The Swan-Canning Estuary in 2050 - oceans.uwa.edu.au · The Swan-Canning Estuary in 2050 Baseline...

The Swan-Canning Estuary in 2050

Baseline Emily Barnes Pritam Patil Shaokun Song Yunhan Wang Chris Whitwell Sarah Rui Zang Extreme Pablo Islas Arrioja Marley Butler Harrison Davis Zhenheng Fu Anthony Joseph Qing Zhang Jiakang Zhao Management Holly Child

Xiaolin Hu

Han Jiang Sarah McCulloch Yiru Shi Aneesh Toolsee Patrick Zhang

UWA Environmental Engineering Design 5552 Class (2018)

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ACKNOWLEDGEMENTS

This research was partially supported by RiverLab, a collaboration between

Woodside and the University of Western Australia.

We would like to thank the following people for their insight and time which all greatly

assisted in research for this project:

Mark Cugley - Rivers Estuaries Division, DBCA

Catherine Thomson – Department of Water and Environmental Regulation

Anas Ghadouani - CRC for Water Sensitive Cities

Peta Kelsey - Department of Water and Environmental Regulation

Kerry Trayler - Rivers Estuaries Division, DBCA

Greg Ryan - CRC for Water Sensitive Cities

Joanne Woodbridge - Eastern Metropolitan Regional Council

Karl Henning - Department of Water and Environmental Regulation

In particular, we would also like to thank Dr Matt Hipsey and Dr Peishing Huang (UWA) for access to and for assistance with the Swan Canning Estuarine Response Model who, without their help, this project would not have been possible. As well as Dr Gregory Ivey (UWA) for his vital guidance and leadership throughout this project.

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Table of Contents

Acknowledgements .................................................................................................................................. i

List of Figures ......................................................................................................................................... iv

List of Tables ........................................................................................................................................ viii

Executive Summary ................................................................................................................................ ix

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

2 Background ..................................................................................................................................... 3

2.1 The Swan-Canning Estuary System ......................................................................................... 3

2.2 Population Changes ................................................................................................................ 6

2.3 Management and Policy ......................................................................................................... 6

2.4 Water Quality Indicators ......................................................................................................... 8

2.4.1 Dissolved Oxygen ............................................................................................................ 8

2.4.2 Salinity ............................................................................................................................. 9

2.4.3 Phytoplankton and Chlorophyll-a ................................................................................. 10

2.5 Literature Review .................................................................................................................. 11

2.5.1 Nutrient Loads ............................................................................................................... 11

2.5.2 Climate Change ............................................................................................................. 12

2.6 The Swan-Canning Estuary Response Model ........................................................................ 14

3 Methods ........................................................................................................................................ 16

3.1 Description of SCERM ........................................................................................................... 16

3.1.1 Boundary Conditions and Anthropogenic Inputs .......................................................... 17

3.2 Aims and Outcomes .............................................................................................................. 18

3.3 Reference Conditions ............................................................................................................ 21

3.3.1 Catchment Conditions in 2008 ...................................................................................... 21

3.3.2 Simulation 1: The Characteristic Baseline ..................................................................... 24

3.3.3 Simulation 2: 2050 Baseline (No Oxygenation) ............................................................ 26

3.4 Extreme Simulations ............................................................................................................. 32

3.4.1 The Runs from the Extreme conditions group .............................................................. 32

3.4.2 Method for determining “2050 high extreme” and “2050 low extreme” variables .... 34

3.4.3 Key Parameters ............................................................................................................. 40

3.5 Management Simulations ..................................................................................................... 41

3.5.1 Current Oxygenation Strategy ...................................................................................... 41

3.5.2 Enhanced Oxygenation Strategy ................................................................................... 45

3.5.3 Nutrient Reduction ....................................................................................................... 48

4 Results and Analysis ...................................................................................................................... 52

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4.1 Reference Conditions ............................................................................................................ 52


4.2.1 2050 Low and High Extreme (sim 4 and 5) ................................................................... 57

4.2.2 Year 2050 Summer Flood Event (sim 6) ........................................................................ 65

4.2.3 “Mean Year 2050” No Inflow (sim 7) ............................................................................ 71


4.3.1 Oxygenation (sim 3 and 8) ............................................................................................ 79

4.3.2 Nutrients Reduction (sim 9) .......................................................................................... 89

4.3.3 Future Work .................................................................................................................. 90

4.3.3.1 Zero Sediment Flux ........................................................................................................ 90

4.3.3.2 Banking in Constructed Wetlands ................................................................................. 91

5 Discussion ...................................................................................................................................... 94

5.1 Reference Simulations .......................................................................................................... 94


5.2.1 Saltwater intrusion and stratification ........................................................................... 96

5.2.2 Increase severity and occurrence of low DO events ..................................................... 97


5.3.1 Oxygenation .................................................................................................................. 98

5.3.2 Nutrients Reduction ...................................................................................................... 99

6 Conclusions ................................................................................................................................. 100

7 References .................................................................................................................................. 101

8 Appendices .................................................................................................................................. 108

Appendix A: List of Acronyms ......................................................................................................... 108

Appendix B: Tributary Boundary Conditions Parameters ............................................................... 109

Appendix C: Flow Normalised Nutrient Duration Curves ............................................................... 110

Appendix D: Flow Normalised Nutrient Concentrations & Scale Factors ....................................... 114

Appendix E: Time Series Plots for Dissolved Oxygen ...................................................................... 115

Appendix E: Time Series Plots for Salinity ....................................................................................... 116

Appendix F: Normalised Nutrient Curves ....................................................................................... 119

Appendix J: “Mean Year 2050” no inflow: Results (sim 7) .............................................................. 123

Dissolved Oxygen Time Series ..................................................................................................... 123

Phytoplankton Time Series ......................................................................................................... 128

Appendix K: Oxygenation (sim 3 and 8) ......................................................................................... 132

Appendix L : Nutrients reduction ( Sim 9) ....................................................................................... 137

Dissolved Oxygen Time Series ..................................................................................................... 137

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LIST OF FIGURES

FIGURE 1: THE CATCHMENT OF THE SWAN AND CANNING ESTUARIES (KELSEY ET AL. 2011). ....................... 1

FIGURE 2: THE SEASONAL STATES OF THE SWAN-CANNING RIVER SYSTEM (SWAN RIVER TRUST 2008). ...... 5

FIGURE 3: POPULATION TREND OF WESTERN AUSTRALIA (AUSTRALIAN BUREAU OF STATISTICS 2014). NOTE:

INDIGENOUS POPULATIONS WERE NOT INCLUDED IN THE CENSUS UNTIL 1961 AND HISTORICAL

POPULATION ESTIMATES IN WESTERN AUSTRALIA DO NOT EXTEND TO 1788, WHEN EUROPEAN

SETTLEMENT FIRST OCCURRED. .................................................................................................... 6

FIGURE 4: AVERAGE ANNUAL TOTAL NITROGEN (LEFT) AND PHOSPHOROUS (RIGHT) LOADS IN TONNES, FROM

THE SWAN CANNING SUB-CATCHMENTS (KELSEY ET AL. 2011). ...................................................... 12

FIGURE 5: THE RESULTS OF THE MODELLING ANALYSIS BY SMITH & POWER (2014), THE OBSERVED PATTERNS

(DARK BLUE) ARE COMPARED TO THE CIMP5 MULTI-MODEL FOR THE SWWA INFLOWS (GL), THE

VARIOUS COLOURED LINES SHOW THE RESULTS OF THE SEVEN MODELS USED AND THE GREY LINES

INDICATE THE ENSEMBLE MAXIMUM, MEAN AND MINIMUMS. .............................................................. 13

FIGURE 6: A MAP OF THE DOMAIN AND ITS CORRESPONDING BOUNDARY CONDITIONS AND VALIDATION SITES

(HIPSEY ET AL. 2014) ................................................................................................................. 16

FIGURE 7 A MAP OF THE FINAL OUTPUT LOCATIONS FOR THE MODEL ........................................................ 17

FIGURE 8: FLOW DURATION CURVE FOR WALYUNGA MONITORING STATION OR “UPPER SWAN RIVER”, AS

GIVEN IN FIGURE 1. TOTAL RECORDED YEARS 1976-2016. DATA FROM THE DEPARTMENT OF WATER

(2018). ..................................................................................................................................... 21

FIGURE 9: RELATIVE FLOW CONTRIBUTIONS TO THE SCE IN 2008. DATA FROM THE DEPARTMENT OF WATER

(2018). ..................................................................................................................................... 22

FIGURE 10: HYDROGRAPH (ORANGE) AND HYETOGRAPH (BLUE) FOR THE SWAN-CANNING ESTUARY IN 2008.

STREAMFLOW DATA FROM THE DEPARTMENT OF WATER (2018) AND RAINFALL DATA FROM DAFWA

SOUTH PERTH MET. STATION. ..................................................................................................... 23

FIGURE 11: FLOW NORMALIZED AVERAGE NUTRIENT CONCENTRATIONS FOR EACH OF THE SCE TRIBUTARIES

IN 2008. FLOW AND NUTRIENT CONCENTRATION DATA FROM DEPARTMENT OF WATER (2018). ........... 23

FIGURE 12: NUTRIENT CONCENTRATION DURATION CURVE FOR WALYUNGA (UPPER SWAN). A FDC METHOD

WAS APPLIED TO NUTRIENT CONCENTRATIONS TO CREATE A NORMALISED CONCENTRATION BY FLOW FOR

EACH OF THE EIGHT SITES. .......................................................................................................... 25

FIGURE 13: 50TH PERCENTILE FLOW NORMALIZED AVERAGE NUTRIENT CONCENTRATIONS FOR EACH OF THE

SCE TRIBUTARIES. FLOW AND NUTRIENT CONCENTRATION DATA FROM DEPARTMENT OF WATER (2018).

................................................................................................................................................ 26

FIGURE 14: AN EXTRACT FROM THE IPCC 5TH ASSESSMENT REPORT OF THE OBSERVED AND PROJECTED

CHANGE IN MEAN SEA LEVEL FOR FREMANTLE, AS ONE OF THE REPRESENTATIVE COASTAL LOCATIONS

(CHURCH ET AL. 2015). THE OBSERVED IN SITU RELATIVE MEAN SEA LEVEL RECORDS FROM TIDE

GAUGES (SINCE 1970) ARE PLOTTED IN YELLOW, AND THE SATELLITE RECORD (SINCE 1993) IS PROVIDED

AS PURPLE LINES. THE PROJECTED RANGE FROM 21 CMIP5 RCP 4.5 SCENARIO RUNS (90%

UNCERTAINTY) IS SHOWN BY THE SHADED REGION FOR THE PERIOD 2006-2100, WITH THE BOLD LINE

SHOWING THE ENSEMBLE MEAN. VERTICAL BAR AT THE RIGHT SIDES OF EACH PANEL REPRESENT THE

ENSEMBLE MEAN AND ENSEMBLE SPREAD (5 TO 95%) OF THE LIKELY (MEDIUM CONFIDENCE) SEA LEVEL

CHANGE AT EACH RESPECTIVE LOCATION AT THE YEAR 2100 INFERRED FROM RCP2.6 (DARK BLUE), 4.5

(LIGHT BLUE), 6.0 (YELLOW) AND 8.5 (RED) ................................................................................... 29

FIGURE 15: THE RESULT OF A 3-POINT MOVING AVERAGE OF DAILY AVERAGE TEMPERATURE AND THE

RESULTING REGRESSION LINE. DATA TAKEN FROM THE PERTH AIRPORT STATION (BOM 2018), THE TIME

GIVEN ON THE X AXIS IS THE NUMBER OF DAYS FROM THE START OF THE RECORD TO PRESENT DAY

(1944-2018). ............................................................................................................................ 30

FIGURE 16: FLOW VOLUMES (GL/DAY) MEASURED AT WALYUNGA CANYON SHOWING THE JANUARY 2000

FLOW EVENT. ADAPTED FROM (ATKINS, ROSE, BROWN, & ROBB, 2001) .......................................... 33

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FIGURE 17: TIME SERIES OF MEDIAN FREMANTLE SEA LEVEL (1986-2050: ONE YEAR RUNNING MEAN). ...... 36

FIGURE 18: HISTORICAL INFLOW TO PERTH DAMS (WATER CORPORATION (2016). ................................... 38

FIGURE 19. THE LOCATIONS OF THE GUILFORD, CAVERSHAM, BACON, CAMSELL AND NICHOLSON ARTIFICIAL

OXYGENATION FACILITIES IN THE SWAN-CANNING ESTUARY (DWER 2015). .................................... 43

FIGURE 20 OXYGEN LEVELS AT THE MIDDLE SWAN BRIDGE FROM JANUARY UNTIL MARCH. AS CAN BE SEEN

FROM THE FIGURE, LEVELS OF OXYGEN FALL BELOW 4MG/L BETWEEN JANUARY 21ST AND FEBRUARY 7TH.

................................................................................................................................................ 45

FIGURE 21 DISSOLVED OXYGEN LEVELS AT NILE STREET ....................................................................... 46

FIGURE 22 LOCATION OF THE ARTIFICIAL OXYGENATION SYSTEMS, INCLUDING THE SIMULATED “ADDITIONAL

OXYGENATION SYSTEM”, IN THE SWAN-CANNING ESTUARY ALONG WITH THE LOCATION OF THE SEVEN

STUDY AREAS. ........................................................................................................................... 47

FIGURE 23 TOTAL NITROGEN AND TOTAL PHOSPHORUS TARGET CONCENTRATIONS FOR THE THIRTY SUB-

CATCHMENTS WITHIN THE SWAN-CANNING ESTUARY (KELSEY ET. AL. 2010). ................................... 50

FIGURE 24: SIMULATION 1 (THE CHARACTERISTIC BASELINE) VS SIMULATION 2 (2050 BASELINE) DISSOLVED

OXYGEN (DO) PLOTS. THE TOP PLOT (A) SHOWS THE VALUES FOR THE SITES IN THE SURFACE LAYER OF

THE DOMAIN. SIMILARLY, THE BOTTOM PLOT (B) SHOWS THE OXYGEN CONCENTRATIONS IN THE BENTHIC

CELLS (BOTTOM LAYER). FROM LEFT TO RIGHT THE X-AXIS SHOWS THE VARIOUS MONITORING SITES

ACROSS THE RIVER, BLACKWALL REACH (BLA), ARMSTRONG SPIT (ARM), HEATHCOTE (HEA), NILE ST

(NIL), ST JOHN OF GOD HOSPITAL (STJ), SUCCESS HILL (SUC) AND MIDDLE SWAN BRIDGE (MSB),

FROM WEST TO EAST OF THE MODEL DOMAIN. THE RED LINE SHOWS THE HYPOXIC THRESHOLD OF 2MG/L.

THE ERRORS BARS SHOW THE MAXIMUM AND MINIMUM VALUES FOR DO CONCENTRATIONS FOR EACH

SITE. ......................................................................................................................................... 52

FIGURE 25: SIMULATION 1 (THE CHARACTERISTIC BASELINE) VS SIMULATION 2 (THE 2050 BASELINE) TIME

SERIES PLOTS OVER ONE YEAR AT MSB (MIDDLE SWAN BRIDGE), IN UPPER SWAN. THE TOP PLOT (A)

SHOWS THE DISSOLVED OXYGEN TIME SERIES AND THE BOTTOM (B) SHOWS THE SALINITY TIME SERIES

PLOT. THE RED LINE SHOWS THE HYPOXIC THRESHOLD VALUE OF 2MG/L. ......................................... 53

FIGURE 26: SIMULATION 1 (THE CHARACTERISTIC BASELINE) VS SIMULATION 2 (THE 2050 BASELINE)

SALINITY PLOTS. THE TOP PLOT (A) SHOWS THE VALUES FOR THE SITES IN THE SURFACE LAYER OF THE

DOMAIN. SIMILARLY, THE BOTTOM PLOT (B) SHOWS THE SALINITY IN THE BENTHIC CELLS (BOTTOM

LAYER). FROM LEFT TO RIGHT THE X-AXIS SHOWS THE VARIOUS MONITORING SITES ACROSS THE RIVER,

BLACKWALL REACH (BLA), ARMSTRONG SPIT (ARM), HEATHCOTE (HEA), NILE ST (NIL), ST JOHN OF

GOD HOSPITAL (STJ), SUCCESS HILL (SUC) AND MIDDLE SWAN BRIDGE (MSB), FROM WEST TO EAST

OF THE MODEL DOMAIN. THE ERRORS BARS SHOW THE MAXIMUM AND MINIMUM VALUES FOR SALINITY

(PSU) FOR EACH SITE. ................................................................................................................. 54

FIGURE 27: SALINITY (PSU) RESULTS FOR UPPER SWAN RIVER. ALL THREE SIMULATION SCENARIOS ARE

DISPLAYED, SHOWN BY THE LOWER EXTREME (SIM 4), MEAN (SIM 2) AND UPPER EXTREME (SIM 5). ...... 58

FIGURE 28: SALINTY (PSU) FOR MIDDLE SWAN RIVER. ALL THREE SIMULATION SCENARIOS ARE DISPLAYED,

SHOWN BY THE LOWER EXTREME (SIM 4), MEAN (SIM 2) AND UPPER EXTREME (SIM 5). ....................... 59

FIGURE 29: SALINTY (PSU) FOR LOWER SWAN RIVER. ALL THREE SIMULATION SCENARIOS ARE DISPLAYED,


FIGURE 30: SALINTY (PSU) BOX PLOTS FOR SURFACE. ALL THREE SIMULATION SCENARIOS ARE DISPLAYED,


FIGURE 31: SALINTY (PSU) BOX PLOTS FOR BOTTOM. ALL THREE SIMULATION SCENARIOS ARE DISPLAYED,


FIGURE 32: DISSOLVED OXYGEN (MG/L) FOR UPPER SWAN RIVER. ALL THREE SIMULATION SCENARIOS ARE


FIGURE 33: DISSOLVED OXYGEN (MG/L) FOR UPPER/ MIDDLE SWAN RIVER. ALL THREE SIMULATION

SCENARIOS ARE DISPLAYED, SHOWN BY THE LOWER EXTREME (SIM 4), MEAN (SIM 2) AND UPPER

EXTREME (SIM 5). ....................................................................................................................... 62

FIGURE 34: DISSOLVED OXYGEN (MG/L) FOR MIDDLE SWAN RIVER. ALL THREE SIMULATION SCENARIOS ARE


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FIGURE 35: DISSOLVED OXYGEN (MG/L) FOR LOWER SWAN RIVER. ALL THREE SIMULATION SCENARIOS ARE


FIGURE 36: DISSOLVED OXYGEN BOX PLOTS FOR SURFACE. ALL THREE SIMULATION SCENARIOS ARE


FIGURE 37: DISSOLVED OXYGEN BOX PLOTS FOR BOTTOM. ALL THREE SIMULATION SCENARIOS ARE


FIGURE 38: OBSERVED FLOW DATA FORM THE UPPER SWAN USED IN SIM 6. ............................................. 65

FIGURE 39. THE SALINITY RESULTS INDICATE A RAPID FLUSHING OF THE SYSTEM AND DECREASE IN

STRATIFICATION UNTIL THE END OF FEBRUARY AS CONDITIONS BEGIN TO RETURN TO NORMAL. THIS

PATTERN IS COMMON TO SIMULATED SITES ALONG THE SWAN CANNING ........................................... 66

FIGURE 40. THE OBSERVED MEASUREMENTS OF SALINITY STRONGLY MATCH THE RAPID FLUSHING AND SLOW

RECOVERY OF SALINE WATERS SEEN IN THE MODELLED SCENARIO (FIGURE 33) (DWER 2018). ......... 67

FIGURE 41. TOTAL PHOSPHOROUS FOR STJ DID NOT REACH EXCESSIVE VALUES THAT WOULD SUGGEST A

EUTROPHIED SYSTEM NOR WOULD IT SUGGEST A BLOOM IS PARTICULARLY LIKELY. ............................ 68

FIGURE 42. OBSERVED DATA FOR TOTAL PHOSPHOROUS TAKEN AT VARIED DEPTHS (DWER 2018) ........... 69

FIGURE 43.TOTAL CHLOROPHYLL-A COUNTERINTUITIVELY DECREASES AS CONDITIONS FOR ALGAL GROWTH

IMPROVES, AND DECREASES FURTHER POST FLOOD EVENT. ............................................................ 69

FIGURE 44. LOW LEVELS OF TCHLA WERE SIMULATED RELATIVE TO OBSERVED DATA TAKEN IN THE SAME

PERIOD (DWER 2018) ............................................................................................................... 70

FIGURE 45. RAPID DECLINE IN BOTTOM DISSOLVED OXYGEN (DOTTED LINE) MAY SEEM TO INDICATE

ANAEROBIC DECOMPOSITION OF ALGAL BIOMASS. HOWEVER, IT IS MORE LIKELY THAT THIS IS DUE TO

SALINITY STRATIFICATION OR OTHER EXTERNAL FORCINGS AS ONLY A SMALL AMOUNT OF BIOMASS

(TCHLA) WAS SIMULATED. ........................................................................................................... 71

FIGURE 46: NILE STREET SALINITY TIME SERIES (MIDDLE SWAN RIVER). NOTE THE INITIAL DRAMATIC

INCREASE IN SIMULATED SALINITY (RED) APPROACHING 2008 LEVELS (BLUE) BY FEBUARY DESPITE AN

ARTIFICALLY LOWER INITIAL SALINITY DUE TO MODELLING ERROR. .................................................... 72

FIGURE 47: SALINITY TIME SERIES FOR ST JOHN HOSPITAL IN THE MIDDLE SWAN. AS THE INITIAL 2008

SALINITY IS NEAR THE ERRONEOUS INITIAL 20 PSU OF THE SIMULATION THE ST JOHN HOSPITAL

PROVIDES THE ONLY ACCURATE INDICATION OF EARLY SALINITY RISE AND DEVIATION DUE TO SPRING

RAINFALL. .................................................................................................................................. 72

FIGURE 48: BOX PLOT OF BOTTOM SALINITY THROUGHOUT THE EXTENT OF THE SWAN RIVER FOR THE “MEAN

2050” RUN (BLUE) AND THE “NO INFLOW 2050” RUN (RED). ............................................................. 73

FIGURE 49: THE SIMULATED SALINITY REACHES A PLATEAU AT NEAR MARINE LEVELS, THEN, AT THE END OF

THE SIMULATION, IS SEEN EXCEEDING THIS CAP. ............................................................................ 74

FIGURE 50: SUCCESS HILL DO LEVELS. NOTE THE SEVERE LOW DO LEVELS (<2MG/L) IN SIM 1 BOTTOM IN

FEBRUARY AND FROM APRIL TO JULY. .......................................................................................... 76

FIGURE 51: NILE STREET DO LEVELS. NOTE THE SEVERE LOW DO LEVELS IN SIM 1 BOTTOM. .................... 76

FIGURE 52: SUCCESS HILL DYNO CONCENTRATIONS. ............................................................................ 78

FIGURE 53: BLACKWALL REACH DYNO CONCENTRATIONS. ..................................................................... 78

FIGURE 54. OXYGEN CONCENTRATIONS RECORDED AT NILE STREET FOR SIMULATION 2, SIMULATION 3 AND

SIMULATION 8. ........................................................................................................................... 80

