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Controls on solute transport in large spring-fed karst rivers

Robert T. Hensley,a and Matthew J. Cohenb,*

aSchool of Natural Resources and Environment, University of Florida, Gainesville, Florida

bSchool of Forest Resources and Conservation, University of Florida, Gainesville, Florida

Abstract

The spring-fed rivers of North Florida flow across a karst plain, leading us to predict significant hyporheictransient storage within the secondary porosity of the carbonate media. They also span a gradient of submergedmacrophyte cover and density, offering an opportunity to investigate reach-scale vegetation effects on dispersionand transient storage. Conservative tracer tests using Rhodamine WT were conducted on nine rivers spanning theextant range of vegetative and geomorphologic characteristics. A one-dimensional advection-dispersion-storagemodel with variable configurations of storage zones was fit to tracer breakthrough curves to determine optimalmodel coefficients as well as storage-zone configuration (i.e., single, two in parallel, two in series). In most cases,two zones in series best fit the observed breakthrough curves. Moreover, fitted storage-zone attributes weresignificantly correlated across springs with field-measured analogs (i.e., vegetation area for storage zone 1,sediment area for zone 2), supporting inference that this model configuration physically represents the riversystems. While the hyporheic zone area was large, its effect on transient storage was not, presumably because low-hydraulic-conductivity sediments and weak hydraulic gradients limit exchange with the secondary porosity of karst matrix; tracer mass recovery uniformly near 100% underscores the absence of significant hydraulic turnover.Vegetation was a significant predictor of transient storage, mean velocity, and mean residence time, suggestingthat plant beds exert important controls on reach-scale hydraulics in these systems.

The hydraulic properties of a river exert primary controlover the nutrient spiraling process (Newbold et al. 1982;Ensign and Doyle 2006; Wollheim et al. 2006). Conse-quently, characterization of riverine hydraulics is aprerequisite to understanding nutrient uptake and retentionin river networks. Studies of solute transport dynamicsusing tracer breakthrough experiments traditionally havefocused on small, first- and second-order streams (Day

1975; Bencala and Walters 1983; Bencala et al. 1990), wherethere is evidence for high rates of biogeochemical reactivity(Alexander et al. 2000; Peterson et al. 2001). However,more recent work (Ensign and Doyle 2006; Wollheim et al.2006; Tank et al. 2008) has suggested that larger rivers exertsignificant control over network removal rates in spite of their relatively small contribution to total network channellength (Seitzinger et al. 2002). This has prompted a need toexpand our understanding of river solute transport dyna-mics to fourth-order and larger river systems (Wondzelland Swanson 1996; Fernaid et al. 2001; Laenen andBencala 2001). In the course of these studies, it has beenobserved that certain channel characteristics such as chan-nel topography (Harvey and Bencala 1993) and geomor-phology (Wondzell 2006; Gooseff et al. 2007; Briggs et al.2009), and the presence of vegetation and other organicdebris (Mulholland et al. 1994; Hart et al. 1999; Harveyet al. 2003) exert a significant influence on the hydraulicproperties.

Solute transport studies have also overwhelminglyfocused on alluvial systems, while our understanding of karst systems is more limited. Karst rivers may exhibitamplified hydraulic exchange between river and hyporheicstorages, and these storages may be of greater magnitude,

because of the existence of secondary porosity (e.g.,siphons, sinks, and conduits) in the soluble matrix thatunderlies the channel. For example, discharge associatedwith large floods on the lower Suwannee River declineswith distance downstream as the river passes over a karstplain, suggesting significant flood-water storage in theadjacent rocks (Giese and Franklin 1996). Surface water isgenerally undersaturated with respect to carbonate miner-

als, leading to surface-water control over the evolution of channels and adjacent secondary porosity (Gulley et al.2011). This is likely to be important for regulating riverhydraulics, but it has yet to be quantified. Part of ourobjective was to determine whether karst rivers exhibit ahigher degree of transient storage and extended residencetimes because of extensive hyporheic exchange.

North Florida possesses the highest abundance of spring-fed karst rivers anywhere in the world, with over30 first-magnitude (i.e., greater than 2.8 m3 s21 meandischarge) springs (Scott et al. 2004). Discharge, waterchemistry, and temperature from these springs are remark-ably constant, with a coefficient of variation over long

periods of record less than 10%

across all analytes (Brownet al. 2008). This stability, coupled with generally highprimary production due to water clarity, makes theresulting rivers useful model systems for understandingreach-scale biotic processes (Odum 1957a; Duarte andCanfield 1990; Heffernan and Cohen 2010). However,despite a 60-yr legacy of ecological study, no rigorousattempt has been made to quantify solute transportproperties in these rivers, a knowledge gap this study seeksto fill.

