Assessment of spatial and temporal variation in surface water quality in Jerrabomberra Wetlands, Australian Capital Territory
Rahnum Tasnuva Nazmul
A thesis in fulfillment of the requirements for the degree of Master of Philosophy
School of Physical Environmental and Mathematical Sciences
UNSW Canberra
October 2016
THE UNIVERSITY OF NEW SOUTH WALES Thesis/Dissertation Sheet Surname or Family name: Nazmul First name: Rahnum Other name/s:Tasnuva Abbreviation for degree as given in the University calendar: MPhil School: School of Physical Environmental and Mathematical Faculty: UNSW Canberra Sciences Title: Assessment of spatial and temporal variation in surface water quality in Jerrabomberra Wetlands, Australian Capital Territory
This Masters thesis aims to add to the knowledge of the spatio-temporal variation in surface water quality in Jerrabomberra Wetlands in order to provide information for managers as they seek to protect the values of the wetland, improve water quality and manage pollutants from the Fyshwick catchment. Located in the heart of Australian Capital Territory (ACT), Jerrabomberra Wetlands is a habitat for a variety of animals and plants. The Basin Priority Project (BPP), undertaken by the ACT and Commonwealth Governments to improve the quality of water flowing through the ACT includes this Fyshwick-Jerrabomberra catchment as a key site of mixed urban and agricultural land usage. Current study outcomes will add to the knowledge of the ACT wide water quality monitoring program. This project studied eight water quality parameters: water temperature, pH, turbidity, electrical conductivity, dissolved oxygen, total phosphorus and nitrate, and zinc using surface water samples collected from six locations at the south eastern corner of Jerrabomberra Wetlands on a weekly basis for four months in 2015. Results from spatial data plotted using a geographic information system found these parameters changing downstream in four different wetlands. Upstream wetlands were found to contain an elevated nitrate and zinc level compared to that in the downstream wetlands and exceed the guideline values provided by ACT Parliamentary Counsel (2005). Results from comparative assessment of the water quality data, tested using statistical parametric and non-parametric approaches, indicate that both of Wetland 22 and Wetland 24 are mostly dependent on catchment discharge, while the Billabong and Kellys Swamp were significantly different in nature with prominent groundwater effect with high electrical conductivity. An elevated total phosphorus level in two large bird habitats namely, the Billabong and Kellys Swamp was observed while it was considerably low in the upstream wetlands such as Wetland 22 and 24. These variations in water quality data in each wetland was mostly related to their characteristic feature and major water sources feeding the wetlands. Time series analysis of the observed weekly sampled data, when compared to archived data and event sampled results from BPP, indicate that the nutrient and zinc levels carried during storm events are higher than the observed values in the wetlands.. An important observation from this study is to find the upstream wetlands efficiently functioning in retaining zinc and other pollutants from flowing to the downstream. However, the pollutants present in the wetlands were lower than the amount predicted by the MUSIC model used by BPP indicating the need to include the fraction deposited as sediment and accumulated in plant species in future nutrient balance estimates. In conclusion, the spatio-temporal and comparative assessment of the data, contributes to understanding the effect of urban polluted stormwater from industrial areas in Jerrabomberra Wetlands and identified suitable locations for further monitoring sites, providing managers with information to assist decision- making.
Declaration relating to disposition of project thesis/dissertation
I hereby grant to the University of New South Wales or its agents the right to archive and to make available my thesis or dissertation in whole or in part in the University libraries in all forms of media, now or here after known, subject to the provisions of the Copyright Act 1968. I retain all property rights, such as patent rights. I also retain the right to use in future works (such as articles or books) all or part of this thesis or dissertation. I also authorise University Microfilms to use the 350 word abstract of my thesis in Dissertation Abstracts International (this is applicable to doctoral theses only).
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Originality Statement
‘I hereby declare that this submission is my own work and to the best of my knowledge it contains no materials previously published or written by another person, or substantial proportions of material which have been accepted for the award of any other degree or diploma at UNSW or any other educational institution, except where due acknowledgement is made in the thesis. Any contribution made to the research by others, with whom I have worked at UNSW or elsewhere, is explicitly acknowledged in the thesis. I also declare that the intellectual content of this thesis is the product of my own work, except to the extent that assistance from others in the project's design and conception or in style, presentation and linguistic expression is acknowledged.’
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Date……………………………..
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Authenticity Statement
‘I certify that the Library deposit digital copy is a direct equivalent of the final officially approved version of my thesis. No emendation of content has occurred and if there are any minor variations in formatting, they are the result of the conversion to digital format.’
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Dedication
To my beloved parents
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Acknowledgements
My sincere gratitude goes to the Almighty Allah for blessing me with the opportunity of this Master of Philosophy program in a renowned university like University of New South Wales, Canberra.
I would like to express my special thanks and gratefulness to my supervisor Associate Professor Stuart Pearson for his cooperation during this project. It was his careful supervision, encouragement and continuous guidance which helped to make the research possible. His mental support during the course work of the program was a driving force for me to pass through the hurdles. I am thankful to my co supervisor Professor Hans Riesen for his support and advice during the project, especially with the critical part of zinc determination.
I am grateful to School of Physical, Environmental and Mathematical Sciences for providing me with the scholarship and the excellent research facilities. In this regard, my special thanks to head of school Professor Warrick Lawson and Dean of Research Professor Hans Riesen. I thank all the staff of UNSW Canberra for their support and cooperation during my candidature. My thanks to Ms. Annabelle Boag for her support in academic administration needs. I am deeply thankful to Ms. Kate Badek, for her guidance and assistance in field work and laboratory analysis in this project. Academic Language and Learning (ALL) unit, UNSW Canberra, provided critical suggestions about my writing and Dr. Amy Griffin helped me a lot with GIS. My heartfelt thanks to them.
I would like to acknowledge the contribution of Jerrabomberra Wetland Management Authority and its members for their cooperation in this research project. Without their cooperation and necessary resources this project would not be possible. I am especially thankful to Dr. Ian Lawrence for his time and valuable suggestions during the project.
Many thanks to my dear colleagues Anh, Amerita, Amanda, Bobby and James for their support and for being very good friend during my life in Australia. I am thankful to my friends Shaker Subarna, Rakib and Nawrin for being very helpful during my study here. Special thanks to my undergraduate teacher Mr. iv
Shameem Ahmed, who stayed as a continual support with my family. Also thanks to Dr. Mahmud Ashraf and Dr. Safat Al-Deen for their help, advice and suggestions during my study.
My gratitude and endless thanks to my beloved parents and my dear husband, my dear sister, brother in law, my niece Adeepta, mother in law and father in law for their love and support in my life. I am here today just because of their love and blessings. The encouragement I received from my parents was the main inspiration for me in this journey. Since childhood in each of my achievements the satisfying smile I saw in their faces brought me this far. I am truly grateful and thankful to my loving husband for everything he did during my academic life. I am very lucky to have a supportive husband like him. My words may fall short to thank him in this acknowledgment. It was truly impossible to complete my study without his consistent support and love.
In a word, many thanks and my gratefulness to everyone for helping me to accomplish my goals this far.
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Abstract This Masters thesis aims to add to the knowledge of the spatio-temporal variation in surface water quality in Jerrabomberra Wetlands in order to provide information for managers as they seek to protect the values of the wetland, improve water quality and manage pollutants from the Fyshwick catchment. Located in the heart of Australian Capital Territory (ACT), Jerrabomberra Wetlands is a habitat for a variety of animals and plants. The Basin Priority Project (BPP), undertaken by the ACT and Commonwealth Governments to improve the quality of water flowing through the ACT includes this Fyshwick-Jerrabomberra catchment as a key site of mixed urban and agricultural land usage. Current study outcomes will add to the knowledge of the ACT wide water quality monitoring program. This project studied eight water quality parameters: water temperature, pH, turbidity, electrical conductivity, dissolved oxygen, total phosphorus and nitrate, and zinc using surface water samples collected from six locations at the south eastern corner of Jerrabomberra Wetlands on a weekly basis for four months in 2015. Results from spatial data plotted using a geographic information system found these parameters changing downstream in four different wetlands. Upstream wetlands were found to contain an elevated nitrate and zinc level compared to that in the downstream wetlands and exceed the guideline values provided by ACT Parliamentary Counsel (2005). Results from comparative assessment of the water quality data, tested using statistical parametric and non-parametric approaches, indicate that both of Wetland 22 and Wetland 24 are mostly dependent on catchment discharge, while the Billabong and Kellys Swamp were significantly different in nature with prominent groundwater effect with high electrical conductivity. An elevated total phosphorus level in two large bird habitats namely, the Billabong and Kellys Swamp was observed while it was considerably low in the upstream wetlands such as Wetland 22 and 24. These variations in water quality data in each wetland was mostly related to their characteristic feature and major water sources feeding the wetlands. Time series analysis of the observed weekly sampled data, when compared to archived data and event sampled results from BPP, indicate that the nutrient and zinc levels carried during storm events are higher than the observed values vii in the wetlands. An important observation from this study is to find the upstream wetlands efficiently functioning in retaining zinc and other pollutants from flowing to the downstream. However, the pollutants present in the wetlands were lower than the amount predicted by the MUSIC model used by BPP indicating the need to include the fraction deposited as sediment and accumulated in plant species in future nutrient balance estimates. In conclusion, the spatio-temporal and comparative assessment of the data, contributes to understanding the effect of urban polluted stormwater from industrial areas in Jerrabomberra Wetlands and identified suitable locations for further monitoring sites, providing managers with information to assist decision- making.
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Table of contents
Originality Statement ...... i Authenticity Statement ...... ii Dedication ...... iii Acknowledgements ...... iv Abstract ...... vii Table of contents ...... ix Table of figures ...... xiii Chapter 1: Introduction...... 1 1.1. Jerrabomberra Wetlands and its challenges ...... 1 1.2. Working with managers to co-develop the knowledge needs ...... 2 1.3. The research questions ...... 4 1.4. Research project: study justification and context ...... 4 1.5. Thesis outline ...... 6 Chapter 2: Knowledge audit of Jerrabomberra Wetlands water and its management ...... 9 2.1. Importance of Jerrabomberra Wetlands Nature Reserve in Australia ..... 9 2.2. History and development of Jerrabomberra Wetlands Nature Reserve 10 2.3. Hydrology of Jerrabomberra Wetlands ...... 11 2.4. Previous studies and management measures in Jerrabomberra Wetlands and Fyshwick catchment in the context of water quality ...... 14 2.5. Conclusion ...... 17 Chapter 3: Literature Review; a knowledge audit to provide the background and directed to the methods and research needs ...... 19 3.1. Research on the function of wetlands and their emerging importance . 19 3.2. Limits to the use of wetlands for water purification ...... 22 3.3. Protection of wetlands ...... 24 3.4. Different approaches used to evaluate the performance of wetlands being used for water treatment and other purposes ...... 25 3.5. Conclusion ...... 28 Chapter 4: Methodology ...... 29 4.1. Description of the study area ...... 30
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4.2. Field water sampling method ...... 33 4.3. Laboratory data analysis ...... 37 4.3.1. Analysing water quality parameters ...... 37 4.3.2. XRF ...... 39 4.4. Collation of existing water quality data ...... 40 4.5. Analytical procedures ...... 41 4.5.1. GIS Analysis ...... 42 4.5.2. Statistical Analysis ...... 42 4.5.2.1. Time series analysis ...... 42 4.5.2.2. Independent sample t-test ...... 43 4.5.2.3. Mann-Whitney U test ...... 44 4.6. Conclusion ...... 44 Chapter 5: Results ...... 45 5.1. Water temperature ...... 45 5.2. pH ...... 46 5.3. Electrical conductivity ...... 49 5.4. Turbidity ...... 52 5.5. Dissolved oxygen ...... 54 5.6. Nitrate ...... 57 5.7. Total phosphorus ...... 58 5.8. Zinc ...... 60 5.9. Conclusion ...... 61 Chapter 6: Discussion and recommendations for future research ...... 63 6.1. Variation of water quality between sites as it travels downstream ...... 63 6.1.1. Point sources or drain outlets ...... 63 6.1.2. Wetlands downstream of the drain outlets: Wetland 22 and Wetland 24 ...... 66 6.1.3. Jerrabomberra Billabong (Wetland 21) and upstream of the silt trap 69 6.1.4. Waterwatch locations: Kellys Swamp and downstream of the silt trap 71 6.2. Implication of this study to management and recommendations for future research ...... 72 6.3. Conclusion ...... 74 x
Chapter 7: Conclusion ...... 77 References ...... 81 Appendix A ...... 89 Appendix B ...... 109 Appendix C ...... 127
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Table of figures
Figure 1 Location of Jerrabomberra Wetlands in Australia (Image Courtesy: ACT Government) ...... 2 Figure 2 Wetlands in Jerrabomberra Wetlands Nature Reserve (Image Courtesy: ACT Government) ...... 12 Figure 3 Classification of wetlands ...... 12 Figure 4 Identified discharge from external catchments ...... 14 Figure 5 Nearby event sampling locations in Basin Priority Project ...... 16 Figure 6 The research methodology of the thesis ...... 30 Figure 7 Water sampling locations in the south eastern corner of Jerrabomberra Wetlands ...... 33 Figure 8 Evidence of flow conditions from the point source A to the wetlands ...... 34 Figure 9 Transferring collected water samples in plastic bottles ...... 36 Figure 10 Collecting samples using a grab sampler ...... 37 Figure 11 Measuring dissolved oxygen in field ...... 38 Figure 12 Zinc measurement using XRF (a) spectrum within 7.5-9.5 keV range (b) Integrated intensity vs. concentration graph for the standard samples ...... 40 Figure 13 Long term rainfall and water temperature data from 2012-2015 ...... 46 Figure 14 Temporal variation of rainfall and pH from Dec 2014 to May 2015 ...... 47 Figure 15 Temporal variation of pH and rainfall with time from 2012-2015 ...... 47 Figure 16 Spatial map of pH variation in the wetlands on 29th January 2015 ...... 48 Figure 17 Box–whisker diagrams of pH data from period Dec 2014 to May 2015 ...... 49 Figure 18 Long term rainfall and Electrical conductivity variation from 2012-2015 ...... 50 Figure 19 Short term rainfall and Electrical conductivity variation from Dec 2014 to May 2015 ...... 51 Figure 20 Box-whisker diagram of Electrical conductivity variation in wetlands from Dec 2014 to May 2015 ...... 51 Figure 21 Spatial variation of Electrical conductivity in the wetlands on 13th March 201552 Figure 22 Short term variation of rainfall and turbidity in the wetlands from Dec 2014 to April 2015 ...... 53 Figure 23 Frequency distribution of turbidity measurements in all samples from Dec 2014 to April 2015 ...... 54 Figure 24 Long term variation of turbidity and rainfall data from 2012-2015...... 54 Figure 25 Short term variation of rainfall and dissolved oxygen from Dec 2014 to April 2015 ...... 55 Figure 26 Long term variation of rainfall and dissolved oxygen levels from 2012-2015 .... 56 Figure 27 Box-Whisker diagram of dissolved oxygen variation in wetlands from Dec 2014 to April 2015 ...... 56 Figure 28 Short term variation of rainfall and nitrate levels in the wetlands from Dec 2014 to April 2015 ...... 57 Figure 29 Long term variation of rainfall and nitrate levels from 2012-2015 ...... 58 Figure 30 Short term variation of total phosphorus levels in the wetlands from Dec 2014 to April 2015 ...... 59 Figure 31 Long term variation of total phosphorus in the wetlands from 2012-2015 ...... 59 xiii
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Chapter 1: Introduction
Water quality plays an important role in controlling wetland functions, which are critically linked to its ecological characteristics (Verhoeven 2014). Wetlands are at the intersection of the terrestrial and the aquatic environment, wetland water quality and the associated ecosystems get substantially affected by the untreated runoff from upstream catchments, as shown by previous research (Herbert et al. 2015; Yang et al. 2016; Davis et al. 2016). Wetlands are adaptive, however, at the same time they are sensitive to the runoff quality when received beyond a critical loading (Verhoeven et al. 2006). This complicated behaviour has led researchers to critically analyse the response of wetland ecosystems to inflow water quality. In this context, the current research studied the surface water quality variations in Jerrabomberra Wetlands as a case study. These wetlands, situated at the eastern edge of Lake Burley Griffin on an open floodplain of the Molonglo River (Figure 1), receive considerable amounts of stormwater discharge from an upstream light industrial area (Lawrence 2014). The current research, based on a spatial and temporal assessment of selected water quality parameters and the existing hydrological knowledge from the area, investigated the water quality in Jerrabomberra Wetlands. It contributes information to decision making regarding water treatment programs involving the wetlands.
1.1. Jerrabomberra Wetlands and its challenges
The definition of wetlands quoted from the Directory of Important Wetlands in Australia(Environment Australia 2001) is:
“Wetlands are areas of marsh, fen, peatland or water, whether natural or artificial, permanent or temporary, with water that is static or flowing, fresh, brackish or salt, including areas of marine water the depth of which at low tide does not exceed six meters.”
Jerrabomberra Wetlands Nature Reserve includes deliberately constructed wetlands in an area that formed in response to anthropogenic disturbances to the Molonglo River and Jerrabomberra Creek. These disturbances included 1 agriculture, water level controls by dam-filling, silt trap construction, channelized stormwater, infrastructure easements, landfill, weed invasion and earthworks (Jerrabomberra Wetlands Nature Reserve Board of Management 2013). In spite of these changes, it fits the definition of a wetland (Environment Australia 2001; Australian Government 2015b) and moreover provides important ecosystem goods and services. The ACT government and an active management group are deliberately working to maintain the ecosystem functions and values from the surface discharge from the surrounding urbanised catchments. In response to the pollution levels in these wetlands, pro-active management is needed to protect the reserve and deliver outcomes to meet rising social expectations, the expanding population’s demands, associated services and infrastructure needs.
Figure 1 Location of Jerrabomberra Wetlands in Australia (Image Courtesy: ACT Government)
1.2. Working with managers to co-develop the knowledge needs
Working with the existing management provided an opportunity to develop a research project that is also based on the requirements of the management. There is well-developed literature available (ACT Government 2010a; Eco
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Logical Australia 2011; White et al. 2013; Jerrabomberra Wetlands Nature Reserve Board of Management 2013; Batterley and Stone 2013; Lawrence 2014) and a willing group of managers were prepared to co-develop an appropriate research agenda. From the initial meeting with members of The Jerrabomberra Wetlands Management Committee (JWMC), the Billabong and surrounding wetlands were determined as the focus of the study. This was because these areas were expected to be polluted from the surface water runoff from Fyshwick area where further information on water quality is clearly needed.
