C Rainfall RunoffModelling

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Francis Chiew, Jai Vaze, Neil Viney, Phillip Jordan, Jean-Michel Perraud,

Lu Zang, Jin Teng, Jorge Pena Arancibia, Robert Morden, Andrew Freebairn,

Jenet Austin, Peter Hill, Chloe Wiesemfeld and Rachel Murphy

June 2008

Rainfall-runoff modelling across

the Murray-Darling BasinA report to the Australian Government from the

CSIRO Murray-Darling Basin Sustainable Yields Project


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Murray-Darling Basin Sustainable Yields Project acknowledgments

The Murray-Darling Basin Sustainable Yields project is being undertaken by CSIRO under the Australian Government's Raising

National Water Standards Program, administered by the National Water Commission. Important aspects of the work were

undertaken by Sinclair Knight Merz; Resource & Environmental Management Pty Ltd; Department of Water and Energy (New

South Wales); Department of Natural Resources and Water (Queensland); Murray-Darling Basin Commission; Department of

Water, Land and Biodiversity Conservation (South Australia); Bureau of Rural Sciences; Salient Solutions Australia Pty Ltd;

eWater Cooperative Research Centre; University of Melbourne; Webb, McKeown and Associates Pty Ltd; and several individual

sub-contractors.

Murray-Darling Basin Sustainable Yields Project disclaimers

Derived from or contains data and/or software provided by the Organisations. The Organisations give no warranty in relation to

the data and/or software they provided (including accuracy, reliability, completeness, currency or suitability) and accept no

liability (including without limitation, liability in negligence) for any loss, damage or costs (including consequential damage)

relating to any use or reliance on that data or software including any material derived from that data and software. Data must not

be used for direct marketing or be used in breach of the privacy laws. Organisations include: Department of Water, Land and

Biodiversity Conservation (South Australia), Department of Sustainability and Environment (Victoria), Department of Water and

Energy (New South Wales), Department of Natural Resources and Water (Queensland), Murray-Darling Basin Commission.

CSIRO advises that the information contained in this publication comprises general statements based on scientific research.

The reader is advised and needs to be aware that such information may be incomplete or unable to be used in any specific

situation. No reliance or actions must therefore be made on that information without seeking prior expert professional, scientific

and technical advice. To the extent permitted by law, CSIRO (including its employees and consultants) excludes all liability to

any person for any consequences, including but not limited to all losses, damages, costs, expenses and any other

compensation, arising directly or indirectly from using this publication (in part or in whole) and any information or material

contained in it. Data is assumed to be correct as received from the Organisations.

Acknowledgements

The authors wish to thank Prof Roger Grayson, etc who provided technical review of the report, and Becky Schmidt, CSIRO, for

her copy-editing.

Citation

Chiew FHS, Vaze J, Viney NR, Jordan PW, Perraud J-M, Zhang L, Teng J, Young WJ, Penaarancibia J, Morden RA, FreebairnA, Austin J, Hill PI, Wiesenfeld CR and Murphy R (2008) Rainfall-runoff modelling across the Murray-Darling Basin. A report to

the Australian Government from the CSIRO Murray-Darling Basin Sustainable Yields Project. CSIRO, Australia. 62pp.

Publication Details

Published by CSIRO 2008 all rights reserved. This work is copyright. Apart from any use as permitted under the Copyright Act

1968, no part may be reproduced by any process without prior written permission from CSIRO.

ISSN 1835-095X


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CSIRO 2008 Rainfall-runoff modelling across the Murray-Darling Basin

Preface

This is a report to the Australian Government from CSIRO. It is an output of the Murray-Darling Basin Sustainable

Yields Project which assessed current and potential future water availability in 18 regions across the Murray-

Darling Basin (MDB) considering climate change and other risks to water resources. The project was

commissioned following the Murray-Darling Basin Water Summit convened by the Prime Minister of Australia in

November 2006 to report progressively during the latter half of 2007. The reports for each of the 18 regions and

for the entire MDB are supported by a series of technical reports detailing the modelling and assessment methods

used in the project. This report is one of the supporting technical reports of the project. Project reports can be

accessed at http://www.csiro.au/mdbsy.

Project findings are expected to inform the establishment of a new sustainable diversion limit for surface and

groundwater in the MDB one of the responsibilities of a new Murray-Darling Basin Authority in formulating a new

Murray-Darling Basin Plan, as required under the Commonwealth Water Act 2007. These reforms are a

component of the Australian Governments new national water plan Water for our Future. Amongst other

objectives, the national water plan seeks to (i) address over-allocation in the MDB, helping to put it back on a

sustainable track, significantly improving the health of rivers and wetlands of the MDB and bringing substantial

benefits to irrigators and the community; and (ii) facilitate the modernisation of Australian irrigation, helping to put

it on a more sustainable footing against the background of declining water resources.

Summary

This report is one in a series of technical reports from the CSIRO Murray-Darling Basin Sustainable Yields

Project. This report describes the rainfall-runoff modelling for 0.05o

x 0.05ogrid cells (~ 5 km x 5 km) across the

Murray-Darling Basin (MDB) and presents the runoff estimates for the four modelling scenarios for the 0.05o

x

0.05ogrids and for the 18 MDB regions. The analyses of rainfall and other climate variables are described in a

companion report (Chiew et al., 2008). The key modelling results for the 18 MDB regions defined in the project

are summarised in Appendix A.

Scenario A Historical climate (1895 to 2006) and current development

The mean annual rainfall and modelled runoff, averaged over 1895 to 2006 over the entire MDB, are 457 mm and

27.3 mm respectively. There is a clear eastwest rainfall gradient across the MDB, where rainfall is highest in the

south-east (mean annual rainfall of more than 1500 mm) and along the eastern perimeter, and lowest in the west

(less than 300 mm). The runoff gradient is much more pronounced than the rainfall gradient, with runoff in the

south-east corner (mean annual runoff of more than 200 mm) and eastern perimeter (20 to 80 mm) being much

higher than elsewhere in the MDB (less than 10 mm in the western half). In the northern MDB, most of the rainfalland runoff occurs in the summer half of the year, and in the southernmost MDB, most of the rainfall and runoff

occurs in the winter half of the year.

The runoff estimates in the southern and eastern MDB, where most of the runoff occurs, are relatively good

because there are many gauged catchments there from which to estimate the model parameter values. The

errors in the mean annual runoff estimated for the southern and eastern MDB are generally less than 50 percent

for the 0.05o

x 0.05o

grids, and likely to be less than 10 percent when averaged over the MDB regions. There is

less confidence in the runoff estimates in the dry central and western MDB because there are very few or no

calibration catchments there from which to estimate the model parameter values.


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Rainfall-runoff modelling across the Murray-Darling Basin CSIRO 2008

Scenario B Recent climate (1997 to 2006) and current development

The 1997 to 2006 mean annual runoff averaged over the MDB is 21.7 mm, about 21 percent lower than the 1895

to 2006 long-term mean. The biggest differences are in the southern half of the MDB, where the 1997 to 2006

runoff is more than 30 percent lower than the long-term mean, and up to 50 percent lower in the southernmost

parts. Potter et al. (2008) provide a detailed analysis of recent rainfall and runoff characteristics across the MDB.

Scenario C Future climate (~2030) and current development

The future climate is used to assess the range of likely climate conditions around the year 2030. Forty-five future

climate variants, each with 112 years of daily climate sequences, are used. The future climate variants come from

scaling the 1895 to 2006 climate data to represent the ~2030 climate, based on analyses of 15 global climate

models (GCMs) and three global warming scenarios from the Fourth Assessment Report of the Intergovernmental

Panel on Climate Change.

The majority of the modelling results shows a decrease in mean annual runoff, particularly in the southern MDB

where more than two-thirds of the results show a decrease in mean annual runoff. The best estimate or medianindicates that the future mean annual runoff in the MDB in ~2030 relative to ~1990 will be lower, by 5 to 10

percent in the north-east and southern half, and by about 15 percent in the southernmost parts. Averaged across

the entire MDB, the best estimate or median is a 9 percent decrease in mean annual runoff.

