Targeting Rectification action in the Wingecarribee Catchment · Rectification actions aimed at...

C S I R O L A N D a nd WAT E R

Targeting Rectification action in the

Wingecarribee Catchment

CSIRO Land and Water

Technical Report 47/03, September 2003

A Collaborative Consultancy with the Sydney Catchment Authority

Compiled and edited by Jon Olley and Daniel Deere

Targeting Rectification action in the

Wingecarribee Catchment

CSIRO Land and Water

Technical Report 47/03, September 2003

A Collaborative Consultancy with the Sydney Catchment Authority

Compiled and edited by Jon Olley and Daniel Deere

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Copyright © 2001 CSIRO Land and Water.

To the extent permitted by law, all rights are reserved and no part of this publication covered by copyright may be reproduced or copied in any form or by any means except with the written permission of CSIRO Land and Water.

Important Disclaimer To the extent permitted by law, CSIRO Land and Water (including its employees and consultants) excludes all liability to any person for any consequences, including but not limited to all losses, damages, costs, expenses and any other compensation, arising directly or indirectly from using this publication (in part or in whole) and any information or material contained in it.

ISSN 1446-6163

This report should refer to as:

Olley, J.M. and Deere, D. (2003). Targeting Rectification action in the Wingecarribee Catchment. CSIRO Land and Water technical report 47/03.

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Foreword

In February 2002 ECOWISE Environmental submitted a successful bid to compile a contaminant budget for the Sydney Catchment Authority on the Wingecarribee subcatchment. The project brief required the development of a number of pollutant budgets; natural organic matter, nitrogen, pathogens, phosphorus and sediment.

ECOWISE Environmental is an Australian owned company and part of ActewAGL, a multi-utility providing electricity, gas, water and sewerage to the Australian Capital Territory (ACT) and surrounding region. They are a leading provider of environmental solutions using appropriate technology and skills for total water resource management.

ECOWISE Environmental pulled together a multi-disciplinary team to develop these pollutant budgets drawing on scientists from CSIRO Land and Water, the Australian National University, the Sydney Catchment Authority and ECOWISE itself. The ultimate outcomes of this project could only be achieved through this multi-disciplinary approach.

The pollutant budgets developed by the people drawn together by ECOWISE, and reported in this document clearly identify areas in the Wingecarribee subcatchment for rectification actions. I believe that the development of such budgets should be the first step in any planning of rectification actions.

Ross Benjamin

Manager Business

ECOWISE Environmental

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Executive summary

What we found – Sediment

Recommended Remedial Action- sediments

• Management action to reduce sediment delivery needs to focus on channel erosion, particularly in the eastern catchment areas. The management effort should concentrate on improving and protecting riparian vegetation along channels.

• It should be noted that most sediment will be delivered during infrequent floods, so management strategies must be robust enough to cope with erosion and sediment delivery in the majority of these events.

Hillslope erosion rates are low, producing 340 t/yr or about 5% of the total load

Channel (gully and stream bank) erosion dominates,generating 5735 t/yr or about 95% of the total load

Tracers tell us that most of this sediment comes from soils developed on mudrocks in the eastern catchment

Output 5425 t/yr

Deposition occurs in reservoirs (190 t/yr) and on floodplains (470 t/yr)

• SedNet modeling predicts an annual sediment yield of c.5400 t/yr. This is consistent with the estimates made from water quality and flow data of 804-10,752 t/yr, with a best estimate of c.3000 t/yr.

• The sediment yield is lower than other similar size catchments in southeastern Australia.

• The pre-European sediment yield is estimated to be c.300 t/yr, <10% of the current rate.

• Channel bank erosion is the dominant form of erosion, generating c.95% of the sediment load.

• Hillslope erosion contributes about c.5% of the sediment leaving the catchment.

• Geochemical tracers show that most of the sediment is originates from soils developed on mudstones in the eastern catchment.

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What we found – Nutrients

Recommended Remedial Action - nutrients

• Remedial action should concentrate on reducing nutrient delivery from diffuse sources by establishing stock-free buffer zones along the drainage network.

• The nutrient inputs from point sources should be minimized during low flow periods.

Output 21 t/yr TP; 180 t/yr TN

Diffuse sources dominate, contributing about 90% of the total phosphorus load, and about 88% of the total nitrogen load

Point sources contribute about 10% of the TP load (2 t/yr) and 12% of the TN load (21 t/yr) N

• From the water quality and flow data we estimate a total loads at the outlet of the catchment of 180 t/yr N, and 21 t/yr P.

• About 30% of the total P, and 70% of the total N loads are transported in the dissolved phase.

• Point sources yield c.2 t/yr TP and c.21 t/yr TN, contributing c.10% and c.12% of the total phosphorus and nitrogen annual loads respectively. During low flow conditions these sources could dominate nutrient loads in the catchment.

• Diffuse sources contribute c.90% of both the total P and N loads, most of which is likely to originate from cattle.

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What we found – Pathogens

Giardia

STP

Land

Septic

Cryptosporidium

STP

Land

Septic tanks

Dry Conditions - cryptosporidium

• Modelling shows that sewage treatment plants (29%) and domestic cattle (53%) defecating in the stream lines are the major sources of cryptosporidium during low flow periods

Dry Conditions - giardia

• Domestic cattle (83%) and sewage treatment plants (14%) are also the major sources of giardia during low flow periods

Wet Conditions – giardia

• During wet period sewage treatment plants contribute ~ 39%

• Land sources are the major contributor supplying ~ 60%

Recommended Remedial Action- pathogens

• Remedial action should concentrate on reducing cattle assess to the stream lines by establishing stock-free buffer zones along the drainage network.

• Wild pigs should be eradicated although the infectivity of the pathogens found in pigs for humans is uncertain.

• Releases from STP during wet periods should be minimized

Giardia

Wild pigs

Cattle+

calves

Sew age treatment

plantsSeptic tanks

Wet Conditions - cryptosporidium

• During rain events land sources (78%) (livestock, wild animals, cats and dogs) are the major sources of cryptosporidium to the streams with an equal spilt between rural and urban areas

• Sewage treatment plants are the other major source (21%)

Cryptosporidium

Septic tanks

Sew age treatment

plants

Cattle+

calves

Wild pigs

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What we found – Organic matter

A preliminary investigation in to the sources of organic matter in the Wingecaribee River using carbon and nitrogen isotopes and C/N ratios showed

• organic matter in the sediment samples from below the Wingecarribee Reservoir is not derived from upstream of the Reservoir

• most of the organic matter in the sediment samples is derived from terrestrial plants which use the C3 photosynthetic pathway.

• With the exception of the samples from the lower catchment the data are consistent with soil organic matter being the dominant source.

• In the lower forested are of the catchment fresher plant matter dominates

• The nitrogen isotope data suggests sewage may be a major component of organic matter in some area of the catchment, this finding warrants further investigation

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Contents

FOREWORD.................................................................................................................................................................... 3

EXECUTIVE SUMMARY.............................................................................................................................................. 5

CHAPTER 1: INTRODUCTION ................................................................................................................................. 11

BY DAN DEERE AND JON OLLEY

CHAPTER 2: WATER BUDGET FOR WINGECARRIBEE CATCHMENT........................................................ 17

BY BARRY CROKE

CHAPTER 3 SEDIMENT SOURCES ......................................................................................................................... 25

BY JON OLLEY, BILL YOUNG, GARY CAITCHEON, ANDREW HUGHES, IAN PROSSER, SARA BEAVIS, CHRIS CHAFER, MARTIN KROGH & ROBERT WASSON

CHAPTER 4 NUTRIENTS IN THE WINGECARRIBEE CATCHMENT ............................................................. 43

BY JON OLLEY, BILL YOUNG, GARY CAITCHEON AND CHRIS CHAFER

CHAPTER 5. PATHOGEN BUDGETS....................................................................................................................... 47

BY C. FERGUSON, P. BEATSON, D. DEERE, R. WASSON AND B. CROKE

CHAPTER 6 ORGANIC MATTER SOURCES.........................................................................................................79

BY JON OLLEY AND DECLAN PAGE

APPENDIX 1: GEOCHEMICAL DATA .................................................................................................................... 83

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Chapter 1: Introduction

By Dan Deere and Jon Olley

1.1 Context of this work

If we are to achieve ecologically sustainable land management we need to ensure that the downstream impacts of land uses are minimised. An essential part of this is to reduce the delivery of pollutants to streams. Rectification actions aimed at reducing pollutant delivery need to target the major sources. To do this the major sources, transport pathways and storage areas in a catchment need to be identified. A pollutant budget aims to quantify sources, stores and delivery of a pollutant through a catchment. It enables the relative importance of sources to be assessed and rectification actions to be targeted where they will have most effect. In this study the project team has developed pollutant budgets for fine sediments, nutrients (nitrogen and phosphorus), pathogens (Cryptosporidium and Giardia) and natural organic matter in the Wingecarribee subcatchment. The work provides clear indications of where catchment managers should target rectification actions.

The Sydney Catchment Authority supplies the bulk water for around one fifth of Australia's population. It was formed in 1999 to provide an enhanced mechanism for catchment and source water management with the objective of protecting the quality of the bulk water supply for the greater Sydney region.

The Authority will use a “Regional Plan” as the principal instrument for coordinating water quality protection activities in the Sydney catchments. At the time of writing the details of this Plan are still being developed. However, what is clear is that a key input to the planning process will be an understanding of the priorities for catchment rectification.

In practice, the setting of priorities for catchment rectification is complex and is influenced by many inputs in addition to those from the earth sciences. For example, there are socioeconomic and political pressures within catchments that may influence how and where rectification takes place and these may be just as, or more, significant drivers of change than issues such as water quality or ecological state. A good example of this is the way in which agricultural practices that are known to be unsustainable in the long-term are tolerated in the short-term as a result of their social, economic and political significance.

Prioritizing catchment rectification includes considerations such as drinking-water quality and/or ecological stream health. Those charged with the overall task of prioritization are likely to delegate the detailed work to specialists in particular fields and it important that they understand that what may appear to be straightforward questions might in fact very complex.

The work described here was undertaken to answer certain questions posed by the Sydney Catchment Authority’s planning functions. However, defining those questions was in itself and extensive process. Therefore, part of the introduction to this report is devoted to summarizing how the Authority and its collaborators defined the problem.

Once the problems were defined, factors such as cost, time, technology and expertise availability determined the quality of the answers yielded by the research. The bulk of this report describes what was achieved by a team of scientists from several organizations working collaboratively within those constraints.

Problem Definition

In April 2000 a Science and Research unit was set up within the Sydney Catchment Authority. Between May and August an extensive process of document review and internal and external consultation took place to develop a Research Program, approved by the Board. Importantly the core of this program was to be the construction of pollutant budgets over a five-year period.

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To provide a generalized toolkit for the construction of such budgets, the Authority set up, or enhanced, 5 programs, as follows:

• water quality monitoring program (SCA water quality database);

• GIS data collation and ground-truthing program (SCA GIS database);

• A literature review of catchment export coefficients (AWT 2001);

• Measurement of pollution loads from specific pollution sources within the Sydney catchments (Paterson and Krogh in preparation; Charles in preparation, Cox et al in preparation); and

• A literature review of tracing and tracking tools (ECOWISE 2001).

Once these projects were sufficiently complete, the SCA undertook an extensive consultation process, from July to December 2001, to precisely identify the terms of reference for the construction of pollutant budgets.

Budget endpoint

The first task was to determine the endpoint(s) for the budgeting estimates. There was extensive debate about the basis on which priorities should be set, and whether this should be based on

• contributions to in-stream sinks;

• contributions to the reservoir inflow point;

• contributions to the reservoir offtake for drinking-water supply.

The reservoir inflow was selected as the key point for consideration, with other sinks to be taken into consideration for the purposes of constructing the budget, ie at key stream nodes.

Relative vs actual values

A second task was to decide the extent to which budgets required:

• actual values; or

• relative values.

Since the objective was to prioritise rectification, quantitative but relativistic values were all that was required. Understanding the relative contributions of pollutants reaching the reservoir inflows apportioned according to source would enable priorities to be set.

Source component categories

The third issue relates to the nature and level of discrimination of the source categories. Possible categories included the following:

• Spatial:

o Geomorphological (eg sub-subcatchments or soil regions);

o Political (eg local government area boundaries); or

o Tenure (eg specific landholders’ properties);

• Biophysical Processes (eg gullying, bank erosion);

• Landuse categories;

• Specific categories of distributed sources (eg fauna or flora category);

It was decided that specifically identifying individual spatial components by political or tenure categories was not useful. This information would be too sensitive for the essential community consultation phase of catchment rectification. For a similar reason, singling out landuses was considered relatively risky. Therefore, the budget components were to be limited to:

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• Geomorphological;

• Biophysical Processes; and

• Specific categories of distributed sources.

In addition, specific point sources were to be included where these were well characterized and government-owned.

Resolution

There was debate about the level of resolution required. The limiting factors of cost, time, technology and skill availability, cited above, made it highly unlikely that the resolution found would be unnecessarily fine, or even approach a point of diminishing returns. Therefore, the objective was to maximize the resolution within those constraints.

Pollutants

The range of pollutants considered could have been extensive, since drinking-water can become contaminated by a range of substances. However, it was essential to limit the budget to some manageable number of pollutants, and to focus on the pollutants that were, in fact, causing problems for drinking-water quality rather than future risks or public perception issues. The following pollutants were considered important for the reasons given:

− fine sediment (due to their link to turbidity, their well established carriage of nutrients (see bullets below), and the expectation of their carriage of pathogens and many other pollutants);

− nitrogen and phosphorus (due to their causal link with cyanobacteria, which is an ongoing problem for the SCA, and their link to organic carbon concentration (see bullet below));

− carbon in the form of low molecular weight natural organic matter (NOM) (due to its link to disinfection by products, colour, taste and odour and regrowth potential its poor removal in water treatment); and

− water treatment-resistant pathogens (due to their links to infectious disease in customers).

Climatic influence on budgets

There was debate about the need to produce difference budgets for difference hydrologic and climatic scenarios. Options include:

• Average dry weather flow budgets;

• Long-term annual average budgets;

• Frequent event budgets (eg storms with return frequencies higher than one or a few years); and

• Extreme event budgets (eg millennial floods or thirty-year fire events).

The issue was complicated by the differing time-dependent responses of the various hazards. This is important because the rivers flow into major reservoirs which dilute pollutants and integrate loads over time. As a result, the climatic scenarios considered needed to differ for different pollutants.

For example, pathogens were considered generally short-lived in warm-temperate climates making the need for annual average budgets unnecessary. However, peak events such as storms were required. In contrast, the problems that arise due to nutrients and sediments generally influence reservoir systems over longer periods such that long-term annual averages are an appropriate endpoint. Therefore, the following climatic scenarios were selected as the basic requirements of the budget:

• Long-term annual average budgets for sediments and nutrients (C, P and N); and

• Peak event budgets for pathogens.

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Budgeting framework

It was desirable for there to be a single, common framework for the budget. However, there were a number of different disciplines required (such as hydrology, organic chemistry, geomorphology, nutrient chemistry and microbiology) and not all had the same view as to how a budget should be constructed. To resolve this, a series of workshops were held and a working paper developed to capture perspectives (Workshop 2001). This paper developed a common framework for budgeting, largely based on using existing frameworks for sediment and nutrient budgets and an agreed terminology.

Constructing the budgets

A combination of spatial modelling water quality and flow data, and sediment tracing techniques were used to determine the major sources of sediments and nutrient to the Wingecarribee River. Carbon budgeting and pathogen budgeting were completed with less confidence that other aspects due to the novelty of the approaches applied. Pathogen budgeting was built by using appropriate assumptions based on the same core hydrologic model as the sediment and nutrient budget. However, linkages to sediment transport were not explicitly made.

1.2 Project team and acknowledgements

The project manager was Dr Mick Bales, the project work was undertaken by

• Dr Dan Deere, Ms Christobel Ferguson, Mr Chris Chafer and Mr Martin Krogh of the Sydney Catchment Authority.

• Dr Jon Olley, Dr Ian Prosser, Dr William Young, Dr Gary Caitcheon, and Mr Andrew Hughes from CSIRO Land and Water;

• Prof. Robert Wasson, Dr Barry Croke and Dr Sara Beavis from the Australian National University;

• Dr Peter Beatson;

• Dr Declan Page from ECOWISE

The contract was managed by Mr Ross Benjamin of ECOWISE

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References

AWT, 2001 Contaminant Budgeting - Phase 1 Report. Draft report prepared for the Sydney Catchment Authority.

Paterson P and Krogh M (in preparation) SCA internal report being prepared to summarise sewage treatment plant effluent monitoring data.

Charles, K (in preparation) Risk Assessment of On-site Systems. PhD Thesis, University of New South Wales.

Cox, P, Angles, M, Deere, D and Ferguson, C (in preparation) Pathogen occurrence in faeces from wild and domestic animals inhabiting the Sydney Catchments.

Ecowise 2001. Catchment Analysis Toolkit. Report to the Sydney Catchment Authority.

Workshop 2001. Report on the outcome of workshop discussions held between ANU, Ecowise and SCA. Internal Sydney Catchment Authority

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Chapter 2: Water Budget for Wingecarribee Catchment

By Barry Croke

In order to estimate the budgets for sediment, nutrients and pathogens, information about the streamflow is needed. This chapter presents a water budget for the catchment estimating mean annual flow, and the influence of broad scale changes in the percentage of woody cover. The current status of hydrological science prevents reliable estimation of the influence of more detailed land use change on the distribution of flows.

Within those limits, estimates of the mean annual rainfall, potential evaporation and land use (represented as woody cover) were used to estimate the mean annual flow for the 52 sample collection sites in the Wingecarribee catchment (see Figure 3.5 for locations). These estimates do not include evaporation from dams/lakes, nor do they include anthropogenic effects such as irrigation. The flow estimates are in reasonable agreement with the mean annual values obtained from gauged sites.

2.1 Key Assumptions

The method of estimating the hydrological budget for the Wingecarribee catchment is based on a method developed for catchments spanning the Murray-Darling Basin, from the Condamine catchment in southern Queensland, to the Goulburn-Broken catchment in Victoria. This assumes that the relationships derived for the MDB hold in the Wingecarribee catchment. The resulting mean annual flows are natural flows. That is, extractions from the stream are not taken into account. Only loss component considered is evaporative loss from Wingecarribee dam.

2.2 Data used

The spatial data sets used are listed in Table 2.1 and the temporal datasets in Table 2.2. After deriving the internal catchment area for each sample site, the catchment mean rainfall, potential evaporation and woody cover corresponding to each sample site were derived.

Table 2.1: Spatial datasets

Dataset Resolution Source

DEM 25 m Land and Property Information, Sydney

Rainfall 25 m Bureau of Meteorology

Potential Evaporation 0.05 deg NLWRA (1980-1999)

Woody cover 0.05 deg Lu et al., 2002

Table 2.2: Temporal datasets

Dataset Gauges Resolution Source

Rainfall 7 15 min SCA

Streamflow 5 15 min SCA

Streamflow 5 Daily Pinneena DLWC

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2.3 Statistical Analysis of Rainfall and Streamflow Data.

Data for 7 rainfall and 6 stream gauges were used to study the distribution of rainfall and streamflow events in the Wingecarribbee catchment. The data had a temporal resolution of 15 minutes.

Rainfall analysis The length of record and percentage of dry 15min periods for each rainfall gauge are shown in Table 2.3. In addition, the distribution of event peak rainfall was also investigated (locating events with peak rainfall in 15min exceeding 1mm). Average rainfall peak was 3.3mm/15min period, with a standard deviation of 2.7mm. Peak event was 25mm/15min. Mean separation between events was 5.9 days, with a standard deviation of 12 days, and a peak separation of 99 days. Selecting only events with separation greater than 1 day gave a mean separation of 12.3 days, and a standard deviation of 15.3 days. The rainfall exceedence for one of the gauges (gauge 1) is shown in Figure 2.1.

Table 2.3. Preliminary analysis of 15 minute rainfall data

Gauge Location Number of timesteps with valid data Years % without rain

568070 East Kangaloon 664494 18.95 4.27 568082 Colyers Creek 671299 19.15 3.20 568113 Wingecarribee Dam 345177 9.84 2.87 568163 Joadja (Greenstead) 371058 10.58 2.04 568165 Berrima Junction 387003 11.04 2.70 568183 Burrawang (Amgrow) 315804 9.01 3.54 568184 Robertson (Crowes) 339209 9.67 4.09

Figure 2.1: Rainfall exceedence plot for gauge 568070.

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Streamflow analysis Table 2.4 shows the length of available streamflow record for each gauge, as well as the fraction of time with no flow. Analysis of the flow events measured by the gauge at Berrima yields an average time between events of 32 days, with a standard deviation of 73 days, and a maximum separation of 530 days. Average event peak is 2573ML/day with a standard deviation of 5678ML/day, and peak flow of 54520ML/day. The flow exceedence plot for the gauge at Berrima is shown in Figure 2.2.

