Future impacts of climate variability, climate change …...Basin is under threat from three...

Final: 10/07/02

Future impacts of climate variability, climate changeand land use change on water resources in theMurray Darling Basin

Overview and Draft Program of Research

Roger Jones, Peter Whetton, Kevin Walsh and Cher PageCSIRO Atmospheric Research

Future impacts of climate variability, climate change and land use change on water resources in the Murray Darling Basin

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Table of ContentsTable of Contents .................................................................................................................................................... 2Executive Summary................................................................................................................................................. 3

Projected climate change ..................................................................................................................................... 3Macquarie River water resources ........................................................................................................................ 3Climate variability and land-use change.............................................................................................................. 3Catchment-wide implications .............................................................................................................................. 4Future directions.................................................................................................................................................. 4

Part One................................................................................................................................................................... 5Introduction ............................................................................................................................................................. 5Policy background................................................................................................................................................... 5Climate change projections for the Murray-Darling Basin...................................................................................... 6Previous assessments............................................................................................................................................... 7Future impacts on the Murray-Darling Basin .......................................................................................................... 8

Projected regional climate ................................................................................................................................... 8Macquarie River risk assessment implications for the MDB .............................................................................. 8

Climate variability and land-use change.......................................................................................................... 9Critical thresholds.......................................................................................................................................... 10

Part Two ................................................................................................................................................................ 11The Macquarie Study ............................................................................................................................................ 11

The Region ........................................................................................................................................................ 11Model structure.................................................................................................................................................. 12Baseline climate and results .............................................................................................................................. 12Climate change scenarios .................................................................................................................................. 13Climate model runs............................................................................................................................................ 15Probability distributions .................................................................................................................................... 15Uncertainty analysis .......................................................................................................................................... 16Bayesian analysis............................................................................................................................................... 17Critical thresholds.............................................................................................................................................. 18Risk assessment ................................................................................................................................................. 19Potential impact on water resources of reforestation......................................................................................... 20Summary ........................................................................................................................................................... 21

Draft research plan ................................................................................................................................................ 21Program aim ...................................................................................................................................................... 21Method .............................................................................................................................................................. 21Modelling system .............................................................................................................................................. 22Attribution ......................................................................................................................................................... 22Resources........................................................................................................................................................... 22

Appendix A ........................................................................................................................................................... 24Transfer functions.............................................................................................................................................. 24

Appendix B ........................................................................................................................................................... 24Artificial scenarios for sensitivity analysis of climate change on environmental flows .................................... 24

References ............................................................................................................................................................. 26

IMPORTANT DISCLAIMER

This report relates to climate simulations based on computer modelling. Models involve simplifications of realphysical processes that are not fully understood. Accordingly, no responsibility will be accepted by CSIRO orthe clients (the South Pacific Regional Environment Programme) for the accuracy of forecasts or predictionsinferred from this report or for any person's interpretations, deductions, conclusions or actions in reliance of thisreport.

Address for correspondence:Dr Roger JonesCSIRO Atmospheric ResearchPB No.1 AspendaleVictoria 3195Phone: 61 3 9239 4555Fax: 61 3 9239 4688Email: [email protected]


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Executive Summary

The Intergovernmental Panel on Climate Change has nominated the impacts of climate change on water as a keyissue for Australia. Under current climate, the Murray-Darling Basin (MDB) is affected by large climatevariability, high water-use as a proportion of total streamflow and competing demands for water. Climate changeis expected to impact on runoff and streamflow, but large uncertainties have hampered planning of practicalsteps to manage these impacts. The possibility that already limited environmental flows and plans to recoversome of these flows in the MDB may be threatened by climate change is investigated through three avenues:• Climate change projections for Australia, recently released by CSIRO, are re-investigated for the MDB.• Conclusions from recent impact modelling and risk assessment on flows and water use in the Macquarie

River catchment are used to infer possible basin-wide changes.• The impacts of climate variability and land-use change in combination with climate change are described.

Projected climate change

Recent projections of rainfall change for the MDB suggest a decline in winter and spring rainfall by the year2030. In summer, rainfall may either decrease or increase, with increases slightly more likely, while in autumnthe direction of rainfall change is uncertain. Possible rainfall increases are largest towards the north of the MDBand decreases are largest to the south. Temperature is expected to increase in all areas. Potential evaporation isalso highly likely to increase in all areas due to higher temperatures. These increases will be larger in regions andseasons in which rainfall decreases. Increases in open water evaporation will affect wetlands and water storages.

Macquarie River water resources

Water resources in the Macquarie River system were investigated by coupling CSIRO’s climate scenariogenerator to the IQQM river management model of the NSW Department of Land and Water Conservation. Thestudy investigated possible changes to flows, irrigation allocation and environmental flow allocations using thefull quantifiable range of possible precipitation and potential evaporation change from nine climate models. Thebaseline climate period was 1980–1996.

The results indicate decreases in streamflow in a warmer world into the Burrendong Dam, the main storage forthe catchment. The simulated change in storage range from +1% to –30% in 2030 and from +6 to –55% in 2070.A risk assessment suggests that the most likely outcomes in flow are from about 0 to –15% in 2030 and 0 % to –35% in 2070. Uncertainty analysis showed that about two thirds of the uncertainty in water resources impactswas due to uncertainty in projected rainfall change as a function of global warming and about one quarter wasdue to uncertainty regarding the rate of global warming itself. This demonstrates the importance ofunderstanding how future rainfall may change, and in correctly attributing observed changes over time.

The Macquarie study identified two critical water management thresholds. A critical threshold marks the point atwhich an activity or system faces an unacceptable level of harm. The two thresholds were constructed: theminimum amount of flow required to ensure the continued breeding of water birds in the Macquarie Marshes,and irrigation allocations falling below a level of 50% for five consecutive years. The results suggest that thelikelihood of exceeding both thresholds is about 1% in 2030, and 30–40% in 2070.

Climate variability and land-use change

Simulated flow for the Macquarie River based on 1890–1947 input was much less than that for the period 1948–1996. The rainfall climate was therefore classified as a “drought-dominated” regime before 1948 and a “flood-dominated” regime after 1948. The shift between regimes was abrupt, having a significant affect on thesimulation of flows, shifting from about 25% less than the long-term mean to about 25% greater after 1948. Thisis supported by observed flows elsewhere in the MDB.

These shifts in rainfall regime also considerably affected the risk of critical threshold exceedance. In 2030, therisk of exceeding both thresholds under a drought-dominated regime increases from 1% to about 30%. In 2070,these probabilities are 60–70% in a drought-dominated regime and 10–20% in a flood-dominated regime.However, these regimes of decadal rainfall variability are poorly understood and cannot be predicted, nor canchanges in regime be diagnosed in the short term.

Reforestation of the upper Macquarie catchment was also considered. Three scenarios of reforestation coveringfrom 2% to 10% of the upper catchment reduced flows by 4% to 17%. Under climate change, these reductions


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were mainly additive, suggesting that the joint effect of reforestation and climate change further increases therisk to streamflow. This highlights the need for tree-planting programs to be carefully targeted to maximisesalinity benefits while minimising streamflow losses.

Catchment-wide implications

Broader conclusions applicable to the wider basin can be drawn from the Macquarie study. Projected changes inrainfall and evaporation are broadly similar. Two factors are important: in the south of the MDB, rainfallreductions appear to be slightly more likely, and streamflow is more reliant on winter-spring rainfall, which islikely to decrease. Therefore, it is very likely that streamflow throughout the MDB will be reduced underenhanced greenhouse conditions. Reductions in flow in southern catchments may be larger than those in theMacquarie, while flow reductions in northern catchments may be more moderate.

The water resources in the Basin have been developed and operated in the flood-dominated climate of the latter20th century. Most changes in rainfall variability are likely to reduce those resources. The risk to water resourcesfrom climate change is far greater under a drought-dominated climate than it would be under a normal or flood-dominated climate. Further decreases from reforestation and afforestation are probable but cannot be quantifiedbecause future planting and regrowth rates and patterns have not been investigated on a large scale. Increases infuture flows from climate change and variability cannot be discounted but appear unlikely.

