JULY 2015
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
978-922188-95-3 (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
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
Insecticide use in Cotton
31
Step 1 Calculations
31
Step 2 Calculations
31
Step 3 Calculations
33
Herbicide Use in Chickpeas
35
Step 1 Calculations
35
Step 2 Calculations
35
6.3
7.2
7.3
iv
8
AQUATIC EXPOSURE ESTIMATES – DRYLAND CROPPING
Step 3 Calculations
37
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 25 th 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
Model: Sigmoidal Model
Distribution type: Pearson 6
100
Probability Density Function
0.96
25th percentile flow rate
0.88
0.8
80
0.72
70
0.64
60
0.56
f(x)
Cumulative Frequency, %
90
50
0.48
0.4
40
0.32
30
0.24
20
0.16
0.08
10
0
0
0
800
1600
2400
3200
4000
4800
5600
6400
7200
x
1
100
In Stream Concentration, ppb
10000
Histogram
Pearson 6
α1 = 91.753 α2 = 0.37329 β = 0.011
90% exceed 0.92 ML/d
90% exceed 0.94 ML/d
5 Parameter Logistic Model
Distribution Type: Levy (2P)
100
Probability Density Function
0.96
75th percentile flow rate
0.88
80
0.8
0.72
70
0.64
60
0.56
f(x)
Cumulative Frequency, %
90
50
0.48
0.4
40
0.32
30
0.24
20
0.16
0.08
10
0
0
0
2000
4000
6000
8000
10000
12000
x
10
1000
In-Stream Concentration, ppb
Histogram
Levy (2P)
σ = 36.028 ƴ = -3.1233
90% exceed 9.9 ML/d
90% exceed 10.2 ML/d
14000
16000
18000
20000
22000
PROPOSED APPROACH
New South Wales
Curve-fitting approach
Probability Density Functions
Model: 5 Parameter Logistic Model
Distribution type: Log Normal
100
Probability Density Function
1
90
th
25 percentile flow rate
80
0.8
70
0.7
60
0.6
f(x)
Cumulative Frequency, %
0.9
50
0.5
40
0.4
30
0.3
0.2
20
0.1
10
0
0
0
400
800
1200
1600
2000
2400
2800
3200
3600
4000
4400
x
1
100
In Stream Concentration, ppb
Histogram
Lognormal
α = 1.889 µ = 3.594
90% exceed 3.0 ML/d
90% exceed 3.25 ML/d
5 Parameter Logistic Model
Distribution Type: Log-Logistic (3P)
100
Probability Density Function
0.96
th
75 percentile flow rate
0.88
0.8
80
0.72
70
0.64
60
0.56
f(x)
Cumulative Frequency, %
90
50
0.48
0.4
40
0.32
30
0.24
20
0.16
0.08
10
0
0
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
11000
12000
13000
x
10
1000
In-Stream Concentration, ppb
Histogram
Log-Logistic (3P)
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.
7
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
Table 1: Catchments and number of sites in data libraries for stream flow
State
Catchment)
Queensland
Balonne-Condamine
38
Border Rivers
14
Burnett
27
Fitzroy
37
Moonie River and Burdekin River
20
Total for Queensland
New South Wales
38
Murrumbidgee
68
Lachlan
33
Namoi
55
Macquarie-Bogan
44
Total for New South Wales
Avoca
4
240
5
Broken River
11
Campaspe River
17
Goulburn River
39
Loddon River
34
Mallee
5
Murray Riverina
9
Wimmera
Total for Victoria
Western Australia
136
Gwydir
Castlereagh
Victoria
Number of sites
Esperance Coast Basin
25
145
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).
