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HOW DOES RIPARIAN REVEGETATION AFFECT SUSPENDED SEDIMENT IN A SOUTHEAST QUEENSLAND STREAM? TECHNICAL REPORT Report 04/13 December 2004 Nick Marsh / Ian Rutherfurd / Stuart Bunn COOPERATIVE RESEARCH CENTRE FOR CATCHMENT HYDROLOGY

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Page 1: HOW DOES RIPARIAN REVEGETATION AFFECT SUSPENDED … · rehabilitation strategy on a small stream in southeast Queensland. The stream rehabilitation strategy was to exclude stock by

HOW DOES RIPARIAN REVEGETATION AFFECTSUSPENDED SEDIMENT IN A SOUTHEASTQUEENSLAND STREAM?

TECHNICAL REPORTReport 04/13

December 2004

Nick Marsh / Ian Rutherfurd / Stuart Bunn

C O O P E R A T I V E R E S E A R C H C E N T R E F O R C A T C H M E N T H Y D R O L O G Y

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Marsh, Nicholas

How does Riparian Revegetation Affect SuspendedSediment in a Southeast Queensland Stream?

KeywordsRiparian vegetation

Revegetation

Sedimentation

Streams

Rehabilitation

Sediment yield

Erosion control

Runoff

River management

Land use

Hydrology

Grasses

Trees

Channels

Hydraulics

Turbidity

© Cooperative Research Centre for Catchment Hydrology, 2004

Bibliography

ISBN 1 920813 18 7

1. River sediments - Queensland - Echidna Creek. 2. Stream restoration- Queensland - Echidna Creek. 3. Riparian restoration - Queensland -Echidna Creek. 4. Restoration ecology - Queensland - Echidna Creek. I.Rutherfurd, I. D. (Ian D.). II. Bunn, Stuart E. III. Cooperative ResearchCentre for Catchment Hydrology. IV. Title. (Series: Report (CooperativeResearch Centre for Catchment Hydrology); 04/13).

333.9162153099432

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How doesRiparianRevegetationAffect SuspendedSediment in aSoutheastQueenslandStream?

Nick Marsh1 / Ian Rutherfurd2 / Stuart Bunn3

1 Queensland Environmental Protection Agency2 School of Anthropology, Geography and

Environmental Science, The University of Melbourne, Victoria

3 Centre for Riverine Landscapes, Griffith University, Queensland

Technical Report 04/13December 2004

Preface

Despite the considerable government and privateresources invested in the rehabilitation of damagedenvironments, little is known about the success ofsuch projects. The Cooperative Research Centre(CRC) for Catchment Hydrology conducted a project(2000-2003) in collaboration with the CRC forFreshwater Ecology and the Moreton Bay andCatchments Healthy Waterways Partnership to assessthe impact of stream rehabilitation on a few keyelements of stream health. The project aimed toquantify the affect of a commonly adopted streamrehabilitation strategy on a small stream in southeastQueensland. The stream rehabilitation strategy was toexclude stock by fencing the stream, provide off-stream stock watering and to revegetate the riparianzone using endemic native species for a 1.5 km2

catchment (Echidna Creek) near Nambour in southeastQueensland. Four key elements were monitoredthrough the life of the project:

1. Suspended sediment load;

2. Channel morphology;

3. Water temperature;

4. Aquatic macrophyte growth.

The results of the suspended sediment response torevegetation are presented in this report. The otherkey research areas are presented in separate CRC forCatchment Hydrology technical reports.

Mike Stewardson Program Leader, River Restoration CRC for Catchment Hydrology

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Executive Summary

This report presents the results of suspended sediment(SS) response to the revegetation of a small stream insoutheast Queensland.

Suspended sediment was measured indirectly byconverting half hourly turbidity values into suspendedsolids concentrations using a suspended solids-turbidity relationship developed for the study site.Suspended solids were logged from December 2000until March 2004 in the treatment stream that wassubject to revegetation in early 2001, a control streamthat remained cleared of trees and a fully forestedreference stream. All streams had a catchment area ofapproximately 1.5km2 and were located within 3 km ofeach other. The discharge of each of three streams wasgauged for the duration of the study, hence we canconvert suspended sediment concentrations tosuspended sediment loads for the three streams.

The mean annual suspended sediment yield for thecatchments was 14.5-87.8 t/km2/a for the controlstream, 12.3-212.2 t/km2/a for the treatment streamand 3.0-78.0 t/km2/a for the reference stream. Theseloads are low by world standards. 85% of the totalsuspended sediment load for the three streams wasdelivered during storm events.

The treatment stream initially had a similar suspendedsediment yield to the control stream, howeverfollowing revegetation the SS yield in the treatmentstream increased by around 100% due to disturbanceof bank material and clearing of riparian weeds. Thesuspended sediment yield at the treatment site was stillelevated at the completion of the three-year study,however we would anticipate a gradual decline insuspended sediment yield to the same or lower levelsthan prior to revegetation. Stream managers need tobe mindful of the likely initial increase in suspendedsediment yield following rehabilitation work.

We compared the suspended sediment yield betweenthe three streams on an event-to-event basis, andillustrated how the relative suspended sedimentyielded from forested and grassed catchments willvary according to storm event size. For very large

storm events (with overland flow), the forested siteyielded more sediment than the grassed catchment,however for small storm events when most dischargewas within the stream banks, the grassed catchmentyielded more sediment.

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Table of Contents

Preface i

Executive Summary ii

List of Figures iv

List of Tables v

1. Introduction 1

1.1. Role of Vegetation in Controlling Channel Morphology 2

1.2. Southeast Queensland Channel Geometry 4

2. Conceptual Models of Sediment Response to

Revegetation 7

3. Methods 9

3.1. Experimental Design 9

3.1.1 Turbidity Logger Installation 9

3.2. Site Description 10

3.2.1 Treatment Site (Echidna Creek) 10

3.2.2 Control Site (Dulong Creek) 11

3.2.3 Reference Site (Piccabeen Creek) 11

4. Results and Discussion 13

4.1. Suspended Sediment - Turbidity Relationship 13

4.2. Discharge Calculation 16

4.3. Suspended Sediment Yield Results and Discussion 19

4.3.1 Variation in Control Streams 19

5. Conclusions 29

6. References 31

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List of Figures

Figure 1. Hypothesised Relationship Between Riparian Vegetation Type and Channel Width 3

Figure 2. Comparison of the Cross-sectional Area of Treed and Grassed Streams 4

Figure 3. Conceptual Model of Changes in Gross ChannelMorphology 7

Figure 4. Turbidity Meter and Logger Installation 9

Figure 5. Field Site Locations 10

Figure 6. Echidna Creek, Downsteam Monitoring Site - April 2001 and March 2004 11

Figure 7. Dulong Creek (Control Site), Piccabeen Creek(Reference Site) 11

Figure 8. Turbidity-SS Relationship for Echidna Creek AreaShowing Range of Data foreach Sample 13

Figure 9. Bioperturbation in Dulong Creek 14

Figure 10. Bioperturbation Caused by Turtles on Dulong Creek 15

Figure 11. Example of Turbidity Data Corrected for Drift (Dulong Creek) 15

Figure 12. 900 V-Notch Weir Installed on Echidna Creek 16

Figure 13. Stage Discharge Relationship for Echidna Creek. 16

Figure 14. Sediment Load for Echidna Creek Dec 2000-Mar 2004 17

Figure 15. Sediment load for Dulong Creek Dec 2000-Mar 2004 17

Figure 16. Sediment Load for Piccabeen Creek Dec 2000-Mar 2004 18

Figure 17. Seven Day Running Mean of Suspended Sediment Load for the Three Streams 18