Originally this project was intended to study the water quality of the entire reserve. However considering the project duration and limited availability of water quality data, the study area was defined within the Billabong and surrounding wetlands. The managers generally acknowledged that there is no existing study of the surface water quality of the wetlands with exception of the Waterwatch voluntary water health-monitoring program. This has two sampling locations in the Kelly Swamp and in the downstream of the silt trap on the Jerrabomberra Creek (Jerrabomberra Wetlands Nature Reserve Board of Management 2013). The water quality parameters measured by Waterwatch in the two locations mentioned above are water temperature, pH, electrical conductivity, turbidity, total phosphorus, dissolved oxygen, nitrates and algal abundance. Therefore, in response to the gap in the water quality database in this area, the current research contributed additional water sampling data as a part of the project.
In this project, eight parameters were chosen for analysis to be scientifically appropriate and useful to management. These parameters include water temperature, pH, turbidity, electrical conductivity, dissolved oxygen, nitrate, total phosphorus and zinc. The water sampling sites were selected during two field visits and designed to achieve systemic understanding of the hydrological and water quality situation. The management agreed the sites selected were of interest and provided the archived water data to enable an integration of new knowledge with the existing knowledge available to the JWMC.
Spatial mapping of the water quality dataset with a Geographic Information
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System (GIS) developed a spatial understanding of the surface water quality within the wetlands. A temporal variation of the data provided the management with an understanding of the variation of water quality with time. Moreover, the historical data from the two Waterwatch sites inside the reserve contributed to a broader picture of water quality information from previous years. As suggested by the managers, this project also offered an opportunity to revaluate the water quality dynamics in the study area, previously described by Lawrence (2014). Thus, this research seeks to make a scientific contribution to knowledge on the water quality of wetlands subjected to urban runoff.
1.3. The research questions
The current project identified the following research questions to be answered from a short Master Philosophy research project on Jerrabomberra Wetlands:
1. To what extent are the wetlands contributing to improving the key components of water quality? 2. How do the pollutant levels vary in different wetlands?
By answering these questions, knowledge of the existing water quality data will grow; GIS layers of surface water quality parameters will be constructed; and these will contribute to the existing systems of spatial data held by the ACT government. The case study will further add to the knowledge of water quality in wetlands subjected to excessive nutrients and heavy metals carried with runoff from urban catchments.
1.4. Research project: study justification and context
Previous research was conducted by the Jerrabomberra Wetlands Nature Reserve Board of Management (2013) to assess the potential health risks to aquatic fauna and other values in Jerrabomberra Wetlands and Lake Burley Griffin. The study reported that stormwater discharges from the surrounding sub-catchments probably include heavy metals, toxicants, nutrients and oxygen depleting substances that may jeopardise the health of ecosystems in wetlands and Lake Burley Griffin.
The vulnerability of the southern part of Jerrabomberra Wetlands to 4 stormwater runoff was further explained in a separate study by Batterley and Stone (2013). Consultants identified, using predictive water quality models, that the wetland will receive elevated nutrients and heavy metals. They recommended further water quality assessment.
The current project will help to develop an understanding of the distribution of nutrients and heavy metals in the wetlands in response to the point and non- point source discharges and will inform management endeavours. As a management initiative to investigate the groundwater quality, groundwater monitoring boreholes were installed in association with the Board and Territory and Municipal Services (TAMS) in early 2013 (Lawrence 2014). The groundwater observations satisfied all the water quality criteria except for an elevated nitrate content adjacent to Kellys Swamp (Jerrabomberra Wetlands Nature Reserve Board of Management 2013). In some areas, such as in the southern corner of the Reserve, the absence of surface water quality data meant that groundwater data served as the only way to understand the water sources, pathways and the pollution extent across the reserve. From Lawrence (2014), the Billabong was identified to have a direct linkage with the groundwater aquifer and also with the silt trap. However, that research could neither determine to what extent the Billabong is affected by this interlinkage, nor the qualitative performance of the silt trap in mitigating pollutants. As such, an understanding of the underpinning dynamics of change of water quality in this specific area of Jerrabomberra Wetlands will aid in evaluating the proposed diversion of the Creek through the former channel pathway through the Billabong and will help predict the consequences. In addition, the plan to introduce a deliberate biofilter role in this area (Jerrabomberra Wetlands Nature Reserve Board of Management 2013) has made it an important time for this site to be studied. This will also provide the managers with a benchmark of water quality in the area surrounding the Billabong prior to its use for water purification.
This is an ideal time to conduct this research. The ACT government, with over $85 million of Australian Government funding, is currently working on a massive investment project called the Basin Priority Project (BPP) with an aim 5 to improve the water quality of Canberra’s lakes and water bodies (GHD 2014). Among other study sites of this project, Fyshwick and Jerrabomberra catchment is considered as a representative of the industrial, urbanised and developed catchments that contribute mostly to the pollutant loads to ACT waterways (GHD 2014). As a result, there is an opportunity to provide more water quality information as treatment measures, such as biofilter and wetlands, as these has been proposed for Jerrabomberra Wetlands to become part of the water treatment train.
Based upon the identified knowledge needs, this research project intends to accomplish the following aims and objectives.
1. To map and synthesise the spatial variation in water quality parameters in the study area. 2. To statistically analyse the temporal trends of water quality for the study area mentioned above. 3. To better describe the wetlands in relation to their major water sources and pollution levels. 4. To develop comparative frameworks for study of the water quality parameters from upstream to downstream and through time as impacts occur. 5. To provide foundations and feasibility trials for necessary future research opportunities in this area such as future monitoring sites and event-based sampling at suitable locations.
The key outputs of this research project include GIS maps, graphical representation and statistically tested comparisons of water quality data for the southern corner of Jerrabomberra Wetlands. There is a peer reviewed conference publication from this thesis.
1.5. Thesis outline
The thesis comprises of seven chapters. The supplementary materials, such as spatial maps and tables, are provided in the appendices and are also mentioned in the text. The content of each chapter is briefly outlined below.
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This chapter (one) provided an introduction on Jerrabomberra Wetlands in the context of water quality management, the co-development of research needs in this area, research questions followed by justification of the research project to meet the knowledge needs followed and thesis aims and objectives. This chapter also includes the arrangement of the thesis.
Chapter two provides a review of the existing knowledge in Jerrabomberra Wetlands that encompasses the historical background of the area and previous studies on water quality in the wetlands.
Chapter three describes the national and international studies that are related to the current project; and connects the research outcomes to the context of international research in this field.
Chapter four contains a description of the study area, followed by details of the project methodology that includes the experimental design, analytical procedures, and statistical approaches adopted in this research project.
Chapter five illustrates the analytical results of chemical, biological and heavy metal analyses. It has a description of the observed values and the anomalies present in the dataset.
Chapter six critically evaluates the outcomes from the viewpoints of the scientists, managers and engineers and their potential effect on wetland functionality. It also recommends future research in this area and suggests some ideas for consideration by the managers.
Finally, chapter seven provides a conclusion and summarises the important outputs of the study and scope for future Jerrabomberra water quality knowledge projects.
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Chapter 2: Knowledge audit of Jerrabomberra Wetlands water and its management
This chapter includes a synthesis and record of the grey literature and discussions to provide the necessary background to Jerrabomberra Wetland management in order to identify the broader research (to be covered in the literature review). It will provide additional guidance to the research questions and methods.
2.1. Importance of Jerrabomberra Wetlands Nature Reserve in Australia
Australia is endowed with approximately 57904254 ha of wetlands that include 65 listed as internationally important and 904 listed nationally important because of their cultural and environmental significance. The Australian Capital Territory, although the smallest of all the territories and states in Australia, accommodates 1257 ha of wetlands including 13 nationally important wetlands (ACT Government 2010b). Jerrabomberra Wetlands Nature Reserve is the largest of the nationally important wetlands in the Australian Capital Territory and covers an area of 201 ha (Finlayson and Spiers 1999; ACT Government 2010b; Australian Government 2015d). The wetlands in the Reserve satisfy the criteria 3 and 6 of the Directory of Important Wetlands in Australia (Australian Government 2015c) and therefore were enlisted in the Directory in 1996. These criteria include their value as a habitat of 177 different birds as well as a refuge island during dry period, and their archived contribution to humans using the floodplain resources (White et al. 2013; Jerrabomberra Wetlands Nature Reserve Board of Management 2013). The Australian National Heritage Charter refers to the cultural importance of the Reserve for the aesthetic, historical, scientific as well as social or spiritual values it provide for antecedent, current and future generations (Jerrabomberra Wetlands Nature Reserve Board of Management 2013). In this regard, it was enlisted on the ACT heritage Register in 1998, considering its natural and heritage values, particularly for those as the 9 habitat of birds and other wildlife, and its importance as the singular extensive area in ACT made of riverine floodplains with a palaeochannel.
2.2. History and development of Jerrabomberra Wetlands Nature Reserve
Jerrabomberra Wetlands Nature Reserve is enriched with ecological and cultural values. The Reserve floodplain sediments carry historical records from the past evidencing the change in river morphology. As described by White et al. (2013), the earlier geomorphological studies in the Reserve revealed that most of the sediments were released with gully incision during the late 1800s and early 1900, caused by European agriculture. Most of the sediment deposition occurred during the late 1800s and was significantly reduced after 1950.
Besides the sediment deposition, the Reserve has an archaeological record of Aboriginal use for hunting, fishing and social life, as evidenced by the Aboriginal stone tools identified in the sediment of the large palaeochannel of Molonglo river floodplain (ACT Government 2010a; White et al. 2013). Previously, during the post European settlement, the area was used for dairy purposes, major arterial transport thoroughfares, landfills and industrial purposes (ACT Government 2010a).
With the course of time, different anthropogenic activities acted to alter the wetland landscape. As described by White et al. (2013), there was construction of railway track, electrical infrastructures, sewer pipelines through the Molonglo floodplain, intrusion of native plants vegetated on the garden beds along the margin of Kellys Swamp and south of Jerrabomberra Creek, construction of silt trap on the Jerrabomberra Creek channel and landfill on the West of the Reserve. The dairy floodplains and Quaternary dunes have gone through significant modification with pasture grass for agricultural and grazing purposes. The alteration of the Jerrabomberra Creek and inundation of the flood channel in Dairy Flat were found favourable replacement for unique habitat types that were previously found in the upland south-east Australia. During the construction of new Parliament House, the south western corner of the Reserve was filled with considerable amount of construction residuals. This favoured for
10 dry woodland plant species and moisture tolerant species; it also improved the moisture holding capacity of the soil during prolonged dry period (ACT Government 2010a). As a result, a number of changes to the wetland community were caused by the modified hydrological and soil condition. As an instance, there is a modification and replacement of the original wetland vegetation community with invasive species, weeds and planted species (Jerrabomberra Wetlands Nature Reserve Board of Management 2013).
2.3. Hydrology of Jerrabomberra Wetlands
The Molonglo River and the Jerrabomberra Creek surrounding the Reserve on its North and South respectively, created a complex hydrological pattern. A number of wetlands emerged in the floodplain depressions that are permanent and ephemeral in nature depending on their water sources. Currently there are 25 natural and deliberately constructed wetlands in the Reserve (Figure 2). Among those, a few wetlands were named as Jerrabomberra Billabong (Wetland 21), Kellys Swamp (Wetland 19), Jerrabomberra pool (Wetland 16c), Jerrabomberra silt trap (Wetland 18), and Shoveler Pool (Wetland 12, 13). The wetlands depend on the Lake water, surface discharge from Fyshwick and turf farms, direct rainfall, and flooding of the Molonglo River and Jerrabomberra Creek as their major water source. Those depending on Lake water are perennial in nature while the ephemeral wetlands depend either on the surface runoff or surface overflow from the Molonglo River or the Creek. Based upon the source of water and water depth , the Reserve wetlands are classified as demonstrated in Figure 3. The variation of the water level in the wetlands was identified as a function of groundwater, rainfall, evaporation, catchment discharge and the lake water level (Lawrence 2014).
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Figure 2 Wetlands in Jerrabomberra Wetlands Nature Reserve (Image Courtesy: ACT Government)
Figure 3 Classification of wetlands
(Source: Jerrabomberra Wetlands Nature Reserve Board of Management 2013, p. 36)
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Further investigation by Jerrabomberra Wetlands Nature Reserve Board of Management in partnership with TAMS (Jerrabomberra Wetlands Nature Reserve Board of Management 2013), identified three major hydrogeological systems active in the Reserve. These are: 1) the Molonglo river floodplain with 2.5- 4.0 m silty loam soil which is situated on a shallow sandy loam aquifer; 2) Jerrabomberra Creek floodplain with 0.1-1 m of loamy soil cover on a loamy sand and gravel aquifer; 3) southern Jerrabomberra Creek floodplain with 0.1m sandy loam soil cover on sand and gravel aquifer. The intersection of the Molonglo and Jerrabomberra Creek floodplain is a sand dune system which acts as the groundwater recharge point (Lawrence 2014). Besides this, Lawrence identified window wetlands (connected to the groundwater aquifer) and perched wetlands (detached to the groundwater aquifer) in the Reserve that also aid in groundwater recharge seasonally. Accordingly, Kellys Swamp (Wetland 19), Shoveler Pool (Wetland 12, 13), the Billabong (Wetland 21) and south eastern wetlands were found to act as the window wetlands during dry periods and perched wetlands during wet periods. However, groundwater was found to contribute 0.1 percent to the total amount of water in the Reserve during 2013- 14 (Lawrence 2014). Lawrence further added that the major water source in the wetlands was rainfall (91.4%) whereas external catchments discharge account for 8.5 percent of the total water in the wetlands. Eight external sub-catchments were identified to discharge into the wetland based on their elevation of the area. These include stormwater discharges from Fyshwick sewage treatment plant and Dairy Road corridor, discharge from impervious areas of Fyshwick industrial zone, turf farm, contaminated landfill and residential urban areas (Figure 4). Growing concerns of the potential effects on the wetlands by the pollutants carried by stormwater runoff and industrial spill is held by the managers and community.
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Figure 4 Identified discharge from external catchments
(Source: Jerrabomberra Wetlands Nature Reserve Board of Management 2013, p. 27)
2.4. Previous studies and management measures in Jerrabomberra Wetlands and Fyshwick catchment in the context of water quality
Jerrabomberra Wetlands have been subjected to modification to the hydrology in almost all of the wetlands with changed floodplain arrangements caused by excavations and soil dumps. Native plant species compete with invasive plant species. Although it is impossible to revive the historical ecological values of the Reserve, ACT Government and management of Jerrabomberra Wetlands Nature Reserve are actively working to protect the resources and values in the reserve as documented by management reports (ACT Government 2010a; Eco Logical Australia 2011; Jerrabomberra Wetlands Nature Reserve Board of Management 2013; Lawrence 2014). As an example, the managers permitted grazing in the 14 reserve considering the number of benefits this offered in return (e.g., reducing public access in the bird refuge area, reducing fire fuel load, and contributing to bird diversity and to maintaining their habitat). There are activities to reduce and remove pest plants and species in the reserve such as removing willows and woody species. To reduce the intrusion of carp, there is planning to harvest carp in Jerrabomberra Reach and to remove them manually (Jerrabomberra Wetlands Nature Reserve Board of Management 2013). Besides that, installations of water flow controlling measures (e.g., valves, levees) in the Reserve are under consideration. As mentioned in Eco Logical Australia (2011), the future management plans in the reserve include construction of a visitor centre with parking and entertainment facilities; a trail network that will focus on the habitat and seasonal variation of birds aided with traffic noise control facilities; addition of new bird habitat types with segregation of their habitats according to their needs; and finally develop a transition zone from the wetlands and East Lake development site with constructed wetlands and biofilter system.
A groundwater quality assessment project was conducted within the Reserve in 2013 (Lawrence 2014) (Table C 1 in Appendix C). The study indicated the absence of wastewater seepage from the neighbouring sewage treatment plant in Fyshwick. A stormwater drainage study was conducted on Fyshwick catchment to test the feasibility purpose of the site for the East Lake Urban Renewal project (Batterley and Stone 2013). Aiming to contribute more to the limited knowledge of the drainage system and the stormwater quality delivered from this area, it provided a detailed outline of the sub-catchments that discharge into the Reserve and the potential risk factors associated with those areas. However, water sampling and testing were beyond the project’s scope. Instead, a predictive numerical approach (MUSIC) was followed to anticipate the possible amount of runoff and pollutants associated with the runoff. The study results revealed (Table C 2 in Appendix C) that the possible pollutant loads for total suspended solids, nutrients and heavy metal in the estimated amount of runoff from Fyshwick catchment exceed the water quality criteria mentioned by ANZECC and ARMCANZ (2000) that would be expected from the same amount of runoff, which would be a matter of concern. As a part of Basin Priority Project
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(GHD 2014) (section 1.4), stormwater samples were collected and analysed from rainfall events from Fyshwick catchment along with the other priority catchments (i.e., Lake Tuggeranong, Yarralumla , Mid-Molonglo, Lower Molonglo and Riverview West Belconnen). In Fyshwick catchment, the nearest event- sampling stations to Jerrabomberra Wetlands were located downstream of the gross pollutant trap (site: WIL110) and upstream of the silt trap (site: JER120) (Figure 5). Event-sampling results from a brief wet period (11 February 2015, 12 February 2015 and 14 February 2015) confirm that indeed, the nutrients
(NOx and total phosphorus) and heavy metals (zinc) carried through the wetlands through the Creek is undoubtedly high (GHD 2015) as also predicted by Batterley and Stone (2013). Therefore, considering the expected low quality of runoff from upstream in the south-eastern corner of the Reserve, this area is likely to receive attention in water quality remediation program.
Figure 5 Nearby event sampling locations in Basin Priority Project
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2.5. Conclusion
This chapter provided a brief background of the historical, cultural and ecological values of Jerrabomberra Wetlands Nature Reserve. These recent initiatives discussed, highlight the importance of conservations of these multiple values of the Reserve. The management plans, initiatives and previous studies in the Reserve, as well as in Fyshwick catchment provide the context for this research project. The previous study outcomes, while conveying the information of the water quality, also create a platform for the current project on the surface water quality in the Reserve.
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Chapter 3: Literature Review; a knowledge audit to provide the background and directed to the methods and research needs
This chapter provides a brief literature and knowledge review and provides the necessary scientific and management background to wetland values, functions and water qualities of wetlands like Jerrabomberra. This includes existing international literature on: the functionality of wetlands and their importance; an integrated analysis of management and the data and recommendations that were made internationally to achieve wetland conservation. This is followed by a discussion of the common approaches used by researchers to assess the condition of existing wetlands.
