Low-flow hydrological classification of
Australia
S. Mackay1, N. Marsh2, F. Sheldon1 and M. Kennard1
1. Australian Rivers Institute, Griffith University
2. Yorb Pty Ltd
Low flows report series – March 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 The Australian Rivers Institute, Griffith
University and Yorb Pty Ltd on behalf of the National Water Commission.
NATIONAL WATER COMMISSION — Low flows report series
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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: 978-1-921853-66-1
Published by the National Water Commission
95 Northbourne Avenue
Canberra ACT 2600
Tel: 02 6102 6000
Email: enquiries@nwc.gov.au
Date of publication: March 2012
An appropriate citation for this report is:
Mackay S, Marsh N, Sheldon F & Kennard M 2012, Low-flow hydrological classification of Australia,
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
Acknowledgements.......................................................................................... ............................. .....viii
Acronyms and abbreviations ................................................................................................................ ix
Executive summary ............................................................................................................................... x
1. Introduction ....................................................................................................................................... 1
1.1 Background ........................................................................................................................... 1
1.2 Aim and structure ................................................................................................................. 1
2. Approach ........................................................................................................................................... 2
2.1 Classifying low flows ............................................................................................................. 2
2.2 Mapping single low-flow metrics ........................................................................................... 2
2.3 Testing the long-term Normalised Difference Vegetation Index (NDVI) for
predicting low-flow class ............................................................................................................. 3
3. Methods............................................................................................................................................. 4
3.1 Classifying low flows ............................................................................................................. 4
3.2 Mapping single low-flow metrics ........................................................................................... 7
3.3Testing the long-term NDVI for predicting low-flow class ...................................................... 7
4. Results .............................................................................................................................................. 9
4.1 Classifying low flows ............................................................................................................. 9
4.2 Mapping single low-flow metrics ......................................................................................... 15
4.3 Testing the long-term NDVI for predicting low-flow class ................................................... 21
5. Interpretation ................................................................................................................................... 23
5.1 Classifying low flows ........................................................................................................... 23
5.2 Mapping single low-flow metrics ......................................................................................... 27
5.3 Testing the long-term NDVI for predicting low-flow class ................................................... 27
6. Conclusions ..................................................................................................................................... 36
References .......................................................................................................................................... 37
Tables
Table 1: Low-flow metrics included in the low-flow classification....................................................... 5
Table 2: Summary of hydrological attributes of low-flow classes. Flow classes
are arranged from perennial to highly ephemeral. Colours correspond to those
used in figures 3, 4 and 13. See Table 1 for definition of flow-metric acronyms......................11
Table 3: Summary of hydrologic attributes of flow classes for the simplified
low-flow classification. Flow classes are listed from highly ephemeral to strongly
perennial. Colours correspond to those used in figures 6 and 15. ‘Most at risk’ refers
to areas where there is a high risk of ecological change from a small change in discharge....14
Table 4: Parameter estimates for the multinomial logistic regression models describing
the relationship between simplified low-flow class and NDVI. Flow classes were
grouped for analysis: class 1 = simplified low-flow class 1; class 2 = simplified low-flow
class 2; class 3 = simplified low-flow classes 3 to 7; class 4 = simplified low-flow class 8.
The models compare each flow class to flow class 1 (the reference class). Thus for
flow class 2, a one unit increase in NDVI will result in a 14.606 unit change in the
log-odds of flow class 2 relative to flow class 1 (where 14.606 is in log-odds units).
Nagelkerke pseudo-R2 0.491. Chi-square for the model fit (comparing the null
model and the fitted model) = 506.8, p<0.001. These models were used to calculate
probabilities of class membership for 0.25x0.25-degree grid cells...........................................22
Table 5: Confusion matrix showing the number of gauges correctly assigned to
low-flow classes by the multinomial logistic regression model.................................................22
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Figures
Figure S1: Context of reports produced for the National Water Commission's Low Flow
Ecological Response and Recovery Project. The circles represent the location of
individual case studies and the size of each circle represents the spatial extent
of each case study......................................................................................................................xi
Figure 1: A change in a flow metric of a given magnitude resulting from flow regulation
e in flow class occurs but in the bottom example a change in flow-class
membership occurs.....................................................................................................................8
Figure 2: Bayes Information Criterion (BIC) versus number of clusters for classification
of low-flow metrics using Mclust. The model and number of clusters that maximises
BIC is the best classification of the data. Each plot represents an individual model
implemented by Mclust. Note that BIC for models EVI, VVI and VVV could only be
calculated for single-cluster solutions..........................................................................................9
Figure 3: Box-and-whisker plots of log-transformed low-flow metrics identified by
90th (upper) percentiles respectively. See Figure 4 for n for each low-flow class..................... 10
Figure 4: Cluster-wise stability for the six-class solution identified by Mclust. Horizontal
l
6 – unstable clusters (Hennig 2010)...........................................................................................12
Figure 5: Box-and-whisker plots of log-transformed low-flow metrics used in the four-metric
classification. See Table 1 for flow metric acronyms and Figure 3 for description of plots........ 13
Figure 6: Cluster-wise stability for the eight-class solution identified by Mclust.
Horizontal lines i.6 – unstable clusters (Hennig 2010)......................................................................... 15
Figure 7: P90 (90th percentile exceedence flow divided by the mean daily flow) values
for stream gauges used in the simplified low-flow classification................................................ 16
Figure 8: Baseflow index values (baseflow divided by total streamflow) for stream gauges
used in the low-flow classification...............................................................................................17
Figure 9: Inter-annual variability (as coefficient of variation) in the baseflow index for
stream gauges used in the low-flow classification...................................................................18
Figure 10: Number of zero-flow days for stream gauges used in the simplified
low-flow classification.................................................................................................................19
Figure 11: Variability (as coefficient of variation) in the number of zero-flow days for
stream gauges used in the simplified low-flow classification..................................................... 20
Figure 12: Range of NDVI values in each flow class, based on the simplified
four-metric classification. Numbers above boxes are the number of sites in each
low-flow class. Colours correspond to those used in Table 3.....................................................21
Figure 14: Location of flow class 2 (weakly perennial) and flow class 3
(marginally ephemeral) from the classification of 35 low-flow metrics.....................................25
Figure 16: Predicted low-flow classification based on simplified flow classification
extrapolated using the long-term normalised difference vegetation index (NDVI). The
eight flow classes from the simplified low-flow classification were grouped into
four classes for this analysis (see text for explanation). Class 3 represents ‘at risk’
areas; that is, areas where there is a relatively high risk of ecological change for a
small change in flow. Class 3 includes simplified low-flow classes 3 to 7……………………...28
Figure 17: Simplified low-flow classes predicted from long-term NDVI scores for
New South Wales. For analysis the eight simplified low-flow classes were grouped
into four classes (see text for explanation). Class 3 represents ‘at risk’ areas; that is,
areas where there is a relatively high risk of ecological change for a small change in
flow. Class 3 includes simplified low-flow classes 3 to 7. White patches represent
missing data……………………………………………………………………………………..........29
Figure 18: Simplified low-flow classes predicted from long-term NDVI scores for
Victoria. For analysis the eight simplified low-flow classes were grouped into
four classes (see text for explanation).Class 3 represents ‘at risk’ areas; that is, areas
where there is a relatively high risk of ecological change for a small change in flow.
Class 3 includes simplified low-flow classes 3 to 7. White patches represent
missing data…………………………………………………………………………………………..30
Figure 19: Simplified low-flow classes predicted from long-term NDVI scores for Tasmania.
For analysis the eight simplified low-flow classes were grouped into four classes
(see text for explanation). Class 3 represents ‘at risk’ areas; that is, areas where there
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is a relatively high risk of ecological change for a small change in flow. Class 3
includes simplified low-flow classes 3 to 7. White patches represent missing data.................31
Figure 20: Simplified low-flow classes predicted from long-term NDVI scores for
South Australia. For analysis the eight simplified low-flow classes were grouped into
four classes (see text for explanation). Class 3 represents ‘at risk’ areas; that is, areas
where there is a relatively high risk of ecological change for a small change in flow.
Class 3 includes simplified low-flow classes 3 to 7. White patches represent
missing data.............................................................................................................................32
Figure 21: Simplified low-flow classes predicted from long-term NDVI scores for
Western Australia. For analysis the eight simplified low-flow classes were grouped
into four classes (see text for explanation). Class 3 represents ‘at risk’ areas; that is,
areas where there is a relatively high risk of ecological change for a small change in
flow. Class 3 includes simplified low-flow classes 3 to 7. White patches represent
missing data.............................................................................................................................33
Figure 22: Simplified low-flow classes predicted from long-term NDVI scores for the
Northern Territory. For analysis the eight simplified low-flow classes were grouped
into four classes (see text for explanation). Class 3 represents ‘at risk’ areas; that is,
areas where there is a relatively high risk of ecological change for a small change in
flow. Class 3 includes simplified low-flow classes 3 to 7. White patches represent
missing data.............................................................................................................................34
Figure 23: Simplified low-flow classes predicted from long-term NDVI scores for
Queensland. For analysis the eight simplified low-flow classes were grouped into
four classes (see text for explanation). Class 3 represents ‘at risk’ areas; that is,
areas where there is a relatively high risk of ecological change for a small change in
flow. Class 3 includes simplified low-flow classes 3 to 7. White patches represent
missing data.............................................................................................................................35
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Acknowledgements
We thank the National Water Commission for funding and support and the helpful comments of
reviewers. We would further like to thank the project advisory group, jurisdictional agency staff and
review workshop participants for their input and useful comments throughout the project. We thank
Daren Barma (Barma Water Resources) for helpful feedback on the suitability of metrics to inform
hydrological modelling. We kindly acknowledge Tory Grice (Griffith University) for the preparation of
figures and maps and the Bureau of Meteorology for the supply of Normalised Vegetation Difference
Index data.
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Acronyms and abbreviations
ANZECC
Australia New Zealand Environment Conservation Council
BIC
Bayes Information Criterion
BoM
Bureau of Meteorology
BFI
Baseflow index
EM
Expectation-maximisation
EPT
Ephemeroptera (mayfly), Plecoptera (stonefly) and Trichoptera (caddisfly)
GIS
Geographic Information System
IBI
Index of Biotic Integrity
NDVI
Normalised Difference Vegetation Index
NWI
National Water Initiative
SIGNAL
Stream Invertebrate Grade Number-Average Level
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Executive summary
This report presents a national classification of low-flow hydrology in Australia. It outlines key low-flow
metrics that may be used as suitable calibration metrics for testing hydrological models and provides
a basis for extrapolating the results of site- or region-based hydro-ecological studies.
A preliminary low-flow classification based on 35 low-flow metrics was calculated for 830 stream
gauge records. These 35 metrics are a subset of the 120 flow metrics used by Kennard et al. (2010a)
for the classification of Australian flow regimes. A six-group classification was developed and the
robustness of the group membership tested. The six-group low-flow classification shows a strong
latitudinal gradient relating to climatic patterns and a strong secondary gradient related to position in
catchment – which may reflect catchment size and its effect on measures of low flow (i.e. smaller
streams cease to flow more often than larger streams). We used a novel technique of cluster stability
to assess the ‘validity’ of the low-flow classes. These results can be used by water managers to
determine where hydro-ecological studies should be focused, since ‘weak’ flow classes may not yield
robust flow-ecology relationships.
The low-flow classification process was revised in the light of a series of site-specific hydro-ecological
studies that tested how the flow metrics correlated with the biological traits of aquatic taxa (see Marsh
et al. 2012). A second and simpler classification using only four low-flow metrics (the flow exceeded
90 per cent of the time, the baseflow index, the number of zero-flow days and the average of the
annual minimum flow divided by catchment area) yielded the same general patterns as for the 35 lowflow metrics classification.
In acknowledgement of the limitations of using categorical class membership, we also produced a
series of maps demonstrating the spatial distribution of five key low-flow metrics across Australia.
These metrics were the flow exceeded 90 percent of the time, the baseflow index, the coefficient of
variation of the baseflow index, the number of zero-flow days and the coefficient of variation in the
number of zero-flow days.
In addition to the classification, we compared the point-based class membership for the 830 sites with
the long-term Normalised Vegetation Difference Index (NDVI). The NDVI is a satellite-derived
vegetation vigour index which has been shown to be a good predictor of water deficit. Using the longterm NDVI, sites could be allocated to the correct low-flow class (as identified by the simplified
classification) with an error of approximately 50 per cent of the time (and within the correct or adjacent
class greater than 90 per cent of the time). This site allocation is clearly not precise but provides a
useful illustration of the spatial distribution of flow classes within a region. We therefore used the
national long-term NDVI scores to spatially extrapolate the low-flow classification to generate a map
for helping managers transpose the results of site- or region-specific studies and identify areas of
potential ecological risk through alteration to low-flow hydrology.
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Report context
This report is part of a larger series of reports produced for the National Water Commission’s Low
Flow Ecological Response and Recovery Project (Figure S1). The report presents key low-flow
metrics and the classification provides a basis for extrapolating the results of site- or region-based
hydro-ecological studies.
Guidance on ecological response and hydrological modelling for low -flow
water planning
Low-flow hydrological classification of Australia
Review of literature quantifying ecological responses to low flows
Early warning, compliance and diagnostic monitoring of ecological
responses to low flows
Synthesis of case studies quantifying ecological responses to low flows
Figure S1: Context of reports produced for the National Water Commission's Low Flow Ecological
Response and Recovery Project. The circles represent the location of individual case studies and the
size of each circle represents the spatial extent of each case study.
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1. Introduction
1.1. Background
Low flows play a fundamental role in structuring river ecosystems (Bunn & Arthington 2002; Poff et al.
2010). In perennial systems they are critical to maintaining populations of water-dependent plants and
animals between periods of high flow (Thoms & Sheldon 2000; Sheldon et al. 2002). The ecological
importance of low flows is expected to be particularly pronounced in Australia where varying degrees
of intermittency are a widespread feature of natural riverine flow regimes (Kennard et al. 2010a).
Ecologically, the hydrological definition of ‘low flow’ will depend on the spatial extent and temporal
aspect of a river’s hydrograph and the varying flow requirements of water-dependent biota and
ecological processes (Smakhtin 2001). For example, the duration of low-flow periods may be critical
for the persistence of a species of fish in a river reach, whereas the frequency of low-flow periods may
determine the abundance of a macroinvertebrate species by controlling recruitment. As such,
identifying hydrologic metrics that are ecologically meaningful (i.e. on which biota or ecosystem
processes depend), and classifying them to allow appropriate transposition of results from site to site,
is important for water resource management that seeks to balance the needs of aquatic ecosystems
and human uses.
1.2. Aim and structure
This report presents a national classification of low-flow hydrology. Its purpose is to provide a basis
for extrapolating the results of site- or region-based hydro-ecological studies, and to present key lowflow metrics that are suitable performance measures for testing hydrological models at the low-flow
end of the hydrograph.
The report is traditionally structured by approach, methods, results, interpretation and conclusions.
Each of these sections is organised into three parts:

