Refinement of Aquatic Exposure Estimates in Australian

Pesticide Environmental Assessments

Use of Real World Data to Characterise Receiving Waters in Australia’s Dryland

Cropping Regions - Runoff Risk Assessment.

© Australian Pesticides and Veterinary Medicines Authority 2015

ISBN 978-1-922188-95-3 (electronic)

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CONTENTS iii

CONTENTS

ACKNOWLEDGEMENTS 1

EXECUTIVE SUMMARY 2

1 INTRODUCTION 3

1.1 Focus on Dryland Cropping Regions of Australia 3

2 DESCRIPTION OF THE PROPOSED APPROACH 5

2.1 Application of the data libraries 5

3 DEVELOPMENT OF THE DATA LIBRARIES 8

3.1 Treatment of data 8

3.2 Number of Sites in Data Libraries 8

4 CURRENT RUNOFF ASSESSMENT APPROACH 10

4.1 Limitations of the Current Approach 11

5 EXTENSION OF CURRENT RUNOFF MODEL 13

5.1 Added Scenario 4 14

6 NEW RUNOFF RISK ASSESSMENT FRAMEWORK 16

6.1 Step 1 Calculations 18

6.2 Step 2 Calculations 18

Calculating the combined probability, P(Com) 19

Calculation of the combined rainfall probability value 20

6.3 Step 3 Calculations 22

Baseflow calculations 23

In-Stream Calculation Module 24

River Flow Rates used for Probability Distributions of In-Stream Concentrations 25

Rainfall Values for use in the In-Stream Analysis 26

Determining the theoretical distribution of in-stream concentrations 27

7 CASE STUDIES 29

7.1 Herbicide use in Winter Cereals, Western Australia 29

Step 1 Calculations 29

Step 2 Calculations 29

7.2 Insecticide use in Cotton 31

Step 1 Calculations 31

Step 2 Calculations 31

Step 3 Calculations 33

7.3 Herbicide Use in Chickpeas 35

Step 1 Calculations 35

Step 2 Calculations 35

iv AQUATIC EXPOSURE ESTIMATES – DRYLAND CROPPING

Step 3 Calculations 37

8 CONCLUSION 41

REFERENCES 42

List of tables

Table 1: Catchments and number of sites in data libraries for stream flow 9

Table 2: Comparison of Receiving Water Concentrations – Current Department of the Environment Model and with

Model Extensions Described Below 12

Table 3: Summary of Base Flow Index Values for Dryland Cropping Regions in the Different States 24

Table 4: Receiving water concentrations (µg/L) at 25th and 75th percent stream flow rates exceeded by 90% of receiving

waters. 25

Table 5: Runoff model predictions for rainfall required to generate runoff 26

Table 6: Distribution of theoretical in-stream concentrations, Queensland 25th and 75th percentile flow rates, winter

months, Scenario 4 (covered, moist soils), loamy soils 28

Table 7: Runoff concentrations and risk quotients, winter cereals, pre-emergent application 29

Table 8: Calculation of P(com), Western Australia Winter Cereals Growing Regions 30

Table 9: Runoff concentrations and risk quotients, cotton, post-emergent application 31

Table 10: P(com) values for spring and summer in major Australian cotton growing districts 32

Table 11: P(com) values for spring and summer in major Australian cotton growing districts 33

Table 12: 25th and 75th percentile rainfall values (mm/d) of positive rainfall >5.1 mm/d, Autumn and percent of receiving

waters potentially affected. 34

Table 13: Runoff concentrations and risk quotients, chickpeas, pre-emergent application 35

Table 14: P(Com) values for Chickpea Regions, Autumn Application 36

Table 15: P(Com) values for Chickpea Regions, Winter Application 37

Table 16: Test for P(Com) at 2 X PNEC for Chickpea Regions 37

Table 17: 25th and 75th percentile rainfall values (mm/d) and percent of receiving waters potentially affected 38

Table 18: 25th and 75th percentile rainfall values (mm/d) of positive rainfall >5.1 mm/d and prediction of potentially

affected percent of receiving waters at the 25th and 75th percentile flow rates 40

ACKNOWLEDGEMENTS 1

ACKNOWLEDGEMENTS

The data libraries relating to stream flow for use in the assessment approach described in this document have been obtained from Australian State Governments using their long-term monitoring information. Without the availability and quality of this information, the proposed approach would not be possible. This paper focuses on dryland cropping regions in Australia, and this has been assessed for Queensland, New South Wales, Victoria, South Australia and Western Australia. The following list acknowledges, with thanks, the State Government agencies from which stream flow and river height data have been obtained:

Queensland Water Monitoring Data Portal. Available at: http://watermonitoring.derm.qld.gov.au/host.htm

© State of Queensland, Department of Natural Resources and Mines, 2015

New South Wales

NSW Office of Water, Real Time Data – Rivers and Streams. Available at: http://realtimedata.water.nsw.gov.au/water.stm?ppbm=SURFACE_WATER&rs&3&rskm_url

© State of New South Wales, Department of Primary Industries, Office of Water 2015

Victoria Department of Environment and Primary Industries. Available at http://data.water.vic.gov.au/monitoring.htm

© State of Victoria, Department of Environment and Primary Industries, 2015

South Australia

WaterConnect, Government of South Australia. Available at: https://www.waterconnect.sa.gov.au/Pages/default.aspx

Government of South Australia, Department of the Environment, Water and Natural Resources, 2015 (Creative Commons Attribution 3.0 Australia licence).

Western Australia

Department of Water. Department of Regional Development (Water Information Reporting) Available at: http://wir.water.wa.gov.au/SitePages/SiteExplorer.aspx

© State of Western Australia, Department of Water, 2015

The methodology developed and described in this document is the initiative of Chris Lee Steere, Australian

Environment Agency Pty Ltd.

2 AQUATIC EXPOSURE ESTIMATES – DRYLAND CROPPING

EXECUTIVE SUMMARY

This document sets out a framework and methodology for refining aquatic exposure assessment for application in

environmental risk assessments of pesticides undertaken for the APVMA. The approach is designed to move

away from the current default-based deterministic assessment method to a more evidence based approach

through use of long-term river (stream flow) and rainfall data.

The process still applies the currently-used Department of the Environment runoff model accepted by the APVMA.

However extensions to this model have been undertaken to increase its flexibility within the runoff risk

assessment.

This paper describes the development of data libraries for stream flow from dryland cropping regions in Australia.

In total, long-term daily data from between 570 and 580 stream monitoring stations from Queensland, New South

Wales, Victoria and Western Australia were assessed. The values obtained for each monitoring station allow the

development of cumulative frequency distributions for receiving water concentrations resulting from runoff, and

thereby allow a more quantitative assessments of the risk. These assessments can now be undertaken in terms of

both spatial and temporal differences.

For runoff risk assessment, a new framework is proposed with several steps. The first step still relies on a

standard default receiving water body as is applied in current assessments. The second step considers probability

of rainfall events that may result in an unacceptable risk in the standard water body, with a particular focus on

persistence of substances. This allows identification of regions within the country where a runoff risk assessment

is best focussed, and identification of those regions where, based on the probability of rainfall, a runoff risk is

unlikely. The final step involves the in-stream assessment through application of the long-term rainfall and stream

flow data.

This final step applies several conservative assumptions to ensure adequate environmental protection is

maintained. These assumptions include:

• Application of the pesticide to 20% of the catchment area;

• The full treated area contributes to runoff;

• Daily rainfall is assumed to fall over a period of one hour.

INTRODUCTION 3

1 INTRODUCTION

The current methodology employed by the Department of the Environment, when undertaking environmental risk

assessments of agricultural chemicals and veterinary medicines for the Australian Pesticides and Veterinary

Medicines Authority (APVMA) is explained in detail in SCEW (2009). This manual describes the general

deterministic risk assessment approach employed, not only in Australia, but in international jurisdictions. In

essence, the outcome of this deterministic approach is an estimate of the likelihood of an adverse effect through

development of a risk quotient (RQ), which is the ratio of an estimated environmental concentration with a relevant

eco-toxicity end-point. These RQ values are then compared to levels of concern (LOC) to come to a conclusion on

the risk potential. In aquatic ecosystems, for example, where the RQ is ≤0.1 for acute toxicity data, the risk is

acceptable and where it is ≤1.0 for chronic toxicity data, the risk is acceptable.

The current method of estimating environmental concentrations of pesticides arising from runoff is limited in its

ability to consider temporal and spatial variability that will occur in the environment. Such considerations may be

given in a qualitative form within a pesticide assessment in Australia. However, to date, very limited use of

available real-world data have been relied on to refine environmental exposure calculations.

Tools and data sets do exist to allow significant refinement in the current approach to environmental risk

assessments undertaken within the national chemicals regulatory framework in Australia. This paper sets out a

framework and methodology for refining aquatic exposure assessments for runoff using cumulative frequency

distributions of theoretical receiving water concentrations in different use regions. A new runoff risk assessment

framework is discussed which allows incorporation of real-world data sets for both long term stream flow and

rainfall data for different use regions. Aquatic exposure to chemicals is assessed using these data sets, which

allow consideration of both temporal and spatial differences in these parameters.

1.1 Focus on Dryland Cropping Regions of Australia

Dryland cropping has been chosen as the first case for developing data libraries for refining aquatic exposure

assessment in Australia. The primary reason for focussing on dryland cropping is that it is considered to

encompass a very large number of cropping situations likely to be considered in assessing applications for

approval or registration of new pesticides and their products.

Dryland cropping regions have been determined using the MCAS-S tool (Multi-Criteria Analysis Shell for

Spatial Decision Support) down to individual river basin level. MCAS can be obtained from

www.daff.gov.au/abares/data/mcass.

