Identifying rainfall decline and land use impacts on runoff from catchments in southwest Western Australia

Melissa Wilson

Supervisors: Dr Keith Smettem1 and Dr Richard Silberstein2

1School of Environmental Systems Engineering

Faculty of Engineering, Computing and Mathematics

The University of Western Australia

2CSIRO Land and Water

November 2012

Cover Photo – The Warren River at Barker Road Crossing (X Mayer)

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Abstract

Catchment runoff is largely influenced by the balance of precipitation and evapotranspiration. As stream yields tend to increase with mean annual rainfall, one of the most significant climate changes has been the below-average rainfall observed in many areas in southwest Western Australia between 1975 and the present.

For Perth’s surface water supply catchments in the Darling Plateau, a 300m elevation ridge that runs north‐south 20 km inland from the coast hills inland from Perth, the decline is associated with decreases in both the frequency of daily precipitation and in wet‐day amounts. This decline in regional precipitation is strongly associated with a marked decrease in moisture content in the lower troposphere, an increase in regionally averaged sea level pressure in the first half of the season, and intraseasonal changes in the regional north‐south sea level pressure gradient (Bates et al. 2010).

Across the forested water supply catchments of the Darling Plateau, declining rainfall has led to a shift from perennial to ephemeral streams (Bari & Smettem 2004) and a decline in the runoff coefficient (runoff/rainfall) in the last decade suggests a new hydrologic regime has developed with important implications for future surface water supply to Perth (Petrone et al. 2010).

However, as the synoptic influences on declining rainfall have a regional extent, this may affect the greater southwest region. Statistical analysis of rainfall records suggest that the decrease in rainfall declines from a maximum on the coast to much lower values inland. This change in precipitation is amplified in the runoff data. Analysis of streamflow data has shown a decline in runoff with declines in runoff coefficients demonstrating that the rainfall-runoff regime has changed over time. Many rivers have experienced a decadal decline in summer flows since 2000, even in catchments where land clearing had previously led to increased flows due to rising groundwater. This suggests that baseflow sustained by groundwater is declining, which could result in increased stresses on the ecology of these rivers.

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Acknowledgements

Firstly, thank you to my supervisors, Keith Smettem and Richard Silberstein for their guidance throughout the project. Thank you for making time for me, taking all my questions, and helping me interpret the results.

Thank you to Kevin Petrone from CSIRO Land and Water for obtaining rainfall and streamflow data and to Nik Callow for help performing the GIS analysis of cleared land.

I am very grateful to everyone in SESE for their support over the years. I consider myself lucky to have known such an awesome group of people. I would not change our experiences together for anything.

I would also like to thank Daniel Paraska and Jeremy Mullan for their kindness, friendly advice and assistance despite their hectic schedules.

Finally thank you to Mum, Dad, Melanie and Geoffrey for supporting me during this period and for telling me to take breaks.

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Contents

1 Introduction 1

2 Background 3

2.1 Catchment Hydrology 3

2.1.1 Water Balance 3

2.2 Climate Change 6

3 Definition and Features of Southwest Western Australia 7

3.1 Geomorphological description 7

3.2 Climate 7

3.2.1 Mechanisms Governing Southwest Climate 8

3.2.2 Climate Variation in the Southwest 9

4 Chapter 1 – Precipitation Analysis 12

4.1 Rationale 12

4.2 Method 12

4.2.1 Regression Analysis of Annual Precipitation over Time 12

4.2.2 Hypothesis Test for Regression Slope 13

4.2.3 Jarque-Bera Test for Normality 13

4.3 Results 14

4.3.1 Data Quality 14

4.3.2 Rainfall Anomaly Plots 15

4.3.3 Visual Inspection 17

4.3.4 Regression Analysis of Precipitation 18

4.4 Discussion 23

5 Chapter 2 – Streamflow Analysis 24

5.1 Rationale 24

5.2 Method 24

5.2.1 Regression Analysis between Time (year) and Streamflow 24

5.2.2 Runoff coefficient 24

5.3 Results 25 iv

5.3.1 Quality of Data 25

5.3.2 Regression analysis of streamflow data 25

5.3.3 Reaches of specific land use 27

5.4 Discussion 32

6 Chapter 3 – Baseflow Analysis 34

6.1 Rationale 34

6.2 Method 34

6.3 Results 34

6.3.1 Warren River 37

6.3.2 Kent River – Styx Junction 39

6.3.3 Denmark River 40

6.3.4 Margaret River 42

6.4 Discussion 44

7 Conclusions 46

8 Recommendations 46

9 References 48

10 Appendices 51

10.1 Appendix 1 51

10.2 Appendix 2 54

10.3 Appendix 3 56

10.4 Appendix 4 59

10.5 Appendix 5 62

10.6 Appendix 6 63

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Figures

Figure 1 Southwest region of Western Australia (Sterrett 2008) ...... 7 Figure 2 Location of rainfall gauges with more than 50 years of record ...... 14 Figure 3 Rainfall anomaly plot for Jarrahdale using a base 1961-1990 average ...... 16 Figure 4 Rainfall anomaly plot for Barooga using a base 1961-1990 average ...... 16 Figure 5 Rainfall anomaly plot for Kwobrup using a base 1961-1990 average ...... 17 Figure 6 Rainfall anomaly plot for Pemberton using a base 1961-1990 average ...... 17 Figure 7 The location of rain gauging stations. Upon visual inspection, gauges experiencing a drop in high rainfall events since 2000 are highlighted in red, with others exhibiting no clear visual trend in green...... 18 Figure 8 Location of rainfall sites showing significant downward trends in precipitation (green) ...... 20 Figure 9 Location of rainfall gauges with continuous record that can be reliably tested statistically ...... 20 Figure 10 Residual plot for the Pemberton dataset ...... 21 Figure 11 Residual plot for the Dwellingup dataset ...... 21 Figure 12 Rainfall sites showing significant decrease in precipitation. Larger decreases are in red ...... 22 Figure 13 Location of streamflow gauges demonstrating significant decline ...... 27 Figure 14 Location of the Warren River catchment in the southwest region of Western Australia ...... 28 Figure 15 Isolated reaches of the Warren River catchment. Forest (green) and cleared (yellow) ...... 28 Figure 16 Annual runoff against time for the forested and cleared parts of the Warren River catchment ...... 29 Figure 17 Annual runoff coefficients against time for the forested reach of the Warren River catchment ...... 29 Figure 18 Location of the Blackwood River catchment in the southwest region of Western Australia ...... 30 Figure 19 Isolated reaches of the Blackwood River catchment. Forest (green) and cleared (yellow) ...... 30 Figure 20 Annual runoff against time for the forested and cleared parts of the Blackwood River catchment ...... 31 Figure 21 Annual runoff coefficients against time for the forested reach of the Warren River catchment ...... 31

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Figure 22 Locations of gauges showing significant decline in summer baseflow ...... 36 Figure 23 Location of the Warren River catchment in the southwest region of Western Australia ...... 37 Figure 24 Isolated reaches of the Warren River catchment. Forest (green) and cleared (yellow) ...... 37 Figure 25 Annual summer baseflow against time for the forested reach of the Warren River catchment ...... 38 Figure 26 Location of the Kent River catchment in the southwest region of Western Australia ...... 39 Figure 27 Annual summer baseflow against time for the forested of the Kent River catchment before revegetation ...... 40 Figure 28 Annual summer baseflow against time for the forested of the Kent River catchment after revegetation ...... 40 Figure 29 Location of the Denmark River catchment in the southwest region of Western Australia ...... 41 Figure 30 Annual summer baseflow against time for the forested of the Denmark River catchment before 2000 ...... 41 Figure 31 Annual summer baseflow against time for the forested of the Denmark River catchment after 2000 ...... 42 Figure 32 Location of the Margaret River gauge in the southwest region of Western Australia ...... 42 Figure 33 Annual summer baseflow against time for Margaret River ...... 43 Figure 34 Rainfall anomaly plot for Cowaramup using a base 1961-1990 average ...... 56 Figure 35 Rainfall anomaly plot for Dwellingup using a base 1961-1990 average ...... 56 Figure 36 Rainfall anomaly plot for Kuranda using a base 1961-1990 average ...... 57 Figure 37 Rainfall anomaly plot for Kurrara Park e using a base 1961-1990 average ...... 57 Figure 38 Rainfall anomaly plot for Marbling using a base 1961-1990 average ...... 57 Figure 39 Rainfall anomaly plot for Perth Airport using a base 1961-1990 average ...... 58 Figure 40 Rainfall anomaly plot for Roelands using a base 1961-1990 average ...... 58 Figure 41 Rainfall anomaly plot for Yoongarillup using a base 1961-1990 average ...... 58 Figure 42 Location of streamflow gauges used in the analysis ...... 59 Figure 43 Location of streamflow gauges used in the baseflow analysis ...... 63 Figure 44 Annual summer baseflow against time for Kalgan River ...... 63 Figure 45 Annual summer baseflow against time for Frankland River ...... 64 Figure 46 Annual summer baseflow against time for Deep River ...... 64 Figure 47 Annual summer baseflow against time for the Upper Warren River catchment ..... 65

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Figure 48 Annual summer baseflow against time for Perup River ...... 65 Figure 49 Annual summer baseflow against time for Tone River ...... 66 Figure 50 Annual summer baseflow against time for Lefroy Brook ...... 66 Figure 51 Annual summer baseflow against time for Carey Brook ...... 67 Figure 52 Annual summer baseflow against time for Donnelly River ...... 67 Figure 53 Annual summer baseflow against time for the Upper Blackwood River catchment68 Figure 54 Annual summer baseflow against time for Blackwood River ...... 68 Figure 55 Annual summer baseflow against time for Wilyabrup Brook ...... 69 Figure 56 Annual summer baseflow against time for Brunswick River ...... 69 Figure 57 Annual summer baseflow against time for Harvey River ...... 70

Tables

Table 1 Rainfall gauges with more than 50 years of record ...... 15 Table 2 Results of the regression analysis, with significant results highlighted in yellow ...... 19 Table 3 Result of the regression analysis of annual runoff vs. time ...... 25 Table 4 Result of the regression analysis of annual summer baseflow vs. time ...... 34 Table 5 Rivers with significant long-term declines in summer baseflow. Note the level of decline after 2000...... 35 Table 6 Period of record for the rainfall stations used in the analysis ...... 51 Table 7 Results from the regression analysis and Jarque-Bera test ...... 54 Table 8 Streamflow gauges with continuous long period of record that can be tested ...... 60 Table 9 Streamflow gauges used in the baseflow analysis ...... 62

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Glossary

BOM Bureau of Meteorology, Australia

Catchment The area of land from which rainfall becomes runoff contributing to a single watercourse or wetland or recharge to an aquifer.

Isohyet A line on a map joining places of equal rainfall.

IOCI Indian Ocean Climate Initiative

Recharge Water that infiltrates into the soil to replenish an aquifer.

Reforestation Planting trees as a forest on land previously cleared of native forest overstorey.

Runoff coefficient The ratio of runoff to rainfall, also known as the ‘runoff ratio’.

Salinity The effects on land and in water of the build-up of salts near the surface as a result of rising or discharging groundwater.

SOI Southern Oscillation Index

SWWA Southwest of Western Australia

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1 Introduction It has been widely reported that Southern Australia is experiencing an extended period of drought, with southwest Western Australia (SWWA) experiencing significant declines in rainfall since the mid‐1970s (Power, Sadler & Nicholls 2005; Bates et al. 2008; Murphy & Timbal 2007).

For Perth’s surface water supply catchments in the Darling Plateau, a 300m elevation ridge that runs north‐south 20 km inland from the coast hills inland from Perth, the decline is associated with decreases in both the frequency of daily precipitation and in wet‐day amounts. This decline in regional precipitation is strongly associated with a marked decrease in moisture content in the lower troposphere, an increase in regionally averaged sea level pressure in the first half of the season, and intraseasonal changes in the regional north‐south sea level pressure gradient (Bates et al. 2010).

Across the forested water supply catchments of the Darling Plateau, declining rainfall has led to a shift from perennial to ephemeral streams (Bari & Smettem 2004) and the decline in the runoff coefficient (runoff/rainfall) in the last decade suggests a new hydrologic regime has developed with important implications for future surface water supply to Perth (Petrone et al. 2010).

Since the synoptic influences on declining rainfall have a regional extent, it is possible that catchments along the south coast of Western Australia will also experience declining rainfall and hence, declines in runoff. However, the situation is complicated by increased runoff from cleared agricultural land (Bari & Smettem 2006a). Rainfall also declines from a maximum on the south coast to much lower values in the catchment headwaters. The clearing of catchment headwaters has resulted in perennial saline groundwater flows in many watercourses (Bari & Smettem 2006b) and this increase in groundwater flow from the agricultural reaches in watercourses may be one identifiable flow signature from such areas.

The purpose of this study is to: 1) Test the hypothesis that rainfall is declining across the catchments of the south coast of Western Australia and 2) Identify the impacts of any significant rainfall decline and the counter trends of land clearing on runoff from south coast catchments.