A sentinel feature of most of Florida’s spring-fed rivers isthe density of submerged aquatic vegetation (SAV;Fig. 1A) (Odum 1957b; Canfield and Hoyer 1988; Hoyer* Corresponding author: mjc@ufl.edu

Limnol. Oceanogr.,  57(4), 2012, 912–924

E 2012, by the Association for the Sciences of Limnology and Oceanography, Inc.doi:10.4319/lo.2012.57.4.0912

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et al. 2004), composed primarily of   Sagittaria kurziana(springtape) and   Vallisneria americana   (tape grass). Thishigh abundance of benthic macrophytes likely influencesriverine transport properties, biogeochemistry, and thedynamics of autochthonous sediment production. Theserivers therefore represent the extreme end of a continuumof rivers with regard to the relative importance of biologicalprocesses in solute transport dynamics. It has been welldocumented that vegetation decreases the mean velocity

(Manning 1890, 1895) and increases the magnitude of dispersion (Nepf et al. 1997; Nepf 1999; Lightbody andNepf 2006). Additionally, the low velocity observed withindense vegetation beds (Sand-Jensen and Mebus 1996) raisesthe question of whether these areas might also function astransient storage zones. We sought to evaluate whethervegetation effects on hydraulics previously detected fromsmall-scale mechanistic studies will also be observable atthe reach scale.

First, we hypothesized that channel geometry andvegetation would act as controls on the mean velocity aspredicted by both the continuity equation and by Man-ning’s equation. For channel geometry, we therefore

expected a correlation between measured discharge dividedby measured channel area and mean velocity. Forvegetation, we expected a negative correlation betweenthe percentage of the channel cross section obstructed byvegetation and mean velocity.

Second, we hypothesized that channel geometry and

vegetation would act as controls on the magnitude odispersion. Because shallower channels force a greaterproportion of flow to experience shear stress separation atthe boundary layer, we expected to observe a negativecorrelation between the hydraulic radius (normalized fordischarge) and the longitudinal dispersion coefficientBecause vegetation adds additional shear stress, we alsoexpected a positive correlation between the percentage ofchannel cross-sectional area obstructed by vegetation andthe longitudinal dispersion coefficient.

Finally, we hypothesized that both hyporheic sedimentsand vegetation beds act as transient storage zones. Wetherefore expected the storage-zone cross-sectional area tobe positively correlated with the hyporheic sediment cross-sectional area and the vegetative frontal area. Because ofthe karstic nature of the hyporheic zone, and theabundance of submerged vegetation, we also predicted alarger magnitude of transient storage compared with otherrivers.

As a prerequisite to testing these hypotheses, the solutetransport properties must be quantified using the onedimensional advection-dispersion-storage (ADS) equation(Bencala and Walters 1983; S.S.W. 1990; Runkel 1998)The ADS equation relates the rate of change of conserva-tive solute concentration with respect to time to a stream’sadvective, dispersive, and transient storage properties.

LC

Lt~{

Q

A

LC

LxzD

L2C

Lx2 za   CS{Cð Þ ð1Þ

The variable Q is discharge, A is the channel cross-sectionalarea, D is the dispersion coefficient,   a   is the storageexchange coefficient, and CS   is the storage-zone concentration. A second equation relates the rate of change inconcentration within the transient storage zone to afunction of the concentration gradient between the channeland the storage zone (Fig. 1B).

LCS

Lt  ~a

A

AS

C{CSð Þ ð2Þ

This equation is used by the widely implemented one

dimensional transport with inflow and storage (OTIS) mode(Runkel 1998). A shortcoming of this equation is that itimplicitly assumes an exponentially distributed residencetime distribution (RTD). Recent studies have shown that, fora wide variety of streams, an exponential RTD does a poor

 job of fitting the tail end of observed breakthrough curvesand that a nonexponential distribution (e.g., power functionmay be more appropriate (Haggerty et al. 2000, 2002Gooseff et al. 2005). Accurately describing the breakthroughcurve tail is relevant for understanding stream biogeochem-istry because these flow paths likely represent those in whichmost reactive solute processing has occurred. More generaRTD models such as the solute transport and multirate

Fig. 1. (A) Spring-fed rivers are widely dominated by densesubmerged macrophyte beds. (B) The standard ADS modelformulation has one transient storage zone that interacts withthe channel water. Additional storage zones can operate (C) inparallel, where each zone interacts independently with thechannel, or (D) in series, where the second storage zone interacts

with the first. Based on the physical arrangement of these riversystems, shown in (A), we predict that the serial storage-zoneconfiguration is optimal. Photo credit L. V. Korhnak.

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Each tracer release consisted of a single pulse of Rhodamine WT (RWT; Keystone Aniline Corporation),a conservative dye that fluoresces at 580 nm under light at550 nm. Because of its smaller size, a continuous tracerinjection was performed in Mill Pond Spring. In this test,RWT was released at a constant rate for 80 min. In allcases, the mass of tracer released was set to achieve a down-stream peak concentration of 20  mg L21 based on estimateddischarge and expected dispersion. Tracer breakthroughwas measured at the downstream end of each reach usinga Turner Design C3 fluorometer calibrated prior to eachdeployment using a two-point curve with 0   mg L21 and10   mg L21 standards. The fluorometers sampled everyminute and were deployed for sufficient time followingtracer release to ensure most of dye had been exported; thisvaried from a few hours in smaller systems to a full day ormore in larger rivers. The deployment in Rock Springs Runwas cut short due to sampling logistics and failed to capturethe entire breakthrough tail at the downstream monitoringstation.