MCAS can be used to identify individual river catchments for general cropping situations. A map detailing

Australia’s drainage divisions and river basins is available from the Bureau of Meteorology at

www.bom.gov.au/water/about/image/basin-hi_grid.jpg. The river basins identified in this map are the same as

those used in MCAS, and importantly, are the same as those used by the different Australian states to report river

flow and height data. This allows particular catchments to be readily identified and obtain the necessary data to

develop data libraries for the refined exposure assessment.

These maps can be considered on different levels including by country, state or river catchment. The following

series of maps demonstrates this for dryland cropping zones in Australia, in NSW, and in the Gwydir River

catchment in NSW as examples:

4 AQUATIC EXPOSURE ESTIMATES – DRYLAND CROPPING

Figure 1: Dryland cropping areas of Australia, New South Wales and the Gwydir River Catchment

Australia – Black shading = dryland cropping areas

New South Wales – Black shading = dryland cropping areas

Gwydir Catchment – Black shading = dryland cropping areas

PROPOSED APPROACH 5

2 DESCRIPTION OF THE PROPOSED APPROACH

The APVMA and their advisory agency for environmental risk assessment, the Department of the Environment

(DotE), have established models to predict receiving water environmental concentrations in the risk assessment.

The ‘front-end’ model pertaining to runoff as developed by DotE is described in the APVMA’s agricultural Manual

of Requirements and Guidelines (AgMORAG Volume 3, Part 7–Environment)1. Aspects of this model have been

extended to allow for greater flexibility with different scenarios and soil types, but the underlying model remains the

same. This model is accepted by the APVMA for regulatory use already as a first tier approach. The current

proposal continues to apply this model in its extended form in initial exposure calculations. The focus of this paper

is to describe the method developed to better estimate the concentrations of agricultural chemicals in the receiving

aquatic environment, rather than using standard default values to approximate receiving water concentrations.

To do this, data libraries on stream flow rates from around the country have been developed. These data libraries

are based on monitoring stations in identified river basins that are found in the dryland cropping region. For each

monitoring gauge station that meets acceptability criteria (see Section 3 for discussion on treatment of data), the

full range of daily values for stream flow have been downloaded. These data can be obtained from 10 years to

>120 years of daily data depending on how long the monitoring station has been in operation; hence, many

stations contain over 30000 data points. These data have then been analysed to determine a single value per

monitoring station, for example, a stream flow percentile value for that site within any given season. The single

values from all monitoring stations within a particular region (state in this case) are then combined into a data

library, which can be used to characterise the receiving waters in that particular region.

This approach can be applied quantitatively to deal with probabilities and allows great flexibility in that it can

assess spatial differences between regions and temporal differences both within and between regions.

2.1 Application of the data libraries

The data libraries are designed to inform the risk assessment through allowing real-world data to be used in

estimating exposure concentrations in the receiving environment. For each data library, the value obtained from a

single gauge monitoring station represents one data point, with that one data point based on analysis of the long

term daily data assessed for that particular station.

The range of data (number of gauge monitoring stations within a region) can be used to develop a cumulative

frequency distribution for receiving water characteristics within that region. Use of these data can be done through

development of probability density functions. However, for this proposed method, the cumulative frequency

distributions have been fitted with curve-fitting software and the requisite values have been obtained from the

mathematical equations underpinning the curves. There are several reasons for this. First, there are a large

number of data points within any one region (ranging from 60 points for Western Australia to 240 for New South

Wales). This allows a higher level of confidence in the fit of the data, and this is readily shown visually in the

accompanying graphs. Secondly, when probability density functions are used, communication of complex results

1 archive.apvma.gov.au/morag_ag/vol_3/part_07_environment.php

6 AQUATIC EXPOSURE ESTIMATES – DRYLAND CROPPING

becomes more difficult and hence community confidence in the outcomes from the application of such a method

may be reduced. The choice of distribution may not be straightforward, and different distributions can result in

quite different outcomes.

Nonetheless, to ensure that the more simplified approach adopted here does not seriously impact the results, a

comparison of the curve-fitting outcomes and those using probability density functions has been undertaken. For

this analysis, 25th and 75th percentile stream flow data for Victoria and New South Wales have been compared

(see Figure 2). The curve-fitting has been performed using IBDS XLfit software while the probability density

functions have been calculated using EasyFit v5.5 software applying the function that gave the best fit to the data.

Figure 2: Findings from the Stream Flow Rate Analysis

Victoria Curve-fitting approach Probability Density Functions

25th percentile flow rate

Model: Sigmoidal Model Distribution type: Pearson 6

In Stream Concentration, ppb1 100 10000

Cu

mu

lative

Fre

qu

en

cy,

%

0

10

20

30

40

50

60

70

80

90

100

Probability Density Function

Histogram Pearson 6

x

720064005600480040003200240016008000

f(x)

0.96

0.88

0.8

0.72

0.64

0.56

0.48

0.4

0.32

0.24

0.16

0.08

0

α1 = 91.753 α2 = 0.37329 β = 0.011

90% exceed 0.92 ML/d 90% exceed 0.94 ML/d

75th percentile flow rate

5 Parameter Logistic Model Distribution Type: Levy (2P)

In-Stream Concentration, ppb10 1000

Cu

mu

lative

Fre

qu

en

cy,

%

0

10

20

30

40

50

60

70

80

90

100

Probability Density Function

Histogram Levy (2P)

x

2200020000180001600014000120001000080006000400020000

f(x)

0.96

0.88

0.8

0.72

0.64

0.56

0.48

0.4

0.32

0.24

0.16

0.08

0

σ = 36.028 ƴ = -3.1233

90% exceed 9.9 ML/d 90% exceed 10.2 ML/d

PROPOSED APPROACH 7

New South Wales Curve-fitting approach Probability Density Functions

25th percentile flow rate

Model: 5 Parameter Logistic Model Distribution type: Log Normal

In Stream Concentration, ppb1 100

Cu

mu

lative

Fre

qu

en

cy,

%

0

10

20

30

40

50

60

70

80

90

100

Probability Density Function

Histogram Lognormal

x

4400400036003200280024002000160012008004000

f(x)

1

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

α = 1.889 µ = 3.594

90% exceed 3.0 ML/d 90% exceed 3.25 ML/d

75th percentile flow rate

5 Parameter Logistic Model Distribution Type: Log-Logistic (3P)

In-Stream Concentration, ppb10 1000

Cu

mu

lative

Fre

qu

en

cy,

%

0

10

20

30

40

50

60

70

80

90

100

Probability Density Function

Histogram Log-Logistic (3P)

x

130001200011000100009000800070006000500040003000200010000

f(x)

0.96

0.88

0.8

0.72

0.64

0.56

0.48

0.4

0.32

0.24

0.16

0.08

0

α = 1.0074 β = 242.25 ƴ =-1.7565

90% exceed 29.3 ML/d 90% exceed 29.11 ML/d

The results show quite good agreement between the curve-fitting approach and the probability density function

approach. Importantly, however, the use of probability density functions resulted in (slightly) higher values for

stream flow than the curve-fitting approach (with the exception of NSW 75th percentile flow rates), meaning the

curve-fitting approach outlined in this document is likely to be at least as protective.

8 AQUATIC EXPOSURE ESTIMATES – DRYLAND CROPPING

3 DEVELOPMENT OF THE DATA LIBRARIES

Results from stream monitoring sites cannot be applied for refining the exposure modelling without appropriate

data filtering. For each site, data have been assessed for quality and relevance. Care has been taken to ensure

that artificial drains and channels are omitted from the data set.

3.1 Treatment of data

Rules have been applied in the use of the available data, and include:

1. Only monitoring sites with 10 years or more daily values have been used wherever possible. The object of

this approach is to use long term data to have more confidence in distributions. Further, because the data

are then sorted by season, there become much fewer data points associated with individual seasons and 10

years data may still result in <1000 data points for a season where stream flow is being assessed. While this

has been applied as a general rule, there are occasions when sites with <10 years data were included,

particularly where catchments have few sites available for consideration.

2. If possible, only currently active sites were considered. This was a general rule, but for Western Australia,

due to the somewhat limited number of sites, information from closed monitoring stations as provided by the

Western Australian Government were also used in the data set, provided they had >10 years monitoring

information available.

3. The characteristics of the individual site were carefully considered. While artificial drains and channels have

been excluded, it is also important to remove sites that do not reflect more natural conditions. For example,

sites immediately downstream from dams and weirs may have long periods of no or artificially low water

levels due to a lack of water being released.

4. The analysis of stream flow data is undertaken to allow a comparison of water flow rates that can be

compared to concentrations in runoff waters resulting from rainfall events. Stream flow adjacent to fields

where chemicals are being applied can come from other sources such as aquifers or runoff upstream in the

catchment.

3.2 Number of Sites in Data Libraries

Due to a lack of sites in South Australia, the Victorian stream flow data has been applied as a surrogate. The

data libraries have been constructed on a catchment level basis. The following table summarises the monitoring

sites considered in developing the stream flow data libraries. For each site a separate record has been

constructed that allows stream flow to be assessed. The temporal trends in stream flow in these data libraries

are undertaken by different seasons, but it is possible to assess for any time period if required.

DATA LIBRARIES 9

Table 1: Catchments and number of sites in data libraries for stream flow

State Catchment) Number of sites

Queensland Balonne-Condamine 38

Border Rivers 14

Burnett 27

Fitzroy 37

Moonie River and Burdekin River 20

Total for Queensland 136

New South Wales Gwydir 38

Murrumbidgee 68

Lachlan 33

Namoi 55

Macquarie-Bogan 44

Castlereagh 4

Total for New South Wales 240

Victoria Avoca 5

Broken River 11

Campaspe River 17

Goulburn River 39

Loddon River 34

Mallee 5

Murray Riverina 9

Wimmera 25

Total for Victoria 145

Western Australia Esperance Coast Basin 7

Albany Coast Basin 14

Avon River 16

Moore-Hill Rivers Basin 11

Greenough/Murchison Basin 14

Total for Western Australia 60

In total, 581 individual monitoring stations have been considered for stream flow based on daily records from

long-term monitoring. These data were separated by season to assess temporal trends with respect to stream

flow, and the 25th, 75th and 90th percentile flow rates for each site and season were compiled (total of ~7,000

individual values).

