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C S I R O L A N D a nd WAT E R
Minimum Impact Logging Systems in
Forest ErosionPart A: Impact of Logging and Natural Fires on Runoff and
Soil Loss in Erodible Granite Terrain, Northeast Tasmania
CSIRO Land and Water, Canberra
Technical Report 19/99, June 1999
Cathy Wilson, Peter Wallbrink and Andrew Murray
Minimum Impact Logging Systems in
Forest ErosionPart A: Impact of Logging and Natural Fires on Runoff and
Soil Loss in Erodible Granite Terrain, Northeast Tasmania
SECTION 1: The Effects of Logging and Fire on Runoff and Erosion on
Highly Erodible Granitic Soils in Tasmania
SECTION 2: "Determining Soil Loss Using the Inventory Ratio of
Excess Pb to Cs”210 137
CSIRO Land and Water, Canberra
Technical Report 19/99, June 1999
Cathy Wilson, Peter Wallbrink and Andrew Murray
C S I R O L A N D a nd WAT E R
This report is available as a pdf file on the web at:
http://www.clw.csiro.au/publications/technical99/
i
EXECUTIVE SUMMARY
Background
In 1989 the Forestry Commission Tasmania, now Forestry Tasmania,established monitoring programs aimed at quantifying the impact of forestryoperations on water quality and yield in Tasmanian Rivers. ForestryTasmania identified two initial regions of primary interest for the monitoringprogram. These were the dry sclerophyll forests on highly erodible granitesoils in NE Tasmania near St. Helens, and the wet sclerophyll forests onstable dolerite soils near Launceston. Flow and water quality monitoringstations were installed in three catchments: Gentle Annie and DeaconsCreeks in the erodible granite area, and Musselboro Creek in the Doleritearea. Forestry Tasmania engaged the Australian Centre for CatchmentHydrology (ACCH), now the CRC for Catchment Hydrology (CRCCH), tocollaborate in the experimental program by assisting in site selection anddevelopment, monitoring, data analysis and technology transfer. Funding forthe research program came from both the Tasmanian Forest ResearchCouncil and the National Soil Conservation Program.
The issues to be addressed at the sites were different. At Musselboro Creekthe main issue was that of water quality in the North Esk River which servesas Launceston’s water supply. Turbidity in runoff from undisturbedcatchments in this are very low, (1-5 NTU). However, Dolerite soils havehigh clay content and forestry operations undertaken during rainfall eventsproduce runoff with high turbidity values (100’s of NTU). Monitoring atMusselboro Creek during a winter logging operation aimed to quantify theimpacts of forestry relative to agriculture on water quality and yield in anarea affected by in-stream turbidity.
In contrast, on-site erosion was the primary issue in NE Tasmania. Attemptsto log in the erodible granite soils in the 1980’s resulted in widespread rillingand gullying which were difficult to ameliorate. Forestry Tasmaniaresponded to this problem by developing a trial code of practice tailored to thearea aimed at minimising the impact of logging on erosion in the forest.Logging trials were carried out at Gentle Annie and Deacons Creek to testthe Minimum Impact Code of Practice. The in-stream effects of these trialswere assessed through storm-based water quality monitoring in logged andunlogged sub-catchments at the site.
The initial water quality data suggested that logging had little impact onwater quality in the Gentle Annie Catchment, but this result was confoundedby the loss of all water quality data during the period immediately followinglogging. This period included a large flood event in the catchment. Inaddition, a natural bush fire burned a small portion of the logged catchmentduring the logging operation. Bush fires are common in this area, and may
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trigger natural episodes of accelerated erosion. Given these circumstances,the CRCCH and Forestry Tasmania decided to carry out a set of rainfallsimulation experiments to better quantify the impact of logging and fire onrunoff and erosion on the erodible granite soils, to augment the in-streammonitoring.
The results of the monitoring and research programs described above will bepresented in two parts, A and B. Here we present Part A: Impact of loggingand natural fires on runoff and soil loss in erodible granite terrain, NortheastTasmania. This report covers the results and analysis of the large rainfallsimulator experiments carried out in the Gentle Annie Research Catchmentin northeast Tasmania. The results are presented in the context of simpleempirical predictive models that can be used to estimate the amount oferosion that may occur on logged and burnt hillslopes on erodible granitesoils in NE Tasmaina. The report also contains an analysis of the total post-logging erosion at the site based on a radionuclide tracer study in thecatchment.
Part B: Impact of logging on water quality and yield in granitic and doleriteterrains in Tasmania, analyses the results of in-stream water quality andyield monitoring for all sites. Part B is reported under separate cover.
Summary of Results
Section 1
A set of rainfall simulator experiments was carried out in a logged forest inNE Tasmania to quantify the impact of logging and fire on runoff and soilloss. A large, portable rainfall simulator was developed for the experimentsto help overcome problems associated with spatially varying hydraulic anderosion properties of the soil. Simulated rainfall events with intensities ofapproximately 35, 75 and 150 mm hr-1 were applied to four 300 m2 plots withdifferent levels of surface disturbance: severely burnt, logged and burnt withhigh mechanical disturbance, logged with low mechanical disturbance, andundisturbed. The experiments indicated that the amount of runoff and soilloss from the plots depended on the type and degree of disturbance to thenatural biotic crust developed on this soil. Erodibility per unit runoff wasgreatest on the severely burnt plot; but runoff on the mechanically disturbedsite was so great that it produced the highest total sediment yield on an eventbasis. Runoff production was similar on the three plots where the naturalbiotic crust was largely intact, and low relative to the plot with highmechanical disturbance. Total sediment yield was typically higher on loggedplots than on unlogged plots.
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Section 2
The ratio of fallout 210Pb to 137Cs can be used to determine soil loss indisturbed landscapes. The technique was applied to the undisturbed andlogged rainfall simulator plots described above. The average inventory ratiosof fallout 210Pb to 137Cs from the two logged plots, which had experiencedminimal disturbance and normal disturbance, were measured and comparedto the undisturbed plot. The average core inventory ratio at the undisturbedlocations was 2.24 + 0.14 (n=18), compared to means of 0.74 + 0.09 (n=10)and 1.73 + 0.29 (n=10) from the ’normal’ and ’minimal impact’ sites. Theaverage depth of soil removed from the logged sites was then calculated bycomparing these ratios with the inventory activity ratio curve from theirrespective ’control’ sites (40 + 6 mm, ’normal’ site; 17 + 5 mm ’minimal’ site).It is concluded that, compared with areal concentrations alone, ratios offallout nuclides are likely to provide a less randomly variable (and thus moresensitive) method for investigating surface erosion in landscapes wherevertical soil mixing is not sufficiently recent to be of concern.
TABLE OF CONTENTS
Page
EXECUTIVE SUMMARY
SECTION 1. THE EFFECTS OF LOGGING AND FIRE ON
RUNOFF AND EROSION ON HIGHLY ERODIBLE GRANITIC
SOILS IN TASMANIA. By Cathy J. Wilson
1.1 INTRODUCTION 1
1.2 SITE DESCRIPTION 2
1.2.1 Geology and Soils 2
1.2.2 Climate 2
1.2.3 Vegetation 4
1.2.4 Fire 4
1.2.5 Plot Description 4
1.3 METHODS 7
1.3.1 Rainfall Simulator 7
1.3.2 Instrumentation 8
1.3.3 Rain Events 11
1.4 RESULTS 14
1.4.1 Soil Hydraulic Properties 14
1.4.2 Rainfall – Runoff 14
1.4.3 Erosion Processes and Measurements 18
1.4.4 Erosion Data 18
Page
1.5 DISCUSSION 27
1.5.1 Role of Disturbance in Runoff Generation 27
1.5.2 Role of Disturbance and Sediment Transport 28
1.5.3 A Simple Erosion Model 29
1.5.4 Implications of Results for Post LoggingSediment Yields and Sediment Deliveryto Streams 30
1.6 CONCLUSIONS 32
1.7 ACKNOWLEDGEMENTS 33
1.8 REFERENCES 34
SECTION 2. “DETERMINING SOIL LOSS USING THEINVENTORY RATIO OF EXCESS 210PB TO 137CS”.By P.J. Wallbrink and A.S. Murray 38
2.1 INTRODUCTION 39
2.2 MATERIALS AND METHODS 40
2.3 RESULTS 43
2.3.1 Discrimination between forest loggingtreatments using 137Cs 43
2.3.2 Discrimination between forest loggingtreatments using excess 137Cs 43
2.3.3 A new approach using 210Pbexcess/137Cs ratios 47
2.3.4 Comparison of reference profile with disturbed sites 47
Page
2.4 DISCUSSION 50
2.4.1 Caveats on the radionuclide estimates of soil loss 50
2.4.2 Evidence supporting the radionuclide estimatesof soil loss 50
2.4.3 Differential depth dependency of 210PBexcess and 137Cs 50
2.4.4 Causes of variability 50
2.4.5 The influence of sample surface area on spatialvariability 51
2.4.6 Possible influence of vegetation on spatial variability 51
2.4.7 Other factors affecting radionuclide variability 51
2.4.8 Potential for futher applications 52
2.5 CONCLUSIONS 53
2.6 ACKNOWLEDGEMENTS 54
2.7 REFERENCES 55
SECTION 1
The Effects of Logging and Fire on Runoff and Erosion on
Highly Erodible Granitic Soils in Tasmania
By
Cathy J. Wilson
email: [email protected]
1.1 INTRODUCTION
Much attention in Australia and around theworld is focussed on how forestry operationsgenerate and redistribute sediment in catch-ments. Logging roads, extraction tracks,landings and harvested areas have beenshown to produce sediment at higher thanbackground levels across a large range of for-ested environments [eg: Fredriksen, 1970;Megahan and Kidd 1972; Douglass andSwank, 1975, 1976; Swanston and Swanson,1976; Langford and O’Shaughnessy, 1977;Brown, 1980; Gray and Megahan, 1981;Burgess et al. 1981; Cornish and Binns, 1987;Doeg and Koehn, 1990; Megahan et al. 1995].Forestry related erosion, including landslides,has been shown to affect channel morphology[Lyons and Beschta, 1983, Ryan and Grant,1991], in-stream and riparian habitat [Daviesand Nelson, 1993, 1994], water quality[Brown, 1980], site productivity and siteaccess [Williamson, 1990].
In 1989 the Forestry Commission of Tas-mania (now Forestry Tasmania) established aset of monitoring programs to assess the on-site and off-site impacts of logging in Tas-mania, Australia. This included a seven yearlong monitoring project in the Gentle Annieexperimental catchment in northeasternTasmania, which is characterized by itshighly erodible granitic soils. Logging in thearea had resulted in serious rill and gullyerosion that required extensive reclamationwork. Reclamation was only partially suc-cessful and many areas have re-incised overthe past eight years. The Gentle Annie pro-ject aimed to determine how low impactforestry practice would protect site produc-tivity and limit soil and nutrient losses tostreams. The main feature of the practicewas the exclusion of logging vehicles from allriparian areas up to and including the headsof first-order ephemeral streams.
A primary objective of the Gentle Annieproject was to assess if low impact loggingpractice caused a significant increase inerosion relative to background rates. Naturalforest fires occur frequently in this part ofTasmania, and studies in many forestedcatchments show that fire temporarily
increases sediment yields from forests[Megahan and Molitor, 1975; Cornish andBinns, 1987; Burgess et al. 1991; Megahan etal. 1995]. Since fire is more frequent thanlogging in this catchment, the sediment yieldassociated with logging may be small relativeto the natural average annual sediment yieldfrom the catchment. To interpret the impactof logging on runoff and soil loss, the impactof fire also needs to be determined.
To quantify the effects of logging and fire onrunoff and erosion in the Gentle Annie catch-ment, a set of rainfall simulator experimentswas carried out in May 1992. The objectivesof the experiment were to:
1) determine the dominant hydrologic anderosion processes and responses onlogged, burnt and undisturbed plots,
2) identify the rain intensities required toproduce runoff and mobilize surfacesediment on the plots, and
3) quantify the amount of erosion on thedifferent plots under a range of artificialstorm events.
The results of the rainfall simulation experi-ment provide a new and comprehensive set ofdata on runoff and erosion rates from largeplots in logged forests. The problem of char-acterising spatial variability in soil hydraulicand surface properties is minimised in thisexperiment by the use of a very large plotsize. Since spatial variability is integrated,these data may prove useful in hillslope tosmall catchment scale models of runoff anderosion. The results also shed light on theroles of surface cover, plot roughness and soilprofile disturbance in determining theamount of erosion and sediment transportthat occurs in areas affected by logging andfire. In particular, a biotic crust caps the soilsin the Gentle Annie catchment. This naturalcrust plays an important role in limitingrunoff and erosion at this site, and may needto be considered when designing forestharvest operations elsewhere.
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1.2 SITE DESCRIPTION
1.2.1 Geology and Soils
The Gentle Annie experimental catchment islocated about 15 km northeast of St. Helens,Tasmania (Fig. 1). The geology of the regionis dominated by granite plutons of UpperDevonian age. The Gentle Annie site is lo-cated within the Mt Pierson Pluton, a por-phoritic or coarse grained biotite granite/adamellite [Davies and Neilsen, 1987]. Thisademellite spalls and disintegrates readily,and the soil surface is coated with a lag oflarge feldspar and quartz phenocrysts. Soilprofiles are characterised by high gravel, sandand silt fractions. Significant amounts of un-altered mica and illite are present, and are anindication of the low clay content, low co-hesion, poor aggregation and high erodibilityof these soils [Davies and Neilsen, 1987].