Table 2.4. Preliminary Analysis of 15 minute streamflow data

Gauge Timesteps with valid data years % without flow

Berrima 909846 25.9 9.39

Bong Bong 236925 6.8 7.18

Bong Bong2 425016 12.1 6.10

Greenstead 400045 11.4 0

Maugers 519434 14.8 0

Sheepwash 531452 15.2 0

Figure 2.2: Flow exceedence plot for Gauge at Berrima

2.4 Method of Estimating Mean Annual Flow

The water balance for a catchment over some time δt can be written as:

SQETEQP s ∆++++=

where P is the rainfall, Q is the streamflow out of the catchment, E is the evaporation from wet surfaces, ET is the evapotranspiration, Qs is the subsurface outflow/inflow and ∆S is the change in the water stored within the catchment. This ignores any pumping of water into or out of the catchment. For a sufficiently long period (several years), assuming no significant change in

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groundwater levels between start and end of period, and insignificant subsurface flow and evaporation from wet surfaces (no large dams, wetlands or lakes), the water balance becomes:

ETQP +=

Therefore, in order to estimate streamflow at each sampling point, an estimate of the evapotranspiration is needed. This was achieved using a regional model for actual evapotranspiration developed for the eastern Murray-Darling Basin (Croke, 2002). This regionalisation uses the P-ET relationships derived by Zhang et al., (2001):

⎥⎥⎦

⎤

⎢⎢⎣

⎡

+++

=xxx

xxx EPPEw

PEwPAET

,0,0

,0

1

1

where x indicates either forest or grassland, w=2 for forest, and 0.5 for grassland, and E0 = 1410mm for forest, and 1100mm for grassland. This was used to derive an initial estimate of the runoff coefficient:

⎟⎟⎠

⎞⎜⎜⎝

⎛

⎥⎥⎦

⎤

⎢⎢⎣

⎡

+++

−=−

==xxx

xxxxxc EPPEw

PEw

P

AETP

P

Qr

,0,0

,0, 1

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A correction for variation in potential evaporation (using the mean annual potential evaporation surface from the National Land and Water Resources Audit) was derived by comparing long-term mean annual streamflow and rainfall for a number of gauges in the Goulburn-Broken and Condamine basins. The resulting fit was tested against gauges in the Upper Murrumbidgee, with a good agreement being found between the average estimated and observed of the mean annual flows (20% error), though the model underestimates the scatter between catchments (resulting in an error of approximately 40% for individual gauges). The resulting runoff coefficient is:

( ) ( )( )GcFcc rffrPEr ,, 1168.2exp64.21 −−−−=

where PE is the potential evaporation in m/yr. This method gives the natural flow, assuming no anthropogenic effects, and no significant evaporative losses from dams and lakes. The later can be included by considering the potential evaporation, and the surface area of the dam/lake.

2.5 Modelling Results

The predicted mean annual discharge values for each sampling site are given in Table 2.6. Note that site 7 is just below Wingecarribee Dam, and the DEM fails to represent the entire area draining to this point. A comparison of the observed and modelled mean annual discharge is given in Table 2.5. For the gauges at Maugers and Sheepwash, the model reproduces the observed flows providing that evaporative loss from Wingecarribee dam is taken into account for Sheepwash. The flows at Berrima and Greenstead are overestimated. This is likely to be due to extractions from the river not being included in the model. Providing that the model is estimating the hydrological response correctly (see Key Assumptions section), the mean annual extraction for the period from 1990 to 2001 upstream of Berrima were 16GL/yr, and between Berrima and Greenstead, extractions are estimated at 36GL/yr. These extractions from the river will not significantly influence event flows, and so will not influence the transport capacity of the river during these times. From the analysis of the results for gauges in the Upper Murrumbidgee, the uncertainty in the estimates of MAF are approximately 40%. This includes uncertainty in the mean annual rainfall and potential evaporation as well as the uncertainty in the parameterisation of the model.

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2.5 Conclusions

The mean annual flow for each sampling site has been estimated using a water balance model. The uncertainty in the estimates based on testing of the model in the Upper Murrumbidgee catchment is approximately 40%. This includes the uncertainty in the mean annual rainfall and potential evaporation, as well as the errors in the model (both errors in the model parameters, and the structure of the model). Comparison with gauged flows in the Wingecarribee catchment suggests that the uncertainty is considerably less than this, though this is based only on the results for two sites. The runoff coefficient varies across the catchment from approximately 15% for site 50 (catchment outlet, mean annual rainfall = 814mm) to 37% near Robertson (mean annual rainfall site sampling site 1 is 1640 mm).

Table 2.6. Mean annual flow estimates for sampling sites

site Internal

area (km2) Total area

(km2) Woody cover

Mean P

Mean PE

Internal MAF (mm)

Internal MAF (ML)

MAF (ML)

1 1.1 1.1 0.78 1640 1126 606 685 685

2 3.3 3.3 0.79 1663 1142 594 1932 1932

3 2.6 7.0 0.78 1598 1131 568 1491 4108

4 1.8 1.8 0.77 1524 1130 524 946 946

5 1.1 1.1 0.77 1353 1149 390 433 433

6 5.3 6.4 0.77 1324 1149 374 1984 2417

7 0.4 39.4 0.76 1278 1150 347 150 18463

Dam 30.2 39.0 0.77 1417 1141 440 13258 18312

8 14.8 54.2 0.76 1253 1149 335 4952 23414

9 41.3 47.7 0.71 1211 1156 319 13173 15590

10 18.8 120.6 0.76 1160 1156 281 5266 44270

11 8.5 8.5 0.76 1164 1154 285 2430 2430

12 12.3 20.9 0.74 1060 1165 229 2826 5257

13 30.4 151.0 0.70 1099 1163 258 7838 52108

14 0.8 28.6 0.72 1030 1166 220 182 7016

15 1.0 180.6 0.72 1028 1166 219 224 59349

16 6.7 6.7 0.67 1100 1161 267 1780 1780

17 4.3 11.0 0.67 1081 1164 256 1097 2878

18 4.7 4.7 0.70 1145 1161 283 1319 1319

Table 2.5. Comparison of observed and modelled flows for the period from 1990 to 2001 (flows are in GL/yr)

Gauge nearest sampling site

Qobs Qmodel dam evap

corrected Qq Qs Qs/Qobs

Maugers 3 4.9 4.1 0 4.1 2 2.9 0.59

Sheepwash 7 12.9 18.5 6.8 11.7 5.8 7.1 0.55

Berrima 25/26 41.3 64 6.8 57 29.7 11.5 0.28

Greenstead 49 74.1 133 6.8 126 53.2 20.9 0.28

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Table 2.6. Mean annual flow estimates for sampling sites (cont.)

site Internal area (km2)

Total area (km2)

Woody cover

Mean P Mean PE Internal MAF (mm)

Internal MAF (ML)

MAF (ML)

19 10.7 15.4 0.70 1116 1164 267 2867 4186

20 8.1 23.5 0.70 1086 1164 252 2039 6225

21 9.9 33.3 0.68 1056 1166 241 2384 8609

22 16.4 60.7 0.66 994 1171 212 3479 14966

23 11.6 11.6 0.67 1013 1169 220 2559 2559

24 11.0 11.0 0.70 1032 1170 221 2427 2427

25 10.2 201.8 0.72 1009 1169 209 2140 63916

26 3.5 205.3 0.71 963 1172 188 659 64575

27 9.3 9.3 0.73 986 1170 195 1810 1810

28 2.0 2.0 0.69 1086 1164 253 510 510

29 18.7 20.7 0.69 1026 1167 222 4143 4653

30 0.4 0.4 0.69 928 1185 172 63 63

31 32.3 53.3 0.68 900 1181 163 5281 9997

32 5.1 58.4 0.68 824 1188 131 664 10661

33 35.4 35.4 0.65 833 1194 137 4838 4838

34 1.4 95.1 0.68 815 1188 128 177 15676

35 30.1 125.2 0.67 825 1190 134 4016 19692

36 28.2 427.2 0.68 840 1187 138 3903 106874

37 4.5 556.9 0.67 819 1186 131 593 127159

38 7.0 7.0 0.68 894 1185 160 1109 1109

39 44.4 123.6 0.67 934 1178 180 8007 26640

40 1.3 399.0 0.68 865 1186 148 197 102970

41 7.9 274.1 0.68 874 1181 154 1208 76133

42 26.4 266.2 0.71 914 1173 168 4434 74925

43 22.0 22.0 0.71 893 1170 161 3537 3537

44 9.4 9.4 0.73 918 1162 171 1600 1600

45 13.8 23.1 0.71 902 1162 167 2296 3896

46 2.6 2.6 0.74 875 1177 146 372 372

47 67.4 93.1 0.71 860 1174 146 9856 14124

48 10.6 103.7 0.68 820 1196 128 1361 15485

49 42.1 599.0 0.66 805 1193 127 5333 132492

50 59.5 762.2 0.70 814 1208 120 7122 155100

51 3.2 208.5 0.73 949 1171 179 570 65145

52 6.9 27.8 0.73 1053 1165 229 1577 6834

23

2.6 References

Croke, B., 2002. Hydrological Parameterisation of SedNet, Technical Report I for the Basin-wide mapping of sediment and nutrient exports in dryland regions of the Murray-Darling Basin (Project D10012, MDBC)

Lu, H., M.R. Raupach, T.R. McVicar and D.J. Barrett 2002. Decomposition of vegetation cover into woody and herbaceous components using AVHRR NDVI time series, Remote Sensing of Environment (in review).

Zhang, Dawes and Walker 2001. The response of mean annual evapotranspiration to vegetation changes at the catchment scale, Water Resources Research, 37, 701-708.

25

Chapter 3 Sediment sources

By Jon Olley, Bill Young, Gary Caitcheon, Andrew Hughes, Ian Prosser, Sara Beavis, Chris Chafer, Martin Krogh & Robert Wasson

In this chapter a combination of spatial modelling (Prosser et al., 2001), analysis of water quality and flow data, and sediment tracing techniques are used to determine the major source of sediments to the Wingecarribee River.

3.1 Where Does the Sediment Come From?

Sediments originate from erosion of hillslopes and from gully and stream bank erosion (channel erosion). At the local level, it is common for either hillslope or channel erosion to clearly be the dominant source. The management of these two erosion types differs. Channel erosion is best managed by preventing stock access to streams, protecting vegetation cover in areas prone to channel erosion, revegetating bare banks, and reducing sub-surface seepage in areas with erodible sub-soils. Hillslope erosion is best managed by promoting groundcover, maintaining soil structure, and promoting deposition of eroded sediment before it reaches the stream. It is therefore important to be aware of the dominant type of erosion before attempting local or catchment-wide management to control it.

The assessment reported below is divided into three parts: hillslope erosion; channel erosion (gully and stream/river bank); and river sediment transport.

3.2 Assessment of Hillslope erosion

The controls on hillslope erosion by surface wash and rilling are well understood and incorporated in several models. The best known model, and the only one suitable for broad-scale application is the Universal Soil Loss Equation (USLE) (Wischmeier, 1978) and its derivatives such as the Revised USLE (Renard et al., 1997), SOILOSS (Rosewell, 1993) and PERFECT (Littleboy et al., 1992). Detailed process models of erosion and transport exist, but cannot be applied at regional scales because they require parameter data that are unavailable for large areas. The empirical form of the USLE is, however, consistent with the mechanics of sediment transport and detachment encapsulated in the more detailed models (Moore and Burch, 1986; McCool et al., 1989).

Hillslope erosion from sheet and rill erosion processes was estimated using the Revised Soil Loss Equation (RUSLE; Renard et al., 1997) as applied in the National Land and Water Resources Audit (NLWRA) (Lu et al., 2001). The RUSLE calculates mean annual soil loss (Y, tonnes ha-1 y-1) as a product of six factors: rainfall erosivity factor (R), soil erodibility factor (K), hillslope length factor (L), hillslope gradient factor (S), ground cover factor (C) and land use practice factor (P):

Y = RKLSCP

The precise form of each factor is based on soil loss measurements on hillslope plots, mainly in the USA. Limited local calibration of the RUSLE factors, particularly the C factor, has been undertaken in some catchments using plot scale measurements of erosion (McIvor et al., 1995; Scanlan et al., 1996).

Most of the factors included in the RUSLE vary significantly across catchments, enabling assessment of the spatial patterns of erosion. The land use practice factor (P), which is generally applied to disturbed lands where contour cultivation, bank systems and other land use practices are used to reduce erosion (Rosewell, 1997), was removed from the analysis. This was because it was not expected to be highly variable, and a simple method for estimating the factor was not available.

The data for the R, K, L and C factors were derived from the NLWRA database. Detailed descriptions of how these were derived are given in Lu et al. (2001). While the C factor was

26

derived from the NLWRA database it was based on the high resolution land use map supplied by the Sydney Catchment Authority. The S factor was derived by using the 25 metre DEM of the catchment and applying the same methodology as that carried out by Rosewell (1997) and Lu et al. (2001).

The rate of hillslope erosion predicted across the catchment is generally low <0.1 t/ha/yr (Figure 3.1). Slope is the primary factor controlling the variation in hillslope erosion hazard across the catchment, with the highest rates being predicted in the steeper country toward the lower end of the catchment. Total hillslope erosion in the catchment is estimated to be 6750 t/yr. Only a small proportion (typically 5%) of sediment eroded on hillslopes is delivered to the stream lines. The total fine sediment yield from hillslopes to the drainage network is therefore estimated to be c. 340 t/yr.

Figure 3.1: Hillslope erosion hazard predictions from RUSLE for the Wingecarribee Catchment.

3.3 Assessment of gully and stream bank erosion

Gully and stream bank erosion are significant land degradation processes and sources of sediment in Australian rivers. Erosion from gully and stream banks can generate up to 90% of the total sediment yield from a catchment (Olley et al., 1993; Prosser and Winchester, 1996, Wallbrink et al., 1998, Wasson, et al., 1998). Sediment that has been eroded from gullies since European settlement is still present in many rivers and continues to impact upon river ecosystems.

The extent of gully erosion in the catchment has been mapped by the New South Wales Department of Land and Water Conservation (DLWC) (Figure 3.2). There are 24.4 km of active gullies in the 763 km2 catchment. Assuming a gully cross sectional area of 10 m2, we calculate a total gully volume of c.244,000 m3, or c.367,000 t of sediment generated (assuming a soil density of 1.5 t/m3; subsoil densities in this region typically range from 1.3 to 1.7 t/m3) from the gullies since their formation. If we assume that these gullies formed c.100 years ago and that 50% of the sediment

27

generated contributes to the fine sediment load (this assumption is necessary because particle size distribution of the bank material is unknown, but typically in this region fines contribute between 30 and 70%), then the average fine sediment yield per year from the gully network is 1835 t/yr.

Figure 3.2: Gullies in the Wingecarribee catchment mapped by the New South Wales Department of Land and Water Conservation

Stream/river bank erosion is the most uncertain of the sediment source terms in the catchment budget modelling. It is known that degradation of riparian vegetation and other impacts on our rivers have resulted in greatly increased rates of erosion, to the extent that this form of erosion cannot be ignored as a sediment source in regional assessments. There is, however, very little data on the rates of bank erosion and the environmental factors controlling those rates. Rutherfurd (2000), following a review of global literature, proposed the following rule for bank erosion:

60.058.1016.0 QBE =

where BE is the bank erosion rate in metres of recession per year, and Q1.58 is the discharge (m3/s) of the 1.58 y recurrence interval flood event, assumed to represent bank-full discharge. This rule essentially scales the rate of bank erosion to the size of the river as both size and discharge increase with catchment area. Other factors, such as the extent of riparian vegetation (Brooks, 1999; Abernethy and Rutherfurd, 2000; Prosser et al. 2001), stream power, and the proportion of erodible material (bedrock vs. alluvium) also influence bank erosion. Incorporating these factors Prosser el al., (in press) developed the following bank erosion rule:

( )( )xFxxxx ePRSgQBE 008.01100002.0 −−−= ρ

28

where ρ is the density of water, g is the acceleration, Q is mean annual flow in Ml/y, S is the river bed slope, PR is the proportion of bank with intact riparian vegetation, and F is the floodplain width in m. This rule has been used to estimate bank erosion in the Wingecarribee Catchment.

Riverbed slopes were derived from the 25m x 25m DEM. Floodplain extent was mapped from aerial photographs (Figure 3.3). Floodplains are defined as areas adjacent to the stream line with a slope of < 2o. The extent of riparian vegetation was determined from the land use map supplied by the SCA. Riverbank heights in the alluvial plain areas were determined by field inspection to be 1.5-2 m. Channel banks in the lower sandstone area generally consist of exposed bedrock.

Figure 3.3: Floodplain extent (shown in purple) in the Wingecarribee catchment mapped from aerial photographs

The predicted spatial pattern of bank erosion is shown in Figure 3.4. The highest erosion rates are predicted for the areas with the most extensive floodplains and the steeper lower reaches of the catchment. Over the entire catchment stream bank erosion is predicted to generate 7800 t/yr of sediment, 50% or 3900 t/yr of which is assumed (as it was for gullies discussed above) to contribute to the fine sediment load. Stream bank erosion rates are predicted to be highest in the alluvial plains area downstream of the Wingecarribee Reservoir. Numerous examples of stream bank erosion were observed in this area during a field inspection carried out in February 2003, as illustrated in Photographs 3.1 and 3.2.

29

Photograph 3.1. Bank erosion downstream of Wingecarribee Reservoir

Photograph 3.2: Bank erosion downstream near Bong Bong Bridge

30

Figure 3.4: The predicted spatial pattern of river bank erosion in the Wingecarribee Catchment

3.4 The relative contributions of hillslope and channel erosion

The estimates given above predict that gully and stream bank erosion contribute c.5735 t of fine sediment to the drainage network each year. Hillslope erosion is predicted to contribute c.340t/yr, or c.6% of the total sediment input. In this section we show that this estimation is supported by fallout radionuclide data obtained from sediment samples collected from throughout the stream network.

Fallout radionuclides (Cs-137 and Pb-210) have been widely used to determine the relative contribution of hillslope and channel erosion to stream sediments (Wallbrink et al., 1993, 1999; Walling and Woodward, 1992). More information about the application of these methods is given in the report prepared for the SCA by CLW (Tracing Techniques for Sediment and Associated Substances, 2001).

Fallout 210Pb is a naturally occurring radionuclide, formed through the radioactive decay of 222Rn gas. The parent of 222Rn is 226Ra, part of the 238U decay series. These radionuclides are present in all soils. Some 222Rn gas escapes from the soil into the atmosphere where it decays to 210Pb. This 210Pb is then deposited on the soil surface, primarily by rain (Wise 1980). The maximum concentrations of fallout 210Pb in soils are found at the surface. Concentrations then decrease to detection limits at about 100 mm depth.

Cs-137 is a product of atmospheric nuclear weapons testing that occurred during the 1950-70s. Initially the distribution of this nuclide in the soil decreased exponentially with depth, with the maximum concentration at the surface. However, due to processes of diffusion the maximum concentration is now generally found just below the surface in undisturbed soils. The bulk of the activity of this nuclide is retained within the top 100 mm of the soil profile.

As both fallout radionuclides are concentrated in the surface soil, sediments derived from sheet and rill erosion will have high concentrations of both nuclides, while sediment eroded from gullies or channels have little or no fallout nuclides present. By measuring the concentration in suspended

31

sediments moving down the river, and comparing them with concentrations in sediments produced by the different erosion processes, the relative contributions of each process can be determined.

Recently deposited sediment samples were collected from 52 locations in the stream network during low flow conditions. Site numbers and locations are show in Figure 3.5. Cs-137 and Pb-210 activity concentrations were measured in 14 samples (Table 3.1) by high resolution gamma spectrometry. The measurement methods are described in detail by Murray et al., (1987). Measurements were made on samples fractionated to recover the <10µm fraction (clay and fine silt). Most of the nutrients and contaminants associated with sediment are in this fraction.

Concentrations of both fallout nuclides in all of the samples, except those from sites 31 and 34 (discussed below), are low (Figure 3.6). Typical concentrations for SE Australia measured in fine sediments eroded from pasture land, forested areas, cultivated land and channel banks are also shown in this figure and in Table 3.2 (data supplied by Dr Wallbrink, CSIRO). With the exception of samples from sites 31 and 34, values range from 0.2 + 0.1 to 4.0 + 0.9 Bq/kg, with a mean of 1.7 + 0.3 Bq/kg for 137Cs, and -7.8 + 3.1 to 29.4 + 9.3 Bq/kg with a mean of 6.9 + 3.0 Bq/kg for 210Pb excess.

Figure 3.5: Map of the Wingecarribee catchment shown sediment sampling sites and location numbers

32

Table 3.1: Radionuclide measurements on the <10um fraction from sediment samples collected from the Wingecarribee Catchment. Results are reported in Bq kg-1. Uncertainties (se) are one standard error.