3.1. Research on the function of wetlands and their emerging importance
The ecological functions, and the environmental, economic and cultural benefits wetlands provide to humankind are well understood and are also still being revealed (Whiteoak and Binney 2012). These values are often used in decision making to prioritise their protection (Fisher et al. 2009). The benefits include: maintaining biodiversity and wildlife; water quality improvement by supporting function of nitrogen, phosphorus and carbon cycles and sinks (Keddy 2010); climate stabilisation by ameliorating flood and drought conditions, shoreline protection and in recharging the groundwater aquifer (Mitsch and Gosselink 2007); as a component in the popular Water Sensitive Urban Design (WSUD) approach to control non-point source pollutants (Brix 1994; Mitchell et al. 1995; Deletic et al. 2014); and to provide aesthetic value to people. Their contribution as a receiver of wastewater and a source of clean water often means they are described as the kidney of the landscape (Mitsch and Gosselink 2007). Besides that, wetlands are also described as landscape factories or ecological supermarkets (Keddy 2010) for generating complex goods and services from catchment inputs and delivering them to the ecosystem.
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Among the multiple benefits wetlands provide to humankind, their contribution in ameliorating the quality of water flow has always been considered an important one (Mitsch and Gosselink 2007). There are a number of biological, physical and chemical processes in wetlands that serve to trap the organic particles and debris, and they transform the nutrients flowing with water. These processes have been well described for decades (Vymazal et al. 1998; Khanijo 2002; Vymazal 2007; Mitsch and Gosselink 2007; Kadlec and Wallace 2009). Besides that there are a number of environmental factors that have indirect control over these processes such as wetland soil type (Vymazal 2007), hydrological regime (Fisher and Acreman 2004), retention time, amount of nutrients already present in the system (Kim et al. 2016), wetland age (Beebe et al. 2014), and type of vegetation and biota (Read et al. 2008). Furthermore seasonal effects such as rainfall, evapotranspiration (Białowiec et al. 2014), along with the seasonal change of inflows and concentrations, control wetland performance in water treatment (Kadlec 1999). Based upon these factors, the performance of wetlands varies in reducing the concentration of dissolved constituents.
Humans have relied on the benefits of wetlands either intentionally or unintentionally for water quality improvement (Brix 1994; Mitsch and Gosselink 2007). In the early 1950s in German experiments, wetland plants were first used for wastewater treatment as reviewed by Brix (1994). Since then, a number of experiments led to a full scale development of wetlands in use for water treatment. During 1990, the use of wetlands gained international popularity through the exchange of knowledge and over the last two decades has extended to a wide range of purposes (Vymazal 2011). Wetlands are used in various roles including: treating industrial and agricultural effluent (Zhao et al. 2004; Wood et al. 2007; Calheiros et al. 2012; Vymazal 2014); landfill leachate (Justin and Zupančič 2009); urban and highway stormwater runoff (Vymazal 2011; Scholes et al. 1999; Istenič et al. 2012); and in upgrading the water quality from agricultural, industrial and municipal sources (Brix 1995; Vymazal 2007, 2014). In developing countries, as reviewed by Zhang et al. (2014), the use of constructed wetlands is a popular low cost, simple to operate way to achieve higher water quality.
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From the classification delineated by Duraiappah et al. (2005), the goods and services provided by wetlands are classified as supporting (e.g., nutrient cycling), regulatory (e.g., water flow regulation) and cultural services (e.g., recreational). These values are evaluated economically (Woodward and Wui 2001) and incorporate the social, environmental and economical values in different ways (Woodward and Wui 2001; Brander et al. 2006; Ghermandi et al. 2010; Chaikumbung et al. 2016). Regardless of the method of calculation, the studies consistently agreed that: small wetlands with bird watching facilities are more highly valued; urban and marine wetlands were valued more (Chaikumbung et al. 2016); wetlands located in areas with high human population densities; and in richer countries have a larger economic value. There has been a gradual growth of concern around the governance of wetlands as a public good and protected asset.
In Australia wetlands are highly valued as a less expensive, reliable and sustainable technology for water treatment. Realisation of the important values of the wetlands and a desire to avoid the costs of not having their services has made people appreciate the multiple benefits of wetlands in treatment of industrial and domestic wastewater. Failure to protect Australian Alpines wetlands, those used for sedimentation purposes in protection of dams used for hydroelectric purposes, agriculture and drinking water supplies, fostered awareness of the value for catchment protection in water supply areas (Wells 2015). The connection between disease and dirty water took longer to develop but the link between vegetation, sediment trapping and water clarity was quickly made. Currently, constructed wetlands are one of the most popular Water Sensitive Urban Design (WSUD) approaches used in Australia (Deletic et al. 2014). A comprehensive review, provided by Mitchell et al. (1995) on the development of wetlands for water pollution management in Australia, showed the successful management of BOD (Biochemical oxygen demand) (97%) from sewage water using some selected plants (Myriophyllum aquaticum, Spirodela spp., Typha spp.) during 1981. A separate study conducted by Commonwealth Scientific and Industrial Research Organisation (CSIRO), as reviewed by Mitchell et al. (1995), succeeded in using wetlands planted with Typha latifolia in a gravel-based trench to treat effluents (60% total nitrogen, 70% total phosphorus, almost all total suspended solids) with
21 high pollutant concentration from piggeries, abattoirs and wineries. This shows researchers’ efforts to replicate wetlands by design to ensure the maximum sustainable use of this valuable environmental resource. At the present stage, use of constructed wetlands, as a subset of stormwater management options in water sensitive urban design, has significantly expanded with knowledge contributed from both scientists and experience from managers (Wong et al. 1999; Deletic et al. 2014). In this regard, the suitability of Jerrabomberra Billabong for use as a biofilter to treat the polluted stormwater runoff needs to be carefully considered and identified by additional water quality information from the Billabong.
3.2. Limits to the use of wetlands for water purification
While the use of wetlands for water purification has gained widespread recognition, there is a rising global concern centred on the losses and impacts associated with the uncontrolled use of wetlands. Anthropogenic activities are a major cause of widespread loss and degradation of wetlands worldwide (Davis and Froend 1999; Finlayson and Rea 1999; Gopal 1999). Palaeoecologists, using indicators such as chemicals and biological information in sediments show that rising populations are responsible for the deterioration of the wetlands. This generalisation is supported by recent urban research (Lintern et al. 2015) in Melbourne that linked residential growth to pollution levels and demonstrated that there exists a positive relationship between urban growth and pollutant concentrations deposited as sediments in urban wetlands.
A myriad of studies document the effects of urban development on wetlands. Osborne and Totome (1994) studied the effect of long term (25 years) use of a tropical wetland for nutrient removal and concluded that wetlands have a limited capacity for water purification. Comparing the long term (1966 - 1991) aerial photographs of that study area revealed a remarkable loss of submerged and floating leaves along with a decreased extent of littoral and emergent vegetation. The limits of using wetlands for nutrient removal was further documented by Verhoeven et al. (2006) who identified ‘critical loads’ that, if exceeded in a certain wetland, may result in an increased undesirable productivity, emission of greenhouse gases as well as nutrient leaching. Although rare, exceeding these ‘critical loads’ can be responsible for a sudden 22 shift of the ecosystem into a different stable condition and thereby results in an altered species composition (Verhoeven et al. 2006). A strong correlation was observed between soil pH and growth of bacterial community and biogeochemical processes. A decrease in bacterial diversity in the wetland was also identified as a result of wetland restoration (Hartman et al. 2008).
Toxic substances carried by stormwater runoff (such as metals), are also documented as of concern to wetland ecologists. Among the metal contaminants of urban runoff, copper, lead and zinc are considered to be important because of their strong correlation with urbanisation (Kennedy 2003; Kayhanian et al. 2007) and their environmental significance (ANZECC and ARMCANZ 2000). Metals accumulated on the freshwater biofilm in the aquatic ecosystem can be consumed by the biota and can be transferred into the food chain (Ancion et al. 2010). Such bioaccumulation of metals in the food chain exposes the aquatic macro-organisms (Clearwater et al. 2002) and vertebrates to hazards. Metal tolerant bacterial and algal populations in metal polluted sites (Admiraal et al. 1999; Morin et al. 2008) are reported.
Zinc is abundant in oil, tyres, concrete, and gutter dust. An estimation from Kennedy (2003) shows the highest amount of zinc to be derived from a combination of residential and industrial land use. The median zinc concentration present in urban sediments in Australia (477 mg/kg) is double than that present in the sediment from Europe, North America and Asia (107- 183 mg/kg), as documented by a recent study by Centre for Aquatic Pollution Identification and Management (Allinson et al. 2015). Sediment and water samples from 24 wetlands in Melbourne were tested for trace metals and detected considerably high zinc concentration in the sediment (Allinson et al. 2015). The zinc concentration observed in these wetlands were similar to the finding from the sediment in Dandenong Creek in Melbourne (Marshall et al. 2010). A separate research in Melbourne studied stormwater samples in five aquatic systems for six events and from October 2011 to March 2012 (Allinson et al. 2017). The results detected zinc in 87% of the samples exceeding the guideline values. In Canberra, Fyshwick catchment, being mostly light industrial uses is expected to be the source of zinc in stormwater runoff along with other 23 heavy metals. According to the MUSIC model predicted results, 302 kg of zinc could be generated from this catchment per year (Batterley and Stone 2013). This amount is the highest among the four heavy metals studied (i.e., lead, zinc, copper, cadmium) and is 20 times higher than the guideline values (ANZECC and ARMCANZ 2000). Therefore, a study of the empirical evidence of zinc in the downstream water is an important contribution.
In addition to the nutrient and heavy metals load, the increased salinity level in wetlands is considered one of the greatest causes of wetland degradation by Duraiappah et al. (2005). The phenomena of wetland salinization is a consequence of anthropogenic activities (known as secondary salinity) as described by (Cañedo-Argüelles et al. 2013). The potential reason of secondary salinity is the rise in groundwater table due to extraction of groundwater for different purposes (Herbert et al. 2015). Additionally, in Australia, the salinity of groundwater may also result from the climatic and geomorphological aspects (known as primary salinity). Although the Australian native species of lowland rivers show flexibility to the rising salinity level up to a certain extent (threshold value from specific species) (James et al. 2003), however, the rapid rise in salinity has been reported to be too fast for the biota to adopt. Considering the extent of wetland salinization and its adverse effects (Baldwin et al. 2006; Herbert et al. 2015), an estimation of the salinity level in Jerrabomberra Wetlands would provide a benchmark of salinity and would inform in management decision making for necessary salinization management.
3.3. Protection of wetlands
The protection of wetlands by careful governance aims to avoid wetland losses and to ensure ‘wise use’ (Verhoeven 2014) of wetlands. Several global and national agreements and commitments were made in order to protect the wetlands. Among those was the Ramsar Convention, this remarkable international agreement which was signed in 1971 in Iran, with an aim to arrest wetland loss and to protect important wetland remnants. This resulted in recognition of 65 Ramsar wetlands in Australia that cover more than 8.3 million hectares of land and require special consideration in their management (Australian Government 2015b). Moreover, legal frameworks under the 24
Environment Protection and Biodiversity Conservation Act 1999 protect ecological habitats including wetlands (ACT Government 2010b; Australian Government 2015a).
There are a number of natural (for instance Nursery Swamp in Namadgi National Park, Gungahlin's Horse Park Wetland, and Lake George, NSW) and constructed wetlands in the Australian Capital Territory (ACT). Some help in improving the water quality derived from the surrounding urban catchments while also providing additional environmental values (for example, O’Connor wetland, Dickson and Lyneham wetlands) (Robinson 2012). In recent years the ACT Government augmented the urban wetlands (ACT Government 2010b). It has, invested in the protection, conservation, restoration and construction of wetlands and there is a role for research to inform the management so that social, legal and international agreement needs are also achieved. The Basin Priority Project (paragraph 1.4) extends that commitment, having assessed the quality of flowing water in Canberra’s waterways and based upon the assessment results, it proposes water treatment measures that include wetlands of various kinds.
3.4. Different approaches used to evaluate the performance of wetlands being used for water treatment and other purposes
It is worthwhile to review the approaches followed by the scientists, engineers and decision-makers that will aid in developing the framework of analysis for this current study. This includes setting goals, selecting appropriate measures, conducting analysis and supporting decision-making about possible actions.
This project is part of a broader goal setting purpose that is ongoing. The literature consistently proposes that setting goals by scientific, community and legal knowledge is important. In this respect an integration of interdisciplinary knowledge is often needed for adaptive management. For example, for the effective management of the upland swamps in the Blue Mountains, NSW, Australia (Kohlhagen et al. 2013), a combination of knowledge from surveying rehabilitators and geomorphologists served to develop essential baseline information. Apart from the physical assessment of the environment,
25 involvement of volunteer community through conversation and citizen science has served to link the past, present and anticipation for the future change (Pearson et al. 2015). Given the importance of community involvement, the current project involves the data derived from Waterwatch (Upper Murrumbidgee Waterwatch 2015b) and JWMC as important knowledge to inform an understanding of water quality data in Jerrabomberra Wetlands. This thesis will provide complementary information to the existing knowledge.
Jerrabomberra Wetlands relies on community support and community participation to do water quality assessment. Evidence suggests that the skills and knowledge of measuring water enhances the community’s value of wetlands (Australian Government 2016). Volunteers, with their diverse levels of knowledge and experience can contribute to protective activities in wetlands. This was reported from the knowledge exchange between “Bushcare” volunteer groups with the rehabilitators with the information that was unattainable from physical assessments alone (Kohlhagen et al. 2013; Bushcare Volunteers of Sydney 2016). The contribution from the volunteer organisation “Waterwatch” is notable because it develops an exemplar of a historical water quality database (Upper Murrumbidgee Waterwatch 2015a). There are several examples of such volunteer groups contributing to the welfare of the community such as Conservation Volunteers and Bush Heritage Australia (Conservation Volunteers Australia 2016; Bushcare Volunteers of Sydney 2016). In this research the Waterwatch database is the basic historical water quality data at two locations within the reserve. In addition, the methods, training, tools and data gathered in this research project (discussed in detail in methodology chapter in paragraph 4.2) followed the Waterwatch protocols to ensure it would contribute to the community-based long- term projects. Although the Waterwatch knowledge falls outside the peer- reviewed literature, often it stands as the best and only information available (Upper Murrumbidgee Waterwatch 2015a). Considering these facts, the water quality parameters studied in this research project were kept similar to that of the Waterwatch, except with the addition of zinc, to ensure the credibility of the work to the managers and also to contribute to the existing database in the area of study.
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To manage the available water quality data and to plot maps for visualisation, ArcGIS is frequently used by researchers to demonstrate the distribution of pollutant concentrations (Pongpetch and Suwanwaree 2012; Li et al. 2014), for different spatial analysis (Chang 2008) and also to plan nonpoint source pollution control (Yang and Jin 2010). A combination of GIS and statistical methods often serves as a powerful tool for analysis. The changes in landscape pattern and the landscape characteristics can be revealed through GIS and can be further correlated with water quality assessment (Zhou et al. 2007; Yang et al. 2010; Chebud et al. 2012; Li et al. 2014). The ACT Government uses ArcGIS for mapping and visualising data and to provide actionable information. Thus, GIS can provide decision-makers and audiences with authentic and envisioned information. Considering the acceptability of GIS to both the researchers and the managers, the current project used ArcGIS for mapping and data management (extant, new and synthesis) for the water quality parameters.
This research applies to the relatively well known Jerrabomberra Wetland that has only a few water quality sample points and a short-term data series. To evaluate the spatio-temporal variation of water quality parameters, application of Multivariate Statistical Analysis including Principal Component Analysis (PCA), Cluster Analysis (CA), Factor Analysis (FA) and Discriminant Analysis (DA) are the prevailing statistical techniques that are used by the scholars to extract meaningful information from complex datasets (Kazi et al. 2009; Yang et al. 2010; Wang et al. 2012; Li et al. 2015; Duan et al. 2016). However, all of these methods are usually applied on a long term data series. For example, Li et al. (2015) analysed the water quality data in the controlling points of rivers on a dataset from 1991 to 2011 with PCA, DA and CA. Kazi et al. (2009) analysed 36 water quality parameters collected for two years of study period (2005-2006) with PCA and CA technique to reveal important water quality information. Wang et al. (2012) applied the multivariate statistical techniques on 5 years (2002- 2006) of water quality data collected from 12 locations. Nevertheless long term data series are not always available in most cases where management decisions are being made. As such to analyse the relatively small data series as those from the current project in Jerrabomberra Wetlands, t-test, Mann-Whitney U test are
27 suitable significance tests that could be used (Bhat et al. 2014; Bogdał et al. 2016). As long as the assumptions are correctly attained, an independent sample t test with unequal variance is a widely used parametric approach of measuring the significance of similarity of two individual datasets. This test gives reliable results for small sample size as well. However, when assumptions cannot be met, non-parametric approaches such as Mann-Whitney U test could be used. To avoid the ambiguity of results from a Multivariate Statistical Analysis on small scale dataset from current project parametric significance test such as t-test and Mann-Whitney U tests were selected for current project, as the dataset satisfactorily met the test assumptions. Therefore, from the peer reviewed knowledge on different techniques to analyse water quality data, statistical and comparative assessment methods were selected to fit the data series from Jerrabomberra Wetlands.
3.5. Conclusion
The ideas and knowledge derived from the international literature contribute to the Jerrabomberra Wetlands project by providing a background, needs and methods to pursue the gradual emergence of wetland use for water quality improvement, followed by the associated limitations of such use, was described in brief. This, apart from providing the historical background of wetland use, indicates the need for ‘wise use’ is an important but threatened ecological feature (Maltby 1991). In response, wetlands receive attention under global treaties regulations and also spur local and regional actions for environmental protection (Kermode et al. 2016) that were briefly described. For determining the performance of these management plans, assessment of the effect of anthropogenic activities on wetland ecosystems is treated as an important preliminary step (Kermode et al. 2016). In this regard, the knowledge derived from this chapter, helps describe the applicability of this local project in Jerrabomberra Wetlands as a response to the management needs. It is believed that the knowledge derived from this chapter will serve as useful frame of reference to achieve the objectives of the current project.
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Chapter 4: Methodology
This chapter describes the procedure followed in the current research project to answer the research questions about surface water quality in Jerrabomberra Wetlands. The methodology was designed by integrating data from field monitoring, citizen science and existing event sampling data. Spatial and temporal analysis of the dataset with statistical techniques, and spatial maps of the water quality parameters with a geographic information system, were applied to critically evaluate the variation of the parameters based upon the wetland characteristics. The project methodology, being a combination of interdisciplinary knowledge with spatial and temporal analytical techniques, served as a powerful tool to assess of the effect and extent of stormwater runoff from Fyshwick on downstream Jerrabomberra Wetlands.
The description of the procedure followed in this project includes; a depiction of the study area, field water sampling and analysing procedures, collection of existing water quality data in the surrounding areas, combining new data with the existing data, and spatial and temporal analytical procedures. The entire procedure of the current research project is shown in Figure 6.