Hillslopes and ridges in the gentler parts ofthis landscape (<15o) have soils with verysharp contrast in texture between the B hori-zon and overlying A1 and E horizons. Surfacesoils in this area are generally light texturedloamy sands, often divided into shallow (100to 300 mm) A1 and E horizons where the Ehorizon is significantly coarser. Soils arecharacterised as an apedal mass of gravel andsand particles, except at depth where yel-lowish brown, and grey gravelly, sandy clayhorizons have developed (10 YR 5/8 to 5 Y 6/1)[Davies and Neilsen, 1987]. Organic contentin these horizons is low and the soils are con-sidered infertile.
Steep hillslopes have unconsolidated sandy/gravelly soils about 1 m deep with a lessmarked contrast in texture throughout thesoil profile. The steepest areas (>25o) aredominated by outcrops of bedrock andgravelly shallow soils on ridges. Foot-slopes,drier flats and hollows are characterised bydeep deposits (2-5 m) of accumulated quartzgravel, sand and silt, which are highly un-stable and are often deeply gullied if dis-turbed. Low angle fans composed of sand andgravel are prevalent where first orderstreams join the low gradient, broad, swampyvalleys of second and third order streams inthe catchment. These fans are also highlysusceptible to gullying when disturbed.
Soils in the swampy valley bottoms have highorganic content and support stable, narrowand deep channels with widths of 0.5 to 1 mand depths of 0.5 to 1.5 m. Only smallpatches of sand sized and coarser sedimentcan be found in the bed of the main streamchannel in this catchment. Though fresh de-posits of coarse sediment are found on thefoot-slopes, fans and edges of the swampyvalleys after large rain events.
Most of the catchment has a biotic surfacewith a typical depth of about 10 mm. Thisstable crust is comprised of sediment and or-ganic matter bound together by fine roots,fungal hyphae and organic secretions [Bondand Harris, 1964]. Australian investigationshave shown that biotic crusts absorb and re-tain light to moderate rainfalls but inhibitinfiltration into sandy soils during intenserain events [Chartres, 1992]. Disruption ofthese crusts by both fire and trampling hasled to substantial increases in runoff anderosion in a range of environments [Chartresand Mucher, 1989; Kinnell et al. 1990].
1.2.2 Climate
Average annual rainfall in the region is about700-800 mm. The Gentle Annie catchment islocated within the low rainfall zone of north-eastern Tasmania. Areas nearby have anaverage annual rainfall of 1250 mm yr-1.However, the low rainfall zone experiencesmore severe thunderstorms than most ofTasmania, and is therefore subjected to highintensity, erosive rainfall. Intensity-fre-quency-duration (IFD) values derived for theGentle Annie area [Canterford et al. 1987]suggests that intensities of around 100 mmhr-1 are maintained for periods of 6 to 10 min-utes in storms with a return period of be-tween 5 and 10 years. Estimated IFD valuesfor the area are summarised in Table 1.
3
Fig. 1. Location diagram of St Helens field study area, Tasmania, Australia, showing relativepositions of experimental plots within the St Helens state forest.
4
Table 1. Estimated precipitation Intensity-frequency-duration (IFD) data for Gentle AnnieCatchment.
Duration 6 min 10 min 20 min 30 min 1 hr 12 hr 72 hrFrequency (yr) Intensity (mm hr-1)_______________________________________________________________________________________________________________
1 62 51 38 31 22 5 2 2 82 68 50 42 29 6 2 5 110 90 66 54 37 8 2 10 128 105 76 62 42 9 3 50 185 150 108 88 59 13 4 100 213 172 123 99 66 15 4
1.2.3 Vegetation
The area is characterised as a dry sclerophyllforest. The soils have poor fertility and sup-port an open forest to woodland dominated byEucalyptus sieberi and Eucalyptus amyg-dalina [Davies and Neilsen, 1987]. The rootsof most trees in the area are mainly withinthe A horizon, with some penetration into theclay rich B horizon. Hillslope areas are alsovegetated with a medium height, sparseheath understory [Walker and Hopkins,1990]. Understory species become extremelydense along moist drainage lines. Thesepatches of dense vegetation correspond to thewet areas where the valley bottoms widenand flatten.
In drought years the ground surface is largelydevoid of humus or ground cover except forpatches of charcoal, leaves and twigs. It islocally described as “sugar country” due to thewidespread, crusted surface areas coated withquartz and feldspar sands and gravels.Crusting of the soil surface is pronounced andwidespread throughout dry periods. After aseries of wet years however, large areas ofground surface become covered with lichens,fungi, algae and other small plants. It islikely that these plants aid the formation ofthe crust, and contribute to their observedhydrophobic properties [Bond and Harris,1964, Green et al. 1990].
1.2.4 Fire
Bush fires are thought to have a recurrenceinterval of about 9 years in the Gentle Anniecatchment (G. Brown, pers. com., Forestry
Tasmania). An intense fire burnt a smallportion of the Gentle Annie catchment aboutone month after the completion of logging.
1.2.5 Plot Description
The experiments were carried out on four300m2 plots. Plot size was chosen to be largeenough to account for some portion of thevariability in soil hydraulic properties anderodibility that exists at the hillslope scale.This plot size also accommodates a range oflarge roughness features found in natural anddisturbed forests such as trees, tree throwmounds, stumps and branches, and soilmounds constructed as barriers to overlandflow. The maximum size of the plot was con-strained by the amount of water and type ofequipment that was required to generate a150 mm hr-1 storm intensity.
The plots were chosen to represent the rangeof conditions that occur across a large propor-tion of the experimental catchment. A surveyof hillslopes in the catchment was carried outto find four plot areas that had the samegradients, an appropriate site for water tanksand tanker access, and the desired surfacetreatments. About 40% of the catchment fallsin a slope class between 11o and 17º. The ex-perimental plots were sited on 15º slopes,which was typical of much of the logged area.No logging occurred on slopes with gradientsgreater than 18º.
Disturbance to the soil surface on the fourplots ranged from patchy disruption of theshallow biotic surface, to deep mixing of top-soil and subsoil by logging machinery. The
5
impact of fire was also incorporated into theexperiments, and the four plots were selectedand classified as follows:
Plot 1) not logged and recently burnt by anintense wildfire
Plot 2) logged with heavy disturbance frommachinery and burnt
Plot 3) logged with little disturbance frommachinery and not burnt
Plot 4) not logged and not burnt (control).
The degree of surface disturbance varied sig-nificantly between plots. Each plot was
ranked on site with respect to the amount ofsurface cover, soil profile disturbance, burnintensity and roughness. These qualitativerankings are given in Table 2. Figs 2a and 2bprovide views of two plots: the heavily dis-turbed, logged and burnt plot 2, and the un-disturbed plot 4 respectively. The rainfallsimulation experiments were carried out 6months after the logging operation and fire. Itshould be noted that these simulations pro-vide only an indication of how the differencein surface disturbance between the plots im-pacts on the runoff and erosion response inthis forest. Replication of the plots was notpossible, and inter-plot variability unrelatedto the treatments may effect the plot re-sponses.
Table 2. Plot characteristics.
Plot number Treatment Surface cover Mechanical Burn intensity Roughnessdisturbance
1 burned, unlogged <15% low high low2 burned, logged <15% high moderate high3 unburned, logged 30% moderate nil high4 unburned, unlogged >50% nil nil moderate
6
Fig. 2a. The logged and burned section of the experimental catchment with plot 2 in the foreground.
Fig. 2b. Close up view of the sprinkler risers on plot 4 (low intensity event).
7
1.3 METHODS
A large, portable rainfall simulator was builtto apply a set of artificial rain events to thefour plots within the Gentle Annie experimen-tal catchment. The plots were instrumentedto measure rainfall, runoff and erosion on theplots as well as the subsurface hydrologicresponse.
1.3.1 Rainfall Simulator
Rainfall simulation has been used widely as amethod for assessing runoff and erosion ratesand processes in agricultural and forestedenvironments [Dunne and Dietrich, 1980a,1980b; Hart, 1984; Abrahams et al. 1988,1995; Elliot et al. 1995]. Results from theseexperiments have been used to characteriseresponses from sites with different soil types,vegetation cover and treatment, and toparameterise predictive models such as theWater Erosion Prediction Project (WEPP)model [Laflen et al. 1991; Luce and Cundy,1994].
The rainfall simulator used in these experi-ments was designed to be portable, easy to re-construct on flat or sloping experimentalsites, able to produce a wide range of rainfallintensities, yield uniform and reproduciblerain events, and represent the energyspectrum of natural rain drops. The design ofthe CSIRO simulator was based on the FieldEfficient Colorado State Rainfall Simulator[Holland, 1969; Neff, 1983, Riley et al.1997 ].
The simulator has ten 3 m risers which sup-port one or two spray nozzles to achievedesired rain intensities and drop size charac-teristics. The sprinkler risers are arrangedon the plot in a triangular pattern (Fig. 3).The riser spacing and configuration waschosen to cover plots 15 m wide by 20 m long,but the number of risers can be altered tocover plots of any size.
A series of nozzles was tested to determinerain intensity, spray geometry and drop sizeproperties (Lemin, unpub.). The flour pelletmethod was used to characterize rain dropmass and size. Flour pellet masses were con-verted to drop sizes using techniques anddata found in the literature [Laws, 1941;
Laws and Parsons, 1943; Hudson, 1963;Langford, 1969].
15m
20
m
1.5m
7.5m
Plot
Hoses
Water inflow
Piezometer
TDR
Tensiometer
Sprinkler risers
Camlock reducers
Runoff trough
Tipping bucket
Rain gauge
R R RR
R R RR
R
R R RR
R R RR
R R RR
R R RR
P PTT
T
h
h
h hTP
h h h
P PTT T
P
P PTT T
P
P PTT T
P
P
Fig. 3. Layout of the sprinkler risers, hoses,hydrologic instrumentation and runofftrough that also served as the sedi-ment trap. Position of instrumen-tation was roughly similar for allplots.
The calculated drop diameters from thesimulated rainfall at a range of intensitiesand pressures were compared with natural
8
rainfall data for similar intensities [Laws andParsons, 1943; Hudson, 1963; Langford,1970]. These data were used to choose threecombinations of nozzles and pressures to pro-vide rainfall at the desired intensities anddrop sizes. Drop diameter characteristics forthe two selected nozzles are presented withdata from natural rain events in Fig. 4. Com-parison of natural rain data from Laws andParsons [1943] and Hudson [1963] shows a
high degree of variability in median drop sizeand drop size distribution for a given rainintensity. Hudson’s [1963] median drop di-ameter of 2 mm for high intensity rain eventscorresponds better with the rainfall simulatordata, but could not be presented here becauseraw data was not supplied in the paper. Nodata on natural drop size were available forthe Gentle Annie site.
0
20
40
60
80
100
0 1 2 3 4 5 6
25 mm hr-1
natural
51 mm hr-1
natural
100 mm hr-1
naturalmed. & high intensitylow intensity
Cu
mu
lativ
e p
erc
en
tag
e
Drop diameter (mm)
Fig. 4. Comparison of drop size distribution between natural rainfall reported by Laws andParsons [1943] and simulated rainfall.
Other nozzle properties taken into consid-eration were spray height and spray pattern.At the running pressures of 12 to 20 psi,drops achieved heights of between 5.3 and 6.8m before falling to the ground. Thistranslates to impact velocities between 89%and 94% of terminal values. Spray patterncharacteristics were used to calculate theoptimal distance between risers in an attemptto achieve relatively uniform intensitiesacross the plot.
The variation in rain intensity during thehigh intensity event on the undisturbed plot 4is apparent in Fig. 5. A network of 24 raingauges was used to measure the spatial dis-tribution of total rainfall across a plot for eachevent. These data were used to calculate thespatial distribution of average rainfall inten-sity on each plot for each event (Figs 6 a, band c). Significant variation in rain intensity
occurred on all the plots for all events, butwas most pronounced at the highest intensity.Although this variation may be consistentwith throughfall distribution in open forests,it highlights a problem with riser-based rain-fall simulators.
1.3.2 Instrumentation
Rainfall input, surface runoff, sediment yieldand suspended sediment concentrations weremeasured for each simulated rain event. To-tal rainfall on an event basis was determinedwith the network of rain gauges describedabove. A gently inclined trough (2o to 3o) waslocated at the base of each plot to collect run-off and eroded sediment. All surface runoffgenerated on the plot was routed through the
9
Fig. 5. High intensity event on plot 4.
trough to a large tipping bucket with a 6.5liters per tip capacity. Surface runoff wasmeasured by periodically reading the numberof tips recorded on the tipping bucketmechanical counter.