Figure 18. Annual Summaries of Total Suspended Sediment Yield 21

Figure 19. Cumulative SS Yield Per Effective Catchment Area 21

Figure 20. Suspended Sediment Yield for 28 Identified Storm Events 22

Figure 21. Comparison of the Relative SS Yield per ha Between the Reference and Control Streams 24

Figure 22. The SS Yield for Each of the Streams is Closely Related to the Event Size 24

Figure 23. Event Size for the Three Streams 25

Figure 24. Event-mean Suspended Sediment Yield Per Hectare of Contributing Catchment 25

Figure 25. Updated Conceptual Model of SS Response toRevegetation 29

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List of Tables

Table 1. Selected Sediment Yield Results 20

Table 2. Key Variables considered in Developing Multiple Linear Regression Models of Event Mean SS Yield 26

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1. Introduction

A current trend in stream management is to controlsediment delivery to receiving waters, and to this endextensive revegetation projects are under way acrossAustralia. With all riparian zones intact, the netsediment delivery would be less than for clearedchannel banks. However to achieve this end point offully restored riparian zones we require anintermediate period for vegetation to become re-established. During the re-establishment ofvegetation, the shading out and killing of invasivepasture grass may result in the liberation of largequantities of sediment to be delivered to receivingwaters. A key consideration for this study was todetermine if the sediment load of a small stream didincrease following revegetation, and over what periodwas the sediment load elevated. The implications arethat stream rehabilitation work can be staged to reducethe impacts of increased sediment supply frommultiple rehabilitation projects being undertakensimultaneously.

The South East Queensland Water Quality MonitoringStrategy's Moreton Bay Study found that the greatestthreat to Moreton Bay was sediment delivery to theBay (Dennison and Abal, 1999). Sediment not onlysmothers sea-grass but the continual re-suspension offine particles by wave and tidal action increasesturbidity and limits seagrass depth range. Suspendedsediment (SS) can also increase nutrient delivery bytransferring adhered phosphorus to the Bay. There isa worldwide emphasis on controlling nutrientenrichment by controlling sediment input to streams.

Previous studies on the role of riparian vegetation inrestricting channel dimensions or controlling sedimentdelivery are effectively "space-for-time" typeexperiments where the differences in channelcharacteristics or suspended sediment load areassumed to be driven by the difference in the riparianvegetation density. An example study was by Hossain(2002) who showed a reduced SS load for the heavilyforested Bungawalbin Creek catchment of theRichmond River compared to the upper RichmondRiver and Wilson River. Neil et al., (2002) showed aclear positive relationship between percent catchment

cleared and median suspended sediment concentrationfor the Tully River in North Queensland. Sutherland(2002) showed that turbidity was higher in disturbedstreams compared to reference streams in NorthCarolina (USA) during baseflow conditions, howeverthere was no clear relationship for turbidity duringstorm flow.

To date there have been limited long term studieswhere changes in sediment delivery due to land usechange have been measured. McKergow et al., (2001)measured the suspended sediment response to a small(5.9 km2) catchment near Albany in Western Australiathat was monitored for six years prior to revegetationand four years after the commencement of stockexclusion and revegetation. This study found a largedecrease in total SS following revegetation (94% dropin event mean SS concentration). However there wasno control for climate before and after therevegetation. Comparing the annual rainfall andrunoff from before and after the revegetation shows nosignificant difference (t-test P>0.05), however SSdelivery to streams is largely during individual stormevents, hence it is important to consider sub-annualrainfall and discharge data to illustrate the consistencyin climate both pre and post revegetation. It isunlikely that any difference in storm activity pre andpost revegetation, could explain the huge 94% drop insediment delivery, yet still deliver the same annualdischarge. It is not clear whether the reduction in SSyield is mostly related to the removal of stock,whereby stock tracks can provide a preferred flowpath for sediment to the stream (Hairsine et al., 2001;Trimble and Mendel, 1995), or the role of vegetationin trapping sediment. However the restoration projecthas clearly had a dramatic effect on sediment delivery,although the precise impact is not clear withoutcomparing the individual storm events before and afterrevegetation.

Saiakeu et al.,(2004) compared the SS concentrationat 57 sites on major rivers in central Japan over atwenty year period. The patterns in SS were primarilylinked to changes in agricultural land, construction,and point sources in populated areas. As you mayexpect there was a negative relationship between meansuspended sediment concentration and percent ofcatchment under forest, although the same comparison

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is not presented for event based SS concentrations.From the above two studies the low flow SSconcentration forms a strong relationship with percentcatchment cleared. This is important from a streamhabitat perspective where the dominant conditions inthe stream are low flow. However in terms ofsediment yield, most sediment is transported duringstorm events. The available data sets comparingforested and cleared sites during the peak sedimentdelivery period (storm events) is limited.

1.1. Role of Vegetation in ControllingChannel Morphology

The replanting of riparian vegetation is commonstream rehabilitation activity largely because of therelatively low cost, limited expertise required andlarge range of potential benefits. One of the potentialbenefits of riparian vegetation is its ability to controlchannel erosion (e.g. Beeson and Doyle, 1996;Anderson, 1985; Gurnell, 1995; Henderson, 1986;Shields et al., 1995; Stott, 1997). Riparian vegetationis often claimed to have a dramatic effect on thegeomorphology of streams (Abernethy andRutherfurd, 2000); (Anderson, 1985); (Beeson andDoyle, 1996); (Bradbury et al., 1995). Riparianvegetation is usually associated with a reducedchannel cross-sectional area. Hydraulic geometryrelations used for predicting coarse channel geometry(bankfull width and depth) usually predict a reductionin channel width of around 20% for heavily vegetatedstreams (e.g. Church, 1996; Hey and Heritage, 1988;Huang and Warner, 1995; Huang and Nanson, 1997;Millar and Quick, 1998). Based on this literaturealone, one would be keen to plant riparian vegetationfor the sole purpose of restricting channel erosion andsediment delivery to receiving waters. However someliterature clearly illustrates an increase in channelcross-section for streams with a dense riparian forest(Beeson and Doyle, 1996; Davies-Colley, 1997;Gregory, 1992; Gurnell, 1995).

If one considers a regional stream restoration objectivebased on reduced sediment delivery, a change inchannel width of ± 10% can result in an enormousquantity of either sediment delivered to receivingwaters or stored within the channel. For example,consider the Brisbane River catchment with say 425 kmof stream length, assume an average width of 15 m and

depth of 2 m, a 10% reduction in channel width wouldtrap around 2 million tonnes of sediment. Converselya channel widening of 10% would deliver the sameamount to receiving waters. Whilst vegetationprovides many beneficial elements other than erosioncontrol it is important that we understand the role thatvegetation plays in controlling sediment deliverybefore we construct conceptual models of the expectedchannel response to the replacement of grass withtrees.