FIGURE 55 OXYGEN CONCENTRATIONS RECORDED AT ST JOHN HOSPITAL FOR SIMULATION 2, SIMULATION 3

AND SIMULATION 8. .................................................................................................................... 80

FIGURE 56 OXYGEN CONCENTRATIONS RECORDED AT SUCCESS HILL FOR SIMULATION 2, SIMULATION 3 AND

SIMULATION 8. ........................................................................................................................... 81

FIGURE 57 OXYGEN CONCENTRATIONS RECORDED AT MIDDLE SWAN BRIDGE FOR SIMULATION 2, SIMULATION

3 AND SIMULATION 8. ................................................................................................................. 81

FIGURE 58 BOX PLOT REPRESENTING THE CONCENTRATION OF OXYGEN RECORDED AT THE SURFACE WATER

LAYER OVER JANUARY 2050 TO MARCH 2050 FOR THE SEVEN STUDY AREAS. .................................. 82

FIGURE 59. BOX PLOT REPRESENTING THE CONCENTRATION OF OXYGEN RECORDED AT THE BOTTOM WATER

LAYER OVER JANUARY 2050 TO MARCH 2050 FOR THE SEVEN STUDY AREAS. .................................. 82

FIGURE 60 DISCREPANCIES IN SALINITY AS A RESULT OF INCONSISTENT RUN-UP TIME. .............................. 84

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FIGURE 61 OPERATIONAL SCENARIO SIMULATIONS WITH 2010 FLOW REGIME AND COMPUTED BY AVERAGING

OVER 12 MONTHS (DOW 2015). .................................................................................................. 87

FIGURE 62 DISSOLVED OXYGEN CONCENTRATION IN NILE STREET REGION .............................................. 88

FIGURE 63 DISSOLVED OXYGEN LEVELS AT MIDDLE SWAN BRIDGE ......................................................... 89

FIGURE 64 DISSOLVED OXYGEN AT SUCCESS HILL ............................................................................... 90

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LIST OF TABLES

TABLE 1: SIMULATION SUMMARY TABLE ............................................................................................................... IX

TABLE 2 OXYGENATION CLASSIFICATIONS FOR THE SWAN-CANNING ESTUARY (HIPSEY ET. AL 2014)............. 8

TABLE 3: SIMULATIONS OVERVIEW ...................................................................................................................... 20

TABLE 4: MEDIAN GLOBAL MSLR PROJECTIONS RELATIVE TO SEA LEVEL AVERAGED OVER 1986-2005). 2050

AND 2100 PROJECTION ARE ADOPTED FROM (CHURCH, 2013). 2150 PROJECTION IS FROM LINEAR

EXTRAPOLATION. ......................................................................................................................................... 35

TABLE 5: MEDIAN SLR PROJECTIONS (M) RELATIVE TO MSL AVERAGED OVER 1986-2005. 2008 AND 2050

PROJECTIONS ARE FROM LINEAR REGRESSION........................................................................................... 36

TABLE 6: SUMMARY OF RESULTS FROM TEMPERATURE REGRESSION AND VARIABILITY ANALYSIS. THE FINAL

PREDICTED MEAN, LOWER AND UPPER BOUND OFF SET VALUES ARE SHOWN IN RED. ............................... 37

TABLE 7: SUMMARY OF RESULTS FROM FLOW VOLUME REGRESSION AND VARIABILITY ANALYSIS. THE FINAL

PREDICTED MEAN, LOWER AND UPPER BOUND SCALING VALUES ARE SHOWN IN RED. .............................. 39

TABLES 8: SCALING FACTORS FOR BOTH NITROGEN AND PHOSPHOROUS APPLIED TO THE 2008

CONCENTRATION DATA TO ACHIEVE 10% AND 90% PROBABILITY OF EXCEEDANCE AT ALL SITES. .......... 40

TABLE 9. OXYGENATION CLASSIFICATIONS FOR THE SWAN-CANNING ESTUARY (HIPSEY ET. AL 2014). ......... 44

TABLE 10 DESCRIPTION OF ALL OXYGENATION SIMULATIONS ............................................................................ 79

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EXECUTIVE SUMMARY

Since European’s settled its banks in 1829, the Swan-Canning Estuary has been

exposed to an array of anthropogenic pressures. With increasing populations and

changing land use practices in the catchment, these pressures have increased in

recent decades. In order to protect the environmental values of this iconic system,

Western Australian Government bodies have begun to put into place management

strategies in an attempt to ensure the system’s long-term sustainability. In general,

these response strategies have been reactive, rather than predictive. In order to

understand and plan for the management of the system into the future, predictive

tools can provide a means to further the effectiveness and suitability of management

efforts.

To support the development of management plans that aim to ensure the

sustainability of the system, the 2018 UWA Environmental Engineering Design Class

has forecast the state of the Swan-Canning Estuary system for the year 2050. The

Swan Canning Estuarine Response Model (SCERM), a model whose development

was led by Dr. Matt Hipsey, of the University of Western Australia and his

collaborators, was utilised by the class to predict the state of the system in 2050. To

encompass the uncertainty in both modelling and ecological prediction, eight

different future scenarios were modelled, with an additional simulation serving as a

baseline. The project was split up into three streams, each focused on a key area of

study. These areas, or so-called phases, are the baseline phase, the extrema phase

and the management phase. Each phase analysed a few different representative

scenarios, summarised in Table 1.

Table 1: Simulation Summary Table

In the future scenarios, the three main factors considered were the effects of changes

in inflow as a result of reduction in rainfall; the effects of changes in nutrient levels

entering the estuary system; changes in air temperature; and the effects of rise in

mean sea level. Best estimates, to represent the most likely scenario due to climate

Baseline Phase

Simulation 1 Baseline 2008 Simulation

Simulation 2 Baseline 2050 Simulation

Extrema Phase

Simulation 4 2050 Low Extreme Year

Simulation 5 2050 High Extreme Year

Simulation 6 2050 Summer Flood Event

Simulation 7 2050 Mean Year No Inflow

Management Phase

Simulation 3 2050 Current Oxygenation Management Strategy

Simulation 8 2050 Enhanced Oxygenation Management Strategy

Simulation 9 2050 Nutrient Reduction

x

change, included a 39% reduction in inflow, an air temperature rise of 1.04°C, and a

mean sea level rise of 0.19m.

The reference simulations within the baseline phase of this project gave a

representation of the likely effect of climate change on the system in 2050. The results

of which indicated that it can be expected for climate change to reduce dissolve oxygen

concentrations in the river noticeably. Hypoxic, low dissolved oxygen events will likely

be associated with an increase in duration and intensity of stratification events. The

seasonal timings of which will be important from a management perspective, the

results of this project have suggested that climate change will lead to an increase in

intensity and duration of summer low dissolved oxygen events, historically associated

with poor water quality and algal bloom events.

The results from the extrema phase of this project indicated that with increasing

drought events we can expect a more marine system, with the migration of the salt

wedge even further upstream for an extended period. This phase also showed a likely

reduction in dissolved oxygen concentrations, particularly in the bottom layers of the

Estuary, with an increase in the occurrence of hypoxic events. Modelling of drought

events exhibited an increase in the spatial extent of salinity throughout the system.

The effect of the increase extent of salinity will be particularly relevant to regions in the

Upper Swan, where modelling showed a greater temporal variation in salinity. An

increase in flood events resulted in conditions suited to longer and more intense algal

bloom events, however the degree of this is not clear.

The results of the management phase showed that, though attempts to reduce

nutrients or mechanically oxygenate sections were largely ineffective, alternative

responses with a focus on the sensitive Upper Swan may be required. The addition of

an oxygenation plant was found to have a limited effect on the river, within the context

of the changing conditions represented by climate change. It was recommended that

further analysis be conducted to validate the results of the simulation with an additional

oxygenation plant, and that further simulations are run over a more extensive time

period in order to more accurately represent the possible long-term benefits of

oxygenation.

Results from all three phases showed that the effects of climate change may provide

significant and complex challenges for the management of the system, and by

extension the Swan-Avon catchment. It is recommended that further investigation be

conducted into the potential alleviation of persistent stratification and salt intrusion,

with a focus on the Upper Swan. Future avenues for investigation could include

coupling the Swan Canning Estuarine Response Model to a Swan-Avon catchment

model to be able to include catchment land use and the effect on the health of the

system, in informing management decisions.

.

1

1 INTRODUCTION

The Swan-Canning Estuary extends 60km from Fremantle and 11km from the

Canning Highway Bridge, covering approximately 40𝑘𝑚2 (Kelsey et al. 2011). The

Swan Avon River system has a total catchment area of 124,000𝑘𝑚2. However, the

scope of this project will focus on the Swan-Canning Estuary (indicated in red, see

Figure 1). The Swan-Canning system contains crucial environmental value within its

riparian vegetation and diverse aquatic ecosystem.

Figure 1: The catchment of the Swan and Canning estuaries (Kelsey et al. 2011).

The original custodians of the river, the Nyoongar people, ensured their local

environment and ecology remained healthy through a variety of historical land use

practices. Following British settlement in 1831, the Swan-Canning system has shown

signs of environmental degradation, most notably through loss of biodiversity, algal

blooms and fish deaths. In response to the detrimental effect of human activities,

water quality and the biological health of the system have become key concerns for

those involved in the management of the waterway. It is of increasing concern that

the effects of climate change, rapid population growth and increased urban

development and local anthropogenic forcing and land use practices will continue to

2

cause extensive detriment to the values of the system if management practices do

not become more advanced, focussed and effective.

The River is utilized widely and has significant cultural heritage, recreational,

ecological and commercial value (Department of Agriculture and Water Resources

2016), all of which rely in some form on the continued health of the system. In the

21st century, the river was announced as the first heritage icon of Perth, signifying

the river systems inherent value and bringing awareness to the importance of

appropriate stewardship. While sewerage and industrial waste have ceased to be

disposed of into the system, untreated stormwater and diffuse nutrient sources from

rural catchments and sedimentation, still enter the waterway and are issues of

concern. However, with the increasing pressures of population growth and urban

development in the Swan Canning catchment, the future of the water quality of the

system is uncertain.

This report will present the results of this modelling study, focussed on predicting the

water quality of the Swan-Canning Estuary system out to 2050. Within this scope,

the possible influences of both climate change and management strategies will also

be considered. To do so a total of nine modelling scenarios have been investigated

through the adaption of a numerical model which consists of a hydrodynamic driver,

Tuflow-FV, coupled with a biogeochemical model, AED2 (the Swan-Canning

Estuarine Response model, SCERM). This allowed for the water quality to be

forecasted for the given timeframe and, by extension, the review of existing

management tools and the assessment of possible future strategies.

3

2 BACKGROUND

2.1 THE SWAN-CANNING ESTUARY SYSTEM

The Swan-Canning Estuary (SCE) system has undergone a myriad of changes from

the times of European settlement. It can be expected, based on current trends, that

there will be further increases in residential and commercial land uses, such as

farming, intensive horticulture practises, engineering construction, export industries

and urban development, which have had a significant impact on the Swan-Canning

catchment and estuary to date. Development in these areas changed the nature of

what was previously a mostly freshwater system into a seasonally fluctuating marine

environment. A significant example of such change is the removal of the limestone bar

at the mouth of the estuary. This transformed the estuary into a fresh/brackish system

during winter/spring and saline during summer/autumn. In order to prevent tidal

encroachment up the Canning River, the Kent Street Weir was constructed, reducing

longitudinal flow back up the river (Department of Parks and Wildlife 2016a).

As such the system can be divided into two characteristic halves, defined by these

seasonal fluctuations. The Upper Swan is frequently dominated by fresh water, driven

by stream flow and rainfall. The tidal portion of the river, the lower Swan, experiences

profound changes in salinity. During summer the excursion of the salt wedge upstream

creates a virtually salt-water environment, in winter the system oscillates from fresh to

brackish water (Rose 2005). Hence, fresh or brackish conditions largely occur in the

middle Swan during winter/spring and saline conditions dominate in summer/autumn

months (Rose 2005). These seasonal fluctuations contribute to the complexity of the

system, and significantly influence water quality patterns, shown diagrammatically in

Figure 2. The natural seasonal fluxes in nutrients and algae growth, match the

seasonal fluxes of the system (Rose, Brown & Robb 2000).

As a heavily populated estuary it is expected the Swan-Canning River system would

have a history of environmental stress. This is represented clearly in the recurring

events of algal blooms, low dissolved oxygen and fish deaths; symptoms of the

persistent water quality issues of the system. Nutrient enrichment, or eutrophication,

can be a natural or human induced occurrence and drives the primary productivity in

waterways (Department of Water 2018). Through changes to the land use and water

movement the salinity, biology and nutrient availability in the Swan-Canning River, and

so the health of the system has been altered and has subsequently lead to changes

in algal growth patterns (Rose 2005, Kelsey et al. 2011). The increase in nutrient

inputs, namely of phosphorus and nitrogen, into the river system essentially provides

excess food for algae (Rose 2005). This increase in algal biota leads to the reduction

in oxygen availability, therefore there has been an increase in the intensity and

occurrence of algal blooms in the Swan River (Thompson & Tracey 2002).

4

The Swan-Canning River is naturally vulnerable to algal bloom events due to a range

of factors. Firstly, the leached sandy soils of the region have limited capacity for

nutrient storage (Rose 2005). This is coupled with other conditions, ideal for algal

growth, which are prevalent in the system such as the abundance of sunlight, warm

water temperatures and the extended periods of relatively slow moving and calm water

(Rose 2005). Though there is distinct variation spatially between the upper and lower

Swan in terms of nutrients and hydrodynamics, as mentioned previously, the greater

variation to be considered is seasonal (Thompson & Tracey 2002). The combination

of a reliable winter rainfall and a deep and open estuary (Thompson & Tracey 2002)

leads to the seasonal fluxes in salinity. The cycling can be distinctly separated into the

winter and summer months, as matches the flux of inflow of freshwater. Human activity

has merely accelerated processes within the sensitive SCE system (Rose 2005).

5

Figure 2: The seasonal states of the Swan-Canning River system (Swan River Trust 2008).

6

2.2 POPULATION CHANGES

According to the earliest historical records, the population of Western Australia (WA)

was just under 800 (see Figure 3). Following the effects of colony growth, and the

influx due to the gold rush and the mining boom, the population has grown to

approximately 2 million as of 2017. This is expected to grow to 3.5 million by 2050

(Western Australian Planning Commission 2015).

Figure 3: Population trend of Western Australia (Australian Bureau of Statistics 2014). Note: indigenous

populations were not included in the census until 1961 and historical population estimates in Western Australia

do not extend to 1788, when European Settlement first occurred.

The population increase has been accompanied by the clearing of land and changing

hydrological conditions in sub-catchments, for housing and agriculture. This change in

land use has led to the continued use of fertilisers and has altered the ability of the

sub-catchments to store and attenuate the nutrients, see section 2.4.1. Furthermore,

much of the land development has required the use of artificial drains to make the

wetlands on either side of the Swan suitable for construction.

2.3 MANAGEMENT AND POLICY

Since the 1950’s the health of the Swan-Canning River system has undergone various

management initiatives and changes in regulatory authority. It wasn’t until 1988 that

the Swan River Trust Act was enacted, creating one body responsible for the planning,

protection and management of the River. The legislation established an eight-member

body representing the interested of the community, State and Local governments

regarding the Swan and Canning Rivers (State Library of Western Australia 2010).

This act was surpassed by the Swan and Canning Rivers Management Act 2006, an

Act for the preservation of the Swan and Canning Rivers and associated land, to

0.0

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7

ensure the maintenance of ecological and community benefits and amenity (Cugley

2017). Under the Act the Swan-Canning River Park was established, the facilitation of

community involvement was initiated, whilst still including the function of the Swan

River Trust body, and the River Protection Strategy and the Riverpark Management

Program were introduced.

The Swan Canning River Protection Strategy (SCRPS) is a long-term strategy

developed by 21 local government authorities, State Government agencies and

natural resource management groups aiming to coordinate management efforts

(Department of Parks and Wildlife 2015). Since 2008 the State Government has

invested $11 million on foreshore restoration, $10 million to build and maintain

oxygenation plants, $7 million into nutrient stripping wetlands, and $4 million of funding

to develop community-based management groups (Department of Parks and Wildlife

2015, Cugley 2017).

As of July 2015, the role and functions of the Swan River Trust were merged with the

Department of Biodiversity, Conservation and Attractions (DBCA), creating a new

Rivers and Estuaries Division (State Library of Western Australia 2010). However, the

Swan River Trust still remains as an advisory body, providing independent, high level

advice to the Minister for Environment and to the Director General of DBCA on matters

affecting the rivers (Swan River Trust 2015a).

Recently, nutrient reduction targets have been a major driver in management plans

for the improvement to the water quality of the system, these are discussed further in

section 2.5.1. In creating reduction goals, targets for inputs of total nitrogen (TN) and

phosphorus (TP) were established from costal catchment modelling analysis by the

Department of Water (DWER 2010). This modelling supported the Swan Canning

Water Quality Improvement Plan (SCWQIP), the Department of Water (2009), which

highlighted reasonable reduction goals for inputs, with a 49% reduction in nitrogen and

a 46% for phosphorus.

At present, there are two oxygenation plants located in the Upper Swan, in the suburbs

of Caversham and Guilford and there are three oxygenation plants located in the

Canning River, the Bacon, Camsell and Nicholson oxygenation facilities, that each

cycle water from the Swan-Canning through the system and supersaturate the

estuarine water with dissolved oxygen to enhance oxygenation in the system. This

supersaturated oxygenated estuarine water is then released back into the system via

instruments that sit at the bottom of the Estuary to diffuse the oxygen into the system

effectively (DoW 2015).

The artificial oxygenation facilities located in the Upper Swan Estuary each release a

base load of 30kg/hr of O2 and a heightened load of 60kg/hr (Hipsey et. al. 2014). The

facilities begin releasing oxygen into the system when oxygen levels fall below 4mg/L,

8

as levels of poor oxygen conditions within the Swan-Canning Estuary can be defined

as any value less than 4mg/L, see Table 2

Sampling and previous model simulations have shown that oxygenation plants in the

Upper Swan improve oxygen conditions in 39-92% of the 10km target zones

(Department of Water, 2015).

Classification Concentration

Low Oxygen < 4mg/L

Hypoxia < 2mg/L

Anoxia 0 mg/L

Table 2 Oxygenation classifications for the Swan-Canning Estuary (Hipsey et. al 2014).

2.4 WATER QUALITY INDICATORS

The DBCA has identified a range of factors believed to influence the water quality in

the SCE. These include total nitrogen, ammonium nitrogen, total oxidised nitrogen,

dissolved organic nitrogen, total phosphorus, soluble reactive phosphorus, silica,

dissolved organic carbon, total suspended solids, alkalinity, chlorophyll-a, secchi

depth and phytoplankton. Given the difficulty of modelling to capture biological

processes, and the already significant uncertainty introduced by forecasting decades

into the future, the decision was made to look primarily at quantities that have high

certainty in the model validations, namely Dissolved Oxygen and salinity (with R2

ranging from 0.6-0.74 and 0.9-0.97 respectively), (Hipsey et al. 2016). These two

parameters are key in considering the water quality of the SCE and are, to a certain

extent, interdependent. Salinity is the key driver dominating the density of the estuary

(Hipsey et al. 2014) and density stratification in turns effects the Dissolved Oxygen

concentrations, which in itself is also influenced by the biology and chemistry of the

system.

2.4.1 Dissolved Oxygen

One of the key indicators used to assess water quality is the concentration of Dissolved

Oxygen (DO), which is necessary for sustaining aquatic organisms and for the

decomposition of organic matter. The concentration of DO is influenced by

photosynthesis, organic decomposition, nitrification, surface exchange, and sediment

oxygen demand (Hipsey et al. 2014). Studies have shown the strong relationship

between DO concentrations and temperature (Green et al. 2007) as well as the

9

relationship between DO concentrations and nutrient cycling (Hipsey et al. 2014,

Kelsey et al. 2010), as shown in Figure 2. As discussed in Section 2.1 and will be

further in the following section, equally important to consider is the influence of salinity

and stratification on the DO concentrations in the river, with climate change (Section

2.5.2), leading to increasing stratification (following section), the reduction in

occurrence of convective overturn, which will contribute to exacerbating low oxygen

events (Green et al. 2007). Previous research has also shown the significance of the

relationship between the thermocline, oxycline and halocline (Anderson & Morrison

1989).

The benthic layers of the Upper Swan and Upper Canning River experience low DO

levels (less than 4mg/L), for approximately a third of the year and for more than two-

thirds of the year, respectively (Rose 2005, Green et al. 2007). Conditions are

considered hypoxic when below the threshold value of 2mg/L within the water column

and anoxic when at 0mg/L (The Government of Western Australia 2000, Swan River

Trust 2000, Department of Biodiversity Conservation and Attractions 2017).

Sections of the SCE are increasingly experiencing hypoxic and anoxic conditions, due

to eutrophication, increased biochemical oxygen demand (BOD), stratification and

climate change, see section 2.5.2 (Auditor General 2010). Hypoxic conditions are

often exacerbated by algal blooms, which are caused by excess nutrients in the

estuary, and further deplete oxygen in the system (Swan River Trust 2005).

2.4.2 Salinity

Understanding the extent and distribution of salinity in the SCE is an important part of

understanding ecological stressors including salt intrusion and stratification induced

periods of low oxygen. The unique coexistence of marine, brackish and fresh

ecosystems in estuaries contributes to the significance of their biodiversity. Particularly

in the SCE, the effect of increasing salt-wedge intrusion as a result of climate change

is of particular interest to stratification salt determining possible changes to the species

that inhabit the currently fresh Upper Swan regions.

Furthermore, salinity can directly affect the health of the estuary on shorter time

scales, usually via the process of stratification. Salinity driven stratification occurs

when large differences in the salinity of water masses prevent them from mixing,

resulting in the formation of distinct fluid layers (Stewart 2003). Given the differences

in density between the two water masses, this results in a relatively salty layer of water

underneath a layer of fresh water at the surface. The inability of the stratified estuary

to mix vertically means that oxygen that diffuses into the water at the air-water interface

has difficulty reaching the benthic layers, leading to particularly low levels of oxygen

in the benthic layers of the estuary.

10

2.4.3 Phytoplankton and Chlorophyll-a

There are hundreds of naturally occurring phytoplankton, or microscopic algal species,

within the SCE and as primary producers, they are a vital component of the Estuary

ecosystem (Rose 2000). Phytoplankton typically undergo seasonal fluctuations and

bloom cycles where the extent of a particular bloom depends on nutrient availability

(Rose 2000). As discussed in section 2.1, these blooms can be detrimental to the

functioning and health of the system. Different species of algae or phytoplankton

typically prefer different conditions, which is often reflected in algal bloom events and

the distribution of species across the SCE. For example, dinoflagellates are a species

well known to be affiliated with mostly marine environments though can be found in

freshwater, therefore the presence of high concentrations of dinoflagellates can be an

indication of the relatively saline nature of portions of the SCE (Green et al. 2007).