10 AQUATIC EXPOSURE ESTIMATES – DRYLAND CROPPING

4 CURRENT RUNOFF ASSESSMENT APPROACH

Currently in Australian environmental risk assessments performed for the APVMA, potential exposure through

runoff is considered using the Department of the Environment runoff model. This model is described in the

APVMA’s agricultural Manual of Requirements and Guidelines (AgMORAG Volume 3, Part 7–Environment) and

readers should refer to that document for a description. An estimation of the amount of active substance in run-

off water is calculated using a sub-model of the REXTOX model proposed by the OECD2 and described further

in Probst et al. (2005), which has been adopted as a working model by the Department of the Environment. The

model considers rainfall and run-off water, topography of the land (slope), degradation of the pesticide, mobility

of the pesticide and buffer zones. In addition to the REXTOX sub-model the Department of the Environment

considers heterogeneity of fields, interception and retention of the pesticide by crops/weeds and sediment

transport of the pesticide.

The model is based on the following equations:

L%run-off = (R/P) × Crsoil_surface ×f1slope × f2bufferzone × f3foliar_application x heterogeneity_factor x

100 + suspended_pesticide. (1)

L% run-off is the percentage of application dose available in runoff water as dissolved substance, R is the

quantity of run-off water (mm/day) and P is the daily precipitation (mm/day). Currently the Department of the

Environment considers a rainfall event of 100 mm with 20 mm of run-off water in its worst case scenario. On a

hectare basis, the assumption of 100 mm rainfall with 20% running off results in a run-off water volume of 200 m3

per hectare, or 200 000 L/ha.

Topography, especially slope, is an important consideration in assessing run-off. The model predicts the effect of

slope according to:

f1slope = 0.02153 × slope + 0.001423 × slope2 for slope <20%. Where slope ≥20%; f1 = 1. (2)

In order to take into account the many topographical situations in which a pesticide may be applied, the

Department of the Environment generally considers the worst case for two scenarios. Most cropping is expected

to be performed on gentler slopes ≤12.5% (7º) although some crops may be grown on steeper slopes >12.5% -

<20% (>7º - <11º). Solving equation 2 results in a value of ≤0.5 for f1slope with a slope of 12.5% or less, and 1.0

for a slope of 20%. Therefore, with all other factors equalling 1, f1slope will range from 0.5 to 1.0, resulting in a

prediction of 10% applied chemical in run-off from a 12.5% slope and 20% applied chemical in run-off from a

20% slope, based on equation 1.

The Department of the Environment estimates that <50% of an area effectively contributes to runoff in most

realistic circumstances (based on Dunne and Black, 1970). Accordingly, Equation 1 is multiplied by 0.5 to reflect

the heterogeneity of real fields.

2 www.oecd.org/env/ehs/pesticides-biocides/2078654.pdf

CURRENT APPROACH 11

The model is further refined by considering the fate of the pesticide. The model assumes that in the worst case,

the run-off event occurs three days after the application of the pesticide. The mobility of the pesticide is also

taken into account. The fraction of the pesticide available for run-off is related by the equation:

Crsoil-surface = e(-3 ln2/DT50) x (1/(1+Kd) for three days of degradation. (3)

Where: Crsoil-surface is the amount of pesticide relative to the dose applied that is available for run-off three

days after application;

DT50 is the half-life of the chemical on soil in days;

Kd is the solid/water partition coefficient (L/kg)

4.1 Limitations of the Current Approach

While the Department of the Environment model discusses refinements in terms of tiers, the most refined option

available uses chemical specific data and calculates a receiving water concentration based on a standard water

body of 1 ha surface area and 15 cm depth. Chemical specific data for field half-lives and adsorption coefficients

are applied.

This should not be considered a high tier assessment however, as the current approach does not readily allow

for refinements of slopes (default slope of 12.5%), soil types, soil cover and moisture characteristics, and

importantly, the model uses a single rainfall event of 100 mm in a day with 20% of this running off. Hence, even

at the most refined stage of the Department of the Environment model, it is still largely a conservative, generic

approach and further refinement is possible.

In order to allow a more flexible approach, that model has been enhanced to allow consideration of two different

soil types, and using runoff/rainfall relationships for these soils and different scenarios depending on soil

moisture and soil cover. Nonetheless, these enhancements make the model more conservative than the current

DotE model. The assumption of 100 mm rainfall can result in additional dilution in the standard water body which

actually results in lower concentrations than may be found at lower rainfalls. This is demonstrated in the following

table where a chemical with Kd = 2.5, half-life = 60 days, application rate = 500 g ac/ha and Predicted No Effect

Concentration (PNEC) = 10 µg/L have been modelled. Two different land slopes have been considered:

12 AQUATIC EXPOSURE ESTIMATES – DRYLAND CROPPING

Table 2: Comparison of Receiving Water Concentrations – Current Department of the Environment Model and

with Model Extensions Described Below

Model Slope (%) Scenario

Peak concentration

(µg/L)

Risk Quotient

Department of the

Environment model

12.5 Not applicable 19.4 1.9

8 10.4 1.0

Extended model 12.5 1 – Bare, moist

soils

45.9 4.6

8 24.6 2.5

12.5 4 – Covered,

moist soils

36.6 3.7

8 19.6 2.0

This demonstrates that the move to an extended front-end model does not detract from the overall

protectiveness of the approach as the extended model predicts higher initial receiving water concentrations than

the current Department of the Environment in-house model.

EXTENSION OF CURRENT MODEL 13

5 EXTENSION OF CURRENT RUNOFF MODEL

The Department of the Environment model (MORAG) describes an approach and many of the corresponding

formulae from OECD (2000). However, as pointed out by Probst et al (2005), rainfall-induced surface runoff is

the most important source for input of matter and pesticides from arable land and is often associated with

biological effects in the stream. The Department of the Environment model, by defaulting to a 100 mm rain event

with 20 mm runoff, does not allow an assessment of the change that levels of rainfall will have on rainfall induced

surface runoff. The OECD (2000) paper addresses this issue by calculating the runoff amount according to look-

up tables described by Lutz (1984) and Maniak (1992). These data are found in Annex 2 to that report, and are

available online at www.oecd.org/chemicalsafety/pesticides-biocides/2078678.pdf. Two soil types (sandy soil and

loamy soil) are considered under three separate scenarios being:

Scenario 1: bare soil with high soil moisture

Scenario 2: bare soil with low soil moisture

Scenario 3: covered soil with low soil moisture.

Based on the lookup tables provided in Annex 2 of the Report of the OECD Pesticide Aquatic Risk Indicators

Expert Group (OECD 2000), the following equations for estimating runoff amounts (R, mm) have been

determined using polynomial curve-fitting where P = the 24 hour rainfall (mm):

Figure 3: Scenario 1 – Bare soils, high soil moisture

0

10

20

30

40

50

60

0 20 40 60 80 100

Ru

no

ff (

mm

)

Rainfall (mm)

Scenario 1, Sandy soil

0

10

20

30

40

50

60

70

0 20 40 60 80 100

Ru

no

ff (

mm

)

Rainfall (mm)

Scenario 1, Loamy soil

R = -3E-5(P3) + 0.0075(P2) + 0.0511(P)-0.7234 R = -4E-5(P3) + 0.009(P2) + 0.1398(P)-1.0094

14 AQUATIC EXPOSURE ESTIMATES – DRYLAND CROPPING

Figure 4: Scenario 2 – Bare soils, low soil moisture

0

5

10

15

20

25

30

35

0 20 40 60 80 100

Ru

no

ff (

mm

)

Rainfall (mm)

Scenario 2, Sandy soil

0

5

10

15

20

25

30

35

40

45

0 20 40 60 80 100

Ru

no

ff (

mm

)

Rainfall (mm)

Scenario 2, Loamy soil

R= -1E-5(P3) + 0.0045(P2) - 0.0143(P)-0.0682 R= -2E-5(P3) + 0.0056(P2) + 0.0142(P)-0.142

Figure 5: Scenario 3 – Covered soils, low soil moisture

0.00

5.00

10.00

15.00

20.00

25.00

0 20 40 60 80 100

Ru

no

ff (

mm

)

Rainfall (mm)

Scenario 3, Sandy soil

0

5

10

15

20

25

30

35

0 20 40 60 80 100

Ru

no

ff (

mm

)

Rainfall (mm)

Scenario 3, Loamy soil

R= -6E-6(P3) + 0.0026(P2) - 0.0114(P)-0.0164 R= -9E-6(P3) + 0.004(P2) + 0.0042(P)-0.0611

5.1 Added Scenario 4

Unfortunately, no scenario exists for covered soil with high soil moisture. This is a scenario that may be

commonly encountered in use situations in Australia, for example, post-emergence application during winter. The

following scenarios for sandy and loamy soils have been determined based on the ratio of runoff from the look up

tables between bare dry and moist soils, and applying these ratios to the look up results from covered dry soils.

EXTENSION OF CURRENT MODEL 15

Figure 6: Scenario 4 – Covered soils, high soil moisture

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

0 20 40 60 80 100

Ru

no

ff (

mm

)

Rainfall (mm)

Scenario 4, Sandy soil

0.00

10.00

20.00

30.00

40.00

50.00

60.00

0 20 40 60 80 100

Ru

no

ff (

mm

)

Rainfall (mm)

Scenario 4, Loamy soil

R = -2E-5(P3) + 0.0046(P2) + 0.0175(P)-0.3277 R = -3E-5(P3) + 0.0067(P2) + 0.0771(P)-0.5624

16 AQUATIC EXPOSURE ESTIMATES – DRYLAND CROPPING

6 NEW RUNOFF RISK ASSESSMENT FRAMEWORK

An initial version of the following runoff risk assessment framework was developed for the recent review of diuron

(APVMA, 2012). In the initial assessment using the existing method, the application of a number of (generally)

conservative assumptions meant that it was not possible to conclude, with any degree of certainty, that the risks

arising from diuron’s use were acceptable. The revised framework involved several additional steps, with

individual in-stream risk quotients calculated for a select number of streams and creeks in the different use

areas; where in-stream risk quotients exceeded a value of 1.0, the risk was considered unacceptable.