Site 226Ra se 232Th se 210Pbex se 137Cs se

3 31.0 1.2 34.5 1.4 7.0 6.8 4.0 0.9

6 51.4 0.5 71.2 0.6 -2.8 2.2 0.2 0.1

8 48.5 1.3 72.5 1.3 6.3 7.5 3.3 1.0

11 50.8 0.9 62.8 1.1 6.3 7.5 0.9 0.4

13 45.8 0.8 62.4 0.8 4.5 4.6 0.9 0.5

14 46.3 1.3 68.4 1.7 -7.2 6.3 0.6 0.8

15 49.9 0.7 65.8 1.0 -4.2 3.7 0.8 0.4

18 46.5 0.8 60.4 1.0 19.6 4.4 2.0 0.6

19 50.1 0.9 67.5 1.1 14.6 4.9 2.3 0.7

23 57.9 1.4 89.7 1.2 29.4 9.3 1.3 0.6

28 49.1 0.6 63.0 0.7 -7.8 3.1 2.4 0.3

31 81.9 5.6 127.4 5.4 79.1 37.0 2.4 2.7

34 71.2 2.7 124.4 3.8 129.4 17.8 0.0 1.3

45 55.1 0.9 103.9 1.3 16.6 4.6 1.3 0.6

Figure 3.6: Concentrations of fallout radionuclides measured in the <10um size fraction of sediment samples collected from the stream network, and typical values from SE Australia for forest, pasture, cultivated land and channel banks. Data from samples 31 and 34 are identified on the plot. See text below for discussion.

137Cs Bq kg-1

-10 0 10 20 30 40 50 60

210 P

b B

q kg

-1

-100

0

100

200

300

400

500

basalt

mudstone

sandstone

main channel

sources

Forested

pasture land

channel erosion

cultivated land

34

31

33

Using the mixing model developed by Wallbrink et al. (1998), the relative contribution from uncultivated lands, cultivated lands, and channel banks can be determined as follows. If Cu, Cc, Cb and Pu, Pc, Pb represent the 137Cs and 210Pbex concentrations from uncultivated, cultivated, and channel bank sources and Cs and Ps represent the respective total concentrations of 137Cs and 210Pbex on suspended sediments, then

A.Cu + B.Cc + C.Cb = Cs

A.Pu + B.Pc + C.Pb = Ps

A + B + C = 1

where A, B, and C represent the relative contributions from uncultivated lands, cultivated lands and channel banks, respectively.

The radionuclide concentrations in fine sediment from uncultivated lands, cultivated lands, and channels/gullies used to calculate the relative contributions are given in Table 2. While uncultivated areas of the catchment include pasture land and forested areas, the typical pasture land value has been used for the uncultivated lands (consequently estimates of the hillslope component will be a maximum). As the typical radionuclide concentration in fine sediment eroded from forested areas is higher than those from pasture land, the estimated contribution from the uncultivated areas will be a maximum.

Using these parameter values we calculate that c.95+5% of the sediment in the stream network is derived from channel erosion, which is consistent with the estimate of c. 94% predicted by the SedNet model as previously discussed.

Table 3.2. Typical radionuclide concentrations in fine sediment from uncultivated lands, cultivated lands, and channels/gullies used to calculate relative contributions to sediment sampled from the stream network. The subscripts are standard errors on the mean.

Location Radionuclide Parameter Concentration (Bq kg-1)

Uncultivated lands 137Cs Cu 30 3

Cultivated fields Cc 18 2

Channel erosion Cb 0.6 0.1

Wingecarribee Sediments Cs 1.5 0.32

Uncultivated lands 210Pbex Pu 370 26.0

Cultivated fields Pc 120 5.9

Channel erosion Pb 2.7 0.3

Wingecarribee Sediments Ps 3.6 2.7

Activity concentrations of 137Cs in the samples from sites 31 and 34 are also low (2.4 + 2.7 and 0.0 + 1.3 Bq/kg respectively), again indicating a dominance of channel erosion (>90%). However, the 210Pbex values are higher (79.1 + 37.0 to 129.1 + 17.8 Bq/kg respectively) than would be expected from any of the sources given the 137Cs activity concentrations. Wallbrink et al., (2002) made a similar observation about sediment in the Brisbane River, and argued that the elevated 210Pbex values were due to the addition of 210Pb by direct fallout in the channel. The higher 210Pb values are indicative of these sediments having resided in the channel for several years because, as described above, 137Cs production ceased after the 1970s, while 210Pb deposition is continuous. These samples

34

come from a relatively undisturbed forested sandstone area of the catchment. It would be expected that sediment residence times would be longer in this region.

In summary, we conclude that channel erosion is the dominant source of sediment.

3.5 The spatial sources of sediment

A variety of chemical and physical tracer techniques can be used to investigate the sources of sediments and nutrients delivered to river systems. These tracing techniques all involve measuring parameters that provide a 'fingerprint' to distinguish one source of sediment/nutrient from another. In this study we have used major and trace element geochemistry and radiochemistry to distinguish sediment derived from different areas of the catchment. The use of geochemistry as a fingerprinting tool is founded on the premise that differences in parent rock material produce soils and sediment with distinctly different minerals, and thus different elemental composition (Dyer 1998). These differences in geochemical composition provide a means of differentiating source areas based on different rock types.

The distribution of rock types in the Wingecarribee catchment is dominated by Triassic age sandstones in the west, and mudstones in the east (Figure 3.7). The other main rock type in the eastern part of the catchment is Tertiary basalt that mainly outcrops around the catchment boundaries. The western margin of the catchment has a deeply incised drainage network where older sedimentary rocks (shale and siltstone) are exposed.

Figure 3.7: Geology of the Wingecarribee Catchment

The sediment samples collected from the stream network have been grouped into four categories based on sampling location and rock type:

• Main channel: samples collected from along the main channel of the Wingecarribee River below the reservoir.

35

• Main source areas –

o Basalt: samples collected from areas that drain soils formed from basaltic rock

o Mudstone: samples collected from areas that drain soils primarily formed from mudrock

o Sandstone: samples collected from areas that drain soils primarily formed from sandstone

Note that further subdivision of the catchment on the basis of rock type would be possible with a more intensive sampling exercise.

Major element concentrations were determined in the <10um fraction from all of the samples (Appendix 1). Trace element (Appendix 1) and radionuclide concentrations (Table 3.1) were determined on all of the samples in which sufficient <10 um material remained following the major element determination.

The 226Ra to 232Th data show a clear separation between samples collected from each of the source areas (Figure 3.8). These data clearly illustrate the potential for distinguishing sediment from these three source areas using lithogenic radionuclides. Unfortunately, sufficient <10 um material to enable gamma spectral analysis was recovered from only three of the main channel sediments (sites 8, 13 and 15, Figure 3.5). These are from the reach just below the Reservoir to just downstream of Berrima. All of the samples fall within the concentration ranges of samples collected from the

232Th Bq kg-1

0 20 40 60 80 100 120 140

226 R

a B

q kg

-1

0

20

40

60

80

100

basalt

mudstone

sandstone

main channel

Figure 3.8: The 226Ra to 232Th data from the <10um fraction of sediment samples collected from the Wingecarribee Catchment

36

mudstone source area, indicating little or no contribution from the basalt areas, and a dominance of material derived from the mudstone area of the catchment.

This conclusion is supported by the major element and trace element data (Appendix 1: Figure A2 a, b and c). Sediment samples from the basalt areas are clearly distinguishable from those from the mudstone and sandstone areas by higher Fe2O3, TiO2, ZrO2, CuO, Nb2O, and lower SiO2 and Rb2O. Major and trace element concentrations in samples from the mudstone and sandstone area tend to overlap.

However, these source areas are separated in the plots of Rb2O vs. Al2O3 (Figure 3.9). Data from the main channel sediments, with the exception of concentrations of MnO, CaO, K2O and Na2O, tend to fall in the ranges defined by the sediment from the mudstone area (shown in Appendix 1 Figure A1). Differences between MnO, CaO, K2O and Na2O concentrations in the main channel sediment, and those in the source area sediments, results from mineral fractionation. The main channel sediment samples contain less weathered mineral components as indicated by the chemical index of alteration (Table A1).

Rb2O vs. Al2O3 (Figure 3.9) provides the best means of separating the three source areas using trace elements. These elements are not significantly affected by the mineral fractionation that is occurring in the main channel. Data from the 7 main channel sediments analyzed for trace elements from sites 7, 8, 9, 10, 13, 26 and 50 (at the outlet of the catchment) all fall in the field defined by the mudstone data. These data indicate that sediments derived from the mudstone area of the catchment dominate the supply of sediment to the main channel below the Reservoir, and that the basalt areas, and the forested sandstone areas are not significant sources of fine grained sediment.

In summary the lithogenic radionuclide, major and trace element data indicate that the <10 um fraction in the sediment samples collected from along the main channel below the Reservoir are not derived from the basalt soils. The radionuclide data and Rb2O vs Al2O3 data indicate that the samples are dominated by sediment derived from the mudstone areas of the catchment.

Al2O3 wt%

0 5 10 15 20 25 30 35

Rb2

O u

g/g

0

50

100

150

200

250

basalt

mudstone

sandstone

main channel

Figure 3.9: Rb2O and Al2O3 concentrations in the <10um fraction of sediment samples collected from the Wingecarribee Catchment

37

3.6 Assessment of the fine sediment load leaving the catchment

Gully and stream bank erosion are predicted to input c.5735 t of fine sediment to the drainage network each year. Hillslope erosion is predicted to contribute c.340t/yr, giving a total sediment input of 6075 t/yr. Not all of this sediment will be transported out of the catchment. Some will be trapped in the Wingecarribee Reservoir, in the weir pools at Berrima, Bong Bong and Merima, and some will be deposited on the flood plains.

These losses have been predicted using the SedNet budget model to be 660t/yr deposited in the weirs, Wingecarribee Reservoir, and on floodplains. The total fine sediment yield from the catchment is therefore predicted to be 5425 t/yr.

The water quality data can also be used to estimate the sediment load. There are 155 measurements of suspended sediment which can be coupled with instantaneous flow data at Berrima Weir. Flow at the time of sampling was not record, so we have used the instantaneous flow values for 9.00am each day to estimate the sediment load, such that

sediment load (kg/d) = flow at 9am (Ml/d) x suspended sediment concentration (mg/l)

This equation has been used to convert the sediment concentration data to daily sediment load and is plotted against daily flow in Figure 3.10. A power law fit to the data presented in Figure 3.10 (equation below) was used with the daily flow record at Berrima Weir to estimate an the annual sediment yield at Berrima Weir to be in the range of 335 to 4480 t/yr with a best estimate of 1270 t/yr .

Log (sediment load) = 1.31±0.05 x (Log (flow)) -2.71±0.43 R2= 0.83

Daily Flow Ml/day

0.1 1 10 100 1000 10000

Sed

imen

t loa

d t/d

ay

0.0001

0.001

0.01

0.1

1

10

100

1000

Figure 3.10: Suspended sediment load vs instantaneous flow at site E322, Berrima weir Solid lines show the linear regression and 95% confidence limits.

38

This result agrees well with the SedNet predicted load at Berrima weir of 1100 t/yr. The mean annual flow at the outlet of the whole catchment is 2.4 times that at Berrima Weir. If we assume that the sediment yield increases in proportional with flow, the annual sediment yield for the entire catchment is estimated to be in the range of 804 to 10752 t/yr with a best estimate being 3050 t/yr. This is consistent with the SedNet prediction of 5425 t/yr.

3.7 Comparison with other catchments in SE Australia

The catchment load estimates are lower than sediment yields determined for other catchments in the southeastern region (plotted in Figure 3.11; Wasson, 1994). For this region pre-European sediment yields (SY, t/yr) as a function of catchment area (A, km2) have been estimated to be:

SY=1.6 A0.79 (r2=0.85) (7)

This relationship was determined using stratigraphic records from 11 catchments (all less than 500 km2) in the Southern Tablelands of New South Wales (Wasson 1994). From this relationship we estimate the pre-European sediment yield from the Wingecarribee catchment was c.300 t/yr, or c.10 times less than the present yield.

Catchment area (km2)

0.01 0.1 1 10 100 1000 10000 100000

Sed

imen

t yie

ld (

t/yr)

1

10

100

1000

10000

100000

1000000

a. Pre-1820

b. 1945-1994 SEDNET

Water Quality data

Figure 3.11: Estimates of sediment yield (t/yr) plotted against catchment area (km2) for the periods (curve a) pre-1820 (Wasson, 1994), (curve b) and 1945 to 1994 (a subset of the Southern Tablelands data Wasson, 1994). Solid lines are lines of best fit. Dashed lines show 95% confidence limits. Sediment yield estimates from the Wingeecarribee catchment are shown in green and brown.

39

3.8 The sediment budget and implications for management

The sediment budget is summarized in Figure 3.12. Gully and stream bank erosion are predicted to input c.5735 t of fine sediment to the stream network each year, hillslope erosion is predicted to contribute c.340t/yr, c.190 t/yr is deposited in the Reservoir and weirs, and c.470 t/yr on the floodplains. The net export from the catchment is c. 5400 t/yr. .

Management action to reduce sediment delivery needs to focus on channel erosion (gullies and stream banks) particularly in the mudstone areas of the catchment. The management effort should concentrate on improving and protecting riparian vegetation along channels in the principal source areas. However, it should be noted that most sediment will be delivered during large infrequent floods (see Figure 3.13), so management strategies must be robust enough to cope with erosion and sediment delivery in the majority of these events.

Figure 3.13.

Daily Flow data at Berrima weir since 22-08-75

Estimated daily sediment loads at Berrima for the same period. Note that most of the sediment is transported during infrequent flood events

Dis

char

ge M

l/day

0

10000

20000

30000

40000

50000

Days since 22-08-75

0 2000 4000 6000 8000 10000

Sed

imen

t loa

d t/d

ay

0

500

1000

1500

2000

2500

Sediment (t yr-1)

-6000 -4000 -2000 0 2000 4000 6000

Hillslope

Gully

Riverbank

Net export

Flood plains

Reservoir

Figure 3.12: Sediment budget for the Wingecarribee catchment

40

3.9 References

Abernethy, B. and I.D. Rutherfurd. (2000). "The effect of riparian tree roots on riverbank stability." Earth Surface Processes and Landforms. 25, 921-937.

Brooks, A. (1999), "Lessons for river managers from the fluvial tardis." In I.D. Rutherfurd and R. Bartley, editors, Second Australian Stream Management Conference: The Challenge of Rehabilitating Australia's Streams. Cooperative Research Centre for Catchment Hydrology. Melbourne.121-128.

Dyer, F., (1998). Source of sediment and associated phosphorus for Tarago reservoir. Phd thesis, Dept. Civil and Environmental Engineering, Univ. of Melbourne

Littleboy, M., Silburn, D.M., Freebairn, D.M., Woodruff, D.R., Hammer, G.L., and Leslie, J.K., 1992. Impact of soil erosion on production in cropping systems. I. Development and validation of a simulation model. Australian Journal of Soil Research, 30(5): 757-774.

Lu, H., Gallant, J., Prosser, I., Moran, C., and Priestly, G., 2001. Prediction of sheet and rill erosion over the Australian Continent, incorporating monthly soil loss distribution. Technical Report 13/01, CSIRO Land and Water, Canberra.

Moore, I.D. and Burch, G.J., 1986. Physical basis for the length-slope factor in the universal soil loss equation. Soil Science Society of America Journal, 50, 1294-1298.

McCool, D.K., Foster, G.R., Mutchler, C.K., and Meyer, L.D., 1989. Revised slope length factor in the Universal Soil Loss Equation. Transactions of the American Society of Agricultural Engineers, 32: 1571-1576.

McIvor, J., Williams, J., and Gardener, C., 1995. Pasture management influences runoff and soil movement in the semi-arid tropics. Australian Journal of Experimental Agriculture, 35, 55-65.

Murray A. S., Marten R., Johnston A., and Martin P. (1987) Analysis for naturally occurring radionuclides at environmental levels by gamma spectrometry. Journal of Radioactive and Nuclear Chemistry 115, 263-288.

Olley, J.M., Murray, A.S., Mackenzie, D.M., Edwards, K. (1993) “Identifying sediment sources in a gullied catchment using natural and anthropogenic radioactivity.” Water Resources Research, 29, 1037-1043

Prosser, I.P., Winchester, S.J. (1996) “History and processes of gully initiation and development in Australia.” Zeitschrift für Geomorphologie Supplement Band, 105, 91-109.

Prosser, I.P., I.D. Rutherfurd, J. Olley, W.J. Young, P.J. Wallbrink, and C.J. Moran. (2001), "Large-scale patterns of erosion and sediment transport in river networks, with examples from Australia." Marine and Freshwater Research. 52, 81-99.

Prosser, I., and Hughes, A., in press. Gully and channel bank erosion. CSIRO Technical Report.

Renard, K.G., Foster, G.A., Weesies, D.K., McCool, D.K., and Yoder, D.C., 1997. Predicting soil erosion by water: A guide to conservation planning with the revised universal soil loss equation . Agriculture Handbook 703, United States Department of Agriculture, Washington DC.

Rosewell, C.J., 1993. SOILOSS - A program to assist in the selection of management practices to reduce erosion. Technical Handbook No. 11 (2nd edition) Soil Conservation Service of NSW, Sydney.

Rutherfurd, I. (2000), “Some human impacts on Australian stream channel morphology”. In Brizga, S. and Finlayson, B. River Management: The Australasian Experience. Chichester, John Wiley & Sons, 2-52.

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Rosewell, C.J., 1997. Potential Sources of Sediments and Nutrients: Sheet and Rill Erosion and Phosphorus Sources. Australia: State of the Environment Technical Paper Series, Department of the Environment Sport and Territories, Canberra.

Scanlan, J., Pressland, A., and Myles, D., 1996. Runoff and soil movement on mid-slopes in north-east Queensland grazed areas. Rangeland Journal, 18, 33-46.

Wischmeier, W.H., 1978. Predicting Rainfall Erosion - A Guide to Conservation Planning. Agriculture Handbook 537, United States Department of Agriculture, Washington DC.

Wallbrink, P.J., and Murray A.S. (1993) Use of fallout radionuclides as indicators of erosion processes. Hyd. Proc., 7, 297-304.

Wallbrink, P.J., Murray, A.S. and Olley, J.M. (1999) Relating suspended sediment to its original soil depth using fallout radionuclides. Soil Sci. Soc. Am. J, 63/2, 369-378.

Wallbrink, P.J., Murray, A.S., Olley, J.M. & Olive, L.J. (1998). Determining sources and transit times of suspended sediment in the Murrumbidgee River, New South Wales, Australia, using fallout 137Cs and 210Pb. Water Resour. Res. 34(4), 879-887

Wallbrink, P.J. Olley, J.M. and Hancock, G. (2002) Estimating residence times of sediment in river channels using fallout Pb-210, In The structure function and management implications of fluvial sedimentary systems, eds. Dyer, F. Thoms, M. and Olley, J.M IAHS red book series, No 276. p 425-432

Walling, D.E., and Woodward, J.C. (1992) Use of radiometric fingerprints to derive information on suspended sediment sources. In Erosion and Sediment Transport Monitoring Programmes in River Basins, J. Bogen, D.E. Walling and T.Day (eds.) IAHS Publ. 210: 153-164.

Wise, S.M. (1980) Caesium-137 and Lead-210: A review of the techniques and some applications in geomorphology. In Timescales in Geomorphology. (eds. Cullingford, R.A. Davidson, D.A. and Lewin, J.) John Wiley and Sons Ltd. 109-127.

Wasson RJ. 1994. Annual and decadal variation of sediment yield in Australia, and some global comparisons. IAHS publication 224: 269-279.

Wasson, R.J., Mazari, R.K., Starr, B. & Clifton, G. (1998). The recent history of erosion and sedimentation on the Southern Tablelands of southeastern Australia: sediment flux dominated by channel incision. Geomorphology 24, 291-308

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Chapter 4 Nutrients in the Wingecarribee Catchment

By Jon Olley, Bill Young, Gary Caitcheon and Chris Chafer

In this chapter analysis of water quality and flow data, and an assessment of the point and diffuse nutrient sources are used to determine the major source of nutrients to the Wingecarribee River.

4.1 Where do the nutrients come from?

Nutrients can be delivered to streams in either in a dissolved form or in association with organic or mineral particles. The fraction delivered in each of these forms depends on the land-use, land-cover, hydrology and the nature of the drainage. Water and sediment chemistries and biological activity in the stream then control the form in which the nutrients are transported through the drainage network.

Nutrient inputs into the aquatic environment are classified as point or non-point/diffuse sources. Point sources such as municipal sewage treatment plants, and effluent release from stock feedlots discharge into the aquatic environment at defined points, usually through drains or pipes. In contrast, non-point sources such as runoff from agricultural land enter the aquatic environment by numerous pathways and over wide areas. In heavily populated or industrialized areas point sources can dominate nutrient delivery. In catchments such as the Wingecarribee with significant areas of both urban and rural land use, it is more difficult to determine the dominate nutrients sources. We have used a combination of water quality data, modelling and isotopic tracing techniques to determine the loads and sources of nutrients in the Wingecarribee catchment. In the following sections we first determine the nutrient load leaving the catchment, then assess the nutrient sources.

4.2 Assessment of the particulate nutrient load

The sediment load estimated above was 5725 t/yr. Average phosphorus and nitrogen concentrations on the <10µm fraction from the sediment samples collected from along the main channel are 0.15±0.02 wt% P and 0.04±0.01 wt% N. From these values we calculate that the particulate nutrient loads leaving the catchment are 8.6±1.1 t/yr P and 2.3±0.6 t/yr N. The estimate for the nitrogen load is considered to be a minimum for total particulate nitrogen because much of the particulate nitrogen is expected to move as lighter, coarse, organic matter, that will not have been included in the low flow sediment samples.