There is considerable uncertainty in these estimates, with the extreme dry and extreme wet values in the northern

half of the MDB ranging from a 30 percent decrease to a 30 percent increase in mean annual runoff. In the

southern half of the MDB, the extreme estimates range from a 40 percent decrease to a 20 percent increase in

mean annual runoff, and in the southernmost MDB, the extreme estimates range from a decrease in mean annual

runoff of up to 50 percent to little change in mean annual runoff. Averaged over the entire MDB, the extreme

estimates range from a 33 percent decrease to a 16 percent increase in mean annual runoff.

The biggest uncertainty in Scenario C modelling is in the global warming projections and the GCM modelling of

the impact of this global warming on rainfall in the MDB. The uncertainty in the rainfall-runoff modelling of the

impact of climate change on runoff is small compared to the uncertainty in the climate change projections. The

Scenario C modelling only considers the impact of changes in rainfall and potential evapotranspiration on runoff.

The modelling does not take into account the potential effect of global warming and enhanced CO2

concentrations on forest water use. This impact could be significant, but it is difficult to estimate the net effect

because of the compensating positive and negative impacts and the complex climate-biosphere-atmosphere

interactions and feedbacks.

Scenario D Future climate (~2030) and future development (~2030)

Plantations can significantly affect local runoff, but for the Bureau of Rural Sciences projections of commercialforestry plantations assessed here, the impact on runoff averaged over an entire region is negligible. The impact

of the projected increases in farm dams varies from zero to a 1.5 percent reduction in mean annual runoff

averaged over 17 of the 18 MDB regions and about 3 percent reduction in mean annual runoff in the Eastern

Mount Lofty Ranges region.

After the uncertainty in the Scenario C climate change projections, the biggest uncertainty in Scenario D

modelling is in the projections of future increases in commercial forestry plantations and farm dam development

and the impact of these developments on runoff. The increase in farm dams is estimated by considering trends in

historical farm dam growth and current policy controls. There is considerable uncertainty both as to how

landholders will respond to development policies and how governments may set policies in the future.


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CSIRO 2008 Rainfall-runoff modelling across the Murray-Darling Basin

Table of Contents

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

2 Rainfall-runoff modelling method ...........................................................................................22.1 Rainfall-runoff modelling...............................................................................................................................................22.2 Climate scenarios.........................................................................................................................................................3

3 Summary of modelling results .................................................................................................53.1 Reporting regions, subcatchments and calibration catchments ..................... .................... .................... .................... ...53.2 Scenario A results (historical climate, recent development)..........................................................................................73.3 Scenario B results (recent climate, recent development)..................... .................... .................... .................... ........... 113.4 Scenario C results (future climate, recent development).............................................................................................133.5 Scenario D results (future climate, future development) .................. .................... .................... ..................... .............. 28

4 Rainfall-runoff modelling for Scenario A ..............................................................................304.1 Rainfall-runoff models .................. .................... .................... .................... ..................... .................... .................... .....304.2 Model calibration and verification .................. .................... .................... .................... ..................... .................... ........ 32

5 Rainfall-runoff modelling for Scenario C ..............................................................................415.1 Modelling climate change impact on runoff.................................................................................................................415.2 Global warming and forest water use ................... .................... .................... .................... .................... ..................... .435.3 Future bushfire risk and impact on runoff ................... ..................... .................... .................... .................... ............... 44

6 Rainfall-runoff modelling for Scenario D...............................................................................486.1 Commercial forestry plantations... .................... ..................... .................... .................... .................... .................... .....486.2 Farm dams.................................................................................................................................................................496.3 Estimation of future farm dam development ................... ..................... .................... .................... .................... ........... 536.4 Results.......................................................................................................................................................................56

7 References...............................................................................................................................60

8 Appendix A: Summary of key modelling results .................................................................62

Tables

Table 5-1. Summary of broad assessment of impact of increased future bushfire risk on future runoff..................... ........ 45Table 6-1. Existing areas of commercial forestry plantations in the Murray-Darling Basin and the projected increases by2030................... .................... .................... .................... .................... ..................... .................... .................... ................. 49Table 6-2. Existing farm dam storage capacity (GL), listed by data source, region and state .................. .................... ..... 53Table 6-3. Summary of projected increases in farm dam storage capacity (~2030 relative to ~2005) in Murray-DarlingBasin regions in New South Wales.... .................... .................... ..................... .................... .................... .................... ...... 55

Figures

Figure 3-1. Map showing 18 reporting regions, subcatchments and calibration catchments............. .................... .............. 6Figure 3-2. Mean annual rainfall, areal potential evapotranspiration and modelled runoff ................... .................... ........... 8Figure 3-3. Mean summer (DJF) rainfall, areal potential evapotranspiration and modelled runoff.................................... ... 9Figure 3-4. Mean winter (JJA) rainfall, areal potential evapotranspiration and modelled runoff .................. .................... .. 10Figure 3-5. Percent difference between 19972006 mean annual runoff and 18952006 long-term mean for 0.05

ox 0.05

o

grid cells (left) and averaged over each of the 18 MDB regions (right) .................. .................... .................... .................... 12Figure 3-6. Absolute difference (in mm) between 19972006 mean annual runoff and 18952006 long-term mean for0.05

ox 0.05

ogrid cells (left) and averaged over each of the 18 MDB regions (right) .................. ..................... .................. 12

Figure 3-7. Percent change in mean annual runoff across the Murray-Darling Basin (~2030 relative to ~1990) from 15global climate models under the medium global warming scenario.... .................... .................... ..................... .................. 14Figure 3-8. Percent change in mean summer (DJF) runoff across the Murray-Darling Basin (~2030 relative to ~1990) from15 global climate models under the medium global warming scenario................... .................... ..................... .................. 15

Figure 3-9. Percent change in mean winter (JJA) runoff across the Murray-Darling Basin (~2030 relative to ~1990) from15 global climate models under the medium global warming scenario................... .................... ..................... .................. 16


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Rainfall-runoff modelling across the Murray-Darling Basin CSIRO 2008

Figure 3-10. Number of rainfall-runoff modelling results (using projections from 15 global climate models) showing adecrease (or increase) in future mean annual, summer (DJF), and winter (JJA) runoff.... .................... .................... ......... 17Figure 3-11. Percent change in modelled mean annual runoff across the Murray-Darling Basin (~2030 relative to ~1990)for the best estimate or median and the extreme dry and extreme wet scenarios ................... .................... .................... .. 18Figure 3-12. Percent change in modelled mean summer (DJF) runoff across the Murray-Darling Basin (~2030 relative to~1990) for the best estimate or median and the extreme dry and extreme wet scenarios .................... ..................... ........ 19Figure 3-13. Percent change in modelled mean winter (JJA) runoff across the Murray-Darling Basin (~2030 relative to

~1990) for the best estimate or median and the extreme dry and extreme wet scenarios .................... ..................... ........ 20Figure 3-14. Absolute change (in mm) in modelled annual runoff across the Murray-Darling Basin (~2030 relative to~1990) for the best estimate or median and the extreme dry and extreme wet scenarios .................... ..................... ........ 21Figure 3-15. Absolute change (in mm) in modelled mean summer (DJF) runoff across the Murray-Darling Basin (~2030relative to ~1990) for the best estimate or median and the extreme dry and extreme wet scenarios..... .................... ........ 22Figure 3-16. Absolute change (in mm) in modelled mean winter (JJA) runoff across the Murray-Darling Basin (~2030relative to ~1990) for the best estimate or median and the extreme dry and extreme wet scenarios..... .................... ........ 23Figure 3-17. Percent change in modelled mean annual runoff for the 18 Murray-Darling Basin regions (~2030 relative to~1990) for the best estimate or median and the extreme dry and extreme wet scenarios (results are obtained using climatechange projections from a single global climate model run for the entire region, in contrast to Figure 3-11 where thechanges are shown for 0.05

ox 0.05

ogrids).............. .................... .................... ..................... .................... .................... .... 24