Future directions

Risk assessment in the Macquarie catchment, and climate projections for the MDB show that streamflow in theBasin is under threat from three sources: climate change, decadal-scale rainfall variability and land-use change.The Macquarie assessment needs to be extended across the Basin so that planning to ameliorate the impacts ofthese reductions can commence. The modelling system developed for the Macquarie study can be applied to theother catchments of the MDB in a new program of research. This program would aim to incorporate as manyclimatic and hydrological uncertainties as possible to estimate likely future changes in flow for the Murray andDarling Rivers. Avenues for changing water management strategies, involving management, legislative andeconomic options, would be investigated. Critical thresholds for a number of key systems would be constructedand linked to climate (e.g. critical environmental flow thresholds) and subject to a risk assessment. The aim ofthis program would be to produce options for the sustainable long-term management of environmental flows inthe Murray-Darling Basin.

This program would require a consortium of partners involving Federal and State government agencies, researchorganisations and the Tertiary research sector. The co-operation of each State government through their interestin individual catchments and current role in water management is critical.


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Part One

Introduction

This paper provides an overview of how climate change, land-use change and climatic variability may affectwater resources and the allocation of environmental flows in the Murray-Darling Basin (MDB). It was preparedfor the Murray-Darling Basin Commission as part of the wider project Environmental Flows and Water QualityObjectives for the River Murray. The paper is based on recent research conducted by CSIRO and collaboratorsand is divided into two parts. Part One discusses Australia’s international treaty obligations towards climatechange and the resulting national policy response, previous research into water resources in Australia, climateprojections for the MDB and likely outcomes in flows based on the research to date. Part Two summarises theresults of a recent study of climate change impacts on water resources in the Macquarie River Catchment thatcontribute to the wider conclusions for the MDB, and draft proposals for a future research program.

Policy background

Australia has international obligations under the United Nations Framework Convention on Climate Change,ratified in December 1992. The main goal of the convention is as follows:

“The ultimate objective … is to achieve … stabilization of greenhouse gas concentrationsin the atmosphere at a level that would prevent dangerous anthropogenic interference withthe climate system. Such a level should be achieved within a time-frame sufficient to allowecosystems to adapt naturally to climate change, to ensure that food production is notthreatened and to enable economic development to proceed in a sustainable manner.”

The convention indicates that this requires a precautionary approach:

“The Parties should take precautionary measures to anticipate, prevent or minimize thecauses of climate change and mitigate its adverse effects. Where there are threats of seriousor irreversible damage, lack of full scientific certainty should not be used as a reason forpostponing such measures, taking into account that policies and measures to deal withclimate change should be cost-effective so as to ensure global benefits at the lowestpossible cost.”

Of relevance to the management of water resources are the following:

“The Parties have a right to, and should, promote sustainable development. Policies andmeasures to protect the climate system against human-induced change should beappropriate for the specific conditions of each Party and should be integrated with nationaldevelopment programmes, taking into account that economic development is essential foradopting measures to address climate change.”

“[The Parties should] cooperate in preparing for adaptation to the impacts of climatechange; develop and elaborate appropriate and integrated plans for coastal zonemanagement, water resources and agriculture, and for the protection and rehabilitation ofareas … affected by drought and desertification, as well as floods…[and] take climatechange considerations into account, to the extent feasible, in their relevant social, economicand environmental policies and actions, … employ appropriate methods, for exampleimpact assessments, formulated and determined nationally, with a view to minimizingadverse effects on the economy, on public health and on the quality of the environment, ofprojects or measures undertaken by them to mitigate or adapt to climate change…”

This convention entered into force in March 1994. It is currently being addressed through the NationalGreenhouse Strategy (AGO, 1998). Module Eight of the NGS addresses adaptation strategies for climate changebut to date, limited progress has been made (AGO, 2000). In many sectors a number of scientific issues areunresolved. Here, we address the issues relevant to the management of water resources in the Murray-DarlingBasin in a warmer world.


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Climate change projections for the Murray-Darling Basin

Recently, CSIRO prepared ranges of projected future climate change for the Australian region (CSIRO, 2001).Ranges of temperature and rainfall change were based on the full range of projected global warming as given byIntergovernmental Panel on Climate Change (IPCC, 2001) in combination with projected regional changesobtained from nine climate models. The ranges of change allow for uncertainty in human behaviour (uncertaintyin future emissions of greenhouse gases), as well as climate science uncertainty (differences in the response ofclimate models).

Ranges of annual average warmings across the MDB are 0.4 to 2.0°C by 2030 and 1.0 to 6.0°C by 2070 relativeto 1990. Variations in the warming range across the Basin and over the four seasons are minor, although there isa tendency for the warming to be weaker in the south of the basin in winter (e.g. around 0.8 to 5.0°C in 2070).

Ranges of rainfall change given in CSIRO (2001) have been re-analysed to provide more detailed informationover the MDB (Figure 1). Uncertainty in projected regional rainfall change is large. In summer, ranges ofchange are biased slightly toward increase over most of the basin (–8% to +13% by 2030 and –25% to +40% by2070). In autumn the direction of change is uncertain over most of the basin (–8% to +8% in 2030 and –25% to+25% in 2070). However in winter and spring there is a strong bias toward rainfall decrease. In winter, most ofthe basin is in the range is –8% to +3% in 2030 and –25% to +8% in 2070, and in spring –13% to +3% and –40% to +8%. Projected rainfall decreases are more evident in the southern parts of the basin. Regional climatemodelling simulating changes typical of the above patterns strongly indicate an increase in dry springs. Modelresults also indicate that extreme daily rainfall events are likely to become more extreme, where average rainfallincreases, stays the same or decreases slightly. Seasonal changes in extreme daily rainfall have not beeninvestigated.

Figure 1: Ranges of change in average rainfall (%) for around 2030 and 2070 relative to 1990. Thecoloured bars show ranges of change for areas with corresponding colours in the seasonal and annualmaps.

Winter

Spring

Annual

Summer

Autumn

-40 -20 0 20 40Rainfall Change (%)

-40 -20 0 20 40Rainfall Change (%)

2030

2070


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Annual average potential evaporation increases by 0% to 8% per degree of global warming (pdgw) over most ofAustralia and up to 12% pdgw over the upper catchment areas of the MDB. All models simulated increases inpotential evaporation over the MDB. Increases are highest where rainfall decreases and is least in regions andseasons where rainfall increases.

Previous assessments

Previous impact assessments on water resources in the MDB were undertaken by Chiew et al. (1995), Schreideret al. (1996, 1997) and Wang et al. (1999). An earlier study on the Macquarie catchment integrating watersupply, environmental flows and economic outcomes for agriculture is described in Hassall and Associates(1998). These studies utilised earlier climate change scenarios (CSIRO 1992, 1996) which contained bothsubstantial increases and decreases in rainfall, or single model outputs. The CSIRO (1992) scenarios were basedon an earlier generation of climate models that utilised simplified “mixed-layer” oceans and lacked oceancurrents and dynamic phenomena such as the El Niño – Southern Oscillation (e.g. Allan et al., 1996). The 1996regional climate change scenarios (CSIRO, 1996) summarised both mixed layer and more realistic coupledocean atmosphere-models. These scenarios also encompassed a broad range of rainfall uncertainty.

The resulting impact studies listed above produced conflicting outcomes. Using rainfall changes based onCSIRO (1992) and estimating potential evaporation (Ep) from changes in temperature, Chiew et al. (1995)simulated both substantial increases and decreases in runoff and streamflow for several small, ungaugedcatchments in the upper Murray region. Schreider et al. (1996, 1997) investigated changes in the snow-freeGoulburn and Ovens catchments and the snow-affected Mitta Mitta and Kiewa catchments using “most wet”(temperature increase of +1.5°C, rainfall increase of +20% in summer, +10% winter) and “most dry” (+2°C, nosummer rainfall change, –10% winter rainfall change) scenarios for 2030, based on CSIRO (1992). Theseassumptions produced neutral to negative changes in streamflow in the snow-free catchments and slightlypositive to negative changes in the snow-affected catchments (Table 1). Despite using similar scenarios to Chiewet al. (1995), these results were substantially drier. The differences between these results may be related to thedifferent representation in their respective models of the effect of temperature and Ep changes on runoff.

Table 1. Climate scenario impact on precipitation and streamflow in snow-free and snow-affectedcatchments in Victoria in 2030. From Schreider et al. (1996, 1997), based on CSIRO (1992).