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Figure 6 The research methodology of the thesis
4.1. Description of the study area
The present study selected the south eastern corner of Jerrabomberra Wetlands for surface water sample collection. This area is located immediately downstream of Fyshwick sub-catchments covered with industrial and commercial uses such as warehouses, industries, car parks and offices. The surrounding areas are 50% impervious (Batterley and Stone 2013) and that runoff includes pollutants and increased amounts of runoff. Currently there is no
30 stormwater treatment present upstream (e.g., sediment detention ponds, gross pollutant traps) other than the only gross pollutant trap in Fyshwick. The runoff conveyed by the stormwater network to Jerrabomberra Wetlands is untreated. The Billabong and surrounding wetlands are suspected to be vulnerable to pollution from upstream and were chosen as important areas for surface water quality assessment. Therefore, the water sampling site selection criteria were set such as the water samples represent the quality of incoming water as well as a gradual change in its quality as it moves through the wetlands to the Billabong. In addition, the connection of underlying aquifer to the wetlands were also important to considered in order to observe the groundwater effect on the surface water of the wetlands. These locations were kept different and upstream to those from the Waterwatch (KEL 010 and JER 120) as shown in Figure 7 and serve as an extension of the water quality data. Thus it will also help to compare the wetlands with each other in respect of water quality parameters.
Water quality monitoring and site selection is a complicated to design, as there is chance for the test results and research outcomes to be affected by an inappropriate monitoring site selection. The Australian Guidelines for Water Quality Monitoring and Reporting is a standard report of the National Water Quality Management Strategy (NWQMS) (ANZECC 2000). Following this standard, a defined set of objectives and list of sought information are required for a successful water quality monitoring program. Therefore, considering the project objectives, site selection criteria, available time (12 months) and limited resources, a short term surface water quality monitoring project was designed. Categorically this was designed as preliminary survey with a short study duration as classified by (Chapman 1996). In agreement with the Jerrabomberra Wetlands Management Authority and after two field visits, six sampling sites (Figure 7) were selected for water sample collection that successfully met the selection criteria. Geographic coordinates of the sampling sites were determined using GPS and are provided in Table B 1 in Appendix B. Among the sites two stormwater drain outlets were identified and named as Point source A and Point source C. The other sampling locations, as shown on Figure 7, include Wetland 22, Wetland 24, Wetland 21 and Wetland 18 as named by the management.
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These locations are representative of different hydrological and ecological characteristics as described below. Point source A and Point source C are considered, as the name implies, point sources of stormwater discharge into the wetlands. Field observations showed water flowed from Point source A and Point source C to Wetland 22 and Wetland 24 respectively. These wetlands probably receive the ‘first flush’ of stormwater runoff through their respective point sources upstream.
Water samples were collected from Wetland 21 (commonly known as the Billabong), Wetland 22 and Wetland 24. These are described as important examples of ‘window’ wetlands (Lawrence, 2014) that reflect groundwater levels. The Billabong, with its fluctuating water level, is a habitat for small waders, frogs and Latham’s Snipe. The sixth sampling site is upstream of the silt trap (Wetland 18) which is a wetland constructed to protect Lake Burley Griffin from sedimentation. Also, because the discharge from the gross pollutant trap (GPT) gets merged with the Jerrabomberra Creek before entering the Jerrabomberra Wetland Nature Reserve, a sampling site upstream of the silt trap was used to record the water quality coming from Fyshwick and upstream of the Jerrabomberra Wetlands. Water samples collected from these locations provided an insight into the pollutant levels carried with the stormwater runoff through the stormwater drains, their distribution into different wetlands and also the pollutants carried and trapped in the silt trap.
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Water flow direction
Figure 7 Water sampling locations in the south eastern corner of Jerrabomberra Wetlands
4.2. Field water sampling method
The water sampling project was designed to collect samples from the selected sites on a weekly basis from January 2015 to April 2015. Water samples were collected between 8 am to 10 am each week during the four month study. In the absence of a rainfall event in the previous week, the water in Point source A and C was inadequate to collect and hence were excluded. For example, for Point source A and C, there were 4 and 5 water sampling events (out of 13 events) respectively when they were completely dry. For the rest of the samples collected from the small Point source A and C pools, they represented the runoff that was the last flush of rain events. Peak flows flatten the vegetation (Figure 8) provide evidence of high volume of surface runoff coming in with probably high
33 level of contaminants. Low or no flow waters (Figure 8) are trapped in pools and these may be less concentrated than the downstream wetlands. As a result, those were probably low in suspended solids and other nutrients (NO3-, Total phosphorus and zinc) and were not accurately representing the pollutant concentration delivered during the first flush or flood flow (Figure 8). Instead those were probably cleaner (chemical and sediment load is less) than storm events. Furthermore the nitrate loading plausibly indicates there are sewage leaks or cross connections which may enter into the flow later in the event.
Point source A Flattened vegetation Flattened vegetation
Figure 8 Evidence of flow conditions from the point source A to the wetlands
Water sampling in the wetlands was conducted using a similar approach to the Waterwatchers with the same sampling kits (Upper Murrumbidgee Waterwatch 2015b). In this way the results are comparable with the longer data set and credible to decision-makers alongside the historical Waterwatch data. Weekly sampling was carried out to demonstrate the gradual change of the parameters parallel to a monthly variation observed from the Waterwatch data.
A bucket (Figure 9) was used in water sample collection which was replaced on the third week with an extended grab pole sampler of 1.5 L considering the ease of a representative sample collection (Figure 10). The water samples were transferred to 500ml polyethylene acid washed and pre-rinsed plastic sampling bottles to carry the samples to the laboratory. A duplicate sample from each site was collected in plastic bags to verify the results. Water samples were collected
34 from approximately 15cm under the water to avoid surface effects. In flowing water (i.e., Wetland 18) the sampler was moved forward to the flowing water to have a representative sample of inflows. Locations that were filled with macrophytes or other organisms (i.e. peripheral areas of the wetlands) were avoided. After sample collection the sampling bottles were labelled with the location name, date and time of collection. Then those were carried to the laboratory and were kept refrigerated until being tested within 24 hours.
To include the standard error associated with the mean values of the water quality parameters, ten sub-sampling sites were selected from virtual grids formed over each wetland and samples were collected from the selected locations on the same day. Multiple sub-sampling was done at Wetland 22, Wetland 24 and Wetland 21 (Billabong). In the case of water upstream of the silt trap (Wetland 18), samples were collected from immediately upstream of the silt trap and in the trap to include the turbulence effect in the calculation. Standard errors of each parameter for the ten sub-samples from the single wetland on the same day were calculated to measure how accurately a single sample represents its population. The small errors at the mean relative to the measurements suggest one sample from the site was a reliable indicator of the water quality parameter. Calculated standard error associated with each parameter is presented in the following table (Table 1).
Table 1 Standard error of the mean value of each parameter for n = 10
Parameter Water pH Electrical Turbidity Total Dissolved Nitrate Location temp conductivity (NTU) Phosphorus oxygen (mg/l) (ºC) (µS/cm) (mg/l) (mg/l) Wetland 22 15.6± 7.5± 52± 8.3± 0± 4.8 0.3± 0.07 0.11 1.33 0.60 0 ±0.27 0.3 Wetland 24 16.2± 7.0± 50±0 2.4± 0.005± 6.2± 0± 0.06 0.07 0.13 0.003 0.16 0 Wetland 21 15.4± 7.6± 358± 15.7± 0.1± 5.5± 0± 0.07 0.03 6.5 2.1 0.02 0.3 0
The mean value of different physiochemical parameters from the wetlands located in the study area are presented in the Appendix B (Table B 2, Table B 3, Table B 4, Table B 5, Table B 6, Table B 7 in Appendix B). As observed from Table 1, the samples are reliably representing the water in a wetland with an
35 average variation of ±0.06 ºC for water temperature, ±0.9 NTU for turbidity, ±0.07 for pH, ±0.1 mg/l for nitrate and ±0.24 mg/l for dissolved oxygen. The electrical conductivity in the Billabong (Wetland 21) was found to vary within ±6.5 which can be considered as minor compared to the electrical conductivity range (i.e., upper value – lower value). The variation was highest in Wetland 22 regarding water pH level (±.106) and nitrate content (±0.3 mg/l) in comparison to Wetland 24 and Wetland 21.
Figure 9 Transferring collected water samples in plastic bottles
36
Figure 10 Collecting samples using a grab sampler
4.3. Laboratory data analysis
4.3.1. Analysing water quality parameters
The samples were analysed for seven parameters selected to match those studied by Waterwatch (Upper Murrumbidgee Waterwatch 2015b). These are water temperature (WT), pH, turbidity, dissolved oxygen (DO), electrical conductivity (EC), total phosphorus (TP), and nitrate (NO3-N). Along with these seven parameters, the concentration of zinc was tested in the samples collected. Among all the parameters, water temperature, dissolved oxygen, pH and electrical conductivity were measured on the spot whereas total phosphorus, turbidity, nitrate and zinc were measured in the laboratory following standard methods (Table 2). Dissolved oxygen and temperature was measured using a dissolved oxygen meter (Model: YSI 55/25) (Figure 11). A handheld conductivity meter (Model: WP-81) was used to measure electrical conductivity on site. The pH was measured with a pH meter (Model: WP-81) on site. The turbidity was measured both on-site using turbidity tubes and in the laboratory (Table 2).
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Figure 11 Measuring dissolved oxygen in field
The samples in the laboratory were first filtered and divided into four subsamples.
Three of them were used for analysing NO3-N, total phosphorus and turbidity while the fourth part was used for analysis of zinc. NO3-N was analysed following colorimetry (Visocolor HE) using the determination procedure used by Waterwatch volunteers. Total phosphorus was measured following the
Phosphomolybdenum blue method with 45% sulphuric acid (H2SO4) in the laboratory. In the laboratory turbidity was measured by nephelometric method using the HACH 2100AN turbidimeter. Dissolved oxygen measurement in the field was occasionally so unstable it had to be measured by the titration method on the same day. The water quality parameters, associated abbreviations, units and analytical methods are summarised in Table 2.
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Table 2 Water quality parameters, associated abbreviations, units and analytical methods
Parameter Abbreviations Units Analytical methods Specifications
Water temperature WT ºC Dissolved oxygen meter YSI 55/25 pH pH Conductivity meter WP-81 Electrical EC µS/cm Conductivity meter WP-81 conductivity Turbidity Turbidity NTU Turbidimeter HACH 2100 AN Turbidity tube Range 10-400 Dissolved oxygen DO mg/l Dissolved oxygen meter YSI 55/25 Titration Total phosphorus TP mg/l Phosphomolybdenum blue Nitrate Nitrate µg/l Colorimetry Visocolor HE Zinc Zn µg/l X-ray fluorescence (XRF) EDX-800HS spectrometry SHIMADZU
4.3.2. XRF
Zinc concentration was measured in the water samples using X-ray fluorescence spectrometry (XRF) which is a non-destructive and cost effective analytical technique used to determine the composition of materials. A set of standards within the range from 5-50 ppm were prepared based on the anticipated concentration of the unknown samples. Figure 12a) shows the intensity spectrum for the four samples of 5 ppm, 10 ppm, 20 ppm and 50 ppm. Integrated intensity was plotted against concentration for standard samples (Figure 12b). In Figure 12b, the red and black line refers to peak height method and peak area method respectively. In the peak height method, the height of the peak intensity is considered whereas in the peak area method the area underneath the peak is considered. Though both methods give similar results for lower concentrations, the area method is considered to be more accurate for higher concentrations. Therefore, in this study, the peak area method (black line) was adopted for determining the Zn concentration in the samples. The line coefficient was determined from the graph for the known concentrations using a linear regression.
To measure Zn concentration, water samples were firstly filtered to remove any organic particles and were reduced to 5mL and a few drops of 1M HNO3 were 39 added. As XRF cannot detect Zn in very low concentrations, it was necessary to increase the concentration of zinc in the samples through evaporation. The dilution factor of the respective samples was recorded and used later to calculate the concentration of zinc in their original volume. Analysis was run using an X-ray fluorescence spectrometer (SHIMADZU, model no. EDX-800HS) following the instrument user manual instructions. The energy spectrum diagram of the water samples was then calculated using the calibration curve in Figure 12b.
Figure 12 Zinc measurement using XRF (a) spectrum within 7.5-9.5 keV range (b) Integrated intensity vs. concentration graph for the standard samples
4.4. Collation of existing water quality data
Existing surface water quality data in Jerrabomberra Wetlands and surrounding areas (Fyshwick catchment) provides a broader picture of the historical water quality. The comparison of weekly sampled data to the existing monthly data helped ensure the results could contribute to analysis of upstream and downstream historical water quality data.
Discussion with the Jerrabomberra Wetlands Management Authority, and a search of Waterwatch’s citizen science gathered water quality database revealed that surface water quality data was collected from 2012 at Jerrabomberra 40
Wetlands. Since 2012 Waterwatch monitored the surface water quality in Jerrabomberra Wetlands at two locations, named Kellys Swamp (KEL010) and downstream of the feature called the “silt trap” (JER 240) (shown in Figure 5). Waterwatch volunteers collect water samples on the third week of every month and test the samples for basic water quality parameters necessary to develop a comprehensive idea of water quality. Thereby, an adoption of the Waterwatch data from 2012 to 2016 as the historical water quality database delivered necessary information about the long term changing pattern of the parameters. This information formed a baseline for the parameters which is necessary to compare wetlands.
To develop an understanding about the variation of pollutants during storm events, an event-based sampling of storm water runoff in the study area was desirable. However an event-based sample collection was beyond the project limits. In this regard and as a substitute, the event-based sampling results for Fyshwick catchment, conducted as a part of the Basin Priority Project (GHD 2015) on the upstream of silt trap and downstream of gross pollutant trap (Figure 7), were used to serve the purpose. Event-based sampled results are tabulated in Table C 3 in Appendix C. These locations were selected because of their proximity to the study area. The event-based sampling period was concurrent to the sampling period of current project the water quality parameters in water samples during storm events demonstrated an hourly variation with the hydrograph. Event sampling was done by grab sampling (JER 120) and rising stage (WIL 110) method. The current research project also compares the observed data with the pollutant loads estimated for Fyshwick North and Central catchment by MUSIC model tabulated in Table C 2.
4.5. Analytical procedures
The datasets obtained from water sampling and historical data were analysed using a geographical information system, descriptive and inferential statistics. All graphical, mathematical and statistical analyses were performed using Microsoft Office Excel 2010. The following sections will describe the analytical procedures sequentially.
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4.5.1. GIS Analysis
ArcGIS 10.0 (ESRI 1995) was used to develop spatial maps to show the average concentration distribution of water quality parameters along the wetlands in the study area. The basemap and data layers were provided by ACT Government in accordance with licensed agreements. The data belongs to the period of March-April 2014 and represents present time without significant change of the area.
Shapefiles were developed for each parameter using the collected water quality data. With the shapefiles and the basemap, spatial maps were developed for each individual water quality parameter. This provided the management Authority of Jerrabomberra Wetlands with spatial maps to delineate the distribution of water quality parameters in different wetlands to determine the level of pollution. In these maps, a weekly concentration variation of the associated parameters was demonstrated with graduated colours on the location specific symbols.
4.5.2. Statistical Analysis
4.5.2.1. Time series analysis
Graphical analyses were carried out to identify the major changes that occurred in the wetland water during the study period. These assisted with the assessment of the temporal variation of surface water quality for the study period and made a lasting contribution to the surface water quality database.
The variation of the parameters through time and across the six sites and two Waterwatch locations were plotted to provide the preliminary understanding of surface water quality. Descriptive statistics were used on all 66 observation events for the entire study period and Waterwatch dataset. The average, mode, standard deviation, minimum and maximum values for each parameter were used to reveal the basic characteristics of the dataset. The water quality dataset was reanalysed after being subdivided into four seasonal datasets: summer, autumn, winter and spring (Australian Government 2013). A seasonal mean value and coefficient of variation of the datasets was calculated for each parameter. The coefficient of variation was used to assess the dispersion of the 42 observed parameters relative to their respective mean values in a particular season. Waterwatch data from 2012 to 2015 identified seasonal pattern in water quality.
To synthesise the data analysis graphical representations, box-and-whisker plots were used as an effective and concise way to illustrate the basic characteristics (Helsel and Hirsch 2002) and identify difference between the sites for the seven parameters (except Zinc). Histograms were constructed to observe the frequency of the data and to identify anomalous results. The data analysis identified a few anomalies during particular events when a sudden change in total phosphorus was caused by resuspension or high turbidity in a storm event.
4.5.2.2. Independent sample t-test
Inferential statistics were then used to deduce relationships between different sampling sites regarding their water quality. In this case, the datasets were tested with an independent sample t-test with unequal variances (p < 0.05) to determine if there is any significant difference of water quality between two sampling sites (Helsel and Hirsch 2002). The null hypothesis was: that there is no significant difference between the water qualities of the pair of locations. The pair wise analysis was between Point source A and C; Point source A and Wetland 22; Point source C and Wetland 24; Wetland 22 and Wetland 24; Wetland 22 and Wetland 21; Wetland 24 and Wetland 21; Wetland 21 and Wetland 18. Prior to analysis, it was necessary to ensure that the datasets met the assumptions of a parametric test for the accuracy of the test results. The assumptions are that: 1) the two datasets are normally distributed around their respective means; and that 2) the two groups do not have the significantly different variance. As in the case of most environmental data, it is hard to satisfy these two strong assumptions, therefore the modified t-test for unequal variances by Satterthwaite’s approximation, as described by Helsel and Hirsch (2002), was used in this research project. A Normal Probability Plot Method (Helsel and Hirsch 2002, p. 26) validated the assumption of normality for all parameters except for total phosphorus values in the wetlands. For nitrate, turbidity and electrical conductivity values, the dataset was found to be skewed 43 and was then log-transformed and satisfied the assumption of normality. However, for total phosphorus the distribution failed to satisfy the criteria. Moreover, as a result of many zero values in that dataset (no total phosphorus in water) a log-transformation was not possible for total phosphorus and t-test analysis was not done.
4.5.2.3. Mann-Whitney U test
As previously mentioned, total phosphorus and some other parameters did not meet the parametric assumptions and also may include the potential error of the prediction of population distribution due to small sample size. Therefore a non- parametric significance test was conducted on the dataset. In this research Mann-Whitney-U test was selected (Helsel and Hirsch 2002, p. 118). It is a non- parametric alternative to the t-test for independent samples. The test was conducted on the same pairs as the t-test. Finally both the parametric and non- parametric test results were compared with each other.
4.6. Conclusion
These methods are a combination of empirical geographical and statistical analyses. To fulfil the project objectives a small scale dataset was developed to fit within the time limit of the project and to compliment a longer Waterwatch dataset. The descriptive statistical techniques applied to the dataset revealed seasonal and temporal patterns and addressed the second and third project objectives. Maps and box-whisker plots illustrated the water quality parameters and tests identified several statistically significant properties of the water quality data. This information adds to the managers’ knowledge and confidence to plan for future management measures. Prior to this project, there was apparently no study on the heavy metals accumulation in these wetlands. The current study provided the managers with a preliminary distribution of zinc in these wetlands and demonstrates the necessity to include these in plan for future interventions.