The thesis commences by introducing and defining the key components of the annual water balance (Chapter 2) and the definition of climate change, then defines and describes the southwest region of Western Australia and reviews literature on climate change in the region (Chapter 3). Methods of analysis and results for determination of rainfall, streamflow and 1 Melissa Wilson baseflow trends are covered in chapters 4-6 and concluding remarks and recommendations are made in chapters 7 and 8.

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2 Background

2.1 Catchment Hydrology

2.1.1 Water Balance The water balance represents the key storage and flux volumes associated with the movement of water within the terresphere. The water balance equation accounts for all the inputs and outputs of a system.

(1)

Where Q is runoff, P is precipitation, E is evaporation, T is transpiration, I is interception, D is deep drainage and ΔS is change in storage.

The water balance is a means of quantifying the hydrologic cycle at any scale of interest, as it is dependent on the universal law of conservation of mass. In its simplest form, the water balance equation is applied to a simple storage such as a catchment, where water is subject to processes of storage and exchange.

2.1.1.1 Precipitation Precipitation results from the condensation of moisture in the atmosphere. For condensation to occur and raindrops to form, water vapour releases its latent heat and becomes liquid water again (Manning 1987). This change of phase occurs when moisture-laden air is cooled until it reaches saturation, and a surface (condensation nucleus) is present for the moisture to condense onto. The small water droplets must then grow until they reach a size that enables them to fall (Hjelmfelt & Cassidy 1975).

The cooling associated with condensation and precipitation is almost always due to the uplift of air (Ward & Robinson 1990). The three methods of elevating air masses are convectional lifting, cyclonic lifting and orographic lifting (Manning 1987; Davie 2008).

Cyclonic lifting occurs when warm light air is forced up over a denser cold air mass (Hjelmfelt & Cassidy 1975). Cyclonic precipitation comprises both frontal and non-frontal types. In frontal precipitation, warm moist air is forced to rise over a wedge of denser cold air. Non-frontal precipitation is a result of the convergence and uplift of air within a low-pressure area (Ward & Robinson 1990).

Convectional lifting results from when air over a particular locality becomes warmer than the surrounding air and are lifted in cells. As it rises, it expands and cools producing thunderstorms (Hjelmfelt & Cassidy 1975; Manning 1987)

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Orographic lifting occurs when air is forced up against a mountain where it cools, condenses and precipitates. The result is increased precipitation on windward slopes, and significantly less precipitation on leeward, inland slopes (Brutsaert 2005). In Western Australia the Darling scarp exerts an orographic effect, with annual rainfall typically 200-300 mm greater on the scarp than on the Swan coastal plain.

2.1.1.2 Evaporation Evaporation is the transition of water from the liquid phase to the vapour phase. This process requires thermal energy, so that the kinetic energy of the water molecules is increased to the extent that they can escape from the liquid surface (Brutsaert 2005; Hjelmfelt & Cassidy 1975). The process also requires some mechanism to remove the escaped water molecules from the vicinity so they do not recondense. Evaporation is highest in warm, dry, windy conditions and least in cold, humid conditions (Ward & Robinson 1990).

2.1.1.3 Transpiration In the water balance transpiration is typically combined with evaporation and called evapotranspiration. Vascular plants have conducting tissues that transport water, minerals and photosynthetic materials throughout the plant. Transpiration is the process where liquid water in plants is released as water vapour through open stomata. Many plants can regulate the degree of stomatal opening according to changing conditions of light intensity, water balance and carbon dioxide concentration (Palmer et al. 2010). The rate at which the stomata open and close controls the flow of water upward from the roots into the plant (Manning 1987).

2.1.1.3.1 Interception Interception refers to precipitation that does not reach the ground, but is instead intercepted by the leaves and branches of vegetation. The intercepted water is then evaporated to the atmosphere or transpired by the vegetation (Brutsaert 2005).

The amount of interception that occurs is dependent on the duration and type of precipitation, as well as the type of vegetation, as it taller, denser vegetation tends to intercept more than sparse vegetation (Brutsaert 2005).

2.1.1.4 Runoff Runoff refers to the movement of water to a channelised stream under gravity. Catchment runoff is largely influenced by the balance of precipitation and evapotranspiration, with changes in precipitation being amplified in runoff (Chiew et al. 1995). Runoff mechanisms include overland flow and throughflow (Davie 2008).

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Infiltration excess overland flow, also known as Hortonian overland flow, occurs when the precipitation rate exceeds the rate at which water can infiltrate the ground. Hydrophobic soils also contribute to overland flow as they decrease the amount of water able to infiltrate into the ground. Saturation excess overland flow occurs when the soil becomes saturated, therefore no additional water can infiltrate into the ground and it is forced to run off (Manning 1987).

Throughflow refers to flow which takes place through the unsaturated zone, physically within the soil profile (Davie 2008). Once the water has infiltrated the ground surface, it continues to move through the soil matrix or along lateral flow paths.

Terrain features such as basin elevation and orientation, topography, soil type, and geology affect the runoff of a catchment (Manning 1987). Vegetation type and amount play a significant role in how much precipitation is intercepted above the ground, and how much infiltrates the soil. This is because of increased transpiration and because vegetation retards overland flow, giving the water more time to infiltrate the soil (Manning 1987).

Increases in streamflow following clearing due to the reduction in transpiration and interception loss, may be followed by a second phase of streamflow rise when the groundwater level reaches the streambed. The increase in runoff caused by clearing is a counter trend to the decrease expected with declining precipitation.

2.1.1.5 Groundwater Groundwater is a vital part of the hydrologic cycle and dominates available water resources in arid areas like Australia. An aquifer is a saturated permeable geologic unit that can yield a usable quantity of groundwater. The surface of an unconfined aquifer where the pore water pressure is atmospheric is called the water table. It has the same general shape as the surface topography, with less relief change (Freeze & Cherry 1979).

Groundwater is naturally replenished by surface water from precipitation, and streams when this recharge reaches the water table. It is either stored in the aquifer or passes through as baseflow. Baseflow refers to the portion of streamflow discharged from shallow groundwater, in a non-recharge period (Brutsaert & Sugita 2008). It is noticeable in the dry season when precipitation does not occur, but there is still runoff.

In general groundwater discharge occurs in low zones and recharge in the high zones, with groundwater typically flowing from topographic highs to lows, although this may reverse on a small scale. Groundwater flow is much slower than surface runoff, and therefore lags behind the initial occurrence of precipitation, acting as a memory of past rainstorms.

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2.2 Climate Change Climate refers to the atmospheric conditions (such as solar radiation, temperature, humidity, precipitation, atmospheric pressure and wind) that prevail in a given region (May 2008). The historical record, built up by analysing indirect measures of climate such as ice cores, tree rings and ocean sediments, show that climate systems vary naturally over periods ranging from decades to millions of years. Climate change is a significant, permanent change in the statistical distribution of weather patterns over any of this wide range of time scales (May 2008). While the change could be in altered average weather conditions, it can also be in the distribution of weather such that there are more frequent or fewer extreme weather events.

Climate change is caused by factors that include external influences such as explosive volcanic eruptions, natural variations in the output of solar radiation, and slow changes in the configuration of Earth’s orbit relative to the sun (May 2008). However, human-induced alterations of the natural world, such as the increase in atmospheric CO2 levels due to emission from fossil fuel combustion, also to contribute substantially to recent climate change.

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3 Definition and Features of Southwest Western Australia The southwest of Western Australia (SWWA) is defined as southwest of a line connecting 30°S, 115°E and 35°S, 120°E (Pitman et al. 2004) as seen in Figure 1.

Figure 1 Southwest region of Western Australia (Sterrett 2008)

3.1 Geomorphological description The main topographical feature of SWWA is the Darling Scarp. The Darling Scarp is a north- south linear structure which extends some 900 km northwards from Cape Naturaliste to the area east of Shark Bay. The southern segment of the scarp, inland from Bunbury and Perth, has a maximum scarp relief of about 300 m and diminishes gradually northwards (Jakica et al. 2011).

3.2 Climate SWWA has a Mediterranean climate with mild, wet winters and hot, dry summers (Charles et al. 1999). The average rainfall is 600 mm, with a local maximum >1000 mm on the western

7 Melissa Wilson escarpment (Timbal 2004). Off the coast of Perth, the annual mean sea surface temperature is around 20 °C.

3.2.1 Mechanisms Governing Southwest Climate This annual cycle of heavy winter rainfall and low summer rainfall is the result of the annual cycle of planetary winds (Gentilli 1972; IOCI 2002). The presence of a subtropical belt of high pressure which extends across the region, reaching a southernmost extension in January or February, results in summers which are dry and hot (Bates et al. 2008). This high pressure belt moves northward during autumn, and is almost completely out of the region during the winter months, but returns south during spring (Gentilli 1972).

Most of the annual precipitation falls as rain within the cooler winter months (June and July), with over eighty per cent falling between May and October (Wright 1974; Bates et al. 2008). Wright (1974) found that in SWWA, the dominant mechanisms in producing rainfall in early winter are different to those producing rainfall in late winter. Wright (1974) described the early winter the dominant mechanism as a result of “widespread ascent in mid-troposphere associated with an upper tropospheric jet stream and surface winds both from north of west”. The late winter showery rains were described as coming mainly on surface winds from between west and south-west contribute a greater proportion (Wright 1974).

A cold front forms when warm air is displaced and forced upward by an advancing mass of cold air (Manning 1987). Charles et al. (1999) describe the high winter rainfall as a result of cold fronts lodged between high pressure systems at latitudes 30°S to 35°S, with frontal rains spreading rapidly northwards and increasing in intensity throughout May, and then retreating southwards and decreasing in intensity from August to October. Wright (1974) described heavy winter rainfall as being generated by moist unstable westerly winds, with progressive troughs within the westerly airstream. Wright (1974) also states that this heavy precipitation is enhanced by convergence induced by friction on the air as it reaches the high ground of the Darling Scarp.

On average, 20 – 25% of the April to October rainfall is associated with interactions with ‘northwest cloudbands’ (IOCI 2002), with most of it occurring in the early months. The northwest cloudbands are extensive bands extending from off the northwest coast and into the mid-latitudes. When they interact with approaching cold fronts in the southwest, which are associated with mid-latitude synoptic depressions, they can form pre-frontal rainbands (Wright 1997; IOCI 2002).

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Wright (1974) found summer rainfall to be “associated with ‘meridional troughs’ which involve a linkage between tropical and temperate low-pressure. The rain may frequently originate from moisture injected into the upper atmosphere by tropical cyclones, and its spatial distribution in notably uniform”.

In SWWA there is a gradient in total winter rainfall from east to west, and a gradient in temperature from south to north. Wright (1974) described that, “From south to north, there is a rapid decrease of rainfall with distance over the first 80 km followed by a much slower, steady decrease. From west to east the rainfall increases with distance inland over the first 30 km or so, especially in the south over the Darling Scarp, and then decreases steadily”.

3.2.2 Climate Variation in the Southwest Since the 1970s, Bates et al. (2008) found that annual mean temperatures in SWWA have increased at a rate of about 0.15 °C per decade. These increases have occurred in all seasons except summer, where cooling of about 0.1 °C per decade has occurred (Bates et al. 2008). As an increase in surface temperature would increase evaporation, temperature has considerable influence on the hydrologic cycle.

However, one of the most important climate changes has been the sharp and sudden decrease in early winter rainfall which took place around the mid-1970s (IOCI 2002). From 1975 to 2004, the mean totals over the period are close to 14% less than the means for the period from the mid-1900s to 1974 (Bates et al. 2008). Although there has been little decline in late winter rainfall, recent data shows that winter half-year rainfall is still relatively low (Bates et al. 2008).

This step change also sees the loss of very high rainfall years that were a relatively common feature throughout the 20th century (Bates et al. 2008). It appears that the decline was not gradual, but rather a switch to an alternative rainfall regime (IOCI 2002).

However, Bates et al. (2010) found that over the past three decades, annual inflows to dams in the Darling scarp were more consistent with a smooth nonlinear trend than with successive periods of relatively constant levels, separated by abrupt changes. Bates et al. (2010) also found that substantial decreases in precipitation have occurred over most of the main water supply catchments, with the occurrence of precipitation varying seasonally and regionally, and the intraseasonal variation in intensity varying regionally.

The decrease in rainfall of 15-20% has led to a stream flow reduction of about 50%, a large decrease relative to the rainfall decline (Bates et al. 2008). This disproportion reflects the lack of very wet years and runoff sensitivity to decreases in rainfall (Hope & Foster 2005). Factors 9 Melissa Wilson such as increased evaporation and decreased soil moisture are combining with the decreased rainfall to produce a much greater decrease in inflow.

3.2.2.1 Synoptic Causality of Climate Change From May – July, Bates et al. (2010) found evidence of an increasing trend in the probability of the weather type associated with dry conditions across the study region. The increase was accompanied by a decrease in the combined probabilities of weather types associated with widespread wet conditions across the southwest of Western Australia. Bates et al. (2010) believed these trends to be strongly associated with a decrease in moisture content in the lower troposphere, an increase in regionally averaged sea level pressure in the first half of the season, and intraseasonal changes in the regional north‐south sea level pressure gradient (Bates et al. 2010).