classifying low flows

mapping single low-flow metrics

testing the long-term Normalised Difference Vegetation Index (NDVI) to predict low-flow class.
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2. Approach
2.1. Classifying low flows
To date there are no consistent or agreed summary hydrological measures or metrics of the
ecologically relevant characteristics of a low-flow regime that can be applied on a continental scale.
To establish these metrics and their spatial applicability, coordinated ecological research across
different ecosystems and biological measures is required. While substantial research has been
undertaken for river systems that experience extended periods of low-flow stress, these studies have
been conducted largely with a single study focus (Rolls et al. 2012). This limits the extent to which the
results can be applied to water resource management in general. We have therefore developed a
national low-flow classification (this report) based initially on the 35 low-flow metrics (selected from
120 flow metrics) and 830 gauge sites used for the classification of Australian flow regimes (Kennard
et al. 2010a) (Table 1) so that regional hydro-ecological relationships can be more broadly
generalised by considering areas of similar hydrology.
We later repeated the initial low-flow classification using a simplified set of four metrics – following a
series of case studies where biological datasets were compared against low-flow hydrological
conditions (see Marsh et al. 2012). Many of the case studies tested or hypothesised that only a few
key hydrological metrics were critical for consideration of the potential biological consequences of low
flows. We therefore completed a second low-flow classification with only four low-flow metrics:
1. Average number of zero-flow or cease-to-flow days per year
2. Baseflow index
3. Average of annual specific mean annual minimum (the average of the annual minimum flow divided
by catchment area)
4. 90th percentile exceedance flow (flow exceeded 90 per cent of the time)
It is important to note that ecological conditions are locally specific and the broad generalisations
attempted here (through flow classification) should be used only as a general guide and as an indirect
predictor of likely ecological conditions.
2.2. Mapping single low-flow metrics
The classification of a given site is based on all of the low-flow metrics used in that classification.
However, many hydro-ecological analyses (e.g. Marsh et al. 2012) only consider a subset of the most
ecologically relevant low-flow metrics. Furthermore, different low-flow metrics emerge as being the
most important for different ecological functions in each hydro-ecological analysis. For example, the
number of cease-to-flow days may be important for the persistence of a given fish species, but the
long-term inter-annual variation in the length of cease-to-flow spells (the predictability of ephemerality)
may determine the likely presence of a sensitive macroinvertebrate species. To spatially extrapolate
the results of the first example, we are primarily interested in the number of cease-to-flow days and
how that varies spatially (other hydrological metrics being less important). Hence, for this study, as
well as classifying sites with similar low-flow regime characteristics, we have also produced a series of
maps showing values for individual low-flow metrics that have been shown or are hypothesised to be
primary drivers in hydro-ecological analyses (so that their results can be extrapolated spatially to
areas with similar values for those metrics). The key metrics are:

P90: the flow exceeded 90 per cent of the time

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)
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
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)

number of zero-flow days: the mean annual number of zero-flow days used as a continuous
function to consider how ephemeral a stream is.

