Hydrological modelling practices for
estimating low flows – guidelines
D. Barma and I. Varley
Low flows report series – June 2012
Low flows report series
This paper is part of a series of works commissioned by the National Water Commission on key water
issues. This work was undertaken by Barma Water Resources and Sinclair Knight Merz on behalf of
the National Water Commission.
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© Commonwealth of Australia 2012
This work is copyright.
Apart from any use as permitted under the Copyright Act 1968, no part may be reproduced by any
process without prior written permission.
Requests and enquiries concerning reproduction and rights should be addressed to the
Communications Director, National Water Commission, 95 Northbourne Avenue, Canberra ACT 2600
or email bookshop@nwc.gov.au.
Online/print: ISBN: 978-1-921853-83-8
Hydrological modelling practices for estimating low flows – guidelines
June 2012
Authors: D Barma and I Varley
Published by the National Water Commission
95 Northbourne Avenue
Canberra ACT 2600
Tel: 02 6102 6000
Email: enquiries@nwc.gov.au
Date of publication: June 2012
An appropriate citation for this report is:
Barma D and Varley I 2012b, Hydrological modelling practices for estimating low flows – guidelines,
National Water Commission, Canberra.
Disclaimer
This paper is presented by the National Water Commission for the purpose of informing discussion
and does not necessarily reflect the views or opinions of the Commission.
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Contents
Executive summary
1.
Introduction
1.1
Aim and scope
1.2.
Models
1.3.
Ecologically-significant low-flow metrics
1.4.
General modelling guidelines
1.5.
Report structure
2.
Data considerations
2.1
Principles
2.2
Issues
2.2.1 Primary input data
2.2.2 Secondary input data
2.3
Data guidelines
3.
Model selection and configuration (in gauged catchments)
3.1.
Principles
3.2.
Model selection
3.2.1 Rainfall-runoff models
3.2.2 Regression models
3.2.3 Water balance methods
3.2.4 River system models
3.2.5 Linked hydrodynamic and groundwater models
3.3
Model configuration
3.3.1. Interception by hillside dams
3.3.2. Baseflow
3.3.3. Channel transmission losses
3.3.4. Groundwater/surface water interactions
3.4
Model selection and configuration guidelines
4.
Model calibration
4.1
Principles
4.2
Calibration process – inflow estimates
4.1.1. General approach
4.2.1 Automatic calibration
4.2.2 Manual calibration
4.2.3 Calibrating to separate parts of the flow regime
4.3
Calibration process – river system models
4.4
Uncertainty analysis
4.5
Model calibration guidelines
5.
Transposition methods (for ungauged catchments)
5.1.
Principles
5.2.
Transposition of gauged flows
5.2.1. Empirical transposition methods
5.2.2. Regional regression models
5.2.3 .Use of similarity criteria for selecting suitable analogue catchments
5.3.
Transposition of rainfall-runoff model parameter values
5.4.
Estimating low-flow indicators directly
5.5.
Testing estimates in ungauged catchments
5.6.
Short-term gauging data
5.7.
Ungauged catchment guidelines
Appendix A: Description of rainfall-runoff models commonly used in Australia
Appendix B: Case study summaries
1. Recalibration of low flows in the Snug and upper north Esk catchments, Tasmania
2. Recalibration of low flows in the Daly River, Northern Territory, catchment using
an integrated groundwater/surface water model
3. Derivation of residuals and flow generation in the Paroo and Burdekin catchments,
Queensland, using various conceptual models and climatic data
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4. Recalibration of Currency Creek, East Mount Lofty ranges, South Australia
Shortened forms
References
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Tables
Table 1: Guide to acceptable calibration ............................................................................................. 21
Table 2: Low-flow metrics ................................................................................................................... 22
Table 3: Relevance of four key ecologically-relevant hydrological indicators to different
stream classes identified .............................................................................................................. 24
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Acknowledgements
We sincerely thank the project advisory group, jurisdictional agency staff and associates, and review
workshop participants for their advice and input to the project. We also thank report reviewers for their
thoughtful comments.
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Executive summary
These guidelines aim to promote best practice in estimating low flows, including cease-to-flow, in
rainfall-runoff and river system models. They provide advice on data considerations, model selection,
model configurations and model calibration strategies. The guidelines also provide advice on
approaches to estimated flows in ungauged catchments.
The guidelines draw on a stocktake and review of existing modelling practices in Australia (Barma &
Varley 2012) as well as case studies that tested a number of methods recommended. The guidelines
complement the more extensive suite of eWater modelling guidelines and should be used in
conjunction with them.
Twelve principles underpin the 30 recommendations provided:
Data
1. The model used to estimate low flows should contain sufficient data of adequate spatial and
temporal coverage to describe the major physical processes that influence the generation of
low flows. The data period should be sufficient to represent climate variability and in particular
include low-flow and cease-to-flow periods.
2. Data should be checked for outliers, trends and errors that need to be corrected or removed.
3. Uncertainties associated with the data should, if possible, be determined before use.
Model selection and configuration
4. The hydrological characteristics important in generating and describing low flows should be
identified before selecting a model(s). Ecologically-relevant hydrological characteristics
should be included.
5. Models should be configured to ensure these important hydrological characteristics are
adequately represented.
6. Models best able to represent the identified low-flow hydrological characteristics should be
selected for use in estimating low flows. Model selection should be based on the simplest
model that will satisfy the representation of important low-flow hydrologic characteristics, as
well as overall modelling objectives and project requirements.
Model calibration
7. The calibration period should be sufficient to represent climate variability and in particular
include low-flow and cease-to-flow periods.
8. Where possible, validation using separate sets of data and appropriate criteria should be
used to assess and report calibration robustness.
9. Consistent calibration processes should be applied when calibrating a model to observed
flows. Objective functions used for automatic calibration should provide suitable weighting to
the low-flow portion of the flow regime. Manual calibration should adopt suitable low-flow
metrics (e.g. tables 2 and 3 in section 4).
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10. Uncertainty analysis should be undertaken to evaluate the reliability and robustness of the
model, particularly its ability to estimate low flows.
Model transposition
11. Flows in ungauged catchments should be estimated using data from gauged catchments in
the region. Information from more than one regional gauging station should be used in
deriving the estimate.
12. The reliability of flow estimates in ungauged catchments should be tested by applying the
method adopted to gauged catchments in the region and assessing the quality of calibration
using suitable objective functions and low-flow metrics. This may include testing different
methods and selecting the one that performs best.
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Report context
This report is part of a larger series of reports produced for the National Water Commission’s Low
Flows Project (Figure S1).
Hydrological modelling practices for estimating low flows – guidelines
Hydrological modelling practices for estimating low flows – stocktake, review and
case studies
Low flow monitoring and modelling gaps
Guidance on ecological responses and hydrological modelling for low-flow water planning
Low flow hydrological classification of Australia
Early warning, compliance and diagnostic monitoring of ecological responses to low flows
Review of literature quantifying ecological responses to low flows
Synthesis of case studies quantifying ecological responses to low flows
Eleven case study reports quantifying ecological responses to low flows
Figure S1: Context of reports produced in the National Water Commission's Low Flows Project (group
one, teal = modelling-related reports; group two, green = Waterlines report; group three, orange =
ecology-related reports).
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1. Introduction
1.1 Aim and scope
These guidelines aim to promote a best practice approach to estimating low flows, including cease-toflow conditions. They cover runoff generation models and river system models and provide advice on
model selection, data requirements, improved model configurations to represent low-flow processes,
and improved calibration strategies for matching low-flow behaviour in gauged catchments. The
guidelines also provide advice on approaches to model flows in ungauged catchments.
A key driver for these guidelines is an acknowledgement that most models have been developed and
calibrated with an emphasis on reliably assessing long-term water availability and the impact of
proposed water allocations on consumptive users. As a result, model calibrations have tended to
focus on replicating long-term averages and overall flow variability. To date the emphasis has not
been on low flows and so existing models do not generally represent low flows accurately. Models are
also used to determine and assess environmental flow rules and in their current form many may not
be wholly suited to this purpose because many ecologically-relevant processes and indicators
associated with low flows are not well-represented in or accommodated by models.
In the low flows report series, low flow is defined as the volume of water that occurs over a given
frequency and duration that is responsible for a mechanistic change in the processes and structure of
aquatic ecosystems relative to the average or median discharge for an individual river (or river reach).
Low flows in this context have a broad definition and vary from catchment to catchment. While the
focus of these guidelines is to provide advice on how to better model low flows, it is important the
models continue to accurately represent long-term averages and medium to high flows if water
planners and decision-makers can continue using them to assess the impact of water sharing rules on
consumptive users. Case studies show that existing models can be modified to greatly improve their
ability to estimate low flows without compromising their ability to estimate medium and high flows
however, in some instances, it may be necessary to implement separate models to represent different
parts of the flow regime.
These guidelines draw on a stocktake and review of existing modelling practices in Australia (Barma
& Varley 2012a). The stocktake documented information on model types and calibration and
validation techniques (see summary in Appendix A). The review assessed how well existing models
represented low flows and identified potential improvements, including a better understanding of
processes that drive low flows, model selection, model configurations, data acquisition and calibration
processes.
The guidelines also draw on case studies undertaken by jurisdictions that tested a number of methods
recommended. Summaries of these are reported in Appendix B and full reports are on the National
Water Commission’s website at www.nwc.gov.au/publications.
An associated report Guidance on ecological responses and hydrological modelling for low-flow water
planning (Marsh et al. 2012a) places the key points made in this report into a water planning
framework.
These guidelines should be viewed as a starting point. While every effort has been made to verify
recommendations and ensure consistency with other relevant guidelines, it is important they are
reviewed and strengthen over time.
1.2. Models
Most water resource planning is underpinned by hydrologic modelling, even in catchments which are
well-gauged where hydrologic parameter values could be derived from available data. This is because
models enable:

the impact of past and future changes in water management arrangements and land use to
be assessed

hydrologic parameter values (or flow regimes) to be estimated at locations that are not
gauged – either within, or adjacent to gauged catchments

the time series of historical flow data to be extended (using other inputs, such as rainfalls,
which typically have longer records) to provide a longer climate record and better
representation of variability and extreme conditions

the impact of future climate changes to be assessed.
A common objective of most models is to inform the appropriate allocation of water between different
users, including the environment and the ecosystem services it provides.
For the purposes of these guidelines, models estimating low flows are considered to include flowestimation methods such as rainfall-runoff models, regression models and flow transposition methods.
These flow estimation methods may serve as stand-alone models or provide flow time series input to
river system models (including river operation models and water resource planning models). They
may incorporate a range of processes such as: irrigation demands and diversions; town water
demands and diversions; return flows from irrigation drainage and sewerage plants; the impact of
storage operation and river regulation; channel routing impacts; transmission losses and gains.
1.3. Ecologically-significant low-flow metrics
A key purpose of these guidelines is to ensure models provide accurate assessments of low flows
because low flows are critically important to assessing environmental flow requirements and
environmental impacts of water allocation and management decisions. A related study by Mackay et
al. (2012), Low-flow hydrological classification of Australia, identifies 35 ecologically-relevant low-flow
metrics from an original set of 120 (Kennard et al. 2010) (Table 2). Of these 35 low-flow metrics, four
were selected as most relevant to most catchments based on the results of hydro-ecological case
studies (Marsh et al. 2012b), and for ease of calculation and potential application to support the
calibration and testing of hydrological models:

number of zero-flow or cease-to-flow days per year (the mean annual number of zero-flow or
cease-to-flow days per year used as a continuous function to consider stream ephemerality)

baseflow index (the proportion of flow attributable to baseflow using the Lyne and Hollick
digital baseflow filter method (Nathan & McMahon 1990; Grayson et al. 2004)

90th percentile exceedance flow (flow exceeded 90 per cent of the time)

specific mean annual minimum (the average of the annual minimum flow divided by
catchment area).
A classification using these four metrics and 830 unregulated site records identified eight classes of
streams ranging from highly ephemeral to strongly perennial. A series of single-layer metric maps of
Australia were also produced to illustrate the absolute value of three of the four metrics, as well as of
two additional metrics (the long-term inter-annual variability in both the baseflow and the number of
cease-to-flow days). More information is provided in Chapter 4.
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1.4. General modelling guidelines
General guidelines on developing rainfall-runoff models and river system models can be found in the
following guidelines developed by eWater CRC:

Black DC, Wallbrink PJ, Jordan PW, Waters D, Carroll C & Blackmore, JM 2011, Guidelines
for water management modelling: towards best practice model application, eWater
Cooperative Research Centre, Canberra, Australia. ISBN: 978-1-921543-46-3. Available at:
www.ewater.com.au.

Rassam DW, Jolly I & Pickett T 2011, Guidelines for modelling groundwater-surface water
interactions in eWater Source: towards best practice model application, eWater Cooperative
Research Centre, Canberra, Australia. ISBN 978-1-921543-59-3. Interim version 1.0 available
at: www.ewater.com.au/uploads/files/eWater-Guidelines-Source-GWSW-(v1-Interim-Nov2011).pdf.