Figure 3-18. Mean monthly rainfall and modelled runoff averaged over each of the 18 Murray-Darling Basin regions forthe historical climate, with the extreme range for future climate shown in orange ................... .................... .................... .. 25Figure 3-19. Projected increases in commercial forestry plantations and farm dam storage capacity in the 18 Murray-

Darling Basin regions (~2030 relative to ~2005) .................. .................... .................... .................... .................... ............. 28Figure 3-20. Percent change in modelled mean annual runoff for the 18 Murray-Darling Basin regions for the bestestimate or median and the extreme dry and extreme wet scenarios (the impacts of both climate change and developmentare included, in contrast to Figure 3-17 where only impacts from climate change are shown)...................... .................... . 29Figure 4-1. Structure of SIMHYD rainfall-runoff model .................. .................... ..................... .................... .................... .. 31Figure 4-2. Structure of Sacramento rainfall-runoff model......... ..................... .................... .................... .................... ...... 32Figure 4-3. Summary of model calibration and verification results ..................... .................... .................... .................... .. 34Figure 4-4. Typical plots comparing modelled and observed monthly runoffs and daily runoff characteristics .................. 35Figure 4-5. Comparison of mean annual runoff estimated by SIMHYD and Sacramento models for the verification resultswith the observed runoff ................... .................... ..................... .................... .................... .................... .................... ....... 38Figure 4-6. Mean annual runoff estimated by the SIMHYD and Sacramento models across the Murray-Darling Basin .... 38Figure 4-7. Mean summer (DJF) runoff estimated by the SIMHYD and Sacramento models across the Murray-DarlingBasin. .................... .................... ..................... .................... .................... .................... .................... ..................... ............. 39

Figure 4-8. Mean winter (JJA) runoff estimated by the SIMHYD and Sacramento models across the Murray-Darling Basin.................... .................... .................... .................... ..................... .................... .................... .................... .................... .... 39Figure 4-9. Summary of model verification results across the Murray-Darling Basin .................. .................... .................. 40Figure 5-1. Comparison of changes in runoff characteristics in ~2030 relative to ~1990 estimated by the SIMHYD andSacramento models using climate change projections from the IPSL global climate model under the medium globalwarming scenario ................... .................... ..................... .................... .................... .................... .................... ................. 42Figure 5-2. Comparison of changes in runoff characteristics in ~2030 relative to ~1990 estimated by the SIMHYD andSacramento models using climate change projections from the INMCM global climate model under the medium globalwarming scenario ................... .................... ..................... .................... .................... .................... .................... ................. 42Figure 5-3. Comparison of changes in runoff characteristics in ~2030 relative to ~1990 estimated by the SIMHYD andSacramento models using climate change projections from the CCCMA T47 global climate model under the mediumglobal warming scenario...... .................... .................... ..................... .................... .................... .................... .................... 43Figure 5-4. The effect of increased CO2 on catchment water balance processes (adapted from Field et al., 1995) (upwardarrow next to a process indicates an increase, downward arrow next to a process indicates a decrease)......................... 44Figure 5-5. Mean annual cumulative forest fire danger index (FFDI) for the historical climate (19802006) and percentchange by ~2030 (second lowest, median and second highest using future climate data informed by six global climatemodels) ..................... .................... .................... .................... .................... ..................... .................... .................... .......... 46Figure 5-6. Relationship between forest area burnt and mean annual cumulative FFDI based on data from Victoria....... 47Figure 5-7. Average change in mean annual streamflow for different species following bushfires.................. .................. 47Figure 6-1. Sources of data on existing farm dam storage capacity .................. ..................... .................... .................... .. 51Figure 6-2. Spatial density of existing farm dam storage capacity........... ..................... .................... .................... ............ 52Figure 6-3. Spatial density of projected increase in farm dam storage capacity (~2030 relative to ~2005) .................. ..... 55Figure 6-4. Percent reduction in mean annual runoff due to the projected increase in farm dams (~2030 relative to ~2005).................... .................... .................... .................... ..................... .................... .................... .................... .................... .... 58


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CSIRO 2008 Rainfall-runoff modelling across the Murray-Darling Basin 1

1 Introduction

This report is one in a series of technical reports from the CSIRO Murray-Darling Basin Sustainable Yields Project. The

terms of reference for the project are to estimate current and future water availability in each catchment and aquifer in

the Murray-Darling Basin (MDB) considering climate change and other risks and surface-groundwater interactions, andcompare the estimated current and future water availability to that required to meet the current levels of extractive use.

Results from the project were reported progressively for 18 contiguous regions across the entire MDB.

The purpose of this report is to describe in more detail the rainfall-runoff modelling undertaken in the project. The main

objective of the rainfall-runoff modelling is to use a consistent MDB-wide modelling approach to estimate daily runoff for

0.05o x 0.05o grids (~ 5 km x 5 km) across the MDB for four scenarios. The four scenarios are:

Scenario A Historical climate (1895 to 2006) and current development

Scenario B Recent climate (1997 to 2006) and current development

Scenario C Future climate (~2030) and current development

Scenario D Future climate (~2030) and future development (~2030).

The rainfall-runoff modelling method is described in Chapter 2 and the key modelling results are summarised in Chapter

3. The remaining chapters present more details on the modelling: calibration and assessment of the rainfall-runoff

models in Chapter 4; application of the models to estimate climate change impact on runoff in Chapter 5; and modelling

the impact of development (commercial forestry plantations and farm dams) on future runoff in Chapter 6.


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2 Rainfall-runoff modelling across the Murray-Darling Basin CSIRO 2008

2 Rainfall-runoff modelling method

2.1 Rainfall-runoff modelling

The adopted rainfall-runoff modelling method provides a consistent way of modelling historical runoff across the Murray-

Darling Basin (MDB) and assessing the potential impacts of climate change and development on future runoff.

The lumped conceptual rainfall-runoff model, SIMHYD, with a Muskingum routing method is used to estimate daily runoff

for 0.05o

x 0.05o

grid cells (~ 5 km x 5 km) across the entire MDB for the four scenarios. The use of 0.05o

x 0.05o

grid

cells allows a better representation of the spatial patterns and gradients in rainfall. The rainfall-runoff model is calibrated

against 1975 to 2006 streamflow data from 183 small and medium size unregulated gauged catchments (50 to 2000

km2) across the MDB (referred to as calibration catchments). Although unregulated, streamflow in these catchments may

reflect low levels of water diversion and will include the effects of historical land use change. The calibration period is a

compromise between a shorter period that would better represent current development and a longer period that would

better account for climatic variability.

In the model calibration, the six parameters of SIMHYD are optimised to maximise an objective function that incorporatesthe Nash-Sutcliffe efficiency of monthly runoff and daily flow duration curve, together with a constraint to ensure that the

total modelled runoff over the calibration period is within 5 percent of the total recorded runoff (see Chapter 4). The

resulting optimised parameter values are therefore identical for all grid cells within a calibration catchment.

The runoff for a non-calibration or ungauged subcatchment is modelled using optimised parameter values from the

geographically closest calibration catchment, provided there is a calibration catchment within 250 km (subcatchments are

defined by the river system models for the 18 MDB regions). Once again, the parameter values for each grid cell within a

subcatchment are identical. For subcatchments more than 250 km from a calibration catchment, default parameter

values are used. The default parameter values are identical across the entire MDB and are chosen to ensure a realistic

runoff gradient across the drier parts of the MDB. The places these default values are used are therefore all areas of very

low runoff. There is an exception for the Paroo and Warrego regions in the north-west MDB. In these regions, analyses

of local long-term rainfall and runoff data justified the use of optimised parameter values from a single calibration

catchment in the Paroo for the entire Paroo region and two calibration catchments in the Warrego for the Warrego region

(CSIRO, 2007a; CSIRO 2007b; Young et al., 2006).