Since early winter rainfall is associated with winds from the northwest and northwest cloudbands, it is suggested that the rainfall decline observed in this period is associated with a decrease in the frequency, intensity or preferred track of these cloudbands, or their interactions with cold fronts and mid-latitude depressions (IOCI 2002).

IOCI (2002) associated the decline in winter rainfall with a change in the large-scale global atmospheric circulation, finding that the drop in winter precipitation was coincident with an increase in atmospheric pressure. Over the greater Australian region, high precipitation years correspond with low pressure years with the strongest correlations (between -0.8 to -1.0) over SWWA (IOCI 2002).

The Southern Oscillation Index (SOI) gives an indication of the development and intensity of El Niño or La Niña events in the Pacific Ocean and is calculated using the pressure differences between Tahiti and Darwin. As atmospheric pressures across the Australia region are related to the El Niño Southern Oscillation, the climate of SWWA is also related to the El Niño Southern Oscillation (IOCI 2002). However, the relationship between the El Niño Southern Oscillation and Australian rainfall changes on multidecadal timescales, with the relationship with southwest rainfall changing in the mid-1950s (IOCI 2002). According to (IOCI 2002) the rainfall is now lower for any value of the SOI than it has been in the past, suggesting that other factors are involved in the rainfall decrease, rather than it simply reflecting a change in the character of the El Niño Southern Oscillation.

The roles of other possible contributing factors to the rainfall decline have been investigated. The Indian Ocean has warmed through the last few decades as SWWA rainfall has decreased. While the surface temperature of the southern Indian Ocean appears to be correlated with 10 Melissa Wilson southwest rainfall, the correlations are not consistent across time or seasons, suggesting no physical or causal link between the trends (IOCI 2002; Smith et al. 2000).

Land clearing can be significant at regional scales. The replacement of native vegetation for agriculture leads to significant changes in land surface characteristics such a albedo, surface roughness and canopy resistance, which induce changes in the atmospheric boundary layer (Lyons 2002). However the observations of slight rainfall decreases during spring and summer do not fit with this theory (Bates et al. 2008)

It is possible that the recent decline in precipitation is a result of natural climatic variability, and is returning SWWA rainfall to levels observed at the end of the 19th century (IOCI 2002). The Federation Drought which occurred at the end of the 19th century (1895-1902) mainly affected eastern Australia and saw a decline in spring/summer rainfall with a reduction in the number of wet days. It was primarily driven by the El Niño Southern Oscillation (Verdon- Kidd & Kiem 2010). Since the climate of SWWA is also related to the El Niño Southern Oscillation (IOCI 2002) the low precipitation observed at the end of the 19th century in SWWA could be related to the Federation Drought.

Since the industrial revolution, there has been a build-up in the amount of carbon dioxide in the atmosphere. The build-up of carbon dioxide has caused an enhanced greenhouse effect, which is contributing to the warming of the Earth. The global temperature increase has consequences for climate, which include changes to rainfall conditions and storm intensity or frequency.

It is likely that both natural variability and the enhanced greenhouse effect are the major factors in the SWWA rainfall decrease, with other local factors such as land-use change being secondary contributors (Bates et al. 2008; IOCI 2002).

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4 Chapter 1 – Precipitation Analysis

4.1 Rationale It has been widely reported that southwest Western Australia has experienced significant declines in rainfall since the mid‐1970s (Power, Sadler & Nicholls 2005; Bates et al. 2008; Murphy & Timbal 2007). The Darling Plateau has seen decreases in both the frequency of daily precipitation and in wet‐day amounts. Since the synoptic influences on rainfall have a regional extent, statistical analysis of precipitation was undertaken to determine if rainfall actually was declining across the catchments of the south coast of Western Australia.

4.2 Method The quality of the data was examined before statistical testing could be undertaken. Precipitation data was taken from gauges with continuous daily records. Daily records were summed to create monthly and annual records.

Initial exploratory analysis was undertaken to visually identify any trends. This was done by creating rainfall anomaly plots of the long term data, using a 1961 to 1990 base average. Eleven-year moving averages were used to smooth data and identify variations or trends in the data.

Following this, a more sophisticated statistical analysis was carried out to test the hypothesis that rainfall was declining as a result of regional synoptic shifts identified by Bates et al. (2010).

4.2.1 Regression Analysis of Annual Precipitation over Time Regression analysis is a statistical technique for investigating and modelling the relationship between variables. In this study, the relationship between annual precipitation and time was assessed using the method of least-squares. This calculates the best-fitting line for the observed data by minimising the sum of the squares of the vertical deviations from each data point to the line.

Linear regression of precipitation against time was determined using the following equations (Montgomery & Peck 1992)

(2) where x represents the year, y represents the annual precipitation (mm), β0 is the intercept

(equation 3), and β1 is the slope (equation 4).

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∑ ( ̅)( ̅) (3) ∑ ( ̅)

̅ ̅ (4)

The coefficient of determination, also called the R-squared term, refers to the fraction of variance explained by the model. It is used to understand how well the line fits the model.

4.2.2 Hypothesis Test for Regression Slope The fitted line is the mathematical model describing the functional response of y to x. To determine whether there is a significant linear relationship between x and y, the hypothesis that the slope of the regression line is significantly different from zero is tested.

The resulting p value is a measure the probability that the values for the slope and intercept are not derived by chance. These p values state the confidence that one can have in the estimated values being correct. The p value was compared to the chosen significance level, and the null hypothesis was rejected if the p value was less than the significance level. This statistical analysis was undertaken using Excel.

Initially, the tests were performed on data from stations with very long (>50 years) records. However, there is a paucity of such stations across SWWA and in order to identify regional trends it was necessary to resort to data from stations with only thirty to forty years of record. The slopes of the rainfall vs. time regressions from these shorter record stations were compared in order to assess the regional distribution of rainfall trends.

4.2.3 Jarque-Bera Test for Normality Examination of the residuals can validate the assumption that a linear relationship exists. While a histogram of the residuals can suggest the distribution of the errors, the Jarque-Bera test statistic can be formally used to test for normality (Ogunc & Hill 2011). The Jarque-Bera test uses skewness (a measure of asymmetry of a distribution about its mean) and kurtosis (which measures the peakedness of a distribution) in the following equation (Ogunc & Hill 2011)

[ ( ) ] (5)

If the data comes from a normal distribution, the JB statistic asymptotically has a chi-squared distribution with two degrees of freedom, and so it can be used to test the hypothesis that the residuals are from a normal distribution.

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4.3 Results

4.3.1 Data Quality Upon inspection, there were very few rainfall stations of continuous, long term record on which to reliably test statistically for climate change. There were twelve Bureau of Meteorology rainfall stations with more than fifty years of continuous record. The locations and details of the rain gauging stations are shown in Figure 2 and Table 1 respectively.

Figure 2 Location of rainfall gauges with more than 50 years of record

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Table 1 Rainfall gauges with more than 50 years of record BoM Period of BoM Name Longitude Latitude Reference Record 1 010510 BAROOGA 116.7992 -33.1642 1911-2011 2 009636 COWARAMUP 115.0747 -33.8358 1941-2011 3 009538 DWELLINGUP FORESTRY 116.0594 -32.7103 1935-2011 4 009023 JARRAHDALE 116.0755 -32.3342 1900-2011 5 009668 KURANDA 116.6475 -33.6739 1957-2011 6 010691 KURRARA PARK 117.4047 -33.0964 1953-2011 7 010589 KWOBRUP 117.9878 -33.61 1916-2010 8 009024 MARBLING 116.0842 -31.5608 1943-2011 9 009592 PEMBERTON 116.0433 -34.4478 1941-2011 10 009021 PERTH AIRPORT 115.9764 -31.9275 1945-2011 11 009657 ROELANDS 115.7789 -33.2964 1943-2011 12 009771 YOONGARILLUP 115.4697 -33.7411 1957-2011

One of the longest recording gauges was the Jarrahdale dataset, but it was missing recent data. Data was filled in with interpolated data from SILO data drill to the nearest twentieth of a degree location (http://www.longpaddock.qld.gov.au/silo/) (Petrone et al. 2010). Rainfall trends from the 20th century onwards were analysed as most of the stations began after this point. The Jarrahdale data prior to 1900 included the Federation Drought which was interestingly the driest period on record, however as no other rainfall site had records to support this only records past 1900 were used.

4.3.2 Rainfall Anomaly Plots An anomaly plot is used to show deviation from a long-term average. In this study, anomaly plots were produced to show deviation in rainfall from the expected 1961-1990 average.

The Jarrahdale rainfall anomaly plot (Figure 3) shows a large number of very high rainfall years in the beginning of the 20th century. However, there is a clear decrease in the number of high rainfall years and an increase in the frequency of low rainfall years since 2000.

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Jarrahdale Rainfall Anomaly 100

80

60 40 20 0

-20 Rainfall Rainfall Anomaly (mm) -401900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 -60 Year

Figure 3 Rainfall anomaly plot for Jarrahdale using a base 1961-1990 average

Barooga and Kwobrup are both located inland in the low rainfall zone. They also appear to show a similar decrease in the number of high rainfall years and an increase in the frequency of low rainfall years since the later part of the 20th century (Figure 4 and Figure 5).

Barooga Rainfall Anomaly 100

80 60 40 20 0 -20

Rainfall Rainfall Anomaly (mm) -40 -60 1911 1921 1931 1941 1951 1961 1971 1981 1991 2001 2011 Year

Figure 4 Rainfall anomaly plot for Barooga using a base 1961-1990 average

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Kwobrup Rainfall Anomaly (1961-90 base) 80

60 40 20 0 -20

Rainfall Rainfall Anomaly (mm) -40 -60 1916 1926 1936 1946 1956 1966 1976 1986 1996 2006 Year

Figure 5 Rainfall anomaly plot for Kwobrup using a base 1961-1990 average

Pemberton, which is in the high rainfall zone on the south coast, has a decrease in very high rainfall years (Figure 6). There is an increase in the number of low rainfall years towards the later part of the 20th century, but this increase in frequency is not as great as it is for Jarrahdale and Barooga.

Pemberton Rainfall Anomaly (base 1961-90)

60

40

20

0

-20

-40 Rainfall Rainfall Anomaly (mm)

-60 1941 1951 1961 1971 1981 1991 2001 2011 Year

Figure 6 Rainfall anomaly plot for Pemberton using a base 1961-1990 average

While the anomaly plots appear to show a declining trend, it is still necessary to test the trend statistically for significance. The remaining rainfall anomaly plots can be found in Appendix 3.

4.3.3 Visual Inspection Due to the small number of sites with long (>50 years) records, sites with thirty to forty years of continuous record were also tested. The locations and details of the rain gauging stations

17 Melissa Wilson are shown in Appendix 1. Visual inspection of rainfall over time plots found an absence of rainfall events exceeding the long term mean by at least 40%, since the year 2000. A map was produced (Figure 7) showing the locations of stations where this loss of high rainfall years has occurred in red, and where it has not occurred in green.

Figure 7 The location of rain gauging stations. Upon visual inspection, gauges experiencing a drop in high rainfall events since 2000 are highlighted in red, with others exhibiting no clear visual trend in green.

Results show the loss in high rainfall years after the year 2000 is mostly restricted to the high rainfall areas along the coastal plain, scarp and southwest forests, where annual rainfall exceeds 800 mm. The majority of inland areas do not show this trend, with the exception of Barooga, Kurrara Park and Kwobrup.

4.3.4 Regression Analysis of Precipitation Regression analysis was carried out to determine the trend of annual precipitation over time; these results are included in Table 2. Although R-squared values are low, there are some instances where the hypothesis test for regression slope yielded significant p values. Stations 18 Melissa Wilson at Pemberton, Jarrahwood, Kwobrup, Cowaramup, Yoongarillup, Roelands, Barooga, Dwellingup Forestry, Jarrahdale, Medina Research Centre, Marbling, Perth Airport, Wooroloo Brook, Serpentine Drain, and Margaret River showed these significant decreasing trends.