Vaze J, Jordan P, Beecham R, Frost A & Summerell G 2011, Guidelines for rainfall-runoff
modelling: towards best practice model application, eWater Cooperative Research Centre,
Canberra, Australia. ISBN 978-1-921543-51-7. Interim version 1.0 available at:
www.ewater.com.au/uploads/files/eWater-Guidelines-RRM-(v1_0-Interim-Dec-2011).pdf.
1.5. Report structure
These guidelines are presented in four sections covering the following topics:

Data considerations (Chapter 2)

Model selection and configuration (in gauged catchments) (Chapter 3)

Model calibration processes and calibration performance (in gauged catchments) (Chapter 4)

Transposition methods (for ungauged catchments) (Chapter 5).
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2. Data considerations
2.1 Principles
Principle 1: The model used to estimate low flows should contain sufficient data of adequate spatial
and temporal coverage to describe the major physical processes that influence the generation of
low flows. The data period should be sufficient to represent climate variability and in particular
include low-flow and cease-to-flow periods.
Principle 2: Data should be checked for outliers, trends and errors that need to be corrected or
removed.
Principle 3: Uncertainties associated with the data should, if possible, be determined before use.
2.2 Issues
The accuracy of rainfall-runoff and river system models substantially depends on the quality of input
data. Low flows are particularly sensitive, as relatively small errors in some inputs can significantly
impact low flows. While not covered in these guidelines, it is essential that standard checks on the
suitability and accuracy of the available data be conducted before use, and corrections made where
feasible. Otherwise erroneous data should be discarded. Relevant checks may include measures
such as:

checking for outliers, trends and non stationarity (using double mass curve plots etc.) and
consistency between different stations

infilling missing data

disaggregating data

checking the timing and relationship between rainfall and runoff (errors if runoff occurs in
wrong days or runoff volumes are not sensible).
A more general consideration of data issues is provided in the eWater guidelines (Black et al. 2011;
Rassam et al. 2011; Vaze et al. 2011) referenced in the Introduction.
2.2.1 Primary input data
The most significant inputs to rainfall-runoff and river system models are rainfall, flow and potential
evapotranspiration. Issues related to these inputs are discussed below from a low-flows perspective.
Rainfall
Rainfall-runoff models are often used to estimate a time series of flow inputs for river system models.
The reliability of these models is highly dependent on accurate representations of spatial and
temporal variations in rainfall. Typical river system models are applied over long periods, spanning
100 years or more. Adequate information on rainfall is generally available for recent decades, but
fewer rainfall stations are available as the models are extended back in time, resulting in a loss of
detail in spatial and temporal rainfall variability. This affects the ability of the models to reproduce low
flows. Low-flow estimates can be more accurate if the models are calibrated and run for only periods
where sufficient stations exist to provide adequate spatial representation of rainfall. While this may
limit the ability to examine the impact of long-term changes in the climate regime (important for
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assessing supply reliability), the improvement in low-flow estimates may be significant. One approach
may be to run the models over longer time periods for assessing supply reliability, and to base lowflow assessments on models run over shorter time periods when there is better spatial coverage.
Alternatively, recent data may be used to generate extended datasets, ensuring that relevant statistics
such as the mean and variance are preserved.
According to Boughton (2005): It is a cliché among catchment modellers that most modern water
balance models will give good results with good quality input data and poor results with poor quality
input data. In other words, the quality of the results is more dependent on the quality of the input data
than on the model chosen for use. The biggest problem is sampling errors when the selected input
data are not representative of the entire catchment. The standard 203 mm (8-inch) rain gauge has a
catch area of 1/3,000,000 sq. km and the rain gauge network in Australia is one gauge to about 10 to
100 km2. Rainfall has much more spatial and temporal variability than evaporation, and it is the
sampling errors of the spatial variability of rainfall that is the biggest problem with catchment-scale
water balance modelling.
Streamflows
Investigations such as those by Ozbey et al. (2008) have shown that conventional streamflowgauging stations typically exhibit errors of 5 to 15 per cent when measuring medium to high flows, but
the error when measuring low flows may be considerably higher. It is difficult with current technology
to measure low flows accurately without constructing a flume or weir in the channel, which in many
instances is considered unacceptable because of adverse environmental impacts. This means that
models are being calibrated to data with high levels of uncertainty. In South Australia, V-notch weirs
have recently been constructed on many waterways where accurate low-flow measurements are
required. Potential adverse environmental impacts of the structures may be compensated by the
improved information on low-flow behaviour, which informs water management and allocation
decisions.
When constructing river system models, the accuracy of existing low-flow measurement should be
investigated and options to improve low-flow measurements should be explored (where feasible),
particularly when existing structures could be modified without further adverse environmental impacts.
When assessing the accuracy of low-flow measurements, the following may be considered:

the stability of the site

sensitivity of the measurements (stage and rating curve)

can accuracy be improved by additional low-flow gaugings or is a structure or new technology
required?
Potential evapotranspiration
Potential evapotranspiration is less variable than rainfall or streamflow, both in space and time. As a
result, reasonable estimates of potential evapotranspiration can generally be derived from available
data.
2.2.2 Secondary input data
The stocktake has shown that accuracy of low-flow estimates is hampered by poor information in key
areas of:

diversions and extractions

distribution, size and historical development of farm dams
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
types and distribution of crops (used to estimate crop water demands and hence
consumption)

on-farm practices such as use of storage dams, recycling, planting decisions and water
application technologies (efficiencies)

in-stream transmission gains and losses.
These issues are discussed below.
Diversion data
Historical diversions (for irrigation, industrial and town water use) are a key input to river system
models and have a significant influence on calibration. The accuracy of information on diversions is
often poor due to inaccurate meters. In many instances, recorded data is only available for limited
time periods or for a subset of users. Diversion data is often only available at monthly or quarterly
intervals, and since low flows may be significantly affected by daily diversions this limits the ability of
models to reliably estimate daily low flows.
There is little that can be done to improve information on diversions if the existing data is poor. It is
recommended that metering programs for diversions are improved where appropriate by installing
accurate meters capable of recording data at daily or sub-daily intervals. The accuracy of low-flow
estimates could be greatly improved with the addition of just a few years of reliable diversion data.
A number of government-funded programs have recently been undertaken or are in the planning
stages (particularly in the Murray-Darling Basin) to install accurate diversion meters across irrigation
districts that are capable of measuring diversions at daily and sub-daily time steps. In addition,
national standards are currently being developed for accurately measuring diversions for irrigation
and town water use. When these standards are introduced, land holders and water authorities will be
required to install complying meters. These measures will improve information on diversions in future.
It is recommended that river system models are recalibrated as this improved diversion information
becomes available, as this will improve low-flow estimates.
Irrigation demands and consumption
Historical irrigation demands can be based on metered diversions. However, where metered data is
not available they need to be estimated. Two approaches are used to estimate irrigation demands for
a given climate. The first uses relationships derived from correlations between climate data and
metered diversions, either from the area of interest or a region with similar conditions (climate, crops,
farm practices). The second (and most common approach) uses a crop demand model which
simulates relevant factors such as crop water demand at different stages of the growth cycle and soil
moisture in response to climate and irrigation practices. Crop demand models require information on
the meteorological data, type and distribution of crops, soil properties and farming practices (watering
practices, application technologies, drainage or recycling practices). Available information on soils
and climate is generally adequate for modelling purposes, but information on the types and
distribution of crops is limited in terms of spatial and temporal coverage. Information on farming
practices is often very limited.
It is recommended that crop demand models are developed where possible to estimate irrigation
demands since they are more suited to capturing the effects of changes in resource availability and
water resource management. Consequently, improved information on crop types and distribution and
on-farm practices needs to be collected (where appropriate) to assist in improving low-flow estimates.
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Farm dams
In some catchments, farm dams may be responsible for intercepting significant volumes of runoff,
which reduces available flow. This matter has gained considerable attention in Victoria and concerted
effort has been made to estimate the impact of farm dams on flows. This has involved using satellite
imagery, LIDAR and photogrammetry information to identify the number and extent of farm dams at
different points in time. Ground survey has measured the size of hundreds of dams and typical
volume surface area relationships have been developed. Information on typical storage use behaviour
has also been collected and a simulation model (STEDI, Nathan et al. 2005) has been developed to
estimate the impact of farm dams on flows. A number of other jurisdictions (including New South
Wales, South Australia and Western Australia) have applied similar approaches to estimate the
impact of farm dams on flows in selected catchments.
Where farm dams are assessed as significant, it is recommended the relevant information is collected
and a simulation tool like STEDI used to assess the impact on flows, especially low flows. If farm
dams are assessed as significant and little or no information can be obtained, then some attempt to
assess uncertainty associated with the low-flow estimate should be made through an inflow sensitivity
analysis.
In-stream transmission gains and losses
Channel transmission losses are derived from several sources including:

water that infiltrates through the stream bed and migrates to a groundwater aquifer that lies
below the stream bed (representing a permanent loss)

water that infiltrates into the stream banks which may be lost to riparian evapotranspiration

water lost to evaporation from the stream water surface.
Current river system models typically lump all components of transmission loss into a single value
which is varied on an annual basis. This approach does not simulate the processes involved in
transmission losses and is not able to reproduce the variations that occur on a seasonal or daily
basis. Given that transmission losses can be of the same magnitude as low flows, this approach
makes it difficult for current river system models to accurately represent low flows.
Historically, transmission losses have not been well understood, with a lack of data to quantify and
describe them. However, a recent water balance study of the Murrumbidgee River (SKM 2011) has
provided valuable information on the behaviour of the components of transmission losses and how to
estimate these components. Current modelling platforms (REALM, IQQM, Source Catchments,
WaterCress) can incorporate complex functions capable of representing the actual loss processes,
provided sufficient data is available to describe the loss behaviour. It is therefore recommended that
appropriate data is collected (either from literature or the field) to describe transmission losses and
that appropriate representations of transmission losses are included in future river system models so
daily and seasonal transmission losses can be reliably estimated.
Most existing river system models apply transmission loss functions based on average losses over a
year. However, losses vary seasonally due to climate, local groundwater level variations and river
operation patterns. Losses can vary significantly on a daily basis in response to changing climate,
with hot days producing high evaporation losses from the water surface, as well as high
evapotranspiration losses from riparian vegetation.
Furthermore, most current water resources planning models lump transmission losses into a single
term, whereas more detailed water balance models separate transmission losses (and gains) into
components such as:

losses to evaporation or gains from rainfall on the water surface
NATIONAL WATER COMMISSION — Low flows report series
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
losses to evapotranspiration by riparian vegetation

seepage to, or return flows from the groundwater table.
The averaging and lumping of losses introduces modelling errors that limit a model’s ability to
accurately reproduce low flows. It is recommended that impact of transmission losses on low flows
are assessed and if deemed significant, data should be collected on the processes which influence
each of these losses so transmission losses can be specifically modelled.
Note the eWater Source Catchments product has functionality to specifically represent separate loss
components. The eWater Guidelines for modelling groundwater-surface water interactions in eWater
Source: towards best practice model application (Rassam et al. 2011) also provides guidance with
respect to modelling of seepage to and return flows from the groundwater table.
2.3 Data guidelines

The physical processes that influence the generation of low flows in a particular catchment
should be identified and all relevant data on these processes collected before model
configuration. Where data is unavailable, consideration should be given to implementing
appropriate data collection programs. Data required to model low flows include:
–
rainfall (need for adequate spatial coverage)
–
streamflow (need for accurate low-flow measurement)
–
potential evaporation data
–
land use (including changes) and water use, including information on crop distribution and
water user behaviour as well as the operation, volume, and distribution over time of
hillside dams and on-farm storages
–
flux changes between surface and groundwater
–
data to allow transmission loss and/or gain components to be represented separately
–
detailed cross section information for rivers and floodplains, including locations associated
with water holes
–
specific low-flow-dependent characteristics of key ecological assets and functions.

All data should ideally be collected and disaggregated to a spatial and temporal resolution
that aligns with the model being used to estimate low flows, and which allows for assessment
of ecologically- relevant outcomes.

Data should be checked for outliers, step changes, trends and errors which need to be
corrected or removed.

The uncertainty associated with the historic streamflow data used to calibrate and validate
low-flow reproduction should be determined before model calibration and validation.
NATIONAL WATER COMMISSION — Low flows report series
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Case study 1: Primary input data - the Daly River
The Daly River catchment lies in the wet/dry tropics of northern Australia. Its rainfall and runoff is
characterised by extreme seasonality in rainfall patterns, with 95 per cent (975 mm) of rainfall falling
in the wet season between November and May. The Daly has highly-connected ground and surface
water with flow in the dry season fed by baseflow from connected aquifers. The Daly River catchment
has been modelled by a suite of linked rainfall-runoff, hydrodynamic and groundwater models to
reproduce these connected flows.
It had been assumed that inflows were relatively evenly distributed upstream of Low Level gauge
(G8140222). However, a case study adopted additional streamflow gauging stations and improved
rating tables (note the case study also evaluated if low-flow reproduction could be improved by
applying better calibration methods, see Chapter 4). The inclusion of new gauge sites and re-rating of
the gauging station at the Railway Bridge (G8140001) upstream on the Katherine River resulted in a
significantly improved understanding of how the aquifer-river connectivity affected flows, especially
low flows.
More details are provided in Appendix B. A complete description for the case study is on the National
Water Commission website at www.nwc.gov.au/publications.
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3. Model selection and configuration (in
gauged catchments)
3.1. Principles
Principle 4: The hydrological characteristics important in generating and describing the low flows to be
modelled should be identified before a model(s) is selected. Ecologically-relevant hydrological
characteristics should be included.
Principle 5: Models should be configured to ensure these important hydrological characteristics are
adequately represented.
Principle 6: Models best able to represent the identified low-flow hydrological characteristics should
be selected for use in estimating low flows. For example, coupled surface and groundwater models
may be more appropriate than rainfall-runoff models in systems where regional groundwater
systems primarily feed low flows. Model selection should be based on the simplest model that will
satisfy the representation of the low-flow hydrologic characteristics, as well as overall modelling
objectives and project requirements.
3.2. Model selection
3.2.1 Rainfall-runoff models
Rainfall-runoff models are often used to estimate catchment inflows for river system models (although
other methods are occasionally applied). A range of different rainfall-runoff models are used across
Australia. Those most commonly used historically for river system models in Australia are listed below
and described in Appendix A:

Sacramento model

AWBM

SymHyd

MODHYDROLOG

WC1

LASCAM (LArge Scale CAtchment Model).
Other rainfall-runoff models applied in Australia include IHACRES and SMARG, and more recently
GR4J. New models continue to be introduced and improvements made to existing models.
While the models listed above differ in structure, each can be described as one-dimensional lumped
models that imply uniformity of rainfall, catchment characteristics and hydrologic response within a
subcatchment. Most include soil stores with surface runoff generated when the store is saturated or
when the rainfall intensity exceeds the soil infiltration capacity. These models represent interflow and
baseflow behaviour to different levels of complexity. Distributed GIS-based models have been around
more than 15 years but have not been widely applied due to their complexity, the lack of suitable data
for calibration and because they do not generally perform any better than lumped models.
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The six models listed above are available in stand-alone form, but several have been incorporated
into other modelling platforms that provide additional features and capabilities, such as the ability to
incorporate channel routing, transmission losses and automatic calibration routines. The Sacramento
model, AWBM and SymHyd are included in the eWater Rainfall Runoff Library (RRL). They are also
incorporated in eWater’s Source IMS modelling framework 1 and WaterCress. MODHYDROLOG and
WC1 are also incorporated in WaterCress (Cresswell et al. 2002). Short descriptions of these models
are provided in Appendix A. (Note: Many rainfall-runoff water balance models currently in use in
Australia are derivatives of earlier models. For example, SymHyd is a derivative of MODHYDROLOG,
which is a derivative of HYDROLOG. For a more complete description of the development of rainfallrunoff water balance models in Australia, see Boughton 2005.)
The strengths and weaknesses of these models affect their selection for projects. New South Wales
and Queensland use the Sacramento rainfall-runoff model incorporated in IQQM. In Victoria, rainfallrunoff models such as AWBM, SymHyd or MODHYDROLOG typically compute inflows to REALM.
AWBM has historically been the preferred model because it has fewer parameters and is simpler to
calibrate, but MODHYDROLG has been applied in catchments where baseflow is important, or in
catchments with ephemeral streams, as it is better able to reproduce flow behaviour in these settings.
More recently, SIMHYD has gained acceptance as another model with a relative small number of
parameters easy to apply. In Tasmania, the adapted Hydstra database uses AWBM to compute
inflows. However, AWBM has been modified to allow different monthly values for the CapAve
parameter to better reproduce seasonal influences on flow behaviour. In South Australia, the
WaterCress framework with inflows computed using WC1 is generally used. LASCAM has been
widely applied in Western Australia, although a variety of other models have been used including
LUCICAT, SQUARE (both derivatives of LASCAM) and the eWater RRL suite.
The choice of preferred rainfall-runoff model will vary depending on circumstances. To date, no
systematic studies have assessed and compared the ability of the various models to perform across a
range of climate zones and geographic settings. Based on feedback from jurisdictions, some broad
conclusions have been drawn and are discussed below. SIMHYD and AWBM are the simplest models
and the easiest to calibrate. They have been found to work well across a wide variety of different
catchment types and climate zones. However, they may be unsuitable in applications with ephemeral
streams, arid climates, or where there are strong seasonal variations in runoff response.
MODHYDROLOG has been found to perform well for applications with a strong baseflow component
and ephemeral streams, but may not perform well in arid climates or applications with a strong
seasonal variation in response. The Sacramento model is a complex model shown to perform well in
a wide variety of situations, including catchments with a strong baseflow component and seasonal
response. It has been applied successfully to arid climates in New South Wales and Queensland, but
did not perform well when tested in arid catchments in South Australia. WC1 was specifically
developed for arid catchments in South Australia that demonstrated a strong seasonal climatic pattern
and a significant spatial variation in runoff response across subcatchments. WC1 varies from other
models in that it applies different soil moisture capacities across a subcatchment. Soil moisture
capacity is described by a catchment average moisture capacity and a distribution which represents
how moisture capacity varies across the catchment (i.e. what percentage of the catchment has higher
and lower capacities). The model tracks the variation in moisture capacity (saturation) across the
catchment. When a portion of the catchment becomes saturated, all further rainfall is converted to
runoff. LASCAM has been demonstrated to perform well in a wide variety of catchment and climate
zones in Western Australia. It also performed well when tested (as part of this project) in Tasmania.
General guidance on rainfall-runoff model choice can be found in the eWater rainfall-runoff modelling
guidelines (Vaze et al. 2012).
To date, jurisdictions have not paid particular attention to assessing how well their models reproduce
low flows. Evaluation of the application of existing models has shown that low flows are generally not
well reproduced, but there is insufficient information to assess if any models have a superior ability to
simulate low flows. In terms of low-flow reproduction, it would appear there have been few
1
Source IMS includes Source Catchments, Source Rivers and River Operator.
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comparisons during the past 20 years between models using common data, common calibration
processes and optimisation strategies and common assessment techniques. Consequently, the only
guidance that can be provided in regards to rainfall-runoff model selection is:

models which are superior at replicating baseflows are most likely to be the best at
reproducing low flows

models able to emulate hydrological characteristics including ecologically-relevant
characteristics such as those highlighted in Mackay et al. (2012) and Marsh et al. (2012b) that
are known to be relevant to low flows in the region of interest are preferred (this might include
seasonal rainfall-runoff response, or spatial variability in soil capacity).
3.2.2 Regression models
A time series of catchment inflows (usually at a daily or monthly time scale) can be estimated using a
statistical model developed by applying multivariate regression techniques. They are of most use in
situations where catchment conditions remain similar to those the statistical model is based on. It is
difficult to develop regression relationships capable of accounting for changed catchment conditions,
water management conditions or future climate change. Regression relationships are therefore very
limited in their ability to investigate different scenarios. If it was necessary to investigate scenarios,
physically-based models are more appropriate. Another issue with regression relationships is that the
estimated time series typically exhibit less variance than the raw time series.
As with rainfall-runoff models, the key data inputs are rainfall (daily or monthly) and evaporation. The
steps in the process are:
i.
Data Acquisition – Acquire data for streamflows, rainfall and other parameters (such as
evaporation) if required. Partition the data into model development and verification datasets.
ii.
Model design – Develop a statistical model of the relationship between the input parameters
and flow. Runoff will be primarily driven by the rainfall on that day, but also influenced by
antecedent conditions, which may generally be expressed by accounting for the previous 30
days of rainfall. Evaporation may also have an influence on antecedent conditions and are
normally expressed in terms of the previous 30 days of potential evaporation. Runoff may be
influenced by seasonal factors which may be incorporated in the model by including the
month or season. Statistical rainfall-runoff models may take many forms. A typical relationship
which relies on observed rainfall as the main driver of the flow estimate in the target
catchment may take the form:
Qi = fn(Ri, Rpm, Epm, Mn)
Where: Qi
= discharge on day ‘i’
Ri
= rainfall on day ‘i’
Rpm
= cumulative rainfall in the previous 30 days
Epm
= cumulative potential evaporation in the previous 30 days
Mn
= current month
fn
= a function
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This relationship can be further refined by adding a soil moisture term. Alternatively, a typical
relationship which derives an estimate of runoff in a target catchment, based on observed
flows in an adjoining catchment may take the form:
Qti = a + b Qai
or
Qti = a(Qai)b
Where: Qti
Qai
= Discharge in the target catchment on day ‘i’
= Discharge in the analogue catchment on day ‘i’
a and b are constants estimated by regression.
(Note: Other relationships may be applied and are limited only by the knowledge and
imagination of the practitioner.)
iii.
Using an appropriate statistical package, develop the model using step wise regression
analysis.
iv.
Assess the fit of the model against the development dataset and validate the performance of
the model using the validation dataset (see Chapter 4 below for details on assessing the
performance of a model).
3.2.3 Water balance methods
Inflows from some areas within a river system model are often computed using water balance
techniques. This is particularly the case for residual flows from intermediate catchments located
between two gauging stations on a main watercourse. In this case, runoff from the intermediate
catchment is not directly gauged, but can be inferred by examining the difference in gauged flows
between the upstream and downstream catchments. However, simply subtracting the upstream flows
from the downstream flows may not yield an accurate result because:

Channel routing can modify flows as they move through a system, with attenuation of flow
occurring on the rising limb of a hydrograph (water goes into storage reducing the
downstream flow) and the opposite occurring on the falling limb (water leaving storage adding
to the downstream flows).

Travel time between gauging stations needs to be taken into account when subtracting flows.

Extractions by irrigators and other consumptive users; return flows (such as irrigation
drainage water and sewerage treatment plant discharges); residual catchment inflows; and
channel losses (and gains) may introduce discrepancies.
As a general rule, channel routing and travel time are not important factors for estimating monthly
flows, but they do need to be specifically addressed for estimating daily flows.
The upstream and downstream gauging stations are both likely to have errors. Therefore, even when
the channel routing and travel time effects are correctly accounted for, there may be situations where
subtracting the upstream flow from the downstream flow results in a negative flow, which is not
possible in a system which has no transmission loss. Low flows will be sensitive to these errors, which
are likely to be of similar magnitude to the low flows themselves. Water balance methods may be
suitable for estimating residual flows at a monthly or annual time scale, where daily flow measurement
errors are likely to cancel out, but may be unsuitable for estimating low flows at a daily time scale.
NATIONAL WATER COMMISSION — Low flows report series
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It should also be noted that water balance issues are merely one manifestation of many potential
problems due to gauging station errors. More information on these is provided in the eWater
guidelines (e.g. Black et al. 2011; Rassam et al. 2011; Vaze et al. 2011).
3.2.4 River system models
The most widely applied river system models in Australia are IQQM and REALM, with IQQM the
model of choice in New South Wales and Queensland, while REALM is the model of choice in
Victoria. REALM has also been applied in Western Australia and the Australian Capital Territory. It is
intended both these models will be replaced by Source IMS, currently under development by eWater
CRC. Source IMS incorporates many features and capabilities of IQQM and REALM, as well as
significant additional capabilities. Other river system models used in Australia include WaterCress
widely applied in South Australia and MIKE Basin in Western Australia.
IQQM incorporates Sacramento as a rainfall-runoff model for estimating catchment inflows, while
WaterCress allows the user to select from a range of rainfall-runoff models, including Sacramento,
HYDROLOG, AWBM, WC1 and SIMHYD. REALM does not include a rainfall-runoff model, with
inflows (which need to be calculated independently) added as a time series input.
River system models incorporate functionality for modelling a number of key catchment processes
that affect low flows not included in rainfall-runoff models, such as:

channel process including:
–
channel routing (some rainfall-runoff models have some provisions for this)
–
diversions
–
reservoir operations
–
transmission losses, including groundwater/surface water interactions

complex rules relating to:
–
the passing of environmental flows
–
extractions for irrigation and town water use.
A strength of REALM is that it includes a network linear optimising algorithm to allocated water to
different demand centres in space and time. It ensures the most efficient use of available delivery
systems and storages to meet demands and is especially efficient in optimising the performance of
systems with multiple storages and delivery pathways 2. REALM is generally configured to operate on
a monthly time step3 and does not include channel routing (which is not relevant at a monthly time
step). REALM can be configured to operate at a daily time step with channel routing, but the
optimising algorithm does not function at a daily time step and so this option is rarely used. This has
important implications, because one of the most important purposes for computing low flows is to
assess environmental impacts and these are sensitive to low flows at a daily time step. Historically
agencies that use REALM have computed information on daily low flows using a disaggregation
process (using the observed flows from selected streamflow-gauging stations or the results of daily
rainfall-runoff models) to develop daily patterns of flow. In future it is expected that Source
Catchments, a new-generation model recently released by eWater CRC, will supersede REALM and
IQQM and possibly WaterCress. Source Catchments will incorporate the individual strengths of each
of these models and introduce new capabilities.
2
These systems are common in Victoria, but not in New South Wales or Queensland, which tend to have one or two storages
in the headwaters and a linear supply system.
3
IQQM and WaterCress both operate at a daily time scale.
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3.2.5 Linked hydrodynamic and groundwater models
Water resource planning agencies in Western Australia and the Northern Territory have developed
modelling systems which combine hydrodynamic and groundwater models to accurately represent
systems with highly connected groundwater and surface water. In each application, the DHI modelling
platform is adopted, using a combination of models including MIKE 11, MIKE SHE and FEFLOW.
MIKE11 uses an implicit, finite difference scheme for the computation of unsteady one-dimensional
flows in rivers and estuaries (DHI 2007). The model also possesses rainfall-runoff generation
capability in the form of a NAM model. FEFLOW uses a finite element solution for the computation of
three-dimensional flows and pressures in groundwater systems (DHI 2010). MIKE SHE combines
surface and groundwater models.
This modelling approach has been applied to systems in climates with distinct wet and dry seasons,
where dry season streamflows are fed by groundwater systems and channel flows in the wet season
are significant contributors to groundwater recharge.
3.3 Model configuration
The stocktake has shown a number of hydrologic processes important to the generation of low flows
are often not represented, or are poorly represented, in existing river system models, including:

interception by hillside dams

baseflow behaviour

channel transmission gains or losses

groundwater/surface water interactions.
While it can be argued that all but the first point above are related phenomena, from a modelling
perspective there are important distinctions, which are discussed below.
3.3.1. Interception by hillside dams
The effect of hillside dams has historically been assumed to be insignificant and they have not been
included in river system models. However, there is now a strong body of evidence that proliferation of
hillside dams in many catchments profoundly affects surface water runoff across the entire flow
regime, and particularly low flows.
Interception by hillside dams is not represented in any standard rainfall-runoff models. In principle, the
effect of hillside dams could be approximately simulated within existing river system models by
incorporating lumped storages at the end of individual sub-areas that represent the combined
storages of upstream farm dams. However, the best approach would be to employ a model
specifically developed for use in Victoria to simulate the behaviour of farm dams (and their effect on
runoff), such as STEDI (Spatial Tool for Estimating Dam Impacts, Nathan et al. 2005) or CHEAT
(Jordan et al. 2004). (Note: Source Catchments includes an adaptation of the STEDI model so the
effect of farm dams on runoff response can be seamlessly incorporated into the river system model.)
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3.3.2. Baseflow
Baseflow is generated from water that infiltrates into the soil stores and groundwater stores and
makes its way to a stream. This process is represented in many rainfall-runoff models, mostly as a
one-way process, but some models (such as MODHYDROLOG) allow two-way flow. Only a portion of
the water that infiltrates makes its way to the stream, as some is lost to evapotranspiration and to
deep groundwater stores.
Baseflow is particularly important when it comes to reproducing low flows. Baseflow is represented by
all standard rainfall-runoff models, but a number of models have been shown to better represent
baseflow behaviour (see Rassam et al. 2011).
3.3.3. Channel transmission losses
Lumping all components of transmission loss into a single value which is varied on an annual basis is
a common feature of many river system models. This form of configuration means these models do
not produce the majority of loss variations that occur on a seasonal or daily basis. Given that
transmission losses can be the same magnitude as low flows, this approach makes it difficult for
current river system models to accurately represent low flows.
As stated in Chapter 2, current modelling platforms (REALM, IQQM, Source Catchments,
WaterCress) can incorporate complex functions capable of representing the actual loss processes,
provided there is sufficient data to describe the loss behaviour. It is therefore recommended that
future models are configured to represent the individual components that make up transmission
losses so they can be reliably estimated on both an intra and inter-year basis.
Case study 2: Residuals and flow generation – Modelling of the Paroo and Burdekin
catchments
The Paroo is located in an arid to semi-arid zone in south-west Queensland and north-west New
South Wales. The Paroo River is a dryland river consisting of a series of disconnected waterholes
most of the time.
A case study was undertaken to evaluate if low-flow reproduction can be improved through:

incorporating pools in the model to represent the water captured and stored in the pools

computing evapotranspiration losses from the river and pools.
No clear conclusion could be drawn from the results, and processes need to be better understood
and specifically represented in the model. Use of a hydrodynamic model better able to represent
the movement of water into the floodplain and connected storages (with associated infiltration and
evaporation losses) may improve results.
More details are provided in Appendix B. A complete description for the case study is on the
National Water Commission website at www.nwc.gov.au/publications.
3.3.4. Groundwater/surface water interactions
Groundwater/surface water interactions could be argued to include baseflows and losses to deep
aquifers (described above), but in this context refer to a situation where the relative levels between a
stream and groundwater source fluctuate due to variations in the streamflow (and corresponding
water level) or variations in the groundwater level. This creates a situation where the stream may
NATIONAL WATER COMMISSION — Low flows report series
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incur losses to the aquifer for a period of time followed by periods of gaining flow, or vice versa.
Variations can be in response to seasonal or long-term drivers. Seasonal variations can be influenced
by climate drivers such as rainfall and potential evaporation that affect streamflows and aquifer
recharge and depletion, or by river operations aimed at meeting seasonal irrigation and urban water
demands. Long-term variations may be driven by climate cycles that endure for periods of years or
decades or changes in water use behaviour. These changes may cause ground water levels to rise or
fall over extended periods. Flux changes between surface and ground water can be approximately
represented in modelling platforms such as REALM, IQQM and Source Rivers using a function that
relates the groundwater/surface water flux to the relative water levels. However, these models do not
simulate the hydrogeological processes and in some situations it may be necessary to use a linked
groundwater/surface water model such as MIKE 11 and FEFLOW or MIKE SHE.
More information is provided in Guidelines for modelling groundwater-surface water interactions in
eWater Source: towards best practice model application (Rassam et al. 2011).
3.4 Model selection and configuration guidelines

It is recommended the choice of model used to estimate low flows is governed by the
hydrological characteristics of the catchment and the level and type of information required. A
good conceptualisation of the system should be completed before model selection to determine
(and roughly quantify) the importance of factors such as groundwater interaction, changing land
use (including farm dam construction) and other components of the flow regime (i.e. interflow,
baseflow, quickflow etc.).

The model should be configured to ensure it appropriately represents key hydrological processes
(including ecologically-relevant hydrological processes) that influence low flows, such as:
–
interception by hillside dams
–
baseflow behaviour
–
channel transmission gains and losses (including riparian evapotranspiration)
–
groundwater/surface water interactions.

Model selection should be based on the simplest model that will satisfy the conceptual model
and the project requirements.

Many water resouce planning models in Australia are currently based on a monthly time step.
This is a suitable time step for assessing water availablilty for irrigation and town water use.
However, it is not a suitable time step for estimating low flows or assessing environmental
processes. It is therefore recommended that models aimed at estimating low flows are based on
a daily time step.

In cases where residual inflows are determined using water balance techniques, the water
balance should take into account all inflows and outflows for overlapping periods of observed
data and incorporate the effects of channel routing (including storage in weirs and water holes),
travel time, extractions, return flows and transmission losses. Procedures to eliminate negative
flows and other problems due to gauging and measuring instrument errors should be
incorporated without distorting the total flow.

Water balance methods may be suitable for estimating residual flows at a monthly or annual time
scale, where daily flow measurement errors are likely to cancel out, but may be unsuitable for
estimating low flows at a daily time scale.
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Case study 3: Model selection
Modelling of the upper north Esk catchment
The north Esk catchment discharges into the Tamar Estuary on the Tasmanian north coast and has a
catchment area of 1065 km2. The dominant land use is low-density sheep and cattle grazing with
some cropping and forestry plantations. The lower reaches also include urban development. A portion
of the water supply for Launceston is drawn from the upper reaches. This catchment had previously
been modelled using the Hydstra modelling platform with AWBM as the rainfall-runoff model.
A case study was undertaken to evaluate if low-flow reproduction can be improved by applying an
alternative model (in this case LASCAM version 2.6), additional calibration methods, and performance
metrics. An automatic calibration optimiser was applied using the Nash-Sutcliffe coefficient of
efficiency on daily flows as the objective function. Flows were transformed by raising the modelled as
well as observed flows by an exponent that ranged from 0.5 to 0.9 (see Chapter 4), providing a range
of model estimates. Low-flow metrics were assessed using the eWater product River Analysis
Package v3.0.3 (RAP).
The performance assessment metrics indicate the LASCAM model performed better than AWBM at
estimating the mean daily flow, the P5, P10, P90 (see Chapter 4 below for definitions), minimum and
median flows. LASCAM also did better at reproducing the baseflow statistics. The flow duration
curves support these statistics showing that LASCAM reproduced the observed flow duration curve
across the entire flow regime, whereas AWBM underestimated the median and low flows. LASCAM
was also successful at reproducing the low trough values and the duration of event. The best overall
results were achieved with an exponent of 0.65.
More details are provided in Appendix B. A complete description for the case study is found on the
National Water Commission website at www.nwc.gov.au/publications.
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4. Model calibration
4.1 Principles
Principle 7: The calibration period should be sufficient to represent climate variability and in
particular include low-flow and cease-to-flow periods.
Principle 8: Where possible, validation using separate sets of data and appropriate criteria should be
used to assess and report calibration robustness.
Principle 9: Consistent calibration processes should be applied when calibrating a model to observed
low flows. Objective functions used for automatic calibration should provide suitable weighting to
the low-flow portion of the flow regime. Manual calibration should adopt suitable low-flow
metrics, such as those described in tables 2 and 3.
Principle 10: Uncertainty analysis should be undertaken to evaluate the reliability and robustness of
the model, particularly its ability to estimate low flows.
Note - model calibration and validation for all flows are extensively discussed in the eWater
guidelines (Black et al. 2011; Rassam et al. 2011; Vaze et al. 2011).
4.2 Calibration process – inflow estimates
4.1.1. General approach
Models should be calibrated against historical data that cover a period sufficiently long enough to
represent climate variability. When models are targeting low flows, the calibration data should include
periods of low flows, including cease-to-flows (where appropriate) and the transition to and from lowflow periods. There are no clear guidelines as to what represents a suitable length of record for
calibration, which will depend on the variability of flows and data availability. State jurisdictions specify
minimum calibration periods ranging from three to 20 years, but these criteria are probably a reflection
of data availability.
It is recommended that validation (to further confirm model calibration robustness) is undertaken
where sufficient data exists to allow its separation into subsets of calibration and validation data. Two
thirds of this data is typically used for calibration and the remainder for validation.
A two-step calibration approach which uses automatic routines to establish an initial calibration is
recommended, followed by manual adjustment to further improve the calibration using selected lowflow metrics, as well as comparison of graphical outputs (such as time series plots, scatter diagrams
and flow duration curves).
Most existing river system models have been shown to poorly reproduce low flows, but experience
from the case studies shows that low-flow performance can be improved significantly without
compromising high-flow performance when attention is given to low flows, by adopting an objective
function that gives additional weight to low flows, and paying attention to low flows during manual
calibration.
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4.2.1 Automatic calibration
Most rainfall-runoff models that underpin water resource plans in Australia have automatic routines
that can calibrate to various objective functions. More details of these are provided in Appendix A and
Guidelines for rainfall-runoff modelling: towards best practice model application (Vaze et al. 2011).
Where automatic routines are not incorporated into the models, other tools such as PEST
(www.parameter-estimation.com) may be applied to help with automatic calibration. Most automatic
routines allow the modeller to calibrate to a primary objective function (such as minimising the sum of
the squares of the difference between observed and modelled flows) and a secondary objective (such
as difference in cumulative runoff volume). Optimisers typically give the user the option to vary the
weighting between the primary and secondary objective. Typical objective functions include:

difference in total runoff (over entire period of record)

difference in total runoff over different seasons

mean square error between observed and modelled flow

Nash-Sutcliffe coefficient of efficiency on daily or monthly flows

Nash-Sutcliffe coefficient of efficiency calculated using transformed flows

match to flow duration curve.
Objective functions which allow the user to give emphasis to low flows are preferred for models where
low flows are of priority. One approach is to apply the Nash-Sutcliffe coefficient of efficiency to
transformed flows. The Nash-Sutcliffe coefficient of efficiency is given by the relationship:
__
Where: O is mean of the observed discharge
Mi is modelled discharge at time t
Oi is observed discharge at time t
n is number of observed flow values
 transforms the flows
While the standard Nash-Sutcliffe relationship gives greater weight to higher flows, modifying this
relationship to allow transformation of the flows (as below) gives the user the option to give greater
weight to different parts of the flow regime:
Setting  to a value less than 1 will give greater weight to lower flows. Suitable values of  typically
range from 0.2–0.6, when calibrating to low flows.
Suitable definitions of what constitute acceptable model calibrations for monthly models are
suggested in Ladson (2008) and provided in Table 1.
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Table 1: Guide to acceptable calibration
Classification
Coefficient of efficiency
Coefficient of efficiency
(calibration)
(validation)
Excellent
E ≥ 0.93
E ≥ 0.93
Good
0.8 ≤ E < 0.93
0.8 ≤ E < 0.93
Satisfactory
0.7 ≤ E < 0.8
0.6 ≤ E < 0.8
Passable
0.6 ≤ E < 0.7
0.3 ≤ E < 0.6
Poor
E < 0.6
E < 0.3
Note: These values have been developed for monthly flow models and should not be applied to daily flows.
4.2.2 Manual calibration
Model calibration will be improved by manually adjusting the calibration parameters to better
represent the targeted components of the flow regime. The quality of the calibration is assessed using
a number of approaches, including by:

visually comparing a time series plot of modelled and observed flows, specifically considering
the timing, duration, frequency and magnitude of low-flow events and ephemeral streams
zero-flow days

visually comparing modelled and observed flow duration curves, paying attention to the lowflow portion

visually comparing a scatter plot of modelled versus observed flows

compare low-flow metrics relevant to the region.
In the case of reproduction of low flows, it is suggested that ecologically-relevant performance metrics
are used to assess model performance. A related study Low-flow hydrological classification of
Australia (Mackay et.al. 2012) developed 35 ecologically-relevant low-flow indicators which aim to
describe the following key flow characteristics:

magnitude of (low) flow events

frequency of (low) flow events

duration of (low) flow events

timing of (low) flow events.
These indicators are listed in Table 2.
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Table 2: Low-flow metrics
Metric
Unit
Acronym
Median of annual minimum flows
Mlday-1
MedAnnMin
Baseflow index
No unit
BFI
Variability in baseflow index
No unit
CV_BFI
Low-flow discharge 75th percentile
Mlday-1
P75
Low-flow discharge 90th percentile
Mlday-1
P90
Low-flow discharge 99th percentile
Mlday-1
P99
Specific mean annual minimum runoff
Ml/day/km2
Sp_MeanAnnMin
Low-flow pulse count (<75th percentile)
year-1
LSNum_P75
Low-flow pulse count (<90th percentile)
year-1
LSNum_P90
Low-flow pulse count (<99th percentile)
year-1
LSNum_P99
Variability in low-flow pulse count (<75th percentile)
No unit
CV_ LSNum_P75
Variability in low-flow pulse count (<90th percentile)
No unit
CV_ LSNum_P90
Variability in low-flow pulse count (<99th percentile)
No unit
CV_ LSNum_P99
Annual minima of 1-day means of daily discharge
Mlday-1
AnnMin1day
Annual minima of 3-day means of daily discharge
Mlday-1
AnnMin3day
Annual minima of 7-day means of daily discharge
Mlday-1
AnnMin7day
Annual minima of 30-day means of daily discharge
Mlday-1
AnnMin30day
Annual minima of 90-day means of daily discharge
Mlday-1
AnnMin90day
Variability in annual minima of 1-day means of daily discharge
No unit
CV_AnnMin1day
Variability in annual minima of 3-day means of daily discharge
No unit
CV_AnnMin3day
Variability in annual minima of 7-day means of daily discharge
No unit
CV_AnnMin7day
Variability in annual minima of 30-day means of daily discharge
No unit
CV_AnnMin30day
Variability in annual minima of 90-day means of daily discharge
No unit
CV_AnnMin90day
Low-flow pulse duration (<75th percentile)
Days
LSDur_P75
Low-flow pulse duration (<90th percentile)
Days
LSDur_P90
Low-flow pulse duration (<99th percentile)
Days
LSDur_P99
Variability in low-flow pulse duration (<75th percentile)
No unit
CV_LSDur_P75
Variability in low-flow pulse duration (<90th percentile)
No unit
CV_LSDur_P90
Variability in low-flow pulse duration (<99th percentile)
No unit
CV_LSDur_P99
Number of zero-flow days
Days
NumZeroDay
Variability in number of zero-flow days
No unit
CV_NumZeroDay
Julian date of annual minimum
No unit
JDMin
Variability in Julian date of annual minimum
No unit
CV_JDMin
Seasonality (M/P) of minimum instantaneous flow (month)
No unit
SEASON
Predictability (P=C+M) of minimum instantaneous flow (month)
No unit
PREDICT
Magnitude of low flows
Frequency of low flows
Duration of low flows
Timing of low flows
The classification of sites based on the 35 flow metrics provided a method to distinguish between
highly intermittent streams, those which ceased to flow rarely and those that were perennial. The
value of the distinction between the low-flow classes is that ecological processes are likely to be
different in streams of different flow regimes. To make the flow classification more useful, a second
classification based on four metrics was conducted. This simplified classification has similar spatial
patterns in terms of the distinction between flow regimes as the more complicated 35 metric
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classification and identifies eight stream classes (see Table 3). However, the simpler four metric
classification is more straightforward to apply in considering future studies and in selecting
appropriate hydrological modelling performance metrics. The four metrics used in the simplified
classification were chosen pragmatically, based on the results of hydro-ecological case studies
whereby a range of flow metrics were considered, ease of calculation and potential application to
support the calibration and testing of hydrological models. The four metrics are:

Average number of zero-flow or cease-to-flow days per year – the mean annual number of
zero-flow or cease-to-flow days per year is used as a continuous function to consider how
ephemeral a stream is.