The key role that vegetation plays in reducing channelerosion is through the reinforcement of channel banksby tree roots [e.g. (Abernethy and Rutherfurd, 1998;Montgomery, 1997)]. However in the absence ofriparian trees, the stream bank is not necessarily bare,grasses can colonise the stream bank providing a highlevel of strength. It is streams with dense, invasivepasture grass that effectively restrict the channel size,hence the debate about what is best on the banks is notabout comparing vegetation and no vegetation butcomparing grass with trees.

Davies-Colley (1997) found that small New Zealandstreams (<1 km2 catchment area) that had forestedriparian zones were double the width of the samestream where the riparian zone consisted solely ofgrazing pasture. This effect of pasture grass restrictingchannel size reduced until a catchment area of around30 km2. Catchments of 30 km2 had a similar width inboth forest and pastures. That is, streams up to around10 m wide were reduced in size when passing throughpasture (compared to native forest).

A similar finding has been illustrated in Coon CreekWisconson, USA by Trimble (1997), where streamsflowing from pasture to forest increased in width (andcross-sectional area). Trimble (1997) does not statethe catchment area, however the reach mean streamwidths are from 8.8-11.8 m, indicating that thecatchment areas were likely to be small (<50 km2). Amuch earlier study by Zimmerman et al., (1967) foundthat meadow streams in Vermont USA were narrowerthan streams in forest.

These differences in channel width do not appear to bethe result of changes in catchment hydrology wherebyforested catchments produce a longer time-of-concentration and reduced peak for any given stormevent than cleared catchments because the observed

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differences in channel width occurs in both forest-to-pasture and pasture-to-forest transitions (Davies-Colley, 1997). The differences in channel dimensionsappear to be primarily a function of the bankvegetation. Davies-Colley (1997) suggests that themechanism by which pasture streams becomenarrower than forest streams is largely due to pastures'ability to colonise freshly deposited sediment morerapidly than trees, effectively narrowing the channelthrough pasture grass invasion into the channel. Thismechanism explains the observed phenomenon insmall streams, but does not describe why thephenomenon should only occur in small streams.

We think that the differentiation between grass andtrees being the most effective at restricting sedimentdelivery to the stream is largely a function of channelhydraulics. We hypothesise that where streams arevegetated only by pasture grass and the erosivepotential of the stream is less than that required toscour the pasture grasses, then the pasture grasseffectively controls the channel width and can also actto decrease the channel width by trapping finesediment (Bunn et al., 1997). For streams wherepasture grasses can be scoured at bankfull flow, thepasture grass does not control the lateral extent of thechannel. For these streams with a greater erosivepotential, the lateral extent of the channel is defined bythe effective cohesiveness of the underlying soil. For

the case of streams that that have dense riparianvegetation there is usually limited pasture growth dueto shading, hence the control of the channel widthwould be due to the effective cohesiveness of theunderlying soil. The roots of riparian vegetationincrease the effective cohesiveness (and hence shearresistance) of the underlying soil (Abernethy andRutherfurd, 1998). Whilst dense riparian vegetationeffectively restricts the lateral expansion of thechannel, the bare soil under a dense tree canopyprovides limited structure to enhance sedimentdeposition, hence one would not expect channelnarrowing in small streams with a dense riparianforest. The underlying hypothesis therefore is thatpasture streams are narrower than densely forestedstreams where the erosive potential of the stream islow. However where the erosive potential of thestream is high, densely forested streams are narrowerthan those vegetated only by pasture (Figure 1). Theterm erosive potential is used rather broadly here, andis intended to encapsulate any method of measuringthe ability of a stream to modify its form. The conceptof defining the channel forming discharge or dominantdischarge (the discharge that would produce the samechannel form as the natural hydrograph) (Copeland etal., 2000) or effective discharge (maximum sedimentdelivery discharge) (Sichingabula, 1999) would meetour criteria for quantifying the erosive potential.

Figure 1. Hypothesised Relationship between Riparian Vegetation Type and Channel Width.

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1.2. Southeast Queensland ChannelGeometry

We can test the hypothesis in Figure 1 using 42alluvially controlled reaches in southeast Queensland.These reaches were included in the Design andImplementation of Baseline Monitoring Project(Smith and Storey, 2001). For details of site selectionand reach description please refer to chapter 2 of theDIBM3 report. To test the hypothesis illustrated inFigure 1, reaches were classified as having riparianzones dominated by grass (herbaceous vegetation) ortrees. To quantify the stream erosion potential (Figure1) we combined a measure of the streams ability tomobilise sediment (stream power per unit streamlength [Equation 1]) and an indicator of the likelyduration of stream mobilising events.

For channel change to occur, high stream powerconditions must persist for long enough to affectmorphological change. We do not have dischargerecords for all of the reaches in Figure 2 to conduct afrequency distribution of the duration of the erosiveevents in each reach. We have instead used a coarsesurrogate of catchment size instead of flood duration.Flood duration is a function of catchment size (andshape and topography and land use and stormcharacteristics) with small catchments generallyhaving shorter duration floods than large catchmentsfor the same storm event. The streams used here allwere located in southeast Queensland with generallysimilar climatic conditions (e.g. similar stormintensities and durations), hence catchment area wasassumed to be a suitable surrogate for event duration.

Figure 2. Comparison of the Cross-sectional Area of Treed and Grassed Streamsusing Bankfull Stream Power and Catchment Area to Describe 'ErosionPotential'.

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The measure of erosion potential used in Figure 2 is tosimply multiply the stream power and catchment sizetogether.

ϖ = ρgQS (1)

where:

ρ = density of water (kg/m3)

g = gravity (9.8 m/s2)

S = water surface slops (channel slope used as a surrogate)

Q = bankfull discharge (m3/s)

ϖ = stream power (kgm/s3)

Unfortunately we had a relatively limited data set withwhich to test the vegetation effect on channel size, andvery few streams with a catchment area less than 50km2. For each reach, five cross-sections weremeasured (spaced at random intervals between 5 and15 m). For each of these five cross-sections thebankfull stream power was calculated and a reachaverage combination of stream power times catchmentarea were used to represent the erosive potential of thestreams. To calculate stream power, we calculated thebankfull discharge using Manning's equation, wherebychannel surveys were used to determine channelgeometry variables (Cross-sectional area, hydraulicradius and bed slope which was used to approximatewater surface slope), and Manning's 'n' was estimatedfrom tables and figures of representative streams(Chow, 1959). Previous studies relating channelgeometry (width, depth or cross-sectional area) tobankfull discharge or catchment area have tended tofind that power functions are robust for curve-fitting todescribe the data trends (Annable, 1996; Hey, 1978;Hey, 1988; Huang and Nanson, 1998; Thorne et al.,1988). In Figure 2 we have applied power functions(fit based on least squares) to describe the relationshipbetween channel size and erosive potential.

Figure 2 is not conclusive in its support for smallercross-sectional area pasture streams (for low streamenergy) due to the spread of the data and limitedreplication. However the grassed streams do appear tohave a slightly reduced cross-sectional area for smallstreams than the treed streams. Figure 2 illustrates thatthis hypothesis is worth pursuing with a larger data setand more robust quantification of erosive potential.

Based on the hypothesis of smaller streams in lowenergy grassy channels compared to forested channelsin the same environment we would expect somechannel change in a small stream where grass isreplaced with trees. The stream which is the subject ofthis stream restoration monitoring project (EchidnaCreek) has a small catchment (1.5 km2), hence wewould expect the channel response due to revegetationto be one of widening and potential shallowing with anet increase in channel cross-sectional area. We mighttherefore expect an increased sediment delivery fromEchidna Creek (compared to control streams) inresponse to the replacement of grass with trees.