Table 2 Results of the regression analysis, with significant results highlighted in yellow BoM Period of BoM Context Name Linear Regression R-square p value Reference Record 509022 YATE FLAT CREEK 1983-2011 y=0.2639x+190.66 0.000355 0.922719 009592 PEMBERTON 1941-2011 y=-2.9139x+6947.38 0.096394 0.008411 509042 SMITHS BROOK TRIB 1973-2011 y=-2.9215x+6684.91 0.057293 0.142215 509210 PERUP RIVER 1975-2011 y=0.0689x+517.10 4.88E-05 0.967271 509212 PERUP RIVER 1975-2011 y=-1.4788x+3663.26 0.018387 0.423591 509375 TONE RIVER 1979-2011 y=-0.6773x-743.18 0.004217 0.719557 509383 TONE RIVER CATCHMENT 1979-2011 y=1.5095x-2445.00 0.023103 0.398445 509053 BARLEE BROOK 1973-2011 y=-2.9636x+7012.88 0.041457 0.213777 509296 CAREY BROOK 1975-2011 y=-2.2859x+5871.00 0.018788 0.418533 009801 ALEXANDRA BRIDGE 1971-2011 y=-3.2797x+7589.46 0.059871 0.123113 009862 CAPERCUP 1965-2011 y=-1.4340x+3359.59 0.048128 0.138441 009842 JARRAHWOOD 1976-2011 y=-0.9325x+1349.05 0.144593 0.02424 009668 KURANDA 1957-2011 y=-1.7501x+4041.39 0.069464 0.051865 010691 KURRARA PARK 1953-2011 y=-1.0728x+2530.07 0.046276 0.101795 010589 KWOBRUP 1916-2010 y=-1.1880x+2743.07 0.122256 0.000514 509199 SCOTT RIVER 1975-2011 y=-1.9965x+4937.26 0.029663 0.308052 510040 BALGARUP RIVER 1976-2011 y=-0.6261x+1726.00 0.005292 0.673282 509062 MARGARET RIVER 1973-2011 y=-1.1963x+3383.89 0.006564 0.623911 509063 VASSE RIVER 1973-2011 y=-2.6064x+5459.43 0.05548 0.148878 509065 MARGARET RIVER 1973-2011 y=-4.8546x+10698.63 0.11667 0.033323 509355 MARGARET RIVER NORTH 1978-2011 y=-2.1172x+514421 0.018179 0.447098 009636 COWARAMUP 1941-2011 y=-4.0889x+9199.71 0.206774 6.78E-05 009771 YOONGARILLUP 1957-2011 y=-3.5322x+7846.09 0.112868 0.012153 009657 ROELANDS 1943-2011 y=-5.1769x+11122.67 0.337408 2.07E-07 509081 HARRIS RIVER 1972-2011 y=-2.1297x+5143.71 0.019576 0.389189 509082 HARRIS RIVER 1973-2011 =-2.5714x+6056.59 0.023804 0.348353 509108 COLLIE R EAST BRANCH 1973-2011 y=-0.4930x+1576.21 0.003175 0.733338 509109 HAMILTON RIVER 1973-2011 y=-2.7687x+6531.59 0.026211 0.324785 509220 SALMON BROOK TRIB 1974-2011 -0.7190x+2385.42 0.001885 0.795751 509237 COLLIE RIVER 1975-2010 y=-1.0008x+2694.80 0.008506 0.592657 509248 BINGHAM RIVER TRIB 1975-2008 y=-0.3331x+1333.79 0.001024 0.857414 509249 BINGHAM RIVER TRIB 1975-2011 y=-2.0572x+4813.74 0.021936 0.434759 509321 BATALLING CREEK 1977-2011 y=-0.2333x+1060.33 0.000543 0.894315

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509370 BRUNSWICK RIVER 1981-2011 y=-4.8800x+10720.85 0.068227 0.155802 509408 CAMBALLIN CREEK 1983-2011 y=-2.0464x+4729.84 0.016642 0.504801 509119 HARVEY RIVER 1973-2011 y=-4.0903x+9266.96 0.053621 0.156068 509214 BANCELL BROOK 1975-2011 y=-3.1566x+7332.20 0.041825 0.224676 509444 HARVEY RIVER 1986-2011 y=-8.1176x+17052.90 0.137904 0.061784 010510 BAROOGA 1911-2011 y=-1.3704x+3200.10 0.127424 0.000248 010888 DWARDA DOWNS 1983-2011 y=-2.4977x+5499.77 0.045233 0.268004 009538 DWELLINGUP FORESTRY 1935-2011 y=-2.8816x+6922.32 0.069479 0.020544 009023 JARRAHDALE 1900-2011 y=-2.8406x+6733.43 0.133255 7.03E-05 009194 MEDINA RESEARCH CENTRE 1984-2011 y=-8.9225x+18584.79 0.304309 0.002342 509295 SERPENTINE DRAIN 1984-2011 y=-7.6552x+16075.00 0.186083 0.021907 509306 WILLIAMS RIVER 1986-2010 y=-5.6017x+11785.59 0.136866 0.068718 509345 LITTLE DANDALUP TRIB 1984-2011 y=-6.8128x+14734.32 0.103631 0.094807 509349 NORTH DANDALUP TRIB 1978-2011 y=-3.6107x+8328.69 0.035535 0.285666 510042 MORTLOCK RIVER NORTH 1985-2010 y=-2.3974x+5164.94 0.037651 0.342209 009168 KARRAGULLEN 1973-2011 y=-2.9072x+6801.47 0.042835 0.206162 009024 MARBLING 1943-2011 y=-3.1246x+6934.01 0.178715 0.000296 009021 PERTH AIRPORT 1945-2011 y=-3.1474+7000.64 0.176707 0.000398 509156 WOOROLOO BROOK 1981-2011 y=-5.9308x+12608.24 0.1725 0.020152 509271 WATERFALL GULLY 1985-2011 y=-5.9544x+12897.02 0.081091 0.149961

Out of the 53 rain gauging stations tested (Figure 9), 15 were found to show significant downward trends in precipitation (Figure 8).

Figure 9 Location of rainfall gauges Figure 8 Location of rainfall sites with continuous record that can be showing significant downward trends reliably tested statistically in precipitation (green)

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The residuals were inspected for trends. The residuals from the Pemberton dataset had no observable pattern (Figure 10), however other datasets such as Dwellingup (Figure 11) were not as clear.

Pemberton Residual Plot 600 500 400 300

200 100 0

Residuals -1001930 1940 1950 1960 1970 1980 1990 2000 2010 2020 -200 -300 -400 -500 X Variable 1

Figure 10 Residual plot for the Pemberton dataset

Dwellingup Residual Plot 800 600 400

200 0 1930 1940 1950 1960 1970 1980 1990 2000 2010 2020 Residuals -200 -400 -600 -800 X Variable 1

Figure 11 Residual plot for the Dwellingup dataset

The Jarque-Bera test was then used to validate formally the assumption of normality in the residuals of these fifteen datasets. All datasets, except Jarrahdale and Barooga were found to come from normally distributed data.

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Comparison of the slopes from the regression highlighted the regional distribution of the trends. Figure 12 shows sites of greater rainfall decrease in red, and sites with less decrease in lighter shades. Regression slopes are smaller inland than on the coast, suggesting that rainfall decline is at a maximum on the coast, with smaller decreases evident inland.

Figure 12 Rainfall sites showing significant decrease in precipitation. Larger decreases are in red

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4.4 Discussion Bates et al. (2010) found substantial decreases in precipitation associated with changes in both occurrence and intensity, to have occurred over most of the main water supply catchments in the Darling Range. From the rainfall anomaly plots there was a clear decrease in the number of high rainfall years and an increase in the frequency of low rainfall years since 2000.

Results show that the loss of high rainfall years after the year 2000 is restricted to the high rainfall areas along the coastal plain, scarp and southwest forests. The majority of inland areas do not show this trend, except for Barooga, Kurrara Park and Kwobrup which come from datasets with long periods of record. Bates et al. (2010) state that “during winter the mean track of low pressure systems is always south of the region and moves away to the southeast. Thus precipitation decreases from west to east and from south to north”. The drop in precipitation after 2000 is likely to be an effect of changes in the atmospheric circulation factors that cause the precipitation gradient.

Fifteen of the fifty-three rain gauging stations showed statistically significant downward trends in precipitation. The trends that showed a higher level of significance were more often derived from data sets recorded by stations that were operational for a longer period of time than those with a shorter record. This may suggest that the longer data sets provide a greater level of significance, and hence the lack significance in some station data records may be primarily due to the shorter operational period.

In SWWA winter precipitation is typically either in the form of showers associated with convection, or continuous precipitation due to the uplift in the mid-troposphere associated with surface winds from north of west (Wright 1974). Changes to the mid-troposphere could therefore impact on regional precipitation.

The results suggest that the downward trend in precipitation is restricted to the coastal plain, scarp and southwest forests, where annual rainfall exceeds 800 mm. In these areas, the decrease is much greater than the inland areas, between the 600 to 800 mm isohyets, there is limited significant decline in rainfall. This is similar to the rainfall sites which saw a drop in precipitation after 2000.

As changes in precipitation are typically amplified in runoff, it is prudent to see how this downward trend in precipitation has affected the south coast river flows. However, land use change in these catchments means that the situation is complicated by rising water tables.

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5 Chapter 2 – Streamflow Analysis

5.1 Rationale In SWWA, surface water has been the traditional method of meeting water demands as it is the easiest and most cost efficient way of supplying water to the public. Since catchments along the south coast of Western Australia are experiencing a decline in rainfall, it is presumed that this would also see a decline in runoff putting increased pressure on these water resources. However the situation is complicated by increased runoff from cleared agricultural land (Bari & Smettem 2006a), a result of reduced interception loss and reduced evapotranspiration loss from the pasture. To identify the impacts of rainfall decline and the counter trends of land clearing on runoff from south coast catchments, a statistical analysis of streamflow was undertaken.

5.2 Method The quality of the data was examined before statistical testing could be undertaken. Runoff data was taken from gauges with continuous daily records. Daily records were summed to create monthly and annual records.

5.2.1 Regression Analysis between Time (year) and Streamflow Initially annual streamflow data was analysed using the regression procedure described for rainfall.

However, to minimise effects of land clearing on the annual data the runoff behaviour in major catchments was then analysed by isolating reaches of cleared and uncleared (forested) land identified from LANDSAT imagery. For these specific reaches, the annual runoff was obtained by subtracting the upstream gauge flow from the downstream gauge. The area of the reach was obtained by subtracting the catchment area above the upstream gauge from the area above the downstream gauge.

This procedure resulted in identification of runoff over an area for a specific land use and rainfall zone. Linear regression was performed again on runoff against time for each identified reach in order to identify any significant trends.

5.2.2 Runoff coefficient The runoff coefficient refers to the proportion of total annual rainfall that becomes streamflow. Following major clearing in a rural catchment, often the first change noticed is one in the runoff coefficient. Cleared catchments tend to show an increase in runoff coefficient immediately following clearing, which would be a result of reduced interception loss and reduced evapotranspiration loss from the pasture (Ruprecht & Schofield 1989). 24 Melissa Wilson

Plots of runoff coefficients against time were produced in order to investigate how the rainfall-runoff regime has changed over time, as studies have found that runoff coefficients have been declining across the forested catchments of the Darling Plateau (Petrone et al. 2010).

5.3 Results

5.3.1 Quality of Data Upon inspection, there were very few streamflow gauges of continuous, long term record on which to reliably test statistically for trends. There were 39 Bureau of Meteorology rainfall stations with more than 20 years of continuous record. The locations and details of the streamflow gauges are shown in Appendix 4.

5.3.2 Regression analysis of streamflow data Regression analysis was carried out to determine the trend of annual runoff over time; these results are included in Table 3. Although R-squared values are low, there are some instances where the hypothesis test for regression slope yielded significant p values. These streamflow gauges were located at Brunswick River, Carey Brook, Clarke Brook, Denmark River, Donnelly River, Harvey River, Lefroy Brook, Little Dandalup Trib, Margaret River, Perup River, Serpentine River, Sleeman River, South Dandalup Trib, Warren River, Wilyabrup Brook and Yate Flat Creek. The analysis of residuals was conducted as per the rainfall analysis and the datasets were found to be normally distributed.

Table 3 Result of the regression analysis of annual runoff vs. time AWRC AWRC Context AWRC Name Linear Regression R-square p value Reference Name 609014 ARTHUR RIVER MOUNT BROWN y=-0.3682x+763.30 0.030886259 0.344310 609005 BALGARUP MANDELUP y=0.3733x-707.48 0.016016868 0.462027 RIVER POOL 609017 BALINGUP BROOKLANDS y=-1.0585x+2163.23 0.077928211 0.150267 BROOK 613007 BANCELL WATEROUS y=-4.0211x+8294.64 0.159703494 0.014266 BROOK 609015 BEAUFORT MANYWATERS y=-0.5709x+1156.45 0.103861649 0.088195 RIVER 614037 BIG BROOK ONEIL ROAD y=-0.5743x+1172.94 0.053403637 0.236741 612014 BINGHAM RIVER PALMER y=-0.0691x+151.79 0.002827544 0.758078 612008 BINGHAM RIVER ERNIES y=-0.0716x+147.80 0.00849849 0.587361 TRIB CATCHMENT 609012 BLACKWOOD WINNEJUP y=-0.3682x+763.30 0.030886259 0.344310 RIVER 609019 BLACKWOOD HUT POOL y=-0.5121x+1063.81 0.034921218 0.341008 RIVER 612022 BRUNSWICK SANDALWOOD y=-5.1562x+10495.69 0.246791884 0.004471 RIVER 612025 CAMBALLAN JAMES WELL y=-0.8903x+1809.30 0.077374516 0.143994 CREEK