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 ceaseto-flow events, with high values indicating highly variability in the length of cease-to-flow events.
Note that three of these metrics are used in the four-metric low-flow classification (see above).
2.3. Testing the long-term Normalised Difference
Vegetation Index for predicting low-flow class
One of the challenges in undertaking a hydrological classification is obtaining suitable streamflow
data. For this study the gauge data were from gauges largely unimpacted by flow-regulating
structures (see Kennard et al. 2010a, 2010b). As a consequence, gauges in the main channels of
river systems are not well-represented in the dataset, particularly those in the Murray-Darling Basin.
This problem could be avoided by hydrological modelling with flow-regulating structures removed (i.e.
essentially modelling a ‘pre-development’ case). This modelling approach is suitable when the
hydrological model has a sound representation of the low-flow part of the hydrograph. However, in
most cases, existing hydrological models have been developed to predict yield and, as such, their
calibration is heavily influenced by average and high flow-values rather than low flows (Barmah &
Varley 2012a).
As an alternative to hydrological modelling, and to make up for the paucity of flow-gauge data and
sites, we investigated how the NDVI varies across low-flow classes as a basis for spatially
extrapolating the low-flow classification. The NDVI is a satellite-derived vegetation vigour index that
has been shown to be a good predictor of water deficit (McVicar et al 2003; Box 1). A resulting map
illustrates low-flow classes across Australia and can be used by water planners and managers in
desktop analyses to transpose results of site- or region-based hydro-ecological studies and to identify
areas of potential ecological risk through alteration to low-flow hydrology.
Box 1: NDVI summary (Bureau of Meteorology 2011)
Live green vegetation absorbs visible light (solar radiation) as part of photosynthesis. At the same
time plants scatter (reflect) solar energy in the near infrared. This difference in absorption is quite
unique to live vegetation and provides a measure of the greenness of the vegetation.
NDVI measures this difference, providing a measure of vegetation density and condition. It is
influenced by the fractional cover of the ground by vegetation, the vegetation density and the
vegetation greenness. It indicates the photosynthetic capacity of the land surface cover.
NDVI is calculated from the red and near-infrared reflectances rRed and rNIR as
NDVI = (rNIR - rRed) / (rNIR + rRed)
Its value is always between -1 and +1. Vegetation NDVI in Australia typically ranges from 0.1 up to
0.7, with higher values associated with greater density and greenness of the plant canopy. NDVI
decreases as leaves come under water stress, become diseased or die. Bare soil and snow values
are close to zero, while waterbodies have negative values.
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3. Methods
3.1. Classifying low flows
3.1.1. Hydrologic data and metrics
We used the hydrologic dataset of Kennard et al. (2010a) to undertake the low-flow classification. In
summary, this dataset comprises 120 hydrologic metrics and 830 stream gauges representing 11 of
the 12 Australian Water Resources Commission drainage divisions. The hydrologic metrics were
calculated for a minimum of 15 years of hydrologic record. Justification for the record length used,
methods for infilling missing data and selection of flow metrics can be found in Kennard et al. (2010a,
2010b). The low-flow classification presented in this report is based on the 35 hydrologic metrics
listed in Appendix S1 of Kennard et al. (2010a) as representing low-flow attributes of the flow regime
(Table 1). These attributes were:

magnitude of flow events

frequency of flow events

duration of flow events

timing of flow events.
Hydrologic metrics were defined as representing low-flow attributes if they represented flow minima or
were associated with the 75th, 90th and 95th percentiles of flow (see Olden & Poff 2003). All
magnitude metrics were standardised by dividing by the mean daily flow calculated for the entire flow
record to downweight their influence on the classification. Following Kennard et al. (2010a)
standardised low-flow metrics were log10(x+1)-transformed before analysis and modelled as normallydistributed variables (see below).
3.1.2. Thirty-five metric classification
The low-flow classification was undertaken using the Mclust package for R (R Development Core
Team 2010; Fraley & Raftery 2008). Mclust is a model-based hierarchical agglomerative clustering
procedure based on Gaussian finite mixture models. Model-based clustering assumes that the
observed data come from a population composed of several subpopulations (Raftery & Dean 2006).
Each subpopulation is modelled separately and hence the entire population is composed of a mixture
of models. In the context of flow classification each subpopulation represents a flow class. Individual
models are probability density functions, usually multivariate normal distributions, parameterised by
their means and covariances (Fraley & Raftery 2007). Covariance properties determine cluster
geometry (i.e. shape, volume and orientation; Fraley & Raftery 2002). These attributes can be fixed or
allowed to vary between clusters.
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Table 1: Low-flow metrics included in the low-flow classification
Metric
Unit
Acronym
Median of annual minimum flows
No unit
MedAnnMin
Baseflow index
No unit
BFI
Coefficient of variation 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
Mlday-1km2
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
Coefficient of variation in low-flow pulse count (<75th percentile)
No unit
CV_ LSNum_P75
Coefficient of variation in low-flow pulse count (<90th percentile)
No unit
CV_ LSNum_P90
Coefficient of variation 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
Coefficient of variation in annual minima of 1-day means of daily
discharge
No unit
CV_AnnMin1day
Coefficient of variation in annual minima of 3-day means of daily
discharge
No unit
CV_AnnMin3day
Coefficient of variation in annual minima of 7-day means of daily
discharge
No unit
CV_AnnMin7day
Coefficient of variation in annual minima of 30-day means of daily
discharge
No unit
CV_AnnMin30day
Coefficient of variation 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
Coefficient of variation in low-flow pulse duration (<75th percentile)
No unit
CV_LSDur_P75
Coefficient of variation in low-flow pulse duration (<90th percentile)
No unit
CV_LSDur_P90
Coefficient of variation in low-flow pulse duration (<99th percentile)
No unit
CV_LSDur_P99
Number of zero-flow days
Days
NumZeroDay
Coefficient of variation in number of zero-flow days
No unit
CV_NumZeroDay
Julian date of annual minimum
No unit
JDMin
Coefficient of variation in Julian date of annual minimum flow
No unit
CV_JDMin
Seasonality (contingency/predictability) of minimum instantaneous
flow (month)
No unit
SEASON
Predictability (constancy+contingency) of minimum instantaneous flow
(month)
No unit
PREDICT
Magnitude of low flows
Frequency of low flows
Duration of low flows
Timing of low flows
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Clustering is undertaken by merging pairs of clusters so that the classification likelihood (i.e. the
probability of observing the data given a set of parameter values; Bolker 2008) is maximised, as
summarised below (Fraley & Raftery 2002):
(a) Determine the maximum number of clusters (M) and set of mixture models to consider (in
Mclust, the maximum number of clusters is 9).
(b) Perform hierarchical agglomeration to provide an approximate classification likelihood for
each model and obtain the corresponding classifications for a maximum of M clusters.
(c) Apply the EM (expectation-maximisation) algorithm to estimate parameters for each model
and each number of clusters, using the classification likelihood obtained from step (b) to
provide starting values for the EM algorithm.
(d) Compute Bayes Information Criterion (BIC) using optimal parameters determined from the EM
algorithm for 2-M clusters. The model and number of clusters that maximises BIC is the
optimal classification. An information criterion is used compare all possible candidate models
from a set of variables (Bolker 2008). BIC has been found to perform well in several studies
(see Fraley & Raftery 2007).
The clustvarsel package for R was used to identify a subset of low-flow metrics in which all metrics
contain classification information (Raftery & Dean 2006; Dean & Raftery 2009). The clustvarsel
algorithm identifies a flow metric to add to the flow-metric subset that best improves the classification
and then determines whether an existing flow metric in the subset can be dropped (Raftery & Dean
2006). The algorithm begins by identifying the flow metric that has the most evidence of univariate
clustering, next identifies the second clustering variable that has the most evidence of bivariate
clustering and then selects the next clustering variable as the one that shows the best evidence for
multivariate clustering (while including the first two variables). The algorithm then searches for a flow
metric to drop from the subset, based on change in BIC. The procedure is repeated until no metric
can be found to include or drop (Raftery & Dean 2006).
Next, the clusterboot function in the fpc package for R (Hennig 2010) was used to determine the
cluster-wise stability of the low-flow classification. Stability refers to the capacity of a ‘valid’ cluster to
be retained in a classification if the original dataset is changed in a non-essential manner and reclassified (Hennig 2007). Jaccard similarity is used as a measure of cluster similarity. We chose the
sub-setting option to assess cluster-wise stability. This method classifies random subsets of the
original dataset and compares each cluster in the subsets with the most similar cluster in the original
classification. This procedure is repeated and the mean Jaccard similarity for each cluster is used as
a measure of cluster stability (Hennig 2010). We used 100 random subsets, with each representing
75 per cent of the original dataset (i.e. 623 gauges in each subset). Valid, stable clusters should have
a mean Jaccard similarity of 0.85 or greater. A mean Jaccard similarity of 0.75 to 0.85 indicates a
stable cluster and a mean Jaccard similarity of 0.6 to 0.75 indicates pattern in the data but uncertain
cluster membership (Hennig 2010).
3.1.3. Four-metric classification
The same classification procedure as described above was used to classify the 830 stream gauges
using four log-transformed low-flow metrics (baseflow index, mean number of zero-flow days,
Spec_MeanAnnMin and 90th percentile flow). These metrics were identified in the case studies
(technical reports in this series) as being key metrics describing patterns in aquatic biota or were
metrics discriminating between low-flow classes in the full classification (above). This analysis (termed
the simplified low-flow classification) identified eight low-flow classes (Figure 6; Table 3).
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3.2. Mapping single low-flow metrics
Maps showing values for the five individual low-flow metrics selected were produced, again using the
hydrologic dataset of Kennard et al. (2010a) (see Section 3.1.1).
3.3. Testing the long-term NDVI for predicting lowflow class
Monthly NDVI data are available for Australia from 1992, and the average monthly NDVI values
(1992–2011) provide a long-term summary of average vegetation vigour across the country. We
hypothesised that areas of low long-term NDVI values (i.e. low vegetation vigour) should correlate
with areas of low water availability (highly ephemeral streams) and, as such, the NDVI may be a
useful surrogate for extending the low-flow stream classification to locations without available stream
gauge data.
The Bureau of Meteorology (BoM) provides monthly NDVI coverage for Australia on a 0.25 x 0.25degree grid (cells of approximately 25 km x 25 km), and for each state this is provided on a 0.05 x
0.05-degree grid (cells of approximately 5 km x 5 km). As this project is largely a pilot analysis, only
the national scale (0.25 x 0.25-degree grid) layers have been used, although one would expect a
better result with higher-resolution data. As well as the monthly NDVI scores, BoM also produces the
NDVI standardised anomaly for periods of one, three and six months. This score is a measure of the
NDVI score’s departure for the reporting period from the long-term average NDVI value for that grid.
The long-term average values are determined for the period 1992 to 2008, excluding April to
September 1994 due to excessive ground shadows caused by low sun elevations and September
2003 due to instrument malfunction (BoM 2011). These 16 years of averaged monthly NDVI scores
represent a spatially explicit long-term vegetation vigour score. The hydrological classification
described above similarly represents long-term hydrological characteristics for low-flow conditions.
