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Streamflow trends in south-west Western Australia

Looking after all our water needs

Department of Water

Surface water hydrology series

Report no. HY32

August 2009

Department of Water 168 St Georges Terrace Perth Western Australia 6000 Telephone +61 8 6364 7600 Facsimile +61 8 6364 7601 www.water.wa.gov.au

© Government of Western Australia 2009

August 2009

This work is copyright. You may download, display, print and reproduce this material in unaltered form only (retaining this notice) for your personal, non-commercial use or use within your organisation. Apart from any use as permitted under the Copyright Act 1968, all other rights are reserved. Requests and inquiries concerning reproduction and rights should be addressed to the Department of Water.

ISSN 1836-9626 (print) ISSN 1836-9634 (online)

ISBN 978-1-921675-00-3 (online)

Acknowledgements

This report was prepared by Jacqueline Durrant. The data analysis was completed by Samantha Byleveld and Jacqueline Durrant.

For more information about this report, contact the Surface Water Assessment section of the Department of Water.

Streamflow trends in south-west Western Australia Surface water hydrology HY32

Department of Water iii

Contents Contents ..................................................................................................................... iii

Summary .....................................................................................................................v

1 Introduction..............................................................................................................1

1.1 Streamflow..........................................................................................................................2 1.2 Rainfall ................................................................................................................................6

2 Methods of trend analysis......................................................................................11

2.1 Statistical trend analysis ...................................................................................................11 2.1.1 General requirements for trend analysis...............................................................................11 2.1.2 Adopted tests........................................................................................................................12 2.1.3 Statistical significance...........................................................................................................13

2.2 Graphical trend analysis ...................................................................................................14 2.2.1 Trend analysis maps.............................................................................................................14 2.2.2 Time-series plots and curve fitting ........................................................................................14 2.2.3 Additional graphical analysis.................................................................................................15

3 Results ..................................................................................................................17

3.1 Independence...................................................................................................................17 3.2 Trend analysis maps ........................................................................................................17 3.3 Step-change analysis .......................................................................................................25 3.4 Further investigation .........................................................................................................28

4 Possible causes of streamflow change .................................................................33

4.1 What caused the 1975 step-change? ..............................................................................33 4.2 Climate variability and period of record............................................................................33 4.3 What causes can be attributed to post-1975 changes?...................................................34

5 Concluding remarks ..............................................................................................36

Appendices................................................................................................................37

References ................................................................................................................78

Appendices

Appendix A: Time-series for trend analysis ...............................................................38

Appendix B: Decadal flow duration curves ................................................................68

Appendix C: Monthly streamflow distributions ...........................................................74

Surface water hydrology HY32 Streamflow trends in south-west Western Australia

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iv Department of Water

Figures

Figure 1 Streamflow gauging stations in south-west Western Australia that were used in this study ..................................................................................................................... 3

Figure 2 Period of record of streamflow gauging stations in south-west Western Australia that were used in this study .............................................................................. 4

Figure 3 Comparison of the mean annual rainfall for 1997 to 2007 with the mean annual rainfall for 1975 to 1996 (percentage difference) ................................................. 8

Figure 4 Comparison of the maximum daily rainfall for each year, averaged over two periods: 1997 to 2007 and 1975 to 1996 (percentage difference) ................................... 9

Figure 5 Comparison of the 90th percentile of daily rainfall for each year, averaged over two periods: 1997 to 2007 and 1975 to 1996 (percentage difference) .................. 10

Figure 6 Legend used on streamflow trend analysis maps................................................ 14 Figure 7 Time-series for Yarragil Brook at Yarragil Formation (614044) with a) linear

trend b) step-change c) loess curve .............................................................................. 15 Figure 8 Trends for decadal periods and the overall period of record for Donnelly

River at Strickland (608151).......................................................................................... 16 Figure 9 Trends in the annual total streamflow since 1975 ............................................... 18 Figure 10 Trends in the annual maximum daily streamflow since 1975 ............................ 19 Figure 11 Trends in the 10th percentile daily streamflow (high flows) since 1975............. 20 Figure 12 Trends in the 50th percentile daily streamflow (median flows) since 1975 ........ 21 Figure 13 Trends in the 90th percentile daily streamflow (low flows) since 1975 .............. 22 Figure 14 Streamflow gauging stations that show a decrease (or no trend) in all

streamflow indices......................................................................................................... 24 Figure 15 Carey Brook with step-change identified in 1999 shown for (a) long term

and (b) 1975 to 2006 ..................................................................................................... 26 Figure 16 Gingin Brook with step-change identified in 1999 shown for (a) long term

and (b) 1975 to 2007 ..................................................................................................... 26 Figure 17 Bancell Brook with step-change identified in 1996 shown for (a) long term

and (b) 1975 to 2007 ..................................................................................................... 26 Figure 18 Flow duration curve for Donnelly River at Strickland ......................................... 29

Tables

Table 1 Summary of the catchment and data characteristics for the standard period of record (1975–2003)..................................................................................................... 5

Table 2 Summary of the variation in decadal mean annual flow with the standard period of record (1975–2003)........................................................................................ 31

Table 3 Summary of the variability in annual streamflow with varying periods .................. 32


Department of Water v

Summary

In this study streamflow characteristics were analysed for the period of record post-1975 at 29 sites across south-west Western Australia. The aim was to see whether streamflow records exhibited evidence of change – either gradual (trend) or abrupt (step-change). Findings from the study included the following:

• Some individual sites show an additional step-change, but a consistent regional reduction in streamflow – similar to the step-change observed in 1975 in south-west Western Australia – has not been observed.

• The majority of sites show a negative trend indicating a decrease in streamflow, yet very few of these are statistically significant.

• A shift in the peak flow month (one month later) has been observed across all sites in the south-west.

• Further interpretation of the data for possible causes of streamflow change is needed at the local study level.

Using these findings as a reference, it is recommended that all data post-1975 be used in making surface-water management decisions. It is also important to consider the historical variability and observed trends along with future predictions and scenarios of climate and streamflow in forming surface-water management decisions.


Department of Water 1

1 Introduction

South-west Western Australia has experienced a reduction in annual rainfall since 1975 in comparison with the long-term mean annual rainfall. A subsequent reduction in runoff has been observed. Rodgers and Ruprecht (1999) investigated the impacts of climate variability on streamflow in the region and concluded that any changes over the long term were part of natural climate variability. However, they also stated that the lower streamflow observed since 1975 needed to be accounted for in future water resource management.

In November 2004, the Water Resources Allocation Committee (of the then Water and Rivers Commission) endorsed an allocation note outlining the use of a standard, 28-year data period (1975–2003) for surface-water management decisions in south-west Western Australia (I Loh 2004, internal memo). This period was selected to provide reliable and unbiased estimates of the statistical properties of hydrologic data. Inclusion of data pre-1975 would have biased the statistics to the previous wetter conditions.

There has been a suggestion of a further decline in streamflows from 1975 (nominally 1997) that would be fundamental to future planning of surface-water resources. This originated through the Water Corporation’s source-development planning that considered three climate scenarios: a post-1975 sequence, a post-1997 sequence and a post-2001 climate average (WC 2005).

I Loh’s internal memo concluded that, as of 2004, the adoption of a period shorter than 1975 to 2003 (i.e. 1997–2003) risked significantly underestimating the variability of the expected streamflow and potentially biasing the statistics to the particular years of the selected record.

Another decade of streamflow record is now available since the Rodgers and Ruprecht (1999) study, so it is necessary to analyse streamflow post-1975 to detect any changes that may impact on future surface-water management decisions. This study aimed to repeat the previous streamflow trend analysis using the additional years of data, and identify whether:

• there had been another reduction in streamflow in south-west Western Australia, similar to the step-change observed in 1975

• the additional years of data influenced the previous conclusions reached on long-term trends in streamflow across south-west Western Australia.

This report provides a summary of the results for the study area including a summary of the streamflow and rainfall, the method used to carry out the trend analysis (Chapter 2), a description of the model results (Chapter 3), a discussion of potential causes in any streamflow changes (Chapter 4) and concluding remarks (Chapter 5).


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2 Department of Water

1.1 Streamflow

Streamflow data was analysed for a selection of Department of Water streamflow gauging stations across the south-west region – from Australian Water Resources Council (ARWC) basins 602 to 617. Where possible, the same set of streamflow gauging stations was used as the Rodgers and Ruprecht (1999) study to relate this assessment with the previous work. Stations were excluded if the record did not continue past 2003; however, alternative stations were chosen to ensure a representative coverage over the south-west. Missing values in the daily data were filled post-1975.

Figure 1 shows the streamflow gauging stations selected for this study and Figure 2 shows the period of record (as extracted from the Department of Water database as at July 2008). A summary of the catchment and data characteristics of the gauged streams examined in this study is shown in Table 1 for the current standard period, 1975 to 2003.



Figure 1 Streamflow gauging stations in south-west Western Australia that were

used in this study



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Waychinicup River — Cheynes Beach Road (602031)

Goodga River — Black Cat (602199)

Denmark River — Kompup (603173/003)

Denmark River — Mt Lindesay (603136)

Yate Flat Creek — Woonanup (603190)

Kent River — Styx Junction (604053)

Frankland River — Mount Frankland (605012)

Weld River — Ordnance Road Crossing (606195)

Perup River — Quabicup Hill (607145/004)

Lefroy Brook — Pemberton Weir & Rainbow Trail (607009/013)

Wilgarup River — Quintarrup (607144)

Warren River — Barker Rd Crossing (607008/220)

Carey Brook — Staircase Road (608147/002)

Donnelly River — Strickland (608151)

Blackwood River — Darradup & Hut Pool (609025/019)

Margaret River — Willmots Farm (610001)

Vasse River — Chapman Hill (610003)

Wilyabrup Brook — Woodlands (610006)

Thomson Brook — Woodperry Homestead (611111)

Collie River East — Coolangatta Farm (612001)

Bingham River — Palmer (612014)

Collie River — South Branch (612034)

Bancell Brook — Waterous (613013/007)

Murray River — Baden Powell Wtr Spout (614006)

Yarragil Brook — Yarragil Formation (614044)

Williams River — Saddleback Road Bridge (614196)

Wooroloo Brook — Karls Ranch (616001)

Helena River — Poison Lease GS (616216)

Gingin Brook — Gingin (617058)

Record has gaps

Record has no gaps

Figure 2 Period of record of streamflow gauging stations in south-west Western Australia that were used in this study



Table 1 Summary of the catchment and data characteristics for the standard period of record (1975–2003)

Station number

Location Catchment area (km 2)

Mean annual rainfall (mm)

Clearing # (%)

Mean annual flow (ML)

CV*

602031 Waychinicup Creek – Cheynes Beach Road 238 700 48 8760 0.65 602199 Goodga River – Black Cat 49.2 790 54 4280 0.35

603173/003 Denmark River – Kompup 242 720 31 11 600 0.86 603136 Denmark River – Mt Lindesay 502 790 17 28 500 0.66 603190 Yate Flat Creek – Woonanup 56.3 760 59 4760 0.66 604053 Kent River – Styx Junction 1810 680 39 79 200 0.62 605012 Frankland River – Mount Frankland 4510 510 68 155 000 0.49 606195 Weld River – Ordnance Road Crossing 250 990 1 41 000 0.38

607145/004 Perup River – Quabicup Hill 667 690 14 13 300 0.61 607009/013 Lefroy Brook – Rainbow Trail 249 1040 35 39 900 0.35

607144 Wilgarup River – Quintarrup 460 800 28 27 100 0.51 607008/220 Warren River – Barker Road Crossing 3930 720 30 264 000 0.43 608147/002 Carey Brook – Staircase Road 30.3 1150 1 7050 0.28

608151 Donnelly River – Strickland 782 890 24 102 000 0.41 609025/019 Blackwood River – Darradup & Hut Pool 12400 520 65 535 000 0.55

610001 Margaret River – Willmots Farm 443 970 21 86 100 0.41 610003 Vasse River – Chapman Hill 47.7 900 63 10 600 0.49 610006 Wilyabrup Brook – Woodlands 82.3 1020 79 23 900 0.36 611111 Thomson Brook – Woodperry Homestead 102 860 26 10 800 0.55 612001 Collie River East Branch – Coolangatta Farm 1350 640 21 40 700 0.73 612014 Bingham River – Palmer 366 650 4 5450 0.99 612034 Collie River – South Branch 662 700 22 24 200 0.85

613013/007 Bancell Brook – Waterous 13.6 990 8 4180 0.33 614006 Murray River – Baden Powell Water Spout 6760 510 59 224 000 0.60 614044 Yarragil Brook – Yarragil Formation 73.5 930 0 1620 0.63 614196 Williams River – Saddleback Road Bridge 1410 490 78 61 900 0.59 616001 Wooroloo Brook – Karls Ranch 515 750 44 45 000 0.43 616216 Helena River – Poison Lease 591 670 6 5030 1.04 617058 Gingin Brook – Gingin 106 650 68 12 500 0.12

# Clearing percentage is calculated from the National Land and Water Resources Audit Extent of Native Vegetation (2001) * CV (coefficient of variation) is the standard deviation divided by the mean



1.2 Rainfall

The Department of Water previously developed mean annual rainfall isohyets for the period 1975 to 2003 to account for the observed step-change in rainfall and to enable surface-water management decisions to be made on this standard data period (DoW 2007).