4.3 Estimating nutrient loads

There are 72 paired filterable and total phosphorus measurements on samples collected from Berrima Weir (water quality station E332). Filterable P concentrations range from 0.003 to 0.6 mg/L with an average of 0.026±0.008 mg/L (n=72). Total P concentrations range from 0.008 to 0.62 mg/L with an average of 0.075±0.008 mg/L (n=99). The filterable to total P ratio, which ranges from 0.075 to 1.00, with an average of 0.31±0.02 (n=72), is not significantly correlated with flow. If we assume that the filterable P equates to dissolved P, this then indicates that on average c.31±2 % of the P load is transported in the dissolved phase. The particulate P load was calculated from the suspended sediment load and major element chemical data to be 8.6±1.1 t/yr. Combining this with the dissolved to particulate ratio gives a dissolved P load of c. 3.6 t/yr, and a total P load of c. 12 t/yr at the catchment outlet.

There are only 8 paired filterable and total nitrogen measurements on samples collected from Berrima Weir. Filterable N concentrations range from 0.11 to 1.1 mg/L, with an average of 0.58±0.10 mg/L (n=8). Total N concentrations range from 0.36 to 2.60 mg/L with an average of 1.12±0.04 mg/L (n=99). The filterable to total N ratio ranges from 0.12 to 0.95 with an average of 0.70±0.10 (n=8). If we assume that the filterable N is dissolved and there is no correlation with flow, this then equates to about c.70±10 % of the N load being dissolved. Given the uncertainties

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about the estimated particulate N load, we have not used these data to estimate the total or dissolved nitrogen load.

4.4 Nutrient loads determined from the water quality data

Estimates of mean annual P and N loads for the catchment were determined using nutrient concentration data from E332 and the flow record from the Berrima Weir gauge for the period August 1975 to December 2001. Flow at the time of sampling was not recorded, so we have used the instantaneous flow values for 9.00am each day to estimate the nutrient load such that

nutrient load (kg/d) = flow at 9am (Ml/d) x nutrient concentration (mg/l)

This equation has been used to convert the nutrient concentration data to daily nutrient loads, and these are plotted against daily flow in Figure 4.1a and b. A power law fit to the data presented in figure 4.1a and b (using the equations below) was used with the daily flow record at Berrima Weir to estimate an the annual TN load at Berrima weir to be in the range of 45 to 125 t/yr, with a best estimate of 75 t/yr; and an annual TP load in the range of 2.8 to 27.5 t/yr with a best estimate of 8.7 t/yr.

Log (N load) = 1.066±0.0.017 x (Log (flow)) -0.107±0.170 R2= 0.96

Log (P load) = 1.201±0.038 x (Log (flow)) -1.482±0.373 R2= 0.86

The ratio of estimated mean annual flows between the whole catchment and Berrima Weir is 2.4, giving an estimated annual catchment TP load in the range of 6.7 to 66 t/yr with a best estimate of c.21 t/yr. The estimate from the suspended sediment load, major element chemical data, and the dissolved to particulate ratio calculated above, is c. 12 t/yr, which falls within this range. Similarly the TN annual load at the outlet of the catchment is estimated to be 180 t/yr, with a range of 108 to 300 t/yr.

The estimates based on the water quality data (21 t/yr TP and 180 t/yr TN) are taken to be the best estimates of nutrient loads available at present.

Flow Ml/d

0.1 1 10 100 1000 10000

TN

load

kg/

d

0.1

1

10

100

1000

10000

Flow Ml/d

0.1 1 10 100 1000 10000

TP

load

kg/

d

0.01

0.1

1

10

100

1000

10000

Figure 4.1: (a) TN load (b) TP load versus instantaneous flow at Berrima Weir (water quality station E332)

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4.5 Assessment of the nutrient sources

Point sources There are three sewage treatment plants in the catchment that discharge into the streams, located at Berrima, Bowral, and Moss Vale. Average annual nutrient loads from each of these have been calculated using measured treated waste discharge rates and nutrient concentrations (Table 4.1). There are two other major point sources in the catchment: the Moss Vale saleyards and the Boen Boe Piggery. No data has been obtained for the former point source, and the animal waste contributions from stock passing through the saleyards are assumed to be adequately accounted for in the calculations for diffuse sources detailed below. The piggery is in the northern half of the lower catchment. There are an estimated 7,700 pigs in the catchment (AWT, 2001), and it is assumed that these are all at the Boen Boe piggery. Intensive piggeries produce large volumes of waste with high levels of ammonia and phosphorus. The average annual load for the piggery was estimated from the number of pigs, the nutrient load per animal, the proportion of this nutrient load contained in surface runoff, and the nutrient reduction achieved by runoff management (Table 4.2). No specific details on runoff treatment for the piggery have been obtained, but it is assumed that in line with required practice, wastes are treated in aerobic ponds and then spray irrigated onto pastures.

In total, point sources yield c.2 t/yr TP and c.21t/yr TN, primarily as dissolved load. Point sources therefore account for c.30% of the dissolved phosphorus load and 17 % of the dissolve nitrogen load leaving the catchment. During low flow conditions these sources probably dominate the nutrient loads.

Table 4.1: Calculated nutrient loads from sewage treatment plants

NO3 NO2 NH3 TKN TN TP

Mean daily load (kg) 0.27 0.01 0.11 0.35 0.69 0.03

Stdev daily load (kg) 0.49 0.01 0.16 0.33 0.75 0.03

Berrima

Estimate mean annual load (T) 0.10 0.00 0.04 0.13 0.25 0.01



Bowral




Moss Vale


Table 4.2: Estimation of nutrient loads originating from the Boen Boe piggery

Piggery Nutrient Loads TN TP Reference

Rate (kg/pig/yr) 19 6.6 http://www.ext.nodak.edu/extpubs/ansci/dairy/as1023w.htm

Total Load (kg/yr) 146300 50820

% of load in runoff 7 7

Runoff load (kg/yr) 10241 3557.4

%Reductionvia management 80 80

Export (t/yr) 2.0 0.7

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Diffuse sources Total annual nutrient loads at the outlet of the catchment are estimated to be 180 t/yr N and 21 t/yr P, with 31±2% of the P and 70±10% N loads transported as dissolved phases. Point sources contribute c.2 t/yr TP and c.21 t/yr TN, primarily as dissolve loads. Diffuse sources therefore contribute c.15.0 t/yr particulate P, c.4 t/yr dissolved P, c.50t/yr particulate N and c.100 t/yr dissolved N. Potential sources for these diffuse loads include soil erosion, livestock, septic tanks, runoff from urban areas, and fertilizer from cropping land.

There are an estimated 5000 separate septic tank systems in the catchment (SWI, 1997), each handling, on average, the wastes for 2.5 people. There are 370 deer, 10150 sheep, 52900 cattle and c. 3580 horses in the catchment (AWT, 2001). Estimates of livestock waste nutrient content from http://www.ext.nodak.edu/extpubs/ansci/dairy/as1023w.htm have been used to determine the source loading from each of these sources (Table 4.3).

Table 4.3. Nutrient loads generated by humans (via septic tanks) and livestock in the Wingecarribee catchment Source Type

Number TP Loading kg/hd/yr

TN Loading kg/hd/yr

Total TP Production t/yr

Total TN Production t/yr

Human 12500 0.9 4.4 11 55 Sheep 10150 1.59 7.67 16 78 Cattle 52900 16.97 72.09 898 3814 Deer 370 3.18 15.33 1 6 Horses 3580 12.96 54.75 46 196 Total 972 4150

It is clear that the largest potential diffuse contributions from these sources to the nutrient budget of the Wingecarribee are likely to be from cattle, for which the total phosphorus load in wastes is 40 times the catchment export. Only a very small fraction of the nutrient load in livestock wastes is delivered to streams, however, if just 2% of the cattle waste was delivered to the stream lines this would account for all of the P exported from the catchment. In terms of risk management, controlling cattle access to the stream lines is an appropriate and important measure. At present we do not know how much each of these sources contributes to the diffuse load. Further work is required to determine the yield from each of these potential sources to the stream network.

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Chapter 5. Pathogen Budgets

By C. Ferguson, P. Beatson, D. Deere, R. Wasson and B. Croke

This chapter sets out the context and methodology used to develop a pathogen budget for catchments managed by the Sydney Catchment Authority (SCA). It summarises the development of the methodology and its application to the construction of a pathogen budget for the Wingecarribee sub-catchment.

5.1 Why construct a pathogen budget?

Construction of a pathogen budget enables watershed managers to quantify the origin, inactivation, deposition and movement of pathogens within a watershed, and is pivotal to sound pathogen management. It requires detailed knowledge and understanding of the sources of pathogens, the processes that influence their mobilisation and transport, and the factors that cause their inactivation/loss. These processes can be described in terms of “stocks” (pathogens) and “flows” (movement of pathogens). The flows within the watershed are influenced by the processes identified in the conceptual model and by proximity to the hydrological channel. Primary stocks and flows describe the processes related to the origin of pathogens within the watershed and the factors that determine their initial survival and transport. Secondary stocks and flows describe the processes that can store pathogens (sinks) and influence their subsequent fate and transport within the watershed (such as re-suspension of pathogen-bound sediments).

The conceptual model of catchment processes that affect pathogen fate and transport is used as the basis for the development of a mathematical model. The assumptions used in the calculation of the model are discussed in the context of existing data constraints. The methodology developed herein will subsequently be used as a template for the construction of pathogen budgets in other SCA sub-catchments.

5.2 Concept of constructing a Pathogen Budget

Stocks are sources and accumulations of contaminants (pathogens). The state of a catchment is best described in terms of stocks because they modulate the behaviour of the catchment by accumulating the difference between inflows and outflows to a particular part of a catchment or a particular process. By this means stocks create delays because they provide inertia to the system. Stocks therefore are the cause of disequilibrium in a catchment by decoupling rates of flow.

Flows are the rates of increase or decrease in stocks. The net flow into a stock is the rate of change of the stock. This statement has a precise meaning, and can be represented mathematically in the following integral equation:

Stock (t) = [ ]Outflow(s)Inflow(s) −∫t

t o

ds + Stock (to)

Where, Inflow(s) are the quantity of the inflow at any time between the initial time (to) and the current time (t). In differential form, the net change of a stock is the inflow less the outflow:

d (Stock) / dt = Inflow (t) - Outflow (t)

For example, a reservoir accumulates a stock of water (or pathogens) at a rate given by the difference between its inflows and outflows, beginning with an initial stock (to). If the inflow and outflow are constant, so too is the stock. If the outflow is greater than the inflow, the stock will be reduced, until eventually the initial stock (to) is also reduced to a new value which is in equilibrium with the inflow and outflow; assuming that the inflows and outflows are constant. If, however, the stock alone is known, there is a very large range of variation in inflows and outflows that can

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produce a particular behaviour of the stock. Information about inflows and outflows cannot therefore be derived from stocks alone.

The definition of sources and sinks raises an important matter of definition, and of use of budgets for management decisions. A catchment can be decomposed into any size, as required for a particular problem or issue. For example, a stock and flow analysis could be performed for an individual development application in the Warragamba Catchment, or for the entire Warragamba Catchment. In the case of the whole catchment, the sink may be Lake Burragorang, or stream sediment upstream of Warragamba Dam. For a development application for a feedlot, for example, on a hillside, the source may be domestic animals on the proposed feedlot, while the sink may be the creek at the bottom of the hill.

For sediment and nutrient budgets stocks are usually measured as amounts (e.g. kg or numbers) and flows as amounts per time (e.g. kg or number/second). Many budgets are mean annual accounts, averaged over decades to smooth out variability in individual flow events. However, for acute-acting hazards such as pathogens time series should be constructed from flows and therefore for changes in stocks. Such time series can be derived from monitoring known relationships with events, for example rainfall.

In constructing stock and flow analyses it is important to identify feedbacks as a step towards deeper understanding and to better decision rules for management. The most pervasive feedback control in a catchment is what is called a state-determined system. Stocks change only by inflows and outflows, but stocks also determine flows. For example, the stock of pathogens can become so depleted by the absence of domestic animals on a paddock that the flow (transport of pathogens to the stream) decreases the concentration of pathogens in the water. The stock controls the flow, with the flow rate in individual events being a function of both the stock size and the magnitude of the transporting event. Feedbacks and the resulting dynamics are only clearly seen where flow (and stock change) time series are available. Feedbacks in highly averaged budgets can be inferred but not readily visualised or analysed.

Delays occur when outflows lag behind inflows. In spatial systems such as catchments, delays are created by stocks and travel times. Stocks accumulate the difference between input and outputs. Travel times simply delay the arrival of, for example, an outflow from a stock as it travels down a river to become an inflow to a reservoir. Travel times can be thought of in terms of stocks and flows, as each reach of a river is a stock. Unless time series can be constructed for flows and stock changes, then delays, like feedback, can only be inferred. Delays come in various types, because a river network can be conceptualised as a large number of links, each being a stock and delay, a series of first-order material delays can be coupled together to analyse the entire flow system spatially. A second order material delay consists of two first-order delays in which the input to the second delay is the output from the first; and so on to higher order systems. While it is in principle possible to model a whole river network in this way, link by link, the accumulation of errors is likely to destroy its usefulness.

By decomposing a river network into links or into mega-links between large river junctions (so-called nodes), the stock and flows of the river and adjacent hillslopes, floodplains, fans, and point sources of contaminants can be analysed as a spatially distributed system. This is important for decision rules to determine the future of old land uses and the wisdom of new ones. By mapping, and storing in a GIS, the river network, floodplain widths and lengths, hillslope gradients, point sources and land uses, spatially distributed stock and flow analysis will provide managers with a significant input to decision rules. But even a lower spatial resolution, such as prioritising major sub-catchments in terms of their input of contaminants to downstream channels, is of use. This can be achieved by water quality monitoring and the application of tracers.

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5.3 Pathogen Stocks and Flows

Pathogen budgets should be constructed in two phases. The first phase addresses the total stocks of pathogens and their flows within the watershed. The second phase focuses on the proportion of the total stock that represents infectious pathogen units (IPU) capable of causing infection and/or illness in humans.

The primary sources of pathogen stock within watersheds are:

• Animals, including domestic, native and feral species; and,

• Human faecal effluent, including sewage treatment plants (STPs) and on-site septic systems.

The importance of these stocks are mediated by many factors including host prevalence and excretion rates, inter-host transfer rates, the presence of zoonotic pathogens, number of animals and the amount of faecal material generated. Primary flows are dominated by natural and riparian processes such as: preferential flow paths at the soil surface and sub-surface, rainfall intensity and duration, and surface runoff. Some of these processes also contribute to the inactivation of pathogens (accounted for as a decrease in stock) influenced by the factors listed in Table 5.1. Further complexity arises from the variable effect of factors such as natural organic matter (NOM), freezing and thawing, and pH, that can either increase or decrease stock.

The majority of secondary processes that generate stocks and flows within a watershed are human generated; farm management and urban development, but also include natural processes such as sediment re-suspension and interflow. Physical factors such as soil type, slope and distance from sources to waterways will determine the spatial location of sinks for pathogens. Secondary sinks for stocks include sediment in watercourses, retention ponds, ephemeral streams, wetlands, soil infiltration and ingestion by organisms higher in the food chain. Secondary flows include sediment re-suspension, overflow of retention ponds and wetlands, interflow, surface runoff, manure spreading and the introduction of new animal species to the watershed. Table 5.1 summarises the primary and secondary stocks and flows for pathogens in watersheds. The pathogen budget can be constructed by quantifying the primary stocks and flows and their subsequent loss as secondary stocks and flows as outlined in the equation below;

Yield = Primary Stock - Primary flows - Secondary Stock - Secondary flows

The initial pathogen budget should determine the total stock of microorganisms present in the catchment. Then the proportion of the total stock that represents infectious pathogen units (IPU) capable of causing infection in humans should be used to calculate an IPU budget. The analysis of watershed samples for pathogens can present difficulties for the construction of an IPU budget. The presence of viable-non-detectable (VND) states can lead to the underestimation of stock while lack of knowledge regarding the relationship between viability and infectivity may lead to overestimation of IPU stock. In a watershed management and risk assessment framework it is the IPU budget that should be used to guide the implementation of best management practices within the watershed.

In Table 5.1 the stocks in the left column are affected by the primary flow processes as represented by the arrows in the effect column. Stocks in the centre panel are affected by the secondary flow processes as represented in the effects column on the far right. Stocks and flows highlighted in bold are those selected as being the most important to quantify for the preparation of a first cut pathogen budget for the Wingecarribee sub-catchment.

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Table 5.1: Summary of stocks and flow processes for infectious pathogen units (IPU)

Primary Stock Primary flow processes Effect Stock Secondary flow processes Effect Native animals Visible light ↓ Channel sediment Sediment resuspension ↑ Feral animals Ultraviolet radiation ↓ Retention ponds Overflow ↑ Domestic animals Temperature ↓ Ephemeral ponds Interflow ↑ Humans STP’s Freeze/thaw ↑ or ↓ Dams Stormwater runoff ↑ or ↓ Humans Septics Moisture content ↑ Wetlands New host arrives ↑ Natural organic matter ↑ or ↓ Soil Imported animal feed ↑ Nutrients ↑ Food chain Manure/effluent treatment ↓ Vegetative matter ↑ Manure spreading ↑ PH ↑ or ↓ Riparian buffer zones ↓ Ammonia ↓ Animal access ↑ Soil type ↑ or ↓ Human access ↑ Preferential flow paths ↑ or ↓ Leakage of On-site systems ↑ Rainfall ↑ Tillage practices ↑ or ↓ Surface runoff ↑ Competition & predation ↓ Aggregation ↑ or ↓ Mesofauna ↓ Viable non detectable → Viable not infective ↓ Distance / Time ↓ Settling rate ↓

Bold text indicates those factors that were considered most significant, and that needed to be accounted for in a first-cut pathogen budget

5.4 Data Assumptions

The conceptual model highlighted the catchment processes that drive fate and transport (Figure 5.1), however not all of these processes need to be quantified to derive a first cut pathogen budget. Table 5.1 defined the stocks and flows of each catchment process and those deemed to be most significant were highlighted in bold. These stocks and flows needed to be quantified.

Dry Weather Conditions The model for low flows assumes no mobilisation of pathogens from land sources. The only contributing sources are STPs, septic tanks, and animals defecating into the stream. For low flow conditions, the transit time is sufficiently long that instream processes become important. The basic assumptions are:

1. Dry weather load = number of animals x daily manure production x protozoan concentration x access to stream x defecation in stream

2. Animal numbers are based on Wingecarribee land use allocation figures from Table 5.2 and estimates of stocking density. Wild pig estimates are 1 pig/km2 with area = 70km2. Calves are 10% of cattle numbers.

3. Manure production is based on Australian and / or US data, see Tables 5.4 and 5.5.

4. Protozoan numbers in faeces based on SCA Hotspots results see Tables 5.7 and 5.8.

5. Access to streams, Cows - 0.1 based on rough stream length per hectare estimate, wild pigs occupy unfenced areas, and always have access.

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6. Defecation into stream, cows and wild pigs - based on review in the following. Other animals tend to defecate away from stream. Direct input of faecal matter to the stream is dominated by wild pigs and cattle.

7. Survival (inactivation rate) coefficient (k:log10) of each pathogen in water = 0.01 for Cryptosporidium and 0.1 for Giardia. Survival coefficient in soil or manure 0.02 and 0.2 respectively.

8. Total number of on-site systems in Wingecarribee subcatchment = 5000. Assume 1% of tanks in the catchment are community tanks and the remainder residential.

9. Septic tanks for individual residences are used by 2.5 people, each using 160L of water per day. Based on Robertson/Oakdale study and AS1547. Assume water use per person of 16L, and an average use of 25 people per day for community systems.

10. Data available for concentrations of pathogens in septic tanks are representative of the normal conditions of the tanks. Protozoan concentrations in on-site systems as per Table 5.13.

11. Assume 50% of on-site systems fail, and 10% of these have connection to streams under low flow conditions. Assume volume of effluent surcharging is half the inflow in failing systems. Assume that all working on-site systems do not reach streams.

12. Protozoan production per day in raw effluent as per Table 5.9.

13. STPs have an effective constant release of treated effluent, with an effective constant concentration of pathogens, Table 5.11.

14. STP mean daily flow based on historical data, summarised in Table 5.12.

15. Settling of material is important – but only (oo)cysts bound to larger particles, assume total removal of such oocysts. Assume attachment rate of 70% for both Cryptosporidium and Giardia.

16. Assume no resuspension from sediments during low flow conditions.

17. Long travel time – of the order of 1 week to 1 month. For a 100km channel length (order of magnitude estimate) this corresponds to a flow velocity of 0.6 to 0.15km/hr (0.16 to 0.04m/s).

18. Total catchment daily flow based on single result of Ecowise snapshot sampling (measured at site 50).

Wet Weather Event-based budget The event-based budget estimates the material mobilised during significant rainfall events. Due to the short travel time during events, the instream processes are ignored; including resuspension. Assumptions from low flow conditions hold unless indicated otherwise below.