We used multinomial logistic regression (SPSS version 17) to determine how well long-term NDVI
scores could be used to predict low-flow classes from the simplified low-flow classification.
Multinomial logistic regression is an extension of logistic regression for situations where the
dependent (categorical) variable has more than two possible values. One category is designated as
the reference or baseline category and the probability of membership in the other categories is
compared with the probability of membership in the baseline category (UCLA 2011). For this analysis,
the 0.25 x 0.25-degree grid cell values of the long-term NDVI score were used as predictors of lowflow class for gauges located within those grids. NDVI scores were unavailable for five of the original
830 gauges used in the classification.
3.4. Note on extrapolation from classifications
A critical issue in using a spatial classification approach as the basis for extrapolating point- or regionbased results is that the classification necessarily forms distinct groups of sites with similar low-flow
characteristics. Within a given hydrological group, there may be a large range in values for any
particular metric and the boundary for discriminating between classification groups is a statistical
exercise that may have no bearing on the ecological processes associated with the metric. The issue
is essentially one of trying to classify ecological processes occurring over a continuous range into
arbitrary groupings with fixed boundaries. The transition from one flow class to another means less
ecologically than the overall change in the parameter. In the hypothetical example in Figure 1, for the
same absolute change in the number of cease-to-flow days per year, we could have either no change
in flow classification group membership or a change of up to two classification groups. This is an
inherent problem with classification, and requires expert interpretation when using a classification
scheme to infer ecological consequences from a change in group membership. For this project we
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acknowledge this problem and present the continuous distribution of raw values of the key hydrologic
metrics in addition to the multi-metric hydrological classification. We also used a soft classification
procedure (Olden et al. in press) where each gauge or site is assigned a probability of flow-class
membership.
Flow-classification group membership
Number of cease-to-flow days per year
Figure 1: A change in a flow metric of a given magnitude resulting from flow regulation may have
different outcomes on flow-class membership. In the top example, no change in flow class occurs but
in the bottom example a change in flow-class membership occurs.
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4. Results
4.1. Classifying low flows
4.1.1. Thirty-five metric classification
50000
BIC
100000
150000
Mclust was run initially with cluster geometry limited to models appropriate for multidimensional data
following the recommendation of Fraley & Raftery (2007). However, the number of clusters reached
the maximum of nine allowed by the function. When Mclust was allowed to select the best model type
from all possible model types (default option) a six-cluster solution was obtained (Figure 2; Table 2).
The probability of low-flow-class membership was greater than 99.8 per cent for all gauges (i.e. there
was low error associated with allocation of gauges to low-flow classes).
0
EII
VII
EEI
VEI
EVI
2
4
6
VVI
EEE
EEV
VEV
VVV
8
Number of clusters
Figure 2: Bayes Information Criterion (BIC) versus number of clusters for classification of low-flow
metrics using Mclust. The model and number of clusters that maximises BIC is the best classification
of the data. Each plot represents an individual model implemented by Mclust. Note that BIC for
models EVI, VVI and VVV could only be calculated for single-cluster solutions.
Clustvarsel identified eight metrics as best discriminating between low-flow classes (Figure 3; Table
2). These metrics were related to discharge magnitude. Hence, metrics describing low-flow spell
duration, frequency and timing were relatively unimportant in discriminating between low-flow classes.
Much of the variation in the eight key metrics was expressed by low-flow classes 1, 2 and 3 (Figure
3). Low-flow classes 3 to 6 had high values for NumZeroDays, suggesting ephemeral to highly
ephemeral flow regimes.
Cluster-wise stability for the six-class solution ranged from 0.411 to 0.907 (Figure 4). Clusters 1 and 2
were highly stable, clusters 3 and 4 unstable, cluster 5 indicated pattern in the data but uncertain
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2
3
4
5
0.12
0.06
log(MedAnnMin+1)
1
0.00
0.10
0.00
log(Spec_MeanAnnMin+1)
class membership, and cluster 6 was stable (Figure 4). This suggests that caution should be used
when interpreting hydro-ecological relationships related to flow classes 3 to 5.
6
1
2
6
5
6
5
6
5
6
0.12
0.00
2
3
4
5
6
1
2
2
3
4
5
2.0
1.0
6
1
2
3
4
Low-flow class
4
5
6
0.12
0.06
0.00
0.12
0.06
0.00
2
3
Low-flow class
log(AnnMin7Day+1)
Low-flow class
1
4
0.0
0.10
0.00
1
3
Low-flow class
log(NumZeroDays+1)
Low-flow class
log(P75+1)
5
0.06
log(P90+1)
0.06
0.03
1
log(AnnMin3Day+1)
4
Low-flow class
0.00
log(P99+1)
Low-flow class
3
1
2
3
4
Low-flow class
Figure 3: Box-and-whisker plots of log-transformed low-flow metrics identified by clustvarsel as best
discriminating between low-flow classes. See Table 1 for flow-metric acronyms. The box represents
the 25th (lower line), median (thick line) and 75th (upper line) percentiles respectively; the whiskers
represent the 10th (lower) and 90th (upper) percentiles respectively. See Figure 4 for n for each lowflow class.
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Table 2: Summary of hydrological attributes of low-flow classes. Flow classes are arranged from
perennial to highly ephemeral. Colours correspond to those used in figures 3, 4 and 13. See Table 1
for definition of flow-metric acronyms.
Low-flow
Description
Dominant flow metrics
1
Strongly perennial
P90 – high
P75 –high
AnnMin3Day – high
AnnMin7Day – high
CV_AnnMin30Day – low
2
Weakly perennial
Spec_MeanAnnMin – high
MedAnnMin – moderate
P90 – moderate
P75 – moderate
AnnMin7Day – moderate
3
Marginally ephemeral
MedAnnMin – moderate
P90 – moderate
P75 – moderate
AnnMin7Day – moderate
4
Ephemeral
NumZeroDays – high
P75 – moderate
5
Moderately ephemeral
NumZeroDays – high
P75 – low
6
Highly ephemeral
NumZeroDays –highest
P75 - low
class
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1.0
n = 79
n =117
n =127
n =158
n =217
0.6
0.5
0.4
0.0
0.1
0.2
0.3
Clusterwise stability
0.7
0.8
0.9
n =132
1
2
3
4
5
6
Low
flow class
Low-flow
class
Figure 4: Cluster-wise stability for the six-class solution identified by Mclust. Horizontal lines indicate
levels of cluster-wise stability: >0.85 – highly stable clusters; 0.75 to 0.85 – stable clusters; 0.60 to
0.75 – pattern exists but cluster membership uncertain; <0.6 – unstable clusters (Hennig 2010)
4.1.2. Four-metric classification
The greatest discrimination between the simplified low-flow classes based on four metrics occurred
for the log-transformed mean number of zero-flow days per year (Figure 5). In particular, classes 1 to
3 and classes 4 to 8 formed two distinct groups on the basis of this metric (Figure 5). Cluster-wise
stability for the eight-class solution ranged from about 0.3 to 0.85 (Figure 6).
We used the adjusted Rand Index (Hubert & Arabie 1985) to compare the low-flow classification using
35 low-flow metrics with the simplified classification. The adjusted Rand index value for this
comparison (0.435) suggests moderate concordance between these classification schemes.
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2.0
1.5
1.0
0.0
0.5
log(NumZeroDays+1)
2.5
0.15
0.10
0.05
0.00
log(BFI+1)
1
2
3
4
5
6
7
8
1
2
4
5
6
7
8
7
8
0.08
0.04
0.00
0.05
0.10
log(P90+1)
0.15
Low-flow class
0.00
log(Spec_MeanAnnMin+1)
Low-flow class
3
1
2
3
4
5
6
Low-flow class
7
8
1
2
3
4
5
6
Low-flow class
Figure 5: Box-and-whisker plots of log-transformed low-flow metrics used in the four-metric
classification. See Table 1 for flow metric acronyms and Figure 3 for description of plots.
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Table 3: Summary of hydrologic attributes of flow classes for the simplified low-flow classification.
Flow classes are listed from highly ephemeral to strongly perennial. Colours correspond to those used
in figures 6 and 15. ‘Most at risk’ refers to areas where there is a high risk of ecological change from
a small change in discharge.
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 – Low -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
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Low-flow class
Figure 6: Cluster-wise stability for the eight-class solution identified by Mclust. Horizontal lines
indicate levels of cluster-wise stability: >0.85 – highly stable clusters; 0.75 to 0.85 – stable clusters;
0.60 to 0.75 – pattern exists but cluster membership uncertain; <0.6 – unstable clusters (Hennig
2010)
4.2. Mapping single low-flow metrics
Maps of the five individual hydrologic metrics that were shown or hypothesised by the case study
reports (Marsh et al. 2012) to be ecologically relevant are presented in figures 7 to 11. The key
hydrologic metrics are:

P90 (Figure 7): the flow exceeded 90 per cent of the time and represents a common and simple
representation of baseflow.

Baseflow index (Figure 8): the proportion of flow attributable to baseflow (i.e. baseflow divided by
total streamflow) using the Lyne and Hollick digital baseflow filter method (Nathan & McMahon
1990; Grayson et al. 2004).

Variability of the baseflow index (Figure 9): the inter-annual variability in the annual proportion of
flow due to baseflow. Sites of high variability in baseflow are indicative of unpredictable low-flow
conditions.

Number of zero-flow days (Figure 10): this is simply the mean annual number of zero-flow days
and is used as a continuous function to consider how ephemeral a stream is. A stream with a
higher average number of zero-flow days has a longer cease-to-flow period (on average).

Variability in number of zero-flow days (Figure 11): 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. Sites of high values have a highly variable length of cease-to-flow period.
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Low-flow discharge 90th
percentile (MLday-1)
Figure 7: P90 (90th percentile exceedence flow divided by the mean daily flow) values for stream gauges used in the simplified low-flow classification
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Baseflow
index
Base flow
index
0.008 - 0.081
0.081 - 0.147
0.147 - 0.219
0.219 - 0.299
0.299 - 0.426
Figure 8: Baseflow index values (baseflow divided by total streamflow) for stream gauges used in the low-flow classification
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Variability in baseflow
Variability
base flowindex
index
0.036 - 0.174
0.174 - 0.274
0.274 - 0.406
0.406 - 0.585
0.585 - 2.475
Figure 9: Inter-annual variability (as coefficient of variation) in the baseflow index for stream gauges used in the low-flow classification
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Figure 10: Number of zero-flow days for stream gauges used in the simplified low-flow classification
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Figure 11: Variability (as coefficient of variation) in the number of zero-flow days for stream gauges used in the simplified low-flow classification
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4.3. Testing the long-term NDVI for predicting lowflow class
n=140
n=121
n=124 n=88
n=67
n=67
n=26
6
7
n=197
0.4
0.3
0.1
0.2
Mean Monthly NDVI
0.5
0.6
Mean monthly NDVI increased from ephemeral to perennial low-flow classes (i.e. classes 1 to 8 in
Figure 12). The variation in mean monthly NDVI within low-flow classes was generally high (but see
flow class 7 in Figure 12).
1
2
3
4
5
8
Low flow
Low-flow
classclass
Figure 12: Range of NDVI values in each flow class, based on the simplified four-metric classification.
Numbers above boxes are the number of sites in each low-flow class. Colours correspond to those
used in Table 3
To simplify the multinomial logistic regression we combined some of the flow classes from the
simplified low-flow classification (UCLA 2011). Initial analysis indicated that simplified low-flow classes
3 to 7 could not be predicted from NDVI scores (error rate 100 per cent). We therefore combined
these flow classes for analysis. Thus the analysis was based on four low-flow classes: class 1, class
2, classes 3 to 7 combined and class 8. We designated low-flow class 1 as the reference class. All
other classes are compared with the reference class. Typically, the reference class is the class with
the highest number of observations (UCLA 2011) but we chose low-flow class 1 as the reference
class. Class 1 had the lowest median monthly NDVI score and the smallest degree of overlap with
other flow classes (Figure 12).
The model fit was statistically significant (Table 4). Class 1 (corresponding to low-flow class 1) had the
highest percentage of correctly allocated gauges. The model parameters and confusion matrix
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indicate a moderate capacity to predict low-flow class from the mean long-term monthly NDVI with an
error of approximately 50 per cent. Capacity to predict within adjacent classes (i.e. within the correct
class or one adjacent to it) is better with an error of <10 per cent (Table 5). This site allocation is
clearly not precise but the resulting map (Section 5.3) provides a useful illustration of the spatial
distribution of flow classes within a region and can usefully inform desktop water planning and
management.
Table 4: Parameter estimates for the multinomial logistic regression models describing the
relationship between simplified low-flow class and NDVI. Flow classes were grouped for analysis:
class 1 = simplified low-flow class 1; class 2 = simplified low-flow class 2; class 3 = simplified low-flow
classes 3 to 7; class 4 = simplified low-flow class 8. The models compare each flow class to flow class
1 (the reference class). Thus for flow class 2, a one unit increase in NDVI will result in a 14.606 unit
change in the log-odds of flow class 2 relative to flow class 1 (where 14.606 is in log-odds units).
Nagelkerke pseudo-R2 0.491. Chi-square for the model fit (comparing the null model and the fitted
model) = 506.8, p<0.001. These models were used to calculate probabilities of class membership for
0.25x0.25-degree grid cells.
Class
Parameters
β
Std error
Significance
Class 2
Intercept
-3.723
0.472
p<0.001
NDVI
14.606
1.639
p<0.001
Intercept
-7.296
0.597
p<0.001
NDVI
23.804
1.869
p<0.001
Intercept
-11.500
0.797
p<0.001
NDVI
32.544
2.178
p<0.001
Class 3
Class 4
Note – log-odds is the natural logarithm of the ratio of the number of subjects with a given attribute to the number of subjects
without the attribute.
Table 5: Confusion matrix showing the number of gauges correctly assigned to low-flow classes by
the multinomial logistic regression model.
Predicted low-flow class
True class
Class 1
Class 2
Class 3
Class 4
Per cent correct
Class 1
78
57
4
0
56.1%
Class 2
37
116
70
21
47.5%
Class 3
9
61
103
74
41.7%
Class 4
4
22
54
115
59.0%
Overall
percentage
15.5%
31.0%
28.0%
25.5%
49.9%
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5. Interpretation
5.1. Classifying low flows
5.1.1. Thirty-five metric classification
Classification of the 35 low-flow metrics identified six low-flow classes. This compares with the 12 flow
classes identified by Kennard et al. (2010a) for the continental classification. Since the principal
hydrologic gradient within the continental classification was flow permanence (intermittent-perennial)
the distribution of low-flow classes shows high concordance with the continental classification (Figure
13). There are clearly some large geographic drivers with much of Tasmania, Victoria and some of
south-west Western Australia dominated by flow class 1 which has a high minimum flow and a low
number of zero-flow days. This implies that flow class 1 streams are perennial with a relatively high
baseflow. This group can be compared with class 6 which has a low minimum flow and a high number
of zero-flow days; that is, an ephemeral flow regime.
A second feature in Figure 13 is that within a single geographic region, several flow classes are
present. Kennard et al. (2010a) noted that the continental classification showed varying degrees of
‘spatial cohesion’. This is particularly obvious in south-western Western Australia, where all six lowflow classes are present. This second feature demonstrates that position in catchment is critical in
defining low-flow hydrology and that caution should be used in extrapolating hydro-ecological
relationships to adjacent systems (see Kennard et al. 2010a). Where a lowland site may be a stable
perennial stream, an upstream tributary stream may be unpredictably ephemeral and as such support
a very different ecological community. A key point for the modelling of low-flow hydrology is that
quantifying the hydrology in small sub-catchments is important for being able to interpret likely
ecological consequences. This is a particular challenge in hydrological modelling as small catchments
are often ungauged and thus developing calibrated hydrologic models is problematic.
It is expected that the biota found in low-flow classes 1 and 6 would be adapted to very different
hydrological conditions (predictably perennial versus strongly ephemeral). As such, through a series
of site-based flow-ecology studies in each of the flow-classification sites, one can then extrapolate the
likely traits of organisms in each of these regions and how the flow regime influences their life history.
Using a series of site-based hydro-ecological studies combined with a large-scale hydrologic
classification, one can infer the likely ecological features in areas where little biological data are
available. This, of course, is a poor substitute for studying each site specifically, but can help in
preliminary desktop analysis to identify areas at risk.
Several of the hydro-ecological case studies conducted as part of this larger project demonstrated the
distinct difference in biological communities between ephemeral and perennial streams. Flow class 2
(Figure 13; Table 2) has a low 90th percentile flow but no zero-flow days; thus it is perennial, but most
similar to the ephemeral classes (classes 3–6) and therefore at risk of becoming ephemeral through
water resource use. Flow class 2 is represented in New South Wales coastal streams and streams in
far north Queensland and the Northern Territory (figures 13 and 14).
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Low-flow classification
Figure 13: Location of low-flow classes identified by classification of 35 low-flow metrics. Symbols represent stream gauges.
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Low-flow classification
Figure 14: Location of flow class 2 (weakly perennial) and flow class 3 (marginally ephemeral) from the classification of 35 low-flow metrics.
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Figure 15: Simplified low-flow classification based on four hydrologic metrics (baseflow index, specific mean annual minimum runoff, 90th percentile flow,
mean number of zero-flow days per year). Symbols represent stream gauges.
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5.1.2. Simplified low-flow classification
The results of the simplified classification based on only four low-flow metrics (Figure 15; Table 3)
mirror those of the classification based on all 35 low-flow metrics. The most ephemeral of the flow
classes (classes 1–3) are characterised by high numbers of cease-to-flow days. Only flow class 8
(strongly perennial) has no cease-to-flow days. In terms of application of the classification to inform
water resource planning, flow classes 4 to 7 are susceptible to the most dramatic ecological change
through transition from perennial to ephemeral. Of these classes, classes 4 and 5 are the most
susceptible as they have the lowest baseflow proportion, P90 and specific mean annual minimum
runoff.
5.2. Mapping single low-flow metrics
We have not interpreted the single metric maps – these are provided because the combined
classification result can mask some sites with a single low score for a single metric.
5.3. Testing the long-term NDVI for predicting lowflow class
From the simplified classification, flow classes 3 to 7 include sites that have some cease-to-flow days,
but these classes are characterised by a low P90, low baseflows and a low specific mean annual
minima. This implies that a relatively small reduction in flow during the low-flow period could
dramatically increase the number of cease-to-flow days and alter streams in these classes to strongly
ephemeral streams. The multinomial logistic regression using the NDVI has been used to spatially
extrapolate four classes (Figure 16) where flow class 3 is most ‘at risk’ of ecological alteration in
response to a relatively small volumetric flow change.
The state-based representations of the NDVI extrapolations (figures 17 to 23) show a general pattern
where class 3 is mostly mid-catchment sites downstream of perennial sites (class 4). This
corresponds with areas of moderate to low rainfall in many regions. Remembering that the NDVI is a
measure of vegetation vigour and is a surrogate for soil moisture (made up of rainfall and