Two problems were encountered with the tip-ping bucket during the experiments. First,the tipping bucket was originally configuredwith an electronic logging device that failedduring the first two rain events on plot 1.The total number of tips for the two eventswas recorded on the mechanical counter onthe tipping bucket however, and this valuewas used to calculate an average runoff ratefor the combined events. Second, the tippingbucket was underrated to cope with the largeamount of runoff produced during the highintensity event on plot 2. Peak runoff fromthis event was crudely estimated using theinfiltration theory developed by Hawkins[1982] and described in the Results section.
The inclined trough acted as a sediment trapfor all bedload transported to the bottom ofthe plot. At the end of each event all of thesediment deposited in the trough was col-lected and bagged for further analysis in thelaboratory. Bedload sediment was dried at50ºC and weighed, and grainsize distributionwas determined. Organic content was deter-mined by the loss on ignition technique at atemperature of 400ºC [Allen et. al., 1974].Much of the organic material at this site con-sists of leaves, twigs, and charcoal. The or-ganic matter production rates presented hereare likely to be only slightly overestimated bythis technique, since the soil minerals are notprone to desiccation at high temperatures.Organic matter was measured in both thebedload and suspended sediment samples.Suspended sediment samples were collectedperiodically throughout each event at the endof the inclined trough as water droppedthrough a stilling pond into the tippingbucket.
10
6 8 10 12 14 160 42-2
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Low Intensity (47mm/hr)
Distance along bottom of plot (m)
Dis
tanc
e al
ong
leng
th o
f plo
t (m
)
a
Fig. 6a. Average simulated rainfall intensity distribution for the low intensity events on plot 4.Intensity (mm hr-1 ) contours are derived from the total rainfall collected in 24 raingauges distributed in a grid (shown as dots) on the plot during each event. The edge ofplot 4 runs from 0 to 15 m along the x-axis and from 0 to 20 m along the y-axis.
Subsurface hydrologic data was also collectedduring each event. Nests of piezometers, ten-siometers and Time Domain Reflectometer(TDR) probes were installed in each plot andmonitored on a continuous basis during thesimulated rain events. Tensiometers wereinstalled within the A, E and B horizons to amaximum depth of 0.9m. TDR probes werelocated at depths of 0.1 to 0.4 m within the Aand E horizons on each plot. The deepest pie-zometers were located at the A-B horizonboundary to monitor the rise and fall of thewater table that develops on top of the clayrich B horizon. Subsurface hydrologic datawere used to ensure that initial conditions ofsoil moisture content were similar betweenruns. The position of instrumentation is ap-proximately the same on each plot (Fig. 3).
Saturated hydraulic conductivity, Ks, wasmeasured in a range of geomorphic positionsin the Gentle Annie catchment using fallinghead infiltrometers [Talsma and Hallam,1980; Talsma, 1987]. The theory for con-verting infiltrometer data to Ks valuesprohibits shallow measurements over shortprofile intervals. For this reason, theshallowest soil profile interval measured was200 to 400 mm below the soil surface. Pointmeasurements of surface infiltration were notmade at the site. Instead, apparentinfiltration rates were calculated for thewhole plot surface as the difference betweenrainfall intensity and peak instantaneousrunoff rate for each event on each plot[Hawkins, 1982].
11
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Distance along bottom of plot (m)
Dis
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)
b
Fig. 6b. Average simulated rainfall intensity distribution for the medium intensity events on plot4. Intensity (mm hr-1 ) contours are derived from the total rainfall collected in 24 raingauges distributed in a grid (shown as dots) on the plot during each event. The edge ofplot 4 runs from 0 to 15 m along the x-axis and from 0 to 20 m along the y-axis.
1.3.3 Rain Events
Three rain events in the sequence of low,medium and high rainfall intensity, wereapplied to each of the four plots. Duplicateevents were applied on plot 1 to sort outteething problems and to examine the effectof antecedent rainfall on sediment and runoffproduction.
An attempt was made to apply water at a rateof 40 mm hr-1 for the low intensity event, and
70 and 150 mm hr-1 for the medium and highintensity events respectively. It was not pos-sible to achieve these rates exactly with oursimulator, as will be shown in the resultsbelow. These rates represent events withreturn periods of 1-5, 10-20 and 50-100 years.Table 3 lists the natural events measuredwithin the Gentle Annie catchment during a 4year period that equaled or exceeded an in-tensity of 30 mm hr-1 for a 10 minute period.
12
Table 3. Natural rainfall intensitiesgreater than 30 mm hr-1.
Intensitydate (mm hr-1)
23/01/87 3021/03/87 3528/04/88 6124/06/88 31 5/03/89 115 7/07/89 3021/12/89 58 4/02/90 7027/10/90 3828/01/91 56
Summary of natural rain events which hadintensities greater than or equal to 30 mm hr-1 forat least 10 minutes during the Gentle Annie waterquality monitoring program.
The simulated rainfall experiments werecarried out directly after a two week period oflow intensity, natural rainfall. SubsurfaceTDR data showed that volumetric moisturecontent varied from 0.15 to 0.25 at 0.1 m, and0.2 to 0.25 at 0.3 m below the surface on allplots prior to the application of simulatedrain. Saturated volumetric moisture contentis about 0.4 near the surface and 0.35 nearthe top of the B horizon. No perched watertable was present above the B horizon on anyplot before the experimental runs. Durationand intensity information for the simulatedrain events is presented in Table 4.
100
120
120 140
100
140
160
180
180
180
200
200
220
220
220
220
220
240
130
3060
6080
6 8 10 12 14 160 42-2
2
8
10
12
14
16
18
20
22
6
4
0
-2
160140
200
160
120
160180
200180160
240
260
High Intensity (162mm/hr)
Distance along bottom of plot (m)
Dis
tanc
e al
ong
leng
th o
f plo
t (m
)
c
Fig. 6c. Average simulated rainfall intensity distribution for the high intensity events on plot 4.Intensity (mm hr-1 ) contours are derived from the total rainfall collected in 24 raingauges distributed in a grid (shown as dots) on the plot during each event. The edge ofplot 4 runs from 0 to 15 m along the x-axis and from 0 to 20 m along the y-axis.
13
Table 4. Summary of Results
Plot Type Plot Run Day Duration Average Return Total Total Peak Apparent Suspended Suspended Trough Trough Suspended(min) rain int. period rainfall runoff runoff infiltration sediment organic sediment organic / trough
(mm hr-1) (years) (l) (l) (mm hr-1) (mm hr-1) (kg m-2) (kg m-2) (kg m-2) (kg m-2)
Unlogged 1 1 14 35 35 1-2 6150 63 Trough yield values shown for run 2 represent average of runs 1 and 2& burned 1 2 14 49 38 2-5 9330 120 1 37 2.8E-05 3.3E-05 3.3E-04 3.0E-05 0.14
1 3 15 10 143 20-50 7170 1241 43 114 8.1E-02 1.8E-02 1.1E-01 1.0E-02 0.441 4 15 21 70 5-10 7380 266 4 66 1.5E-03 5.9E-04 3.8E-03 5.8E-04 0.321 5 15 21 68 5-10 7170 351 4 64 1.2E-03 8.3E-04 4.6E-03 7.5E-04 0.281 6 15 14 110 10-20 7710 2318 36 74 1.0E-01 3.8E-02 2.8E-02 2.7E-03 0.82
Logged 2 7 18 48 39 2-5 9390 1047 9 31 1.2E-02 1.5E-03 1.5E-02 8.8E-03 0.36& burned 2 8 18 22 73 5-10 8040 3231 35 39 2.3E-02 7.7E-03 7.8E-02 10.0E-04 0.28
2 9 18 11 158 50 8700 3281 101* 57* 7.7E-02 1.7E-02 2.4E-01 3.9E-03 0.28
Logged & 3 10 20 39 54 5 10470 607 5 49 1.1E-03 4.3E-04 3.2E-03 3.7E-04 0.30unburned 3 11 20 22 73 5-10 8010 878 12 61 6.5E-03 1.8E-03 1.9E-02 1.0E-03 0.30
3 12 20 11 145 50 7950 1767 39 106 3.2E-02 8.8E-03 9.6E-02 3.7E-03 0.29
Unlogged 4 13 21 44 47 5 10410 210 2 45 9.2E-05 2.9E-05 2.0E-04 1.6E-05 0.36unburned 4 14 21 21 75 10 7890 531 8 67 1.3E-03 2.9E-04 2.4E-03 2.0E-04 0.37
4 15 21 10 162 50-100 8070 1702 45 117 2.0E-02 2.9E-03 1.8E-02 3.3E-04 0.55
*Values estimated using apparent infiltration theory [Hawkins, 1982].
14
1.4 RESULTS
1.4.1 Soil Hydraulic Properties
Water poured onto the dry soil surface crusttends to puddle and form beads before infil-trating. This hydrophobic behavior is charac-teristic of biotic crusts [Bond and Harris,1964, Green et al. 1990]. The data presentedin this paper show that the soil crust limitsthe amount of water that infiltrates into thesubsurface, and in general has a lower con-ductivity than the soil horizons directly below.Infiltration of rainfall through the soil crust is
patchy after extended dry periods. A trenchdug near plot 1 just before commencement ofthe simulation rain events exposed irregularbroad (approximately 0.25 m wide and 0.1-0.3m deep) lobes of wet zones in the A and E ho-rizons.
Ks values measured for the A and E horizonswere high and varied between 50 and 400 mmhr-1. In some cases Ks was too fast to bemeasured. Ks values in the B and C horizonswere between 3 and 9 mm hr-1, and typicallydecreased with depth (Table 5).
Table 5. Saturated Hydraulic Conductivity In Gentle Annie Catchment
Test interval Ks Test interval Ks Landscapewithin A A Horizon within B & C B & C Positionhorizon (mm) (mm hr-1) horizons (mm) (mm hr-1)
200-400 122 440-640 8 footslope740-960 impermeable footslope
400-600 158 hollow200-400 99 800-1000 3 sideslope200-400 395 600-880 impermeable sideslope200-400 >625 400-700 9 nose100-440 47 450-800 3 nose
370-770 4 hollow
1.4.2 Rainfall - Runoff
Surface runoff varied between plots for simi-lar rainfall intensities and increased withincreasing rainfall intensity on a given plot(Figs 7 a, b, c). Some of the variation betweenplots is due to differences in the actual rain-fall intensity that was applied to each plot.Irrespective of variations in input, a relation-ship emerged between runoff behavior andthe type of plot disturbance.
In all runs, the logged and burned site with ahigh degree of soil disturbance, plot 2, pro-duced the highest amount of runoff for a givenrainfall intensity. Runoff did not approachsteady state values for any event on plot 2.Runoff doubled in the last 10 minutes of thelow intensity, 39 mm hr-1, event from 4 mmhr-1 to a peak value of 8 mm hr-1 (Fig. 7a). Themedium intensity event on plot 2 producedpeak runoff of 35 mm hr-1, nearly half the rainintensity (Fig. 7b).
Peak runoff rates from the low and mediumintensity events on plot 2 were used to esti-mate a peak instantaneous runoff of 101 mmhr-1 for the high intensity event on that plot.Runoff data with values between 45 mm hr-1
and the estimated peak value of 101 mm hr-1
were removed from the high intensity hydrog-raph (Fig. 7c), since the tipping bucket wasnot reliable in this range.
The logged plot 3 experienced soil disturbancerelated to the felling and dragging of treeswithin the area, but only small patches of theB horizon were exposed on this plot. Al-though the surface crust was disturbed inplaces on plot 3, the A horizon was largely intact. The logged plot 3 had higher runoffrates than the burnt plot 4 for all event inten-sities, though the higher runoff rate on plot 3for the low intensity event is partly due to thehigh rain intensity it received. Runoff on thelogged plot 3 was also more intense than run-off on the control plot 4 for the low and
15
Fig. 7a. Runoff intensity as a function of time for the low intensity events. The average rainfallintensity for each plot is shown to the right of the plot number in the legends.
Fig. 7b. Runoff intensity as a function of time for the medium intensity events. The averagerainfall intensity for each plot is shown to the right of the plot number in the legends.
0
5
10
15
0 10 20 30 40 50 60
Low Intensity Events
logged, burnt (2) 39 mm hr-1
logged (3), 54 mm hr-1
control (4), 47 mm hr-1
Ru
no
ff (
mm
hr-1
)
a
0
10
20
30
40
0 10 20 30 40 50 60
Medium Intensity Events
burnt (1), 68 mm hr-1
logged, burnt (2), 73 mm hr-1
logged (3), 73 mm hr-1
control (4), 75 mm hr-1
Ru
no
ff (
mm
hr-1
)
b
16
0
20
40
60
80
100
0 5 10 15 20 25 30
H igh I n t ensi t y Event s
bur n t (1), 143 m m hr-1
logged, bur n t (2), 158 m m hr-1
logged (3), 145 m m hr-1
con t r ol (4 ), 162 m m hr-1
Ru
nof
f (m
m h
r-1)
c
Ti m e (m in)
Fig. 7c. Runoff intensity as a function of time for the high intensity events. The average rainfallintensity for each plot is shown to the right of the plot number in the legends.
medium intensity events. Plot 3 producedrunoff at rates of about 3.5 mm hr-1 and 10mm hr-1 for the low and medium intensityevents respectively, while the burnt and con-trol plots 1 and 4 produced runoff at rates ofabout 1 mm hr-1 and 5 mm hr-1 for the lowand medium intensity events respectively(Figs 7a,b). Unlike plot 2, runoff tended to-ward steady state on each of the other threeplots for both the low and medium intensityrainfall events (Fig. 7a,b). Runoff rates onplot 4 surpassed rates on plot 3 for the highintensity event. This was probably largelydue to the much higher rain intensity on plot4 for this event.