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608002 CAREY BROOK STAIRCASE y=-2.3462x+4898.19 0.12629959 0.033416 ROAD 613146 CLARKE BROOK HILLVIEW FARM y=-6.2442x+12664.62 0.45236974 0.000000 612002 COLLIE RIVER MUNGALUP y=-0.7144x+1462.30 0.082583476 0.064990 TOWER 612001 COLLIE RIVER COOLANGATTA y=-0.2525x+531.43 0.018689835 0.382025 EAST FARM 612230 COLLIE RIVER JAMES y=-0.0175x+75.43 0.000121417 0.943406 EAST TRIB CROSSING 606001 DEEP RIVER TEDS POOL y=-0.6621x+1395.11 0.02420523 0.3796094 19 603136 DENMARK MT LINDESAY y=-0.8038+1648.51 0.119046629 0.013153 RIVER 608151 DONNELLY STRICKLAND y=-2.1803x+4466.18 0.267364937 0.000023 RIVER 605012 FRANKLAND MOUNT y=-0.1707x+374.60 0.01600791 0.3396344 RIVER FRANKLAND 13 612004 HAMILTON WORSLEY y=-2.7361x+5605.62 0.097330543 0.053177 RIVER 613002 HARVEY RIVER DINGO ROAD y=-3.4177x+7011.93 0.191436223 0.004227 603004 HAY RIVER SUNNY GLEN y=-1.3389x+2719.13 0.120920979 0.069798 602004 KALGAN RIVER STEVENS FARM y=-0.0959x+211.10 0.004367448 0.7103836 38 604053 KENT RIVER STYX JUNCTION y=-0.3159x+670.33 0.033525507 0.1894098 94 607013 LEFROY BROOK RAINBOW TRAIL y=-2.2988x+4727.88 0.139249377 0.035414 614062 LITTLE BATES y=-11.4930x+23136.33 0.775112021 0.000000 DANDALUP TRIB CATCHMENT 610001 MARGARET WILLMOTS y=-3.2768x+6715.64 0.164640981 0.008486 RIVER FARM 614021 NORTH LEWIS y=-0.0792x-60.84 0.00016154 0.943126 DANDALUP TRIB CATCHMENT 609010 NORTHERN LAKE TOOLIBIN y=-0.1886x+378.56 0.080383789 0.109841 ARTHUR RIVER INFLOW 607004 PERUP RIVER QUABICUP HILL y=-0.3316x+678.38 0.087479404 0.075511 614030 SERPENTINE DOG HILL y=-4.3456x+8811.58 0.358921978 0.000291 DRAIN 603007 SLEEMAN RIVER SLEEMAN ROAD y=-2.0629+4252.49 0.057606525 0.237601 BRIDGE 614060 SOUTH GORDON y=-0.4303x+868.90 0.151735891 0.066165 DANDALUP R. CATCHMENT TRIB 614007 SOUTH DEL PARK y=-3.3645x+6857.63 0.231058296 0.002601 DANDALUP TRIB 609018 ST JOHN BROOK BARRABUP y=-1.7213x+3504.73 0.109239853 0.085826 POOL 607007 TONE RIVER BULLILUP y=0.2881x-539.97 0.011964808 0.544544 607003 WARREN RIVER WHEATLEY y=-0.2283x+479.19 0.019829043 0.379855 FARM 607220 WARREN RIVER BARKER RD y=-0.9372x+1930.10 0.147297534 0.009251 CROSSING 607144 WILGARUP QUINTARRUP y=-1.2296x+2502.59 0.261579641 0.000147 RIVER 610006 WILYABRUP WOODLANDS y=-4.8564x+9950.68 0.187947697 0.006547 BROOK 603190 YATE FLAT WOONANUP y=-1.3655x+2792.92 0.134605037 0.009514 CREEK

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Figure 13 Location of streamflow gauges demonstrating significant decline

5.3.3 Reaches of specific land use

5.3.3.1 Warren River The Warren River catchment (Figure 14) was divided so that the forested reach was isolated from the cleared upper catchment (Figure 15).

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Figure 14 Location of the Warren River catchment in the southwest region of Western Australia

Figure 15 Isolated reaches of the Warren River catchment. Forest (green) and cleared (yellow)

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Warren River Runoff 350 Forested 300 Cleared

250 Linear (Forested) 200 Linear (Cleared )

150 y = -2.3628x + 4851.9

Runoff(mm) 100 R² = 0.2039 50 y = -0.2253x + 479.19 0 R² = 0.0198 1970 1980 1990 2000 2010 2020 Years

Figure 16 Annual runoff against time for the forested and cleared parts of the Warren River catchment

The forested reach of the catchment was found to have a statistically significant decrease in runoff over the period of record. The cleared part of the catchment did not show any significant trend (Figure 16).

Runoff coefficients for the forested reach of the Warren River were found using the closest rainfall gauge with suitable period of record. A significant decrease in the runoff coefficients for this isolated reach of the Warren River (Figure 17) was found.

Forested Warren Runoff Coefficient 0.3

0.25

0.2

0.15

0.1 y = -0.0024x + 4.9091

Rainfall/Runoff R² = 0.2453 0.05

0 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 Year

Figure 17 Annual runoff coefficients against time for the forested reach of the Warren River catchment

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5.3.3.2 Blackwood River The Blackwood River catchment (Figure 18) was divided so that the forested reach was isolated from the cleared upper catchment (Figure 19).

Figure 18 Location of the Blackwood River catchment in the southwest region of Western Australia

Figure 19 Isolated reaches of the Blackwood River catchment. Forest (green) and cleared (yellow) 30 Melissa Wilson

Blackwood River Runoff 200 180 Forested 160 Cleared 140 Linear (Forested) 120 100 Linear (Cleared) 80

Runoff(mm) 60 y = -1.8117x + 3695.7 40 R² = 0.1285 20 y = 0.0303x - 34.454 0 R² = 0.0002 1980 1985 1990 1995 2000 2005 2010 2015 Years

Figure 20 Annual runoff against time for the forested and cleared parts of the Blackwood River catchment

The forested reach of the catchment did not have a significant decrease in runoff over the period of record (Figure 20); however, the dataset is not very long. The cleared part of the catchment did not show any significant trend.

0.16 Blackwood Runoff Coefficients 0.14

0.12

0.1 0.08 y = -0.0016x + 3.3255 R² = 0.1811

0.06 Runoff/Rainfall 0.04 0.02 y = 1E-04x - 0.1707 R² = 0.0037 0 1980 1985 1990 1995 2000 2005 2010 2015 Years

Figure 21 Annual runoff coefficients against time for the forested reach of the Warren River catchment

Runoff coefficients for the Blackwood River were found using the closest rainfall gauge with suitable period of record. There was a significant decrease in the runoff coefficients for the forested each of the Blackwood River (Figure 21). However, the slight increase in the cleared part of the catchment was not statistically significant.

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5.4 Discussion Changes in precipitation are always amplified in runoff with the amplification higher in drier catchments. In wet catchments, the change in rainfall has little effect on the soil moisture but in drier catchments, the percentage change in soil moisture levels can be greater than the percentage change in rainfall (Chiew et al. 1995).

Forest clearing for agricultural development has greatly increased catchment water and salt yields in the jarrah forest (Schofield, Stoneman & Loh 1989). In the Wights and Lemon catchments, an immediate increase in streamflow following clearing was observed (Ruprecht & Schofield 1989), due to the reduction in transpiration and interception loss (Williamson, Stokes & Ruprecht 1987). A second phase of streamflow rise occurred when the groundwater level reached the streambed. This may be why the cleared parts of the Warren and Blackwood catchments appeared to have no trend (Figure 16 and Figure 20), despite decreased rainfall. However, isolating the forested area in the Warren and Blackwood catchments has removed the complication of land clearing. While the Blackwood River result is not statistically significant, the significance of the decline in streamflow from the forested part of the Warren catchment indicates that streamflow is declining as expected with climate change.

Even without isolating the forested reach, the Warren River showed a significant decrease in streamflow, suggesting that the increase from land clearing does not fully counter the decrease from declining precipitation. The 1996 Salinity Action Plan (Government of Western Australia 1996) identified parts of the Collie, Warren, Denmark and Kent River basins as Water Resource Recovery Catchments, as these were existing or potential water supply sources expected to deteriorate beyond recover without further management. A decline in streamflow would limit the use of these basins for water supply, even if the salinity problem was adequately managed.

Following major clearing in a rural catchment, often the first change noticed is one in the runoff coefficient. Silberstein et al. (2005) adopt a simple empirical approach to examine the runoff coefficient of the stream over time, comparing the treated catchments with their forested control counterparts. It was found that cleared catchments all showed an increase in runoff coefficient immediately following clearing, which would be a result of reduced interception loss and reduced evapotranspiration loss from the pasture (Ruprecht & Schofield 1989). The cleared part of the Blackwood catchment showed no significant trend in runoff coefficients over time. This may be because the anticipated increase due to clearing was countered by a decrease in rainfall.

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Petrone et al. (2010) found that runoff coefficients declined over time across forested water supply catchments on the Darling Plateau. The runoff coefficients for the isolated reach of the Warren and Blackwood catchments have also shown a significant decline demonstrating that the rainfall-runoff regime has changed over time (Figure 17 and Figure 21), furthering their hypothesis that the mechanisms driving runoff change are regional rather than catchment- specific (Petrone et al. 2010).

Since rainfall and streamflow are decreasing, the lack of recharge combined with the forest transpiring at the same rate, may cause groundwater levels to decline.

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6 Chapter 3 – Baseflow Analysis

6.1 Rationale Relating groundwater storage with available streamflow data is a simple approach for analysing groundwater trends in large catchments. Baseflow refers to the portion of streamflow discharged from shallow groundwater, in a non-recharge period (Brutsaert & Sugita 2008). It can also be called low flow, sustained flow, dry-weather flow or fair-weather flow. Baseflow data can show the underlying response to climate change and draw down by vegetation.

6.2 Method Baseflow separation techniques use the time-series record of streamflow (hydrograph) (Tallaksen 1995) to define groundwater-surface water relations. White and Sloto (1990) found that manual methods of separation are slow, subjective and cannot be replicated among investigators, and several computer programs have been written to automate the manual methods.

For simplicity, in this study the baseflow over the summer low flow period was represented by summing the January to April runoff, after the removal of highly infrequent large summer storm events. This was done for whole catchments as well as some isolated reaches of cleared and uncleared (forest) land.

6.3 Results A list of streamflow gauges used in the analysis of summer baseflow, their locations and conditions is found in Appendix 5.

The regression analysis of the baseflow found significant long-term trends for many of the sites. These are detailed in Table 4, with additional graphs in Appendix 5.

Table 4 Result of the regression analysis of annual summer baseflow vs. time AWRC AWRC Context AWRC Name Regression R-square p value Reference Name 602004 KALGAN RIVER STEVENS FARM y=46.0980x-89903.80 0.267649 0.003415 603004 HAY RIVER SUNNY GLEN y=-3.0660x+6589.02 0.002408 0.815814 DENMARK 603136 MT LINDESAY y=0.8208x-1510.67 0.017701 0.383611 RIVER 604053 KENT RIVER STYX JUNCTION y=1.2947x-2107.02 0.00524 0.624742 FRANKLAND MOUNT 605012 y=-13.1985x+27318.91 0.128735 0.006618 RIVER FRANKLAND 606001 DEEP RIVER TEDS POOL y=-0.7069x+1433.78 0.045630 0.248566 WHEATLEY 607003 WARREN RIVER y=-12.6572x+25690.31 0.095235 0.067063 FARM 607004 PERUP RIVER QUABICUP HILL y=-8.0795x+16390.26 0.162637 0.016296 34 Melissa Wilson

607007 TONE RIVER BULLILUP y=1.5354x-2912.59 0.004672 0.724601 607013 LEFROY BROOK RAINBOW TRAIL y=-10.2804x+21243.28 0.047239 0.248614 BARKER RD 607220* WARREN RIVER y=-80.7912x+164699.20 0.242386 0.001691 CROSSING STAIRCASE 608002 CAREY BROOK y=-10.01x+20499.88 0.197870 0.007419 ROAD DONNELLY 608151 STRICKLAND y=-51.1589x+102988 0.567810 7.46E-11 RIVER BLACKWOOD 609012 WINNEJUP y=-4.2066x+10121.36 0.000170 0.947447 RIVER BLACKWOOD 609019 HUT POOL y=-238.144x+486497.4 0.136140 0.058231 RIVER MARGARET WILLMOTS 610001 y=-3.3059x+6668.21 0.155688 0.014229 RIVER FARM WILYABRUP 610006 WOODLANDS y=-0.1671x+338.22 0.083409 0.097612 BROOK BRUNSWICK 612022 SANDALWOOD y=-49.4910x+99827.02 0.426165 9.22E-05 RIVER 613002 HARVEY RIVER DINGO ROAD y=-45.5356x+92140.2 0.249636 0.001029 *Refers to the isolated forest reach of the catchment

The rivers exhibiting significant declines in summer flow are shown in Table 5, and their locations in Figure 22.

Table 5 Rivers with significant long-term declines in summer baseflow. Note the level of decline after 2000.

AWRC AWRC Context % decline AWRC Name Regression p value Reference Name since 2000

605012 FRANKLAND RIVER MOUNT FRANKLAND y=-13.1985x+27318.91 0.006618 13 607004 PERUP RIVER QUABICUP HILL y=-8.0795x+16390.26 0.016296 45 607220 WARREN RIVER BARKER RD CROSSING y=-80.7912x+164699.20 0.001691 27 608002 CAREY BROOK STAIRCASE ROAD y=-10.01x+20499.88 0.007419 25 608151 DONNELLY RIVER STRICKLAND y=-51.1589x+102988 7.46E-11 83 610001 MARGARET RIVER WILLMOTS FARM y=-3.3059x+6668.21 0.014229 60 612022 BRUNSWICK RIVER SANDALWOOD y=-49.4910x+99827.02 9.22E-05 63 613002 HARVEY RIVER DINGO ROAD y=-45.5356x+92140.2 0.001029 40

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Figure 22 Locations of gauges showing significant decline in summer baseflow

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6.3.1 Warren River The Warren River catchment was divided (Figure 23) so that the forested reach was isolated from the cleared upper catchment (Figure 24).