Despite high water clarity at the spring vents (Duarteet al. 2010), several downstream sites had sufficient fluo-

rescent dissolved organic matter (fDOM) to create anonzero prerelease RWT concentration. To correct this,linear regressions were developed between baseline readingsof fDOM and RWT prior to each tracer release, and thenfDOM values were used to subtract background interfer-ence during the tracer test.

We performed moment analyses (Kadlec and Knight1996) on each breakthrough curve to estimate tracer massrecovery (area under the breakthrough curve multiplied bydischarge) and fractional mass recovered (mass recovereddivided by mass injected). Mass recovery was used to verifymeasured discharge, fluorometer calibration, DOM filter-ing, and, most importantly, that the fluorometer was

deployed long enough to capture the entire measureablebreakthrough curve.

Moment analysis also provided median (tmed) and meanresidence time (tmean) estimates. The value of   tmed   wascalculated as the time when 50%   of injected tracer warecovered. The value of  tmean was calculated by dividing thefirst breakthrough curve moment by the total area underthe curve to determine the centroid. The thalweg length (L)of each reach was determined using aerial images, andmean velocity (u) in each reach was obtained by dividingthis length by   tmean.

u~L

tmean

ð9Þ

Because fitting the ADS model to the observedbreakthrough curve and obtaining the ADS modecoefficients are prerequisite to testing the other hypothesesthe first hypothesis tested focused on ADS modeconfiguration. The ADS partial derivatives were solvedusing a finite-difference approach (Runkel 1998) whereinthe concentration at the current time and reach segment

depends on the concentration at that segment in theprevious time step, the upstream segment concentration inthe previous time step, and the upstream segmentconcentration in the current time step, as well as the modelparameters that describe hydraulic transport and exchangeAdditionally, a lateral input component was added to theADS equations to account for the observed (albeit minimal) lateral inputs observed in some of the rivers. Whilethis approach is used by existing software (e.g., OTIS andOTIS-P), we developed our own model using MicrosofExcel (2007), to allow for easier manipulation of modestructure (e.g., two storage zones in parallel or series) andto provide improved visual feedback on model fitting. Note

Table 1.   Summary of mean measured vegetative and geomorphic characteristics. US indicates the upstream reach and DS indicatesthe downstream reach. L is the reach length, N refers to the number of lateral transects in each reach, Q is mean discharge, R is the meanhydraulic radius, W is mean reach width, Ac  is the mean measured channel cross section, Av is the mean vegetation cross-sectional areaAB  is the mean benthic cross-sectional area, and  K   is the mean sediment hydraulic conductivity. Ranges are for one standard deviation.Asterisks represent a statistical difference between upstream and downstream reach.

River reach L (m) N Q (m3 s21) R (m) W (m) AC

 (m2) AV

  (m2) AB

  (m2)   K  (m d21)

Alexander US 1300 4 3.8 1.060.2 34.6611.5*** 33.763.0*** 4.165.7*** 55.6615.6*** 4.462.8Alexander DS 1800 4 4.5 0.860.1 62.867.5*** 46.462.6*** 22.764.6*** 82.8624.2*** 4.061.6Blue Spring US 140 1 0.9 1.060.0 28.060.0 26.760.0 25.960.0 27.160.0 2.760.0Blue Spring DS 210 2 1.1 0.660.1 18.861.7 10.860.3 7.461.4 27.165.0 2.760.0Ichetucknee US 1800 5 6.5 0.760.3** 62.6635.5* 33.269.7** 17.368.6 86.4669.1* 4.661.9Ichetucknee DS 2500 5 6.5 1.260.1** 24.061.4* 31.364.0** 10.763.0 19.461.8* 5.461.6Juniper Creek US 1700 5 1.3 0.560.1* 9.061.9 4.161.2 0.260.4 16.369.3 4.261.4Juniper Creek DS 1000 5 1.9 1.060.1* 9.861.2 10.262.1 0.060.0 19.964.1 8.164.4Mill Pond Spring 160 7 0.9 0.460.0 10.864.0 4.862.1 3.261.8 8.764.6 -Rainbow River US 1500 4 14.7 1.560.3 65.7614.5* 106.2634.0* 32.6611.5** 28.4620.0 6.161.6Rainbow River DS 4250 4 16.8 1.460.2 47.866.3* 65.964.8* 12.464.5** 20.764.7 4.663.6Rock Springs US 700 3 1.3 0.660.2 8.061.0** 5.261.5 0.060.0 11.863.7** 15.464.8**Rock Springs DS 2300 4 1.3 0.660.3 35.3614.1** 23.6618.6 13.2612.0 47.4615.2** 4.063.9**Silver River US 1550 3 14.5 2.260.6 47.169.8* 101.968.9* 34.064.7** 114.3652.0 4.262.3

Silver River DS 5300 6 15.5 2.26

0.6 30.96

8.0* 71.36

30.1* 20.76

7.0** 63.26

38.6 3.56

2.5Weeki Wachee US 1300 6 3.1 0.760.1 21.564.6** 15.062.8*** 1.562.7 48.1614.0** 5.660.9Weeki Wachee DS 2000 5 3.1 0.760.1 12.061.0** 8.861.0*** 0.0060.0 26.665.3** 9.664.8

* p,0.10, **  p,0.05, ***  p,0.01.