To investigate the effect of additional years of rainfall data, mean annual rainfall for the period 1975 to 2007 was calculated at 61 Bureau of Meteorology rainfall stations. Maximum daily rainfall totals and percentiles of annual daily rainfall were also assessed for each year. The rainfall stations were chosen to provide an even coverage over the south-west study area and the data was sourced from the silo patched point dataset.

The mean annual rainfall for the last decade of data (1997–2007) was compared with the mean annual rainfall statistic for 1975 to 1996 (Figure 3) and reported as a percentage difference. More than 50 per cent of the rainfall stations analysed have a difference smaller than ± 5 per cent. Eight rainfall stations have a decrease of more than 10 per cent and 10 rainfall stations show an increase in mean annual rainfall for the last decade. Spatially there appears to be a decline in mean annual rainfall for the last decade across most of south-west Western Australia. Basic statistical testing showed the declines were generally not significant.

The maximum daily rainfall for each year was averaged over the last decade of data (1997–2007) and compared with the maximum daily rainfall for each year averaged over the period 1975 to 1996 (Figure 4) and reported as a percentage difference. Overall, the decrease in this statistic is greater than the decrease in mean annual rainfall. The Darling Scarp and wheatbelt areas of the south-west show a clearer spatial pattern than the south coast, with this area displaying an increase at some stations.

The maximum daily rainfall is a simple statistic to assess and can be assumed to parallel extreme rainfall events – although the 95th or 99th percentile of daily rainfall is typically used to define extremes. Studies have shown that the trend in extreme daily rainfall generally follows the same direction as the mean rainfall trend, but with a greater magnitude (CSIRO 2007). However, analysis of observed maximum daily rainfall across south-west Western Australia has shown a more complex situation with no uniform trend. Further work is required to assess the implications of observed changes in maximum daily rainfall across the south-west region. The decline would be expected to have a larger impact on inflows to the dams, particularly as recent studies have shown that for a given change in rainfall, there is generally a threefold change in streamflow (Berti et al. 2004; Chiew 2006; Kitsios et al. 2008).

The spatial plot of the 90th percentile of annual daily rainfall for each year, averaged over the last decade of data (1997–2007) in comparison with the average over 1975



to 1996, shows more increases (particularly across the south coast area) and a smaller overall percentage difference (Figure 5). This may explain some of the consistency seen in the mean annual rainfalls. Lower percentiles were assessed, although these were often skewed by days of no rainfall, resulting in no obvious visual trend.

The reduction in rainfall that was observed in the mid 1970s in south-west Western Australia has been studied extensively (CSIRO 2007). In comparison to that period, this rainfall analysis shows there does not appear to be a further significant reduction in rainfall across the south-west. This confirms the statement made in Climate change in Australia (CSIRO 2007) that the observed rainfall declines reflect the step-change in the mid 1970s and are exacerbated by the very low rainfall of recent years.



Figure 3 Comparison of the mean annual rainfall for 1997 to 2007 with the mean

annual rainfall for 1975 to 1996 (percentage difference)



Figure 4 Comparison of the maximum daily rainfall for each year, averaged over

two periods: 1997 to 2007 and 1975 to 1996 (percentage difference)



Figure 5 Comparison of the 90th percentile of daily rainfall for each year,

averaged over two periods: 1997 to 2007 and 1975 to 1996 (percentage

difference)



2 Methods of trend analysis

Changes in streamflow data generally occur gradually (a trend) or abruptly (a step-change – as seen in the reduction of rainfall since 1975). The change may affect any aspect of the data including the mean, median or variance (Kundzewicz & Robson 2004). A number of different statistics where chosen to describe the characteristics of, and to test for any change in, the flow regime for each streamflow gauging station. Commonly used flow statistics include the following:

• annual total

• mean annual flow (MAF): the average flow over a particular period, typically calculated in decades (1957–66, 1967–76, 1977–86, 1987–96, 1997–2006)

• annual maximum daily flow: the maximum daily flow in each year (referred to in this report as annual maxima)

• median daily flow (Q50): the flow for which 50 per cent of days in a year have greater flow (or the probability of exceedance is 50 per cent)

• 90th percentile of daily flow (Q90): a measure of the low flow – the flow for which 90 per cent of days in a year have greater flow (or the probability of exceedance is 90 per cent)

• 10th percentile of daily flow (Q10): a measure of the high flow – the flow for which 10 per cent of days in a year have greater flow (or the probability of exceedance is 10 per cent)

• coefficient of variation (CV): a measure of the variability of flow from year to year (ratio of the standard deviation to the mean).

2.1 Statistical trend analysis

Many techniques are available to analyse trends in hydrologic data. Statistically, the aim is to identify a trend as the increase or decrease of streamflow over time. To identify the presence of a significant trend, the TREND software package by the Cooperative Research Centre for Catchment Hydrology (Chiew & Siriwardena 2005) was used.

2.1.1 General requirements for trend analysis

Parametric trend detection techniques assume:

1 a normal distribution

2 a constant variance/distribution

3 independence.

Streamflow data can often violate these assumptions because the data:

1 is generally highly skewed (i.e. not normally distributed)

2 is markedly seasonal



3 can show serial correlation (autocorrelation) and/or spatial correlation (particularly when using data collected at monthly or more frequent intervals (Kundzewicz & Robson 2004).

Distribution-free testing methods (commonly called non-parametric tests) can be used to ensure the underlying test assumptions are met. These do not require an assumption about the form of distribution that the data derives from. This includes rank-based tests, which use the ranks of the data values (not the actual data values), and resampling methods (Kundzewicz & Robson 2004).

The premise behind resampling is that if there is no trend in the data, then the order of the data values should make little difference. Time-series are generated by randomly selecting data values from the original data series to give a new series with the same number of values and same distribution as the observed data. Bootstrapping is used so the series generated may contain more than one of some values from the original series and none of any other values. The original test statistic is then compared with the test statistics for the generated series. If the original test statistic is largely different to the generated results then this suggests that the ordering of the data does affect the statistic and there is a trend (Kundzewicz & Robson 2004; Chiew & Siriwardena 2005).

2.1.2 Adopted tests

The TREND software package (Chiew & Siriwardena 2005) provides various statistical methods for detecting trends, step-changes, differences in means/medians between two data periods and randomness in hydrological time-series data. The following tests were selected.

Median crossing test (test for randomness)

Most of the streamflow characteristics assessed for change are at an annual time-step, which means they are generally non-normal (typically they have a positively skewed distribution) but are independent and non-seasonal. The seasonal variation in flow is removed by the use of an annual series rather than a continuous daily series. In this case, any of the following distribution-free tests should be suitable; however, resampling methods were used if the test assumption of independence was not met for each series (i.e. if the median crossing test indicated that the time-series was not from a random process).

Mann-Kendall and linear regression tests (tests for trend)

Two different methods were used to detect whether there was a trend in the streamflow series. The Mann-Kendall test, which is a rank-based distribution-free method, was used to detect a trend and determine the significance. Non-parametric tests detect a trend but do not quantify it; therefore linear regression was used to determine the magnitude of the trend.



CUSUM and cumulative deviation tests (tests for step-change in mean/median)

The CUSUM and cumulative deviation tests were used to test for, and determine the significance of, a step-change in the mean and median of two periods where the change point is unknown. The cumulative deviation test (parametric) also provides the year where the change in the median between the two periods is determined to be most significantly different (the CUSUM test detects a change but does not quantify the year).

Rank-sum and students t-test (tests for difference in mean/median in two different data periods)

The rank-sum and students t-test were used to test for the difference in mean and median between two data periods with the change points set as 1975 to 1996, 1997 to 2007 and 1975 to 2000, 2001 to 2007.

Further information on the tests is available in Chiew and Siriwardena (2005).

Although running multiple tests is good practice it can also make interpretation of the results complex. The presence of just one significant test result may only be weak evidence of change – even if the test if considered robust and powerful. If more tests are significant then this can provide stronger evidence of change (unless the tests have very similar properties) (Kundzewicz & Robson 2004). Priority was given to the more powerful non-parametric test results.

2.1.3 Statistical significance

The statistical significance of trends describes the probability that the observed trend is actually occurring and is distinguishable from random variability in the data (i.e. the slope of the fitted trend line is likely to be different from zero).

For example, a 5 per cent significance level means there is a 95 per cent chance that the trend line fitted through the data has a slope that is different from zero. It is important to note that assuming this significance level means an error will be made, on average, for the same percentage of the time.

Statistical significance for this project is assessed at four levels:

• statistically significant at the 1 per cent level – interpreted here as representing a very strong trend

• statistically significant at the 5 per cent level – interpreted here as representing a strong trend

• statistically significant at the 10 per cent level – interpreted here as representing a moderate trend

• not statistically significant at the 10 per cent level – interpreted here as having ‘nil’ significance (this indicates that any trends identified cannot be confidently attributed to an actual trend and may be due to random variability in the data).



Trends that are statistically significant at the 1, 5 or 10 per cent level of significance are collectively referred to as statistically significant trends in this report.

2.2 Graphical trend analysis

The statistical assessment was performed in conjunction with a visual assessment to confirm whether any trend is actually an observed pattern in the data. As streamflow exhibits strong natural variability, a weak trend – even if it exists – cannot always be detected by statistical testing (Kundzewicz & Robson 2004).

2.2.1 Trend analysis maps

Visual examination of the results of the statistical analysis was used to interpret and present the results of the streamflow trends (i.e. trend gradients and significance levels) across south-west Western Australia.

The trend maps produced use arrows to indicate the nature of the trend; with the magnitude, direction and significance of the trend being illustrated by the size, direction and shading of the arrows (Figure 6). The direction and significance of the trend is determined by the Mann-Kendall test and the magnitude of change is determined using linear regression, standardised to make the change comparable across stations (slope divided by the mean over the data period from 1975 to 2007).

Figure 6 Legend used on streamflow trend analysis maps

2.2.2 Time-series plots and curve fitting

Time-series plots were created for all streamflow indices. These were used for visually identifying any data problems (i.e. outliers) and temporal patterns (i.e. trend or step-change).

To help identify a trend or step-change through time, a number of curve fits were applied. These included linear regression, means over different periods and the loess curve (Figure 7). Loess models fit a curve generated by a polynomial fitting. They include a procedure to smooth out seasonality in the data over the whole data period in order to fit a trend line to the data.



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Figure 7 Time-series for Yarragil Brook at Yarragil Formation (614044) with a)

linear trend b) step-change c) loess curve

2.2.3 Additional graphical analysis

Period of record

Trend analysis is only completed for the period of record since 1975.

The long-term records were not used in this analysis because the step-change observed in 1975 is the key feature identified statistically and visually (i.e. the step-change observed in 1975 is larger and more significant than any change post-1975).

This can be seen in a visual examination of the long-term time-series for Yarragil Brook (Figure 7). These figures also explain why a number of different curve fits are applied to each series. The step-change is not reflected in the linear regression curve, which only shows a decreasing trend. Thus the loess curve and the means over two periods are necessary to show a step-change.

Decadal variability

The trend over the period of record since 1975 will not necessarily be representative of the trend over a shorter period of time, such as the last 10 years. This is illustrated in the annual time-series for Donnelly River at Strickland (608151) in Figure 8, where an overall downward trend in streamflow has occurred over the period 1975 to 2007. However, the trend in streamflow over decadal periods within this record is varied (i.e. no trend, increasing, then decreasing).



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Figure 8 Trends for decadal periods and the overall period of record for Donnelly

River at Strickland (608151)

The long-term decadal variations in flow are assessed graphically (Section 2.2.2) and in tables.

Flow duration curves

Flow duration curves have been constructed using available daily flow data for each decade in order to visually assess changes in the flow regime between the decades.

Flow duration curves show the relationship between the magnitude and frequency of streamflow. They provide a measure of the percentage of time a given streamflow is equalled or exceeded over the daily time interval.

A number of key streamflow features are highlighted in the curves; such as the variability in the range of flows, changes in cease-to-flow periods, changes in baseflow (the slope of the low-flow end of the curve) and an indication of the catchment’s response to rainfall (the slope of the high-flow end of the curve).

To further analyse changes in the slope of the flow duration curves, the ratio of high flows to median stream flow (Q10/Q50 or high-flow index) and low flows to median stream flow (Q90/Q50 or low-flow index) were also assessed.

Monthly streamflow

To investigate any changes in seasonality, average monthly streamflow graphs were also compared for the periods 1975 to 1996 and 1997 to current. The monthly streamflow distributions show the consistency of baseflows, the timing of the onset of winter flows and the peak flow month.