The main assumptions are:

1. Species contributing to land load are cattle, wild pigs, dogs, cats, horses and kangaroos. Assume 1 pet per household, 2.5 people per household and 39000 people in catchment – approx 16000 pets, of which ~ half are dogs, and half are cats. No land use allocation for sheep according to available data. 10 kangaroos/km2 over entire catchment – 1500 animals. Ignore horticulture – assume potatoes with no significant manure input. Calves are 10% of cattle numbers.

2. Faecal loads accumulate during antecedent dry period, protozoan content decays according to k values for soil and manure 0.02 for Cryptosporidium and 0.2 for Giardia.

3. Wet weather load = number of animals x daily manure production x antecedent

4. dry period length x protozoan concentration x die off rate x overland transport factor.

5. For rural areas, 5% of deposited material is transported into the stream network, for urban areas, 20% is transported (allowing for higher impervious fraction in urban areas).

6. For septics, 50% are connected to the stream during events. Assume 100% of effluent in failing systems is not absorbed during events.

7. STP overflow frequency and volume calculated with reference to estimated maximum STP capacity, see Table 5.12.

8. That part of protozoan load entering overflow is based on per volume proportion of the total inflow ie. overflow plus maximum capacity (see Table 5.12).

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9. Instream transit time is short – instream processes can be neglected. Assume load mobilised by surface runoff is sufficiently high to swamp any signal from resuspended material).

10. Catchment discharge for events from calculated runoff based on antecedent soil moisture, using the catchment moisture deficit (CMD) version of the IHACRES model.

11. Event load is calculated, not peak load during event. It assumes that the event was big enough to deposit into the stream anything that could be mobilised. Any extra flow would then be a dilution flow.

Worst Case Scenario budgets Assumptions from low flow and event conditions outlined previously remain unchanged unless indicated otherwise below.

1. Moss Vale RLPB total numbers x 0.67 (Wingecarribee proportion by area). Sheep included (note - no land use allocation for sheep in Table 5.3). Horses increased to compensate for unreported stock (where < 6 animals/property).

2. Access to streams, Cows - 0.5 seems as high as is possible.

3. Mobilisation and transport worst case for rural 10%, more seems unlikely. For urban areas estimated proportion of impermeable area equivalent to 50%.

4. Assume number of persons visiting community on-site systems doubles ie. 50 people

5. Assume protozoan concentrations in on-site tanks are equivalent to sewage treatment plant primary effluent concentrations, see Table 5.9.

6. Assume in dry weather conditions that 50% of systems are connected to streams.

7. Assume during wet weather events that all on-site system absorption trenches are completely flushed and all are connected to streams.

8. STP’s – Assume protozoan concentrations in treated effluent are equivalent to highest measured values for each plant.

9. Assume no binding of protozoans to particles and subsequently no loss due to settling.

10. Assume maximum antecedent dry period of 100 days based on rainfall recurrence of 1mm in 15 minute event size.

5.5 Application to Wingecarribee Sub-catchment

Protozoan parasites - Cryptosporidium and Giardia Cryptosporidium and Giardia are waterborne parasites that inhabit the intestinal tract of humans and animals, and can cause gastrointestinal illness. They are released into the environment (via faeces) in inactive forms that are resistant to many natural and artificial stressors (including disinfection). These are called oocysts for Cryptosporidium and cysts for Giardia. New hosts are infected by ingestion of (oo)cysts. As the number (dose) of (oo)cysts required to cause infection is small (about 10 organisms), and infected individuals shed (oo)cysts in very high numbers (typically 1010 over the course of the disease), and the (oo)cysts are highly persistent outside the host, their disease causing potential is quite high. Cryptosporidium oocysts are spherical, diameter 4 to 6 µm. Giardia cysts are larger and elliptical, size 8-12 by 7-10 µm.

Numerous methods of enumerating (oo)cysts have been used by researchers, but unless otherwise noted the numbers in this report refer to total counts, usually determined by immunofluorescent staining. Total counts may however include a proportion of non-viable (oo)cysts which are presumably non-infectious and therefore not of health significance. As criteria for determining viability/infectivity are disputed, it is prudent to adopt total counts as a conservative measure of protozoan contamination levels for a first attempt.

Organism counts are reported as a number of (oo)cysts detected in the sample. A variable proportion of (oo)cysts are lost by extensive preprocessing and (for water) concentration of samples, making determination of a recovery efficiency (Rrec) necessary. This is usually performed by inclusion of an internal standard in each sample. The base count is adjusted by Rrec to calculate the original concentration of organisms in the sample. Counts from samples with Rrec < 10% were

53

excluded from analysis. Recovery efficiencies were frequently low, especially for solid matrices such as faeces and sediments. In some of the remaining samples (n = ntot) the number of (oo)cysts was below the detection limit. These were scored as negative results (n = nneg), and were excluded from calculation of the mean and standard deviation. Geometric means (G.M., x) and standard deviations were generated from the remaining positive results after log transformation. Concentration of (oo)cysts in the overall population was estimated by multiplying the G.M. of positive samples by the fraction (F) of samples that were scored positive (npos / ntot). Some samples were positive but no numerical value could be determined as (oo)cysts were too numerous to count. Some attempt was made to incorporate these observations: F was adjusted by incrementing both npos and ntot .

Both parasites are present in surface waters. Disposal of human waste (via sewage effluent) is an obvious source for water contamination, but wastes from the non-human hosts that inhabit drinking water catchments (domestic livestock, feral and native animals) may prove more significant as much of it is untreated, and can enter streams directly or via runoff.

Loads in animal faeces - Domestic animals From preliminary calculations it seems probable that the most significant microbial input from stock will be from the proportion of waste defecated directly into the stream, which will probably overshadow the amount washed overland from pastures. To model this it is necessary to identify pastures which include a watercourse within their fence-line, and know how many of what type of animals would be in each of the pastures along the watercourses, and also know whether these pastures have other watering sources (dams, troughs) as this will affect the amount of time that animals spend at the stream. Finally, the mean concentration of the two protozoans in faeces of different host animals must be determined, and factored by standard manure production rates to determine the protozoan loads shed.

Stock numbers and density/distribution The Moss Vale Rural Lands Protection Board region subsumes but does not coincide with Wingecarribee catchment boundaries. Inquiries are in progress to obtain property boundaries, hectareage, stock numbers, and stock types for each of the farms that are intersected by a watercourse. CRES are determining fence lines and farm dams from aerial photography. In the 'Hotspots' Stage 3 report, animal populations were estimated on an area-proportional basis for the entire overlap of Moss Vale RLPB with the entire Warragamba Catchment (Table 2.1). Note that this area does not coincide with Wingecaribbee Catchment, it appears to exceed it by perhaps 50%. Land area devoted to primary production in Wingecarribee catchment is broken down in Table 5.3.

Table 5.2: Numbers of domestic animals (Australian Water Technologies, 2002)

Domestic Animals Number

Sheep 10,150

Beef cattle 43,800

Dairy cattle 9,100

Horses 3,580

Pigs 7,000

Goats 1,000

Deer (farmed) 370

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Table 5.3: Agricultural land use in Wingecarribee catchment (CSIRO Land and Water, 1999)

Land use Hectares

Extensive beef 0

Mixed sheep / beef 0

Dairy / intensive beef 1,839

Horticulture 1,644

Forestry 0

Manure production by domestic animals

Table 5.4: Manure production, Australia (Australian Water Technologies, 2002)

Animal kg manure.animal-1.d-1

Cattle* 27.25

Pigs 6.2

Sheep 1.0

Dogs 0.5

* average, includes meat and dairy, and young animals

Table 5.5: Manure production, USA (American Society of Agricultural Engineers, 1999)

Cattle Pigs Sheep/Goat Horse Fowl

A: g manure.kg animal-1.d-1 86 (dairy) 84 40 (sheep) 51 64 (layer)

58 (beef) 41 (goat) 85 (broiler)

62 (veal) 110 (duck)

B: mean live animal mass (kg) 640 (dairy) 61 27 (sheep) 450 1.8 (layer)

360 (beef) 64 (goat) 0.9 (broiler)

91 (veal) 1.4 (duck)

A*B: kg manure.animal-1.d-1

55 (dairy) 5.1 1.1 (sheep) 23 0.12 (layer)

21 (beef) 2.6 (goat) 0.078 (broiler)

5.6 (veal) 0.15 (duck)

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Table 5.6: Manure production, Netherlands (van Eerdt, 1998)

Manure Production Cattle Chickens

kg manure.animal-1.d-1

63 (dairy) 0.044 (layer <18wks)

9.6* (veal ≤24wks) 0.088 (layer ≥18wks)

0.030 (broiler ≤6wks)

* combined mass of urine and faeces (calf slurry)

Concentration of (oo)cysts in faeces, and daily production by domestic animals These Sydney Catchment figures apply to very small numbers of samples obtained over two short periods and therefore may not be representative of the true values. Dutch figures are annual means for large sample numbers.

Table 5.7: Concentrations of Cryptosporidium in faeces of domestic animals

Animal Incidence of

positives Count * log

G.M.(log S.D.)

Average content (oocysts.kg-1)

**

Per animal production (oocysts.animal-1.d-1)

Ref.***

CATTLE 33% (3/9) 1.1848 (0.7475) 5.1x103 1.4x105 1

Dairy cow 25% (1/4) 7.2x104 2.0x106 2

Dairy (>3yr) †1.2% (0/55) 3

Calf 83% (5/6) 2.3821 (1.7843) 2.0x105 1.1x106 (est) 1

Veal calf (≤24wks) ††69% (56/81) 2.82 4.6x105 4.7x106 3

SHEEP 100% (10/10) 1.9107 (0.6639) 8.1x104 8.1x104 1

DOMESTIC PIGS 100% (2/2) 2.4998 (0.0420) 3.2x105 2.0x106 1

HORSES 20% (1/5) 0.6882 (N.A.) 9.8x102 2.2x104 1

CHICKENS # 0% (0/2) 1

Broilers † 1.6% (0/42) 3

Layers (<18wks) 27% (4/16) 3.89 2.1x106 9.3x104 3

Layers (>18wks) 5% (2/50) 3.11 6.5x104 5.8x103 3

DOGS 50% (5/10) 2.8168 (0.9317) 3.3x105 1.6x105 1

CATS 17% (1/6) 1.2291 (N.A.) 2.8x103 5.6x102 §§ 1

* per gram, mean count among positive manure samples, negatives excluded (see Section 4.1). ** Estimate: average content = [incidence of infected herds x mean concentrations in infected manure samples], i.e. accounts for negative herds.*** 1 = Sydney Catchment Authority, combined data: 'Hotspots' stage 3 (Australian Water Technologies, 2002) plus preliminary results; 'Hotspots' stage 4 (Australian Water Technologies, 2002); 2 = (Australian Water Technologies, 2001);3 = Netherlands, (Medema et al., 2001).† Estimate - no positive samples, i.e. insufficient number of herds sampled to determine infection rate, and therefore oocyst content of faeces could not be determined either.†† Incidence varied by age - at 1-6 weeks 90% positive for Cryptosporidium; at 19-24 weeks 30%. For Giardia 97% and 57% respectively.# May be

avian Cryptosporidium species not infectious to humans.§§ Based on estimation of 200g faecal production per animal per day.

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Table 5.8: Concentrations of Giardia in faeces of domestic animals

Animal Incidence of positives

Count * log G.M.(log S.D.)

Average content

(cysts.kg-1) **

Per animal production

(cysts.animal-1.d-1)

Ref.***

CATTLE 70% (7/10) 2.3920 (0.9086) 1.7x105 4.7x106 1

Dairy cow 0% (0/4) 0 2

Dairy (>3yr) †3% (1/55) 2.30 6.0x103 3.8x105 3

Calf 75% (6/8) 1.8871 (1.9079) 5.8x104 3.3x105 (est.) 1

Veal calf (≤24wks) ††84% (68/81) 2.91 6.9x105 7.1x106 3

SHEEP 80% (8/10) 2.7364 (0.5201) 4.4x105 4.4x105 1

DOMESTIC PIGS 63% (5/8) 2.9222 (1.2548) 5.2x105 3.3x106 1

HORSES 25% (1/4) 0.9788 (N.A.) 2.4x103 5.5x104 1

CHICKENS # 25% (1/4) 0.4750 7.5x102 74 1

Broilers † 1.6% (0/42) 3

Layers (<18wks) 4.0% (0/16) 3

Layers (>18wks) 1.3% (0/50) 3

DOGS 89% (8/9) 3.4550 (1.9772) 2.5x106 1.3x106 1

CATS 20% (1/5) >3.602 (N.A.) >8x105 >1.6x105 §§ 1

* per gram, mean count among positive manure samples, negatives excluded.** Estimate: average content = [incidence of infected herds x mean concentrations in infected manure samples], i.e. accounts for negative herds.*** 1 = Sydney Catchment Authority, combined data: 'Hotspots' stage 3 (Australian Water Technologies, 2002) plus preliminary results; 'Hotspots' stage 4 (Australian Water Technologies, 2002); 2 = (Australian Water Technologies, 2001) 3 = Netherlands, (Medema et al., 2001).† Estimate - no positive samples, i.e. insufficient number of herds sampled to determine infection rate, and therefore oocyst content of faeces could not be determined either.†† Incidence varied by age - at 1-6 weeks 90% positive for Cryptosporidium; at 19-24 weeks 30%. For Giardia 97%

and 57% respectively. # May be avian Cryptosporidium species not infectious to humans.§§ Based on estimation of

200g faecal production per animal per day.

Calves: The Australian per animal shedding rate was similar to the Dutch. Concurrent analysis of bulk liquid calf pen wastes (calf slurry) at a Dutch treatment plant did however appear to indicate that measurements in manure were underestimating Cryptosporidium 15-fold, and Giardia by a factor of 2 (Medema et al., 2001). Waste from intensively farmed calves is usually collected and treated in the Netherlands, with ~80% removal of both parasites (Medema et al., 2001).

Swine: Only two samples provide this measurement. Surveys (Fleming et al., 1999) suggest that Cryptosporidium is at least as if not more prevalent in young pigs as in lambs. All piggery waste in Wingecarribee catchment is supposed to be contained for treatment on site. This presumably involves digestion in manure pits, with solids later spread to land and supernatant to effluent lagoons. Although treatment of swine waste reduces protozoan content, piggeries may be significant point sources in lagoon overflow events, or act as diffuse source via runoff from manure spreading. The major piggery in the district (Joadja, 7,700 head) is outside Wingecarribee catchment (Nattai R.).

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Sheep: As with cattle, the carriage of Cryptosporidium peaks in newborns and is associated with diarrhoeal disease. The mean total excretion during the course of illness is 4.83x109 oocysts per animal (Bukhari and Smith, 1997).

Horses: As a ruminant, horses are presumably susceptible to cryptosporidial diarrhoeal illness. Official numbers (Table 2.1) are a serious underestimate - recreational horse ownership in the region is high but properties with less than six horses are not recorded in stock surveys (Moss Vale RLPB, pers. comm.).

Loads in animal faeces – Native and Feral animals Cryptosporidium has been isolated from 79 mammalian species, usually identified as C. parvum (the human pathogen). Other cryptosporidia infest birds (30 species), reptiles (57), fish (9), amphibians (2) and molluscs (1) (Mager et al., 1998). It is likely that any warm-blooded animal can be considered a reservoir of Cryptosporidium capable of being passed to humans, whereas Giardia species seem more host-group specific, although zoonotic infections can occur - eg. ‘beaver fever’. Medema (1999) suggested this model for approximating the average concentration of parasites contributed by an animal species to a steady state, perfectly mixed water body:

C = L / Vd * (1 - e-t / τ)

Where, C = concentration of (oo)cysts in the water ( l-1 ) L = load of (oo)cysts from faeces (see below), per day ( d-1 ) Vd = volume of water entering reservoir, per day ( l.d-1 )

Vtot = total volume of reservoir ( l )

τ = mean retention time of water ( d ) = Vtot / Vd

t = length of contamination event ( d ), ie. Presence of animals in the catchment, as some animals are only present seasonally. For

permanent residents (where t > τ ) , C -> L / Vd .

Total daily load of oocysts (L) can be estimated as: L = number of animals * faecal output (g.d-1) * transport factor * oocyst conc. (g-1 faeces)

Transport factors were estimated from animal habits: eg. 0.5 to 1.0 for aquatic fowl; 0.01 for forest mammals that only approach water to drink.

An initial study (Long et al., 1994) of 644 animal scats in the protected catchment zone included both native and feral mammals, birds, reptiles and fish. It yielded no detections of Cryptosporidium, and only 11 detections of Giardia among waterbirds. Sensitivity of the detection methodology used was not reported, but was likely to be poor by today’s standards. A new survey has recently been commissioned by the SCA ('Hotspots' 4). The Eastern grey kangaroo is the most common large native mammal in the Sydney catchment zone. An 18 month study of over 2000 faecal samples indicated a prevalence rate of 5% for Cryptosporidium in winter to summer, with peaks during two autumns of 32% and 10.6% (Power et al., 2001). The range of concentrations in positives was from 1x104 to 2x1010 oocysts.kg-1. Annual means of 10% prevalence and concentration of 3x107 oocysts.kg-1 in positive samples were not provided but can be estimated from the data presented. The average daily excretion per animal is 0.2 kg (Australian Water Technologies, 2002), yielding an estimated load of 6x105 oocysts.animal-1.d-1.

Pigs are probably the most significant contributors among feral animals due to their large body size, significant populations, and habit of foraging and wallowing in and around water bodies. The population has been estimated at around 1 pig.km-2 in the protected catchment zone (SCA Pest

58

Control Group pers. comm.;(Australian Water Technologies, 2002). Atwill (1997) sampled Californian feral pig populations and determined overall prevalence of infection (during winter) of 5.4% for Cryptosporidium and 7.6% for Giardia. The prevalence among young pigs was higher, birth occurs all year around but peaks in spring and autumn. Infection was restricted to areas of high pig population (≥2 km-2). Concentrations in faeces were not measured, but the little data on domestic pigs may be relevant (see Table 5.7 and 5.8). Other feral animals of (economic) consequence in the catchment include rabbits, foxes, goats, dogs and cats (Australian Water Technologies, 2002). Waterfowl can carry both Giardia and Cryptosporidium. Carriage of anthropogenic strains by birds seems rare, and human infectivity of avian strains is debatable but the types are indistinguishable under routine monitoring and both will contribute to total counts - and as direct deposition of faeces is the norm, water birds may represent significant inputs to water bodies (Medema et al., 2001). A study of wild ducks (n = 69) in the southwestern United States (Kuhn et al., 2002) showed a carriage rate of 49% for Cryptosporidium sp. (mean 4.75x104, S.D. 2.70 x105 oocysts.kg-1); and 28% for Giardia sp. (mean 4.36x105, S.D. 3.53 x106 cysts.kg-1).

Loads in raw and treated sewage effluent Domestic sewage is treated on site or in STP’s, but some fraction enters waterways untreated (Figure 5.1). Data for efficiency of removal of protozoans by sewage treatment processes exists, which should allow rough estimation of loads in untreated discharges by back-calculation from treated effluent data (or vice versa).

STP's in Wingecarribee Catchment: There are 39,453 persons in Wingecarribee catchment (June 2002: Planning NSW estimate). Most residents are connected to one of five municipal sewage works, of which Bowral STP (capacity: 10,000 person-equivalents), Berrima STP (2,000 P.E.) and Bundanoon STP (2,000 P.E.) discharge to the Wingecarribee River (Wingecarribee Shire Council). Historical flow rate data for these STP's has already been provided (SCA, from Wingecarribee Shire Council). Additionally, many households rely on on-site treatment (see Section 2.4, below). Septic tank pump outs are treated at Berrima STP.

All three works treat effluent to tertiary levels. Both Berrima and Bundanoon use Pasveer channels in the secondary stage. At Bowral STP the capacity is equally divided between an older activated sludge plant and two more recent Pasveer channels. Tertiary treatment is accomplished in sludge settling lagoons. Water residence times in the lagoons are Bowral STP - 12 days; Berrima STP - 30 days and Bundanoon STP - 15 days (Wingecarribee Council data).

None of the works have any significant storage ahead of treatment. Flows in excess of capacity are diverted straight to the tertiary treatment lagoons. Sewage bypasses (overflows) are estimated to occur once or twice a year at Bowral STP, when input exceeds 17.5 KL.d-1 (7 x mean dry weather flow: Martin Krogh, SCA: pers. comm.). Overflows do not occur at Berrima STP, as sewerage and stormwater networks are well isolated. Bypass frequency data at Bundanoon STP has not been provided but as average and maximum daily flows (Wingecarribee Council data) are quite high compared to the design capacity of the works, overflows may occur more often than at Bowral STP.

Primary effluent: Raw sewage (primary effluent) was collected from Mittagong STP, (oo)cysts were enumerated in one ml subsamples, and the results were used to calculate contribution of (oo)cysts from untreated human waste (Table 5.9).