Scenario Precipitation(% change)

Streamflow(% change)

Snow free - 7 - 36Snow affected

Most dry- 6 - 30

Snow free + 13 0Snow affected

Most wet+ 13 + 9

Wang et al. (1999) investigated the Campaspe system using a scenario derived from the CSIRO regional modelnested in a mixed-layer global climate model (GCM), which produced rainfall decreases in the first half of theyear and increases in the second half, resulting in a net annual rainfall decrease. This was used to investigate theimpact of climate change on security of water right. Irrigation water is allocated on the basis of 100% water rightand a further 120% of sales water, giving a total of 220% of the water right in a year when supply is not limited.Security of the actual water right is measured as the percentage of years that 100% of the water right can besupplied. This percentage was reduced by only 1% in 2030 (0.8°C global warming), 4% in 2070 (1.8°C globalwarming) and 16% for a 4.1°C global warming, but the relatively effective maintenance of security was at theexpense of downstream environmental flows.

Hassall & Associates (1998) reported on an extensive study into the effects of climate change on the economyand ecology of the Lower Macquarie Valley. Precipitation and (Ep) scenarios from CSIRO’s regional climatemodel nested in a mixed-layer GCM were used to provide estimates for 2030 that were towards the dry end ofthe CSIRO (1996) scenarios. The changes in river flow simulated by the Integrated Quantity and Quality Model(IQQM) Macquarie Model (Department of Land and Water Conservation, 1995ab) are shown in Table 2.Average sub-catchment streamflow decreases for the most wet and most dry scenarios were 12% and 32%respectively. Accumulated economic losses for the livestock, cotton and wheat industries were $38 million and$152 million respectively. Most losses were in the livestock industry and all sectors took productivity increasesdue to higher concentrations of CO2 into account (Hassall & Associates, 1998). These losses can be consideredas relevant within the scope of the latest research described in Part Two of this document.


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Table 2. Total Annual Yields of the Macquarie River (GL)Catchment Most wet Most dry

Rainfall(% change)

Change(%)

Rainfall(% change)

Change(%)

Upstream Burrendong Dam -4 -11 -7 -30Downstream Burrendong Dam -8 -14 -11 -37

Overall -6 -12 -9 -32

Future impacts on the Murray-Darling Basin

Planning for climate change in the MDB has been hampered by the large uncertainties associated with how thegreenhouse effect may affect the regional climate, particularly for the direction and magnitude of rainfall change.However, we believe the latest research described in this paper and in the papers it draws upon, is sufficient towarrant the development and implementation of adaptation strategies to cope with climate change consistentwith the policies described earlier.

Under current climate, the Murray-Darling Basin (MDB) is affected by large climate variability, high water-useas a proportion of total streamflow and competing demands for water. Plans to secure environmental flows in theMDB would be threatened by possible reductions in flow in coming decades. This possibility is investigatedthrough three avenues:• Climate change projections for Australia, recently released by CSIRO, have been re-investigated for the

MDB.• Conclusions from recent impact modelling and risk assessment on flows and water use in the Macquarie

River catchment have been used to infer possible basin-wide changes.• The impacts of climate variability and land-use change.

Projected regional climate

The patterns of seasonal P and Ep changes investigated in detail for the Macquarie catchment are similar to thebroader changes simulated over the MDB as shown in Figure 1. The pattern of rainfall change is one of increasesor decreases in summer and autumn, with increases dominating over the northern half of the Basin, andpredominantly decrease in winter and spring. Winter-spring decreases are more strongly evident in the southernhalf of the basin. Cool season rainfall (and runoff) is proportionally more important relative to the annual total insouthern areas of the basin (Table B2). This pattern of rainfall change may be at least partially explained in termsof simulated changes in atmospheric circulation. Pressure patterns show an increase in the mid southern latitudesthat would be consistent with a southward shift in the westerly rain belt, an important source of rainfall insouthern Australia, particularly in the cooler months (not shown). Whether this is a robust aspect of simulatedregional rainfall change within coupled ocean-atmosphere models needs to be further examined.

Increases in Ep as observed in the Macquarie study are also generally applicable across the MDB (CSIRO,2001). Increases in potential evaporation in winter and spring are typically stronger in southern parts of thebasin, as would be expected given the stronger tendency for rainfall decreases in these regions. Along with therainfall changes discussed above, this would contribute to greater flow decreases in southern catchmentscompared to those simulated in the Macquarie study. Open water evaporation over lakes, reservoirs and wetlandswould also be expected to increase. Such changes have not yet been quantified.

Ranges of temperature increases are lower in the southern half of the MDB and tend to be less in winter. Highertemperatures will affect water temperature, possible leading to increased algal blooms. Higher temperatures inthe uplands of the MDB will change the ratio of solid precipitation to rainfall. Schreider et al. (1997) indicatedthat snow-affected catchments were a little less sensitive to evaporation increases than neighbouring snow-freecatchments. Changes in seasonality of flow (with the peak occurring earlier in the year) may also be expected insnow-affected catchments (although in the Schreider et al. study, this effect was quite small).

Macquarie River risk assessment implications for the MDB

Many of the results from Macquarie River risk assessment detailed in Part Two can be applied more broadly tothe MDB. Similar patterns of regional P and Ep change across the MDB show that similar outcomes in terms ofstreamflow are likely. However, quantified outcomes for the MDB cannot be provided until each significant


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catchment in terms of water supply, has been modelled directly. Furthermore, several important uncertaintieswere not addressed in the Macquarie risk assessment, and these also need to be explored within a modellingsystem to determine whether they would significantly alter the conclusions.

Risk analysis indicated the most likely 90% of the total range of change in streamflow and water supply is 0% to15% in 2030 and 0% to 35% in 2070. Changing the input assumptions alters the extremes of the total range ofoutcomes but has relatively little effect on the central 90%. Taking into account the climate projections for theMDB described above, this range may be slightly more favourable to the north, where summer and autumnrainfall increases are possible. The range is likely to become more negative (larger decreases) to the southbecause of the dependence of streamflow on winter-spring rainfall which is expected to decrease, based on thecurrent evidence.

Two areas of uncertainty, that have not bee simulated, concern daily rainfall changes and hydrologicaluncertainties. Daily rainfall is expected to become more intense in most areas where mean rainfall increases orremains the same, and may possibly become more intense even with slight decreases in mean rainfall. How thesetrends may combine with the seasonal pattern of mean rainfall change is uncertain. It may be expected thatsummer-autumn increases in intensity could be larger than winter-spring changes, which may vary betweenincrease and decrease. However, this needs to be investigated using daily model data output from a number ofclimate models, a data- and time-intensive procedure. This effect is likely to most significant in small catchmentsand those with a relatively high proportion of runoff compared to rainfall. How changes in extreme rainfall mayaffect flood distributions (an important part of environmental flows) and flood risks remains unknown.

Hydrological uncertainties are many, and are probably less significant than climatic uncertainties (Arnell andLiu, 2001) but need to be accounted for in planning. The type of rainfall-runoff used in this study falls into agroup of conceptual rainfall runoff-models that approximate hydrological processes within a series of optimisedparameters, ranging from several to about two dozen. Whether these parameters adequately represent processesunder climate change is poorly known, and few studies have been carried out where several such models havebeen compared within a single catchment (e.g. Boorman and Sefton, 1997). This would be a very valuableexercise to carry out within the MDB.

Topographic variations, especially in the eastern highlands, are generally poorly represented in climate modelsbecause of their large grid size. Since, the interaction between mountains and various weather systems influencesthe distribution of rainfall, poor topographic representation remains an uncertainty. However, the 60km and125km CSIRO regional models used in this study contain a better representation of topography and did not havevastly different results to the GCMs. Also, poor soil development in a number of rainshadow areas in south-eastern Australia suggests that these types of topographic controls have persisted throughout a series of pastclimate changes.

The Bayesian and uncertainty analysis carried out for the Macquarie catchment also indicate that, under thepresent modelling structure, the probability distribution functions (PDFs) of streamflow are little affected bychanges in the input range or distribution of uncertainties. Most of the uncertainty is due to rainfall andmagnitude and change, and changes in the ranges and distribution of such changes produced similar PDFs tothose produced using altered input assumptions.

In summary, it is very likely that the general tendency for reduced flows under enhanced greenhouse conditionssimulated for the Macquarie catchment will apply throughout the wider MDB. With regard to the effect ofrainfall change, reductions in flow in catchments in southern areas of the Basin may be larger than those fromthe Macquarie study, whereas flow reductions in northern catchments may be more moderate. More reliableestimates of changes in water resources and allocation implications for other catchments in the MDB wouldrequire explicit modelling for these catchments (see draft program of research below).

