Diversity and temporal sequences of forms of DOC and NO3-discharge

responses in an intermittent stream: Predictable or random

succession?

Andrea Butturini,1 Marta Alvarez,1 Susana Bernal,2 Eusebi Vazquez,1

and Francesc Sabater1

Received 3 March 2008; revised 16 May 2008; accepted 9 June 2008; published 5 August 2008.

[1] Storm events have major implications for biogeochemical cycles at local and regionalscales and they provide an excellent opportunity to study the hydro-biogeochemicalfunctioning of catchments. However, concentration-discharge (C-Q) responses have onlybeen studied in detail for short periods or a few selected events. In consequence, it isdifficult to quantify the diversity of C-Q responses in a hydrological system andimpossible to assess whether the succession of forms of C-Q responses follows apredictable sequence or not. Bearing in mind these shortfalls, the variability of dissolvedorganic carbon (DOC) and nitrate (NO3) pulses during storms is analyzed in a detailed4-year series from an intermittent Mediterranean stream. In this study, each DOC andNO3-Q response is synthesized by two descriptors that summarize its trend (DC; dilution/flushing/no change) and shape (DR; linear/nonlinear response). We observe that C-Qresponses are widely distributed along the two-dimensional DR versus DC continuum.Furthermore, the temporal succession of forms of DOC and NO3-Q responses follow arandom pattern, and only the dynamics of the DR(NO3) descriptor show periodicity. Thelong-term data set reveals that it is impossible to predict with reasonable precision thefull properties of DOC and NO3-Q responses. Thus, a ‘‘typical’’ C-Q response does notreally exist at our study site, and this apparent diversity of responses has to be handledwith a probabilistic approach that allows synthesis of the complexity of the hydro-biogeochemical functioning of a specific catchment.

Citation: Butturini, A., M. Alvarez, S. Bernal, E. Vazquez, and F. Sabater (2008), Diversity and temporal sequences of forms of

DOC and NO3-discharge responses in an intermittent stream: Predictable or random succession?, J. Geophys. Res., 113, G03016,

doi:10.1029/2008JG000721.

1. Introduction

[2] Storm events are the most effective cause of soluteflushing in streams on a short timescale (from hours toweeks). Their occurrence, frequency and magnitude havemajor implications for biogeochemical cycles at local andregional scales and for management of inland waters[McClain et al., 2004].[3] Concentration-discharge (C-Q) responses related to

storms span from linear to nonlinear relationships [Evansand Davies, 1998]. These patterns reflect the complexhydro-chemical processes in watersheds and provide crucialinformation for determining the origin and fate of solutes/pollutants in running waters.[4] The visual characteristics of C-Q responses (slope,

shape and rotational pattern if hysteresis appears) facilitatetheir classification with a few, simple parameters [Johnson

and East, 1982; Evans and Davies, 1998; House andWarwick, 1998; Butturini et al., 2005]. For instance, Evansand Davies [1998] identified 6 discrete C-Q hysteresis typeswithin the framework of the mixing hydrological model[Christophersen et al., 1990]. Being conscious that astraightforward hydro-chemical interpretation of solute pat-terns during storms requires caution [Butturini et al., 2005;Rice et al., 2004; Chanat et al., 2002], the typification ofthese specific C-Q patterns represents a promising startingpoint for the study of concentration fluctuations duringstorms in terms of ‘‘diversity’’ of C-Q responses.[5] To date, the diversity of C-Q responses has barely

been studied by hydro-biogeochemists. Due to obviousmethodological and/or human resource constraints, mostof the research focused on C-Q responses has gatheredthe information from small catchments (preferentially intemperate regions) during a few selected events or forrelatively short periods. These studies might convey theperception that a satisfactory description of C-Q responsesfor a specific solute could be obtained by monitoring only afew events in detail. Nevertheless, the increasing evidencethat forms of C-Q responses show significant variability[Soulsby, 1995; Biron et al., 1999; Evans and Davies, 1998;

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 113, G03016, doi:10.1029/2008JG000721, 2008ClickHere

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1Department d’Ecologia, Universitat de Barcelona, Barcelona, Spain.2Department of Ecology and Evolutionary Biology, Princeton

University, Princeton, New Jersey, USA.

