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\begin{document}

\title{Simplified environmental flow metrics and environmental water
requirements for Mediterranean seasonal rivers}



\author[1]{Douglas Green}%
\author[1]{Kumar Savadamuthu}%
\affil[1]{South Australia Department for Environment and Water}%


\vspace{-1em}



  
  \date{May 24, 2024}


\begingroup
\let\center\flushleft
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\selectlanguage{english}
\begin{abstract}
1. The flow regime of a river is well established as being one of the
key drivers of riverine ecosystem type, diversity and condition. This is
especially true of seasonal rivers that experience a cease to flow
period over the dry months of the year. 2. In order to effectively
assess changes to the flow regime, it is required that flow data be
quantified into metrics for ease of assessment and to effectively relate
changes to environmental outcomes. 3. Previous methods have used large
numbers of, often complex, flow metrics to assess the flow regime. These
metrics are often highly internally correlated with each other which may
pose problems when considering how these metrics are assessed. The
metrics are also often complicated which introduces issues for
communication of results. We suggest that due to high internal
correlation between metrics, significantly fewer metrics are required to
describe the flow regime, and owing to the high correlation within same
season flow metrics, simple metrics can be selected. 4. We report on a
series of six flow metrics that cover the whole of the flow regime, that
are reported annually and that are simple to assess and interpret. We
then apply those six metrics to establish environmental water
requirements for the North Para River in the Barossa Valley of South
Australia. 5. Environmental water requirements are defined using upper
and lower bounds of a moving average for each metric, rather than a
defined threshold. We suggest this better reflects the highly variable
nature of seasonal rivers, and the subsequent tolerances of the flora
and fauna that inhabit them.%
\end{abstract}%



\sloppy


\section*{Introduction}

{\label{introduction}}

The flow regime of a river, defined as the magnitude, timing, duration
and frequency of flow events, has long been established as one of the
key driving influences on riverine ecosystems (Naiman et al., 2008, Poff
et al., 1997). Previous efforts to define the flow regime has provided a
multitude of flow metrics that can be used to quantify nearly any aspect
of the flow regime (Kennard et al., 2010, VanLaarhoven and van der
Wielen, 2009). The altering of the flow regime through water resource
development (e.g. dams) has been linked to declines in riverine
ecosystem condition, most commonly associated with reductions in flows
(Poff et al., 2007, Poff et al., 2010, Poff and Zimmerman, 2010). It has
been further identified that some aspects of the flow regime have a
disproportionate impact on riverine ecosystems, in particular for
intermittent rivers (Datry et al., 2014a, Datry et al., 2014b). It
follows that the assessment of impacts of changes to the flow regime of
intermittent rivers can be summarised using a suite of key, ecologically
relevant flow metrics, and that these changes in flow metrics can be
meaningfully translated into a level of potential ecological impact.

Seasonal rivers, as a class of intermittent rivers, are defined as
rivers that have a predictable cease to flow period. The vast majority
of these follow the typical Mediterranean flow regime of cease to flow
periods over the hot and dry summer months and a predictable flowing
period over the cooler, wetter winter months (Kennard et al., 2010).
These seasonal rivers are the dominant type of river found in southern
Australia and are often heavily developed for their water resources to
provide water for agricultural activities (Malerba et al., 2021). The
level of development in some of these regions has led to the need to
regulate and manage the capture, extraction and use of water through
legislation (e.g. Landscape South Australia Act, Government of South
Australia, 2019).

The sustainable management of these water resources depends on archiving
an equitable balance between users, while keeping the total requirements
for water below a sustainable level. Generally these requirements are
broken down into social, economic, cultural and environmental water
requirements. For water planning in South Australia, there is a
significant emphasis placed on environmental water requirements as a
priority user for water resources. Generally, environmental water
requirements (EWRs) are defined as the water regime needed to sustain
the ecological values of aquatic ecosystems, including their process and
biological diversity, at a low level of risk (VanLaarhoven and van der
Wielen, 2009). Previous assessments of environmental water needs have
used various different methods for describing the EWRs for seasonal
rivers, ranging from qualitative descriptions (e.g. Barossa, Natural
Resources AMLR, 2009) through to complex series of 50+ flow metrics
(e.g. Western and Eastern Mt Lofty Ranges, Natural Resources AMLR, 2013,
Natural Resources SAMDB, 2013).