Figure 23 Location of the Warren River catchment in the southwest region of Western Australia

Figure 24 Isolated reaches of the Warren River catchment. Forest (green) and cleared (yellow) 37 Melissa Wilson

Forested Warren River Baseflow 9000 8000 7000 6000 y = -80.791x + 164699 R² = 0.2424

5000 April Flow Flow April (ML)

4000 - 3000 2000

Total Jan 1000 0 1970 1975 1980 1985 1990 1995 2000 2005 2010 Year

Figure 25 Annual summer baseflow against time for the forested reach of the Warren River catchment

The forested reach of the Warren River catchment exhibits a statistically significant decrease in baseflow over the period of record (Figure 25).

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6.3.2 Kent River – Styx Junction The Kent River, as located in Figure 26, saw a significant increase in baseflow up until 1996 (Figure 27). Following this, a decrease in baseflow was observed, however, it was not statistically significant (Figure 28).

Figure 26 Location of the Kent River catchment in the southwest region of Western Australia

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Kent River Baseflow 1957-1996 1400

1200

1000 y = 7.3114x - 13970 R² = 0.0838 800

600

April April Runoff(ML) - 400

TotalJan 200

0 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000

Figure 27 Annual summer baseflow against time for the forested of the Kent River catchment before revegetation

y = -5.8012x + 12009 Kent River Baseflow 1997-2009 R² = 0.0182

800

700 600 500

400 April April Runoff(ML)

- 300 200

100 TotalJan 0 1996 1998 2000 2002 2004 2006 2008 2010 Years

Figure 28 Annual summer baseflow against time for the forested of the Kent River catchment after revegetation

6.3.3 Denmark River The Denmark River, as located in Figure 29, saw a significant increase in baseflow up until 2000 (Figure 30). Following this, a decrease in baseflow was observed, however, it was also not statistically significant (Figure 31).

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Figure 29 Location of the Denmark River catchment in the southwest region of Western Australia

Denmark River 1962-2000

350

300 250 200

April Rnoff (ML) 150 y = 2.5963x - 5018.7 - R² = 0.1016 100

50 TotalJan 0 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 Years

Figure 30 Annual summer baseflow against time for the forested of the Denmark River catchment before 2000

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Denmark River 2000-2011

300

250

200

150 April Runoff (ML) - 100 y = -4.1877x + 8510.1

50 R² = 0.028 TotalJan 0 1998 2000 2002 2004 2006 2008 2010 2012 Years

Figure 31 Annual summer baseflow against time for the forested of the Denmark River catchment after 2000

6.3.4 Margaret River Margaret River, as located in Figure 32, was found to have a significant decrease in baseflow over the period of record. Since 2000, there has been an increase in the frequency of very low flow years (Figure 33).

Figure 32 Location of the Margaret River gauge in the southwest region of Western Australia 42 Melissa Wilson

Margaret River Baseflow

400

350 300 250

200 April Runoff (ML) - 150 y = -3.1973x + 6447.9 100 R² = 0.138

50 TotalJan 0 1970 1975 1980 1985 1990 1995 2000 2005 2010 Years

Figure 33 Annual summer baseflow against time for Margaret River

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6.4 Discussion The forested part of the Warren River catchment has exhibited a significant decline in annual runoff as a result of decreased rainfall. A reduction in recharge combined with the forest transpiring at the same rate, has caused the groundwater to decline. This is evidenced by the continual decline in summer baseflow over the period of record. This is consistent with results reported by Hughes, Petrone and Silberstein (2012) for catchments on the Darling Plateau and highlights the important role of catchment groundwater storage in the water balance of these deep-rooted forests.

Continual supplementation of transpiration from groundwater is not sustainable in the long term and increased water stress on the forest could result in environmental damage. Trees respond to water stress in a number of ways. Low levels of stress can reduce stem and root growth. If water levels drop to a critical level trees can be irreversibly damaged and branches, portions of the crown, or entire trees may suddenly die. Also, when trees are under stress their ability to withstand attacks by insect and disease agents is decreased, causing further damage (Mattson & Haack 1987).

As bush fires are strongly influenced by the moisture content of the fuel, increased tree stress could see an increase in fires (Billing 2003). During drought, aquatic plants and animals survive in refuge habitats. Isolated pools can act as critically important local refuges for more tolerant species. However, with decreased streamflow and baseflow, the existence of such pools could be under threat.

For catchments that have been cleared and revegetated, such as the Kent and Denmark River catchments, an initial baseflow increase has been observed. With the continued rainfall decline causing a lack of recharge, and revegetation causing drawdown of groundwater, this trend has now reversed and declining summer flows are evident (Figure 28 and Figure 31), although a longer period of record would be required to check if this trend is significant. At present the trends are not significant and it is only possible to conclude that the previous increases in summer baseflow have reached a plateau since 2000.

In Western Australia, stream and land salinity problems have developed following the clearing of deep-rooted, native vegetation for agriculture. The land use change causes a rise in water tables which mobilises the salt stored in the unsaturated zone of the soil profile. Dryland salinity has been a problem in the Kent and Denmark River catchments, and stream salinity has been affected. The decrease in baseflow could therefore cause a decrease in the amount of salt that is mobilised, and have a positive effect.

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While there are many dams in the Margaret River region, it is expected that the effect of these on runoff and summer baseflow would be minimal. Margaret River was found to have a significant decrease in summer baseflow over the period of record. The groundwater-surface water connectivity that is crucial in maintaining summer baseflow has undergone change. Since 2000, there has been an increase in the frequency of very low flow years, suggesting that Margaret River has become more ephemeral than previously. This is consistent with results from the forested water supply catchments of the Darling Plateau, where a shift from perennial to ephemeral streams has occurred (Bari & Smettem 2004).

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7 Conclusions A statistical analysis of precipitation was undertaken to determine if rainfall actually was declining across the catchments of the south coast of Western Australia. The analysis of rainfall records found that rainfall decline is at a maximum at the coast and decreases inland, becoming statistically not significant between the 600-800 mm isohyets.

As changes to precipitation are typically magnified in the runoff, a statistical analysis was used to check if the decline in precipitation had caused a decline in runoff. To avoid complications from increased streamflow due to agricultural clearing, reaches of specific land use were identified and isolated. Subsequent statistical analysis of streamflow showed a decline in runoff for forested reaches on the wettest areas of the south coast, which could have major impacts on the use of these basins for water supply.

Summer baseflow is also declining, suggesting that groundwater levels are diminishing. A lack of recharge combined with the forest transpiring at the same rate, would cause the groundwater to decline. This could have implications for the maintenance of summer low flows leading to increased stresses on the ecology of these waterways.

8 Recommendations The aim of this thesis was to assess whether rainfall actually was declining across the catchments of the south coast of Western Australia and whether there were any impacts of this decline on runoff. As the situation is complicated by increased runoff from cleared agricultural land, reaches of specific land use were isolated. However, as some of the clearing occurred during the period of record, this would have an impact on the results of the cleared areas. Further research into the runoff behaviour of these cleared areas should take this into account. LANDSAT imagery could be used to determine the amount and dates of clearing.

Since forested catchments along the south coast of Western Australia are experiencing a decline in runoff, this puts increased pressure on these precious water supply sources. Therefore it is imperative that the water management implications of the observed runoff decline be investigated. It could also be worthwhile looking into the various silvicultural options as a means to maintain streamflow in this changed climate, while minimising salinity problems.

Future work could also focus on the magnitude and timing of rainfall required to initiate winter runoff. Diminishing summer baseflow suggests that catchments are drying out and so there is a greater soil moisture deficit to satisfy prior to initiation of runoff. Groundwater use 46 Melissa Wilson may be buffering the southern forests from more deleterious impacts of climate change but the clear evidence of diminished summer flows may be a harbinger that changes to forest structure induced by water stress may occur in the future. An assessment of the coupling between groundwater level and transpiration by vegetation would be useful in order to understand feedbacks that may influence the future water balance of these catchments.

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9 References Bari, M & Smettem, K 2006a, 'A conceptual model of daily water balance following partial clearing from forest to pasture', Hydrology and Earth System Sciences Discussions, vol. 10, no. 3, pp. 321-337.

Bari, M & Smettem, K 2006b, 'A daily salt balance model for stream salinity generation processes following partial clearing from forest to pasture', Hydrology and Earth System Sciences Discussions, vol. 10, no. 4, pp. 519-534.

Bari, M & Smettem, KRJ 2004, 'Modelling monthly runoff generation processes following land use changes: groundwater–surface runoff interactions', Hydrol. Earth Syst. Sci., vol. 8, no. 5, pp. 903-922.

Bates, B, Hope, P, Ryan, B, Smith, I & Charles, S 2008, 'Key findings from the Indian Ocean Climate Initiative and their impact on policy development in Australia', Climatic Change, vol. 89, no. 3, pp. 339-354.

Bates, BC, Chandler, RE, Charles, SP & Campbell, EP 2010, 'Assessment of apparent nonstationarity in time series of annual inflow, daily precipitation, and atmospheric circulation indices: A case study from southwest Western Australia', Water Resources Research, vol. 46, no. null, p. W00H02.

Billing, P, Impact of the drought on forest fires, Bureau of Meteorology. Available from: . [29th October 2012].

Brutsaert, W 2005, Hydrology - An Introduction, Cambridge University Press.

Brutsaert, W & Sugita, M 2008, 'Is Mongolia's groundwater increasing or decreasing? The case of the Kherlen River basin', Hydrological Sciences Journal, vol. 53, no. 6, pp. 1221- 1229. [2012/04/15].

Charles, SP, Bates, BC, Whetton, PH & Hughes, JP 1999, 'Validation of downscaling models for changed climate conditions: case study of southwestern Australia', Climate Research, vol. 12, pp. 1-14.

Chiew, F, Whetton, P, McMahon, T & Pittock, A 1995, 'Simulation of the impacts of climate change on runoff and soil moisture in Australian catchments', Journal of Hydrology, vol. 167, no. 1, pp. 121-147.

Davie, T 2008, Fundamentals of Hydrology, Taylor & Francis.

Freeze, RA & Cherry, JA 1979, Groundwater, Prentice-Hall.

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Hjelmfelt, AT & Cassidy, JJ 1975, Hydrology for Engineers and Planners, Iowa State University Press.

Hope, P & Foster, I 2005, 'How our rainfall has changed–The south-west', Indian Ocean Climate Initiative (IOCI) Perth, Western Australia< www. ioci. org. au/pdf/IOCIclimatenotes_5. pdf.

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Hughes, JD, Petrone, KC & Silberstein, RP 2012, 'Drought, groundwater storage and stream flow decline in southwestern Australia', Geophys. Res. Lett., vol. 39, no. 3, p. L03408.

IOCI 2002, 'Climate variability and change in south west Western Australia', Perth, WA: Indian Ocean Climate Initiative Panel.

Jakica, S, Quigley, MC, Sandiford, M, Clark, D, Fifield, LK & Alimanovic, A 2011, 'Geomorphic and cosmogenic nuclide constraints on escarpment evolution in an intraplate setting, Darling Escarpment, Western Australia', Earth Surface Processes and Landforms, vol. 36, no. 4, pp. 449-459.

Lyons, T 2002, 'Clouds prefer native vegetation', Meteorology and Atmospheric Physics, vol. 80, no. 1, pp. 131-140.

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Mattson, WJ & Haack, RA 1987, 'The role of drought in outbreaks of plant-eating insects', BioScience, vol. 37, no. 2, pp. 110-118.

May, RM 2008, The Britannica Guide to Climate Change: An Unbiased Guide to the Key Issue of Our Age, Robinson.

Mayer, X, Ruprecht, J & Bari, M 2005, Stream salinity status and trends in south-west Western Australia, Report No. SLUI 38, Perth, Australia.

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Ogunc, A & Hill, R 2011, Using Excel for Principles of Econometrics, John Wiley & Sons.

Palmer, AR, Fuentes, S, Taylor, D, Macinnis‐Ng, C, Zeppel, M, Yunusa, I & Eamus, D 2010, 'Towards a spatial understanding of water use of several land‐cover classes: an examination of relationships amongst pre‐dawn leaf water potential, vegetation water use, aridity and MODIS LAI', Ecohydrology, vol. 3, no. 1, pp. 1-10.

Petrone, KC, Hughes, JD, Van Niel, TG & Silberstein, RP 2010, 'Streamflow decline in southwestern Australia, 1950-2008', Geophys. Res. Lett., vol. 37, no. 11, p. L11401.

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Schofield, N, Stoneman, G & Loh, I 1989, 'Hydrology of the jarrah forest', in The Jarrah Forest: a Complex Mediterranean Ecosystem, ed. B Dell, Havel, JJ, Malajczuk, N, Kluwer, Dordrecht, pp. 179-201.