Copyright 2008 by the American Geophysical Union.0148-0227/08/2008JG000721$09.00

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Rice et al., 2004; Andrea et al., 2006; Inamdar et al., 2006;Ocampo et al., 2006] and the opportunity to automate long-term high resolution sampling programmes [Kirchner et al.,2004] will encourage a more exhaustive analysis of solutebehavior associated with storm events under a wide spectraof hydro-climatic conditions.[6] No one has explored the characteristics of a large

series of C-Q responses as a concatenation of storm epi-sodes of different magnitudes spaced at irregular intervals.Furthermore, an attempt to synthesize the description offorms of C-Q responses from the point of view of their‘‘diversity’’ is, to our knowledge, missing. In this context,the questions that this study attempts to answer are thefollowing: (1) How diverse are the C-Q responses ofnutrients? (2) Does the temporal succession of types of C-Q response occur in a predictable sequence? And finally, alogical question that arises from question 2 is: (3) If apredictable sequence is detected, what controls it?[7] Here we focus on nitrate (NO3) and dissolved organic

carbon (DOC)-Q responses. Both solutes are reactive andtheir patterns integrate the hydrological mechanisms andbiotic processes that occur in catchments [Mulholland andHill, 1997]. Furthermore, NO3 and DOC are studied widelyby biogeochemists because of their relevance to the actualnitrogen and carbon cycles in catchments and their ongoingalteration as well as to improving the management of waterquality for human consumption [Houghton, 2003;Galloway,2003].[8] In order to provide a complete view of the diversity of

DOC and NO3-Q responses and their temporal succession,both solutes were monitored in detail over 4 years in anintermittent Mediterranean stream. The DOC and NO3-Qresponses for each storm event are described with two simpledescriptors successively plotted in a two-dimensional unityplane. The diversity of DOC and NO3-Q responses(question 1) is explored in terms of the dispersion of datain the unit plane and by means of the classic Shannondiversity index. Contingency periodograms analysis[Legendre et al., 1981] is used to explore the temporalsuccession of types of C-Q response (question 2). Finally, ifcyclicities in C-Q responses are detected, they are over-lapped with those observed for environmental variablescharacterizing hydrological and climatic conditions prevail-ing in the catchment (i.e., the magnitude of the storm events,the wetness in the catchment prior to the storm event, andthe seasonal changes in temperature) (question 3).

2. Hydrological and BiogeochemicalCharacterization of the Study Site

[9] The hydro-chemical data set used in this research isfrom the Fuirosos stream in the Montnegre natural park(41� 420N; 2� 340E; 50–700 m a.s.l.). Fuirosos drains aforested, granitic catchment of 13 km2. The forest (oakholm, coniferous and deciduous) covers 90% of the totalcatchment area. Climate is Mediterranean with air temper-ature ranging from �2 to 28�C. Streamflow is typicallyintermittent with a no flow period from July to Septem-ber, followed by a dry-wet hydrological transition phase(September–October) and a humid period with permanentflow (October–May) [Butturini et al., 2003]. Stream basalflow discharge ranged between 0 and 25 L s�1 (Figure 1a).

[10] The hydro-biogeochemical data set covers nearly4 years, from September 1999 to April 2003. This temporalseries includes a wide spectrum of hydro-climatic condi-tions during which the frequency and magnitude of stormswas extremely erratic [Bernal et al., 2006]. The timeelapsing between rain episodes ranged typically from 4 to30 days, but longer periods without precipitation were farfrom being sporadic. For instance, no-rain periods ofbetween 3 and 5 months occurred in summers 2000 and2001 and spring 2000. On the other hand, the heavyprecipitation episodes recorded in spring and summer2002 prevented the summer dry period (Figure 1a). Duringthe study 105 rain events ranging from 2 to 153 l m�2

occurred. Storm hydrographs were negligible when totalprecipitation (RainTot) was lower than 8 l m�2. Thus, weattempted to obtain as much hydro-chemical data as possi-ble from the 63 storm events with RainTot � 8 l m�2.[11] The magnitude of storms (DQ) ranged from 1 to