Duplicate events for the low, medium andhigh intensity rainfall rates were applied tothe burnt plot 1 to determine if the sequentialapplication of storm events on a single plotinfluenced runoff or sediment production onthe plot. Both medium intensity events onplot 1 had similar runoff behavior. In con-trast, the duplicate high intensity event thatwas applied at 110 mm hr-1 produced runoffequal to the 143 mm hr-1 event on the sameplot. This anomaly can’t be explained by dif-ferences in antecedent soil moisture betweenthe runs. Volumetric moisture content at0.1 m below the surface was 0.3 for both
events, and the peak moisture content of 0.40was slightly higher for the 143 mm hr-1 event.
In spite of the differences noted above be-tween plots 1, 3 and 4, runoff rates on thesethree plots were similar compared to runoffrates on the heavily disturbed plot 2. In aplot of peak runoff rates versus rainfall inten-sity (Fig. 8a) variation is low around a LeastSquared error linear fit to the data for plots 1,3 and 4.
Apparent infiltration rates were calculatedfor each event on each plot as the differencebetween peak rainfall intensity andinstantaneous runoff [Hawkins, 1982]. Theactual average infiltration capacity of theplots can not be calculated for the eventspresented here because steady state runoffwas not achieved during any of the highintensity events. Apparent infiltration, f,increased with increasing rainfall intensity, i,on all plots. Values of f ranged from about 30mm hr-1 for the low intensity event on thelogged and burnt plot 2 to 117 mm hr-1 for thehigh intensity event on the control plot 4 (Fig.8b). The apparent infiltration rate values forplots 1,3 and 4 lie along a similar trend. Thelinear fit to this
17
Fig. 8a. Peak runoff intensity as a function of average rainfall intensity for each simulated rainevent. Apparent infiltration capacity for the high intensity event on plot 2 was estimatedusing the theory of Hawkins [1982], and peak runoff for this event was calculated as i – f.
0
50
100
150
200
0 50 100 150 200
burnt (1)logged, burnt (2)logged (3)control (4)f = i estimated i (2)f = 16.4 + 0.63if= 2.0 + 0.34i
Ap
pa
ren
t in
filtr
atio
n r
ate
, f
(mm
/hr)
Rainfall intensity, i (mm/hr)
b
Dunne et al., 1991
plots 1, 3, 4
Fig. 8b. Apparent infiltration capacity as a function of average rainfall intensity for eachsimulated rain event. Apparent infiltration capacity for the high intensity event on plot2 was estimated using the theory of Hawkins [1982], and peak runoff for this event wascalculated as i – f.
0
20
40
60
80
100
120
0 50 100 150 200
burnt (1)logged, burnt (2)logged (3)control (4)estimated runoff (2)plots 1,3,4
Ru
no
ff r
ate
(m
m/h
r)a
18
data intersects the line where i = f at about45 mm hr-1, indicating that no surface runoffwill be produced on this plot for rain inten-sities lower than this value. Apparent infil-tration rates on the heavily disturbed plot 2are lower than the other plots for all rainintensities. Values of f for the low and medium intensity events on plot 2 were used toestimate f for the high intensity event, usingthe assumption that the three f values for plot2 would fall on the same line (Fig. 8b). A peakrunoff rate of 101 mm hr-1 (Fig. 8a) was thencalculated for plot 2 as the difference betweenactual i (158 mm hr-1) and estimated f (57 mmhr-1).
These results conform to theory and data pre-sented in the literature [Hawkins, 1982;Hawkins and Cundy, 1987; Dunne et al.1991]. Hawkins’ theory states that the maxi-mum average infiltration capacity for a plotwill only be achieved when the whole plotsurface is producing runoff. The theory isbased on observations that there is spatialvariation in infiltration capacity on experi-mental plots and hillslopes [Loague andGander, 1990; Dunne et al. 1991]. For agiven rainfall intensity only the portion of theplot or hillslope with Ks > i will produce run-off. Runoff on the Gentle Annie plots contin-ued to increase throughout the high intensityevents. This means that true peak dischargeswere not achieved and the apparent infiltra-tion rates for these events are probably higherthan the actual average infiltration capacitiesof the plots.
Apparent infiltration rates on all plots areless than the maximum Ks values measuredin the A horizon around the catchment (Table5). Peizometer data showed that perchedwater tables formed within the soil profile onall plots during the simulated rain events, buteven during the high intensity events watertables were 0.1 to 0.3 m below the soil sur-face.
1.4.3 Erosion Processes and Measure-ments
Material was transported from the plots dur-ing the simulated rain events by sheet washand rilling. Overland flow occurred in local-ized sheets during both the medium and highintensity events, concentrating downslopeinto rills that ranged in depth from less than
5 mm to greater than 100 mm. Coarser grainsof quartz and feldspar, up to 10 mm diameter,were rolled downslope for distances up toseveral meters and deposited in small debrisfans throughout the plot. Some of thismaterial was moved across the bottom of theplot into the trough. Finer material, includ-ing some fine to medium sand, was carried insuspension along the trough to the plot outlet.
1.4.4 Erosion Data
The material yield (organic and inorganic)from each plot for each event is summarizedin Figs 9 a, b and c. The yield data showerosion increased with event intensity onevery plot, and most of the eroded materialwas deposited in the trough at the base of theplot (Fig. 9a). The heavily disturbed plot 2and control plot 4 show the upper and lowerlimits of erosion during the simulated rainevents (Figs 9a and 9c) respectively.Suspended yield was greatest from the burntplot 1 for the high intensity events, with amaximum yield of 0.14 kg m-2 for the 110 mmhr-1 event (Fig. 9b). Plot 2 however, had thehighest total erosion for all events with yieldsranging from 0.03 to 0.33 kg m-2 (Fig. 9c).The logged plot 3 and burnt plot 1 producedtrough yields with indistinguishable trendswhen fit with a line, but yield from plot 1appears to behave in a non-linear mannerrelative to all other plots (Fig. 9a). The burntplot 1 had material yields similar to thecontrol plot 4 for the low and mediumintensity events. Although some of thevariation in yield values between plots is dueto differences in the duration of the events,the above trends remain unchanged when theyield data is normalised by event duration.
The relative proportion of material that wasdeposited in the trough varied between plotsand events. During the low and medium in-tensity events 25% to 40% of the erodedmaterial remained in suspension to thetrough outlet on plots 1, 3 and 4 (Figs 10a, b).As event intensity increased, the proportion ofsuspended material increased on the burntplot 1 and control plot 4 to about 50%, butremained about 30% on the logged plot 3 (Fig.10c). The heavily disturbed plot 2 started offwith the highest fraction of suspended mat-erial, 45%, but as rill erosion increased withhigher intensity events, a greater proportionof material was deposited in the trough at the
19
Fig. 9a. Material yield (organic + inorganic) from each plots as a function of simulated rainintensity: Bedload yield deposited in the trough at the bottom of the plot. Linesrepresent the linear best fit using the Least Squared error regression method.
Fig. 9b. Material yield (organic + inorganic) from each plot as a function of simulated rainintensity. Suspended yield derived from instantaneous concentration of runoff. Linesrepresent the linear best fit using the Least Squared error regression method.
0
0.05
0.1
0.15
0.2
0.25
20 40 60 80 100 120 140 160 180
burnt (1)logged, burnt (2)logged (3)control (4)
Tro
ug
h y
ield
(kg
m-2
)a
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
20 40 60 80 100 120 140 160 180
burnt (1)logged, burnt (2)logged (3)control (4)
Su
spe
nd
ed
yie
ld (
kg m
-2)
b
20
Fig. 9c. Material yield (organic + inorganic) from each plot as a function of simulated rainintensity. C) The sum of trough yield and suspended yield. Lines represent the linearbest fit using the Least Squared error regression method.
bottom of the plot. The sixth and final eventapplied to plot 1 produced the highestproportion of suspended material; this was85% of the total yield of approximately 0.19kg m-2.
Although large pieces of organic matter werefound deposited with sediment in the troughat the bottom of the plot, this material repre-sented only a small portion of the totalerosion for each event (Figs10a, b, c).Typically about 10% of the trough yield wasorganic matter for the events on all plotsexcept the burnt plot 1. Organic mattercomprised up to 17% of the trough materialon plot 1. In contrast, suspended samplescontained between 20% and 40% organicmaterial, with a peak value of 50% for the lowintensity event on plot 1.
For each plot, the organic fraction in suspen-sion tended to decline as event intensity andtotal erosion increased. This decline wasmost pronounced on plot 1 where charcoalwas abundant following the natural, hot bushfire that passed through the area. However,total organic yield (trough + suspended)typically increased with increasing event
intensity, achieving maximum values of 0.003kg m-2 on the undisturbed plot 4, and 0.04 kgm-2 on the burned plot 1 (Table 4).
Sediment (inorganic fraction) yields exhibitedsimilar trends to those described for organicmatter. The main difference between the twofractions is that generally at least an order ofmagnitude more sediment than organic mat-ter is removed from each plot for each event.Sediment (trough + suspended) yields rangedfrom 2.9 x 10-4 kg m-2 to 3.8 x 10-2 kg m-2 onplot 4, and from 2.7 x 10-2 kg m-2 to 3.0 x 10-1
kg m-2 on plot 2 (Table 4, Fig. 10).
Trough sediment from all plots had similargrainsize distributions with grain diametersranging from 0.001 to 10 mm (Fig. 11). Dif-ferences in grainsize distributions weregreater between events on any single plotthan between plots, with D50 falling between1 mm and 2 mm for every event. The sedi-ment deposited in the trough coarsened withincreasing rainfall intensity on plots 2, 3, and4 (Fig. 11). Only the burnt plot 1 differedfrom this pattern. Here eroded sediment wasfiner for the two medium intensity events
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
20 40 60 80 100 120 140 160 180
burnt (1)logged, burnt (2)logged (3)control (4)
To
tal y
ield
(kg
m-2
)
Event intensity (mm hr -1)
c
21
Fig. 10a. The amount of material transported off the plot is shown separated into mineral andorganic components for both the bedload (trough) and suspended fractions from eachplot. a) Low intensity events. The event intensity (mm hr-2) is show in parentheses onthe x-axis.
Fig. 10b. The amount of material transported off the plot is shown separated into mineral andorganic components for both the bedload (trough) and suspended fractions from eachplot. b) Medium intensity events. The event intensity (mm hr-2) is shown inparentheses on the x-axis.
0
0.002
0.004
0.006
0.008
0.01
0.012
0.014
0.016
plot 1 (38) plot 2 (39) plot 3 (54) plot 4 (47)
Low Intensity Events
sus. sedimentsus. organictrough sedimenttrough organic
Yie
ld (
kg m
-2)
a
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
plot 1 (70) plot 1 (68) plot 2 (73) plot 3 (73) plot 4 (75)
Medium Intensity Events
sus. sedimentsus. organictrough sedimenttrough organic
Yie
ld (
kg m
-2)
b
22
0
0.05
0.1
0.15
0.2
0.25
plot 1 (110) plot 1 (143) plot 2 (158) plot 3 (145) plot 4 (162)
High Intensity Events
sus. sedimentsus. organictrough sedimenttrough organic
Yie
ld (
kg m
-2)
c
Event intensity (mm hr -1)
Fig.10c. The amount of material transported off the plot is shown separated into mineral andorganic components for both the bedload (trough and suspended fractions from each plot.c) High intensity events. The event intensity (mm hr-1) is shown in parentheses on the x-axis.
than for either the low or high intensityevents.
A large proportion of the eroded silt and claywas transported off the plots in suspension.Suspended sediment concentration hoveredaround a single value shortly after the onsetof runoff for many events (Figs 12a, b, c).Sediment concentrations clearly increasedwith time for the low intensity event on thelogged and burnt plot 2, and the high inten-sity events for the burnt plot 1. The morerandom fluctuations in suspended sedimentover time appeared to be related to the peri-odic suspension of sand, possibly derived from
the trough sediment (Figs 12 b, c). However,the early peak in sediment concentration onplot 2 may be due to a flushing of fine mate-rial at the start of the medium intensity event(Fig. 12b).
Suspended sediment yields were highest onthe logged and burned plot 2 and logged plot 3for the low and medium intensity events(Table 4). However, the largest suspendedsediment yield of 0.1 kg m-2 occurred on plot 1during the sixth event (Fig. 12c, Table 4).The control plot had the lowest suspendedsediment yields for the medium and high in-tensity events.
23
0
20
40
60
80
100
0.0010.010.1110
Plot 1
36 (mm/hr)70 (mm/hr)68 (mm/hr)143 (mm/hr)110 (mm/hr)
Pe
rce
nt
fine
r
a
Fig. 11a. The grainsize distribution of sediment that was trapped in the trough for each event onthe unlogged and burnt plot 1. Event intensities are listed in the legends.