groundwater contributions), the NDVI scores are closely related to rainfall regions. As such, the lowflow classes extrapolated using the NDVI also reflect this association with rainfall. The low-flow
hydrology is more closely related to groundwater contribution than rainfall and hence it is important to
note that extrapolation of the classes using the NDVI is likely to have about a 50 per cent
misallocation rate, generally to an adjacent class. However, inclusion of variables such as longitude
and latitude and elevation may improve the predictive capacity of this approach. The specific locations
represented in the maps should not be directly used for water planning but rather as broad indicators
of where the greatest threats to changes in low-flow regimes are located in the landscape.
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Figure 16: Predicted low-flow classification based on simplified flow classification extrapolated using the long-term NDVI. The eight flow classes from the
simplified low-flow classification were grouped into four classes for this analysis (see text for explanation). Class 3 represents ‘at relative risk’ areas; that is,
areas where there is a relatively high risk of ecological change for a small change in flow. Class 3 includes simplified low-flow classes 3 to 7.
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Predicted low-flow classification – NSW
Figure 17: Simplified low-flow classes predicted from long-term NDVI scores for New South Wales. For analysis the eight simplified low-flow classes were
grouped into four classes (see text for explanation). Class 3 represents ‘at relative risk’ areas; that is, areas where there is a relatively high risk of ecological
change for a small change in flow. Class 3 includes simplified low-flow classes 3 to 7. White patches represent missing data.
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Predicted low-flow classification – Vic
Figure 18: Simplified low-flow classes predicted from long-term NDVI scores for Victoria. For analysis the eight simplified low-flow classes were grouped into
four classes (see text for explanation).Class 3 represents ‘at relative risk’ areas; that is, areas where there is a relatively high risk of ecological change for a
small change in flow. Class 3 includes simplified low-flow classes 3 to 7. White patches represent missing data.
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Predicted low-flow classification – Tas
Figure 19: Simplified low-flow classes predicted from long-term NDVI scores for Tasmania. For
analysis the eight simplified low-flow classes were grouped into four classes (see text for
explanation). Class 3 represents ‘at relative risk’ areas; that is, areas where there is a relatively high
risk of ecological change for a small change in flow. Class 3 includes simplified low-flow classes 3 to
7. White patches represent missing data.
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Predicted low-flow classification – SA
Figure 20: Simplified low-flow classes predicted from long-term NDVI scores for South Australia. For
analysis the eight simplified low-flow classes were grouped into four classes (see text for
explanation). Class 3 represents ‘at relative risk’ areas; that is, areas where there is a relatively high
risk of ecological change for a small change in flow. Class 3 includes simplified low-flow classes 3 to
7. White patches represent missing data.
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Predicted low-flow classification – WA
Figure 21: Simplified low-flow classes predicted from long-term NDVI scores for Western Australia.
For analysis the eight simplified low-flow classes were grouped into four classes (see text for
explanation). Class 3 represents ‘at relative risk’ areas; that is, areas where there is a relatively high
risk of ecological change for a small change in flow. Class 3 includes simplified low-flow classes 3 to
7. White patches represent missing data.
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Predicted low-flow classification – NT
Figure 22: Simplified low-flow classes predicted from long-term NDVI scores for the Northern
Territory. For analysis the eight simplified low-flow classes were grouped into four classes (see text
for explanation). Class 3 represents ‘at relative risk’ areas; that is, areas where there is a relatively
high risk of ecological change for a small change in flow. Class 3 includes simplified low-flow classes
3 to 7. White patches represent missing data.
.
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Predicted low-flow
classification – Qld
Figure 23: Simplified low-flow classes predicted from long-term NDVI scores for Queensland. For
analysis the eight simplified low-flow classes were grouped into four classes (see text for
explanation). Class 3 represents ‘at relative risk’ areas; that is, areas where there is a relatively high
risk of ecological change for a small change in flow. Class 3 includes simplified low-flow classes 3 to
7. White patches represent missing data.
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6. Conclusions
A classification of low-flow hydrology using 35 low-flow metrics (hypothesised to be ecologically
relevant) identified six low-flow classes across continental Australia. These low-flow classes varied in
terms of flow permanence from perennial to ephemeral. A second classification was conducted using
only four low-flow metrics (baseflow index, specific mean annual minimum runoff, 90th percentile flow,
mean number of zero-flow days per year) considering the low-flow metrics used in this project’s 11
accompanying case studies (see Marsh et al. 2012) and metrics suitable for hydrological modelling
(Barma & Varley 2012a, b). This much simpler pragmatic collection of flow metrics displayed
moderate concordance with the full classification based on 35 low-flow metrics, showing that a small
set of low-flow metrics can represent the patterns in a classification derived from a large metric set.
Five individual hydrologic metrics that were shown or hypothesised by case studies (Marsh et al.
2012) to be ecologically relevant were also individually mapped since classification results can mask
some sites with a single low score for a single metric.
The low-flow classification performed in this report provides a basis to describe the range in low-flow
conditions across Australia. For example, perceived low-flow conditions in eastern Tasmanian
streams do not have the same hydrological characteristics as those in South Australia; as such, the
flow classes provide a basic form of taxonomy for coarsely differentiating flow regimes. The
classification also allows the conceptual extrapolation of existing hydro-ecological understanding; in
other words, when first considering the likely hydro-ecological interactions for a new site, you can
consider the hydro-ecological interactions from sites that have similar hydrology (and other habitat
characteristics). The low-flow classification provides this basic spatial distribution of different
hydrological conditions, and the case studies provide a collection of similarly considered hydroecological analyses.
We extended the utility of the low-flow classification in two ways. Firstly, we assessed cluster stability
for both classifications. This approach provides a means for stream managers to determine where
hydro-ecological studies should be focused, since ‘weak’ flow classes (e.g. classes 3 to 5 in the 35metric classification) may not yield robust flow-ecology relationships. Secondly, we examined whether
the long-term NDVI could be used as a predictor of low-flow class. We used the national long-term
NDVI scores to spatially extrapolate the low-flow classification to provide a map of areas of potential
ecological risk through alteration to low-flow hydrology. The inclusion of variables such as longitude
and latitude and elevation may improve the predictive capacity of this approach.
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Glossary
Abundance: frequency of occurrence. A species present in great numbers is abundant.
Aerophily: pertaining to air or oxygen. Aerophilic species have a preference for well-oxygenated
waters.
Alien species: a species that would not have occurred at a location without anthropogenic input.
Allochthonous: pertaining to substances, materials or organisms originating outside the stream.
Usual context is to refer to the source of carbon or nutrients from terrestrial sources that are washed
into the stream.
ANZECC guidelines: Australia New Zealand Environment Conservation Council water quality
guideline values which are frequently used to assess water quality.
Australian drainage divisions: regions of broadly similar hydrology, originally defined by the
Australian Water Resources Commission on the basis of climate and topography.
Autochthonous: pertaining to substances, materials or organisms originating within the stream.
Usual context is to refer to the source of carbon or nutrients as derived from the stream (as opposed
to terrestrial inputs – see Allochthonous)
Benthic: bottom. Refers to organisms that live on the stream bed (as opposed to those that live in the
water column).
Baseflow index (BFI): the proportion of a stream’s discharge due to baseflow or groundwater.
Community diversity: a community is a collection of organisms within a specified area. The number
of different taxa represents the diversity of the community.
Coefficient of variation: mean divided by the standard deviation of a dataset. A unitless measure of
the distribution of a dataset.
Decomposition: the process by which organic matter is broken down into simpler forms of matter.
Diatom: a major group of algae and one of the most common types of phytoplankton.
Disturbance (ecological): a change in environmental condition(s) that produce or cause a response
in an aspect (either structural or functional) of an ecosystem (see, for example, Resh et al. 1988; Lake
2000).
Drainage division: the major hydrologic drainage areas of Australia are divided into 12 drainage
divisions (e.g. Murray-Darling Division, Tasmanian Division).
Drought: difficult to define (Humphries & Baldwin 2003) due to significant variability in flow regimes
(Kennard et al. 2010). A period of significantly lower water availability (flow) when compared with
long-term central (median) or average conditions for a particular geographic region.
Ecosystem recovery trajectories: explains the path that an ecosystem takes as it recovers from a
disturbance (see Disturbance).
Ephemeral: Streams and rivers that discharge water during and immediately after rainfall. These
streams do not have significant baseflow, meaning that such streams are generally dry (apart from
isolated waterholes) when not flowing.
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Ephemeroptera (mayfly), Plecoptera (stonefly) and Trichoptera (caddisfly) (EPT taxa): these
taxa are sensitive to low dissolved oxygen, high salinity and high water temperatures and scores of
their collective relative abundance are often used to demonstrate the prevailing stream conditions.
Expectation-maximisation (EM): algorithm: an iterative method for estimating the parameters of a
statistical model.
Family richness: the number of different taxonomic families in a sample. A site of high family
richness has many different families of species present. A site of high species richness has many
different species, but these may be from only a few families. Macroinvertebrate datasets are
frequently only identified to family level.
Flow regime: the long-term (tens of years) combination of the magnitude, timing, duration, frequency,
and rate of change of streamflow that characterises the hydrology for a stream.
Fluvial: pertaining to water (fluvial geomorphology is the study of landforms created by water such as
river channels).
Fulton’s Condition Factor (K): a commonly used indicator of fish health which is computed as a
fish’s body mass divided by the cube of its length.
Higher-order consumers: a ‘first order consumer’ is any organism that feeds on a plant. Higherorder consumers feed on first order consumers.
Hydraulic conditions/hydraulic habitat: the characteristic habitat features created by water (e.g.
velocity, depth, shear stress).
Hyporheic zone: the wetted interstitial zone within the sediments below and alongside rivers. This