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that we initially also fit the ADS model with dispersiononly (i.e., no transient storage); because it failed to captureobserved breakthrough curve geometry in any of the rivers,we omitted that model from further consideration.

Modeled breakthrough curves (predicted concentrationvs. time) were compared with observed breakthrough

curves from each fluorometer location. Initial boundaryconcentrations in the upstream-most segment (tracerrelease point), and ADS model variables (Q, A, D,   a, andAS) determined modeled breakthrough curve position andshape. Initial boundary concentrations were known basedon tracer mass release and measured river discharge (Q).While channel cross-sectional area was measured severaltimes along each reach, this variable was left unconstrainedto see if it would converge on measured channel area or asmaller value reflecting displacement effects of vegetation.We used the solver function in Microsoft Excel, whichemploys a generalized reduced gradient algorithm tominimize the sum of squared errors between modeled andobserved breakthrough curves, to estimate the four model

coefficients (A, D,  a, and As) for each reach. As verificationof model performance, parameter estimates for the singletransient storage condition obtained using this approachwere identical to OTIS-P output.

Inclusion of a second storage zone added two new modelparameters: an additional cross-sectional area (AS2) andexchange coefficient (a2). To determine whether addition of these parameters was justified, we used the AkaikeInformation Criterion (AIC; Akaike 1974), which penalizesmore complex model formulation based on the number of parameters (k), the number of observations (n), and theeffect of additional parameters on model residual sum of squares (RSS).

AIC~2kzn:

ln(RSS)   ð10Þ

The model (single storage zone, two storage zones inparallel, two storage zones in series) with the lowest AICwas selected. Harvey et al. (2004) used a similar method todetermine the best model configuration in a wetland. Weused this approach to test the hypothesis that the modelwith two storage zones in series would best simulate theobserved residence time distribution.

A metric commonly used to quantify the relative im-portance of transient storage across rivers of different size isF med, the fraction of median travel time due to retention intransient storage zones (Runkel 2002; Ensign and Doyle2006).   F med   was obtained by calculating the difference in

median residence time between the observed breakthroughand a simulated breakthrough in which advection anddispersion remain the same but no transient storage occurs(tmed

a50). This difference in median residence times wasdivided by the observed median residence to give the fractionof the observed residence time due to transient storage.

F med~tmed{tmed

a~0

tmed

ð11Þ

Finally, to evaluate observational support for our hypo-theses regarding the vegetative and geomorphic featuresthat act as controls on solute transport properties, ordinary

least-squares regressions were performed, with statisticalsignificance set at   p   ,   0.05. The   p-value, coefficient of determination (r2), and slope were the outputs that we usedto evaluate each prediction.

Results

The summary of discharge, geomorphic, and vegetativeattributes (Table 1) underscores significant variation inflow (0.9 m3 s21 to 16.8 m3 s21), mean channel cross-sectional area (4.1 m2 to 106.2 m2), mean width (8.0 to67.5 m), mean hydraulic radius (0.4 m to 2.2 m), and plantcover (0%   to 97%  of channel cross-sectional area) acrossrivers. We also confirmed significant variability betweenupstream and downstream reaches within rivers. The widthof Alexander Springs Creek, for example, nearly doubled inthe downstream reach, while the Ichetucknee width wasreduced by more than half. In general, width and cross-sectional area decreased while velocity increased in down-stream reaches. Cross-sectional profiles from the Ichetuck-nee River (Fig. 2) in upstream (transects 8, 9) and down-stream (transects 5, 6) reaches illustrate the dramatic changesin channel geometry that, notably, occur without changes indischarge.

The cross-sectional area of benthic sediments rangedfrom 8.7 m2 to 114.3 m2, which was generally of com-parable size to channel area (between 27%   and 398%   of measured channel area, with Rainbow River a notableoutlier). Measured hydraulic conductivity was ranged from3 to 15 m/d, suggesting that hyporheic exchange may berelatively slow.

Vegetation was generally dominated by macrophytes(Fig. 1A), and frontal area ranged from 0 to 34.0 m2 (0% to

96.9%

of the channel cross-sectional area obstructed); somereaches (e.g., upstream Alexander) were dominated bythick algal mats, which may exert different hydrauliceffects. Generally, the total area and fraction of the channeloccluded by vegetation declined significantly in the lowerreaches, though some rivers (Alexander Springs Creek andRock Springs Run) had different patterns.