Climate variability and land-use change

Decadal scale variability in rainfall in this project is a period of rainfall where an average higher, lower or closeto the long-term mean is sustained for several or more decades. These periods can switch between drought-dominated and flood-dominated modes in a relatively short time. Little is known about their dynamics, but theyaffect many parts of the world, especially in the tropics and mid-latitudes. Drought-dominated and flood-dominated regimes observed in the Macquarie River catchment persisted across the MDB switching fromdrought- to flood-dominated mode in 1948. The shift between regimes was abrupt, having a significant affect onthe simulation of flows, shifting from about 25% less than the long-term mean to about 25% greater after 1948.


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This is supported by observed flow changes elsewhere in the MDB. These modes of rainfall variability are likelyto continue in the future but they are unpredictable, their dynamics are poorly understood and it is difficult at anytime to be certain which mode is current. However, the research in the Macquarie showed that the combinationof climate change and decadal rainfall variability is much more important than either mechanism on its own.

Tree-planting and other forms of revegetation across the MDB are being planned for the following purposes:Commercial plantations: Australian governments have committed themselves to a tripling of plantation forestsby 2020.Carbon sequestration: Increases in vegetation biomass as an allowed mechanism to meet CO2 emission targets(1990 levels plus 8%), have been set by the Kyoto Protocol.Salinity remediation: Late in 2000, the federal government announced a plan to spend $1.4 billion over 10years on salinity remediation. A principal remediation activity is the revegetation of recharge areas.Biodiversity management and ecosystem services: Re-establishment of indigenous species and communities isaiming to halt the decline in biodiversity and ecosystem services.

Three scenarios of reforestation covering from 2% to 10% of the upper Macquarie catchment reduced flows by4% to 17%. Under climate change, these reductions were mainly additive, suggesting that the joint effect ofreforestation and climate change further increases the risk to streamflow. The revegetation programs listedabove, all valuable in their own right, could potentially lead to similar scale reductions over the MDB. Thishighlights the need for tree-planting programs to be carefully targeted to maximise the above benefits whileminimising streamflow losses.

Critical thresholds

Critical thresholds marking an unacceptable level of harm for a system or aspect of a system can be used toassess the risk associated with a driver, or combination of drivers such as climate change and variability. Twothresholds were constructed for the Macquarie catchment: the minimum amount of flow required to ensure thecontinued breeding of water birds in the Macquarie Marshes, and irrigation allocations falling below a level of50% for five consecutive years. The risk of exceeding both thresholds under a drought-dominated regimeincreases from 1% in a normal rainfall regime to about 30%. In 2070, these probabilities are 60–70% in adrought-dominated regime, 30–40% in a normal climate and 10–20% in a flood-dominated regime.

Critical thresholds and other forms of thresholds can, and are set up for monitoring environmental flows. Forinstance, River Red Gum forests need a particular level of flood frequency to remain healthy. Other well knownthresholds in the MDB are the 500 Ec limit for total dissolved salts in the Murray River at Morgan and theproportion of current flow compared to natural flow.

We recommend measuring the impacts on important functions in the MDB in two ways:1. Using risk and uncertainty analysis to determine the most likely outcomes for important impacts, e.g. 0% to

–15% changes in streamflow for the Macquarie by 2030, to use in general planning. A monitoring andattribution program can determine observational rules that signal if climate is changing in such a way thatthese limits may be exceeded (e.g. rainfall trends having a likelihood of being above or below a certainlimit).

2. Determine important thresholds within the system that are associated with legislated targets, plans andpolicies, sustainable performance or an unacceptable level of harm. Use risk assessment to assess how likelysuch thresholds are to be met or avoided within a certain range of conditions. Again, a monitoring programcan determine whether thresholds are more or less likely to be exceeded over time. Bayesian analysis canalso look how risk profiles may change when new information becomes available.


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Burrendong Dam

Windamere Dam

Macquarie Marshes

Irrigationareas

Part Two

The Macquarie Study

Based on the earlier Macquarie Study (Hassall and Associates (1998), and with funding from the RuralIndustries Research and Development Corporation, CSIRO and research partners, the NSW Department of Landand Water Conservation (DLWC) and Hassall and Associates, carried out a risk assessment of climate change onthe water resources of the Macquarie River catchment (Jones et al., 2001a and b; Jones and Page, 2001). Thiswork is the most relevant available regarding the issue of climate change impacts on water resources and theirmanagement in the MDB. Relevant methods and outcomes are described in Part Two of this report.

The Region

The Macquarie River Catchment is situated in the central eastern part of the Murray-Darling Basin and coversabout 75,000 km2 (Fig. 2). It rises on the western slopes of the Great Dividing Range, 100 km west of Sydney,and flows WNW into the Darling River. Rainfall ranges from about 1,200 mm pa in the upper catchment to <400mm pa in the lower catchment. Potential evapotranspiration follows an inverse spatial pattern to rainfall, rangingfrom about 1,200 mm pa in the upper catchment to 2,200 mm pa in the lower catchment. The bulk of runoff isgenerated in the headwater catchments and flows into the Burrendong Dam, the main storage in the catchmentwith a capacity of 1,189 GL (NSW EPA, 1995). Downstream, there is a net loss of streamflow due toevaporation, recharge of shallow aquifers and abstractions for irrigation. Competition for water is high, withsupply to towns, irrigation, wetlands and streamflow.

Water allocations are classed as either high security or general security. General security water is allocatedaccording to water availability and most irrigation water is of this type. Every irrigator is licensed for a fixedvolume of water, but the proportion of their entitlement that they actually receive in any year is calculated after

dead storage and high security waterallocations have been allocated.Irrigators are more vulnerable toreductions in catchment runoff thanrecipients of high security water, andmay receive less than 100% of theirlicensed allocation in a water year.

The Macquarie Marshes, located inthe lower reaches of the catchment,are a large and diverse system ofwetlands that has particularsignificance as a refuge and breedingarea for waterbirds (Kingsford andJohnson, 1998). The MacquarieMarshes Nature Reserve is listedunder the RAMSAR Convention ofWetlands of International Importance.The Marshes are very sensitive tochanges in flow regime, having shrunkby more than 40% since theconstruction of Burrendong Dam in1967 (Kingsford and Thomas, 1995).Under the Water Management Plan forthe Macquarie Marshes 1996 (DLWCand NWPS, 1996), 50 GL of highsecurity water and 75 GL of generalsecurity water have been allocated tothe Marshes to ensure their ecologicalsustainability.

Figure 2. Macquarie River basin.


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Model structure

The Australian climate scenario generator, OzClim, was coupled to the Integrated Quality Quantity Model(IQQM), a stream flow model developed by the DLWC to investigate possible changes in flow, flow allocationsand climatic uncertainties. OzClim contains regional climate patterns for P and Ep measured as percentagechange per degree of global warming. Regional scenarios of P and Ep change were created by multiplying thesepatterns by projections of global warming. Precipitation was taken directly from climate model output, but Epwas calculated using the Complementary Relationship Areal Evaporation (CRAE) Method (Morton 1983), toprovide estimates of changes in A-Class pan equivalent Ep for input into IQQM. IQQM consists of theSacramento model to simulate runoff (Burnash et al., 1984), accompanied by river routing routines and extensivemanagement rules, including those for water supply, allocations, crop demand, crop area and wetlandmanagement (DLWC, 1995a and b). IQQM uses the 1996 management rules developed by DLWC to managethe Macquarie catchment more sustainably (DLWC and NWPS, 1996).

The IQQM output is a simulated record based on how historical climate may have affected the Macquarie underthe 1996 development and management rules and does not simulate historical changes in water supply. Becauseobserved historical flow is dominated by catchment development rather than climate, historical climate is usedestablish a baseline under current development and management structure. OzClim “drives” IQQM by applyingclimate change scenarios to these historical climate series, which are then used to simulate changes in rainfall–runoff, streamflow and allocations.

Baseline climate and results

Three aspects of the baseline precipitation climate (mean rainfall, decadal-scale variability and interannualvariability) were investigated with regard to their impact on water supply and how they may change over thecoming decades. Mean climate is the historical mean over the length of record (1890–1996). Decadal-scalerainfall variability consists of extended periods of rainfall below, above or consistent with the long-term mean.High interannual rainfall variability, which is strongly influenced by ENSO, results in the Macquarie Riverhaving one of the most variable records of annual streamflow in Australia (NSW EPA, 1995). Figure 3 showsthe time series of annual rainfall anomalies averaged over three high-quality recording stations within thecatchment. This record is divided into drought-dominated and flood-dominated decadal rainfall regimes.