0
20
40
60
80
100
0.0010.010.1110
Plot 2
39 (mm/hr)
73 (mm/hr)
158 (mm/hr)
Pe
rce
nt
fine
r
Grain diameter (mm)
b
Fig. 11b. The grainsize distribution of sediment that was trapped in the trough for each event onthe logged and burnt plot 2. Event intensities are listed in the legends.
24
0
20
40
60
80
100
0.0010.010.1110
Plot 3
54 (mm/hr)
73 (mm/hr)
145 (mm/hr)
Pe
rce
nt
fine
rc
Fig. 11c. The grainsize distribution of sediment that was trapped in the trough for each event onthe logged and unburnt plot 3. Event intensities are listed in the legends.
0
20
40
60
80
100
0.0010.010.1110
Plot 4
47 (mm/hr)
75 (mm/hr)
162 (mm/hr)
Pe
rce
nt
fine
r
Grain diameter (mm)
d
Fig. 11d. The grainsize distribution of sediment that was trapped in the trough for each event onthe control plot 4. Event intensities are listed in the legends.
25
0
1000
2000
3000
4000
5000
6000
7000
0 10 20 30 40 50
Low Intensity Events
burnt (1)logged,burnt (2)logged (3)control (4)
Co
nce
ntr
atio
n (
mg
l-1
)
a
Fig. 12a. Suspended sediment concentrations as a function of time for the low intensity events.Runs 3, 4, 5 and 6 on plot 1 are indicated in the legend as follows: (1-3), (1-4), etc.
0
2000
4000
6000
8000
10000
12000
0 5 10 15 20 25
Medium Intensity Events
burnt (1-4)burnt (1-5)logged, burnt (2)logged (3)control (4)
Co
nce
ntr
atio
n (
mg
l-1
)
b
Fig. 12b. Suspended sediment concentrations as a function of time for the medium intensityevents. Runs 3, 4, 5 and 6 on plot 1 are indicated in the legend as follows: (1-3), (1-4), etc.
26
0
10000
20000
30000
40000
50000
0 5 10 15 20 25
High Intensity Events
burnt (1-3)
burnt (1-6)
logged, burnt (2)
logged (3)
control (4)
Co
nce
ntr
atio
n (
mg
l-1
)
Time (min)
c
Fig. 12c. Suspended sediment concentrations as a function of time for the high intensity events.Runs 3, 4, 5 and 6 on plot 1 are indicated in the legend as follows: (1-3), (1-4), etc.
27
1.5 DISCUSSION
1.5.1 Role of Disburbance in RunoffGeneration
The runoff data suggest that surface runoff onhillslopes in the Gentle Annie catchment isproduced by the Horton mechanism (infil-tration excess), and that the natural bioticcrust plays a critical role in limiting infil-tration in these highly permeable soils. Arainfall intensity of at least 30 to 45 mm hr-1
is required to generate Horton overland flowon the hillslopes (Fig. 8b). However, stream-flow in the catchment can be generated withas little as 20 mm of rainfall. Streamflowgeneration for small, low intensity eventsmust therefore be due to saturation excessmechanisms such as return flow and rain onsaturated areas in the low gradient swampyvalleys. Both infiltration excess and satu-ration excess mechanisms play a role instreamflow generation during large, intenseevents.
The results of our study suggest that both thedegree of disturbance to the soil profile andthe amount of surface cover play importantroles in controlling hillslope runoff at thissite. Mechanical mixing of the soil profile bylogging machinery appears to have thegreatest impact on runoff production at thissite. The data shown in Fig. 8 indicate thelogged plots 2 and 3 had most of the highestpeak runoff values measured at this site. Thehighly disturbed plot 2 produced the mostrunoff of all the plots for all event intensities.The less disturbed logged and unburnt plot 3produced higher runoff than the burnt orcontrol plots for both the low and mediumintensity events (Fig. 8).
Disturbance of the soil profile by machineryexposed the low conductivity B-horizon, mixedclay from B-hroizon into the A horizon andcompacted the A and B horizons. All of theseprocesses cause a reduction in the infiltrationcapacity of the soil surface. This finding issupported by Rab [1996], who showed infil-tration rates decreased by up 100% on similargranitic soils disturbed by logging machineryin south eastern Australia.
Logging machinery also disrupts the fragile,well-structured biotic surface crust found atthis site. Although the crust was observed to
restrict infiltration into the A horizon, it con-tains root holes and other pores that are con-nected to the highly permeable sandy soil be-low. Even during intense rain events thesepassages appear to maintain their integrity.In the event that the crust is disturbed, suchpassages can quickly become packed withfines, and infiltration is impaired [Moss,1991; Chartres, 1992]. Mucher et al. [1988]found that surface sealing increased withincreasing disruption (trampling by cattle) ofbiotic crusts on red earth soils in a semi-aridEucalypt woodland. This process of bioticcrust degradation is not well described in theforestry literature, but may be important insome forested environments.
The runoff behavior of the burned plot issomewhat paradoxical, in that lower infil-tration rates were expected on the burnedplot for all events. This is becausehydrophobicity is often induced in forest soilsafter wildfires (see review by Zierholtz et.al,[1995]), and has been reported for graniticsoils by Megahan and Molitor, [1975].However, the Gentle Annie data is consistentwith results by presented by Greene et al.[1990] and Chartres and Mucher [1989] forbiotic crusts on red earth soils in a semi-aridEucalypt woodland. Greene et al. [1990]showed that saturated surface infiltrationrates decreased with increasing number offires at their site, but the first fire on the siteincreased infiltration. They do not explainthis phenomenon, but do note that the bioticsurface crust is destroyed by the first fire.Destruction of the top-most layer of organicmaterial, to a depth of 0.150 mm may exposeotherwise sealed pathways through the crust.Photomicrographs by Greene et al. [1990]show that the soil just below the surface (3-7mm) remains porous and well structuredafter one fire, but becomes severely degradedwhen burned on an annual basis.
It should be noted that Greene et al. [1990]used a disc permeameter to measure infil-tration, and did not apply rainfall to the site.Chartres and Mucher [1989] performed rain-fall simulation experiments on monoliths ofthe same soils. Their work showed that firecaused little increase in runoff at low rainfallintensities. As rainfall intensity and durationincreased, runoff on the burned plots tended
28
to surpass runoff on the unburned plots.They attributed the increased runoff to in-creasing degradation of the surface crust dueto rain-drop impact and erosion over subse-quent events. The burned plot 1 at GentleAnnie behaved in a similar manner to thosedescribed by Chartres and Mucher [1989].Runoff from plot 1 surpassed runoff from boththe logged and unburnt plot 3 and control plot4 during the high intensity events. The veryhigh runoff observed during the sixth, 110mm hr-1, event on plot 1 may reflect thecumulative degradation of the soil surface andsurface sealing due to rain splash describedby Chartres [1992].
The role of surface cover on runoff generationis less well defined than the role of crustaldisturbance. Both the unlogged and burntplot 1 and logged and burnt plot 2 had similarlow cover, but their runoff responses werevery different. Peak runoff for the low andmedium intensity events on the low cover,burned plot 1 was similar to, but less than,runoff for comparable events on the undis-turbed plot 4 which had the highest amountof surface cover. Surface cover probablyplayed and important role in protecting thebiotic crusts and limiting surface sealing onthe logged and unburnt plot 3 and control plot4. Lack of cover on the burnt plot 1 probablypromoted degradation of the crust, surfacesealing and increased runoff on the plot dur-ing the final high intensity event.
1.5.2 Role of Disturbance in Erosion andSediment Transport
Fig. 9 shows total sediment yield (trough andsuspended) responds in a similar manner torunoff. That is, sediment yield is highest onthe logged plots 2 and 3 for most events.However, the burnt plot 1 yielded signifi-cantly more sediment than the logged andunburnt plot 3 and the control plot 4 duringits high intensity events (Fig. 9a). This isbecause plot 1 had the highest suspendedsediment concentrations of all the plots, in-cluding the heavily disturbed plot 2, duringits high intensity events (Fig. 9b).
The high suspended yield from the burnt plot1 is probably the result of fire induceddegradation of the biotic crust described inthe section above. It is also likely that thethree extra events on plot 1 led to further
degradation of the biotic crust relative to theother plots. This is supported by Chartres etal. [1989], who showed that fire increasedsediment concentrations in runoff from soilswith biotic crusts by two to five times. Whenthe remains of the burnt biotic crust disinte-grated during rainfall experiments by Kinnellet al. [1990], sediment concentrations in-creased by an order of magnitude relative tocontrol monoliths. Sediment yield from theburnt plot 1 was about 5 times higher thanthe control plot for the high intensity events(Fig. 9c).
The difference in erosion response betweenthe unlogged and burnt plot 1 and the loggedand burnt plot 2 may be due in part to thedifference in roughness on the two plots.Both plots had similar low cover, but plot 1was flat and smooth while plot 2 was bumpy.Plot 2 had a number of large mounds andtroughs formed by machinery to act asbarriers to overland flow. Ponded waterspilled out from behind several mounds andcoalesced into pathways of concentratedrunoff which rapidly rilled to depths of100mm. The rills efficiently delivered erodedsediment into the trough at the bottom of plot2. In contrast, deep rills never formed on theburnt plot 1 and overland flow was welldispersed across the plot surface. Thoughboth plots had degraded surfaces, loggingpromoted concentrated flow pathways withhigh localised sediment transport capacity,rilling, and coarse sediment transport. Firepromoted dispersed sheetflow with moderatesediment transport capacity, and finesediment transport.
Fig. 11a suggests that plot 1 experienced acycle of coarse and fine grain flushing whichprogressed as follows:
1) a coarse lag of quartz and feldspar pheno-crysts were flushed off plot 1 during theearly 143 mm hr-1 event,
2) interstitial fines were excavated duringthe following two back-to-back medium in-tensity events, and
3) coarser sediment was exhumed andflushed during the final 110 mm hr-1
event. This cycle can be seen in the in thegrainsize data shown in Fig. 11a, and wasobserved to occur on the plot at the time ofthe experiments.
29
1.5.3 A Simple Erosion Model
The data presented and discussed above showthat disturbance to the biotic crust by eithermechanical processes or fire can enhance ero-sion at the Gentle Annie site. The data alsoshow that high sediment yields are associatedwith high runoff rates on the mechanicallydisturbed site, but that the burnt site hashigh sediment yields with runoff values simi-lar to the control plot (Figs 8a &9c). The
logged and burnt plot 2, which had the high-est level of mechanical disturbance, producedboth the highest runoff and highest totalmaterial yield for a given rainfall intensity.In contrast, Fig. 13, which presents transportrate as a function of flow power, shows thatthe unlogged and burnt plot 1 had the highesterosion for a given discharge. That is, theamount of material moved per unit of runoffwas greatest for all events on plot 1.
Fig. 13. Material transport rate (including organic and mineral factions of both trapped andsuspended material) as a function of stream power. The two lines through each set ofdata show the small degree of deviation between a power function fitted to the data andpredicted transport proportional to the square of stream power.
Fig. 13 also provides a method for examiningthe response of the Gentle Annie plots withinthe context of simple erosion theory. A powerfunction, using the Least Squared errormethod, was fitted to the runoff-erosion datafor each experimental plot. In each case theexponent of the power law was very close to 2,and a second set of equations was fitted to thedata to give the empirical relationship:
T = kP2,
where P = tan(15º)q.
T is the transport rate (kg m-1 min-1), P isstream power (l m-1 min-1), q is discharge(l m-1 min-1), and k is a coefficient oferodibility and transport. Each plot had thesame slope of 15º. The coefficients for bothsets of equations are given in Table 6.
10-4
10-3
10-2
10-1
10-2
10-1
100
101
plot 1 (fitted)
plot 2 (fitted)
plot 3 (fitted)
plot 4 (fitted)
plot 1 (predicted)
plot 2 (predicted)
plot 3 (predicted)
plot 4 (predicted)
tra
nsp
ort
ra
te (
kg m
-1 m
in-1
)
flow power (l m -1 min-1 * tan(15 o))
30
Table 6. Values of Coefficients used inerosion equations
T = aPb(best fit)
T = kP2
Plot a b r2 k
1 0.046 1.9 0.94 0.052 0.022 1.9 0.98 0.023 0.018 2.2 0.99 0.0184 0.006 2.0 0.99 0.0055
Value of coefficients, a and b, for the power functionswhich best fit erosion data as a function of stream power,and estimated constant of proportionality, k, used to fit asimple erosion equation to the same data.
Kirkby [1980] demonstrated that the re-lationship given in equation (1) correspondswell to erosion theory under conditions of uni-form runoff production. This condition holdsfor most of the simulated events reportedhere (Fig. 9). Experimental data reported byMeyer and Monke [1965], and Kramer andMeyer [1969] for artificial soils of uniformgrainsize fit this relationship. The streampower values calculated for our plots areconsistent with the predicted and measuredcritical stream power values given by Meyerand Monke [1965] that are required totransport the coarsest material from theGentle Annie plots.