Baseflow index – the proportion of flow attributable to baseflow using the Lyne & Hollick
digital baseflow filter method (Nathan & McMahon 1990; Grayson et al. 2004).

Average of annual specific mean annual minimum – the average of the annual minimum flow
divided by catchment area.

90th percentile exceedance flow – flow exceeded 90 per cent of the time.
The individual flow components at a site and how they may be affected by flow regulation should be
considered along with any description provided by the classification’s multiple flow metrics. To this
end, a series of single-layer metric maps were produced by Mackay et al. (2012) to demonstrate the
absolute value of the underlying metrics for 830 unregulated sites. The single metric maps include the
four metrics in the simplified classification plus two additional metrics of the long-term inter-annual
variability in baseflow and in the number of cease-to-flow days, i.e. the:

Coefficient of variation of the baseflow index – a measure of the inter-annual variability in the
annual proportion of flow due to baseflow. Sites of high variability in baseflow indicate
unpredictable low-flow conditions.

Coefficient of variation in the number of zero-flow days – this is the coefficient of variation of
the annual total number of zero-flow days and is essentially a measure of the predictability of
cease-to-flow events, with high values indicating highly variability in the length of cease-toflow events.
The six metrics are likely to be important predictors of the ecological response and long-term ecology
of a site, yet may only be affected by relatively small volumetric changes in the flow regime. For
example, a change in the average number of cease-to-flow days or a change in its inter-annual
variability under water regulation may have significant ecological impacts despite representing only a
relatively small volume of monthly or annual flow.
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Table 3: Relevance of four key ecologically-relevant hydrological indicators to different stream classes
identified
Class
Description
Dominant flow metrics
1
Highly ephemeral
BFI – Lowest
Num Zero days – Highest
Spec_meanAnnMin – Low
P90 – Low
2
Ephemeral
BFI – Low
Num Zero days – High
Spec_meanAnnMin – Low
P90 – Low
3
Weakly ephemeral
BFI – Low/medium
Num Zero days – High
Spec_meanAnnMin – Low
P90 – Low
4
Weakly ephemeral – most at risk
BFI – Medium
Num Zero days – Low
Spec_meanAnnMin – Low
P90 – Low
5
Weakly perennial – most at risk
BFI – Medium
Num Zero days – Medium – Large range
Spec_meanAnnMin – Low
P90 – Medium
6
Weakly perennial
BFI – High
Num Zero days – Low
Spec_meanAnnMin – Low/medium
P90 – Medium
7
Perennial
BFI – High
Num Zero days – Low
Spec_meanAnnMin – Medium
P90 – High
8
Strongly perennial
BFI – High
Num Zero days – Low
Spec_meanAnnMin – Large range
P90 – High
4.2.3 Calibrating to separate parts of the flow regime
Experience has shown it is sometimes difficult to achieve good calibration for an entire flow regime
using the same set of parameter values. In these situations, it is recommended models are separately
calibrated to different parts of the flow regime and that separate models are used to generate
estimates of the relevant parts of the flow regime. If this strategy is required, two different approaches
can be adopted which are described below:
Method 1: (fixed  value) – Identify elements of the flow regimes for which separate models are
required. For example, assume one model for the low-flow regime and another for the average to high
flows. In this case a threshold value, such as the median flow, is selected to differentiate the two
regimes. Filter the historical time series to separate the low-flow periods from the average to high
flows. Calibrate separate models for the two separated time series using the automatic optimiser with
the selected objective functions. To extend the flow record, run both models for the full-time period of
interest. Then develop a combined estimated flow time series by splicing the results from the two
records for the low-flow and average/high-flow periods as required.
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Where there are distinctive seasons (such as the wet and dry season in northern Australia) the time
series may be best filtered according to the seasons. (Note: It may be necessary to treat the transition
periods as separate seasons, giving up to four seasons.)
Method 2: (variable  value) – As above, filter the historical time series into the low and high-flow
components. Run the automatic calibration routine for the average/high-flow time series with the value
of  set to 1. Run the automatic calibration routine for the low-flow time series with  set to a lower
value (say 0.3 initially) to provide greater weight to low flows. Different values for  (0.1 to 0.5) should
be trialled to establish the value most appropriate for low flows in a given region or climatic zone.
Case Study 4: Calibration processes: Modelling the Currency Creek catchments
The Currency Creek catchment is located in the East Mount Lofty ranges and has a catchment area
of approximately 99 km2. The mean annual rainfall varies from 450 mm in the lower reaches to 900
mm in the upper reaches.
The objective of this case study was to see if low-flow estimations could be improved by refining the
calibration method. Modelling was based on an existing WaterCress model that used WC1 as the
rainfall-runoff model.
Runoff model calibration to low flows is a priority in South Australia and the original Currency Creek
model was generally well-calibrated. Hence, only limited improvements were found by splitting the
sample into high and low-flow datasets and calibrating these separately.
More details are provided in Appendix B. A complete description for the case study is included on the
National Water Commission website at www.nwc.gov.au/publications.
4.3 Calibration process – river system models
River system models typically incorporate complex river system networks with subcatchment inflows
estimated by models calibrated as described above. However, they also include a range of other
processes such as storage behaviour, irrigation extraction, irrigation return flows, channel routing and
transmission losses, which also need to be calibrated. These are generally calibrated using
streamflow-gauging stations and other data where available (such as irrigation extractions), but
calibration is complicated because many inputs and outputs are not directly measured. These various
processes should be specifically represented in the model and, as far as possible, data should be
collected to assist in developing reasonable estimates of the separate inputs and outputs.
An overall assessment of the calibration of river system network models should be made by
connecting individual contributory subcatchment areas to form the river system model, starting from
the most upstream subcatchment and progressively working towards the most downstream
subcatchment. As each subcatchment is joined, the inclusion of sites with historic streamflows that
span longer periods of time may lead to opportunities, where previously unadjusted rainfall-runoffmodel-generated flows for upstream subcatchments can be adjusted to produce better reproductions
of observed downstream reach flows. A process similar to that outlined in the Queensland Data
Modification Module (see Queensland case study and summary in Barma & Varley 2012a) should be
considered for inflow adjustment. This process should continue until all contributory subcatchment
inflows are entered, after which an overall assessment of model calibration performance can be
made. More detailed information on approaches to calibration of river system models is in the eWater
guidelines (Black et al. 2011; Rassam et al. 2011; Vaze et al. 2011).
4.4 Uncertainty analysis
While the previous sections assume that measured streamflows and other data used for calibration
are reliable, this is not always the case. Modellers should appreciate the reliability of underlying data
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the model is based on, which will inform how results are interpreted and applied and influence the
level of effort for calibration.
Uncertainty in the data, processes and models used to estimate low flows leads to uncertainty in the
low-flow estimate itself. While a formal assessment of methods for quantifying this uncertainty is
beyond the scope of these guidelines, various eWater guidelines offer guidance about the range of
considerations that should occur when using models to estimate flows (including low flows).
4.5 Model calibration guidelines

The calibration period should be sufficient to represent climate variability and include periods
of low flows and cease-to-flow (where appropriate), and the transition to and from low-flow
ranges.

Validation (to further confirm model calibration robustness) should be undertaken if there is
sufficient data, in which case the data is separated into a subset of calibration and validation
data. Appropriate calibration criteria and acceptability criteria should be applied. The criteria
should adequately capture the success of the model in reproducing low flows.

Calibration should report quantitative as well as qualitative measures.

Validation should be assessed using the same criteria as calibration.

Calibration should be described, including gauging stations or periods and ranges of flow data
that did not calibrate well and possible reasons why.

A two-step calibration approach for low flows is recommended that uses automatic
optimisation calibration routines to establish an initial calibration, which are then manually
adjusted to further improve the calibration using selected ecologically-important low-flow
statistical metrics (tables 2 and 3), as well as comparison of graphical outputs (such as time
series plots and scatter diagrams). Slight variations in the approach may be needed to
accommodate differences in hydrologic behaviour across regions.

When using automatic optimisation calibration routines, modellers should ensure the adopted
objective function appropriately weights the flow range of most interest. This can be achieved
by applying the Nash-Sutcliffe coefficient of efficiency to transformed flows.

Record length permitting, calibration should be conducted for sub-periods within the observed
flow record for which the spatial density of rainfall stations is at a maximum.

Where subcatchment inflows generated by rainfall-runoff models are fed into river system
network models, an overall assessment of calibration should be made by connecting individual
contributory subcatchment areas to form the river system model, starting from the most
upstream subcatchment and progressively working towards the most downstream
subcatchment. As each subcatchment is joined, the inclusion of sites with historic streamflows
that span longer periods of time may lead to opportunities, where previously unadjusted
rainfall-runoff-model-generated flows for upstream subcatchments can be adjusted to produce
better reproductions of observed downstream reach flows. A process similar to that outlined in
the Queensland Data Modification Module (Barma & Varley 2012a) should be considered for
inflow adjustment. This process should continue until all contributory subcatchment inflows are
entered, after which an overall assessment of model calibration performance can be made.
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Case Study 5: Calibration methods – modelling of the Daly River
The Daly River catchment lies in the wet/dry tropics of northern Australia. Its rainfall and runoff is
characterised by extreme seasonality in rainfall patterns, with 95 per cent (975 mm) of rainfall falling
in the wet season between November and May. Flow in the dry season is fed by baseflow from highly
connected aquifers.
The Daly River catchment has been modelled by a suite of linked rainfall-runoff, hydrodynamic and
groundwater models to reproduce flows. A case study was undertaken to evaluate if low-flow
reproduction could be improved by applying the calibration methods described in this section. The
PEST optimiser was used to assist in optimising the calibration (note the study also applied improved
rating tables and additional streamflow gauging stations, see Chapter 2).
Visually the recalibrated discharge hydrographs show a closer match to the observed flows than the
initial calibration, which consistently underestimated flows. The Nash-Sutcliffe coefficient of efficiency
was greatly improved for both the NAM and integrated models. The recalibration of the FEFLOW
model resulted in a distribution of groundwater inflow which more closely reflects the observed data.
The Nash-Sutcliffe coefficient of efficiency for flows at station G8140001 improved dramatically from
0.364 to 0.813.
More details are provided in Appendix B. A complete description of the case study is on the National
Water Commission website at www.nwc.gov.au/publications.
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5. Transposition methods (for ungauged
catchments)
Estimating flows in ungauged catchments is particularly challenging and has been researched for
more than 30 years. Lack of progress in this important area prompted the International Association of
Hydrological Science (IAHS) to initiate the Decade for Prediction in Ungauged Basins (2003–2012),
and it has sponsored meetings throughout the world to coordinate and report on relevant research
relating to the estimation of low flows.
5.1. Principles
Principle 11: Flows in ungauged catchments should be estimated using data from gauged catchments
in the region. Information from more than one regional gauging station should be used in deriving
the estimate.
Principle 12: The reliability of flow estimates in ungauged catchments should be tested by applying
the method adopted to gauged catchments in the region and assessing the quality of calibration
using suitable objective functions and low-flow metrics. This may include testing different
methods and selecting the one that best performs.
5.2. Transposition of gauged flows
5.2.1. Empirical transposition methods
Empirical methods modify gauged flows from an adjacent analogue catchment to provide an estimate
of flows at a target ungauged catchment. The analogue catchment should have similar hydrologic
response to rainfall and similar evaporation demand. When trying to estimate a time series of flows, it
is imperative that flows in the analogue catchment and target catchment are concurrent. This requires
the analogue catchment is (WMO 2008):

geographically close to the ungauged catchment (otherwise rainfall inputs which drive the
flows will be dissimilar)