The notion of establishing flow assessment methods is not novel, with
hundreds of methods developed and reported (Tharme, 2003). The most
recent attempt at quantifying EWRs for seasonal rivers in South
Australia provides a pass/fail style threshold for 56 flow metrics.
Subsequent assessment of these flow metrics identified significant
correlation between some of the flow metrics, especially between flow
metrics within the same flow season (Maxwell et al., 2015). This
correlation was indicative of potential bias within the metrics,
especially when using percentage passing/failing as the overall
indicator, as metrics would fail in batches rather than independently.
There are also a considerable number of metrics using non-zero daily
flows which has been identified as an issue when comparing datasets. The
changing proportion of data between different scenarios included in a
non-zero flow assessment results in metrics that are not directly
comparable.

The goal was to define a suite flow metrics that were representative of
all aspects of the flow regime that were ecologically relevant and not
correlated with each other. This is in a similar vain to the functional
flows approach used by Yarnell et al. (2020) with a focus both on
function (e.g. intermittency, high flows) and season (e.g. low flow
season, transition seasons). The number of metrics was determined by a
combination of consideration of the aspects of the flow regime that were
deemed to be important along with assessment of previous suites of
metrics. A PCA of the metrics (normalised) from VanLaarhoven and van der
Wielen (2009) suggested that the first six principal components
contained \textasciitilde{}91\% of the variance of the overall dataset.
While not directly relatable to individual metrics, six was chosen as
the number of metrics to develop.

Previous assessments of flow data from seasonal rivers in South
Australia has shown that there is significant correlation between flow
metrics per season (Maxwell et al., 2015) so single metrics per season
were identified to avoid this correlation. Based on assessment of
previous suites of flow metrics used (e.g. Kennard et al., 2010, Poff
and Zimmerman, 2010, VanLaarhoven and van der Wielen, 2009, Yarnell et
al., 2020), the six key areas of interest were identified as (1)
intermittency, (2) low flows, (3) break of season, (4) spring flows, (5)
medium flows and, (6) high flows. The links between these flow metrics
and riverine ecosystem function is summarised in Table 1.

Table 1: summary of ecological functions identified for each of the flow
regime areas summarised from conceptual understanding of seasonal rivers
and literature review.\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\begin{minipage}[b]{0.47\columnwidth}\raggedright\strut
Flow regime area\strut
\end{minipage} & \begin{minipage}[b]{0.47\columnwidth}\raggedright\strut
Ecological functions\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Intermittency\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Considered the master variable for intermittent rivers (Datry et al.,
2014a) Longer cease to flow periods leads to deteriorating water quality
in refuge habitat (Chapin et al., 2014, Schmarr et al., 2014) Length of
flow period dictates habitat availability and expected lifecycle
completion (Bonada et al., 2007)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Low flows over the low flow season (Dec -- April)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Flushing of permanent pools Maintenance of habitat (Vander Vorste et
al., 2020) Watering of in channel riparian vegetation over low flow
season (Nicol, 2013) Opportunities for dispersal (Baumgartner et al.,
2014)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Break of season\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Cues for migration and breeding (Lucas and Baras, 2008, Mackay, 1992,
Pires et al., 2014) Increased stress on refuge habitats (Vander Vorste
et al., 2020) Likelihood of lifecycle completion (Mackay, 1992)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Spring flows\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Promotes resilience leading into the low flow/cease to flow period
(eWater, 2022) Promotes fish recruitment success (Green et al., 2014)
Migration of obligate aquatic fauna (Lucas and Baras, 2008) Discourages
exotic fish species (Seebacher and Kazerouni-Ghanizadeh, 2021)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Medium flows\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Promotes large-scale fish migration (Lucas and Baras, 2008) Discourages
exotic fish species (Moore et al., 2008) Expand riffle habitat for
macroinvertebrate species (Bonada et al., 2007) Inundate vegetation on
benches and lower banks (Maxwell et al., 2015) Control terrestrial
vegetation in channel (Maxwell et al., 2015)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
High flows\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Inundate vegetation higher on banks (Maxwell et al., 2015) Habitat
maintenance including silt removal and algae scouring (Fuller et al.,
2010, Loire et al., 2019) Entrain organic material from banks (Caraco
and Cole, 2004) Plant propagule transport (Stromberg et al., 2007)
Management of reed beds (Stromberg et al., 2007)\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

Flow metrics for each flow regime area were identified by examining
existing flow metrics for each key part of the flow regime. Learning
from the issues identified in previous flow metric assessments and
considering the communicability of the resulting metrics, simple metrics
were preferred, generally revolving around a number of days. Further to
this, metrics that encompass longer time periods (i.e. annual metrics)
were identified as being preferred as they combined many of the more
specific and ultimately correlated metrics (Datry et al., 2014a).