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2. Conceptual Models of Sediment Response to Revegetation

Before revegetation Echidna Creek was wider onaverage in forested sections than in cleared sections.We hypothesised that the narrower channel in pasturewas due to the lack of riparian forest vegetation, andeffective encroachment of pasture grass (Figure 3).Our conceptual model for channel activity in EchidnaCreek following revegetation was that:

1. When canopy closure is achieved by the replantedvegetation, the light levels in the riparian zone willbe too low for the continued vigorous growth ofpasture grass.

2. The pasture grass within the channel will die due toshading.

3. The lack of pasture grass will decrease the bankstrength.

4. The channel will widen in response to the effectivedecrease in cohesive strength in the stream banks.

5. During the period of no grass and establishment ofvegetation there is effectively no buffering effectdue to streamside vegetation with hillslope andfloodplain generated sediment able to be delivereddirectly to the stream without sediment settling dueto the roughness effect of ground cover. Hence thesuspended sediment load would increase asvegetation becomes established and the channelreaches a new wider morphology.

6. As vegetation becomes established the channelmargins will become stabilised and the suspendedsediment load will return to the pre-revegetationlevel.

7. A wider channel will be established.

Figure 3. Conceptual Model of Changes in Gross Channel Morphology. (Channel expected to widen as woody vegetation becomes established).

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3. Methods

3.1. Experimental Design

The approach for testing the suspended solidshypotheses was by way of a 'BACI' type experimentaldesign (Before, After, Control, Impact). The relevantparameters were measured simultaneously at both ahigh quality reference site and a poor quality (limitedvegetation) control site as well as the treatment site.The relative difference between the treatment site andthe reference and control sites can be tracked throughtime from before the revegetation commenced. Thiscomparison of the relative difference in suspendedsediment load between the control and treatment sitesreduces ambiguity due to seasonal differences in SSfrom inter-annual variation in rainfall.

3.1.1 Turbidity Logger Installation

Turbidity is being used as an indirect measure ofsuspended solids because it is an inexpensive way tocontinuously monitor sediment concentration. Thereare several turbidity loggers on the market, each withspecific benefits such as robustness, battery life, datastorage, or recalibration requirements. We choseturbidity loggers manufactured by GreenspanTechnologies (TS 300 model) because they wererelatively inexpensive and had a lens cleaning jetattached. The fouling of the optical lens byfilamentous algae has proven to be a major limitationto the remote measurement of turbidity (Gippel 1989).There are two basic designs to facilitate the removal ofalgae from the optics, 1) abrasive removal such as by

using a small brush, and 2) jet removal such asspraying the optics with a high velocity jet (Figure 4).An electronic controller was used to control theduration of each jet and the frequency of lens cleaning.The pump (for the jet) was attached to a 12 volt gelcell battery, hence the operation of the optics cleaningjet was a compromise between the frequency andduration of water jet for lense cleaning and the batterylife.

Greenspan turbidity loggers were installed at twolocations in December 2000 (Figure 5). The loggerlocated at Echidna Creek is at the downstream end ofthe stream rehabilitation project (the treatmentlogger). Dulong Creek (control site) has little riparianvegetation and had continuous cattle access to thestream for the duration of the experiment. In additionto these two sites a third logger was installed inPiccabeen Creek in March 2002. Piccabeen Creek isa reference site located in nearby Mapleton StateForest.

The intended analysis was to compare both referenceand control sites with the treatment site for a series ofstorm events during the establishment of vegetation.The SS load is likely to be heavily influenced byantecedent conditions such as a recent flood peak(Rieger and Olive, 1988), hence one must be careful inpresenting before and after comparisons for a singlesite. By comparing across nearby sites the antecedentconditions can be assumed to be very similar for anygiven event hence the difference in SS would indicatea real difference between the streams rather than ananomaly of the pre-event conditions and stormintensity.

Figure 4. Turbidity Meter (left) and Logger Installation (right).

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3.2. Site Description

3.2.1 Treatment Site (Echidna Creek)

The target stream, Echidna Creek is small with abankfull width of around 1-5 m and a catchment areaof 1.5 km2. Echidna Creek is a tributary of the SouthMaroochy River near Nambour in southeastQueensland. The catchments near Echidna Creekwere previously used for intensive dairy farming,although the present land use is largely hobby farmingand low intensity cattle grazing. The area has fertilevolcanic soils, and is about 250 m above sea level.The climate in Echidna Creek is subtropical, with alate summer dominated rainfall (Average annualrainfall since 1952, 1732.6 mm/a). The catchment hadbeen mostly cleared. Echidna Creek has two largefarm dams in the upstream section (Figure 5). Thechannel bed is cobbly with bedrock outcrops. Theriparian vegetation for this length of stream is patchyand varies from dense over-storey to pasture grass.

Rehabilitation commenced in February 2001, withmost rehabilitation complete by May 2001. Stockexclusion was achieved using a four-strand barbedwire fence with solar powered off-stream stockwatering points. Points where stock or vehiclestraverse the stream were constructed into low levelconcrete fords. The riparian revegetation consists ofspecies grown from locally collected riparian seedstock. The revegetation process was firstly poisoningthe grass and weeds in the riparian zone using anorganophosphate pesticide, and secondly plantingtube-stock by digging a single hole for each tubestock. The tube stock was watered-in and the wholeriparian zone (approximately 5 m) either side of thestream was covered in a thick layer of mulch hay. Asmall section of fencing and revegetation wasconducted in November 2001, and some secondaryplanting to replace non-viable plants in February-March 2002. Figure 6 compares the downstreamsampling site at the beginning and end of the project.

Figure 5. Field Site Locations.

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3.2.2 Control Site (Dulong Creek)

Dulong Creek is located 2 km from Echidna Creek,and has a catchment area of 1.53 km2. The land use isimproved pasture (mostly Kikuyu grass (Pennisetumclandestinum)) for dairy and beef cattle grazing.Dulong Creek has two similar sized tributaries thatjoin approximately 200 m upstream of the samplingsite. A large dam is located on one of these tributaries.

3.2.3 Reference Site (Piccabeen Creek)

Piccabeen Creek is located approximately 2 km fromEchidna Creek and has a similar catchment area of1.55 km2. Piccabeen Creek is located within theMapleton state forest area and has a fully forestedcatchment. The catchment has been subject to pastlogging, but regrowth within the catchment appears tobe mature trees older than 30 years, hence thecatchment has not been subject to disturbance inrecent years.

Figure 6. Echidna Creek, Downsteam Monitoring Site - April 2001 (left), March 2004 (right).

Figure 7. Dulong Creek (Control Site) left, Piccabeen Creek (Reference Site) right.