>2000 l s�1 and showed a significant relationship withRainTot (r

2 = 0.6, df = 61, p < 0.001), the data from the dry-wet hydrological transition period being responsible formost of its variability [Butturini et al., 2002]. Storm hydro-graphs lasted from 0.5 to 18 days, and in 80% of cases theywere shorter than a week. Hydrographs were brief with arising discharge limb shorter than 12 hours or 1 day in 66%and 84% of the events, respectively.[12] In stream water, DOC and NO3 concentrations

ranged between 2 and 20 C ppm and 0.01 and 3 NO3–Nppm (Figures 1b and 1c). Both solutes showed clearseasonal patterns. Nitrate basal concentration showed amaximum in winter and a minimum in late spring-summer,which may be a response to seasonal variation in nitrateuptake by terrestrial vegetation and soil microbial organisms[Butturini et al., 2003; Bernal et al., 2005]. In turn, DOChad a typically steep peak during the transition between thedry and wet periods (September–October). Previous studiesattribute the DOC peak to the flushing of abundant organicmatter accumulated in the streambed during the dry period[Bernal et al., 2002; Butturini et al., 2005; Romanı et al.,2006; Vazquez et al., 2007]. The lack of a DOC peak inSeptember–October 2002, preceded by a wet summerperiod, supported this hypothesis.

3. Material and Methods

3.1. Field Monitoring Strategy

[13] Stream water was sampled manually every 7–14 daysduring base flow conditions. Sampling frequency wasincreased up to every 2–5 hours during storms with a stageactuated water sampler (Sigma 900 max).[14] Stream discharges were estimated on each sampling

date by mass balance calculation using the ‘‘slug’’ chlorideaddition method [Gordon et al., 1992]. The stream waterlevel was continuously recorded using a water pressuretransducer connected to the automatic sampler.[15] As previously mentioned, a total of 63 precipitation

events met the condition RainTot3 8 l/m2. A detailed descrip-

tion of C-Q responses was obtained in 49 cases (78% oftotal events). The distribution of the missing values isrelated to the magnitude of the rain events. In fact, mostof the missing C-Q responses are associated with the

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smallest rain episodes, and the percentage of sampled casesincreases up to 90% if RainTot > 20 mm.

3.2. Chemical Water Analysis

[16] All water samples were filtered through pre-ashedGF/F glass fibre filters and stored at 4�C until analyzed.Nitrate (NO3–N) was analyzed colorimetrically with aTechnicon Autoanalyser (Technicon 1976) after reductionof the solute on a copper-doped cadmium column. Dis-solved organic carbon (DOC) was analyzed with a high-temperature catalytic oxidation method (Shimadzu TOCanalyzer) (for additional information, see Bernal et al.[2006]).

3.3. Description of C-Q Responses

[17] Each C-Q response is characterized by two simplesemiquantitative descriptors that summarize solute fluctua-

tion during the storm episode: DC (DC(DOC), DC(NO3)) andDR (DR(DOC), DR(NO3)). DC (%) describes the relativechanges in solute concentration of the C-Q response, by thefollowing formula:

DC ¼

Cs � Cb

Cs

100 if Cs > Cb

Cs � Cb

Cb

100 if Cs < Cb

8>><>>:

9>>=>>;

ð1Þ

where Cb and Cs are the solute concentrations at the baseflow and during the peak of the storm hydrograph,respectively. DC ranges between �100 and 100. NegativeDC values indicate solute dilution. Positive DC valuesindicate solute flushing.

Figure 1. Temporal dynamics of (a) daily precipitation and discharge, (b) NO3 (as N), and (c) DOCduring the study period. Gray lines in Figures 1b and 1c show the air temperature regime. The shadedarea outlines the summer no flow periods.

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[18] The DR descriptor (%) ranges between �100 and100 and provides information about the area and rotationalpattern of the C-Q response:

DR ¼ R * A * 100 ð2Þ

where A is the area of the C-Q response, estimated afterstandardizing discharges and concentrations to a unity scale(0 � A � 1). The term R describes the rotational pattern ofC-Q responses and therefore the timing of solute changesduring storms:

R ¼ 1 clockwise rotational pattern

R ¼ �1 counterclockwise rotational pattern

For ambiguous or nonexistent rotational patterns we setR = 0.

