransport modeling results confirm that terrestrially derived sources of humic DOM are dominant duringsnowmelt and that microbially derived humic and nonhumic DOM are produced in the lake in the summer, whenrates of primary productivity are highest. DOM characterization and modeling results support the interpretationthat losses of terrestrially derived humic DOM are dependent on photochemistry and indicate that the decay ofnonhumic and microbially derived humic DOM is more influenced by heterotrophic degradation. Results suggestthat production of nonhumic DOM can be directly related to chlorophyll a concentrations and that microbiallyderived humic DOM can be produced through condensation reactions. Furthermore, fluorescence and parallelfactor analysis (PARAFAC) of DOM suggest that the rate of decay of microbially derived humic DOM decreasesas the redox state of quinone-like moieties of the DOM becomes more oxidized. This study quantifies theinfluence of DOM source on the chemical and biological reactivity of DOM in lakes. The methods presented inthis study provide a framework for testing hypotheses related to the effects of a changing climate on lakeecosystem structure and function.

Recently, increases in dissolved organic carbon (DOC)concentrations in surface waters have been documented inmany north temperate regions (de Wit et al. 2007; Monteithet al. 2007). These trends have raised concerns because theunderlying processes are not fully understood and becauseof the affects of increasing DOC on aquatic ecosystems anddrinking water quality (Reckhow et al. 1990). To resolveamong potential drivers, it is important to consider that thequality and reactivity of dissolved organic matter (DOM) isdependent on its source (Hood et al. 2003; Mladenov et al.2007; Gondar et al. 2008). For example, humic DOM ofterrestrial origin generally has a higher content of aromaticgroups than does humic DOM of microbial origin(McKnight et al. 1994; Mladenov et al. 2005; Jaffe et al.2008). Additionally, the presence of lakes in catchments hasbeen shown to influence the quantity and quality of DOMat downstream sites (Larson et al. 2007). Sources of DOMto surface waters include (1) the flushing of humic DOMfrom catchment soils and vegetation (Thurman 1985), (2)the release of nonhumic microbial exudates and othernonhumic plant and soil products (Giroldo and Vieira2005; Kawasaki and Benner 2006), and (3) the productionof microbially derived humic DOM from biomass(McKnight et al. 1994). Furthermore, it is anticipated thatthe relative contribution of DOM from these differentsources will change in response to a changing climate (L. J.Tranvik et al. unpubl. data).

In the Green Lakes Valley in the Front Range of theColorado Rocky Mountains, yearly monitoring has shownthat a pulse of terrestrially derived, aromatic humic DOMis transported into alpine and subalpine lakes duringsnowmelt (Hood et al. 2003). This pulse is characterizedby a low fluorescence index (FI) and high specificultraviolet absorbance (SUVA). Later in the summer,during the annual phytoplankton bloom, microbial sourcescontribute humic DOM with a high FI and low SUVA andthe percentage of humic DOM decreases. Based on theseresults, Hood et al. (2003) proposed a conceptual model ofDOM cycling in the Green Lakes Valley in which the alpinelakes function as wide points in the stream duringsnowmelt, with minimal in-lake modification of the DOMpool. In contrast, later in summer, on the descending limbof the snowmelt hydrograph, alpine lakes provide autoch-thonous DOM to the lower stream reaches. Using the FI inan end-member mixing analysis, they calculated that 30–40% of the DOC yield at the outlet of an alpine lake wasderived from in-lake production.

The conceptual model of Hood et al. (2003) provides aframework for understanding future changes in these lakesbut requires quantification of the biogeochemical reactivityof DOM to be useful in predicting the extent of changes inDOM quality that may occur. Several trends observed inthe Colorado Front Range may be drivers of change inalgal production and in-lake DOM quality, includingdecreasing lake ice thickness, earlier occurrence of ice outand snowmelt, and increasing atmospheric nitrogen depo-sition (Williams and Tonnessen 2000; Caine 2002). Thesechanges will likely result in greater phytoplankton and

1 Present address: School of Forest Resources, PennsylvaniaState University, University Park, Pennsylvania

* Corresponding author: [email protected]

Limnol. Oceanogr., 54(6), 2009, 2213–2227

E 2009, by the American Society of Limnology and Oceanography, Inc.

2213

benthic algal production and therefore greater autochtho-nous production of nonhumic and microbially derivedhumic DOM in lakes. Terrestrially derived DOM inputsmay also change with the eventual arrival of the mountainpine beetle (Dendroctonus ponderosae). Quantitative anal-ysis of the potential changes in DOM quality requires areactive transport model that considers allochthonoussources of DOM and the decay and production of DOMwithin the lake. Recently there have been advances in thereactive transport modeling of DOM of varying source andquality in wetlands (Mladenov et al. 2007), streams (Milleret al. 2006), and lakes (Hanson et al. 2004). In some of thesestudies, DOM has been separated into pools based onsource and/or lability; however, information gained fromchemical characterization of DOM quality was not used asa basis to perform the separation.

This study focuses on quantitatively evaluating andextending the hypotheses put forward in the conceptualmodel of Hood et al. (2003) for an alpine lake in theColorado Front Range. A lake model was developed thataccounts for DOM in three pools of differing source enteringthe epilimnion via surface water runoff and precipitation, aswell as lake stratification, and time-variable water residencetimes. This model allows for comparison of measured valuesfor the three DOM pools with simulated values at the lake’soutlet, assuming conservative transport. Additionally, thedevelopment of a reactive transport model has allowed for amore detailed investigation into the biological and chemicalconditions that drive the reactivity of the three pools ofDOM in the lake.

Methods

Site description—The research site is the Green LakesValley, which is described elsewhere (Williams et al. 2001;Hood et al. 2003). Beginning in May 2006 and ending inSeptember 2006 samples were collected on a weekly basisfrom the inlet and outlet of Green Lake 4, an oligotrophicalpine lake in the Green Lakes Valley (Fig. 1; Table 1). Thewatershed above Green Lake 4 drains approximately2.2 km2 and has steep talus slopes. About 20% of thevalley floor is covered by vegetation with well-developedsoils, and the rest of the valley floor is glaciated bedrock(Williams et al. 2001). The inlet to Green Lake 4 is braidedand divides into multiple channels, while the outlet is awell-defined channel. Water level at the lake outlet ismonitored continuously with a pressure transducer, anddischarge is calculated using a rating curve.