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4. Results and Discussion

4.1. Suspended Sediment - TurbidityRelationship

The direct measurement of suspended solids is timeconsuming and requires expensive samplingequipment. To permit a high frequency of suspendedsediment (SS) sampling, a continuous turbiditymeasurement was selected as a suitable surrogatemeasure of SS. To convert the turbidity values to anequivalent SS value, a calibration curve for the givencatchment must be developed. A risk of this type ofexperiment is that most of the variability in the opticalproperties measured by nephelometric turbidity unitsis not explained by SS. Based on available literature,an R2 value of 0.5-0.9 (SS versus turbidity - linearmodel) would be expected. There is potential that theoptical properties of the stream are significantlyaffected by variables other than SS such as colour, anddense growth of filamentous algae that may precludethe successful use of turbidity as a surrogate measureof suspended sediment.

For each SS-turbidity datum point three instantaneousturbidity recordings were taken using a TPS brandhandheld turbidity meter, and three filtered suspendedsamples were collected. The turbidity readings weretaken in the field to reduce the potential errorassociated with delayed turbidity sampling asdescribed by Gippel (1989). Suspended sedimentfilter samples were collected by first oven drying filterpapers at 105ºC for 45 minutes and pre-weighing. Aknown volume of water was filtered through the 0.45micron filter papers (120-400 ml depending on SSconcentration). The used filter papers were oven-driedand re-weighed on a 5-point balance. The dry pre andpost filtration paperweights were used to calculate theSS concentration.

The small catchments used in the study have a veryrapid response to storm events, and it was difficult tobe on site during a storm event to sample elevatedturbidity values. In an effort to collect high turbiditydata, artificially high turbidity conditions weresimulated by mechanically agitating the streamsediment then collecting SS and turbidity datadownstream from this point over approximately a 45-

Figure 8. Turbidity-SS Relationship for Echidna Creek Area Showing Range of Data for Each Sample.

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minute period as the water settles to the ambientturbidity value.

The 70 turbidity-SS datum points (Figure 8) gave areasonable correlation (R2=0.79) using a linear modelforced through zero. This correlation compares wellwith the range normally reported in the literature (R2=0.5-0.9). This relationship is adequate to compute SSload from turbidity data at the selected streams. Therange bars on Figure 8 show the total range of valuescollected for each sample (Three replicates for eachsample). The error range increased with increasing SSconcentration, illustrating the difficulty in sampling inthe high turbidity range where the SS concentration isnot uniformly mixed through the entire water column.

The SS-turbidity relationship in Figure 8 was used toconvert turbidity data to SS concentration. This SSconcentration was then converted to SS load by usingstream discharge. Stream discharge was calculated byapplying stage discharge curves to recoded stage levelin the three streams.

The collection of turbidity data was not asstraightforward as initially hoped. There was a largedegree of temporal variation in turbidity data, withirregular spikes in turbidity values up to 20 times thebase levels. We think that these high values were due

to submerged macrophytes, or dense algal matsdrifting past the turbidity detection probe. In additionto the spiky turbidity data, a distinct diurnalfluctuation of up to 10 NTU was detected for severalmonths on Dulong Creek (Figure 9). This fluctuationappears to peak mid-morning and we think that it waslikely to be due to bioperturbation. The pool in whichthe turbidity logger is located on Dulong Creek ishome to several turtles, one of which has beenobserved scratching at the bank material above thewater surface next to the logger installation (Figure10). The diurnal fluctuations in turbidity have littlebearing on the total sediment loads because mostsediment is delivered during short duration stormevents. However bioperturbation may be relevant forwater quality sampling programs that have infrequentspot samples that take little account of daily variation.Bioperturbation may also be an important control forthe instream habitat during the dominant baseflowconditions, where suspended sediment levels may bedoubled by the action of bioperturbation, although theabsolute maximum potential increase in turbidity dueto bioperturbation would be limited by bed and bankmaterial composition (e.g. in a gravel bed stream thepotential maximum turbidity due to bioperturbationwould be less than for a silt lined channel).

Figure 9. Bioperturbation in Dulong Creek (mid-morning peak about 7 NTU above baseline turbidity).

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In order to remove the spikes in turbidity that were nottruly a reflection of changes in SS concentration (e.g.bioperturbation, floating algal mats, electronic spikesin the logger) a filtering algorithm was written. Thealgorithm removed spikes in the turbidity data thatwere more than two times the values either side, andnot part of a continuous rise or fall (minimum threeconsecutive recordings) by applying a linearinterpolation between turbidity values either side ofthe peak. Turbidity data was initially recorded half

hourly from December 2000 until March 2001, thenthe recording was changed to quarter hourly. Thequarter hourly data is a single measure logged every15 minutes, this sampling regime was altered furtherin January 2002 to record the average of 15 turbidityvalues every 15 minutes. The recording of 15 minuteaveraged turbidity values produced far fewerunexplained spikes in the turbidity values than theinstantaneous recordings.

Water depth, used for calculating discharge wasrecorded half hourly, hence turbidity data wassummarised from 15 minute recordings to correspondto the half hourly recording of water depth byaveraging two adjacent recordings for each half hourlytime step.

In addition to this correction for spiky turbidity values,the turbidity data also had to be corrected for drift dueto diatom growth on the sensing lens for some periods.The turbidity probes were equipped with lens cleaningpumps that were actuated to pump for 30 secondsevery two hours. The pumps were reasonablysuccessful in removing algal growth from the turbidityprobe lens (minor algal growth near lens edge).However the growth of a more hardy film of diatomscontinued to develop on the lens. The lens wascleaned with a toothbrush during the monthly logger

Figure 10. Bioperturbation Caused by Turtles on DulongCreek.

Figure 11. Example of Turbidity Data Corrected for Drift (Dulong Creek).

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download, and the turbidity measured using a handheld turbidity meter. The hand held turbidity metervalues are used to correct for drift in the turbidityvalues by assuming a linear relationship between theturbidity effect due to diatom growth and time. Figure11 shows a sample of turbidity data before and afterbeing corrected for drift.

The turbidity logger for the reference stream(Piccabeen Creek) was not installed until March 2002.The third logger was not installed because we firstwanted to test the suitability of the turbidity loggers,and to determine if a suitable SS-turbidity relationshipcould be developed for these three streams. We choseto install the turbidity logger in the reference site lastbecause we assumed that this site was the most likelyto have a predictable SS response to storm eventsbecause of the limited anthropogenic impact in thecatchment. Once installed, we constructed a stage-turbidity relationship using 2791 turbidity readingsfrom 27/04/02-24/06/02. The turbidity stagerelationship was reasonable (R2=0.59, linear leastsquares fit). We then used this turbidity stagerelationship to back calculate the turbidity from stagerecorded from December 2000. Thus the turbiditydata for the reference site has a potential error of 41%for December 2000 until March 2002.

4.2. Discharge Calculation

In order to convert the turbidity data to a mass load ofSS, the water depth data was converted to dischargedata. In order to achieve this, a stage discharge curvewas required for each site being monitored. The smallcatchment areas, peak response to rainfall andinaccessibility of Piccabeen Creek during wet weatherhas necessitated the development of theoretical stage-discharge curves for the three streams. For EchidnaCreek the theoretical stage discharge curve wasconstructed by installing temporary ninety-degree V-

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Figure 12. 900 V-Notch Weir Installed on Echidna Creek

depth (mm)

disc

harg

e (l/

s)

Figure 13. Stage Discharge Relationship for Echidna Creek.

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Figure 14. Sediment Load for EchidnaCreek (Dec 2000-Mar 2004).

Figure 15 Sediment Load for DulongCreek (Dec 2000-Mar 2004).

