Rainfall-runoff modelling across the Murray-Darling Basin
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Rainfall-runoff modelling across the Murray-Darling Basin A report to the Australian Government from the CSIRO Murray-Darling Basin Sustainable Yields Project 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
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, Freebairn A, 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
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 Government’s 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
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Project. This report describes the rainfall-runoff modelling for 0.05 x 0.05 grid cells (~ 5 km x 5 km) across the
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Murray-Darling Basin (MDB) and presents the runoff estimates for the four modelling scenarios for the 0.05 x
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0.05 grids 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 east–west 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.
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
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for the 0.05 x 0.05 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.
© CSIRO 2008 Rainfall-runoff modelling across the Murray-Darling BasinScenario 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 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. 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 commercial forestry 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. Rainfall-runoff modelling across the Murray-Darling Basin © CSIRO 2008
Table of Contents
1 Introduction................................................................................................................................1
2 Rainfall-runoff modelling method ...........................................................................................2
2.1 Rainfall-runoff modelling...............................................................................................................................................2
2.2 Climate scenarios.........................................................................................................................................................3
3 Summary of modelling results .................................................................................................5
3.1 Reporting regions, subcatchments and calibration catchments ....................................................................................5
3.2 Scenario A results (historical climate, recent development)..........................................................................................7
3.3 Scenario B results (recent climate, recent development) ............................................................................................ 11
3.4 Scenario C results (future climate, recent development)............................................................................................. 13
3.5 Scenario D results (future climate, future development) ............................................................................................. 28
4 Rainfall-runoff modelling for Scenario A ..............................................................................30
4.1 Rainfall-runoff models ................................................................................................................................................ 30
4.2 Model calibration and verification ............................................................................................................................... 32
5 Rainfall-runoff modelling for Scenario C ..............................................................................41
5.1 Modelling climate change impact on runoff................................................................................................................. 41
5.2 Global warming and forest water use ......................................................................................................................... 43
5.3 Future bushfire risk and impact on runoff ................................................................................................................... 44
6 Rainfall-runoff modelling for Scenario D...............................................................................48
6.1 Commercial forestry plantations ................................................................................................................................. 48
6.2 Farm dams................................................................................................................................................................. 49
6.3 Estimation of future farm dam development ............................................................................................................... 53
6.4 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............................. 45
Table 6-1. Existing areas of commercial forestry plantations in the Murray-Darling Basin and the projected increases by
2030 ................................................................................................................................................................................. 49
Table 6-2. Existing farm dam storage capacity (GL), listed by data source, region and state ........................................... 53
Table 6-3. Summary of projected increases in farm dam storage capacity (~2030 relative to ~2005) in Murray-Darling
Basin regions in New South Wales................................................................................................................................... 55
Figures
Figure 3-1. Map showing 18 reporting regions, subcatchments and calibration catchments............................................... 6
Figure 3-2. Mean annual rainfall, areal potential evapotranspiration and modelled runoff .................................................. 8
Figure 3-3. Mean summer (DJF) rainfall, areal potential evapotranspiration and modelled runoff....................................... 9
Figure 3-4. Mean winter (JJA) rainfall, areal potential evapotranspiration and modelled runoff ........................................ 10
Figure 3-5. Percent difference between 1997–2006 mean annual runoff and 1895–2006 long-term mean for 0.05o x 0.05o
grid cells (left) and averaged over each of the 18 MDB regions (right) .............................................................................. 12
Figure 3-6. Absolute difference (in mm) between 1997–2006 mean annual runoff and 1895–2006 long-term mean for
0.05o x 0.05o grid 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 15
global climate models under the medium global warming scenario ................................................................................... 14
Figure 3-8. Percent change in mean summer (DJF) runoff across the Murray-Darling Basin (~2030 relative to ~1990) from
15 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) from
15 global climate models under the medium global warming scenario .............................................................................. 16
© CSIRO 2008 Rainfall-runoff modelling across the Murray-Darling BasinFigure 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 ..................................................... 17
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 ............................................................. 18
Figure 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 ................................................. 19
Figure 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 ................................................. 20
Figure 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 ................................................. 21
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 ................................. 22
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 ................................. 23
Figure 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 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)....................................................................................................................... 24