3 Results

3.1 Independence

The median crossing test showed that non-randomness was indicated in very few series of annual totals (four of 29), annual maxima (two of 29) and 10th percentile flows (three of 29). For the median and 90th percentile flows, a larger number of series did not meet the assumption of independence (10 and 13 of 29 respectively). This is due to these series containing a large number of zero flows, resulting in high serial correlation. For the series that violated the assumption of independence, the resampling technique was used to obtain an appropriate value of the significance level for all the statistical tests applied.

3.2 Trend analysis maps

The trend analysis maps show the direction and significance of a trend, as determined by the Mann-Kendall test; and the magnitude of change, as determined using linear regression – standardised to make the change comparable across stations (slope divided by the mean over the data period from 1975 to 2007).

Generally the results of the Mann-Kendall and linear regression tests were in good agreement. In cases where they were not in agreement; for example, the statistics detected a negative non-parametric trend (Mann-Kendall) but the parametric slope estimate was positive (linear regression), priority was given to the Mann-Kendall test results because these were deemed more powerful and robust. If the results were opposing but close to zero, a decision was made to assign a ‘nil’ trend to the data.

Trend analysis maps are shown for annual total streamflow (Figure 9), annual maximum daily flow (Figure 10), 10th percentile of daily flows (Figure 11), median daily flows (Figure 12) and the 90th percentile of daily flows (Figure 13). A brief description of each map is given, followed by more detailed comments on specific sites and a regional perspective.

The time-series for each streamflow index is shown graphically, and the results of the TREND software are tabulated – in Appendix A – for each streamflow gauging station.



Annual total streamflow

Figure 9 Trends in the annual total streamflow since 1975

For annual streamflow, all stations show evidence of a trend but only five show statistically significant changes (one increase and four decreases). Although 22 of the 29 stations show a decrease in total streamflow, seven stations spread across the south-west show a possible increase.

Although mean annual rainfall in the Warren River basin has shown an increase for the last decade, the streamflow in this area has decreased.



Annual maximum daily streamflow

Figure 10 Trends in the annual maximum daily streamflow since 1975

Regional trend studies rarely result in a homogenous spatial pattern; however, the maxima showed decreasing trends in 26 of the 29 stations, with five of these being significant. Of these, 21 stations had a rate of decline in maximum streamflows of between 1 and 5 per cent. Two stations on the south coast had no trend in annual maxima and Vasse River showed a significant increasing trend.

The analysis of maximum flows can give a parallel to extreme flows. As with rainfall, the reduction in extreme events may greatly contribute to the reduction in annual streamflow totals. It is important to note that the time-series of annual maxima



contains only one maximum value for each year; so if some years contain no extremes at all, values in the series may not be very high. Additionally, it is likely that there are more days with higher flow or more than one high-flow event in a year. To account for this, longer records and partial flow-series analysis are recommended to give a better indication of extreme flows.

10th percentile of daily streamflows

Figure 11 Trends in the 10th percentile daily streamflow (high flows) since 1975

The 10th percentile flows show little consistency in trends across the whole region. Rivers on the south coast typically exhibit downward trends (the exception being Frankland River), with the remainder showing a roughly equal mix of increasing and



decreasing trends. The magnitudes of the decreases are typically larger (eight stations with up to 5 per cent change) than the magnitude of the increases (only two stations with up to 5 per cent change). Overall, 19 stations show a decreasing trend in high flows (with five of these being statistically significant), eight stations show an increasing trend (with one statistically significant) and two stations show no trend.


Figure 12 Trends in the 50th percentile daily streamflow (median flows) since 1975

Annual median streamflow is largely decreasing (20 stations show a decrease) and there is a clear dominance of significant trends (14 of the 20 are statistically significant).




Figure 13 Trends in the 90th percentile daily streamflow (low flows) since 1975

The low flows are typically used as a surrogate measure of baseflow conditions. These flows show mainly decreasing trends with 14 cases of decreasing low streamflows identified (six significant). However, there were also six increases (four significant) and one third of the stations showed no change at all (due to the low flows being mainly zero flows). For stations with zero flows, the 50th percentile may be more representative of baseflows. These showed a clear dominance in significantly decreasing trends (Figure 12).



Overview of trend analysis maps

The broad pattern from the trend maps is toward decreasing streamflows with a few significant trends. A decrease (or no trend) in all streamflow indices is seen at 17 out of the 29 sites (59 per cent) (Figure 14). The remainder of the sites show trends of varying direction and significance.

Streamflow gauging stations on the south coast show a clear decrease in streamflow for all characteristics. This is contrary to what would be expected based on the rainfall analysis, which in the last decade showed an increase for all the rainfall indices at a number of rainfall stations on the south coast. Frankland River at Mount Frankland (605012) is the only site analysed on the south coast that shows increasing trends. The streamflow data for this site is current in the database to 2005, which corresponds with the occurrence of a large rainfall and subsequent flow event. This event is not represented at a number of the south coast streamflow gauging stations, as the records are only current in the database until the end of 2004. If the additional years of streamflow data were added (to 2007), it would be interesting to see if the additional data – in particular this single event – would affect the statistics (especially the significant decline in the median flows) or if decreasing trends would still be observed.

Gingin Brook is the only location where all the decreases are statistically significant. Further investigation into this area is required to confirm if the reduction in streamflow can be attributed to climate changes, particularly as some rainfall stations surrounding Gingin show an increase in some rainfall statistics. Potential other causes include the agricultural sector’s increased use of surface water and groundwater in recent years. As there is strong surface-groundwater interaction, a reduction in baseflows from increased summer use is also evident (Lindsay et al. 2007).

For streamflow gauging stations in the coastal scarp area (AWRC basins 611 to 616), there is a mix of increasing and decreasing trends. Only the trends in the median flows are significant. This indicates that the trends identified in the high flows, maximum flows and annual totals may be due to random variability in the data. As most stations have no trend in the low flows (due to the presence of zero flows), the median flows could be used as a surrogate for baseflow, indicating a possible reduction in baseflows for this area since 1975.

The exception in the annual maximum daily flows is Vasse River, with a significant increasing trend. This catchment experienced flood events in 1997 and 1999. As a result of these events, compensating basins have been installed for flood mitigation purposes and these would also be expected to mitigate future large events.

It is not unexpected that gauges located close to each other can behave in a different way. This occurs for the Yarragil Brook station (614044), which has decreasing streamflow trends, and Murray River at Baden Powell Water Spout (614006), which exhibits increasing trends (with the exception of maximum flows). This may be



attributed to different geology, land uses and clearing levels in the catchments with 59 per cent of the Murray River catchment cleared and the Yarragil Brook catchment being fully forested.

Figure 14 Streamflow gauging stations that show a decrease (or no trend) in all

streamflow indices



3.3 Step-change analysis

CUSUM and cumulative deviation tests

The CUSUM and cumulative deviation tests were used to test for a step-change in the mean and median of two periods where the change point was unknown. The CUSUM test was used to identify the presence of a step-change, with the cumulative deviation test providing the year of change. Values were reported if they were identified at or above the 10 per cent significance level in both tests.

The step-change results are tabulated for each streamflow index in Appendix A for each streamflow gauging station. A step-change was identified at very few stations; so it could be concluded that a new streamflow regime, similar to the step-change observed in 1975, has not been established.

For the annual totals, a step-change was only identified at three locations – Carey Brook (moderate trend), Bancell Brook (moderate trend) and Gingin Brook (strong to very strong trend) – with the streamflow data for all three being higher in the earlier years than the later years. The change point was identified as 1999 for Carey Brook and Gingin Brook and 1996 for Bancell Brook.

The statistical testing for Carey Brook and Gingin Brook also identified a trend. For Carey Brook, the Mann-Kendall test had a higher significance level (5 per cent) than the CUSUM or cumulative deviation tests identified (10 per cent). If both step-change and trend results were significant at more locations, it would have been necessary to investigate additional information in order to determine which of these provided the best change description. The long-term time-series and the post-1975 time-series were visually assessed to see if a step-change or trend was more evident.

The data post-1999 for Carey Brook was still within the range of annual variability already experienced since 1961, with the exception of 2001, which was the lowest year on record (Figure 15). The annual flows for Gingin Brook post-1999 have been some of the lowest since 1958 (Figure 16). For both stations, the annual flows post-1999 were all below the mean annual flow for 1975 to 1999. The loess curves post-1975 show a relatively constant slope with a sharper decline in the late 1990s. Visually, the long-term time-series indicate a step-change rather than a linear decline. Therefore, a step-change may be more evident than a trend at these two sites.

The step-change for Bancell Brook was identified as occurring in 1996. The loess curve post-1975 shows a period of increased flows in the 1980s before a decrease in the last decade (Figure 17). With the exception of 2001 and 2006, the observed flows post-1996 are within the variability of previously observed flows post-1975 and long term.

At several gauging stations, a change point was identified for the median to low flows in either the 1980s (with the average of 1980 onwards larger than the average of the



earlier period) or the early 1990s (with a decline following the period of larger flows in the 1980s). These stations all showed a decade (1980s) of high flows within the period post-1975. This change is also evident in the decadal mean annual flow analysis in Table 2.

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Figure 15 Carey Brook with step-change identified in 1999 shown for (a) long term

and (b) 1975 to 2006

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Figure 16 Gingin Brook with step-change identified in 1999 shown for (a) long term

and (b) 1975 to 2007

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Figure 17 Bancell Brook with step-change identified in 1996 shown for (a) long

term and (b) 1975 to 2007



Rank-sum and students t-test

The rank-sum and students t-test were used to determine whether the medians (rank-sum) and the means (students t-test) between two defined periods were statistically different, as opposed to the CUSUM and cumulative deviation tests which identified the statistical change point. The data periods were specified as 1975 to 1996, 1997 to 2007 and 1976 to 2000, 2001 to 2007 to coincide with the Water Corporation climate sequences. Values were reported if they were identified at or above the 10 per cent significance level in both tests.

About one third of the sites showed a decline in the means and a quarter of the sites showed a decline in the medians for the 1997 change point for the annual total flows, annual maximum daily flows and the high flows (10th percentile daily flows). The exception was Vasse River with an increase in maximum flows. A decline occurred at more sites for the 2000 change point with, on average, about a quarter of the sites showing a decline in the means and half the sites showing a decline in the medians. The sites were not always the same between the 1997 and 2000 change points.

The low flows (90th percentile daily flows) were more consistent across both time periods. There were more significant differences between the means (about 40 per cent of sites) than the medians (about 30 per cent). Most sites showed a decline in the later period; however, Goodga River, Denmark River at Kompup, Yate Flat Creek, Wilyabrup Brook and Williams River had significant increases.

The prevalent difference was seen in the median flows (50th percentile daily flows), with significant differences between the means and medians in both time periods at about 65 per cent of sites. The only increases were Goodga River, Frankland River and Williams River.

Comparison of mean and median flows indicates the distribution of streamflow totals around the mean. On a monthly basis, the largest differences typically occur in the winter months, indicating that large flows resulting from weather events such as thunderstorms significantly increase the total for these months, skewing the distribution. In areas with perennial streamflow, the smallest differences occur in the summer months, indicating the consistency of the groundwater contribution. This analysis was completed using annual time-series; however, the larger decline in the medians than the means would alter the skew of the distribution and could be caused by:

• less frequent extreme winter flow events (extreme events distort the mean and not the median, which is based on ranks)

• declining summer baseflows.

Although the rank-sum and student’s t-test identified a number of significant differences in the means and medians with the change points specified at 1997 and 2000, these were not the main years of change identified in the step-change tests.



Although differences may be observed post-1997 or post-2000, they are considerably weaker than the step-change observed in 1975 by Rodgers and Ruprecht (1999).

In addition to the significance of a change, the duration of a trend or step-change is important. Statistical tests are unlikely to detect a trend that has only continued for a short time or they may detect an apparent trend that would disappear once more data was collected. The 10 years post-1997 and seven years post-2000 are probably not long enough to confidently detect a change.

3.4 Further investigation

The trend analysis maps show the trend over the whole period of available record since 1975. However, the inter-decadal means and variability can display different trend results compared with the whole record.

Decadal mean annual flow

Table 2 shows the percentage change in the decadal mean annual flow from the mean annual flow of the standard period (1975–2003). In the decade before 1975, streamflow was greater than the mean annual flow of the standard period for all areas except the south coast region. Between 1977 and 1986 there was a roughly equal mix of upward and downward trends in annual totals from the mean. From 1987 to 1996 there was an increase in streamflows (this was also identified in the step-change tests); however, in most instances this was still below the flows experienced before 1975. Since 1997 this pattern has reversed, with a substantial drop in streamflow (the largest decrease on record). This pattern is consistent across the stations with the exception of Vasse River and Frankland River (see discussion for these two sites with the trend analysis maps).