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Figure 17 Seven Day Running Mean ofSuspended Sediment Load forthe Three Streams.

Figure 16. Sediment Load for PiccabeenCreek (Dec 2000-Mar 2004).

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notch weirs (Figure 12) and relating stage to dischargethrough several storm events. Weirs could not beinstalled in Piccabeen and Dulong Creeks due toaccess and land use restrictions, hence the stagedischarge curve for these streams was a theoreticaldischarge curve based on cross-sectional survey datausing a 1D flow routing model (US Army Corps ofEngineers HEC-RAS) to compute a theoretical stagedischarge curve. The validity of the stage dischargecurve for low flow was checked manually when theloggers were downloaded.

Figure 13 illustrates the stage discharge curve forEchidna Creek. The calculated suspended sedimentload for the three streams for the duration of the study(December 2000 - March 2004) is presented in asummarised form in Figures 14, 15, and 16. Theseven-day running mean suspended sediment load(Figure 17) illustrates a similar shaped response fromeach stream for each event, although the magnitude ofthe response for each stream (relative to the otherstreams) varies from event to event.

4.3. Suspended Sediment Yield Results andDiscussion

The closest long-term rainfall gauge to the treatmentsite is located in Nambour (approximately 5 km fromthe site). The long term rainfall for the Nambourgauge is 1736 mm/year (since 1952) The first twoyears of the study were quite dry 1180 mm in 2001,and 1055 mm in 2002 compared to a slightly aboveaverage rainfall for 2003 of 1974 mm. As aconsequence, the suspended sediment yield was muchhigher in 2003 than the first two years of the study forall three streams (Figure 18). The SS yield for thereference stream was consistently lower than thecontrol stream. The treatment stream produced asimilar SS load to the control stream for 2001 and2002 but had more than double the SS yield of thecontrol stream in 2003. To calculate the suspendedsediment yield we have only considered suspendedsediment yield per unit of potential source catchmentarea. To calculate the potential source area we have toremove the effect of sediment trapping by farm damsin the Echidna Creek and Dulong Creek catchments.There are several ways to consider trap efficiencybased on pond volume, pond surface area or period ofretention (see [Haan et al., 1994] for a review). Thedam volumes for both streams is larger than the

measured mean annual discharge for both streams (forthe three complete years of this study), according to(Brune, 1953), the trap efficiency is likely to bebetween 90-100% where the reservoir volume exceedsthe annual inflow. We have assumed 100% trapefficiency for the farm dams in Echidna and Dulongcreeks. The contributing area is therefore only thecatchment area downstream of the dams. Theeffective contributing catchment areas are 82.5 ha forEchidna Creek, 101 ha for Dulong Creek and 155 hafor Piccabeen Creek (no dams in Piccabeen Creek).The cumulative suspended sediment yield percatchment area for the study period shows the deliveryof sediment from the treatment site to be similar to thecontrol site until large storm events in March 2003(Figure 19).

The annual suspended sediment yields are within therange reported for Australian streams which are lowby world standards (Table 1).

4.3.1 Variation in Control Streams

To overcome the complication of inter-annualvariations in rainfall affecting our ability to interpretthe response of the treatment stream to revegetationwe have considered the suspended sediment yieldfrom the treatment stream relative to the controlstreams. We compared the relative deviation of thetreatment stream from the control stream andreference stream through time. The hypothesisedresponse (Figure 3) was that the suspended sedimentload at the treatment site (Echidna Creek) shouldinitially be similar to that of the negative control site(Dulong Creek), go through some transition phasefollowing revegetation and eventually have asuspended sediment regime resembling the referencesite (Piccabeen Creek).

Most sediment transport (85.4% of total load)occurred during storm events, to compare the variationin SS load through time we defined a storm event interms of discharge. When the discharge in any creekexceeded a threshold that was approximately doublethe baseflow discharge (threshold flows for Echidnaand Piccabeen Creeks were 10 l/s and 15 l/s forDulong Creek). The event was assumed to start whenthe threshold was exceeded by one stream and finishwhen the flow passed back below the threshold of thelast stream. A storm event had to occur in at least twostreams to be considered and the period between storm

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Site Catchment t/km2/a SourceArea (km2)

Australia

Echidna Creek 1.5 12.3-212.2 (This study)

Dulong Creek 1.5 14.5-87.8 (This study)

Piccabeen Creek 1.5 3.0-78.0 (This study)

Modelled North Queensland 6410-202610 1000- 5800 (Neil et al., 2002)(Natural catchments)

Modelled North Queensland 6410-202610 1900-76000(Disturbed catchments)

Tully River, Queensland 44-585 8-45 (Rieger and Olive, 1988)

Babinda, Queensland 15 480 (Rieger and Olive, 1988)

Macleay River, NSW 7.5-20 138-179 Loughran (1969) and Field0.33-12.52 (1985) in (Rieger and Olive,

1988)

Southern Tablelands (NSW, ACT) <10 km2 2-24 (Wasson et al., 1998)Native forest and pasture

Southern Tablelands (NSW, ACT) <10 km2 42-72 (Wasson et al., 1998)Cropped

Southern Tablelands (NSW, ACT), <10 km2 42-126 (Wasson et al., 1998)Pine plantation

China

Upper Yangzi 363-974881 68-1770 (Lu and Higgitt, 1999)

USA

Major USA Basins 29500-2979000 0.16-633 (Horowitz et al., 2001)

Continental Scale

Europe 35-43 (Rieger and Olive, 1988)

Asia 600-166 (Rieger and Olive, 1988)

Africa 27-37 (Rieger and Olive, 1988)

North and Central America 96-73 (Rieger and Olive, 1988)

South America 63-93 (Rieger and Olive, 1988)

Australia 45-32 (Rieger and Olive, 1988)

Table 1. Selected Sediment Yield Results.

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Figure 18. Annual Summaries of Total Suspended Sediment Yield.

Figure 19. Cumulative SS Yield Per Effective Catchment Area (area below dams).

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mm

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events had to exceed 24 hours, otherwise they wereconsidered a single storm event.

Using these criteria we identified 28 independentstorm events during the three-year data-samplingperiod December 2000 until March 2004. Figure 20illustrates each event-mean sediment concentrationper unit discharge (average of all instantaneous SSconcentrations for the event).

To test our hypothesis that the SS yield in thetreatment stream would increase followingrevegetation we need to be able to compare therelative sediment yield for each event between sites.To compare between sites requires that we furtherstandardise the SS yield data to remove the effect oftime variant parameters (e.g. localised differences inrainfall) that can vary between the sites. One way toinvestigate the effect of time variant parameters is tocompare our control and treatment sites over theperiod of the study, if there are no time variantparameters, the relative SS yield for each event shouldbe similar between sites.

Figure 21 shows that the relative SS yield betweenDulong (control) and Piccabeen (reference) is highlyvariable through time with a range of up to six ordersof magnitude for a single storm event. This range inresponse appears to be largely due to the largedifference in discharge for the same event between thereference and control streams. As one would expect,SS yield is strongly related to event size (Figure 22),such that larger discharge events, deliver moresediment. It is not valid to use the curve functions in

Figure 22 to predict SS yield because instantaneousdischarge features in the calculation of yield (Y axis)and total event discharge (X axis), however it is usefulto consider the shapes of the curves in Figure 22because a different shape would indicate a different SSyield response mechanism. The power function curvefor the treatment and reference streams have a verysimilar slope (on the log-log plot), but with a higher Yintercept for the treatment stream, indicating similarSS delivery mechanisms, but a consistently higher SSyield from the treatment stream. By comparison, thecontrol stream has a larger Y intercept and flatter slopethan the treatment and reference streams indicatingthat the SS delivery mechanisms are different in thiscatchment. The different slope for the controlcatchment indicates a higher SS yield for small stormevents, however for large storm events the SS Yield/m3

was less than the reference and treatment streams.