Also, dinoflagellates, as a motile and buoyancy-regulating species, are one of the

species of phytoplankton that tend to favour high nutrient levels in the bottom waters

(Green et al. 2007). As such, this particular species was chosen as a water quality

indicator in this modelling study, the results of which will be discussed further in section

4.2.3

As discussed in section 2.1, increases in phytoplankton concentrations can be

representative of increases in nutrient availability and can subsequently lead to

reductions in oxygen concentrations in the river (Rose 2000). The cycle perpetuates,

with reductions in oxygen concentrations leading to increased biomass and

accumulation of organic matter in the sediments which will then, after microbial

decomposition, become available as fuel for the further growth of phytoplankton (Rose

2000, Green et al. 2007). The subsequent growth of phytoplankton then leading to the

further reduction in DO concentrations. Climate change will also likely further

exacerbate these trends, for example with increasing water temperatures likely to

increase nutrient fluxes from the sediments (Green et al. 2007), further discussed in

section 2.5.2.

SCERM has the ability to capture the drivers of phytoplankton growth through the

interrelationships between temperature, salinity, light and nutrients (Hipsey et al.

2016), all essential factors in considering and modelling algal growth (Rose 2005).

Though, at the time of writing, there were acknowledged limitations in the confidence

of the ability of SCERM to accurately forecast algal blooms and that further validation

of the phytoplankton module of the model was necessary against more recent data

(Hipsey et al. 2016). The model is found to have a moderate degree of accuracy when

predicting the concentration of chlorophyll-a denoted by the R-value of 0.42 when

comparing 2014 modelled chlorophyll-a concentrations with observed chlorophyll-a

concentrations. A lack of vertical migration configured in the model for certain algal

species means that the model has a tendency to over-predict the chlorophyll-a

concentrations at the bottom of the system thus leading to discrepancies in observed

and predicted results (Hipsey et. al. 2016).

11

Predicting these water quality indicators in a complex system like the SCE is

particularly difficult due to the non-linearity of the system and the many interdependent

relationships that a responsible for water quality. Thus, this study will use modelling

as a tool to predict these water quality indicators as a response to climate change.

2.5 LITERATURE REVIEW

This section will summarise key information in the literature regarding the main climate

change parameters considered for this modelling study and nutrient loads into the

system, as they relate to modelling the water quality of the system out to 2050.

2.5.1 Nutrient Loads

Excess nutrient loads into the SCE have historically been a significant driver of water

quality issues. As discussed in section 2.1, nutrients are key factors for plant growth,

but excess can promote eutrophication, algal blooms and low oxygen conditions,

which have historically caused fish deaths and other negative effects on aquatic life

(Department of Biodiversity, Conservation and Attractions 2017 & 2018).

Nutrient loading into the estuary has been a combination of both point and diffuse

sources. The point source contaminants, namely sewerage and industrial waste, were

removed by the Swan River Conservation Board from the 1950’s to the 1970’s.

Conversely, diffuse source nutrient loads continue to enter the SCE and are

significantly more complicated to manage.

Agriculture is the largest contributor to diffuse nutrient loads for both phosphorous (P)

and nitrogen (N), which typically enter the estuary through contaminated run-off and

groundwater flow from water soluble fertilizers (Swan River Trust 2005). This source

of nutrient loading usually coincides with the high flow winter months, which flushes

and hence mitigates the possible harmful effects of the nutrients. Conversely, urban

diffuse contaminants tend to provide year-round nutrient loading into the Estuary

through the artificial drainage systems implemented in urban developments (Swan

River Trust 2005).

One of the sub-catchments of particular concern is Ellen Brook, as it is the greatest

nutrient exporter (see Figure 4). While this is primarily due to its large size and

extensive farming, it also has some of the longest periods of no flows from December

through to May (Swan River Trust 2005). This infers a greater build-up of nutrients

over dry months with the potential to contribute significant loads during extreme rainfall

events in summer, which are expected to increase in frequency and intensity under

current climate change predictions (Ruprecht et al. 2005).

As discussed in section 2.3, water quality targets have been set for each catchment,

regarding their total nitrogen (TN) and total phosphorus (TP) inputs into the Swan-

Canning Estuary, established on the back of management strategies from the

12

SCWQIP. Hydrological and nutrient modelling estimated that 26 tonnes of P and 251

tonnes of N entered the Swan-Canning River System in 2009. However, modelling as

part of SCWQIP estimated that maximum acceptable load for the Swan Canning

catchment per year is 28 tonnes of N and 14 tonnes of P. Thus, in order to make

substantial improvements in water quality, SCWQIP provide recommendation to

reduce the N load by 123 tonnes and P load by 12 tonnes per year (DBCA 2018). As

of 2016, only 8 out of 15 catchments met all of their targets, with all 8 falling short on

total nitrogen targets (Department of Parks and Wildlife 2016b).

2.5.2 Climate Change

2.5.2.1 Rainfall and Inflow

Perth experiences a Mediterranean-type climate, characterised by wet winters and dry

summers, with over 85% of rainfall falling between May and October. Since the early

1970s, there has been prolonged decline in rainfall resulting in serious reductions to

inflows (Smith & Power 2014). This is expected to continue under drying climate

change scenarios in the south-west of WA. By 2050, it is predicted that the inflow

volumes entering the Swan system will be half of those recorded in 1960 (Smith &

Figure 4: Average annual total nitrogen (left) and phosphorous (right) loads in tonnes, from the Swan Canning

sub-catchments (Kelsey et al. 2011).

13

Power 2014). Studies on patterns of decreasing rainfall and runoff highlight the

potential effect of climate change on the whole system's ecological function and

human interaction (Graham-Taylor 2011).

The primary threat driven by climate change to the functioning of the system, is the

decrease in rainfall and consequently the increase in the extent of saline conditions,

modifying the hydrodynamics of the system (Green et al. 2007). The system is

expected to undergo a reduction in the vital flushing forcing, with coastal catchment

flows accounting for larger contributions than previously (Kelsey et al. 2010, Green et

al. 2007).

Through a Couple Model Intercomparison Project Phase 5 (CMIP5), a multi-model

approach for the south west WA region, Smith & Power (2014) predicted annual

inflows (GL), under a high-end greenhouse gas emissions scenario (RCP 8.5). The

high-end scenario, RCP 8.5, represents a scenario of comparatively high greenhouse

gas emissions, which combines assumptions involving high population, modest rates

of technology change, slow income growth and energy intensity improvements

(Church et al. 2013). Smith & Power (2014) identified a significant shift in the

relationship between rainfall and inflows for the southwest of WA, a shift from relatively

wet conditions to relatively dry after 1976. The resultant trend of decreasing inflows

into the system from Smith & Power is shown in Figure 5 below.

Figure 5: The results of the modelling analysis by Smith & Power (2014), the observed patterns (dark blue) are

compared to the CIMP5 multi-model for the SWWA inflows (GL), the various coloured lines show the results of

the seven models used and the grey lines indicate the ensemble maximum, mean and minimums.

2.5.2.2 Mean Sea Level Rising (MSLR)

The Swan-Canning River system, as an interface between a river catchment and a

tidal estuary, is an area susceptible to both storm surges from the ocean boundary

14

and riverine flooding (Rodgers & Bretnall 2015). Climate change induced sea level rise

and increasing storm surge events, are also expected to affect the Swan-Canning

system (Swan River Trust Technical Advisory Panel 2007). There is expected to be

an increase in intensity and frequency of extreme storm event driven by climate

change (Climate Council of Australia 2017), though due to limited reliable predictions

and data for the south west region of WA, this was not considered further in this study.

MSLR is occurring at an increasing rate (Pattiaratchi & Eliot 2005). A research study

and literature review conducted in 2017 by a group of Master of Professional

Engineering students at the University of Western Australia, found that MSLR will

increase the likelihood of coastal flooding events (ENVE5502 2017), and that flooding

was likely to increase in magnitude and frequency. Furthermore, summer flooding is

likely to increase alongside changes in the upper reaches of the system’s erosion and

sediment regimes, further increasing flooding (Green et al. 2007). The

summer/autumn floods will bring organic matter, sediments and nutrients into the

system during a time of year with an already greater chance of low dissolved oxygen

events (Green et al. 2007). The specific impact of this will depend on where rain falls

and the health of the catchments (Green et al. 2007).

2.5.2.3 Increasing Air Temperature

Mean global surface temperatures have increased by 0.76° C since 1850 (IPCC 2007)

and it was reported that the average surface water temperature has increased by

0.76°C since 1850 in the Perth region (Green et al. 2007). The possible effects of

increasing temperature on the system are complex and difficult to separate from other

phenomena, for example Australian regional trends in evaporation are not simply

reflective of the rises in temperature but potentially could be more representative of

changes in cloud cover or wind (Green et al. 2007). The influence of temperature on

DO is just one example of the need to include climate change driven temperature

changes for predicting the water quality of the system to 2050. The solubility of oxygen

decreases with increasing temperature and the metabolic rates of aquatic plants will

increase as temperatures rise, altering the BOD. As such, temperature within the

hydrodynamic model is subject to surface heating and cooling processes (Hipsey et

al. 2014). It is a parameter with significant influence on biochemical processes,

influencing the water quality of the system and therefore it is necessary to adapt this

parameter within the meteorological forcing of the model (see section 3.1.1) to reflect

potential changes driven by climate change.

2.6 THE SWAN-CANNING ESTUARY RESPONSE MODEL

The complexity of the SCE necessitates the use of advanced modelling techniques in

order to make predictions about the water quality in the system as a response to

climate change. Predicting the water quality in the estuary using a model requires that

both a hydrodynamic and biogeochemical processes, and their interdependence, are

captured. These capabilities, which are vital to understanding the drivers of water

15

quality in highly complex non-linear environments, can be found in the Swan-Canning

Estuary Response Model (SCERM).

The SCERM was initially developed as a tool to support decision makers combat

challenges to the systems health by providing a holistic view of the estuarine response

to both short and long-term stressors (Hipsey et al. 2016). In the past, the model has

simulated the time period from 2008 to 2012, with the results validated against real

measurements taken in the estuary. In order to meet the aims of this particular study,

the existing SCERM infrastructure was adapted so as to allow for estuarine conditions

to be forecast out to 2050. Specifically, this was achieved by predicting the model

forcing in 2050 and creating a set of boundary conditions that reflect this.

16

3 METHODS

3.1 DESCRIPTION OF SCERM

The Swan-Canning Estuary Response Model (SCERM) is a model of the Swan-

Canning Estuary developed to assist in understanding the drivers of water quality in a

highly complex non-linear environment. SCERM consists of a hydrodynamic driver,

Tuflow-FV, coupled with a biogeochemical model, AED2. The model domain uses a

finite volume mesh that extends from Fremantle eastwards as far as the Kent Street

Weir along the Canning, and up to the Great Northern Highway along the Swan (Figure

6).

Figure 6: A map of the domain and its corresponding boundary conditions and validation sites (Hipsey et al. 2014)

17

Furthermore, Figure 6 shows the locations where the boundary conditions are applied

in the model domain and the sites used for model validation. Hipsey et al. (2016)

describes the validation of the SCERM for these sites over the period from 2008 to

2012.

The SCERM is highly influenced by forcing provided by boundary conditions that can

be divided into tributary inflows, tidal forcing and meteorological data. Furthermore,

the model forcing also includes anthropogenic inputs, namely oxygenation plants.

Largely, input data is in hourly timesteps which TUFLOW interpolates onto the model

calculation timesteps, typically a few seconds. The raw data and methodology that is

used for creating these boundary conditions is described by Hipsey et al. (2016).

Figure 7 A map of the final output locations for the model

3.1.1 Boundary Conditions and Anthropogenic Inputs

3.1.1.1 Tributary Inflows

The model domain contains 8 tributaries captured as boundary conditions in the

model. These include Bayswater Drain, Bennet Brook, Helena River, Ellen Brook,

Susannah Brook, Canning River, Upper Swan (Walyunga), and Jane Brook. The

boundary conditions for each tributary consist of a flow rate and a host of water

properties associated with the flow as described in Appendix D. The ephemerality of

tributaries is captured by turning on and off the flow rate in the boundary condition

input file.

3.1.1.2 Tidal Forcing

The tidal forcing is read into the model as a change in water level relative to mean sea

level, and corresponding water quality parameters, at the point where the domain

18

intersects the open ocean. Tuflow, the hydrodynamics driver, then calculates the

corresponding flow caused by the tidal file depending on the existing water surface

elevations in the model relative to tidal height. Furthermore, density differences

between the estuary and the ocean also drive salt water intrusion into the estuary.

Tidal time series data, as input into boundary condition files, were obtained from the

Bureau of Meteorology (BOM 2018), from the Fremantle tidal gauge, as described in

Hipsey et al. (2016).

3.1.1.3 Meteorological Forcing

Meteorological forces play a significant impact in driving the hydrodynamic and

biogeochemical forces in the model, and as such, the model also requires these forces

to be captured in the model. Specifically, the model has two input boundary condition

files concerned with meteorology. The first contains the daily rainfall volume, and the

second contains hourly readings (at 10m above ground) of air surface temperature,

relative humidity, wind velocity and solar radiation. Meteorological time series data, as

input into boundary condition files, were obtained from the Bureau of Meteorology

(BOM 2018), from the South Perth Meteorological Station, as described in Hipsey et

al. (2016).

3.1.1.4 Artificial Oxygenation

Within the model domain exist oxygenation plants that operate by removing water from

the estuary, adding oxygen into it and then pumping it back into the river. From a

modelling perspective, this process is essentially captured by an inflow of oxygen rich

water at the location of the plant, and thus, its format closely resembles the tributary

inflows. Given the plant does not treat the water it oxygenates, the oxygenated water

input by the oxygenation plant boundary conditions has the same water quality

properties, with the exception of oxygen, of the surrounding water.

3.2 AIMS AND OUTCOMES

The aim of this study is to forecast the response of the water quality in the Swan-

Canning Estuary (SCE) to climate change, so as to inform decision making for the

management of the Swan-Avon catchment and its associated waterways. As such,

nine modelling scenarios have been investigated, including simulations for modelling

the influences of both climate change and management strategies and are

summarised in Table 1.

Specifically, the aim for the Reference Simulations is to model the SCE in both

contemporary conditions as well as those in the proposed forecast date, 2050. These

simulations will then form the comparative basis for other forecasting events and

simulations to be benchmarked against. Simulation 1, the Characteristic Baseline,

aims to capture the historical norm of the system by simulating mean estuarine

characteristics or the parameters that influence the water quality of the system.

Alternately, Simulation 2, the 2050 Baseline, aims to predict the most likely condition

19

of the SCE in 2050 by making assumptions about the expected conditions that impact

the system.

Specifically, these general aims for the Characteristic Baseline Simulation require:

• The hydrological ‘norm’ of the Swan-Canning Estuary using a Flow Duration

Curve

• The nutrient loading ‘norm’ for the SCE using Nutrient Duration Curves

• Determining if other meteorological/catchment conditions should be

accounted for

• Reflecting these conditions in the model inputs

Likewise, the aims for the 2050 Baseline Simulation include:

• Predictions of changes to estuary inflow in 2050

• Predictions of Mean Sea Level Rise in 2050

• Predictions of air surface temperature in 2050

• Reflecting these conditions in the model inputs

The extreme runs aim to capture a range of conditions the Swan-Canning estuary may

encounter as conditions change towards the forecast date of 2050. The low and high

extreme runs aim to provide the upper and lower bounds of conditions relating to water

quality in the Swan-Canning River, and results are observed in combination with the

“2050 baseline” simulation. This aims to identify how each key variable (sea level,

temperature, flow volume and flow concentration) impact the state of the Swan-

Canning system as they become more extreme.

The team also develop two specific extreme events; a drought and a flood. The context

of such runs is explained in detail under section 3.4. The runs aim to create scenarios

that are not necessarily realistic, but instead provide useful information in terms of

observing the system responses under extreme scenarios.

The aim of management runs is to model effectiveness of various management

strategies applied to the Swan-Canning estuary in the year 2050. The current

oxygenation run aims to determine the effectiveness of artificial oxygenation in 2050

based on current management strategy, Guildford and Caversham oxygenation at the

Upper Swan. An enhanced oxygenation run, aims to identify location for placing an

enhanced oxygenation based on the current oxygenation and to capture effectiveness

of oxygenation plant by adding an enhanced oxygenation plant includes two current

oxygenation plants. Alternatively, the aim of a nutrient reduction is to predict the

reduction load of nitrogen and phosphorus, then to determine the effectiveness of

reducing the nutrient concentrations for the year 2050.

The key water quality indicators for the system, as identified in Section 2.4, can then

be analysed for each of these simulations. This is achieved by graphically displaying

the model outputs for multiple location across the model domain.

20

Simulation Name and

Description

Changes to 2008 Data

Simulation 1 Characteristic

Baseline an average

case scenario

• Nutrient Inflow concentrations scaled to

historical 50th percentile.

Simulation 2 2050 Baseline

2050 Most Likely

Conditions

• Nutrient Inflow concentrations scaled to

historical 50th percentile.

• Tributary Inflow decreased

• Mean Sea Level increased

• Air-surface temperature increased

Simulation 4 Low Extreme

An optimistic

scenario for 2050

• Nutrient inflow concentrations, inflow

volume, sea level rise and air surface

temperature scaled to historical 90th and

10th percentiles to represent favorable

conditions.

Simulation 5 High Extreme

A pessimistic

scenario for 2050

• Nutrient inflow concentrations, inflow

volume, sea level rise and air surface

temperature scaled to historical 90th and

10th percentiles to represent adverse

conditions.

Simulation 6 Summer Flood

Event

Unseasonable

inflow

• 2000 historical values for meteorological

data, tidal, inflow concentration and

volume

• 2050 mean (simulation 2) values for sea

level rise and surface air temperature.

Simulation 7 No Flow Inflow

Extended drought of

up to a year.

• 2050 mean (simulation 2) conditions

• No rainfall

• No inflow

Simulation 3 Current

Oxygenation

• Run Guildford and Caversham

oxygenation plant from January to

March 2050 at a base load of 30kg/hr of

O2 per hour each day.

Simulation 8 Enhanced

Oxygenation

• Current oxygenation (simulation 3)

conditions

• An enhance oxygenation added next to

the Nile Street monitoring site

Simulation 9 Nutrient Reduction • A reduction in nitrogen and phosphorus

loads into the system by 48%and 46%

respectively

Table 3: Simulations Overview

21

3.3 REFERENCE CONDITIONS

The aim of the first two simulations is to establish reference conditions, for both

contemporary and future catchment scenarios. The following sections outline the

motivations and methodology associated with developing each of the simulations.

Furthermore, Table 3 in the previous section, contains an overview of the reference

conditions simulations.

3.3.1 Catchment Conditions in 2008

Given the significant amounts of data required for the SCERM to accurately represent

the estuary, the decision was made to use a set of input data derived from real

measurements that represented normal estuary conditions. Specifically, the

hydrological conditions, in terms of total flow and temporal distribution, were the main

factors considered when representing normal estuary conditions. Furthermore, it was

decided that the year from which the characteristic baseline is formed should be

selected from the timeframe over which the SCERM had been validated, restricting

the possible choices from years in between 2008 and 2012 (Hipsey et al. 2014).

Figure 8: Flow Duration Curve for Walyunga monitoring station or “Upper Swan River”, as given in Figure 1. Total

recorded years 1976-2016. Data from the Department of Water (2018).

In order to determine which year of recorded data to use for our characteristic baseline,

a flow duration curve (FDC) was constructed for the Swan River. This curve ranks

years based on their annual flow volume, in an attempt to determine what is

considered to be a normal year, in this case one which had a 50% exceedance

22

probability. In layman’s terms, there would be an equal likelihood of any given year to

have more or less flow than the ‘normal’ year.

Figure 8 above shows a flow duration curve constructed from flow observations at the

Walyunga monitoring station, at the northern-extent of the model domain, for years

between 1976 and 2016. From Figure 7, it can be seen that the 2008 is the closest

year to the 50th percentile exceedance probability from those which have been

validated. It is worthwhile noting that Walyunga (also referred to as ‘Upper Swan’) was

chosen for the analysis given that it provides a dominant contribution to the estuaries

total inflow, as shown in Figure 9 below.

Furthermore, it was important to capture the typical temporal variability present in the

system. In the case of the Swan-Avon catchment, a normal year would contain dry

and warm summer months progressing to frontal rainfall events throughout the middle

of the year. Figure 10 below shows the hydrograph and hyetograph for the Swan-

Canning in 2008.

From this, it can be seen that the dry summer months lead to periods of low flow whilst

winter rainfall leads to increased streamflow, especially as catchment wetness

increases. Of particular interest is the first significant rainfall events that occur in April

provide an initial ‘flush’ of the estuary that may break stratification and decrease

nutrient concentrations.

Figure 9: Relative flow contributions to the SCE in 2008. Data from the Department of

Water (2018).

23

Given the large focus on nutrient loading in the SCE, understanding the distribution of

nutrient fluxes across each of the domain tributaries assists in defining which sub-

catchments and by extension, which tributaries, contribute to estuary nutrient loading

(Swan River Trust 2009).

Figure 11: Flow normalized average nutrient concentrations for each of the SCE tributaries in 2008. Flow and

nutrient concentration data from Department of Water (2018).

Figure 11 above shows the flow normalized nitrogen and phosphorous concentrations

for each of the SCE tributaries. From this, it can be seen that Ellenbrook contains the

highest inflow concentrations for both nitrogen and phosphorous, with the latter over

5 times the concentration of any other sub catchment.

Nitrogen concentration is considerably more uniform across the tributaries in 2008.

Nonetheless, the Ellenbrook inflow nitrogen concentration is still noticeably higher

(~1.5-2 times) than its counterparts.

Figure 10: Hydrograph (orange) and hyetograph (blue) for the Swan-Canning Estuary in 2008. Streamflow data from the Department of Water (2018) and rainfall data from DAFWA South Perth

Met. Station.

24

3.3.2 Simulation 1: The Characteristic Baseline

3.3.2.1 Motivation

The Characteristic Baseline Simulation is a vital component of this modelling study,

providing the comparative baseline for the further simulations. Analysis of possible and

current management strategies and the effect of climate change on the system

requires reference to some baseline, representing an average case contemporary

scenario for the main drivers of the system. The Characteristic Baseline aims to

provide a sense of perspective to forecasting simulations by contrasting them against

the SCE’s current state.

As discussed above, the year 2008 was selected due it is approximating a 50th

percentile of annual flow volume, whilst also showing a typical temporal flow

distribution. Nonetheless, although 2008 is a normal year hydrodynamically, it was not

necessarily normal in all conditions influencing water quality in the estuary, thus the

decision was made to alter 2008 from its historical conditions to represent a 50 th

percentile year across multiple factors. The result of adopting a characteristic baseline

as opposed to using a historical year is that forecasting simulation can then be

compared against a ‘historically-average’ baseline simulation as opposed to a single

year.

3.3.2.2 Nutrient Inflow

Nutrient concentrations (Nitrogen and Phosphorus) are considered a driver of water

quality of the system and historically have fluctuated significantly over recorded time.

The relationship between nutrient concentrations and inflows is not a novel concept

and has long been observed in the system (Henning & Kelsey 2015, Kelsey et al. 2011

& Jacowyna et al. 2000), and was further investigated in the preliminary work towards

establishing nutrient inflow values for a characteristic year. Using historical data

available did indeed confirm a strong relationship between inflow and nutrient

concentration for a number of the monitoring locations within the model domain. Given

this, as well as the clear limitations in taking an arbitrary nutrient quantity based on a

one year of average loads and dividing this load for all input locations across the model

domain, a duration curve technique was also applied for nutrient loads. An example of

such can be found in Figure 12.