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Chapter 5: Results
The new dataset compliments the long term water quality data from Waterwatch, indicating that the water quality in the wetlands is relatively uniform in nature except for some sudden changes in response to specific events. The rainfall pattern during the period under consideration has been represented with daily rainfall (mm) data recorded from Canberra airport. The observed variation of water quality parameters were compared with the guideline trigger values defined by Environmental Protection Guideline for Australian Capital Territory (ACT Parliamentary Counsel 2005) as permitted for aquatic ecosystem of urban wetlands. Guideline trigger values are the methods to assess the physical and chemical water quality of ecosystems. The guideline trigger values are concentrations that indicates possible environmental risk if exceeded. They act as a ‘trigger’ to the managements in requirement of further investigation depending on local regulations. The results demonstrated the concentration of zinc in different wetlands as an indicator of the presence and distribution of heavy metals in the wetlands. Results from the current study, when compared to those observed from event-based water samples, illustrate the residual fraction of the pollutants (i.e., nutrients and heavy metal) in the wetlands after stormwater discharge. These observations will be discussed briefly in the following sections.
5.1. Water temperature
Long term water temperature variation reflected a seasonal cycle in the wetlands (Figure 13). Higher temperatures were observed in summer (maximum 30ºC) and lower temperatures were observed in winter (minimum 10ºC). However, there are exceptions, such as an unusual rise (35ºC) in water temperature observed in Kellys Swamp during the summer of 2013 by followed it drying out until May 2014. During this period, there was a drawdown of Lake water level that caused lowering of the swamp water levels. Mean temperature in the wetlands was 19ºC (17 Dec 2014 to 24 April 2015) which is similar to the mean temperature observed from the long term Waterwatch data (2012-2015).
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70 40
60 35
)
C °
30 50 25 40 20 30 15
Rainfall(mm/d) 20
10 Water Water temperature( 10 5 0 0 2012 2013 2014 2015 Rainfall D/S Silt Trap Kellys Swampswamp Point A Point C Wetland 21 Wetland 24 Wetland 22 Wetland 18
Figure 13 Long term rainfall and water temperature data from 2012-2015
Spatial maps and statistical tests indicate no significant difference in water temperature in different wetlands (P<0.05, and t-test, n=13). In contrast, water temperature upstream of the silt trap was found to be significantly different (i.e., higher) in comparison to the Billabong water (P<0.05, Mann-Whitney U test, n=13) (Table B 10 in Appendix B). Upstream of the silt trap water temperature likely reflects the behaviour of a flowing water body with a higher water temperature than the settled water in the Billabong.
5.2. pH
The pH values observed in the wetlands were found to vary from 6.5 to 8.5 during the study period (Figure 14) and are within the permissible range of pH mentioned for the aquatic ecosystem in urban wetlands (ACT Parliamentary Counsel 2005). When the observed pH data was plotted alongside the historical Waterwatch data (Figure 15), it was seen that the water in the upstream wetlands is slightly acidic in nature and becomes more alkaline towards downstream. This can be observed from the increase in water pH level as it flows to the downstream through the silt trap (Figure 14).
46
70 10
60 9.5 EPR upper limit 9 50
8.5
40
8 pH 30 7.5 20
7 Rainfall(mm/d) 10 6.5 EPR lower limit 0 6 December January February March April May Rainfall Point A Pont C D/S Silt Trap KellysKellys Swampswamp Wetland 21 Wetland 24 Wetland 22 Wetland 18
Figure 14 Temporal variation of rainfall and pH from Dec 2014 to May 2015
70 10
60 9.5 9 50 8.5
40 8 pH 30 7.5 7
Rainfall(mm/d) 20 6.5 10 6 0 5.5 2012 2013 2014 2015 Rainfall Point A Point C Wetland 21 Wetland 24 Wetland 22 Wetland 18 D/S Silt Trap Kellys swampS
Figure 15 Temporal variation of pH and rainfall with time from 2012-2015
Spatial maps (an example is shown in Figure 16, the others are attached in Appendix A) show that both the Billabong and the silt trap water have a higher pH level compared to the upstream wetlands in the study area (Wetland 22, 24). However, from temporal plots (Figure 14, Figure 15), it can be seen that pH is the highest in both Kellys Swamp and downstream of the silt trap compared to all the wetlands under consideration.
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Figure 16 Spatial map of pH variation in the wetlands on 29th January 2015
The similarity and dissimilarity in water pH level observed from the spatial maps and temporal plots was further tested by both the parametric (independent sample t-test) and non-parametric (Mann-Whitney U test) test results (p<0.05 and n = 13) (Table B 10 in Appendix B). There were no significant differences observed between the pH level in water from Wetland 22 and 24; Billabong and the silt trap; and Point source A and C.
The box-whisker plots (Figure 17) show, the water inlets (i.e., Point source A, C and Jerrabomberra Creek) contained water with higher pH values (7.5 to 8) compared to the immediately downstream wetlands (Wetland 22 and 24). The event sampling results (Table C 3 in Appendix C) by Basin Priority Project also indicated a similar pH level in water samples collected during rainfall events
48 from upstream of the silt trap (JER 120) to that observed during the current project. However, based on investigation of water pH level, water coming through the upstream catchments was seldom found to contain very high pH, which would pose a threat to ecosystem habitats.
9
8.5 Max
8 75 %
Median
7.5
pH 25 %
7 Min 6.5
6 Point source Point source Wetland 22 Wetland 24 Wetland 21 Wetland 18 A C
Figure 17 Box–whisker diagrams of pH data from period Dec 2014 to May 2015
5.3. Electrical conductivity
A variation in electrical conductivity in the wetlands was observed from the current study results. Descriptive statistical analysis of the datasets (Table B 8) indicate that the electrical conductivity in the wetlands usually varies within 30 to 560 µS/cm (mean = 168 µS/cm for n = 66). The most commonly observed values were found to vary between 30 to 90 µS/cm from frequency distribution diagram (Figure B 7 in appendix B). These observations are similar to the long term salinity values observed downstream of the silt trap which varies within 160-500 µS/cm (Figure B 9) between 2012 to 2015. In contrast, the salinity level in Kellys Swamp is higher compared to any other wetland under consideration and ranges from 900-1770 µS/cm (Figure B 8 in Appendix B). This variation can be seen from the graphical plot of salinity level in the wetlands (Figure 18), which shows that the salinity in Kellys Swamp is consistently high from 2012 to 2015.
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A graph of the electrical conductivity during the study period (Figure 19) shows that the stormwater drain outlets upstream (i.e., Point source A and C) contain water with lower salinity. As a result, a lower salinity level was also observed in the wetlands located immediately downstream to the point sources (Wetland 22, 24). The salinity of water in these wetlands was not significantly different (p<0.05, Mann-Whitney U test and t-test, n=13) (Table B 10 in Appendix B) from the water arriving through the point sources. However, these values are significantly different (p<0.05) from the Billabong (Wetland 21) and upstream of the silt trap. Figure 19 shows the salinity level in the Billabong and the silt trap is higher than that observed in the upstream wetlands (Wetland 22, 24) and the point sources. However, there was no change observed in the salinity level of the water flowing through the silt trap as it flowed downstream (Figure 19).
70 2000
1800 60 1600 50 1400
40 1200 1000 30 800
Rainfall(mm/d) 20 600 400 10 ElectricalConductivity(µS/cm) 200 0 0 2012 2013 2014 2015 Rainfall Point A Point C Wetland 21 Wetland 24 Wetland 22 Wetland 18 D/S Silt Trap Kellys swampS
Figure 18 Long term rainfall and Electrical conductivity variation from 2012-2015
The spatial variations in salinity can be observed from both the box-whisker plot (Figure 20) and spatial maps (Figure 21 and the other maps in Appendix A). Both spatial maps and box-whisker plot results indicate that salinity from both the Billabong and the silt trap has a large range. The lower electrical conductivity indicates similar levels to that observed in the point sources whereas higher electrical conductivity levels are also observed representing
50 those events when high saline water passes through the Jerrabomberra Creek.
70 2000
1800 60 1600
50 1400
40 1200 1000 30 800 20 600 Rainfall(mm/d) 400 10 200 ElectricalConductivity(µS/cm) 0 0 December January February March April May Rainfall D/S Silt trap Kellys Swamp Point A Point C Wetland 18 Wetland 24 Wetland 21 Wetland 22
Figure 19 Short term rainfall and Electrical conductivity variation from Dec 2014 to May 2015
600
500
400
300
200
Electricalconductivity (µS/cm) 100
0 Point source Point source Wetland 22 Wetland 24 Wetland 21 Wetland 18 A C
Figure 20 Box-whisker diagram of Electrical conductivity variation in wetlands from Dec 2014 to May 2015
The results from the current study, when compared to the event-based water sampling results (Table C 3), are found to resemble the salinity levels observed in water from the point sources. Therefore, it is clear that the stormwater runoff from the upstream sub-catchments is not responsible for high electrical 51 conductivity in the wetlands. Instead the electrical conductivity in the wetlands is more likely to be contributed by internal sources other than the stormwater runoff.
Figure 21 Spatial variation of Electrical conductivity in the wetlands on 13th March 2015
5.4. Turbidity
From the descriptive statistical analysis (Table B 8 Descriptive statistics results for surface water quality parameters for n = 66) and the time series plot (Figure 22), the turbidity in the wetlands was found to vary between 1 to 150 NTU with most frequent results between 0-30 NTU (Figure 23). The turbidity values (Figure 22) for the weekly samples are below the maximum permissible turbidity level for an aquatic ecosystem in urban wetlands mentioned by the EPR (ACT Parliamentary Counsel 2005). However, during and after rainfall 52 events, water turbidity was high (90 to 151 NTU), as observed from the event- sampling data (Table C 3 in Appendix C). As such, higher turbidity levels were observed in water from the point source A (17 December 2015, 150NTU) from the current study (Table B 2 in Appendix B). This indicates that highly turbid water flows through the drain outlets into the wetlands but eventually with the passage of time the turbidity level declines below 30 NTU in the wetlands. Among the wetlands and water inlet points, point source A and Wetland 24 were found to experience high turbidity as indicated by the outliers present in the box-whisker plot (Figure B 24 in Appendix B).
70 160
60 140
120 50 100 40 80 30
60 Rainfall(mm/d) 20 Turbidity (NTUs) EPR upper limit 40 10 20
0 0 December February April Rainfall Point A Wetland 18 Wetland 24 Kellys swamp D/S Silt Trap Point C Wetland 21 Wetland 22
Figure 22 Short term variation of rainfall and turbidity in the wetlands from Dec 2014 to April 2015
Similar to the observations from the current datasets, historical observation indicates that (Figure 24) turbidity level in Kellys Swamp and downstream of silt trap usually are below 30 NTU. Kellys Swamp was found to experience a long period of high turbidity between October 2012 to January 2013 (15-500 NTU).
In spite of the occasional rise in turbidity values, box-whisker plot (Figure B 24) and hypothesis test results (p<0.05 and n = 13 for Mann-Whitney U test and t- test) (Table B 10 in Appendix B) indicate that the turbidity pattern in different wetlands and the point sources are not significantly different. Although highly
53 turbid water enters the wetland during storm events, it gradually reduces and maintains a lower turbidity level.
60
50
40
30 56
20 Number Number of days
10
6 2 0 0 0 1 1 0 0 0-15 15-30 30-45 45-60 60-75 75-90 90-120 120-151 Turbdity (NTU)
Figure 23 Frequency distribution of turbidity measurements in all samples from Dec 2014 to April 2015
70 600
60 500 50
400 40 300 30 200
20 Turbidity (NTUs) Rainfall(mm/d) 10 100 EPR upper limit 0 0 2012 2013 2014 2015 Rainfall Point A Point C Wetland 18 Wetland 21 Wetland 22 Wetland 24 D/S Silt Trap Kellys swamp
Figure 24 Long term variation of turbidity and rainfall data from 2012-2015
5.5. Dissolved oxygen
Time series graphs of dissolved oxygen in the wetlands under observation show a gradual decrease in dissolved oxygen level during the summer (December 54
2014 to April 2015) (Figure 25). During this period, the dissolved oxygen level was found to exceed the minimum permissible limit of dissolved oxygen for urban wetlands by EPR (ACT Parliamentary Counsel 2005). In this regard, dissolved oxygen level in wetlands located upstream (i.e., Wetland 22, 24, 21, 18) were found to be lower than the Kellys Swamp and downstream of the silt trap) (Figure 25). A comparison of present data to the archival dataset (Figure 26) indicates that the fluctuation in dissolved oxygen in the wetlands is common with a few occasional drops below EPR levels.
60 12
50 10
40 8
30 6 EPR lower limit
20 4 Raiinfall(mm/d)
10 2 Dissolved Oxygen (mg/l)
0 0 December February April Rainfall d/s silt trap Kellys swamp Point source A Point source C Wetland 21 Wetland 24 Wetland 22 Wetland 18
Figure 25 Short term variation of rainfall and dissolved oxygen from Dec 2014 to April 2015
55
70 12
60 10
50 8 40 6 30 EPR lower limit 4
Rainfall(mm/d) 20
10 2 Oxygen Dissolved (mg/l)
0 0 2012 2013 2014 2015 Rainfall Wetland 24 d/s silt trap Kellys swamp Point source A Point source C Wetland 21 Wetland 22 Wetland 18
Figure 26 Long term variation of rainfall and dissolved oxygen levels from 2012-2015
Spatial maps (Appendix A) and a box-whisker diagram plotted with the weekly samples (Figure 27) identified the drain outlets as areas that are more likely to carry low levels of dissolved oxygen into the wetland. However, in water flowing through the Creek this effect is considerably diminished, possibly because of the aeration effect of the flowing water.
12
10
8
6
4 Dissolved Oxygen (mg/l)
2
0 Point source Point source Wetland 22 Wetland 24 Wetland 21 Wetland 18 A C
Figure 27 Box-Whisker diagram of dissolved oxygen variation in wetlands from Dec 2014 to April 2015
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5.6. Nitrate
The results of this study reveal considerable amounts of nitrate present in the stormwater runoff from the upstream catchment. From graphical plots (Figure 28) of the nitrate content in the wetlands during the study period and from the spatial maps, the highest nitrate content as found to be present in the water coming through the water inlets (i.e., Point source A and C, the silt trap) in highest concentration (5mg/l). As a result, the upstream wetlands (i.e., Wetland 22, 24) were mostly affected with nitrate content at concentrations 33 times (60 µg/l) higher than the allowable limit for urban wetlands aquatic ecosystem (ACT Parliamentary Counsel 2005) (Figure 28). Moreover, the Billabong was also found to contain similar nitrate levels to that observed in the upstream water, indicating the source of nitrate contamination in the Billabong. However, from the historical results (Figure 29), Kellys Swamp and downstream of the silt trap water have never been considerably affected by excessive nutrient content from upstream.
70 7
60 6
50 5
40 4
30 3 Nitrate Nitrate (mg/l)
Rainfall(mm/d) 20 2
10 EPR upper limit 1
0 0 December January February March April May Rainfall d/s silt trap Kellys swamp Point source A Point source C Wetland 21 Wetland 24 Wetland 22 Wetland 18
Figure 28 Short term variation of rainfall and nitrate levels in the wetlands from Dec 2014 to April 2015
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70 7
60 6
50 5
40 4
30 3 Nitrate Nitrate (mg/l)
Rainfall(mm/d) 20 2
10 EPR upper limit 1
0 0 2012 2013 2014 2015 Rainfall Point source A Point source C Wetland 21 Wetland 24 Wetland 22 Wetland 18 d/s silt trap Kellys swamp
Figure 29 Long term variation of rainfall and nitrate levels from 2012-2015
Event-based sampling results (Table C 3 in Appendix C), when compared to the observed nitrate level in the wetlands, show that the nitrate levels present in the wetlands are higher than that measured during storm events.
5.7. Total phosphorus
Stormwater discharged into the wetlands from the upstream was found to contain very low levels of total phosphorus. As such, total phosphorus levels in the wetlands that depend on stormwater discharge were low (Figure 30) and below guideline values (100 µg/l) (ACT Parliamentary Counsel 2005). The descriptive analysis results (Table B 8 in appendix B) shows that total phosphorus levels in the wetlands (Wetland 22, 24, 21, 18) usually vary within 0 to 0.2 mg/l with most frequent value within 0-0.05 mg/l range (Figure B 13 in Appendix B). In comparison to archived data (Figure 31), these results were found to be in a similar range to that recorded for the site downstream of the silt trap. In contrast, the results were different from the total phosphorus level in Kellys Swamp, where total phosphorus levels typically vary within 0.1-2.0 mg/l.
58
70 2 1.8 60 1.6 50 1.4 40 1.2 1 30 0.8
Rainfall(mm/d) 20 0.6 0.4 10 Total (mg/l)Phosphorus EPR upper limit 0.2 0 0 December February April Rainfall Kellys swamp Point source C Wetland 21 Wetland 24 Wetland 22 Wetland 18 d/s silt trap Point source A
Figure 30 Short term variation of total phosphorus levels in the wetlands from Dec 2014 to April 2015
The total phosphorus level observed from the event-sampled data (Table C 3 in Appendix C) in the gross pollutant trap was high, however upstream of the silt trap it was found to be in a similar range (0.096 mg/l) to the wetlands.
70 1.9
60
50 1.4 40 0.9 30 20 Rainfall(mm/d) 0.4
10 EPP upper limit Total Phosphorus (mg/l) 0 -0.1 2012 2013 2014 2015 Rainfall d/s silt trap Kellys swamp Point C Wetland 21 Point A Wetland 24 Wetland 22 Wetland 18
Figure 31 Long term variation of total phosphorus in the wetlands from 2012-2015
The results show very low levels of total phosphorus present in water coming through the silt trap and stormwater drains. Therefore, instead of stormwater, there are probably other sources that contribute to increased total phosphorus levels in the wetlands. 59
5.8. Zinc
Zinc, as indicative of other heavy metals, was carried by stormwater runoff to the wetlands. Zinc was mostly found in water samples from the point sources. Zinc concentration measured in water samples from the wetlands was above the guideline value (ANZECC and ARMCANZ 2000; ACT Parliamentary Counsel 2005) mentioned for aquatic ecosystem in urban wetlands.
Zinc was detected in Wetland 22 several times above the guideline indicating vulnerability of the wetland to exposure to other heavy metals (e.g., copper, lead). In the Billabong zinc was detected once during the study period (20 March 2015). From the event-sampled results (Table C 3 in Appendix C) the amount of zinc carried during storm events was somewhat lower in the silt trap and samples from the gross pollutant trap. Results from the current study (e.g., 521 µg/l in Wetland 22) resemble the zinc levels observed in the gross pollutant trap and thereby confirm that the wetlands are exposed to pollution by heavy metals.