The practical application of these flow metrics revolves around linking
them to ecological responses. In order to demonstrate the practicality
of these metrics, EWRs were developed for each for a seasonal river
system. The notion of a pass/fail threshold for ecological systems is
somewhat nonsensical as all ecological systems have a degree of
resilience (Poff, 2018). This is especially true of seasonal riverine
ecosystems the exist in a highly variable environment (Datry et al.,
2014b). Rather than identify a single threshold based on a long-term
average or individual year, a moving average approach was used to define
boundaries within which the environmental water requirement was
considered to be achieved.

In order to evaluate the metrics, the Barossa Valley Prescribed Water
Resource Area (the PWRA) was used as a case study. The Barossa Valley is
an internationally renowned wine region in South Australia that derives
it water resources from the North Para River and groundwater resources
of the PWRA. The PWRA's mild, wet winters and hot, dry summers, are
typical of a Mediterranean climate. Annual rainfall varies from more
than 850 mm at high points in the Flaxman Range to about 300 mm north of
Angaston. The surface water resources of the area are heavily developed
with an estimated 1790 dams with an estimated total capacity of 8.1 GL
(Jones-Gill and Savadamathu, 2014, Montazeri and Savadamathu, 2018).
This represents approximately 52\% of the resource capacity (total
modelled runoff with the impacts of water extraction/capture and use
removed (1997 - 2022) (Savadamathu et al., 2023). Overall about 10\% of
these dams are licensed under the existing WAP and account for
approximately 60\% of the dam capacity.

Ultimately, this report documents the six key ecologically relevant flow
metrics used to quantify the flow regime and the upper and lower limits
of the moving averages for each metrics used to describe the EWRs for
the degraded but stable riverine ecosystems of the Barossa Valley
seasonal rivers.

\section*{Methods}

{\label{methods}}

Data for this assessment as sourced from two key locations. Observed
flow data was accessed for a series of flow monitoring stations across
the PWRA. A total of six flow stations were used for the assessment with
data accessed from Water Data SA (Department of Environment and Water,
2023). Only verified data was used for the assessment with no
interpolated data included. Modelled flow data was sourced from a
surface water model run using eWater Source (eWater, 2022) developed for
the Barossa by Jones-Gill and Savadamathu (2014) and updated by
Montazeri and Savadamathu (2018) run for the period 1969 -- early 2023.
Two modelled scenarios used in this assessment; the modelled current
scenario that represents the current hydrological situation in the PWRA,
and the no dams/extraction scenario which reflects a scenario with water
capture and extraction removed, referred to as the `no dams' scenario.

All metrics calculated are based on years with a minimum of 95\% of data
present to avoid missing data being interpreted as days below relevant
thresholds. The flow year is considered to be from December -- November
to capture the full flow seasons as the low flow season spans December -
April.

\subsection*{Zero flow threshold}

{\label{zero-flow-threshold}}

The zero flow threshold is the cut off used to describe a flow being
zero in the modelled data. The modelling software will attenuate daily
flows down to 1 x 10\textsuperscript{-5} ML/day (10 litres per day).
Flows this low are nonsensical in reality, therefore a threshold is
applied. Previous assessments in the Barossa have used the 0.05 ML/day
threshold based on Green et al. (2014). Other environmental water
planning assessments in the Mt. Lofty Ranges have used no threshold
(e.g. VanLaarhoven, 2012, VanLaarhoven and van der Wielen, 2009). The
zero flow threshold used for this assessment was established by
comparing the number of flowing days in the observed flow data and the
modelled current data from the eWater Source model. Several thresholds
were assessed including zero (no threshold), 0.015 ML/day, 0.03 ML/day,
0.04 ML/day, 0.05 ML/day and 0.1 ML/day. The threshold used was selected
based on the highest correlation (Pearson correlation coefficient) and
the degree of overlap with the observed data.

\subsection*{Flow metric assessment}

{\label{flow-metric-assessment}}

All of the flow metrics developed applied the zero flow threshold
identified for modelled data. Observed data was used with no
modification. Where suitable observed data was available, this was used
in preference to the modelled current data.