25

Figure 12: Nutrient Concentration Duration Curve for Walyunga (Upper Swan). A FDC method was applied to

nutrient concentrations to create a normalised concentration by flow for each of the eight sites.

The analysis was conducted across each of the tributaries that included in the SCERM

as boundary conditions, as described in Section 3.1. The nutrient and flow data for

each tributary was sourced from Department of Water (DoW 2018) using the same

stations used to generate each of the tributary boundary conditions (Hipsey et al.

2016). Exceedance probability graphs for flow normalised concentrations were made

for each tributary so as to calculate the 50th percentile nutrient concentrations for each

inflow.

For each tributary, an annual mass flux for both nitrogen and phosphorous was

calculated and divided by the corresponding annual flow volume, resulting in a flow-

normalised nutrient concentration for each year. This was achieved by interpolating

fortnightly nutrient measurements onto daily timesteps, whereby they were multiplied

by daily flow volumes in order to obtain a daily mass flux, which are then summed over

the year. This process was repeated for each year with available data, and then

displayed graphically against their exceedance probability. Given these results, a

scaling factor was calculated for each tributary such that multiplying each

concentration reading for 2008 by the factor would change it to a 50th percentile. The

resultant nutrient duration curves for each of the estuary inflows can be found in

Appendix D, with Appendix E showing the 50th percentile scaling factors applied to

each of the boundary conditions.

26

Figure 13 shows the effect of scaling the flow normalized concentrations of nitrogen

and phosphorous from 2008 values to the 50th percentiles calculated as per the

above method. When comparing these results to Figure 7 (Section 3.2.1), Ellenbrook

is still the clearly the inflow with the highest concentrations of both nitrogen and

phosphorous. Furthermore, scaling the nitrogen concentrations to their 50th

percentile results in each of the remaining catchments showing almost uniform

nitrogen inflow concentrations.

3.3.2.3 Other Boundary Conditions and Anthropogenic Forces

Many of the boundary conditions were left unaltered from the actual conditions present

in 2008. Some model input parameters, such as mean sea level, were considered to

have much less annual temporal variability than flow and nutrients and as such, were

not changed.

Furthermore, given the interdependence of some boundary conditions (such as

meteorology and flowrate), many of the meteorological inputs could not be altered

without making complex and unreliable assumptions about the relationships between

interdependent parameters. For example, the interdependence of flow on rainfall is

not determined by a simple linear relationship (Hipsey et al. 2014, Kelsey et al. 2011,

Smith & Power 2014). Therefore, altering inflows from actual 2008 towards creating a

characteristic baseline year that reflect the contribution of an annual typical rainfall,

would not be feasible.

Although the Swan-Canning Estuary currently has 2 oxygenation plants that fall within

the model domain (as discussed in Section 3.1), neither of these plants were in

operation during 2008 and as such, are not present in the Characteristic Baseline

Simulation.

3.3.3 Simulation 2: 2050 Baseline (No Oxygenation)

3.3.3.1 Motivation

The 2050 Baseline Simulation is the created 2050 scenario to obtain a long-term

indication of the behaviour of system if no additional management strategies or

programs have been implemented from the Characteristic Baseline Simulation. In this

Figure 13: 50th Percentile flow normalized average nutrient concentrations for each of the SCE tributaries. Flow and nutrient concentration data from Department of Water (2018).

27

Simulation, a study was undertaken into how each of the models input parameters is

expected to change between the time of the baseline reference year (2008) and the

target year 2050 as a result of climate change. Specifically, the study investigated the

effects of mean sea level rise, tributary inflow, rainfall and air surface temperature.

Different methods of predicting conditions in 2050 were used for each parameter, as

discussed in the following sections.

Given that this Simulation aims to capture the ‘most likely’ scenario, the RCP 4.5

scenario was used to predict the effects of climate change on boundary conditions. As

many of the predictions for the model parameters have only been predicted on a global

scale, the creation of this Simulation often required the use of a simple regression

analysis to extract a trend from local data. This trend was then used to predict by

extrapolation the 2050 conditions, the results of which were then compared to the

global predictions.

Using the reference year (The Characteristic Baseline Simulation) as a template and

scaling the input parameters means that it is possible to assume that the yearlong time

series will be of the same form as the reference case where only the amplitude of each

variable may change. For example, the inflow resulted in a reduction in amplitude (see

the following section). Nonetheless, the non-linearity of the system means that the

model output may not necessarily directly reflect the change in the input boundary

conditions.

This Simulation does not include the two oxygenation plants (Guilford and Cavesham),

functioning within the bounds of the model as of 2018. To run a forecasting Simulation

for 2050 that does include the oxygenation plants, a baseline 2050 Simulation without

oxygenation would be necessary to determine within model time when the plants

would be theoretically in action. This would be to determine at what points in time the

conditions in the estuary are classified as anoxic, at the specific locations of the two

oxygenation plants. Simulation three will be ran with the two oxygenation plants turned

on as discussed in section 3.5.1

3.3.3.2 Estuary Inflow

To predict and model a most likely 2050 scenario, with expected patterns of climate

change included, it was necessary to obtain reasonable predictions for the declining

inflow into the Swan Estuary. Measured climate trends so far have indicated that the

non-linear relationship between streamflow and rainfall is known to have exaggerated

the impact of the patterns of declining inflow. For example, a 10-15% reduction in

rainfall during the period from 1975 to 2001 saw an approximate 50% decrease in

inflow into the reservoirs of Perth (Green et al. 2007). Due to the difficulties in obtaining

suitably statistically significant results for this non-linear relationship in order to make

predictions of the annual inflow for 2050, predictions for changing rainfall and potential

evaporation were not considered.

Though this study intended to focus on an average climate change scenario, namely

the RCP 4.5 pathway, the results found by Smith & Power (2014), as discussed in

28

section 2.2.3 were used as guidance for predicting inflows in the year 2050. The

resultant trend for predicting the reduction in inflows for southwest WA by Smith &

Power (2014) was obtained from a baseline range of years from 1911 to 2013.The

level of uncertainty in predicting these climate change trends should not be

overlooked. The majority of studies to date on the region have not incorporated the

effects of future warming as well as the increase in potential evaporation and the

response on streamflow. This is except for a study on the Stirling catchment by Berti

et al. (2004), which showed through sensitivity analysis, that when a 10% decline in

rainfall is coupled with an 11% increase in potential evaporation, a 40% decline in

streamflow is expected. Another study (Marillier et al. 2015) indicated that as the south

west region of WA has been reported as a region of the globe relatively susceptible to

climate change. As of such, this study found that the region showed a greater decline

in rainfall than interior and southern coastline regions (Marillier et al. 2015).

Henceforth, with all factors considered, assumptions will be held for the use of the

trend from Smith & Power for all consistently in all Simulations.

Altering flow in the boundary files from the baseline year, to then model a 2050 year,

required a scaling factor for each time step. This was done for each of the eight sites

as historically these eight sites consist of the majority of significant estuary inflow

within the model domain collectively, see section 3.3.1.

Baseline data set for this study only included the years ranging from 1976-2016. The

predicted mean trend (Figure 6, section 2.5.2) from Smith & Power (2014) was utilised

to predict from the 2008 annual inflow (GL) to that of 2050. The trend from Smith &

Power gave a 39% decrease in inflows from the 2008 to 2050. Though this modelling

was done under a high-end emission scenario, this predictive decline still fits within

that expected by global predictions.

Predictions obtained from Smith & Power (2014) trends are supported by global and

localised predictions from other such studies. For example, Green et al. (2007)

predicted a 22-55% decrease in streamflow to 2030, and a 45-75% decrease to 2070,

from a baseline ranging from 1925-1975. Therefore, the 39% decrease in inflows was

converted to individual scaling factors for each of the sites to alter the input boundary

condition files for the 2050 Baseline Simulation.

3.3.3.3 Mean Sea Level

The need to incorporate the influence of MSLR to a 2050 predicted Simulation is clear

when considering the strong tidal influence on the system. The rate of global MSLR

has experienced substantial growth from 0 mm/year to 0.013 mm/year, since the early

20th century (Church et al. 2013). For locations such as Perth this seemingly small

increase in height could translate to large increases in volume for the SCE. Particularly

for coastal locations, a local change of sea level over a relatively short time span could

be affected by tidal influences, storms and climatic variability. When scaling for longer

term patterns, climate change is considered a contributing factor (Church et al. 2013).

Thus, it was deemed necessary to confirm these global trends were applicable on a

29

localised scale. The International Panel on Climate Change (IPCC) predicted the

global mean sea level rising for Fremantle as one of the representative coastal

locations for the 5th Assessment Report (ICCP 2018), see Figure 14.

The trend from the 5th Assessment Report was used to provide an offset value to be

applied to the tidal time series data, as input into boundary condition files, to represent

the mean sea level rise from the baseline year (2008) to 2050. The trend for 2050 sea

level rise was selected from the RCP 4.5 prediction shown as the light blue line in

Figure 13, considering 1986-2005 as a baseline. For the mean sea level tidal data as

input to the model for this study, 2008 was chosen as the baseline year. Thus, the

value for the 2008 mean sea level rise, as predicted by modelling of the RCP 4.5 by

Church et al. (2015), was used as a baseline. The predicted 2050 value for mean sea

level under this scenario was also taken from this source and the increasing trend

based between these two values was then be applied to the 2008.

From Figure 13, a mean sea level of 0.04m was found for 2008 and of 0.23m for 2050

respectability. Thus, from the ICCP predictions for Fremantle, a MSLR between 2008

and the year 2050 can be estimated at a total of 0.19m. This value was used to

positively offset each input surface water elevation in the tidal boundary condition, to

then represent the change in mean sea level from the base case to the predicted most

likely climate change scenario in 2050.

Figure 14: An extract from the IPCC 5th Assessment Report of the observed and projected change in mean sea level for Fremantle, as one of the representative coastal locations (Church et al. 2015). The observed in situ relative mean sea level records from tide gauges (since 1970) are plotted in yellow, and the satellite record (since 1993) is provided as purple lines. The projected range from 21 CMIP5 RCP 4.5 scenario runs (90% uncertainty) is shown by the shaded region for the period 2006-2100, with the bold line showing the ensemble mean. Vertical bar at the right sides of each panel represent the ensemble mean and ensemble spread (5 to 95%) of the likely (medium confidence) sea level change at each respective location at the year 2100 inferred from RCP2.6 (dark blue), 4.5 (light blue), 6.0 (yellow) and 8.5 (red)

30

3.3.3.4 Air Temperature

The effect of climate change on air temperature can have substantial effects on the

water quality within the estuary, see Section 2.5.2. Specifically, the diffusion of oxygen

through the water surface is dependent on, among other things, the air surface

temperature. Average annual air temperatures for Perth have increased by

approximately 0.6°C in the last century to 1990 (Green et al. 2007). Though this trend

matched global predictions, it cannot be considered consistent in a prediction out to

2050 due to the influence of local effects (Green et al. 2007). Therefore, for this study

a simple linear regression analysis of data available was conducted to predict air

temperature in 2050. Data was taken from the longest recorded data set, Perth Airport

which maintained a daily temperature record from 1944 to current day. A 3-point

moving average was applied to the daily time step data, to replicate the average

duration for storm and heat wave events in the region, the results of which can be

seen in Figure 15.

Figure 15: The result of a 3-point moving average of daily average temperature and the resulting regression line. Data

taken from the Perth Airport Station (BOM 2018), the time given on the x axis is the number of days from the start of the

record to present day (1944-2018).

From this simple analysis and the trend found in Figure 15, daily average temperature

values predicted by the trend for 2008 were compared to those predicted for 2050. An

annual average for each was found and an offset representing the rise in average

annual temperature from 2008 to 2050 was established. Based on this method, the

projected warming in 2050 from 2008 was 1.04°C.

y = 7E-05x + 23.57R² = 0.0079

0

5

10

15

20

25

30

35

40

45

50

0 5000 10000 15000 20000 25000 30000

Tem

pe

ratu

re ℃

Days

3 Point Moving Average Temperature Time Series

3days moving average

regression line

31

To reinforce that this predicted rise was reasonable, other localised predictions were

assessed for comparison purposed. For a local prediction, we compared with the

results of Marillier et al. (2015). This prediction was made relative to a 1961-1990

baseline, which gave an estimated increase in temperatures of 1.0-1.3°C to 2050,

varying within different potential future climate regimes (wet, medium or a dry

scenario).

The result from the regression analysis were also compared to those found by Green

et al. (2007) who predicted air temperature risings for 2030 and 2070 based on a 1990

baseline year. The result of which was a projected mean warming in 2030 of 0.8 °C

and 1.4°C in 2070. Therefore, the predicted rise in air temperatures used as an offset

value for the 2050 Baseline Simulation was considered within range as to be expected

based on the collection of current work into the localised effect of climate change on

the system.

3.3.3.5 Nutrient Inflow

The factors that influence nutrient concentrations in the system are complex, with

rainfall and catchment land use being considered dominant. A strong correlation has

been shown between catchment rainfall and total nutrient load into the system,

whereby an increase in rainfall in the catchment mobilises more nutrients stored in the

catchment itself (Thompson 2017). Furthermore, changes to catchment land use

(typically driven by urbanisation, and by extension, population) are also expected to

affect the amount of nutrients entering the system. Ideally, the influence of these

factors would be considered when trying to predict nutrient inflows in a future scenario.

An analysis was conducted during the early stages of this study to consider the

possible influence of increasing population and changing land use in the catchment.

The SCERM does not contain any elements of catchment modelling, though work has

been done in the past of this nature (Kelsey et al. 2010). It was considered that the

possible extent of urban development could be partly factored in when considering the

effect of increasing population, as the dominating influence of this within the model

domain would be the increasing urbanisation within the coastal catchment region.

Ultimately, there was no significant relationship found between increasing population

and nutrient loads. Using predictions of the likely population increase for Perth, as

given by the Australian Bureau of Statistics a simple comparison showed the trend of

increasing population was of significantly smaller magnitudes to the trends of

decreasing flow. The influence of population on nutrient inflow was considered

negligible given the clearly dominant influence of flow. Furthermore, without

conducting extensive catchment modelling, attempts at quantifying the effect of land

use changes on the systems nutrient budget were unreliable. Given the complex non-

linear relationship between catchment land use, rainfall and nutrient influxes and the

use of catchment modelling outside the scope of this study, the nutrient concentrations

were not changed in the 2050 scenario.

32

3.4 EXTREME SIMULATIONS

3.4.1 The Runs from the Extreme conditions group

In previous sections the ‘mean’, or ‘most likely’ conditions of the Swan-Canning River

Estuary were discussed. From a design and management perspective, it is also

pertinent to determine the extreme extent of conditions that are likely to be

experienced, as well as some particularly poor but probable scenarios. In order to

predict the impacts of these extreme and poor conditions as well as the implications

of the changing climate, the study group decided on simulating four ‘Extreme’ condition

scenarios set in the year 2050. These are described in detail below.

3.4.1.1 2050 Low Extreme

The 2050 low extreme run aims to characterise an optimistic scenario by representing

a more favourable sector of the spectrum of environmental trends. The 2050 low

extreme run will aim to describe a “good year” in terms of certain conditional variables

relating to water quality in the Swan-Canning system in 2050. When making decisions

about each conditional variable the team applied the philosophy of the 2050 low

extreme run as “an optimistic approach to climate forecasting and annual variability,

resulting in a year that is unlikely but entirely possible”.

In order to create such a scenario, the team applied two main predictive tools to each

key variable. The first was to develop a local trend for each variable using real data.

Various sources were used and are explained in detail in the description of the

methodology of each key variable (section 3.3.0). The low and high extreme runs can

be thought of as a ‘one off’ forecast that predicts a trend beyond the timeframe of

available observed data. When applying regression analysis or trends to data we

adopted the same data sources and approach that was implemented to create the

“mean” 2050 condition (section 3.2.2). Following this trend prediction, statistical

analysis was used to determine an upper and lower bound based on the variation of

such variables. For each conditional variable with an adverse effect on the system the

team took the value from the lower quartile for the low extreme run, and the higher

quartile for high extreme run. The opposite quartiles were taken for beneficial

variables.

3.4.1.2 2050 High Extreme

The philosophy behind the 2050 high extreme run is the opposite of the 2050 low

extreme run. The 2050 high extreme run will aim to describe a “bad year” in terms of

certain conditional variables relating to water quality in the Swan-Canning system in

2050. It applies the philosophy of “a pessimistic approach to climate forecasting and

annual variability, resulting in a year that is unlikely but entirely possible”. The

approach reflects that of the 2050 low extreme run, using a regression and then

applying upper and lower bounds based on annual variation of data. However, in the

case of the 2050 high extreme run, the upper quartile of adverse variables are

considered.

33

The combination of the low extreme and high extreme runs aim to allow comparison

between the results of the “2050 low extreme, mean and 2050 high extreme” runs, in

a sense, creating a full range of possible scenarios for comparison.

3.4.1.3 2050 Summer Flood Event

The summer flood run aims to reproduce conditions that in the past caused major algal

blooms in the Swan-Canning system, but under 2050 scenarios. Two unseasonably

large rainfall events in January 2000 resulted in an estimated 270GL of freshwater

runoff entering the Swan-Canning estuary (Figure 16: Flow volumes (GL/day) measured

at Walyunga Canyon showing the January 2000 flow event. Adapted from (Atkins, Rose,

Brown, & Robb, 2001)Figure 16). Nutrient concentrations of inflow reached 7mg/L for

nitrogen and 0.3mg/L for phosphorus. In the following weeks the system saw the

largest bloom of toxic cyanobacteria Microcystis ever recorded, causing a closure of

the river to commercial fishing and recreational use of the river for 12 days (Atkins,

Rose, Brown, & Robb, 2001).

Figure 16: Flow volumes (GL/day) measured at Walyunga Canyon showing the January 2000 flow event. Adapted

from (Atkins, Rose, Brown, & Robb, 2001)

There is a concern that the magnitude and frequency of unseasonal rainfall events will

increase as a result of the larger storm events and tropical cyclones expected in the

future. When combined with other climatic changes predicted in 2050, these conditions

may result in the increased severity and duration of algal bloom events. It is for this

reason that the flood run aims to reproduce the flow conditions leading up to the

February 2000 algal bloom but observe the system responses under the predicted

mean 2050 conditions.

The flood run uses observed tidal, meteorological and inflow data from the Upper

Swan recorded from December 1st, 1999 to March 31st 2000. This data is

superimposed onto the predicted mean 2050 conditions in terms of air temperature

34

and sea level rise as outlined in detail below section 3.2.2. The model is then run with

the February 2000 algal bloom data under 2050 mean conditions, and the system

responses are observed. The timeframe of three months was chosen to allow ample

time for the system to respond to the inflow event, by running for two months after the

event occurs we expect the system will have responded fully. This is supported by the

observed algal bloom in February 2000 lasting only 12 days before settling.

3.4.1.4 “Mean Year 2050” No Inflow

The no inflow run considers a highly extreme scenario in which freshwater inflows to

the system are nil. Although a highly extreme scenario, the run is not aiming to provide

a “probable scenario”, but instead a look at what happens to the Swan-Canning

system when inflow is reduced to zero. In a sense this run uses the no flow as an

extreme limiting condition in the system.

The results can be used to explore what happens to the Swan-Canning system as a

drought persists over time. There is a strong understanding that the frequency and

severity of drought events in Western Australia is likely to increase (Dai, 2011; Perkins-

Kirkpatrick et al., 2016; Spinoni, Naumann, Carrao, Barbosa, & Vogt, 2014). It is

predicted by 2030 there will be a decrease in winter rainfall of 7 to 20 per cent from

records between 1925 and 1975 (Smith & Power, 2014). The effect of reduced rainfall

to actual inflow into the estuary system is magnified by lowering water tables.

Although conditions such as a drought leading to no inflow to the system is not

expected to last a full twelve months in 2050, by modelling an entire year we aim to

provide relevant information towards how the Swan-Canning system may respond

over time as a drought situation progresses. Observing how the system responds to

such an event provides useful insight to the effect of the combination of increased

drought events and a general trend of reduced rainfall in the catchment. The response

of the Swan-Canning estuary to the no inflow run provides an interesting extreme

benchmark.

3.4.2 Method for determining “2050 high extreme” and “2050 low extreme”

variables

As above, for each of the Extreme runs we considered the influence and impact of four

primary boundary conditions. These were Sea Level, Temperature, Inflow Volume and

Inflow Concentration. In each case, predictive and statistical approaches were

implemented in order to determine an offset value which would be used to scale the

previously identified 2050 mean conditions. This process as well as the context will be

discussed in subsequent sections.

3.4.2.1 Mean Sea Level Rise

As part of the 2017 UWA environmental engineering design project (ENVE 5552,

2017), reliable projections of sea level out to 2050, 2100 and 2150 were required. The

confidence range in this case is set as 90%, which considers the contribution of

35

increased ice flow from Greenland and Antarctica observed for 1993-2003 but doesn’t

include the full potential effects of changes in ice sheet flow and climate-carbon cycle

feedback due to a lack of published literature on these aspects. In addition to this past

research, the process of developing the “mean 2050” sea level conditions contributed

to a firm understanding towards the most likely mean sea level at Fremantle in 2050.

The methods used to determine the mean sea level in 2050 are explained in detail in

section 3.2.2, and were based on extrapolating the Bureau of Meteorology sea level

data from 1897 to 2018 (Bureau of Meteorology, 2018), and comparing it to various

sources of literature and climate projections.

To set the 2050 high extreme and 2050 low extreme levels for 2050 the team adopted

the same method to determine a mean level in 2050. We then analyzed the variance

between annual means to gain an understanding of the annual variability in mean sea

levels due to event such as storm surges or variable swell climate. The standard

deviation between annual means in the data was 0.06m, almost twice the difference

between various IPCC scenarios expect (Table 4).

Table 4: Median global MSLR projections relative to sea level averaged over 1986-2005). 2050 and

2100 projection are adopted from (Church, 2013). 2150 projection is from linear extrapolation.

As the standard annual variation between means was greater than the variation

between climate scenarios, the team used confidence intervals based on the data

variation to determine upper and lower bounds for the 2050 high extreme and 2050

low extreme sea level conditions. The linear trend of MSL Projections from 2005-2050 is

presented inFigure 17 Figure 17, which predicts MSL of 2008 and 2050 with its lower and

upper confidence bound. The lower and upper confidence bound represent minimum

and maximum sea level respectively. The confidence interval for the upper and lower

bound is 90%.

36

Figure 17: Time series of median Fremantle Sea Level (1986-2050: one year running mean).

Results of the projections are shown in the table below. The upper and lower bound

mean sea level rise between 2008 and 2050 were found to be 0.09m and 0.27m

respectively. We believe the range of sea levels described by the lower and upper

bounds represents mean sea levels on the extreme side of both forecasting and

annual variation but remain within what is absolutely possible to see in the year 2050.

Table 5: Median SLR projections (m) relative to MSL averaged over 1986-2005. 2008 and 2050 projections are

from linear regression.