This document describes an amended runoff risk assessment framework to that developed for diuron, with

characterisation of receiving waters in different regions now utilising a probabilistic approach in the exposure

assessment. While quantitative Australian protection goals have not yet been established for such an approach,

for the case studies below the goal of protecting 90% or more of receiving waters has been adopted; that is, the

proportion of potentially affected streams, rivers, creeks etc. cannot exceed 10%. This is taken to also represent

lentic water bodies (ponds and lakes).

The following runoff risk assessment framework has been applied in the case studies:

NEW FRAMEWORK 17

Figure 7: Runoff Risk Assessment Framework

Where PAF = Potentially Affected Fraction of receiving waters, that is, the fraction where in-stream

concentrations may exceed the acceptable aquatic toxicity value. The process involved with each of these steps

is described below.

At Step 1 and throughout the framework, additional considerations can be made to mitigate runoff exposure such

as placing slope restrictions in the modelling, considering cropping practices that may allow a change in the

scenario being modelled (for example, no till farming practices allowing the use of covered soils in the

modelling), and reducing application rates when possible.

18 AQUATIC EXPOSURE ESTIMATES – DRYLAND CROPPING

6.1 Step 1 Calculations

Step 1 calculations use the enhanced runoff model to predict concentrations in the standard water body, viz. a

1 ha surface area pond with 15 cm water depth being fed by a 10 ha catchment. Half the treated area is

assumed to contribute to runoff. At this step, restrictions on slopes can be used to mitigate runoff where

appropriate (for example, through a label restriction on slopes where the product can be sprayed) without the

need to move to the next step of the framework.

The model is such that increased rainfall does not necessarily translate to increased receiving water

concentrations as dilution increases. There will be a rainfall value that results in peak concentrations in the

standard water body before increased rainfall sees these concentrations decrease due to dilution. At the Step 1

calculations, the rainfall value resulting in peak concentrations should be used. This value will differ depending

on the chemical specific values including the ecotoxicity end-point applied in the model.

If the receiving water concentrations result in risk quotients exceeding the level of concern (1.0 where chronic

data have been relied on), the assessment proceeds to the step 2 calculations.

6.2 Step 2 Calculations

The step 2 calculations involve quantifying the likelihood that a rainfall event of sufficient magnitude to result in a

potential risk from runoff will fall in a given region.

The receiving water concentration is dictated by many different factors including the application rate, mobility and

ecotoxicity of the substance. For two substances with the same application rate and mobility, a more toxic

substance will have a lower allowable concentration (=PNEC) than a less toxic substance. Similarly, all other

things being equal, a more mobile substance will have a lower allowable concentration compared to a less

mobile substance.

The lower the allowable concentration, the lower the rainfall value required to result in an unacceptable risk in the

standard water body. The lower this rainfall value, the higher the P(com), that is, the higher the frequency in days

that such a rainfall event may occur.

For a substance to pass the step 2 process, the P(com) value is considered in two separate tests.

a) Initial P(com) is <10%. Broadly speaking, this translates to a frequency of less than 3 days per month where

such a rainfall event could occur that may result in an unacceptable risk (receiving water concentration

exceeds the PNEC).

b) To account for persistent chemicals, which can run-off with similar concentrations even after a long interval

between application and run-off, the second test is that the implied days between run-off events [1/P(com)]

for rainfall to exceed 2 x PNEC is longer than the half-life of the chemical. This will mean that sufficient

amount of chemical will degrade before a rainfall event occurs, which can cause the concentration to exceed

1 x PNEC.

Failing either of these tests requires an in-stream analysis (Step 3). In addition, and as an extra precaution when

assessing P(Com), if the half-life of the chemical exceeds 90 days (approximately 1 half-life in a given season),

NEW FRAMEWORK 19

then the P(com) value cannot be based on the single season and must also be considered for the following

season.

Calculating the combined probability, P(Com)

To maintain consistency with the current regulatory approach, the runoff event in the Step 1 calculation is

assumed to occur three days after application. This assumption applies whether application is in a dry region of

the country, or a wet region. Where this Step 1 deterministic assessment approach identifies a potential risk from

runoff, it is important to put the chance of a runoff event into context. To this end, the use of long term rainfall

data is used to better quantify the likelihood of such an event. In reality, farmers are unlikely to apply pesticides if

significant rainfall events are forecast. However, it is not possible to use the long-term rainfall data to determine

probabilities relating to rainfall on a given day after pesticide application. Therefore, the probability values are

based on the total data set regardless of when the pesticide is actually applied. The likelihood value calculated is

based on two separate probabilities:

1. Probability of rain actually falling on a given day. To determine this, Bureau of Meteorology (BoM) data

for representative weather stations were interrogated for long term rainfall characteristics. It is common for

such stations to have in excess of 100 years of daily rainfall data. While northern parts of Australia

experience a wet and dry season rather than summer, autumn, winter and spring, it is preferable to group

the data by seasons rather than as a single data set. This allows a further assessment of temporal trends

since the likelihood of a runoff event is diminished during drier seasons compared to wetter seasons. From

the seasonal data, a probability of rain falling in each season can be determined simply as a comparison of

the number of days with positive rainfall over the total number of days for which data were available for that

season.

2. Probability of a particular rain event on days of positive rainfall. The second probability value is based

on the amount of rain that falls on wet days, that is, days of positive rainfall. This value (percentile) is

determined from the cumulative probability curve of positive rain values for the particular weather station

being assessed.

These two independent probability values are combined to give a final probability value by which to assess the

likelihood of rain. While this value combines both discrete and continuous variables, it is considered a fair

measure of the chance of unacceptable risk from runoff across regions. If, for example, a particular area has a

lower percentage of days of rain in a pesticide use season, then the potential for subsequent runoff events is

diminished. This is not to say follow up runoff events will not happen, rather, that their probability of happening is

lower. This in turn allows a higher rainfall value (higher percentile on the cumulative probability curve) to be used

in the rainfall combined probability value.

Similarly, runoff events are dictated by the amount of rain. It is possible for regions to have a high probability of a

rainy day, but the actual amount of rain that may fall on such a day can be vastly different. The methodology

outlined here takes account of that factor through use of cumulative frequency distributions of positive rainfall

values to determine likelihood of exceeding a particular rainfall level.

This concept is not unprecedented. For example, in estimating precipitation probability values the US EPA takes

account of both the probability of precipitation amount and the probability of a precipitation event as part of their

wider methodology for stormwater best management practice design (Clar et al, 2004).

20 AQUATIC EXPOSURE ESTIMATES – DRYLAND CROPPING

Calculation of the combined rainfall probability value

The following terminology is used:

P(com) = rainfall combined probability value;

P(rf) = Probability of positive rainfall;

P(re) = Probability of rain exceeding a particular value;

P(rv) = Probability of a particular rainfall value (percentile of positive rainfall distribution)

P(com) is the combined probability of P(rf) and P(re). P(re) in turn needs to be calculated from P(rv).

Because this is centred around the probability of a particular rain value exceeding an allowable level,

P(re) = 1 – P(rv).

Therefore:

P(com) = P(rf) x P(re) = P(rf) x (1-P(rv))

P(re) is calculated as a function of the maximum allowable rainfall (mm/d) predicted by the model that will still

result in an acceptable RQ in the standard receiving water body. This value is then compared to the distribution

of positive rain values for a particular area and the chance of this level being exceeded is calculated. This value

is in terms of a percentile (= P(rv) from above equation).

For example, consider the following two areas (one “wet” and one “dry”) where the model predicts a maximum

allowable rainfall value of 12 mm to still result in an acceptable risk quotient in the standard water body. The

combined probability value is used to determine the overall probability of a rain event this size occurring. As two

examples, long term rainfall data from a town in a wet tropical area (Innisfail, QLD) and a town in a southern

winter cereal growing district (Hay in NSW) are considered.

The following two cumulative probability density curves show probability of rain amount (wet days only) for these

two towns up to the cut off value of 12 mm/d for use during a wet season (winter in Hay; December to February

in Innisfail):

NEW FRAMEWORK 21

Rainfall, mm/d0.1 10

Cu

mu

lative

Fre

qu

en

cy,

%

0

10

20

30

40

50

60

70

80

90

100

InnisfailHay

Figure 8: Innisfail (QLD) and Hay (NSW) – Probability of exceeding a rainfall value

Hay has the bulk of its rain in winter, and the likelihood of rain in these months (P(rf)) = 0.273 (27.3%). Innisfail,

in the QLD Wet Tropics, has a likelihood of rain in the December to February wet season of 0.516 (51.6%) on

any given day.

The percentile value for 12 mm/d on wet days in the seasons being considered, (P(rv)), is 0.93 (93%) for Hay

and 0.51 (51%) for Innisfail. Therefore, the percent chance of 12 mm/d being exceeded (on any particular day of

rain) for both these towns is P(re) = 1-P(rv) = 0.07 (7%) for Hay and 0.49 (49%) for Innisfail.

The P(com) can therefore be calculated as follows:

Hay Innisfail

P(com) = P(rf) X P(re) P(com) = P(rf) X P(re)

= 0.273 X 0.07 = 0.516 X 0.49

= 0.019 (1.9%) = 0.25 (25%)

A P(com) trigger value of 10% is applied (assumed to represent realistic worst case), that is, P(com) ≥10% will

result in a presumption of risk and hence further refinement of the assessment would be required.