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Figure 3. Historical rainfall anomalies averaged for three high quality rainfall stations in the MacquarieCatchment, divided into drought-dominated and flood-dominated regimes, after Warner (1987).

A change between drought-dominated and flood-dominated regimes occurred in 1948. Similar changes alsooccur elsewhere in south-eastern Australia and the historical rainfall record in the MDB can be divided into twoperiods: a drought-dominated and a flood-dominated regime dating from 1890–1948 and 1949–1998respectively (Pittock 1975; Warner 1987). This shift is distinguished from other forms of decadal variability,such as the Interdecadal Pacific Oscillation and the Indian Ocean Dipole, that appear to affect interannualvariability through the modulation of phenomena such as ENSO (Power et al., 1999).

Simulated streamflow (neglecting changes caused by catchment modification) also reflects this change in rainfallregime. Table 3 shows the summary statistics for P and mean annual storage in the Burrendong Dam undercurrent climate conditions. Simulated streamflow from IQQM increases in the second half of the 20th century,


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whereas historical streamflow has decreased over time due to increasing extractions of water for irrigation andother purposes (Kingsford and Thomas, 1995). The simulated flow during the drought-dominated regime is 23%below the long-term average, while during the flood-dominated regime it is 27% above average. Table 3 showsthat both rainfall and streamflow during the two regimes deviate by >20% from the long-term historical mean.The flood-dominated regime contains both the wettest and driest years for the entire series.

Table 3. Average rainfall anomalies and mean annual storage volume in the Burrendong Dam fordrought-dominated and flood-dominated periods under current climate simulated using the 1996 flowmanagement rules.

Drought-dominated period Flood-dominated period1890–1948 1949–1996

Rainfall anomaly –20% +23%Flow anomaly –23% +27%

Figure 4 shows Burrendong Dam and Macquarie Marshes inflows along with the percentage of irrigationallocations met within a water year (July 1 to June 30) for the period 1890/91–1995/96. The effect of decadalvariability is obvious. Flows and irrigation allocations are much lower for the first half of the 20th century thanthey are for the second half. During the period 1947/48 to 1978/79, irrigation allocations only fall below half onone occasion, and 100% allocations were supplied almost 80% of the time. Between 1947 and 1996, threeextreme shortfalls (<25%) in allocations are simulated, whereas in the first half of the century, simulatedirrigation allocations fall below 25% on fifteen occasions.

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Figure 4. Simulated flows into Burrendong Dam and the Macquarie Marshes (in gigalitres ×××× 10) and bulkirrigation allocations in percentage for the Macquarie River based on 1890/91 to 1995/96 climate and 1996management rules.

Water supply in the Macquarie catchment is now fully allocated to over-allocated when all competing needs,such as irrigation, environmental flows and water quality, are taken into account (see NSW EPA, 2000).Although recent droughts and the imposition of irrigation caps have limited supply, it is likely that sustainedperiods of low rainfall typical of the first half of the 20th century would cause severe hardship if they were tooccur again. Severe years of drought, including several years in succession such as occurred in 1979 to 1983, canbe managed within the current coping range. However, could a longer sequence of dry years reminiscent of1900–1910 or 1935–1945 cross the threshold into vulnerability? In future, different modes of climate variabilityoccurring on daily to decadal timescales may continue independently of climate change, or may be modulated byit. Changes in mean climate may occur gradually or may be abrupt. These phenomena will need to be managedin combination and is why climate scenarios must explicitly incorporate both climate variability and meanclimate change.

Climate change scenarios

Output from nine climate models was used to explore the fullest possible range of regional change in P and Ep.These models are the same as used in CSIRO (2001) except that HADCM2 is omitted and the CSIRO DARLAM60km model is added. All of the models were forced by greenhouse gases only, omitting the regional coolingeffects of sulphate aerosols. However, this effect is small over Australia.


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Precipitation patterns in change per degree of global warming were linearly interpolated from model grids tomatch the OzClim grid for the Murray-Darling Basin. Morton’s (1983) CRAE model was used to calculate Ep,requiring inputs of modelled average temperature, dewpoint temperature (or an equivalent moisture variable) anddownward shortwave radiation. The results were compared with an evaporation climatology for 1961-1990prepared for the Bureau of Meteorology Climatic Atlas of Australia (Wang et al., 2001) calculated using thesame method. The methods for calculating Ep from GCM output are described and assessed in Walsh et al.(2000).

Within OzClim, each model pattern was multiplied by the projected global warming for a particular date (e.g.2030) to produce individual estimates of precipitation change (δP) and change in potential evapotranspiration(δEp) in percent, which were then used to scale the input data in the IQQM model.

Precipitation and Potential Evaporation Relationships under Climate Change

Figure 5 shows the relationship between average δP and δEp for the Macquarie catchment taken from all nineclimate models for the summer, autumn, winter and spring quarters. This is consistent with the complementaryrelationship of Bouchet (1963), where annual values of P and Ep are inversely related in moisture-limitedenvironments. Increases in P are associated with small increases in Ep, whereas decreases in P are associatedwith large increases in Ep. This is consistent with increasing cloud cover (decreasing solar radiation) andhumidity accompanying rainfall increases, limiting the affects of increasing temperature. The correlationbetween the two is high (-0.77) with an R2 of 0.59. This relationship has been confirmed for all of easternAustralia showing that P and Ep are co-dependent under a changing climate (Walsh et al., 2001). This co-dependency allows δEp to be re-scaled from estimates of P using a linear regression based on the relationshipshown in Figure 5.

R2 = 0.59

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Figure 5. Relationship between δδδδP and δδδδEp per degree of global warming from nine models on a quarterlybasis for the Macquarie River catchment, for the simulated period 1961-1990.

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Figure 6. Average monthly changes in P and Ep with ±1 standard deviation for nine climate models forthe Macquarie River catchment. Change is percent per degree of global warming.


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The annual average P–Ep deficit increases (i.e. becomes drier), showing a fairly similar seasonal pattern for allmodels (Fig. 6). As described earlier, rainfall increases dominate over the summer–autumn period and decreasesdominate over winter–spring. Only the GFDL and NCAR models do not fully conform to this pattern.

Climate model runs

Multiple climate change simulations were run:1. For 2030 based on IS92a–f emission scenarios and IPCC (1996) warmings, nine climate models at low,

moderate and high sensitivity, for a total of 27 simulations.2. For 2070 based on the SRES emission scenarios and IPCC (2001) warmings, nine climate models at

low, moderate (2 runs) and high sensitivity for a total of 36 simulations.

The results produced changes to mean annual storage in Burrendong Dam ranging from +1% to –22% in 2030and +6% to –55% in 2070. The results for 2030 where then adjusted to the later IPCC (2001) emissionsscenarios, using the procedure detailed in Appendix A. Allowing for the higher IPCC (2001) warmingscompared with IPCC (1996), the updated results for 2030 are +1% to –30% (Figure 7). Changes to flows into theMacquarie Marshes are slightly more sensitive and irrigation allocations are slightly less sensitive.

Utilising individual scenarios based on the full range of δP and δEp changes from a number of models andglobal warming projections can at best give a range of outcomes, similar to those produced by Chiew et al.(1995) and Schreider et al. (1996, 1997). However, the flow changes simulated here at best maintain currentwater supply, and at worst, produce substantial reductions. This reduced range of outcomes compared withprevious studies is due to reduced uncertainty in rainfall changes and the development of Ep scenarios.

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Figure 7. Range of change in mean annual storage into Burrendong Dam at low, mid and high warmingsfor 2030 based on IPCC (2001).

Probability distributions

According to the central limit theorem of statistics, if multiple ranges of uncertainty are combined, then thecentral values are favoured at the expense of the extremes. Uncertainty needs to be estimated for three variablesto describe the total uncertainty range: global warming and regional P and Ep change. Here, we applied MonteCarlo methods (repeated random sampling) to sample the IPCC (2001) range of global warming for 2030 and2070. These were then used to scale a range of change per degree of global warming on a quarterly basis for P,sampling Ep using the regression relationship established for the Macquarie catchment (Figure 5). The quarterlychanges for P and Ep were then totalled to determine annual δP and δEp. The transfer function in Equation A1(see Appendix A) was used to estimate the probability distribution for Burrendong Dam storage, MacquarieMarshes inflows and irrigation allocations in 2030 and 2070.