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 ............................................................. 25
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) ............................................................................................................... 28
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)........................................... 29
Figure 4-1. Structure of SIMHYD rainfall-runoff model ..................................................................................................... 31
Figure 4-2. Structure of Sacramento rainfall-runoff model................................................................................................ 32
Figure 4-3. Summary of model calibration and verification results ................................................................................... 34
Figure 4-4. Typical plots comparing modelled and observed monthly runoffs and daily runoff characteristics .................. 35
Figure 4-5. Comparison of mean annual runoff estimated by SIMHYD and Sacramento models for the verification results
with the observed runoff ................................................................................................................................................... 38
Figure 4-6. Mean annual runoff estimated by the SIMHYD and Sacramento models across the Murray-Darling Basin .... 38
Figure 4-7. Mean summer (DJF) runoff estimated by the SIMHYD and Sacramento models across the Murray-Darling
Basin ................................................................................................................................................................................ 39
Figure 4-8. Mean winter (JJA) runoff estimated by the SIMHYD and Sacramento models across the Murray-Darling Basin
......................................................................................................................................................................................... 39
Figure 4-9. Summary of model verification results across the Murray-Darling Basin ........................................................ 40
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 ............................................................................................................................................................. 42
Figure 5-2. Comparison of changes in runoff characteristics in ~2030 relative to ~1990 estimated by the SIMHYD and
Sacramento models using climate change projections from the INMCM global climate model under the medium global
warming scenario ............................................................................................................................................................. 42
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................................................................................................................................................... 43
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 process indicates a decrease)......................... 44
Figure 5-5. Mean annual cumulative forest fire danger index (FFDI) for the historical climate (1980–2006) and percent
change by ~2030 (second lowest, median and second highest using future climate data informed by six global climate
models) ............................................................................................................................................................................ 46
Figure 5-6. Relationship between forest area burnt and mean annual cumulative FFDI based on data from Victoria ....... 47
Figure 5-7. Average change in mean annual streamflow for different species following bushfires .................................... 47
Figure 6-1. Sources of data on existing farm dam storage capacity ................................................................................. 51
Figure 6-2. Spatial density of existing farm dam storage capacity.................................................................................... 52
Figure 6-3. Spatial density of projected increase in farm dam storage capacity (~2030 relative to ~2005) ....................... 55
Figure 6-4. Percent reduction in mean annual runoff due to the projected increase in farm dams (~2030 relative to ~2005)
......................................................................................................................................................................................... 58
Rainfall-runoff modelling across the Murray-Darling Basin © CSIRO 20081 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, and
compare 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.
© CSIRO 2008 Rainfall-runoff modelling across the Murray-Darling Basin ▪ 12 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
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for 0.05 x 0.05 grid cells (~ 5 km x 5 km) across the entire MDB for the four scenarios. The use of 0.05 x 0.05 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
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km ) 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 incorporates
the 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).
2
As the parameter values come from calibration against streamflow from 50 to 2000 km 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
2 ▪ Rainfall-runoff modelling across the Murray-Darling Basin © CSIRO 2008objectives 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 differences
between 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
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SILO Data Drill provides surfaces of daily rainfall and other climate data for 0.05 x 0.05 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 Morton’s 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 important
because 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
© CSIRO 2008 Rainfall-runoff modelling across the Murray-Darling Basin ▪ 3traditional methods that assume the entire rainfall distribution to change in the same way would lead to an underestimation of total runoff. 4 ▪ Rainfall-runoff modelling across the Murray-Darling Basin © CSIRO 2008
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. © CSIRO 2008 Rainfall-runoff modelling across the Murray-Darling Basin ▪ 5
Figure 3-1. Map showing 18 reporting regions, subcatchments and calibration catchments 6 ▪ Rainfall-runoff modelling across the Murray-Darling Basin © CSIRO 2008
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 east–west 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). © CSIRO 2008 Rainfall-runoff modelling across the Murray-Darling Basin ▪ 7
Figure 3-2. Mean annual rainfall, areal potential evapotranspiration and modelled runoff 8 ▪ Rainfall-runoff modelling across the Murray-Darling Basin © CSIRO 2008
Figure 3-3. Mean summer (DJF) rainfall, areal potential evapotranspiration and modelled runoff © CSIRO 2008 Rainfall-runoff modelling across the Murray-Darling Basin ▪ 9
Figure 3-4. Mean winter (JJA) rainfall, areal potential evapotranspiration and modelled runoff 10 ▪ Rainfall-runoff modelling across the Murray-Darling Basin © CSIRO 2008
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 α = 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. © CSIRO 2008 Rainfall-runoff modelling across the Murray-Darling Basin ▪ 11
Figure 3-5. Percent difference between 1997–2006 mean annual runoff and 1895–2006 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 1997–2006 mean annual runoff and 1895–2006 long-term mean
for 0.05o x 0.05o grid cells (left) and averaged over each of the 18 MDB regions (right)
12 ▪ Rainfall-runoff modelling across the Murray-Darling Basin © CSIRO 20083.4 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
th th
driest and second wettest results are used because they represent about the 10 and 90 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
o o
0.05 x 0.05 grid cells across the MDB, the results in Figure 3-17 are obtained using climate change projections from a
single 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.