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10 Appendices

10.1 Appendix 1 Table 6 Period of record for the rainfall stations used in the analysis BoM BoM Context BoM Name Longitude Latitude Period of Reference Name Record 509022 YATE FLAT WOONANUP 117.292107008 -34.703752779 1973-2011 CREEK 509042 SMITHS BROOK MANJIMUP 116.2101193 -34.37185395 1973-2011 TRIB RESEARCH STN 509053 BARLEE BROOK DICKSON 115.85888289 -34.21877385 1973-2011 TOWER ROAD 509062 MARGARET GEORGE ROAD 115.269603835 -33.906212799 1973-2011 RIVER 509063 VASSE RIVER CHAPMAN HILL 115.33008783 -33.75552368 1973-2011 509065 MARGARET WILLMOTS 115.05488596 -33.94220659 1973-2011 RIVER FARM 509081 HARRIS RIVER BALINGHALLS 116.155289408 -33.224550442 1972-2011 FARM 509082 HARRIS RIVER SANDY ROAD 116.18455085 -33.10323772 1973-2011 509108 COLLIE R EAST JAMES 116.57621459 -33.37907206 1973-2011 BRANCH CROSSING 509109 HAMILTON WORSLEY 116.04760888 -33.30934943 1973-2011 RIVER 509119 HARVEY RIVER DINGO ROAD 116.03565735 -33.09184675 1973-2011 509156 WOOROLOO KARLS RANCH 116.11571452 -31.73441216 1981-2011* BROOK 509199 SCOTT RIVER BRENNANS 115.30192414 -34.27679931 1975-2011 FORD 509210 PERUP RIVER CORBALLUP 116.45100569 -34.14709862 1975-2011 ROAD 509212 PERUP RIVER QUABICUP HILL 116.45850702 -34.33110655 1975-2011 509214 BANCELL WATEROUS 115.94760207 -32.94490713 1975-2011 BROOK 509220 SALMON BROOK WIGHTS 115.98955225 -33.42073922 1974-2011 TRIB CATCHMENT 509237 COLLIE RIVER GODFREY 116.468044411 -33.300892978 1975-2010 509248 BINGHAM RIVER DONS 116.47149774 -33.2787886 1975-2008 TRIB CATCHMENT 509249 BINGHAM RIVER ERNIES 116.4451037 -33.29212216 1982-2011* TRIB CATCHMENT

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509271 WATERFALL MT CURTIS 116.0786 -32.2094 1985-2011* GULLY 509295 SERPENTINE DOG HILL 115.8615 -32.3417 1984-2011 DRAIN 509296 CAREY BROOK GIBLETT 115.8972606 -34.35574617 1975-2011 509306 WILLIAMS SADDLEBACK 116.42898515 -32.99378538 1986-2011* RIVER ROAD BRIDGE 509321 BATALLING MAXON FARM 116.57371132 -33.31462486 1977-2011 CREEK 509345 LITTLE WARREN 116.0286 -32.5933 1984-2011* DANDALUP TRIB CATCHMENT 509349 NORTH LEWIS 116.0629 -32.5673 1978-2011 DANDALUP TRIB CATCHMENT 509355 MARGARET WHICHER 115.44360394 -33.80660677 1978-2011 RIVER NORTH RANGE 509370 BRUNSWICK SANDALWOOD 115.92299982 -33.21962561 1981-2011 RIVER 509375 TONE RIVER BULLILUP 116.67895108 -34.25026748 1979-2011 509383 TONE RIVER METTABINUP 116.80011243 -33.99184958 1979-2011 CATCHMENT 509408 CAMBALLIN JAMES WELL 116.4258097 -33.45568185 1983-2011 CREEK 509444 HARVEY RIVER KARINGA 115.757949449 -32.955606046 1986-2011 510040 BALGARUP MANDELUP 117.142350022 -33.907193731 1976-2011 RIVER POOL 510042 MORTLOCK FRENCHES 116.655475348 -31.556353572 1985-2011* RIVER NORTH 009021 PERTH AIRPORT PERTH AIRPORT 115.9764 -31.9275 1945-2011 009023 JARRAHDALE JARRAHDALE 116.0755 -32.3342 1900-2011 009024 MARBLING MARBLING 116.0842 -31.5608 1943-2011 009168 KARRAGULLEN KARRAGULLEN 116.1203 -32.1189 1973-2011 009194 MEDINA MEDINA 115.8075 -32.2208 1984-2011 RESEARCH RESEARCH CENTRE CENTRE 009538 DWELLINGUP DWELLINGUP 116.0594 -32.7103 1935-2011 FORESTRY FORESTRY 009592 PEMBERTON PEMBERTON 116.0433 -34.4478 1941-2011 009636 COWARAMUP COWARAMUP 115.0747 -33.8358 1941-2011 009657 ROELANDS ROELANDS 115.7789 -33.2964 1943-2011 009668 KURANDA KURANDA 116.6475 -33.6739 1957-2011 009771 YOONGARILLUP YOONGARILLUP 115.4697 -33.7411 1957-2011

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009801 ALEXANDRA ALEXANDRA 115.1892 -34.1597 1971-2011 BRIDGE BRIDGE 009842 JARRAHWOOD JARRAHWOOD 115.6658 -33.7964 1976-2011 009862 CAPERCUP CAPERCUP 116.7386 -33.5083 1965-2011 010510 BAROOGA BAROOGA 116.7992 -33.1642 1911-2010 010589 KWOBRUP KWOBRUP 117.9878 -33.61 1916-2011 010691 KURRARA PARK KURRARA PARK 117.4047 -33.0964 1953-2011 010888 DWARDA DWARDA 116.6931 -32.8125 1983-2011 DOWNS DOWNS *The station was in operation before this point, but many records were missing. The period of record refers to a continuous period.

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10.2 Appendix 2 Table 7 Results from the regression analysis and Jarque-Bera test Jarque-Bera p BoM BoM Context Jarque-Bera BoM Name Longitude Latitude Slope Intercept R-square t-stat p value value from chi- Reference Name Statistic distribution YATE FLAT 509022 WOONANUP 117.292107008 -34.703752779 0.263873 190.6599 0.000355 0.09792 0.922719 0.367769882 0.832031531 CREEK SMITHS MANJIMUP 509042 116.2101193 -34.37185395 -2.92149 6684.915 0.057293 -1.49956 0.142215 1.440930226 0.486525914 BROOK TRIB RESEARCH STN BARLEE DICKSON 509053 115.85888289 -34.21877385 -2.96362 7012.881 0.041457 -1.26501 0.213777 0.53095028 0.766841506 BROOK TOWER ROAD MARGARET 509062 GEORGE ROAD 115.269603835 -33.906212799 -1.19633 3383.891 0.006564 -0.49445 0.623911 0.576451376 0.749592399 RIVER CHAPMAN 509063 VASSE RIVER 115.33008783 -33.75552368 -2.60641 5949.429 0.05548 -1.47423 0.148878 1.211657991 0.545621921 HILL MARGARET WILLMOTS 509065 115.05488596 -33.94220659 -4.85461 10698.63 0.11667 -2.21064 0.033323 2.192502391 0.334121294 RIVER FARM HARRIS BALINGHALLS 509081 116.155289408 -33.224550442 -2.1297 5143.711 0.019576 -0.87107 0.389189 0.259993591 0.878098245 RIVER FARM HARRIS 509082 SANDY ROAD 116.18455085 -33.10323772 -2.57139 6056.588 0.023804 -0.94985 0.348353 0.584566306 0.746557116 RIVER COLLIE R JAMES 509108 EAST 116.57621459 -33.37907206 -0.49297 1576.206 0.003175 -0.34327 0.733338 1.759122098 0.414965021 CROSSING BRANCH HAMILTON 509109 WORSLEY 116.04760888 -33.30934943 -2.76871 6531.586 0.026211 -0.99795 0.324785 0.758931746 0.684226776 RIVER HARVEY 509119 DINGO ROAD 116.03565735 -33.09184675 -4.09026 9266.963 0.053621 -1.44789 0.156068 2.462392469 0.291943136 RIVER WOOROLOO 509156 KARLS RANCH 116.11571452 -31.73441216 -5.93083 12608.24 0.1725 -2.45872 0.020152 2.010554004 0.365943254 BROOK BRENNANS 509199 SCOTT RIVER 115.30192414 -34.27679931 -1.9965 4937.257 0.029663 -1.03438 0.308052 0.537629631 0.764284776 FORD CORBALLUP 509210 PERUP RIVER 116.45100569 -34.14709862 0.068874 517.1041 4.88E-05 0.041326 0.967271 3.150354736 0.206970839 ROAD QUABICUP 509212 PERUP RIVER 116.45850702 -34.33110655 -1.47883 3663.263 0.018387 -0.80969 0.423591 3.396704603 0.18298478 HILL BANCELL 509214 WATEROUS 115.94760207 -32.94490713 -3.1566 7332.204 0.041825 -1.23604 0.224676 0.547781866 0.760415007 BROOK SALMON WIGHTS 509220 115.98955225 -33.42073922 -0.71904 2385.422 0.001885 -0.26078 0.795751 0.148942589 0.928234119 BROOK TRIB CATCHMENT

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COLLIE 509237 GODFREY 116.468044411 -33.300892978 -1.00083 2694.801 0.008506 -0.54009 0.592657 2.426562502 0.297220423 RIVER BINGHAM DONS 509248 116.47149774 -33.2787886 -0.33307 1333.79 0.001024 -0.18112 0.857414 0.847543646 0.654573223 RIVER TRIB CATCHMENT BINGHAM ERNIES 509249 116.4451037 -33.29212216 -2.05719 4813.741 0.021936 -0.79245 0.434759 2.467424625 0.291209508 RIVER TRIB CATCHMENT WATERFALL 509271 MT CURTIS 116.0786 -32.2094 -5.95437 12897.02 0.081091 -1.48532 0.149961 1.235875768 0.539054886 GULLY SERPENTINE 509295 DOG HILL 115.8615 -32.3417 -7.65518 16075 0.186083 -2.43809 0.021907 1.2891536 0.524884626 DRAIN CAREY 509296 GIBLETT 115.8972606 -34.35574617 -2.28591 5871.002 0.018788 -0.81864 0.418533 0.049120384 0.975738955 BROOK WILLIAMS SADDLEBACK 509306 116.42898515 -32.99378538 -5.60167 11785.59 0.136866 -1.90973 0.068718 2.032335166 0.361979542 RIVER ROAD BRIDGE BATALLING 509321 MAXON FARM 116.57371132 -33.31462486 -0.2333 1060.331 0.000543 -0.13388 0.894315 2.122646139 0.345997728 CREEK LITTLE WARREN 509345 DANDALUP 116.0286 -32.5933 -6.8128 14734.32 0.103631 -1.73375 0.094807 1.53056329 0.46520289 CATCHMENT TRIB NORTH LEWIS 509349 DANDALUP 116.0629 -32.5673 -3.61073 8328.693 0.035535 -1.08583 0.285666 4.108222662 0.128206718 CATCHMENT TRIB MARGARET WHICHER 509355 RIVER 115.44360394 -33.80660677 -2.11718 5144.211 0.018179 -0.76974 0.447098 0.02114723 0.989482089 RANGE NORTH BRUNSWICK 509370 SANDALWOOD 115.92299982 -33.21962561 -4.87999 10720.85 0.068227 -1.45721 0.155802 1.029702558 0.597589462 RIVER 509375 TONE RIVER BULLILUP 116.67895108 -34.25026748 0.677285 -743.176 0.004217 0.362339 0.719557 1.176737071 0.555232388 TONE RIVER 509383 METTABINUP 116.80011243 -33.99184958 1.509529 -2445 0.023103 0.856222 0.398445 0.194085895 0.90751703 CATCHMENT CAMBALLIN 509408 JAMES WELL 116.4258097 -33.45568185 -2.04644 4729.844 0.016642 -0.67598 0.504801 0.160711571 0.922787973 CREEK HARVEY 509444 KARINGA 115.757949449 -32.955606046 -8.11757 17052.9 0.137904 -1.95937 0.061784 0.308263934 0.857158897 RIVER BALGARUP MANDELUP 510040 117.142350022 -33.907193731 -0.62611 1725.999 0.005292 -0.42532 0.673282 2.241845141 0.325978917 RIVER POOL MORTLOCK 510042 RIVER FRENCHES 116.655475348 -31.556353572 -2.39741 5164.939 0.037651 -0.969 0.342209 1.987312582 0.370220579 NORTH

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10.3 Appendix 3

40 Cowaramup Rainfall Anomaly (base 1961-90)

30

20 10 0 -10

-20 Rainfall Rainfall Anomaly (mm) -30

-40

1941 1951 1961 1971 1981 1991 2001 2011 Years

Figure 34 Rainfall anomaly plot for Cowaramup using a base 1961-1990 average

Dwellingup Rainfall Anomaly (base 1961-90)

80

60 40 20 0 -20

-40 Rainfall Rainfall Anomaly (mm) -60 1935 1945 1955 1965 1975 1985 1995 2005 Years