Decadal variability

Table 3 shows the coefficient of variation for the periods 1975 to 2003, 1975 to current and the last 10 years (1997 to current). As would be expected, the variability in streamflow decreases for the last decade in comparison with the standard period. For a few stations, the coefficient of variation actually increases for the last decade. Interestingly, these sites correspond with a decreasing trend in annual streamflow – so it would be expected that the variability would also decrease. However, at some of these sites, the start of the last decade incorporates the increased streamflows observed since the late 1980s and also include record low annual flows in the more recent years, resulting in a large range of streamflows and hence increased variability.

Decadal flow duration

In Appendix B, the decadal flow duration curves for the 29 stations analysed in this study are presented. The ratio of high flows to median stream flow (Q10/Q50 or high-flow index) and low flows to median stream flow (Q90/Q50 or low-flow index) are also tabulated.



A common feature of the flow duration curves is the progressive reduction in flow over time. As a general rule, the decrease appears to be uniform across the flow regime; however, in a number of cases the reduction in low flows appears greater. This is consistent with the trend analysis results and indicates that baseflows are decreasing. As shown in the decadal flow tables, the general increase in streamflow for the period 1987 to 1996 is reflected in the flow duration curves, with a subsequent greater reduction in the last decade.

The sites on the south coast are again the exception, where the flow duration curves display a different pattern. Comparison of the daily flow duration curves for decadal periods is very useful for these sites as the overall trend statistics showed a decrease in the annual statistics. It is evident that this is actually the combined effect of a decrease in the mid to high flows and an increase in the mid to low flows.

A major change is seen for the Donnelly River at Strickland where the period from 1986 onwards shows the occurrence of cease-to-flow periods in a once perennial river (Figure 18).


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Figure 18 Flow duration curve for Donnelly River at Strickland

To further examine streamflow variability and duration, the high-flow index and low-flow index were calculated. If the low-flow index is zero then the site ceases to flow at or below 90 per cent of the time. The closer the low-flow index is to one, the smaller the variability in the baseflow. The least variability between the 90th and 50th percentile of flows is seen at Waychinicup River and Gingin Brook, with low-flow ratios of 0.6 and 0.5. Although this ratio has been consistent across the decades, the flow at Waychinicup River has uniformly increased and the flow at Gingin Brook has uniformly decreased across this flow range. The larger the high-flow index, the larger the range of variability in the mid to high flows. The largest variability is seen at Yarragil Brook, with a decrease in the 10th percentile flows and a consistent



decrease in the low flows (in the last decade flows have occurred less than 40 per cent of the time).

Monthly streamflow

In Appendix C, average monthly streamflows for the periods 1975 to 1996 and 1997 to current are presented for the 29 gauging stations analysed in this study.

The key components in the monthly graphs were reductions in average summer flows, signalling a reduction in baseflow and a general reduction in average monthly flows. The most noticeable change in the monthly graphs is the shift in the peak flow month. In most cases, the shift is by one month: peak flows that occurred in July are now occurring in August (similarly August peak flows now occur in September). This indicates either drier antecedent conditions affecting the processes and timing of runoff and/or a change in the peak rainfall month.



Table 2 Summary of the variation in decadal mean annual flow with the standard period of record (1975–2003)

Variation in decadal mean annual flow with standard period (%)* Station number

Location MAF (ML) (1975–2003) 1957–66 1967–76 1977–86 1987–96 1997–2006 2007

602031 Waychinicup River 8761 -17.9 (6) 13.5 1.2 -11.8 (9) 602199 Goodga River 4279 -16.3 4.5 13.1 -21.0 (8)

603173/003 Denmark River – Kompup 11 649 -21.5 7.7 27.1 -43.9 (8) 603136 Denmark River – Mt Lindesay 28 474 21.1 (6) -7.9 10.0 13.6 -19.6 -83.6 603190 Yate Flat Creek 4758 -1.6 12.7 17.5 -42.0 (8) 604053 Kent River 79 233 14.9 -1.2 -1.1 20.6 -26.6 (8) 605012 Frankland River 154 633 36.2 7.3 -11.7 19.0 7.0 (9) 606195 Weld River 40 982 41.2 0.6 6.5 -12.1 (9)

607145/004 Perup River 13 251 15.2 (7) -8.8 25.6 -28.8 607009/013 Lefroy Brook 39 921 9.9 -0.7 -18.9 -38.5

607144 Wilgarup River 27 103 20.7 (9) -2.1 15.0 -27.3 607008/220 Warren River 264 329 24.1 -7.4 12.6 -12.2 -45.3 608147/002 Carey Brook 7046 32.1 (9) 3.9 4.5 -16.7

608151 Donnelly River 102 076 78.0 (6) 25.3 -0.2 11.4 -21.7 -47.0 609025/019 Blackwood River 535 093 24.0 -7.7 19.7 -11.5 -33.0

610001 Margaret River 86 135 33.2 (6) 3.7 5.2 -23.1 610003 Vasse River 10 645 -11.4 -2.4 13.7 16.7 610006 Wilyabrup Brook 23 947 4.0 4.1 -17.9 -37.7 611111 Thomson Brook 10 837 47.8 (9) 12.9 -8.7 18.3 -15.5 612001 Collie River East 40 737 12.0 (8) -7.6 35.2 -20.3 2.7 612014 Bingham River 5447 -6.7 44.9 -32.8 8.7 612034 Collie River 24 175 -1.3 40.9 -37.7 -21.3

613013/007 Bancell Brook 4178 13.7 2.2 15.8 -22.5 -30.8 614006 Murray River 224 323 32.7 (7) -7.0 24.1 -17.0 614044 Yarragil Brook 1616 159.3 (6) 165.5 -2.2 21.0 -31.6 614196 Williams River 61 909 12.3 (6) -10.6 20.8 -1.0 616001 Wooroloo Brook 44 997 1.5 (7) -7.1 19.3 -21.4 -18.0 616216 Helena River 5032 93.8 7.1 18.4 -39.6 -38.5 617058 Gingin Brook 12 477 7.7 (7) 20.0 (7) 1.0 1.4 -9.5 (9) -25.5

*Percentage change in the MAF from the MAF between 1975–2003 for decades with greater than five years of data. Brackets indicate the number of years of data available if less than a decade. Negative values indicate a percentage decrease from the 1975–2003 MAF.



Table 3 Summary of the variability in annual streamflow with varying periods

Coefficient of variation* Station number

Location Period of record 1975 to 1975–2003 1975–current 1997–current

602031 Waychinicup River 2005 0.65 0.64 0.46 602199 Goodga River 2004 0.35 0.36 0.20

603173/003 Denmark River – Kompup 2004 0.86 0.88 0.71 603136 Denmark River – Mt Lindesay 2007 0.66 0.73 0.97 603190 Yate Flat Creek 2004 0.66 0.68 0.65 604053 Kent River 2004 0.62 0.63 0.49 605012 Frankland River 2005 0.49 0.53 0.53 606195 Weld River 2005 0.38 0.37 0.31

607145/004 Perup River 2006 0.61 0.64 0.75 607009/013 Lefroy Brook 2007 0.35 0.37 0.41

607144 Wilgarup River 2006 0.51 0.55 0.66 607008/220 Warren River 2007 0.43 0.45 0.48 608147/002 Carey Brook 2006 0.28 0.29 0.31

608151 Donnelly River 2007 0.41 0.45 0.52 609025/019 Blackwood River 2007 0.55 0.56 0.49

610001 Margaret River 2006 0.41 0.44 0.61 610003 Vasse River 2007 0.49 0.48 0.58 610006 Wilyabrup Brook 2007 0.36 0.40 0.49 611111 Thomson Brook 2006 0.55 0.56 0.63 612001 Collie River East 2007 0.73 0.71 0.55 612014 Bingham River 2007 0.99 0.96 0.67 612034 Collie River 2007 0.85 0.83 0.59

613013/007 Bancell Brook 2007 0.33 0.34 0.27 614006 Murray River 2006 0.60 0.59 0.47 614044 Yarragil Brook 2006 0.63 0.66 0.71 614196 Williams River 2006 0.59 0.59 0.47 616001 Wooroloo Brook 2007 0.43 0.44 0.37 616216 Helena River 2007 1.04 1.05 0.68 617058 Gingin Brook 2007 0.12 0.14 0.13

* Coefficient of variation is the standard deviation divided by the mean



4 Possible causes of streamflow change

Detection refers to the process of demonstrating that a variable has changed in a statistical sense, without providing a reason for that change (IPCC 2001). This chapter discusses potential causes in any detected streamflow change; however, it does not attempt to attribute detected changes to most likely causes. Further analysis of the data, as well as examination of data for factors influencing streamflow, would be required to explore the possible reasons for a trend.

It is not implicit that climate change is the cause of a significant trend in a data series: there may be many other explanations. Streamflow data can be affected by many factors such as urbanisation, deforestation, changes in agricultural practices, reservoirs, drainage systems, water abstraction, land-use change, natural catchment changes such as changes in channel morphology, climate variability and/or climate change (e.g. anthropogenic increases in greenhouse gases, solar activity, ENSO cycle) and data problems (Kundzewicz et al. 2004).

4.1 What caused the 1975 step-change?

The rainfall decline that was observed in the mid 1970s has been studied extensively and has been linked to changes in the large-scale winter weather systems that bring rain to south-west Western Australia (CSIRO 2007). The climate changes, particularly in extremes, were also strongly linked to the relationship with ENSO and the Southern Oscillation Index (SOI) with a more frequent, persistent and intense warm phase of ENSO (El Nino) observed since the mid 1970s, when compared with those of the previous 100 years (IPCC 2001; Kundzewicz et al. 2004).

The subsequent decline in runoff showed a strong regional trend. As the data showed similar patterns at all sites, then the cause was considered to be widespread and attributed to this period of reduced rainfall (Rodgers & Ruprecht 1999). Although statistical support for the rainfall and runoff decline has been shown, the relative contribution of climate change and/or natural variability has been difficult to quantify.

4.2 Climate variability and period of record

Climate variability is the natural variation in the climate from one period to the next, whereas climate change is the long-term alteration in the climate. Climate variability can occur on a range of time-scales, from daily to multi-decadal. The variability can have two effects:

1. Trends in a streamflow record (due to climate variability) may be mistakenly attributed to climate change, particularly if the record is short (e.g. Figure 8).

2. Underlying changes resulting from climate change or other causes can be obscured by climate variability, particularly as the natural streamflow variability is typically large (Kundzewicz et al. 2004).



I Loh’s internal memo (2004) indicated that the period 1975 to 2003 was selected for Department of Water studies to provide reliable and unbiased estimates of the statistical properties of hydrologic data, which would be expected to continue at least 10 years into the future (nominally 2014). This is consistent with the approach that the World Meteorological Organisation recommends: 30-year averages with updates each decade. Livezey et al. (2007) concluded that this technique was suitable for very weak climatic trends but was generally not useful for design, planning and decision-making purposes in areas with a strong underlying trend.

4.3 What causes can be attributed to post-1975 changes?

It is possible that because the analysed data length is small (around 31 years), any changes identified post-1975 are due to climate variability rather than a change in the climate. It is also possible that the statistical tests are not able to detect short-term or weak changes. The variability can be seen in Table 2: for sites that showed a decrease in streamflow for the last decade, 90 per cent indicated a positive trend in the decade before. One effect of a longer record can be seen for the south coast region, where the gauging station on Frankland River had an additional year of data available compared with other sites analysed in this area and displayed an increasing trend as opposed to the decreasing trend at other sites. The effect of a longer record would not be uniform across all sites.

Any declines in rainfall and runoff post-1975 are only now beginning to be studied. Justification for the previous step-change is clear in records such as ENSO and SOI but there has been no current reporting on these climate statistics. This study showed a basic rainfall analysis of mean annual rainfall, maximum one-day rainfalls and percentiles of daily rainfall, and the percentage change in these indices of the last decade of data (1997–2007) in comparison with 1975 to 1996. Spatially there was a decline in mean annual rainfall and maximum rainfall across most of south-west Western Australia. Basic statistical testing showed the declines were generally not significant and that the rainfall declines reflected the step-change in the mid 1970s and were exacerbated by very low rainfall in recent years. Despite the rainfall reduction not being large, rainfall can still have an effect on streamflow because the distribution and intensity of rainfall can also impact on the streamflow regime.

This study shows that the trends in runoff do not appear to be as uniformly regionalised, or as significant, as that observed in 1975. Therefore, they cannot be attributed solely to lower rainfall. For locations that show a trend or step-change, other causes need to be investigated. The process of streamflow generation is the integrated result of several factors. At the simplest level, runoff is the difference between rainfall and evapotranspiration; however, it also includes factors such as catchment storage and soil water storage. The effect of any differences in these factors and the response of these factors are difficult to isolate. In addition to rainfall decline, it is expected that the most common impact on streamflows in south-west Western Australia would be:



• increased surface-water and groundwater use

• declining groundwater levels

• land-use changes

• farm dams and regulation.

Increased surface-water and groundwater use

For individual sites, it would be useful to have accurate data on use in the catchment. This could explain anomalies in the trend results, such as Lefroy Brook which showed an increase in the rainfall statistics, yet a significant decline in all streamflow indices. There is a high level of surface-water development in Lefroy Brook and this use may be a contributing factor to the apparent streamflow decline.