The Green Lakes Valley receives the majority of itsprecipitation as winter snowfall, and the hydrology isdominated by spring snowmelt. An annual peak in thehydrograph occurs in late May or early June. As the snowmelts, primary productivity is generally low, with chloro-phyll a (Chl a) concentrations ,1 mg L21 (Gardner et al.2008). After snowmelt, residence time in the lake graduallyreturns to baseflow conditions (,30 d). Monitoring ofGreen Lake 4 shows that as snowmelt ends, the percentagecontribution of fulvic acids decreases, nitrate (NO{

3 )concentrations decrease, total dissolved phosphorus(TDP) concentrations are consistently low, and primary

productivity increases (peak Chl a concentrations of,8 mg L21; Gardner et al. 2008). Conservative transportsimulations have shown that nitrate and total dissolvedphosphorus are consumed in the lake coincident with apeak in phytoplankton productivity (Miller 2008).

Field sampling—Water samples were collected from arepresentative inlet stream and the outlet of Green Lake 4.Samples were collected on 15 dates during the summer of2006. The water column of Green Lake 4 was sampled atthree depths (surface, 3 m, and 9 m) on 06 July 2006. Acomposite rainwater sample was collected as part of theNational Atmospheric Deposition Program (http://nadp.sws.uiuc.edu/) during a rain event from 07 to 09 July 2006.Samples for analysis of DOC, percentage of fulvic acidcontent (%FA), Chl a, absorbance, oxygen isotopes (d18O),and fluorescence spectroscopy were collected in precom-busted 500-mL amber glass bottles and filtered within 4 hof collection with precombusted 0.7-mm, 47-mm WhatmanGF/F glass-fiber filters with a hand-pump filtration system.The rainwater sample was collected in a 60-mL Nalgenehigh-density polyethylene bottle, filtered with a 1-mm, 47-mm Gelman A/E glass-fiber filter, and analyzed for DOC,absorbance, fluorescence, and d18O. Table 2 lists thevariables that were measured, calculated, or estimated inthis study and their abbreviations.

Laboratory methods—Filters were immediately processedfor analysis of Chl a concentrations following the methodsof Marker et al. (1980). The filtrate was acidified to pH 2and stored at 4uC. Within 1 week, samples were analyzed for

Fig. 1. Map of Colorado showing the location of the GreenLakes Valley. The inset shows a bathymetric map of Green Lake4, and the solid star represents the sampling location for collectionof water column samples.

2214 Miller et al.

DOC concentration, percentage of fulvic acid, absorbance,and fluorescence characteristics. DOC concentrations weremeasured by high temperature catalytic oxidation with aShimadzu TOC-550A total organic carbon analyzer, with aconfidence interval of 0.02 mg L21. Absorbance values forall whole water samples and humic fractions were obtainedwith an Agilent 8453 ultraviolet (UV)–visible spectroscopysystem. A 3-mL quartz cuvette with a path length of 1 cmwas used, and absorbance was measured at 1-nm intervalsover a range of 190 nm to 1100 nm. Specific ultravioletabsorbance (SUVA254; m2 g21) of the humic fraction wascalculated as the ratio of the decadic absorption coefficientat 254 nm (a) to the DOC concentration of the humicfraction. Analysis for d18O was done at the Stable IsotopeLaboratory at the Institute of Arctic and Alpine Research(INSTAAR) (Epstein and Mayeda 1953). The 1-s precisionwas 60.05%.

A 200-mL subsample of each 500-mL sample wasfractionated into nonhumic and humic fractions usingcolumns with a 10-mL bed volume packed with XAD-8resin following the methods of Thurman and Malcolm(1981). The nonhumic fraction, which includes low molec-ular weight compounds such as carbohydrates, amino acids,and carboxylic acids (Thurman 1985), does not sorb to theXAD-8 resin at low pH and was collected as the effluentfrom the column. The humic fraction sorbs to the XAD-8resin and was obtained by back elution with 20 mL of 0.1NaOH. The humic fraction was immediately acidified topH 2 with concentrated HCl. The percentage of the DOMthat was humic was determined using the mass balanceapproach described by Hood et al. (2003), which typicallyyields near complete DOC recovery (65%). Thurman (1985)reported that the majority of the humic DOM in surfacewaters is fulvic acid. Therefore, the percentage of humicDOM is referred to as the percentage of fulvic acid followingthe terminology of Baron et al. (1991) and Hood et al.(2003). It was not possible to fractionate the rainwatersample given the small sample volume.

All samples collected for fluorescence analysis wereanalyzed using a JY-Horiba/Spex Fluoromax-3 spectro-fluorometer. To ensure stable instrument function, a lampscan, cuvette check, and water raman scan were run daily.Scans collected every few months included a quinine sulfatescan and scans of Suwannee River fulvic acid (SRFA) andPony Lake fulvic acid (PLFA). To correct for decay in thelamp output over time, the area underneath the waterraman scan was calculated and used to normalize allsample intensities.

Three-dimensional fluorescence scans were run on allfiltered whole water and humic samples. Scans were blanksubtracted and corrected for excitation and emission inMatlab (Cory 2005) using the instrument specific excitationand emission correction files, and were corrected for theinner-filter effect. The FI for each sample was calculated asthe ratio of intensity of emission at 470 nm to the intensityof emission at 520 nm for an excitation scan at 370 nm(McKnight et al. 2001; Cory 2005). The standard deviationassociated with triplicate analyses was 0.01 (Cory 2005).

Corrected three-dimensional excitation-emission matri-ces (EEMs) were modeled using the parallel factor analysis(PARAFAC) model presented in Cory and McKnight(2005). PARAFAC corresponds to a three-way version ofprincipal component analysis, and when applied to theEEMs of samples containing DOM, identifies differentclasses of fluorophores herein referred to as ‘‘components’’(Stedmon et al. 2003). An EEM can be represented as thesum of the components. The redox state of the quinone-likefluorophores was determined using the redox index (RI;Miller et al. 2006).

Table 1. Morphometric characteristics of Green Lake 4.

Characteristic Green Lake 4

Elevation (m) 3550Surface area (km2) 0.05Volume (m3) 214,960Maximum depth (m) 13.1Depth of stratification (m) 8.0Thickness of thermocline (m) 2.0Approx. date of ice out 06 Jul 2006

Table 2. Variables and their corresponding abbreviationsthat were used in the study. Also shown are the subscripts thatwere used in combination with a subset of the variables.