9
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 runoff 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 (%)
Department of the
Environment model
12.5
Scenario
Not applicable
8
Extended model
12.5
1 – Bare, moist
soils
8
12.5
8
4 – Covered,
moist soils
Peak concentration
(µg/L)
Risk Quotient
19.4
1.9
10.4
1.0
45.9
4.6
24.6
2.5
36.6
3.7
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 lookup 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
Scenario 1, Loamy soil
70
50
60
40
Runoff (mm)
Runoff (mm)
Scenario 1, Sandy soil
60
30
20
10
50
40
30
20
10
0
0
0
20
40
60
80
Rainfall (mm)
R = -3E-5(P3) + 0.0075(P2) + 0.0511(P)-0.7234
100
0
20
40
60
Rainfall (mm)
R = -4E-5(P3) + 0.009(P2) + 0.1398(P)-1.0094
80
100
AQUATIC EXPOSURE ESTIMATES – DRYLAND CROPPING
14
Figure 4: Scenario 2 – Bare soils, low soil moisture
Scenario 2, Loamy soil
45
30
40
35
25
Runoff (mm)
Runoff (mm)
Scenario 2, Sandy soil
35
20
15
10
30
25
20
15
10
5
5
0
0
0
20
40
60
80
100
0
20
Rainfall (mm)
40
60
80
100
80
100
Rainfall (mm)
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
Scenario 3, Sandy soil
Scenario 3, Loamy soil
25.00
35
30
Runoff (mm)
Runoff (mm)
20.00
15.00
10.00
5.00
20
15
10
5
0.00
0
0
20
40
60
Rainfall (mm)
R= -6E-6(P3) + 0.0026(P2) - 0.0114(P)-0.0164
5.1
25
80
100
0
20
40
60
Rainfall (mm)
R= -9E-6(P3) + 0.004(P2) + 0.0042(P)-0.0611
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
Figure 6: Scenario 4 – Covered soils, high soil moisture
Scenario 4, Loamy soil
60.00
30.00
50.00
25.00
Runoff (mm)
Runoff (mm)
Scenario 4, Sandy soil
35.00
20.00
15.00
10.00
40.00
30.00
20.00
10.00
5.00
0.00
0.00
0
20
40
60
80
Rainfall (mm)
R = -2E-5(P3) + 0.0046(P2) + 0.0175(P)-0.3277
100
0
20
40
60
80
Rainfall (mm)
R = -3E-5(P3) + 0.0067(P2) + 0.0771(P)-0.5624
100
15
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
6.1
AQUATIC EXPOSURE ESTIMATES – DRYLAND CROPPING
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
Figure 8: Innisfail (QLD) and Hay (NSW) – Probability of exceeding a rainfall value
100
Cumulative Frequency, %
90
80
70
60
50
Innisfail
Hay
40
30
20
10
0
0.1
10
Rainfall, mm/d
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
P(com)
Innisfail
= 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.
AQUATIC EXPOSURE ESTIMATES – DRYLAND CROPPING
22
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 10 th
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
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
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
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
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
New South Wales
Victoria
Queensland
Western Australia
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
WA 1
25 th
75 th
25 th
75 th
25 th
75 th
25 th
75 th
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, regionspecific 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 25 th
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
90 th
percentile
values for
region
Soil Type
Season
Chance of rain on
any day (%)
P(re), %
P(Com), %
Autumn
24.6
13.4
2.8
Winter
50.0
20.3
9.7
Autumn
24.6
6.1
1.2
Winter
50.0
6.7
3.2
Loamy
Sandy
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
Tenterfield
Walgett
Narrabri
Quirindi
Tamworth
Mitchell
Miles
QMDC
Stanthorpe
St. George
Chinchilla
Dalby
Condamine
Spring
24.7
29.5
7.3
Summer
29.3
36.0
10.6
Spring
27.6
30.5
8.4
Summer
31.0
36.8
11.4
Spring
25.4
33.9
8.6
Summer
33.4
35.3
11.8
Spring
28.0
32.6
9.1
Summer
34.3
34.8
11.9
Spring
14.6
26.8
3.9
Summer
16.4
35.3
5.8
Spring
16.8
35.4
5.9
Summer
18.1
43.5
7.9
Spring
22.9
30.8
7.1
Summer
22.1
37.4
8.3
Spring
28.2
30.5
8.6
Summer
26.2
30.9
8.1
Spring
16.8
30.1
5.1
Summer
22.7
34.7
7.9
Spring
18.9
31.5
6.0
Summer
25.4
36.5
9.3
Spring
25.7
30.8
7.9
Summer
32.7
32.5
10.6
Spring
15.0
30.7
4.6
Summer
17.4
39.6
6.9
Spring
16.4
54.4
8.9
Summer
16.8
55.8
9.4
Spring
21.4
27.3
5.8
Summer
25.3
37.5
9.5
Spring
21.9
28.5
6.3
Summer
26.2
33.0
8.7
Spring
21.8
33.3
7.3
Summer
27.8
35.4
9.8
Moree
Glen Innes
Namoi
P(re), %
Season
Inverell
Gwydir
Chance of rain on
any day (%)
Town
Oakey
Warwick
P(Com), %
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)
th
Gwydir
Namoi
QMDC
Condamine
th
25 percentile
75 percentile
Moree
9.1
23.6
Inverell
9.0
24.1
Tenterfield
8.6
23.9
Glen Innes
8.4
23.4
Walgett
8.4
23.4
Narrabri
9.4
26.4
Quirindi
8.1
24.1
Tamworth
8.0
21.6
Mitchell
8.4
24.7
Miles
8.9
25.9
Stanthorpe
8.6
22.4
St George
9.2
28.1
Chinchilla
8.6
26.4
Dalby
8.8
26.1
Oakey
8.6
29.2
Warwick
8.4
23.6
Mean for Region (mm/d)
th
25 percentile
75th percentile
8.8
23.8
8.5
23.9
8.8
25.3
8.6
26.3
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.