3.4. Classification and Diversity of C-Q Responses

[19] The combination of the DC and DR descriptors(D(CR)) synthesizes the variability of the geometricalproperties of the C-Q responses in the two-dimensionalcontinuum unity plane DR versus DC.[20] A discrete qualitative classification of C-Q responses

can be obtained by splitting the continuum unity plane into9 regions (labeled from ‘‘1’’ to ‘‘9’’). Each region identifiesa C-Q response type. This implies classifying the DC andDR descriptors into three qualitative categories (‘‘�1,’’ ‘‘0’’

and ‘‘1’’). The threshold of ±10% is used to generate thesediscrete categories:

DC < �10% �1 solute dilutionð Þ�10% � DC � 10% 0 neutralð Þ

DC > 10% 1 solute releaseð ÞDR < �10% �1 counterclockwise loopð Þ

�10 � DR � 10% 0 no loopð ÞDR > 10% 1 clockwise loopð Þ

[21] The unity plane is then divided into 32 regions. Inthis way the DR versus DC plane includes the six C-Qhysteresis types (i.e. DR 6¼ 0) proposed by Evans andDavies [1998] plus the simple lineal C-Q responses (i.e.DR 0) (Figure 2). Having defined the C-Q hysteresisinto the nine discrete types, synthesis of their diversity isestimated by means of the classic diversity Shannon index(H) which is widely used in ecology, applied mathematics,statistics and physics [Shannon, 1948]:

H ¼ �X9i¼1

pi ln pið Þ ð3Þ

where pi is the relative abundance of each C-Q type.

4. Time Analysis

4.1. Contingency Periodogram Analysis

[22] Periodicities of the temporal succession of C-Qresponse types and of three environmental parameters wereexplored with contingency periodograms (hereafter CP), amethod for analyzing the presence/absence of periodicity inshort series that requires the input of categorical data[Legendre et al., 1981].[23] For each data series a list of contingency statistic Hcs

values were estimated for T periods ranging from 2 to 63/2storm events. Hcs values were calculated according toShannon [1948].[24] Graphically a CP consists of a plot relating the values

of Hcs to the investigated T periods (Figure 3). Thesignificance of a period T is tested successively by deter-mining the probability that the associated Hcs value differsfrom zero, using the following formula [Legendre et al.,1981]:

Hcs > c2=2N ð4Þ

where c2 is the value of Chi-Square at the selectedprobability level (see below); N is the number of stormevents in the data series. Thus, a hypothetical significant oflength T indicates a cycle that comprises a sequence of Tstorm events (Figure 3).[25] The presence/absence of periodicity in the DC and

DR descriptors were estimated by using in each case thethree qualitative categories described previously (i.e., ‘‘1,’’‘‘0’’ and ‘‘�1’’). Similarly, the periodicity of the combina-tion of DC and DR in the unity plane (D(CR)) wasestimated using the 9 qualitative types.

Figure 2. Schematic representation of the unity plane DCversus DR that describes the diversity continuum across thegeometrical forms of C-Q responses. In this plane, thevertical and horizontal dotted lines delimit the nine discretedifferent types of C-Q response (see text for additionalinformation).

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4.2. Contingency Periodogram Matrices for C-QResponse Descriptors

[26] Given that C-Q descriptors (DC, DR and D(CR))were missing in 22% of cases, in order to improve therobustness of the contingency statistic Hcs estimations weproceeded as follows:[27] 1. A random value of DC, DR and D(CR) was

assigned to the events without chemical information.

[28] 2. To reduce the uncertainty generated by randomvalues, Hcs was calculated 104 times and the mean±standarddeviation was calculated for each T period investigated.[29] 3. Being aware that the percent of sampled storm

events increased with RainTot, we assembled a matrix ofcontingency periodograms for each C-Q descriptor. Theconcatenation of the individual CPs that composed a matrixwas obtained after eliminating those storm events caused bya rain episode lower than a selected RainTot threshold fromthe data series. As the RainTot threshold increased, thenumber (N) of storm cases in the data series decreasedbut the percent of sampled storm events increased. In ourcase the RainTot threshold increased progressively from 8 to30 l/m2. Conversely the number of storm episodes (N) ineach data series decreased gradually from 63 to 29 and thepercent of sampled storm events increased from 78% to93% (see Figure 3 for an example of individual CP graphsthat composed a CP matrix).[30] 4. For each CP matrix only T periods with Chi-

Square probabilities p < 0.05, p < 0.001 and p < 0.005(equation (4)) were extracted and plotted on a surface graph.[31] 5. Having obtained the surface graph face for each C-

Q descriptor, a cycle is considered robust solely when wecan draw an oblique line that connects the significant Tperiods detected at different RainTot thresholds. Otherwisewe assumed that the data series was randomly assorted.