The following assumptions were applied:1. The range of global warming in 2030 was 0.55–1.27°C with a uniform distribution, and 1.16–3.02°C in

2070.2. Changes in P were taken from the full range of change for each quarter taken from the sample of nine

climate models.3. Changes in P for each quarter were assumed to be independent of each other (seasonally dependent changes

were tested in each of the climate models but no lag correlation between seasons could be found).


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4. The difference between samples in any consecutive quarter could not exceed the largest difference simulatedin the nine climate models.

5. Ep was assumed to be partially dependent on P (δEp = 5.75 – 0.53δP, standard error = 2.00, randomlysampled using a Gaussian distribution).

The results for 2030 and 2070 are shown in Figures 8 and 9, calculating the probability distribution from thewettest (best) to the driest (worst) outcomes. Although there is an increased flood risk for wetter outcomes, thedrier outcomes are considered worse in terms of lost productivity and environmental function. The driest andwettest outcomes are less likely than the central outcomes where the line is steepest. The most likely outcomes(in the steepest areas of the graphs) give flow changes ranging from about 0% to –15% in 2030 and –0% to –35% in 2070.

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Figure 8. Probability distribution for changes to mean annual Burrendong Dam storage, MacquarieMarsh inflows and irrigation allocations based on Monte Carlo sampling of input ranges of globalwarming, δδδδP and δδδδEp in 2030.

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Figure 9. Probability distribution for changes to mean annual Burrendong Dam storage, MacquarieMarsh inflows and irrigation allocations based on Monte Carlo sampling of input ranges of globalwarming, δδδδP and δδδδEp in 2070.

Uncertainty analysis

Uncertainty analysis was carried out to understand how much the component uncertainties contributed to therange of outcomes. Three ranges of input uncertainty, global warming and local changes in P and Ep, wereassessed by keeping each input constant within a Monte Carlo assessment while allowing the others free play,consistent with Visser et al. (2000). Global warming was kept constant at 0.91°C in 2030 and 2.09°C in 2070. δPwas taken as the average of the nine models in degrees per °C global warming for each quarter. δEp was linearlyregressed from δP, omitting the sampling of a standard deviation. The results (Table 5) show that in both 2030and 2070, δP provides almost two-thirds of the total uncertainty, global warming almost 30% and δEp just over10%.


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The key uncertainty for water supply in 2030 and 2070, at 64% of the total, is the direction and magnitude ofrainfall change. δEp as a co-dependent variable at 12% adds little further uncertainty. Global warminguncertainty comprises only 25% of the total uncertainty. The rate of global warming itself is therefore asecondary consideration to the rate and direction of rainfall change. δEp as a function of both P and globalwarming is least important. However, given that δEp is likely to be easier than δP to diagnose due to its smallervariability, the rate of Ep change may provide some indication of P change through inverse the modelling of theco-dependent P and Ep relationship.

Table 5. Results of uncertainty analysis for water storage in Burrendong Dam in 2030 and 2070.2030 Limits of Range Range Contribution to UncertaintyAll +10.3 to –28.4 38.7Constant global warming +7.7 to –21.4 29.1 25%Constant P –1.9 to –15.9 14.0 64%Constant Ep 7.2 to –26.7 33.9 12%

101%2070All +23.8 to –60.1 83.9Constant global warming +17.3 to –45.8 63.1 25%Constant P –4.6 to –34.0 29.4 65%Constant Ep 16.3 to –57.7 74.0 12%

102%

Bayesian analysis

Bayesian analysis was used to assess the impact of initial assumptions of uncertainty and to help determine thelevel of confidence of predictions. Bayesian analysis involves testing the effect of changes in input assumptionson the resulting probabilities. For example, what if other climate models increased the range of rainfalluncertainty? How are the results affected by increases in the projected range of global warming in IPCC (2001)compared to IPCC (1996)? Two different sets of assumptions were tested. One was to sample δP on a six-monthly and annual basis to determine whether independent sampling of rainfall changes on a quarterly basisdetailed in the previous section affected the results. The other was to test the impact on the results of assumingdifferent probability distributions of the input uncertainties.

Figure 10 shows the impact of quarterly, six monthly and annual sampling of δP and δEp on the probabilitydistribution of changes to mean annual Burrendong storage in 2030. Also shown are the results of the 27individual model-based scenario runs, which are given equal probability of occurring. The resultant probabilitydistributions for six-monthly and annual sampling produce higher flows, but the results do not change by morethan 10% from the standard values in most cases. It is concluded that the probability distribution function isrelatively insensitive to changed seasonal sampling strategies for P and Ep.

The second test was to determine the impact of a non-uniform distribution of global warming, compared with theuniform distribution used in the previous sections, on the results. Wigley and Raper (2001) produced non-linearPDFs for global warming in 2030 and 2070. The results using these distributions are shown in Figures 11 and12, which give the impact on changes to mean annual Burrendong storage. The results show that altering theinput assumptions for global warming does not greatly change the PDFs for Burrendong storage. Such minorchanges would be expected because global warming comprises only 25% of the input uncertainties. Only verylarge changes in that range, or in its distribution, could be expected to significantly affect the result.

We also tested the effect of altered distributions of rainfall change by applying nonlinear regressions to the rangeprovided by the nine models, counting the lowest and highest samples as the 10th and 90th percentilesrespectively (thereby extending the possible range of rainfall change). These were added to the non-linearWigley and Raper PDFs for global warming and are also shown in Figures 11 and 12. In 2030, the use of non-linear PDFs increases the range of streamflow (the difference between upper and low limits of change in supply)by only 2%, while adding in the larger possible rainfall range increases the streamflow range by 31%. For 2070,the respective figures are 20% and 55%. While these increases are fairly large, they mostly affect the extremes ofthe distribution, while the PDFs remain fairly similar for the major part of the range. Thus the most likelychanges in streamflow are not much affected by these differing assumptions.


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Figure 10. Impact of scenario-based, quarterly (Standard), six monthly and annual sampling of δδδδP andδδδδEp on the probability distribution for changes to mean annual Burrendong storage in 2030.

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Figure 11. Impact of the standard approach, non-linear sampling of global warming (Wigley and Raper,2001) and non-linear sampling of rainfall change (All) on the probability distribution for changes to meanannual Burrendong storage in 2030.

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Figure 12. Impact of the standard approach, non-linear sampling of global warming (Wigley and Raper,2001) and non-linear sampling of rainfall change (All) on the probability distribution for changes to meanannual Burrendong storage in 2070.

Critical thresholds

A critical threshold marks the point at which an activity or system faces an unacceptable level of harm. Itdelineates the coping range of climate from that part of the range where an activity is vulnerable, providing acriterion or hazard by which to measure risk. If a critical threshold can be expressed in climatic terms andquantified under climate change, it then becomes possible to estimate risk if a probability distribution for the


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climate inputs can be determined. Two critical thresholds for the Macquarie Basin estimated as representing anunacceptable degree of hazard were established:1. Bird breeding events in the Macquarie Marshes, where inflows were below the volume needed for colonial

water birds to breed over the expected lifetime of the waterbird population, taken as 10 consecutive years ofinflows below 350 GL.

2. Irrigation allocations falling below a level of 50% for five consecutive years, resulting in severe hardshipand economic failure for the bulk of irrigators.

Both thresholds are a measure of accumulated stress rather than a single extreme event. This indicates thatindividual years, such as the 1982/83 drought are currently well managed, i.e. the adaptive capacity tointerannual variability is high. Analysis showed that both thresholds were exceeded if mean annual flows fellbelow –10% in a drought-dominated climate, –20% in a normal climate and –30% in a flood-dominated climate.The risk of these thresholds being exceeded in 2030 and 2070 was assessed by relating the critical thresholds tothe probability of a change in flow such as those in Figures 8 and 9.

Risk assessment

The critical thresholds as they relate to mean changes in flow and rainfall regime were imposed on probabilitydistributions for change in mean annual flow in 2030 and 2070. Figure 13 illustrates the risk of criticalthresholds being exceeded as a function of the probability distribution shown in Figure 8 and assuming eitherdrought-dominated, long-term historical (normal) and flood-dominated baseline climates. In a normal climate,where decadal variability is neutral and P is close to the long-term average, then in 2030 both thresholds haveonly a ~1% probability of being exceeded. Under a drought-dominated regime, then the probability of a climatechange occurring in which the critical thresholds is exceeded is close to 30%. Taking full account of theuncertainties assessed in the above section the risk is between 20–30%. Under a flood-dominated climate the riskis present but negligible.