Figure 35 Rainfall anomaly plot for Dwellingup using a base 1961-1990 average

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Kuranda Rainfall Anomaly (base 1961-90) 50

40

30 20 10 0 -10 -20

-30 Rainfall Rainfall Anomaly (m) -40 -50 1957 1967 1977 1987 1997 2007 Years

Figure 36 Rainfall anomaly plot for Kuranda using a base 1961-1990 average

Kurrara Park Rainfall Anomaly (base 1961-90) 60

40

20

0

-20 1953 1963 1973 1983 1993 2003 Rainfall Rainfall Anomaly (mm) -40

-60 Year

Figure 37 Rainfall anomaly plot for Kurrara Park e using a base 1961-1990 average

Marbling Rainfall Anomaly (base 1961-90)

60

40 20 0 -20

-40 Rainfall Rainfall Anomaly (mm) -60 1943 1953 1963 1973 1983 1993 2003 Years

Figure 38 Rainfall anomaly plot for Marbling using a base 1961-1990 average

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Perth Airport Rainfall Anomaly (base 1961-90)

60

40 20 0 -20

-40 Rainfall Rainfall Anomaly (mm) -60 1945 1955 1965 1975 1985 1995 2005 Years

Figure 39 Rainfall anomaly plot for Perth Airport using a base 1961-1990 average

Roelands Rainfall Anomaly (base 1961-90)

80

60 40 20 0 -20

-40 Rainfall Rainfall Anomaly (mm) -60 1943 1953 1963 1973 1983 1993 2003 Years

Figure 40 Rainfall anomaly plot for Roelands using a base 1961-1990 average

Yoongarillup Rainfall Anomaly (base 1961-90)

80

60 40 20 0 -20

-40 Rainfall Rainfall Anomaly (mm) -60 1957 1967 1977 1987 1997 2007 Years

Figure 41 Rainfall anomaly plot for Yoongarillup using a base 1961-1990 average

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10.4 Appendix 4

Figure 42 Location of streamflow gauges used in the analysis

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Table 8 Streamflow gauges with continuous long period of record that can be tested AWRC AWRC Context Catchment AWRC Name Longitude Latitude Reference Name Area(km²) 609014 ARTHUR RIVER MOUNT BROWN 116.93860928 -33.36814281 2,117.60 BALGARUP MANDELUP 609005 117.142242303 -33.907464427 82.43 RIVER POOL BALINGUP 609017 BROOKLANDS 115.95362189 -33.79755883 548.94 BROOK BANCELL 613007 WATEROUS 115.94826732 -32.94564336 13.57 BROOK BEAUFORT 609015 MANYWATERS 116.9643599 -33.49307499 1,565.24 RIVER 614037 BIG BROOK ONEIL ROAD 116.1902 -32.5113 149.44 BINGHAM 612014 PALMER 116.27557148 -33.27764801 366.11 RIVER BINGHAM ERNIES 612008 116.44492031 -33.29394336 2.68 RIVER TRIB CATCHMENT BLACKWOOD 609012 WINNEJUP 116.31405168 -33.98001623 8,729.47 RIVER BLACKWOOD 609019 HUT POOL 115.29264011 -34.09091544 12,372.21 RIVER BRUNSWICK 612022 SANDALWOOD 115.92192692 -33.21961612 116.17 RIVER CAMBALLAN 612025 JAMES WELL 116.4258097 -33.45568185 169.96 CREEK STAIRCASE 608002 CAREY BROOK 115.84348005 -34.39222307 30.29 ROAD CLARKE 613146 HILLVIEW FARM 115.92195912 -32.99773281 17.11 BROOK MUNGALUP 612002 COLLIE RIVER 116.09743219 -33.37073728 2,546.17 TOWER COLLIE RIVER COOLANGATTA 612001 116.26320981 -33.3302504 1,345.25 EAST FARM COLLIE RIVER JAMES 612230 116.58111685 -33.38143352 170.55 EAST TRIB CROSSING 606001 DEEP RIVER TEDS POOL 116.61577126 -34.7668237 467.80 DENMARK 603136 MT LINDESAY 117.3149841 -34.86667125 502.40 RIVER DONNELLY 608151 STRICKLAND 115.78459072 -34.32717576 782.13 RIVER FRANKLAND MOUNT 605012 116.788519063 -34.906186609 4,508.89 RIVER FRANKLAND HAMILTON 612004 WORSLEY 116.05024572 -33.31085785 32.28 RIVER 613002 HARVEY RIVER DINGO ROAD 116.0389452 -33.08708274 147.21 603004 HAY RIVER SUNNY GLEN 117.478309491 -34.910839908 1,210.61 KALGAN 602004 STEVENS FARM 117.999972305 -34.886107214 2,179.84 RIVER 604053 KENT RIVER STYX JUNCTION 117.08742007 -34.88884503 1,806.04 LEFROY 607013 RAINBOW TRAIL 116.01602937 -34.42754843 249.38 BROOK LITTLE BATES 614062 DANDALUP 116.0273 -32.5829 2.23 CATCHMENT TRIB MARGARET WILLMOTS 610001 115.05488596 -33.94220659 443.00 RIVER FARM NORTH LEWIS 614021 DANDALUP 116.0576 -32.5657 2.01 CATCHMENT TRIB 60 Melissa Wilson

NORTHERN LAKE TOOLIBIN 609010 117.61400543 -32.9048133 438.50 ARTHUR RIVER INFLOW 607004 PERUP RIVER QUABICUP HILL 116.45850702 -34.33110655 666.66 SERPENTINE 614030 DOG HILL 115.8616 -32.3419 469.72 DRAIN SLEEMAN SLEEMAN ROAD 603007 117.503163732 -34.958315447 75.67 RIVER BRIDGE SOUTH GORDON 614060 DANDALUP R. 116.2552 -32.6321 2.10 CATCHMENT TRIB SOUTH 614007 DANDALUP DEL PARK 116.0423 -32.6671 1.33 TRIB ST JOHN BARRABUP 609018 115.691446755 -33.943495199 552.26 BROOK POOL 607007 TONE RIVER BULLILUP 116.67895108 -34.25026748 983.13 WARREN WHEATLEY 607003 116.27772334 -34.3694038 2,821.12 RIVER FARM WARREN BARKER RD 607220 115.89943583 -34.52169322 3,933.67 RIVER CROSSING WILGARUP 607144 QUINTARRUP 116.34748906 -34.3486032 460.53 RIVER WILYABRUP 610006 WOODLANDS 115.02201697 -33.79605664 82.26 BROOK YATE FLAT 603190 WOONANUP 117.292107008 -34.703752779 56.32 CREEK

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10.5 Appendix 5 Table 9 Streamflow gauges used in the baseflow analysis AWRC Reference AWRC Context Name AWRC Name Longitude Latitude Condition*

602004 KALGAN RIVER STEVENS FARM 117.9999723 -34.88610721 62% cleared 603004 HAY RIVER SUNNY GLEN 117.4783095 -34.91083991 52% cleared 603136 DENMARK RIVER MT LINDESAY 117.3149841 -34.86667125 17% cleared by 1982, revegetation started in 1991 604053 KENT RIVER STYX JUNCTION 117.08742007 -34.88884503 39% cleared until revegetation started in 1996 605012 FRANKLAND RIVER MOUNT FRANKLAND 116.7885191 -34.90618661 69% cleared 606001 DEEP RIVER TEDS POOL 116.6157713 -34.7668237 1% cleared 607003 WARREN RIVER WHEATLEY FARM 116.2777233 -34.3694038 35% cleared by 1982, revegetated from the 1990's. Clearing down to 24% by 2002. 607004 PERUP RIVER QUABICUP HILL 116.458507 -34.33110655 16% cleared 607007 TONE RIVER BULLILUP 116.6789511 -34.25026748 65% cleared 607013 LEFROY BROOK RAINBOW TRAIL 116.0160294 -34.42754843 30% cleared 607220 WARREN RIVER BARKER RD CROSSING 115.8994358 -34.52169322 35% cleared by 1982, revegetated from the 1990's. Clearing down to 24% by 2002. 608002 CAREY BROOK STAIRCASE ROAD 115.8434801 -34.39222307 0% cleared 608151 DONNELLY RIVER STRICKLAND 115.7845907 -34.32717576 20% cleared 609012 BLACKWOOD RIVER WINNEJUP 116.3140517 -33.98001623 84% cleared, relatively unchanged since the early 1980s 609019 BLACKWOOD RIVER HUT POOL 115.2926401 -34.09091544 64% cleared 610001 MARGARET RIVER WILLMOTS FARM 115.054886 -33.94220659 23% cleared 610006 WILYABRUP BROOK WOODLANDS 115.022017 -33.79605664 72% cleared 612022 BRUNSWICK RIVER SANDALWOOD 115.9219269 -33.21961612 11% cleared 613002 HARVEY RIVER DINGO ROAD 116.0389452 -33.08708274 0% cleared *Unless more details are given, the cleared values are from the year 1996 (Mayer, Ruprecht & Bari 2005).

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Figure 43 Location of streamflow gauges used in the baseflow analysis

10.6 Appendix 6

Kalgan River Baseflow

4500

4000 y = 46.098x - 89904 3500 R² = 0.2676 3000 2500

2000 April April Runoff(ML) - 1500 1000

500 TotalJan 0 1975 1980 1985 1990 1995 2000 2005 2010 2015 Years

Figure 44 Annual summer baseflow against time for Kalgan River

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Frankland River Baseflow

3500

3000 2500 y = -13.198x + 27319 2000 R² = 0.1287

1500

April April Runoff(ML) - 1000

500 TotalJan 0 1940 1950 1960 1970 1980 1990 2000 2010 2020 Years

Figure 45 Annual summer baseflow against time for Frankland River

Deep River Baseflow

160

140 120 100

80 April April Runoff(ML)

- 60 40 y = -0.7069x + 1433.8 20

TotalJan R² = 0.0456 0 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 Years

Figure 46 Annual summer baseflow against time for Deep River

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Upper Warren River (Wheatley Farm) Baseflow

2500

2000

1500 April April Flow (ML) - 1000 y = -12.657x + 25690 R² = 0.0952

500 TotalJan 0 1966 1971 1976 1981 1986 1991 1996 2001 2006 2011 Year

Figure 47 Annual summer baseflow against time for the Upper Warren River catchment

Perup River Baseflow

800

700 600 500

400 April April Runoff(ML) - 300 y = -8.0795x + 16390 200 R² = 0.1626

100 TotalJan 0 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 Years

Figure 48 Annual summer baseflow against time for Perup River

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Tone River Baseflow

900

800 700 600 500

400 April April Runoff(ML) - 300 200 y = 1.5354x - 2912.6 R² = 0.0047

100 TotalJan 0 1975 1980 1985 1990 1995 2000 2005 2010 2015 Years

Figure 49 Annual summer baseflow against time for Tone River

Lefroy Brook Baseflow

2000

1800 1600 1400 1200 y = -10.28x + 21243 1000 R² = 0.0472

April April Runoff(ML) 800 - 600 400

TotalJan 200 0 1975 1980 1985 1990 1995 2000 2005 2010 2015 Years

Figure 50 Annual summer baseflow against time for Lefroy Brook

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Carey Brook Baseflow

1200

1000 y = -11.437x + 23358 800 R² = 0.226

600 April April Runoff(ML) - 400

200 TotalJan 0 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 Years

Figure 51 Annual summer baseflow against time for Carey Brook

Donnelly River Baseflow

4500

4000 3500 3000 2500

2000 April April Runoff(ML) - 1500 y = -51.159x + 102988 1000 R² = 0.5678

500 TotalJan 0 1950 1960 1970 1980 1990 2000 2010 2020 Years

Figure 52 Annual summer baseflow against time for Donnelly River

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Blackwood River (Winnejup) Baseflow

12000

10000

8000

6000 April April Runoff(ML) - 4000

2000 y = -4.2066x + 10121

TotalJan R² = 0.0002 0 1975 1980 1985 1990 1995 2000 2005 2010 2015 Years

Figure 53 Annual summer baseflow against time for the Upper Blackwood River catchment

Blackwood River (Hut Pool) Baseflow

30000

25000

20000

15000 y = -238.14x + 486497

April April Runoff(ML) R² = 0.1361 - 10000

5000 TotalJan 0 1980 1985 1990 1995 2000 2005 2010 2015 Years

Figure 54 Annual summer baseflow against time for Blackwood River

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Wilyabrup Brook Baseflow

25

20

15

April April Runoff(ML) 10 - y = -0.1671x + 338.22

5 R² = 0.0834 TotalJan 0 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 Years

Figure 55 Annual summer baseflow against time for Wilyabrup Brook

Brunswick River Baseflow

3000

2500

2000

1500 April April Runoff(ML) - 1000 y = -49.491x + 99827

500 R² = 0.4262 TotalJan 0 1975 1980 1985 1990 1995 2000 2005 2010 2015 Years

Figure 56 Annual summer baseflow against time for Brunswick River

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Harvey River Baseflow

4000

3500 3000 2500

2000 y = -45.536x + 92140 April April Runoff(ML)

- 1500 R² = 0.2496 1000

500 TotalJan 0 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 Years

Figure 57 Annual summer baseflow against time for Harvey River

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