The lower SS yield/m3 from the fully grassedcatchment for large storm events might be expectedbased on the research into buffer strips in northQueensland, where remnant forest buffer stripstrapped less sediment (and were sometimes a source ofsediment) compared to grass buffer strips (McKergowet al., 2004). Considering the potential sedimentsources as either in channel (channel bed and banks)or out of channel (hillslope, floodplain). The forestfloor in the reference site is covered in leaf litter, butlacks dense ground cover. For large storm events,once overland flow occurs in the forest, there is anenormous potential to generate sediment runoff. Thepotential to generate overland flow is lower in the

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Figure 20. Suspended Sediment Yield for 28 Identified Storm Events (lines connecting events shown for clarity).

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forested stream than pasture stream because of thegreater infiltration rates in forests (Bonell andGilmour, 1978; Talsma and Hallam, 1980). In theforested catchment, once overland flow commences,the relatively smooth forest floor does little to enhanceresettlement of suspended material, and the bare soilallows preferential flow paths to develop. Anysediment that is deposited on the forest floor can beeasily remobilised in subsequent events, compared toa grassed area where deposited sediment is rapidlyvegetated (McKergow et al., 2004). Once overlandflow has commenced in the control catchment, thecontinuous carpet of pasture grass provides nopreferential flow paths for gullying to commence, andit also provides a hydraulically rough surface (relativeto water depth and Froude number of overland flow),to allow deposition of sediment. The net SS yield islower in the forested stream because these large stormevents are relatively rare; in this study 6 of the 28events were large enough for SS yield/m3 to be greaterin the forested site than the pasture site.

The variation in event size between streams is aroundone order of magnitude for most events (Figure 23),although the discharge of some events vary by up tothree orders of magnitude. From Figure 22, one orderof magnitude difference in discharge produces aroundone order of magnitude difference in SS yield.Combining Figures 22 and 23 we would then expectaround one order of magnitude difference in SS yieldfor each of the 28 storm events due to the difference indischarge alone. To explore the land use impacts onSS yield at an event-by-event time scale we need toremove the effect of discharge on SS yield.

A common approach for removing the effect of totalevent discharge is to consider the event-mean SSconcentration rather than total yield. We can computethe event-mean SS concentration in two ways; 1) the SS concentration that when multiplied with total

discharge gives the total SS load (Figure 24), and;2) the mean of many instantaneous measurements of

SS concentration throughout the event (Figure 20).

The first method of determining event-mean SSconcentrations is effectively the second method whereeach instantaneous SS concentration is weighted bydischarge. The second method is sensitive to SSconcentration because it is not weighted by flow. We

used the first method (Figure 24) to calculate eventmean SS concentration, and further divided this by thesource catchment area to produce the event-mean SSyield which can be compared across-catchmentsbecause it has been standardised by catchment area.

The range of event-mean SS concentration is quitesimilar between the three streams, with many points ofoverlap in Figure 24 indicating an inconsistentresponse of each stream relative to the other streams.Variation in inter-event SS yield has been observedelsewhere; a study of suspended sediment loads in theNorthern Territory found that an effective way todetermine total sediment load was to develop arelationship between SS load and discharge for eachevent, indicating a different sediment load responsedepending on the nature of the storm event (Moliere etal., 2004). This varying SS load response to differentstorm events is well recognised and is often describedin terms of hysteresis curves, that compare SSconcentration to instantaneous discharge throughout asingle storm event (Rieger and Olive, 1988). Theoften poor relationships between discharge and SSconcentrations are illustrated in several SS loadstudies (Rieger and Olive, 1988; Walling, 1974;Walling, 1977a; Walling, 1977b; Walling and Webb,1981); (Brasington and Richards, 2000). The poorrelationships between SS and discharge are becausethere are several key variables other thaninstantaneous discharge that control SS load (Walling,1974).

Whilst the absolute event to event SS yield values varyfor each stream there are some general trends that canbe observed by smoothing the data using a runningmean of three consecutive events (Figure 24). Basedon the running mean of Figure 24, the reference sitehad a consistent suspended sediment yield/m3 ofaround 1x10-3 kg/ha/m3 for the full study period. Thecontrol site initially had a higher running mean ofaround 4x10-6kg/ha/m3 which dropped below that ofthe reference site in early 2003 to 1x10-4 kg/ha/m3, andhas remained below or similar to the reference site.This reduction in suspended sediment yield at thecontrol site has occurred during a period of larger,more intense storm events (late 2002 and 2003)whereby a greater proportion of the event dischargehas come directly from overland flow. The completely

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grassed catchment of the control site has yielded lesssuspended sediment for these large events that arelikely to have had overland flows than the other siteswhich have more bare soil. We suspect that thedifferent event responses between the grassed andtreed streams are due to differences in the erosionprocesses due to the different ground covers. Thedense, and continuous ground cover of the control sitemay preclude flow concentration and gully formation.

To test the apparent change in SS yield at the controland reference sites due to changes in the SS responsemechanisms because of an increased rainfall rate weused a nonparametric test (Wilcoxon signed-rank testin the SPSS package) to compare the control andreference sites before October 2002 (14 events) andafter October 2002 (14 events). The Wilcoxon signed-rank test computes the difference between the twosites for each event and classifies them as either

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Figure 21. Comparison of the Relative SS Yield per ha Between theReference and Control Streams.

Figure 22. The SS Yield for Each of the Streams is Closely Related to the Event Size.

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positive, negative or tied, whereby sites for with asimilar distribution of events the number of positiveand negative differences will be similar and thedifferences not considered as significant. The controland reference streams for the first 14 events (dryperiod), were close to being significantly different(P=0.064), with the control stream having a higheryield/m3. For the 14 events in the wetter period (afterOctober 2002) the control and reference sites weresignificantly different (p=0.009), with the referencestream having a higher yield/m3.

We have shown that the change in relative SS yield/m3

for the control and reference sites is likely to be due tothe differing SS delivery mechanisms in wet vs. dryperiods. It would be useful to develop models toquantify the relative importance of rainfall eventcharacteristics that discriminate between thealternative SS delivery processes. Key characteristicsthat are likely to influence the SS load response tostorm events are related to catchment characteristicsand to storm event characteristics (Table 2).

Figure 23. Event Size for the Three Streams (usually a range of around 1 order of magnitude).

Figure 24. Event-mean Suspended Sediment Yield Per Hectare of Contributing Catchment (Y axis = eventtotal SS load divided by total event discharge divided by contributing catchment area).