Declining groundwater levels

Declining groundwater levels, and the impact on streamflow, could result from abstraction/use or a change in antecedent conditions. The rainfall analysis post-1975 showed a reduction in maximum daily rainfalls averaged over the last decade. This could impact on infiltration rates and general ‘wetting up’ of the catchment, resulting in a reduction of baseflow contribution to a stream. It is also possible that any decreases in rainfall take time to reach an equilibrium in the groundwater signature.

Land-use changes

For sites that exhibit a trend, historical information about catchment changes need to be collated. Impacts on streamflow from disturbances such as severe drought, bush fires, clearing and reforestation/plantations may be identified through changes in streamflow over time.

Farm dams and regulation

Associated with increased surface-water use is the development of farm dams and regulated catchments. Ideally, to detect a climate change component in streamflow, it is necessary to eliminate the effect of other causes or reconstruct natural flows (Kundzewicz et al. 2004). Pre-dam flows have been constructed at a number of locations across south-west Western Australia (Sinclair Knight Merz 2008); however, the effect of vegetation changes would also need to be removed.



5 Concluding remarks

This report has provided evidence that areas in south-west Western Australia have experienced a further decline in streamflow since 1975, with only a few areas showing a significant decline. There was also a period of increased flows in the 1980s. The decline may be largely attributed to climate variability, such that more data may see the trend at many sites disappear; however, plausible other causes need to be investigated at each individual site. Evidence of a change in the streamflow regime is seen with the shift in the peak flow month (one month later) that has been observed across all sites in the south-west.

Livezey et al. (2007) suggests that 30-year averages may no longer be appropriate for climatic variables. The use of a set period (1975–2003) assumes hydrologic variables remain constant through time, which is applicable to the entire south-west of Western Australia. The statistical evidence in this current study is not strong enough to base a change for a shorter period since 1975; however, the standard period of analysis should be extended to include all available data post-1975 for surface-water resource management. To put this issue into context, it would be useful to assess the impact of differing time periods, post-1975, on current surface-water management approaches (e.g. sustainable diversion limits).

In addition, studies suggest that south-west Western Australia is likely to experience further decreases in rainfall and increases in temperature over the next 50 years. Consequently, further declines in streamflow, at rates greater than the projected declines in rainfall, could be expected over the same time period (Berti et al. 2004; Kitsios et al. 2008). It is therefore important that appropriate data periods for surface-water assessments are determined that consider historical averages and variability in conjunction with future predictions.



Appendices



Appendix A: Time-series for trend analysis

For each gauging station, time-series plots are shown for the annual totals, daily maxima and the 10th, 50th and 90th percentile of daily flows.

The figure below provides an example of the information provided on the plots.

y = 0.0481x + 1.047

R2 = 0.5056

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- Mann-Kendall 1% sig level • Increasing step-change

- CUSUM 1% sig level - cumulative deviation 1% sig level (1988)

The mean, median, standard deviation and coefficient of variation are given for the whole period of record used in the analysis. Results from the TREND software (Chiew & Siriwardena 2005) are also provided.

If the data is not independent, this indicates that the median crossing test showed that the time-series was not from a random process and therefore resampling was used to apply the statistical tests.

If a trend is identified, the linear regression trendline (orange dashed line) and equation is plotted on the time-series. If the trend is significant, the significance level of the Mann-Kendall test is also provided. If the trend is not significant, this indicates that any trend identified may be due to random variability in the data.

A loess curve is also plotted on the time-series to aid in visually identifying the trend. If any of the data points are zero then a loess curve cannot be created.

If a step-change is identified, the significance level of the CUSUM and cumulative deviation tests are provided. The cumulative deviation test also provides the year where the change in the median between the two periods is determined to be most significantly different (provided in brackets). This year is used in plotting the means of the two periods on the time-series plot, with the red line indicating the period of lower flow and the blue line indicating the period of higher flow.




y = -117.56x + 10638

R2 = 0.036

0

5000

10000

15000

20000

25000

30000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

Annual total Mean 8760 ML, Median 7350 ML Standard deviation 5630 ML, CV 0.64 • Decreasing trend but not significant • No step-change

y = -5.1639x + 863.23

R2 = 0.0031

0

500

1000

1500

2000

2500

3000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

Daily maxima Mean 781 ML, Median 406 ML Standard deviation 849 ML, CV 1.09 • Decreasing trend but not significant • No step-change

y = -0.9821x + 60.782

R2 = 0.0674

0

20

40

60

80

100

120

140

160

180

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

10th percentile Mean 45.1 ML, Median 34.5 ML Standard deviation 34.4 ML, CV 0.76 • Decreasing trend but not significant • No step-change

y = 0.0559x + 7.8635

R2 = 0.0869

0

2

4

6

8

10

12

14

16

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

50th percentile Mean 8.76 ML, Median 8.36 ML Standard deviation 1.72 ML, CV 0.20 • Increasing trend

- Mann-Kendall 10% sig level • No step-change

y = 0.0366x + 4.5069

R2 = 0.3525

0

1

2

3

4

5

6

7

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)







y = -36.381x + 4786.4

R2 = 0.0448

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

1975 1980 1985 1990 1995 2000

Stre

amflo

w (

ML)


y = -1.4628x + 292.2

R2 = 0.0027

0

100

200

300

400

500

600

700

800

900

1000

1975 1980 1985 1990 1995 2000

Stre

amflo

w (

ML)


y = -0.3572x + 29.97

R2 = 0.1104

0

5

10

15

20

25

30

35

40

45

50

1975 1980 1985 1990 1995 2000

Stre

amflo

w (

ML)

10th percentile Mean 24.4 ML, Median 22.2 ML Standard deviation 9.46 ML, CV 0.39 • Decreasing trend

- Mann-Kendall 10% sig level • Decreasing step-change


y = 0.0158x + 5.3596

R2 = 0.0117

0

1

2

3

4

5

6

7

8

9

10

1975 1980 1985 1990 1995 2000

Stre

amflo

w (

ML)

50th percentile Mean 5.61 ML, Median 5.44 ML Standard deviation 1.29 ML, CV 0.23 • Data is not independent • Increasing trend but not significant • Increasing step-change


y = 0.0481x + 1.047

R2 = 0.5056

0

0.5

1

1.5

2

2.5

3

1975 1980 1985 1990 1995 2000

Stre

amflo

w (

ML)






Denmark River — Kompup (603003)

y = -259.31x + 15368

R2 = 0.0519

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

50000

1975 1980 1985 1990 1995 2000

Stre

amflo

w (

ML)

Annual total Mean 11 300 ML, Median 9070 ML Standard deviation 10 000 ML, CV 0.88 • Decreasing trend but not significant • No step-change

y = -0.4368x + 932.14

R2 = 2E-05

0

500

1000

1500

2000

2500

3000

3500

4000

1975 1980 1985 1990 1995 2000

Stre

amflo

w (

ML)


y = -1.9232x + 111.23

R2 = 0.0673

0

50

100

150

200

250

300

350

1975 1980 1985 1990 1995 2000

Stre

amflo

w (

ML)



y = -0.0205x + 1.5167

R2 = 0.0172

0

1

2

3

4

5

6

1975 1980 1985 1990 1995 2000

Stre

amflo

w (

ML)


0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

1975 1980 1985 1990 1995 2000

Stre

amflo

w (

ML)

90th percentile Mean 0.002 ML, Median 0 ML Standard deviation 0.006 ML, CV 3.0 • Data is not independent • No trend • No step-change




y = -458.39x + 35436

R2 = 0.0479

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

Annual total Mean 27 600 ML, Median 22 200 ML Standard deviation 20 200 ML, CV 0.73 • Decreasing trend


0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

Daily maxima Mean 1380 ML, Median 934 ML Standard deviation 1260 ML, CV 0.91 • No trend • No step-change

y = -4.7393x + 286.94

R2 = 0.0844

0

100

200

300

400

500

600

700

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

10th percentile Mean 206 ML, Median 185 ML Standard deviation 158 ML, CV 0.77 • Decreasing trend


y = -0.1374x + 15.811

R2 = 0.0116

0

10

20

30

40

50

60

70

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

50th percentile Mean 13.5 ML, Median 10 ML Standard deviation 12.3 ML, CV 0.91 • Decreasing trend


y = -0.0122x + 0.7264

R2 = 0.0355

0

0.5

1

1.5

2

2.5

3

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

90th percentile Mean 0.52 ML, Median 0.31 ML Standard deviation 0.63 ML, CV 1.21 • Data is not independent • Decreasing trend but not significant • No step-change




y = -111.63x + 6361

R2 = 0.0976

0

2000

4000

6000

8000

10000

12000

14000

1975 1980 1985 1990 1995 2000

Stre

amflo

w (

ML)


y = -8.1294x + 520.31

R2 = 0.042

0

200

400

600

800

1000

1200

1400

1600

1800

1975 1980 1985 1990 1995 2000

Stre

amflo

w (

ML)


y = -0.7224x + 44.241

R2 = 0.0856

0

10

20

30

40

50

60

70

80

90

100

1975 1980 1985 1990 1995 2000

Stre

amflo

w (

ML)

10th percentile Mean 33 ML, Median 29.8 ML Standard deviation 21.7 ML, CV 0.66 • Decreasing trend but not significant • No step-change

y = 0.005x + 0.4109

R2 = 0.0152

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

1975 1980 1985 1990 1995 2000

Stre

amflo

w (

ML)

50th percentile Mean 0.49 ML, Median 0.43 ML Standard deviation 0.36 ML, CV 0.73 • Increasing trend but not significant • Increasing step-change


y = 0.0018x + 0.005

R2 = 0.1031

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

1975 1980 1985 1990 1995 2000

Stre

amflo

w (

ML)

90th percentile Mean 0.03 ML, Median 0 ML Standard deviation 0.05 ML, CV 1.67 • Data is not independent • Increasing trend but not significant • No step-change




y = -1013.7x + 93404

R2 = 0.0332

0

50000

100000

150000

200000

250000

1975 1980 1985 1990 1995 2000

Stre

amflo

w (

ML)

Annual total Mean 77 700 ML, Median 67 800 ML Standard deviation 49 000 ML, CV 0.63 • Decreasing trend but not significant • No step-change

y = -26.905x + 3215.6

R2 = 0.0117

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

1975 1980 1985 1990 1995 2000

Stre

amflo

w (

ML)


y = -7.3329x + 769.81

R2 = 0.0265

0

200

400

600

800

1000

1200

1400

1600

1800

2000

1975 1980 1985 1990 1995 2000

Stre

amflo

w (

ML)

10th percentile Mean 656 ML, Median 584 ML Standard deviation 397 ML, CV 0.61 • Decreasing trend but not significant • No step-change

y = -0.8467x + 44.006

R2 = 0.1469

0

20

40

60

80

100

120

1975 1980 1985 1990 1995 2000

Stre

amflo

w (

ML)

50th percentile Mean 30.9 ML, Median 24.6 ML Standard deviation 19.4 ML, CV 0.63 • Data is not independent • Decreasing trend



y = -0.0356x + 3.2089

R2 = 0.0304

0

1

2

3

4

5

6

7

8

9

1975 1980 1985 1990 1995 2000

Stre

amflo

w (

ML)





y = 1668.1x + 132666

R2 = 0.0325

0

50000

100000

150000

200000

250000

300000

350000

400000

450000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

Annual total Mean 159 000 ML, Median 144 000 ML Standard deviation 84 100 ML, CV 0.53 • Increasing trend but not significant • No step-change

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

50000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

Daily maxima Mean 5980 ML, Median 2980 ML Standard deviation 8110 ML, CV 1.36 • No trend • No step-change

y = 16.186x + 1040.5

R2 = 0.0433

0

500

1000

1500

2000

2500

3000

3500

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

10th percentile Mean 1300 ML, Median 1270 ML Standard deviation 707 ML, CV 0.54 • Increasing trend but not significant • No step-change

y = 2.3927x + 59.526

R2 = 0.0655

0

100

200

300

400

500

600

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

50th percentile Mean 97.8 ML, Median 81 ML Standard deviation 85 ML, CV 0.87 • Increasing trend but not significant • No step-change

y = -0.1096x + 10.477

R2 = 0.0306

0

5

10

15

20

25

30

35

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)





y = -345.46x + 46363

R2 = 0.044

0

10000

20000

30000

40000

50000

60000

70000

80000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

Annual total Mean 40 800 ML, Median 41 200 ML Standard deviation 1470 ML, CV 0.04 • Decreasing trend but not significant • No step-change

y = -13.354x + 1468.7

R2 = 0.0354

0

500

1000

1500

2000

2500

3000

3500

4000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)


y = -1.7868x + 385.97

R2 = 0.0146

0

100

200

300

400

500

600

700

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)


y = -0.6453x + 28.885

R2 = 0.1862

0

10

20

30

40

50

60

70

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)