hydrologically similar

similar in size.
Ideally, the analogue catchment should be located upstream or downstream of the ungauged
catchment, but nested within the same river basin. This will ensure a strong spatial correlation
between the flow measured at the analogue catchment and flows at the ungauged catchment. It is
also likely to provide similar underlying geological properties, which will enhance hydrologic similarity.
However, there will be situations where adopting an analogue catchment that is not nested in the
same river basin will be necessary.
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Flow at the ungauged catchment is usually estimated using a relationship similar to:
QXt
= fn(AtRt/AaRa)*QXa
Where: QXt
= flow in the target catchment
QXa
= the corresponding flow in the analogue catchment
At
= catchment area for the ungauged catchment
Rt
= mean annual rainfall in target catchment
Aa
= catchment area for the analogue catchment
Ra
= mean annual rainfall in analogue catchment
fn
= a scaling constant or function
In simple applications the scaling constant is set equal to 1. Variations of the scaling function may be
developed and tested using gauged catchments located in the same region as the target catchment,
provided they have similar hydrologic properties.
Lowe & Nathan (2005) determined the mean annual rainfall and area ratio did not always provide an
accurate estimate of how the flow from the analogue catchment should be adjusted to represent the
target catchment. Instead, they recommended the flow in the target ungauged catchment is adjusted
based on the ratio of mean annual flows:
QUG
= QA (MAFUG/MAFA)
QUG
= discharge in ungauged catchment
QA
= discharge in analogue catchment
MAFUG = mean annual flow ungauged catchment
MAFA
= mean annual flow analogue catchment
They developed a relationship for estimating mean annual flow (MAF) in Victoria:
MAFUG = -4.73 + 0.91 ln (AREA) + 0.002(RAIN) + 0.003 (AEVAP)
RAIN
= mean annual flow (ML)
AREA
= catchment area (km2)
Rain
= mean annual rainfall (mm)
AEVAP = average actual annual evaporation (mm)
5.2.2. Regional regression models
Regional regression models can be developed to estimate flows in ungauged catchments, based on
relationships developed from gauged catchments in the region. Ideally, the gauged catchments used
to develop the regression models will be hydrologically similar and located in the same river basin.
The model adopted for the regression may estimate flows in the ungauged catchment based on a
transformation of observed flows in one or more analogue catchments, using a relationship similar to
that described in Section 5.2.1 above. The regression process can be used to refine the transform
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function. Alternatively, the model may estimate flows in the ungauged catchment based on observed
rainfalls and evaporation using a relationship similar to that described in Section 3.2.2 above, which
would be developed using data from regional gauged catchments.
5.2.3. Use of similarity criteria for selecting suitable analogue
catchments
A considerable number of researchers around the world have investigated how to assess hydrological
similarity between catchments. It can be concluded that catchment characteristics are so variable it is
impossible to develop a common set of indicators to determine if two catchments are hydrologically
similar. It is therefore necessary to develop regional-specific indicators. Factors found to be relevant
in assessing hydrologic similarity in different settings include:

mean annual rainfall or mean annual flow

base flow index (proportion of total annual flow which is generated from baseflow)

mean annual or mean monthly evaporation

vegetation cover

soil characteristics

seasonality of rainfall and flow

standard deviation of annual flow, or a low-flow value such as the 80th percentile

elevation.
Lowe & Nathan (2006) developed a methodology for assessing hydrologic similarity with a view of
using this approach to provide a better method for selecting analogue catchments. Their study was
based on 165 gauged catchments in Victoria which varied in catchment size from 10 km 2 to 1000 km2
and encompassed a wide variety of climate and geographic characteristics. They developed a
number of measures for catchment characteristics including:

catchment area

catchment perimeter

elevation

vegetation cover

soil

monthly and annual rainfall

monthly and annual evapotranspiration

depth the water table

stream density (length of stream divided by area)

stream frequency (number of stream junctions divided by catchment area).
Three indicators were selected to represent key elements of the flow regime:
1. MAF.
2. Baseflow index (BFI – i.e. the proportion of the mean annual runoff which is contributed from
baseflow).
3. Median summer flow (MSF).
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Regression relationships were then developed to describe these three hydrologic indicators using the
above catchment characteristics. It was found that MAF was best described with a relationship based
on average annual rainfall and the percentage of vegetation cover. The BFI was best described with a
relationship based on mean annual rainfall, soil permeability, vegetation cover and stream frequency.
MSF was best described with a relationship based on average annual rainfall, soil permeability and
vegetation cover. In essence, the hydrologic properties could be described using four catchment
characteristics, namely:
1. Mean annual rainfall.
2. Percentage of vegetation cover.
3. Soil permeability.
4. Stream frequency.
Intuitively, the best catchment for transposition would be one that is geographically close that also
demonstrates hydrologic similarity. Lowe & Nathan (2006) tested which of the 165 catchments was
the best analogue catchment for each other catchment and examined the importance of geographic
proximity and hydrologic similarity in selecting the best analogue catchment. They determined that
selecting the analogue catchment based only on hydrologic similarity yielded acceptable results, but
on average the closest catchment gave better results. The best selection of analogue catchment was
achieved by using both measures, but giving geographic proximity (measured as the distance
between catchment centroids) a higher linear weighting. The best results were achieved with a
weighting of 0.85 for geographic proximity. Other investigators (Merz & Bloschl 2004; Oudin et al.
2008; Parajka et al. 2005; Zhang & Chiew 2009) have also shown that the geographically-closest
catchment provides similar or better results than the most hydrologically-similar catchment, but
considering both factors in selecting the analogue catchment will likely provide the best results.
5.3. Transposition of rainfall-runoff model parameter
values
Transposition of rainfall-runoff model parameter values are discussed in detail in the Guidelines for
rainfall-runoff modelling: towards best practice model application (Vaze et al. 2011). The following
sections complement these guidelines.
Researchers have been investigating methods to enable the application of rainfall-runoff water
balance models to ungauged catchments for many years and most have tried to develop regional
relationships to estimate the parameter values of specific rainfall-runoff models. The first attempt to
generate regional relationships for model parameters in Australia was by Johnson & Pilgrim (1976).
They were unsuccessful and instead concluded that inter-relationships between model parameters
confounded attempts to develop such relationships because different sets of parameters produced
almost identical runoff estimates. This led to a series of studies aimed at simplifying the existing
rainfall-runoff water balance models, leading to improved models. For instance, the Boughton model
was simplified to become the SFB model. Chiew & McMahon (1993,1994) made a sensitivity analysis
of the 17 parameters in MODHYDROLOG and found the calibrations were insensitive to many
parameters. They simplified the model down to seven parameters, leading to the development of
SIMHYD.
Nathan & McMahon (1990a) applied the SFB model to 168 catchments in New South Wales and
Victoria to achieve good calibration results. However, they were still unable to find useful correlations
between measurable catchment characteristics and model parameters. Nathan et al. (1996) was
successful in developing regional relationships for the two-parameter MOSAZ model when applied to
195 catchments in Victoria, but concluded there was no physical basis for the relationships and they
should be applied with considerable caution outside the region they were derived from.
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Boughton & Chiew (2003) calibrated a daily version of the AWBM (UGAWBM) on data from 221
catchments located over a wide range of mainland Australia. The datasets were from the same group
used with the SIMHYD model for the National Land and Water Audit. In this case, the calibrations
have been presented for use by others because of the wide coverage of the data, instead of
attempting to relate parameters to catchment characteristics. Some 80 per cent of calibrations had a
value of Nash-Sutcliffe efficiency of 0.75 or greater (considered satisfactory). The very lowest values
< 0.55 were mainly associated with the drier catchments. These catchments typically have partialarea storms and runoff. It was concluded that where the calibrated parameter values of several
nearby catchments were similar and the physical characteristics matched the target catchment, the
parameter values of the nearby catchments could be used on the ungauged catchment with
reasonable confidence. Conversely, where there was a spread of calibrated parameter values among
nearby catchments, the different values could be used to indicate the likely range of error in the
estimated runoff.
Chiew & Siriwardena (2005) applied SIMHYD to 300 catchments across Australia (using data collated
for the Australian Land and Water Resources Audit) and investigated if the calibration parameters
could be related to catchment characteristics. They adopted six catchment characteristics for
regionalisation:

Mean annual rainfall.

Mean annual rainfall and annual potential evapotranspiration.

90th percentile minus 10th percentile elevation.

Fraction of woody vegetation coverage.

Plant-available water-holding capacity.

Transmissivity.
Regression relationships were developed for model parameters based on the catchment
characteristics. Models based on parameters derived from these regional relationships provided
results that were generally acceptable, but inferior to those from calibrated models. For instance, the
Nash–Sutcliff coefficient of efficiency was within 0.1 of the value from the calibrated model for
approximately half of the catchments and was within 0.2 for 80 per cent of the catchments. However,
it was found that estimates based on the nearest gauge were generally of a similar standard.
Various researchers have investigated ‘ensemble’ approaches where multiple donor catchments are
used to derive estimates of flow at ungauged catchments (Parajka et al.; Merz & Bloschl 2004, etc.).
In some instances these studies have also applied multiple rainfall-runoff models (typically six
models) for each location. The results from the multiple donor catchments and multiple models are
then combined (sometimes with a weighting factor applied) to develop an estimate of flows for the
ungauged catchment. These ‘ensemble’ approaches have been found to provide slightly improved
flow estimates compared with methods based solely on hydrologic similarity and geographic
proximity. However, this approach involves considerable additional effort and yields only marginal
improvements.
In conclusion, it has been found that developing regional relationships to select model parameters for
ungauged catchments is difficult and is in fact next to impossible for models with large numbers of
parameters. Suitable relationships have been developed to estimate SIMHYD parameter values from
catchment characteristics, which have applicability in a wide range of catchments across Australia.
These parameter values can be used for models in ungauged catchments. However, it is
recommended the approach preferred by Boughton & Chiew (2003) is applied, which is described
below.
Step 1:
Prepare rainfall and evaporation files for the chosen model (AWBM, SIMHYD or other).
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Step 2:
If there are gauged catchments in the location of interest (known to have similar rainfall-runoff
characteristics as the ungauged catchment), then establish and calibrate models for each catchment.
The derived model parameters can then be used as a guide for selecting parameters for the
ungauged catchment. If no gauged catchments are available for calibration, then proceed to Step 3.
Step 3
Find the nearest calibrated catchments to the ungauged catchment. If the nearest catchments – either
from Step 2 or from published data such as Boughton & Chiew (2003) or Chiew & Siriwardena – have
similar calibrated parameter values, then averages of these values are the best values to use for the
ungauged catchment. Check the catchment characteristic of the nearby catchments (i.e. elevation,
leaf area index, percentage of area that is woody vegetation, plant water holding capacity, and
transmissivity of the soil.) If the ungauged catchment has similar characteristics to one or more
nearby catchments, then the calibrated parameter values from those catchments can be given more
weight in selecting values for use.
5.4. Estimating low-flow indicators directly
Since low-flow estimates are primarily used to assess the impact of water sharing and management
decisions on the environment and this is generally done by assessing changes in key low-flow
indicators, there is an argument for deriving estimates of the low-flow indicators directly. However, the
limited value of this approach in terms of assessing future management or climate scenarios should
be recognised.
Many investigators have developed regional relationships for estimating key low-flow indicators. In
Australia, Nathan & McMahon (1990c) undertook a study which developed relationships to describe
key low-flow indicators in south-eastern Australia. The relationships were developed using data from
184 catchments that ranged in size from 1 km2 to 250 km2 covering a wide range of climatic and
physiographic features. They used multiple regression techniques to developed relationships for the
following low-flow indicators:

mean annual flow

standard deviation of annual flow

river regime group (based on Haines et al. 1998)

baseflow index

recession constant

flow duration curve

low-flow frequency curve (for the 1, 7, 15, 30, 60, 120, 183 and 284 day annual minima)

spell durations.
A total of 21 catchment characteristics were assessed including:

shape

area

slope

vegetation cover

density of drainage network

rainfall depth
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
rainfall variability

nine geological variables.
The performance of the derived relationships was considered to be acceptable with R 2 values ranging
from 0.56 to 0.97. Software was developed and made available to end users to allow them to assess
the confidence limits of each prediction relationship in any given application.
If low-flow indicators are required in other regions they will need to be developed using a similar
approach to Nathan & McMahon (1990c). The approach would be:

Collect flow data from gauging stations in the area of interest.

Identify the key low-flow indicators for the region (guidance may be taken from Nathan &
McMahon and from tables 2 and 3).

Identify catchment characteristics that may relate to the flow regime (such as those listed in
section 5.2.3 above).

Obtain data to describe the catchment characteristics from the previous step.

Apply regression techniques to develop relationships between catchment characteristics and
low-flow indicators.

Apply the derived relationships to estimate low-flow indicators for the target ungauged
catchment.
5.5. Testing estimates in ungauged catchments
The reliability of estimates in ungauged catchments cannot be tested directly, as there is no data at
the site. However, the reliability of the estimate can be evaluated by applying the method to adjoining
gauged catchments and assessing the quality of the estimate against observed data using the
methods described in Chapter 4 above.
5.6. Short-term gauging data
While the previous section provides a number of different approaches for estimating flows in
ungauged catchments, every opportunity should be taken to obtain flow data, as a short period of
data or spot streamflow gaugings can greatly enhance the estimation of low flows and the reliability of
estimation procedures.
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5.7. Ungauged catchment guidelines

Flows in ungauged catchments should be estimated using data from gauged catchments in the
region. Information from more than one regional gauging station should be used in deriving the
estimate.

Various methods for estimating flows in ungauged catchments have been described including:
–
transposing gauged flows using empirical methods
–
transposing flows using regional relationships derived from regression
–
applying a rainfall-runoff model using transposed model parameters.
The choice of method should consider the type and quantity of data available and the skill of the
hydrologist applying the method.

If transposition of flows is the adopted method, selecting the analogue catchment should take
into account geographical proximity and hydrologic similarity. Gauging stations located in the
same river system should be preferred, especially those located upstream or downstream on
the same river (unless there is a large difference in catchment area or characteristics).

If a rainfall-runoff model is adopted, parameters should be taken from calibrated models
developed from gauged catchments in the region. Parameters should generally be averaged,
although catchments with greater hydrologic similarity should be given a higher weighting.