4.3. Contingency Periodogram Matricesfor Environmental Parameters

[32] Contingency periodograms can be used to explorethe periodicity of environmental variables after reducing thequantitative series into three categories according to thecriteria of Legendre et al. [1981].[33] In this study, we included three intuitive environ-

mental variables frequently used to explore potential cause-effect relationships in hydro-biogeochemical catchmentstudies [Rice et al., 2004] and that also synthesized thehydro-climatic properties of the Fuirosos catchment appro-priately [Bernal et al., 2002]: (1) magnitude of each stormevent (DQ); (2) basal discharge (Qbas) immediately preced-ing each storm, indicating the antecedent wetness of thecatchment; (3) average air temperature during each storm(Tday), capturing the regularity of seasons over time andrepresenting a proxy for biotic activity in the catchment[Hobbs et al., 1995]. After the categorization of environ-mental parameters, CP matrices were assembled followingthe same criteria used for C-Q descriptors. Environmentaldata were available for all storms, and so there was no needto include random values (i.e., the previously describedsteps 1 and 2 were unnecessary).

5. Results

5.1. Time Analysis of Environmental Variables

[34] The CP analysis detected a significant cycle with aperiod that comprised 16 storm events (T = 16; N = 63 andits replica at T = 31; N = 63) for Qbas and Tday (Figures 4aand 4b, respectively). These cycles are robust. For instance,for Qbas, the cycle persisted as the RainTot thresholdincreased, though the length of T declined progressivelyas the number of N decreased (from 16 to 6), until itvanished at N < 30. An oblique line connecting these

Figure 3. Selected contingency periodograms of DR(NO3)

obtained after eliminating the storm events below a selectedRainTot threshold from the data set. Broken lines delimit thecritical values at the 0.05, 0.01, and 0.005 probability levels.Arrows show the significant T periods detected by thecontingency analysis (small arrow: p < 0.05; medium arrow:p < 0.01; large arrow: p < 0.005). For example, in Figure 3bthe analysis detected an Hcs value that differed from zero atp < 0.005 at a period T = 18, i.e., a cycle that comprised18 storm events. In Figures 3c and 3d the cycles emergedevery 15 and 11 storm events, respectively (see the TimeAnalysis section for additional explanation). N is thenumber of storm events in the data series. In parenthesisis the percent of sampled events.

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significant periods at different RainTot thresholds is drawnin the surface graph (Figure 4a). The cycle observed forTday does not overlap exactly with that for Qbas. In fact,from N < 60, this cycle is always longer than that for Qbas

(Figure 4d).[35] The magnitude of storm events (DQ) shows a clear

and persistent cycle. This cycle starts with a length of T = 21(N = 63), and decreases to T = 13 before disappearing (N <39) (Figure 4c). Therefore, the period of this cycle is longerthan those estimated for Qbas and Tday (Figure 4d) at aRainTot threshold lower than 12 l m�2.

5.2. Diversity and Time Analysis of DOCand NO3-Q Responses

[36] Dispersion of DOC-Q response data in the DRversus DC unit plane clearly differed from that of NO3.DOC data covered 16% of the unit plane (Figure 5a), whilethe NO3 data were spread over 48% (Figure 5b). In moredetail, DOC data fall in 6 regions of the unity plane. A totalof 40% of events are type 2 and the remaining regions rangebetween 27% (type 1) and 2% (types 3 and 7). H(DR,DC)

values, measured sequentially during the events series, peakat 2.4 after 9 events (Figure 5a, inset). DOC release (DC >10%, 73% of events) clearly predominates over dilution. Atotal of 51% of cases are linear DOC-Q responses (�10% <DR < 10%), while 45% are clockwise DOC-Q hysteresis(DR > 10%).[37] NO3 data fall in all the 9 potential regions of the

unity plane. The most probable NO3-Q response is that oftype 3 (37%) and the contribution of the remaining NO3-Qtypes ranges between 14% (type 2) and 2% (type 5). AllNO3-Q responses are rather well distributed over time.Consequently, H(DR,DC) values, measured sequentially overthe events sequence, increase more slowly than DOC andpeak at a value of H(DR,DC) 2.9 after 18 events (Figure 5b,inset). NO3 release (69% of events) predominates overdilution. Counterclockwise, clockwise, and linear NO3-Qresponses are 47%, 24% and 27%, respectively.[38] The CP analysis indicated that D(CR)(DOC) and