Measured dye concentrations over time, and the fittedADS model breakthrough curves (Fig. 3) are shown in log-space to accentuate the geometry of curve tails, which occurat low concentrations and are therefore not readily visiblein linear-space. Note that at Mill Pond Spring, weperformed a continuous plateau tracer injection instead of a pulse, and, because of its short length, we monitored only

a single reach.Moment analyses of the observed breakthrough curves(Table 2) yielded uniformly high mass recovery, suggestinglittle evidence of major mass loss between the injectionpoint and downstream locations. The major exception wasthe downstream reach of Rock Springs Run, where lowermass recovery was almost certainly due to the fluorometerbeing retrieved too early to record the entire tail-end of the breakthrough curve. The mean residence time rangedfrom 19 min to 685 min, though this spans reaches of dramatically different length; dividing reach length by themean residence time yields mean velocity, which rangedby nearly an order of magnitude from 0.03 m3 s21 to

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0.28 m3 s21. Within rivers, the mean velocity of the down-stream reach was generally higher than the mean velocity of the upstream reach. The mean velocity from the momentanalysis correlated strongly ( p  ,  0.001) with velocity esti-mated by dividing measured discharge by measuredchannel area; however, the slope was 0.84, indicating effectsof transient storage on mean velocity that the continuityequation does not take into account.

AIC values for each ADS model configuration for eachreach (Table 3) indicate that in four cases—downstreamBlue Spring, Mill Pond Spring, downstream Rainbow River,and downstream Weeki Wachee River—only a single

storage zone (Fig. 1A) was justified. In three of these cases,neither two-zone configuration reduced model error. In MilPond Spring, however, addition of a second storage zoneslightly improved model fit, but not enough to justifyadditional parameters. In all other cases (n 5 14), a secondstorage zone yielded significant, often dramatic, modeimprovement, and the serial model was always better theparallel model.

A summary of ADS model coefficients for each reach(Table 4) shows wide variation in dispersion (D), transientstorage-zone areas (AS1   and AS2), and exchange coefficients. Notably, by leaving channel area (A) as a variable

Fig. 2. Sample cross-sectional profiles of the Ichetucknee River, showing the cross section of water, vegetation, and sediment (to bedrock) in (A, B) two upper river transects and (C, D) threelower river transects.

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we could compare model and measured values (Fig. 4A); thestrongly significant ( p , 0.001) correlation indicates modelconvergence with measured geometry, but the slope of the best-fit line (0.86) indicates that transport is occurring

only in a subset of the total channel, an important resultpreviously observed by Briggs et al. (2009). When modelprimary storage-zone area is added to model channel area,there is an even stronger correlation with measured channelarea (including vegetation), and the slope of the best-fit lineis very close to 1.0 (Fig. 4B). This supports the hypothesisthat some fraction of the measured channel, such as the areaobstructed by vegetation, is acting as a storage zone ratherthan an advective zone. Mean velocity was negativelycorrelated ( p   5   0.024) with the fraction of channel areaobstructed by vegetation (Fig. 4C), as predicted by Man-ning’s equation. However, model dispersion (D) was notcorrelated ( p 5 0.750) with the fraction of the channel that

was vegetated, as expected (Fig. 4D). It should be noted,however, that the dispersion coefficient was correlated withmean velocity (Fig. 4E), albeit not significantly ( p 5 0.131).This is likely a result of greater shear stress separation at the

boundary layer at higher velocities. An unforeseen conse-quence is that while the obstructive properties of vegetationmay induce mechanical dispersion, vegetation also decreasesvelocity, which results in less shear stress separation, andultimately less total net dispersion. The dispersion coefficientwas also negatively correlated ( p 5 0.033) with the hydraulicradius normalized for discharge (Fig. 4F), likely as a resultof more shear stress separation when more of the flowinteracts with the benthic boundary layer.

By comparing model estimates of transient storage-zoneareas with measured vegetation and sediment cross-sectional areas, we sought to evaluate our conceptualmodel of hydraulics in these highly vegetated systems.

Fig. 3. Upstream (gray) and downstream (black) Rhodamine WT (RWT) breakthrough curves for each of the nine rivers viewed inlog-concentration space. The solid lines are for the best fit of the three possible storage-zone configurations of the ADS models. Note thatthe time axis varies among rivers because of differences in discharge, reach length, and mean residence time. A plateau rather than pulseinjection was used at Mill Pond.