As the parameter values come from calibration against streamflow from 50 to 2000 km2

catchments, the runoff defined

here is different, and can be much higher than streamflow recorded over very large catchments where there can be

significant transmission losses (particularly in the western and northwestern MDB). Almost all the catchments available

for model calibration are in the higher runoff areas in the southern and eastern MDB. Runoff estimates are therefore

generally good in the southern and eastern MDB and are comparatively poor elsewhere.

The same set of parameter values are used to model runoff across the MDB for Scenarios A, B and C using 112 years of

daily climate inputs described in Section 2.2. The future climate scenario simulations therefore do not take into account

the effect of global warming and enhanced CO2 concentrations on forest water use. This effect could be significant, but it

is difficult to estimate the net effect because of the compensating positive and negative impact and the complex climate-

biosphere-atmosphere interactions and feedbacks (see Section 5.2 for more discussion). Bushfire risk is also likely to

increase under the future climate scenario. In areas where bushfires occur, runoff would reduce significantly as forests

regrow. However, the impact on runoff averaged over an entire region is unlikely to be significant (see Section 5.3 for

more discussion and broad analysis).

The projection of growth in commercial forestry plantations and farm dams and the modelling of the impact of these

developments on future runoff are described in Chapter 6.

The rainfall-runoff model SIMHYD is used because it is simple and has relatively few parameters and, for the purpose of

this project, provides a consistent basis (that is automated and reproducible) for modelling historical runoff across the

entire MDB and for assessing the potential impacts of climate change and development on future runoff. It is possible

that in data-rich areas, specific calibration of SIMHYD or more complex rainfall-runoff models based on expert judgement

and local knowledge, as carried out by some agencies, would lead to better model calibration for the specific modelling


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objectives of the area. Chapter 4 describes in more detail the model calibration and assessment, and the comparison of

SIMHYD with another commonly used rainfall-runoff model (the Sacramento model). The simulations from the two

rainfall-runoff models are relatively similar in the context of the application here.

The modelled runoff series are modified and used as inputs to drive the river system models to estimate the impact of

climate change and development on water availability and water use across the 18 MDB regions. The modelled runoff

series are not used directly as subcatchment inflows in the river system models as this would violate the calibrations of

the river system models already undertaken by state agencies to different runoff series. Instead, the relative differencesbetween the daily flow duration curves of the historical climate scenario and the other scenarios are used to modify the

existing inflows series in the river system models. All the scenario inflow series for the river system modelling therefore

have the same daily sequence, but different amounts.

2.2 Climate scenarios

Daily rainfall and potential evapotranspiration (PET) are required to run the rainfall-runoff models. The climate data for

the hydrologic scenario modelling across the MDB are described in detail in Chiew et al. (2008). A brief summary is given

here.

The historical climate scenario (Scenario A) is the baseline against which other scenarios are compared. It is based on

observed climate data from 1895 to 2006. The source of the historical climate data is the SILO Data Drill of the

Queensland Department of Natural Resources and Water (http://www.nrw.qld.gov.au/silo and Jeffrey et al., 2001). The

SILO Data Drill provides surfaces of daily rainfall and other climate data for 0.05o

x 0.05o

grid cells across Australia,

interpolated from point measurements made by the Australian Bureau of Meteorology. Areal potential evapotranspiration

is calculated from the SILO climate surface using Mortons wet environment evapotranspiration algorithms

(http://www.bom.gov.au/climate/averages; Morton, 1983; Chiew and Leahy, 2003).

The recent climate scenario (Scenario B) is used to assess future water availability should the climate in the future prove

to be similar to that of the last ten years. Climate data for 1997 to 2006 are used to generate stochastic replicates of 112-

year daily climate sequences. The replicate which produces a mean annual runoff closest to that observed in 1997 to

2006 is selected to define this scenario.

The future climate scenario (Scenario C) is used to assess the range of likely climate conditions around the year 2030.

Forty-five future climate variants, each with 112 years of daily climate sequences, are used. The future climate variants

come from scaling the 1895 to 2006 climate data to represent ~2030 climate, based on analyses of 15 global climate

models (GCMs) and three global warming scenarios (the 15 GCMs used are listed in Chiew et al., 2008). The scenario

variants are derived from the latest modelling for the Intergovernmental Panel on Climate Change Fourth Assessment

Report (IPCC, 2007; CSIRO and Australian Bureau of Meteorology, 2007).

The method used here takes into account two types of uncertainties. The first uncertainty is in the global warming

projection, due to the uncertainties associated with projecting greenhouse gas emissions and predicting how sensitive

the global climate is to greenhouse gas concentrations. The second uncertainty is in GCM modelling of local climate in

the MDB.

Results from each GCM are analysed separately to estimate the percent change per degree global warming in rainfall

and other climate variables required to calculate PET. The percent change per degree global warming is then multiplied

by the temperature change projected for high, medium and low global warming. The result is the percent change in the

climate variables expected in ~2030 relative to ~1990 under high, medium and low global warming scenarios.

As the future climate series (Scenario C) is obtained by scaling the historical daily climate series from 1895 to 2006

(Scenario A), the daily climate series for Scenarios A and C have the same length of data (112 years) and the same

sequence of daily climate. Scenario C is therefore not a forecast climate at 2030, but a 112-year daily climate series

based on 1895 to 2006 data adjusted to match projected global temperatures at ~2030 relative to ~1990.

The method used to obtain the future climate series also takes into account different changes in each of the four seasons

as well as changes in the daily rainfall distribution. Considering changes in the daily rainfall distribution is importantbecause many GCMs indicate that future extreme rainfall is likely to be more intense, even in some regions where a

decrease in mean seasonal or annual rainfall is projected. As the high rainfall events generate large runoff, the use of


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traditional methods that assume the entire rainfall distribution to change in the same way would lead to an

underestimation of total runoff.


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3 Summary of modelling results

This chapter summarises the key modelling results, in particular the runoff estimates for the different scenarios. Chiew et

al. (2008) provide a similar presentation for rainfall and other climate variables.

3.1 Reporting regions, subcatchments and calibration catchments

Figure 3-1 shows the boundaries of the 18 regions defined for the CSIRO Murray-Darling Basin Sustainable Yields

Project, the subcatchments defined for the river system modelling in the 18 regions, and the gauged catchments used to

calibrate the rainfall-runoff models. The map also highlights where default model parameter values are used to model

areas where the closest calibration catchment is more than 250 km away. Almost all the gauged catchments available for

model calibration are in the higher runoff areas in the southern and eastern Murray-Darling Basin (MDB). Runoff

estimates are therefore generally good in the higher runoff areas but comparatively poor elsewhere.


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Figure 3-1. Map showing 18 reporting regions, subcatchments and calibration catchments


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3.2 Scenario A results (historical climate, recent development)

Figure 3-2 shows the mean annual rainfall, areal potential evapotranspiration (APET) and modelled runoff, averaged

over 1895 to 2006. Figures 3-3 and 3-4 show this same information for summer (December-January-February) and

winter (June-July-August), respectively. The mean annual rainfall, APET and runoff averaged over the entire MDB are

457 mm, 1443 mm and 27.3 mm, respectively.

There is a clear eastwest rainfall gradient across the MDB, where rainfall is highest in the south-east (mean annual

rainfall of more than 1500 mm) and along the eastern perimeter, and lowest in the west (less than 300 mm). The runoff

gradient is much more pronounced than the rainfall gradient, with runoff in the south-east corner (mean annual runoff of

more than 200 mm) and eastern perimeter (20 to 80 mm) being much higher than elsewhere in the MDB (less than

10 mm in the western half). In the northern MDB, most of the rainfall and runoff occurs in the summer half of the year,

and in the southernmost MDB, most of the rainfall and runoff occurs in the winter half of the year (see Figure 3-14).