Table 3 Zinc concentration in the wetlands
Zn Concentration(µg/l) Guideline values (µg/l) Date Location (zinc detection limit 10 µg/l) ANZECC EPR 17 Dec 2014 Point source A 31.85 Wetland 22 23 9 Jan 2015 Point source C 53.35 21 Jan 2015 Wetland 22 59.8 29 Jan 2015 Wetland 22 521 13 Feb 2015 below detection limit-- 20 Feb 2015 below detection limit-- 26 Feb 2015 below detection limit-- Wetland 22 23 6 Mar 2015 Wetland 18/ Upstream of 53.35 ≤15 ≤5 silt trap 13 Mar 2015 Wetland 22 below detection limit-- Point source A 42.6 Wetland 22 64 20 Mar 2015 Point source C 42.6 Billabong/ 53.35 Wetland 21 27 Mar 2015 below detection limit -- 10 Apr 2015 below detection limit -- 24 Apr 2015 below detection limit
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5.9. Conclusion
This chapter described the variation of water quality in Jerrabomberra Wetlands. It revealed that data from the current project is successfully reflecting the historical pattern and the estimates from event-based sampling. The new dataset, along with the previous data, shows a relatively uniform water quality in the wetlands except for rises in the specific water quality parameters in response to specific events. An important finding from this chapter is the detection of zinc in excess of the trigger level which indicates the wetlands are subjected to heavy metals. The following chapter will provide a discussion on the most important observations from the study.
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Chapter 6: Discussion and recommendations for future research
The project was designed to assess the spatial and temporal variation of water quality parameters in Jerrabomberra Wetlands. The current study shows that elevated levels of nutrients and heavy metals (zinc) are carried to the wetlands with the stormwater runoff from adjacent light industrial catchments. The observed variations of different parameters in the wetlands are possibly being controlled by stormwater runoff, groundwater contributions as well as their other characteristic features (e.g., waterbirds, aquatic habitat). These factors control the surface water quality of the wetlands affecting their function and water processes. In this regard, while the water quality of upstream wetlands were shown to be affected by stormwater runoff constituents such as nutrients (especially nitrate) and heavy metals (such as zinc), the downstream wetlands (Billabong and Kellys Swamp) were more likely to be governed by groundwater and the wetland's characteristic ecological features. These observations are useful to the managers and decision-makers as they set priorities for necessary management actions. In addition, it further validates the role wetlands play in light industrial catchments as predicted by the model results (Batterley and Stone 2013) and those observed by event sampling (GHD 2015). This chapter will discuss the major findings of this research in the sequence from upstream to downstream. A discussion of the implications of this research to possible management approaches and future research opportunities to support the management goals will be discussed in parallel.
6.1. Variation of water quality between sites as it travels downstream
6.1.1. Point sources or drain outlets
Current study results found Point sources A and C contribute untreated water with excessive nitrate and zinc content into the wetlands, as suspected by the managers (Jerrabomberra Wetlands Nature Reserve Board of Management 2013). The intermittent behaviour of the point sources indicates that they are independent of base flow and are solely carrying stormwater runoff. As such, the 63 excessive nutrients and zinc found in the point sources are mainly transported by stormwater runoff. Additionally, the runoff quality in both of the point sources are not significantly different (p<0.05) from each other representing a similar catchment behaviour with similar run-off.
In Jerrabomberra Wetlands these heavy metals and nutrients are probably being washed-off from the industrial effluents from Fyshwick catchment, possibly highway runoff from Monaro Highway, and seepage from Fyshwick Sewerage Treatment Plant (STP). The elevation contour of the Reserve wetlands and surrounding areas (Figure A 22) also support the discharge of runoff from these areas into the wetland. In this case, zinc can be expected in Jerrabomberra Wetlands in the wash off process from the abundant parking areas associated with 15.8 hectares of industrial blocks (Batterley and Stone 2013) located in Fyshwick, with prevailing uses for automobile recycle services car wash, construction materials and paints, fuel stations and warehouses. Disposal of heavy metals by both atmospheric deposition and runoff from industrial land use has been proven by previous researchers (Guittonny-Philippe et al. 2014). Highway dust (Duong and Lee 2011), worn dust of automobile tyres, galvanised parts and brake pads are notable sources of zinc in urban watersheds (Councell et al. 2004; Hwang et al. 2016).
This observation from the current project is further supported by research in other urban wetlands such as Towra Point Nature Reserve, located in Sydney’s Botany Bay, which evaluated the effect of the industrial and urbanised land use on the adjacent wetlands. Arsenic, lead and zinc were detected in the sediment from parts of those wetlands adjacent to urban areas (Kermode et al. 2016) whereas the remote parts were found to be less affected. Sediment contamination by zinc, cadmium and nickel were detected in the Pearl River Estuary, China that receives discharge from the surrounding major cities with industries (Li et al. 2007). Supporting these observations, the current study results indicate that the wetlands in south-eastern corner of the Reserve are exposed to the nutrient and heavy metal rich catchment discharge through the point sources along with other non-point sources. The zinc loaded water after being carried to the downstream waterbody, can be accumulated by aquatic 64 habitats, plants or deposited onto the sediment in particulate format whereas a minimum amount remains in water Zhang et al. (2009).
Current study results (Table 3) from the wetlands and point sources, when compared with the literature, are found consistent with the observations from a recent study on heavy metals in water and sediment in constructed wetland located in Melbourne (0.008 mg/l to 0.069 mg/l) (Mackintosh et al. 2016). This observation is also consistent with the findings from a separate study (Zn, 0.004 -0.074 mg/l) (Sekomo et al. 2011) on metal concentration in the Nyabugogo natural wetland (Kigali City, Rwanda) that receives untreated urban runoff including that from industrial areas. However, comparison of weekly sampled zinc values in the wetlands to the event-based samples indicate that zinc carried through the point sources is considerably lower (12%) than that observed during rainfall events (Table C 3 in Appendix C). This could plausibly be the result of three possible mechanisms at work: 1) the water coming through the outlets is carrying a lower concentration of zinc in this area than the event- based samples measured at the Gross Pollutant Trap (GPT); 2) the weekly samples collected here are not accurately representing the actual amount of zinc coming through the drains under all flow conditions; 3) zinc has already been deposited into the sediment and is therefore depleted in the surface water. All of these mechanisms could result in lower amounts of zinc in water compared to the actual amount of zinc content imported in the wetlands, fraction deposited on sediments or its potential impact on the environment (i.e., accumulation in plant and aquatic species). Zinc flows to the wetland 22 from the point sources probably at higher levels during flood events. The event sampled data results (GHD 2015) demonstrate that the concentration of pollutants in stormwater runoff varies hourly. In addition, these concentrations are higher than the site mean concentration of zinc (0.345 – 0.591mg/l) observed in untreated stormwater in Melbourne in the beginning of this century as reviewed by (Allinson et al. 2017). Therefore, an elevated zinc concentration in the wetlands during rainfall events is likely. Event sampling at the drain outlets to measure the heavy metals arriving in the wetlands could resolve this question. This aspect is necessary to consider in case of the use of wetlands for Water Sensitive
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Urban Design purposes such as biofilter, as its design requires to consider the system characteristics and associated site conditions (Deletic et al. 2014).
Except for an elevated nitrate and zinc content in the stormwater runoff, other parameters such as turbidity, electrical conductivity and total phosphorus were found to be within acceptable levels. However, while the groundwater quality was found to be satisfactory in relation to desired water quality criteria (Jerrabomberra Wetlands Nature Reserve Board of Management 2013) (Table C 1 in Appendix C) indicating no seepage from nearby STP, elevated nutrient content in surface water samples from the current project suggests reconsidering this as a possible source.
6.1.2. Wetlands downstream of the drain outlets: Wetland 22 and Wetland 24
Wetland 22 and Wetland 24 receive stormwater discharge from the catchment and hold it; they function as retention basins. The nutrient levels in these wetlands were found to be 16 times higher than the desired limit for nitrate (ANZECC and ARMCANZ 2000). As nitrate is one of the most suitable forms for plant assimilation of nitrogen (Seitzinger et al. 2002), the nitrate rich water in Wetland 22 and 24 is not only suitable for the growth of microorganisms and algae, but also is suitable for invasive plant species to establish in the wetlands. An algal bloom in Wetland 24 during the period of field observations and the elevated nitrates supports the connection between nutrients and susceptibility of the algal blooms.
The decreasing nitrate concentration downstream through the wetlands indicates that it is gradually depleted as it moves downstream. This treatment of nitrogen is an important function provided by wetlands, including constructed wetlands (Liu et al. 2015). The functions of assimilation, denitrification or leaching reduce the impact of elevated nitrogen (Keddy 2010). A review study from earlier this century on 57 wetlands all over the world documented that 80 percent of the wetlands serve in retaining nitrogen species (Fisher and Acreman 2004). An approximately constant nitrate level observed in wetland 22 and 24 (Figure 28) indicates that there is a balance between the inflow and outflow
66 concentration of nitrate. The underlying reason of this phenomenon can be clarified according to the explanation described by the previous researchers.
Nitrate (NO3-), being a negatively charged ion, is not retained by negatively charged soil particles and hence remains in the solution. This results in the higher mobility of nitrate (NO3-) in the solution than the positively charged ammonium (NH4+) ion. As a result it can be lost in groundwater flow in the absence of assimilatory nitrate reduction (a nitrate reduction process caused by plant and microbe assimilation). Besides that, it can also be reduced by dissimilatory nitrogenous oxide reduction (different pathways of nitrogen reduction other than the assimilation process in biological cell). An illustration of this is the conversion of nitrate into ammonia, and denitrification process where nitrate serving as the terminal electron acceptor converts predominantly into nitrogen gas (N2) (Mitsch and Gosselink 2007). In addition, a higher nitrate concentration gradient in surface water and thereby upper oxidised layer of wetland soil causes transformation of NH4+ to NO3- and downward diffusion of
NO3- into the deeper anoxic region from the upper oxidised layer (Keddy 2010). All these mechanisms are probably at work at Jerrabomberra Wetlands in reducing the nitrate concentration in water. However, despite the observed nitrate balance in Wetland 22 and 24, the nitrate level in these wetlands is considerably high and needs further attention from the management. Therefore, considering its availability to the plants and organisms and the possibility to diffuse in groundwater, suitable management measures are necessary in this regard. The addition of a bio filtration system with an anaerobic zone would be helpful to aid in reducing the nutrient concentration in the inflow.
Total phosphorus levels in downstream wetlands (e.g., Wetland 24 and Kellys Swamp) were found to be higher than that observed in the point sources (Figure 30). In contrast, the total phosphorus concentration in the Wetland 22 and the point sources was well below the trigger values. Moreover, a major portion (<95%) of the lower amount of total phosphorus carried with stormwater runoff in these wetlands, may also be mediated through sediment deposition followed by a smaller fraction reduced through microbial and plant uptake (Vymazal 2007). As a result a smaller amount of total phosphorus remains in the
67 water. However, although rare in these wetlands, sudden rises in total phosphorus level were observed indicating the possible resuspension of total phosphorus from sediments and decomposed macrophytes into water column (Reddy et al. 1999; Mitsch and Gosselink 2007; Keddy 2010).
The preliminary zinc analyses indicated that heavy metals (zinc being an indicator) are elevated Wetland 22. The likely explanation is that stormwater’s “first flush” directly flows into Wetland 22 and then the passage through the wetlands and through time means that metals are either diluted, or deposited. The flattened vegetation (as a footprint of water damage) observed from point source A to Wetland 22 recorded the extent of floodwater and the potential importance of this vegetation surface to deposition during peak and high velocity stormwater runoff events. However, although similar evidence from vegetation flattening from flood wave were also present from point source C to Wetland 24, no zinc was detected in this wetland (Table 3). Possibly this outlet drains an area with less transportable zinc or there may be other sedimentation of zinc upstream. Additionally, the fraction of zinc deposited in sediments as well as the fraction accumulated in the plants needs to be considered to determine the actual amount of zinc moving to the wetlands.
The pH level in both Wetland 22 and 24 (Figure 14) may instigate the transformation of dissolved zinc fractions into particulate forms and increase deposition on sediments. This amount increases with a rise in water pH as previously found by Lintern (2015) for a water pH variation from 5.8 to 6.6. The difference between the observed zinc concentration from the current project to the predicted amount zinc to be discharged from the upstream area (Table C 2) emphasizes the need to consider the amount of particulate fraction of zinc along with the dissolved concentration as also suggested by Lintern (2015). The current research identifies the presence and the extent of heavy metals in the wetlands and suggests that a further systematic research is necessary including the amount deposited on sediments. Event sampling on the point sources may also help in determining the actual zinc load coming to the wetland through point discharge for the design of efficient sediment traps and vegetated aprons to improve heavy metals management. 68
In addition, the low water volume, high evaporation effect and independence from groundwater means wetland 22 is more likely to experience elevated zinc levels in water at some times. Wetland 22 is shallower (less than 0.5m) than the other wetlands in Jerrabomberra. The observed salinity level (electrical conductivity) in Wetland 22 and Wetland 24 is interpreted by Lawrence (2014) as having ‘limited’ or ‘no’ contribution of groundwater. Groundwater contains total dissolved solids (TDS) that can also be measured with electrical conductivity of water (Harter 2003). The electrical conductivity of water measures the concentration of dissolved ions indicating the groundwater contribution. Groundwater discharge can be detected by a sudden change in the electrical conductivity level along the water-sediment interface (Oxtobee and Novakowski 2002). The water levels in Wetland 22 and 24 were lower than the others and in some cases wetland 24 dried-up. The electrical conductivity measured in those wetlands during this sampling period did not show any remarkable change. These wetlands appear to be independent of groundwater contribution and their characteristics depend on precipitation and external catchment discharge.
The discussion clarifies that Wetland 22 and 24 are fed by stormwater runoff and rainfall when their source was previously from “an unknown origin” (Jerrabomberra Wetlands Nature Reserve Board of Management 2013). The rainfall dilutes the pollutants carried with stormwater and reduces their effect on the ecosystem. Moreover, Wetland 22 is found to act an important water body that receives the pollutants coming from upstream and prevents them from being transferred downstream. Among all the wetlands under consideration in this project, Wetland 22 was found to be mostly affected by zinc and may need a suitable upstream intervention (e.g., bio retention system).
6.1.3. Jerrabomberra Billabong (Wetland 21) and upstream of the silt trap
Jerrabomberra Billabong is an important ecological feature of the reserve and is valued as an important waterbird habitat and for its contribution to aquatic wildlife. The difference in the salinity level from the upstream wetlands supports the interpretation by Lawrence (2014), that groundwater contributes to the Billabong’s hydrology. The water balance model by Lawrence (2014) 69 described the Billabong as a ‘window wetland’ that intersects the groundwater aquifer in the Southern Billabong groundwater zone. As such the interconnection with the groundwater aquifer possibly increases salinity levels in the Billabong compared to other wetlands (Wetland 22, 24, 18). The elevated salinity level, while giving the Billabong distinctive ecological characteristics, also marks the possibility of infiltration of other pollutants in the groundwater. It should be noted that an elevated nitrate level was observed previously in groundwater next to Kellys Swamp (Jerrabomberra Wetlands Nature Reserve Board of Management 2013). As discussed nitrate, because of its fast mobility, can easily be dissolved in groundwater, therefore the identification of higher nitrate in groundwater could be sign that nitrate in stormwater runoff is percolating into the aquifer.
In respect to total phosphorus levels in water, the Billabong was different to other wetlands because of its rich habitat values for birds (including Latham’s Snipe) and frogs (Jerrabomberra Wetlands Nature Reserve Board of Management 2013), probably increases the amount of total phosphorus. Waterbird excrement has previously been documented by researchers (Scherer et al. 1995) and droppings affect the nutrient cycle of the wetlands by addition of total phosphorus, nitrogen and carbon that results in increased productivity. The observed total phosphorus level in the Billabong is approximately 2.5 times higher than the stormwater inputs indicating that internal factors are predominantly providing the total phosphorus carried by stormwater. It also supports the observation from groundwater monitoring results which raise the possibility of seepage from Fyshwick Sewage treatment plant (Jerrabomberra Wetlands Nature Reserve Board of Management 2013).
The water upstream of the silt trap shares characteristics with both the Billabong and the flowing water body. For example, on one hand a similar pH pattern was observed in the silt trap and the Billabong; on the other hand the aeration effect and a subsequent increase in the amount of dissolved oxygen is evident in the silt trap water (Figure 26). Moreover, the water has low turbidity (< 30 NTU) and the total phosphorus value tends to be zero in the absence of storm events. The increased amount of total phosphorus carried by stormwater 70 runoff through the silt trap gradually gets reduced to zero with time and movement through the wetland. The wetland helps to reduce the total phosphorus concentration in the downstream reach as shown by the water quality data downstream of the silt trap (collected by the Waterwatch citizen science group). As observed the total phosphorus in water, by the time it flows through the silt trap to the downstream area has been reduced to below the detectable level. It is necessary to mention that both Wetland 18 and downstream of the silt trap are not rich bird wetlands and therefore the total phosphorus present in the water is mainly coming from stormwater runoff. Also the deep silt trap probably traps nutrients in the sediment.
As the silt trap is the largest and deepest wetland in the reserve, its potential to dilute concentrated flows and store sediments is higher in comparison to the other wetlands. As observed from the results (Figure 31), the total phosphorus value downstream of the trap is consistently under the detection limit. As such, the dilution effect in the silt trap is possibly contributing to reducing values of total phosphorus, nitrate and zinc more than those observed in the point sources. Also the similarity in salinity (electrical conductivity) levels in both wetland (silt trap and the Billabong) is probably indicating the role of groundwater. However the possibility of movement of nitrate due to its mobility through either subsurface or overland flow from the upstream wetlands and the silt trap, and the interconnection of the Billabong to the groundwater aquifer, together point to the Billabong being affected by higher nitrate content from upstream sources.
6.1.4. Waterwatch locations: Kellys Swamp and downstream of the silt trap
Kellys Swamp and downstream of the silt trap are regularly studied by the citizen science volunteers (Waterwatchers). Their dataset serves as a baseline, providing information about the improvement or deterioration of the water after flowing through the silt trap.
Waterwatch and this newly collected data show water quality in Kellys Swamp is mostly governed by the groundwater and its ecological features rather than the stormwater runoff. Kellys Swamp hosts three of the five bird-hides in the
71 wetlands reflecting its popularity with birds (and birdwatchers). The large numbers of birds residing in this area are presumably following resources and also contributing to the increased nutrient level in the wetlands. Consequently total phosphorus level was found to be considerably higher in Kellys Swamp since mid-2014. As much as 25-34% annual variation in total phosphorus is expected to either be directly deposited in water or gradually released from sediments (Scherer et al. 1995). However, this observation contradicts the groundwater quality results next to the Kellys Swamp, which did not detect any phosphorus contamination that could be leakage from Fyshwick STP in groundwater.