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Associations for the primary (AS1) and secondary (AS2)model storage zones vs. measured vegetation frontal area(AV) and benthic sediment cross-sectional area (AB) weresignificant in all cases, but higher for AV vs. AS1 ( p 5 0.005)than for AV vs. AS2 ( p 5 0.015), and higher for AB vs. AS2

( p 5 0.006) than for AB  vs. AS1  ( p  5  0.026); this supportsthe contention that the shorter residence time modelstorage is linked to vegetation, and the longer residencetime storage is linked to hyporheic sediments. Analysis of absolute cross-sectional areas, however, ignores covarianceexpected as a result of scale. To control for this, we dividedmeasured and modeled cross-sectional areas by meanchannel width (W), effectively comparing the average depthof measured and modeled storage zones. The results lendfurther support to the prediction that model storage zone 1(AS1) is linked to vegetation bed storage (AV), while thesecond storage zone (AS2) is related to benthic storage (AB).Specifically, AS1   W21 is significantly correlated with AV

W21 (Fig. 5A), but not with AB  W21 (Fig. 5B), while AS2

W21 is more strongly (albeit not significantly) correlatedwith AB  W21 (Fig. 5C) than with AV  W21 (Fig. 5D). It isalso notable that the best-fit slopes in all cases were muchless than 1, implying that transient storage is limited to arelatively small fraction of the measured vegetation beds

and underlying sediments.An opportunity arose to conduct a second tracer test atGilchrist Blue Springs. The initial test was conducted underalmost completely vegetated conditions, while the secondtest was conducted under dramatically lower vegetationconditions resulting from recreation effects during thesummer. A comparison of resulting breakthrough curvesfrom the downstream reach (Fig. 6) illustrates the dramaticeffects of vegetation on solute transport. The meanresidence time with dense vegetation was substantiallylonger than for sparse vegetation conditions (145 min vs.105 min), and the dense vegetation breakthrough curve tailis visibly longer, indicative of greater transient storage. The

ADS model coefficients for this comparison of vegetativeconditions (Table 5) reveal that a second storage zone didnot improve model fit under low vegetation conditions(where the tail is slightly concave down), but it made asignificant improvement under high vegetation conditionsThe conversion to a two-storage-zone model from a single-storage-zone model under higher vegetation conditionssupports the general contention that vegetation exertscritical control on riverine hydraulics at the reach scale.

Discussion

This study represents the first systematic characterization of geomorphic and vegetative features of northFlorida’s spring-fed rivers; as useful model systems for riverecology, these results will be of significant utility. Thisstudy is also among the first to examine riverine hydraulicsin a karst setting. Our hypothesis that karst rivers wouldexhibit greater transient storage than alluvial rivers due tointeractions with secondary porosity was not supported. Acomparison of observed  F med values with those reported inother studies (Runkel 2002) suggests that these rivers doexhibit large, though not exceptional transient storageMoreover, we obtained nearly 100% mass recovery in every

tracer test (except Rock Springs Run, where the fluorometerwas not deployed long enough). Many previous studies usingRhodamine WT failed to achieve complete mass recoverywhich has been, at least in part, attributed to RWT having atendency to sorb to hyporheic sediments (Smart and Laidlaw1977; Bencala el al. 1983; Sabatini and Austin 1991), limit-ing its utility as a conservative tracer. However, anothercommon explanation for failing achieve complete massrecovery is the effects of long time-scale storage (such asdeep within hyporheic sediments) and either release oveextended periods at concentrations below the detection limiof the fluorometer or loss from the system via complexgroundwater flow paths that lead to significant hydraulic

Table 2.   Summary of breakthrough curve moment deriveddata for each reach.   tmean   and   tmed   are the mean and medianresidence time, respectively, and u is the estimated mean velocity.

River reachRecovery

(%)tmean

(min)tmed

(min)u

(m s21)

Alexander Creek US 99.89 294 262 0.07Alexander Creek DS 100.15 314 321 0.10Blue Springs US 104.54 86 79 0.03Blue Springs DS 99.10 59 47 0.06Ichetucknee River US 100.24 193 173 0.16Ichetucknee River DS 99.68 168 165 0.25Juniper Creek US 99.58 172 154 0.16Juniper Creek DS 99.46 135 132 0.12Mill Pond Spring 99.21 19 18 0.14Rainbow River US 98.93 387 375 0.06Rainbow River DS 97.45 298 286 0.24Rock Springs Run US 100.46 43 39 0.27Rock Springs Run DS 79.48 418 400 0.09Silver River US 99.65 157 140 0.16Silver River DS 96.08 456 440 0.19

Weeki Wachee River US 99.26 125 113 0.17Weeki Wachee River DS 101.32 118 117 0.28

Table 3.   Summary of AIC values for each reach for threedifferent model configurations. Bold values indicate the lowesAIC (best fit).

River reachSingle

storageTwo storage

(parallel)Two storage

(series)

Alexander Creek US 5782 4051   3932Alexander Creek DS 2102 2106   1545

Blue Springs US 3611 3615   2837

Blue Springs DS   898   902 902Ichetucknee River US 5082 4620   4400

Ichetucknee River DS 4221 2723   2568

Juniper Creek US 7836 7236   6098

Juniper Creek DS 5068 2972   2551

Mill Pond Spring   1124   1128 1128Rainbow River US 4314 4318   4066

Rainbow River DS   4318   4322 4322Rock Springs Run US 5735 5375   5375

Rock Springs Run DS 3493 1737   1734

Silver River US 8919 7648   7646

Silver River DS 5584 2729   2116

Weeki Wachee River US 4236 4240  3134

Weeki Wachee River DS   1464   1468 1468

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turnover (Payn et al. 2009). Measured mass recoverysuggests that long-term hyporheic storage and hydraulicturnover are negligible in these rivers, underscoring theunexpectedly minor effect of karst features on hydraulics.