© CSIRO 2008 Rainfall-runoff modelling across the Murray-Darling Basin ▪ 13Figure 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
14 ▪ Rainfall-runoff modelling across the Murray-Darling Basin © CSIRO 2008Figure 3-8. Percent change in mean summer (DJF) runoff across the Murray-Darling Basin (~2030 relative to ~1990)
from 15 global climate models under the medium global warming scenario
© CSIRO 2008 Rainfall-runoff modelling across the Murray-Darling Basin ▪ 15Figure 3-9. Percent change in mean winter (JJA) runoff across the Murray-Darling Basin (~2030 relative to ~1990)
from 15 global climate models under the medium global warming scenario
16 ▪ Rainfall-runoff modelling across the Murray-Darling Basin © CSIRO 2008Figure 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
© CSIRO 2008 Rainfall-runoff modelling across the Murray-Darling Basin ▪ 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
18 ▪ Rainfall-runoff modelling across the Murray-Darling Basin © CSIRO 2008Figure 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
© CSIRO 2008 Rainfall-runoff modelling across the Murray-Darling Basin ▪ 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
20 ▪ Rainfall-runoff modelling across the Murray-Darling Basin © CSIRO 2008Figure 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
© CSIRO 2008 Rainfall-runoff modelling across the Murray-Darling Basin ▪ 21Figure 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
22 ▪ Rainfall-runoff modelling across the Murray-Darling Basin © CSIRO 2008Figure 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
© CSIRO 2008 Rainfall-runoff modelling across the Murray-Darling Basin ▪ 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 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)
24 ▪ Rainfall-runoff modelling across the Murray-Darling Basin © CSIRO 200860 6
Mean monthly rainfall (mm)
Mean monthly runoff (mm)
40 4
Paroo
20 2
0 0
J F M A M J J A S O N D J F M A M J J A S O N D
80 3
Mean monthly rainfall (mm)
Mean monthly runoff (mm)
60
2
40
Warrego
1
20
0 0
J F M A M J J A S O N D J F M A M J J A S O N D
100 6
Mean monthly rainfall (mm)
Mean monthly runoff (mm)
80
4
60
Condamine-Balonne 40
2
20
0 0
J F M A M J J A S O N D J F M A M J J A S O N D
100 6
Mean monthly rainfall (mm)
Mean monthly runoff (mm)
80
4
60
Moonie 40
2
20
0 0
J F M A M J J A S O N D J F M A M J J A S O N D
120 8
Mean monthly rainfall (mm)
Mean monthly runoff (mm)
100
6
80
Border 60 4
40
2
20
0 0
J F M A M J J A S O N D J F M A M J J A S O N D
100 12
Mean monthly rainfall (mm)
Mean monthly runoff (mm)
80
9
60
Gwydir 40
6
3
20
0 0
J F M A M J J A S O N D J F M A M J J A S O N D
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
© CSIRO 2008 Rainfall-runoff modelling across the Murray-Darling Basin ▪ 25100 8
Mean monthly rainfall (mm)
Mean monthly runoff (mm)
80
6
60
Namoi 40
4
2
20
0 0
J F M A M J J A S O N D J F M A M J J A S O N D
100 8
Mean monthly rainfall (mm)
Mean monthly runoff (mm)
80
6
60
Macquarie-Castlereagh 40
4
2
20
0 0
J F M A M J J A S O N D J F M A M J J A S O N D
60 2.0
Mean monthly rainfall (mm)
Mean monthly runoff (mm)
1.5
40
Barwon-Darling 1.0
20
0.5
0 0.0
J F M A M J J A S O N D J F M A M J J A S O N D
80 6
Mean monthly rainfall (mm)
Mean monthly runoff (mm)
60
4
40
Lachlan
2
20
0 0
J F M A M J J A S O N D J F M A M J J A S O N D
80 10
Mean monthly rainfall (mm)
Mean monthly runoff (mm)
60 8
6
40
Murrumbidgee
4
20
2
0 0
J F M A M J J A S O N D J F M A M J J A S O N D
60 4
Mean monthly rainfall (mm)
Mean monthly runoff (mm)
3
40
Murray 2
20
1
0 0
J F M A M J J A S O N D J F M A M J J A S O N D
Figure 3-18. (continued) 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
26 ▪ Rainfall-runoff modelling across the Murray-Darling Basin © CSIRO 2008150 60
Mean monthly rainfall (mm)
Mean monthly runoff (mm)
100 40
Ovens
50 20
0 0
J F M A M J J A S O N D J F M A M J J A S O N D
120 40
Mean monthly rainfall (mm)
Mean monthly runoff (mm)
100
30
80
60
Goulburn-Broken 20
40
10
20
0 0
J F M A M J J A S O N D J F M A M J J A S O N D
100 20
Mean monthly rainfall (mm)
Mean monthly runoff (mm)
80
15
60
Campaspe 40
10
5
20
0 0
J F M A M J J A S O N D J F M A M J J A S O N D
60 6
Mean monthly rainfall (mm)
Mean monthly runoff (mm)
40 4
Loddon-Avoca
20 2
0 0
J F M A M J J A S O N D J F M A M J J A S O N D
60 4
Mean monthly rainfall (mm)
Mean monthly runoff (mm)
3
40
Wimmera 2
20
1
0 0
J F M A M J J A S O N D J F M A M J J A S O N D
80 10
Mean monthly rainfall (mm)
Mean monthly runoff (mm)
8
60
6
40
Eastern Mount Lofty Ranges
4
20
2
0 0
J F M A M J J A S O N D J F M A M J J A S O N D
Figure 3-18. (continued) 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
© CSIRO 2008 Rainfall-runoff modelling across the Murray-Darling Basin ▪ 273.5 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 about
3 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)
28 ▪ Rainfall-runoff modelling across the Murray-Darling Basin © CSIRO 2008You can also read