D(CR)(NO3) do not show any predictable pattern (data notshown). Therefore, the displacement of DOC and NO3 datain the DR versus DC unit planes follows a random pattern.

Figure 4. Contingency periodogram matrix for (a) basal discharge preceding the storm events (Qbas),(b) air temperature during storms (Tday), and (c) magnitude of the storm events (DQ). Solid oblique linesthat overlap the periodogram matrices connect the statistically significant T periods at different RainTotthresholds. (d) Location of the oblique lines of the three environmental variables in a single periodogrammatrix.

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Some significant periods with short lengths (2 < T < 8 forDOC and 2 < T < 6 for NO3) were detected (in most casesp < 0.05), though such cycles were inconsistent becausethey did not re-emerge regularly during longer periods(i.e., at T between 8 and 31). Furthermore, the observedsignificant cycles of T are shorter than the minimumnumber of episodes required to obtain a full picture of

C-Q response variety (9 and 18 cases for DOC and NO3,respectively).[39] On the other hand, when temporal sequences of DR

and DC descriptors are analyzed separately, a periodicity isobserved for DR(NO3) and, to a lesser degree for DC(NO3)

(Figures 6a and 6b). In more detail, the CP matrix ofDR(NO3) detects numerous significant periods (at least p <0.05) at different RainTot thresholds. Nevertheless, only onecycle persists at different RainTot thresholds (the solidoblique line in Figure 6a). This cycle persists until a RainTotthreshold of 18 l m�2 is reached (N = 40). Initially, thecycle is characterized by a period of T = 22 storm episodesthat declines progressively with RainTot until it disappearsat T = 11.[40] A cycle for DC(NO3) is also detected, however its

periodicity is slightly longer and patently weaker than thatobserved for DR(NO3). It is longer because initially itcomprises 24 storm episodes (T = 24; N = 63, p < 0.05),and vanishes at T = 17 (RainTot threshold of 13 l m�2; N =40, p < 0.05). It is weaker because the oblique line that linksthese significant periods T is much shorter than that ob-served for DR(NO3) (Figure 6b).[41] The DR(DOC) and DC(DOC) series lack any consistent

cycles, indicating that their temporal succession is randomlyassorted. In both cases, some significant periods aredetected at different RainTot thresholds. However, distribu-tion of these significant periods in the periodogram matricesdoes not show any consistent pattern (Figures 6c and 6d).

6. Discussion

[42] The data set, obtained from 4 years of intense hydro-chemical monitoring, demonstrates the need to describe thediversity of forms of C-Q responses in probabilistic terms. Itis worth noting that the most probable C-Q response types(i.e., types 2 and 3 from Figure 5, for DOC and NO3,respectively) represent as much as 40% of all cases. Thus,the majority of the DOC and NO3-Q responses fall withinsome low probability C-Q response types. These resultspoint to the large degree of uncertainty in the depiction of a‘‘typical’’ DOC and NO3-Q response and underline theimportance of identifying the ‘‘typical’’ probability distri-bution of a set of C-Q responses within the DR versus DCunity plane.[43] Under the proposed probabilistic approximation,

DOC and NO3 data from our study show different distri-butions and limits in the DR versus DC unity plane. TheDOC data set, although intersecting several C-Q responseregions, lies in a relatively small portion of the DR versusDC unity plane. Thus, a minimum set of 9 events isrequired to describe the diversity of DOC-Q responsessatisfactorily. In contrast, the NO3 data set is homogenouslydistributed in a large portion of the DR versus DC unityplane. This implies that a set of at least 18 events isnecessary to capture the variety of NO3-Q responsessatisfactorily. An evident consequence of this result is thatif we ignore the magnitude of diversity of C-Q responsesof a determined solute, we do not know if the hydro-biogeochemical interpretation obtained with an arbitrarynumber of storm episodes is representative or not of thehydro-chemical functioning of the watershed studied.