Abbreviation

Variable

Chlorophyll a Chl aDecadic absorption coefficient aDecay rate kDelta oxygen-18 d18ODissolved organic carbon DOCDissolved organic matter DOMFluorescence index FIPercentage fulvic acid %FAPony Lake fulvic acid PLFAProduction rate lRatio of carbon to chlorophyll a in algae rca

Reactive transformation SRedox index RISpecific ultraviolet absorbance SUVASuwannee River fulvic acid SRFATemperature TThermocline area At

Thermocline mixing rate E9tThermocline thickness zt

Vertical eddy diffusivity Et

Volume VVolumetric flow rate Q

Subscript

Epilimnion eHypolimnion hInflow stream inMicrobially derived humic fraction MHNonhumic fraction NHPrecipitation pTerrestrially derived humic fraction THTime tTotal humic fraction HWhole water WW

Alpine lake DOM decay and production 2215

Calculation of DOM source pools—For each samplecollected at the inlet, outlet, and in the water column, theDOM was separated into three pools of differing source. Thethree source pools are (1) terrestrially derived humic DOM(DOCTH; mg L21), (2) nonhumic DOM (DOCNH; mg L21),and (3) microbially derived humic DOM (DOCMH; mg L21).Based on prior monitoring results, the three DOM poolswere calculated using a mixing model approach similar tothat of McKnight et al. (1992). The whole water DOCconcentrations can be broken into two pools as follows:

DOCWW~DOCNHzDOCH ð1Þ

where DOCWW (mg L21) is the measured whole water DOCconcentration and DOCH (mg L21) is the measured humicfraction concentration. DOCH can be separated further intothe terrestrially derived and microbially derived humic poolsdescribed above by

DOCH~DOCTHzDOCMH ð2Þ

Likewise, the decadic absorption coefficient balance is asfollows:

aH~aTHzaMH ð3Þ

where aH (m21), aTH (m21), and aMH (m21) represent thedecadic absorption coefficients at 254 nm of the total humicfraction of the DOM, the terrestrially derived humic fraction,and the microbially derived humic fraction, respectively.

Because it is not possible to directly measure DOCTH,DOCMH, aTH, or aMH, end-member SUVA254 values canbe used to rewrite Eq. 3, which can then be solved forDOCTH and DOCMH. Given that the SUVA254 value for agiven sample is defined as the decadic absorption coeffi-cient normalized to the DOC concentration, Eq. 3 can berewritten as follows:

SUVAHDOCH~SUVATHDOCTHzSUVAMHDOCMH ð4Þ

where SUVAH (m2 g21) is the measured SUVA254 value ofthe humic fraction, and SUVATH (m2 g21) and SUVAMH

(m2 g21) represent the SUVA254 values of terrestriallyderived humic DOM and microbially derived humic DOMend members, respectively. The values of SUVATH andSUVAMH end members are parameters of the model, andthe expected ranges of both values are known fromprevious studies. SUVATH is expected to be similar to theSUVA254 of SRFA and SUVAMH is expected to be similarto the SUVA254 of PLFA. SRFA and PLFA are end-member fulvic acids available from the InternationalHumic Substances Society. The values of SUVATH andSUVAMH were set based on comparison of the maximumand minimum humic SUVA254 values measured in thisstudy with those of SRFA and PLFA, respectively, and tomeet the requirement that

SUVAMHƒSUVAHƒSUVATH ð5Þ

It should be noted that SUVATH and SUVAMH areconstant parameters in the model, while DOCTH, DOCMH,aTH, aMH, and SUVAH are all time-variable values.Therefore, each sample has unique DOCTH and DOCMH

concentrations. DOCTH can be solved for any sample bycombining Eqs. 2 and 4 as follows:

DOCTH~DOCH(SUVAH{SUVAMH)

(SUVATH{SUVAMH)ð6Þ

It follows that

DOCMH~DOCH{DOCTH ð7Þ

The DOM separation technique and the choice of SUVATH

and SUVAMH were evaluated by running a linearregression analysis between the ratio of DOCMH : DOCH

and the FI of the humic fraction of the samples collected atthe outlet of the lake. Although FI and SUVA254 aregenerally inversely related, processes that may decreaseSUVATH, such as photobleaching and sorption on ironoxides, do not cause an increase in FI, but may cause aslight decrease (McKnight et al. 2001; Cory et al. 2007).Thus, examination of the production and loss of DOCH

and temporal trends in the FI of the humic fraction is arobust test that differentiates between a decrease in SUVAH

resulting from addition of microbially derived humic DOMand a decrease resulting from a change in SUVA254 of theterrestrially derived humic end member.

Model development—A reactive transport model wasdeveloped to investigate hydrologic and in-lake biologicalcontrols on the three DOM pools described above. Thelake was represented as two well-mixed constant-volumelayers (epilimnion and hypolimnion) with one surface waterinlet, a precipitation input, exchange between the epilim-nion and hypolimnion, and one surface water outlet. Directgroundwater input to the lake and evaporative losses fromthe lake were not considered in the model.

Mass balance equations can be written for each of theDOM pools for the two layers as

Ve

dDOCi,e

dt~Qin(t)DOCi,inzQp(t)DOCi,p

{ Qin(t)zQp(t)� �

DOCi,e

zE 0t(t)(DOCi,h{DOCi,e)zSiVe ð8Þ

and

Vh

dDOCi,h

dt~Et

0(t)(DOCi,e{DOCi,h)zSiVh ð9Þ

where V 5 volume (m3), DOCi 5 DOC concentration (mgL–1) for DOC species i, t 5 time (d), Q 5 volumetric flowrate (m3 d–1), E0t 5 thermocline mixing rate (m3 d–1), and Si

5 transformations (mg L–1 d–1) for DOC species i. The (t)denotes that the parameter is a time-variable inputfunction. The subscripts designate the epilimnion (e),hypolimnion (h), inflow stream (in), and precipitation (p).In addition to the three DOM pools, d18O was simulated asa conservative substance. That is, d18O was modeled usingEqs. 8 and 9 with transformations, S, set to zero.