\emph{Low flows}

The low flow rate was identified by assessing the flow of water required
to turn over the entire volume of water in an average permanent pool in
five days. Five days was selected as this was a reasonable approximation
of the flow duration of a pulse flow in response to a large summer storm
based on the observed data. Pool volumes were estimated using a simple
ellipsoid formula with length and width estimated from aerial imagery
and depth based on data collected during previous surveys (unpub. data,
Schmarr et al., 2014). The PWRA was divided into four areas based on
understanding of the runoff generation and watercourse characteristics
across the landscape as well as the location of key monitoring stations
(Penrice for the Flaxman and Yaldara for the Barossa) (Hancock et al.,
2014). The flow rate at the end of each of these areas was defined based
on the flow rate required to turn over pools in the upstream area. Each
of the individual management zones in each area were given a flow rate
based on its proportion of the total catchment.

\emph{Break of Season}

The break of season represents the period of change from the low flow
season to the medium/high flow season, generally considered to be
between the start of May and the end of June (commonly referred to as
the transition 1 season). In order to identify the point at which the
season breaks, a threshold value was identified by looking at the
average inflection point of the cumulative flow plots for the two key
flow monitoring stations. The cumulative flow calculation excluded flow
from January through March to exclude the impacts of large summer
storms.

As with the low flow calculation, the threshold value for the individual
zones was apportioned by the total catchment area. The break of season
value was reported as the number of days after the first of April that
the cumulative flow (from the start of April) reached the break of
season threshold for each zone.

\emph{Spring flows}

Spring flows were calculated based on the Mountain Galaxias response
model reported in Green et al. (2014). The model used mean daily flow
between August and November (inclusive) calculated as the average daily
flow in ML divided by the catchment area in square kilometres.

\emph{Median and high flows}

Flow metrics representing the medium and high flows were developed based
on values reported in Jones-Gill and Savadamathu (2014). The threshold
for medium flows was defined as the median (50\textsuperscript{th}
percentile exceedance) of non-zero daily flows for the modelled current
scenario. Likewise, the threshold for high flows was defined as the
20\textsuperscript{th} percentile exceedance of non-zero daily flows for
the modelled current scenario. These thresholds were applied across all
scenarios, i.e. the threshold was not recalculated for different
scenarios. Using the same threshold regardless of scenario removes the
issue with using non-zero flow metrics relating the changing portions of
zero flow (changing baselines).

\subsection*{Establishment of environmental water
requirements}

{\label{establishment-of-environmental-water-requirements}}

The EWRs for the current ecosystems present in the PWRA was identified
by examining the results of the flow metrics over the last approximately
two decades. Concurrent work has identified 1997 -- 2022 as a time
window reflective of modern flow conditions that includes the Millennium
Drought, several high rainfall years and the recent extreme variability
(Savadamathu et al., 2023). The environment was considered stable up
until the start of the extreme variability observed post 2017.
Therefore, a baseline period of 1997-2016 was used to establish the
expected and acceptable range of variability within each of the metrics,
characterised as the upper and lower limits of a three-year moving
average for each metric. Three years was identified as it is the general
maximum life expectancy of Mountain Galaxias (McNeil and Hammer, 2007),
a high priority ecological asset in the region. Post 2016, metrics were
assessed as either meeting or failing the EWR by the three year moving
average rather than the individual metric result for each year.

\section*{Results}

{\label{results}}

\subsection*{Zero flow threshold}

{\label{zero-flow-threshold-1}}

The assessment supported the use of a threshold of 0.04 ML/day as the
zero flow threshold with a Pearson correlation coefficient of 0.64
(Table 2). Thresholds of 0.03, 0.04 and 0.05 ML/day all showed good
overlap with observed flow data so the higher correlation score was used
to identify the best value. A higher correlation was not expected from
this data due to the accuracies associated with Source Modelling and the
issues with limiting the data at 365 days.