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Figure 5.1: Model for sewage discharge of pathogens

Table 5.9: Protozoan parasites in raw effluent, Mittagong STP (Australian Water Technologies, 2002)

Positive samples* (oo)cysts.l-1in +ve samples

Per person volume of

raw sewage ( l.d-1 )

Per person contribution

of (oo)cysts ( d-1 )

Cryptosporidium 2/7 (29%) 1.08x104 240 7.5x105

Giardia 6/7 (86%) 6.07x104 240 1.3x106

* sampling weekly for single eight week summer period - seasonal effects unknown

For comparison, using combined data from two STP’s (KRAL and WEST, see below) the average annual protozoan loads shed by humans in the Netherlands were estimated to be Cryptosporidium 4.4x105 oocysts.person-1.d-1 and Giardia 1.4x106 cysts.person-1.d-1.

STP discharge (treated effluent): Protozoans in treated sewage from all eleven STP's discharging to the SCA catchment are being enumerated as part of the 'Hotspots' project, preliminary results are presented in Table 5.10.

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Table 5.10: Protozoan parasites in treated effluent, NSW (AWT/SCA unpublished)

Positive samples† (oo)cysts.l-1 in positive samples* Average content **

(oo)cysts.l-1

Geometric mean

x

Log equivalents

x (S.D.)

Cryptosporidium 14/20 (70%) 2.50 0.3981 (0.9838) 1.75

Giardia 22/22 (100%) 4.22 0.6257 (0.9525) 4.22

* Negative results (not detected) not included.** Estimate of average content = (% positive * G.M of positives)† Final number of grab sample analysed per STP will be three (ie. n = 33). Note some samples yet to be analysed, or excluded as recovery <10%.

For another SCA project, Cryptosporidium and Giardia testing are being conducted daily during the month of May 2002 for eight of these STPs, including the three in Wingecarribee catchment. The full results (SCA/AWT, unpubished) will become available shortly. Three grab samples were also collected from Bowral outfall during the Ecowise field study in April. Combined results for Wingecarribee catchment from these three studies to date are shown in Table 5.11.

Table 5.11: Protozoan parasites in STP treated effluent, Wingecarribee catchment

Positive samples (oo)cysts.l-1 in positive samples* Average content **

(oo)cysts.l-1

Geometric mean x Log equivalents

x (S.D.)

Bowral STP

Cryptosporidium 13/13 (100%) 5.17 0.7132 (0.6606) 5.17

Giardia 14/14 (100%) 28.3 0.6257 (0.5432) 28.3

Berrima STP

Cryptosporidium 3/11 (27%) 0.83 -0.0809 (1.3531) 0.23

Giardia 5/11 (45%) 0.43 -0.3645 (0.8803) 0.20

Bundanoon STP


Giardia 8/10 (80%) 0.62 -0.2066 (0.2023) 0.50

* Negative results (not detected) not included. ** Estimate of average content: % positive x G.M. [positives]

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Calculations (Table 5.12) show that the contribution of protozoan parasites by Bowral STP should far outweigh those of the other two, smaller plants in dry weather, but that overflows would have major consequences for water quality.

Table 5.12: Estimated protozoan loads from STP's in Wingecarribee catchment

Bowral Berrima Bundanoon

Design capacity (person equivalents) 10,000 2,000 2,000

Current catchment (P.E.) 8,000? 1,450 1,900

G.M. Effluent flow ( KL.d-1) * 1,460 260 590

Design capacity ( KL.d-1) ** 2,500 500 500

Maximum capacity ( KL.d-1) *** 17,500 3,500 3,500

Days when max. capacity exceeded † 0.33% 0.00% 1.28%

Mean overflow volume ( KL.d-1) †† 4,660 nil 1,145

Crypto in treated effluent (l-1) § 5.17 0.23 0.025

Giardia in treated effluent (l-1) § 28.3 0.20 0.50

Crypto load in raw effluent (d-1) §§ 6.0x109 1.1x109 1.4x109

Giardia load in raw effluent (d-1) §§ 1.0x1010 1.9x109 2.5x109

Crypto Export (oocysts.d-1) 1.7x107 6.0x104 4.5x106

Dry weather flows: 1.3x107 (75%) 6.0x104 (100%) 1.5x104 (0.3%)

In overflows: §§§ 4.1x106 (25%) nil (0%) 4.5x106 (99.7%)

Giardia Export (cysts.d-1) 7.7x107 5.2x104 8.1x106

Dry weather flows: 6.9x107 (91%) 5.2x104 (100%) 3.0x105 (4%)

In overflows: §§§ 7.2x106 (9%) nil (0%) 7.8x106 (96%)

* Geometric mean calculated from actual records - Bowral: May 1995-April 2000; Berrima: Feb 1996-Apr 2000; Bundanoon May 1997-April 2000 (data from Wingecarribee Shire Council). ** For average dry weather flows, based on 250 litres per person per day.*** Estimate: design capacity x 7.† From actual records (see ** above).†† Geometric mean of all daily flows in excess of maximum daily capacity.§ Data from Table 2.9 above.§§ Estimate: protozoan load per person data from Table 2.7 above, multiplied by current number of persons served by the STP (catchment size). §§§ That part of protozoan load (see §§) entering overflow, as a proportion of the total inflow (ie. overflow plus maximum capacity).

Comparisons of STP efficiency: Protozoan removal efficiency of Bowral STP is likely to be rather lower than the figures quoted below, due to its outmoded technology and operation at close to capacity even under normal conditions (Australian Water Technologies, 2002).

In a brief study of 5 STPs in the Netherlands, the median concentration of Cryptosporidium in primary effluent was 17 oocysts.l-1 (range: <1 to 3.9x105). Removal of oocysts by secondary treatment was 75%, logarithmic removal value (LRV) equal to 0.60. Concentration of Giardia in primary effluent was 200 cysts.l-1 (range: 21 to 2.6x103). Removal of cysts by secondary treatment was 99% (LRV = 2). As the ratio of the two parasites in receiving waters was about 1:1, it was

62

obvious that the easily removed Giardia was mostly derived from a minor volume of untreated wastewater, whereas most Cryptosporidium had passed through treatment (Medema, 1999). A follow-up study of 2 STPs was performed (Medema et al., 2001). In the first plant (KRAL) that received combined sewage and urban runoff, the annual mean concentration of Cryptosporidium in primary effluent was 540 oocysts.l-1 (range: <50 to 6,000). Removal of oocysts was 97% (LRV = 1.5). Concentration of Giardia in primary effluent was 3,900 cysts.l-1 (range: <50 to 1.3x104). Removal of cysts by secondary treatment was 99% (LRV = 2.0). In the second plant (WEST) that received domestic sewage only, the annual mean concentration of Cryptosporidium in primary effluent was 4,650 oocysts.l-1 (range: <50 to 2.9x104). Removal of oocysts was 95% (LRV = 1.3). Concentration of Giardia in primary effluent was 2.1x104 cysts.l-1 (range: 1.0x104 to 6.8x105). Removal of cysts by secondary treatment was 98.8% (LRV = 1.9).

At an advanced treatment STP in St. Petersburg Florida (Rose et al., 1996) the annual mean concentration of Cryptosporidium in primary effluent was 370 oocysts.l-1 (range: <6.1 to 1.2x104). Removal of oocysts was 91% (LRV = 1.0). Concentration of Giardia in primary effluent was 3,900 cysts.l-1 (range: 100 to 1.3x104). Removal of cysts by secondary treatment was 98% (LRV = 1.6). At an STP in Israel (Nasser and Molgen, 1998) the annual concentration range of Cryptosporidium in primary effluent was 300 to 7,700 oocysts.l-1. Removal of oocysts by secondary treatment was 93% (LRV = 1.15). Concentration of Giardia in primary effluent ranged from 1.8x104 to 2.8x104

cysts.l-1. Removal of cysts by secondary treatment was 99% (LRV = 2).

Loads in septic tank contents and drainage Three main types of on-site sewage treatment systems are used. These are:

a. Septic tank plus soil absorption (STSA), 79%;

b. Pump out septic tank (POST), 1%;

c. Aerated water treatment systems (AWTS), 20%.

A breakdown by percentage of septic systems in Wingecarribee catchment (total approx. 5,000) is also shown, above (Katrina Charles, UNSW pers. comm.). STSA is the standard septic system, a primary sedimentation tank where supernatant overflows to a gravel-filled absorption trench, where it infiltrates to the underlying soil layer. AS 1546.1:1998 suggests minimum tank volumes for STSA systems, 3,000 litres for 1 to 5 persons, 4,000 l for 6 to 10. Most extant systems are 2050 litres. Capacity is reduced over time by accumulation of sedimented solids. The tank is supposed to be pumped out when capacity has been reduced such that detention time is less than 24 hours, this may be required every 3 to 5 years - AS 1547:2000 assumes a sludge build up rate of 80 l.yr-1. Protozoan removal rates in the septic tank by either death or sedimentation would be negligible considering the short detention time (days), but if death is to be considered, k values for water may be appropriate.

The minimum trench area required is determined by the effluent volume and the loading rate (mm.day-1) which can be absorbed under local soil conditions, guidelines for sizing absorption systems are provided in AS 1547:2000, Appendix 4.2A (particularly Table 4.2A-1). Note that tank size upgrades to accommodate higher loads are common, but often unaccompanied by extra absorption capacity. Protozoans would not be physically retained by the coarse media in the absorption trench, but might be effectively trapped in the surrounding soil. Absorption trenches do however vary in effectiveness, and effluent may surface if a system's fluid capacity is exceeded. This can be caused by heavy precipitation, inadequate trench area, or if the permeability of the surrounding soil is or becomes insufficient (for example by growth of 'biomats'). Protozoa in surfacing effluent may enter nearby watercourses via overland flow. It is likely that effluent dispersal areas of most on-site systems in Robertson have a significant hydrological connection (overland or seepage) to local watercourses (K. Charles, UNSW pers. comm.).

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A time-step model was described by Jelliffe (1997) whereby water balance in the absorption trench was used to predict effluent surfacing. Inputs were flow from the tank and infiltration of precipitation, versus losses by percolation into the subsoil and evapo-transpiration (the latter was effectively negligible). Failing systems were considered to have half the fluid absorption capacity required under AS 1547, and failure rates were estimated to be 5 to 10% - this might be reasonable for recently installed septics designed to AS 1547, however an earlier NSW survey reported visible surfacing effluent in 45% of septic systems (O'Neill et al., 1993) which might be more representative of older areas. The model predicted that effluent surfacing should occur rarely from properly functioning STSA systems, whereas contaminant release from failing systems was frequent, substantial and dominated the overall export budget.

POST's should not release effluent to the surrounding environment, tank contents are periodically removed and disposed via municipal sewerage at the owner's cost. A high level of delinquency is however to be expected, with overflows or effluent illegally pumped out to local stormwater or watercourses.

AWTS are multi-stage biological treatment systems where effluent is degraded and clarified, and usually chlorinated before discharge, via sub-surface or (more typically) surface irrigation. Protozoan removal rates by AWTS are unquantified to date, but might be anticipated to be somewhat less effective than the equivalent municipal-scale secondary treatment process (activated sludge with post-settling), especially considering that in practice reliability of AWTS's is low, a very high percentage (70-95%) fail to meet regulatory discharge standards (Charles et al., 2001) based on all quality requirements. Surface application may also promote protozoan transport in runoff. Protozoan data from septic systems in Wingecarribee catchment are not yet available. The results of a recent small study in the SCA catchment are shown in Table 5.13.

Table 5.13: Protozoans in septic tank contents, Oakland NSW (SCA/UNSW, unpublished)

Positive samples

(co)cysts.l-1 in positive samples* Average content †

Geometric mean x

Log equivalents x

(S.D.)

(oo)cysts.l-1

All septics (n = 24)

Cryptosporidium 5/24 (21%) 155 2.1896 2.5018 32.2

Giardia 8/24 (33%) 807 2.9067 0.5147 269

Community septics (n = 5)

Cryptosporidium 2/5 (40%) 3.72x104 4.5709 2.4768 1.49x104

Giardia 3/5 (60%) 589 2.7699 0.7937 353

Single residence septics (n = 19)

Cryptosporidium 3/19 (16%) 4.0 0.6021 0.0000 0.632

Giardia 5/19 (26%) 975 2.9888 0.3552 256

* negative results (not detected) not included. † Estimate: average contents = ( % positive x G.M. of positives )

The number of persons per household in Robertson, from in the small study above was G.M. 2.35, S.D. 0.16 (n=16). Usage rate of community tanks could not be estimated. AS 1547:2000 provides general estimates of per capita wastewater production. For residences this is 180 (reticulated or bore water supply) or 140 l.person-1.d-1 (rainwater tank). If standard water-saving fixtures are used,

64

this is reduced to 145 or 115 l.person-1.d-1 respectively. Ratings for public systems range from 10-20 l.person-1.d-1 for community halls, to 30 l.person-1.d-1 for schools, and 100 l.person-1.d-1 for fully services camping/caravan parks (multiply by 1.5 if piped water is used). As mentioned the total number of septic systems in Wingecarribee is estimated to be approx. 5,000.

Loads in urban runoff Protozoan loading on urban stormwater catchments might be estimated from density of urban animals (dogs, cats, rodents). A simple model for predicting single-event contaminant export from urban catchments was developed in a Melbourne-based study (Gutteridge and Victoria, 1981). In their model export was linearly proportional to total runoff (mm) during a rainfall event. Runoff was linearly related to rainfall by the proportion of impermeable surface in each catchment. Only the first 20 mm of rainfall was included in the contaminant budget, to simulate washout of sources during large rain events. No attempt was made to account for antecedent dry period and its effect on the maximum amount of contaminant build up that could occur. Runoff from unsewered urban areas is believed to be the fourth largest source of Cryptosporidium in the SCA catchment areas (Australian Water Technologies, 2001). Dry weather drainage from an unsewered township in the Sydney catchment was measured, Table 5.14.

Table 5.14:Protozoan parasites in unsewered urban runoff (Australian Water Technologies, 2002)

Positive samples Protozoan concentration (oo)cysts.l-1

Median Range

Cryptosporidium 2/6 (33%) 0 0 - 1

Giardia 6/6 (100%) 43 12 - 392

Loads from recreational use of water bodies Direct input of faecal material can be expected from bathers and other recreational users of waterways. The incidence and impact of actual defecation are difficult to evaluate, Anderson et al. (1998) assigned a probability of 0.001 and faecal mass of 50 to 200 g per bather.

Release from animal faeces

Direct input to waterways Cattle defecate 12 times daily on average (Thelin and Gifford, 1983). With unrestricted access to waterways, free ranging cattle defecated in streams 3.4% of the time in August (late summer) and 1.7% in November (late autumn) (Larsen et al., 1988). Where streams are the only water source the rate may be higher, 6.7 to 10.5% (Gary et al., 1983). Sheep do not tend to defecate into water.

Release to soil Faecal deposits cover approximately 0.4 to 2% of cattle pasture, with highest density on fence lines, bedding and watering areas (Larsen et al., 1994). In one study the location of randomly sampled pats was recorded, 45.4% were on grazing areas, 23.2% at feed/watering stations, 11.4% on riparian areas, 10.4% along trails, and 9.8% under trees (Hoar et al., 1999). Tate et al. (2000) simulated the release of Cryptosporidium from fresh calf pats by storms. Model pats (4 x 0.2 kg: 1.5x108 oocysts.kg-1) were placed on a 0.5 m2 soil plot (equivalent pat density to areas of cattle concentration) and subjected to intense artificial rainfall of 7.62 cm.h-1 (a 1 in 100 yr 30 min storm) for 90 min. About 1.2% of total oocysts appeared in overland flow (most in the first 30 minutes).

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Runoff however accounted for only 4% of the volume added by precipitation, and oocysts were not enumerated in the remainder which appeared as leachate (the plot had been pre-wetted so that precipitation and runoff-plus-subsurface flow were in equilibrium).

Transport in overland flow and subsurface flow Mawdsley et al. (1996) investigated the horizontal and vertical movement of Cryptosporidium in soil over a 70 day period using 0.8 m long sloping soil blocks and regular artificial rainfall. Cattle slurry containing 1.67x109 oocysts was applied at the top of the slope. After each 0.7 cm ‘rainfall’ event, drainage was collected. Volumetric yields of runoff and subsurface flow averaged 17% and 59% of precipitation respectively. In the ‘worst’ case total oocysts per event removed by runoff declined from 5x105 at day 1 to 2x103 at day 70, in subsurface drainage from 8x106 to 2x104. Oocyst loss totalled for all drainage over 70 days was 1.1x107 (0.68%). At the end of the experiment cores were examined to follow the infiltration of oocysts into the soil. Oocysts were concentrated (2x104 oocysts.g-1 dry wt.) in the surface layer (0-6cm) close to the point of origin, numbers decreasing sharply with depth. At 40 cm downslope surface counts had decreased 100-fold, and no oocysts could be detected in the soil at 70 cm from the point of origin.

Trask et al. (2001) also investigated efficacy of vegetated filter strips (VFS) to trap Cryptosporidium present in runoff (ie. free of manure matrix). The short-term yield was 2.9% of the oocysts applied in suspension for a grassed 3.6 m VFS versus 14.6% for bare soil (1.5% slope, 3

x 0.73 h rainfall at 2.54 cm.h-1), both overland and near-surface flow included. Fairly extreme rainfall regimes were used in the experiments of Tate et al. (2000) and Trask et al. (2001), which generated high (short-term) transport of oocysts in runoff compared to Mawdsley (1996). Tate et al. (2000) showed that slope affected the concentration of Cryptosporidium released from model calf pats (see above) appearing in runoff - for a 20% slope 23 times greater (not significant), and a 30% slope 84 times greater (significant) than for a 10% slope, when pooled over 4 natural rainfall events in the same small grassed catchment. As the runoff volume also increased with slope, the effect on total yields would be even greater (not measured). Trask et al. (2001) observed a less pronounced slope effect using 3% and 1.5% gradients.

Attempts have been made to model the movement of protozoans through aquifers using packed columns (Brush et al., 1999;Marly et al., 2001). Briefly, Cryptosporidium did not absorb to mineral surfaces tested, and it moved through porous materials at about the same rate as the pore water, however it was quickly removed by filtration except in very coarse media (gravel). In a subsurface flow constructed wetland (length 61 m) where all water is forced to pass through the gravel bed in which macrophytes are rooted, the LRV of a settled secondary effluent feed was 0.17 to 1.8 for Cryptosporidium and 0.48 to 2.7 for Giardia. The residence time of the water was 3.8 days (Thurston et al., 2001).

In natural soils Cryptosporidium can infiltrate soil to depths of at least 30 cm within 20 days but, in accordance with column studies, the vast majority (73%) of oocysts are immobilised in the top 2 cm (Mawdsley et al., 1996). Vegetation (Trask et al., 2001) increases vertical transfer of oocysts - probably by increasing detention time of runoff, but root surfaces and macropores may also act as conduits from the surface.

In summary, (oo)cysts should move efficiently with flowing water, depending on conditions either overland with runoff or down via pores into the soil substructure. The latter group are likely to then be immobilised within the soil, and are unlikely to be released (except by erosion). The transport of pathogens via subsurface flow to waterways may therefore be insignificant. Migration is not necessarily greater in better-drained soils (Mawdsley et al., 1996). Presence of preferential flow paths, whether natural conduits (macropores, roots, worm burrows, drying fissures) or artificial subsurface drainage systems may however facilitate rapid movement of oocysts over longer distances.

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Direct deposition of faeces in watercourses is probably the chief means by which animal waste enters surface waters, and may serve as a first approximation to the total load. Surface transport may occasionally be significant in areas where a continuous liquid layer connection to the watercourse can exist under extreme conditions, this also facilitates the delivery of manure solids and eroded soil, both containing (oo)cysts - the Universal Soil Loss Equation might be applicable if average oocyst concentrations in soils can be estimated.

Survival (Oo)cysts can appear in the environment as viable (presumed infectious), and non-viable (intact, but presumed non-infectious) forms, as well as empty shells. All three forms are detectable by standard methods and are usually lumped to produce a total count in a water (sewage, soil, manure) sample, and to determine the effectiveness (as removal) of a treatment process. Survival studies however, have been focussed mainly on the (presumed) viable forms, usually data on the actual number of (oo)cysts are not reported, and the survival rate is expressed as a percentage (viable organisms) of whatever (oo)cysts are present. These k values below may therefore not be appropriate to describe loss of total (oo)cyst numbers, as the persistence of 'dead' (empty or non-viable) (oo)cysts is usually not considered.

Persistence in water Both parasites follow first-order decay kinetics in water (Equation 5):

N / No = 10-k.t or log10 (N / No) = -k.t (5)

Where, No = initial number of (oo)cysts present

N = number of (oo)cysts present after time interval t t = time interval (d) between initial and final measurements of N

k = death rate coefficient (log10[(oo)cysts].d-1) Medema (1999) cites values of k ranging from 0.005 to 0.037 for Cryptosporidium in fresh water under natural conditions, and used a conservative value of k = 0.003 for modelling purposes. The value of k is affected by several environmental factors, notably temperature and the presence/activity of other predating or competing microorganisms. Medema et al. (1997) showed that although Cryptosporidium survival was not directly influenced by low temperature, the detrimental activity of other microorganisms was repressed at 5°C, as the following k values were obtained:

k = 0.010 at either 5°C and 15°C in absence of other microorganisms (filtered river water) k = 0.010 at 5°C in presence of other microorganisms (natural river water) k = 0.024 at 15°C in presence of other microorganisms

High temperatures promote die-off of Cryptosporidium, for example Jenkins et al. (1999) measured survival in distilled water over 5 days at 30 to 55°C (Table 5.15).