0

0.5

1

1.5

2

2.5

3

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

90th percentile Mean 0.17 ML, Median 0.01 ML Standard deviation 0.48 ML, CV 2.82 • No trend • No step-change



Perup River — Quabicup Hill (607004)

y = -106.04x + 14332

R2 = 0.0152

0

5000

10000

15000

20000

25000

30000

35000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)


y = -11.061x + 681.37

R2 = 0.0521

0

200

400

600

800

1000

1200

1400

1600

1800

2000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)


y = -0.4125x + 103.99

R2 = 0.0032

0

50

100

150

200

250

300

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)


y = -0.1217x + 10.113

R2 = 0.1257

0

2

4

6

8

10

12

14

16

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)




y = -0.037x + 1.6765

R2 = 0.2651

0

0.5

1

1.5

2

2.5

3

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)






Lefroy Brook — Pemberton Weir & Rainbow Trail (607009/013)

y = -504.67x + 46699

R2 = 0.117

0

10000

20000

30000

40000

50000

60000

70000

80000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

Annual total Mean 38 100 ML, Median 36 000 ML Standard deviation 14 300 ML, CV 0.38 • Decreasing trend


y = -17.552x + 1154

R2 = 0.112

0

500

1000

1500

2000

2500

3000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

Daily maxima Mean 856 ML, Median 762 ML Standard deviation 507 ML, CV 0.59 • Decreasing trend


y = -3.1986x + 344.63

R2 = 0.0811

0

100

200

300

400

500

600

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

10th percentile Mean 290 ML, Median 276 ML Standard deviation 109 ML, CV 0.38 • Decreasing trend


y = -1.5695x + 72.684

R2 = 0.2924

0

20

40

60

80

100

120

140

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

50th percentile Mean 46 ML, Median 41.1 ML Standard deviation 28.1 ML, CV 0.61 • Decreasing trend



y = -0.0775x + 5.6109

R2 = 0.0326

0

5

10

15

20

25

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)





y = -319.01x + 30949

R2 = 0.0448

0

10000

20000

30000

40000

50000

60000

70000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)


y = -15.949x + 1080.6

R2 = 0.0536

0

500

1000

1500

2000

2500

3000

3500

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)


y = -1.8997x + 244.32

R2 = 0.0207

0

50

100

150

200

250

300

350

400

450

500

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)


y = -0.5431x + 24.143

R2 = 0.3779

0

5

10

15

20

25

30

35

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)




y = -0.0197x + 0.8664

R2 = 0.0559

0.0

0.5

1.0

1.5

2.0

2.5

3.0

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)




Warren River — Barker Rd Crossing (607220)

y = -1233.1x + 274736

R2 = 0.0111

0

100000

200000

300000

400000

500000

600000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)


y = -82.781x + 7404.1

R2 = 0.0501

0

2000

4000

6000

8000

10000

12000

14000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)


y = -5.3837x + 2158.9

R2 = 0.0025

0

1000

2000

3000

4000

5000

6000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)


y = -2.8959x + 264.4

R2 = 0.0862

0

100

200

300

400

500

600

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)



y = -0.0778x + 28.493

R2 = 0.002

0

10

20

30

40

50

60

70

80

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)




Carey Brook — Staircase Road (608002)

y = -70.619x + 8052.1

R2 = 0.1114

0

2000

4000

6000

8000

10000

12000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

Annual total Mean 6890 ML, Median 6940 ML Standard deviation 1990 ML, CV 0.29 • Data is not independent • Decreasing trend



y = -1.4233x + 167.89

R2 = 0.0667

0

50

100

150

200

250

300

350

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

Daily maxima Mean 144 ML, Median 140 ML Standard deviation 51.7 ML, CV 0.36 • Data is not independent • Decreasing trend but not significant • No step-change

y = -0.3193x + 56.866

R2 = 0.0329

0

10

20

30

40

50

60

70

80

90

100

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

10th percentile Mean 51.6 ML, Median 50 ML Standard deviation 16.5 ML, CV 0.32 • Decreasing trend but not significant • No step-change

y = -0.2132x + 16.029

R2 = 0.177

0

5

10

15

20

25

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)



y = -0.0546x + 4.6068

R2 = 0.0896

0

1

2

3

4

5

6

7

8

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)






y = -1029x + 113913

R2 = 0.0536

0

50000

100000

150000

200000

250000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)


y = -38.375x + 2953.7

R2 = 0.0815

0

1000

2000

3000

4000

5000

6000

7000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)



y = -5.8701x + 919.03

R2 = 0.0207

0

200

400

600

800

1000

1200

1400

1600

1800

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)


y = -1.936x + 99.92

R2 = 0.2154

0

20

40

60

80

100

120

140

160

180

200

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

50th percentile Mean 67 ML, Median 59.1 ML Standard deviation 40.3 ML, CV 0.60 • Decreasing trend



y = -0.2774x + 9.8684

R2 = 0.3534

0

2

4

6

8

10

12

14

16

18

20

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)







y = 946.83x + 505730

R2 = 0.001

0

200000

400000

600000

800000

1000000

1200000

1400000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)


y = -372.4x + 21254

R2 = 0.039

0

20000

40000

60000

80000

100000

120000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

Daily maxima Mean 14 900 ML, Median 8800 ML Standard deviation 18 200 ML, CV 1.22 • Decreasing trend but not significant • No step-change

y = 3.5672x + 4745.8

R2 = 0.0001

0

2000

4000

6000

8000

10000

12000

14000

16000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

10th percentile Mean 4810 ML, Median 4040 ML Standard deviation 3130 ML, CV 0.65 • Data is not independent • Increasing trend but not significant • No step-change

0

200

400

600

800

1000

1200

1400

1600

1800

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

50th percentile Mean 442 ML, Median 363 ML Standard deviation 281 ML, CV 0.64 • No trend • No step-change

y = 0.815x + 44.686

R2 = 0.1113

0

20

40

60

80

100

120

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

90th percentile Mean 58.5 ML, Median 56 ML Standard deviation 23.6 ML, CV 0.40 • Increasing trend but not significant • No step-change




y = -981.3x + 98279

R2 = 0.0642

0

20000

40000

60000

80000

100000

120000

140000

160000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)


y = -35.76x + 2785.6

R2 = 0.0756

0

1000

2000

3000

4000

5000

6000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)




y = -7.0034x + 782.35

R2 = 0.0488

0

200

400

600

800

1000

1200

1400

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)


y = -1.7694x + 84.258

R2 = 0.1852

0

20

40

60

80

100

120

140

160

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

50th percentile Mean 55.1 ML, Median 53 ML Standard deviation 38.6 ML, CV 0.70 • Decreasing trend


y = -0.0065x + 0.1728

R2 = 0.4716

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)






y = 108.22x + 8702.8

R2 = 0.043

0

5000

10000

15000

20000

25000

30000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

Annual total Mean 10 500 ML, Median 9670 ML Standard deviation 5050 ML, CV 0.48 • Increasing trend but not significant • No step-change

y = 27.073x + 438.97

R2 = 0.1252

0

500

1000

1500

2000

2500

3000

3500

4000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

Daily maxima Mean 899 ML, Median 631 ML Standard deviation 740 ML, CV 0.82 • Increasing trend



y = 0.0567x + 67.968

R2 = 0.0004

0

20

40

60

80

100

120

140

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

10th percentile Mean 68.9 ML, Median 65.5 ML Standard deviation 27.2 ML, CV 0.39 • Increasing trend but not significant • No step-change

y = -0.0548x + 5.5829

R2 = 0.0337

0

2

4

6

8

10

12

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)


0

1

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

90th percentile Mean 0 ML, Median 0 ML Standard deviation 0 ML, CV 0 • No trend • No step-change




y = -238.91x + 26844

R2 = 0.0648

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)


y = -15.451x + 1369.2

R2 = 0.0592

0

500

1000

1500

2000

2500

3000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)



y = -1.4926x + 211.98

R2 = 0.0426

0

50

100

150

200

250

300

350

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)


y = -0.1682x + 6.6418

R2 = 0.1878

0

2

4

6

8

10

12

14

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)




y = 0.0003x + 0.0009

R2 = 0.0771

0.00

0.01

0.01

0.02

0.02

0.03

0.03

0.04

0.04

0.05

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

90th percentile Mean 0.01 ML, Median 0 ML Standard deviation 0.01 ML, CV 1.0 • Data is not independent • Increasing trend





y = -1.6257x + 10478

R2 = 7E-06

0

5000

10000

15000

20000

25000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

Annual total Mean 10 500 ML, Median 9320 ML Standard deviation 5840 ML, CV 0.56 • Decreasing trend but not significant • No step-change

y = -5.1927x + 624.81

R2 = 0.0246

0

200

400

600

800

1000

1200

1400

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)


y = 0.1506x + 82.392

R2 = 0.0008

0

50

100

150

200

250

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)


y = -0.0445x + 3.9325

R2 = 0.042

0

1

2

3

4

5

6

7

8

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

50th percentile Mean 3.3 ML, Median 2.82 ML Standard deviation 1.99 ML, CV 0.60 • Decreasing trend but not significant • Decreasing step-change


0.000

0.002

0.004

0.006

0.008

0.010

0.012

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

90th percentile Mean 0.0003 ML, Median 0 ML Standard deviation 0.002 ML, CV 6.67 • No trend • No step-change




y = 266.15x + 35965

R2 = 0.008

0

20000

40000

60000

80000

100000

120000

140000

160000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)


y = -55.654x + 3709.5

R2 = 0.0248

0

3000

6000

9000

12000

15000

18000

21000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)


y = 3.0835x + 274.98

R2 = 0.0121

0

200

400

600

800

1000

1200

1400

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

10th percentile Mean 327 ML, Median 277 ML Standard deviation 271 ML, CV 0.83 • Data is not independent • Increasing trend but not significant • No step-change

0

5

10

15

20

25

30

35

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

50th percentile Mean 8.88 ML, Median 6.82 ML Standard deviation 8.41 ML, CV 0.95 • No trend • No step-change

y = -0.0128x + 1.8842

R2 = 0.003

0

1

2

3

4

5

6

7

8

9

10

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)





y = 20.78x + 5014.4

R2 = 0.0015

0

5000

10000

15000

20000

25000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

Annual total Mean 5370 ML, Median 4890 ML Standard deviation 5130 ML, CV 0.96 • Increasing trend but not significant • No step-change

y = -8.0782x + 585.06

R2 = 0.0305

0

500

1000

1500

2000

2500

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)


y = 0.3297x + 40.775

R2 = 0.0034

0

50

100

150

200

250

300

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)


0.00

0.02

0.04

0.06

0.08

0.10

0.12

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

50th percentile Mean 0.004 ML, Median 0 ML Standard deviation 0.02 ML, CV 5.0 • No trend • No step-change

0

1

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)





y = -82.195x + 24953

R2 = 0.0016

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)


y = -28.492x + 1907.2

R2 = 0.0326

0

1000

2000

3000

4000

5000

6000

7000

8000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)


0

100

200

300

400

500

600

700

800

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

10th percentile Mean 183 ML, Median 149 ML Standard deviation 161 ML, CV 0.88 • No trend • No step-change

y = -0.0808x + 9.6381

R2 = 0.01

0

5

10

15

20

25

30

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)


0

1

2

3

4

5

6

7

8

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

90th percentile Mean 0.85 ML, Median 0.13 ML Standard deviation 1.43 ML, CV 1.68 • Data is not independent • No trend • No step-change



Bancell Brook — Waterous (613007)

y = -30.933x + 4554.2

R2 = 0.0468

0

1000

2000

3000

4000

5000

6000

7000

8000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

Annual total Mean 4030 ML, Median 3820 ML Standard deviation 1380 ML, CV 0.34 • Decreasing trend but not significant • Decreasing step-change


y = -0.4018x + 71.812

R2 = 0.0306

0

20

40

60

80

100

120

140

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

Daily maxima Mean 65 ML, Median 62.4 ML Standard deviation 22.2 ML, CV 0.34 • Decreasing trend but not significant • No step-change

y = -0.0975x + 24.918

R2 = 0.0113

0

5

10

15

20

25

30

35

40

45

50

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)


y = -0.0863x + 9.3896

R2 = 0.0729

0

2

4

6

8

10

12

14

16

18

20

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)




y = -0.0471x + 3.7105

R2 = 0.1166

0

1

2

3

4

5

6

7

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)






Murray River — Baden Powell Water Spout (614006)

y = 1003.9x + 203380

R2 = 0.0052

0

100000

200000

300000

400000

500000

600000

700000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

Annual total Mean 220 000 ML, Median 206 000 ML Standard deviation 131 000 ML, CV 0.60 • Data is not independent • Increasing trend but not significant • No step-change

y = -117.99x + 10813

R2 = 0.0264

0

5000

10000

15000

20000

25000

30000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)


y = 12.149x + 1531.9

R2 = 0.0087

0

1000

2000

3000

4000

5000

6000

7000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

10th percentile Mean 1730 ML, Median 1420 ML Standard deviation 1220 ML, CV 0.71 • Increasing trend but not significant • No step-change

y = 1.7857x + 99.409

R2 = 0.0776

0

50

100

150

200

250

300

350

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

50th percentile Mean 129 ML, Median 125 ML Standard deviation 60.1 ML, CV 0.47 • Increasing trend but not significant • No step-change