Table 2: Pearson correlation coefficient results for the different flow
thresholds used when compared back to the observed flow data for the
number of flowing days.\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
Threshold (ML/day) & Pearson correlation coefficient\tabularnewline
\midrule
\endhead
No threshold & -0.0804\tabularnewline
0.015 & 0.5614\tabularnewline
0.03 & 0.6345\tabularnewline
0.04 & 0.6390\tabularnewline
0.05 & 0.6127\tabularnewline
0.1 & 0.5692\tabularnewline
\bottomrule
\end{longtable}

\subsection*{Flow metric assessment}

{\label{flow-metric-assessment-1}}

\emph{Low flows}

Twenty pools were assessed in the Flaxman Valley and Barossa Valley area
for estimated volume. Pools in the Flaxman Valley ranged from
approximately 0.7 ML to 3.2 ML with a mean value of 1.2ML. Pools in the
Barossa Valley ranged from approximately 1.1 ML to 7.8 ML with a mean
value of 4.8ML. The larger pools were towards the end of the valley in
both cases as expected. Based on these volumes, flow rates of 0.2 ML per
day and 1 ML per day were established for the end of the Flaxman Valley
and Barossa Valley respectively. These values represented the
approximate complete turn over of an average pool near the end of the
valley in approximately five days. Flow rates for the remaining
management areas are described in Table 3.

These values were also compared to the flow duration curves calculated
from the observed flow data from the Penrice gauge (Flaxman Valley) and
the Yaldara gauge (Barossa Valley). In both instances the flow values
identified were close to the lower inflection point of the curves
suggesting that the values were a good reflection of flows at the low
end of the curve (Figure 1).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Figure 1: Flow duration curves for the Yaldara gauging station data
(left) and the Penrice gauging station (right) showing the low flow
thresholds identified for the two gauging stations (red lines).

The flow rate for Lyndoch Creek was set at 0.22 ML per day as it has
similar area and rainfall as Jacob Creek. The flow rate for Duck Ponds
was set at 0.1 ML per day in line with Upper Flaxman Valley which is of
comparable size. The flow rate for Stone Chimney Creek was set at 0.05
ML per day as the flow rate established by portioning was below the zero
flow threshold established.

In general there was a good distribution of the number of days per low
flow season that the flow exceeded the threshold with a clear
distinction between the modelled no-dams scenario and the modelled
current scenario (Figure 3).

\emph{Break of season}

Assessment of the cumulative flow from the first of April for the
Yaldara gauge suggested that the inflection point occurred at
approximately 500 ML of cumulative flow in most years (Figure 2). There
were two years where the 500 ML threshold was not reached by the end of
September (2018 and 2019). Assessment from the Penrice gauge suggested
that the inflection point occurred at approximately 250 ML of cumulative
flow. There were 11 years that did not reach this threshold by the end
of September including a series of dry years from 2018 to 2021. The
apportioned flow thresholds are reported in Table 3.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Figure : example years of cumulative flow from April
1\textsuperscript{st} and the identified 500ML threshold identified for
the Yaldara Gauging station (Barossa Valley Gorge).

As for the low flow threshold assessment, the value of Lyndoch Creek was
based on Jacob Creek and set to 95 ML. Duckponds Creek was initially set
to 95 ML in line with upper Flaxman Valley, however, this value was
deemed to be too high with the majority of years not `breaking'. A lower
value of 70 ML produced more sensible results. Based on the thresholds
reported in Table 3, the date of the break of season was identified for
each year and the number of days between the break of season and the
first of April calculated for each of the zones (Figure 3). While the
data showed great variability in the delay in the break of season, there
is a clear trend of increasing delay, especially since the end of the
Millennium Drought.

\emph{Spring flows}

Spring flows calculated according to the Mountain Galaxias runoff
response model from Green et al. (2014) showed a good level of
variability and the same declining trend as observed for the rest of the
metrics (Figure 3).

\emph{Medium and high flows}

The thresholds developed for the medium and high flows are reported in
Table 3. The requirement to base these thresholds on non-zero flow was
due to the proportion of zero flows being greater than 50\% of the flow
in some zones (Duck Ponds Creek and Stone Chimney Creek under the
modelled current scenario) and close to the low flow threshold
identified in other zones (Lower Flaxman, Angaston Creek etc.).