3.4.2.2 Temperature

According to the baseline scenarios forecast for the increase in the mean temperature

out to the year 2050, it was calculated that compared to 2008 (our base year), 2050

would be on average 1.04 degrees hotter. Verification with a few external sources

gave us an indication that this was an acceptable value to use:

• The Departments of Water’s Water Science Technical Series report no. 72

‘Selection of future climate projections for Western Australia’ made a prediction

of temperature in South-west region of WA. Relative on the 1961-1990

baseline, in 2050 the temperature will increase by 1.0-1.3

• The ‘Potential impacts of Climate Change on the Swan and Canning rivers

2007’ publication by the Swan River Trust gave a prediction from 1990 (base

year) for the projected annual mean warming in 2030 to be 0.8 degrees and in

2070 1.4 degrees. 2050 lies in between these years and a value of 1.04 sits in

between the forecasted temperatures.

0.5

0.6

0.7

0.8

0.9

1

1.1

198

6

198

8

199

0

199

2

199

4

199

6

199

8

200

0

200

2

200

4

200

6

200

8

201

0

201

2

201

4

201

6

201

8

202

0

202

2

202

4

202

6

202

8

203

0

203

2

203

4

203

6

203

8

204

0

204

2

204

4

204

6

204

8

205

0

Sea

Leve

l(m

)

Year

Time series of Median Fremantle sea level (1986-2050)

MSL Predicted Mean SL

Lower Confidence Bound(Min SL) Upper Confidence Bound(Max SL)

Year Mean Lower confidence bound Upper confidence bound

2008 0.79 0.70 0.88

2050 0.96 0.87 1.06

Offset 0.18 0.09 0.27

37

• Lastly, the International Panel on Climate Change compiled a special report on

climate emission scenarios (SRES) which predicted a global temperature

change by 2050 of 0.8-2.6 oC.

A consequence of long term forecasting (40+ years) is the amplification of uncertainty,

which can lead to a somewhat large range of possible values. To take this into account

in our simulations, the group has decided to use two ‘extreme’ scenarios to

supplement the mean scenario of a 1.04-degree increase. These two extreme

scenarios will cover what happens when the mean surface air temperature for 2050 is

in the upper bound of the baseline 2050 prediction, and another when the temperature

is at the lower bound of the predicted baseline 2050 temperature.

To do this, it was decided that we would take the previously predicted baseline mean

temperature for 2050 and we would alter this by selecting a mean that was 1.281

standard deviations above and below the baseline mean. This 1.281 standard

deviations provided us with a confidence interval of 80% which means 10% probability

of exceedance for the upper region and 90% probability of exceedance for the lower

region. Put simply, there is a 90% chance that the lower extreme case for mean

surface air temperature will occur in 2050, and there is a 10% chance that the high

extreme case will occur.

In order to reach a representative result data from the Perth airport site was analysed

due to its long running temperature measurements. An annual mean regression

analysis was then produced for this data. Using only the historical data (not the

predicted part of the regression) the team found the standard deviation of the annual

mean values from the previously calculated regression line, giving the standard

deviation from a moving mean. Using this value for standard deviation, we took the

2050 predicted mean temperature and added (or subtracted depending on which the

scenario) 1.281 times the standard deviation from the regression line. This gives us

two new values for the offset to be used as inputs for the model, the upper value which

in theory has a 10% chance of occurring and the lower value which in theory has a

90% chance of occurring.

Table 6: summary of results from temperature regression and variability analysis. The final predicted

mean, lower and upper bound off set values are shown in red.

3.4.2.3 Flow Volume

In order to predict the flow volumes for the 2050 high extreme and 2050 low extreme

scenarios we needed to quantify the change in flow conditions due to climate change

as well as specify what exactly a ‘best’ and ‘worst’ scenario would entail.

Percentage z z*SD

Maximum expected

Temperature 2050

Offset from (forecast)

2008 value

90 1.281 1.009172 27.1 2.0

50 0 0 26.0 1.0

10 -1.281 -1.00917 25.00 0.0

38

Following from 3.2.2, we use the same regression relationship found from Smith and

Power (2014). However, instead of using the mean annual flow in 2008 as a baseline

we adjusted this starting value to represent either a ‘2050 high extreme’ and ‘2050 low

extreme’ scenario. Upon analysis of the Water Corporation (2016) data on observed

inflow to Perth dams, a strong decline in the magnitude and variance of inflow was

found (Figure 18).

Figure 18: Historical inflow to Perth Dams (Water Corporation (2016).

In consideration of the strong trend, data from post 1975 was used to determine the

upper and lower bounds of the 2050 predicted inflow. By determining the recent (post

1975) standard deviation of inflow into Perth damns from (Smith & Power, 2014),

probability of exceedance plots used to determine the upper and lower bounds of the

10% and 90% exceedance values.

The upper value (10% probability of exceedance) is used as a starting point from which

the regression will continue in the ‘2050 low extreme’ scenario, likewise the lower

value (90% probability of exceedance) is used as a baseline for the ‘2050 high

extreme’ scenario. Applying such method means that we can simulate an extreme

year, but one that is still likely to occur and therefore would bear consideration in the

future. Each case holds a 10% probability of reaching an annual flow of that value, or

more extreme. Over the 42 years from the baseline year up until 2050 the probability

of annual flow reaching or exceeding either scenario’s critical value at least once is

98.8%, based on recent data. The results of the analysis are shown in Table 7 below.

39

Percentage z z*SD

Flow Volume at Walunga

(2050)

Scaling factor from 2008

(181 GL)

90 1.281 15.7 126.1 0.70

50 0 0 110.4 0.61

10

-

1.281 -15.7 94.7 0.52

Table 7: summary of results from flow volume regression and variability analysis. The final predicted

mean, lower and upper bound scaling values are shown in red.

In order to reach our final offset, amount the regression gradient is used to forecast

forward from each scenario specific starting point to 2050. This 2050 value is

compared to the mean 2008 value to reach a final scaling factor.

3.4.2.4 Nutrient Concentration

As described in section 3.2.2, many factors influencing potential trends in nutrient

concentration were considered. Anthropogenic factors such as population rise or

changes in land use were key considerations. Nutrient concentration in runoff form

catchments has also been shown to proportional to rainfall and flow volume (P Kelsey,

2010). Analysis of the influence of such variables on concentration data form the

catchments confirmed that the major influence was rainfall and inflow, and so the team

decided to neglect the influence of population rise and catchment use when setting

2050 high extreme and 2050 low extreme bounds for nutrient concentration. Instead,

the team used normalised concentrations by flow to determine bounds for 2050 high

extreme and 2050 low extreme.

The method used follows from section 3.2.2, where final selection of Nitrogen and

Phosphorous concentrations were based on the normalized concentrations by flow for

all years of the data. Nutrient data of each site location was sourced from the

Department of Water (2018).

We then applied 10% and 90% exceedance probabilities to the normalized data to

give upper and lower bounds for N and P at each site. Each site location/tributary was

scaled to the 10th and 90th percentile for both nitrogen and phosphorous by diving the

percentile by the 2008 concentration at each tributary. The result is a scaling factor for

10th and 90th percentiles that we apply to all nutrient concentrations in the 2008 model

year to achieve a final nutrient concentration with 10% and 90% probability of

exceedance. The normalized concentration curves can be found in the appendices, a

table summarising the nutrient concentration percentile for 2050 high extreme and

2050 low extreme in 2050 are shown below. Note the units in the following tables are

scaling factors, meaning the ratio of concentration of the 10th or 90th percentile over

the 2008 concentration of nitrogen and phosphorus. Mathematically this is represented

as 𝐶10𝑡ℎ 𝑝𝑒𝑟𝑐𝑒𝑑𝑛𝑡𝑖𝑙𝑒

𝐶2008 𝑣𝑎𝑙𝑢𝑒 and

𝐶90𝑡ℎ 𝑝𝑒𝑟𝑐𝑒𝑑𝑛𝑡𝑖𝑙𝑒

𝐶2008 𝑣𝑎𝑙𝑢𝑒.

40

10%

exceedance

Bayswater

Drain

Bennet

Brook

Canning

River

Ellen

Brook

Helena

River

Jane

Brook

Susannah

Brook

Upper

Swan

N 0.852 0.572 0.584 0.692 0.573 0.349 0.423 0.445

P 0.81 0.783 0.763 0.526 0.634 0.356 0.690 0.404

90%

exceedance

Bayswater

Drain

Bennet

Brook

Canning

River

Ellen

Brook

Helena

River

Jane

Brook

Susannah

Brook

Upper

Swan

N 1.189 0.833 0.969 1.036 0.987 0.896 1.307 1.07

P 2.361 1.286 1.254 1.235 2.154 1.400 2.075 1.357

Tables 8: Scaling factors for both nitrogen and phosphorous applied to the 2008 concentration

data to achieve 10% and 90% probability of exceedance at all sites.

3.4.3 Key Parameters

3.4.3.1 2050 Low Extreme (sim 4)

Boundary Conditions:

Air Surface Temperature (OFFSET) -0.0

Sea Level Rise (OFFSET) +0.09m

Flow rate (SCALAR) Scaling factor of 0.697 to all inflow

Nutrient load (SCALAR) 90% scaling factor specific to each drain

3.4.3.2 2050 High Extreme (sim 5)


Air Surface Temperature (OFFSET) +2.01

Sea Level Rise (OFFSET) +0.27

Flow rate (SCALAR) Scaling factor of 0.523 to all inflow

Nutrient load (SCALAR) 10% scaling factor specific to each drain

3.4.3.3 2050 Summer Flood Event (sim 6)




Flow rate Superimpose Dec 1st to March 31st 2000

Nutrient load Superimpose Dec 1st to March 31st 2000

41

3.4.3.4 “Mean Year 2050” No Inflow (sim 7)




Flow rate (SCALAR) 0

Nutrient load (SCALAR) 0 0

3.5 MANAGEMENT SIMULATIONS

The methodologies for the management scenarios focus upon the inputs of the

SCERM model. By altering minimal inputs of the SCERM model, from the baseline

2050 simulation, analysis of these changes and identification of key drivers of change,

thus ensuring an effective and conclusive evaluation of the outputs.

3.5.1 Current Oxygenation Strategy

3.5.1.1 Motivation

The current oxygenation simulation is to demonstrate how the current oxygenation

management strategy will impart the Swan-Canning Estuary in the year 2050. This

simulation uses simulation 2: 2050 baseline as the reference simulation for modelling,

by adding the two implemented artificial oxygenation plants (Guildford and

Caversham) back to the model.

A study was undertaken to determine oxygen load and running hour of the current

oxygenation plant. The oxygen levels in the bottom layer of the Swan-Canning in

Upper Swan will fall below the bench line 4mg/L during the seventeen-day time period

between the 21st of January to 7th of February 2050 by predicting from simulation 2.

The decision has therefore been made to run the two artificial oxygenation plants at a

base oxygen load of 30kg per hour for all 24 hours in a day from 21st of January to 7th

of February 2050 to interpret the effect that these plants are likely to have on the

oxygen levels in the year 2050.

3.5.1.2 Method

Maintaining elevated levels of dissolved oxygen (DO) within estuarine environments

is essential to sustaining the health of the aquatic organisms within the ecosystem

(Bailey & Ahmadi 2014). An artificial oxygenation strategy was developed by the

Department of Parks and Wildlife in collaboration with Department of Water and the

Swan River Trust in response to increasing hypoxic and anoxic conditions within the

Swan-Canning Estuary caused by a number of environmental factors (DBCA

2015). At present, there are five oxygenation facilities located within in the Swan-

Canning Estuary that each cycle water from the Swan-Canning through the system

42

and supersaturate the estuarine water with dissolved oxygen to enhance oxygenation

in the system. This supersaturated oxygenated estuarine water is then released back

into the system via instruments that sit at the bottom of the Estuary to diffuse the

oxygen into the system effectively (DoW 2015). It is understood that hypoxia develops

when vertical stratification and warm water occur simultaneously and as temperatures

are expected to rise in the year 2050 we expect to see significantly higher levels of

hypoxia in the system (Stanley & Nixon 1992). The aim of the oxygenation plants is to

enhance the oxygen content throughout the stratified layers from the top layer to the

bottom layer (i.e. throughout the water column) in order to reduce the stressed areas.

These oxygenation facilities are located in areas within the Swan-Canning that have

been identified by the Department of Parks and Wildlife to be impacted by significant

levels of anoxia, there are two oxygenation plants located in the Upper Swan, in the

suburbs of Caversham and Guilford and there are three oxygenation plants located in

the Canning River, the Bacon, Camsell and Nicholson oxygenation facilities (Figure

19). The oxygenation management strategy began in 1998 in the Canning River

system where two artificial oxygenation plants were installed to combat the anoxic

conditions in the system. The systems were monitored to determine their effectiveness

in the Estuary and it was found that oxygen levels were enhanced within a 5km radius

of the plants (DBCA 2015). The strategy was extended to the Upper Swan Estuary

where another two artificial oxygenation plants were installed in 2008 to combat the

anoxic conditions in the Upper Swan caused by the high nutrient load and poor

flushing abilities that are present in the upper reaches of the Estuary. These systems

are understood to increase the oxygen levels in the Estuary within a 10km impact zone

of the plants, 10km upstream and downstream of these facilities. A third oxygenation

plant was installed upstream from the Kent Street Weir in the Canning River in 2014

as there was further need for oxygenation as anoxic conditions were becoming more

common (Hipsey et. al. 2014). Sampling and previous model simulations have shown

that oxygenation plants in the Upper Swan improve oxygen conditions in 39-92% of

the 10km target zones (Department of Water, 2015).

43

Figure 19. The locations of the Guilford, Caversham, Bacon, Camsell and Nicholson artificial oxygenation

facilities in the Swan-Canning Estuary (DWER 2015).

The purpose of this model run is to demonstrate how the current oxygenation

management strategy will impact the Swan-Canning Estuary in the year 2050. To

allow for an effective interpretation of the impact that is had on the system by these

oxygenation facilities it is assumed that the only management strategy undertaken for

the Swan-Canning in 2050 is the oxygenation strategy that has been in place since

2014. The model does not extend past the man-made barrier at Kent street weir and

therefore the area of the canning river where the oxygenation plants are located is not

included in the model. Only the two oxygenation plants that are located in the Upper

Swan are in included in this analysis.

The time period input concentrations of these artificial oxygenation facilities where

used by Hipsey et al (2014) when determining the benefit of the facilities in 2014. This

time series concentration data was added to the baseline 2050 model inputs

(simulation two). The inputs of these artificial oxygenation facilities were deciphered

for the purpose of a study prepared by the Swan River Trust that modelled in the

oxygen dynamics of the Upper Swan Estuary and Canning Pool to create an optimal

oxygenation strategy for the system (Hipsey et. al. 2014). The oxygenation plant input

rates were calculated based on oxygenation consumption data logs that are monitored

as part of the oxygenation management program run by the Western Australian

Government. The specifications of each plant flow rate were incorporated into the

model inputs, and it was configured so that the supersaturated oxygenated flow would

44

be released over the bottom 1m of the centre cell across the appropriate locations in

the estuary (Hipsey et. al. 2014). This approach was validated using oxygen and

salinity data profiles of the area for the dates at which they were simulated (2008 and

2010) and was found to be an effective with a mean absolute error of 25% for predicted

oxygen values over the simulated years. We can therefore be confident of our results

for oxygenation in the Swan-Canning Estuary for 2050.

The artificial oxygenation facilities located in the Upper Swan Estuary each release a

base load of 30kg/hr of O2 and a heightened load of 60kg/hr (Hipsey et. al. 2014). The

facilities begin releasing oxygen into the system when oxygen levels fall below 4mg/L,

as levels of poor oxygen conditions within the Swan-Canning Estuary can be defined

as any value less than 4mg/L (

Table 9)

Table 9. Oxygenation classifications for the Swan-Canning Estuary (Hipsey et. al 2014).

The model does not allow for these artificial oxygenation facilities to be systematically

turned on and off when the oxygen levels in the system fall below 4mg/L. It is for this

reason that the results of simulation two were inspected to decipher an appropriate

time to “turn on” the artificial oxygenation facilities in the Upper Swan Estuary. Due to

time constraints it was only feasible to model the first three months of year 2050 to

allow for analysis. Figure 20 shows the results from Simulation Two at Middle Swan

Bridge. Using Figure 20 we have predicted that the oxygen levels in the bottom layer

of the Swan-Canning in this area will fall below the bench line 4mg/L during the

seventeen-day time period between the 21st of January to the 7th of February 2050.

The decision has therefore been made to “turn on” the artificial oxygenation facilities

during this period (with an estimated base loaf of 30kg/hr) to interpret the effect that

Classification Concentration

Low Oxygen < 4mg/L

Hypoxia < 2mg/L

Anoxia 0 mg/L

45

these facilities are likely to have on the oxygen levels in this area in the year 2050. As

can also be seen in Figure 20 there seems to be frequent occasions where the bottom

oxygen levels fall below the benchmark 4mg/L, it was decided that the artificial

oxygenation facilities would not be “turned on” during this time to allow for defined

analysis. Through our analysis we were able to determine the effectiveness that the

artificial oxygenation facilities will have over time by isolating a time of activation and

analysing the residual results from the input. The facilities were “turn on” permanently

and maintained a constant inflow during this period to combat the low oxygen levels

in the system at the time.

Figure 20 Oxygen levels at the Middle Swan Bridge from January until March. As can be seen from the figure,

levels of oxygen fall below 4mg/L between January 21st and February 7th.

3.5.2 Enhanced Oxygenation Strategy

3.5.2.1 Motivation

Oxygen levels in areas of the Upper Swan are still likely to fall below 4mg/L in 2050

regardless of the two artificial oxygenation plants in the Upper-Swan Estuary from the

results obtained for simulation 3. An enhanced oxygenation simulation was decided to

run for assessing the benefit that an enhanced oxygenation strategy is likely to have

on the Swan-Canning Estuary in 2050.

One additional artificial oxygenation plant was decided to add next to the Nile Street

monitoring site. All initial conditions and operation conditions of this additional

46

artificial oxygenation plant were consistent with the conditions for simulation 3, this

will allow for an accurate interpretation of the impact that an additional artificial

oxygenation plant is likely to have on the oxygen levels within the Swan-Canning

Estuary for the year 2050

3.5.2.2 Method

The results from simulation three, section 4.3.1, were analysed and it was concluded

that regardless of the two artificial oxygenation systems installed the Upper Swan

estuary oxygen levels in the areas of the Upper Swan are still likely to fall below 4mg/L

in 2050, Figure 21. It was decided that a simulation will be run of an enhanced

oxygenation strategy, allowing for an assessment of the benefit that an enhanced

oxygenation strategy is likely to have on the Swan-Canning Estuary in 2050.

Figure 21 Dissolved oxygen levels at Nile street

The oxygen outputs of simulation three were analysed to determine the most

appropriate location of the additional oxygenation plant(s). It is understood that high

salinity and high levels of stratification in the Lower Swan Area (from Blackwall reach

to Heathcote) will negatively impact the ability of the oxygen to mix throughout the

water column (Hipsey at. al. 2014). It is also understood that the inundation of

terrestrial vegetation and sediments into the system is increased in the Upper-Swan

Estuary, the decay of this vegetation and introduction of sedimentation is likely to lead

to a dramatic decrease in dissolved oxygen (Kneis, Forster & Bronstert 2009). It is for

47

these reasons that the decision was made to keep the artificial oxygenation strategy

within the Upper-Swan area and as these artificial oxygenation systems are estimated

to have an impact within the 10km impact zone of the system only one additional

artificial oxygenation system was added. It is important to note however, that the

impact zone is highly dependent on the location and seasonal changes in the system

and this will likely affect the impact of the new oxygen plant (Hipsey et al, 2014). This

oxygenation facility was “added” just next to the Nile Street monitoring site to ensure

that the result analysis will present the maximum impact on the area.

Figure 22 Location of the artificial oxygenation systems, including the simulated “additional oxygenation system”,

in the Swan-Canning Estuary along with the location of the seven study areas.

All initial conditions were consistent with the initial conditions for simulation three,

aside from the addition of the single artificial oxygenation system at Nile Street. The

inputs for the artificial oxygenation system are the same for the oxygen plant flow rate

as quantified by Hipsey et. al. (2014) and where combined with the estuarine water

specifications at Nile Street to simulate the addition of the third oxygenation facility. It

is assumed, as it was for simulation three, that the oxygenation facility is producing a

base load of 30kg/hr of dissolved oxygen and it will also be run for the same duration,

24 hours a day from January 21st until February 7th, 2050. The simplistic approach

will allow for an accurate interpretation of the impact that an additional artificial

oxygenation facility is likely to have on the oxygen levels within the Swan-Canning

Estuary for the year 2050.

48

3.5.3 Nutrient Reduction

3.5.3.1 Motivation

In this simulation, a study was undertaken on how each of the models input parameters

expected to change between the time of baseline reference year 2008 and the target

year 2050 as a result of nutrient change. Specifically, the study investigates the effects

of changes in Nitrogen and Phosphorus levels. It was planned to reduce Nitrogen and

Phosphorus levels by 48% and 46% respectively. This reduction target is based on

projections of the Streamflow Quality Affecting Rivers and Estuaries (SQUARE) Model

and the framework for the Nutrient Offset Contributions Scheme for the Swan-

Canning catchment (Kelsey et. al. 2010; BDA Group 2008).

The simulation does not include oxygenation plants. To run a forecasting simulation

that has the combined effects of both oxygenation plant and nutrient reduction, which

is very likely to be applied in reality, the 2050 forecasting simulation of nutrient

reduction without oxygenation plant is necessary. Furthermore, through a forecasting

simulation that has the effects of both oxygenation plant and nutrient reduction, how

the oxygenation plant affects the nutrients level could be investigated.

3.5.3.2 Method

Changes in the nutrients levels of the Swan-Canning Estuary will impact the growth

rates of various algae species thus impacting the overall health of the system (De

Roach 2006; Turner et. al. 2006). With nitrogen and phosphorous values increasing in

the Swan-Canning Estuary through the effects of an increasing population and climatic

responses the Swan River Trust along with the Government of Western Australia and

the Department of Water (DoW) have outlined a plan to reduce the Nitrogen and

Phosphorus levels in the Swan-Canning Estuary by 48% and 46% respectively

(Kelsey et. al. 2010). This reduction target is based on projections of the Streamflow

Quality Affecting Rivers and Estuaries (SQUARE) Model and the framework for the

Nutrient Offset Contributions Scheme for the Swan- Canning catchment (Kelsey et. al.

2010; BDA Group 2008).

The Nutrient Offset Contributions Scheme was developed for the Swan River Trust by

the BDA Group in 2008. The Scheme investigates the financial, legislative and

implementation constraints that surround offsetting residual loads of nutrients from

new developments into the Swan-Canning Estuary in an attempt to improve water of

quality of the system. A reduction in nutrient inflow into the system is proposed by

ensuring that all new land developments within the Swan-Canning Catchment area

manage the runoff caused by the development by making offset contributions

equivalent to the residual loads contributed by the developer. It is proposed that these

contributions be collected into a ‘Nutrient Management Fund’, run by the Swan River

Trust that is used to mitigate and manage nutrient runoff caused by new land

developments to deliver equivalent nutrient reductions (BDA Group 2008).