0

5

10

15

20

25

30

35

40

45

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)





y = -15.332x + 1786.7

R2 = 0.0203

0

500

1000

1500

2000

2500

3000

3500

4000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)


y = -0.5653x + 96.719

R2 = 0.0084

0

50

100

150

200

250

300

350

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

Daily maxima Mean 87.4 ML, Median 74.8 ML Standard deviation 57.8 ML, CV 0.66 • Data is not independent • Decreasing trend but not significant • No step-change

y = -0.107x + 14.713

R2 = 0.01

0

5

10

15

20

25

30

35

40

45

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)


y = -0.0095x + 0.2768

R2 = 0.2096

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)




0

1

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)





y = 811.86x + 48880

R2 = 0.0433

0

20000

40000

60000

80000

100000

120000

140000

160000

180000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

Annual total Mean 62 300 ML, Median 61 600 ML Standard deviation 36 600 ML, CV 0.59 • Increasing trend


y = -68.677x + 4744.9

R2 = 0.029

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)


y = 7.7572x + 340.58

R2 = 0.0552

0

200

400

600

800

1000

1200

1400

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

10th percentile Mean 469 ML, Median 404 ML Standard deviation 310 ML, CV 0.66 • Increasing trend


y = 0.9185x + 16.887

R2 = 0.1963

0

20

40

60

80

100

120

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

50th percentile Mean 32 ML, Median 26.9 ML Standard deviation 19.4 ML, CV 0.61 • Data is not independent • Increasing trend


y = 0.0488x + 0.6169

R2 = 0.082

0

1

2

3

4

5

6

7

8

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

90th percentile Mean 1.42 ML, Median 0.98 ML Standard deviation 1.6 ML, CV 1.13 • Increasing trend





y = -135.86x + 45534

R2 = 0.0047

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

100000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

Annual total Mean 43 200 ML, Median 37 200 ML Standard deviation 19 100 ML, CV 0.44 • Data is not independent • Decreasing trend but not significant • No step-change

y = -26.1x + 2132.3

R2 = 0.1239

0

500

1000

1500

2000

2500

3000

3500

4000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)


y = -0.3243x + 353.53

R2 = 0.0003

0

100

200

300

400

500

600

700

800

900

1000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)


y = -0.3418x + 29.377

R2 = 0.0612

0

10

20

30

40

50

60

70

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)


y = -0.0121x + 0.6691

R2 = 0.011

0

1

2

3

4

5

6

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

90th percentile Mean 0.46 ML, Median 0 ML Standard deviation 1.12 ML, CV 2.43 • Data is not independent • Decreasing trend but not significant • No step-change




y = -59.319x + 5767.9

R2 = 0.0132

0

5000

10000

15000

20000

25000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

Annual total Mean 4760 ML, Median 2330 ML Standard deviation 5000 ML, CV 1.05 • Data is not independent • Decreasing trend but not significant • No step-change

y = -8.5162x + 420.18

R2 = 0.0859

0

200

400

600

800

1000

1200

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)


y = -0.3847x + 44.599

R2 = 0.008

0

20

40

60

80

100

120

140

160

180

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)


y = -0.0083x + 0.3933

R2 = 0.0246

0.0

0.5

1.0

1.5

2.0

2.5

3.0

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

50th percentile Mean 0.25 ML, Median 0 ML Standard deviation 0.51 ML, CV 2.04 • Data is not independent • Decreasing trend but not significant • No step-change

0

1

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)





y = -85.832x + 13659

R2 = 0.2529

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

Annual total Mean 12 200 ML, Median 11 800 ML Standard deviation 1650 ML, CV 0.14 • Decreasing trend



y = -3.5652x + 237.26

R2 = 0.278

0

50

100

150

200

250

300

350

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)

Daily maxima Mean 177 ML, Median 159 ML Standard deviation 65.4 ML, CV 0.37 • Decreasing trend



y = -0.3239x + 61.41

R2 = 0.0969

0

10

20

30

40

50

60

70

80

90

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)



y = -0.1146x + 30.606

R2 = 0.1414

0

5

10

15

20

25

30

35

40

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)



y = -0.0853x + 16.051

R2 = 0.1449

0

5

10

15

20

25

1975 1980 1985 1990 1995 2000 2005

Stre

amflo

w (

ML)





Appendix B: Decadal flow duration curves

For each gauging station, flow duration curves are shown and median (Q50), high-flow (Q10/Q50) and low-flow (Q90/Q50) indices are tabulated for each decade. If a dash is shown for the ratio, this indicates that the median flow is equal to zero. If the low-flow index is zero then the site ceases to flow at or below 90 per cent of the time. The closer the low-flow index is to one, the smaller the variability in the baseflow. The larger the high-flow index, the larger the range of variability in the mid to high flows.


1

10

100

1000

10000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly s

trea

mflo

w (

ML)

1970-19761977-19861987-19961997-2005

Q50 Q10/Q50 Q90/Q50

1957–1966

1967–1976 8.0 4.2 0.5

1977–1986 7.8 6.2 0.6

1987–1996 8.3 4.9 0.6

1997–2006 9.0 3.8 0.6


0.01

0.1

1

10

100

1000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly s

trea

mflo

w (

ML)

1964-19661967-1976 1977-19861987-19961997-2004

Q50 Q10/Q50 Q90/Q50

1957–1966 2.9 5.4 0.1

1967–1976 4.4 4.8 0.2

1977–1986 5.0 5.4 0.3

1987–1996 6.4 4.2 0.4

1997–2006 5.1 3.5 0.4

Denmark River — Kompup (603173/003)

0.01

0.1

1

10

100

1000

10000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly s

trea

mflo

w (

ML)

1964-19661967-1976 1977-19861987-19961997-2004

Q50 Q10/Q50 Q90/Q50

1957–1966

1967–1976 0.0 - -

1977–1986 0.4 200.4 0.0

1987–1996 1.4 68.8 0.0

1997–2006 0.3 121.8 0.0


0.01

0.1

1

10

100

1000

10000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly s

trea

mflo

w (

ML)

1961-19661967-1976 1977-19861987-19961997-2006

Q50 Q10/Q50 Q90/Q50

1957–1966 9.8 24.4 0.0

1967–1976 8.9 22.2 0.01

1977–1986 8.0 30.1 0.03

1987–1996 11.4 19.6 0.03

1997–2006 6.4 19.9 0.02




0.01

0.1

1

10

100

1000

10000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly s

trea

mflo

w (

ML)

1963-19661967-1976 1977-19861987-19961997-2004

Q50 Q10/Q50 Q90/Q50

1957–1966 0.1 491.8 0.0

1967–1976 0.2 214.7 0.0

1977–1986 0.2 177.8 0.0

1987–1996 0.6 70.9 0.02

1997–2006 0.4 41.0 0.0


0.1

1

10

100

1000

10000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly s

trea

mflo

w (

ML)

1957-19661967-1976 1977-19861987-19961997-2004

Q50 Q10/Q50 Q90/Q50

1957–1966 14.4 50.3 0.03

1967–1976 23.5 29.7 0.07

1977–1986 22.8 24.9 0.1

1987–1996 32.2 23.7 0.07

1997–2006 16.9 27.2 0.1


1

10

100

1000

10000

100000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly s

trea

mflo

w (

ML)

1952-19561957-19661967-1976 1977-19861987-19961997-2005

Q50 Q10/Q50 Q90/Q50

1957–1966 69.5 18.2 0.2

1967–1976 59.0 22.0 0.1

1977–1986 64.4 14.1 0.1

1987–1996 80.3 18.6 0.08

1997–2006 81.9 16.3 0.07


0.01

0.1

1

10

100

1000

10000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly s

trea

mflo

w (

ML)

1964-19661967-1976 1977-19861987-19961997-2005

Q50 Q10/Q50 Q90/Q50

1957–1966 32.9 16.6 0.0

1967–1976 31.0 16.5 0.0

1977–1986 15.8 21.5 0.0

1987–1996 15.2 25.5 0.0

1997–2006 10.2 32.6 0.0

Perup River — Quabicup Hill (607145/004)

0.001

0.01

0.1

1

10

100

1000

10000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly s

trea

mflo

w (

ML)

1961-1966 (4)1967-1976 (7)1977-19861987-19961997-2006

Q50 Q10/Q50 Q90/Q50

1957–1966 19.7 12.8 0.04

1967–1976 9.4 12.2 0.1

1977–1986 7.7 9.3 0.2

1987–1996 9.0 13.9 0.09

1997–2006 5.4 13.7 0.07



Lefroy Brook — Pemberton Weir & Rainbow Trail (6070 09/013)

0.01

0.1

1

10

100

1000

10000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly s

trea

mflo

w (

ML)

1957-1966 (3)1967-1976 (4)1977-19861987-19961997-2006

Q50 Q10/Q50 Q90/Q50

1957–1966 77.2 6.4 0.2

1967–1976 77.7 5.8 0.2

1977–1986 52.5 6.0 0.04

1987–1996 37.2 8.2 0.09

1997–2006 34.5 7.4 0.08


0.01

0.1

1

10

100

1000

10000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly s

trea

mflo

w (

ML)

1962-1966 (4)1967-1976 (9)1977-19861987-19961997-2006

Q50 Q10/Q50 Q90/Q50

1957–1966 25.5 14.0 0.04

1967–1976 19.1 13.6 0.02

1977–1986 17.2 10.6 0.01

1987–1996 13.8 19.8 0.01

1997–2006 6.7 24.5 0.03

Warren River — Barker Rd Crossing (607008/220)

1

10

100

1000

10000

100000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly s

trea

mflo

w (

ML)

1957-1966 (0)1967-1976 1977-19861987-19961997-2006

Q50 Q10/Q50 Q90/Q50

1957–1966 456.8 6.6 0.2

1967–1976 251.7 10.5 0.1

1977–1986 201.9 8.8 0.1

1987–1996 184.2 13.4 0.1

1997–2006 168.5 11.2 0.1

Carey Brook — Staircase Road (608147/002)

0.1

1

10

100

1000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly s

trea

mflo

w (

ML)

1961-1966 (2)1967-1976 (9)1977-19861987-19961997-2006

Q50 Q10/Q50 Q90/Q50

1957–1966 13.4 7.1 0.3

1967–1976 9.6 6.9 0.2

1977–1986 11.9 3.7 0.3

1987–1996 10.7 4.4 0.3

1997–2006 8.5 4.6 0.3


0.01

0.1

1

10

100

1000

10000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly s

trea

mflo

w (

ML)

1957-1966 (6)1967-1976 1977-19861987-19961997-2006

Q50 Q10/Q50 Q90/Q50

1957–1966 113.0 11.6 0.1

1967–1976 90.6 11.9 0.1

1977–1986 68.8 11.3 0.1

1987–1996 56.5 17.6 0.05

1997–2006 39.2 17.9 0.03




1

10

100

1000

10000

100000

1000000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly s

trea

mflo

w (

ML)

1957-1966 (4)1967-19761977-19861987-19961997-2006

Q50 Q10/Q50 Q90/Q50

1957–1966 423.5 16.2 0.1

1967–1976 226.5 23.9 0.1

1977–1986 256.2 12.6 0.1

1987–1996 425.3 12.0 0.1

1997–2006 273.1 14.7 0.2


0.01

0.1

1

10

100

1000

10000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly s

trea

mflo

w (

ML)

1971-1976 1977-19861987-19961997-2006

Q50 Q10/Q50 Q90/Q50

1957–1966

1967–1976 70.8 13.8 0.0

1977–1986 64.0 10.8 0.0

1987–1996 39.0 19.7 0.0

1997–2006 21.9 26.7 0.0


0.01

0.1

1

10

100

1000

10000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly s

trea

mflo

w (

ML)

1973-19761977-19861987-19961997-2006

Q50 Q10/Q50 Q90/Q50

1957–1966

1967–1976 6.4 16.5 0.0

1977–1986 4.4 15.3 0.0

1987–1996 4.6 16.0 0.0

1997–2006 3.2 20.6 0.0


0.01

0.1

1

10

100

1000

10000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly s

trea

mflo

w (

ML)

1974-19761977-19861987-19961997-2006

Q50 Q10/Q50 Q90/Q50

1957–1966

1967–1976 5.8 41.6 0.0

1977–1986 4.8 40.5 0.0

1987–1996 1.8 118.3 0.0

1997–2006 1.0 157.8 0.01


0.01

0.1

1

10

100

1000

10000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly s

trea

mflo

w (

ML)