Table 3: Threshold values used for the calculation of the flow metrics
for the Barossa Valley PWRA\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\textbf{Zone} & \textbf{Area} & \textbf{Percentage of area} &
\textbf{Low flow rate (ML/d)} & \textbf{Break of season cumulative flow
threshold (ML)} & \textbf{Medium flow rate (ML/day)} & \textbf{High flow
rate (ML/day)}\tabularnewline
\midrule
\endhead
Barossa Valley Gorge & Barossa & 100\% & 1.000 & 500 & 6.4 &
37.92\tabularnewline
Barossa Valley Floor & Barossa & 88\% & 0.884 & 440 & 5.84 &
29.59\tabularnewline
Lower Jacobs Creek & Barossa & 22\% & 0.222 & 110 & 3.68 &
15.14\tabularnewline
Upper Jacob Creek & Barossa & 19\% & 0.189 & 95 & 0.87 &
8.25\tabularnewline
Lower Tanunda Creek & Barossa & 15\% & 0.146 & 75 & 1.94 &
8.15\tabularnewline
Transition Zone & Barossa & 14\% & 0.139 & 70 & 3.11 &
17.16\tabularnewline
Upper Tanunda Creek & Barossa & 10\% & 0.100 & 50 & 1.48 &
7.08\tabularnewline
Lower Angaston Creek & Barossa & 8\% & 0.082 & 40 & 1.04 &
4.08\tabularnewline
Upper Angaston Creek & Barossa & 6\% & 0.059 & 30 & 0.46 &
2.62\tabularnewline
Duck Ponds Creek & Duck Ponds & 100\% & 0.100 & 70 & 0.33 &
2.04\tabularnewline
Lower Flaxman Valley & Flaxman & 100\% & 0.200 & 250 & 0.53 &
7.92\tabularnewline
Mid Flaxman Valley & Flaxman & 68\% & 0.135 & 170 & 3.76 &
16.45\tabularnewline
Upper Flaxman Valley & Flaxman & 38\% & 0.076 & 95 & 0.59 &
5.52\tabularnewline
Stone Chimney Creek & Flaxman & 13\% & 0.050 & 32.5 & 0.29 &
1.47\tabularnewline
Lyndoch Creek & Lyndoch & 100\% & 0.220 & 95 & 3.16 &
14.98\tabularnewline
\bottomrule
\end{longtable}

Inspection of the number of days of medium and high flows showed that
the same declining trend observed in other metrics is observed in these
metrics (Figure 3). In general this decline is more pronounced in the
observed flow data rather than the modelled flow data, especially in the
lower Flaxman Valley (Penrice gauge). It is generally considered that
dam development does not have a significant impact on medium and high
flows as these predominatly occur during the high flow season when dams
are full and spilling (VanLaarhoven, 2012, VanLaarhoven and van der
Wielen, 2012). The large differences noted in the Flaxman Valley,
especially in the medium flows, likely reflects the very heavy dam
development in this area.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Figure 3: Example outputs of the metrics from the two key locations used
in the development of the metrics. Barossa Valley gorge uses data from
the Yaldara gauging station, Lower Flaxman Valley uses data from the
Penrice gauging station. Red -- observed data, blue -- modelled no dams
data, green -- current modelled data.

\subsection*{Metric correlation}

{\label{metric-correlation}}

To ensure that there was no excessive correlation between metrics, a
Pearson's correlation was undertaken between the six metrics. While a
degree of correlation between the metrics was expected driven by
climatic drivers (e.g. wet/dry years), the objective was to produce
metrics that were no more that 75\% correlated. The results of the
correlation assessment (Table 4) showed that the highest correlation was
70.4\% between medium and high flow days. This was followed by flowing
days and low flow days over the low flow season with a correlation of
67.9\%. The remaining metrics were well below the level of concern.

Table 4: Correlation assessment between the six identified metrics using
Pearson correlation.\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
& Flowing days & Low flow days & Break of season & Runoff & Medium flows
& High flows\tabularnewline
\midrule
\endhead
Flowing days & 1.000 & & & & &\tabularnewline
Low flow days & 0.679 & 1.000 & & & &\tabularnewline
Break of season & -0.454 & -0.414 & 1.000 & & &\tabularnewline
Runoff & 0.266 & 0.195 & -0.376 & 1.000 & &\tabularnewline
Medium flows & 0.542 & 0.419 & -0.511 & 0.314 & 1.000 &\tabularnewline
High flows & 0.328 & 0.127 & -0.510 & 0.537 & 0.704 &
1.000\tabularnewline
\bottomrule
\end{longtable}

\subsection*{Establishment of environmental water
requirements}

{\label{establishment-of-environmental-water-requirements-1}}

The moving average window of three years provided a good degree of
variability while not being too responsive to individual year results
(see Yaldara gauge example, Figure 4). There was a considerable amount
of variability between zones, both in regards to the bounds and the
range between the bounds, as expected given the variability in rainfall
and dam development across the PWRA.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

Figure 4: Combined plots of the six identified flow metrics assessed for
the Barossa Valley Gorge (Yaldara gauging station) in the Barossa PWRA
showing the environmental water requirement bounds (blue dashed lines)
identified as the upper and lower limits of the three year moving
average between 1997 and 2016 (orange dashed line). Grey points are
metric results for the baseline period. The moving average post 2016
(black dashed line) showing current variability in the flow regime. For
years where the moving average is outside the expected bounds, the data
point is red, if it is inside the bounds, the data point is green,
regardless of the actual position of the data point relative to the
bounds.