In this case, the 10% trigger value of P(com) would not be exceeded at Hay, but would be at Innisfail. The initial

runoff modelling may calculate risk quotients exceeding levels of concern, which would apply to both the wet and

dry regions. However, this first level of refinement using real-world data can essentially permit a conclusion,

based on probability, that the risk from runoff is acceptable in the drier region.

22 AQUATIC EXPOSURE ESTIMATES – DRYLAND CROPPING

6.3 Step 3 Calculations

Where the Step 2 calculations still do not allow a conclusion of acceptable risk from runoff, the assessment

proceeds to Step 3 calculations, which comprises the in-stream analysis.

At this stage, the interest is in determining in-stream concentrations adjacent to the treated area, which will be

the result of rainfall-induced runoff.

In addition to runoff waters entering the stream/river, flow rates can already exist from other sources such as

stream base flow and runoff waters originating from elsewhere in the catchment. In rainfall runoff models used to

estimate design floods, rainfall runoff/filtration is classified as quick flow (surface runoff) and base flow is

essentially the result of groundwater flow (Tularam and Ilahee, 2005). The methodology described for refined

runoff assessments in this document focuses on in-stream concentrations that result from quick flow, which is a

direct result of rainfall.

The approach proposed remains conservative, but allows the use of the regional specific stream flow data. The

general approach is based on that implemented in the European FOCUS surface water stream scenario

(FOCUS, 2001, 2011a). These scenarios are considered to be complex, but FOCUS adopts a recognised

extremely conservative approach which is underpinned by the following considerations:

Flows within any water body are dynamic, reflecting the various base flow, runoff and drainage responses

to rainfall events in the water body catchment. In the methodology described here, stream flow rates are

historical and real-world and therefore already reflect these variables.

Stream scenarios modelled in FOCUS are the most complex. They receive drainage or runoff fluxes from

a 1 ha field adjacent to the ditch and from a 100 ha upstream catchment. It is assumed that, in addition

to the adjacent 1 ha field, pesticide will be applied on the same day to 20% of the area of the upstream

catchment. The stream thus receives pesticide solute in the drainage of runoff waters from all of the 1

ha adjacent field and 20 ha of the upstream catchment. However, in order to adopt an extremely

conservative approach to the exposure calculation, it is assumed that all pesticide solute deriving from

the treated area of the up-stream catchment impacts upon the surface water body at exactly the same

time as that deriving from the treated field adjacent to it.

No pesticide solute is present in the base flow fluxes that contribute water to the stream.

While the Step 3 calculations in the methodology described in this paper do not put a restriction on catchment

size (real value stream monitoring data are used, which are already reflective of their actual catchments), the two

conservative FOCUS assumptions of 20% of the catchment treated on the same day and all the treated area

contributing to runoff are adopted in the runoff methodology for Step 3 calculations described here. In addition,

FOCUS applies the concept of “hydraulic residence time”. In the methodology described here, residence time is

not taken into account. Rather, the concentrations described in the theoretical cumulative frequency distributions

of in-stream concentrations are taken as peak modelled concentrations. These are also designed to act as a

surrogate for lentic water bodies (ponds and lakes). Further refinement in terms of use of time-weighted average

concentrations and residence time can be applied on a case-by-case basis if required.

In considering flow rates, FOCUS uses a “mean annual minimum 7-day flow” (MAM7) value. For the framework

proposed here, the long-term flow rate data are used for the different regions.

NEW FRAMEWORK 23

Baseflow calculations

An important component of the stream analysis, both in the proposed methodology here and that described in

the FOCUS stream scenarios, relates to base flow. If there was no consideration of a baseflow component,

stream flow rate percentiles would be based on the cumulative frequency distribution curves using the total

dataset and therefore overestimation of in-stream concentrations would be likely. This is because the increased

flow resulting from rainfall induced runoff would not be considered as an additional flow rate. However, the

rainfall value used to predict runoff concentrations would remain the same, so in-stream concentrations would be

overestimated.

In the FOCUS scenarios, in order to derive a baseflow component to the hydrological flows feeding the surface

water bodies, parameters quantifying the catchment “Base Flow Index” (BFI) were needed. The BFI quantifies

the fraction of long-term total flow in a catchment that is represented by base flow. This parameter was derived

from an estimated soil hydrological class at each representative field site for the scenarios available in Europe.

Estimated soil hydrological classes have associated set of empirically-derived coefficients describing stream flow

characteristics. Based on the soil hydrological characteristics for each of the FOCUS surface water scenarios,

BFI values of between 0.17-0.79 were adopted.

Such a classification tool is not available in Australia. In order to remain conservative, a simple method for

calculating an individual baseflow index for each monitoring station during each separate season has been

applied. For the analysis, positive flow is considered to be >0.001 m/s (~0.09 km/h). Using the standard stream

of 3 m wide and 15 cm deep, this equates to a flow rate in terms of volume of around 1.15 L/s (0.1 ML/d) and this

low rate has been used as the positive flow cut off in an attempt to reduce possible inconsistencies in measuring

low flow conditions between the different monitoring stations.

All the long term monitoring flow data have been separated by season and the time (%) of positive flow

determined for each season.

The probability of rainfall within the season of interest for each region has been obtained by taking the lower 10th

percentile of rainfall probability for each season. This is a likelihood of ANY rainfall, not rainfall that could

generate runoff as that value will be highly variable depending on other factors such as soil type and slopes

within different catchments.

A unique BFI has then been calculated for each monitoring station for each season as the difference between

the time (%) in positive low and the probability of any rainfall. In many cases, particularly in drier seasons, the

stream flow data showed positive flow periods to be less than the frequency of rainfall. This is not surprising as

the rainfall likelihood was for any rainfall, not the amount resulting in runoff (which would be a lower likelihood). In

these instances, the BFI was set at 0 meaning that any flow in these systems is assumed to be the result of

quick-flow only. It demonstrates the conservatism of the approach. If the probability of rainfall resulting in runoff

was considered, the probability is significantly reduced which would in turn lead to a higher BFI (less

conservative for in-stream concentrations).

The following table summarises the BFI values obtained for application in assessing stream flow data:

24 AQUATIC EXPOSURE ESTIMATES – DRYLAND CROPPING

Table 3: Summary of Base Flow Index Values for Dryland Cropping Regions in the Different States

Model Slope (%) Summer Autumn Winter Spring

New South Wales

Minimum BFI 0.00 0.00 0.00 0.00

Maximum BFI 0.82 0.82 0.83 0.83

Median BFI 0.79 0.79 0.82 0.80

10th percent 0.34 0.35 0.55 0.49

Victoria

Minimum BFI 0.00 0.00 0.00 0.00

Maximum BFI 0.89 0.84 0.72 0.79

Median BFI 0.54 0.62 0.65 0.64

10th percent 0.00 0.01 0.09 0.07

Queensland

Minimum BFI 0.00 0.00 0.00 0.00

Maximum BFI 0.87 0.86 0.87 0.86

Median BFI 0.66 0.54 0.50 0.50

10th percent 0.39 0.18 0.09 0.21

Western Australia

Minimum BFI 0.00 0.00 0.00 0.00

Maximum BFI 0.93 0.85 0.67 0.85

Median BFI 0.25 0.58 0.66 0.45

10th percent 0.00 0.00 0.04 0.00

In-Stream Calculation Module

To model the in-stream concentrations, the in-stream equation in Probst (2005) has been applied as an

extension to the runoff model described in Section 6.1 and 6.3:

where:

Pc = simulated mean pesticide in-stream concentration (µg/L);

L% runoff = percentage of application dose available in runoff water as dissolved substance; (separate

equation – see Section 6.1)

Pa = amount of pesticides applied to the simulation area (µg)

Qstream = peak stream flow during heavy rain events (L/s);

∆T = duration of heavy rain event (seconds).

NEW FRAMEWORK 25

An important consideration in using this approach is the duration of a heavy rain event. The rainfall used for the

initial runoff modelling is a daily rainfall rate. Further, the stream discharge rates reported in the data libraries are

in terms of discharge in ML/day (converted to L/s). However, it is not expected that the rain falls at a constant

rate during the whole 24 hours. The standard assumption is that the intensity over short periods is much more

than the intensity determined over a 24 h period, and in this method, the 24 h rainfall event is assumed to all

occur over a duration of 1 hour for purposes of mixing with in-stream flow rates to predict the in-stream

concentration (that is, ∆T = 3600 seconds). This is considered to be a very conservative assumption.

Further, while the initial runoff to the standard water body assumes a catchment of 10 ha with an assumption that

50% of the area contributes to runoff, the in-stream analysis should assume direct runoff from the treated area to

the receiving stream, that is, 100% of the treated area contributes to runoff.

The impact on receiving water concentrations as a result of moving from the default receiving water body at Step

1 (maximum concentration) to a system where the real-world data are relied on for both rainfall and stream flow

is demonstrated in the following example.

Modelling a chemical with an application rate of 1000 g ac/ha, 5% slope, Kd of 1.5 L/kg and field half-life of 50

days results in a peak concentration in the standard receiving water body at Step 1 of 29.6 µg/L. The following

table shows the receiving water concentrations at the 25th and 75th percentile stream flow rates for the different

states based on the stream flow data libraries where the flow rate is exceeded by 90% of receiving waters. In

modelling these concentrations, unique rainfall values have been developed for each state and each flow rate

percentile (values not reported here) for application in the model. The values relate to the wettest periods for

each state (winter months for Western Australia, Victoria/South Australia and New South Wales; and summer

months for Queensland):

Table 4: Receiving water concentrations (µg/L) at 25th and 75th percent stream flow rates exceeded by 90% of

receiving waters.