zone often contains invertebrates specialised for a hypogean existence.
Impact (pressure): a disturbance (see definition of Disturbance) either natural or anthropogenic that
creates a resultant affect. Usually considered in the context of a pressure (such as water extraction)
resulting in an effect such as water quality decline.
Index of Biotic Integrity: the IBI was initially developed as a multi-metric index reflecting fish health
(based on 12 metrics including species richness, composition and abundance). Many alternative
region-specific IBI approaches are now used, and the term generally means a multi-metric index.
Intermittent: streams and rivers that have seasonally predictable flow that occurs between a few
months to multiple years depending on climate conditions. These streams are placed in the middle of
a continuum from ephemeral to permanent.
Lag effect: a delayed response to an impact or disturbance (see definition of Disturbance and
Impact).
Low-flow regime: ecologically, the hydrological definition of ‘low flow’ will depend on the spatial
extent and temporal aspect of a river’s hydrograph and the varying flow requirements of waterdependent biota and ecological processes (Smakhtin 2001).
Low flow (working definition): difficult to define broadly across geographic and climatic regions
(Smahktin 2001), hence the likely reason why the term ‘low flow’ is often not defined in the
ecohydrological literature, although this lack of a clear definition is generally acknowledged (e.g.
Dewson et al. 2007; Larned et al. 2010; Suren & Riis 2010). Frequently ‘low flow’ is defined in
hydrologic contexts based on flow exceedence probabilities (e.g. 95 per cent exceedence flow) or
deviations from baseflow (see, for example, Suren & Riis 2010). For the purpose of this work, we
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define low flow as the volume (i.e. magnitude) 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 (including surface water, groundwater and estuaries), relative to the average or median
discharge for an individual river (or river reach). We emphasise that this implies that definitions of low
flow that are ecologically relevant will never be universally applicable given spatial variation in river
flow regimes and also the temporal scale at which ‘low flow’ events are quantified (sensu Biggs et al.
2005)
Low-flow spell/low-flow event: a continuous period where low-flow conditions continuously prevail.
In tropical Australia the low-flow spell typically coincides with the dry season and is a period of
continuous drying or decrease in streamflow.
Macroinvertebrate indicators:
–
predators: a functional feeding group that consumes other animals
–
scrapers: a functional feeding group that feeds by scraping algae from surfaces
–
collector-gatherers: a functional feeding group that feeds on fine organic matter by brushing it
off surfaces or from by burrowing in soft sediments
–
filter-feeders: a functional feeding group that filter-feeds on fine organic matter.
Multi-metric classification: pertaining to a classification or reporting method whereby more than one
measurement is combined to give a single score.
Normalised Difference Vegetation Index: a satellite-derived vegetation vigour index (2003).
NDVI anomaly: this is a measure of the deviation in vegetation vigour from the long-term vegetation
vigour. The NDVI Anomaly can be used to compare the relative changes in vegetation vigour between
regions (e.g. compare relative drought severity in different locations) that otherwise have different
long-term NDVI scores.
Num. zero days: number of consecutive days where there is no measured streamflow.
National Water Initiative: the Intergovernmental Agreement on a National Water Initiative was
signed at the 25 June 2004 Council of Australian Governments meeting. The Tasmanian Government
joined the Agreement in June 2005 and the Western Australian Government joined in April 2006. The
NWI represents a shared commitment by governments to increase the efficiency of Australia's water
use, leading to greater certainty for investment and productivity, for rural and urban communities, and
for the environment (http://www.nwc.gov.au/reform/nwi).
Nutrient cycling: movement and exchange of organic and inorganic matter back into the production
of living matter, and through this process the transformation of nutrients to different compound forms
which are more or less bioavailable (e.g. ammonium, nitrites, nitrates, nitrogen).
Perennial: streams and rivers that consistently discharge water sourced from runoff and groundwater
in all but extreme drought periods where groundwater availability is so low that connection to the main
channel of the stream is lost.
Population metrics: the combined range of measures used to describe the dynamics of a
population, such as fecundity and survivorship.
P90: 90th percentile flow. This is the flow value, below which 90 per cent of flow values fall (i.e. a high
flow-value). The 90th percentile exceedence is the flow which is exceeded by 90 per cent of flow
values (i.e. a low flow). 90th percentile = 10th percentile exceedence; 10th percentile = 90th
percentile exceedence.
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Recruitment: a reproductive opportunity that results in the production of juveniles. A fish may lay
eggs that hatch (i.e. reproduce), but this is not considered recruitment until the larvae reach a juvenile
stage.
Refuges/refugia: habitat remnants that provide refuge for species during periods of environmental
stress. In the context of these reports, refuges are pools where animals retreat during extended dry
periods.
Resistance: the capacity to tolerate or withstand a disturbance. That is, to show little or no change in
response to a disturbance (see Lake 2000).
Resilience: the ability to recover from a disturbance (see Disturbance definition and Lake 2000).
Response (ecological): the ecological effect of a disturbance (see Disturbance definition and Lake
2000). Response can be measured using a range of different indicators (e.g. species richness,
decomposition etc.).
Rheophilic: having a preference for running water; for example, filter-feeding macroinvertebrates
have a preference for flowing waters.
Sensitive taxa: taxa sensitive to periods of stress (in this case from low flow).
Short-term flow conditions: those prevailing during the 90 days before sampling).
Stream Invertebrate Grade Number – Average Level (SIGNAL): an indication of water quality
based on the presence and abundance of different macroinvertebrates. Streams with high SIGNAL
scores are likely to have low levels of salinity, turbidity, and nutrients such as nitrogen and
phosphorus and are likely to be high in dissolved oxygen.
SPEAR (species at risk): a response indicator used to measure impact of a stressor (in this case low
flow).
Specific mean annual min: the mean of the minimum flow values for each year of the record.
Temporary: often used interchangeably between ephemeral and intermittent flow regimes (see
Larned et al. 2010).
Thermophily: being able to tolerate a large temperature range. This particularly applies to
macroinvertebrates that can tolerate higher temperatures that occur in shallow pools.
Tolerant taxa: taxa able to withstand periods of stress (in this case from low flow).
Traits: characteristics or properties of an organism. Rheophily (preference for flowing water) is an
example of a trait.
Trophic relationships: also called feeding relationships. Can be presented as a foodweb or a food
chain representing links between different trophic levels (producers, consumers, decomposers).
Water quality: the chemical and physical characteristics of the water. In the context of these reports,
the principal water quality components are temperature, dissolved oxygen, salinity (reported as
conductivity) and water clarity (often reported as turbidity).
Glossary terms specific to this report
Confusion matrix: a matrix comparing observed class membership with predicted class membership.
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Constancy: a measure of the degree to which the state of a variable stays the same. Constancy is
one of three indices called Colwells Indices.
Contingency: a measure of how different states of a variable correspond to different time periods.
Contingency is one of three indices called Colwells Indices.
Flow class: a group of sites, identified by classification, with similar flow regime attributes.
Hierarchical agglomerative classification: a classification approach where each site or gauge
starts as a single cluster; clusters are merged successively until a single cluster containing all sites is
obtained.
Likelihood: probability of observing the data given a set of parameter values.
Soft classification: a classification scheme where each site is assigned a probability of class
membership for all classes. The class with the highest probability of membership is assigned as the
class for each site. This compares with a hard classification scheme where classes are assumed to
have distinct boundaries and each site is assigned to a single class only.
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Other reports in this low flows report series
Australian Rivers Institute 2012, Low-flow ecological knowledge gaps, National Water Commission,
Canberra.
Balcombe SR & Sternberg D 2012, Fish response to low flows in dryland rivers of western
Queensland, National Water Commission, Canberra.
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modelling gaps, National Water Commission, Canberra.
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review and case studies, National Water Commission, Canberra
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National Water Commission, Canberra
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the Granite Creeks system, Victoria, National Water Commission, Canberra.
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regime characteristics and landscape context in Victorian rivers, National Water Commission,
Canberra.
Chessman B, Haeusler T & Brooks A 2012, Macroinvertebrate responses to low-flow conditions in
New South Wales rivers, National Water Commission, Canberra.
Deane D 2012, Macroinvertebrate and fish responses to low flows in South Australian rivers, National
Water Commission, Canberra.
Dostine PL & Humphrey CL 2012, Macroinvertebrate responses to reduced baseflow in a stream in
the monsoonal tropics of northern Australia, National Water Commission, Canberra.
Hardie, SA, Bobbi, CJ & Barmuta, LA 2012, Macroinvertebrate and water quality responses to low
flows in Tasmanian rivers, National Water Commission, Canberra.
Kitsios A, Galvin L, Leigh C & Storer T 2012, Fish and invertebrate responses to dry season and
antecedent flow in south-west Western Australian streams, National Water Commission,
Canberra.
Leigh, C 2012, Macroinvertebrate responses to dry season and antecedent flow in highly seasonal
streams and rivers of the wet-dry tropics, Northern Territory, National Water Commission,
Canberra.
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Mackay S, Marsh N, Sheldon F & Kennard M 2012, Low-flow hydrological classification of Australia,
National Water Commission, Canberra.
Marsh N, Sheldon F & Rolls R 2012, Synthesis of case studies quantifying ecological responses to
low flows, National Water Commission, Canberra
Marsh N, Sheldon F, Wettin P, Taylor C & Barma D 2012, Guidance on ecological responses and
hydrological modelling for low-flow water planning, National Water Commission, Canberra
Rolls R, Marsh N & Sheldon F 2012, Review of literature quantifying ecological responses to low
flows, National Water Commission, Canberra.
Rolls R, Sheldon F & Marsh N 2012, Macroinvertebrate responses to prolonged low flow in subtropical Australia, National Water Commission, Canberra.
Sheldon F, Marsh N & Rolls R 2012, Early warning, compliance and diagnostic monitoring of
ecological responses to low flows, National Water Commission, Canberra.
Smythe-McGuiness Y. Lobegeiger J, Marshall J, Prasad R, Steward A, Negus P, McGregor G & Choy
S 2012, Macroinvertebrate responses to altered low-flow hydrology in Queensland rivers,
National Water Commission, Canberra.
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