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Figure 3-2. Mean annual rainfall, areal potential evapotranspiration and modelled runoff


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Figure 3-3. Mean summer (DJF) rainfall, areal potential evapotranspiration and modelled runoff


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Figure 3-4. Mean winter (JJA) rainfall, areal potential evapotranspiration and modelled runoff


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3.3 Scenario B results (recent climate, recent development)

Figures 3-5 and 3-6 show the percent difference, and absolute difference (in mm), respectively, between the modelled

mean annual runoff averaged over the past ten years (1997 to 2006) compared to the 1895 to 2006 long-term mean. The

1997 to 2006 mean annual runoff averaged over the MDB is 21.7 mm, about 21 percent lower than the 1895 to 2006

long-term mean. The biggest differences are in the southern half of the MDB, where the 1997 to 2006 runoff is more than

30 percent lower than the long-term mean, and up to 50 percent lower in the southernmost parts. The difference between

the 1997 to 2006 runoff and the 1895 to 1996 runoff in the eight southernmost regions (Murray, Murrumbidgee, Eastern

Mount Lofty Ranges, Wimmera, Loddon-Avoca, Campaspe, Goulburn-Broken and Ovens) are statistically significant at

= 0.2 (with the Student-t and Rank-Sum tests). The difference between the 1997 to 2006 and 1895 to 2006 runoff

averaged over the MDB is dominated by the values in south-east MDB where runoff is highest and where the biggest

differences occur (see Figure 3-6). Potter et al. (2008) provide a detailed analysis of recent rainfall and runoff and a

discussion of annual rainfall and runoff characteristics across the MDB.


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Figure 3-5. Percent difference between 19972006 mean annual runoff and 18952006 long-term mean

for 0.05o

x 0.05o

grid cells (left) and averaged over each of the 18 MDB regions (right)

Figure 3-6. Absolute difference (in mm) between 19972006 mean annual runoff and 18952006 long-term mean

for 0.05o

x 0.05o

grid cells (left) and averaged over each of the 18 MDB regions (right)


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3.4 Scenario C results (future climate, recent development)

Figures 3-7, 3-8 and 3-9 show the percent change in mean annual, summer, and winter runoff, respectively, for ~2030

relative to ~1990. These results were obtained from the rainfall-runoff modelling using climate change projections from

the 15 global climate models (GCMs) under the medium global warming scenario (see Chiew et al. (2008) for description

of the GCMs). Figure 3-10 shows the number of GCMs that indicate a decrease (or increase) in mean annual, summer,

and winter runoff. The results indicate that the potential changes in runoff as a result of global warming can be very

significant. However, there can be considerable differences in the runoff modelling results using climate change

projections from the different GCMs. Nevertheless, the majority of the results show a decrease in mean annual runoff,

particularly in the southern MDB where more than two-thirds of the results show a decrease in mean annual runoff

(Figures 3-7 and 3-10).

The majority of the results indicate that the future summer runoff will increase except in the southernmost MDB (Figures

3-8 and 3-10). The results also indicate that future winter runoff is likely to be lower across the MDB, with more than two-

thirds of the results showing a decrease in winter runoff (Figures 3-9 and 3-10). As most of the runoff in the southernmost

MDB occurs in winter, the decrease in winter runoff translates to a significant decrease in mean annual runoff there.

The seasonal scaling factors for the 45 future climate variants are obtained by multiplying the percent change per degree

global warming (obtained from the 15 GCMs) by the temperature change projected under the low, medium and high

global warming scenarios. Thus, the driest and wettest variants will come from the high global warming scenario. The

best estimate or median of the change in mean annual, summer, and winter runoff and the extreme range of changes

across the MDB are shown as percent changes in Figures 3-11, 3-12 and 3-13, and as changes in runoff amounts (in

mm) in Figures 3-14, 3-15 and 3-16. For the best estimate or median, the median result from the medium global warming

scenario is used. For the extreme dry estimate, the second driest result from the high global warming scenario is used.

For the extreme wet estimate, the second wettest result from the high global warming scenario is used. The second

driest and second wettest results are used because they represent about the 10th

and 90th

percentile results.

The best estimate or median indicates that the future mean annual runoff in the MDB in ~2030 relative to ~1990 will be

lower, by 5 to 10 percent in the north-east and southern half, and by about 15 percent in the southernmost parts.

Averaged across the entire MDB, the best estimate or median is a 9 percent decrease in mean annual runoff. The

change in future runoff averaged over the entire MDB is dominated by the change in runoff in south-east and eastern

perimeter of the MDB where most of the runoff occurs. There is considerable uncertainty in the estimates, with the

extreme dry and extreme wet estimates in the northern half of the MDB ranging from a 30 percent decrease to a 30

percent increase in mean annual runoff. In the southern half of the MDB, the extreme estimates range from a 40 percent

decrease to a 20 percent increase in mean annual runoff, and in the southernmost MDB, the extreme estimates range

from a decrease in mean annual runoff of up to 50 percent to little change in mean annual runoff (Figure 3-11). Averaged

across the MDB, the extreme estimates range from a 33 percent decrease to a 16 percent increase in mean annual

runoff.

Figure 3-17 shows the best estimate or median of the change in mean annual runoff and the extreme range of changes

for the 18 MDB regions. Figure 3-17 is similar to Figure 3-11, but unlike Figure 3-11 where the changes are shown for

0.05o

x 0.05o

grid cells across the MDB, the results in Figure 3-17 are obtained using climate change projections from asingle GCM run for the entire region (one for each of the extreme dry, median and extreme wet scenarios). These results

are used for the whole-of-region modelling presented in the MDBSY regional reports.

Figure 3-18 shows the mean monthly rainfall and modelled runoff averaged over the 18 MDB regions, and the extreme

range for the ~2030 climate. The extreme range is determined separately for each month from the high global warming

scenario, with the second driest and the second wettest monthly result defining the lower and the upper bound,

respectively. In the northern regions, most of the rainfall and runoff occurs in the summer half of the year, and in the

southernmost regions, most of the rainfall and runoff occurs in the winter half of the year. The plots also highlight the

considerable differences between the climate change projections from the GCMs and the modelled runoff results,

particularly in the northern regions. In the southernmost regions (Eastern Mount Lofty Ranges, Wimmera, Loddon-Avoca,

Campaspe, Goulburn-Broken and Ovens), almost all GCMs predict a decrease in winter rainfall which translates to an

even bigger percent decrease in winter runoff when most the runoff in the region occurs.


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Figure 3-7. Percent change in mean annual runoff across the Murray-Darling Basin (~2030 relative to ~1990)

from 15 global climate models under the medium global warming scenario


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Figure 3-8. Percent change in mean summer (DJF) runoff across the Murray-Darling Basin (~2030 relative to ~1990)



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Figure 3-9. Percent change in mean winter (JJA) runoff across the Murray-Darling Basin (~2030 relative to ~1990)



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Figure 3-10. Number of rainfall-runoff modelling results (using projections from 15 global climate models)

showing a decrease (or increase) in future mean annual, summer (DJF), and winter (JJA) runoff


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Figure 3-11. Percent change in modelled mean annual runoff across the Murray-Darling Basin (~2030 relative to ~1990)

for the best estimate or median and the extreme dry and extreme wet scenarios


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Figure 3-12. Percent change in modelled mean summer (DJF) runoff across the Murray-Darling Basin (~2030 relative to ~1990)



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Figure 3-13. Percent change in modelled mean winter (JJA) runoff across the Murray-Darling Basin (~2030 relative to ~1990)



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Figure 3-14. Absolute change (in mm) in modelled annual runoff across the Murray-Darling Basin (~2030 relative to ~1990)



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Figure 3-15. Absolute change (in mm) in modelled mean summer (DJF) runoff across the Murray-Darling Basin

(~2030 relative to ~1990) for the best estimate or median and the extreme dry and extreme wet scenarios


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Figure 3-16. Absolute change (in mm) in modelled mean winter (JJA) runoff across the Murray-Darling Basin

(~2030 relative to ~1990) for the best estimate or median and the extreme dry and extreme wet scenarios