Step 1 QLD NSW VIC/SA WA1

25th 75th 25th 75th 25th 75th 25th 75th

Flow (ML/d) 14.2 128.7 31.5 141 4.05 34.6 1.74 5.92

Concentration (µg/L) 3.18 1.01 1.55 0.9 9.29 3.11 5.21 6.60

1) Modelled for sandy soils

This example shows that significant reductions in estimated receiving-water concentrations can be achieved

through the use of the real-world data about the receiving environment.

River Flow Rates used for Probability Distributions of In-Stream Concentrations

As this refinement step focuses on runoff, the summary values for 25th, 75th and 90th percentile flow rates in the

data libraries are based on flow rates exceeding the base flow for each individual monitoring station (as reported

in the data libraries).

26 AQUATIC EXPOSURE ESTIMATES – DRYLAND CROPPING

As highlighted earlier, lower levels of rainfall can produce higher predicted concentrations at both edge of field

and in the standard receiving water body due to lower dilution.

The US Geological Survey (USGS) applies the following guidance for river flows

(waterdata.usgs.gov/nd/nwis/?percentile_help):

• a percentile greater than 75 is considered above normal

• a percentile between 25 and 75 is considered normal

• a percentile less than 25 is considered below normal

Below normal conditions were not considered here with the exception of determining a representative baseflow

index for each station, since it is assumed that stream flow following rainfall events leading to runoff will be at

least normal to above normal (with below normal flow conditions found following drier spells).

Rainfall Values for use in the In-Stream Analysis

The choice of a rainfall value for use in in-stream modelling is very important. Unlike the Step 1 calculations

indicating that increased rainfall can result in decreased receiving water concentrations because of dilution, the

stream flow rates are fixed, based on long-term monitoring data. These rates are independent of the specific

rainfall value applied in the model, and increasing rainfall will always increase the in-stream concentrations.

It is assumed that an amount of rain can fall prior to runoff actually occurring. The runoff model can be used to

set these rainfall values in that a minimum amount of rainfall is required in the model for each scenario prior to

runoff waters being generated. The rainfall values in the following table have been determined by the extended

Department of the Environment model and is the amount of rainfall that the model predicts as being required

prior to runoff (Q/P >0) occurring.

The following table shows the minimum rainfall required for each soil type and each scenario as determined by

the extended runoff model described in Section 5:

Table 5: Runoff model predictions for rainfall required to generate runoff

Scenario Soil cover Soil moisture Rainfall prior to runoff (mm)

Sandy soil Loamy soil

1 Bare High moisture 7.0 5.4

2 Bare Low moisture 5.9 3.9

3 Covered Low moisture 5.6 3.4

4 Covered High moisture 10.9 5.1

Despite the model predicting higher runoff from high moisture soils, the required rainfall prior to generating runoff

is initially higher in moist soils suggesting initial infiltration is higher. Further, the model predicts higher runoff

NEW FRAMEWORK 27

from loamy soil. In these soils, estimated initial rainfall values are always lower than sandy soils, probably as a

result of lower infiltration in these soils.

In order to construct the cumulative frequency distributions for theoretical in-stream concentrations, region-

specific rainfall values are used. These values differ between regions, seasons, the scenario being modelled and

the stream flow percentile being considered. They are extremely important in the context of the modelling. A too

high a value will increase the likelihood of assuming a risk where there may not be one, while a value which is

too low will increase the likelihood of assuming an acceptable risk when in fact the risk may not be acceptable.

For each scenario being modelled, the minimum rainfall value resulting in a prediction of runoff commencing is

obtained and the percentile rainfall corresponding to the river flow percentile being considered is obtained. For

example, if the model predicts 5.9 mm of rain is required prior to runoff waters being generated, and the 25th

percentile stream flow is being modelled, the 25th percentile rainfall value on the rainfall cumulative frequency

distribution where 5.9 mm is exceeded is applied in the model.

The rainfall values will be influenced by the time period during which application occurs, thus different rainfall

values are derived for different seasons of application to allow for this temporal variability.

Determining the theoretical distribution of in-stream concentrations

In order to make a conclusion about the potential for aquatic risk, a predicted environmental concentration in the

water is required. With the distribution of flow rates for streams in the representative area, it is possible to obtain

a theoretical distribution of in-stream concentrations. The enhanced runoff model is used to predict these

concentrations using different stream flow rates as input.

It is stressed here that the front-end model predicting edge-of-field concentrations currently used by the

Department of the Environment (as described above, with extensions for scenarios and soil types) is still used for

this process. However, the edge-of-field concentration, instead of entering a standard receiving water body, can

now be modelled to enter the range of receiving waters assessed for the region being considered. The front-end

model requires a rainfall value to predict the edge-of-field concentration. This rainfall value is matched with the

stream flow rate being assessed, for example, at the 25th percentile flow rate the corresponding 25th percentile

rainfall value is used in the model to predict edge-of-field concentrations.

In order to construct the theoretical distribution of in-stream concentrations, an in-stream concentration is

calculated for each station within a particular data library. For example, there are 136 stations with stream flow

data in the Queensland data library. The flow rate for each station has been determined, and these are applied

as the Qstream input parameter which would give 136 different theoretical in-stream values to construct the

cumulative frequency distribution.

Consider a stream with a 25th percentile river flow rate of 10 ML/d and a 75th percentile flow rate of 45 ML/d.

Runoff concentrations were calculated for a pesticide with Kd of 8.0 L/kg, a field half-life of 50 days, an

application rate of 2000 g ac/ha, a slope restriction of 3%, and a PNEC of 6 µg/L. For this example, arbitrary

rainfall input values of 9 mm/d and 30 mm/d for the 25th and 75th percentile flow rates respectively were

modelled using Scenario 1 (bare, moist soils).

Using Qstream values of 10 ML/d and 45 ML/d for the 25th and 75th percentile flow rates respectively, with

rainfall values of 9 mm/d and 30 mm/d (assumed to occur over a period of 1 h such that ∆T = 3600 seconds), the

28 AQUATIC EXPOSURE ESTIMATES – DRYLAND CROPPING

model predicts an in-stream concentration of 4.2 µg/L and 3.0 µg/L at the 25th and 75th percentile flow rates

respectively.

Undertaking these calculations for all stations in the region allows a theoretical distribution of in-stream

concentrations to be constructed. This is illustrated in the following figure where a theoretical in-stream

concentration has been calculated for each monitoring station in the assessed Queensland catchments (n = 158

monitoring stations). Rainfall values have been obtained from 21 weather stations throughout these catchments

and the 90th percentile values for 25th and 75th percentile flow rates in Scenario 4 (9.0 mm/d and 26.3 mm/d

respectively) have been applied with an application rate of 2000 g ac/ha, Kd = 8.0 L/kg and DT50 = 50 days.

Based on a maximum allowable concentration of 6.0 µg/L, the theoretical distributions and percent receiving

waters protected levels are shown in the following figure for the winter months:

Table 6: Distribution of theoretical in-stream concentrations, Queensland 25th and 75th percentile flow rates,

winter months, Scenario 4 (covered, moist soils), loamy soils

Highest concentrations can be associated with lower rainfall and stream flows because of less dilution. When

compared to the PNEC of 6 µg/L, this example shows that 86.4% and 95.4% of streams at the 25th and 75th

percentile flow rates respectively will remain protected, that is, the in-stream concentrations in these receiving

waters are predicted to be lower than the ecotoxicity end-point. This example indicates that risk may be

considered unacceptable at the lower flow rate (for example, indicative of first flush runoff), but may be

considered acceptable with higher stream flow rates.

CASE STUDIES 29

7 CASE STUDIES

Three case studies are described below demonstrating how the runoff risk assessment framework can be used

applying probabilistic distributions of in-stream risk quotients from the data libraries developed as part of this

project.

The case studies are provided, each with different outcomes. The first demonstrates how the framework can be

applied with a conclusion of acceptable risk based on Step 2 calculations. The other two examples require Step

3 calculations, with one of these examples showing how management practices can be used to further refine the

runoff risk where the initial Step 3 calculations indicate that an unacceptable risk still remains.

7.1 Herbicide use in Winter Cereals, Western Australia

This case study is performed for an herbicide with a field half-life of 26 days, a Kd of 0.5 L/kg and an ecotoxicity

end-point of 20 µg/L. The modelled application rate is 1000 g ac/ha.

Step 1 Calculations

A slope restriction of 3% is considered practicable in Australian dryland cropping regions and has been applied in

the model. Additionally, it is common practice by Australian farmers to practice no-till or limited-till farming, since

herbicides are used in preference to cultivation. However, for this assessment, the worst case scenario of bare,

moist soil (Scenario 1 in the enhanced runoff model) was used.

The runoff model predicts that the highest receiving water concentration using the standard water body will occur

with a rainfall event of 36 mm/d. The following Step 1 risk quotients are derived:

Table 7: Runoff concentrations and risk quotients, winter cereals, pre-emergent application

Soil Type L%

Receiving water

concentration (µg/L)

Risk quotient

Sandy soil 0.62 25.6 1.28

Loamy soil 0.92 31.8 1.59

These step 1 calculations show that risk quotients are above the level of concern, so the assessment moves to a

Step 2 analysis which determines the combined rainfall probability (P(com)).

Step 2 Calculations

Based on the enhanced runoff model, the lowest rainfall value to result in an acceptable RQ in the standard

water body was 10.8 mm/d for loamy soil and 19.7 mm/d for sandy soil. Soils in the Western Australian wheat

belt and the south west are overwhelmingly sandy in nature (see Figure 10 below), so both loamy and sandy

soils were modelled.

30 AQUATIC EXPOSURE ESTIMATES – DRYLAND CROPPING

Figure 9: Sands and Yellow Duplex Soils (in yellow) in Western Australia (MCAS-S)

Long-term weather data were obtained from town centres (n = 45) throughout the Western Australian wheat belt.

The 90th percentile values for probability of rainfall, probability of exceeding a particular rainfall value and the

combined probability were used to obtain the following generate P(com) values.