Several observations merit consideration for why thishypothesis about transient storage in karst rivers wasrejected. First, the underlying limestone was rarely ex-posed, precluding unimpeded riverine exchange with the

Table 4.   Summary of best-fit ADS model coefficients. A is the channel cross-sectional area, D is dispersion, AS1 and AS2 are thefitted areas of storage zones 1 and 2, respectively,  a1 and  a2 are the fitted storage-zone exchange coefficients, and F med is the fraction of residence time due to transient storage.

River reach A (m2) D (m2 s21) AS1  (m2)   a1  (s21) AS2 (m2)   a2  (s21)   F med (%)

Alexander Creek US 31.0 0.6 14.5 0.00032 8.3 0.00017 31.3

Alexander Creek DS 41.7 2.8 1.7 0.00009 1.3 0.00010 5.0Blue Springs US 16.1 0.0 14.2 0.00218 3.6 0.00010 43.7Blue Springs DS 11.6 0.7 7.7 0.00012 — — 17.0Ichetucknee River US 25.4 7.8 5.4 0.00012 3.4 0.00002 13.3Ichetucknee River DS 21.7 5.3 1.9 0.00010 1.8 0.00004 8.5Juniper Creek US 5.4 1.0 1.7 0.00087 0.9 0.00029 22.1Juniper Creek DS 10.5 0.4 1.7 0.00044 1.8 0.00004 16.7Mill Pond Spring 5.8 1.0 0.8 0.00015 — — 9.5Rainbow River US 124.0 3.4 9.1 0.00006 5.9 0.00007 6.7Rainbow River DS 53.0 0.0 13.1 0.00007 — — 16.4Rock Springs Run US 2.8 2.2 1.3 0.00419 0.6 0.00108 28.6Rock Springs Run DS 10.6 1.3 5.2 0.00020 8.9 0.00002 26.4Silver River US 57.7 1.8 23.9 0.00066 11.5 0.00016 27.1Silver River DS 57.0 2.2 17.1 0.00029 10.1 0.00004 23.5Weeki Wachee River US 8.9 3.6 5.3 0.00057 2.0 0.00020 33.2

Weeki Wachee River DS 8.3 4.2 0.7 0.00014 — — 8.6

Fig. 4. Relationships among (A) measured and modeled channel area, (B) measured channel area vs. modeled channel and primarystorage-zone area, (C) vegetation frontal area as a fraction of total channel area vs. mean velocity, (D) vegetation frontal area as afraction of total channel area vs. model dispersion, (E) velocity vs. model dispersion, and (F) hydraulic radius, normalized to discharge,vs. model dispersion.

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significant secondary porosity in the adjacent rock.Second, the meter or more of fine calcitic sedimentsbetween the river and the rock exhibited only moderatehydraulic conductivities. Though previous studies sug-gested hydraulic conductivity acted as a control onsurface-hyporheic exchange (Morrice et al. 1997), wedid not observe a correlation between hydraulic conduc-tivity and transient storage once a statistical outlier wasremoved. Third, there are few settings in these low-relief rivers where hydraulic gradients to drive water into the

subsurface would be present. Moreover, as settings of major groundwater outflow, extant hydraulic gradients inspring-fed river are likely out of the sediments, not in, afinding verified along the entire length of the IchetuckneeRiver (M. Kurz unpubl. data).

These observations help to explain why we can reject ourhypothesis that these spring-fed rivers possess exceptionallylarger hyporheic storage, but they also underscore why wehesitate to draw generalizations about other karst riversfrom these results. Rivers with more exposed limestone,thinner and/or coarser sediments, greater bed slopes andturbulence, and different exfiltration hydraulic gradientswould be likely to exhibit markedly different behavior.

Fig. 5. Relationships between measured cross-sectional areas (AV 5 submerged vegetation,AB   5   benthic sediments) normalized for channel width and ADS model storage-zone cross-

sectional areas (AS1 and AS2) normalized for channel width.

Fig. 6. Comparison of observed and fitted breakthroughcurves for Blue Springs under low (gray) and high vegetativeconditions (black). The low vegetation condition, which is inducedby recreational effects, is best fit by a single transient storage ADSconfiguration, while the high vegetation condition, which returnswhen recreation effects decline in the fall, was best fit by a two-zone serial ADS configuration, supporting inference that vegetation is an important and distinct second transient storage zone.

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Indeed, the presence of flow reversals (i.e., river water entryinto the subsurface) in springs along the Suwannee andSanta Fe Rivers in north Florida during flood stage (Gulleyet al. 2011) indicates potentially tremendous hyporheicstorage within the aquifer, with significant consequencesfor karst conduit development. While such reversals canand do occur for some of our sites, such conditions werenot present during our sampling.