The mixing rate, E9, quantifies mass transfer due to two-way flows across open boundaries (Chapra 1979). Asapplied to the thermocline, it is related to more fundamen-

2216 Miller et al.

tal parameters by

E0t~EtAt

zt

ð10Þ

where Et 5 vertical eddy diffusivity across the thermocline(m2 d–1), At 5 thermocline area (m2), and zt 5 the thermo-cline thickness (m).

Kinetic transformations for the DOM pools weredeveloped to test specific hypotheses about the reactivetransport of the three pools of DOM. First, to test theimportance of photochemical and microbial decay of theterrestrially derived humic DOM pool, the followingexpression for reactive transformation was used:

STH~{kDOCTH1:07T{20DOCTH ð11Þ

where T 5 layer temperature (uC) and ki 5 the decay rate(d–1) for DOC species i. The value of 1.07 is a constant thatcorresponds to a Q10 value of 2 (a doubling of the decay orproduction rate for an increase in water temperature from10uC to 20uC) (Chapra 1997). Second, the dependence ofthe nonhumic DOM pool on primary productivity wastested with the transformation

SNH~{kDOCNH1:07(T{20)DOCNH

zlDOCNH1:07(T{20)rcaChl a ð12Þ

where li 5 the production rate (d–1) for DOC species i, rca

is the ratio of carbon to Chl a in the algae (mg C mg Chl a–1),and Chl a is the Chl a concentration measured at the outlet(mg L–1). Finally, the role of condensation reactions in theproduction of microbially derived humic DOM and redox-dependent decay of the microbially derived humic DOMpool were tested with the transformation

SMH~{2kDOCMH1:07(T{20)DOCMHRI

zlDOCMH1:07(T{20)DOCNH ð13Þ

where RI is the unitless redox index of the whole waterDOM.

The three DOM pools and d18O were modeled from 01June 2006 through 07 September 2006. Initial conditions inthe epilimnion were set as measured values from the outlet

to the lake on 01 June 2006. Discharge measured from thegage at the lake outlet, the volume of rainfall during the 07–09 July 2006 rain event, and knowledge of the mixingregime in the lake were used to estimate surface water flowat the inlet. Values for E0t(t) were based on watertemperature data at the outlet to Green Lake 4 in concertwith water column temperature profiles and knowledge ofwhen the lake was ice covered and ice free.

All model parameters that were not measured directlywere estimated (Table 3). The kDOC (d–1) terms are user-defined, first-order decay coefficients for the respectiveDOM pools and were obtained from the literature(Mladenov et al. 2007). The RI of the whole water DOMwas scaled from 0 to 1, where 1 corresponded to themaximum value measured at the outlet during the course ofthe season (0.51 on 01 June 2006) and 0 corresponded tothe minimum value measured at the outlet (0.27 on 17August 2006). Values for the production coefficients, l, forthe DOCMH and DOCNH pools were determined by modelcalibration to fit the measured data. The sensitivity of themodel to these assumptions was tested over the course ofthe season (Miller 2008). Forcing functions and parametersused in the reactive transport model are shown in Table 4.

Because the hypolimnion d18O and DOC concentrationswere not measured over the course of the summer, theywere modeled where the only sources to or from thehypolimnion were due to mixing with the epilimnion. Thedifferential equations represented in Eqs. 8 and 9 weresolved using the classical fourth-order Runge-Kuttamethod. For all model results the root mean square error(RMSE) between observed and simulated values wascalculated.

Results

Hydrology—The hydrograph at the outlet to GreenLake 4 in early summer was characteristic of snowmeltdominated catchments (Fig. 2). Discharge began to in-crease in mid May and peaked in late May at just over26,000 m3 d21, with a second peak in early June. Asreported by Hood et al. (2003), this second peak may havebeen caused by failure of an upstream ice dam on GreenLake 5. Additionally, there was a peak in discharge in earlyJuly of approximately 60,000 m3 d21, which was over twice

Table 3. Parameters used in the lake model for values that were estimated.

Parameter Units Value Source

Thermocline mixing coefficient m3 d–1 Time variable Water column temperature profilesInitial d18O value in hypolimnion % 215.5 Value measured in hypolimnion during lake

stratification on 06 July 2006Humic DOC from terrestrial sources in precipitation mg L–1 0.25 1/3 of whole water DOCNonhumic DOC in precipitation mg L–1 0.25 1/3 of whole water DOCHumic DOC from microbial sources in precipitation mg L–1 0.25 1/3 of whole water DOCInitial humic DOC from terrestrial

sources in hypolimnionmg L–1 0.11 Value measured from hypolimnion sample during

lake stratification on 06 July 2006Initial nonhumic DOC in hypolimnion mg L–1 0.5 Value measured from hypolimnion sample during

lake stratification on 06 July 2006Initial humic DOC from microbial

sources in hypolimnionmg L–1 0.22 Value measured from hypolimnion sample during

lake stratification on 06 July 2006


the peak during snowmelt. This peak resulted from anunusual 3-d midsummer rainstorm that produced 9 cm ofprecipitation. Following this peak, the flow from the lakequickly returned to pre–rain event conditions.

When the assumptions described in Table 3 were used,the conservative transport model results matched well withthe measured d18O values (Fig. 3). Prior to the rain event,the simulated d18O values were essentially identical to themeasured values. Following the rain event the model resultsdeviated slightly from the measured values, with differencesbetween the simulated and measured values being less than2%. Late in the season the measured values wereconsistently more enriched than the modeled values. Thegreatest deviation of measured values from simulatedvalues occurred at the end of the summer (1.7%) whenthe residence time, and subsequently the evaporation rate,was greatest. Moreover, sensitivity analyses showed thatthe model was not sensitive to the initial d18O values in thehypolimnion (Miller 2008).

Primary productivity—Chl a concentrations at the lakeinlet were low (,1 mg L21) throughout the summer(Fig. 4). In contrast, the Chl a concentrations at the lakeoutlet were low in May and then began to increase.Following the rain event, the Chl a concentration at theoutlet decreased as the phytoplankton were flushed fromthe lake (Miller et al. 2009). Later in the summer, the Chl aconcentration at the outlet rapidly increased and remainedhigh (5–6 mg L21) for a period of 5 weeks.