Table 8: Calculation of P(com), Western Australia Winter Cereals Growing Regions

Town Soil Type Season

Chance of rain on

any day (%)

P(re), % P(Com), %

90th

percentile

values for

region

Loamy

Autumn 24.6 13.4 2.8

Winter 50.0 20.3 9.7

Sandy

Autumn 24.6 6.1 1.2

Winter 50.0 6.7 3.2

The combined probability of a rainfall event sufficient to result in an unacceptable risk in the standard water body

is greatly reduced in sandy soils given the high rainfall needed in this example, and the rainfall patterns of the

region. Even with loamy soils, the P(com) does not exceed 10% indicating an acceptable risk from this first

measure.

For the second test at these step 2 calculations, the rainfall required to generate a receiving water concentration

of 2 X PNEC is calculated using the standard water body. This value was determined in loamy soils to exceed 36

mm/d, and in sandy soils to exceed 45 mm/d. Daily rainfall data have been collected for over 45 town centres

throughout the WA wheat belt, with well in excess of 100 years of data for many of the centres. It is highly

unusual even in winter (high rainfall months) for centres to receive daily rainfall approaching 45 mm. For

example, using the whole data set of rainfall from this region, the probability of exceeding 26 mm/d in either

autumn or winter is only 3%. The P(Com) associated with a rainfall of 26 mm/d is 0.7% in autumn and 1.6% in

winter. For the active constituent to pass the 2nd test for P(com), this requires a half-life of 62.5 days based on

the winter rainfall data or 142 days for the autumn rainfall data. The chemical being modelled has a half-life of 26

CASE STUDIES 31

days so passes this second test. Consequently, the tier 2 calculations for P(com) pass in this case and the risk

from runoff is considered acceptable.

In this case no further assessment would be required.

7.2 Insecticide use in Cotton

This case study was undertaken for an insecticide with a field half-life of 200 days, a Kd of 5.6 L/kg and an

ecotoxicity end-point of 0.1 µg/L. The application rate modelled was 50 g ac/ha

Insect pressure can occur early in the growing season, and full foliage cover is needed for control. Application

occurs predominantly in the spring and summer months. Crop interception values were taken from those

reported in FOCUS (2011b). For this case study, an interception value of 30% was used.

Step 1 Calculations

The modelled rainfall value resulting in the highest concentration flowing to the standard water body was 35 mm

per day. The following Step 1 calculations were based on a slope at application of 3% and using Scenario 4

(covered, moist soil):

Table 9: Runoff concentrations and risk quotients, cotton, post-emergent application

Soil Type L%

Receiving water

concentration (µg/L)

Risk quotient

Sandy soil 0.04 0.12 1.18

Loamy soil 0.11 0.22 2.19

The Step 1 calculations show the risk quotient in the standard water body exceeds the level of concern of 1.0, so

the runoff assessment proceeded to the Step 2 calculations.

Step 2 Calculations

The maximum rainfall to retain an acceptable risk quotient in the standard body of receiving water for loamy soil

is 9.3 mm. Based on this rainfall, the following P(com) values were calculated for the major cotton-growing

districts:

32 AQUATIC EXPOSURE ESTIMATES – DRYLAND CROPPING

Table 10: P(com) values for spring and summer in major Australian cotton growing districts

Region/

Description Town Season

Chance of rain on

any day (%)

P(re), % P(Com), %

Gwydir

Moree Spring 24.7 29.5 7.3

Summer 29.3 36.0 10.6

Inverell Spring 27.6 30.5 8.4

Summer 31.0 36.8 11.4

Tenterfield Spring 25.4 33.9 8.6

Summer 33.4 35.3 11.8

Glen Innes Spring 28.0 32.6 9.1

Summer 34.3 34.8 11.9

Namoi

Walgett Spring 14.6 26.8 3.9

Summer 16.4 35.3 5.8

Narrabri Spring 16.8 35.4 5.9

Summer 18.1 43.5 7.9

Quirindi Spring 22.9 30.8 7.1

Summer 22.1 37.4 8.3

Tamworth Spring 28.2 30.5 8.6

Summer 26.2 30.9 8.1

QMDC

Mitchell Spring 16.8 30.1 5.1

Summer 22.7 34.7 7.9

Miles Spring 18.9 31.5 6.0

Summer 25.4 36.5 9.3

Stanthorpe Spring 25.7 30.8 7.9

Summer 32.7 32.5 10.6

St. George Spring 15.0 30.7 4.6

Summer 17.4 39.6 6.9

Condamine

Chinchilla Spring 16.4 54.4 8.9

Summer 16.8 55.8 9.4

Dalby Spring 21.4 27.3 5.8

Summer 25.3 37.5 9.5

Oakey Spring 21.9 28.5 6.3

Summer 26.2 33.0 8.7

Warwick Spring 21.8 33.3 7.3

Summer 27.8 35.4 9.8

The highest rainfall probability and rainfall values are found in summer in this region of Australia so it is not

surprising that the highest P(com) values are associated with summer.

The second test required for Step 2 has not been undertaken in this case. Even though the P(com) for spring

months did not exceed the trigger value of 10%, the half-life of >90 days required spring application to also be

modelled for summer months. The P(Com) has been shown to exceed the 10% trigger value for summer months

in some centres in most regions thereby already requiring an in-stream analysis. Further, because of the long

half-life of this substance, runoff during the following season (autumn) is also required.

CASE STUDIES 33

Step 3 Calculations

While the major cotton growing districts in NSW are found in the Namoi and Gwydir catchments, cotton can also

be grown further south in NSW, so in the first instance, the full range of NSW stream flow data was used.

For Scenario 4 with loamy soils the model predicts that 5.1 mm rain is required prior to runoff occurring. Rainfall

values for the different centres in the major cotton growing regions, and the mean value for each region were

calculated from the long-term daily rainfall data available from Bureau of Meteorology as follows:

Table 11: P(com) values for spring and summer in major Australian cotton growing districts

Region/

Description Town Percentile Rain Value (mm/d) Mean for Region (mm/d)

25th percentile 75th percentile 25th percentile 75th percentile

Gwydir

Moree 9.1 23.6

8.8 23.8 Inverell 9.0 24.1

Tenterfield 8.6 23.9

Glen Innes 8.4 23.4

Namoi

Walgett 8.4 23.4

8.5 23.9 Narrabri 9.4 26.4

Quirindi 8.1 24.1

Tamworth 8.0 21.6

QMDC

Mitchell 8.4 24.7

8.8 25.3 Miles 8.9 25.9

Stanthorpe 8.6 22.4

St George 9.2 28.1

Condamine

Chinchilla 8.6 26.4

8.6 26.3 Dalby 8.8 26.1

Oakey 8.6 29.2

Warwick 8.4 23.6

For this example, modelled NSW stream concentrations were, based on the Gwydir rainfall values and QLD

stream concentrations were based on the Queensland Murray Darling Catchment (QMDC) values because of the

higher 25th percentile rainfall value in this region.

34 AQUATIC EXPOSURE ESTIMATES – DRYLAND CROPPING

Figure 10: Percent potentially affected receiving waters due to runoff at 25th and 75th percentile flow rates,

NSW and QLD, Summer months. 95% confidence intervals indicated on distributions.

NSW, 25th percentile stream flow NSW, 75th percentile stream flow

In Stream Concentration, ppb0.0001 0.01

Cu

mu

lative

Fre

qu

en

cy,

%

0

10

20

30

40

50

60

70

80

90

100

In-Stream Concentration, ppb0.0001 0.01

Cu

mu

lative

Fre

qu

en

cy,

%0

10

20

30

40

50

60

70

80

90

100

2.3% of streams potentially affected <1% of streams potentially affected

QLD, 25th percentile stream flow QLD, 75th percentile stream flow

In-Stream Concentration, ppb1x10

-50.01

Cum

ula

tive

Fre

quency,

%

0

20

40

60

80

100

In Stream Concentration, ppb0.0001 0.01

Cu

mu

lative

Fre

qu

en

cy,

%

0

10

20

30

40

50

60

70

80

90

100

1.5% of streams potentially affected 1.0% of streams potentially affected

Having proceeded to the in-stream analysis, it can be concluded that runoff risk from this use pattern in cotton

would be acceptable since <10% of receiving waters were deemed to be potentially affected. However, due to

the long half-life of this substance, an in-stream analysis is also required for runoff events that may occur in

autumn even though application is unlikely in these months. The distribution graphs are not shown here. The

outcomes of the in-stream analysis are described in the following table. The NSW catchments have been

modelled using the higher rainfall value for this season for the Namoi catchment while the QLD catchments have

been modelled using the higher rainfall value from the QMDC catchment.

Table 12: 25th and 75th percentile rainfall values (mm/d) of positive rainfall >5.1 mm/d, Autumn and percent

of receiving waters potentially affected.

Region/

Description Percentile Rain Value (mm/d)

Percent receiving waters potentially

affected

25th percentile 75th percentile 25th percentile 75th percentile

Gwydir 7.9 20.2 1.7% <1%

Namoi 8.4 23.4

QMDC 8.6 23.8 6.7% 2.1%

Condamine 8.1 21.8

CASE STUDIES 35

Interestingly, in the case of autumn rainfall following summer application, the prediction of potentially affected

receiving waters is higher in Queensland than the wetter summer seasons with 6.7% (25th percentile flow rates)

and 2.1% (75% percentile flow rates) potentially affected. This compares with 1.5% and 1.0% potentially affected

at the 25th and 75th percentile both flow rates respectively in the summer conditions. Nonetheless, the overall

values still indicate <10% of receiving waters potentially affected supporting a conclusion of acceptable use.

7.3 Herbicide Use in Chickpeas

This case study was undertaken for an herbicide with a field half-life of 40 days, a Kd of 0.1 L/kg and an

ecotoxicity end-point of 0.06 µg/L. The application rate modelled was 4 g ac/ha.