Table 5.15: Effect of high temperature on viability of Cryptosporidium (over 5 days)

Temperature (°C) k (mean ± 95% CI: log10.d-1)

30 0

35 0.061 ± 0.018

40 0.192 ± 0.031

45 0.428 ± 0.117

50 1.668 ± 0.284

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Giardia is generally more fragile than Cryptosporidium. Survival of Giardia is poor even at moderate temperatures - at 23°C, k = 0.28; whereas at 1°C k = 0.015. For environmental modelling, Medema (1999) recommended this temperature relationship for Giardia:

k = 0.01 x T

Where T = temperature (ºC)

Sunlight is known to strongly prejudice survival of other microorganisms in surface waters (Johnson et al., 1997). Both protozoans are however fairly resistant to UV irradiation, at 110-120 mJ.cm-2 survival of Giardia was 3%, and Cryptosporidium 1% (Medema, 1999). Irradiation is subsumed into the broad death rates for natural waters mentioned above. However; as intensity would be greater in Australian conditions than in a European climate, some adjustment might be required.

Persistence in soil Little is known about the survival of (oo)cysts in soil, although they might be expected to survive on time scales of months or even years. Increased levels of Cryptosporidium can continue to appear in drainage for some months after stock have been removed from pastures. Olson et al. (1999) compared viability of both parasites in sterilised and unsterilized soil over 12 weeks at different temperatures (Table 5.16). The presence of soil biota had only a small impact on oocyst survival.

Table 5.16: Viability of protozoans in soil at 4°C and 25°C

k at 25°C (log10.d-1) k at 4°C (log10.d-1)

Cryptosporidium

Sterilized 0.02 0.006

Unsterilized 0.02 0.014

Giardia (muris)

Sterilized 0.1 0.02

Unsterilized 0.08 0.03

In a separate study under field conditions and daily average soil temperatures of 5 to 15°C viability loss of Cryptosporidium was slow (45 days: k = 0.018 approx.), and at lower soil temperature (< 5°C) viability was unaffected (40 days) until freeze-thawing cycles began (Jenkins et al., 1999). Unlike bacterial spores, oocysts and cysts are not very resistant to desiccation. Jenkins et al. (1999) demonstrated that the inactivation rates of oocysts in soil increased with decreasing water potential. While at the extreme low range of moisture content Robertson et al. (1992) showed rapid inactivation of oocysts allowed to dry on glass slides (100% within 4 hours). Such dessication is unlikely to occur in natural situations as oocysts are protected from rapid drying by being associated with faecal matrices (Anderson, 1986). Jenkins et al. (1999) measured survival in sterilized soil over 20 days for different water potentials (Table 5.17).

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Table 5.17: Effect of soil water potential on viability of Cryptosporidium (at 25°C) (Jenkins et al., 1999)

Water potential (Mpa) Soil specific values k (mean ± 95% CI)

% water by weight % field capacity (log10.d-1)

-0.003 43.1 100 0.0

-0.018 34.5 80 0.0

-0.100 25.9 60 0.006 ± 0.001

-0.580 17.2 40 0.107 ± 0.023

-3.20 8.6 20 0.181 ± 0.085

Persistence in animal faeces Tate et al. (2000) demonstrated that Cryptosporidium was released from small model calf pats (see table 5. 18) for at least six weeks in a Californian rangeland with a Mediterranean climate (springtime, intermittent storms), though in steadily lesser quantities. On rangelands full-sized pats (2 to 3 kg) are crusted within 48 hours and dry throughout in 15 days (Thelin and Gifford, 1983). Kemp et al. (1995) found steady levels of oocysts throughout the year in drainage from pasture improved by manure spreading, though with a peak following application of the manure.

Olson et al. (1999) measured viability of both parasites in artificial calf pats over 12 weeks at different temperatures (at constant moisture levels). At both 4°C and 25°C Cryptosporidium viability loss was similar (k = 0.03 approx.). For Giardia, k = 0.04 and >0.14 (approx.) at 4°C and 25°C respectively. Jenkins et al. (1999) studied the viability of Cryptosporidium in stored calf manure, using manure-filled diffusion chambers inoculated with oocysts and buried in the pile. Control were sealed in vials of distilled water. Viability loss was about twice as rapid in manure as in water (Table 5.18).

Table 5. 18 Viability of Cryptosporidium in manure (measured over approx. 50 days) (Jenkins et al., 1999)

Location Temperature Inactivation rate, k (log10.d-1)

°C Manure Water

Pile A Fell steadily from 30 to 5 0.018 0.008

Pile B Varied between 22 and 30 0.006 0.003

Ammonia released during decomposition of manure, particularly when stored as slurry in pits, has been shown to influence survival of Cryptosporidium, as shown in Table 5.19 (Jenkins et al., 1998).

Table 5.19: Effect of ammonia on viability of Cryptosporidium (measured over 1 day) (Jenkins et al., 1998) Ammonia (mM) k (mean ± 95% CI: log10.d-1)

7 0.146 ± 0.042 26 0.281 ± 0.073 39 0.521 ± 0.052 60 0.490 ± 0.146 104 0.615 ± 0.354 148 0.688 ± 0.313

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Ammonia content of manure slurries can range from 9 to 175 mM (Fleming et al., 1999). In swine waste treatment pits ammonia-N levels were 932 to 4911 mg.kg-1, though free ammonia levels were not reported (Fleming et al., 1999).

The results of Mawdsley et al. (1996) may give an indication of Cryptosporidium survival in cow slurry. They measured the number of oocysts appearing in runoff over 70 days, a decrease was observed. If it is assumed that the decrease was only due to decline in the number of oocysts available for transport (ie. that the trapping efficiency of the downslope surface remained constant, and the mass of source faecal material did not change due to erosion), then death rate coefficients of k = 0.0150 to 0.0545 were obtained. The amount of oocysts released by the first rainfall event was 0.35 to 1.8 logs higher than by the following one, this suggests that flushing out of easily-removed oocysts is caused by the first significant rain event. This suggests an alternate modelling possibility, using a step function. To represent both die-off in the antecedent dry period (before first rainfall) and also subsequent losses over time, a combination of a first order decay with a step function might be suitable, assuming that (say) an instantaneous 1 log reduction occurs upon the first rainfall event.

Aquatic sediments as secondary stocks Sedimentation rates Giardia and Cryptosporidium will not sediment significantly in moving water unless attached to other particles (Medema, 1999). Sedimentation velocity (vs) for free (oo)cysts was determined to

be 3.5x10-7 m.s-1 for Cryptosporidium (diameter: 4.9 µm) and 1.4x10-6 m.s-1 for Giardia (size: 12.2 by 9.3 µm). This corresponds to densities of 1.0454 and 1.0362 g.cm-3 respectively (Medema et al., 1998). (Oo)cysts can associate with larger particles that are removed from the aqueous phase more quickly. When Cryptosporidium was mixed with clarified (settled) secondary effluent, 30% of oocysts bound immediately to sewage flocs, reaching a plateau at 70% after 24 hours (Medema et al., 1998). For average sized (10 µm) sewage/oocyst aggregates, vs = 1x10-6 m.s-1, or up to

1x10-5 m.s-1 for the largest flocs (~100 µm). Sewage flocs, especially post- clarification, have a very low density (1.003 - 1.004 g.cm-3). Particles from runoff sources may be mineral-based and denser. Another study of bacteria in surface runoff measured a higher mean vs , 1.35x10-5 m.s-1 for

the particles with which most bacteria were associated (0.45µm to 10 µm size range) (Auer and Neihaus, 1993). These were presumably particles of terragenous origin, with a derived density (Stoke’s Law) of 1.86 g.cm-3. Ingestion by mesofaunal predators such as rotifers may consolidate (oo)cysts into larger faecal pellets, though the effect on their viability is unknown (Fayer et al., 2000). Persistence in sediment, concentrations in sediment It is widely assumed that protozoans concentrate in sediment layers. The concentration of bacterial pathogens and indicators in polluted sediments is frequently quoted as 100-1000 fold higher than in the overlying water column (Meyer-Reil et al., 1978;Erkenbrecher, 1981). Protozoans in sediments from four rivers (Wingecarribee, Upper Cox's, Wollondilly, and Braidwood) within the SCA catchment are being enumerated as part of the 'Hotspots' project, Stage 4. Composite samples were taken from near the headwaters as well as the final confluence. Preliminary results (Table 5.20) appear to contraindicate large reservoirs of protozoans in these river sediments, but the amount of sediment analysed was small, and the magnitude and proximity of contaminant sources to each sample site are unknown. Notably, the only detections in sediment were obtained from the Wingecarribee River.

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Table 5.20: Protozoan parasites in river sediments, NSW (Australian Water Technologies, 2002)

Location Positive samples (oo)cysts.g-1 in positive samples * Average content **

(oo)cysts.kg-1

Geometric mean

x

Log equivalents

x (S.D.)

All headwaters combined †

Cryptosporidium 2/9 (22%) 6.0 0.8539 (N.A) 1.3x103

Giardia 0/9 (0%) 0

All confluences combined

Cryptosporidium 1/8 (12%) 25.8 1.4117 (N.A) 3.1x103

Giardia 0/8 (0%) 0

* Negative results (not detected) not included. Sample size: 0.5 gram. ** Estimate: average content = ( % positive x G.M. of positives). † Note one sample excluded as recovery <10%.

Unlike some faecal bacteria, (oo)cysts do not reproduce in sediment, so their numbers might be estimated from the inputs (sedimentation rate) and losses (death) under steady-state conditions:

Ns => 1 / ( 1 - b ) * Ni (7)

Where, Ns = steady-state number of (oo)cysts in sediment

Ni = number of (oo)cysts input to sediment, daily ( (oo)cysts.d-1 )

b = fraction of (oo)cysts surviving, daily ( (oo)cysts.d-1 ), where b = 10-k

k = logarithmic death rate coefficient (log10[(oo)cysts].d-1) Survival of the two protozoans in aquatic sediments has not been assessed. As (oo)cysts are resistant to most stress factors it should be fair to assume similar (or possibly lower) k values to the overlying water. The fate of oocysts trapped in peat materials is unknown, however a study of acidified plant material by Merry et al. (1997) may give some indication. Total counts of Cryptosporidium oocysts were measured during ensilage of ryegrass in a lab-scale model. After 14 days 17% of the oocysts remained. In treatments where a pH of 4 or less was maintained, oocyst loss continued (1.7% remained at 106 d). In other treatments, pH returned towards neutral and no significant further loss of oocysts occurred. Viability of the remaining oocysts was about 50% at 14 days and remained at a similar proportion for the remainder of the experiment.

Resuspension/release from sediment Disturbance of stream sediment increases bacterial concentrations in the overlying water and this can be linked to animal activity (Sherer et al., 1988).

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Concentrations in surface waters (for calibration purposes) Protozoans in four rivers (Wingecarribee, Upper Cox's, Wollondilly, and Braidwood) within the SCA catchment are being enumerated as part of the 'Hotspots' project, stage 4. Samples were taken from near the headwaters as well as the final confluence. Preliminary results are shown in Table 5.21.

Table 5.21 Protozoan parasites in river waters, NSW (Australian Water Technologies, 2002)

Location Positive samples (oo)cysts.l-1 in positive samples * Average content **

Geometric mean

x

Log equivalents

x (S.D.)

(oo)cysts.l-1

All headwaters combined †


Giardia 3/8 (38%) 0.16 -0.8073 (0.4087) 0.06

All confluences combined

Cryptosporidium 1/6 (17%) 0.175 -0.7559 (N.A.) 0.03

Giardia 0/4 (0%) 0

* Negative results (not detected) not included. Sample volumes 6 to 20 litres.** Estimate: average content = ( % positive x G.M. of positives).† Note one sample excluded as recovery <10%.

Regular monitoring for both parasites is conducted at the outlet of Wingecarribee reservoir (site DW11), results for the last available year are presented in Table 5.22.

Table 5.22::Protozoan parasites in Wingecarribee reservoir, NSW (Sydney Catchment Authority, 2000)

1999-2000 (n = 49) Positive samples (Oo)cysts.l-1 in positive samples * Average content **

Geometric mean

x

Log equivalents

x (S.D.)

(oo)cysts.l-1

Crypto 1/49 (2.0%) 0.10 -1.00 (N.A.) 0.002

Giardia 0/49 (0%)

* Sample volume 100 litres. Negative results (ie. not detected) not included.** Estimate: average content = ( % positive x G.M. of positives )

Table 5.23 shows the results of a survey conducted by Champion (1998) that examined the concentration of Cryptosporidium and Giardia in surface waters at 26 drinking water catchment sites in Australia. There was significantly lower water contamination in completely protected catchments compared to those with some agricultural and/or recreational landuse. For example the percentage of Cryptosporidium positive samples was 7% and 29% respectively (mean

concentrations 0.27 and 0.52 oocysts.l-1). While the percentage of Giardia positive samples was 1% and 11% for protected and unprotected catchments, respectively (mean concentration 0.5 and

0.42 cysts.l-1). Results were on the low end of the scale compared to others worldwide, possibly affected by summer and drought conditions during the survey period.

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Table 5.23: Protozoan parasites in Australian raw drinking waters (Champion, 1998)

Positive samples Protozoan concentration, in positive samples only:

(oo)cysts.l-1

(10 litres) x maximum

Cryptosporidium 19.1% * 1.26 3.0

Giardia 6.4% 0.47 2.0

* 188 samples tested in total. 16 of 26 sites were tested repeatedly (≥5 times) - of these 69% and 50% were positive on at least one occasion for Cryptosporidium and Giardia respectively

Recent reports show similar results elsewhere, for example a study of 6 raw water intakes in Germany (Table 5.24) (Karanis and Seitz, 1996).

Table 5.24: Protozoan parasites in German raw drinking waters (Karanis and Seitz, 1996)

Positive samples Protozoan concentration, in positive samples only:

(oo)cysts.l-1

x Maximum

Cryptosporidium 45% 1.0 8.3

Giardia 61% 0.5 3.2

In the Netherlands, raw drinking water abstracted at eleven locations on the (heavily pollution-impacted) Rhine and Meuse rivers had average Cryptosporidium concentrations ranging from 1.4 to 87 oocysts.l-1; and average Giardia concentrations ranging from 1.5 to 95 cysts.l-1 (Medema et al., 2001).

Pathogen budgets

Tables 5.25 and 5.26 show the calculations for the dry weather and wet weather event budgets for the Wingecarribee subcatchment.

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Table 5.25: Dry weather pathogen budget for the Wingecarribee Catchment Direct input to stream from animals

species Cryptosporidium relative strength Giardia

relative strength

cattle 1.25E+07 0.5276 4.17E+08 0.9923 calves 1.12E+07 0.4724 3.25E+06 0.0077 pigs (wild) 0.00E+00 0.0000 0.00E+00 0.0000 kangaroos 0.00E+00 0.0000 0.00E+00 0.0000 dogs 0.00E+00 0.0000 0.00E+00 0.0000 cats 0.00E+00 0.0000 0.00E+00 0.0000 horses 0.00E+00 0.0000 0.00E+00 0.0000 total 2.37E+07 4.20E+08 Septic tanks

type Cryptosporidium relative strength Giardia

relative strength

private 3.13E+04 0.0042 1.27E+07 0.9863 community 7.50E+06 0.9958 1.77E+05 0.0137 total 7.53E+06 1.28E+07 STP plants

Cryptosporidium relative strength Giardia

relative strength

Bowral 1.27E+07 0.994182 6.86E+07 0.994967 Berrima 5.98E+04 0.004667 5.20E+04 0.000754 Bundanoon 1.48E+04 0.001151 2.95E+05 0.004279 1.28E+07 6.89E+07 Total number of (oo)cysts delivered to stream network Cryptosporidium Giardia land 2.37E+07 53.82% 4.20E+08 83. 71% septic tanks 7.53E+06 17.10% 1.28E+07 2.56% STP 1.28E+07 29.09% 6.89E+07 13.74% Instream processes (steady state values) Cryptosporidium Giardia Input 4. 41E+07 5.02E+08 settling 3.08E+07 3.51E+08 Decay 4.88E+06 1.49E+08

EXPORT Cryptosporidium Giardia (oo)cysts total 8.34E+06 1.51E+06 (oo)cysts/L 0.35 0.06

Table 5.26: Wet weather pathogen budget for the Wingecarribee Catchment Land budget Species Cryptosporidium relative

strength Giardia relative

strength cattle 1.96E+10 0.2676 7.13E+10 0.9371 calves 1.75E+10 0.2396 5.55E+08 0.0073 pigs (wild) 0.00E+00 0.0000 0.00E+00 0.0000 kangaroos 1.41E+10 0.1925 0.00E+00 0.0000 dogs 2.06E+10 0.2824 1.71E+09 0.0225 cats 7.01E+07 0.0010 2.19E+09 0.0288 horses 1.23E+09 0.0169 3.30E+08 0.0043

Total 7.31E+10 7.61E+10 Mobilisation Urban 4.14E+09 0.7038 7.80E+08 0.1788 Rural 1.74E+09 0.2962 3.58E+09 0.8212

Total 5.89E+09 4.36E+09

Septic tanks Type Private 3.13E+05 0.0042 1.27E+08 0.9863 Community 7.50E+07 0.9958 1.77E+06 0.0137

Total 7.53E+07 1.28E+08

STP plants

Bowral 1.28E+09 0.7901 2.27E+09 0.7935 Berrima 5.98E+04 0.0000 5.20E+04 0.0000 Bundanoon 3.41E+08 0.2099 5.91E+08 0.2065

Total 1.62E+09 2.86E+09 Total number of (oo)cysts delivered to stream network

Cryptosporidium Giardia Land 5.89E+09 77.60% 4.36E+09 59.32% septic tanks 7.53E+07 0.99% 1.28E+08 1.75% STP 1.62E+09 21.41% 2.86E+09 38.93%

EXPORT Cryptosporidium Giardia

(oo)cysts total 7.58E+09 7.35E+09 (oo)cysts/L 4.90 4.74

75

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Champion, A., 1998. Survey of Australian Waters for Cryptosporidium and GiardiaReport No. 128.

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Croke, B. F. W. and A. J. Jakeman, 2001. Predictions in catchment hydrology: an Australian perspective. Marine and Freshwater Research, 52, 65-79.

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Erkenbrecher, C. W., 1981. Sediment bacterial indicators in an urban shellfishing subestuary of the lower Chesapeake bay. Applied and Environmental Microbiology, 42, 484-492.

Fayer, R., J. M. Trout, E. Walsh, and R. Cole, 2000. Rotifers ingest oocysts of Cryptosporidium parvum. Journal of Eukaryotic Microbiology, 47(2), 161-163.

Fleming, R., D. Hocking, H. Fraser, and D. Alves, 1999. Extent and magnitude of

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agricultural sources of Cryptosporidium in surface water. Final Report Project #40, 33 pp.

Gary, H. L., S. R. Johnson, and S. L. Ponce, 1983. Cattle grazing impact on surface water quality in a Colorado front range stream. Journal of Soil and Water Conservation, 38, 124-128.

Gutteridge, H. a. D. P. L. and E. P. A. o. Victoria, 1981. Characterisation of pollution in urban stormwater runoff.Australian Water Resources Council Technical Paper No. 60.

Hoar, B. R., E. R. Atwill, C. Elmi, W. W. Utterback, and A. J. Edmondson, 1999. Comparison of fecal samples collected per rectum and off the ground for estimation of environmental contamination attributable to beef cattle. American Journal of Veterinary Research, 60(11), 1352-1356.

Jakeman, A. J., I. G. Littlewood, and P. G. Whitehead, 1990. Computation of the instantaneous unit hydrograph and identifiable component flows with application to two small upland catchments. Journal of Hydrology, 177, 275-300.

Jelliffe, P. A., 1997. Predicting stormwater quality from unsewered development. Clear Water, A technical response. Stormwater Industry Association Conference, Coffs Harbour, Australia.

Jenkins, M. B., W. C. Ghiorse, L. J. Anguish, D. D. Bowman, and M. J. Walker, 1998. Viability of Cryptosporidium parvum oocysts - Assessment by the dye permeability assay - Authors reply. Applied & Environmental Microbiology, 64(9), 3544-3545.

Jenkins, M. B., M. J. Walker, D. D. Bowman, L. C. Anthony, and W. C. Ghiorse, 1999. Use of a sentinel system for field measurements of Cryptosporidium parvum oocyst inactivation in soil and animal waste. Applied and Environmental Microbiology, 65(5), 1998-2005.

Johnson, D. C., C. E. Entriquez, I. L. Pepper, T. L. Davis, C. P. Gerber, and J. B. Rose, 1997. Survival of Giardia, Cryptosporidium, Poliovirus and Salmonella in marine waters. Water Science and Technology, 35(11-12), 261-268.