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Figure 3-17. Percent change in modelled mean annual runoff for the 18 Murray-Darling Basin regions (~2030 relative to ~1990)


(results are obtained using climate change projections from a single global climate model run for the entire region,

in contrast to Figure 3-11 where the changes are shown for 0.05o

x 0.05o

grids)


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Paroo

0

20

40

60

J F M A M J J A S O N D

Meanmonthlyrainfall(mm)

02

4

6


Meanmonthlyrunoff(mm)

Warrego

0

20

40

60

80



01

2

3



Condamine-Balonne

0

20

40

60

80

100


Meanmonthlyrainfall(m

m)

02

4

6


Meanmonthlyrunoff(mm

)

Moonie

0

20

40

60

80

100


Meanmon

thlyrainfall(mm)

02

4

6


Meanmon

thlyrunoff(mm)

Border

0

20

40

60

80

100

120



0

2

4

6

8



Gwydir

0

20

40

60

80

100



03

6

9

12



Figure 3-18. Mean monthly rainfall and modelled runoff averaged over each of the 18 Murray-Darling Basin regions

for the historical climate, with the extreme range for future climate shown in orange


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Namoi

0

20

40

60

80

100



02

4

6

8



Macquarie-Castlereagh

0

20

40

60

80

100



02

4

6

8



Barwon-Darling

0

20

40

60


Meanmonthlyrainfall

(mm)

0.00.5

1.0

1.5

2.0



Lachlan

0

20

40

60

80


Mean

monthlyrainfall(mm)

02

4

6


Mean

monthlyrunoff(mm)

Murrumbidgee

0

20

40

60

80



02

4

6

8

10



Murray

0

20

40

60



01

2

3

4



Figure 3-18. (continued) Mean monthly rainfall and modelled runoff averaged over each of the 18 Murray-Darling Basin regions



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Ovens

0

50

100

150



0

20

40

60



Goulburn-Broken

0

20

40

60

80

100

120



0

10

20

30

40



Campaspe

0

20

40

60

80

100


Meanmonthlyrainfall(m

m)

0

5

10

15

20



Loddon-Avoca

0

20

40

60



0

2

4

6


Meanmo

nthlyrunoff(mm)

Wimmera

0

20

40

60



0

1

2

3

4



Eastern Mount Lofty Ranges

0

20

40

60

80



0

2

4

6

8

10



Figure 3-18. (continued) Mean monthly rainfall and modelled runoff averaged over each of the 18 Murray-Darling Basin regions



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3.5 Scenario D results (future climate, future development)

Figure 3-19 shows the projected increases in commercial forestry plantations and farm dam storage capacities by ~2030

relative to ~2005. Commercial forestry plantations are projected to increase significantly in only three of the 18 MDB

regions, but they have negligible impact on the mean annual runoff averaged over a region.

The projected increase in farm dams in the Eastern Mount Lofty Ranges will reduce mean annual runoff there by about3 percent. In New South Wales and Victoria, the projected increases in farm dams will reduce mean annual runoff over a

region by 0.5 to 1.5 percent, a relatively small impact compared to that from climate change. The projected increase in

farm dams has negligible impact on future runoff in the Queensland regions.

There is considerable uncertainty in the future projections of commercial forestry plantations and farm dam storage

capacity, and the uncertainty in the projections and the resulting impact on runoff are discussed in more detail in Chapter

6.

Figure 3-20 shows the impact of both climate change and development on future runoff for the best estimate or median

and the extreme dry and extreme wet scenarios. Because the impact of development on runoff is small compared to the

impact from climate change, the plots in Figure 3-20 are almost identical to the plots in Figure 3-17 which show only the

climate change impact on future runoff.

Figure 3-19. Projected increases in commercial forestry plantations and

farm dam storage capacity in the 18 Murray-Darling Basin regions (~2030 relative to ~2005)


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Figure 3-20. Percent change in modelled mean annual runoff for the 18 Murray-Darling Basin regions for the best estimate or median

and the extreme dry and extreme wet scenarios (the impacts of both climate change and development are included, in contrast to

Figure 3-17 where only impacts from climate change are shown)


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4 Rainfall-runoff modelling for Scenario A

The purpose of Chapter 4 and Section 5.1 is to assess and compare the performance of SIMHYD, the rainfall-runoff

model used for the CSIRO Murray-Darling Basin Sustainable Yields Project with another commonly used rainfall-runoff

model, Sacramento model, and discuss the implications of the choice of rainfall-runoff model for this project.

4.1 Rainfall-runoff models

SIMHYD is a simple lumped conceptual daily rainfall-runoff model with seven parameters (Chiew et al., 2002). Figure 4-1

shows the model structure of SIMHYD and the equations used to model the rainfall-runoff processes. SIMHYD has been

used successfully across Australia for various applications, including the estimation of runoff in the National Land and

Water Resources Audit (Peel et al., 2002), the estimation of cl imate change impact on runoff (Chiew and McMahon,

2002), and in various regionalisation studies (Chiew and Siriwardena, 2005).

A Muskingum routing algorithm with two parameters (KMUSK and XMUSK), as described in Tan et al. (2005), is used to rout

the daily runoff simulated by SIMHYD to the catchment outlet. For the application here, XMUSK is set to 0 (thereforerouting with a linear storage), and the two relatively insensitive infiltration capacity parameters, COEFF and SQ (see

Figure 4-1), are set to 150 and 2 respectively. There are therefore six parameters in SIMHYD that require optimisation in

the application here.

The Sacramento model is also a lumped conceptual daily rainfall-runoff model (Burnash et al., 1973), but it is

considerably more complex than SIMHYD. Figure 4-2 shows the structure of the Sacramento model. The Sacramento

model has been used widely, in particular as part of the river system model implementations in New South Wales and

Queensland and for flow forecasting worldwide. The Sacramento model has 17 parameters, but in the application here,

only 13 parameters are optimised (ADIMP, LZFPM, LZFSM, LZPK, LZSK, LZTWM, PFREE, REXP, SARVA, IZFWM,

UZK, UZTWM, ZPERC) plus one unit hydrograph parameter, with the other four parameters set to default values

(PCTIM=0, RSERV=0.3, SIDE=0, SSOUT=0).


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Figure 4-1. Structure of SIMHYD rainfall-runoff model


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Figure 4-2. Structure of Sacramento rainfall-runoff model

4.2 Model calibration and verification

The daily rainfall and APET data required to run the two rainfall-runoff models have been described in Section 2.2. The

models are calibrated and verified using 1975 to 2006 observed streamflow data from 183 small and medium sized

unregulated gauged catchments (50 to 2000 km2) (Figure 3-1). The models are run for an extra year before the

calibration period to allow the model stores to reach the appropriate levels. The modelling is carried out for 0.05o

x 0.05o

grid cells to allow a better representation of the spatial patterns and gradients in rainfall. The same set of parameter

values are used for all 0.05o

x 0.05o

grid cells within a catchment.

The objectives of the modelling are to estimate, as reliably as possible: the mean annual runoff, the monthly runoff

series, and the daily runoff characteristics. The modelling exercise over the 183 catchments indicated that calibrating the

models to reproduce the daily runoff series led to monthly runoff simulations that are almost equally as good as

calibrating the models to reproduce the monthly runoff series directly. For this reason, the models are calibrated to

optimise the Nash-Sutcliffe efficiency (Nash and Sutcliffe, 1970) of daily runoff, with a volumetric constraint used to

ensure that the total modelled runoff is within 5 percent of the total observed runoff. The Nash-Sutcliffe efficiency (E) is

defined as follows:

E =

( ) ( )

( )

=

==

n

i

i

n

i

ii

n

i

i

1

2

1

2

1

2

OBSOBS

OBSMODOBSOBS


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where MOD is modelled runoff, OBS is observed runoff, OBS is mean of all the observed runoff, and n is the number of

observations.