Consideration should be given to estimating hydrologic indicators directly.
The reliability of flow estimates in ungauged catchments should be tested by applying the method to
gauged catchments in the region. This may include testing different methods and choosing the one
that provides the best performance.
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Appendix A: Description of rainfall-runoff
models commonly used in Australia
AWBM
AWBM is an hourly or daily water balance model that estimates runoff from rainfall and
evapotranspiration. The model uses three surface stores to simulate partial areas of runoff. The water
balance of each surface store is calculated independently of the others. At each time step, rainfall is
added to each of the three surface moisture stores and evapotranspiration is subtracted from each
store. The water balance equation is:
storen = storen + rain - evap (n = 1 to 3) (1)
If the value of moisture in the store exceeds the capacity of the store, the moisture in excess of the
capacity becomes runoff and the store is reset to the capacity. When runoff occurs from any store,
part of the runoff becomes recharge of the baseflow store if there is baseflow in the streamflow. The
fraction of the runoff used to recharge the baseflow store is calculated as BFI*runoff, where BFI is the
base flow index (i.e. the ratio of baseflow to total flow in the streamflow). The remainder of the runoff
becomes surface runoff. Baseflow is generated from the baseflow store at the rate of (1.0 - K)*BS
where BS is the current moisture in the baseflow store and K is the baseflow recession constant
(chosen to account for the time step; i.e. daily or hourly).
The surface runoff can be routed through a store to simulate the delay of surface runoff reaching the
outlet. The surface store acts in the same way as the baseflow store, and is depleted at the rate of
(1.0 - KS)*SS, where SS is the current moisture in the surface runoff store and KS is the surface
runoff recession constant of the time step being used. Baseflow is added to the routed surface runoff
to generate the total catchment runoff at the outlet.
AWBM has nine calibration coefficients:

six to define the surface stores (i.e. three to define the area of each store and three to define
the capacity of each store)

baseflow index

baseflow recession constant

surface flow recession constant.
Sacramento
Sacramento uses soil moisture accounting to simulate the water balance within the catchment. Soil
moisture storage is increased by rainfall and reduced by evaporation. The size and relative wetness
of the storage determines the depth of rainfall absorbed, actual evapotranspiration and the amount of
water moving vertically or laterally out of the store. Rainfall in excess of the infiltration capacity
becomes runoff and is transformed through an empirical unit hydrograph. Lateral water inputs from
the soil moisture stores are added to this runoff to give streamflow.
Sacramento includes five stores:
1. Upper zone tension water (UZTW).
2. Upper zone free water (UZFW).
3. Lower zone tension water (LZTW).
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4. Lower zone primary free water (LSFWP).
5. Lower zone supplementary free water (LZFWS).
A total of 16 parameters are used to simulate the water balance. Of these:

five define the size of soil moisture stores

three calculate the rate of lateral outflows

three calculate the percolation water from the upper to the lower soil moisture stores

two calculate direct runoff

three calculate losses in the system.
Tension water stores represent the volume of water held in the soil matrix by surface tension. Water
can only be removed from these stores by evapotranspiration. Water can move through the free water
stores vertically to other stores, or laterally as interflow in the upper zone or as baseflow in the lower
zone.
The UZTW store receives the rain first. When this store is filled, water passes to the UZFW store. The
UZFW store then supplies water to the lower stores with a user-determined split between the free
water and tension water stores. When the LZFWS is filled, water passes to the tension water stores.
Streamflow generated by the Sacramento model is made up of three flow components:
1. Surface runoff.
2. Interflow.
3. Baseflow.
Surface runoff occurs when UZTWS is full and the rainfall exceeds the sum of the percolation rate
and the maximum interflow drainage capacity. Interflow is generated from the UZFWS and is a
function of the volume of water in the store and the drainage rate parameter, UZK. Baseflow is a
function of the volume of water in the lower zone free stores and their drainage rate parameters,
LZPK and LZSK. Baseflow is reduced by channel loss parameters, SIDE and SSOUT.
Evapotranspiration can only take place from upper and lower tension water stores and upper free
water stores. The upper limit of evaporation is the evaporative demand, and is the product of the pan
evaporation modified by the (user-specified) pan factor. Evaporation occurs firstly from the UZTWS,
then the UZFWS, and lastly from the LZTWS.
The driving force for percolation is the relative wetness of the UZFWS as moderated by the relative
wetness of the lower zone stores. Percolation increases when either the storage in the UZFW store
increases or the storage in the lower zone stores decrease.
SIMHYD
SIMHYD is a daily conceptual rainfall-runoff model that estimates streamflow from rainfall and areal
potential evapotranspiration data. It is a simplified version of HYDROLOG with seven parameters,
compared with the 17 parameters required for HYDROLOG. The model estimates runoff generation
from three sources:
1. Infiltration excess runoff.
2. Interflow (and saturation excess runoff).
3. Baseflow.
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SIMHYD first fills the interception store, which is emptied each day by evaporation. The excess
rainfall is then subjected to an infiltration function that determines the infiltration capacity. The excess
rainfall that exceeds the infiltration capacity becomes infiltration excess runoff.
Moisture that infiltrates is subjected to a soil moisture function that diverts the water to the stream
(interflow), groundwater store (recharge) and soil moisture store. Interflow is first estimated as a linear
function of the soil wetness (soil moisture level divided by soil moisture capacity). The equation used
to simulate interflow attempts to mimic both the interflow and saturation excess runoff processes (with
the soil wetness used to reflect parts of the catchment that are saturated, from which saturation
excess runoff can occur). Groundwater recharge is then estimated, also as a linear function of the soil
wetness. The remaining moisture flows into the soil moisture store.
Evapotranspiration from the soil moisture store is estimated as a linear function of the soil wetness,
but cannot exceed the atmospherically-controlled rate of areal potential evapotranspiration. The soil
moisture store has a finite capacity and overflows into the groundwater store. Baseflow from the
groundwater store is simulated as a linear recession from the store.
LASCAM
LASCAM is a general purpose hydrologic and water quality model developed by the Centre for Water
Research (University of Western Australia) to predict the impact of land use change and climate
change on daily trends in streamflow and water quality.
Hydrologic and water quality processes are modelled at the subcatchment scale and are then
aggregated via stream network routing to estimate flows (water quality) at the catchment outlet and
intermediate points. Each subcatchment is effectively represented by a lumped hydrologic model, but
including subcatchments allows spatial variability in rainfall and catchment properties to be
represented. LASCAM has been successfully applied to catchments ranging in size from 0.8 km 2 to
120,000 km2.
At a subcatchment scale, LASCAM is built around three interconnected subsurface stores
representing:
1. The near stream perched aquifer system – A store.
2. The permanent deeper groundwater system – B store.
3. An intermediate unsaturated infiltration zone – F store.
The proportion of rainfall that infiltrates into the A store is estimated as a proportion of rainfall.
Relationships that define losses to evapotranspiration from the B and F stores depend upon the
extent of deep-rooted vegetation. The channel network allows for re-infiltration of runoff to the A
stores, as water is routed through the channel network.
LASCAM operates on a daily time step. Inputs include daily rainfall, annual pan evaporation (which is
disaggregated to daily values based on a fixed harmonic function), vegetation cover and land use
information. Measured streamflow is required for calibration. The model generates estimates surface
runoff, subsurface runoff, actual evaporation, recharge to the permanent groundwater table and
baseflow for each subcatchment. LASCAM uses 30 parameters to define the subcatchment water
balances and has up to 57 water quality parameters.
MODHYDROLOG
MODHYDROLOG is a daily conceptual rainfall-runoff model that estimates daily streamflow from
rainfall and potential evapotranspiration data. It is a modified version of HYDROLOG, incorporating
modified groundwater algorithms that improve the representation of stream–aquifer interactions and
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groundwater seepage. MODHYDROLOG has demonstrated that it performs better than HYDROLOG
for ephemeral streams, otherwise the two models provide similar performance.
MODHYDROLOG has five storages:
1. An interception store which represents rainfall intercepted by the vegetation canopy.
2. A depression store that represent rainfall taken up by surface depressions.
3. A soil moisture store.
4. A groundwater store.
5. A channel store.
Runoff is generated from three sources:
1. Surface runoff which occurs after the interception store and depression store capacities are
satisfied and when the rate of rainfall exceeds the soil infiltration capacity.
2. Interflow generated from the soil moisture store.
3. Baseflow generated from the groundwater store.
Runoff is routed through the channel store, using a non-linear function. The interception store,
depression store and soil moisture store can lose water to evaporation. Water in the depression store
can infiltrate to the soil moisture store. The infiltration capacity varies as a function of the soil moisture
(relative to the soil moisture capacity) as does the rate of interflow. The rate of baseflow varies as a
function of the water held in the groundwater store, relative to the groundwater store capacity. The
groundwater store can be recharged from the soil moisture store and the channel store. Water can be
lost from the groundwater store to deep aquifers, which do not generate baseflow.
Different monthly values can be applied to the coefficients that influence the rate of infiltration,
interflow response and groundwater recharge.
WC1 model
WC1 was developed by the South Australian Government after other models failed to reproduce
runoff behaviour from arid catchments in the Mt Lofty Ranges, Barossa Valley and mid north where
rainfall is in the range of 450–650 mm per year. It has been found the soil moisture capacity varies
across these catchments and most runoff is generated by portions of the catchment that become
saturated.
WC1 varies from other models in that it applies different soil moisture capacities across a
subcatchment. Soil moisture capacity is described by a catchment average moisture capacity and a
distribution which represents how moisture capacity varies across the catchment (i.e. what
percentage of the catchment has higher and lower capacities). The model tracks the variation in
moisture capacity (saturation) across the catchment. When a portion of the catchment becomes
saturated, all further rainfall is converted to runoff.
The model has 12 calibration parameters and incorporates three storages:
1. An interception store.
2. A soil moisture store.
3. A groundwater store.
Rainfall first fills the interception store which typically has a capacity of approximately 30 mm. Rainfall
in excess of the interception store capacity is termed excess rainfall. The interception store is
depleted by evaporation and seepage into the soil store. Excess rainfall can infiltrate into the soil store
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or become surface runoff, if the excess rainfall rate exceeds the infiltration capacity, which varies a
function of the soil moisture store (relative to the soil moisture store capacity).
The soil store generates interflow, which contributes to runoff and can contribute to groundwater
recharge. It can also be depleted by evaporation. The groundwater store contributes baseflow to the
runoff. The interception store and soil store can be depleted by evaporation. Groundwater recharge in
these arid climates is typically dominated by recharge from the streams. Hence, WC1 allows
groundwater recharge as a function of the rate of runoff and recharge from the soil store is generally
minor. WC1 Includes loss functions that simulate losses in runoff and the groundwater store.
Typically, WC1 is implemented via the WaterCress framework. A separate WC1 model can thus be
developed for individual subcatchments. These can be combined and routed through a channel
network using the WaterCress framework.
Automatic calibration
Different approaches are applied to optimising these rainfall-runoff models depending on which
framework they are applied under. For instance, the RRL provides several automatic calibration
routines for these rainfall-runoff models, with the user having a choice of optimisers including:

uniform random sampling

pattern search

multi-start pattern search

Rosenbrock search

Rosenbrock multi-start search

genetic algorithm

Shuffled Complex Evolution (SCE-UA).
The RRL allows calibration to be made against a primary and secondary objective function with userdefined weightings between the two functions. Available primary objective functions include:

Nash-Sutcliffe criterion (coefficient of efficiency)

sum of square errors

root mean square error (RMSE)

root mean square difference about bias

absolute value of bias

sum of square roots

sum of square of the difference of square root

sum of absolute difference of the log.
The following secondary objective functions are available:

runoff difference in percentage

flow duration curve

baseflow method 2.
WaterCress allows for automatic calibration using NLFIT or RBgenetic. LASCAM includes an
automatic calibration routine based on the Shuffled Complex Evolution algorithm, which allows
simultaneous optimisation of parameters against any number of observed response time series, using
a weighted objective function. The scheme uses a transformation parameter, Lambda (Box & Cox
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1964) to de-trend the relationship between model residuals and predictions. Experience in Western
Australia suggest that a value of 0.5 will provide a better match to the overall flow regime, whereas
higher values put more emphasis on reproduction of high flows.
Some jurisdictions also used PEST, a stand-alone optimisation software, to assist with model
calibration.
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Appendix B: Case study summaries
The information following is a summary of case studies conducted as part of this report. The full case
studies can be found on the Commission's website – www.nwc.gov.au/publications.
1. Recalibration of low flows in the Snug and upper north Esk
catchments, Tasmania
Acknowledgements
This study was undertaken by Bryce Graham, Kate Hoyle and Shivaraj Gurung of the Department of
Primary Industries, Parks, Water and Environment (Tasmania) with assistance from Joel Hall of the
Department of Water (Western Australia).
Introduction
The objective was to evaluate if low-flow reproduction within the Snug and upper north Esk
catchments could be improved by applying an alternative model (in this case LASCAM version 2.6),
additional calibration methods, and performance metrics.
These catchments had previously been modelled using the Hydra modelling platform with AWBM as
the rainfall-runoff model . The LASCAM modelling was performed for varying Lambda values (0.5,
0.65, 0.75 and 0.9) providing a range of model estimates. Low-flow metrics were assessed using the
eWater product River Analysis Package v3.0.3 (RAP). Low-flow assessments were based on a oneday duration and a one-day separation of events. The results were also assessed by examining time
series plots of estimated and measured flows and plots of the flow duration curve.
Catchment descriptions
The Snug catchment has an area of 23 km 2 and discharges into the Derwent Estuary. The upper
catchment is mainly eucalypt forest whilst the lower catchment includes two small settlements and
lifestyle residential allotments. The upper catchment has a maximum elevation of 700 m and an
annual average rainfall of 1100 mm, while the lower catchment has an average annual rainfall of 700
mm. The catchment has a single streamflow-gauging station that has been operated since 1975. For
this exercise, calibration and testing of the models was conducted for the period from 1 January 1980
to 31 December 2000, which had no missing data.
The North Esk catchment discharges into the Tamar Estuary on the Tasmanian north coast and has a
catchment area of 1,065 km 2. The dominant land use is low-density sheep and cattle grazing with
some cropping and forestry plantations. The lower reaches also include urban development. A portion
of the water supply for Launceston is drawn from the upper reaches.
The catchment includes a streamflow-gauging station that has operated since 1923, but the
calibration and testing of the models used the period of record from 1 January 1981 to 31 December
2003, which had no missing data.
Results
Snug catchment
The results for the Snug catchment showed very little difference between the two models in regards to
the low-flow metrics. The flow duration curve showed that the two models produced very similar
results for flows above 1 m 3/s that corresponds to the 70th percentile flow. However, the LASCAM
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model was able to better reproduce the flow duration curve for higher percentiles. A time series plot
for flows less than 1 m3/s, shows that neither model was successful at reproducing low-flow
behaviour. The AWBM model significantly overestimated flows, whereas LASCAM did not reproduce
the pattern of flows generating hydrographs that were too peaky. Both models adopted daily time step
and the validity of a daily model for such a small catchment is questionable.
Snug observed data
Tascatch Update modelled data
Tascatch Original modelled data
LASCAM 0.50
LASCAM 0.65
LASCAM 0.75
LASCAM 0.90
Snug catchment low flows
North Esk catchment
The performance assessment metrics indicate the LASCAM model performed better than AWBM at
estimating the mean daily flow, the P5, P10, P90, minimum and median flows. LASCAM also did
better at reproducing the baseflow statistics. The flow duration curves (see below) support these
statistics showing that LASCAM reproduced the observed flow duration curve across the entire flow
regime, whereas AWBM underestimated the median and low flows. LASCAM was also successful at
reproducing the low trough values and the duration of event. Some statistics were best reproduced
using a Lambda value of 0.5, whilst others were best reproduced with Lambda values of 0.65 and
0.75.
North Esk observed data
Tascatch Update modelled data
Tascatch Original modelled data
LASCAM 0.50
LASCAM 0.65
LASCAM 0.75
LASCAM 0.90
North Esk flow duration curves
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2. Recalibration of low flows in the Daly River catchment, Northern
Territory, using an integrated groundwater/surface water model
Acknowledgements
The study was undertaken by Anthony Knapton of the Department of Natural Resources Environment
Arts and Sport (Northern Territory).
Introduction
The Daly River catchment has been modelled by a suit of linked rainfall-runoff, hydrodynamic and
groundwater models4 to be able to reproduce the flows behaviour which has a highly-connected
groundwater/surface water system. The surface water system has a catchment area of 52,000 km 2,
while the connected groundwater system has an extent of 159,000 km 2. Calibration of these models
previously placed an emphasis on being able to reproduce dry season low flows, as this is the period
where water availability is critical to the environment and consumptive users. The PEST optimiser was
used to assist in optimising the calibration results.
The objective of this study was to evaluate if low-flow reproduction can be improved by applying
additional data and calibration methods (see Chapter 4). The study was confined to the Katherine
River subcatchment of the Daly River. Recalibration was limited to the NAM rainfall-runoff and
FEFLOW groundwater components of the integrated model.
The case study had access to additional years of flows data, revised rating tables and additional
gauging stations.
Catchment descriptions
The Daly River catchment lies in the wet/dry tropics of northern Australia. Its rainfall and runoff is
characterised by extreme seasonality in rainfall patterns, with 95 per cent (975 mm) of rainfall falling
in the wet season between November and May. Flow in the dry season is fed by baseflow from
connected aquifers.
The Katherine River catchment represents approximately a quarter of the Daly River catchment and is
connected to the Tindall limestone aquifer. Groundwater inflows to Katherine Creek occur where it
incises the Tindall aquifer, which it does for approximately half its length.
Results
Previously it was assumed that inflows were relatively evenly distributed upstream of Low Level
(G8140222). The inclusion of new gauge sites and re-rating of the gauging station at the Railway
Bridge (G8140001) on the Katherine River, however, have resulted in a change in the understanding
of the aquifer river connectivity. Based on flows at Seventeen Mile Creek (G8140159) and Railway
Bridge (G8140001), virtually no groundwater enters the river from the Tindall limestone upstream of
the Railway Bridge. Most inflows appear to occur between the Railway Bridge (G8140001) and Low
Level (G8140222).
Calibration performance was assessed using visual techniques such as time series plots and
scattergrams and statistical parameters including the correlation coefficient R, Nash-Sutcliffe
efficiency and the metrics described in Chapter 4 above.
4
The adopted rainfall runoff model was NAM, the hydrodynamic model was Mike 11 and the groundwater model was
FEEFLOW (all DHI products).
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Visually, the recalibrated discharge hydrographs show a closer match to the observed flows than the
initial calibration, which consistently underestimate (particularly for G8140001). The Nash–Sutcliffe
coefficient of efficiency was greatly improved for the NAM as well as the integrated models. The
calibration of the FEFLOW model resulted in a distribution of groundwater inflow which more closely
reflects the observed data.
Comparing simulated discharge using the initial and final integrated model parameters for the Railway
Bridge (G8140001) are presented in the figure below. The initial parameters consistently overestimate
the dry season low flows and the transition from wet season to dry season is quite abrupt. The final
parameters generate dry season low flows closer to the observed flows and the transition from wet
season to dry season is more gradual. The NSE for flows at station G8140001 improved dramatically
from 0.297 to 0.806 for the NAM model and from 0.364 to 0.813 for the integrated model. The scatter
plot below further demonstrates the improved performance of the model.
Comparison of simulated discharge using the initial and final integrated model parameters.
Scatter plot of simulated discharge at Railway Bridge (G8140001)
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3. Derivation of residuals and flow generation in the Paroo and
Burdekin catchments, Queensland, using various conceptual
models and climatic data
Acknowledgements
This study was undertaken by the Queensland Department of Environment and Resource
Management.
Introduction
The study aimed to evaluate if low flow in rivers can be better represented by applying alternative
methods for calculating residual flows or by using different climatic data.
Residual flows
Residual flows are ungauged flows that enter a stream between adjoining gauging stations. The
standard approach to estimating residual inflows for existing river system models in Queensland has
been to route flows from the upstream gauging station to the downstream gauging station. Subtracting
the upstream routed hydrograph from the downstream gauged hydrograph gives a residual which is
assumed to equal the ungauged tributary inflow. A rainfall-runoff model is then calibrated to match
these residual inflows. This approach ignores transmission losses and on occasions transmission
losses are so high the computed residuals are negative (meaning that losses were higher than the
inflows from the intermediate catchment). When this occurs, a loss function is added to account for
the negative residuals. Clearly this approach is not accurate as actual transmission losses are not
calculated and the estimated catchment inflows may be in error by a significant amount, especially
during low-flow periods. This study consequently investigated different approaches for improving the
estimate of residual flows, specifically for a reach of the Paroo River. The Paroo has a number of
pools along its length. Flow is intermittent and during zero-flow periods (which occur approximately 60
per cent of the time) the river consists of a sequence of disconnected pools. Losses in some sections
(especially in the lower reaches) are very high with the mean annual flow reducing as the river
progresses downstream (due to water trapped in pools, billabongs and wetlands) and is subsequently
lost to evaporation. The methods investigated for improved estimation of residual flows included:

incorporating pools in the model to represent the water captured and stored in the pools

computing evapotranspiration losses from the river and pools.
Rainfall datasets
The exiting river system models in Queensland used the Bureau of Meteorology rainfall gauging
stations to provide catchments rainfalls for the Sacramento rainfall-runoff models, which were then
used to estimate catchment inflows. Missing data was infilled using correlations established with
nearby gauging stations. These stations are often sparsely distributed and it was proposed a better
estimate of catchment rainfall could be achieved using the Bureau’s SILO Gridded data (Data Drill)
and Patched Point Dataset (PPD).
The PPD uses original Bureau of Meteorology measurements for a particular meteorological station,
but with interpolated data used to fill (‘patch’) any gaps in the observation record. The Data Drill
accesses grids of data derived by interpolating the Bureau of Meteorology's station records.
Interpolations are calculated by splining and kriging techniques, making use of elevation as well as
horizontal position, and taking into account long-term variability of the rainfall at each rainfall station
being used.
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Catchment description
The different methods for estimating residual flows were investigated in the Paroo River, while the
different datasets for describing catchment rainfall were investigated for the Broken River.
Paroo catchment
The Paroo is located in an arid to semi-arid zone in south-west Queensland and north-west New
South Wales. The headwaters are situated in the Warrego and Wallaroo ranges. The river flows
south-west through Queensland and into New South Wales, where it discharges into a complex flood
plain as it approaches the Darling River between Tilpa and Wilcannia. The Paroo River is a dryland
river which most of the time consists of a series of disconnected waterholes. The recorded mean
annual flow in the river at Caiwarro, (the last gauged point in Queensland) is 546,893 ML/yr (1968–
2002), while the mean annual flow discharging into the Darling River is estimated at 140,861 ML/yr
(1889–2002).
This study was confined to reach 3 (Caiwarro to Willara Crossing), which is located in the central part
of the Paroo River system and extends for 96 km. There are a large number of natural waterholes and
lakes in this reach, including Lake Numulla, Lake Wyarra, Bindegolly, Hutchinson, and Tomaroo.
Lakes Hutchinson, Binedegolly and Toomaroo, which are fed by Bundilia Creek. These lakes have
large catchment areas but are often dry. Two tributaries join the Paroo River between Caiwarro and
Willara Crossing: Caiwarro Creek and Barton’s Creek. The average annual rainfall varies from about
200 mm/yr in the central and western areas to a maximum of around 300 mm/yr in the north-west.
Broken River
The Broken River is a tributary of the Burdekin River which discharges into the Coral Sea to the north
of Mackay. The catchment area of the Broken River at the Credation streamflow-gauging station is 41
km2. The Broken River rises in the Clarke Range, which forms the eastern boundary of the catchment
and flows west towards Eungella Dam. The average annual rainfall is relatively uniform across the
catchment, decreasing from 1800 mm to 1600 mm, moving from east to west.
Results
Paroo River – residual flows
The analysis used information from gauging stations at Caiwarro and Willarra Crossing which have
operated since 1967 and 1975, respectively.
No clear conclusion could be drawn from the results. Runoff estimates varied significantly from event
to event. It was concluded there are several processes that influence flows in the reach and
confuscate computation of residual flows, including:

losses in the main channel (including pools) to evaporation, evapotranspiration (riparian
vegetation) and infiltration

losses to the floodplain which incorporates connected pools, wetland and depressions that
lose water to evaporation, infiltration and evapotranspiration

varying connectivity between the mainstream and adjoining pools, wetlands and floodplains.
It was concluded these processes need to be better understood and specifically represented in the
model. Use of a hydrodynamic model better able to represent the movement of water into the
floodplain and connected storages (with associated infiltration and evaporation losses) may provide
improved results.
Broken River
NATIONAL WATER COMMISSION — Low flows report series
47
The streamflow-gauging station at Credation operated from 1955 to 1988. The catchment includes
three rainfall gauging stations. A Sacramento rainfall-runoff model was established for the catchment
and used to compare results using different input rainfall datasets. The results were compared for the
period from 1 January 1956 to 31 December 1987, as this period had no missing streamflow data.
It was expected the gridded rainfall data would provide a better description of the distribution of
rainfall across the catchment and yield the best results. However, the best results were achieved
using the three Bureau gauging stations (with missing data infilled using correlations to adjoining
stations).
4. Recalibration of Currency Creek, East Mount Lofty ranges, South
Australia
Acknowledgements
This study was undertaken by Matt Gibbs, Mark Alcorn, Kumar Savadamuthu and David Deane of the
Department for Water (South Australia).
Introduction
Many recommendations describes in Chapter 4 are already applied in South Australia, but this study
investigated if the estimation of low flows can be further improved by refining the calibration method.
Modelling was based on an existing WaterCress model that used WC1 as the rainfall-runoff model.
Automatic calibration was conducted using a combined objective function consisting of three equallyweighted RMSE objectives, calculated on:

daily flows, weighted based on the flow: 1000 / (Q+0.1)

monthly volumes, weighted based on the volume: 0.01 / sqrt (V)

flow percentiles, in steps of 10 per cent.
Manual calibration was then employed to further refine the flow estimates. The flow was split into
high-flow and low-flow datasets using the following two methods:
1. By date, with the low-flow period from November to April, inclusive.
2. By flow, with the low-flow period below the median flow of 3 ML/day. By adopting a median
value, half the observed data is available for calibration of the high-flow period and half the
data for the low-flow period. It should be noted that for South Australia, lows flows have been
based on the 10th percentile daily flow exceedence for perennial streams, or the 20th
percentile daily flow exceedence of the non-zero-flow period for ephemeral stream, as well as
the representation of baseflow.
The model was separately calibrated to the high and low-flow datasets and the time series of flows
was then spliced together. All calibration runs were undertaken using PEST, which minimises the sum
of squared residuals for the specified objectives. The outcome is assumed to be the same as
calibrating the model to either a daily Nash-Sutcliffe coefficient of efficiency (NSE) with =1 or a R2
value, as the sum of squared residuals is the only term in either equation based on model outputs.
Catchment description
The Currency Creek catchment is located in the East Mount Lofty ranges and has a catchment area
of approximately 99 km2. The mean annual rainfall varies from 450 mm in the lower reaches to 900
mm in the upper reaches. The climate is highly seasonal with approximately 75 per cent of the annual
NATIONAL WATER COMMISSION — Low flows report series
48
rainfall occurring between 1 May and 30 November. Mean annual runoff varies from as low as 10 mm
in the lower reaches to 190 mm in the upper reaches.
Results
Runoff model calibration to low flows is a priority in South Australia and the original Currency Creek
model was generally well-calibrated. Hence, only limited improvements were found by splitting the
sample into high and low-flows datasets and calibrating these separately.
NATIONAL WATER COMMISSION — Low flows report series
49
Shortened forms
AWBM
Australian Water Balance Model
CSIRO
Commonwealth Scientific and Industrial Research Organisation
DAFWA
The Western Australian Department of Agriculture and Food
DERM
Department of Environment and Resource Management
DFW
South Australia Department for Water
DHI
Danish Hydraulic Institute
DPIPWE
Department of Primary Industries Parks, Water and Environment
DSE
Department of Sustainability and Environment
GIS
Geographical Information System
IQQM
Integrated Quantity and Quality Model
LAI
Leaf Area Index
LASCAM
Large Scale Catchment Model
MDB
Murray-Darling Basin
MDBSY
Murray-Darling Basin Sustainable Yields
NLFIT
Non linear fit
NoW
NSW Office of Water
NRETAS
Department of Natural Resources, Environment, The Arts and Sport
NSE
Nash-Sutcliffe Efficiency
NSW
New South Wales
QLD
Queensland
REALM
Resource Allocation Model
RRL
Rainfall Runoff Library
SHE
Système Hydrologique Européen
SKM
Sinclair Knight Merz
STEDI
Spatial Tool for Estimating Dam Impacts
TEDI
Tool for Estimating Dam Impacts
WMO
World Meteorological Organization
WRP
Water Resource Plan
NATIONAL WATER COMMISSION — Low flows report series
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