Karanis, P. and H. M. Seitz, 1996. Vorkommen und verbreitung von Giardia und Cryptosporidium im roh- und trinkwasser von Oberflachenwasserwerken. GWF Wasser Abwasser, 137, 94-100.

Kemp, J. S., S. E. Wright, and Z. Bukhari, 1995. On farm detection of Cryptosporidium parvum in cattle, calves and environmental samples. Protozoan parasites and water, Royal Society of Chemistry.

Kuhn, R. C., C. M. Rock, and K. H. Oshima, 2002. Occurrence of Cryptosporidium and Giardia in Wild Ducks along the Rio Grande River Valley in Southern New Mexico. Appl. Environ. Microbiol., 68(1), 161-165.

Larsen, R. E., J. C. Buckhouse, J. A. Moore, and J. R. Miner, 1988. Rangeland cattle and manure placement: a link to water quality. Oregon Academy of Science, 24, 7-15.

Larsen, R. E., J. C. Miner, J. C. Buckhouse, and J. A. Moore, 1994. Water-quality benefits of having cattle manure deposited away from streams. Bioresource and Technology, 48, 113-118.

Letcher, R. A., A. J. Jakeman, L. J. McKee, W. S. Merritt, B. D. Eyre, and B. Baginska, 1999. Review of techniques to estimate catchment exports99/73.

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Mager, A. L., J. Standridge, S. M. Kluender, and L. L. Peterson, 1998. Source and occurrence of pathogens in watersheds.

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Robertson, L. J., A. T. Campbell, and H. V. Smith, 1992. Survival of Cryptosporidium parvum oocysts under various environmental pressures. Applied & Environmental Microbiology, 58(11), 3494-3500.

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79

Chapter 6 Organic matter sources

By Jon Olley and Declan Page

In this section we examine the major sources of organic matter to the Wingecaribee River

6.1 Stable carbon and nitrogen isotope ratios and total C/N ratios

Stable carbon and nitrogen isotope ratios and total C/N ratios have been widely used to determine the sources of organic matter in aquatic environments (Hedges & Stern, 1984, Pocklington & Tan, 1987; Bird et al., 1992; Thornton & McManus, 1994; Onstrad et al., 2000). Here we have examined the potential for using them to distinguish between different sources of organic matter in the Wingecarribee catchment.

6.2 The data

A subsample of the <10um size fraction from each of the stream sediment samples was subjected to isotope analysis. Not all of the samples contained sufficient organic matter to provide results. The <10µm fraction was separated by settling. All of the samples were air dried at 45oC prior to analysis. Carbon and nitrogen concentrations and stable isotope values were determined using a Europa 20-20 isotope ratio mass spectrometer with an ANCA preparation system. Reference samples were run after every 8 samples. Results are presented in Figure 6.1 and in Table 6.1.

δC13

-32

-30

-28

-26

-24

-22

δN15

0

2

4

6

8

10

12

14

16

~ Distance from catchment outlet km

0 20 40 60 80

C/N

wt r

atio

5

10

15

20

25

30

35

40

Wingecarribee Dam

Berrima

Figure 6.1. δC13, δN15 and C/N wt ratios in the <10um size fraction of sediment samples collected from the Wingecarribee catchment shown against distance from the catchment outlet. Data from the main channel is shown in black, data from the tributary streams in yellow.

80

Table 6.1: Carbon and nitrogen concentrations and stable isotope and C/N ratios in the <10um fraction of sediment samples collected from the Wingecarribee catchment

Site No. %N δ N15AIR %C δ C13 PDB c/n ratio

1 0.31 9.4 4.6 -27.8 15.0

2 0.40 8.7 5.3 -27.7 13.4

3 0.20 7.4 3.0 -28.1 14.8

4 0.14 5.3 2.1 -28.5 15.2

5 0.33 4.3 4.5 -25.7 13.7

6 0.08 1.8 0.9 -26.0 11.2

7 0.12 5.1 1.7 -25.9 14.2

9 0.18 3.2 2.0 -25.9 10.9

10 0.07 3.0 0.9 -25.6 13.3

11 0.31 3.5 3.6 -27.4 11.5

12 0.21 4.3 3.3 -28.0 16.0

14 0.10 8.2 1.5 -25.6 14.1

16 0.07 6.7 1.0 -25.2 14.2

17 0.24 12.3 2.7 -27.1 11.1

18 0.14 6.5 1.7 -26.6 11.5

19 0.08 5.0 1.0 -22.5 11.7

20 0.10 3.1 1.9 -26.5 19.4

21 0.10 4.8 1.3 -23.7 12.9

23 0.11 4.5 1.6 -25.3 14.7

24 0.33 4.8

25 0.63 13.8

28 0.27 3.5 3.9 -28.0 14.5

29 0.09 5.3 1.4 -24.8 15.4

30 0.18 3.8 2.7 -27.1 15.3

33 0.14 2.1 2.3 -27.0 16.7

34 0.06 1.7 1.2 -26.1 21.5

35 0.5 -25.5

36 0.08 3.2 1.7 -27.2 20.9

37

38 0.6 -27.1

39 0.8 -25.4

41 0.4 -29.9

42 0.08 1.6 1.8 -26.4 21.1

43 0.10 0.8 1.4 -27.5 13.2

44 0.10 4.8 1.2 -23.8 12.5

45 0.07 2.2 1.3 -25.2 18.1

46 0.02 0.5 0.8 -26.5 34.7

49 0.05 1.6 0.8 -25.3 15.2

50 0.5 -24.4

51 0.06 6.8 0.7 -23.6 11.3

81

6.3 Assessment of the organic matter sources

The δC13 data clearly show a change in the source of organic matter below Wingecarribee Reservoir, indicating that the organic matter in the sediment samples from below the Reservoir is not derived from upstream of the Reservoir.

In general the δC13 results indicate that most of the organic matter in the sediment samples is derived from terrestrial plants which use the C3 photosynthetic pathway. Organic matter derived from C3 trees and shrubs and temperate grasses typically have δ13C ratios of between -25 and -28‰; grasses which use the C4 pathway have ratios which range from -10 to -14‰ (Smith & Epstein, 1971). The C/N wt ratio of fresh C3 derived organic material is >20, as is that of cow manure.

With the exception of the samples from sites 46, 42, 36, and 34, all of the sediment samples have C/N ratios of <20. As C3 vegetation breaks down in the soil its C/N ratio decreases to below 20, but generally remains above 8 (see for example Olley, 2002). The δC13 and C/N ratios in most of the sediment samples are consistent with that of soil organic matter.

The samples from sites 46, 42, 36 and 34, which are all from the forested sandstone region, have δC13 and C/N ratios consistent with fresher C3 derived organic matter.

Interpretation of the δN15 data is more difficult as there is little literature available on nitrogen behavior in Australia systems. However δN15 ratios near 10 are indicative of sewage (Udy and Bunn, 2001). Samples from sites 1, 2, 17, and 25 show significant enrichment, with values >8.5. The source of this enrichment warrants further investigation.

82

6.4 References

Bird, M.I., Fyfe, W.S., Pinheiro-Dick & Chivas, A.R. (1992) Carbon-isotope indicators of catchment vegetation in the Brazilian Amazon, Global Biogeochem. Cy. 6, 293-306.

Hedges, J.I. & Stern, J. (1984).Carbon and nitrogen determinations of carbonate containing solids. Limnol. Oceanogr. 29, 657-663.

Pocklington R. & Tan, F.C. (1987). Seasonal and annual variations in the organic matter contributed by the St. Lawrence River to the Gulf of St. Lawrence. Geochim. Cosmochim Acta 51, 2579-2586.

Thornton, S.F. & McManus, B. (1994). Application of organic carbon and nitrogen stable isotope and C/N ratios as source indicators of organic matter provenance in estuarine systems: evidence from the Tay Estuary, Scotland, Estuar. Coast. Shelf Sci. 38, 219-233.

Smith, B., and Epstein, S., 1971. Two categories of C13/C12 ratios for plants. Plant Physiology. 47, 380-384.

Udy J.W. & Bunn, S.E. (2001) Elevated δN15 values in aquatic plants from cleared catchments:why? Mar Freshwater Res. 52, 347-51.

83

Appendix 1: Geochemical data

Table A1: Geochemical analyses on the <10um size fraction of sediment samples collected from the Wingecarribee catchment. Data are reported as wt% oxide corrected for loss on ignition.

site Al2O3 Fe2O3 TiO2 MnO CaO K2O P2O5 SiO2 MgO Na2O Sr 1 28.39 24.12 2.99 0.89 1.05 0.27 1.16 39.00 1.41 0.14 0.019 2 27.74 20.81 3.25 0.21 1.57 0.43 0.79 42.11 1.17 0.74 0.021 3 29.68 21.02 3.20 0.27 0.89 0.31 0.68 41.61 1.47 0.32 0.015 4 28.07 21.00 2.89 0.31 1.01 0.37 0.80 43.09 1.62 0.25 0.017 5 32.74 7.76 2.11 0.12 0.49 0.46 0.35 54.82 0.90 0.04 0.014 6 25.58 9.71 1.17 0.11 0.27 2.05 0.20 59.84 0.69 0.17 0.034 7 23.75 16.30 1.29 0.42 0.54 1.36 0.30 54.55 0.74 0.20 0.025 8 24.00 19.58 1.63 0.40 0.76 1.03 0.38 51.08 0.83 0.05 0.018 9 33.12 6.11 1.27 0.11 0.49 1.10 0.36 56.22 0.86 0.13 0.014

10 25.91 14.58 1.42 0.50 0.69 1.04 0.34 53.63 0.96 0.31 0.018 11 27.69 11.66 2.51 0.44 0.63 0.79 0.35 54.38 1.13 0.11 0.015 12 23.91 9.43 1.50 0.08 1.05 1.85 0.40 59.57 1.20 0.43 0.026 13 26.09 13.29 1.31 0.07 0.75 1.51 0.14 55.40 1.04 0.16 0.021 14 25.76 9.77 1.62 0.13 0.66 1.59 0.31 58.72 0.92 0.16 0.022 15 25.70 9.65 1.77 0.04 0.53 1.45 0.23 59.55 0.88 0.05 0.020 16 22.92 16.91 1.07 0.35 1.02 2.11 0.72 53.09 0.90 0.29 0.014 17 22.01 7.96 1.19 0.11 1.44 1.95 0.33 62.46 1.06 0.53 0.018 18 23.62 7.46 1.55 0.28 0.70 1.44 0.23 63.20 0.95 0.21 0.023 19 26.94 10.70 1.55 0.17 0.49 1.12 0.18 57.66 0.93 0.09 0.023 20 22.35 14.73 1.45 0.13 0.94 1.05 0.21 56.20 1.67 0.73 0.019 21 22.72 12.97 1.27 0.23 1.31 1.44 0.23 57.48 1.61 0.40 0.024 22 23.44 12.44 1.45 0.28 1.06 1.31 0.49 58.30 0.83 0.11 0.023 23 25.97 13.93 1.30 0.12 0.80 1.06 0.31 55.18 0.66 0.18 0.025 24 24.84 7.96 1.12 0.15 5.31 1.65 0.42 55.03 1.01 0.66 0.024 25 28.20 8.20 2.07 0.05 0.91 0.62 0.17 57.77 0.51 0.43 0.018 26 22.34 14.72 1.50 0.62 2.39 1.25 0.61 52.24 1.12 0.91 0.026 27 23.53 9.03 1.90 0.07 1.36 1.51 0.33 59.00 1.80 0.47 0.029 28 26.47 14.37 1.43 0.10 0.31 0.97 0.17 55.17 0.71 0.11 0.018 29 27.41 11.05 1.58 0.17 0.56 1.85 0.29 71.98 0.89 0.18 0.04 30 insufficient sample 31 23.99 16.29 1.23 0.39 1.77 1.43 0.39 51.75 1.18 0.48 0.031 32 23.36 18.20 1.12 0.62 1.81 1.63 0.36 50.73 1.20 0.27 0.035 33 27.32 7.49 1.06 0.18 0.41 2.50 0.20 59.66 0.74 0.10 0.023 34 25.14 11.52 1.24 0.28 1.42 1.67 0.42 56.57 1.18 0.12 0.033 35 24.49 12.88 1.13 0.57 2.04 2.04 0.44 52.89 1.29 0.80 0.030 36 23.49 9.51 1.51 0.24 1.66 1.63 0.60 59.33 0.98 0.29 0.023 37 23.44 13.34 1.05 0.94 3.18 2.15 0.48 51.15 1.35 0.96 0.034 38 insufficient sample 39 24.68 12.51 1.45 0.20 2.18 1.22 0.49 54.85 1.02 0.41 0.031 40 26.61 8.29 1.21 0.56 1.70 2.75 0.43 56.40 1.13 0.23 0.033 41 19.20 25.19 1.34 1.38 2.37 1.05 0.98 45.10 1.33 0.65 0.025 42 24.76 16.97 2.21 0.85 1.63 1.19 0.69 49.23 1.04 0.44 0.036 43 29.28 10.26 1.72 0.19 0.75 0.62 0.15 55.16 0.77 0.39 0.016 44 29.80 6.65 1.39 0.04 0.56 1.87 0.23 57.79 0.77 0.37 0.020 45 29.59 8.51 1.85 0.02 0.42 1.15 0.12 57.34 0.55 0.14 0.020 46 24.11 15.91 2.78 0.09 0.78 0.95 0.29 53.19 1.49 0.18 0.024 47 26.94 8.28 1.37 0.07 1.29 2.08 0.22 57.87 0.81 0.22 0.028 48 21.23 11.24 0.99 0.17 1.98 2.07 0.35 58.70 1.28 0.72 0.028 49 insufficient sample 50 22.57 8.10 0.93 0.26 3.67 2.77 0.30 53.94 2.53 2.66 0.018 51 24.23 11.16 1.65 1.17 2.03 1.23 0.55 55.13 1.21 0.49 0.034 52 27.13 12.11 1.42 0.15 0.72 1.26 0.69 55.37 0.77 0.10 0.021

84

Table A1 cont: Geochemical analyses on the <10um size fraction of sediment samples collected from the Wingecarribee catchment. Data for V2O5 and Cr2O3 are reported as wt% oxide corrected for loss on ignition. All other data are reported as ug/g corrected for loss on ignition. The calculated CIA index is also shown (see footnote)

site V2O5 Cr2O3 CuO ZnO Ga Rb2O SrO Y ZrO2 Nb2O5 Ba La Ce Th CIA 1 0.071 0.038 155 250 41 21 185 38 479 111 253 52 79 1 0.92 2 0.057 0.028 216 335 35 25 215 32 473 131 200 56 82 18 0.86 3 0.050 0.035 168 239 42 19 160 32 477 117 174 53 80 11 0.92 4 0.91 5 0.044 0.024 97 220 38 64 150 44 343 74 384 47 60 23 0.96 6 0.035 0.019 94 143 32 154 321 45 264 22 335 32 51 25 0.90 7 0.039 0.016 121 220 27 98 244 36 242 24 273 37 59 17 0.89 8 0.058 0.032 113 257 39 111 232 56 384 49 434 48 112 20 0.90 9 0.038 0.019 120 226 51 143 172 54 293 24 370 44 90 29 0.94

10 0.043 0.020 108 238 33 101 186 49 287 32 398 42 88 22 0.90 11 0.043 0.031 79 283 40 83 154 50 420 92 366 57 101 19 0.93 12 0.043 0.019 131 847 32 135 275 49 335 49 446 41 84 35 0.84 13 0.041 0.021 97 193 33 125 226 41 262 28 338 36 65 24 0.89 14 0.038 0.022 87 205 29 133 224 44 313 46 373 48 63 23 0.89 15 0.045 0.021 83 179 33 119 215 49 335 51 361 43 91 24 0.91 16 0.041 0.021 115 567 31 159 153 48 245 14 320 49 120 19 0.83 17 0.028 0.015 145 329 28 168 173 39 246 18 302 30 60 20 0.80 18 0.037 0.016 93 159 31 132 228 38 301 30 310 32 66 28 0.88 19 0.039 0.023 61 134 35 117 207 46 306 28 301 21 85 25 0.92 20 0.049 0.066 155 609 30 85 192 42 284 26 285 33 74 18 0.85 21 0.045 0.034 75 194 29 112 229 38 259 27 394 37 83 22 0.83 22 0.038 0.021 109 270 37 119 267 43 292 29 398 33 73 29 0.87 23 0.90 24 0.66 25 0.90 26 0.052 0.025 68 151 42 124 202 46 334 35 288 19 62 22 0.76 27 0.037 0.017 109 177 41 163 349 48 299 33 447 40 84 33 0.83 28 0.94 29 0.038 0.018 115 186 43 171.6 366 51 314 34.581 470 42 88 35 0.89 30 31 0.81 32 0.039 0.018 82 310 48 226 271 52 289 31 420 64 112 41 0.81 33 0.88 34 0.85 35 0.77 36 0.81 37 0.70 38 39 0.80 40 0.80 41 0.74 42 0.83 43 0.92 44 0.031 0.017 122 220 44 156 217 40 245 30 398 38 60 34 0.89 45 0.033 0.016 75 96 39 147 136 41 296 39 303 33 55 26 0.93 46 0.04 0.047 109 179 43 94.71 217 42 388 84.254 287 45 91 21 0.90 47 0.84 48 0.75 49 50 0.035 0.022 105 87 53 113 212 47 341 54 318 61 116 37 0.62 51 0.81 52 0.039 0.019 84 249 35 131 206 47 306 36 260 47 72 23 0.91

The chemical index of alteration (CIA) of these sediments is calculated from

3222

32

* OAlOKONaCaO

OAlCIA

+++=

where the concentrations are in molecular proportions, and CaO* represents Ca in the silicate fraction only. The index indicates the degree of weathering that alumino-silicate minerals have undergone. Unweathered alumino-silicate minerals have CIA values of about 0.50, whereas clay minerals typically have values of 0.75-1.0. Many of the sediment samples collected from the lower catchments have relatively low CIA valves (0.65-0.75), indicating that they still contain a significant amount of residual (not fully weathered) feldspar and other aluminosilicates.

85

0 5 10 15 20 25 30 35

Fe2

O3

wt%

0

5

10

15

20

25

30

Basalt

mudrock

sandstone

main channel

0 5 10 15 20 25 30 35

TiO

2 w

t%

0

1

2

3

4

0 5 10 15 20 25 30 35

MnO

wt%

0

1

2

0 5 10 15 20 25 30 35

CaO

wt%

0

1

2

3

4

0 5 10 15 20 25 30 35

K2O

wt%

0

1

2

3

0 5 10 15 20 25 30 35

P2O

5 w

t%

0

1

2

0 5 10 15 20 25 30 35

SiO

2 w

t%

0

20

40

60

80

0 5 10 15 20 25 30 35

MgO

wt%

0

1

2

3

Al2O3 wt%

0 5 10 15 20 25 30 35

Na2

O w

t%

0

1

2

3

Al2O3 wt%

0 5 10 15 20 25 30 35

ZrO

2 w

t%

0.00

0.01

0.02

0.03

0.04

0.05

Figure A1.1a Geochemical data from <10um size fraction of sediment samples collected from the Wingecarribee catchment

86

0 5 10 15 20 25 30 35

Sr

wt%

0.00

0.01

0.02

0.03

0.04

0.05

Basalt

mudrock

sandstone

main channel

0 5 10 15 20 25 30 35

V2O

5 w

t%

0.00

0.02

0.04

0.06

0.08

0 5 10 15 20 25 30 35

Cr2

O5

wt%

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0 5 10 15 20 25 30 35

CuO

ug/

g

0

50

100

150

200

250

0 5 10 15 20 25 30 35

ZnO

ug/

g

0

200

400

600

800

1000

0 5 10 15 20 25 30 35

Ga

ug/g

0

10

20

30

40

50

60

0 5 10 15 20 25 30 35

Rb2

O u

g/g

0

50

100

150

200

250

0 5 10 15 20 25 30 35

SrO

ug/

g

0

100

200

300

400

Al2O3 wt%

0 5 10 15 20 25 30 35

Y u

g/g

0

10

20

30

40

50

60

Al2O3 wt%

0 5 10 15 20 25 30 35

ZrO

2 ug

/g

0

100

200

300

400

500

600

Figure A2.1b Geochemical data from <10um size fraction of sediment samples collected from the Wingecarribee catchment

87

0 5 10 15 20 25 30 35

Nb2

O5

ug/g

0

20

40

60

80

100

120

140

Basalt

mudrock

sandstone

main channel

0 5 10 15 20 25 30 35

Ba

ug/g

0

100

200

300

400

500

0 5 10 15 20 25 30 35

La u

g/g

0

10

20

30

40

50

60

70

Al2O3 wt%

0 5 10 15 20 25 30 35

Ce

ug/g

0

20

40

60

80

100

120

140

Al2O3 wt%

0 5 10 15 20 25 30 35

Th

ug/g

0

10

20

30

40

50

Figure A2.1c Geochemical data from <10um size fraction of sediment samples collected from the Wingecarribee catchment

Targeting Rectification action in the Wingecarribee Catchment · Rectification actions aimed at...

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