The dominant phytoplankton found in Green Lake 4during the monitoring period (06 July–10 August 2006) wereof the divisions Cyanophyta and Chlorophyta. There werealso species of Bacillaryophyta, Dinophyta, Haptophyta, andCryptophyta in lower abundances. The only noticeable shiftin phytoplankton abundance occurred during the period ofhigh Chl a concentrations, as species of Chlorophyta(Chlamydomonas sp. and Chlorella sp.) approximately dou-bled in abundance (www.culter.colorado.edu).

Dissolved organic matter chemistry—Figure 5 shows thebiogeochemical characteristics of the DOM at the inlet and

outlet to Green Lake 4 during the summer. At both theinlet and outlet, DOC concentrations peaked duringsnowmelt (Fig. 5a,b) and then decreased and remainedrelatively steady (0.5–1 mg L21). The percentage of fulvicacid at both sites was high during snowmelt (,80%) anddecreased to approximately 20% during baseflow condi-tions (Fig. 5c,d). Immediately following the rain event, thepercentage of fulvic acid increased.

The SUVAH values of the humic fraction were high earlyin the summer at both sites, indicating that the humicfraction was more aromatic (Fig. 5e,f). Later in the season,during the phytoplankton bloom, the SUVAH valuesdecreased, indicating that the humic substances were lessaromatic. The greatest SUVAH value (SUVATH endmember 5 3.19 m2 g21; whole water FI 5 1.42) wasmeasured at the lake inlet on 06 July 2006. The lowestSUVAH value (SUVAMH end member 5 1.66 m2 g21;whole water FI 5 1.71) was measured at the lake outlet on17 August 2006. These values are nearly identical to theSUVA254 values of SRFA (3.2 m2 g21) and PLFA(1.7 m2 g21) (Weishaar et al. 2003).

During snowmelt, the whole water FI values at both siteswere low and similar to those of an adjacent wetland and tothe SRFA end member, indicating a terrestrial source ofDOM (Fig. 5g,h). Whole water FI values then increased asthe summer progressed. This trend was more pronouncedat the outlet than at the inlet. Following the rain event, thewhole water FI decreased by approximately 0.1 units at theoutlet to the lake but then rapidly increased, indicating amicrobial source of DOM. The peak in whole water FI(1.71) occurred on 17 August 2006 at the outlet and wasgreater than the FI measured for the PLFA end member.After snowmelt, the FI values of the humic fractionincreased consistently, but were lower than the wholewater FI values by more than 0.1 units.

The RI at the inlet was about 0.5 during the entiresummer, indicating that about half of the quinone-likefluorophores were in a reduced state (Fig. 5i). At the lakeoutlet, the RI was high early in the season, and then thequinone-like fluorophores became rapidly oxidized duringthe phytoplankton bloom late in the summer (Fig. 5j).

Table 4. Forcing functions and parameters used in the reactive transport model.

Model inputs Units Value Source

Forcing functions

Te uC Variable Measured in fieldChl a mg L21 Variable Measured in fieldRI Unitless 0–1 Scaled from field measurements

Parameters

kTH, decay coefficient of DOCTH pool d21 0.045 Slowly decaying DOC pool value fromMladenov et al. (2007)

kNH, decay coefficient of DOCNH pool d21 0.14 Rapidly decaying DOC pool value fromMladenov et al. (2007)

kMH, decay coefficient of DOCMH pool d21 0.045 Slowly decaying DOC pool value fromMladenov et al. (2007)

lNH, production coefficient of DOCNH pool d21 0.85 Estimated by calibrationlMH, production coefficient of DOCMH pool d21 0.01 Estimated by calibrationrca mg C mg Chl a21 0.03 Chapra (1997)

2218 Miller et al.

The FI of the humic fraction for the samples from thelake outlet ranged from 1.24 on 01 June 2006 to 1.43 on 24August 2006 (Fig. 5h). The percentage of humic DOM thatwas microbially derived ranged from 3% on 08 June 2006 to100% on 17 August 2006. There was a significant positiverelationship between the ratio of the microbially derivedhumic DOC concentration to total humic DOC concentration(DOCMH : DOCH) and the FI of the humic fraction of theDOM (r2 5 0.513; p , 0.01) (Fig. 6). Furthermore, the twosamples that had low RI values (Fig. 5j) and high concentra-tions of DOCMH at the end of the summer (see Fig. 7c) hadhigh DOCMH : DOCH ratios and high humic FI values.

Conservative and reactive simulations of DOM pools—The measured concentrations of DOCH and the threeDOM pools (DOCTH, DOCMH, and DOCNH) at the outletto Green Lake 4 varied substantially over the course of thesampling period (Fig. 7). In general, the conservativetransport simulation of the total humic pool (DOCH)overestimated the measured concentrations, indicating thathumic DOM was lost in the lake (Fig. 7a). However, thesimulation underestimated the total humic DOM near theend of the phytoplankton bloom, indicating in-lakeproduction of humic DOM.

The conservative transport simulation of the terrestriallyderived humic pool (DOCTH) overpredicted the measuredconcentrations at the outlet prior to the rain event(Fig. 7b). Addition of a decay term resulted in a better fitto the pre–rain event DOCTH data. Both the conservative

and reactive simulations underpredicted the measuredconcentration directly following the rain event. Late inthe summer, the measured DOCTH concentrations wereagain overpredicted by the conservative transport simula-tion, and the addition of the decay term resulted in a moreaccurate simulation of the DOCTH concentrations.

Generally, both the conservative and reactive transportsimulations of the microbially derived humic pool (DOCMH)overpredicted the measured concentrations prior to the rainevent (Fig. 7c). On the other hand, following the rain event,the reactive transport simulation more accurately simulatedthe measured concentrations. The conservative simulationunderpredicted the measured DOCMH concentrations ontwo dates during the Chl a peak, when the quinone-likefluorophores were oxidized (see Fig. 5j), indicating thatmicrobially derived humic DOM was produced in the lake.Addition of the first-order, redox-dependent decay term andthe DOCNH-dependent production term also resulted in anunderprediction of DOCMH on these two dates but provideda better fit than the conservative transport simulation.

The conservative transport simulation of the nonhumicpool (DOCNH) overpredicted the measured DOCNH

concentrations prior to the Chl a peak and underpredictedthe measured DOCNH concentrations during the peak inChl a (Fig. 7d). Addition of the first-order decay and Chl

Fig. 2. Discharge at the outlet to Green Lake 4 during thesummer of 2006. Following the snowmelt peak in late May thehydrograph peaked in early July in response to an unusual 3-drain event.