The predictive SQUARE Model has allowed for the ability to determine the potential

reduction in nutrients in thirty sub-catchments across the Swan-Canning Estuary thus

49

ensuring that realistic and effective nutrient targets are proposed (Kelsey et. al. 2010).

The model was able to identify the sub-catchments with the highest nutrient loads thus

allowing for the effective implementation of management methods in the sub-

catchments of high nutrient contributions and the ensuring that sub-catchments with

acceptable water quality be maintained. The SQUARE Model also allows for the

differentiation between rural and urban sub-catchments as the nutrient contributions

from rural and urban areas is likely to be very different and this allows for the focus of

the management methods to altered for urban and rural areas (Swan River Trust

2009). The nutrient reduction targets are variant across each of the thirty sub-

catchments as can be seen below in Figure 23. The maximum proposed Nitrogen

reduction the Swan-Canning Estuary is 1.0mg/L and which equates to a total reduction

in nitrogen across the system of 48%. The maximum proposed Phosphorus reduction

in the Swan-Canning Estuary is 0.1mg/L which equates to a reduction in phosphorus

across the system of 46%. It is important to note that these reduction targets are higher

than the Estuarine threshold targets proposed by Anders and Schroeder (2003) as

they consider the financial, legislative and implementation constraints associated with

any proposed management methodologies.

50

Figure 23 Total Nitrogen and Total Phosphorus target concentrations for the thirty sub-catchments within the

Swan-Canning Estuary (Kelsey et. al. 2010).

To determine the effectiveness of reducing the nutrient concentrations in the Swan-

Canning Estuary the model will be run for the year 2050 with an overall nutrient

reduction. The Nitrogen and Phosphorus concentrations will be reduced by 48% and

46% respectively for each of the seven regions in respect to the concentration values

noted in the 2008 baseline year, as per the prescribed reductions presented by the

DoW in 2010. That is, we have assumed for this model output that all tributaries in the

Swan-Canning Estuary have had a reduction in Nitrogen and Phosphorus load into

the system by 48% and 46% respectively by the year 2050. A single, overall

percentage reduction for all of the tributaries in the Swan-Canning Estuary was chosen

for simplicity, as the purpose of this assessment is to evaluate the effect that a

51

reduction in nutrients is likely to have on the state of the Swan-Canning Estuary and

a total reduction of nutrients will present an effective result for this purpose. The load

targets have been derived using the climate sequence for the period 1997 to 2006 and

the targets will be different if deduced from a different time period as load is dependent

on local rainfall and inflow patterns (Swan River Trust 2009). The results from this

model output are simplistic approximations of the effects that the proposed nutrient

reduction removal strategy, from the Swan Canning Water Quality Improvement Plan,

will have on the Swan-Canning Estuary by the year 2050.

52

4 RESULTS AND ANALYSIS

4.1 REFERENCE CONDITIONS

Figure 24: Simulation 1 (the Characteristic Baseline) vs Simulation 2 (2050 Baseline) Dissolved Oxygen (DO) Plots. The top plot (a) shows the values for the sites in the surface layer of the domain. Similarly, the bottom plot (b) shows the oxygen concentrations in the benthic cells (bottom layer). From left to right the x-axis shows the various monitoring sites across the river, Blackwall Reach (BLA), Armstrong Spit (ARM), Heathcote (HEA), Nile St (NIL), St John of God Hospital (STJ), Success Hill (SUC) and Middle Swan Bridge (MSB), from west to east of the model domain. The red line shows the hypoxic threshold of 2mg/L. The errors bars show the maximum and minimum values for DO concentrations for each site.

53

Figure 25: Simulation 1 (The Characteristic Baseline) vs Simulation 2 (the 2050 Baseline) time series plots over one year at MSB (Middle Swan Bridge), in Upper Swan. The top plot (a) shows the dissolved oxygen time series and the bottom (b) shows the salinity time series plot. The red line shows the hypoxic threshold value of 2mg/L.

54

Figure 26: Simulation 1 (the Characteristic Baseline) vs Simulation 2 (the 2050 Baseline) salinity plots. The top plot (a) shows the values for the sites in the surface layer of the domain. Similarly, the bottom plot (b) shows the salinity in the benthic cells (bottom layer). From left to right the x-axis shows the various monitoring sites across the river, Blackwall Reach (BLA), Armstrong Spit (ARM), Heathcote (HEA), Nile St (NIL), St John of God Hospital (STJ), Success Hill (SUC) and Middle Swan Bridge (MSB), from west to east of the model domain. The errors bars show the maximum and minimum values for salinity (psu) for each site.

55

Figure 24 shows comparative results of the DO concentrations between the

Characteristic Baseline Simulation and the 2050 Baseline Simulation, for each site

along the river. From left to right the x-axis shows the various monitoring sites across

the river, Blackwall Reach (BLA), Armstrong Spit (ARM), Heathcote (HEA), Nile St

(NIL), St John of God Hospital (STJ), Success Hill (SUC) and Middle Swan Bridge

(MSB), from west to east of the model domain, and are shown in Figure 7

There was an overall decrease in median and Inter Quartile Range (IQR), or the likely

range of DO concentration values for 2050 for the benthic cells (Figure 24b) at each

site. This was also the case for the surface cells (Figure 24a), though to a lesser extent,

with the percentage differences between the two medians no greater than 3% (MSB)

for the surface values. This is in comparison to the DO benthic concentrations for the

2050 Baseline Simulation differing from the median values of the Characteristic

Baseline Simulation by up to approximately 35% (STJ). This significant difference

between the two Simulations suggests that typically values of DO concentrations can

be expected to be reduced by the year 2050.

Also, from the results in Figure 15b, the site MSB (Middle Swan Bridge, Upper Swan),

the eastern extent of the estuary and model bounds, showed the largest change in

IQR from the Characteristic Baseline Simulation to the 2050 Baseline Simulation. This

site showed an increase in the likely range of DO concentrations in the 2050 Baseline

of 47% from the range of DO values in the Characteristic Baseline.

Patterns across the complete time series results for DO (see Appendix E for the plots

for all 7 locations in the system), do not deviate substantially between the two

Simulations, at least for the majority of the locations along the river. The four locations

at the greatest distance from the coast all experienced low oxygen (hypoxic), during

both of the simulations.

To builds on the trends identified for MSB (Upper Swan) in Figure 24, unlike other

locations, MSB experienced significant differences between the DO results of the two

Simulations. As can be seen in Figure 25a, a maximum difference in concentration of

5.5mg/L occurred in June for this site. This was during a prolonged hypoxic event at

this location from the beginning of April until the end of May, where the benthic DO

concentrations fell below 2mg/L for the 2050 Baseline Simulation. Importantly, the

Characteristic Baseline Simulation did not mimic this two month long low oxygen

event, discussed in section 5.1.

Another significant point of interest from Figure 25a, is the hypoxic event during

February (this event occurred in both Simulations). The lowest hypoxic DO

concentration occurs at this time, MSB, falling close to anoxic levels for the 2050

Baseline Simulation, though both Simulations experienced hypoxic levels at this time

period. The February low oxygen event also aligns with a similar timed significant

stratification event, with salinity differences between surface and benthic regions

averaging around 6 psu for the duration, as shown in the bottom plot, Figure 26b. This

56

was the strongest stratification event, meaning the event showing the greatest

difference in salinity between the top and benthic cells, for both Simulations.

During the 2050 Baseline Simulation, other locations, further downstream, also

experienced deviations from the patterns of the Characteristic Baseline, though not to

the magnitude of that shown at MSB. The results of both STJ and SUC showed shorter

stints of low oxygen events, where the DO concentrations for the benthic cells fell

below hypoxic levels for the Characteristic Baseline STJ in September, but not in the

2050 Baseline, see Appendix E.

Additionally, the change in salinity, for each site, between the two Simulations is shown

in Figure 26. The median and IQR of salinity values (psu) were consistently larger for

the 2050 Baseline than the Characteristic Baseline, which was exaggerated for the

benthic cells. The results of Figure 26b show a trend of increasing salinity, particularly

for the Upper Swan, or the eastern extent of the Estuary. The complete set of time

series plots comparing salinity between the two Simulations can be found in Appendix

E. In general, there appeared to be no notable differences in the duration of

stratification events between the two Simulations for the different sites across the river,

stratification events were present at approximately the same time and for similar

durations. The intensity of stratification, or lack of mixing, in the river was clearly

exacerbated slightly for those sites further up river (STJ, SUC and MSB, Appendix E

for the full time series plot). In general, stratification was considered to occur when

there was significant separation in salinity values (psu) between the benthic and

surface layers, at the same point in time, as discussed in section 2.4.

A clear example of stratification can be seen in Figure 25b, for the period from the start

of April to the end of May there was a clear difference in salinity trends between the

two simulations. The Characteristic Baseline shows no distinctive separation between

the salinity surface and benthic values during this time, whereas there was a maximum

difference in salinity of approximately 10 psu for the two layers of the 2050 Baseline

Simulation. This clear stratification event at this location, coincides with the time of

year of the prolonged anoxic conditions identified for the 2050 Baseline Simulation, in

Figure 25a.

57


4.2.1 2050 Low and High Extreme (sim 4 and 5)

4.2.1.1 Salinity

Salinity results display a strong trend of increased vertical stratification as the model

scenario becomes more extreme from the low, mean to the high extreme run (sims

4,2,5). This is most prominent in the Upper Swan Region but is also seen throughout

the lower to middle reaches. Figure 27 shows the vertical stratification at Middle Swan

Bridge from early April through to July, displaying strong stratification for high extreme

(sim 5), but very minor stratification for low extreme (sim 4). Similarly, the level of

vertical stratification at Nile street (Figure 28) in the ‘low extreme’ simulation is much

smaller than the stratification seen in the ‘high extreme’ simulation.

Comparison of the box plot diagrams for surface and bottom waters (Figure 30, Figure

31) helps to explain the increase in stratification seen for the ‘high extreme’ run. The

‘high extreme’ run has a much smaller variability in the surface profile than the bottom

profile for the upstream regions. What this indicates is that the surface layer remains

relatively fresh but the bottom layer, even upstream, is fluctuating between relatively

saline and fresh depending on the time of year. Of the four parameters within the

model that differ between runs (sea level, flow volume, temperature, nutrient

concentration), there are two parameters in the model that are most likely to influence

the contrast in vertical stratification seen; sea level rise and flow volume reduction.

Figure 29 displays the results for salinity in the lower Swan at Blackwall Reach. Very

minor difference between the low, mean and high extreme scenarios are observed,

and this provides insight as to the cause for the increased stratification shown in the

middle to upper reaches of the Swan. The Swan River has been shown to typically

fluctuate between a pattern of becoming a highly vertically stratified salt wedge estuary

and a slightly vertically stratified estuary, where salinity increases towards the sea but

varies little with depth (Thomson, 2001). The latter often occurs when flow is low and

the tidal or wind forces dominate the hydro dynamics. Hence, as expected we see

higher stratification in the upper reaches at times of high flow in the winter months.

What is counter intuitive, is that we see higher vertical stratification for the ‘high

extreme’ run, which has lower inflow.

A suggestion for such observation is that the flow levels are high enough in all runs to

create a clear salt wedge at times of high flow, shown by the freshwater floating on the

surface in the upper reaches for all simulations. The flow has not been reduced to a

point the prevents the formation of the salt wedge and the development of two distinct

layers. The surface salinity profile for the Upper Swan (Figure 27) region shows the

salinity is equal for all runs at times of high flow in (April), but coming out of this into

May and June the high extreme run salinity rises much faster than the low extreme

run. The bottom profiles differ, where the ‘high extreme’ run has higher salinity

throughout the entirety of the run causing increased vertical stratification to the fresh

surface water.

58

As we observe what happens further downstream over the months of April to August,

for example Nile Street (Figure 28), the bottom profile is similar, but the surface profile

now differs between runs. What this suggests is that the lower inflow in the ‘high

extreme’ run allows the salt wedge to creep further upstream during the winter months.

A key point is that the reduction in flow volume is not sufficient to reduce the effect of

the salt wedge and vertical stratification, but instead effects the location in the river

where it may have its greatest effect. The differences in sea level rise are also likely

to have contributed to such changes in hydrodynamics, pushing the salt wedge

upstream with an increased volume of saline water. The implications of this are

significant, and such effects are explored further in ‘no inflow’ simulation, where the

inflow is reduced to zero.

Figure 27: Salinity (psu) results for upper Swan River. All three simulation scenarios are displayed, shown by the

lower extreme (sim 4), mean (sim 2) and upper extreme (sim 5).

59

Figure 28: Salinty (psu) for middle Swan River. All three simulation scenarios are displayed, shown by the lower

extreme (sim 4), mean (sim 2) and upper extreme (sim 5).

Figure 29: Salinty (psu) for lower Swan River. All three simulation scenarios are displayed, shown by the lower


60

Figure 30: Salinty (psu) box plots for surface. All three simulation scenarios are displayed, shown by the lower


Figure 31: Salinty (psu) box plots for bottom. All three simulation scenarios are displayed, shown by the lower


61

4.2.1.2 Dissolved Oxygen

Dissolved Oxygen results for the low, mean and high extreme show similar trends in

the lower to middle upper reaches of the Swan but begin to show contrasting results

as one progresses further upstream. In the upper reaches we see more extreme low

DO events for the bottom water profile of the ‘high extreme’ run, and a greater number

of events where the DO falls below the hypoxic threshold (Figure 32, Figure 33). The

differentiation between scenarios in low DO events is seen more prominently in the

autumn and winter months for the upper region (Figure 32), and in the spring/summer

months for the middle swan (Figure 33). Most notably, at St John Hospital (Figure 33),

from September to December we see seven separate occurrences of DO falling below

the hypoxic threshold in the ‘high extreme run’, and only two events for the ‘low

extreme’ run.

The box plot profiles for the surface and bottom (Figure 36, Figure 37) show the

surface profiles for each run are similar throughout the length of the Swan system.

However, the bottom profile dissolved oxygen in the middle to upper reaches shows

much greater variation between runs, with the ‘high extreme’ run displaying a lower

mean DO and greater variation in dissolved oxygen.

The greatest low DO events occur in phase with the greatest level of stratification. As

has been outlined by Thomson (2001), it is typical to see low DO conditions beneath

highly vertically stratified regions. The differences seen between the low and high

extreme scenarios may in part be explained by this, as regions of more intense

stratification show more extreme low DO events. The difference in temperature offset

between the low and high extreme runs is also likely to contribute to difference in DO

we observe, as the capacity for water to retain oxygen increases with temperature.

Warmer water temperatures also result in increased sediment oxygen demand (Swan

River Trust, 2007).

62

Figure 32: Dissolved Oxygen (mg/L) for upper Swan River. All three simulation scenarios are displayed, shown

by the lower extreme (sim 4), mean (sim 2) and upper extreme (sim 5).

Figure 33: Dissolved Oxygen (mg/L) for upper/ middle Swan River. All three simulation scenarios are displayed,

shown by the lower extreme (sim 4), mean (sim 2) and upper extreme (sim 5).

63

Figure 34: Dissolved Oxygen (mg/L) for middle Swan River. All three simulation scenarios are displayed, shown

by the lower extreme (sim 4), mean (sim 2) and upper extreme (sim 5).

Figure 35: Dissolved Oxygen (mg/L) for lower Swan River. All three simulation scenarios are displayed, shown by

the lower extreme (sim 4), mean (sim 2) and upper extreme (sim 5).

64

Figure 36: Dissolved oxygen box plots for surface. All three simulation scenarios are displayed, shown by the

lower extreme (sim 4), mean (sim 2) and upper extreme (sim 5).

Figure 37: Dissolved oxygen box plots for bottom. All three simulation scenarios are displayed, shown by the lower


65

4.2.2 Year 2050 Summer Flood Event (sim 6)

Figure 38: Observed flow data form the upper Swan used in sim 6.

The observed 2000 flood flow data indicated that the event started around the 22nd of

January, reached a peak on the 25th which was sustained for 4 days until the 29th,

after which it gradually dissipated and returned to normal levels on the around the 25th

of February. The scale of such an event was unprecedented given the

characteristically dry summers of the region.

0

50

100

150

200

250

300

Fow

Vo

lum

e m

3/s

Flow Volume: Upper Swan

66

4.2.2.1 Salinity

Figure 39. The salinity results indicate a rapid flushing of the system and decrease in stratification until the end of

February as conditions begin to return to normal. This pattern is common to simulated sites along the Swan

Canning

Algal species require the absence of highly saline waters for rapid growth. For many

freshwater algal groups such as Microcystis the critical value is around the 10 psu

level (Atkins et al., 2001). A simple check of the simulated results in Figure 39 indicates

that conditions at STJ are well within the required levels for an algal bloom. Cross

referencing with observed values from the same time period affirms that this process

has been suitably reproduced in the simulation. It is interesting to note that after the

flow stops in late February it takes approximately one month for salinity to return to

pre-flood levels following a rapid intrusion of the saltwater wedge.

67

Figure 40. The observed measurements of salinity strongly match the rapid flushing and slow recovery of saline

waters seen in the modelled scenario (Figure 39) (DWER 2018).

4.2.2.2 Total Nutrients

Excessive concentrations of phosphorous is the most common cause of eutrophication

in freshwater lakes, reservoirs, streams, and in the headwaters of estuarine systems

(Correll, 1999; Schindler, Carpenter, Chapra, Hecky, & Orihel, 2016). In the ocean,

nitrogen is generally considered to be the limiting nutrient controlling primary

production (Correll, 1999). Estuaries are a somewhat of a transition zone in regard to

salinity and algal group dominance. In these conditions both excessive nitrogen and

phosphorous can both potentially contribute to algal blooms (Correll, 1999). However

for algal blooms caused by the resulting flow of intense rainfall events form in

predominantly fresh highly flushed conditions. In these cases fresh water algal groups

(such as Microcystis) are more susceptible to rapid growth and formation of bloom

conditions. As such it may be more pertinent to consider total pohospurious as the the

limiting nutrient for scenarios such as the one modelled here.

68

Figure 41. Total Phosphorous for STJ did not reach excessive values that would suggest a eutrophied system

nor would it suggest a bloom is particularly likely.

Comparing to observed data for the same timescale in 2000 (Figure 42) suggests that

levels of phosphorous predicted in the simulation were not uncharacteristic for the

system previous to the flood. A mild peak in surface concentration for STJ was

observed that matches the peak of the flow data (Figure 38), however this spike

returns to normal levels almost half a month before flow rates return to typical values.

This may suggest the system has been flushed to such a degree that initially large

amounts of nutrients have flown out to sea. However, the same flow rates and

concentrations caused a bloom in 2000. It would be intuitive to come to the conclusion

that the sharp and premature decline of phosphorous in the system was caused by

the sequestration of the nutrient by a large bloom of algae. Indeed, after the flow (and

accompanying nutrients) returns to a minimal level the subsequent increase in total

phosphorous on the bottom of the system (dotted line Figure 41) could represent the

detritus sequestered phosphorous of a falling biomass of algae. If this was the case,

we would expect to see a rapid decline in oxygen near to the riverbed as the algal

biomass settles and is anaerobically decomposed. Such a decrease is observed in the

simulation and is displayed in Figure 45, however, this could alternatively indicate a

return of the salt wedge and associated stratified conditions, or a return to pre-flood

quasi-steady conditions (hypoxic conditions were present immediately prior to the

flood).

69

Figure 42. Observed data for total phosphorous taken at varied depths (DWER 2018)

Figure 43.Total Chlorophyll-a counterintuitively decreases as conditions for algal growth improves, and

decreases further post flood event.

70

Figure 44. Low levels of TCHLA were simulated relative to observed data taken in the same period (DWER 2018)

If the model had indeed simulated a large algal bloom a sure indicator of this would be

an increased level of chlorophyll-a (a depth-averaged measurement used to represent

the concentration of algal biomass in the water column). There is a small increase in

total chlorophyll-a after the initial flushing of the system (Figure 43), however this

increase appears small in proportion to the levels of conditions that contribute to algal

bloom risk. Indeed, observed data (although discontinuous) shows a much higher

variability (between 4 and 100 ug/L) and increased level of chlorophyll-a (average of

16 ug/L) for the same period (Figure 44). In explaining this, there exists the possibility

that the model wasn’t able to capture the extent of biogeochemical processes in this

case. These processes are still under development, and particularly for some algal

groups there is a low correlation between predicted and observed values (SCERM

2016). Additionally, in order to save on simulation time, the model used in this study

utilised a limited biogeochemical module, as such plankton group results are highly

speculative. For the resolution required in this case (approximately 1 month)

reproduction of real world results may be variable. This may explain some

discrepancies between results, such as causal factors and algal biomass.

Modelling limitations aside, the quantitative and temporal sensitives that must be

achieved for an algal bloom to occur are highly variable in real world and simulated

conditions. The dynamics in river-estuary conditions are subject to a range of complex

couplings and forcings, not just water quality. Tidal coupling, day night cycling,

meteorological conditions and a variety of local factors all must ‘align’ for a rapid

growth of algae to be possible. The ability to capture the sensitivities and interactions

of these processes are beyond current understanding and as such it is difficult to

71

reproduce single events consistently. As such it is recommended that only the results

of the physical parameters (dissolved oxygen and salinity) be considered to be

representative here.


Figure 45. Rapid decline in bottom dissolved oxygen (dotted line) may seem to indicate anaerobic decomposition

of algal biomass. However, it is more likely that this is due to salinity stratification or other external forcings as

only a small amount of biomass (Tchla) was simulated.

4.2.3 “Mean Year 2050” No Inflow (sim 7)

4.2.3.1 Salinity

Lack of inflow from rivers and drains, as well as excessive evaporation of fresh water

throughout the estuary can result in a highly saline system. We use the Nile Street and

St John Hospital results to make specific comparisons to the mean 2050 run, as they

display similar salinity levels from January to April, but then strongly contrasting levels.

The comparison between the two simulations in Figure 46 clearly demonstrates the

contrast between a riverine flushed estuary and one that does not experience any

fresh water incursion. In the 2008 baseline simulation (blue line in Figure 46) heavy

spring rainfall in April and normal winter rainfall can be seen causing rapid decreases

in salinity through flushing. However, any such flushing is absent in the no inflow

simulation (shown in Figure 46 as the red time series). Other sites show the same

pattern and be viewed under appendices.

72

Figure 46: Nile Street salinity time series (middle Swan River). Note the initial dramatic increase in simulated

salinity (red) approaching 2008 levels (blue) by Febuary despite an artifically lower initial salinity due to modellin g

error.

Figure 47: Salinity time series for St John Hospital in the middle Swan. As the initial 2008 salinity is near the

erroneous initial 20 psu of the simulation the St John Hospital provides the only accurate indication of e arly

salinity rise and deviation due to spring rainfall.

Another key point in the salinity results is the difference in stratification between runs.

Figure 46 and Figure 47 show extreme vertical stratification in sim 1 (2008 mean

73

baseline), most notably from May to August shown by the difference between the top

and bottom salinity levels (dotted and full blue lines). In comparison, the no inflow 2050

run shows enhanced salinity but to no levels of vertical stratification. These differences

in hydrodynamics have significant impacts on the biology and other water quality

parameters of the system, most notably dissolved oxygen (DO) patterns (discussed in

the following section).