Higher electrical conductivity in Kellys Swamp demonstrates that there is a groundwater contribution to the swamp as also suggested by Lawrence (2014). However, the high salinity level makes the wetland a desirable special habitat type for birds, so this water quality variation is desirable. Also the absence of nitrate in the water indicates this wetland is not as severely affected by stormwater constituents as the upstream wetlands.
6.2. Implication of this study to management and recommendations for future research
Based on the knowledge derived from international and local research, this project contributes new knowledge to managers and there is scope to proposing actions to maintain the water quality and ecological aspects of the reserve. This research outcome contributes to management with a surface water quality database in an important part of the Reserve. This database is a fundamental to the knowledge of surface water quality in the wetlands as there was no such previous studies in this area as discussed earlier. The water quality is further discussed in the context of previous literature and data available in this area such as hydrological knowledge, groundwater data, habitat values and soil condition. It helps the managers develop insight on how different aspects are affecting the wetland water quality. Another important aspect to consider from this study is the current water quality situation and pollution extent in the wetlands in response to the surface runoff from the upstream. Previously this was only suspected by the managers that there may be chance of pollution from 72 upstream runoff and presence of heavy metals in the wetlands. However, they did not have knowledge of the extent of pollution and presence of trace elements in the water. Current research documented the spatial variation of the pollutants with their level of pollution and thus, provided the manager with confidence in their decision. While attentions were given on a broader scale to improve Canberra’s water quality by concurrent projects like Basin Priority Project, current study outcome also aimed to contribute to Jerrabomberra Wetlands water quality. The dataset can be further extended and directed to accomplish specific goals such as to study the effect of diversion of the Billabong to its former channel pathway through the creek.
The following future research opportunities were identified that may aid decision-making by the managers.
- To perform an event sampling schedule on the drain outlets It is necessary to conduct targeted water quality studies on the incoming water coming through the point sources and into Wetland 22 during storm events. Knowing the variation of the pollutant concentrations with the hydrology will be helpful to assess the variation of the pollutants and the ways they impact the wetlands. Designs may enable the initial flush to receive additional treatment or dilution for example. As observed from the study that both point sources represented a similar catchment behaviour, so event sampling in either of Point Source A and C could be helpful. However, as Zinc was mostly found to be present in Point source A, it can be chosen as a priority location for event based sampling.
- To measure the water level in the wetlands In order to be more specific in accounting for the evaporation effects and groundwater recharge on the total dissolved concentration observed in the wetland water, the variation of water level with time could be recorded. For example, a weekly measurement of the water level in the wetlands in addition to that after rainfall events will help to account for the actual amount of stormwater runoff. This, in addition to the rainfall (in mm) and evaporation recorded by BoM (Commonwealth of Australia 2016), should
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provide an estimation of groundwater and stormwater contribution in particular wetlands. Incorporation of these contributors would provide a more definite explanation of unanswered questions regarding different parameters. For example, does the evaporation have significant effect on increasing the zinc concentration or it is only stormwater that is carrying increased zinc to the wetlands? Or to what extent does rainfall support dilution of the pollutants? Installation of water depth gauges in the wetlands and also to the point sources would be helpful in this regard. - Estimation of the zinc balance in the study area The zinc detected in the wetlands and also in the point sources is less than that predicted by the MUSIC model and also than the event sampled data. Further work may be needed to tune the MUSIC model given the importance of these estimates to design decision-making. Specific research to close the gap may be desirable. In this regard, to revaluate the pathways of zinc deposited on the sediments and the amounts accumulated by the plant species and other treatment could be useful. - A bio filtration system As observed in the results, zinc and other heavy metals pass through the silt trap, so if the silt trap is replaced by the former channel pathway of the Billabong (as proposed), then a suitable substitute to trap zinc should be contemplated. A viable option for consideration would be the use of a vegetated bio filtration system with aquatic plant species to reduce the heavy metals concentration. The long-term efficacy of this should be carefully considered.
6.3. Conclusion
The spatial and temporal variation of the water quality of Jerrabomberra Wetlands and plausible future research opportunities are described in this chapter. This discussion, in addition to demonstration of the spatial variability of parameters, also specified the vulnerability of different wetlands to different parameters. The upstream wetlands were found to be more affected by stormwater runoff quality than the downstream wetlands. In some cases such as 74 trapping heavy metals, the upstream wetlands were often found to act as retention basins as observed from the current study findings. In this regard, the silt trap was also found to act in reduction of the concentration of water quality constituents downstream. This would potentially be helpful for designing future management intervention in this area.
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Chapter 7: Conclusion
The aim of this Master of Philosophy project was to study the surface water quality of southern wetlands in Jerrabomberra Wetlands Nature Reserve. This chapter will summarise the development of the project, the methodology followed to attain the project objectives, and the key outcomes. It will also acknowledge that this project is not exhaustive and has identified future research ideas.
The initial plan of this project was to study the surface water quality of the entire reserve, which, later on, was focused on the south-eastern corner of the reserve considering the available time and resources. Although a weekly variation of the water quality was reflected by the weekly water sampling project, the sudden changes caused by rainfall events were overlooked. This deficiency was partly alleviated by incorporating the concurrent event-based sampling results upstream of the silt trap that provided insights into the concentration of pollutants carried by stormwater runoff during storm events. The current project reported the nutrients and heavy metals that govern characteristics in different wetlands that are important to ensure safety of aquatic life. However, the nutrient budget calculation was not included in this project. In this regard, a detailed schematic framework is required for estimating the amounts of nutrients and heavy metals deposited on sediments and those accumulated in plant species.
The south-eastern corner of the Reserve was selected as the area of study considering its vulnerability from the runoff generated from the surrounding industrial and agricultural lands. After discussion with the reserve management, and from the detailed literature available from the Reserve wetlands, a set of research questions was identified as necessary to address. Accordingly, a set of objectives and a research plan were developed to fulfil the objectives.
The objectives were set such that, apart from accomplishing the research questions, they can contribute to the existing dataset of the wetland database. In this regard, a water quality data collection project was designed and performed 77 from six locations which were important monitoring needs by the managers. It was done by sampling and analysis of eight water quality parameters inclusive of an indicative heavy metal (Zn), for duration of 13 weeks.
Spatial maps of the water quality dataset were developed using ArcGIS 10.0 software that fulfilled the first objective of the project. The maps developed spatial understanding of the water quality variation in the study area. The software was selected to maintain the consistency of the previous wetland dataset to the newly collected data. The demonstration of distribution of the water quality parameters in different wetlands developed a spatial message of the pollutant level in the wetlands to the managers and indicated the vulnerability of different wetlands to different water quality constituents.
The second objective was to demonstrate the change of the parameters over time. It included a broader data collection by Waterwatch every month since 2012. In this case the deficiency of a long term surface water quality data in the Reserve was managed carefully by incorporating citizen science. A combination of the long term monthly dataset and weekly sampled recent data, in addition to validating its credibility, identified the changes in water quality that have been overlooked in the monthly dataset. As such, these results revealed the presence of nitrates and higher turbidity in the water coming through the stormwater drains and the silt trap (Point source A and C). A gradual decrease in dissolved oxygen and water pH was found during summer 2015 in the wetlands. Moreover, the weekly sampled data showed that catchment inputs from drains discharged runoff including Zn contamination, and at levels where the Zn content was often found to be over the permissible limits of the guidelines (ANZECC and ARMCANZ 2000). The presence of Zn in the water also indicates the likely possibility of the presence of other heavy metals in the water or sediments.
The observed results were verified in accordance with the hydrological behaviour of the wetlands studied by the previous researcher (Lawrence 2014). This accomplished the third and fourth objective that was to enable the water bodies to be well described in relation to their water quality data. Severe Zn
78 contamination was not found in the downstream wetlands, except Wetland 22, thus it will be of interest to investigate whether Zn is accumulated in the sediment or absorbed by the wetland plants. Again, Wetland 22 along with 24 were found to store nitrate rich water, which was associated with an algal bloom and intrusion of invasive plants. Although the Billabong had similar nitrate content, it was not severely affected by the stormwater containing heavy metal. Instead, a characteristically higher salinity level was observed in the Billabong probably because of its interconnection to the groundwater aquifer. Water entering through the drain outlets and the creek in the reserve were not found to contain higher electrical conductivity implying that salinity in the Reserve is not a direct consequence of stormwater runoff. Wetland 22 and 24 were not affected by higher salinity indicating their dependency on the stormwater runoff. Moreover, these wetlands act as retention basins for the downstream wetlands and retain the heavy metals. Another important finding from this study is that the total phosphorus in the wetlands was found to be more affected by internal sources rather than by the stormwater runoff. In this case, the wetlands that serve as major bird habitats (e.g., Kellys Swamp, Jerrabomberra Billabong) were found to contain higher total phosphorus levels in water probably from birds.
Water quality upstream of the silt trap, and evidence from event-sampling results from BPP, clearly show the water is affected with higher nitrate and zinc content. Therefore, the management plan to divert the Creek back to its original track through the Billabong may expose the Billabong ecosystem to increased heavy metals which are presently reduced by the silt trap and Wetland 22. In this regard, it is suggested to consider pre-treatment methods upstream such as the introduction of a bio-filtration system.
In conclusion, the research work successfully accomplished the project aims and added new knowledge to the water quality in Jerrabomberra Wetlands. The limitations of the project were managed carefully with the knowledge derived from scientific literature and also from discussion with the management authority. Necessary future recommendations have been made to meet the knowledge gap. The project has provided the reader with an understanding of 79 the water quality in the wetlands located in the southern corner of Jerrabomberra Wetlands Nature Reserve.
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Appendix A
Figure A 1 Variation of Water Temperature in the wetlands for dates 9 January-13 February 2015
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Figure A 2 Variation of Water Temperature in the wetlands for dates 20 February-13 March 2015
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Figure A 3 Variation of Water Temperature in the wetlands for dates 20-27 March 2015
Figure A 4 Variation of pH in the wetlands for dates 9-21 January 2015
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Figure A 5 Variation of pH in the wetlands for dates 29 January-26 February 2015
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Figure A 6 Variation of pH in the wetlands for dates 6-27 March 2015
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Figure A 7 Variation of electrical conductivity in the wetlands for dates 9 January-13 February 2015
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Figure A 8 Variation of electrical conductivity in the wetlands for dates 20 February-13 March 2015
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Figure A 9 Variation of electrical conductivity in the wetlands for dates 20-27 March 2015
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Figure A 10 Variation of turbidity in the wetlands for dates 9-21 January 2015
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Figure A 11 Variation of turbidity in the wetlands for dates 29 January-26 February 2015
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Figure A 12 Variation of turbidity in the wetlands for dates 6-27 March 2015
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Figure A 13 Variation of dissolved oxygen in the wetlands for dates 9 January-13 February 2015
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Figure A 14 Variation of dissolved oxygen in the wetlands for dates 20 February-13 March 2015
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Figure A 15 Variation of dissolved oxygen in the wetlands for dates 20-27 March 2015
Figure A 16 Variation of nitrate in the wetlands for dates 9-21 January 2015
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Figure A 17 Variation of nitrate in the wetlands for dates 29 January-26 March 2015
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Figure A 18 Variation of nitrate in the wetlands for dates 6-27 March 2015
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Figure A 19 Variation of total phosphorus in the wetlands for dates 9 January-13 February 2015
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Figure A 20 Variation of total phosphorus in the wetlands for dates 20 February-13 March 2015
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Figure A 21 Variation of total phosphorus in the wetlands for dates 20-27 March 2015
Figure A 22 Elevation contour of the study area (Source: Jerrabomberra Wetlands Nature Reserve Board of Management 2013)
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Appendix B
Table B 1 Geographic coordinate and typology of water sampling sites in Jerrabomberra Wetlands; Coordinate system: GCS_GDA_1994; datum Geocentric Datum of Australia 1994
Site name Latitude Longitude Typology Source of water Point source A -35.3204 149.1638 Storm water drain Fyshwick catchment Point source C -35.3224 149.1624 Storm water drain Fyshwick catchment Wetland 22 -35.3201 149.1634 Window wetland Surface drainage Wetland 24 -35.3217 149.1617 Window wetland Surface drainage Billabong/21 -35.3203 149.1619 Window wetland Surface drainage Wetland 18 -35.3217 149.1599 Excavated and designed form River drainage and lake backwater