Fig. 3. Measured (points) and modeled (solid line) d18Ocontent at the outlet to Green Lake 4. The vertical dashed lineindicates the sustained rain event, and the shaded area representsthe timing of the peak in Chl a at the outlet. Also shown are the60.05% 1-s precision error bars. On all dates the error betweenthe measured and modeled values is less than 2%.

Fig. 4. Seasonal variation in Chl a at the (a) inlet and (b) outlet to Green Lake 4. Thevertical dashed lines indicate the sustained rain event, and the shaded areas represent the timingof the peak in Chl a at the outlet.


Fig. 5. Seasonal variation in (a, b) DOC, (c, d) percentage fulvic acid, (e, f) SUVAH, (g, h) whole water FI, and (i, j) whole water RIat the inlet (left) and outlet (right) to Green Lake 4. Also shown in (h) is the FI of the humic fraction of the DOM at the outlet to GreenLake 4 (dashed line with open diamonds). The vertical dashed lines indicate the sustained rain event, and the shaded areas represent thetiming of the peak in Chl a at the outlet. Also shown are the SUVATH and SUVAMH end members that were used in the calculation of thethree DOM pools. PLFA represents the FI of the Pony Lake fulvic acid end member, and SRFA represents the FI of the Suwannee Riverfulvic acid end member. The average FI and RI values from a wetland in the catchment as well as the average RI from an alpine streamsite in the catchment are shown for reference.

2220 Miller et al.

a–dependent production terms improved the simulationboth prior to and during the peak in Chl a. Sensitivityanalyses for all assumptions (as described in Table 3)showed that the model was not sensitive to changes in themixing regime in the lake, the initial DOC concentrationsin the hypolimnion, or the biogeochemical characteristicsof DOM in the precipitation (Miller 2008).

Sensitivity to photochemical degradation of DOCTH—Thesensitivity of the model to a time-variable decay coefficientfor DOCTH was used as a means to investigate theimportance of photochemical degradation of the DOCTH

pool (Fig. 8). When it was assumed that there was aconstant decay rate (kTH 5 0.045 d21) regardless ofwhether the lake was ice covered or ice free and the effectof temperature was removed (Q10 5 1.00) the RMSE was0.064. There was an improved fit to the measured DOCTH

concentrations when the terrestrially derived humic frac-tion was simulated with time-variable decay coefficients(kTH 5 0.022 d21 under ice; kTH 5 0.045 d21 when ice free)and Q10 5 1.00 (RMSE 5 0.052).

Sensitivity to DOCNH-dependent production of DOCMH—There was little overall difference between the simulationsof DOCMH concentrations at the outlet to Green Lake 4when the production of DOCMH was modeled as beingdependent on the concentration of the DOCNH pool or theconcentration of Chl a (Fig. 9). The deviation between thetwo simulations occurred at the end of the sampling period.The simulation in which the production of DOCMH wasdependent on [DOCNH] provided a better fit to themeasured concentrations on 17 and 24 August 2006, whilethe simulations in which DOCMH production was depen-dent on [Chl a] provided a better fit to the measured 07September 2006 concentration.

Sensitivity to humic redox-dependent decay of DOCMH—The model was somewhat sensitive to the dependence of thedecay of the DOCMH pool on the redox state of thequinone-like fluorophores (Fig. 10). The simulation inwhich the decay of DOCMH was dependent on the scaledRI values (RMSE 5 0.100) resulted in a better fit to themeasured concentrations than did the simulation where thedecay was not dependent on the RI (RMSE 5 0.105). Thesimulations were essentially identical early in the season.The deviation between the two simulations occurred duringthe last three sampling dates of the season. On 17 and 24August 2006, when the quinone-like fluorophores wereoxidized (RI 5 0.27 and 0.28, respectively), the simulationin which the decay of DOCMH was dependent on the scaledRI provided a better fit to the measured concentrations.This simulation then deviated from the measured value on07 September 2006 when the quinone-like fluorophoreswere in a moderately oxidized state (RI 5 0.34).

Discussion

Hydrologic modeling—When modeling biogeochemicalprocesses of reactive solutes such as DOM in aquaticsystems, it is necessary to understand hydrologic controls

on the solutes of interest (Bencala 1983). The excellent fitbetween the measured d18O values at the outlet and thosesimulated assuming conservative transport (Fig. 3) indi-cates that the assumption that flow at the outlet is afunction of flow at the inlet and precipitation is valid (e.g.,direct groundwater input and evaporation can be ignored;as in Eq. 8). It follows that the change in the volume of theepilimnion is controlled by the degree of mixing with thehypolimnion. The fact that there was a good fit between themeasured and modeled d18O without modeling directgroundwater input to the lake is not surprising. In thesouthern Sierra Nevada mountains of California, Williamset al. (1990) found that direct seepage of groundwater intoan alpine lake was not an important source of water to thelake. Using the upper value for evaporative loss frommountain lakes reported by Gurrieri and Furniss (2004)(3.8 mm d21) for Green Lake 4 corresponds to anevaporative loss from the surface of Green Lake 4 of203 m3 d21. This value is 2.1% of the average dailydischarge at the outlet to Green Lake 4 measured duringbaseflow conditions in August 2006, when the residencetime of the lake was high. This evaporative loss couldexplain the small (1.7%) deviation between the measuredand modeled d18O values at this time. However, anypotential evapo-concentration of DOC would not beexpected to affect the interpretation of the DOM modelingresults because the differences between measured andmodeled DOC concentrations at this time of year weregreater than 2%.

Separation of DOM into three pools of differing source—The partitioning of DOM into three pools used in thisstudy was based on direct measurement of the total humicand nonhumic fractions and partitioning of the humicfraction based on measured humic SUVA254 values. Theresults of the SUVA-based partitioning and the choice ofSUVATH and SUVAMH were evaluated by comparing theDOCMH concentrations with the measured FI of the humic

Fig. 6. Relationship between the ratio of microbially derivedhumic DOC and total humic DOC (DOCMH : DOCH) and thefluorescence index (FI) of the humic fraction of the DOC fromsamples collected at the outlet to Green Lake 4. There was asignificant positive relationship between DOCMH : DOCH andhumic FI (r2 5 0.51; p , 0.01) when considering all samples at theoutlet. Also shown are the FI values of the Pony Lake fulvic acid(PLFA) and Suwannee River fulvic acid (SRFA) end members.