Figure 46 confirms the increase in salinity from January to April is similar for both runs,

as we would expect, as the mean 2050 run replicates the rainfall pattern of 2008 (dry

summer) are similar to the 2050 no inflow conditions. In other words, the effect of the

2050 predicted mean sea level rise temperature increase is being isolated here, where

the significant increase in volume of saline water to the system. This suggests that the

dramatic deviation in salinity in April is due to seasonal inflow alone. An extension of

a dry summer and a delay of spring rain is shown to increase the duration of

excessively saline waters but does not significantly increase the severity during this

period or throughout winter. However, an autumn drought could increase salinity prior

to an evaporative intense summer which would predispose the system to becoming

hyper saline.

Without seasonal flushing, salinity throughout the system does not experience the

dramatic rise and fall of a standard estuary. The result is a fairly constant raised salinity

level throughout the entirety of the system, as presented by Figure 48 showing a much

smaller variation in salinity when compared to the 2008 baseline year, acting almost

like a “sidearm” to the ocean.

Figure 48: Box plot of bottom salinity throughout the extent of the Swan River for the “mean 2050” run (blue) and

the “no inflow 2050” run (red).

74

In the summer months, when the solar irradiance and temperature is high, the salinity

slowly increases due to the associated evaporation. Fresh water loss from evaporation

leads to more pronounced increases in salinity towards the riverine extents (as the

river surface becomes large relative to the water depth). Generally upstream regions

are fresher due to catchment inflow and their distance from the saline waters nearer

to the ocean inlet. However, the Nile Street and St John Hospital sites in the Middle

Swan reach hyper saline levels and exceed the marine salinity level of 35.5 psu in the

summer (Figure 46 and Figure 47). The Blackwall Reach, Armstrong Spit and

Heathcote sites also experience the same forcing towards hyper salinity, but, as they

are within the tidal influence of the estuary proper, are exposed to the now relatively

fresh ocean water.

The perpetual input of marine water with the tidal estuary mixing provides a soft cap

on the Lower Swan’s ability to become hyper saline. In other words, the Lower Swan

is strongly buffered against any rise in salinity above marine levels despite significantly

detrimental conditions. It is possible that this cap can be breached somewhat as in all

three Lower Swan sites there is a significant rise above this level in the summer

conditions at the tail end of the simulation. One explanation for this is that a critical

point is reached around November wherein the buffering effect of mixing sea water is

overcome by evaporative forcing, thereby allowing increasing salinity.

Figure 49: The simulated salinity reaches a plateau at near marine levels, then, at the end of the simulation, is

seen exceeding this cap.

75

Hyper salinity and low variability of salinity are typical properties are common to so

called inverse estuaries, which have been observed in other areas of low flow and high

evaporation. In these systems, the upstream extents become increasingly dense and

hyper-saline. As this dense water sinks towards the river bed and is pulled by gravity

towards the ocean while the relatively less dense water moves upstream close to the

surface, becoming increasingly saline itself through evaporation.

While the salinity upstream is still lower than the near ocean salinity, the change of

conditions are much more dramatic in the upstream extents and we are confident that

given a longer period of simulation the upstream salinity will continue to rise

dramatically, becoming hypersaline and surpassing the downstream salinity (Schettini,

Valle-Levinson, & Truccolo, 2017) and (Lavın, Godınez, & Alvarez, 1998). Such

conditions would represent an inverse estuary.


The no inflow run shows significantly fewer and less extreme periods of low DO events

(Figure 50 and Figure 51). In sim 1 (2008 baseline), the months of May to August

display periodic states of extremely low DO levels below the hypoxic threshold, most

notably in the bottom section of the water. This can be explained in part by

understanding the salinity data. Extremely low levels of DO in the Swan River have

been shown to correlate with areas of water underneath severe vertical stratification

(Thomson, 2001). The Swan-Canning system (in its typical state) shows strong

stratification where the different densities of freshwater inflow and saline ocean water

interact. In the case where inflow is completely stopped (sim 7), the extent of vertical

stratification is lessened, and the result is that the system displays less extreme low

DO events.

76

Figure 50: Success Hill DO levels. Note the severe low DO levels (<2mg/L) in sim 1 bottom in February and from

April to July.

Figure 51: Nile Street DO levels. Note the severe low DO levels in sim 1 bottom.

77

Overall, dissolved oxygen shows lower variability throughout the year when compared

to the mean 2050 run. The mean 2050 run shows a seasonal peak in DO throughout

winter, whereas the no inflow run tends to show a lower seasonal rise in DO. The no

inflow run shows very similar trends throughout the summer months as the 2050 mean

simulation.

Although the extremely low DO events are shown to be milder and less frequent, the

implications for the water quality of the Swan may not be entirely positive. It is possible

a drying climate will reduce extreme low DO events such as major fish kills, but the

implications for the overall health of the system may be contrasting. The lack of a

seasonal rise in DO over the winter months may have significant effects on the state

of Swan River. Dissolved oxygen is a key concern for aquatic ecosystem health and

water quality. Temperature increases due to climate change will increase the capacity

of the water to hold oxygen, however, this will also increase the amount of bacterial

respiration and rate of decomposition. Increased stratification due to the saline wedge,

and sediment oxygen demand are also likely to cause lower than average dissolved

oxygen. Potential for increased frequency of extreme low dissolved oxygen events is

also apparent as flushing is reduced.

4.2.3.3 Phytoplankton

Phytoplankton concentration up stream of Heathcote show significantly lower levels of

dinoflagellates in the no inflow run (Figure 52). This can be explained by the high

salinity, reducing the ability for the species to reproduce. It was surprising to see that

in the lower Swan, such as Blackwall Reach, the 2050 no inflow run showed very

similar rise and falls to the 2050 mean run (Figure 53).

78

Figure 52: Success Hill Dyno concentrations.

Figure 53: Blackwall Reach dyno concentrations.

79


4.3.1 Oxygenation (sim 3 and 8)

Results are presented in the form of oxygenation concentration plots over time for

each of the seven monitored locations. As the simulations were run from January to

March 2050, oxygen concentrations at the seven locations were plotted for this time

period. Box plots were constructed from the results obtained from the simulations to

decipher the median and quartiles of the oxygen concentrations for the two simulations

at the seven locations. The results from simulation three and simulation eight were

compared to results obtained from simulation two (no oxygenation, baseline 2050

simulation) to evaluate the effect that the artificial oxygen strategies had on the oxygen

concentrations in the Swan-Canning Estuary.

Simulation Description

2 · Using 2050 baseline date;

· Switching off oxygenation plant in the model.

3 · Using 2050 baseline data

· Switching on oxygenation plant by using 2017 oxygenation data.

8 · Using 2050 baseline data

· Addition of an extra oxygenation plant at Nile St river using similar

operating conditions as the two other oxygenation plants.

Table 10 Description of all oxygenation Simulations

80

Figure 54. Oxygen concentrations recorded at Nile Street for Simulation 2, Simulation 3 and Simulation 8.

Figure 55 Oxygen concentrations recorded at St John Hospital for Simulation 2, Simulation 3 and Simulation 8.

81

Figure 56 Oxygen concentrations recorded at Success Hill for Simulation 2, Simulation 3 and Simulation 8.

Figure 57 Oxygen concentrations recorded at Middle Swan Bridge for Simulation 2, Simulation 3 and Simulation

8.

82

Figure 58 Box plot representing the concentration of oxygen recorded at the surface water layer over January

2050 to March 2050 for the seven study areas.

Figure 59. Box plot representing the concentration of oxygen recorded at the bottom water layer over January

2050 to March 2050 for the seven study areas.

83

4.3.1.1 Current Oxygenation Strategies

The impact of the current artificial oxygenation strategy for the year 2050 is analysed

by comparing the oxygenation concentration results from simulation 3 and simulation

2.

It can be seen in Figure 58 that under the implementation of an artificial oxygenation

strategy at Middle Swan Bridge the minimum level of oxygen that can be attained by

the river is above 4mg/L. However, even though there is an approximate increase of

1-1.2 mg/L of oxygen in the bottom layer, the mean and minimum level of oxygen that

the Middle Swan Bridge area can attain is still less than the required 4 mg/L for a

‘healthy’ Swan-Canning system. This can be seen in Figure 59

It can be observed from the Success Hill plot Figure 56 that switching on the oxygen

plants will approximately increase the oxygen level by 1 mg/L at both the bottom layers

and upper layers for what region of time. However, enough oxygen is not being

pumped into the river to increase the levels above 4 mg/L for the bottom layer. The

upper layer is showing satisfactory results since the minimum oxygen level that can

be attained by the river is above 4 mg/L with the oxygenation plants switched on.

A slight increase of oxygen level can be observed on the bottom layers of St John river

however, the levels of increase are not enough to bring up the oxygen content in the

river above 4 mg/L (Figure 55) Negligible changes in the oxygen level is observed as

from Nile St., Heathcote, Armstrong Spit and Blackwall reach.

Lower reaches of the SCE (Blackwall reach to Heathcote) are unaffected by the

artificial oxygenation systems as oxygen levels display minimal to no increase, as seen

in Appendix K.

4.3.1.2 Enhanced Oxygenation

It can be observed in the plots that the introduction of a new oxygenation plant slightly

improves oxygen conditions at Nile St river. It was expected that the oxygen conditions

further downstream would be improved. However, the plant had no influence on the

Middle Swan Bridge, Success, St John, Heathcote, Armstrong and Blackwall Reach

rivers. The new oxygenation plant had minimal effect on the oxygen content at the

bottom layers in the river. The oxygenation plant was unsuccessful in pumping enough

oxygen to bring up the level above 4 mg/L.

A similar pattern is observed at the surface layers compared to bottom layers. The

new oxygenation plant does not affect the Middle Swan Bridge, Success, St John,

Heathcote, Armstrong and Blackwall Reach rivers. However, the plant manages to

bring the level of oxygen on the surface layer above 4 mg/L at Nile St. There is no

significant change in oxygen concentration after the introduction of an additional

artificial oxygenation facility. There is also no significant decrease in the duration of

the low oxygen events that are present in 2050.

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4.3.1.3 Limitations

The shortened run periods for Simulation 3 and Simulation 8 resulted in discrepancies

in the spin up time between Simulation 2. This has resulted in the model displaying a

strong gradient in salinity for the initial conditions along the salt wedge in Simulation

2. This gradient was not held constant for Simulation 3 and 8. The lower Swan is too

fresh in the initial conditions for Simulation 3 and Simulation 8 and the time taken for

the salt wedge to move upstream has taken substantially longer. Examining the salinity

plots seen in Figure 60 it is evident that the difference in salinity levels between

Simulation 2 and Simulation 3 become more apparent as it moves further upstream.

Slightly more saline water will act to impede the oxygen diffusion limiting the dispersion

of oxygen across it target zone. The inconsistencies in time period of analysis between

Simulations 2 and Simulations 3 and 8 also mean that the analysis of the box plots is

not completely reliable as range for different time periods and a different number of

data points.

Another limitation is that the model cannot be run reactive to changes in oxygen it is

unable to represent the true nature of the oxygen plants that operate in the upper

Swan River. This limitation prevents the oxygenation management strategies being

accurately depicted with the model and hence unable to be accurately analysed.

Figure 60 Discrepancies in salinity as a result of inconsistent run-up time.

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4.3.1.4 Cost Benefit Analysis for oxygenation

A cost-benefit analysis is applied in order to understand the performance of the

additional oxygenation plant. Hipsey (2014) undertook an economic assessment for

the two existing oxygenation plants at the upper stream region. The cos-benefit

analysis should be consistent for all the oxygenation plants, including the two existing

plants and the additional plant, hence the cost-benefit analysis for the additional

oxygenation plant will follow the methodology which Hipsey (2014) applied for the two

existing oxygenation plants. In this cost-benefit analysis, we only focus on

performance of plant, the costs such as plant construction cost, indirect costs

associated with operation are not considered.

The operational cost for a plant 𝜃𝑝𝑙𝑎𝑛𝑡 is defined by calculating the electricity cost,

𝜃𝑝𝑜𝑤𝑒𝑟 and the oxygen input cost, 𝜃𝑜𝑥𝑦𝑔𝑒𝑛 (Hipsey et. al., 2014).

𝜃𝑝𝑙𝑎𝑛𝑡 = 𝜃𝑝𝑜𝑤𝑒𝑟 + 𝜃𝑜𝑥𝑦𝑔𝑒𝑛 (1)

The electricity cost:

𝜃𝑝𝑜𝑤𝑒𝑟 = ∑ 𝐸𝑗к𝑡𝑝𝑗,𝑡𝑡 (2)

Where 𝐸𝑗 is the energy units consumed (kWh) for the jth plant, к𝑡 is a binary flag (0,1)

indicating if the plant is operational or not at any time t, 𝑝𝑗,𝑡 is the price of ($ kWh-1).

The energy price is varied between peak and off-peak times.

Hipsey et. al. (2014) assumed the energy consumption of 44kWh for Guildford and 65

kWh for Caversham. He also mentioned that the off-peak time is from 10 pm to 8 am,

and the price of energy is 0.278, 0.100 $ kWh-1 for peak time and off-peak time

respectively. For the additional oxygenation plant, we assume the energy consumption

is the mean energy consumption of two existing oxygenation plants, and rounded to

the nearest integer, which is 55 kWh. Peak time of 12 hr and off peak of 10 hr are

determined by the off-peak time period. The additional oxygenation plant runs 24

hours per day from 21st Jan to 7th Feb.

𝜃𝑝𝑜𝑤𝑒𝑟 𝑝𝑒𝑟 𝑑𝑎𝑦 = 55 kWh ∗ 12 hr ∗ 0.278 $ kWh−1 + 55 kWh ∗ 10 hr ∗ 0.100 $ kWh−1

Therefore, 𝜃𝑝𝑜𝑤𝑒𝑟 𝑝𝑒𝑟 𝑑𝑎𝑦 = $239 (to the nearest integer)

The price of oxygen:

86

𝜃𝑜𝑥𝑦𝑔𝑒𝑛 = ∑ 𝑄𝑜𝑥𝑦(𝑡)𝑝𝑜𝑡 (3)

Where 𝑄𝑜𝑥𝑦(𝑡) is the flow rate of oxygen as a function of time, and 𝑝𝑜 is the prince of

oxygen ($ kg-1). For oxygen cost calculation, we use 0.62 $ kg-1 for 𝑝𝑜 from Hipsey

(2014), and apply the base oxygen load of 30 kg/hr for the additional plant.

𝜃𝑜𝑥𝑦𝑔𝑒𝑛 𝑝𝑒𝑟 𝑑𝑎𝑦 = 24 ∗ 0.62 $ kg−1 ∗ 30 kg/hr

𝜃𝑜𝑥𝑦𝑔𝑒𝑛 𝑝𝑒𝑟 𝑑𝑎𝑦 = 446 $ to the nearest integer

The total cost of the additional plant per day is:

𝜃𝑝𝑙𝑎𝑛𝑡 𝑝𝑒𝑟 𝑑𝑎𝑦 = $ 239 + $446 = $ 685

Therefore, the estimated operational cost of the additional plant is $685 per day.

Hipsey (2014) computed costs for different operational scenario combinations. This

estimation excludes capital and maintenance costs which when compounded over a

32-year period would amount to a significant increase in cost. The combination with

oxygen input at 30kg/hr, and oxygen input constant are similar with the operational

scenario of the additional oxygenation plant. Under that scenario, Figure 61 indicates

that cost for running two existing plants is $400k per year. Hence, the cost for running

two existing plants is approximately $1096 per day.

While running three oxygen plants at the same time, the total cost will be $1781 per

day, excluding maintenance and capital costs.

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Figure 61 Operational scenario simulations with 2010 flow regime and computed by averaging over 12 months

(DoW 2015).

Consequently, the calculation for benefit of the additional oxygenation plant only

considers the effectiveness in Nile Street region. The difference of dissolved oxygen

concentration between the two existing oxygenation plants and three oxygenation

plants, includes the additional oxygenation plant is required for benefit calculation. In

addition, the difference of the bottom layer is more valuable to determine the benefit

of the additional plant due to the one of the main targets for the additional oxygenation

is to reduce the percentage of hypoxic situation. Hence, we focus on the difference

between simulation three, bottom water and simulation eight, bottom water during the

operation period in Figure 62. The increase of dissolved oxygen concentration, ∆∁𝑜𝑥𝑦

by adding an additional oxygenation plant is around 0.4 mg/L during operation period,

which is determined by calculating the difference of area under the two simulation

curves.

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Figure 62 Dissolved oxygen concentration in Nile Street region

By using ∆∁𝑜𝑥𝑦 of the additional oxygenation plant, the cost for increase, 𝐶𝑖𝑛𝑐𝑟𝑒𝑎𝑠𝑒

every mg/L during the operation period can be determined. The operation time is 17

days from 21st Jan to 7th Feb. The cost for each mg/L concentration in the Swan-

Canning Estuary for 2050 is:

𝐶𝑖𝑛𝑐𝑟𝑒𝑎𝑠𝑒 =$685∗17 𝑑𝑎𝑦𝑠

0.4 𝑚𝑔/𝐿= $29,113 to the nearest dollar

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4.3.2 Nutrients Reduction (sim 9)

Figure 63 Dissolved oxygen levels at Middle Swan Bridge

From Figure 63 above, it can be observed that a reduction in nutrient is having an

influence on the oxygen levels in the system during the period of February to April.

However, for the remaining time it is observed that the nutrients is not influencing the

oxygen levels.

An increase of 1 mg/L of oxygen is observed during the period of March to April. This

can be explained by the fact that a decrease in the nutrient level is decreasing the

number of algae inhibiting the area hence increasing the oxygen levels. It can also

be observed from the graphs that a decrease in the levels of nutrient is not affecting

the oxygen levels. This is due to low river flows occurring during the other parts of

the year reducing the dispersion of the nutrients along the river. Therefore, low river

flows would affect the oxygen levels in the river since there will not be dispersion of

nutrients along the whole river.

Success Hill shows a similar behaviour as Middle Swan Bridge. It can be observed

that a reduction in Nutrient is affecting the dissolved oxygen content in between end

of February to middle of March. However, the amount of increase of the oxygen

content due to a decrease in the nutrient concentration is significantly low compared

to Middle Swan Bridge. An increase of approximately 0.5 mg/L is observed. This

might be due to a further reduction of the river flow rate causing less dispersion of

the nutrients along the river and as a result the effect of reducing the nutrient

concentration is not affecting the amount the algae.

St John, Nile St, Heathcote, Armstrong and Blackwall Reach are showing similar

behaviour within the same time period as shown below. As the river moves

Hypoxic

Commented [WU1]: NEEDS TO BE EDITED

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downstream the influence of dispersion decreases and the nutrient reduction has

lesser effect on the system since increase of oxygen levels decreases.

Figure 64 Dissolved Oxygen at Success Hill

4.3.3 Future Work

As part of the analysis for alternative management solutions for the Swan-Canning

Estuary in 2050 a number of alternative management solutions were studied utilising

case studies from Estuaries across the globe. It is recommended that these

management solutions be investigated using the SCERM model to predict their

effectiveness for the Swan-Canning Estuary.

4.3.3.1 Zero Sediment Flux

Sediments are an important part of the ecosystem and can indirectly affect water

pollution. Pollutants are absorbed into the fine particles and deposited in sediments,

forming contaminants. When environmental conditions such as temperature, salinity,

and pH change, the pollutants absorbed into the sediments enter the overlying water

through sediment flux. Organisms are also affected by these pollutants through

flocculation, precipitation, and adsorption, forming secondary pollution sources, and

through bioaccumulation. Sediment is the main accumulation area of carbon,

nitrogen and phosphorus (Chapman (ed) 1992).

From an economic, social and environmental point of view, effective management of

river sediments is incredibly important. Sediments carried by rivers are an important

Hypoxic

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part of the global geochemical cycle (Bogden & Ottesen 2008). Although the

terminology is different, the reservoir sediment management classification is divided

into three categories;

1) Manage sediment passing through the reservoir,

2) The method of removing accumulated sediment in the reservoir to recover the

capacity

3) Minimize the amount of sediment arriving upstream from the reservoir.

The first two methods maintain reservoir capacity and provide sediment for

downstream reaches, but the third type (reducing sediment transport from upstream)

only deals with reservoir capacity issues rather than downstream sediment starvation

(Netzband 2007).

In the Los Angeles River, California the bottom has been covered in concrete to limit

the sediment transfer in the river. The concrete bottom creates an environment

where pollutants are unable to survive in the sediment thus reducing the pollution in

the system (Uyehara 2013). This management strategy cleans the sediment

effectively but can also bring other problems. However, it needs to be noted that

there is a significant cost associated with the construction of the concrete bottom and

therefore, the possibility of applying this extreme example need to be discussed

further. The implied benefits on the ecological health of the system justifies utilising

the Swan-Canning SCERM model to decipher the potential benefits of this

management methodology on the Swan-Canning Estuary. This management

methodology can be modelled using the SCERM model by simply removing the

sediment flux from the model input conditions thus allowing for the simple analysis of

results.

4.3.3.2 Banking in Constructed Wetlands

Wetlands are widely advertised as critical components of our planet providing a wide

variety of ecosystem services: they are considered the ‘kidneys’ of the hydrologic

cycle by removing pollutants, biodiversity hot spots, habitats of rare and endangered

species, groundwater recharge zones, localized areas for flood protection, carbon

sinks, and aesthetic value (Zedler, 2003). The lack of understanding of the role that

wetlands have on the ecosystem has resulted in severe degradation of the planet’s

water resources. Only in last few decades have wetlands been recognized for their

potential role to ameliorate Nonpoint source pollution. Wetlands are often designed

to filter and treat municipal waste water (but also storm runoff, mine waste and

animal waste) and control nonpoint source pollution (Kadlec and Knight, 1996).

There are several mechanisms acting in constructed wetlands that contribute to the

removal of contaminants, including:

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1) Sedimentation and burial (phosphorus, pesticides, particulate organic carbon,

pathogens)

2) Biogeochemical transformations (denitrification, methanogenesis,

dimethylselinide production)

3) Biotic uptake of nutrients and salts

4) Microbial degradation of pesticides and organic matter

5) Redox transformations affecting solubility, sorption, and toxicity (e.g., As, Se,

methyl-Hg)

6) Predation of pathogens

7) Photo degradation of pesticides and organic matter.

As a result of these processes, it is commonly considered that wetlands have a

beneficial effect on water quality (Jordan et al., 2003; Zedler, 2003). Important factors

controlling water purification capacity of wetlands include rate of contaminant inflows,

residence time of water in the wetland, availability of organic matter and other

substrates for growth of microbes, light intensity and penetration, temperature, and

nutrient uptake by plants (Phipps and Crumpton, 1994; Woltemade, 2000).

Nitrogen inputs to constructed wetlands come from field and surface water runoff,

agricultural return flows and tile drains, but can also result from biological fixation, and

wet and dry atmospheric deposition. The dominant nitrogen removal mechanism in

constructed wetlands is respiratory denitrification, the microbially mediated

transformation of nitrate to N2O and N2 gases in the absence of oxygen (Kadlec &

Knight 1996). The low redox potentials in CW soils result in the perfect environment

for denitrification. Studies that have used 15N tracer methods and the acetylene

inhibition technique to measure denitrification in constructed wetlands in agricultural

settings have demonstrated similar findings with rates ranging from 0.02 to 11.8 mg N

m-2 h-1 and average rates around 2 mg N m-2 h-1 (Fleischer et al.,1994; Poe et al.,

2003; Smith et al., 2000; Xue et al., 1999).