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Table B 2 Water quality in Point source A from 17.12.2014 to 24.04.2015
Site ID Latitude Longitude Survey Date WT pH EC Turbidity TP DO NO - observation 3
(deg C) (µS/cm) (NTUs) (mg/L) (mg/L) (mg/L as N)
1a_17.12.14 17-Dec-14 wet 17.9 8.00 125.4 150.00 0.03 5.42 5.0 2a_9.01.15 9-Jan-15 wet 22.1 7.83 32.1 7.83 0.01 7.08 3.0 3a_21.01.15 21-Jan-15 dry
4a_29.01.15 29-Jan-15 wet 22.4 7.98 140.0 1.03 0.00 5.30 5.0 5a_13.02.15 13-Feb-15 wet 20.1 7.40 71.2 3.00 0.00 1.20 1.0 6a_20.02.15 20-Feb-15 dry -35.3204 149.1638 7a_26.02.15 26-Feb-15 wet 18.2 7.57 83.6 2.40 0.10 3.95 5.0
8a_6.03.15 6-Mar-15 dry
9a_13.03.15 13-Mar-15 dry
10a_20.03.15 20-Mar-15 wet 16.2 6.76 79.7 16.90 0.00 2.93 3.0 11a_27.03.15 27-Mar-15 wet 18.0 7.50 140.0 9.69 0.00 3.80 1.0 12A_10.04.15 10-Apr-15 wet 13.1 8.10 200.0 11.60 0.10 7.02 6.0 13A_24.04.15 24-Apr-15 wet 16.5 8.60 80.0 2.00 0.00 2.56 3.0
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Table B 3 Water quality in Wetland 22 from 17.12.2014 to 24.04.2015
Site ID Latitude Longitude Survey Date WT pH EC Turbidity TP DO NO - observation 3
(deg C) (µS/cm) (NTUs) (mg/L) (mg/L) (mg/L as N)
1B_17.12.14 17-Dec-14 wet 20.57 7.60 113.7 10.2 0.00 8.40 1.0 2B_9.01.15 9-Jan-15 wet 25 7.33 32.4 6.32 0.00 9.06 1.0 3B_21.01.15 21-Jan-15 wet 22.1 7.20 69 10.5 0.01 5.60 0.0 4B_29.01.15 29-Jan-15 wet 22.1 7.20 100 10.5 0.01 5.60 0.0 5B_13.02.15 13-Feb-15 wet 20.7 7.00 56.6 2.12 0.00 0.60 1.0 6B_20.02.15 20-Feb-15 wet 20.3 6.63 74.1 7.28 0.00 2.40 1.0 7B_26.02.15 -35.3201 149.1634 26-Feb-15 wet 19 6.81 56.6 2.09 0.00 2.20 1.0 8B_6.03.15 6-Mar-15 wet 14 6.75 67.3 3.86 0.00 4.90 1.0 9B_13.03.15 13-Mar-15 wet 17 7.24 81.9 6.29 0.00 4.69 1.0 10B_20.03.15 20-Mar-15 wet 16.9 6.81 52.1 7.34 0.00 3.30 1.0 11B_27.03.15 27-Mar-15 wet 18 7.75 60 11.6 0.00 6.00 1.0 12B_10.04.15 10-Apr-15 wet 14.3 7.22 280 7.89 0.00 3.93 2.0 13B_24.04.15 24-Apr-15 wet 15.3 7.9 60 7.39 0.00 5.14 3.0
111
Table B 4 Water quality in Point source C from 17.12.2014 to 24.04.2015
Site ID Latitude Longitude Survey Date WT pH EC Turbidity TP DO NO - observation 3
(deg C) (µS/cm) (NTUs) (mg/L) (mg/L) (mg/L as N)
1C_17.12.14 17-Dec-14 wet 19.63 7.14 113.7 30 0.05 2.64 5.0 2C_9.01.15 9-Jan-15 wet 21.6 7.42 32.4 2.47 0.01 7.13 3.0 3C_21.01.15 21-Jan-15 dry
4C_29.01.15 29-Jan-15 wet 20.8 7.72 100.0 0.90 0.05 1.90 3.0 5C_13.02.15 13-Feb-15 wet 21.5 7.66 56.6 1.82 0.10 1.80 3.0 6C_20.02.15 20-Feb-15 dry
7C_26.02.15 -35.3224 149.1624 26-Feb-15 dry
8C_6.03.15 6-Mar-15 dry
9C_13.03.15 13-Mar-15 dry
10C_20.03.15 20-Mar-15 wet 19.4 6.91 52.1 3.41 0.20 1.20 4.0 11C_27.03.15 27-Mar-15 wet 18.0 7.36 60.0 3.64 0.20 2.00 1.0 12C_10.04.15 10-Apr-15 wet 14.6 7.58 280.0 4.00 0.05 4.45 3.0 13C_24.04.15 24-Apr-15 wet 15.8 7.7 70.0 0.796 0.04 6.75 0.0
112
Table B 5 Variation of water quality in Wetland 24 from 17.12.2014 to 24.04.2016
Site - ID Latitude Longitude Survey Date observations WT pH EC Turbidity TP DO NO3 (deg C) (µS/cm) (NTUs) (mg/L) (mg/L) (mg/L as N) 1D_17.12.14 17-Dec-14 wet 21.70 7.07 61.0 10.00 0.01 4.94 1.0 2D_9.01.15 9-Jan-15 wet 26.30 6.93 60.0 3.76 0.05 4.65 1.0 3D_21.01.15 21-Jan-15 wet 20.20 6.80 49.0 1.23 0.03 1.78 1.0 4D_29.01.15 29-Jan-15 wet 26.70 7.40 50.0 1.60 0.00 7.76 1.0 5D_13.02.15 13-Feb-15 wet 22.50 6.70 43.5 2.77 0.00 0.90 1.0 6D_20.02.15 20-Feb-15 wet 20.90 6.95 41.0 14.00 0.00 3.18 1.0 -35.3217 149.1599 7D_26.02.15 26-Feb-15 wet 18.30 6.68 75.9 10.50 0.80 0.20 1.0
8D_6.03.15 6-Mar-15 wet 14.60 6.63 52.9 3.94 0.10 1.00 1.0 9D_13.03.15 13-Mar-15 wet 16.70 7.03 94.3 5.30 0.00 1.94 3.0 10D_20.03.15 20-Mar-15 wet 21.00 6.76 79.7 16.90 0.00 2.93 3.0 11D_27.03.15 27-Mar-15 wet 15.80 7.77 70.0 111.00 0.00 6.00 1.0 12D_10.04.15 10-Apr-15 wet 13.40 6.72 80.0 2.60 0.20 2.55 3.0 13D_24.04.15 24-Apr-15 wet 16.19 7.00 50.0 2.44 0.02 6.22 0.0
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Table B 6 Variation of water quality in Billabong (Wetland 21) from 21.01.2015 to 24.04.2015
Latitude Longitude Survey Site Sample ID WT pH EC Turbidity TP DO NO - Date observations 3
(deg C) (µS/cm) (NTUs) (mg/L) (mg/L) (mg/L as N) 1Billabong_21.01.15 21-Jan-15 wet 22.7 8.02 282.0 6.87 0.20 3.60 1.0 2Billabong_29.01.15 29-Jan-15 wet 26.8 8.41 380.0 8.60 0.07 9.70 0.0 3Billabong_13.02.15 13-Feb-15 wet 21.3 7.5 406.0 7.51 0.10 0.90 1.0 4Billabong_20.02.15 20-Feb-15 wet 21.5 7.33 435.0 17.60 0.20 2.30 1.0 5Billabong_26.02.15 26-Feb-15 wet 20.3 7.10 346.0 10.50 0.20 2.00 1.0 6Billabong_6.03.15 -35.3203 149.1619 6-Mar-15 wet 14.0 6.75 67.3 6.77 0.20 4.82 1.0 7Billabong_13.03.15 13-Mar-15 wet 17.3 7.53 418.0 10.60 0.20 1.70 1.0 8Billabong_20.03.15 20-Mar-15 wet 18.4 7.02 429.0 30.60 0.60 2.92 0.0 9Billabong_27.03.15 27-Mar-15 wet 18.8 8.00 560.0 10.40 0.40 4.60 3.0 10Billabong_10.04.15 10-Apr-15 wet 14.3 7.52 190.0 7.89 0.00 4.08 2.0 11Billabong_24.04.15 24-Apr-15 wet 15.4 7.60 340.0 13.60 0.06 4.35 0.0
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Table B 7 Variation of water quality in upstream of silt trap (Wetland 18) from 9.01.2015 to 24.04.2015
Survey - ID Latitude Longitude Date Site WT pH EC Turbidity TP DO NO3
observation (deg C) (µS/cm) (NTUs) (mg/L) (mg/L) (mg/L as N) 1UST_9.01.15 9-Jan-15 Wet 25.0 7.7 256.0 25.5 0.0 4.9 1.0 2UST_21.01.15 21-Jan-15 Wet 22.7 7.5 305.0 16.5 0.0 6.4 1.0 3UST_29.01.15 29-Jan-15 Wet 20.9 8.2 310.0 12.0 0.0 6.7 1.0 4UST_13.02.15 13-Feb-15 Wet 21.0 7.7 355.0 2.6 0.0 5.1 1.0 5UST_20.02.15 20-Feb-15 Wet 21.3 7.9 441.0 4.8 0.0 5.6 1.0 6UST_26.02.15 26-Feb-15 Wet 18.2 7.6 83.6 2.4 0.1 4.0 5.0 -35.3217 149.1599 7UST_6.03.15 6-Mar-15 Wet 17.1 6.9 356.0 3.0 0.0 5.4 1.0 8UST_13.03.15 13-Mar-15 Wet 21.0 7.6 319.0 2.2 0.0 2.7 1.0 9UST_20.03.15 20-Mar-15 Wet 20.8 7.4 261.0 4.3 0.0 0.8 3.0 10UST_27.03.15 27-Mar-15 Wet 18.2 7.9 370.0 6.4 0.0 4.8 1.0 11UST_10.04.15 10-Apr-15 Wet 13.6 7.2 210.0 18.5 0.0 6.9 3.0 12UST_24.04.15 24-Apr-15 Wet 14.5 7.9 345.0 5.8 0.0 8.6 0.0
115
30
25
20
15 28 28
10 Number Number of days 5 5 5 0 0 10.0 - 15.0 15.0 - 20.0 20.0 - 25.0 25.0 - 30.0 Water temperature (ºC)
Figure B 1 Frequency distribution of water temperature in point sources and Wetland 22, 24, 21, 18 for time period of 17th Dec 2015 to 24th April 2015
9 8
7 6 5 4 8 8 3
Number Number of days 5 2 4 1 2 0 1 0 0 5.0 - 10.0 10.0 - 15.015.0 - 20.020.0 - 25.025.0 - 30.030.0 - 35.035.0 - 40.0 Water temperature (ºC)
Figure B 2 Frequency distribution of water temperature in Kellys Swamp (2012 to 2015)
12
10 8 6 11 4 9 7 8 Number Number of days 2 2 0 0 0 0 5.0 - 10.0 10.0 - 15.015.0 - 20.020.0 - 25.025.0 - 30.030.0 - 35.035.0 - 40.0 Water temperature (ºC)
Figure B 3 Frequency distribution of water temperature in downstream of silt trap (2012- 2015) 116
25
20
15 23 10 20
16 Number Number of days 5 6 1 0 0 6.5-7 7-7.5 7.5-8 8-8.5 8.5-9.0 pH
Figure B 4 Frequency distribution of pH in point sources and Wetland 22, 24, 21, 18 for time period 17th Dec 2015 to 24th April 2015
12
10
8
6 10 4 7 Number Number of days 6 2 1 0 2 2 0 0 0 6.0 - 6.5 6.5-7 7-7.5 7.5-8 8-8.5 8.5-9.0 9.0 - 9.5 9.5 - 10.0 pH
Figure B 5 Frequency distribution of pH in Kellys Swamp (2012-2015)
16
14 12 10 8 14
Number Number of days 6 4 8 7 6 2 0 0 1 1 0 0 6.0 - 6.5 6.5-7 7-7.5 7.5-8 8-8.5 8.5-9.0 9.0 - 9.5 9.5 - 10.0 pH
Figure B 6 Frequency distribution of pH in downstream of silt trap (2012-2015) 117
25
20
15
10 20 14 5
Number Number of days 0 5 3 0 2 1 2 3 3 5 2 2 3 0 0 0 1 0 0
Electrical conductivity (µcm/s)
Figure B 7 Frequency distribution of electrical conductivity in point sources and Wetland 22, 24, 21, 18 for time period 17th Dec 2015 to 24th April 2015
8 7
6 5 4 7 3 5 2 4 3 3
Number Number of days 1 0 0 0 0 0 0 0 0 0 0 2 2 2 0 0
Electrical conductivity (µS/cm)
Figure B 8 Frequency distribution of electrical conductivity in Kellys Swamp (2012-2015)
9 8
7 6 5 4 8 7 3 5 5 2 Number Number of days 3 3 1 0 0 0 0 0 1 0 1 1 1 0 1 1 0 0
Electrical conductivity (µS/cm)
Figure B 9 Frequency distribution of electrical conductivity in downstream of silt trap (2012-2015) 118
60
50
40
30 56
20 Number Number of days 10 6 2 0 0 0 1 1 0 0 0-15 15-30 30-45 45-60 60-75 75-90 90-120 120-151 Turbdity (NTU)
Figure B 10 Frequency distribution of turbidity in point sources and Wetland 22, 24, 21, 18 for time period 17th Dec 2015 to 24th April 2015
18 16
14 12 10 8 17 6
Number Number of days 4 5 2 3 0 1 0 0 0 1 1 0 0 0-50 50 - 100 - 150 - 200 - 250 - 300 - 350 - 400 - 450 - 100 150 200 250 300 350 400 450 501 Turbdity (NTU)
Figure B 11 Frequency distribution of turbidity in Kellys Swamp (2012-2015)
25
20
15
10 22 Number Number of days 5 7 3 3 0 1 0 1 0 0 0-15 15-30 30-45 45-60 60-75 75-90 90-120 120-151 Turbidity (NTU)
Figure B 12 Frequency distribution of turbidity in downstream of silt trap (2012-2015)
119
50 45 40 35 30 25 45 20 15 10 Number Number of days 5 11 0 8 0 0 0 1 0 0 0 1 0 0 1 0 0 0
Total phosphorus(mg/l)
Figure B 13 Frequency distribution of total phosphorus in point sources and Wetland 22, 24, 21, 18 for time period 17th Dec 2015 to 24th April 2015
4.5 4
3.5
3 2.5 2 4 1.5
Number Number of days 1 2 2 2 2 2 0.5 1 1 0 1 1 0 1 0 0 0 0 0 0 0 0-0.1 0.2 - 0.3 0.4 - 0.5 0.6 - 0.7 0.8 - 0.9 1.0 - 1.1 1.2 - 1.3 1.4 - 1.5 1.6 - 1.7 Total phosphorus (mg/l)
Figure B 14 Frequency distribution of total phosphorus in Kellys Swamp (2012-2015)
20 18
16 14 12 10 19 8 6
Number Number of days 4 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Total phosphorus(mg/l)
Figure B 15 Frequency distribution of total phosphorus in downstream of silt trap (2012- 2015)
120
18 16
14 12 10 8 17 16 6
Number Number of days 10 10 4 8 2 3 2 0 0 0-1.5 1.5-3.0 3.0-4.5 4.5-6.0 6.0-7.5 7.5-9.0 9.0-10.5 Dissolved oxygen(mg/l)
Figure B 16 Frequency distribution of dissolved oxygen in point sources and Wetland 22, 24, 21, 18 for time period 17th Dec 2015 to 24th April 2015
10 9
8
7 6 5 9 4 8 8
3 6 Number Number of days 2 3 1 1 0 1 0 0-1.5 1.5-3.0 3.0-4.5 4.5-6.0 6.0-7.5 7.5-9.0 9.0-10.5 Dissolved oxygen(mg/l)
Figure B 17 Frequency distribution of dissolved oxygen in Kellys Swamp (2012-2015)
10 9
8
7 6 5 9 4 8 8 7
3 Number Number of days 2 3 1 1 0 0 0 0-1.5 1.5-3.0 3.0-4.5 4.5-6.0 6.0-7.5 7.5-9.0 9.0-10.5 Dissolved oxygen (mg/l)
Figure B 18 Frequency distribution of dissolved oxygen in downstream of silt trap (2012- 2015)
121
40 35
30
25
20 38 15 10 Number Number of days 13 5 10 0 1 5 0 0 0.0 -1.0 1.0-2.0 2.0 - 3.0 3.0 - 4.0 4.0 - 5.0 5.0 - 6.0 Nitrate(mg/l)
Figure B 19 Frequency distribution of nitrate in point sources and Wetland 22, 24, 21, 18 for time period 17th Dec 2015 to 24th April 2015
30
25
20
15 25
10 Number Number of days 5 0 0 0 0 1 0 0 0.0 -1.0 1.0-2.0 2.0 - 3.0 3.0 - 4.0 4.0 - 5.0 5.0 - 6.0 Nitrate (mg/l)
Figure B 20 Frequency distribution of nitrate in Kellys Swamp (2012-2015)
35 30
25 20 15 32
10 Number Number of days 5 4 0 0 0 0 0 0 0.0 -1.0 1.0-2.0 2.0 - 3.0 3.0 - 4.0 4.0 - 5.0 5.0 - 6.0 Nitrate (mg/l)
Figure B 21 Frequency distribution of nitrate in downstream of silt trap (2012-2015)
122
9
8.5
8
7.5 pH 7
6.5
6 Point source Point source Wetland 22 Wetland 24 Wetland 21 Wetland 18 A C
Figure B 22 Distribution of pH in each of the sampling locations for time period of 17 December 2014 to 24 April 2015
600
500
400
300
200 (µS/cm) 100
Electricalconductivity 0 Point sourcePoint source Wetland 22 Wetland 24 Wetland 21 Wetland 18 A C
Figure B 23 Distribution of electrical conductivity in each of the sampling locations for time period of 17 December 2014 to 24 April 2015
160 140
120 100 80 60
40 Turbidity Turbidity (NTU) 20 0 Point source Point source Wetland 22 Wetland 24 Wetland 21 Wetland 18 A C
Figure B 24 Distribution of turbidity in each of the sampling locations for time period of 17 December 2014 to 24 April 2015
123
1.6
1.4 1.2 1 0.8 0.6 0.4 0.2
Total phosphorus (mg/l) 0 Point source Point source Wetland 22 Wetland 24 Wetland 21 Wetland 18 A C
Figure B 25 Distribution of total phosphorus in each of the sampling locations for time period of 17 December 2014 to 24 April 2015
12
10
8
6
4
2 Dissolved oxygen (mg/l) 0 Point source Point source Wetland 22 Wetland 24 Wetland 21 Wetland 18 A C
Figure B 26 Distribution of dissolved oxygen in each of the sampling locations for time period of 17 December 2014 to 24 April 2015
7
6 5 4 3 2
Nitrate Nitrate (mg/l N) as 1 0 Point source Point source Wetland 22 Wetland 24 Wetland 21 Wetland 18 A C
Figure B 27 Distribution of nitrate in each of the sampling locations for time period of 17 December 2014 to 24 April 2015
124
Table B 8 Descriptive statistics results for surface water quality parameters for n = 66
Water quality parameters Average Min Max Median Mode S.D
Water temperature (ºC) 19.10 31.80 26.80 19.20 22.10 3.38 pH 7.38 6.63 8.60 7.40 7.90 0.47 Electrical conductivity (µS/cm) 168.07 32.10 560.00 83.60 60.00 139.88 Turbidity (NTU) 11.56 0.80 150.00 6.56 10.50 22.39 Total phosphorus (mg/l) 0.07 0.00 0.80 0.00 0.00 0.14 Dissolved oxygen (mg/l) 4.18 0.20 9.70 4.22 1.20 2.27 Nitrate (mg/l) 1.61 0.00 5.00 1.00 1.00 1.39
Table B 9 Coefficient of variation (%CV) of the water quality parameters
Sampling locations Season WT pH EC Turbidity TP DO Nitrate summer 9.35 3.0 42.81 178.43 171.13 81.02 80.51 Point source A autumn 11.17 8.9 39.88 54.65 - 43.10 34.64 summer 3.76 3.1 43.19 139.18 60.80 65.22 24.74 Point source C autumn 11.01 4.1 82.38 42.81 63.33 60.48 79.06 summer 8.30 4.2 35.63 49.24 158.11 61.79 63.25 Wetland 22 autumn 9.30 5.9 80.74 35.31 - 18.52 76.93 summer 12.88 3.3 20.68 75.66 216.49 73.32 0.00 Wetland 24 autumn 14.58 5.5 21.99 149.48 182.58 57.58 74.54 Wetland 21 summer 10.08 6.2 14.28 38.10 37.10 84.35 50.00 (Billabong) autumn 11.63 5.5 48.74 56.69 63.35 31.11 100.00 Wetland 18 summer 9.53 2.9 37.39 79.06 223.61 17.27 89.44 (Upstream of silt trap) autumn 16.13 5.1 18.34 33.20 - 53.08 76.93
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Table B 10 Hypothesis test results of surface water quality samples from the south-eastern corner of Jerrabomberra Wetlands
Note: Test decisions abbreviated as “no significant difference” – NS and “significant difference” – S
Hypothesis test between Test parameter Decision for p<0.05 and n=13 Independent Mann-Whitney U- sample t-test test A C Water temperature NS NS pH NS NS Electrical conductivity NS NS Turbidity NS NS Dissolved oxygen NS NS Nitrate -- S A Wetland Water temperature NS NS 22 pH S S Electrical conductivity NS NS Turbidity NS NS Dissolved oxygen NS NS Nitrate -- S Wetland 22 Wetland Water temperature NS NS 24 pH NS NS Electrical conductivity NS NS Turbidity NS NS Dissolved oxygen NS NS Nitrate -- S C Wetland Water temperature NS NS 24 pH S S Electrical conductivity NS NS Turbidity NS NS Dissolved oxygen NS NS Nitrate -- S Wetland 22 Billabong Water temperature NS NS (Wetland pH NS NS 21) Electrical conductivity S S Turbidity S S Dissolved oxygen NS NS Nitrate -- S Billabong(Wetla Wetland Water temperature NS NS nd 21) 18 pH NS NS (Upstrea Electrical conductivity NS NS m of silt Turbidity NS NS trap) Dissolved oxygen NS S Nitrate -- S Wetland 24 Billabong Water temperature NS NS (Wetland pH S S 21) Electrical conductivity S S Turbidity NS NS Dissolved oxygen NS NS Nitrate -- S
126
Appendix C
Table C 1 Jerrabomberra Wetlands groundwater monitoring results (Source:Jerrabomberra Wetlands Management Authority 2013)
127
Table C 2 Predicted amount of pollutants generated from MUSIC model (Source: Batterley and Stone 2013)
Estimated runoff volume from Fyshwick catchment 836 x 106 L/year
Gross pollutants (kg/year) 28900 kg/year Total Suspended Solids (kg/year) 130000 Total Nitrogen (kg/year) 2250 Total Phosphorus (kg/year) 190 Hydrocarbons (kg/year) 9700 Heavy metals: Lead (kg/year) 90.3 Zinc (kg/year) 302.0 Copper (kg/year) 47.3 Cadmium (kg/year) 1.7
Table C 3 Event-based sampling results from Basin Priority Project (Source: GHD 2015); RS = Rising stage
Date Location Local pH Electrical Turbidity NOx Total Zinc Time conductivity (NTU) (mg/l) phosphorus (µg/l) (µcm/s) (mg/l) 16.11.14 WIL110RS 14.45 7.28 86 34.9 0.180 0.144 446 24.11.14 WIL110RS 22.45 7.13 110 109 0.404 0.261 538 30.11.14 WIL110RS 20.00 7.05 91 38.1 0.758 0.154 424 3.12.14 WIL110RS 16.30 7.29 111 210 0.399 0.662 1310 4.12.14 WIL110RS 16.45 7.5 132 98.6 0.363 0.158 252 WIL110 17.48 7.34 194 62.1 0894 0.098 132 WIL110 13.12 8.03 327 27.3 0.057 0.051 102 06.12.14 JER120 17.45 7.65 132 40.1 0.264 0.093 69 09.01.15 WIL110RS 22.00 7.35 116 173 <0.002 0.320 436 10.01.15 WIL110RS 15.20 7.45 159 71.4 0.430 0.190 655 WIL110 15.28 7.49 58 79.8 0.404 0.230 352 13.01.15 WIL110RS 22.00 7.68 184 18.6 0.416 0.086 378 21.01.15 WIL110RS 16.45 7.35 113 41 0.012 0.340 656 01.02.15 WIL110RS 15.15 7.17 205 158 0.429 0.432 629 23.02.15 WIL110RS 21.00 7.13 82 250 0.350 0.726 1490 24.02.15 WIL110 7.55 7.34 214 8.60 0.145 0.068 204 WIL110RS 14.30 -- -- 66 0.172 0.158 -- 18.03.15 WIL110 8.15 7.06 197 99.7 0.177 0.230 626 06.04.15 WIL110RS 20.30 - - - 0.034 0.073 - 07.04.15 WIL110 12.40 - - - 0.416 0.073 - 09.04.15 JER120 14.18 7.52 161 69.9 0.151 0.096 <5
128