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Figure 13. Cumulative probability diagram for Burrendong storage, Macquarie Marshes inflow andirrigation allocations in 2030 shown with changes in mean water supply needed to exceed criticalthresholds under three modes of decadal rainfall variability. DDR is a drought-dominated regime andFDR, a flood dominated regime.

In 2070 (Figure 14), the risk of the two critical thresholds being exceeded is increased under all modes ofdecadal variability. Under a drought-dominated regime, the probability of threshold exceedance is about 60% to70%, in a normal climate about 30% to 40% and in a flood-dominated regime 10-20% in a flood-dominatedregime. These values are slightly less for irrigation allocations, which are somewhat more flexible.

Both the critical thresholds may be exceeded by a combination of long-term low rainfall and a sequence of dryyears. Therefore, the vulnerability of water supply to climate change in the Macquarie catchment is acombination of climate change, decadal climate variability and interannual climate variability. Climate change,expressed though mean changes in P and Ep, looks at best to maintain current water balance, and at worst toreduce water balance, leading to over 30% reductions in water supply by 2030. This may occur as a trend, or inone or more abrupt changes. How decadal climate variability combines with these changes is critical.


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Figure 14. Cumulative probability diagram for Burrendong storage, Macquarie Marshes inflow andirrigation allocations in 2070 shown with changes in mean water supply needed to exceed criticalthresholds under three modes of decadal rainfall variability. DDR is a drought-dominated regime andFDR, a flood dominated regime.

Potential impact on water resources of reforestation

Herron et al (2001) considered the impact of a range of reforestation programs on flow on the Macquarie systemin combination with the effects of climate change and natural climatic variability. This study employed the samemodelling system (OzClim coupled to IQQM), although the climate change scenarios used were scaled by globalwarming projections based on the IS92 emission scenarios (IPCC, 1996) rather than the more recent SRESemission scenarios (IPCC, 2001). Probabilistic projections were not considered.

Three tree-planting scenarios were applied to the IQQM under current climate (described in greater detail inHerron et al (2001):Scenario 1: All commercially capable land is reforested by 2030 (increases area under trees by 10%).Scenario 2: 20% of the commercially capable land is reforested (increases area under trees by 2%).Scenario 3: Similar to scenario 2 except that 10% of marginal land is also replanted (increases the area undertrees by 5%).

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Figure 15. Change in mean annual storage in Burrendong Dam for three tree planting scenarios undercurrent climate, and the wettest, mid and driest climate model patterns multiplied by a global warming of0.55°C in 2030.

The tree planting scenarios reduced Burrendong Dam storage by 17% for scenario 1 (full commercial capacity),4% for scenario 2 (20% commercial capacity) and 7% for scenario 3 (20% commercial +10% marginal). Whenclimate change applying a warming of 0.55°C was added (the minimum assumed by IPCC (2001) for 2030), therange was –16% to –30% for scenario 1, –3% to –19% for scenario 2 and –6% to –21% for scenario 3. Of these,scenario 3 is most plausible, being a combination of limited commercial forestry and marginal forestry for


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salinity remediation. Both climate change and tree planting losses to streamflow are additive except under severedrying (Figure 15).

When decadal rainfall variability was considered, the uncertainty ranges were significantly expanded. However,the method used to calculate change in evapotranspiration was based on empirical rather than physicalrelationships, and took no account of increased CO2 on plant growth and respiration. It also utilised very generalassumptions about planting area and distribution. In summary, the effect of reforestation is to reduce supply inaddition to the reductions simulated due to climate change. This highlights the need for tree-planting programs tobe carefully targeted to maximise salinity benefits and minimise streamflow losses.

Summary

To our knowledge the Macquarie River project has been the most comprehensive assessment of its type yetundertaken. We have quantified the fullest possible ranges in regional P and Ep change from nine climatemodels, the global warming range from IPCC (2001) and an operational river management model (IQQM)currently used in planning and management of the Macquarie River catchment. Other assessments haveconcentrated on possible global changes to water resources but they tend to be based on the results of a singleclimate model trying to assess the global impacts of a single scenario. Risk assessment using the full range ofuncertainty shows that decreases in streamflow in the Macquarie catchment are highly likely. The project alsodemonstrated that the risk assessment of water resources requires both the decadal mode of rainfall variabilityand climate change acting in combination to be considered. Uncertainty analysis showed that about two-thirds ofthe range of outcomes was due to rainfall change as a function of global warming. Bayesian analysis showed thatthe results are robust with respect to changes in the input assumptions. Land-use change is also an importantfeedback that has not been widely considered in water resource assessments under climate change.

Draft research plan

Water resources in the Murray-Darling Basin are clearly vulnerable to climate change but assessments takingaccount of a broad range of climate and related uncertainties have only been carried out for the Macquarie Rivercatchment. With regard to the MDB, the following considerations should be noted:• The IPCC Third Assessment Report attributes a proportion of currently observed climate change and system

responses to the enhanced greenhouse effect.• CSIRO’s Climate Change Projections for Australia have reduced the uncertainty of regional rainfall

changes from earlier 1996 and 1992 editions showing likely decreases in winter-spring rainfall.• Potential evaporation scenarios from nine climate models show a robust inverse relationship between P and

Ep change under climate change. Rainfall increases show small increases in Ep and P decreases see largerincreases in Ep.

• Application of the full range of uncertainty of possible changes of P and Ep from climate models and globalwarming from the IPCC indicate likely decreases in streamflow for the Macquarie River.

• Possible decreases in streamflow due to reafforestation and due to plausible changes in long-term rainfallvariability away from a flood-dominated regime increase the likelihood of net streamflow decreases by2030.

• Critical system thresholds for the Macquarie River catchment may be exceeded by 2030 and exceedancebecomes increasingly likely by 2070.

• The severity of possible changes will be affected by the combination of climate change, the mode of decadalrainfall variability and land-use changes.

Program aim

Produce options for the sustainable (long-term) management of environmental flows in a climate of uncertainty.Investigate a broad range of uncertainties contributed by climate change, climate variability and land-use changefor key catchments contributing to environmental flows in the Murray-Darling Basin and conduct a riskassessment of environmental flow futures. Test the impact of changed rules on their ability to cope with a broadrange of possible climate and related flow outcomes, and to reduce the risk of critical system thresholds beingexceeded.

Method

The method would expand that used in the RIRDC-funded project led by CSIRO Atmospheric Research thatwould couple existing catchment water management models containing the capacity to model both rainfall-


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runoff and water allocations with OzClim, the climate scenario generator. Critical thresholds for a number of keysystem functions would be constructed and linked to climate. Using current water management rules arelationship between change in rainfall and potential evaporation and key indices of flow would be establishedso that risk analysis could be carried out. Possible changes to water management via several strategies (involvingmanagement, legislative and economic options) would be investigated.

Applying such a method would involve a broad collaboration involving a range of skills. This program wouldrequire a consortium of partners involving Federal and State government agencies, research organisations andthe Tertiary research sector. The co-operation of each state government through their interest in individualcatchments and current role in water management is critical.

Modelling system

This project would require coupling OzClim to water management models on a catchment-by-catchment basis,so that multiple runs can be obtained with a minimum of time and effort. It is likely that models exist for all keycatchments save the upper Murray Catchment, which is largely unregulated and has depended on historicalflows. To save resources, rather than hard-wiring in each catchment (which requires specific programming), anopen modelling system would be investigated where generic river management models from Victoria, NewSouth Wales and Queensland (e.g. IQQM, REALM) could be automatically linked with OzClim. This wouldcreate a system where models could be linked and operated through a graphical user interface, creating a systemthat does not rely on applied programming to undertake each step and which would be available for a range ofinvestigations.