1958-19661967-19761977-19861987-19961997-2006

Q50 Q10/Q50 Q90/Q50

1957–1966 1.9 66.1 0.0

1967–1976 1.6 59.5 0.0

1977–1986 3.2 21.7 0.0

1987–1996 3.6 29.9 0.0

1997–2006 1.4 48.5 0.0




0.01

0.1

1

10

100

1000

10000

100000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly s

trea

mflo

w (

ML)

1969-19761977-19861987-19961997-2006

Q50 Q10/Q50 Q90/Q50

1957–1966

1967–1976 2.2 128.6 0.0

1977–1986 7.0 30.7 0.02

1987–1996 15.7 28.3 0.1

1997–2006 3.0 80.0 0.2


0.01

0.1

1

10

100

1000

10000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly s

trea

mflo

w (

ML)

1977-19861987-19961997-2006

Q50 Q10/Q50 Q90/Q50

1957–1966

1967–1976

1977–1986 0.0 - -

1987–1996 0.0 - -

1997–2006 0.0 - -


0.01

0.1

1

10

100

1000

10000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly s

trea

mflo

w (

ML)

1957-1966 (2)1967-1976 (5)1977-19861987-19961997-2006

Q50 Q10/Q50 Q90/Q50

1957–1966 8.8 31.3 0.1

1967–1976 4.6 55.0 0.03

1977–1986 11.9 10.8 0.01

1987–1996 7.4 34.3 0.0

1997–2006 3.5 34.2 0.0

Bancell Brook — Waterous (613013/007)

0.1

1

10

100

1000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly s

trea

mflo

w (

ML)

1957-1966 (4)1967-19761977-19861987-19961997-2006

Q50 Q10/Q50 Q90/Q50

1957–1966 10.1 3.3 0.2

1967–1976 8.6 3.2 0.3

1977–1986 8.0 3.1 0.3

1987–1996 8.7 3.5 0.3

1997–2006 5.2 3.9 0.4


0.1

1

10

100

1000

10000

100000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly s

trea

mflo

w (

ML)

1967-1976 (7)1977-19861987-19961997-2006

Q50 Q10/Q50 Q90/Q50

1957–1966

1967–1976 166.3 13.7 0.1

1977–1986 90.8 12.9 0.05

1987–1996 145.1 15.1 0.1

1997–2006 111.0 13.1 0.1




0.01

0.1

1

10

100

1000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly s

trea

mflo

w (

ML)

1957-1966 (6)1967-19761977-19861987-19961997-2006

Q50 Q10/Q50 Q90/Q50

1957–1966 2.8 14.5 0.0

1967–1976 1.1 33.1 0.0

1977–1986 0.1 120.8 0.0

1987–1996 0.04 421.8 0.0

1997–2006 0.0 - -


0.01

0.1

1

10

100

1000

10000

100000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly s

trea

mflo

w (

ML)

1967-1976 (6)1977-19861987-19961997-2006

Q50 Q10/Q50 Q90/Q50

1957–1966

1967–1976 32.2 16.9 0.02

1977–1986 22.1 12.8 0.01

1987–1996 32.0 18.3 0.01

1997–2006 33.5 14.3 0.1


0.01

0.1

1

10

100

1000

10000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly s

trea

mflo

w (

ML)

1967-1976 (7)1977-19861987-19961997-2006

Q50 Q10/Q50 Q90/Q50

1957–1966

1967–1976 23.8 17.4 0.0

1977–1986 20.2 15.3 0.0

1987–1996 35.0 11.6 0.0

1997–2006 13.7 20.4 0.0


0.01

0.1

1

10

100

1000

10000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly s

trea

mflo

w (

ML)

1967-19761977-19861987-19961997-2006

Q50 Q10/Q50 Q90/Q50

1957–1966

1967–1976 0.0 - -

1977–1986 0.0 - -

1987–1996 0.0 - -

1997–2006 0.0 - -


1

10

100

1000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


Dai

ly s

trea

mflo

w (

ML)

1958-1966 (7)1967-1976 (7)1977-19861987-19961997-2005

Q50 Q10/Q50 Q90/Q50

1957–1966 30.2 2.4 0.4

1967–1976 35.0 2.0 0.5

1977–1986 28.0 2.1 0.5

1987–1996 29.8 2.0 0.5

1997–2006 27.4 1.9 0.5



Appendix C: Monthly streamflow distributions


0

500

1000

1500

2000

2500

Jan

Feb

Mar

Apr

May Jun

Jul

Aug

Sep Oct

Nov

Dec

Str

eam

flow

(M

L)

Average monthly streamflow 1975 to 1996 Average monthly streamflow 1997 to 2005


0

200

400

600

800

1000

1200

Jan

Feb

Mar

Apr

May Jun

Jul

Aug

Sep Oct

Nov

Dec

Str

eam

flow

(M

L)


Denmark River — Kompup (603003)

0

500

1000

1500

2000

2500

3000

3500

4000

Jan

Feb

Mar

Apr

May Jun

Jul

Aug

Sep Oct

Nov

Dec

Str

eam

flow

(M

L)



0

1000

2000

3000

4000

5000

6000

7000

8000

9000

Jan

Feb

Mar

Apr

May Jun

Jul

Aug

Sep Oct

Nov

Dec

Str

eam

flow

(M

L)



0

200

400

600

800

1000

1200

1400

1600

Jan

Feb

Mar

Apr

May Jun

Jul

Aug

Sep Oct

Nov

Dec

Str

eam

flow

(M

L)



0

5000

10000

15000

20000

25000

Jan

Feb

Mar

Apr

May Jun

Jul

Aug

Sep Oct

Nov

Dec

Str

eam

flow

(M

L)





0

5000

10000

15000

20000

25000

30000

35000

40000

45000

50000Ja

n

Feb

Mar

Apr

May Jun

Jul

Aug

Sep Oct

Nov

Dec

Str

eam

flow

(M

L)



0

2000

4000

6000

8000

10000

12000

14000

Jan

Feb

Mar

Apr

May Jun

Jul

Aug

Sep Oct

Nov

Dec

Str

eam

flow

(M

L)


Perup River — Quabicup Hill (607004)

0

500

1000

1500

2000

2500

3000

3500

4000

4500

Jan

Feb

Mar

Apr

May Jun

Jul

Aug

Sep Oct

Nov

Dec

Str

eam

flow

(M

L)


Lefroy Brook — Pemberton Weir & Rainbow Trail (6070 09/013)

0

2000

4000

6000

8000

10000

12000

Jan

Feb

Mar

Apr

May Jun

Jul

Aug

Sep Oct

Nov

Dec

Str

eam

flow

(M

L)



0

1000

2000

3000

4000

5000

6000

7000

8000

9000

Jan

Feb

Mar

Apr

May Jun

Jul

Aug

Sep Oct

Nov

Dec

Str

eam

flow

(M

L)


Warren River — Barker Rd Crossing (607220)

0

10000

20000

30000

40000

50000

60000

70000

80000

Jan

Feb

Mar

Apr

May Jun

Jul

Aug

Sep Oct

Nov

Dec

Str

eam

flow

(M

L)


Carey Brook — Staircase Road (608002)

0

200

400

600

800

1000

1200

1400

1600

Jan

Feb

Mar

Apr

May Jun

Jul

Aug

Sep Oct

Nov

Dec

Str

eam

flow

(M

L)



0

5000

10000

15000

20000

25000

30000

35000

Jan

Feb

Mar

Apr

May Jun

Jul

Aug

Sep Oct

Nov

Dec

Str

eam

flow

(M

L)





0

20000

40000

60000

80000

100000

120000

140000

160000

180000

200000Ja

n

Feb

Mar

Apr

May Jun

Jul

Aug

Sep Oct

Nov

Dec

Str

eam

flow

(M

L)



0

5000

10000

15000

20000

25000

30000

Jan

Feb

Mar

Apr

May Jun

Jul

Aug

Sep Oct

Nov

Dec

Str

eam

flow

(M

L)



0

500

1000

1500

2000

2500

3000

3500

4000

Jan

Feb

Mar

Apr

May Jun

Jul

Aug

Sep Oct

Nov

Dec

Str

eam

flow

(M

L)



0

1000

2000

3000

4000

5000

6000

7000

8000

9000

Jan

Feb

Mar

Apr

May Jun

Jul

Aug

Sep Oct

Nov

Dec

Str

eam

flow

(M

L)



0

500

1000

1500

2000

2500

3000

3500

4000

Jan

Feb

Mar

Apr

May Jun

Jul

Aug

Sep Oct

Nov

Dec

Str

eam

flow

(M

L)



0

2000

4000

6000

8000

10000

12000

14000

Jan

Feb

Mar

Apr

May Jun

Jul

Aug

Sep Oct

Nov

Dec

Str

eam

flow

(M

L)



0

500

1000

1500

2000

2500

Jan

Feb

Mar

Apr

May Jun

Jul

Aug

Sep Oct

Nov

Dec

Str

eam

flow

(M

L)



0

1000

2000

3000

4000

5000

6000

7000

8000

9000

Jan

Feb

Mar

Apr

May Jun

Jul

Aug

Sep Oct

Nov

Dec

Str

eam

flow

(M

L)




Bancell Brook — Waterous (613007)

0

100

200

300

400

500

600

700

800

900Ja

n

Feb

Mar

Apr

May Jun

Jul

Aug

Sep Oct

Nov

Dec

Str

eam

flow

(M

L)



0

10000

20000

30000

40000

50000

60000

70000

80000

Jan

Feb

Mar

Apr

May Jun

Jul

Aug

Sep Oct

Nov

Dec

Str

eam

flow

(M

L)



0

100

200

300

400

500

600

700

Jan

Feb

Mar

Apr

May Jun

Jul

Aug

Sep Oct

Nov

Dec

Str

eam

flow

(M

L)



0

5000

10000

15000

20000

25000

Jan

Feb

Mar

Apr

May Jun

Jul

Aug

Sep Oct

Nov

Dec

Str

eam

flow

(M

L)



0

2000

4000

6000

8000

10000

12000

14000

16000

Jan

Feb

Mar

Apr

May Jun

Jul

Aug

Sep Oct

Nov

Dec

Str

eam

flow

(M

L)



0

500

1000

1500

2000

2500

Jan

Feb

Mar

Apr

May Jun

Jul

Aug

Sep Oct

Nov

Dec

Str

eam

flow

(M

L)



0

500

1000

1500

2000

2500

Jan

Feb

Mar

Apr

May Jun

Jul

Aug

Sep Oct

Nov

Dec

Str

eam

flow

(M

L)




References

Berti, ML, Bari, MA, Charles, SP & Hauck, EJ 2004, Climate change, catchment runoff and risks to water supply in the south-west of Western Australia, Department of Environment, Western Australia.

Chiew, F & Siriwardena, L 2005, Trend user guide, trend/change detection software, CRC for Catchment Hydrology, Australia.

Chiew, F 2006, ‘An overview of methods for estimating climate change impact on runoff’, 30th Hydrology and Water Resources Symposium, Tasmania.

Commonwealth Scientific and Industrial Research Organisation 2007, Climate change in Australia, CSIRO.

CSIRO – see Commonwealth Scientific and Industrial Research Organisation

Department of Water 2007, REG75 – A tool to estimate mean annual flow for the south west of Western Australia, Department of Water, Western Australia.

DoW – see Department of Water

Intergovernmental Panel on Climate Change 2001, Climate change 2001: The scientific basis: Contribution of Working Group I to the third assessment report of the Intergovernmental Panel on Climate Change, Intergovernmental Panel on Climate Change.

IPCC – see Intergovernmental Panel on Climate Change

Kitsios, A, Bari, MA & Charles, SP 2008, Projected impacts of climate change on the Serpentine catchment – Downscaling from multiple general circulation models, Department of Water, Western Australia.

Kundzewicz, ZW & Robson, AJ 2004, ‘Change detection in hydrological records – a review of the methodology’, Hydrological Sciences, 49 (1), February 2004, pp 7–19.

Kundzewicz, ZW, Graczyk, D, Maurer, T, Przymusińska, I, Radziejewski, M, Svensson, C & Szwed, M 2004, Detection of change in world-wide hydrological time series of maximum annual flow, Global Runoff Data Centre report series no. 32, Germany.

Lindsay, R, Johnson, S & Pigois, J-P 2007, Pre-investigation review of the Gingin Brook project, Hydrogeology report series no. HR258, Department of Water, Western Australia.



Livezey, RE, Vinnikov, KY, Timofeyeva, MM, Tinker, R & van den Dool, HM 2007, ‘Estimation and extrapolation of climate normals and climatic trends’, Journal of Applied Meteorology and Climatology, November 2007, pp 1759–1776.

Rodgers, S & Ruprecht, J 1999, The effect of climate variability on streamflow in south western Australia, Surface water hydrology series no. SWH 25, Water and Rivers Commission, Western Australia.

Sinclair Knight Merz 2007, Impacts of farm dams in seven catchments in Western Australia, Sinclair Knight Merz, Australia.

Water Corporation 2005, Integrated water supply scheme source development plan 2005; planning horizon 2005–2050, Water Corporation, Western Australia.

WC – see Water Corporation

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