1.5.4 Implications of Results for PostLogging Sediment Yields andSediment Delivery to Streams
The total material yield from the logged plots2 and 3 for all three simulated events wasabout 0.5 kg m-2 and 0.2 kg m-2 respectively.The three simulated events on these plots hadhigher intensities and were of longer durationthan the natural events that fell in thecatchment in the post-logging period prior tothe rainfall simulation experiments. In factonly one event with rain intensity greaterthan 30 mm hr-1 fell in the catchment duringthis period, and that event had a peak inten-sity of 61 mm hr-1 (Table 3). Since runoff wasnot produced on these plots for simulated rainintensities less than 30 mm hr-1, it is likelythat this one natural event was responsiblefor most of the post-logging erosion that oc-curred on the plots before the simulation ex-periments. Under these circumstances it isalso likely that the total post-logging sedi-ment yields from plot 2 and plot 3 were less
than the total simulated yields of 0.5 kg m-2
and 0.2 kg m-2 given above.
Wallbrink and Murray [1996] estimated thetotal post-logging soil loss prior to the rainfallsimulation experiments for the logged plotsusing inventory ratios of Lead-210 to Cesium-137. Their results suggest that total soil lossfor this period on plot 2 and plot 3 was about44 kg m-2 and 19 kg m-2 respectively. Thesevalues are two orders of magnitude higherthan the potential post-logging yield esti-mated from the simulation experiments, andrepresent average depths of soil loss on plot 2and plot 3 of 30 mm and 10 mm respectively.They are also much higher than values re-ported for logging roads in current use[Megahan, 1978; Reid and Dunne, 1984;Elliot et al. 1995], and sediment yields fromroads are typically much higher than sedi-ment yields from harvested hillslope areasexcept in areas of active landsliding.
There are a number of possible explanationsfor the inconsistency between the sedimentyields estimated from the rainfall simulatorand the sediment yields calculated by Wall-brink and Murray [1996]. The simulatorestimates of sediment yield would be lowerthan the radionuclide estimates under thefollowing conditions:
1) erosion rates on the plots were muchhigher just after disturbance than at thetime of the rainfall simulation experi-ments, or
2) infiltration rates were much lower justafter logging than during the simulationevents. This would cause runoff produc-tion at lower rain intensities and promoteerosion during smaller events than thoselisted in Table 3.
However, both of these scenarios requirechanges in soil properties of two orders ofmagnitude during the six-month post-loggingperiod.
The preferred reason for the discrepancy, isthat the radionuclide and simulator derivedyields are measures of two different but im-portant processes that occur at logging sites.The radionuclide data provide a measure oftotal soil loss on the plot for the period be-tween disturbance and up to the time the
31
sample was taken. The Wallbrink andMurray [1996] samples incorporate both theone-off mechanical loss of soil from the plotdue to dragging of logs and blading, as well asthe post logging water driven erosion whichcan be determined from rainfall simulation(Wallbrink, pers. Com.). In this respect, mostof the soil loss predicted by radionuclideanalysis from the two logged plots may belocated downslope or outside the boundariesof the plots. Net soil loss across the wholelogging coup as estimated by the radio-nuclides may be low. This finding was latershown by Wallbrink et al. [1997] in a loggedcatchment on granitic soils at Bombala, NewSouth Wales, Australia.
Some portion of the bedload material fromhillslope sources will make its way into
streams during large rain events, particularlywhere rills develop. However, much of thecoarse material produced from hillslopes re-deposits in storage zones before reaching thestream. Suspended sediment is more mobilethan bedload and more likely to be deliveredto streams during rain events. Though manyfactors will affect the proportion of erodedsediment that eventually arrives in thestream, a rough estimate of the sedimentyield to streams is best made using thesuspended sediment yields from the plots. Asa point of reference, the sum of suspendedyield for all six events on the burnt plot 1 was0.24 kg m-2, and the sum of the suspendedyield for all three events on the heavilydisturbed, logged plot 2 was 0.14 kg m-2.
32
1.6. CONCLUSIONS
The rainfall simulator data suggest hillslopeerosion can be accelerated by both logging andfire within the Gentle Annie catchment. Bothprocesses act to disrupt a ubiquitous bioticcrust that plays an important role in limitingrunoff and erosion at the site. However,mechanical disturbance to the soil profile bylogging machinery appeared to cause thegreatest disturbance to the crust and soil pro-file, and resulted in the largest increase inrunoff and sediment yield above backgroundlevels. In general, the highest total sedimentyields were associated with the highest runoffproduction, and the highest level of mechani-cal disturbance to the soil profile by loggingmachinery.
Although fire appears to be less importantthan mechanical disturbance in promotingerosion and sediment transport on hillslopesat this site, suspended sediment yields fromthe plot affected by wildfire were higher thanthose measured on all other plots for the highintensity events. While erosion as a functionof rain intensity was typically highest on bothlogged plots, erosion as a function ofdischarge was highest on the wildfire plot forall event intensities. Erosion was lowest onthe undisturbed control plot for all events,both as a function of rain intensity and afunction of discharge.
The amount of ground cover on a plot ap-peared to have less impact on runoff thanmechanical disturbance to the biotic crust.This finding is supported by results showingboth the wildfire plot and the undisturbedplot had similar runoff rates for most events,but very different cover status. Also, the plot
with heavy mechanical disturbance and thewildfire plot both had similar low cover, butvery different runoff values. However, coverappeared to play an important role in pro-tecting the biotic surface crust. The plotswith good ground cover had lower erosionrates during the high intensity events thanplots with poor ground cover.
The roughness of the soil surface played animportant role in determining runoff anderosion processes on the plots. Mounds andtroughs in the soil surface formed by loggingmachinery promoted concentrated flow path-ways, deep rilling and high sediment yields ofcoarse particles. In contrast, the surface ofthe wildfire plot was smooth, runoff was dis-persed across the plot and high yields of sus-pended fine sediment were observed.
The relative importance of logging or fire inerosion and sediment delivery to streams de-pends on many factors including the timingand size of rain events relative to the timingand extent of the disturbance in a catchment.This study suggests that under some con-ditions logging will cause greater on-siteerosion than wildfire. It also suggests thatduring intense rain events burnt hillslopesmay be a more important sediment source tostreams than logged hillslopes, because sus-pended sediment yields from the burnt plotwere very high. However, burnt areas typi-cally recover quickly in the Australian land-scape. Though erosion rates often declinequickly on harvested hillslopes, other featuresof logged areas, especially roads, may remainimportant sources of sediment to streams formany years after the harvest is complete.
33
1.7 ACKNOWLEDGEMENTS
These experiments were made possiblethrough the work of many people. J. Brophyco-designed and built the rainfall simulatorwith the author, and installed instrumen-tation at the site. W. Reyenga supervised thetransport of the simulator to Tasmania, andkept it running through the experiments. H.Lemin performed the drop energy experi-ments and C. Sogge performed the grain sizeanalyses. T. Lynch and J. Diggle installedinstrumentation on the plots, and along withthe crew at the St. Helens Forestry Depot,helped to run the experiments. Funding for
the project was made available by the Tas-manian Forest Research Council, the(Australian) National Resource ManagementStrategy, the Forestry Commission Tasmania(now Forestry Tasmania), and the CSIRODivision of Water Resources (now CSIROLand and Water). J. Olley, J. Croke and N.McKenzie provided helpful reviews of thispaper, and P. Wallbrink provided the inter-pretation of the radionuclide data for the site.This paper was greatly improved by the com-prehensive and thoughtful comments of C.Luce and another anonymous reviewer.
34
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SECTION 2
“Determining Soil Loss Using the Inventory Ratio of Excess210Pb to 137Cs”
By
P.J. Wallbrink* and A.S. Murray
email: [email protected]
39
2.1 INTRODUCTION
Redistribution of fallout 137Cs has been widelyused to determine patterns and rates of soilloss and sediment accumulation (Longmore etal., 1983; Kachanoski, 1987; Sutherland andde Jong, 1990; Loughran et al., 1990).Generally, an input or reference value of 137Csis determined and compared with soilinventories of 137Cs in erosion or depositionsites; a review of this technique can be foundin Ritchie and McHenry (1990). There areseveral methods available for calculating soilloss rates from 137Cs measurements and theseinclude: empirically derived relationshipsbetween the reduction in 137Cs activity andsoil loss such as those developed by Elliott etal., (1990) and Ritchie and McHenry (1975);theoretical models such as the proportionalmethod of Kachanoski (1987); the gravi-metric method (Brown et al., 1981); profiledistribution models (Zhang et al., 1990); andmass balance models (Fredericks andPerrens, 1988). A review of these is given inWalling and Quine (1990). With theexception of Zhang et al., (1990) thesemethods have been developed for use oncultivated soils, and consequently assumemixing of 137Cs at least to the depth of theplough layer. Generally they are intended tobe used to calculate rates of loss in terms ofan annual average. All these approaches relyon the assumption that the initial falloutdeposition is uniform and that there is noimmediate redistribution before the 137Cs istrapped by soil particles. However, variousauthors (eg. Wallbrink et al., 1994;Sutherland, 1994) have shown that the initialdistribution of 137Cs may not be uniform,indeed the variability can be large, up to 40%.Many samples then need to be analysed toreduce the uncertainties on reference values,(Fredericks and Perrens, 1988; Sutherland,1991) and despite this it is possible or evenprobable that the input value will not beappropriate to the point values fromsuspected eroded or deposition sites.
It is likely that the causes of variability in the137Cs areal concentrations similarly affect theinitial areal concentration of other falloutradionuclides, such as 7Be and 210Pbexcess. (i.e.210Pb derived from fallout). Fallout of 137Cs inthe southern hemisphere essentially stoppedin the mid 1970’s (Longmore et al., 1983) andsince this time about half of the present soilinventory of fallout 210Pb (half life 22.3 years)has been precipitated. Nevertheless, both137Cs and fallout 210Pb are deposited mainly inrainfall, and so will be similarly affected bymicro-climate variations, such as rain-shadowing, and by differences in soil per-meability. Thus it is possible that someinitial correlation between the areal con-centrations of these two fallout nuclidesexists; Megumi et al., (1985) report a re-lationship between concentrations of thesetwo in surface soils. Subsequent redistri-bution by soil movement may or may notaffect the nuclides similarly, depending ontheir relative distribution with respect todepth and particle size. The degree to whichthese characteristics have changed over thelast 40 years or so will also affect thecorrelation.
In this paper we attempt to measure soillosses from different logging treatments usingfallout radionuclides. These losses are firstestimated by comparing 137Cs and 210Pbexcess
inventories with their measured depthdistribution in reference profiles. Evidence isthen presented of a good spatial correlationbetween areal concentrations of these twonuclides. This observation is then used todevelop a new quantitative method fordetermining soil loss. The method is based oncomparing areal averages of fallout 210Pb/137Csinventory ratios from the logged sites withthe activity ratio curve derived from depthdependent measurements of these twonuclides at two ‘control’ locations.
40
2.2 MATERIALS AND METHODS
The St Helens State Forest (latitude 41°S,longitude 148°E, Fig. 1) consists predomi-nantly of Eucalyptus seiberi. There is littleshrub understorey because of frequentburning. The lithology is mainly granite,which is porphyritic, equigranular and coarsegrained, with outcrops occurring on ridgetopsand some slopes. There is a small componentof biotite and a major component of muscoviteadamellite. The soils are yellow podzolics andhave shallow (2–15 cm) sandy A-horizonswhich overlay yellow gravely sandy/loams.They are classified according to theAustralian soil taxonomy classification
system as Uc2.21 (Northcote, 1979), this fallswithin the U.S. soil taxonomy suborderAqualf. Some basic properties of the soil arelisted in Table 1. A major feature of thesurfaces of these soils is a lag layer of whitequartz grains of thickness from one to fivemm thick. In conjunction with the thin Ahorizons this suggests that natural erosionmay be significant. All the plots had slopesless than 15o. Annual rainfall is about 650mm yr-1, with a winter predominance,however high intensity storms (up to 200 mmhr-1) may occur over the summer months.
Table 1. Physical properties of soils within experimental plots, at St Helens, Tasmania. Valuesare representative of the top 30 cm only.
Location Organic matter Texture Bulk density
<63um 63-125 125-250 250-500 >500
(%) (%) (%) (%) (%) (%) (Mg/m3)
Plot 1 2.94 9.72 5.58 5.28 7.98 68.48 1.52Plot 2 4.77 8.79 3.91 4.32 7.23 70.97 1.46Plot 3 2.46 11.37 6.83 6.09 9.27 63.97 1.53Plot 4 1.60 9.00 5.99 5.43 7.44 70.51 1.31
In March and April 1991 the TasmanianForestry Commission logged parts of the StHelens forest as part of an experiment toexamine erosion under different harvestingconditions. After logging operations ceasedfour 350 m2 plots were cordoned off (Fig. 1).The unlogged plots 1 and 4 were adjacent toeach of the harvested areas and left as controlsites. Plot 1 was just outside the catchmentboundary, however it contained similar soilsand vegetation type to the others. Plot 2(normal impact) represented standard loggingpractice and had crown and stem removalfrom the site and soil banks to 60 cm highpushed up by bulldozers to reduce water flowover the disturbed soil. Plot 3 (minimalimpact) was harvested less intensively andthe crowns and waste were left on site,vehicle movement was kept to a minimumand no soil banks were installed. It wasanticipated that soil loss from Plot 2 wouldexceed that of Plot 3.