Figure 5. Dispersion of (a) DOC-Q and (b) NO3-Qresponses in the DC versus DR unity plane. Numberswithin circles illustrate the time succession of the stormevents. Vertical and horizontal dotted lines in the DC versusDR unity plane delimit the nine different types of C-Qresponse. The figures in the inset show the evolution of theDOC-Q and NO3-Q response diversity (H(DR,DC)) measuredsequentially over the event series.

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[44] Within the classical mixing hydrological framework,a plausible hydro-biogeochemical explanation for each C-Qresponse type reported in this study could be given [Riceand Hornberger, 1998]. However, the classical mixingmodel appears to be inadequate to provide a unique andcoherent description of patterns of solutes during storms inan intermittent stream such as our study site. For instance, ifwe constrain our observations to the NO3 counterclockwisehysteresis (47% of all cases) we might conclude thatflushing is transport limited [Burns, 2005]. Contrarily, ifwe analyze the NO3 clockwise hysteresis (24% of cases) wemight conclude that its flushing is supply limited [Burns,2005]. A consequence of this high biochemical variability isthat the use of the classical mixing approach to identify thesources of NO3 in Fuirosos is feasible during the humidperiod but not during the dry-wet transition period [Bernalet al., 2006].[45] Therefore, it is necessary to develop a hydro-biogeo-

chemical framework flexible enough to justify the diversityof C-Q responses and the temporal succession of theseforms over the seasons. Recent theoretical studies increasethe spatial heterogeneity of the hydrological processeswithin the mixing model framework, improving its concep-tual flexibility [Chanat et al., 2002; Butturini et al., 2005].However, the introduction of new boxes and additionalparameters is encouraged, making scientists inquire aboutwhether this approach is really the most appropriate tocapture the nonlinear hydro-biogeochemical behavior ofcatchments [Kirchner et al., 2004].[46] The contingency analysis reveals that DR and DC

values cannot be predicted simultaneously either for DOCor NO3. However, when DR and DC descriptors areanalyzed separately, signals of periodicity emerge for

NO3. This result is especially attractive for DR(NO3) becauseit suggests that the timing of NO3 delivery into the streamover a sequence of storm episodes can be inferred. Theseresults lead to the last question behind our research: whatcontrols the cycles observed for DR(NO3) and DC(NO3)?[47] A preliminary step toward dealing with this question

is to overlap the periodogram matrices of DR(NO3) andDC(NO3), with those obtained for the climatic (Tday) andthe hydrological variables (DQ and Qbas) considered in thisstudy. Figure 7 shows that the period of the cycle ofDC(NO3) is longer than that observed for the hydro-climaticvariables, which show marked seasonality (i.e., cycles ofabout 1 year, Figures 1 and 4). Thus, processes that act atimescales in between 1 and 2 years might influence thecycle of DC(NO3). Being realistic, at the moment, such atimescale cannot be studied satisfactorily by handling a4-year time series only, and we can simply use thisresult to demonstrate the need to generate pluri-annualhydro-biogeochemical series at high resolution.[48] On the other hand, the T period of DR(NO3) overlaps

reasonably well with that of DQ at RainTot < 13 l m�2, andwith Tday at 13 < RainTot < 18 l m�2. This suggests that theperiodicity of the magnitude of storms (DQ) is the mostimportant driver for the succession of linear and nonlinearNO3-Q responses over the entire spectra of rain magnitudes,while the effect of the seasonal temperature change (i.e.,Tday), appears more perceptible after the removal of thesignal of the weaker and more frequent rain episodes.[49] The problem of excess NO3 in running waters is a

recognized problem worldwide [Burgin and Hamilton,2007] and to achieve an accurate simulation of its temporaldynamics constitutes a major challenge for modelers [Wadeet al., 2004], especially in intermittent streams where

Figure 6. Contingency periodogram matrices for (a) DR(NO3), (b) DC(NO3), (c) DR(DOC), and(d) DC(DOC). Oblique lines in the periodogram matrices connect the statistically significant periods.