To calculate Edaily, all the modelled and observed daily runoff series are compared directly. The Nash-Sutcliffe efficiency

(E) expresses the proportion of variance of the observed runoff that can be accounted for by the model and provides a

direct measure of the ability of the model to reproduce the observed runoff with E daily = 1.0 indicating that all the modelled

daily runoffs are the same as all the observed daily runoffs.

As explained earlier, the model calibration to optimise Edaily led to Emonthly values that are similar to when the models are

calibrated to optimise Emonthly directly. As one of the main objectives of the modelling is to estimate as reliably as possible

the monthly runoff series, subsequent results show comparisons of the Emonthly values (and the modelled and observed

monthly runoffs) rather than the Edaily values. The Edfdc values are also shown in subsequent assessments to reflect the

ability of the models to reproduce the observed daily runoff characteristics. To calculate Edfdc, all the modelled daily runoff

values and all the observed daily runoff values are sorted from highest to lowest, and the runoff values at the same ranks

are compared.

Figure 4-3 summarises the Emonthly and Edfdc values from the model calibrations for the 183 catchments. The results

indicate that both the SIMHYD and Sacramento models can reproduce reasonably satisfactorily the observed monthly

runoff series (Emonthly values greater than 0.6 in more than 90 percent of the catchments and greater than 0.8 in more

than 70 percent of the catchments) and the daily runoff characteristics (Edfdc values greater than 0.8 in more than

90 percent of the catchments). The calibration results for the Sacramento model are slightly better than the results for the

SIMHYD model, mainly because there are more parameters in the Sacramento model.

Figure 4-4 shows typical comparisons of the modelled and observed monthly runoffs and the modelled and observed

daily flow duration curves. The calibration to optimise Nash-Sutcliffe E means that more importance is placed on the

simulation of high runoff, and therefore the modelling of the medium and high runoffs are considerably better than the

simulation of low runoff. Nevertheless, an optimisation to reduce overall error variance will result in some

underestimation of high runoff and overestimation of low runoff. This is evident in some of the scatter plots comparing the

modelled and observed monthly runoffs and the daily flow duration curves comparing the modelled and observed daily

runoff characteristics. The discernible disagreement between the modelled and observed daily runoff characteristics only

occurs for runoff that is exceeded less than 0.1 or 1 percent of the time, but is accentuated in the plots because of thelinear scale on the y-axis and normal probability scale on the x-axis. In any case, the volumetric constraint used in the

model calibration ensures that the total modelled runoff is always within 5 percent of the total observed runoff.

Almost all the calibration catchments are in the south-east corner and eastern perimeter of the MDB (Figure 3-1). The

calibration catchments cover less than 5 percent of the south-east corner and eastern perimeter of the MDB, and less

than 1 percent of the entire MDB. As the runoff for 0.05o

x 0.05o

grids in subcatchments across the MDB are estimated

using optimised parameter values from the geographically closest calibration catchment, the ability of the SIMHYD and

Sacramento models to estimate runoff for ungauged catchments is also assessed here. This is done by using the

optimised parameter values from the nearest calibration catchment to estimate runoff for each of the catchments and

comparing the modelled runoff with the observed runoff. This is called model verification here, to distinguish it from the

model calibration discussed above.

The model verification results are also summarised in Figure 4-3. As expected, these results are poorer than the model

calibration results. However, the Emonthly values in the verification results are generally only less than 0.1 lower than the

values in the calibration results in about half the catchments, and less than 0.2 lower in very few catchments (Figure 4-3).

The Emonthly values in the verification results for the Sacramento and SIMHYD models are relatively similar. The

verification results for the Sacramento model are slightly better than the results for the SIMHYD model in more than half

of the catchments (catchments with higher Emonthly values) and slightly poorer than the SIMHYD model in the other

catchments.

The verification results also show that the errors in the mean annual runoff estimated by the SIMHYD and Sacramento

models are less than 20 percent in more than half the catchments and less than 50 percent in more than 85 percent of

the catchments, with no bias towards an underestimation or overestimation (Figures 4-3 and 4-5). Figures 4-6, 4-7 and 4-

8 show the mean annual, summer, and winter runoff estimated by the SIMHYD and Sacramento models for 0.05o

x 0.05o

grids across the MDB. The plots in Figures 4-5 to 4-8 indicate that the mean runoffs estimated by the SIMHYD and

Sacramento models are very similar.


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Figure 4-9 shows the spatial Emonthly values and the errors in the mean annual runoff estimates for the verification results

for the SIMHYD and Sacramento models across the MDB. As the plots do not show any clear regional differences, the

broad conclusions above apply across the MDB. The results therefore indicate that the mean annual runoffs estimated

for ungauged subcatchments using optimised parameter values from a nearby calibration catchment have an error of

less than 20 percent in more than half the subcatchments and less than 50 percent in most of the subcatchments. As

there is little bias towards an underestimation or overestimation, the errors in the mean annual runoff estimates averaged

over each of the 18 MDB regions will be considerably smaller because of compensating positive and negative errors. As

most of the calibration catchments are in the higher runoff areas in the southern and eastern MDB, the runoff estimates

there are generally good. There is less confidence in the runoff estimates in the dry central and western MDB because

there are very few or no calibration catchments there from which to estimate the model parameter values.

As the simulations from the SIMHYD and Sacramento models are similar, particularly in the mean seasonal and annual

runoffs, SIMHYD is used for the modelling in this project because it is simpler and it has been used extensively for

climate change impact on runoff studies. It is likely that the use of better regionalisation and parameterisation methods

can improve the runoff modelling, particularly the daily runoff estimates (e.g., regionalisation based on catchment

similarities, weighted ensemble modelling (of simulations from SIMHYD, Sacramento and other models), and

constraining model calibrations with other data types like remotely-sensed evapotranspiration or soil moisture). A

system-wide optimisation together with the river system models that also use gauged streamflow data in main river

channels can also reduce the uncertainty in the runoff estimates.

0.0

0.2

0.4

0.6

0.8

1.0

0 20 40 60 80 10

0

12

0

14

0

16

0

18

0

SIMHYD verification

Sacramento verification

SIMHYD calibration

Sacramento calibration

Emonthly

0.0

0.2

0.4

0.6

0.8

1.0

0 20 40 60 80 100

% catchments where Emonthly is exceeded

Edfdc

0.0

0.2

0.4

0.6

0.8

1.0

0 20 40 60 80 100

% catchments where Edfdc is exceeded

Bias (% difference between m odelled and

observed m ean annual runoff )

-60

-40

-20

0

20

40

60

0 20 40 60 80 100

% catchments where bias is exceeded

Figure 4-3. Summary of model calibration and verification results


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Figure 4-4. Typical plots comparing modelled and observed monthly runoffs and daily runoff characteristics


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Figure 4-4. (continued) Typical plots comparing modelled and observed monthly runoffs and daily runoff characteristics


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Figure 4-4. (continued) Typical plots comparing modelled and observed monthly runoffs and daily runoff characteristics


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0

200

400

600

800

1000

0 200 400 600 800 1000

Mean annual observed runoff (mm )

MeanannualSIM

HYDrunoff(mm)

0

200

400

600

800

1000

0 200 400 600 800 1000

Mean annua l observed runoff (mm)

MeanannualSacramentorunoff(mm)

Figure 4-5. Comparison of mean annual runoff estimated by SIMHYD and

Sacramento models for the verification results with the observed runoff

Figure 4-6. Mean annual runoff estimated by the SIMHYD and Sacramento models across the Murray-Darling Basin


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Figure 4-7. Mean summer (DJF) runoff estimated by the SIMHYD and Sacramento models across the Murray-Darling Basin

Figure 4-8. Mean winter (JJA) runoff estimated by the SIMHYD and Sacramento models across the Murray-Darling Basin


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Figure 4-9. Summary of model verification results across the Murray-Darling Basin


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5 Rainfall-runoff modelling for Scenario C

There are three sections in this chapter. Section 5.1 compares the climate change impact on runoff predicted by the

SIMHYD and Sacramento rainfall-runoff models. Section 5.2 discusses the potential impact of global warming and

enhanced CO2 concentrations on forest water use, which is not modelled explicitly in the CSIRO Murray-Darling BasinSustainable Yields Project. Section 5.3 presents a broad analysis of the potential increase in future bushfire risk and its

impact on runoff.