The radionuclide concentrations of the soilfrom the logged and unlogged sites weredetermined by dividing each plot into roughly10 x 35 m2 grid cells. Representative soilcores were then taken randomly from withineach cell to a total of 9 or 10 cores per plotalthough obvious features such as trees androck outcrops were avoided. The distancebetween cores was about 5–10 m and thecoring tubes (diameter 100 mm, depth 300mm) each contained approximately 2.5 kg ofsoil. Sampling occurred about 14 monthsafter logging.
Detailed depth sampling was undertaken atthe two ‘control’ sites to determine the initialdistribution of the two nuclides. Theseprofiles were of known area and acquiredusing the method described in Wallbrink andMurray (1996). Briefly, a high speed routerwith a 25 mm diameter tungsten carbide bitwas used to obtain samples from an area of
41
Fig. 1. Location diagram of St Helens field study area, Tasmania, Australia, showing relativepositions of experimental plots within the St Helens state forest.
42
0.16 m2, in depth increments of as little asone mm, down to 30 mm. Below this, thesample area was reduced to 0.04 m2, and ascraper used to obtain depth increments of10 mm down to 300 mm.
All samples were oven dried, ashed at 400°C,and analysed by gamma spectrometry for137Cs, 226Ra and 210Pb as described by Murrayet al., (1987). They were counted for aminimum of 85 ksec. 210Pb is generated fromthe decay of 222Rn both within the soil, and inthe atmosphere and a review of its genesis isgiven in Wise (1980). Generally the fallout210Pb concentration, (210Pbexcess) is calculated
as the difference between the 210Pbconcentration and 226Ra concentration in thesoil (Appleby and Oldfield, 1978), and noallowance is made for the escape of 222Rn gas.In our sites the 210Pb activity concentrationsbecome a constant fraction of 226Ra at depth(about 95%) ie. There is about a 5% loss of222Rn activity to the atmosphere. Conse-quently, our values of 210Pbexcess are calculatedby subtracting 95% of the observed 226Ra ateach depth from the appropriate total 210Pbconcentration. Unless otherwise noted, allmean values are reported plus or minus onestandard error.
43
2.3 RESULTS
All the radionuclide areal concentrations fromthe soil cores within each plot aresummarised in Table 2. The concentrationsof 210Pbexcess and 137Cs measured in thedetailed profiles from Plots 1 and 4 are shownas a function of depth in Figs 2a, b and 3a, brespectively. The cumulative inventories ofeach nuclide have been calculated from depthby summing the areal concentrations withineach increment towards the surface (Figs 2cand 3c; for clarity only the top 150 mm areshown). The deeper penetration of 137Cs isclearly visible. The subsurface maximum of137Cs is not unexpected and has been observedelsewhere (eg. Walling and Woodward, 1992).The 210Pbexcess also has a subsurface peak atabout 5 mm in these soils (Figs 2a and 3a).This can be explained by the predominance ofthe quartz grain lag at the surfaces of thesesites. The relationship between decreasingradionuclide concentration with increasingparticle size has been well documented ingranite soils (Olley et al., 1996). Furtherevidence for this particle size dependence atSt Helens is given in Wallbrink and Murray(1996).
2.3.1 Discrimination between forestlogging treatments using 137Cs
The methods available for relating 137Csmeasurements to soil loss given in the in-troduction are not readily applicable to thisstudy, because loss was essentially instan-taneous (most of it presumably occurred overa period of less than 14 months), the soil wasuncultivated and the nuclide is concentratedtoward the soil surface. Nonetheless, it ispossible to compare 137Cs areal concentrationsfrom the logged plots with those of theunlogged sites, and thus investigate thepotential sensitivity of 137Cs inventories inthis area. The two unlogged plots (1 and 4)are statistically indistinguishable (at p <0.05, one tailed test) and therefore thereference value can be taken as the average ofall these cores, 810 + 90 Bq m-2 (n = 18,Relative Standard Deviation, RSD, 47%).This can be compared to the average arealconcentrations of 710 + 90 (n = 10;RSD = 38%) and 690 + 110 Bq m-2 (n = 10;RSD = 49%) at sites 2 and 3 respectively,giving relative 137Cs depletions of 12 ± 16 and15 ± 18%. These can be converted to depth
losses, of 6 + 8 and 7 + 8 mm respectively, bycomparing them with the known depthdistributions of 137Cs areal concentration attheir respective control sites, Plot 1 and 4(Figs 2c and 3c). There is no statisticaldifference in 137Cs areal concentrations, (p <0.05, one tailed test) between the unloggedcontrol sites and either of the two loggedsites. There is also no statistical differencebetween the two logged sites (p < 0.05, onetailed test). It is concluded that 137Cs arealconcentrations cannot be used to discriminatebetween the effects of the different plottreatments.
2.3.2 Discrimination between forestlogging treatments using excess210Pb
It was thought that 210Pbexcess may be a moresensitive discriminator of surface erosionthan 137Cs within the upper sections of soils.This is because of its greater rate of changewith depth over the top few centimetres,partially resulting from its constant fallout inrainfall and strong particle reactivity(Matthews and Potipin, 1985; Nozaki et al.,1978). Therefore a similar analysis to thatdescribed above for 137Cs was undertakenusing excess 210Pb areal concentrations. Thecontrol plots were again indistinguishable (p< 0.05, one tailed test) and the combinedaverage areal concentration of 210Pbexcess fromthese was 1820 ± 240 Bq m-2 (n=18;RSD=57%). This can be compared to theaverage inventory values of 520 ± 110 (n=10;RSD=66%) and 1010 ± 110 Bq m-2 (n=10;RSD=35%) from Plots 2 and 3. The loggedplots 2 and 3 were statisticallydistinguishable both from one another andfrom the combined control plot value (p <0.05, one tailed test) and their inventoryvalues represent reductions of 71 ± 15 and45 ± 15% respectively. Clearly Plot 2 has losta large fraction of the initial 210Pbexcess
inventory, and Plot 3 has lost a smallerfraction. Comparing these data with theirrespective reference depth profiles at controlPlots 1 and 4, (Figs 2c and 3c) gives depthlosses of about 20 + 4 mm for site 2 and11 + 4 mm for site 3. Nevertheless, it isimportant to note that the inventory valuesfrom the two detailed profiles at Plots 1 and 4(each taken over approximately 0.4 m2)
44
Table. 2. Radionuclide concentrations of soil cores from St Helens, Tasmania.
Coreno.
Site 1 Site 2 Site 3 Site 4
210Pbexcess 137Cs Pb/Cs 210Pbexcess 137Cs Pb/Cs 210Pbexcess 137Cs Pb/Cs 210Pbexcess 137Cs Pb/Cs
(Bq m-2) (Bq m-2) ( - ) (Bq m-2) (Bq m-2) ( - ) (Bq m-2) (Bq-2) ( -) (Bq m-2) (Bq m-2) ( - )
1 2040 90 650 120 3.17 0.62 540 100 570 110 0.95 0.25 970 80 480 130 2.04 0.58 1030 80 440 90 2.33 0.51
2 1360 80 670 45 2.02 0.18 490 70 870 150 0.56 0.13 1180 70 810 60 1.45 0.14 4860 760 1970 100 2.47 0.41
3 1170 900 600 130 1.95 0.61 1380 120 1100 120 1.25 0.18 570 140 1600 70 0.36 0.09 2410 870 770 70 3.15 1.17
4 1900 100 1230 100 1.54 0.16 140 80 180 90 0.8 0.6 1000 140 540 170 1.83 0.63 1040 90 610 100 1.7 0.31
5 1290 70 530 40 2.44 0.24 900 100 900 200 1.01 0.25 1080 100 620 130 1.74 0.41 1810 80 1010 90 1.79 0.17
6 3070 900 940 70 3.26 0.36 450 110 620 130 0.72 0.24 1320 80 810 120 1.63 0.25 480 100 370 50 1.28 0.33
7 2180 100 910 90 2.4 0.26 300 70 750 190 0.4 0.14 270 90 410 150 0.66 0.33 580 70 390 90 1.47 0.39
8 2430 80 830 140 2.94 0.53 280 140 330 160 0.85 0.6 1550 100 380 190 4.03 1.03 1120 70 520 50 2.13 0.24
9 1070 100 850 130 1.25 0.23 240 80 930 80 0.26 0.09 930 90 480 90 1.96 0.44 2940 1280 1270 110 2.31 1.03
10 *1540 80 *640 30 *2.42 0.16 520 110 880 190 0.59 0.18 1220 100 750 90 1.62 0.24 *670 20 *270 10 *2.48 0.1
Mean 1830 800 2.33 520 710 0.74 1010 690 1.73 1810 820 2.1
S.D. 630 200 0.66 350 270 0.28 350 340 0.93 1330 500 0.54
n = 9 10 10 9
Note: Uncertainties given as subscripts, are analytical only and are equivalent to one standard error
*Denotes values from detailed profiles taken within ’control sites’, these are not used in the calculation of site statistics
45
Fig. 2. Detailed ’control’ profile from Plot 1 showing a) 210Pbexcess concentrations with depth b)137Csconcentrations with depth and c) cumulative areal concentrations of 137Cs and 210Pbexcess indiscrete soil increments. Note: i) uncertainties are analytical only, ii) cumulativeinventory for 137Cs starts at 300 mm.
0 30 60 90 120
150
c) C
umul
ativ
e in
vent
ory
(B
q m
-2)
040
080
012
0016
00
137 C
s21
0 Pb ex
Depth (mm)
0 30 60 90 120
150
a) C
once
ntra
tion
(Bq
kg-1
)0
4080
120
160
(Plo
t 1)
010
2030
40
b) C
once
ntra
tion
(Bq
kg-1
)
137 C
s21
0 Pb e
x
46
Fig. 3. Detailed ’control’ profile from Plot 4 showing a) 210Pbexcess concentrations with depth b)137Csconcentrations with depth and c) cumulative areal concentrations of 137Cs and 210Pbexcess indiscrete soil increments.
Depth (mm)
0 30 60 90 120
150
a) C
once
ntra
tion
(
Bq
kg-1
)
010
2030
400 30 60 90 12
0
150
c) C
umul
ativ
e in
vent
ory
(B
q m
-2)
020
040
060
0
210 P
b ex
(Plo
t 4)
210 P
b ex
02
46
810
b) C
once
ntra
tion
(Bq
kg-1
)
137 C
s
137 C
s
47
differ by a factor of about 2. This emphasisesthe possible error in assuming that thecontrol inventories apply to the logged sites.
2.3.3 A new approach using210Pbexcess/137Cs ratios
It was suggested in the introduction thatareal concentrations of 137Cs and 210Pbexcess
may be related, and a clear correlation isobserved between them in the total coreinventories from the control plots 1 and 4(Fig. 4). This correlation explains the overallreduction in variability in the inventory ratio210Pbexcess/137Cs compared with that of theindividual inventories at the control sites(Table 1). Thus an alternative approach isproposed here which is based on theadditional observation that in undisturbedsoils 137Cs and 210Pbexcess also have differentpenetrations into the surface layers of soil.The bulk of 210Pbexcess activity is retainedcloser to the surface than 137Cs (see Figs 2aand 3a). Thus the ratio of the inventory of210Pbexcess (below a particular depth in the soilprofile) to the inventory of 137Cs below thesame depth is unique. This ratio has finitevalues, and thus acts as a useful indicator,over the range of penetration of 210Pbexcess, ie.over a few centimetres.
The ratio of these inventories from the pro-files at Plots 1 and 4 decrease monotonicallywith depth and provides unique values downto about 80 mm (Fig. 5). The curves for thetwo profiles are similar, given the largedifferences in absolute areal concentrations(Figs 2c and 3c). The total inventory ratio (ie.The value at the surface) of profile 1 is2.42 + 0.16 and of Profile 4 is 2.48 + 0.11.These are the values that would be obtainedfrom measurements of a single bulked coretaken to 300 mm from each site, and can thusbe directly compared with the mean of2.22 ± 0.14 obtained from the 18 cores takenfrom the 2 unlogged sites (Plots 1 and 4). Itshould be noted that the variability at site 4in both 210Pbexcess and 137Cs areal concen-trations is about twice that at site 1, althoughthe variability in the 210Pbexcess/137Csinventory ratio at the two sites is similar.The overall RSD in this ratio is 28% for these2 control plots; this is significantly smallerthan the 47% and 57% observed for 137Cs and210Pbexcess alone. Although this result is domi-nated by the contribution from site 4, itconfirms that using the ratio can significantlyreduce the spatial variability, which is
presumed to arise from heterogeneous falloutand infiltration and any redistribution up to1991.
2.3.4 Comparison of reference profilewith disturbed sites
The inventory ratios have been calculated foreach of the ten 300 mm cores taken from thelogged sites 2 and 3, the average of these are0.74 ± 0.09 (n=10, RSD = 38%) and 1.73 ±0.29 (n=10, RSD = 53%) respectively. Thesevalues are statistically different from oneanother, (p< 0.05, one tailed test). Thelocations at which these ratios intersect theactivity ratio curves from the respective‘control’ plots (shown in Fig. 5) correspond tothe depth of soil removal required to leavebehind these inventory ratio values. Averagesoil loss is calculated to be about 40 ± 5 mmfrom the heavily logged Plot 2, and 17 ± 5 mmfrom the more carefully managed Plot 3. Theaverage measured bulk density of these soilswas 1.1 Mg m-3 through the top 100 mm, andso these depths from Plot 2 and 3 correspondto net soil losses of 440 ± 55 and 190±55 t ha-1
respectively. Note that the uncertaintiesgiven are relative to the measurement errorson the initial ratio values only.