Fig. 7. Measured DOC concentrations (points), conservative transport simulation (dashedline), and reactive transport simulation (solid line) for (a) the total humic fraction, (b) theterrestrially derived humic fraction, (c) the microbially derived humic fraction, and (d) thenonhumic fraction at the outlet to Green Lake 4. Note that a reactive transport simulation wasnot run for the total humic fraction. The vertical dashed lines indicate the sustained rain event,and the shaded areas represent the timing of the peak in Chl a at the outlet. The 60.02%confidence interval error bars are shown with the DOCNH measured values. Also shown arethe RMSE.

2222 Miller et al.

fraction. Indeed, the significant positive relationshipbetween DOCMH : DOCH and the FI of the humic fractionof the samples from the lake outlet provided additionalspectroscopic support for the separation approach. Thehigh DOCMH : DOCH ratios and high humic FI values forthe two samples collected at the end of the summer peak inChl a supports the idea that the humic fraction of the DOMof these samples is from a microbial source.

The steady increase in the FI of the humic DOM(Fig. 5h) indicates a microbial source of humic DOM.Additionally, the observed production of total humicDOM in the lake near the end of the phytoplankton bloom(Fig. 7a) can be attributed primarily to in-lake productionof microbially derived humic DOM (Fig. 7c). While themodeling results show that photochemical degradation isan important control on the quantity of terrestrially derivedhumic DOC (Fig. 8), the observed changes in quality of thehumic DOM (e.g., decrease in SUVAH and increase in FI)are not likely due to photochemical degradation. This is

supported by the finding that any photochemical alterationof the humic DOM pool would be expected to decrease theFI (Cory et al. 2007). Therefore, loss of DOC from thehumic pool due to photochemical degradation can likely beattributed to conversion of humic DOM to nonhumicDOM or CO2, rather than an alteration of the quality ofthe humic DOM. Taken together, these results providesupport for the idea that the decrease in SUVAH is drivenby the addition of microbially derived humic DOM insteadof biogeochemical alteration of the SUVA end-membervalues.

The SUVA-based approach may be more robust thanusing the measured humic FI to partition the humic DOMbecause most humic molecules are likely to be chromo-phores and contribute to the SUVA254 of the humicfraction (Weishaar et al. 2003). Using FI would requiresome assumptions about fluorescence intensity per mass ofhumic DOM, which was found to vary by a factor of twoor more for humic samples from similar sources (McKnight

Fig. 8. Measured DOCTH concentrations (points), simulation with a kTH 5 0.045 d21 all season (solid line), and simulation withkTH 5 0.022 d21 when the lake was ice covered and kTH 5 0.045 d21 when the lake was ice free (dashed line) for the terrestrially derivedhumic fraction of the DOC at the outlet to Green Lake 4. Note that the temperature effect on the decay rate was removed for thesesimulations (Q10 5 1.00). The vertical dashed line indicates the sustained rain event, and the shaded area represents the timing of the peakin Chl a at the outlet. Also shown are the RMSE.

Fig. 9. Measured DOCMH concentrations (points), simulation with DOCMH production dependent on [DOCNH] (solid line), andsimulation with DOCMH production dependent on [Chl a] (dashed line) for the microbially derived humic fraction of the DOC at theoutlet to Green Lake 4. The vertical dashed line indicates the sustained rain event, and the shaded area represents the timing of the peakin Chl a at the outlet. Also shown are the RMSE.


et al. 2001). This variability may reflect a lack offluorescent properties for some molecules in the humicfraction (McKnight et al. 2003).

Conservative and reactive transport modeling of DOM—The modeling results confirm that both in-lake decay andproduction of DOM from microbial sources are importantprocesses influencing downstream transport of DOM. Thevalidation of assumptions regarding mixing and distribu-tion of the DOM pools in precipitation and thehypolimnion at the beginning of the season allows fordeviations between conservative and reactive transportsimulations of DOC and measured DOC to be interpretedwith confidence. The addition of reactive transport termsto the transport equations resulted in improved simula-tions at the outlet to Green Lake 4 for all three DOMpools. This indicates that transport of the three DOMpools is not conservative. The nonconservative behavior ofDOM in a wetland has been demonstrated previously(Mladenov et al. 2007). By using decay values for humicDOM from the literature (Mladenov et al. 2007),optimization of the model resulted in a large differencebetween the production coefficients of nonhumic DOMand microbially derived humic DOM (Table 4). Theconcentration of DOCNH measured at the outlet wasgenerally two to four times greater, and is morebiologically labile, than DOCMH. Therefore, it is notsurprising that the coefficient for the production ofDOCNH is nearly two orders of magnitude greater thanthat for the production of DOCMH.

Photochemical controls on terrestrially derived humicDOM—Degradation by both bacteria and UV light canbe important controls on DOM quantity. Amado et al.(2006) showed that bacterial degradation is the main driverof decay of autochthonous DOM, while photochemicaldegradation plays a greater role in the decay of allochtho-nous DOM. Cory et al. (2007) showed that photodegrada-

tion effects on DOM increased with increasing residencetime for fulvic acids isolated from arctic surface waters.This observation suggests that photodegradation may bemore important in Green Lake 4 late in the season whenthe water residence time is two to four times greater thanthe water residence time during snowmelt.

The general overestimation of terrestrially derived humicDOC concentrations by the conservative transport modelsuggests that DOCTH is degraded by microbes and/or UVlight. Terrestrially derived humic DOM that enters surfacewaters during snowmelt has a high aromaticity (Weishaaret al. 2003). Therefore, this fraction absorbs more lightthan the other two DOM pools and may be moresusceptible to photodegradation and more recalcitrant tomicrobial degradation as compared with the less aromatic,less conjugated DOCNH and DOCMH. The importance ofphotochemistry in controlling the concentrations ofDOCTH is further highlighted by the improved simulationwhen a lower decay coefficient was used when the lake wasice covered (microbial degradation only) and a higherdecay coefficient was used when the lake was ice free(microbial degradation and UV degradation).