Application can occur during late autumn to early winter. It was assumed soils are moist but may be bare at the

time of application for this case study. Initial modelling was undertaken using Scenario 1.

Step 1 Calculations

The rainfall value in the model resulting in the highest in stream concentration was 30 mm per day. The following

Step 1 calculations were based on a slope at application of 3%.

Table 13: Runoff concentrations and risk quotients, chickpeas, pre-emergent application

Soil Type L%

Receiving water

concentration (µg/L)

Risk quotient

Sandy soil 0.75 0.14 2.31

Loamy soil 1.14 0.18 3.01

The Step 1 calculations showed that the risk quotient in the standard water body exceeded the level of concern

of 1.0 where application was to both soil types, so the runoff assessment proceeded to the Step 2 calculations.

Step 2 Calculations

The following growing regions for different pulse crops were obtained from Pulse Australia

(www.pulseaus.com.au).

36 AQUATIC EXPOSURE ESTIMATES – DRYLAND CROPPING

Figure 11: Growing areas of chickpeas in Australia

There is minor production in Western Australia, South Australia and Victoria.

The maximum rainfall to remain below an acceptable risk quotient in the standard body of receiving water was

7.5 mm for loamy soil. Western Australia was modelled using a sandy soil and the maximum rainfall to remain

below an acceptable risk quotient in the standard water body for sandy soil was 11.5 mm.

The figures reported in the following tables for rainfall probabilities are the 90th percentile levels from assessment

of a number (n = 8 to 45 per region) of long term weather stations in each of the different dryland regions.

Table 14: P(Com) values for Chickpea Regions, Autumn Application

State Autumn P(rf) Autumn P(re) Autumn P(Com)

Western Australia 24.6 12.3 2.6

South Australia 31.8 23.9 6.3

Victoria 28.0 30.6 7.5

New South Wales 28.7 39.9 9.6

Queensland 22.1 45.9 8.1

P(com) values for autumn application indicate this to generally be acceptable with the trigger value of 10%

exceeded only for one town centre in one region (individual values not shown here).

CASE STUDIES 37

Table 15: P(Com) values for Chickpea Regions, Winter Application

State Winter P(rf) Winter P(re) Winter P(Com)

Western Australia 50.0 18.5 8.9

South Australia 57.8 22.0 12.0

Victoria 51.9 23.5 10.7

New South Wales 27.9 41.0 10.0

Queensland 21.8 44.3 8.2

P(com) values for winter application show the test fails in South Australia, Victoria and New South Wales

requiring an in-stream analysis for these states for winter application.

The second test for the step 2 calculations is also required where the first P(Com) <10% test is passed.

Table 16: Test for P(Com) at 2 X PNEC for Chickpea Regions

State / season P(Com) for 2 X PNEC Time (d) between runoff 2nd test pass/fail

Western Australia, autumn 1.0% 100 Pass

South Australia, autumn 1.5% 67 Pass

Victoria, autumn 2.4% 42 Pass

New South Wales, autumn 3.8% 26 Fail

Queensland, autumn 3.7% 27 Fail

Western Australia, winter 2.4% 42 Pass

Queensland, winter 3.6% 28 Fail

This second test is passed if the half-life of the chemical is greater than the average time between rainfall events

required to produce 2 X PNEC in the standard water body. If this test is failed then an in-stream analysis is

required.

Based on the outcomes of both tests at this step, an in-stream analysis (Step 3) is required for application to

loamy soils in Queensland, New South Wales, Victoria and South Australia. The runoff risk in Western Australia

is considered acceptable so no further assessment is required.

Step 3 Calculations

The first component of the Step 3 calculations is to define the rainfall values needed to input into the runoff

model for calculating individual in-stream concentrations. In this case, due to the much higher risk identified for

winter application, only rainfall for the winter months were used in the modelling. The in-stream analysis for this

case study was restricted to the 25th and 75th percentile flow rates.

Scenario 1 (bare, moist soil) was modelled and with this scenario the model predicts that 5.4 mm of rain (loamy

soils) is required prior to runoff occurring. The following rain values were derived for the different town centres in

the different regions, based on the cumulative distribution of rainfall on wet days exceeding 5.4 mm/d.

38 AQUATIC EXPOSURE ESTIMATES – DRYLAND CROPPING

The stream flow data libraries were used to generate the distributions of in-stream concentrations and these

concentrations are compared to the ecotoxicity end-point to predict the percentage of receiving waters potentially

affected through runoff.

Table 17: 25th and 75th percentile rainfall values (mm/d) and percent of receiving waters potentially affected

Region/

Description Percentile Rain Value (mm/d)

Percent receiving waters potentially

affected

25th percentile 75th percentile 25th percentile 75th percentile

Autumn Application

New South Wales 8.8 23.0 2.7% <1%

Queensland 9.6 30.9 10.5% 3.9%

Winter Application

South Australia 7.2 14.0 4.6% 1.3%

Victoria 7.9 18.9 5.4% 1.7%

New South Wales 8.4 21.8 1.4% <1.0%

Queensland 9.3 26.9 13.8% 4.5%

The theoretical distributions of in-stream concentrations and intersect with the PNEC are shown for winter

application as follows:

CASE STUDIES 39

Figure 12: Percent potentially affected receiving waters due to runoff at 25th and 75th percentile flow rates

SA, Vic, NSW, Runoff model Scenario 1. 95% confidence intervals indicated on distributions.

SA, 25th percentile stream flow SA, 75th percentile stream flow

In-Stream Concentration, ppb1x10

-50.01

Cum

ula

tive

Fre

quency,

%

0

20

40

60

80

100

In-Stream Concentration, ppb1x10

-50.01

Cum

ula

tive

Fre

quency,

%0

20

40

60

80

100

4.6% of streams potentially affected 1.3% of streams potentially affected

VIC, 25th percentile stream flow VIC, 75th percentile stream flow

In-Stream Concentration, ppb1x10

-50.01

Cum

ula

tive

Fre

quency,

%

0

20

40

60

80

100

In-Stream Concentration, ppb1x10

-50.01

Cum

ula

tive

Fre

quency,

%

0

20

40

60

80

100

5.4% of streams potentially affected 1.7% of streams potentially affected

NSW, 25th percentile stream flow NSW, 75th percentile stream flow

In-Stream Concentration, ppb0.0001 0.01

Cu

mu

lative

Fre

qu

en

cy,

%

0

10

20

30

40

50

60

70

80

90

100

In-Stream Concentration, ppb0.0001 0.01

Cu

mu

lative

Fre

qu

en

cy,

%

0

10

20

30

40

50

60

70

80

90

100

1.4% of streams potentially affected <1.0% of streams potentially affected

40 AQUATIC EXPOSURE ESTIMATES – DRYLAND CROPPING

QLD, 25th percentile stream flow QLD, 75th percentile stream flow

In-Stream Concentration, ppb1x10

-50.01

Cum

ula

tive

Fre

quency,

%

0

20

40

60

80

100

In-Stream Concentration, ppb0.0001 0.1

Cum

ula

tive

Fre

quency,

%

0

20

40

60

80

100

13.8% of streams potentially affected 4.5% of streams potentially affected

At the end of these Step 3 calculations, it can be demonstrated that use of the substance in question is not likely

to result in more than 10% of receiving waters being impacted during its use period in most dryland regions in

Australia. In Queensland during both the autumn and winter use periods, potentially >10% of receiving waters

may be impacted at the lower 25th percentile flow rate indicating an unacceptable risk in this region.

It is useful to consider how changes in management practices can influence the outcomes. The modelling

underpinning these calculations was based on application to bare soils. One option to further reduce this risk is

to only allow the substance to be applied under no-till cropping practices. Under such conditions, it is appropriate

to apply a model based on covered and moist soils (Scenario 4), which results in a better runoff profile. The

situation in Queensland is modelled using this assumption to demonstrate the difference in outcomes.

With Scenario 4 model predicts that 5.1 mm rain is required prior to runoff being generated. This means the

overall rainfall values required to model against 25th and 75th percentile flow rates will also be lower than

compared to Scenario 1.

Table 18: 25th and 75th percentile rainfall values (mm/d) of positive rainfall >5.1 mm/d and prediction of

potentially affected percent of receiving waters at the 25th and 75th percentile flow rates

Region/ Description Percentile Rain Value (mm/d)

Percent receiving waters potentially

affected

25th percentile 75th percentile 25th percentile 75th percentile

Queensland, autumn 9.2 30.5 7.7% 2.7%

Queensland, winter 9.0 26.3 9.3% 2.8%

A move to no-till cropping situations is shown therefore to decrease the overall risk profile of the chemical. In

Queensland dryland catchments, the risk is considered acceptable (<10% potentially impacted) at the 25th

percentile flow rates in both autumn and winter compared with >10% where no-till cropping is not practiced.

Risk managers can use this information to consider restrictions on use of the chemical, for example, to only

permit use under no-till farming practices, or where conventional tilling is practiced, to only allow use on farms

that have the ability to retain runoff waters.

CONCLUSION 41

8 CONCLUSION

The current default deterministic approach for undertaking environmental runoff risk assessments in Australia

results in outcomes generally being applicable at a national level with single application to all regions and across

all seasons. These are worst-case estimates but the tools to provide refinements taking into account both spatial

and temporal dimensions have not previously been available.

The use of distributions for real-world data (rainfall patterns and river flow rates with respect to runoff modelling)

now allows significant refinements to the aquatic exposure assessments, including analysis of variability in the

environment both spatially and temporally. The data libraries which have been developed for dryland cropping

regions in Australia, along with the methodologies described in this document for refining exposure estimates for

aquatic environments potentially subject to runoff exposure, can provide a useful tool for regulatory decision-

making in Australian environmental risk assessments. The methodology is applicable to both new and existing

agricultural chemicals.

42 AQUATIC EXPOSURE ESTIMATES – DRYLAND CROPPING

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