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21/1

0/20

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10/1

2/20

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1/20

03

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5/20

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28/0

6/20

03

17/0

8/20

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0/20

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1/20

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We have included these characteristics in a multiplelinear regression model to determine the key controlson event mean SS Yield/m3. Where possible, thevariables have been standardised to make the unitsindependent of discharge magnitude to allowsubsequent comparison between catchments ofvarying discharge. Using the SPSS multiple linearregression model (backward inclusive method) wewere unable to construct significant models (at the0.05 level) to predict event mean SS Yield/m3 for anyof the three streams. This result implies that theinconsistent overlapping of event-mean SS yield inFigure 24 is due to stochastic processes and cannot bedescribed using linear models based on the stormevent variables that we have considered.

We have been unable to quantify the SS yield/m3

response to revegetation at the treatment site becausewe have been unable to explain the variance in the SS

yield/m3 based on storm event variables (other thantotal discharge). There are three potential reasons forthis result:

1. Sites are too similar: the SS yield/m3 for the threestreams considered have an event-to-eventvariation in SS yield/m3 larger than the differencebetween sites, hence further investigation of theeffect of rehabilitation on an even-by-event basiswill not be informative.

2. Reduced time series resolution required: Theevent-based development of predictive modelscannot be achieved because the temporal resolutionof SS yield response may operate at finertimescales (hours or less). To build successfulmodels would require the prediction ofinstantaneous SS concentration based oninstantaneous (or slightly lagged) predictorvariables of the storm event, such as rainfall

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Key Characteristic Representative Measures

Sediment depletion, exhaustion of sediment Number of days since last event (days)sources due to successive runoff events or (Moliere et al., 2004)

Sediment depletiondue to multi-peaked events Number of event peaks (days)(Moliere et al., 2004)

Antecedant conditions, soil moisture at the Number of days since last eventtime of the event

Intensity of rainfall event (Chikita, 1996) Maximum rate of rainfall for the event (mm/h)

Magnitude of the storm event (Sichingabula, 1999) Total discharge for the event transformed by dividing by the mean annual discharge (unitless). Total rainfall for the event (mm)

Seasonality (Walling, 1974) Described by the month in which the event occurs (commencing with the month 1 = September - the minimum mean monthly flow for the experimental period)

Topography Comparing between events with the same catchment therefore this parameter is constant through time

Geology Comparing between events with the same catchment therefore this parameter is constant through time

Land use Comparing between events with the same catchment therefore this parameter is constant through time

Table 2. Key Variables Considered in Developing Multiple Linear Regression Models of Event Mean SS Yield.

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intensity. Walling (1974) was able to constructsuccessful multiple linear regression models topredict instantaneous SS concentration based on arange of storm event variables. However themodels were poor when several events wereconsidered, implying a different SS concentrationresponse model for each event. Walling's failure todevelop multivariate models of SS concentrationthat applied across different storm events impliesthat there were still parameters that had not beenincluded in the model that distinguish betweenevents, hence the models were not particularlyuseful in predicting future SS yield, but rather atexplaining the key variables controlling SS yieldfrom each of the observed events. Furtherexpansion of this modelling technique shouldincorporate ways to capture spatial elements ofeach storm event, such as where and how intenselythe rain falls within the catchment.

3. Inappropriate event lumping: we have combinedevents of all sizes in order to develop themultivariate models. However the SS yieldprocesses for small within-channel events is likelyto be different to those for large floods with a highproportion of overland flow (as illustrated above).To further categorise the 28 floods into a series offlood ranges would reduce the predictive power ofany models produced (increasing the probability ofa type 1 error). A preliminary review of the 28storms indicates that significant predictive models(adjusted R2~0.4) are possible if only the largestevents are considered (we considered events withgreater than 100 mm of rainfall, reducing thenumber of storm events to 11). To construct robustpredictive models based on event based criteriawould require continued monitoring of the threesites to increase the number of events toapproximately 20 times the number of predictors(or 5 as an absolute minimum) (Tabachnick, 1989).

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5. Conclusions

Our conceptual model of suspended sediment (SS)response to revegetation (Figure 3) proposes an initialincrease in SS yield following revegetation, followedby a gradual decrease in SS yield to eventually besimilar to that of the fully forested stream. We haveshown that for a stream revegetation project, the SSyield did increase relative to nearby streams followingrevegetation, although we could not explicitly say thatthis increase was entirely due to revegetation or someinter-annual climatic difference, whereby the sedimentdelivery mechanisms had changed due to climaterather than in response to revegetation activity. Wewill further test the conceptual model of channelresponse to revegetation by comparing a detailedchannel survey completed before replanting, to oneconducted after the channel has had time to respond(3-10 years depending on rainfall).

By comparing forested and grassed (reference andcontrol) streams we have shown that if the SS yield forthe revegetated site does become similar to a forestedstream then we could expect the SS yield to fall toaround two thirds that of a similar, grass lined stream.Whilst unable to fully test the conceptual model ofFigure 3 we can refine the model (Figure 25) to

illustrate the stochastic nature of SS yield, where therunning mean of event mean SS yield effectivelyrepresents the initial conceptual model. Themagnitude of inter-event variation in SS yield overlapswith the long-term running mean of SS yield, makingdiscrimination between sites on the basis of individualevents difficult without a long record to developmultivariate models that account for a significantproportion of the inter-event variation.

We think that the increase in SS yield at the treatmentsite was due to disturbance during the restorationworks (although it may be due to climatic differencesbetween the early and late states of the monitoring).Any disturbance due to the restoration activities islikely to be short lived (1-2 years), and enlarging ofthe channel due to the removal of in-channel grasses islikely to take tens of years depending on thehydrologic regime and sediment characteristics. Wesurveyed the treatment stream in detail beforerestoration work, and will again survey the streamonce the channel has had an opportunity to respond tothe altered riparian vegetation (4-10 years fromrehabilitation depending on climate). The comparisonof survey data before revegetation, and after thechannel has a chance to change form will indicate therole that vegetation plays in controlling the channelform in these small subtropical streams.

Figure 25. Updated Conceptual Model of SS Response to Revegetation.

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The results of this study have provided four mainadvances;

1. We have shown that automatic turbidity loggers canbe successfully used for long-term continuousmonitoring of suspended sediment. We have alsohighlighted some of the difficulties encountered inmeasuring turbidity and how these can beovercome.

2. We have quantified the total SS yield from threesmall subtropical streams of different vegetativecover, yet with little variation in other landscapevariables that are known to influence SS yield. Theresults of this study can be used to test existingmodels of catchment sediment yield such asSedNet (Wilkinson et al., 2004).

3. We have shown that the SS yield from a forestedsubtropical stream is around 30% less than from anadjacent fully cleared (but grassed) catchment, and

4. We have shown that following the channeldisturbance activities associated with revegetation,the SS yield can increase by around 100%.

The rehabilitation work monitored in this study wasmostly out of channel and required no heavymachinery in and around the channel. If softrestoration activities such as presented here can doublethe SS yield, then one would expect a much greatereffect from more invasive work such as willowremoval or physical habitat creation. The primaryconclusion to be drawn from this study is that streamrehabilitation work is likely to at least temporarilycause an increase in SS yield, although ultimately wewould expect a lower SS yield than pre-rehabilitation,hence rehabilitation planning must be mindful of thetemporary increase in SS yield and any effect that mayhave on instream biota. Where stream ecosystems arealready under stress due to a highly degraded stream,stream managers must consider the likely impact ofdramatic but short lived increases in SS yield fromlarge scale works compared to lower magnitude butlonger duration of impacts from staged rehabilitationwork.

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