5.1 Modelling climate change impact on runoff

Figures 5-1, 5-2 and 5-3 compare the changes in runoff characteristics in ~2030 relative to ~1990 estimated by SIMHYD

and Sacramento models using climate change projections from three different GCMs under the medium global warming

scenario. The method used to obtain the 112-year daily rainfall and APET series for a ~2030 climate has been described

in Section 2.2. The comparisons in Figures 5-1 to 5-3 are for the 183 calibration catchments, where the same optimised

parameter values are used to estimate the historical and future runoffs. The three GCMs used here are the IPSL, INMCM

and CCCMA T47 GCMs (see Chiew et al. (2008) for description of the GCMs). These three GCMs are chosen because

the SIMHYD modelling results using the climate change projections from these GCMs show the second driest, median

and second wettest change in future runoff (averaged over the entire MDB) among the results from all 15 GCMs under

the medium global warming scenario (see Figure 3-6).

The impact of climate change on runoff estimated by the SIMHYD and Sacramento rainfall-runoff models shows the

strongest agreement for climate change projections from the wet GCM, CCCMA T47 (Figure 5-3), where the changes in

all the runoff variables estimated by the two models differ by less than 5 percent in almost all the 183 catchments. For

the other two GCM climate change projections, where reduction in future runoff is estimated, the impact on runoff

estimated by SIMHYD is smaller than that estimated by Sacramento (Figures 5-1 and 5-2). The difference in the

modelled climate change impact on mean annual runoff and mean winter runoff estimated by the two models is 5 to

10 percent in the majority of the catchments. This difference is significant, but is relatively small compared to thedisagreement in the rainfall projections for the MDB from different GCMs. For example, the difference between the

median modelled change in the mean annual, summer and winter runoff over the 183 catchments using cl imate change

projections from the dry and wet GCMs (IPSL and CCCMA T47) is about 30 to 40 percent.

The biggest disagreement between the SIMHYD and Sacramento models is in the simulation of climate change impact

on mean summer runoff. For the medium GCM (INMCM), the median difference from the modelling for the

183 catchments is 7 percent, with differences greater than 17 percent in 10 percent of the catchments (Figure 5-1). For

the dry GCM (IPSL), the median difference from the modelling for the 183 catchments is 12 percent, with differences

greater than 24 percent in 10 percent of the catchments (Figure 5-2). The difference in the SIMHYD and Sacramento

modelling results is most likely due to the different methods used to simulate actual evapotranspiration (from soil wetness

and PET), which explains the bigger differences in summer (see Chiew (2006) for more details on estimating hydrologic

sensitivity of climate).

The disagreements between the modelled climate change impacts on extreme runoff (runoff that is exceeded less than

1 percent of the time) and low runoff (number of days with runoff below 0.1 mm) estimated by SIMHYD and Sacramento

are similar to that for mean annual and mean winter runoff.


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% change Sacramento

Mean annual runoff

-60

-40

-20

0

-60 -40 -20 0

Mean summer runoff

-80

-60

-40

-20

0

20

-80 -60 -40 -20 0 20

Mean winter runoff

-60

-40

-20

0

-60 -40 -20 0

Daily runoff that is exceeded

less than 1% of the time

-60

-40

-20

0

-60 -40 -20 0

Number of days with daily

runoff less than 0.1 mm

0

20

40

60

80

100

0 20 40 60 80 100

%c

hangeSIMHYD

Figure 5-1. Comparison of changes in runoff characteristics in ~2030 relative to ~1990 estimated by the SIMHYD and Sacramento

models using climate change projections from the IPSL global climate model under the medium global warming scenario

Mean annual runoff

-40

-30

-20

-10

0

-40 -30 -20 -10 0

Mean summer runoff

-30

-20

-10

0

10

-30 -20 -10 0 10

Mean winter runoff

-60

-40

-20

0

-60 -40 -20 0



-40

-30

-20

-10

0

-40 -30 -20 -10 0

Number of days wi th daily


0

10

20

30

40

0 10 20 30 40

%c

hangeSIMHYD

% change Sacramento

Figure 5-2. Comparison of changes in runoff characteristics in ~2030 relative to ~1990 estimated by the SIMHYD and Sacramentomodels using climate change projections from the INMCM global climate model under the medium global warming scenario


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Mean annual runoff

-20

0

20

40

-20 0 20 40

Mean summer runoff

-40

-20

0

20

40

-40 -20 0 20 40

Mean winter runoff

-20

0

20

40

-20 0 20 40



-20

0

20

40

60

-20 0 20 40 60

Number of days with daily


-20

-10

0

10

20

-20 -10 0 10 20

%c

hangeSIMHYD

% change Sacramento

Figure 5-3. Comparison of changes in runoff characteristics in ~2030 relative to ~1990 estimated by the SIMHYD and Sacramento

models using climate change projections from the CCCMA T47 global climate model under the medium global warming scenario

5.2 Global warming and forest water use

As the same model parameter values are used to estimate the historical and future runoffs, the modelling does not take

into account the potential effect of global warming and enhanced CO2 concentrations on forest water use. There are two

main reasons why changes in forest water use can have a significant impact on future runoff. First, runoff is the

difference between the two larger water balance terms, rainfall and evapotranspiration, and therefore small changes in

forest evapotranspiration can result in large changes in runoff. Second, forests cover a large proportion of the upland

areas where most of the runoff occurs.

The physiological effects of increased CO2 on the hydrological cycle have been shown to be the major cause of

increased runoff for some large river basins around the world (Gedney et al., 2006). However, these effects are generally

not considered in predictions of future runoff changes due to global warming, mainly because the interactions andfeedbacks between the atmosphere and vegetation under increased CO2 are not well understood.

For example, the review of Field et al. (1995) showed that elevated CO2 leads to large decreases in canopy

conductance, resulting in increases in water use efficiencies and reduced transpiration. However, elevated CO2 can also

lead to increases in leaf area index (LAI). The reduced transpiration per leaf/plant due to higher forest water use

efficiency is therefore offset by the higher interception loss and higher transpiration due to higher LAI. Various other

processes also interact to modulate the response of evapotranspiration and runoff to increased CO2 (Figure 5-4). The

summary results of Ainsworth and Long (2005) from 12 large-scale free-air CO2 enrichment (FACE) experiments found

that functional groups differed in their response to elevated CO2 with trees generally more responsive than grass and

crops. Trees showed significant increases in LAI, while there was no significant change in LAI in grass and crops.

The ecohydrological response to elevated CO2 is likely to vary with hydroclimatic conditions. In catchments whereevapotranspiration is limited by energy (e.g. wet catchments in the tropics and northern hemisphere), increased CO2 is

likely to result in reduced evapotranspiration and hence increased runoff. However, in water-limited areas, like most parts


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of the MDB, the lower transpiration per leaf/plant from CO2 fertilisation is likely to be offset by increased LAI, resulting in

little change in evapotranspiration and runoff. Marcar et al. (2006) provide a detailed discussion on the potential impact of

global warming on forest water use for the Murray Uplands area.

In summary, although the potential changes in forest water use under increased CO2 could significantly impact future

runoff, the impact is not estimated in this project. This is because there are compensating positive and negative impacts

of global warming on forest water use, and it is difficult to estimate the net effect because of the complex climate-

biosphere-atmosphere interactions and feedbacks.

CO2Canopy

Conductance

Evapotranspiration Soil Moisture

Soil Evaporation

Runoff

LA

Altered boundary layer

Figure 5-4. The effect of increased CO2 on catchment water balance processes (adapted from Field et al., 1995)

(upward arrow next to a process indicates an increase, downward arrow next to a pro

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