The modelling system could investigate the following aspects of climate change, variability, land-use and watersupply:• Develop a strategy to incorporate changes in rainfall intensity into the simulations rather than just a single

universal scaling factor.• Develop a rainfall-runoff modelling capacity for the Upper Murray Catchment.• Incorporate changes to solid precipitation into water balance models in the eastern Highlands.• For at least one catchment, test several rainfall runoff models to determine hydrological uncertainties under

climate change.• Develop strategies for environmental flow management to incorporate new information regarding climate

risks (e.g. direction and magnitude of rainfall change, new information on variability)• For the Murrumbidgee catchment, investigate the impact of downscaling options for daily rainfall from

climate models being developed by CSIRO Land and Water to determine whether systematic errors exist inthe current methods used

Attribution

At some point in the coming decades it may be possible to attribute changes in flow in the MDB to enhancedgreenhouse climate change. Attribution would require identification of a change in flow or associated changes inclimate which are unusual given our understanding of natural climate variability and which are consistent withsimulated enhanced greenhouse changes. The ability to make such an attribution is affected by the magnitude ofthe climate signal relative to natural variability. Changes are potentially large for regional rainfall, but the naturalvariability of regional rainfall is also large. Signal to noise may emerge earlier in potential evaporation, or bemore quickly detected in a combination of variables. Because the effect of changes in precipitation andevaporation tend to be amplified in streamflow, unusual trends in streamflow may become evident sooner than inthe record of the driving variables. Because of the uncertainty currently associated with estimated enhancinggreenhouse climate change for regions (particularly for the critical variable of precipitation), it is difficult topredict when attribution is likely to be possible.

Early detection and attribution of a climate change signal in the MDB is likely to be achieved through betterunderstanding of regional atmospheric dynamics associated with natural variability and climate change, andthrough careful monitoring and interpretation of regional climate and of streamflow in baseline catchments.Scoping strategies to undertake these tasks would also be an aim of the research program.

Resources

Sufficient resources are needed to cater for the number of catchments needing to be included and to engage theresearchers required. A considerable amount of the research development to carry out this project has been done,


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and much of the continued effort would involve the massive task of gathering the information needed to simulateflow changes in a large number of catchments. The involvement of state agencies is vital to provide resources,including river management models, data, advice and modelling expertise. Stakeholder meetings would be held,initially to plan the project due to its scope and complexity but later to assess risk and explore options for futuremanagement.

Many of the products developed by such an investigation would be of wider interest to State Governments,catchment management bodies, water distribution companies and other industry. A research consortium couldseek funds from interest parties to contribute to the development and analysis of outcomes from particularcatchments. State Governments may also be encouraged to provide in-kind resources, with the understandingthat the results would contribute to planning and strategy.

CSIRO also has a program of strategic research aiming to better understand the links between climate change,land-use change and processes such as salinity. It is expected that in the short-term, this research will not be ableto produce definitive answers for this particular

The proposal is that Phase III of an environmental flows project could provide the core funding for a broaderproject aiming to assess the risk of climate change and variability and robust aspects of land-use change on watersupply and quality.


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Appendix A

Transfer functions

Transfer functions were required to update the 2030 results based on the IPCC (1996) global warmings to thosebased on IPCC (2001) warmings and to carry out risk and uncertainty analyses. This was accomplished asfollows. Streamflow changes for 2030, based on changes in Ep and P simulated by the climate models, were firstcalculated for the IPCC (1996) scenarios. Eight models and three different climate sensitivities were used, giving24 data points (the GFDL model was rejected due to errors affecting Ep calculations for two months).Streamflow changes were also calculated for 2070 using the IPCC (2001) scenarios, again using eight modelsbut four climate sensitivities (giving 32 data points).

Using all 56 samples, a regression relationship was calculated between the change in streamflow and changes inEp and P. A non-linear inverse tan function produced the best physically realistic fit for the distribution:

−

= b

P

Epaflow

δδδ arctan (1)

1where δEp and δP were measured in mm pa and δflow in GL pa and percent, and a and b are constants. Theresults have a standard error ranging from 1 to 2% (Table A1). The streamflow changes for the IPCC (2001)emissions scenarios can now be calculated using eq. 1 and the simulated changes in Ep and P from climatemodels run with these scenarios.

Table A1. Regression relationships for Burrendong Dam storage and Macquarie Marsh inflows in GL andirrigation allocations in percent.Output A B R2 SE (%)Burrendong storage (GL) -98.96 80.10 0.98 1.75Macquarie Marshes inflows (GL) -48.00 79.78 0.98 2.10Irrigation allocations (change in %) -6.17 81.15 0.98 1.19

Appendix B

Artificial scenarios for sensitivity analysis of climate change on environmental flows

Three artificial scenarios of flow, P and Ep change were produced to carry out assess the combined impacts ofclimate change and environmental flow for the Murray River. Although artificial, these scenarios are intended torepresent the scale of changes that might be expected for the MDB, based on the work undertaken for theMacquarie River catchment. However, they are not forecasts and are only intended to test the sensitivity of theenvironmental flows in the Murray River to possible climate change.

The scenarios are based on a climate that has a small rainfall increase in the summer-autumn period and rainfalldecreases in the winter-spring period consistent with CSIRO’s (2001) Climate Change Projections for Australia.They contain (1) small, (2) moderate and (3) large changes in streamflow, and are accompanied by broadlyconsistent changes in P and Ep (Table B1). Increases are greatest in May to June and decreases greatest inSeptember to November, linearly scaled on a monthly basis between these two periods. Based on currentseasonal relationships of flows these changes represent decreases of –5%, –11% and –20% for the Menindee(Darling) inflows and –9%, –18% and –29% for the Hume inflows (Table B2). The larger decrease in meanannual flow for the Hume is due to the dominance of winter-spring flows on the water budget.

Scenario 1 (Table B1) could be considered as roughly equivalent to a mid-case outcome in 2030 (i.e. both wetterand drier outcomes are possible) but is likely to be exceeded by 2070. An outcome on the scale of Scenario 2 isextremely unlikely to be exceeded by 2030, mid-case in 2050 and likely by 2070. An outcome on the scale ofscenario 3 is extremely unlikely to be exceeded by 2030, unlikely in 2050 but possible by 2070.

All three scenarios are highly artificial, having been chosen and adjusted to represent the most robust changes inseasonal flow and magnitude produced by the Macquarie study. The results on which these simulations are basedare only intended to show the sensitivity of environmental flows in the Murray River to the magnitude of flow


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changes that may occur under climate change. Proper modelling and risk assessment of various managementoptions will be required before any such results should be used for planning.

Table 3. Monthly factors in percent for flow changes and potential evaporation comprising Scenario 1,2, and 3.

Flow1 Flow2 Flow3 Evap1 Evap2 Evap3 Rain1 Rain2 Rain3-5 -10 -20 2.4 4.8 8 0.9 1.7 2.90 0 -5 2.4 4.8 8 0.9 1.7 2.95 10 5 2.4 4.8 8 0.9 1.7 2.95 10 5 2.4 4.8 8 0.9 1.7 2.95 10 5 3 6 10 0.1 0.3 0.50 0 -5 3.6 7.2 12 -0.6 -1.2 -2.0-5 -10 -20 4.2 8.4 14 -1.3 -2.6 -4.4

-10 -20 -30 4.2 8.4 14 -1.3 -2.6 -4.4-15 -30 -45 4.2 8.4 14 -1.3 -2.6 -4.4-15 -30 -45 4.2 8.4 14 -1.3 -2.6 -4.4-15 -30 -45 3.6 7.2 12 -0.6 -1.2 -2.0-10 -20 -30 3 6 10 0.1 0.3 0.5

Table B2. Monthly flow as a percentage of annual flow for the Hume inflows (observations aboveDartmouth), Menindee Lakes (observations), and Macquarie River (simulated). Monthly change inpercent of annual flow change (Columns 4 to 9).

Hume Menindee Macquarie Flow1Hume

Flow2Hume

Flow3Hume

Flow1Darl

Flow2Darl

Flow3Darl

3.6 6.4 5.4 -0.2 -0.4 -0.7 -0.3 -0.6 -1.32.3 6.3 6.3 0.0 0.0 -0.1 0.0 0.0 -0.32.4 8.7 5.5 0.1 0.2 0.1 0.4 0.9 0.43.0 10.1 5.8 0.1 0.3 0.1 0.5 1.0 0.54.5 9.6 5.5 0.2 0.5 0.2 0.5 1.0 0.57.8 6.2 10.6 0.0 0.0 -0.4 0.0 0.0 -0.3

12.0 7.4 15.2 -0.6 -1.2 -2.4 -0.4 -0.7 -1.515.0 9.7 14.1 -1.5 -3.0 -4.5 -1.0 -1.9 -2.916.8 10.8 10.3 -2.5 -5.0 -7.6 -1.6 -3.2 -4.917.5 10.6 10.3 -2.6 -5.3 -7.9 -1.6 -3.2 -4.810.2 8.2 6.5 -1.5 -3.1 -4.6 -1.2 -2.4 -3.75.8 7.0 4.6 -0.6 -1.2 -1.8 -0.7 -1.4 -2.1100 100 100 -9 -18 -29 -5 -11 -20


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