AQUATIC EXPOSURE ESTIMATES – DRYLAND CROPPING
34
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, 75th percentile stream flow
100
100
90
90
80
80
Cumulative Frequency, %
Cumulative Frequency, %
NSW, 25th percentile stream flow
70
60
50
40
30
20
10
60
50
40
30
20
10
0
0.0001
0
0.01
In Stream Concentration, ppb
2.3% of streams potentially affected
QLD, 25th percentile stream flow
0.01
In-Stream Concentration, ppb
100
100
90
80
60
40
20
0
0.0001
<1% of streams potentially affected
QLD, 75th percentile stream flow
Cumulative Frequency, %
Cumulative Frequency, %
70
80
70
60
50
40
30
20
10
-5
1x10
0.01
In-Stream Concentration, ppb
1.5% of streams potentially affected
0
0.0001
0.01
In Stream Concentration, ppb
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)
25th percentile
75th percentile
Gwydir
7.9
20.2
Namoi
8.4
23.4
QMDC
8.6
23.8
Condamine
8.1
21.8
Percent receiving waters potentially
affected
25th percentile
75th percentile
1.7%
<1%
6.7%
2.1%
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
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
2 nd 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.
37
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)
25th percentile
75th percentile
Percent receiving waters potentially
affected
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
SA, 25th percentile stream flow
SA, 75th percentile stream flow
Cumulative Frequency, %
Cumulative Frequency, %
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.
100
80
60
40
20
0
100
80
60
40
20
0
-5
-5
4.6% of streams potentially affected
VIC, 25th percentile stream flow
1.3% of streams potentially affected
VIC, 75th percentile stream flow
Cumulative Frequency, %
1x10
0.01
In-Stream Concentration, ppb
Cumulative Frequency, %
1x10
0.01
In-Stream Concentration, ppb
100
80
60
40
20
0
100
80
60
40
20
0
-5
1x10
0.01
In-Stream Concentration, ppb
1.7% of streams potentially affected
NSW, 75th percentile stream flow
100
100
90
90
80
80
Cumulative Frequency, %
Cumulative Frequency, %
5.4% of streams potentially affected
NSW, 25th percentile stream flow
70
60
50
40
30
20
10
0
-5
1x10
0.01
In-Stream Concentration, ppb
70
60
50
40
30
20
10
0.0001
0.01
In-Stream Concentration, ppb
1.4% of streams potentially affected
0
0.0001
0.01
In-Stream Concentration, ppb
<1.0% of streams potentially affected
39
QLD, 25th percentile stream flow
QLD, 75th percentile stream flow
Cumulative Frequency, %
AQUATIC EXPOSURE ESTIMATES – DRYLAND CROPPING
Cumulative Frequency, %
40
100
80
60
40
20
0
-5
1x10
0.01
In-Stream Concentration, ppb
100
80
60
40
20
0
0.0001
0.1
In-Stream Concentration, ppb
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 25 th 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
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 decisionmaking in Australian environmental risk assessments. The methodology is applicable to both new and existing
agricultural chemicals.
41
42
AQUATIC EXPOSURE ESTIMATES – DRYLAND CROPPING
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