While we did not find evidence of exceptional hyporheicstorage, we found strong evidence that vegetation is a majorcontrol on the hydraulic properties of these rivers. Specif-ically, our hypothesis that vegetation would act as a control

on the mean velocity was supported by the strong negativecorrelation between vegetation abundance and mean veloc-ity. This was also observed directly in Gilchrist Blue Springs,where the mean velocity decreased dramatically whenvegetation cover and density were reduced following heavyrecreational activity. The hypothesis that vegetation wouldact as a control on the magnitude of dispersion was notsupported. A possible explanation for this is that while thepresence of vegetation may create mechanical dispersion, italso decreases the velocity, resulting in less shear stressseparation and less total dispersion.

Selection of the appropriate ADS model configurationalso reinforced the case that vegetation acts as anadditional and temporally independent transient storage

zone. In most cases, the two storage zones in seriessignificantly improved model fit over other configurations,suggesting that there are at least two potential storagezones in these rivers. This conclusion is also supported bythe observations in Gilchrist Blue Springs, which showedthat when vegetation was absent, the best fit switched fromtwo storage zones (in series) to a single storage zone.Additionally, the channel cross-sectional area of the ADSmodel, which was left unconstrained, converged on an areathat was more correlated with the measured channel areaonce the area of the primary storage zone was taken intoaccount. This supports the conclusion that some portion of the measured channel area, including vegetation beds, is

acting as a storage zone, while confining the advectivecomponent of transport to a smaller channel area.This conclusion is tempered somewhat by the observation

that in some rivers where a two-storage-zone model fit best,no vegetation was present. In these cases, the primary storagezone may represent other in-channel storage zones such asslack water along the banks and backwater eddies (Briggset al. 2009), which are likely also present when vegetationcover is high. Conversely, vegetation was abundant in threeof the four cases where a second storage zone did not improvemodel fit. In these cases, the breakthrough curve tails werenot as pronounced. While storage in those systems mayplausibly be dominated by vegetation (i.e., AV   is the only

storage), the depth of the hyporheic sediments was notanomalously low. As such, the mechanism that controlswhen addition of a second storage improves model fit, andwhat features lead to that condition, could not be adequatelydetermined.

Similarly, even where two zones in series dramaticallyimproved the fit to observed breakthrough curve tails, therewas evidence that additional storage zones might bewarranted. In those cases, use of an alternative RTD model(Haggerty et al. 2000, 2002; Gooseff et al. 2005) might bedesirable. However, because we physically observed twostorage zones in these systems (vegetation and sediments)

and were attempting to relate these with fitted ADS modelstorage coefficients, this possibility was not formallyinvestigated.

The structure of the ADS model (one vs. two storagezones; serial vs. parallel configuration) is an important testof the conceptual model of storage in these rivers, whichspecifically identifies vegetation beds overlying hyporheicsediments as the two zones. Of particular importance wasthe finding that the fitted ADS model storage zones alignedwith their proposed physical analogs. Specifically, the fittedprimary storage-zone area (AS1) correlated significantlywith measured vegetation frontal area (AV), but not mea-sured benthic sediment area (AB), when the values werenormalized for stream width. The slope of this relationship,

which was 0.36, indicates that transient storage occurs inapproximately one-third of the vegetated cross-sectionalarea. Likewise, the fitted second storage zone (AS2), whereneeded, was more correlated with benthic area (AB) thanwith vegetation (AV). As before, the slope of the line wasfar less than 1 (0.06), suggesting that hyporheic exchangeoccurs only in the surface sediments, and not throughoutthe entire profile. This is the first example of which we areaware that directly links the model fitted zones with theirpresumed physical locations.

In the last 50 yr, many springs have seen nitrate con-centrations increase by an order of magnitude over historicconcentrations due to anthropogenic activities (Katz et al.

2004; Stevenson et al. 2007). Over this same time period,many spring rivers have seen a precipitous drop in theabundance of submerged aquatic vegetation and an increasein benthic filamentous algae. While links to nutrientenrichment are still debated (Heffernan et al. 2010), thepassage of N from fertilizer to aquifer to surface waterremains a major challenge. Insofar as residence time is one of the major factors determining the magnitude of nutrientremoval, it would appear that maintaining high vegetationdensity is an important management and restoration target.In addition to exerting direct effects on nutrient removalthrough assimilation, and first-order indirect effects on Nremoval by providing the organic substrate that fuels

Table 5.   Comparison of ADS coefficients for Blue Spring under varying vegetative (veg.) conditions. Variable definitions are as inTable 4.

River reach A (m2) D (m2 s21) AS1 (m2)   a1  (s21) AS2 (m2)   a2  (s21)   F med (%)

Blue Spring high veg. 12.0 0.0 8.9 0.00169 5.8 0.0001417 39.5Blue Spring low veg. 16.0 0.3 9.7 0.00091 — — 33.3

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Associate editor: H. Maurice Valett

Received: 03 October 2011Accepted: 27 February 2012

Amended: 09 March 2012

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