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simulations are clearly unsatisfactory [Bernal et al., 2004].From this perspective, our results might represent a stimulusfor these scientists, because they suggest that the successionof forms of NO3-Q responses might be coupled to themagnitude of storm events. On the other hand, the sameresults also show that a large portion of uncertainty isinevitable and suggest that a probabilistic modeling approachshould replace the deterministic one [Botter et al., 2006].

7. Conclusions and Perspectives

[50] Abundant scientific literature clearly demonstratesthe impact exerted by storm events on solute patterns andfluxes. However, pollution of inland waters and the plausi-ble alteration of hydrological regimes on a global scale as aconsequence of climate alteration [Intergovernmental Panelon Climate Change (IPCC), 2007] demonstrate the impor-tance of extending these studies. To persuade their col-leagues in this direction, some scientists describe stormevents as ‘‘hot moments’’ [McClain et al., 2004] or comparetheir role to a ‘‘crescendo’’ in a musical piece [Kirchner etal., 2004].[51] In this context, our study is the first to describe the

limits of diversity of DOC and NO3-Q crescendos in astream under a wide spectrum of hydro-climatic conditionsand underlines the need to describe the diversity of C-Qresponses in terms of distribution of C-Q types withdifferent probabilities. Furthermore, the succession of datain the DR versus DC unity planes is random: a priori, it isimpossible to predict the entire pattern of these solutesduring storms with satisfactory precision.[52] In environmental sciences, the use of the adjective

‘‘diversity’’ is immediately related to the biological rich-ness. Thus, at this stage it is natural to inquire to what extentthe diversity of C-Q responses reflects the complexity of

internal hydro-biogeochemical functioning of a specificcatchment. For instance, data from our study came froman intermittent stream with an abrupt autumnal hydrologicaltransition from dry to wet conditions [Butturini et al., 2003]with high and low DOC and NO3 concentration respectivelyin stream waters [Bernal et al., 2005]. Overall, the DOC-Qresponses monitored during this transitional period (33% ofevents) contributed to 41% of the total DOC-Q responsediversity, while in the case of NO3, the autumn NO3-Qresponses did not influence the total C-Q response diversity.Therefore, although the dry-wet transition does not promotea typical C-Q response during the following autumn, itcontributes to enhancing the diversity of DOC-Q (but not ofNO3-Q) responses.[53] For ecologists, understanding the relationship be-

tween biodiversity and ecosystem functioning has been afertile theme since the beginning of the 1990s that has beencontinually fuelled and renewed by the feedback betweenempirical data and new hypotheses [Naeem et al., 2002].Here, in stream hydro-biogeochemistry, we are just starting.In order to strength this theme we need to widen theimplementation of long-term high frequency hydro-chemicalmonitoring programmes [Kirchner et al., 2004]. When thisrequisite is achieved it will be possible to analyze how thediversity of C-Q responses varies among catchments withdifferent hydro-climatic characteristics. Within this context,the DR versus DC unity plane might constitute a synthetic,intuitive and universal framework with which to compare andclassify the dispersion of the C-Q responses of a specificsolute along catchments.

[54] Acknowledgments. The authors thank Sergi Sabater, Ester Ninand Antoni Bombı (Servei Parcs Naturals, Diputacio de Barcelona), fortheir support in the field and Chiara Medici for support with environmentaldata management. We would also like to thank two anonymous reviewers

Figure 7. Location, in a single plot, of the oblique lines that connect the statistically significant Tperiods at different RainTot thresholds for the environmental variables considered in this study (Tday, DQ,and Qbas) and the NO3-Q response descriptors (DC(NO3) and DR(NO3)).

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for their fruitful comments, which greatly helped to improve an earlierversion of this manuscript. This study was supported by the Ministerio deEducacion y Ciencia (CGL2007–60144). The authors are members of theLimnology Group (UB-CEAB_CSIC). A.B. is a member of the Graccienetwork (CSD 2007–00067).

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�����������������������M. Alvarez, A. Butturini, F. Sabater, and E. Vazquez, Department

d’Ecologia, Universitat de Barcelona, 08028 Barcelona, Spain. (abutturini@ub.edu)S. Bernal, Department of Ecology and Evolutionary Biology, Princeton

University, Princeton, NJ 08544 USA.

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