Phosphorus retention in constructed wetlands is controlled by a range of physical,

chemical, and biological processes, including sedimentation, filtration, chemical

sorption and precipitation, redox processes, microbial interactions, and uptake by

vegetation (Reddy and DeLaune, 2008). The three dominant retention mechanisms

include: storage in biomass (biological), sorption to soil (chemical), and formation and

accretion of new mineral and organic soils (physical) (Fig. 4; Kadlec, 1997; Reddy and

DeLaune, 2008). Reported mass phosphorus loads for constructed wetlands treating

agricultural runoff vary widely, ranging from 1 to over 100 g m-2 yr-1 (Debusk et al.,

2005).

A number of management techniques have been evaluated for improving long-term

phosphorus removal performance by constructed wetlands, including routine

vegetation harvesting, removal of accumulated sediment, and chemical immobilization

of P in sediment using amendments (Debusk et al., 2005). Although there are

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disadvantages of constructed wetland including long retention time, large area, low

efficiency in cold seasons, long setting up phase. Constructed wetlands are an

appealing option because they are effective contaminant removal systems that are

relatively inexpensive to develop and maintain (Hammer, 1992; Larson et al., 2000).

Therefore, banking water in wetlands will act to ‘clean’ the water taken from the Swan-

Canning Estuary for it be released in times low water quality acting to ‘flush’ the system

and create a healthier estuary. This can be easily modelled by altering the levels of

nutrients, oxygen and salinity in the ‘banked’ water and then releasing it into the

system at a specified time. The specified time can be determined when examining the

results for the predicted Swan Canning baseline water quality scenario.

4.3.3.3 Barrier Construction at Blackwall Reach

Estuaries trap nutrients and sediment that are carried seaward by rivers, and landward

by tides. A sediment particle is transported away from its initial location due to

advective and diffusive processes, then it settles to riverbed due to gravity (Scully &

Friedrichs 2007). Bed load and suspended load are the two types of sediment

transports in estuaries. Bed load is the case that a sediment particle moves on the

riverbed, and the other case is that a sediment particle becomes suspended by picking

up by boundary layer (Xie 2011). Construction of a barrier on the riverbed can

physically block the two types of sediment transports. Blackwall Reach area is chosen

for this design, where is situated at the top end of a tidal gorge that stretches down to

the ocean of Swan Canning Estuary (DPaW 2017). This location is where freshwater

and seawater start to mix, a barrier that block mixing can reduce nutrients and

sediment transport in the estuary.

Design size of the barrier would be similar to the section of Blackwall Reach area. In

order to maintain the activities on the river, and species that live in the system, a barrier

is with 5 m lower than the average water depth. Fluxes and forces of the section is

required to determine depth of the barrier. Further analysis is required for selecting

material of the barrier, as it depends on the force of the flow that reach to the barrier.

This management strategy can control nutrient, sediment and salinity transport

efficiently. However, some other issues will appear by it, for example, sediments would

settle around both sides of the barrier due to block of movement, arthropods cannot

pass through the barrier. The application of this design should be further discussed by

the relevant parties. The focus of this management methodology is to limit the saline

water travelling further up the estuary. In order to model this efficiently the salinity

inputs into the model can be altered to represent a ‘wall’ at the Black Wall Reach

location.

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5 DISCUSSION

5.1 REFERENCE SIMULATIONS

In light of the findings outlined in Section 4.1, the results of this study indicate that it is

likely that DO concentrations will noticeably decrease as a result of climate change,

supporting the suggestions of Green et al. (2007). This was likely exacerbated by the

increase in overall salinity, as identified in section 4.1 (Figure 26), with regards to the

Upper Swan areas of the Estuary. Increases in salinity will likely create further changes

to the river ecology, affecting biological processes such as biological oxygen demand,

nutrient cycling, and sediment retention (Green et al. 2007).

The significant low oxygen event shown to occur in April-May of 2050 (Figure 26a,

section 4.1), but not during the reference year simulation, is an example of the likely

influences of climate change on the hydrodynamics, and in turn, the water quality of

the system.

The intensity and duration of stratification, as discussed in section 2.1, is particularly

relevant to the state of the water quality of the estuary. Vertical stratification, as shown

in Figure 25 from section 4.1, inhibits mixing between the surface and benthic regions,

as evident in the low DO concentrations in the benthic region shown in Figure 25b.

This shift to hypoxic levels of DO, coinciding with the clear stratification event, is likely

a reflection of the reduction in inflow and the expected shifts in the hydrodynamic

regime of the system (Green et al. 2007, Smith & Power 2014).

The 2050 unique stratification event observed is by no means outside possible

expectations for the influence of climate change on the system. Particularly when

considering the 39% reduction to estuary inflows and the also likely relevant 0.23m

MSLR and 1.04°C increase in air temperatures. The seasonal nature of the circulation

in the Estuary, driven by the seasonal nature of inflows, as discussed in section 2.1,

is reliant upon winter freshwater inflows flushing the system, countering the intrusive

salt wedge (Green et al. 2007), see section 2.4 for a description of this salt wedge

forcing. The low oxygen and stratification event observed at MSB, importantly occurs

at a time of year where the capacity of the system to overcome the stratifying forces

is limited due to low flow conditions. The noticeable drop in salinity in Figure 26b (both

Simulations) immediately before the stratification event in the 2050 Baseline

Simulation in April-May, shows the influence of the flushing effect of the first significant

rainfall of the year discussed in section 3.3.1 and shown in the Hydrograph in Figure

10. This indicates that the freshwater inflow at this time of year will not be sufficient in

2050 to overcome the stratification forcing and the trends of an increasingly saline

river. Hence, this indicates that the greater density differences between the top and

benthic layers of the river will require greater forcing or input freshwater flows to

overcome, than typically occur before the winter flows, and that are expected to occur

in 2050 considering the most likely climate change scenario, as modelled in this study.

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The stratification event highlighted in section 4.1 Figure 26b for the 2050 Baseline

Simulation during February coincided with hypoxic conditions of a greater intensity

than the April-May event and of a longer duration than in the Characteristic Baseline

Simulation at this time. This is consistent with the greater density difference between

the top and benthic regions during February and hence the stronger forcing

stratification experienced. This is consistent with concerns for increases in the duration

of summer stratification events and the persistent penetration of marine water

upstream (Green et al. 2007). The concern for summer stratification events is the lack

of freshwater flow to overcome the stratification forcing, where tidal exchange and

wind action dominate the hydrodynamics, driving the horizontal transport of salinity

across the entire estuary. This particular low oxygen event indicates that it should be

expected that climate change will lead to an increase in intensity and duration of the

summer low oxygen events that have historically been associated with algal bloom

events and poor water quality.

These patterns identified are relevant from management perspective. This shift in

regime, for such an extensive low oxygen event associated with persistent

stratification, could have significant implications for the health of the river and water

quality issues out to 2050. The shift in timing of hypoxic events could have further

implications arising, from changes in sediment fluxes to direct effects on algal bloom

episodes.

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The extreme runs indicated two major outcomes. We are likely to see a more marine

environment and the migration of the salt wedge further upstream. Increased seasonal

vertical stratification is also apparent. Results obtained, also indicated dissolved

oxygen will reduce, most notably in the bottom water layer where an increase in the

occurrence and severity of hypoxic events was simulated.

Additional modelling of flood and drought events indicate that the system can be

sensitive to temporal shifts in climate. Specifically, longer droughts are expected to

lead to increased salinity throughout the system and significantly increase the stresses

on fresh water species which may result in major ecological shifts. Extended drought

events may have a significant effect on the Upper Swan area, as these areas show

the largest variation in salinity, while also hosting an ecology more specifically adapted

to fresh water. Flood events are also likely to result in conditions suited to long and

intense algal events, however the extent of which is unclear.

5.2.1 Saltwater intrusion and stratification

The “2050 no inflow” run displayed increasing salinity along the middle to upper

reaches of the Swan as the no inflow conditions persisted throughout the twelve

months. Although, as discussed, the simulation represents an extreme limiting

condition as opposed to a probable scenario, the results provide insight to the

response of the river as conditions of no flow persist, such as late winter rainfall. The

results of increasing salinity and intrusion of the salt wedge further upstream are

supported by the results of the ‘high extreme’ scenario, where we observed higher

salinity in the surface and bottom profiles as inflow was reduced.

The consequences of increasing salinity in the middle and upper reaches of the Swan

on freshwater biodiversity are likely to be highly significant. Although a full twelve

months of drought is not expected, increases in summer duration, unseasonal

droughts and predicted decrease in overall inflow can cause highly saline waters to

have extended residence time in the middle and upper reaches of the Swan. These

regions are of particular concern as they generally don’t experience high levels of

salinity, but results suggest these areas are at risk of the most dramatic increase in

the case of an extended no inflow event. The biota in these systems are highly

adjusted to fresh water systems and are very unlikely to be able to sustain prolonged

periods of increased salinity. The specie succession in this area under these scenarios

may be an area of future research.

Additionally, the highly saline waters are likely to penetrate the shallow aquifers

surrounding the Swan River. Local plants and animals may not adapt to the increasing

salinity levels and shifts in the special ecology and biodiversity of species along the

Swan and Canning banks are predicted. Most significantly, local freshwater plants

97

species are expected to suffer, and estuarine fish or aquatic species will be pushed to

the upper limit of the Swan-Canning system. However, in the unlikely event of a

yearlong drought, or severely limited flow, these species will be trapped as the once

fresh upper extents will become saline and a catastrophic loss of biodiversity would

be expected.

Another key concern is the spatial distribution of seagrass. Seagrass play a large role

in supporting a variety of marine organisms are a good indicator of ecosystem health.

They provide habitat for mammy small marine organisms such as prawns or juvenile

fish, absorb nutrients and stabilise sediment. The Swan River is home to the tolerant

Halophila ovalis and less tolerant Heterozostera tasmanica. It is expected that the

increasing salinity will support the growth of marine seagrass in the lower regions, but

the reduced dissolved oxygen at the seabed is likely to cause issues to any established

sea grass beds further upstream, again altering the dynamics of nutrient uptake and

water quality (Swan River Trust, 2007).

In summary, a reduction in the spatial extent of the freshwater reaches of the Swan

and a resulting reduction in the biodiversity of both flora and fauna in any freshwater

habitat that does remain is expected.

5.2.2 Increase severity and occurrence of low DO events

Dissolved oxygen is a key concern for aquatic ecosystem health and water quality.

Results from the low and high extreme runs, as well as the no inflow run, indicate we

can expect more intense and frequent low DO events as a result of lowering inflow

and increasing temperatures. We are also likely to see a reduction in the seasonal rise

of DO associated with the winter months. One of the key concerns for low DO events

is mass fish kills. In addition, changes in the nutrient cycling processes are expected.

Nutrient cycling can be thought of composing of three key stages (Swan River Trust,

2007).

1: Uptake of nutrients by plants and their incorporation into organic matter;

2: Release of nutrients from decomposing organic matter; and

3: Conversion of inorganic nutrients into forms unavailable for plant uptake.

Stages one and two require well-oxygenated waters and take place in the sediments

of the estuary system. As nutrient cycling occurs primarily at the water sediment

interface, the lower DO levels shown for the bottom water profile results will reduce

the system capacity to convert inorganic nutrients into forms unavailable for plant

uptake. This is likely to leave more organic matter available for further growth of

phytoplankton.

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5.3.1 Oxygenation

As discussed in section 4.3.1 simulation 3, the 2018 current oxygenation strategies for

the Upper Swan will influence oxygenation levels under 2050 conditions. The artificial

oxygenation systems have the greatest influence on the study areas at Middle Swan

Bridge and Success Hill for which the plants are in the closest proximity. Under the

influence of the oxygenation systems these two sites the oxygen concentrations

remain above the hypoxic threshold (2mg/L) throughout the duration of which the plant

is turned on. Sites at St John’s Hospital were also influenced by the artificial

oxygenation strategy in Simulation 3 however, the influence of the artificial

oxygenation on this site was not as significant with levels of dissolved oxygen dropping

below the hypoxic threshold for periods of time in which the plant was operational. The

results obtained at St John Hospital also display fluctuating levels of oxygenation

around Simulation 2 oxygenation levels for the time in which the oxygenation plant is

running. Simulation 3 oxygen results recorded at St Johns Hospital display oxygen

concentration levels below those observed for Simulation 2 and below the 2 mg/L

hypoxic threshold. These results suggest that the current oxygenation strategies will

not be adequate for this site come 2050.

Simulation 8 models the impacts of adding an additional oxygenation plant to the

Upper Swan based on the site selection identified in section 4.3.1. The results

displayed for Simulation 8 at Nile Street are not consistent with literature produced for

oxygenation as a management strategy. Although, the resultant oxygenation levels

are lower than in simulation 2 it should be noted that the oxygen levels for simulation

8 at Nile Street are higher than simulation 3 for the time period for which the system

is “turned on”. This could suggest that there is an error in the model that could be

explained by the discrepancies with salinity values as discussed in Section 4.3.1

The additional oxygenation plant in Simulation 8 also seems to influence levels of

oxygen at St Johns Hospital however, like in Simulation 3 these results were variable

and did not constantly remain above the hypoxic threshold or remain above the

oxygenation levels for Simulation 2 for the time in which the model was running. This

could suggest that the spatial extent of the oxygen plants will not be as far under the

Estuary conditions in 2050. This could be due to changes in the physical

characteristics of the system such as the increase in temperature as well as the entire

biological and chemical dynamics in the system potentially changing resulting in a

increase in demand for oxygen in the system reducing the area of influence for the

oxygenation plants

It is evident from our data that there is a residual benefit to the system as a result of

an increase in oxygen. This can be seen in the oxygen concentration results for

Success Hill after the oxygenation plants have been “turn off” there continues to be a

higher level of oxygenation at the bottom water throughout the remainder of the run

99

(through to the end of March), with level of dissolved oxygen remaining above the

hypoxic threshold. This relationship however, does not hold for surface oxygen

levels which are substantially lower than in Simulation 2 after the oxygenation plants

have been turned off. Oxygen concentration results at Middle Swan Bridge also

display residual benefits to the system as a result of the introduction of the artificial

oxygenation system. To understand the long-term impact of continued oxygenation,

simulations will need to be run over a number of years to see the long-term benefit of

continuous oxygenation in the Swan-Canning Estuary

It can be concluded from the outputs obtained from simulation 8 that the addition of an

extra plant would have minimal effect on the river system and would not contribute in

the elimination of anoxic and hypoxic regions at the bottom areas of the lake. It can

also be stated after conducting a cost benefit analysis on the addition of a new plant

that the significant cost would not be worth the minimal increase in benefit to the

system. There is need for significant further analysis on the output concentration

values of the SCERM model as the output results for the simulation 8 do not display

predicted outputs for the dissolved oxygen levels. This could be due to the

discrepancies in the spin up of the model. It is therefore recommended that further

simulations of oxygenation plant are ran over a more extensive period of time to

accurately represent the long term benefits of oxygen strategy upon the system as, it

is evident from results in simulation 3 that although target areas of the model are able

to be sustain above hypoxic conditions under the presence of an oxygen plant there

are still site operating in hypoxic conditions. Further analysis will need to be conducted

to validate the results from simulation 3 and 8.

5.3.2 Nutrients Reduction

After analysing the outputs, it can be deduced that reducing the amount of nutrients

input in the system have minimal impact on the oxygen levels. This is because the

nutrient dispersion is dependent on the flow rate of the river and at a low flow rate

there is a low dispersion. Sediment fluxes account for a large input of nutrients in the

river system. Sediment fluxes are normally dominant when the flowrates are low which

is explaining the behaviour of both simulations and the minimal effect of reducing the

nutrients.

100

6 CONCLUSIONS

It can be seen from this study that the effects of climate change will likely result in

significant and complex challenges for the management of the SCE, and by

extension the Swan-Avon catchment. Specifically, potential issues regarding

persistent stratification and salt intrusion into the Upper Swan may prove to be

significant stressors to the estuary’s existing ecosystems. It is recommended that

further study be conducted into the potential alleviation of these stressors, with a

potential focus on the Upper Swan. Future management strategies could focus on

the alteration of upstream sections of the system with the aim of increasing areas

of freshwater, for example the potential use of constructed wetlands as a mitigation

technique to counter the increasingly saline nature of the SCE given that the

introduction of oxygenation plants and reduction of nutrients in the system are

having minimal effect on the estuarine system.

Furthermore, the outcomes of this study highlight several possible areas for future

investigation. Firstly, it was necessary for significant assumptions to be made in

regard to catchment conditions when predicting flow rate, especially given the

dependence of streamflow and nutrient loading on catchment land use. By coupling

the SCERM to a catchment model of the Swan-Avon, more reliable predictions for

future conditions may be made, and potential simulations by analysing more

scenarios may expand to better inform management decisions regarding catchment

land use in the context of the health of the SCE

101

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8 APPENDICES

APPENDIX A: LIST OF ACRONYMS ARM – Armstrong Spit

BLA – Blackwall Reach

BOD – Biochemical Oxygen Demand

BOM – Bureau of Meteorology

CMIP5 - Couple Model Intercomparison Project Phase 5

DBCA – Department of Biodiversity Conservation and Attractions

DO – Dissolved Oxygen

DoW – Department of Water

FDC – Flow Duration Curve

HEA – Heathcote

IPCC – Intergovernmental Panel on Climate Change

IQR – Interquartile Range

MSB – Middle Swan Bridge

MSLR – Mean Sea Level Rising

N – Nitrogen

NAR – Narrows Bridge

NIL – Nile Street

P – Phosphorus

RCP – Representative Concentration Pathway

SCE – Swan Canning Estuary

SCERM - Swan-Canning Estuarine Response model

SCRPS – Swan-Canning River Protection Strategy

SCWQIP - Swan Canning Water Quality Improvement Plan

STJ – Saint John of God Hospital

SUCC – Success Hill

TN – Total Nitrogen

TP – Total Phosphorus

TChla – Total chlorophyll-a

Commented [WU2]: Shouldn’t this go at the start of the report

Commented [mb3R2]: I believe greg wanted it in the appendix

109

WA – Western Australia

APPENDIX B: TRIBUTARY BOUNDARY CONDITIONS PARAMETERS

Property Boundary Condition Header Units

Flowrate Flow 𝑚3 𝑠⁄

Salinity Sal psu

Water Temperature Temp ℃

Flow Tracer TRACE_1 𝑚𝑚𝑜𝑙 𝑚3⁄

Suspended Solids SS 𝑔 𝑚3⁄

Retention Time RET sec

Oxygen Concentration Oxy 𝑚𝑚𝑜𝑙 𝑚3⁄

Silica Sil 𝑚𝑚𝑜𝑙 𝑚3⁄

Ammonium Amm 𝑚𝑚𝑜𝑙 𝑚3⁄

Nitrate Nit 𝑚𝑚𝑜𝑙 𝑚3⁄

Filterable Reactive Phosphorous FRP 𝑚𝑚𝑜𝑙 𝑚3⁄

Particulate Inorganic Phosphorous FRP_ADS 𝑚𝑚𝑜𝑙 𝑚3⁄

Dissolved Organic Carbon DOC 𝑚𝑚𝑜𝑙 𝑚3⁄

Particulate Organic Carbon POC 𝑚𝑚𝑜𝑙 𝑚3⁄

Dissolved Organic Nitrogen DON 𝑚𝑚𝑜𝑙 𝑚3⁄

Particulate Organic Nitrogen PON 𝑚𝑚𝑜𝑙 𝑚3⁄

Dissolved Organic Phosphorous DOP 𝑚𝑚𝑜𝑙 𝑚3⁄

Particulate Organic Phosphorous POP 𝑚𝑚𝑜𝑙 𝑚3⁄

Refractory DOC DOCR 𝑚𝑚𝑜𝑙 𝑚3⁄

Refractory DON DONR 𝑚𝑚𝑜𝑙 𝑚3⁄

Refractory DOP DOPR 𝑚𝑚𝑜𝑙 𝑚3⁄

Coarse Particulate Organic Matter CPOM 𝑚𝑚𝑜𝑙 𝑚3⁄

Chlorophytes GRN 𝑚𝑚𝑜𝑙 𝑚3⁄

Cyanobacteria BGA 𝑚𝑚𝑜𝑙 𝑚3⁄

Cryptophytes CRYPT 𝑚𝑚𝑜𝑙 𝑚3⁄

Diatoms DIATOM 𝑚𝑚𝑜𝑙 𝑚3⁄

Dinoflagellate Group DINO 𝑚𝑚𝑜𝑙 𝑚3⁄

110

APPENDIX C: FLOW NORMALISED NUTRIENT DURATION CURVES

114

APPENDIX D: FLOW NORMALISED NUTRIENT CONCENTRATIONS & SCALE

FACTORS

Table 11. Flow Normalised Nutrient Concentrations (mg/L) for nitrogen and phosphorous. The table contains values for the reference year (2008), and the 50th percentile of recorded years. See Appendix C for corresponding duration curves.𝑁𝑖𝑡2008 is the normalised by flow nitrogen concentration in 2008, 𝑁𝑖𝑡50𝑡ℎ is the 50th percentile value for normalised nitrogen concentration found from the Duration Curve, 𝑆𝑐𝑎𝑙𝑒𝑁𝑖𝑡 is the scalar found from the normalised nitrogen concentration in 2008 and the 50Th percentile concentration for each site. 𝑃ℎ𝑜𝑠2008, 𝑃ℎ𝑜𝑠50𝑡ℎ and 𝑆𝑐𝑎𝑙𝑒𝑃ℎ𝑜𝑠 area the equivalent for phosphorus concentrations.

Nit2008 Nit50th ScaleNit Phos2008 Phos50th ScalePhos

Bayswater

Drain

1.337 1.326 0.992 0.059 0.064 1.082

Bennett

Brook

1.611 1.210 0.751 0.055 0.054 0.974

Canning

River

1.371 1.201 0.876 0.083 0.082 0.986

Ellen Brook 2.762 2.472 0.895 0.508 0.471 0.927

Helena

River

1.123 0.958 0.853 0.017 0.021 1.24

Jane Brook 1.190 0.902 0.758 0.032 0.032 0.991

Susannah

Brook

1.019 0.987 0.969 0.016 0.0159 0.994

Upper

Swan

(Walyunga)

1.837 1.132 0.616 0.053 0.038 0.715

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APPENDIX E: TIME SERIES PLOTS FOR DISSOLVED OXYGEN

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APPENDIX E: TIME SERIES PLOTS FOR SALINITY

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APPENDIX F: NORMALISED NUTRIENT CURVES

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APPENDIX J: “MEAN YEAR 2050” NO INFLOW: RESULTS (SIM 7)

Dissolved Oxygen Time Series

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Box plots: surface level of water DO along Swan River.

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Box plots: Bottom level of water DO along Swan River.

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Phytoplankton Time Series

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APPENDIX K: OXYGENATION (SIM 3 AND 8)


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Dissolved Oxygen Box Plots

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APPENDIX L : NUTRIENTS REDUCTION ( SIM 9)


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