Across all 17 zones assessed, there were two instances where the lower
bound was zero (Low flow days over the low flow season for Lower and Mid
Flaxman Valley). The metrics for these zones was checked against the no
dams modelled data to ensure that the thresholds developed were suitable
and, in both instances, the thresholds were deemed appropriate. The
lower bound of zero suggests that the level and layout of dam
development in these zones is having such an effect that there are
periods of three or more years where there are no low flows over the low
flow season. Similarly, there were two zones, Duck Ponds Creek and Upper
Flaxman Valley, where the upper limit of the delay of break of season
was the maximum 150 days, i.e. the season did not break. These bounds
presents a problem when considering ongoing sustainability of the
riverine ecosystems of the zone and will need to factored into
assessment of environmental water requirement thresholds for water
planning.

\section*{Discussion}

{\label{discussion}}

The ability to represent the entire flow regime in a simple yet
scientifically robust series of six environmentally relevant flow
metrics represents a significant simplification in from the superfluous
manner in which the flow regime of seasonal rivers are represented and
assessed. This simplification is evident both in regards to the number
of flow metrics used (e.g. compared to 56 in VanLaarhoven and van der
Wielen (2009) or 120 in Kennard et al. (2010)) and in regards to the
complexity of the metrics themselves. The results of the correlation
assessment show that there is enough differentiation between the metrics
that they all individually add significant information to the overall
narrative. While it is clear that the additional functions of the flow
regime (e.g. Yarnell et al., 2020) could have been identified (e.g.
overbank flows, variability within flow season etc.), it was determined
that these would have added excess complication to the process without
adding sufficient additional information to the assessment. This was
considered especially important as a significant role of these metrics
will be to communicate flow regime changes to community groups and other
non-scientific stakeholders.

The metrics that been identified are all simple metrics reported as
annual values. This provides benefits over previous complex metrics or
metrics that are calculated over the entire time series. Time series
metrics allow for more detailed investigations into changes through
time, especially relevant when considering the implications of a
changing climate. The impacts of climate change on flow regime are
likely to be a significant focus both for these metrics and in the
broader seasonal rivers field akin to Dhungel et al. (2016).

The use of set threshold values identified from outside the flow data
itself was possible for the Barossa as there was only two locations
where this was required to inform the process, with the remaining values
portioned by catchment area. This provided a tangible link to the
physical environment and a clear line between observed information, the
metrics and associated EWRs. This may prove difficult for larger areas
where information may not be available (e.g. permanent pool depth for
pool volumes) or where the number of locations to be assessed is
prohibitively large to do individual threshold assessments.

For the development of low flow thresholds, it was noted that the
threshold values were close to the lower inflection point of the flow
duration curve for the site. This could provide a method for identifying
the low flow threshold from within the flow data. This will remove the
direct link to a tangible on ground measure but should retain the
overall functions defined for the low flow metric described in table XX.
With the high correlation observed in other flow metric sets within the
same flow season, it is highly likely that any flow threshold used close
to the lower inflection point of the flow duration curve will be highly
correlated with a value identified from the physical assessment used
here. Therefore, they would be likely to convey the same information for
flow regime assessment. The same argument could be made with the
commence-to-flow threshold used for the assessment of the
break-of-season. The identification of the threshold could be achieved
by identifying the inflection point of the cumulative flow post April
1\textsuperscript{st} and averaging the flow value over the time series.

Ultimately, the six flow metrics were able to provide a basis for the
establishment of EWRs for the region based on the premise that the
ecology of the region was stable between 1997 and 2016. This ability to
not only characterise the flow regime, but the relationship of the
environment to it, and quantify thresholds that can be used as
management triggers or levers provides a powerful tool for managers and
researchers alike but is simple enough to be interpreted by the broader
public. By quantifying these thresholds based on a moving average rather
than an individual year, it provides a more meaningful representation of
the impacts on the riverine ecosystems in an inherently highly variable
system.

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