Chlorophyll a–dependent production of nonhumic DOM—In-lake primary production by phytoplankton can be adominant source of autochthonous nonhumic DOM. Thisproduction is a critical link in the microbial loop of aquaticecosystems. In a lake system that experiences a wide rangeof Chl a concentrations, Gondar et al. (2008) report thatconcentrations of autochthonous DOM are significantlycorrelated with Chl a concentrations (a proxy for primaryproduction by phytoplankton). Likewise, in the ArcticOcean, Davis and Benner (2007) have shown that there is astrong seasonal link between primary productivity and theproduction of labile DOM. The overestimation of DOCNH

early in the season and underestimation during the Chl apeak by the conservative transport model suggest acombination of microbial and UV degradation as well as

Fig. 10. Measured DOCMH concentrations (points), simulation with DOCMH decay dependent on scaled RI values (solid line), andsimulation with DOCMH decay not dependent on RI values (dashed line) for the microbially derived humic fraction of the DOC at theoutlet to Green Lake 4. The vertical dashed line indicates the sustained rain event, and the shaded area represents the timing of the peakin Chl a at the outlet. Also shown are the RMSE.

2224 Miller et al.

microbial production of DOCNH. It is reasonable toassume that this production is dependent on the biomass(e.g., Chl a concentration) of the phytoplankton in thesystem. The addition of a Chl a dependent production termto the conservative transport model for the DOCNH poolimproved the simulation significantly. This result indicatesthat although nonhumic DOM comes from both allochtho-nous and autochthonous sources, the in-lake production istightly coupled with the primary productivity of the system.

In-lake production and decay of microbially derivedhumic DOM—Lake and stream systems with terrestrialvegetation in the surrounding watershed receive inputs ofhumic DOM from the terrestrial environment. This isdemonstrated by the pulse of DOCTH to Green Lake 4during snowmelt and following the rain event. However,during the late summer, at least 50% of the measured humicDOM at the outlet to Green Lake 4 was microbiallyderived, indicating an in-lake source of humic DOM. In-lake production of humic DOM during the phytoplanktonbloom is also supported by the underestimation of themeasured total humic DOC by the conservative transportmodel. The SUVA-based DOC separation and the conser-vative transport modeling of the DOCMH fraction indicatethat the in-lake production of humic DOC is microbiallyderived.

Autochthonous production of microbially derived humicDOM has been demonstrated to occur in oceans (Harvey etal. 1983) and lakes without terrestrial plants in thewatershed (McKnight et al. 1994), but it is also likely tooccur in freshwater ecosystems that receive inputs ofterrestrially derived humic DOM. One potential mechanismof in-lake production of microbially derived humic DOM iscondensation reactions, whereby nonhumic DOM condens-es to form humic DOM (Harvey et al. 1983; Kieber et al.1997). The fact that the production of DOCMH at the end ofthe peak growing season corresponded with the timing ofhigh concentrations of DOCNH in the lake suggestscondensation reactions as a possible mechanism for theobserved production. However, this hypothesis was notdirectly supported by the model results over the course of theentire sampling season. When DOCMH was modeled ashaving [DOCNH]-dependent production or as having [Chla]–dependent production, there was little overall differencein how well the simulations matched the data. Thepronounced differences between these two simulations latein the summer indicate that condensation reactions may bedriving DOCMH production primarily when the quinone-like fluorophores in the lake become oxidized. It may be thatany condensation reactions taking place to produce DOCMH

are masked by heterotrophic decay of DOCMH during therest of the summer when the quinone-like fluorophores aremore reduced. It is worth noting that the treatment of thenonhumic DOM fraction as a single pool is a simplification.In actuality, the nonhumic fraction is composed ofcompounds of differing degrees of reactivity such as aminoacids, carbohydrates, and carboxylic acids (Thurman 1985).It is possible that one or more of these subpools of nonhumicDOM could be driving any potential condensation reactionstaking place to form DOCMH.

The observed peak in DOCMH late in the season, whenthe quinone-like fluorophores were oxidized, suggests thatthere may be an oxidation threshold whereby quinone-likefluorophores associated with microbially derived humicDOC become oxidized and are no longer readily decayedby microorganisms. This is further supported by theobservation that the simulations of DOCMH being redox-dependent or not redox-dependent deviate from oneanother during the late season. DOCMH, which is at leastin part produced in the lake, is less conjugated thanDOCTH and therefore more likely to be used as an electrondonor by microorganisms, thereby becoming oxidized, thanis DOCTH. The oxidized quinone-like fluorophores associ-ated with this humic DOM may be more processed thanreduced DOM and are therefore more chemically stableand less susceptible to microbial degradation. Early in theseason when the quinone-like fluorophores are reduced,heterotrophic decay may drive changes in DOCMH. Laterin the season, when oxidized, the DOCMH may behavemore like a large aromatic DOCTH molecule that is difficultfor organisms to use as an electron donor and is moredependent on UV degradation.

The results presented in this study suggest that separat-ing DOM into pools of differing source and reactivityprovides insight into DOM–biota interactions and poten-tially the trend of increasing DOC concentrations seen innorth temperate surface waters around the world (Mon-teith et al. 2007). Through the use of a rigorous modelingapproach, it was demonstrated that lakes in a catchmentmay themselves be sources of increases in DOC concen-trations observed in downstream surface waters. The modelresults presented here provide quantitative support for theconceptual model put forward by Hood et al. (2003).Specifically, it has been demonstrated that lakes in theupper portion of the catchment are essentially wide pointsin the stream during snowmelt and do not contribute DOMto downstream surface waters. Later in the season duringtimes of increased primary productivity, lakes act as asource of autochthonous DOM to downstream surfacewaters. Finally, three pools of DOM from different sourceshave been shown to behave differently in response tochanges in biogeochemical conditions in the lake, such asthe amount of primary productivity, UV light exposure,and the redox state of quinone-like fluorophores.

AcknowledgmentsWe thank B. Vaughn, for isotopic analyses and T. Bell for

identification of phytoplankton. N. Caine and K. Alexanderprovided field assistance, and R. Runkel provided helpfulcomments. We are grateful to two anonymous reviewers for theircomments on earlier versions of the manuscript. This work wasfunded by the National Science Foundation’s Niwot Ridge LongTerm Ecological Research Program (grant DEB-0423662) andCritical Zone Observatory project (grant EAR-0724960).

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Associate editor: Stephen P. Opsahl

Received: 01 October 2008Accepted: 20 July 2009

Amended: 04 August 2009


matthew p. miller, diane m. mcknight, steven c. chapra...

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