These estimates of soil loss are significantlyhigher than long term rates from loggedslopes of about 0.2 t ha-1 yr-1 given inMegahan and Kidd (1972), although they areconsistent with values for logging roadsduring use, of about 120 – 180 t ha-1 yr-1 givenin Reid and Dunne (1984). They are alsoconsistent with a one-off estimate of depthloss on deforested slopes of 41 mm due tostorms associated with cyclone Hilda in NewZealand in 1990 (de Rose et al., 1993).Various other estimates of loss within forestsunder a variety of conditions can be found(Roberts and Church, 1986). However thesetend to be for Northern Hemisphere cooltemperate climate conditions and are pre-sented as annual Figs and thus are difficultto compare with the short term lossesdiscussed here. Possible discrepancies may inpart arise from a difference in theexperimental areas and time scales involved.In this study the plots were only 350 m2 andthe results apply to the 14 monthsimmediately post logging. Losses wouldtherefore be expected to be larger than theannual averages derived from whole slopesreported elsewhere.
48
Fig. 4. Relationship between 210Pbexcess and 137Cs inventories from cores taken within unlogged’control’ Plots 1 and 4, St Helens, Tasmania.
137Cs (Bq m-2)
0 500 1000 1500 2000
210 P
b exce
ss (
Bq
m-2
)
0
1000
2000
3000
4000
5000
49
Fig. 5. Activity ratio with depth from cumulative 137Cs and 210Pbexcess areal concentrations at’control’ profiles, Plot 1 and 4. Graph shows intersection of Plot 2 and 3 average inventoryratio values with respective ’control’ activity ratio curves and estimated depths of soilremoval.
Depth (mm)
0 10 20 30 40 50 60 70 80
0 10 20 30 40 50 60 70 80
Inve
ntor
y R
atio
0.0
0.5
1.0
1.5
2.0
2.5
Ran
ge o
f pr
edic
ted
dept
h of
soi
l rem
oval
Plot 3
Plot 2 ratio 0.74 + 0.09
ratio 1.73 + 0.3
Plo
t 1 c
ore
Plo
t 4 c
ore
Plot
3
Plot
2
50
2.4 DISCUSSION
2.4.1 Caveats on the radionuclideestimates of soil loss
Model predictions are no better than themodel assumptions. In this case, it is as-sumed that erosion at the disturbed sitesremoves soil with nuclide labels described bythe profiles at the undisturbed sites. Thisclearly cannot be exactly true, because thesites were disturbed, and this disturbanceincluded some lateral transport of soil. (Suchcriticisms apply to all techniques which useany form of ‘reference’ profile). Also anylateral movement of soil may involve thepreferential removal of finer particles. Asthese fines carry more of the radionuclidelabel by mass than larger particles, theirpreferential loss may artificially increase theestimates of radionuclide depletion. It shouldalso be noted that the material in the soilbanks at Plot 2 were excavated from asmaller pit immediately upslope of thesefeatures and the possibility remains thatsome topsoil was also used. This may haveleft some areas between the soil banksdepleted in 137Cs and 210Pbexcess. Although thesampling process was intended to average outthese effects, our calculations of loss shouldprobably be regarded as upper estimates.
2.4.2 Evidence supporting the radio-nuclide estimates of soil loss
Ultimately the only reliable test of theapproach is by independent measurement.This was partly achieved by measurements ofsoil loss in an adjacent plot of similar size andsubject to the same intensive logging practiceas Plot 2. In this plot sediment was collectedin a trough over a 2 month period im-mediately prior to the work described in thispaper. Assuming that the trough trapefficiency was close to 100% and that this 2month period was representative of the 15months since logging, the volume of collectedsediment suggests a surface lowering of atleast 30 mm over this latter period (J.C.Wilson, 1/30/93, personal communication).This is broadly consistent with the radio-nuclide predictions. The estimates of soil lossby radionuclides and the collection trough arealso consistent with visual observations atthe time of sampling which found that the 2–
15 cm thick dark organic-rich A horizon wasstill largely intact in plot 3, whereas onlyremnants were visible in plot 2. Mixing of thesoil had also occurred in plot 2, with areas ofbrown clay-rich B horizon exposed. Theseobservations support the finding that plot 3retained more soil following harvesting thanplot 2.
2.4.3 Differential depth dependency of210Pbexcess and 137Cs
The depth losses for plots 2 and 3 respec-tively, determined by 137Cs (6 + 8 and 7 + 8mm ) and 210Pbexcess ( 20 + 6 and 11 + 3 mm)were smaller than the values predicted fromthe ratio data (Fig. 5) of 40 + 6 mm (Plot 2)and 17 + 5 mm (Plot 3). These discrepanciesare thought to arise most probably because ofthe uncertainties in the estimates of 137Cs and210Pbexcess inventories at the logged sites.Control inventories may be systematicallydifferent from those of the logged sites. Incontrast, uncertainties arising from grossdifferences in initial inventories betweenlocations do not affect the ratio technique – inwhich the controlling factors are the ratiobetween the inventories of the two nuclidesand their rate of change with depth. Theseparameters appear to offer greater reliabilityin determining soil loss than either of the twonuclides alone. Further work is requiredhowever, to confirm that the 210Pbexcess/137Csinventory ratio correlation with depth iswidespread and that the relative penetrationdoes not vary markedly across the landscape.The inventory ratio may also be useful ininvestigations of the rates of natural verticalsoil mixing due to soil fauna, such asdescribed by Lee (1985).
2.4.4 Causes of variability
Fredericks et al., (1988), Sutherland (1991,1994) and Wallbrink and Murray (1994) havedemonstrated that 137Cs variability can be asmuch as 40% from studies on uneroded‘control’ sites. These authors speculate thatvariability arising from initial deposition maybe due to a number of factors includingrainshadowing, interception, lateral through-flow and differential run-on/run-off resulting
51
from soil hydrophobicity and compaction.The variability of areal fallout activityconcentrations at the ‘control’ sites reportedhere was considerable (137Cs: RSD = 47%;210Pbexcess : RSD = 57%). It is thought thatthis variability within the reference areas atSt Helens can have two origins (I) variabilityresulting from initial deposition processes(such as described above) and (ii) long termpost depositional redistribution with soilparticle movement. There is evidence fromthis study that the first of these factors maybe more important (see below).
2.4.5 The influence of sample surfacearea on spatial variability
It is surprising that the inventory ratios forthe two detailed profiles at Plots 1 and 4agree so well given that the RSD for sites 1and 4 is about 27%. However, this lattervalue is for 18 cores that each have a surfacearea of about 80 cm2. The detailed profileshave a surface area of 1600 cm2, down to 30mm, and 400 cm2 below that. The top 30 mmcontains about 50% of the 137Cs and 80% ofthe 210Pbexcess. Now, if the spatial frequency ofthe variability in the ratio was high, forinstance the same as the diameter of a 10 cmcore, then the detailed profiles (which containthe areal equivalent of 20 cores, at least downto 30 mm), would have a smaller variabilitythan individual cores. The uncertaintyarising from spatial variability in theinventory ratio value for the detailed profileswould then be reduced to (27% /) = 6%. Itmay be reduced by more than this if the vari-ability arises from local redistribution of thetop few millimetres of soil over lateraldistances of only a few centimetres. In thiscase the scatter would not be random, andmight be almost entirely removed bysampling over a scale large compared withthe redistribution distance. Whether or notthis is this case, this averaging clearly hasnot removed the variability in theconcentration data, as the Plot 1 profile hastotal inventories of 137Cs and 210Pbexcess
approximately 2.5 times greater than those atPlot 4. This suggests that the origin of thisvariability may lie in initial fallout dis-tribution rather than significant subsequentlateral transport.
2.4.6 Possible influence of vegetationon spatial variability
The variability of 137Cs and 210Pbexcess wasgreater at Plot 4 than Plot 1 (Table 2). Thismay be due to differences in their slopeposition. Plot 1 was approximately midslopeand Plot 4 was closer to the slope bottom andthere were slight soil moisture differencesbetween them. This was evident in the moreprofuse vegetation at Plot 4, although theoverstorey species were the same.Furthermore, Plot 1 had undergone morefrequent litter reduction burns; there waslittle or no surface litter and the understoreywas relatively sparse. All of these factorswould result in a greater interception offallout radionuclides at Plot 4, potentiallyleading to a more heterogeneous distribution.
2.4.7 Other factors affecting radio-nuclide variability
The decrease in variability in the inventoryratio at the ‘control’ sites (RSD = 28%) whencompared with 210Pbexcess (RSD = 57%) and137Cs alone (RSD = 47%) also deserves con-sideration. Both nuclides are depositedprimarily by rainfall, and so if 137Cs and 210Pbwere deposited at the same time thevariability in the ratio would presumably bevery small. However, half the 210Pbexcess
inventory has been deposited after fallout of137Cs effectively stopped. Furthermore,because the two nuclides have differentpenetration profiles, the ratio in materialeroded from the surface few millimetres ismarkedly different from the total inventoryratio. Thus local redistribution of sedimentwithin the control site should greatly increasethe variability in the ratio until ultimatelythe ‘memory’ of initial condition would be lost.In addition, changes in the micro-climate andmicro-topography at the site since the 1960’swould again reduce the degree of correlationexpected between the two nuclides. However,a significant reduction in the RSD is observedbetween the 210Pbexcess and 137Cs absoluteconcentrations and the ratio value, and acorrelation persists to the present (see Fig. 4).Therefore it must be concluded that thevariability in the concentration data probablyarose mainly as a result of deposition andinitial redistribution (from mechanismsdescribed earlier) prior to particleattachment, rather than particle re-
52
distribution over the 10-20 years prior tosampling. This also implies that these factorsretained some constancy over this period.
2.4.8 Potential for further applications
The ability of 210Pbexcess/137Cs ratios to reducevariability in ‘control’ sites should beindependent of differences in penetrationdepth. Therefore this approach should alsobe applicable to arable (ploughed) areas.However it is not likely to be of use tocalibrate erosion depth in a ploughed soilbecause this technique does require adifference in penetration depth, which isprobably obscured by the ploughing process.Nonetheless, as long as the last majorphysical disturbance was more than 10 to 20years prior to sampling, then the techniqueshould be applicable.
The method has been shown here to be usefulfor examining erosion following a singledisturbance event which occurred in a shorttime period compared with the 210Pb half life.This allows a direct comparison with the‘control’ sites, because no significant fallout of210Pb had taken place after the disturbingevent. However, the approach is capable ofextension to continuous erosion processes, ifthe steady fallout of 210Pb is taken intoaccount. It is also worth noting that 7Be (halflife 53 days) is also found preferentially verynear the surface (Wallbrink and Murray,1993, 1996) and so a similar approach using7Be to 137Cs or 7Be to 210Pb ratios could beapplicable even on annually ploughed land.
53
2.5 CONCLUSIONS
Areal concentrations of 137Cs were not able todistinguish either the control plots from thedifferently logged plots, or these logged plotsfrom one another. On the other hand it waspossible to discriminate, and then quantify,the effect of different plot treatments usingareal concentrations of 210Pbexcess. However,there were differences in the control site arealconcentration values such that it could not becertain that the control inventories applied tothe logged sites. It has been demonstratedthat areal concentrations of the two nuclidesare spatially correlated. Consequently,taking the ratio of these two nuclides removessome of this variability in arealconcentrations, which is assumed to arisefrom deposition and initial redistributionprocesses. Furthermore most of the 210Pbexcess
activity was retained closer to the soil surfacethan 137Cs and the areal concentration
activity ratio between them decreasesmonotonically with depth. This inventorydepth dependence is very similar at the two’control’ sites, despite more than a factor of 2difference in areal concentrations. Thusdifferences in the inventory 210Pbexcess/137Csratio between the sites could be quantified interms of depth loss. The advantage of thisratio over using 210Pbexcess or 137Cs arealconcentrations alone is that the differences inratio values between sites are less affected bypossible differences in total fallout. It isconcluded that this ratio is a sensitivetechnique for retrospectively comparing soillosses between sites following recentdisturbance in forested or pastured sites.Indeed it may also have the potential to beapplied in any land use where the soil surfacehas been otherwise undisturbed over the lastone or two decades.
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2.6 ACKNOWLEDGEMENTS
The authors would like to thank Dr CathyWilson, from the Cooperative ResearchCentre for Catchment Hydrology (CRCCH),for providing the opportunity and financialsupport to undertake this work. WebeReyenga and Frank Dunin - CSIRO, TomLynch, John Diggle and others from The
Forestry Commission of Tasmania providedvaluable support in the field. Craig Smith -CSIRO processed the soil samples forradionuclide analyses. Drs Olley, Gilliesonand Hairsine provided constructive reviews.The manuscript was further improved bycomments from three anonymous referees.
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